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Migration Prevention of Carbonate Apatite Granules Through Crystal Interlocking Driven by Bassanite-to-Gypsum Transformation on Granule Surface
Multi-Modal Vision Transformer with Explainable Shapley Additive Explanations Value Embedding for Cymbidium goeringii Quality Grading
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Dark Fermentation of Fruit Juice Effluents: Effects of Substrate Concentration
Toughened Vinyl Ester Resin Reinforced with Natural Flax Fabrics

Journal Description

Applied Sciences

Applied Sciences is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.4 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our authors say about Applied Sciences.
Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal, JETA and AI in Medicine.

Impact Factor: 2.5 (2023); 5-Year Impact Factor: 2.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2076-3417

Latest Articles

24 pages, 2822 KiB

Open AccessArticle

Failure Modes Analysis Related to User Experience in Interactive System Design Through a Fuzzy Failure Mode and Effect Analysis-Based Hybrid Approach

by Yongfeng Li and Liping Zhu

Appl. Sci. 2025, 15(6), 2954; https://doi.org/10.3390/app15062954 (registering DOI) - 9 Mar 2025

User experience (UX) is crucial for interactive system design. To improve UX, one method is to identify failure modes related to UX and then take action on the high-priority failure modes to decrease their negative impacts. For the UX of interactive system design, [...] Read more.

User experience (UX) is crucial for interactive system design. To improve UX, one method is to identify failure modes related to UX and then take action on the high-priority failure modes to decrease their negative impacts. For the UX of interactive system design, the failure modes under consideration are human errors or difficulties, and thus the risk factors concerning failure modes are subjective and even subconscious. Existing methods are not sufficient to deal with these issues. In this paper, a fuzzy failure mode and effect analysis (FMEA)-based hybrid approach is proposed to improve the UX of interactive system design. First, hierarchical task analysis (HTA) and systematic human error reduction and prediction approach (SHERPA) are combined to identify potential failure modes concerning UX. Subsequently, fuzzy linguistic variables are employed to assess the risk parameters of the failure modes, and the similarity aggregation method (SAM) is adopted to aggregate the fuzzy opinions. Then, on the basis of the aggregation results, fuzzy logic is adopted to compute the fuzzy risk priority numbers that can prioritize the failure modes. Finally, the failure modes with high priorities are considered for corrective actions. An in-vehicle information system was employed as a case study to illustrate the proposed approach. The findings indicate that, compared with other methods, our approach can provide more accurate results for prioritizing failure modes related to UX, and can successfully deal with the subjective and even subconscious nature of the risk factors associated with failure modes. This approach can be universally utilized to enhance the UX of interactive system design. Full article

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23 pages, 5268 KiB

Open AccessArticle

Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating Shading and Blocking Effects

by José Carlos Garcia Pereira and Luís Guerra Rosa

Appl. Sci. 2025, 15(6), 2953; https://doi.org/10.3390/app15062953 (registering DOI) - 9 Mar 2025

In this work, solar concentrating heliostat fields are modelled using computer ray-tracing techniques to investigate the parameters controlling the optical efficiency of those solar facilities. First, it is explained how the non-trivial problem of heliostat blocking and shading can be efficiently handled in [...] Read more.

In this work, solar concentrating heliostat fields are modelled using computer ray-tracing techniques to investigate the parameters controlling the optical efficiency of those solar facilities. First, it is explained how the non-trivial problem of heliostat blocking and shading can be efficiently handled in ray-tracing simulations. These numerical techniques were implemented in our Light Analysis Modelling (LAM) software, which was then used to study realistic heliostat fields for a range of different geometries. Two locations were chosen, with the highest and the lowest latitudes, from the SFERA-III EU list of solar concentrating facilities with heliostat fields: Jülich (Germany) and Protaras (Cyprus). The results indicate that shading and blocking can substantially reduce the radiation collected during the year (up to 20%). Accurate figures of merit are proposed to quantify the thermal efficiency of a heliostat field, independently of its size. Increasing the tower height mostly reduces blocking (especially when the sun is high and most energy is collected), while increasing the distance between heliostats or increasing the ground slope mostly reduces shading (especially when the sun is low and little energy is collected). Full article

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Figure 1
<p>Overall algorithm to model a single heliostat. When modelling multiple heliostats, the geolocation data and shading/blocking attenuation effects must also be considered.</p> Full article ">Figure 2
<p>Comparison between parabolic (blue) and spherical (red) curvatures for a typical 200 cm × 200 cm (diagonal × diagonal) heliostat as a function of curvature deflection (all distances in cm). For deflections below 30%, parabolic and spherical curvatures are identical.</p> Full article ">Figure 3
<p>(<b>a</b>) Paraboloid curvature <math display="inline"><semantics> <mrow> <mi>a</mi> </mrow> </semantics></math> and focal length <math display="inline"><semantics> <mrow> <mi>f</mi> </mrow> </semantics></math> (see [<a href="#B19-applsci-15-02953" class="html-bibr">19</a>] for details) are functions of heliostat deflection <math display="inline"><semantics> <mrow> <mi>z</mi> </mrow> </semantics></math> shown in (<b>b</b>) and heliostat projected half-diagonal <math display="inline"><semantics> <mrow> <mi>x</mi> </mrow> </semantics></math> depicted in (<b>c</b>). Random reflection points and incident sun rays are generated in the rectangle (in red) at height 0 (lateral and top views) before intercepting the heliostat curvature (in black) below, where the reflection actually occurs.</p> Full article ">Figure 4
<p>(<b>a</b>) Spherical radius <math display="inline"><semantics> <mrow> <mi>r</mi> </mrow> </semantics></math> and focal length <math display="inline"><semantics> <mrow> <mi>f</mi> </mrow> </semantics></math> (see [<a href="#B19-applsci-15-02953" class="html-bibr">19</a>] for details) are functions of heliostat deflection <math display="inline"><semantics> <mrow> <mi>z</mi> </mrow> </semantics></math> shown in (<b>b</b>) and heliostat projected half-diagonal <math display="inline"><semantics> <mrow> <mi>x</mi> </mrow> </semantics></math> depicted in (<b>c</b>). Random reflection points and incident sun rays are generated in the rectangle (in red) at height 0 (lateral and top views) before intercepting the heliostat curvature (in black) below, where the reflection actually occurs.</p> Full article ">Figure 5
<p>Radiation accumulated at the target of a 3 × 2 heliostat field (simulated with 10<sup>8</sup> rays), showing heliostat blocking: the three back-row heliostats (pointing above) are partly blocked by the three front-row heliostats (pointing below). In this example, the target is 6 m higher than the 2 m × 2 m heliostats, which are 2 m apart.</p> Full article ">Figure 6
<p>Adding the cross products between successive edge vectors is an effective way to calculate the normal vector of a convex, flat polygon.</p> Full article ">Figure 7
<p>The successive cross product vectors always point (<b>a</b>) to the same side (inside point) or (<b>b</b>) to opposite sides (outside point), permitting us to distinguish between the two situations.</p> Full article ">Figure 8
<p>Pixel-based target rectangle, perpendicular to average sun radiation coming from the heliostat field (heliostats are represented in the rest position).</p> Full article ">Figure 9
<p>Layout for the simulated heliostat field, with 66 heliostats in a staggered arrangement. The target (T) points north (N), to collect the sun rays (S) reflected. Only the 6 green heliostats are fully simulated. The 20 closer (red) and 8 farther (pink) heliostats must be orientated, to test for possible blocking and shading events. The heliostat mirrors are 2.0 m × 2.0 m, separated by 1.0 m in both the north–south and east–west directions. The two central heliostats are rotated 45° to emphasize that they cannot touch. We also simulated this heliostat field with mirrors 2.5 m (east–west) × 1.6 m (north–south), with 1.0 m separation east–west and 1.9 m north–south.</p> Full article ">Figure 10
<p>Daily heliostat efficiency (DHE) calculated for SW, S, SE, NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Jülich, with rectangular heliostats and flat ground (from 3rd November to 21st January, it is night at 16:00 LCT).</p> Full article ">Figure 11
<p>Daily heliostat efficiency (DHE) calculated for SW, S, SE, NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Jülich, with square heliostats and flat ground (from 3rd November to 21st January, it is night at 16:00 LCT).</p> Full article ">Figure 12
<p>Daily heliostat efficiency (DHE) calculated for SW, S, SE, NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Protaras, with rectangular heliostats and flat ground.</p> Full article ">Figure 13
<p>Daily heliostat efficiency (DHE) calculated for SW, S, SE, NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Protaras, with square heliostats and flat ground.</p> Full article ">Figure 14
<p>Radiation loss (night, shading, and blocking effects) calculated for NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Jülich, with rectangular heliostats and flat ground (from 3rd November to 21st January, it is night at 16:00 LCT).</p> Full article ">Figure 15
<p>Radiation loss (night, shading, and blocking effects) calculated for NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Jülich, with square heliostats and flat ground (from 3rd November to 21st January, it is night at 16:00 LCT).</p> Full article ">Figure 16
<p>Radiation loss (shading and blocking effects) calculated for NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Protaras, with rectangle heliostats and flat ground.</p> Full article ">Figure 17
<p>Radiation loss (shading and blocking effects) calculated for NW, N, and NE representative heliostats (see <a href="#applsci-15-02953-f009" class="html-fig">Figure 9</a>), for Protaras, with square heliostats and flat ground.</p> Full article ">

23 pages, 2404 KiB

Open AccessArticle

Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers

by Shambo Bhattacharjee, Jae Jun Kim and Jennifer Hudson

Appl. Sci. 2025, 15(6), 2952; https://doi.org/10.3390/app15062952 (registering DOI) - 9 Mar 2025

Minimizing the vibrations of flexible structures during maneuvering is a challenging problem. Input shaping control is an effective way to reduce vibration, but in some cases, it does not guarantee a final rest state. In particular, if the initial state is not at [...] Read more.

