Search Results (134)

Background and Objectives: Venous thromboembolic disease (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a major cause of morbidity and mortality worldwide and imposes a substantial financial burden on health systems due to both the direct and indirect costs of acute management and long-term complications. This systematic review aimed to assess patient satisfaction with anticoagulation therapy for VTE and to highlight potential differences according to the type of anticoagulant. The review focused on factors influencing the patient experience, such as perceived efficacy, ease of use, adverse effects, and health-related quality of life. Materials and Methods: A systematic review, without quantitative meta-analysis, was conducted in accordance with PRISMA 2020 guidelines. Articles were identified through searches in major databases (PubMed, Scopus, Cochrane Library and others) using keywords including “patient satisfaction”, “anticoagulation”, “venous thromboembolic disease”, and “quality of life”. In total, 21 studies published between 2009 and 2025 met the inclusion criteria. The studies assessed patient satisfaction with different types of anticoagulation, including vitamin K antagonists (VKAs), direct oral anticoagulants (DOACs), and low-molecular-weight heparin (LMWH) injections. Results: Across the included studies, patients generally reported higher levels of treatment satisfaction with DOACs compared with VKAs, mainly due to the absence of routine laboratory monitoring and fewer dietary restrictions. However, satisfaction varied according to age, sex, and clinical status. In specific patient populations, such as those with cancer-associated thrombosis, factors including fewer drug–drug interactions and perceptions of safety with LMWH appeared to influence treatment choice and satisfaction. Adverse effects, particularly bleeding, were identified as major drivers of dissatisfaction. Several studies suggested that higher treatment satisfaction was associated with better adherence, while quality of life appeared to improve in patients treated with DOACs in comparison with VKAs. Conclusions: Patient satisfaction is a critical component of successful VTE management. Overall, DOACs appear to be associated with higher treatment satisfaction than traditional therapies such as VKAs, although further high-quality research is needed to individualise anticoagulation strategies. Systematic incorporation of patient-reported satisfaction into clinical decision-making and into international guidelines may improve adherence, enhance quality of life, and ultimately increase the effectiveness of anticoagulation therapy. Full article

Yellow fever (YF) remains a re-emerging vector-borne zoonotic disease in tropical regions of the Americas despite the availability of an effective vaccine. In South America, the virus is maintained through a jungle transmission cycle involving Haemagogus and Sabethes mosquitoes and non-human primates (NHPs), which act as amplifying hosts and key epidemiological sentinels. This narrative review examines the current status of YF epizootics in South America, with a focus on the role of NHPs in viral circulation, early detection, and spillover risk to human populations. We synthesize recent evidence on epizootic patterns across endemic countries, the differential susceptibility of neotropical primates, and the ecological and environmental drivers influencing transmission, including deforestation, habitat fragmentation, and human encroachment into forested areas. In addition, we analyze current surveillance strategies, including wildlife monitoring, entomological and genomic surveillance, and their integration within a One Health framework. This review highlights that YF epizootics are expanding geographically and are closely linked to environmental change and human–ecosystem interactions. Strengthening integrated, multidisciplinary surveillance systems is essential to improve early detection, guide vaccination strategies, and prevent human outbreaks. These findings underscore the critical importance of operationalizing the One Health approach to enhance preparedness and response to YF in South America. Full article

Heavy-duty forklifts possess substantial kinetic energy during braking, which is currently wasted due to a lack of recovery in conventional systems. To ensure braking safety, an electro-hydraulic–mechanical compound braking system is necessary. However, the uncoordinated distribution between regenerative and mechanical braking torque leads to braking torque fluctuations, compromising safety, comfort, and recovery efficiency. This paper constructs a parallel hydraulic hybrid power system for heavy-duty forklifts based on a four-quadrant pump/motor, enabling braking energy recovery and reuse via the pump/motor and an accumulator. A compound braking strategy based on the ideal braking force distribution and multi-sensor information fusion is proposed. The system incorporates various sensors, including pressure, speed, flow, and pedal displacement sensors, to monitor system status and driver intention in real time, providing precise data for coordinated control. Feasibility is verified through AMESim simulation and real vehicle tests. The control system based on sensor feedback maximizes braking energy recovery while ensuring braking safety and comfort, achieving a 12.2% energy-saving rate and significantly improving the vehicle’s economy and range. Full article

