Search Results (376)

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Background/Objectives: Severe acquired brain injury (sABI) disrupts early rehabilitation because arousal fluctuates, trunk control is fragile, and agitation limits therapy tolerance; land-based practice is frequently constrained by fall risk and staffing. We aim to reframe aquatic therapy as a programmable multisensory environment to stabilize arousal and support axial alignment before conventional impairment targets are feasible. Here, programmable denotes the deliberate titration and reporting of water depth, turbulence or perturbation, temperature, body orientation, and flotation and manual support as intervention inputs. Methods: This perspective integrates principles from neurobehavioral assessment, motor control, and immersion physiology to propose the Arousal–Alignment–Action loop as a falsifiable model and to define manipulable aquatic inputs (water depth, turbulence or perturbation, temperature, body orientation, and flotation and manual support) as dosing parameters. We outline a pragmatic testing ladder (within-session micro-experiments, feasibility studies, and embedded evaluations) and a minimal outcomes and confounder set to support cumulative evidence. Results: The framework links state regulation to alignment and goal-directed behavior, specifies predictions that can fail, and highlights boundary conditions (sedation, autonomic instability, pain, recent surgery or wounds, and cervical or cardiopulmonary constraints). A minimal outcome package spanning arousal/responsiveness, trunk control, behavioral dysregulation, participation/tolerance, and basic physiology is proposed, with optional objective adjuncts for mechanism-oriented studies. Conclusions: Treating water as a measurable and titratable medium, rather than a generic modality, may reduce early intensity bottlenecks and improve implementability and comparability of aquatic neurorehabilitation research in medically stable sABI; however, the model is intended as hypothesis-generating until supported by stronger direct clinical evidence. Full article

Background: Population aging represents a major public health challenge, accompanied by an increasing prevalence of chronic diseases and age-related functional decline. Declines in lower-extremity physical function are particularly important, as they are strongly associated with mobility limitations, loss of independence, increased risk of falls, hospitalization, and mortality in older adults. Reliable and valid tools to assess physical performance are therefore essential in both clinical and research settings. The Short Physical Performance Battery (SPPB) is a widely used instrument for assessing lower-extremity physical performance in older adults and is recommended within the diagnostic algorithm of the European Working Group on Sarcopenia in Older People (EWGSOP2) for evaluating physical performance severity. However, the SPPB has not yet been psychometrically validated in the Croatian older population. This study aimed to evaluate the reliability and validity of the SPPB in Croatian older adults. Methods: This study examined the metric properties of the SPPB in a sample of 153 older adults recruited from nursing homes and community settings. Results: The SPPB demonstrated acceptable internal consistency (Cronbach’s alpha = 0.74) and good test–retest reliability (ICC = 0.893) for the total score. Convergent and construct validity were supported by significant associations with established measures of functional mobility and muscle strength. Conclusions: The Croatian version of the SPPB is a reliable and valid instrument for assessing lower-extremity physical performance in older adults. Its use is supported in clinical practice and research settings in Croatia. Further studies should examine responsiveness and predictive validity in nationally representative samples. Full article

Background: Motor decline associated with ageing compromises mobility, postural control and the ability, thereby increasing risk among older adults. Biomechanical characterization of movement, particularly using non-linear methods, offers a process-oriented approach capable of detecting subtle changes in motor coordination. The MIND&GAIT programme has previously demonstrated benefits in physical function in frail older individuals; however, its potential to improve motor coordination parameters that underpin fall risk remains insufficiently explored. Objectives: To analyse the impact of the MIND&GAIT program on motor coordination during sit-to-stand (STS) and walking tasks, two daily activities strongly associated with fall risk, using advanced non-linear and biomechanical metrics in institutionalized older adults. Methods: Fourteen institutionalized older adults (82.21 ± 7.14 years) participated. Three-dimensional acceleration and angular velocity were recorded using inertial sensors. Motor variability and predictability were quantified using the multivariate Lyapunov exponent (LyEM) and multivariate incremental entropy (MIE). STS (30 s) and walking-in-place (2 min) tasks were assessed pre- and post-intervention following a three-month, thrice-weekly programme. Results: Although no statistically significant differences emerged (ps > 0.05), trends were observed suggesting increases in LyEM during STS and in both MIE and LyEM during walking were found post-intervention. These exploratory findings may indicate enhanced motor complexity, stability and adaptability, features associated with reduced fall vulnerability. Conclusions: Despite the absence of statistical significance, the biomechanical trends observed suggest improvements in motor coordination patterns relevant to fall risk reduction in institutionalized older adults following the MIND&GAIT programme. These findings highlight the potential of structured exercise-based interventions for promoting safer movement behaviors in frail populations. Full article

