Search Results (5,374)

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R²) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

Efficient and convenient intelligent online detection methods can provide important technical support for the standardization of processing flow in the tea industry. Hence, this study focuses on the key chemical indicators—tea pigments in the rolling process of black tea as the research object, and uses multi-source information fusion methods to predict the changes of tea pigments content. Firstly, the tea pigments content of the samples under different rolling time series of black tea is determined by system analysis methods. Secondly, the spectra and images of the corresponding samples under different rolling time series are simultaneously obtained through the portable near-infrared spectrometer and the machine vision system. Then, by extracting the principal components of the image feature information and screening characteristic wavelengths from the spectral information, low-level and middle-level data fusion strategies are chosen to effectively integrate sensor data from different sources. At last, the linear (PLSR) and nonlinear (SVR and LSSVR) models are established respectively based on the different characteristic data information. The research results show that the LSSVR based on middle-level data fusion strategy have the best effect. In the prediction results of theaflavins, thearubigins, and theabrownins, the correlation coefficients of the testing sets are all greater than 0.98, and the relative percentage deviations are all greater than 5. The complementary fusion of the spectrum and image information effectively compensates for the problems of information redundancy and feature missing in the quantitative analysis of tea pigments content using the single-modal data information. Full article

Background/Objectives: The progression of antibiotic resistance is increasingly recognized as a dynamic and time-dependent phenomenon, challenging conventional diagnostics that define resistance as a binary trait. Methods: Biomolecules have fingerprints in Fourier-transform infrared spectroscopy (FTIR). The targeting of specific molecular groups, combined with principal component analysis (PCA) and machine learning algorithms (ML), enables the identification of bacteria resistant to antibiotics. Results: In this work, we investigate how effective classification depends on the use of different numbers of principal components, spectral regions, and defined resistance thresholds. Additionally, we explore how the time-dependent behavior of certain spectral regions (different biomolecules) may demonstrate behaviors that, independently, do not capture a complete picture of resistance development. FTIR spectra were obtained from Staphylococcus aureus exposed to azithromycin, trimethoprim/sulfamethoxazole, and oxacillin at sequential time points during resistance induction. Combining spectral windows substantially improved model performance, with accuracy reaching up to 96%, depending on the antibiotic and number of components. Early resistance patterns were detected as soon as 24 h post-exposure, and the inclusion of all three biochemical windows outperformed single-window models. Each spectral region contributed distinctively, reflecting biochemical remodeling associated with specific resistance mechanisms. Conclusions: These results indicate that antibiotic resistance should be viewed as a temporally adaptive trajectory rather than a static state. FTIR-based biochemical profiling, when integrated with ML, enables projection of phenotypic transitions and supports real-time therapeutic decision-making. This strategy represents a shift toward adaptive antimicrobial management, with the potential to personalize interventions based on dynamic resistance monitoring through spectral biomarkers. Full article

The increasing demand for accessible and efficient machine learning solutions has led to the development of the Adaptive Learning Framework (ALF) for multi-class, single-label image classification. Unlike existing low-code tools, ALF integrates multiple transfer learning backbones with a guided, adaptive workflow that empowers non-technical users to create custom classification models without specialized expertise. It employs pre-trained models from TensorFlow Hub to significantly reduce computational costs and training times while maintaining high accuracy. The platform’s User Interface (UI), built using Streamlit, enables intuitive operations, such as dataset upload, class definition, and model training, without coding requirements. This research focuses on small-scale image datasets to demonstrate ALF’s accessibility and ease of use. Evaluation metrics highlight the superior performance of transfer learning approaches, with the InceptionV2 model architecture achieving the highest accuracy. By bridging the gap between complex deep learning methods and real-world usability, ALF addresses practical needs across fields like education and industry. Full article

Astringency, a complex oral sensation resulting from interactions between mucin and polyphenols, remains difficult to quantify in portable field settings. Therefore, quantifying the aggregation through interactions can enable the classification of the astringency intensity, and assessing the capillary action driven by the surface tension offers an effective approach for this purpose. This study successfully replicates tannic acid (TA)–mucin aggregation on a paper-based microfluidic chip and utilizes machine learning (ML) to analyze the resulting capillary flow dynamics. Aggregates formed by mixing mucin with TA solutions at three concentrations showed that higher TA levels led to greater aggregation, consequently reducing the capillary flow rates. The flow dynamics were consistently recorded using a smartphone mounted within a custom 3D-printed frame equipped with a motorized sample loading system, ensuring standardized experimental conditions. Among eight trained ML models, the support vector machine (SVM) demonstrated the highest classification accuracy at 95.2% in distinguishing the astringency intensity levels. Furthermore, fitting the flow data to a theoretical capillary flow equation allowed for the extraction of a single coefficient as an input feature, which achieved comparable classification performance, validating the simplified feature extraction strategy. This method was also feasible even with only a portion of the initial data. This approach is simple and cost-effective and can potentially be developed into a portable system, making it useful for field analysis of various liquid samples. Full article

