Search Results (8,158)

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

Photodynamic inactivation (PDI) is an innovative non-thermal sterilization and preservation method that has recently emerged as a safe, effective, cost-effective and environmentally sustainable alternative for biomedical applications. Curcumin (Cur), a commonly used food additive, possesses photosensitizing properties. In this study, we investigated the effect of curcumin-mediated photodynamic treatment (Cur-PDT) on the preservation of fresh wolfberries. Our experimental data revealed that a Cur-PDT treatment using a cur concentration of 500 μmol/L for 30 min, with 20 W irradiation, achieved the best preservation effect on fresh wolfberries. This intervention significantly slowed the decline in post-harvest hardness and delayed the progression of decay. It also reduced the accumulation of malondialdehyde (MDA), hydrogen peroxide (H₂O₂) and superoxide anion (•O₂⁻). Notably, at day 3, the enzymatic activities of catalase (CAT) and ascorbate peroxidase (APX) in Cur-PDT-treated wolfberries were 1.12 and 1.88 times higher, respectively, than those in the control group. These elevated enzyme activities promoted the biosynthesis and recycling of ascorbic acid (AsA) and glutathione (GSH), leading to their substantial accumulation under oxidative stress conditions. By modulating the antioxidant defense system, Cur-PDT has the potential to extend the shelf-life of post-harvest wolfberries and enhance their overall quality attributes, thereby maintaining physiological homeostasis during storage. Full article

Understanding watershed-scale interactions among landscape patterns, river morphology, and water quality is essential for effective water management. However, quantitative assessment of their coupled effects remains challenging. Utilizing water quality observation data, this study analyzed the independent and interactive influences of landscape pattern and river structure on the water quality of inlet rivers in the Chaohu Lake Basin (CLB) using correlation analysis and partial least squares structural equation modelling (PLS-SEM). The main conclusions are as follows: (1) The river water quality showed significant spatial distribution characteristics, and the northwestern part of the CLB formed a pollution aggregation area. (2) Ammonia nitrogen correlated positively with impervious surfaces but negatively with forest cover and patch cohesion, permanganate index linked positively to water surface but negatively to forest cover, and water temperature exhibited a significant negative correlation with network connectivity. (3) PLS-SEM results showed that both river structure (path coefficient = 0.877, p < 0.001) and landscape pattern (path coefficient = 0.177, p < 0.05) significantly influenced CLB water quality, with river structure having a stronger effect. This study supports source-based water quality control for Chaohu Lake Basin. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

Intensified human activities in recent years, such as wastewater discharge and agricultural non-point source pollution have led to a decline in lake water quality, especially in the middle and lower reaches of the Yangtze River Basin, which threaten the stability of lake water ecosystems. Therefore, it is necessary to conduct a scientific assessment of the water eco-environmental quality of shallow lakes and implement targeted management measures. Considering the characteristics of shallow lakes, major ecological and environmental issues, and current standards and guidelines, an indicator system method was employed to establish a water eco-environmental quality evaluation system tailored for typical shallow lakes in the middle and lower reaches of the Yangtze River Basin. This evaluation system comprises three criteria layers (aquatic organism, habitat quality, and water quality) and 10 indicator layers. Using survey data from 2022 to 2024 for evaluation, the results showed that the water eco-environmental quality of Lake Gehu was rated as poor, with the lowest score for macrophyte coverage and the highest score for riparian vegetation coverage. This indicates that the shoreline restoration project in Lake Gehu was effective, while the lake water quality still needs improvement. Remedial measures include increasing aquatic vegetation coverage, reducing nitrogen and phosphorus pollution loads, and controlling the occurrence of algal blooms. This evaluation system combines field surveys with remote sensing monitoring data, fully considering historical and current conditions, and can guide local authorities in evaluating lake water environmental quality. The constructed evaluation system is applicable for the assessment of shallow lakes in the middle and lower reaches of Yangtze River Basin. It provides a scientific basis for the continuous improvement of eco-environmental quality and the construction of Beautiful Lakes Initiative, contributing to the management and protection of lake ecosystems. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

