Search Results (1,156)

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

Background: As the U.S. population ages, the need for gastrointestinal (GI) care and procedures grows. Medicare is a significant payer for these procedures, but declining reimbursements raise concerns about the availability of GIs and thus equitable access to care. This study examines the relationship between Medicare reimbursements for GI procedures and the regional supply and demand of GI physicians. Methods: This study analyzed the Medicare facility and non-facility setting physician reimbursements for the top 10 GI procedures for 2003, 2013, and 2023. Facility reimbursements were compared across four regions (Northeast, Midwest, South, and West) and compared to regional GI physician supply and demand data for 2013 and 2025 projections. Linear regression and mixed-effects models were used to evaluate relationships between reimbursements, physician supply, and demand. Results: The national average adjusted facility setting physician reimbursements for the top 10 GI procedures declined by 45.6% from 2003 to 2023. In 2013 and projected for 2025, the South had the highest GI physician supply and demand, but consistently lower facility setting physician reimbursements compared to the Northeast and West. Associations between supply, demand, and reimbursements were observed, though regional patterns showed paradoxical trends, such as similar low reimbursements in the South and Midwest despite differing supply levels. Conclusions: Regional inconsistencies between physician supply and reimbursements highlight the complexity of economic and healthcare dynamics. Declining Medicare reimbursements for GI procedures are multifactorial and, as the aging population grows, these reductions may widen disparities. Further investigation is needed to address barriers and ensure equitable access to GI care. Full article

This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks. Full article

Unsupervised anomaly detection has been extensively studied. However, most existing methods are designed for either numerical or nominal data, which struggle to detect anomalies effectively in real-world mixed-type datasets. Fuzzy information granulation is a key concept in granular computing, which offers a potent framework for managing uncertainty in mixed-type data and provides a viable pathway for unsupervised anomaly detection. Nevertheless, conventional fuzzy information granulation-based detection methods often model only simple, linear fuzzy relations between samples. This limitation prevents them from capturing the complex, nonlinear structures inherent in the data, leading to a degradation in detection performance. To address these shortcomings, we propose a Hybrid Kernelized Fuzzy Density-based anomaly detector (HKFD). HKFD pioneers a hybrid kernelized fuzzy relation by integrating a hybrid distance metric with kernel methods. This new relation allows us to define a hybrid kernelized fuzzy density for each sample within every feature subspace, effectively capturing the local data dispersion. Crucially, we introduce an information-theoretic weighting mechanism. By calculating the fuzzy information entropy of each feature’s distribution, HKFD automatically assigns higher weights to more informative feature subspaces that contribute more to identifying anomalies. The final anomaly factor is then calculated by the weighted fusion of these densities. Comprehensive experiments on 20 datasets demonstrate that HKFD significantly outperforms state-of-the-art methods, achieving superior anomaly detection performance. Full article

Drying shrinkage cracking of hydraulic cementitious materials, induced by moisture loss under varying environmental conditions, significantly compromises structural durability. The utilization of construction waste powder (CWP) in cement composites presents a sustainability opportunity, but its impact on shrinkage behavior remains poorly understood. This study aims to systematically investigate the drying shrinkage characteristics of cement-CWP composite mortar and to identify optimal mix proportions and curing conditions for shrinkage control. A series of experiments were conducted on mortar specimens with varying water-to-binder ratios (W/B = 0.45, 0.50, 0.55) and CWP incorporation rates (0, 5%, 10%, 20%). Three curing regimes were employed: outdoor curing, standard curing (20 °C, 95% RH), and outdoor film curing. Drying shrinkage was monitored over time. Key findings indicate that the optimal CWP content for shrinkage mitigation is 10%. Excessive CWP (>10%) induces a “weak bonding” effect, leading to an increase in shrinkage due to reduced cohesion. Increasing the W/B ratio to 0.55 effectively reduced shrinkage, with the minimum shrinkage value observed at this ratio. Among curing methods, outdoor film demonstrated superior performance in maintaining moisture and suppressing shrinkage. Predictive modeling revealed that the logarithmic model in accurately capturing the nonlinear evolution of shrinkage over time, effectively reflecting the influences of CWP content, W/B ratio, and curing condition. The drying shrinkage of cement-CWP composite mortar can be effectively optimized by incorporating 10% CWP, utilizing a W/B ratio of 0.55, and implementing outdoor film curing. This paper reveals, for the first time, the dual-mechanism regulation of early-age drying shrinkage behavior in cement-based materials by CWP as a supplementary cementitious material and establishes a shrinkage prediction model applicable to various mix proportions and curing conditions, offering practical strategies for enhancing the durability of sustainable construction materials utilizing construction waste powder. Full article