Background: Acute hepatitis E (AHE) poses escalating risks to older adults (≥65 years), compounded by immunosenescence and comorbidities. Using Global Burden of Disease (GBD) 2021 data, this study analyzes global AHE burden, trends, and projections in aging populations. Methods: Age-standardized rates (ASIR, ASMR, ASDR) for AHE in adults ≥ 65 years were extracted from GBD 2021 across 204 countries (1990–2021). Frontier analysis assessed gaps between observed burdens and sociodemographic index (SDI)-based theoretical minima. Age-period-cohort (APC) modeling evaluated age/period/cohort effects. Bayesian (BAPC), NORDPRED, and ARIMA models projected trends to 2050. Results: Global ASIR increased by 1.5% annually (1990–2021), with ASMR and DALYs declining significantly. Middle SDI regions showed the steepest ASIR rise (net drift: 0.064%/year), while high SDI areas had volatile trends. Age effects peaked in ≥95-year-olds. Frontier analysis revealed persistent ASIR-SDI gaps, particularly in low-middle SDI regions. Projections indicate a ASIR rise by 2050 (113.04/100,000), contrasting with declining ASMR (0.056/100,000) and ASDR (1.31/100,000) and the NORDPRED, ARIMA, and EAPC models exhibit analogous global predictive trends. Conclusions: Diverging trends of rising incidence and falling mortality highlight unmet prevention needs. High-burden regions require SDI-stratified strategies, prioritizing vaccination programs (e.g., HEV 239), zoonotic transmission control, and enhanced surveillance. The Sustainable Development Goals (SDGs) envision hepatitis elimination by 2030 (Target 3.3). However, our analysis projects ongoing AHE burden in aging populations through 2050, indicating the need for post-2030 policy adaptations. Full article

Invasive alien plants (IAPs) pose a severe and escalating threat to biodiversity and ecosystem services in China. However, a systematic nationwide assessment that identifies invasion hotspots, quantifies their overlap with protected area networks, and pinpoints critical conservation gaps is still lacking. This hinders the development of spatially targeted management strategies. To address this, we developed an integrated analytical framework coupling the Maximum Entropy (MaxEnt) model with the InVEST habitat quality model. Using a high-resolution, county-level distribution database of 293 IAPs, we mapped potential species richness and habitat degradation across China. The geo-detector model was further employed to identify the primary environmental factors and their interactions. Spatial overlay analysis was conducted to delineate core invasion habitats (areas of high invasion suitability and high degradation) and assess their coverage within China’s national nature reserves. Nighttime light intensity (DMSP, 34.39%), annual precipitation (Bio12, 14.16%), and mean diurnal range (Bio2, 11.82%) were the factors with the highest contribution in the model, highlighting the statistical interaction between anthropogenic pressure and climatic conditions. The core invasion habitat spanned 20.10 × 10⁴ km², predominantly (66.04%) concentrated in high-intensity human disturbance zones. Notably, only 11.18% of this core habitat falls within existing national nature reserves, revealing a vast conservation gap of 17.85 × 10⁴ km². Our results indicate a profound spatial mismatch between invasion hotspots and the current protected area network in China. We prioritize southeastern coastal urban agglomerations-characterized by high anthropogenic pressure (DMSP), high precipitation (Bio12), and low diurnal temperature range (Bio2)-for immediate monitoring and intervention. This integrated assessment provides a national-scale, spatially explicit prediction of invasion risk for 293 plant species in China, and offers an evidence-based decision-support tool for optimizing invasive species management and biodiversity conservation. Full article