Nomex honeycomb composites (NHCs) are highly sensitive to the abnormal wear state of disc tools during cutting, leading to poor product quality. This paper proposes a real-time anomaly detection method combining a novel CNN–GRU–Attention (CGA) deep learning model with an Exponentially Weighted Moving Average (EWMA) control chart to monitor sensor data from the disc tool. The CGA model integrates an improved CNN layer to extract multidimensional local features, a GRU layer to capture long-term temporal dependencies, and a multi-head attention mechanism to highlight key information and reduce error accumulation. Trained solely on normal operation data to address the scarcity of abnormal samples, the model predicts cutting force time series with an RMSE of 0.5012, MAE of 0.3942, and R² of 0.9128, outperforming mainstream time series data prediction models. The EWMA control chart applied to the prediction residuals detects abnormal tool wear trends promptly and accurately. Experiments on real NHC cutting datasets demonstrate that the proposed method effectively identifies abnormal machining conditions, enabling timely tool replacement and significantly enhancing product quality assurance. Full article

The construction sector, facing a persistent productivity gap compared to other industries, is hindered by fragmented value streams, inconsistent performance metrics, and the limited scalability of process improvements. We introduce a pioneering, four-tiered hierarchical productivity framework to respond to these challenges. This innovative approach integrates operational, tactical, strategic, and normative layers. At its core, the framework applies standardised, repeatable process steps—mapped using Value Stream Mapping (VSM)—to capture key indicators such as input efficiency, output effectiveness, and First-Time Quality (FTQ). These are then aggregated through takt time compliance, schedule reliability, and workload balance to evaluate trade synchronisation and flow stability. Higher-level metrics—flow efficiency, multi-resource utilisation, and ESG-linked performance—are integrated into an Overall Productivity Index (OPI). Building on a modular production model, the proposed framework supports real-time sensing, AI-driven monitoring, and intelligent process control, as demonstrated through an empirical case study of continuous process monitoring for Kelly drilling operations. This validation illustrates how sensor-equipped machinery and machine learning algorithms can automate data capture, map observed activities to standardised process steps, and detect productivity deviations in situ. This paper contributes to a multi-scalar measurement architecture that links micro-level execution with macro-level decision-making. It provides a foundation for real-time monitoring, performance-based coordination, and data-driven innovation. The framework is applicable across modular construction, digital twins, and platform-based delivery models, offering benefits beyond specialised foundation work to all construction trades. Grounded in over a century of productivity research, the approach demonstrates how emerging technologies can deliver measurable and scalable improvements. Framing productivity as an integrative, actionable metric enables sector-wide performance gains. The framework supports construction firms, technology providers, and policymakers in advancing robust, outcome-oriented innovation strategies. Full article

Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The system proposed was compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors were fabricated using silver nanoparticle-functionalized substrates to show high sensitivity and low-cost pollutant detection. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties compared with LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutants at different levels, and executing the models to test their accuracy in detecting pollutants and the latency of data processing. The TCN framework achieved a detection accuracy of 98.7%, which surpassed LSTM by 92.4%. In addition, TCN reduced latency in processing by 38% to enable fast data analysis and decision making. LoRaWAN allowed for perfect packet transmission of up to 15 km, while the loss rate stayed as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks. Full article

Background/Objectives: Diabetic sensorimotor polyneuropathy (DSPN) is a frequent microvascular complication of diabetes mellitus, associated with increased morbidity and reduced quality of life. The existing literature offers a limited understanding of sex-specific cardiovascular risk profiles and their association with DSPN, particularly within Central and Eastern European populations. Methods: A retrospective analysis was conducted using data from 621 individuals with type 1 or type 2 diabetes mellitus who underwent comprehensive neuropathy screening at the University of Debrecen between 2017 and 2021. The diagnosis of DSPN was made in accordance with international criteria, incorporating symptom scores, and electrophysiological measurements. Multivariate logistic regression was applied in order to identify independent predictors. Results: The diagnosis of DSPN was made in 444 individuals (71.5%), of whom 58.2% were female. Despite similar glycemic control (HbA1c: 7.81% in men vs. 7.65% in women, p = 0.297), men had significantly more frequent occurrences of previous myocardial infarction (11.8% vs. 5.0%, p = 0.008), peripheral vascular disease (19.9% vs. 12.7%, p = 0.041) and atherosclerosis (31.7% vs. 22.0%, p = 0.021). Multivariate analysis showed that female gender was independently associated with a lower incidence of DSPN (odds ratio [OR] = 0.592, 95% confidence interval [CI]: 0.369–0.950, p = 0.030), while diabetic retinopathy was a significant predictor (OR = 2.728, 95% CI: 1.300–5.725, p = 0.008). Electrophysiological testing revealed lower nerve conduction amplitudes in females for selected nerves. Conclusions: Our findings highlight sex-specific differences in neuropathy risk and support the implementation of individualized screening strategies in diabetic populations with region-specific risk factors. Full article