In the construction industry, struck-by accidents involving heavy equipment such as crawler excavators are a leading cause of worker fatalities and injuries. Existing vision-based hazard detection methods are limited by approximate evaluations, reliance on specific references, and neglect of spatial relationships between equipment and workers, making them inadequate for complex dynamic construction environments. This study aims to address these limitations by proposing a precise and adaptable struck-by hazard detection method. The method integrates four core modules: object tracking via the YOLOv5-DeepSORT model to detect workers, excavators, and their key components; activity recognition to identify the operational states of excavators, working or static, and workers, driver or field worker; proximity estimation based on stereo vision using the BGNet model and camera calibration to calculate 3D spatial distances; and safety identification to assess worker safety status in real time. Validated through three virtual construction scenarios, flat ground, rugged terrain, slope, the method achieved high safety status identification accuracies of 92.71%, 90.04%, and 94.25% respectively. The results demonstrate its robustness in adapting to diverse construction environments and accurately capturing equipment–worker spatial interactions. This research expands the application scope of hazard monitoring in complex settings, enhances safety identification efficiency, and provides a reliable technical solution for improving construction site safety management. Full article

A seatbelt is an essential aspect of safety in road traffic accidents. Although most traffic regulations enforce drivers and passengers to wear and fasten the seatbelt manually, AI-based techniques have been introduced for monitoring to improve safety standards. In this study, a new approach is proposed to address the monitoring problem of seatbelts. Deep learning (DL) classification based on adaptive Siamese Neural Network (SNN) has been developed utilizing the K-fold method for feature verification. The proposed adaptive K-Fold-based SNN approach utilizes a binary seatbelt dataset, with positive and negative classes, to verify the status of the seatbelt. The network involves sharing a convolutional feature extractor, followed by a distinct-based similarity function. To enhance model reliability, 5-fold cross validation is applied (k = 5), splitting the dataset into 5 subsets, where the model is trained on four sets and validated on the fifth one. The model was trained using binary cross entropy loss, Adam optimization, and performance metrics such as accuracy, precision, recall, and F1 score. The seatbelt dataset is basically designed for object detection models. In this work, we used a dataset in the verification model and achieved high-performance metrics. The model is implemented using a Python-based Jupyter Notebook 7.5.1. It achieved a high performance in seatbelt verification with an average Accuracy = 0.9989, average Precision = 0.9988, average Recall = 0.9990, and average F1 Score = 0.9989. The proposed adaptive K-Fold SNN model can ensure reliability and reduce the risk of over fitting. Full article

Background: Despite recent breakthroughs in pharmacological treatment for Alzheimer’s disease, high costs and the complex procedure to monitor safety have limited access for many patients. Less invasive and more accessible non-pharmacological therapies that support neuroplasticity and slow cognitive decline are needed. Processing Inner Strength Toward Actualization (PISTA) stimulation applies structured tactile input to promote cortical–subcortical activation. This study evaluated the long-term effects of PISTA on cognition and pain in older adults with mild cognitive impairment or early dementia. Methods: This single-arm, prospective trial enrolled 100 outpatients aged 47–70 years at outset (50 women, 50 men) with no control group. Participants received clinician-supervised PISTA stimulation three times weekly for 48 months. Each 30 min session delivered rhythmic tactile input calibrated to individual sensory thresholds. Cognitive performance was assessed monthly using the Mini-Mental State Examination (MMSE). Perceived pain was measured monthly with the Numeric Pain Rating Scale. Outcomes were analyzed using ANCOVA, adjusting for age, sex, and baseline cognitive status. Results: Cognitive scores improved significantly across all age strata, with a mean annual MMSE increase of 0.75 points (95% CI: 0.26–1.21; p < 0.0025). Pain intensity decreased in parallel (mean reduction: 0.56 points; 95% CI: 0.34–0.78; p < 0.001). Improvements in cognition and pain were moderately correlated (r = 0.38). The greatest combined benefits occurred in participants aged 55–62 years. No serious adverse events were observed during the 48-month trial. Conclusions: PISTA stimulation produced sustained improvement in cognition and reduced perceived pain, supporting its promising role as a safe, non-invasive adjunct for neurodegenerative cognitive decline. These findings suggest peripheral sensory activation as a promising driver of functional neuroplasticity and warrant verification in randomized, controlled trials. Full article