Minimizing the vibrations of flexible structures during maneuvering is a challenging problem. Input shaping control is an effective way to reduce vibration, but in some cases, it does not guarantee a final rest state. In particular, if the initial state is not at rest, then it is difficult to bring the final state to rest at the end of a maneuver. This article proposes the use of a single-switch reference control combined with an optimized Zero Vibration Derivative-Derivative (ZVDD) shaper. The ZVDD-shaped torque profile is defined by a small number of parameters, which can be optimized by a standard numerical optimization algorithm. The resulting control is effective in reducing vibration and bringing the final state to rest, regardless of the starting conditions. Two examples related to rest-to-rest control and non-rest-to-rest control are presented. Full article

(This article belongs to the Section Aerospace Science and Engineering)

16 pages, 5731 KiB

Open AccessArticle

Calibration and Analysis of Seeding Parameters of Soaked Cyperus esculentus L. Seeds

by Jianguo Yan, Zhenyu Liu and Fei Liu

Appl. Sci. 2025, 15(6), 2951; https://doi.org/10.3390/app15062951 (registering DOI) - 9 Mar 2025

The seeds of Cyperus esculentus L. exhibit an uneven surface and irregular shape, which adversely affect precision seeding. Pre-sowing seed soaking treatment not only improves seeding performance, but also enhances the germination capability of C. esculentus seeds. However, the intrinsic parameters of the [...] Read more.

The seeds of Cyperus esculentus L. exhibit an uneven surface and irregular shape, which adversely affect precision seeding. Pre-sowing seed soaking treatment not only improves seeding performance, but also enhances the germination capability of C. esculentus seeds. However, the intrinsic parameters of the seeds undergo significant changes after soaking in terms of their physical properties, such as volume, weight, and density. These changes directly influence the fluidity and positioning accuracy of the seeds during the seeding process. Additionally, contact parameters, such as the coefficient of friction and the contact area between the seeds and the seeding apparatus, are altered by soaking. These parameters are crucial for designing efficient seeding devices. Therefore, it is necessary to measure the intrinsic parameters of soaked C. esculentus seeds and their contact parameters with the seeding apparatus to provide parameter support for the precision seeding analysis of pre-soaked C. esculentus. This study focuses on the calibration and experimental investigation of discrete element parameters for soaked C. esculentus seeds. Free-fall collision tests, static friction tests, and rolling friction tests were conducted to calibrate the contact parameters between soaked C. esculentus seeds and between the seeds and steel materials. Using Design-Expert, Plackett–Burman tests, steepest ascent tests, and Box–Behnken response surface tests were designed to obtain the optimal parameter combination for the C. esculentus contact model. The optimal parameters were validated through angle of repose simulation tests and physical experiments. The results indicate that the rolling friction coefficient (F) between seeds, the static friction coefficient (E) between seeds, and the rolling friction coefficient (J) between seeds and steel plates significantly affect the angle of repose. The optimal combination of discrete element parameters is as follows: the static friction coefficient (E) between seeds is 0.675, the rolling friction coefficient (F) between seeds is 0.421, and the rolling friction coefficient (J) between seeds and steel plates is 0.506. Using the calibrated parameters for simulation, the average angle of repose was 32.31°, with a relative error of 1.1% compared to the physical experiments. Full article

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<p>Pre-treatment for soaking of <span class="html-italic">C. esculentus</span> (The soaking began at 19:00 on 8 November 2024).</p> Full article ">Figure 2
<p>Triaxial-dimensional characteristics of <span class="html-italic">C. esculentus</span> deeds.</p> Full article ">Figure 3
<p>Tri-axial size distribution of soaked <span class="html-italic">C. esculentus</span> seeds.</p> Full article ">Figure 4
<p>Measurement of Poisson’s ratio of soaked <span class="html-italic">C. esculentus</span> seeds.</p> Full article ">Figure 5
<p>Measurement of the shear modulus of <span class="html-italic">C. esculentus</span> seeds.</p> Full article ">Figure 6
<p>Determination of static and dynamic coefficients of friction of soaked <span class="html-italic">C. esculentus</span> seeds.</p> Full article ">Figure 7
<p>Determination of collision recovery coefficient of <span class="html-italic">C. esculentus</span> seeds.</p> Full article ">Figure 8
<p>Determination of the physical angle of accretion of soaked <span class="html-italic">C. esculentus</span> seeds: (<b>a</b>) determination of the physical angle of accumulation of the <span class="html-italic">C. esculentus</span> seeds; (<b>b</b>) gray-scale processing; (<b>c</b>) extracting contours to fit straight lines.</p> Full article ">Figure 9
<p>Determination of the physical angle of accretion of <span class="html-italic">C. esculentus</span> seeds: (<b>a</b>) soaked <span class="html-italic">C. esculentus</span> seed; (<b>b</b>) outline model of <span class="html-italic">C. esculentus</span> seed; (<b>c</b>) discrete meta-modeling of <span class="html-italic">C. esculentus</span> seed.</p> Full article ">Figure 10
<p>Determination of the physical angle of accretion of <span class="html-italic">C. esculentus</span> seeds: (<b>a</b>) determination of simulated stacking angle of soaked <span class="html-italic">C. esculentus</span> seeds; (<b>b</b>) gray-scale processing; (<b>c</b>) extracting contours to fit straight lines.</p> Full article ">Figure 11
<p><span class="html-italic">C. esculentus</span> seed accumulation verification experiment: (<b>a</b>) physical stacking test; (<b>b</b>) simulated stacking test.</p> Full article ">Figure 12
<p>Effect of two-way interactions of factors on the angle of repose of <span class="html-italic">C. esculentus</span> seeds: (<b>a</b>) response surface of population stacking angle for interspecies interactions of static and rolling friction coefficients; (<b>b</b>) response surface of population stacking angle for interaction of interspecies static friction coefficient and seed–plate rolling friction coefficient; (<b>c</b>) response surface of population stacking angle for interspecies interaction of rolling friction coefficient and seed–plate rolling friction coefficient.</p> Full article ">

20 pages, 8765 KiB

Open AccessArticle

Mamba-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN

by Xubo Ma, Chuhua Huang, Xin Huang and Wangping Wu

Appl. Sci. 2025, 15(6), 2950; https://doi.org/10.3390/app15062950 (registering DOI) - 9 Mar 2025

The parameter configuration of traditional visual SLAM algorithms usually relies on expert experience and extensive experiments, and the parameter configuration needs to be reset as the scene changes, which is a complex and tedious process. To achieve parameter adaptation in visual SLAM, we [...] Read more.

The parameter configuration of traditional visual SLAM algorithms usually relies on expert experience and extensive experiments, and the parameter configuration needs to be reset as the scene changes, which is a complex and tedious process. To achieve parameter adaptation in visual SLAM, we propose the Mamba-DQN method, which transforms complex parameter adjustment tasks into policy learning assignments for the agent. In this paper, we select the key parameters of visual SLAM to construct the agent action space. The reward function is constructed based on the absolute trajectory error (ATE), and the Mamba history observer is built within the agent to learn the observation trajectory, aiming to improve the quality of the agent’s decisions. Finally, the proposed method was experimented on the EuRoc MAV and TUM-VI datasets. The experimental results show that Mamba-DQN not only enhances the positioning accuracy of visual SLAM and demonstrates good real-time performance but also avoids the tedious parameter adjustment process. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

25 pages, 6769 KiB

Open AccessArticle

NursingXR: Advancing Nursing Education Through Virtual Reality-Based Training

by Mohammad F. Obeid, Ahmed Ewais and Mohammad R. Asia

Appl. Sci. 2025, 15(6), 2949; https://doi.org/10.3390/app15062949 (registering DOI) - 9 Mar 2025

The increasing complexity of healthcare delivery and the advancements in medical technology have highlighted the necessity for improved training in nursing education. While traditional training methods have their merits, they often encounter challenges such as limited access to clinical placements, static physical simulations, [...] Read more.

The increasing complexity of healthcare delivery and the advancements in medical technology have highlighted the necessity for improved training in nursing education. While traditional training methods have their merits, they often encounter challenges such as limited access to clinical placements, static physical simulations, and performance anxiety during hands-on practice. Virtual reality (VR) has been increasingly adopted for immersive and interactive training environments, allowing nursing students to practice essential skills repeatedly in realistic, risk-free settings. This study presents NursingXR, a VR-based platform designed to help nursing students master essential clinical skills. With a scalable and flexible architecture, NursingXR is tailored to support a variety of nursing lessons and adapt to evolving curricula. The platform has a modular design and offers two interactive modes: Training Mode, which provides step-by-step guided instruction, and Evaluation Mode, which allows for independent performance assessment. This article details the development process of the platform, including key design principles, system architecture, and implementation strategies, while emphasizing its utility and scalability. A mixed-methods evaluation involving 78 participants—both novices and experts—was conducted to evaluate the platform’s usability and user satisfaction. The results underscore NursingXR’s effectiveness in fostering an effective and engaging learning environment as well as its potential as a supplementary resource for nursing training. Full article

(This article belongs to the Special Issue Virtual and Augmented Reality: Theory, Methods, and Applications)

