Search Results (4,982)

As global warming intensifies, carbon capture, utilization, and storage (CCUS) technology is widely used to reduce greenhouse gas emissions. CO₂-enhanced oil recovery (CO₂-EOR) technology has, once again, received attention, which can achieve the dual benefits of oil recovery and CO₂ storage. However, flexibly and effectively predicting the CO₂ flooding and storage capacity of potential reservoirs is a major problem. Traditional prediction methods often lack the ability to comprehensively integrate static and dynamic predictions and, thus, cannot fully understand CO₂-EOR and storage capacity. This study proposes a comprehensive deep learning framework, named LightTrans, based on a lightweight gradient boosting machine (LightGBM) and Temporal Fusion Transformers, for dynamic and static prediction of CO₂-EOR and storage capacity. The model predicts cumulative oil production, CO₂ storage amount, and Net Present Value on a test set with an average R-square (R²) of 0.9482 and an average mean absolute percentage error (MAPE) of 0.0143. It shows great static prediction performance. In addition, its average R² of dynamic prediction is 0.9998, and MAPE is 0.0025. It shows excellent dynamic prediction ability. The proposed model successfully captures the time-varying characteristics of CO₂-EOR and storage systems. It is worth noting that our model is 10⁵–10⁶ times faster than traditional numerical simulators, which once again demonstrates the high-efficiency value of the LightTrans model. Our framework provides an efficient, reliable, and intelligent solution for the development and optimization of CO₂ flooding and storage. Full article

Background: The strength of historical buildings built in different centuries with various materials and construction techniques and harboring many structural problems depends on the structural system, geometrical condition, and material properties. Sungurlu clock tower, whose system and geometry are in good condition, has been damaged under environmental and climatic effects, earthquakes, and other loads, and has survived to the present day by preserving its structural integrity to a great extent with the repairs it has undergone. Methods: In addition to static analysis, the robustness and durability of the design of the tower were tested by dynamic analysis with the SAP2000 program. In the model that will represent the actual system behavior of the tower, the lengths of the elements; nodal points; bearings; joints; shapes such as bars, shells, and plates; characteristic values of the materials to be used; as well as the system, element sections, and all loads and combinations of masses or dynamic forces acting on the system are defined. Results: In the reports presented visually, the moment, shear force, axial forces, and other forces to which the tower was exposed after the architectural and structural problems were eliminated were seen in a diagram. Since the effects of the damage could not be predicted, in this study, to measure the reaction of the building against earthquakes and other loads, the numerical model representing its original condition was prepared and analyzed according to the theoretical method and assumptions made by the restitution, survey, and static observation reports. Conclusions: With this program, which allows for the preparation of this model, it was concluded that the loads coming to the structure according to the principles of ductility, rigidity, and strength could be safely transferred to the ground without causing damage to the structural system and its elements. From the deformation, stress, velocity, acceleration, and reaction force graphs obtained, it was understood that the tower exhibited the expected structural behavior under its own weight and live loads. The stress and reaction force graphs showed that the structural materials are adequate for the resistance of the structure and system against the existing loads and possible earthquakes. Full article

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities. Full article

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization. Full article

Macrophytes often live in fluvial backwaters that have a variety of hydrological connections to a main river. Since the ability of these plants to adapt to changing environments may depend on the genetic diversity of the populations, it is important to know whether it can be influenced by habitat characteristics. We examined the microsatellite polymorphism of the submerged macrophyte Ceratophyllum demersum from various backwaters and showed that the genetic diversity of this plant clearly reflects habitat hydrological differences. The greatest genetic variability was found in a canal system where constant water flow maintained a direct connection between the habitats and the river. In contrast, an isolated backwater on the protected side of the river had the lowest plant genetic diversity. Oxbows permanently connected to the branch system with static or flowing water, and former river branches temporarily connected to the main bed contained populations with moderately high or low genetic variability. The results demonstrate that habitat fragmentation can be a result not only of the loss of direct water contact, but also of the lack of flowing water. Adverse hydrological changes can reduce the genetic diversity of populations and thus the ability of this macrophyte to adapt to changing environments. Full article

