Search Results (16,523)

Carbon dots (CDs) derived from mandarin peel biochar (MBC) at different pyrolysis temperatures (200, 400, 600, and 800 °C) have been synthesized and characterized. This high-value transformation of waste materials into fluorescent nanoprobes for environmental monitoring represents a step forward towards a circular economy. In this itinerary, CDs produced via one-pot hydrothermal synthesis were utilized for the detection of copper (II) ions. The study looked at the spectroscopic features of biochar-derived CDs. The selectivity of CDs obtained from biochar following carbonization at 400 °C (MBC400-CDs towards various heavy metal ions resulted in considerable fluorescence quenching with copper (II) ions, showcasing their potential as selective detectors. Transmission electron microscopic (TEM) analysis validated the MBC-CDs’ consistent spherical shape, with a particle size of <3 nm. The Plackett–Burman Design (PBD) was used to study three elements that influence the F₀/F ratio, with the best ratio obtained with a pH of 10, for 10 min, and an aqueous reaction medium. Cu (II) was detected over a dynamic range of 4.9–197.5 μM and limit of detection (LOD) of 0.01 μM. Validation testing proved the accuracy and precision for evaluating tap and mountain waters with great selectivity and no interference from coexisting metal ions. Full article

Heart murmurs are abnormal heart sounds that can indicate various heart diseases. Although traditional auscultation methods are effective, they depend more on specialists’ knowledge, making it difficult to make an accurate diagnosis. This paper presents a machine learning-based framework for the classification of acoustic sounds and heart murmurs using digital signal analysis. Using advanced machine learning algorithms, we aim to improve the accuracy, speed, and accessibility of heart murmur detection. The proposed method includes feature extraction from digital auscultatory recordings, preprocessing using signal processing techniques, and classification using state-of-the-art machine learning models. We evaluated the performance of different machine learning algorithms, such as convolutional neural networks (CNNs), random forests (RFs) and support vector machines (SVMs), on a selected heart noise dataset. The results show that our framework achieves high accuracy in differentiating normal heart sounds from different types of heart murmurs and provides a robust tool for clinical decision-making. Full article

Assessment of the right ventricular outflow tract and pulmonary arteries (RVOT) for percutaneous pulmonary valve implantation (PPVI) uses discrete measurements (diameters and lengths) from medical images. This multi-centre study identified the 3D RVOT shape features prevalent in patients late after surgical repair of congenital heart disease (CHD). A 3D RVOT statistical shape model (SSM) was computed from 81 retrospectively selected CHD patients (14.7 ± 6.8 years) who required pulmonary valve replacement late after surgical repair. A principal component analysis identified prevalent shape features (modes) within the population which were compared with standard geometric measurements (diameter, length and surface area) and between sub-groups of diagnosis, RVOT type and dysfunction. Shape mode 1 and 2 represented RVOT size and curvature and tapering and length, respectively. Shape modes 3–5 related to branch pulmonary artery calibre, conical vs. bulbous RVOTs and RVOT curvature, respectively. Tetralogy of Fallot, transannular patch type and regurgitant RVOTs were larger and straighter while conduit and stenotic types were longer and more cylindrical than other subgroups. This SSM analysed the main 3D shape features present in a population of RVOTs, exploiting the wide 3D anatomical information provided by routine imaging. This morphological information may have implications for PPVI patient selection and device design. Full article

(1) With persistent symptoms emerging as a possible global consequence of COVID-19, the need to understand, diagnose, and treat them is paramount. This systematic review aims to explore the potential of optical coherence tomography (OCT) and/or optical coherence tomography angiography (OCTA) in effectively diagnosing long COVID. (2) The database PubMed and, to reduce selection bias, the AI research assistant Elicit, were used to find relevant publications in the period between February 2021 and March 2024. Included publications on OCT and OCTA analysis of participants with acute COVID symptoms, those after recovery, and participants with long COVID symptoms were organized in a table. Studies with participants under the age of 18, case reports, and unrelated studies, such as pure slit-lamp examinations and subgroup analyses were excluded. (3) A total of 25 studies involving 1243 participants and 960 controls were reviewed, revealing several changes in the posterior eye. Long COVID participants displayed significant thinning in retinal layers in the OCT, including the macular retinal nerve fiber layer (mRNFL), ganglion cell layer (GCL), and inner plexiform layer (IPL). Divergent findings in recovered cohorts featured mRNFL reduction, GCL increase and decrease, and GCL-IPL decrease. Long COVID OCTA results revealed reduced vessel density (VD) in the superficial capillary plexus (SCP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). In recovered patients, SCP consistently showed a reduction, and DCP exhibited a decrease in five out of six publications. The foveal avascular zone (FAZ) was enlarged in five out of nine publications in recovered participants. (4) During various stages of COVID-19, retinal changes were observed, but a comparison between long COVID and recovered cohorts was aggravated by diverse inclusion and exclusion criteria as well as small sample sizes. Changes in long COVID were seen in most OCT examinations as thinning or partial thinning of certain retinal layers, while in OCTA a consistently reduced vessel density was revealed. The results suggest retinal alterations after COVID that are variable in OCT and more reliably visible in OCTA. Further research with larger samples is important for advancing long COVID diagnosis and management. Full article