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<p>Conceptual design and system architecture of NursingXR.</p> Full article ">Figure 2
<p>Layout document for Tracheostomy lesson showing the various interactions and objects involved, as part of the nomenclature translation process.</p> Full article ">Figure 3
<p>Customized assets created for the lesson “Starting an intravenous infusion” including (<b>a</b>) the IV kit and (<b>b</b>) the room and patient avatar.</p> Full article ">Figure 4
<p>Example use of <span class="html-italic">ProcedureEvents</span> script for the Intramuscular Injection lesson, showing a high-level view of the events that were created for this specific lesson’s (<b>a</b>) pre-procedure, (<b>b</b>) procedure, and (<b>c</b>) post-procedure event categories.</p> Full article ">Figure 4 Cont.
<p>Example use of <span class="html-italic">ProcedureEvents</span> script for the Intramuscular Injection lesson, showing a high-level view of the events that were created for this specific lesson’s (<b>a</b>) pre-procedure, (<b>b</b>) procedure, and (<b>c</b>) post-procedure event categories.</p> Full article ">Figure 5
<p>Textual instructions (<b>a</b>) paired with visual effects (<b>b</b>) to guide user through the Intramuscular Injection procedure in the Training Mode.</p> Full article ">Figure 6
<p><span class="html-italic">NursingXR</span> menu and navigation showing the various screens available including (<b>a</b>) Welcome Screen, (<b>b</b>) User ID input, (<b>c</b>) Modules tab, (<b>d</b>), Results tab, (<b>e</b>) Tutorial tab, and (<b>f</b>) Settings tab.</p> Full article ">Figure 6 Cont.
<p><span class="html-italic">NursingXR</span> menu and navigation showing the various screens available including (<b>a</b>) Welcome Screen, (<b>b</b>) User ID input, (<b>c</b>) Modules tab, (<b>d</b>), Results tab, (<b>e</b>) Tutorial tab, and (<b>f</b>) Settings tab.</p> Full article ">Figure 7
<p>Nursing students participating in the study and using NursingXR to supplement their classroom experience on various devices such as the Meta Quest 2 (<b>a</b>) and the HP Reverb (<b>b</b>).</p> Full article ">Figure 8
<p>A sampling of the various steps within the Intramuscular Injection submodule including: (<b>a</b>) user washing their hands, (<b>b</b>) user equipping gloves, (<b>c</b>) filling the syringe with the medication, and (<b>d</b>) injecting the patient’s arm.</p> Full article ">Figure 9
<p>A frequency chart for responses from Experts related to didactic capacity of NursingXR.</p> Full article ">Figure 10
<p>Feedback related to VR comfort from male and female participants.</p> Full article ">

18 pages, 4395 KiB

Open AccessReview

Role of Endoscopic Ultrasound in Diagnosis and Management of Pancreas Divisum: A Case Study and Literature Review

by Paolo Aseni, Ilaria Fanetti, Enrico Ganguzza, Sofia Bosco, Paola Fontana, Antonio Armellino and Pietro Gambitta

Appl. Sci. 2025, 15(6), 2948; https://doi.org/10.3390/app15062948 (registering DOI) - 9 Mar 2025

The long-term efficacy of endoscopic treatment of pancreas divisum is controversial. This review focuses on recent literature on the role of endoscopic ultrasonography (EUS) as effective clinical support in the diagnosis and management of pancreas divisum. A challenging case study in a patient [...] Read more.

The long-term efficacy of endoscopic treatment of pancreas divisum is controversial. This review focuses on recent literature on the role of endoscopic ultrasonography (EUS) as effective clinical support in the diagnosis and management of pancreas divisum. A challenging case study in a patient with pancreas divisum affected by recurrent acute pancreatitis and chronic pain is also reported. Our methodology was developed from a search strategy based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A comprehensive electronic search across multiple databases, including Medline/PubMed, EMBASE, Medline/Metacrawler and the Cochrane Library, to identify relevant publications for this systematic review was conducted. A total of 308 articles were found. According to EMBASE grouping criteria, 31 articles were considered major clinical studies and were analysed, reporting for each study the relevant clinical features. In the majority of studies examined, EUS proved useful in diagnosing a pancreatic divisum. The EUS diagnostic yield was reported to have a sensitivity ranging from 51% to 95% and high diagnostic accuracy up to 97%. EUS has shown high sensitivity and specificity in diagnosing pancreas divisum. Studies indicate that EUS can achieve sensitivity rates ranging from 80% to 100% and specificity rates around 97% to 100% for detecting pancreas divisum. Based on these figures, EUS is the most reliable imaging system in terms of diagnostic capability compared with other imaging systems. Full article

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Figure 1
<p>Drawings illustrate three types of pancreas divisum. (<b>A</b>) Type 1, or classical divisum, with total fusion failure. (<b>B</b>) Type 2 with dominant dorsal drainage in the absence of Wirsung’s duct. (<b>C</b>) Type 3 or incomplete divisum with a small communicating branch (blue arrow).</p> Full article ">Figure 2
<p>PRISMA flow chart diagram.</p> Full article ">Figure 3
<p>MRCP showing necrotic areas of the head of the pancreas and multiple peri-duodenal fluid collections.</p> Full article ">Figure 4
<p>MRCP showing a case of complete pancreas divisum (type 1): absence of communication between the main pancreatic duct and Wirsung’s duct (head of arrow); dominant dorsal duct drainage in the minor papilla (grey arrow). The biliary tract crosses the main pancreatic duct.</p> Full article ">Figure 5
<p>The two pancreatic stents through minor and major papillae are shown.</p> Full article ">Figure 6
<p>Endoscopic view of the lumen-apposing metal stent in place (white arrow, Hot Axios™ (Boston Scientific, Boston, MA, USA)) draining purulent material.</p> Full article ">Figure 7
<p>EUS imaging of pancreas divisum, showing the main pancreatic duct at the pancreatic body (white arrow) without communication with Wirsung’s duct at the isthmus of the pancreas.</p> Full article ">

15 pages, 5513 KiB

Open AccessArticle

Exploration of Travel Patterns of Intercity Metro Passengers—A Case Study in Changsha–Zhuzhou–Xiangtan Metropolitan Area, China

by Yao Xie, Biao Cheng, Wei Ren, Cuizhu Zhou and Chenhui Liu

Appl. Sci. 2025, 15(6), 2947; https://doi.org/10.3390/app15062947 (registering DOI) - 9 Mar 2025

(1) Background: Rapid urban growth in China has extended rail systems to neighboring cities to meet intercity travel needs, making it important to understand intercity travel patterns. (2) Methods: Taking the Changsha–Zhuzhou–Xiangtan Intercity Metro Xihuan Line as an example, intercity ridership patterns are [...] Read more.

(1) Background: Rapid urban growth in China has extended rail systems to neighboring cities to meet intercity travel needs, making it important to understand intercity travel patterns. (2) Methods: Taking the Changsha–Zhuzhou–Xiangtan Intercity Metro Xihuan Line as an example, intercity ridership patterns are analyzed. A Gaussian mixture model is applied to cluster ridership into groups based on travel time and stay time, respectively, and their travel patterns are examined. (3) Results: Weekdays display distinct commuting patterns with morning and evening peaks. On weekends, peak travel times are quite different. Based on travel time, intercity ridership is divided into two groups. The short-travel-time group primarily travels for residential demand, while the long-travel-time group is mainly for leisure. No significant differences were found in weekday and weekend patterns. Based on stay time, ridership is categorized into two groups. Most trips are round trips on the same day, but overnight ridership is higher on weekends. On weekends, the intercity ridership groups with tourism as their travel purpose on weekends have different stay modes, but their main travel destinations are similar. On weekdays, there are large differences in the distribution of main travel destinations between the short-stay-time group and the long-stay-time group, and their travel purposes are more diverse. Full article

(This article belongs to the Section Transportation and Future Mobility)

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<p>The URT network in Changsha and Xiangtan.</p> Full article ">Figure 2
<p>Intercity ridership correlation matrix. Note: * indicates passing the 5% significance level test; ** indicates passing the 1% significance level test; *** indicates passing the significance level test of 0.1%.</p> Full article ">Figure 3
<p>Boarding and alighting intercity ridership on weekends and weekdays.</p> Full article ">Figure 4
<p>Trip counts on weekends and weekdays.</p> Full article ">Figure 5
<p>Travel-time distribution of intercity ridership.</p> Full article ">Figure 6
<p>The clusters of travel-time ridership.</p> Full article ">Figure 7
<p>Distance distributions of different travel-time groups.</p> Full article ">Figure 8
<p>Travel destinations of the short-travel-time group.</p> Full article ">Figure 9
<p>Travel destinations of the long-travel-time group.</p> Full article ">Figure 10
<p>Stay-time distribution of intercity ridership.</p> Full article ">Figure 11
<p>The cluster of stay-time ridership on weekdays and weekends.</p> Full article ">Figure 12
<p>Travel destinations of different stay-time groups on weekends.</p> Full article ">Figure 13
<p>Travel destinations of different stay-time groups on weekdays.</p> Full article ">

23 pages, 3201 KiB

Open AccessArticle

Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning

by Tomás Barros-Everett, Elizabeth Montero and Nicolás Rojas-Morales

Appl. Sci. 2025, 15(6), 2946; https://doi.org/10.3390/app15062946 (registering DOI) - 9 Mar 2025

Setting parameter values is crucial for the performance of metaheuristics. Tuning the parameters of a metaheuristic is a computationally costly task. Moreover, parameter tuning is difficult considering their inherent stochasticity and problem instance dependence. In this work, we explore the application of machine [...] Read more.

Setting parameter values is crucial for the performance of metaheuristics. Tuning the parameters of a metaheuristic is a computationally costly task. Moreover, parameter tuning is difficult considering their inherent stochasticity and problem instance dependence. In this work, we explore the application of machine learning algorithms to suggest suitable parameter values. We propose a methodology to use k-nearest neighbours and artificial neural network algorithms to predict suitable parameter values based on instance features. Here, we evaluate our proposal on the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) using its state-of-the-art algorithm, Hybrid Genetic Search (HGS). Additionally, we use the well-known tuning algorithm ParamILS to obtain suitable parameter configurations for HGS. We use a well-known instance set that considers between 200 and 1000 clients. Three sets of features based on geographical distribution, time windows, and client clustering are obtained. An in-depth exploratory analysis of the clustering features is also presented. The results are promising, demonstrating that the proposed method can successfully predict suitable parameter configurations for unseen instances and suggest configurations that perform better than baseline configurations. Furthermore, we present an explainability analysis to detect which features are more relevant for the prediction of suitable parameter values. Full article

14 pages, 3001 KiB

Open AccessArticle

Effect of Vertical Ground Reaction Force Biofeedback on Knee and Hip Neuromechanical Characteristics During Walking in Older Adults

by Forouzan Foroughi, Soroosh Sadeh and Hao-Yuan Hsiao

Appl. Sci. 2025, 15(6), 2945; https://doi.org/10.3390/app15062945 (registering DOI) - 9 Mar 2025

This study aimed to assess the effect of real-time vertical ground reaction force (VGRF) biofeedback on sagittal plane hip and knee joint biomechanics and extensor muscle activities in older adults. Fifteen healthy older adults (71 ± 5.8 years) walked on a treadmill while [...] Read more.