This study quantifies household-level socioeconomic drivers of food and nutrition insecurity in two rural Lesotho villages and translates those findings into clear monitorable pathways for sustainable development. Lesotho has a population of just over 2 million, of which 580,000 people live below the poverty line, and 24% (about 139,200) in the subset are experiencing extreme poverty, particularly in rural areas. Food and nutrition insecurity affects 61% of the population in rural areas and 39% in urban areas. The study aimed to determine the socioeconomic conditions and food security status of 126 females residing in Mpharane and Maqoala villages in the district of Mohale’s Hoek in Lesotho. A cross-sectional quantitative design was employed, and the measurement instruments included a socio-demographic questionnaire, the Household Hunger Scale (HHS), the Household Food Insecurity Access Scale (HFIAS), and a food frequency questionnaire. Statistical analyses were conducted using descriptive statistics. The results indicated that all the participants were mothers and caregivers living in overcrowded households, with 61% residing in one-room homes. Unemployment was a universal experience among the participants, resulting in severe food insecurity as 100% of the participants reduced the food intake of their children, as well as other household members, due to limited financial resources. The participants’ diet was predominantly cereal-based, with a mean cereal group intake of 26.70 (±8.53), and wheat was the most frequently consumed cereal (59.5%). By linking food security metrics (HHS, HFIAS, and food frequency) to household structure, unemployment, housing density, and cash access, the research produces evidence that can be used to design, prioritize, and evaluate interventions across the social, economic, environmental, and governance dimensions of sustainability. In conclusion, by defining measurable household-level links between socioeconomic conditions and food insecurity, this study provides baseline indicators and practical intervention targets aimed at achieving sustainable food systems, social equity, and economic resilience in rural Lesotho. Full article

The Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD) are the two main European policy instruments for assessing eutrophication in coastal waters, yet their differing assessment architectures often lead to inconsistent classification outcomes. This study provides a scientific comparison of WFD Ecological Status and MSFD Good Environmental Status (GES) classifications for Portuguese coastal waters across three assessment cycles. This is achieved by quantifying the coherence between Eutrophication assessments, by identifying the main drivers of divergence beyond chance, and evaluating where harmonization improved agreement, providing an evidence-based guidance to decision-making and policy regulation. Using officially validated national classifications, we analyzed the methodological drivers of divergence (without reprocessing raw monitoring data) and harmonized both outcomes into a common three-class scheme. Coherence was evaluated using a Discordance Index and Cohen’s kappa coefficient. Results showed that divergence was systematic rather than random, primarily driven by structural (spatial and temporal) misalignment, methodological differences in indicator integration, and contrasting statistical metrics. Both Directives consistently identify eutrophication hotspots associated with major river plumes but differ in how these signals are aggregated and translated into status classes. The study demonstrated that WFD and MSFD provide complementary but only partially aligned assessments, and that coherence improved with methodological harmonization. Full article