Background/Objectives: Individuals with posttraumatic stress disorder (PTSD) tend to show patterns of elevated cardiovascular disease (CVD) risk earlier in life than the general population. The need for effective interventions for CVD risk-reduction in PTSD is increasingly evident. In this paper we present preliminary results from a longitudinal study of a health behavior intervention, as an adjunct to standard trauma therapy in PTSD. The health behavior intervention addresses CVD-related heath behaviors (physical activity, nutrition, sleep, and stress) in a 12-week program delivered individually in 90-min sessions. Behavior change recommendations included: increased aerobic activity; establishing a balanced diet, enhancing consumption of fruits and vegetables and reducing sugars and fat/saturated fat; incorporating strategies to enhance sleep and lower PTSD-related disruptions (e.g., nightmares); and relaxation and cognitive coping skills to reduce general stress. Methods: Participants were randomized to the health behavior intervention plus standard trauma therapy experimental condition or a standard trauma therapy control group. Outcomes were measured at baseline and after the 12-week intervention phase. Sleep efficiency was measured from actigraphy watches. Physical activity was assessed by self-report and blood pressure was measured using an automated device. The preliminary outcomes are for 29 participants to date who have pre-post data. Results: Sleep efficiency was improved in the intervention group compared to controls (p < 0.05). The intervention group also evidenced significant pre-post increases in moderate physical activity compared to the control group (p < 0.05). Changes in vigorous physical activity did not reach statistical significance in this preliminary sample but the pattern of results are similar to those for moderate activity. Trends toward significance were also observed for pre-post changes in systolic (p = 0.06) and diastolic blood pressure (p = 0.07), with small reductions for the intervention group and increases for the control group. Conclusions: These findings provide preliminary information about the effectiveness of the health behavior intervention on multiple parameters for adults with PTSD. The findings suggest that focusing on health behavior change in multidisciplinary treatments for PTSD may enhance outcomes such as sleep and physical activity and potentially result in greater quality of life. However, the small preliminary sample size reported here should be considered when interpreting the outcomes. Further research may also determine how improvements in health parameters impact other indices of long-term cardiovascular health. Full article

Background/Objectives: The complexity of diseases such as cancer and auto-immune disorders drives the need for unique, target-driven therapeutics. A broader arsenal to generate better biologics-based therapeutics is needed to provide more efficient and effective antibody generation technologies. The critical parameter for antibody generation is to generate as much candidate diversity to each target as possible. Method/Results: We present guidelines for having an efficient process using a fully synthetic human single-domain antibody (sdAb) phage display library. Critical milestones for success focused on library quality control (QC) assessments, evaluation of specific biopanning outputs, and construct designs that enabled efficient transition to mammalian expression. The synthetic VHO libraries produced epitope diversity better than an immunized sourced library with candidates possessing nM potencies and monodispersity > 90% via SEC. Conclusions: Synthetic human scaffold sdAb phage display libraries was constructed, biopanned, and selected candidates that could be directly transitioned for mammalian expression. The diverse VHO sets of candidates produced from many targets easily provided opportunities to make a multi-specific biological compound. Both synthetic and immunized phage selection campaign results suggested that these technologies complemented each other to generate therapeutic candidates. Finally, we demonstrated how diverse data produced from a process that used VHO synthetic libraries could accelerate drug discovery. Full article