In the clinical diagnosis of pneumonia, particularly during the COVID-19 pandemic, individuals who progress to a critical stage requiring mechanical ventilation are classified as mechanically ventilated critically ill patients. Accurately predicting the discharge outcomes for this specific cohort, especially those with COVID-19, is of paramount clinical importance. Missing data, a common issue in medical research, can significantly impact the validity of analyses. In this work, we address this challenge by employing two missing data imputation techniques: multiple imputation and missForest, to enhance data completeness. Additionally, we utilize the smoothly clipped absolute deviation (SCAD) penalized logistic regression method to select significant features. Our real data analysis compares the predictive performances of extreme learning machines, random forests, support vector machines, and XGBoost using 10-fold cross-validation. The results consistently show that XGBoost outperforms the other methods in predicting discharge outcomes, making it a reliable tool for clinical decision-making in the treatment of severe pneumonia, including COVID-19 cases. Within this context, the random forest imputation method generally enhances performance, underscoring its effectiveness in managing missing data compared to multiple imputation. Full article

Water is a vital resource, and its quality has a direct impact on human health. Groundwater, as one of the primary water sources, requires careful monitoring to ensure its safety. Although manual methods for testing water quality are accurate, they are often time-consuming, costly, and inefficient when dealing with large and complex data sets. In recent years, machine learning has become an effective alternative for water quality assessment. However, current approaches still face challenges, such as the limited performance of individual models, minimal improvements from optimization algorithms, lack of dynamic feature weighting mechanisms, and potential information loss when simplifying model inputs. To address these challenges, this paper proposes a hybrid model, BS-MLP, which combines GBDT (gradient-boosted decision tree) and MLP (multilayer perceptron). The model leverages GBDT’s strength in feature selection and MLP’s capability to manage nonlinear relationships, enabling it to capture complex interactions between water quality parameters. We employ Bayesian optimization to fine-tune the model’s parameters and introduce a feature-weighting attention mechanism to develop the BS-FAMLP model, which dynamically adjusts feature weights, enhancing generalization and classification accuracy. In addition, a comprehensive parameter selection strategy is employed to maintain data integrity. These innovations significantly improve the model’s classification performance and efficiency in handling complex water quality environments and imbalanced datasets. This model was evaluated using a publicly available groundwater quality dataset consisting of 188,623 samples, each with 15 water quality parameters and corresponding labels. The BS-FAMLP model shows strong classification performance, with optimized hyperparameters and an adjusted feature-weighting attention mechanism. Specifically, it achieved an accuracy of 0.9616, precision of 0.9524, recall of 0.9655, F1 Score of 0.9589, and an AUC score of 0.9834 on the test set. Compared to single models, classification accuracy improved by approximately 10%, and when compared to other hybrid models with additional attention mechanisms, BS-FAMLP achieved an optimal balance between classification performance and computational efficiency. The core objective of this study is to utilize the acquired water quality parameter data for efficient classification and assessment of water samples, with the aim of streamlining traditional laboratory-based water quality analysis processes. By developing a reliable water quality classification model, this research provides robust technical support for water safety management. Full article

This article investigates the application of a deep learning model for evaluating the conformity of model images to types of UML diagrams to be used in self-training and educational settings. Our approach leans on a feature-based dataset that captures a broad range of modeling elements from class, state machine, and sequence diagrams, enhancing the ability to recognize a larger variety of categories selected for this research. The neural network trained with these features representing parts of the UML concrete syntax demonstrates 90% in classification accuracy on average, in respect to our previous research on UML diagrams classification without using a feature-based dataset. This study concludes that a feature-based approach, combined with advanced neural network architectures, can improve the classification of such images, especially in edge cases where diagrams contain similar graphical details but the whole does not represent a UML diagram. For the given research, we obtained a 0.87 F1 score. Full article

The integration of machine learning (ML) in marine engineering has been increasingly subjected to stringent regulatory scrutiny. While environmental regulations aim to reduce harmful emissions and energy consumption, there is also a growing demand for the interpretability of ML models to ensure their reliability and adherence to safety standards. This research highlights the need to develop models that are both transparent and comprehensible to domain experts and regulatory bodies. This paper underscores the importance of transparency in machine learning through a use case involving a VLGC ship two-stroke propulsion engine. By adhering to the CRISP-DM standard, we fostered close collaboration between marine engineers and machine learning experts to circumvent the common pitfalls of automated ML. The methodology included comprehensive data exploration, cleaning, and verification, followed by feature selection and training of linear regression and decision tree models that are not only transparent but also highly interpretable. The linear model achieved an RMSE of 23.16 and an MRAE of 14.7%, while the accuracy of decision trees ranged between 96.4% and 97.69%. This study demonstrates that machine learning models for predicting propulsion engine fuel consumption can be interpretable, adhering to regulatory requirements, while still achieving adequate predictive performance. Full article

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety. Full article

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively. Full article

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study’s methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies. Full article