Before artificial ageing, extruded aluminium profiles are subjected to stretching with a small cold deformation in the range of 0.5–2%. This deformation improves the geometrical stability of the extruded product and causes changes in the microstructure of the profile, which leads to the strain hardening of the material after artificial ageing. The work has resulted in the creation of the prototype of an original device, which is unique in the world, for the dynamic stretching of the extruded profiles after quenching. The semi-industrial unit is equipped with a hydraulic system for stretching and a pneumatic system for cold dynamic deformation. The aim of this research paper is to produce advantageous microstructural changes and increase the strength properties of the extruded material. The solution of the dynamic stretching of the profiles after extrusion is a great challenge and an innovation not yet practised. The paper presents the results of microstructural and mechanical investigations carried out on extruded AlMgSi(Cu) alloys quenched on the run-out table of the press, dynamically stretched under different conditions, and artificially aged for T5 temper. Different stretching conditions were applied: a static deformation of 0.5% at a speed of 0.02 m/s, and dynamic deformation of 0.25%, 0.5%, 1%, and 1.5% at speeds of 0.05 and 2 m/s. After the thermomechanical treatment of the profiles, microstructural observations were carried out using an optical microscope (OM) and a scanning electron microscope (SEM). A tensile test was also carried out on the specimens stretched under different conditions. In all the cases, the dynamically stretched profiles showed higher strength properties, especially those deformed at a higher speed of 2 m/s, where the increase in UTS was observed in the range of 7–18% compared to the classical (static) stretching. The microstructure of the dynamically stretched profiles is more homogeneous with a high proportion of fine dispersoids. Full article

Migraines are one of the most common and expensive neurological diseases worldwide. Non-pharmacological and digitally delivered treatment options have long been used in the treatment of migraines. For instance, migraine management tools, online migraine diagnosis or digitally networked patients have been used. Recently, applications of ChatGPT are used in fields of healthcare ranging from identifying potential research topics to assisting professionals in clinical diagnosis and helping patients in managing their health. Despite advances in migraine management, only a minority of patients are adequately informed and treated. It is important to provide these patients with information to help them manage the symptoms and their daily activities. The primary aim of this case study was to examine the appropriateness of ChatGPT to handle symptom descriptions responsibly, suggest supplementary assistance from credible sources, provide valuable perspectives on treatment options, and exhibit potential influences on daily life for patients with migraines. Using a deductive, qualitative study, ten interactions with ChatGPT on different migraine types were analyzed through semi-structured interviews. ChatGPT provided relevant information aligned with common scientific patient resources. Responses were generally intelligible and situationally appropriate, providing personalized insights despite occasional discrepancies in interaction. ChatGPT’s empathetic tone and linguistic clarity encouraged user engagement. However, source citations were found to be inconsistent and, in some cases, not comprehensible, which affected the overall comprehensibility of the information. ChatGPT might be promising for patients seeking information on migraine conditions. Its user-specific responses demonstrate potential benefits over static web-based sources. However, reproducibility and accuracy issues highlight the need for digital health literacy. The findings underscore the necessity for continuously evaluating AI systems and their broader societal implications in health communication. Full article