Artificial intelligence (AI) has become the leading technology for digital transformation in various industries. However, the digitalization of construction project management (e.g., cost, time, and safety) in the context of AI technology implementation is still limited. Therefore, this paper aims to conduct a systematic literature review of AI technologies in construction project cost, time, and safety management, and identify mainstream application areas, cross-domain synthesis, challenges, research gaps, and future research directions. By adopting the PRISMA approach, a systematic literature review was conducted to retrieve 392 articles from the Scopus database. The results presented mainstream application areas of construction project cost (i.e., cost estimation, cost prediction, cost index forecasting, cost control, cost optimization), time (i.e., planning and scheduling, delay risk prediction, time optimization, cycle time prediction), and safety (i.e., workers’ safety monitoring, on-site safety monitoring, personal protective equipment (PPE) detection, safety report text analysis, fall risk monitoring, safety accident prediction, and safety hazard identification and risk assessment). Moreover, the cross-domain synthesis, challenges, and research gaps of AI technologies in construction project management were discussed. Based on these findings, this paper suggests future directions to extend research in this domain. This paper would contribute to the construction project management research domain by providing key application areas and useful research directions, thus promoting digital transformation in the sector. Full article

Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized exercise prescription, and facilitate scalable monitoring across preventive, community-based, and long-term adapted exercise settings, particularly in populations characterized by functional heterogeneity and variable responses to exercise. The aim of this narrative review is to synthesize and critically discuss current ML applications relevant to the core professional processes of APA practice. A structured narrative review was conducted using searches in PubMed/MEDLINE, Scopus, and Web of Science, complemented by targeted searches in engineering-oriented sources to capture ML methods not consistently indexed in biomedical databases. The search covered the period in which contemporary ML approaches have been increasingly applied to human movement and exercise research and was last updated in January 2026. Evidence was synthesized thematically into application-oriented domains relevant to APA practice. ML applications in APA include markerless motion and gait analysis, wearable-sensor data processing, balance and fall-risk assessment, and functional classification. Predictive and adaptive models support individualized regulation of exercise intensity, progression, and workload, including remote and hybrid delivery models. Applications span oncology, cardiometabolic, respiratory, neuromuscular conditions, and adapted sport contexts. Ethical, legal, and governance issues, such as algorithmic bias, data privacy, and professional accountability, emerge as central considerations for safe and equitable implementation. ML represents a promising decision-support layer for APA, complementing professional expertise through enhanced assessment, personalization, and monitoring. Its effective integration requires robust validation, interpretability, and responsible governance to ensure that ML augments, rather than replaces, professional judgment in APA practice. Full article

Background/Objectives: Overcrowding in the emergency department (ED) is a global health issue. Early prediction of expected hospitalizations, based on parameters available from triage, is essential to enhance patient transfer from the ED to departments, thereby reducing ED congestion. Methods: A historical cohort study included patients who visited two tertiary referral medical centers located in the center of Israel. Data derived from one medical center (MC-A) was used to build the prediction model and to test it, and data from the second medical center (MC-B) was used to validate it. Variables collected included age, sex, triage level, vital signs, initial admitting diagnosis, medical referrals, mode of arrival, time of arrival according to hospital shifts (morning, evening, and night), weekday (workdays/weekend), season, fall risk assessment, and significant comorbidities. Logistic regression was used to build the model, and the area under the ROC curve (AUC) and the discrimination slope (DS) were used to evaluate it. Results: The final cohort included 1436 patients: 1256 patients from MC-A and 180 from MC-B. The patients were divided randomly into a learning group (n = 879), a test group (n = 377), and a validation group (n = 180). We found that higher triage level (urgent+: OR 1.45, p = 0.039), lower O₂ saturation (<95%: OR 3.32, p < 0.001), malignancy (OR 1.81, p = 0.044), cardiovascular disease (OR 2.93, p < 0.001), neurologic illness (OR 2.07, p = 0.014), arrival during the weekend (OR 1.57, p = 0.014), and fall season (OR 1.81, p = 0.003) were associated with higher probability of hospital admission. Our model showed a similar acceptable discrimination ability in all groups (learning: AUC = 0.77, 95%CI 0.73–0.80, and DS = 19%; testing: AUC = 0.76, 95%CI 0.70–0.82, and DS = 17%; validation: AUC = 0.71, 95%CI 0.61–0.80, and DS = 18%). Conclusions: The proposed prediction model can be easily implemented in hospital systems to provide management with an expected number of ED patient hospitalizations in the coming hours. The model can enhance patient flow, thereby reducing crowding in the ED. Full article