Coastal waters worldwide are increasingly threatened by excessive nutrient inputs, a key driver of eutrophication. Dissolved inorganic nitrogen (DIN) serves as a vital indicator for assessing the eutrophic status of nearshore marine environments, underscoring the necessity for precise monitoring to ensure effective protection and restoration of marine ecosystems. To address the current limitations in DIN retrieval methods, this study builds on MODIS satellite imagery data and introduces a novel one-dimensional convolutional neural network (1D-CNN) model synergistically co-optimized by the Bald Eagle Search (BES) and Bayesian Optimization (BO) algorithms. The proposed BES-BO-CNN framework was applied to the retrieval of DIN concentrations in the coastal waters of Shandong Province from 2015 to 2024. Based on the retrieval results, we further investigated the spatiotemporal evolution patterns and dominant environmental drivers. The findings demonstrated that (1) the BES-BO-CNN model substantially outperforms conventional approaches, with the coefficient of determination (R²) reaching 0.81; (2) the ten-year reconstruction reveals distinct land–sea gradient patterns and seasonal variations in DIN concentrations, with the Yellow River Estuary persistently exhibiting elevated levels due to terrestrial inputs; (3) correlation analysis indicated that DIN is significantly negatively correlated with sea surface temperature but positively correlated with sea level pressure. In summary, the proposed BES-BO-CNN framework, via the synergistic optimization of multiple algorithms, enables high-precision DIN monitoring, thus providing scientific support for integrated land–sea management and targeted control of nitrogen pollution in coastal waters. Full article

The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R²) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems. Full article

Dryland grasslands face elevated risks of rapid threshold crossing under a regime of warming, precipitation redistribution, and intensified interannual hydrothermal variability. Using the Ebinur Lake Basin (ELB) as a case, we developed an integrated structure × function assessment—linking land-use/cover change (LUCC) transitions with functional indicators of net primary productivity (NPP), net ecosystem production (NEP), soil conservation (SC), and grass supply (GS)—and coupled it with Bayesian-optimized XGBoost, SHAP, and partial dependence plots (PDPs) at a 30 m pixel scale to identify dominant drivers and ecological thresholds, subsequently translating them into governance zones. From 2003 to 2023, overall grassland status was dominated by degradation (20,160.62 km²; 69.42%), with restoration at 8878.85 km² (30.57%) and stability at 2.79 km² (0.01%). NPP/NEP followed a rise–decline–recovery trajectory, while SC exhibited marked bipolarity. Precipitation and temperature emerged as primary drivers (interaction X3 × X4 = 0.0621), whose effects, together with topography and accessibility, shaped a spatial paradigm of piedmont sensitive–oasis sluggish–lakeshore vulnerable. Key thresholds included an annual precipitation recovery threshold of ~200 mm and an optimal window of 272–429 mm; a road-density divide near ~0.06 km km⁻²; and sustainable grazing windows of ~2.2–4.2 and ~4.65–5.61 livestock units (LU) km⁻². These thresholds underpinned four management units—Priority Control (52.53%), Monitoring and Alert (21.53%), Natural Recovery (20.40%), and Optimized Maintenance (5.55%)—organized within a “two belts–four zones–one axis” spatial framework, closing the loop from threshold detection to adaptive governance. The approach provides a replicable paradigm for climate-adaptive management and ecological risk mitigation of dryland grasslands under warming. Full article

Increasing aridity across the Middle East Region has intensified concerns about the impacts of drought in conflict-affected Northeast Syria (NES). In this study, drought dynamics and their drivers from 2000 to 2023 were analyzed by integrating ERA5-Land meteorological data, MODIS land-surface indicators, FLDAS soil moisture, and ISRIC soil properties at 250 m resolution. The integration of these multisource datasets contributes to a more comprehensive understanding of drought dynamics by combining information on weather conditions, vegetation status, and soil characteristics. The proposed drought analysis framework clarifies independent controls on meteorological, agricultural, and hydrological drought, underscoring the role of land-atmosphere feedback through soil temperature. This workflow provides a transferable approach for drought monitoring and hypothesis generation in arid regions. For this purpose, different XGBoost models were trained for the vegetation health index (VHI), the standardized precipitation-evapotranspiration index (SPEI), and surface soil-moisture anomalies, excluding target-related variables to prevent data leakage. Model interpretability was achieved using SHAP, complemented by time-series, trend, clustering, and spatial autocorrelation analyses. The models performed well (R² = 0.86–0.90), identifying soil temperature, SPEI, relative humidity, precipitation, and soil-moisture anomalies as key predictors. Regionally, soil temperature rose (+0.069 °C yr⁻¹), while rainfall (−1.203 mm yr⁻¹) and relative humidity (−0.075% yr⁻¹) declined. Spatial analyses demonstrated expanding heat hotspots and persistent soil moisture deficits. Although 2018–2019 were anomalously wet, recent years (2021–2023) exhibited severe drought. Full article