This study aimed to assess the effect of real-time vertical ground reaction force (VGRF) biofeedback on sagittal plane hip and knee joint biomechanics and extensor muscle activities in older adults. Fifteen healthy older adults (71 ± 5.8 years) walked on a treadmill while instructed to increase their first peak of VGRF via biofeedback. Whole-body kinetic and kinematic data and electromyography data for the vastus lateralis and gluteus maximus muscles were recorded. A one-way repeated measure ANOVA followed by post hoc analysis was conducted. Results showed increases in peak VGRF (20.95%), knee extension torque (73.7%), knee flexion angle (53.8%), and vastus lateralis muscle activity (72.1%) during the loading response, with percentage changes calculated as the mean of acquisition and recall trials relative to baseline walking. In contrast, no significant effect on peak hip extension torque and hip flexion angle over time was observed. These findings suggest that biofeedback can induce greater vertical support forces production with increased knee extension torque and extensor muscle activity. In addition, older adults adopted higher vertical support without increasing hip joint torque, potentially aiding in mitigating age-related distal-to-proximal joint torque redistribution. These findings suggest that VGRF biofeedback could potentially be an effective intervention to enhance knee extensor activation and mobility in older adults without increasing hip load. Full article

(This article belongs to the Special Issue Advances in the Biomechanics of Sports)

35 pages, 1085 KiB

Open AccessArticle

Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario

by Jayanta Datta, Ali Dehghan Firoozabadi, David Zabala-Blanco and Francisco R. Castillo-Soria

Appl. Sci. 2025, 15(6), 2944; https://doi.org/10.3390/app15062944 (registering DOI) - 8 Mar 2025

In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer [...] Read more.

In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. The input dataset for this neural architecture is constructed by applying multi-source tracking using multi-sensor generalized labeled multi-Bernoulli (MS-GLMB) filtering, which belongs to the labeled RFS framework, to obtain estimations of the sources’ positions and the associated labels (corresponding to each source) at each time frame with high accuracy under the effect of undesirable factors like reverberation and background noise. The tracked sources’ positions and associated labels help to correctly discriminate the target source from the interferers across all time frames and generate time–frequency (T-F) masks corresponding to the target source from the output of a time-varying, minimum variance distortionless response (MVDR) beamformer. These T-F masks constitute the target label set used to train the proposed deep neural architecture to perform target speech enhancement. The exploitation of MS-GLMB filtering and a time-varying MVDR beamformer help in providing the spatial information of the sources, in addition to the spectral information, within the neural speech enhancement framework during the training phase. Moreover, the application of the GAN framework takes advantage of adversarial optimization as an alternative to maximum likelihood (ML)-based frameworks, which further boosts the performance of target speech enhancement under reverberant conditions. The computer simulations demonstrate that the proposed approach leads to better target speech enhancement performance compared with existing state-of-the-art DL-based methodologies which do not incorporate the labeled RFS-based approach, something which is evident from the 75% ESTOI and PESQ of 2.70 achieved by the proposed approach as compared with the 46.74% ESTOI and PESQ of 1.84 achieved by Mask-MVDR with self-attention mechanism at a reverberation time (RT60) of 550 ms. Full article

(This article belongs to the Special Issue Feature Paper Collection in the Section ‘Electrical, Electronics and Communications Engineering’)

27 pages, 12878 KiB

Open AccessArticle

A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering

by Mohamad Shatnawi and Péter Földesi

Appl. Sci. 2025, 15(6), 2943; https://doi.org/10.3390/app15062943 (registering DOI) - 8 Mar 2025

Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties [...] Read more.

Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management. Full article

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Figure 1
<p>Fragments of an internally corroded pipe, illustrating metal loss and wall thinning caused by corrosion. These images are sourced from the research conducted by Beben and Steliga [<a href="#B9-applsci-15-02943" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Illustration of affine transformation on a two-dimensional plane, demonstrating how translation, scaling, and rotation facilitate correspondence between moving and reference sets.</p> Full article ">Figure 3
<p>Pipeline segmentation as proposed by Dann and Dann [<a href="#B13-applsci-15-02943" class="html-bibr">13</a>].</p> Full article ">Figure 4
<p>Pipeline unrolling and moving set double unrolling as proposed by Dann and Dann [<a href="#B13-applsci-15-02943" class="html-bibr">13</a>]—example problem.</p> Full article ">Figure 5
<p>Identification of mixed nearest neighbors using Voronoi tessellations as proposed by Amaya-Gómez et al. [<a href="#B10-applsci-15-02943" class="html-bibr">10</a>] using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.11 m—example problem.</p> Full article ">Figure 6
<p>Two-dimensional presentation of features (length and width), illustrating how interaction between adjacent defects and corrosion variable growth challenge feature matching and influence defect positioning across inspections—example problem.</p> Full article ">Figure 7
<p>Illustration of the matching problems the proposed framework aims to solve, demonstrating how clustering should facilitate isolated correspondence matching, merging defect matching, and localized transformation problems across ILIs.</p> Full article ">Figure 8
<p>Illustration of the proposed model’s workflow and extensibility, highlighting its parameters, data clustering using DENC and DBSCAN, cluster classification into categories, distance-based filtering, point matching using the Voronoi model, and the process of identifying matching and outlier features.</p> Full article ">Figure 9
<p>Establishing adjacency relationships in DENC based on boundaries defined by the directional proximity thresholds <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.300 m and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.150 m—example problem.</p> Full article ">Figure 10
<p>Graphical representation of the directed edges and binary adjacency matrix in DENC using <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.300 m and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.150 m—example problem.</p> Full article ">Figure 11
<p>Clusters (represented by distinct colors) and outliers obtained using DENC with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.300 m and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.150 m—example problem.</p> Full article ">Figure 12
<p>Outlier and cluster classification, illustrating the four density-based categories: (1) one-to-one, (2) one-to-many, (3) many-to-one, and many-to-many—example problem.</p> Full article ">Figure 13
<p>Feature matching results obtained by the proposed model using <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.300 m, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.150 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, α = 0.010, and τ = 0.001—example problem.</p> Full article ">Figure 14
<p>Feature matching results obtained by the Voronoi model [<a href="#B10-applsci-15-02943" class="html-bibr">10</a>] using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> = 0.020, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—example problem.</p> Full article ">Figure 15
<p>Illustration of the pipeline inspection setup from the manned wellhead platform to the unmanned wellhead platform.</p> Full article ">Figure 16
<p>Two-dimensional presentation (length and width) of all features across the six pipeline segments, S1 to S6—case study.</p> Full article ">Figure 17
<p>Sensitivity of the Voronoi model [<a href="#B7-applsci-15-02943" class="html-bibr">7</a>] to outlier proportion parameter <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 18
<p>Sensitivity of the Voronoi model [<a href="#B7-applsci-15-02943" class="html-bibr">7</a>] to defect’s position uncertainty threshold <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> = 0.080 and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 19
<p>Sensitivity of the proposed model to DENC’s directional proximity thresholds <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> = 0.080, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 20
<p>Sensitivity of the proposed model to outlier proportion parameter <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.220 m, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 21
<p>Sensitivity of the proposed model to the merging distance threshold <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.220 m, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> = 0.043 m, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 22
<p>Sensitivity of the proposed DBSCAN-based alternative model to the proximity threshold <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> = 0.080, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 23
<p>Sensitivity of the proposed DBSCAN-based alternative model to outlier proportion parameter <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 24
<p>Sensitivity of the proposed DBSCAN-based alternative model to the merging distance threshold <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">δ</mi> </mrow> </semantics></math> = 0.110 m, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </semantics></math> = 0.055, and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">τ</mi> </mrow> </semantics></math> = 0.001—case study.</p> Full article ">Figure 25
<p>Feature clustering (represented by distinct colors) using DBSCAN (top) and DENC (bottom) using <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> </semantics></math> = 0.250 m, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> = 0.220 m, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">ε</mi> </mrow> <mrow> <mi mathvariant="normal">y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.110 m—case study for segment S5.</p> Full article ">

28 pages, 477 KiB

Open AccessReview

Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation

by Babin Manandhar, Kayode Dunkel Vance, Danda B. Rawat and Nadir Yilmaz

Appl. Sci. 2025, 15(6), 2942; https://doi.org/10.3390/app15062942 (registering DOI) - 8 Mar 2025

Public transportation systems face numerous challenges like traffic congestion, inconsistent schedules, and variable passenger demand. These issues lead to delays, overcrowding, and reduced patron satisfaction. Digital twin (DT) technology is a promising innovation for improving public transportation systems by offering real-time virtual representations [...] Read more.

Public transportation systems face numerous challenges like traffic congestion, inconsistent schedules, and variable passenger demand. These issues lead to delays, overcrowding, and reduced patron satisfaction. Digital twin (DT) technology is a promising innovation for improving public transportation systems by offering real-time virtual representations of physical systems. By integrating real-time data from various sources, digital twins can enable predictive analytics, optimize operations, and improve the overall performance of public transportation networks. This work explores the potential of digital twins to optimize operational efficiency, enhance passenger experiences, and support sustainable urban mobility. A comprehensive review of the existing literature was conducted by analyzing case studies, theoretical models, and practical implementations to assess the effectiveness of DTs in transit systems. While the benefits of DTs are significant, their successful implementation in bus transportation systems is impeded by several challenges like scalability limitations, interoperability issues, and technical complexities involving data integration and IT infrastructure. This paper discusses ways to overcome these challenges, which include using modular designs, microservices, blockchain for security, and standardized communication for better integration. It emphasizes the importance of collaboration in research and practice to effectively apply digital twin technology to public transit systems. Full article

13 pages, 963 KiB

Open AccessArticle

Responsiveness to the Context: Information–Task–Situation Decisional Strategies and Electrophysiological Correlates

by Angelica Daffinà, Carlotta Acconito and Michela Balconi

Appl. Sci. 2025, 15(6), 2941; https://doi.org/10.3390/app15062941 (registering DOI) - 8 Mar 2025

Decision-making, defined as a cognitive process involving the selection of a course of action among several alternatives, is pivotal in personal and professional life and is founded on responsiveness to the context of decisional strategies—in terms of pieces of contextual features collected, evaluated, [...] Read more.