Objectives: Early and accurate identification of frailty is essential for preventing adverse outcomes in older adults. However, existing frailty prediction models often lack reliability, interpretability, and generalizability. Methods: Participants aged 60 years and older between 2011 and 2015 (n = 3419) from the CHARLS were used to develop models, and participants from the CLHLS-HF between 2014 and 2018 (n = 1017) were used for external validation. The frailty was assessed 4 years after baseline in both cohorts by Fried’s Frailty Phenotype (FFP). Six machine learning models were applied to develop prediction models. The SHapley Additive exPlanations (SHAP) method was utilized to explain the final model. Clinical outcomes were evaluated between participants predicted as frail and non-frail by the final model. Results: The XGBoost (AUC = 0.934, 95% CI: 0.921–0.948; F1 = 0.712, 95% CI: 0.686–0.736 in internal validation; AUC = 0.792, 95% CI: 0.750–0.830; F1 = 0.702, 95% CI: 0.652–0.753 in external validation) performed best among six models. Key predictors included lifestyle factors (e.g., instrumental daily living activities, BMI, and self-rated health) and psychological traits (e.g., depression). Participants predicted as frail had significantly elevated risks of falls (OR = 2.11), hospitalization (OR = 1.75), and disability (OR = 1.42). Conclusions: The proposed model provided a robust and interpretable digital tool for predicting frailty among older adults and associated adverse outcomes. Full article

Permanent ground deformation caused by fault movement threatens the safe operation of buried pipelines. Accurate fragility assessment of buried pipelines subjected to faulting is essential for pipeline design and risk management. However, buried pipelines exhibit nonlinear mechanical responses due to the coupled effects of multiple factors. Moreover, the effects of key parameters remain insufficiently quantified, limiting the accuracy and engineering applicability of existing fragility assessments. In this study, a three-dimensional finite element model incorporating large deformation and nonlinear pipe–soil interaction is developed and validated against representative experimental data. Using this model, numerical simulations are performed for 352 parameter combinations covering fault type, dip angle, burial depth, soil type, and pipe material. Nonlinear regression of the simulation results yielded predictive models for pipeline peak axial strain under normal-slip and reverse-slip faulting. A fragility framework is then established with fault displacement as the intensity measure, and fragility curves are derived for both faulting modes. The predicted peak axial strains agree with the finite element results: 78.6% (normal-slip) and 72.5% (reverse-slip) of predictions fall within ±20% error. The fragility curves enable quantitative estimation of fault-displacement thresholds. In the case study, the intact-to-damage displacement threshold is approximately 0.6 m for normal-slip faults but approximately 0.2 m for reverse-slip faults, indicating a higher failure likelihood under reverse-slip faulting. Within the investigated parameter ranges, the fault dip angle is the most significant factor affecting the pipeline failure probability for both normal-slip and reverse-slip faulting. Sandy soil and greater burial depth substantially increase the probability of moderate-to-severe damage, whereas higher steel grade increases the displacement threshold for transition from intact to failure. This study provides a rapid quantitative tool and a theoretical basis for pipeline design and risk quantification of buried pipelines in fault zones. Full article