The early detection of neurological and psychiatric disorders is critical for optimizing patient outcomes and improving the efficacy of healthcare delivery. This study presents a novel multiclass machine learning (ML) framework designed to classify epilepsy, migraine, and schizophrenia simultaneously using electroencephalography (EEG) signals. Unlike conventional approaches that predominantly rely on binary classification (e.g., healthy vs. diseased cohorts), this work addresses a significant gap in the literature by introducing a unified artificial neural network (ANN) architecture capable of discriminating among three distinct neurological and psychiatric conditions. The proposed methodology involves decomposing raw EEG signals into constituent frequency subbands to facilitate robust feature extraction. These discriminative features were subsequently classified using a multilayer ANN, achieving performance metrics of 95% sensitivity, 96% specificity, and a 95% F1-score. To enhance clinical applicability, the model was optimized for potential integration into real-time diagnostic systems, thereby supporting the development of a rapid, reliable, and scalable decision support tool. The results underscore the viability of EEG-based multiclass models as a promising diagnostic aid for neurological and psychiatric disorders. By consolidating the detection of multiple conditions within a single computational framework, this approach offers a scalable and efficient alternative to traditional binary classification paradigms. Full article

Addressing limitations in remote sensing irrigation monitoring (insufficient resolution, single-source constraints, poor terrain adaptability), this study developed a high-precision identification framework for Jianping County, China, a semi-arid region. We integrated Sentinel-1 SAR (VV/VH), Sentinel-2 multispectral, and MOD11A1 land surface temperature data. Savitzky–Golay (S-G) filtering reconstructed time-series datasets for NDVI, SAVI, TVDI, and VV/VH backscatter coefficients. Irrigation mapping employed random forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Key results demonstrate the following. (1) RF achieved superior performance with overall accuracies of 91.00% (2022), 88.33% (2023), and 87.78% (2024), and Kappa coefficients of 86.37%, 80.96%, and 80.40%, showing minimal deviation (0.66–3.44%) from statistical data; (2) SAVI and VH exhibited high irrigation sensitivity, with peak differences between irrigated/non-irrigated areas reaching 0.48 units (SAVI, July–August) and 2.78 dB (VH); (3) cropland extraction accuracy showed <3% discrepancy versus governmental statistics. The “Multi-temporal Feature Fusion + S-G Filtering + RF Optimization” framework provides an effective solution for precision irrigation monitoring in complex semi-arid environments. Full article

Ergonomic assessments for specialized vehicle cockpits are often costly, subjective, or fragmented. To address these issues, this study proposes and validates a quantifiable comprehensive evaluation method combining optical motion capture with simulation. The methodology uses motion capture to acquire accurate, dynamic operator posture data, which drives a digital human model in a virtual environment. A novel assessment framework then integrates the results from six ergonomic tools into a single, comprehensive score using a multi-criteria weighting model, overcoming the ‘information silo’ problem of traditional software. In a case study optimizing a flatbed transporter cockpit, the method guided a redesign that significantly improved the overall ergonomic score from 0.422 to 0.277. The effectiveness of the optimization was validated by a 40% increase in key control accessibility and a significant reduction in electromyography (EMG) signals in the neck, shoulder, and lumbar regions. This study provides an innovative, data-driven methodology for the objective design and evaluation of customized human–machine systems, demonstrating its utility in reducing physical strain and enhancing operator comfort, with broad applicability to other complex industrial environments. Full article

Titanium alloys (Ti-6Al-4V) are widely used in the aerospace field. However, as a typical difficult-to-machine material, titanium alloys have a low thermal conductivity, a high chemical activity, and a significant adiabatic shear effect. In conventional milling (CM), the temperature in the cutting zone rises sharply, leading to tool adhesion, rapid wear, and damage to the workpiece surface. This article systematically investigated the influence of process parameters on the surface roughness, cutting force, and cutting temperature in the ultrasonic-vibration-assisted milling (UAM) process of titanium alloys, based on which multi-objective optimization process of the milling process parameters was conducted, by utilizing the grey relational analysis method. An orthogonal experiment with four factors and four levels was conducted. The effects of various process parameters on the surface roughness, cutting force, and cutting temperature were systematically analyzed for both UAM and CM. The grey relational analysis method was employed to transform the optimization problem of multiple process target parameters into a single-objective grey relational degree optimization problem. The optimized parameter combination was as follows: an ultrasonic amplitude of 6 μm, a spindle speed of 6000 rpm, a cutting depth of 0.20 mm, and a feed rate of 200 mm/min. The experimental results indicated that the surface roughness Sa was 0.268 μm, the cutting temperature was 255.39 °C, the cutting force in the X direction (FX) was 5.2 N, the cutting force in the Y direction (FY) was 7.9 N, and the cutting force in the Z direction (FZ) was 6.4 N. The optimization scheme significantly improved the machining quality and reduced both the cutting forces and the cutting temperature. Full article

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development. Full article