Decision-making, defined as a cognitive process involving the selection of a course of action among several alternatives, is pivotal in personal and professional life and is founded on responsiveness to the context of decisional strategies—in terms of pieces of contextual features collected, evaluated, and integrated. This study explored the behavioral and electrophysiological (EEG) correlates of individual tendencies to rely on three distinct decisional strategies: Information (I-ds), Situation (S-ds), or Task (T-ds). A total of 51 individuals performed a decision-making task that required participants to face real-life decision-making situations, during which an unexpected event prompted them to appraise the situation and rely on different sources of contextual features to make the best decision and manage the problem. The behavioral data and EEG frequency bands (delta, theta, alpha, beta, and gamma) were collected during the decision-making task. The results evidenced a general predisposition to adopt a T-ds. In addition, EEG findings reported a higher increase in theta band power in the right frontal area (AF8) compared to the left temporoparietal site (TP9). Moreover, for the gamma band, higher activity was found in the T-ds compared to the I-ds in AF8. Overall, responsiveness to the context was closely linked to the assignment’s requirements. Additionally, adopting a T-ds requires high levels of multilevel attention control systems and a significant workload on human performance. Nevertheless, the T-ds remain the most employed type of responsiveness to the context approach, when compared to situational and contextual aspects. Full article

(This article belongs to the Special Issue Decision-Making and Decision Support Systems: Methods and Applications)

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37 pages, 13572 KiB

Open AccessArticle

Integrating Biological Domain Knowledge with Machine Learning for Identifying Colorectal-Cancer-Associated Microbial Enzymes in Metagenomic Data

by Burcu Bakir-Gungor, Nur Sebnem Ersoz and Malik Yousef

Appl. Sci. 2025, 15(6), 2940; https://doi.org/10.3390/app15062940 (registering DOI) - 8 Mar 2025

Advances in metagenomics have revolutionized our ability to elucidate links between the microbiome and human diseases. Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, has been associated with dysbiosis of the gut microbiome. This study aims to develop a method for [...] Read more.

Advances in metagenomics have revolutionized our ability to elucidate links between the microbiome and human diseases. Colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide, has been associated with dysbiosis of the gut microbiome. This study aims to develop a method for identifying CRC-associated microbial enzymes by incorporating biological domain knowledge into the feature selection process. Conventional feature selection techniques often evaluate features individually and fail to leverage biological knowledge during metagenomic data analysis. To address this gap, we propose the enzyme commission (EC)-nomenclature-based Grouping-Scoring-Modeling (G-S-M) method, which integrates biological domain knowledge into feature grouping and selection. The proposed method was tested on a CRC-associated metagenomic dataset collected from eight different countries. Community-level relative abundance values of enzymes were considered as features and grouped based on their EC categories to provide biologically informed groupings. Our findings in randomized 10-fold cross-validation experiments imply that glycosidases, CoA-transferases, hydro-lyases, oligo-1,6-glucosidase, crotonobetainyl-CoA hydratase, and citrate CoA-transferase enzymes can be associated with CRC development as part of different molecular pathways. These enzymes are mostly synthesized by Eschericia coli, Salmonella enterica, Klebsiella pneumoniae, Staphylococcus aureus, Streptococcus pneumoniae, and Clostridioides dificile. Comparative evaluation experiments showed that the proposed model consistently outperforms traditional feature selection methods paired with various classifiers. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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<p>Enzyme commission (EC) nomenclature involves seven main enzyme groups with many subclasses, each related to a specific enzyme activity.</p> Full article ">Figure 2
<p>The proposed EC-nomenclature-based G-S-M workflow for analyzing enzyme abundance values obtained from disease-associated metagenomics relative enzyme abundance datasets.</p> Full article ">Figure 3
<p>Detailed description of the grouping, scoring, and modeling components in the proposed EC-nomenclature-based G-S-M approach.</p> Full article ">Figure 4
<p>(<b>A</b>) Top 10 important enzyme groups and (<b>B</b>) top scored enzyme in each enzyme group identified by the EC-nomenclature-based G-S-M method applied to the CRC-associated metagenomic datasets, including relative abundance values of the enzymes. The −log 10 <span class="html-italic">p</span>-values indicate the significance values assigned by the robust rank aggregation method. Each color represents the enzyme commission (EC) activity.</p> Full article ">Figure 5
<p>Performance metrics of the EC-nomenclature-based G-S-M method when applied to population-specific CRC-associated metagenomic dataset, including relative abundance values of the enzymes. (<b>A</b>) AUC values and (<b>B</b>) # of enzymes (features) selected for population-specific datasets.</p> Full article ">Figure 6
<p>AUC values of traditional feature selection methods, including XGB, SKB, IG, MRMR, CMIM, and FCBF, when coupled with different classifiers, including Adaboost, DT, LogitBoost, RF, SVM_opt, Stack_Logitboost_Kmenas, Stack_SVM_Kmeans, and XGBoost, compared with the AUC metrics of the EC-nomenclature-based G-S-M approach when coupled with RF, XGBoost, and DT classifiers and tested on the CRC-associated metagenomic dataset.</p> Full article ">Figure 7
<p>Top 10 most important enzymes detected by (<b>A</b>) XGB and (<b>B</b>) SKB feature selection methods. The colors represent different enzyme functions, as defined by the enzyme commission.</p> Full article ">Figure 8
<p>Top 3 scoring enzyme groups and top 10 scoring enzymes included in these groups, which are identified by the EC-nomenclature-based G-S-M model when applied to the CRC-associated metagenomic dataset. Each color represents the related enzyme commission (EC) activity.</p> Full article ">Figure 9
<p>GO MF terms for the top 3 scoring enzymes that belong to the top 3 scoring enzyme groups, which were identified by the EC-nomenclature-based G-S-M model applied to the CRC-associated metagenomic dataset. GO hierarchy was obtained from the Quick-GO annotations provided by NCBI.</p> Full article ">Figure 10
<p>Top 100 scoring enzymes obtained by the EC-nomenclature-based G-S-M method applied to the CRC-associated metagenomic dataset and their related KEGG pathways.</p> Full article ">Figure 11
<p>The enzymes in the glycosidases (EC: 3.2.1) group and their metabolic pathways, i.e., (<b>A</b>) starch and sucrose metabolism, (<b>B</b>) sphingolipid metabolism, (<b>C</b>) galactose metabolism, (<b>D</b>) <span class="html-italic">N</span>-glycan biosynthesis, and (<b>E</b>) glucuronate interconversions. All pathway information is excerpted from the KEGG database.</p> Full article ">Figure 12
<p>Metabolic pathways of top 3 scoring enzyme groups found using the EC-nomenclature-based G-S-M approach applied to the CRC-associated metagenomic dataset, including relative abundance values of the enzymes. EC: 4.2.1 and EC: 2.8.3 groups of enzymes perform activities in (<b>A</b>) styrene degradation (KEGG database), in (<b>B</b>) butanoate metabolism (KEGG database), in (<b>C</b>) the citric acid cycle (BRENDA database), and in (<b>D</b>) carnitine metabolism (BRENDA database). Each color represents the related enzyme commission (EC) activity.</p> Full article ">Figure 13
<p>Network of top 100 enzymes that have been identified by the EC-nomenclature-based G-S-M on the CRC-associated metagenomic data and associated organisms. A total of 268 species that synthesize the top scoring 100 enzymes are presented. Node size is associated with betweenness centrality.</p> Full article ">Figure 14
<p>Network of top 16 scoring enzymes that were identified either by the XGB or SKB feature selection methods as part of their top 10 scoring lists and their associated organisms. A total of 85 species that synthesize the top scoring enzymes are presented. Node size is associated with betweenness centrality.</p> Full article ">Figure 15
<p>Correlations among the top 10 enzyme groups that were selected by the EC-nomenclature-based G-S-M for different CRC-associated metagenomic datasets, including the relative abundance values of the enzymes obtained from the samples belonging to different populations. The Jaccard index is used to calculate the correlation between the identified EC groups among two populations.</p> Full article ">Figure 16
<p>Commonalities among the top 10 enzyme groups that were selected by the EC-nomenclature-based G-S-M for different CRC-associated metagenomic datasets, including the relative abundance values of the enzymes obtained from the samples belonging to different populations.</p> Full article ">Figure 17
<p>Performance metrics of different feature selection methods (XGB, SKB, IG, MRMR, FCBF, and CMIM) coupled with the RF classifier, RCE with the SVM classifier, and the EC-nomenclature-based G-S-M approach with RF when tested on the CRC-associated metagenomic dataset, including relative abundance values of the enzymes.</p> Full article ">Figure 18
<p>Correlations among the top 100 features that are selected by different feature selection algorithms and the EC-nomenclature-based G-S-M approach when tested on the CRC-associated metagenomic dataset, including relative abundance values of the enzymes.</p> Full article ">Figure 19
<p>Commonalities among the top 100 features that were selected by different feature selection algorithms and the EC-nomenclature-based G-S-M approach when tested on CRC-associated metagenomic dataset, including relative abundance values of the enzymes.</p> Full article ">

25 pages, 6330 KiB

Open AccessArticle

Post-Filtering of Noisy Images Compressed by HEIF

by Sergii Kryvenko, Volodymyr Rebrov, Vladimir Lukin, Vladimir Golovko, Anatoliy Sachenko, Andrii Shelestov and Benoit Vozel

Appl. Sci. 2025, 15(6), 2939; https://doi.org/10.3390/app15062939 (registering DOI) - 8 Mar 2025

Modern imaging systems produce a great volume of image data. In many practical situations, it is necessary to compress them for faster transferring or more efficient storage. Then, a compression has to be applied. If images are noisy, lossless compression is almost useless, [...] Read more.