Discrete manufacturing environments face increasing challenges in managing work-in-process (WIP) inventory due to growing product customization and demand volatility. While Value Stream Mapping (VSM) has been widely used for process improvement, traditional approaches lack the ability to dynamically control WIP levels while optimizing multiple performance dimensions simultaneously. This research addresses this gap by developing an integrated framework that synergizes Multi-Dimensional Value Stream Mapping (MD-VSM) with multi-objective optimization, functioning as a specialized digital twin for dynamic WIP control. The framework employs a four-layer architecture that connects real-time data collection, multi-dimensional modeling, dynamic WIP monitoring, and execution control through closed-loop feedback mechanisms. A mixed-integer optimization model is used to balance time, cost, and quality objectives. Validation using a high-fidelity simulation, parameterized with real-world industrial data, demonstrates that the proposed approach yielded up to a 31% reduction in inventory costs while maintaining production throughput and showed a 42% faster recovery from equipment failures compared to traditional methods. Furthermore, a comprehensive sensitivity analysis confirms the framework’s robustness. The system demonstrated stable performance even when key operational parameters, such as WIP upper limits and buffer capacity coefficients, were varied by up to ±30%, underscoring its reliability for real-world deployment. These findings provide manufacturers with a validated methodology for enhancing operational efficiency and production flexibility, advancing the integration of lean principles with data-driven, digital twin-based control systems. Full article

Background/Objectives: Early diagnosis of Parkinson’s Disease (PD) is essential for initiating interventions that may slow its progression and enhance patient quality of life. Gait analysis provides a non-invasive means of capturing subtle motor disturbances, enabling the prediction of both disease presence and severity. This study evaluates and contrasts Bayesian-optimized convolutional neural network (CNN) and long short-term memory (LSTM) models applied directly to Vertical Ground Reaction Force (VGRF) signals for Parkinson’s disease detection and staging. Methods: VGRF recordings were segmented into fixed-length windows of 5, 10, 15, 20, and 25 s. Each segment was normalized and supplied as input to CNN and LSTM network. Hyperparameters for both architectures were optimized via Bayesian optimization using five-fold cross-validation. Results: The Bayesian-optimized LSTM achieved a peak binary classification accuracy of 99.42% with an AUC of 1.000 for PD versus control at the 10-s window, and 98.24% accuracy with an AUC of 0.999 for Hoehn–Yahr (HY) staging at the 5-s window. The CNN model reached up to 98.46% accuracy (AUC = 0.998) for binary classification and 96.62% accuracy (AUC = 0.998) for multi-class severity assessment. Conclusions: Bayesian-optimized CNN and LSTM models trained on VGRF data both achieved high accuracy in Parkinson’s disease detection and staging, with the LSTM exhibiting a slight edge in capturing temporal patterns while the CNN delivered comparable performance with reduced computational demands. These results underscore the promise of end-to-end deep learning for non-invasive, gait-based assessment in Parkinson’s disease. Full article

Mucositis involving the gastrointestinal, vaginal, and nasal mucosa is one of the primary dose-limiting toxicities of hematopoietic stem cell transplantation (HSCT) and its conditioning regimen. The oropharyngeal mucosa is commonly affected, which can be detrimental to patient health and quality of life. Despite its significant prevalence and deleterious effects, we have an inadequate understanding of the risk factors and outcomes associated with oral mucositis (OM). We performed a literature search through PubMed and EBSCO (inception to 31 March 2024) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data was extracted from eligible studies using a pre-specified data extraction form. Quality of the data was assessed using the Newcastle-Ottawa Scale for non-randomized, observational studies and the Cochrane Collaboration Tool for randomized controlled trials. Our initial search identified 1677 articles, 34 of which were included in our study. Of those 34, 30 were included in the qualitative assessment of clinical risk factors in the development of OM, and 4 were included in the meta-analysis assessing the relationship between OM and infectious complications following HSCT. Across both HSCT modalities and cancer cohorts, female sex and high-intensity conditioning were common risk factors in the development of OM. When stratified by allogeneic and autologous HSCT, methotrexate, younger age, and longer duration of neutropenia were associated with increased OM risk in allogeneic HSCT recipients, while renal dysfunction, HSV-1 reactivation, and longer neutrophil engraftment were associated with increased OM risk in autologous HSCT recipients. Longer neutrophil engraftment was a common risk factor across different cancer cohorts; however, renal dysfunction was a distinct risk factor for OM in multiple myeloma patients. Additionally, our meta-analysis revealed that patients with OM have an increased risk of developing infectious complications following HSCT compared to those without OM, with an odds ratio of 3.84 (95% CI: 2.51–5.86). The development of OM is related to various risk factors, and individuals with OM are at greater risk of infectious complications. Knowledge of these risk factors and outcomes will help clinicians identify high-risk individuals, prevent OM, and protect an immunocompromised population from subsequent life-threatening complications. Full article