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks. Full article

The self-excited oscillation system, owing to its capability of harvesting environmental energy, exhibits immense potential in diverse fields, such as micromachines, biomedicine, communications, and construction, with its adaptability, efficiency, and sustainability being highly regarded. Despite the current interest in track sliders in self-vibrating systems, LCE fiber-propelled track sliders face significant limitations in two-dime nsional movement, especially self-rotation, necessitating the development of more flexible and mobile designs. In this paper, we design a spatial slider system which ensures the self-rotation of the slider propelled by a light-fueled LCE fiber on a rigid circular track. A nonlinear dynamic model is introduced to analyze the system’s dynamic behaviors. The numerical simulations reveal a smooth transition from the static to self-rotating states, supported by ambient illumination. Quantitative analysis shows that increased light intensity, the contraction coefficient, and the elastic coefficient enhance the self-rotating frequency, while more damping decreases it. The track radius exhibits a non-monotonic effect. The initial tangential velocity has no impact. The reliable self-rotating performance under steady light suggests potential applications in periodic motion-demanding fields, especially in the construction industry where energy dissipation and utilization are of utmost urgency. Furthermore, this spatial slider system possesses the ability to rotate and self-vibrate, and it is capable of being adapted to other non-circular curved tracks, thereby highlighting its flexibility and multi-use capabilities. Full article

Background: Myotonic dystrophy type 1 (DM1) is a rare multisystemic genetic disorder with motor hallmarks of myotonia, muscle weakness and wasting. DM1 patients have an increased risk of falling of multifactorial origin, and proprioceptive and vestibular deficits can contribute to this risk. Abnormalities of muscle spindles in DM1 have been known for years. This observational cross-sectional study was based on the hypothesis of impaired cervical proprioception caused by alterations in the neck spindles. Methods: Head position sense was measured in 16 DM1 patients and 16 age- and gender-matched controls. A head-to-target repositioning test was requested from blindfolded participants. Their head was passively rotated approximately 30° leftward or rightward and flexed or extended approximately 25°. Participants had to replicate the imposed positions. An optoelectronic system was adopted to measure the angular differences between the reproduced and the imposed positions (joint position error, JPE, °) concerning the intended (sagittal, horizontal) and unintended (including the frontal) planar projections. In DM1 patients, JPEs were correlated with clinical and balance measures. Static balance in DM1 patients was assessed through dynamic posturography. Results: The accuracy and precision of head repositioning in the intended sagittal and horizontal error components did not differ between DM1 and controls. On the contrary, DM1 patients showed unintended side-bending to the left and the right: the mean [95%CI] of frontal JPE was −1.29° [−1.99°, −0.60°] for left rotation and 0.98° [0.28°, 1.67°] for right rotation. The frontal JPE of controls did not differ significantly from 0° (left rotation: 0.17° [−0.53°, 0.87°]; right rotation: −0.22° [−0.91°, 0.48°]). Frontal JPE differed between left and right rotation trials (p < 0.001) only in DM1 patients. No correlation was found between JPEs and measures from dynamic posturography and clinical scales. Conclusions: Lateral head bending associated with head rotation may reflect a latent impairment of neck proprioception in DM1 patients. Full article

Vision-based techniques have become widely applied in structural displacement monitoring. However, heat haze poses a great threat to the precision of vision systems by creating distortions in the images. This paper proposes a vision-based bridge displacement measurement technique with heat haze mitigation capability. The properties of heat haze-induced errors are illustrated. A dual-tree complex wavelet transform (DT-CWT) is used to mitigate the heat haze in images, and the speeded-up robust features (SURF) algorithm is employed to extract the displacement. The proposed method is validated through indoor experiments on a bridge model. The designed vision system achieves high measurement accuracy in a heat haze-free condition. The proposed mitigation method successfully corrects 61.05% of heat haze-induced errors in static experiments and 95.31% in dynamic experiments. Full article