Modern imaging systems produce a great volume of image data. In many practical situations, it is necessary to compress them for faster transferring or more efficient storage. Then, a compression has to be applied. If images are noisy, lossless compression is almost useless, and lossy compression is characterized by a specific noise filtering effect that depends on the image, noise, and coder properties. Here, we considered a modern HEIF coder applied to grayscale (component) images of different complexity corrupted by additive white Gaussian noise. It has recently been shown that an optimal operation point (OOP) might exist in this case. Note that the OOP is a value of quality factor where the compressed image quality (according to a used quality metric) is the closest to the corresponding noise-free image. The lossy compression of noisy images leads to both noise reduction and distortions introduced into the information component, thus, a compromise should be found between the compressed image quality and compression ratio attained. The OOP is one possible compromise, if it exists, for a given noisy image. However, it has also recently been demonstrated that the compressed image quality can be significantly improved if post-filtering is applied under the condition that the quality factor is slightly larger than the one corresponding to the OOP. Therefore, we considered the efficiency of post-filtering where a block-matching 3-dimensional (BM3D) filter was applied. It was shown that the positive effect of such post-filtering could reach a few dB in terms of the PSNR and PSNR-HVS-M metrics. The largest benefits took place for simple structure images and a high intensity of noise. It was also demonstrated that the filter parameters have to be adapted to the properties of residual noise that become more non-Gaussian if the compression ratio increases. Practical recommendations on the use of compression parameters and post-filtering are given. Full article

(This article belongs to the Special Issue Trends and Prospects in Computer Vision and Pattern Recognition Technology)

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Figure 1

Figure 1
<p>Test single-channel (grayscale) images Frisco (<b>a</b>), Fr03 (<b>b</b>), and Diego (<b>c</b>), all 512 × 512 pixels.</p> Full article ">Figure 2
<p>Dependences of MSE<sub>tc</sub> (calculated between the true and compressed images) for images Diego (<b>a</b>,<b>c</b>) and Frisco (<b>b</b>,<b>d</b>) for <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>σ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>25</mn> <mo> </mo> </mrow> </semantics></math>(<b>a</b>,<b>b</b>) and <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>σ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>=</mo> <mn>196</mn> </mrow> </semantics></math> (<b>c</b>,<b>d</b>) for five coders.</p> Full article ">Figure 3
<p>Visualized <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>I</mi> <mi>m</mi> </mrow> </msub> <mo>,</mo> <mo> </mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <msub> <mrow> <mi>J</mi> </mrow> <mrow> <mi>I</mi> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> for the test images Frisco (<b>a</b>) and Diego (<b>b</b>) corrupted by AWGN with σ<sup>2</sup> = 100 compressed by HEIF with QF = 50.</p> Full article ">Figure 4
<p>Dependences of the output metrics on QF and β for two test images and three metrics, σ<sup>2</sup> = 50. Plots of PSNR (<b>a</b>,<b>b</b>), PSNR-HVS (<b>c</b>,<b>d</b>) and FSIM (<b>e</b>,<b>f</b>) for the test images Frisco (<b>a</b>,<b>c</b>,<b>e</b>) and Fr03 (<b>b</b>,<b>d</b>,<b>f</b>).</p> Full article ">Figure 4 Cont.
<p>Dependences of the output metrics on QF and β for two test images and three metrics, σ<sup>2</sup> = 50. Plots of PSNR (<b>a</b>,<b>b</b>), PSNR-HVS (<b>c</b>,<b>d</b>) and FSIM (<b>e</b>,<b>f</b>) for the test images Frisco (<b>a</b>,<b>c</b>,<b>e</b>) and Fr03 (<b>b</b>,<b>d</b>,<b>f</b>).</p> Full article ">Figure 5
<p>Dependences of the output metrics on QF and β for two test images and three metrics, σ<sup>2</sup> = 100. Plots of PSNR (<b>a</b>,<b>b</b>), PSNR-HVS (<b>c</b>,<b>d</b>) and FSIM (<b>e</b>,<b>f</b>) for the test images Frisco (<b>a</b>,<b>c</b>,<b>e</b>) and Fr03 (<b>b</b>,<b>d</b>,<b>f</b>).</p> Full article ">Figure 5 Cont.
<p>Dependences of the output metrics on QF and β for two test images and three metrics, σ<sup>2</sup> = 100. Plots of PSNR (<b>a</b>,<b>b</b>), PSNR-HVS (<b>c</b>,<b>d</b>) and FSIM (<b>e</b>,<b>f</b>) for the test images Frisco (<b>a</b>,<b>c</b>,<b>e</b>) and Fr03 (<b>b</b>,<b>d</b>,<b>f</b>).</p> Full article ">Figure 6
<p>Dependences of the output metrics on QF and β for two test images and three metrics, σ<sup>2</sup> = 200. Plots of PSNR (<b>a</b>,<b>b</b>), PSNR-HVS (<b>c</b>,<b>d</b>) and FSIM (<b>e</b>,<b>f</b>) for the test images Frisco (<b>a</b>,<b>c</b>,<b>e</b>) and Fr03 (<b>b</b>,<b>d</b>,<b>f</b>).</p> Full article ">Figure 6 Cont.
<p>Dependences of the output metrics on QF and β for two test images and three metrics, σ<sup>2</sup> = 200. Plots of PSNR (<b>a</b>,<b>b</b>), PSNR-HVS (<b>c</b>,<b>d</b>) and FSIM (<b>e</b>,<b>f</b>) for the test images Frisco (<b>a</b>,<b>c</b>,<b>e</b>) and Fr03 (<b>b</b>,<b>d</b>,<b>f</b>).</p> Full article ">Figure 7
<p>Dependences of PSNR<sub>p pf</sub> (CR) and ∆PSNR<sub>p tc</sub> (CR) (<b>a</b>–<b>c</b>) and ∆PSNR-HVS-M<sub>p pf</sub> (CR) and ∆PSNR-HVS-M<sub>p tc</sub> (CR) (<b>d</b>–<b>e</b>) for the test images Fr03 (<b>a</b>,<b>c</b>,<b>d</b>,<b>f</b>) and Frisco (<b>b</b>,<b>e</b>) for σ<sup>2</sup> = 100 (<b>a</b>,<b>b</b>,<b>d</b>,<b>e</b>) and σ<sup>2</sup> = 200 (<b>c</b>,<b>f</b>).</p> Full article ">Figure 8
<p>Test images Med5 (<b>a</b>), Med7 (<b>b</b>), and Med8 (<b>c</b>) used in the additional experiments.</p> Full article ">Figure 9
<p>Noisy image Frisco (<b>a</b>), the results of its compression in the OOP by HEIF (<b>b</b>), compressed with QF = 36 and post-filtered with the optimal β after decompression (<b>c</b>), noisy image Diego (<b>d</b>), the results of its compression in the OOP by HEIF (<b>e</b>), compressed with QF = 36 and post-filtered with the optimal β after decompression (<b>f</b>).</p> Full article ">

30 pages, 22071 KiB

Open AccessArticle

Analysis of Optical Errors in Joint Fabry–Pérot Interferometer–Fourier-Transform Imaging Spectroscopy Interferometric Super-Resolution Systems

by Yu Zhang, Qunbo Lv, Jianwei Wang, Yinhui Tang, Jia Si, Xinwen Chen and Yangyang Liu

Appl. Sci. 2025, 15(6), 2938; https://doi.org/10.3390/app15062938 (registering DOI) - 8 Mar 2025

Fourier-transform imaging spectroscopy (FTIS) faces inherent limitations in spectral resolution due to the maximum optical path difference (OPD) achievable by its interferometer. To overcome this constraint, we propose a novel spectral super-resolution technology integrating a Fabry–Pérot interferometer (FPI) with FTIS, termed multi-component joint [...] Read more.

Fourier-transform imaging spectroscopy (FTIS) faces inherent limitations in spectral resolution due to the maximum optical path difference (OPD) achievable by its interferometer. To overcome this constraint, we propose a novel spectral super-resolution technology integrating a Fabry–Pérot interferometer (FPI) with FTIS, termed multi-component joint interferometric hyperspectral imaging (MJI-HI). This method leverages the FPI to periodically modulate the target spectrum, enabling FTIS to capture a modulated interferogram. By encoding high-frequency spectral interference information into low-frequency interference regions through FPI modulation, an advanced inversion algorithm is developed to reconstruct the encoded high-frequency components, thereby achieving spectral super-resolution. This study analyzes the impact of primary optical errors and tolerance thresholds in the FPI and FTIS on the interferograms and spectral fidelity of MJI-HI, along with proposing algorithmic improvements. Notably, certain errors in the FTIS and FPI exhibit mutual interference. The theoretical framework for error analysis is validated and discussed through numerical simulations, providing critical theoretical support for subsequent instrument development and laying a foundation for advancing novel spectral super-resolution technologies. Full article

(This article belongs to the Special Issue Spectral Detection: Technologies and Applications—2nd Edition)