Organophosphate neuroactive agents represent severe security threats in various scenarios, including military conflicts, terrorist activities and industrial accidents. Addressing these threats necessitates effective protective measures, with a focus on decontamination strategies. Adsorbents such as bentonite have been explored as a preliminary method for chemical warfare agent immobilization, albeit lacking chemical destruction capabilities. Chemical decontamination, on the other hand, involves converting these agents into non-toxic or less toxic forms. In this study, we investigated the hydrolytic activity of a Cu(II) complex, previously studied for phosphate ester hydrolysis, as a potential agent for chemical warfare decontamination. Specifically, we focused on a ligand featuring a thiophene anchor bound through an aliphatic spacer, which exhibited high hydrolytic activity in its Cu(II) complex form in our previous studies. Paraoxon, an efficient insecticide, was selected as a model substrate for hydrolytic studies due to its structural resemblance to specific chemical warfare agents and due to the presence of a chromogenic 4-nitrophenolate moiety. Our findings clearly show the hydrolytic activity of the studied Cu(II) complexes. Additionally, we demonstrate the immobilization of the studied complex onto a solid substrate of Amberlite XAD4 via copolymerization of its thiophene side group with dithiophene. The hydrolytic activity of the resultant material towards paraoxon was studied, indicating its potential utilization in organophosphate neuroactive agent decontamination under mild conditions and the key importance of surface adsorption of paraoxon on the polymer surface. Full article

Introduction: This article provides an overview of several experimental models, including in vivo, genetics, chemical, knock-in, knock-out, electrical, in vitro, and optogenetics models, that have been employed to investigate epileptogenesis. The present review introduces a novel categorization of these models, taking into account the fact that the most recent classification that gained widespread acceptance was established by Fisher in 1989. A significant number of such models have become virtually outdated. Objective: This paper specifically examines the models that have contributed to the investigation of partial seizures, generalized seizures, and status epilepticus. Discussion: A description is provided of the primary features associated with the processes that produce and regulate the symptoms of various epileptogenesis models. Numerous experimental epilepsy models in animals have made substantial contributions to the investigation of particular brain regions that are capable of inducing seizures. Experimental models of epilepsy have also enabled the investigation of the therapeutic mechanisms of anti-epileptic medications. Typically, animals are selected for the development and study of experimental animal models of epilepsy based on the specific form of epilepsy being investigated. Conclusions: Currently, it is established that specific animal species can undergo epileptic seizures that resemble those described in humans. Nevertheless, it is crucial to acknowledge that a comprehensive assessment of all forms of human epilepsy has not been feasible. However, these experimental models, both those derived from channelopathies and others, have provided a limited comprehension of the fundamental mechanisms of this disease. Full article

Background/Objectives: Bimodal stimulation (BS), which combines the use of a cochlear implant (CI) in one ear and a hearing aid (HA) in the opposite ear, is an established strategy to treat hearing loss by exploiting the unique capabilities of each device. CIs stimulate the auditory nerve by bypassing damaged hair cells, while HAs amplify sounds by requiring a functional hearing residual. The aim of this systematic review is to investigate the advantages and disadvantages of BS such as speech perception in noise. Methods: We examined clinical studies published from October 2020 to July 2024, following the PRISMA guidelines, focusing on the advantages and disadvantages of BS on speech perception in noise in adulthood. Results: BS in adult patients significantly improves speech perception in quiet and noisy environments, especially for those with increased residual hearing. Unilateral CIs and BS perform similarly in quiet conditions, but BS significantly improves speech discrimination in noisy environments if loudness between the two devices is appropriately balanced. Conclusions: Directional microphones and programming software are new technologies that succeed in reducing environmental noise and improving verbal perception outcomes, although their features in the literature are controversial. In addition, the individuals using BS may face temporal mismatches mainly due to differing device latencies, affecting sound localization. Compensating for these mismatches can enhance localization accuracy. However, modulated noise remains a significant obstacle to verbal perception in noise. Valuable assessment tools such as music tests provide further information on hearing performance and quality of life. More research is needed to define certain selection criteria. Full article

Federated Learning (FL) enables decentralized data utilization while maintaining edge user privacy, but it faces challenges due to statistical heterogeneity. Existing approaches address client drift and data heterogeneity issues. However, real-world settings often involve low-quality data with noisy features, such as covariate drift or adversarial samples, which are usually ignored. Noisy samples significantly impact the global model’s accuracy and convergence rate. Assessing data quality and selectively aggregating updates from high-quality clients is crucial, but dynamically perceiving data quality without additional computations or data exchanges is challenging. In this paper, we introduce the FedDQA (Federated learning via Data Quality-Aware) (FedDQA) framework. We discover increased data noise leads to slower loss reduction during local model training. We propose a loss sharpness-based Data-Quality-Awareness (DQA) metric to differentiate between high-quality and low-quality data. Based on the DQA, we design a client selection algorithm that strategically selects participant clients to reduce the negative impact of noisy clients. Experiment results indicate that FedDQA significantly outperforms the baselines. Notably, it achieves up to a 4% increase in global model accuracy and demonstrates faster convergence rates. Full article