Road traffic accidents are a major global public health concern, ranking among the top three causes of death worldwide and constituting the leading cause of death among individuals aged 15–29. Monitoring traffic safety status and trends is a vital element of effective road safety management. This study investigates road traffic casualties involving young car drivers (aged 18–24) in the Republic of Serbia from 1997 to 2024, analyzing historical patterns and introducing a predictive model for casualty outcomes. The analytical framework employs machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, to estimate the number of casualties (FSI = Fatal + Serious Injuries) based on various contributing factors. Accurate prediction of accident outcomes is essential for designing targeted road safety measures and reducing casualty numbers. Full article

Enhancing road safety requires an accurate assessment of the drivers’ mental states. The Driver Status Test App (DSTA) is designed to detect conditions such as intoxication, fatigue, and cognitive impairment in simulated driving environments. Utilizing a dual-platform approach, it integrates mobile data collection via React Native and Firebase with web-based management using React and TypeScript. The mobile application conducts real-time assessments of cognitive and motor functions, while the web interface offers data visualization, trend analysis, and results exportation. DSTA evaluates driver impairment through metrics such as tracking, precision, balance, and choice reaction, producing an objective impairment score. These assessments are rapid, scalable, and adaptable for various research and regulatory purposes. The composite scoring framework differentiates between impaired and unimpaired states, making DSTA valuable for driver training programs, regulatory assessments, and autonomous vehicle research, where monitoring human factors is crucial. Full article

The LRRK2+ SAA− cohort of Parkinson’s disease (PD), characterized by the absence of hallmark α-synuclein pathology, remains under-explored. This limits opportunities for early detection and targeted intervention. This study analyzes data from this under-characterized subgroup and compares it with the LRRK2+ SAA+ cohort using longitudinal data from the Parkinson’s Progression Markers Initiative (PPMI). The PPMI dataset includes 115 LRRK2+ patients (70 SAA+, 45 SAA−) across 52 features encompassing clinical assessments, cognitive scores, DaTScan SPECT imaging, and motor severity. DaTScan binding ratios were selected as imaging-based indicators of early dopaminergic loss, while NP3TOT (MDS-UPDRS Part III total score) was used as a gold-standard clinical measure of motor symptom severity. Linear mixed-effects models were then applied to evaluate longitudinal predictors of DaTScan decline and NP3TOT progression, and statistical analyses of group comparisons revealed distinct drivers of symptoms differentiating SAA− from SAA+ patients. In SAA− patients, a decline in DaTScan was significantly associated with thermoregulatory impairment (p-value = 0.019), while NP3TOT progression was predicted by constipation (p-value = 0.030), sleep disturbances (p-value = 0.046), and longitudinal time effects (p-value = 0.043). In contrast, SAA+ patients showed significantly lower DaTScan values compared to SAA− (p-value = 0.0004) and stronger coupling with classical motor impairments, including freezing of gait (p-value = 0.016), rising from a chair (p-value = 0.007), and turning in bed (p-value = 0.016), along with cognitive decline (MoCA clock-hands test, p-value = 0.037). These findings support the hypothesis that LRRK2+ SAA− patients follow a distinct pathophysiological course, where progression is influenced more by autonomic and non-motor symptoms than by typical motor dysfunction. This study establishes a robust, multimodal modeling framework for examining heterogeneity in genetic PD and highlights the utility of combining DaTScan, NP3TOT, and symptom-specific features for early subtype differentiation. These findings have direct clinical implications, as stratifying LRRK2 carriers by SAA status may enhance patient monitoring, improve prognostic accuracy, and guide the design of targeted clinical trials for disease-modifying therapies. Full article