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Figure 1

Figure 1
<p>Schematic diagram of FPI principle [<a href="#B26-applsci-15-02938" class="html-bibr">26</a>].</p> Full article ">Figure 2
<p>Diagram of MJI-HI system structure.</p> Full article ">Figure 3
<p>Schematic diagram of combined interference principle: (<b>a</b>) object spectrum <span class="html-italic">B</span>(<span class="html-italic">ν</span>) and (<b>b</b>) its interferogram <span class="html-italic">I</span><sub>0</sub>(Δ) and (<b>c</b>) FPI-modulated spectrum <span class="html-italic">B</span>(<span class="html-italic">ν</span>)<span class="html-italic">T<sub>FPI</sub></span>(<span class="html-italic">ν</span>) and (<b>d</b>) its interferogram <span class="html-italic">I</span><sub>2</sub>(Δ).</p> Full article ">Figure 4
<p>(<b>a</b>) The schematic diagram of the FPI non-parallelism error in the direction perpendicular to the optical axis and a locally magnified schematic diagram of the flatness error. (<b>b</b>) The schematic diagram of the distribution of the non-parallelism error along the optical axis.</p> Full article ">Figure 5
<p>(<b>a</b>) The comparison and local magnification of the FPI transmittance spectra under ideal conditions (<span class="html-italic">T<sub>FPI</sub></span>) and with parallelism errors (<span class="html-italic">T<sub>FPI</sub></span><sub>-</sub><span class="html-italic"><sub>para</sub></span>); <span class="html-italic">T<sub>FPI</sub></span> and <span class="html-italic">T<sub>FPI</sub></span><sub>-</sub><span class="html-italic"><sub>para</sub></span> are decomposed into (<b>b</b>) the DC component, (<b>c</b>) fundamental frequency component, (<b>d</b>) second harmonic component, and (<b>e</b>) third harmonic component, with corresponding comparisons and local magnifications. Here, <span class="html-italic">z</span> is defined as 2<span class="html-italic">πν</span>2<span class="html-italic">r</span><sub>0</sub><span class="html-italic">β<sub>FPI</sub></span> in Equation (12), and the curves ±2<span class="html-italic">a<sub>m</sub>J</span><sub>1</sub>(<span class="html-italic">mz</span>)/(<span class="html-italic">mz</span>) can be regarded as the envelopes of the fundamental frequency and higher harmonic components of <span class="html-italic">T<sub>FPI</sub></span><sub>-</sub><span class="html-italic"><sub>para</sub></span>.</p> Full article ">Figure 6
<p>(<b>a</b>) The comparison and local magnification of the FPI transmittance spectra under ideal conditions (<span class="html-italic">T<sub>FPI</sub></span>) and with parallelism errors (<span class="html-italic">T<sub>FPI</sub></span><sub>-<span class="html-italic">flat</span></sub>); <span class="html-italic">T<sub>FPI</sub></span> and <span class="html-italic">T<sub>FPI</sub></span><sub>-<span class="html-italic">flat</span></sub> are decomposed into (<b>b</b>) the DC component, (<b>c</b>) fundamental frequency component, (<b>d</b>) second harmonic component, and (<b>e</b>) third harmonic component, with corresponding comparisons and local magnifications. Here, the curves ±2<span class="html-italic">a<sub>m</sub></span>exp[−(<span class="html-italic">mπσ</span>Δ<span class="html-italic"><sub>D</sub>ν</span>)<sup>2</sup>] can be regarded as the envelopes of the fundamental frequency and higher harmonic components of <span class="html-italic">T<sub>FPI</sub></span><sub>-<span class="html-italic">flat</span></sub>.</p> Full article ">Figure 7
<p>The curves of <span class="html-italic">a′</span><sub>0</sub> to <span class="html-italic">a′</span><sub>3</sub> as functions of <span class="html-italic">R</span><sub>0</sub>. The zero points of <span class="html-italic">a′<sub>m</sub></span> with respect to <span class="html-italic">R</span><sub>0</sub> are indicated by arrows, representing that at these points, <span class="html-italic">a<sub>m</sub></span> is less affected by changes in <span class="html-italic">R</span><sub>0</sub>.</p> Full article ">Figure 8
<p>(<b>a</b>) The comparison of the FPI transmittance spectrum under ideal conditions (TFPI) and under reflectance variation errors (<span class="html-italic">T<sub>FPI-R</sub></span>); TFPI and <span class="html-italic">T<sub>FPI-R</sub></span> decomposed into (<b>b</b>) the DC component, (<b>c</b>) the fundamental frequency component, (<b>d</b>) the second harmonic component, and (<b>e</b>) the third harmonic component for comparison.</p> Full article ">Figure 9
<p>Beams with different divergence angles produce different OPDs in the FTIS and FPI. Here, <span class="html-italic">θ</span><sub>0</sub> is the maximum divergence half-angle, and ψ is the azimuth angle. The red arrows indicate the phase differences caused by the central aperture light in the FTIS and FPI, respectively, and the black arrows indicate the phase differences caused by the edge aperture light in the FTIS and FPI, respectively.</p> Full article ">Figure 10
<p>Assuming a divergence angle of 1°, (<b>a</b>) FTIS with an OPD of 1 mm, (<b>b</b>) FTIS with an OPD of 2 mm, and (<b>c</b>) FTIS with an OPD of 3 mm: the overall comparison and local magnification of the FTIS double-beam interferometric spectrum.</p> Full article ">Figure 11
<p>(<b>a</b>) In FTIS, the mirror tilt error causes the wavefront (blue surface) to deviate from the ideal wavefront (yellow surface), tilting in the <span class="html-italic">φ<sub>FTIS</sub></span> direction with a tilt angle of 2<span class="html-italic">β<sub>FTIS</sub></span>; in FPI, the non-parallelism error causes the wavefront (blue surface) to deviate from the ideal wavefront (yellow surface), tilting in the <span class="html-italic">φ<sub>FPI</sub></span> direction with a tilt angle of 2<span class="html-italic">β<sub>FPI</sub></span>, where the Z-axis represents the optical axis. (<b>b</b>) The relative tilt direction along the optical axis between the FTIS mirror tilt error and the FPI non-parallelism error.</p> Full article ">Figure 12
<p>(<b>a</b>) The schematic diagram of the geometric relationship in polar coordinates between the vector (<span class="html-italic">β<sub>m</sub></span>,<span class="html-italic">φ<sub>m</sub></span>) and the vectors <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>x</mi> </mrow> <mo>→</mo> </mover> </mrow> <mrow> <mi>F</mi> <mi>T</mi> <mi>I</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>x</mi> </mrow> <mo>→</mo> </mover> </mrow> <mrow> <mi>F</mi> <mi>P</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) the schematic diagram of the geometric relationship between the vector (<span class="html-italic">β<sub>−m</sub></span>,<span class="html-italic">φ<sub>−m</sub></span>) and the vectors <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>x</mi> </mrow> <mo>→</mo> </mover> </mrow> <mrow> <mi>F</mi> <mi>T</mi> <mi>I</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>x</mi> </mrow> <mo>→</mo> </mover> </mrow> <mrow> <mi>F</mi> <mi>P</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>(<b>a1</b>) Hitran data input spectrum and (<b>a2</b>) the error-free interferograms <span class="html-italic">I</span><sub>1</sub> and <span class="html-italic">I</span><sub>2</sub> obtained using MJI-HI; (<b>b1</b>) Gaussian function input spectrum and (<b>b2</b>) the error-free interferograms <span class="html-italic">I</span><sub>1</sub> and <span class="html-italic">I</span><sub>2</sub>; (<b>c1</b>) cosine function input spectrum and (<b>c2</b>) the error-free interferograms <span class="html-italic">I</span><sub>1</sub> and <span class="html-italic">I</span><sub>2</sub>; (<b>d1</b>) <span class="html-italic">B</span><sub>0</sub> = 1 input spectrum and (<b>d2</b>) the error-free interferograms <span class="html-italic">I</span><sub>1</sub> and <span class="html-italic">I</span><sub>2</sub>.</p> Full article ">Figure 14
<p>(<b>a1</b>–<b>a4</b>). Broadening of the displacement components of orders <span class="html-italic">m</span> = 0, 1, 2, and 3 caused by different non-parallelism; (<b>b1</b>–<b>b4</b>). broadening of the displacement components of orders <span class="html-italic">m</span> = 0, 1, 2, and 3 caused by different non-flatness.</p> Full article ">Figure 15
<p>(<b>a1</b>) Interferogram reconstruction result when the <span class="html-italic">FWHM<sub>D</sub></span> of <span class="html-italic">Defect</span><sub>1</sub> is λ<sub>min</sub>/4; (<b>a2</b>–<b>a4</b>) are its local enlargements; (<b>b1</b>) interferogram reconstruction result when the <span class="html-italic">FWHM<sub>D</sub></span> of <span class="html-italic">Defect</span><sub>1</sub> is λ<sub>min</sub>/2; (<b>b2</b>–<b>b4</b>) are its local enlargements; (<b>c1</b>) interferogram reconstruction result when the <span class="html-italic">FWHM<sub>D</sub></span> of <span class="html-italic">Defect</span><sub>1</sub> is λ<sub>min</sub>; (<b>c2</b>–<b>c4</b>) are its local enlargements. Here, <span class="html-italic">I</span><sub>0</sub> is the ideal interferogram, <span class="html-italic">I<sub>sup</sub></span> is the MJI-HI interferogram reconstruction result without using the improved method, and <span class="html-italic">I<sub>sup−imp</sub></span> is the MJI-HI interferogram reconstruction result using the improved method.</p> Full article ">Figure 16
<p>(<b>a1</b>) Interferogram reconstruction results when the <span class="html-italic">FWHM<sub>tilt</sub></span> of <span class="html-italic">g<sub>FTIS</sub></span> is λ<sub>min</sub>/4; (<b>a2</b>–<b>a5</b>) are its local enlargements; (<b>b1</b>) interferogram reconstruction results when the <span class="html-italic">FWHM<sub>tilt</sub></span> of <span class="html-italic">g<sub>FTIS</sub></span> is λ<sub>min</sub>/2; (<b>b2</b>–<b>b5</b>) are its local enlargements. Here, <span class="html-italic">I</span><sub>0</sub> represents the ideal interferogram, <span class="html-italic">I<sub>sup</sub></span> denotes the MJI-HI reconstructed interferogram under mirror tilt errors, and <span class="html-italic">I<sub>0-tilt</sub></span> is the interferogram obtained using FTIS with the same mirror tilt errors.</p> Full article ">Figure 17
<p>(<b>a</b>) When <span class="html-italic">B</span>(ν) = 1 is used as the input spectrum, the overall situation of the interferogram <span class="html-italic">I</span><sub>2</sub>; (<b>b–e</b>). When the tilt direction angle differences are 0°, 30°, 60°, and 90°, the response function convolution broadening of <span class="html-italic">I</span><sub>2</sub> occurs at ±<span class="html-italic">m</span>Δ<span class="html-italic"><sub>FPI</sub></span>; (<b>b1</b>–<b>b4</b>). When the tilt direction angle difference is <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub> =</span> 0°, the convolutional broadening of the response functions of the <span class="html-italic">m</span> = 0, ±1, ±2, ±3 order caused by the combined errors; (<b>c1</b>–<b>c4</b>). When <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub> =</span> 30°, the convolutional broadening of the response functions of the <span class="html-italic">m</span> = 0, ±1, ±2, ±3 order; (<b>d1</b>–<b>d4</b>). When <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub> =</span> 60°, the convolutional broadening of the response functions of the <span class="html-italic">m</span> = 0, ±1, ±2, ±3 order; (<b>e1</b>–<b>e4</b>). When <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub> =</span> 90°, the convolutional broadening of the response functions of the <span class="html-italic">m</span> = 0, ±1, ±2, ±3 order.</p> Full article ">Figure 18
<p>(<b>a1</b>) Interferogram reconstruction result under combined errors when <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub></span> = 0°, with (<b>a2</b>–<b>a4</b>) showing local magnifications; (<b>b1</b>) interferogram reconstruction result under combined errors when <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub></span> = 30°, with (<b>b2</b>–<b>b4</b>) showing local magnifications; (<b>c1</b>) interferogram reconstruction result under combined errors when <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub></span> = 60°, with (<b>c2</b>–<b>c4</b>) showing local magnifications; (<b>d1</b>) interferogram reconstruction result under combined errors when <span class="html-italic">φ<sub>FTIS</sub></span> − <span class="html-italic">φ<sub>FPI</sub></span> = 90°, with (<b>d2</b>–<b>d4</b>) showing local magnifications; here, <span class="html-italic">I</span><sub>0</sub> represents the ideal interferogram, and <span class="html-italic">I<sub>sup</sub></span> denotes the MJI-HI reconstructed interferogram under joint errors.</p> Full article ">Figure 19
<p>(<b>a</b>) When the input spectrum is a cosine function with a periodic frequency of <span class="html-italic">k</span><sub>0</sub>, the overall interferogram <span class="html-italic">I</span><sub>2</sub> is shown, indicating the specific positions of <span class="html-italic">I</span><sub>2</sub>(<span class="html-italic">m</span>Δ<span class="html-italic"><sub>FPI</sub></span>), <span class="html-italic">I</span><sub>2</sub>(<span class="html-italic">m</span>Δ<span class="html-italic"><sub>FPI</sub></span> − <span class="html-italic">k</span><sub>0</sub>), and <span class="html-italic">I</span><sub>2</sub>(<span class="html-italic">m</span>Δ<span class="html-italic"><sub>FPI</sub></span> + <span class="html-italic">k</span><sub>0</sub>) within <span class="html-italic">I</span><sub>2</sub>. For divergence angles of 0°, 0.5°, 1.0°, 1.5°, and 2.0°, the convolution broadening of <span class="html-italic">δ</span> functions of the <span class="html-italic">m</span> = 0, 1, 2, 3 frequency-shift components due to collimation errors is observed at (<b>b1</b>–<b>b4</b>). Δ = <span class="html-italic">m</span>Δ<span class="html-italic"><sub>FPI</sub></span>, at (<b>c1</b>–<b>c4</b>). Δ = <span class="html-italic">m</span>Δ<span class="html-italic"><sub>FP</sub></span> − <span class="html-italic">k</span><sub>0</sub>, at (<b>d1</b>–<b>d4</b>). Δ = <span class="html-italic">m</span>Δ<span class="html-italic"><sub>FPI</sub></span> + <span class="html-italic">k</span><sub>0</sub>.</p> Full article ">Figure 20
<p>(<b>a</b>) Super-resolution spectra obtained using MJI-HI with divergence angles of 0°, 1°, and 2°; (<b>b</b>) local magnification at wavenumber 1.5 × 10<sup>4</sup> cm<sup>−1</sup>. Here, <span class="html-italic">B</span><sub>0</sub> represents the input spectrum, and <span class="html-italic">B<sub>sup-</sub><sub>θ</sub></span> denotes the super-resolution spectra obtained under different divergence angles using MJI-HI.</p> Full article ">Figure 21
<p>(<b>a1</b>–<b>d1</b>) The FPI reflectance spectra generated by curves 1–4 under PVs of 0%, 2%, 6%, and 10%, respectively. (<b>a2</b>–<b>a5</b>) Correspond to curve 1, (<b>b2</b>–<b>b5</b>) to curve 2, (<b>c2</b>–<b>c5</b>) to curve 3, and (<b>d2</b>–<b>d5</b>) to curve 4, illustrating the changes in the response functions of <span class="html-italic">m</span> = 0, 1, 2, 3 frequency-shift components due to reflectance variations.</p> Full article ">Figure 22
<p>Using <span class="html-italic">B</span><sub>0</sub> = 1 as the input spectrum, the super-resolution spectral results obtained under reflectance variation (<b>a</b>) curve 1, (<b>b</b>) curve 2, (<b>c</b>) curve 3, and (<b>d</b>) curve 4 with PV = 2%, 6%, and 10% are shown. Here, <span class="html-italic">B<sub>sup</sub></span><sub>-PV</sub> represents the super-resolution spectra obtained using MJI-HI under different FPI reflectance error levels.</p> Full article ">