Retrieval-time augmentation can correct hard test samples but may also introduce harmful interference when retrieved neighbors are unreliable. We propose TDI-SF (trustworthy dynamic inference via similarity-gated strict fallback), a safety-oriented dynamic inference strategy that intervenes only when needed and falls back to a frozen baseline when retrieval quality is insufficient. Uncertainty-gated selective retrieval triggers on a hard subset, defined by high entropy or low margin predictions ($q=0.3$), and similarity-gated fusion weights neighbor evidence by maximum similarity with a strict fallback threshold (alpha-mode=maxsim, min_maxsim). We evaluate on ImageNet-100 (ResNet-50) and CICIDS2017 (MLP) and report overall accuracy, hard-subset accuracy, calibration, negative flips, and risk–coverage behavior alongside efficiency. Comprehensive evaluation under both clean and degraded retrieval conditions demonstrates the value of each component. On ImageNet-100, TDI-SF improves hard-subset accuracy by 0.92% and overall accuracy by 0.30%, applying retrieval to only 32.6% of samples with 1.38 ms overhead per triggered sample. On CICIDS2017, the same mechanism yields +1.30% hard-subset gains with only 0.43 ms/hard overhead. These results show a simple, auditable recipe for safer retrieval-augmented inference across heterogeneous domains. Full article

Unconsolidated sandstone reservoirs, particularly in offshore and marine environments, are highly susceptible to sand production, which leads to flow-capacity degradation, plugging evolution, sand-retention instability, and erosion–corrosion damage in gravel-pack completion systems. To address the lack of system-level and quantitative integrity evaluation methods, a unified assessment framework is developed by coupling flow behavior, sand-retention mechanisms, and erosion–corrosion damage processes. The gravel-pack completion system is idealized as a concentric multilayer porous-medium structure under steady-state radial Darcy flow, and an equivalent radial permeability model is established to characterize flow capacity and anti-plugging performance, which enables consistent comparison of different completion schemes under identical plugging conditions. Based on sand-retention mechanisms, a sand-retention capacity index is proposed by integrating formation particle size distribution, screen aperture, gravel size, and sand-leakage risk. An erosion–corrosion coupled damage model is further developed to predict screen damage rates in CO₂-containing environments, and an integrity index is formulated to link damage evolution with long-term service performance. By integrating flow capacity, anti-plugging performance, sand-retention capacity, and structural integrity using a weighted geometric mean, a comprehensive evaluation index is established for overall system integrity assessment. Using the proposed framework, a representative formation sand with d₁₀ = 30  μm, d₅₀ = 180  μm, and d₉₀ = 500 μm  is evaluated. The optimal sand control design corresponds to a gravel median size of 971.53 μm (equivalent to a standard 16/20 mesh gravel) and an optimal screen aperture of 523.11 μm, with a screen porosity of 0.56. Under these conditions, the selected screen aperture and gravel size are well matched with the formation sand size, falling within recommended engineering ranges and achieving a favorable balance among sand retention, flow capacity, anti-plugging performance, and structural integrity. The proposed framework provides a quantitative and engineering-applicable basis for the optimization and integrity classification of offshore gravel-pack sand control completions under multi-constraint operating conditions. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

The margin of stability (MoS) is a metric used to assess dynamic postural stability during walking. Although MoS is typically computed from optical motion capture data, previous studies have shown that MoS can be approximated from six-axis kinematic signals—linear acceleration and angular velocity—measured by inertial measurement units (IMUs). With IMU-equipped devices such as smartphones and smartwatches becoming widespread, it is increasingly common for individuals to carry two or more such devices in daily life. This study aimed to identify combinations of two body locations that most effectively predict MoS. IMU sensors were attached to ten body locations while participants walked on a treadmill. Principal motion analysis, a reductive regression method for multidimensional time-series data, was employed for MoS prediction, and cross-validation was used for reliable model evaluation. Appropriate combinations of two IMU sensors achieved mean errors of approximately 30 mm and 11 mm in anterior and mediolateral MoS, respectively, compared with reference values derived from optical motion capture. These errors were comparable to the intrinsic standard deviations of MoS, suggesting that IMU-based MoS estimation is sufficiently accurate for the classification of individuals at high fall risk. Full article