Ecuador is grappling with a severe energy crisis, marked by frequent power outages. A recent study explored solar energy efficiency in the coastal city of Manta using an IoT real-time monitoring system to compare static photovoltaic (PV) systems with two single-axis solar tracking systems: one based on astronomical programming and the other using light-dependent resistor (LDR) sensors. Results showed that both tracking systems outperformed the static PV system, with net gains of 31.8% and 37.0%, respectively. The astronomical-programming-based system had a slight edge, operating its stepper motor intermittently for two minutes per hour, while the LDR system required continuous motor energization. The single-axis tracker using astronomical programming demonstrated notable advantages in energy efficiency and complexity, making it suitable for equatorial regions like Manta. The study also suggested potential further gains by adjusting solar positioning at shorter intervals, such as every 15 or 30 min. These findings enhance our understanding of solar tracking performance in equatorial environments, offering valuable insights for optimizing solar energy systems in regions with high solar radiation. By emphasizing customized solar tracking mechanisms, this research presents promising solutions to Ecuador’s energy crisis and advances sustainable energy practices. Full article

In light of the limitation that passive suspension can only provide vibration isolation within a specific range, a magnetorheological suspension in extrusion mode was developed. The reliability of structural parameters was ensured through theoretical analysis and numerical simulation, building upon traditional hydraulic suspension. A model linking static stiffness to the diameter of the upper extrusion plate, as well as the heights of the upper and lower liquid chambers, was established using Simulink as an evaluation index. The static stiffness performance of the magnetorheological suspension was then optimized using this model. Results indicate that while meeting the static stiffness requirements, the optimized Magnetorheological Suspension demonstrated a 29.22% increase in static stiffness (approximately 57.71 N/mm) compared to its previous state, validating the effectiveness of stiffness optimization for this system. Full article

The term immersive technology refers to various types of technologies and perspectives that are constantly changing and developing. It can be used for different purposes and domains such as education, healthcare, entertainment, arts, and engineering. This paper aims to compare the effectiveness of immersive technologies used in education, namely mixed reality, generated with Microsoft HoloLens 2, with traditional teaching methods. The experiment involves comparing two groups of students who received different training methods: the first group saw a PowerPoint slide with an image of the human muscular system, while the second group saw a 3D hologram of the human body that showed the same muscle groups as in the PowerPoint (PPT). By integrating the Intelligence Quotient (IQ) levels of the participants as a predictive variable, the study sought to ascertain whether the incorporation of mixed reality technology could significantly influence the learning outcomes and retention capabilities of the learners. This investigation was designed to contribute to the evolving pedagogical landscape by providing empirical evidence on the potential benefits of advanced educational technologies in diverse learning environments. The main finding of this study indicates that while MR has potential, its effectiveness is closely tied to its interactivity. In cases where the content remains static and non-interactive, MR does not significantly enhance in-formation retention compared to traditional PPT methods. Additionally, the study highlights that instructional strategies should be adapted to individual cognitive profiles, as the technology type (MR or PPT) alone does not significantly impact learning outcomes when the information presented is identical. Full article

Body weight support (BWS) systems are crucial in gait rehabilitation for individuals incapacitated due to injuries or medical conditions. Traditional BWS systems typically employ either static mass–rope or dynamic mass–spring–damper configurations, which can result in inadequate support stiffness, thereby leading to compromised gait training. Additionally, these systems often lack the flexibility for easy customization of stiffness, which is vital for personalized rehabilitation treatments. A novel BWS system with online variable stiffness is introduced in this study. This system incorporates a drive mechanism governed by admittance control that dynamically adjusts the stiffness by modulating the tension of a rope wrapped around a drum. An automated control algorithm is integrated to manage a smart anti-gravity dynamic suspension system, which ensures consistent and precise weight unloading adjustments throughout rehabilitation sessions. Walking experiments were performed to evaluate the displacement and load variations within the suspension ropes, thereby validating the variable-stiffness capability of the system. The findings suggest that the online variable-stiffness BWS system can reliably alter the stiffness levels and that it exhibits robust performance, significantly enhancing the effectiveness of gait rehabilitation. The newly developed BWS system represents a significant advancement in personalized gait rehabilitation, offering real-time stiffness adjustments and ongoing weight support customization. It ensures dependable control and robust operation, marking a significant step forward in tailored therapeutic interventions for gait rehabilitation. Full article