15 pages, 5492 KiB

Open AccessArticle

Classification of OCT Images of the Human Eye Using Mobile Devices

by Agnieszka Stankiewicz, Tomasz Marciniak, Nina Budna, Róża Chwałek and Marcin Dziedzic

Appl. Sci. 2025, 15(6), 2937; https://doi.org/10.3390/app15062937 (registering DOI) - 8 Mar 2025

The aim of this study was to develop a mobile application for Android devices dedicated to the classification of pathological changes in human eye optical coherence tomography (OCT) B-scans. The classification process is conducted using convolutional neural networks (CNNs). Six models were trained [...] Read more.

The aim of this study was to develop a mobile application for Android devices dedicated to the classification of pathological changes in human eye optical coherence tomography (OCT) B-scans. The classification process is conducted using convolutional neural networks (CNNs). Six models were trained during the study: a simple convolutional neural network with three convolutional layers, VGG16, InceptionV3, Xception, Joint Attention Network + MobileNetV2 and OpticNet-71. All of these models were converted to TensorFlow Lite format to implement them into a mobile application. For this purpose, three models with the best parameters were chosen, taking accuracy, precision, recall, F1-score and confusion matrix into consideration. The Android application designed for the classification of OCT images was developed using the Kotlin programming language within the Android Studio integrated development environment. With the application, classification can be performed on an image chosen from the user’s files or an image acquired using the photo-taking function. The results of the classification are displayed for three neural networks, along with the respective classification times for each neural network and the associated image undergoing the classification task. The mobile application has been tested using various smartphones. The testing phase included an evaluation of image classification times and score accuracy, considering factors such as image acquisition method, i.e., camera or gallery. Full article

(This article belongs to the Special Issue Machine Learning for Object Detection and Scene Description in Images and Videos)

16 pages, 1884 KiB

Open AccessArticle

Degradation and Ecotoxicity Mitigation of Perfluorooctane Sulfonate by Aeration-Assisted Cold Plasma

by Sengbin Oh, Joo-Youn Nam, Youngpyo Hong, Tae-Hun Lee, Jae-Cheol Lee and Hyun-Woo Kim

Appl. Sci. 2025, 15(6), 2936; https://doi.org/10.3390/app15062936 (registering DOI) - 8 Mar 2025

Various advanced oxidation processes have been used to degrade perfluorooctane sulfonate (PFOS), one of the persistent organic pollutants that dissolves in aquatic ecosystems, but these processes suffer from inherent limitations. This study proposes aeration-assisted cold plasma (CP) technology as an alternative. PFOS removal [...] Read more.

Various advanced oxidation processes have been used to degrade perfluorooctane sulfonate (PFOS), one of the persistent organic pollutants that dissolves in aquatic ecosystems, but these processes suffer from inherent limitations. This study proposes aeration-assisted cold plasma (CP) technology as an alternative. PFOS removal via CP treatment reached 62.5% after 1 h of exposure, with a degradation rate constant of 3.1 h⁻¹. The detection of sulfate (SO₄²⁻) in the solution provides evidence of effective PFOS degradation. The close agreement between the measured and estimated fluoride concentrations further confirms mass balance after degradation. Acute toxicity tests indicate that PFOS degradation may initially increase the acute toxicity, possibly due to the formation of degradation by-products. However, this increased toxicity can be mitigated through additional exposure to the reactive species generated by CP. Furthermore, investigations into the energy per order of CP and the quantification of hydroxyl radicals support its operational effectiveness. This study confirms that aeration-assisted CP has the potential to serve as a viable treatment option for mitigating the environmental threats posed by PFOS. Full article

(This article belongs to the Special Issue New Approaches to Water Treatment: Challenges and Trends)

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5 pages, 147 KiB

Open AccessEditorial

Internet of Things (IoT) Technologies in Cybersecurity: Challenges and Opportunities

by Grzegorz Kołaczek

Appl. Sci. 2025, 15(6), 2935; https://doi.org/10.3390/app15062935 (registering DOI) - 8 Mar 2025

The continuous development and increasing availability of Internet of Things (IoT)[...] Full article

(This article belongs to the Special Issue Internet of Things (IoT) Technologies in Cybersecurity: Challenges and Opportunities)

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