Basketball is an intermittent sport with high neuromuscular and metabolic demands. To optimize specificity, training tasks should replicate competitive loads, but little is known about how drills compare to official matches. This study compared the physiological and biomechanical load of training tasks with official competition in elite U18 basketball players. Twelve male players (16.9 ± 0.8 years) were monitored across two seasons (179 training sessions, 21 matches). A total of 3136 individual records were collected using Catapult Vector S7 LPS units. Training drills were classified by specificity (0–5). Physiological (distance and intensity zones) and biomechanical variables (accelerations, decelerations, jumps, explosive efforts, PlayerLoad™) were analyzed using cluster analysis and linear mixed models. Competition imposed the highest physiological and biomechanical loads. Non-opposition drills (1v0–5v0) showed limited transfer, though 1v0–2v0 accumulated higher jump density. Among opposition formats, 3v3 full-court was the best at replicating match demands. Continuous opposition tasks (3v3v3, 4v4v4, 5v5v5) elicited lower physiological but comparable biomechanical load. Small-sided formats, particularly 3v3 and 4v4, are the most effective training tools for reproducing competition demands, while non-opposition drills are better suited for technical or rehabilitation purposes. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

Early fault vibration signals of rolling bearings are non-stationary and nonlinear, with weak fault signatures easily masked by noise. Traditional denoising methods (e.g., wavelet thresholding, empirical mode decomposition (EMD)) struggle to accurately extract effective features. Although variational mode decomposition (VMD) overcomes mode mixing, its core parameters rely on empirical selection, making it prone to local optima and limiting its denoising performance. To address this critical issue, this study aims to propose a hybrid model with adaptive parameter optimization and efficient denoising capabilities, enhancing the signal-to-noise ratio (SNR) and feature discriminability of early fault signals in rolling bearings. The novelty of this work is reflected in three aspects: (1) An improved edge strategy whale optimization algorithm (IEWOA) is proposed, incorporating six enhancements to balance global exploration and local exploitation. Using the minimum average envelope entropy as the objective function, the IEWOA achieves adaptive global optimization of VMD parameters. (2) A hybrid framework of “IEWOA-VMD + dataset-specific wavelet thresholding for secondary denoising” is constructed. The optimized VMD first decomposes signals to separate noise and effective components, followed by secondary denoising, ensuring both adaptable signal decomposition and precise denoising. (3) Comprehensive validation is conducted across five models using two public datasets (Case Western Reserve University (CWRU) and Paderborn Universität (PU)). Key findings demonstrate that the proposed method achieves a root-mean-square error (RMSE) as low as 0.00013–0.00041 and a Normalized Cross-Correlation (NCC) of 0.9689–0.9798, significantly outperforming EEMD, traditional VMD, and VMD optimized by single algorithms. The model effectively suppresses noise interference, preserves the fundamental and harmonic components of fault features, and exhibits strong robustness under different loads and fault types. This work provides an efficient and reliable signal preprocessing solution for early fault diagnosis of rolling bearings. Full article

Background/Objectives: Specific pharmacological osteoporosis therapy (SPOT) is regarded as a key intervention to reduce fracture risk and improve musculoskeletal function. Real-life data, particularly regarding functional muscular outcomes and pain trajectories, remain limited. This study aimed to longitudinally analyze bone mineral density, laboratory parameters, handgrip strength, functional performance, and pain symptoms under guideline-based SPOT. Methods: In this monocentric prospective real-life observational study, 178 patients (80.9% women; median age 82 years) with confirmed osteoporosis were followed for a median of four years. All patients received guideline-recommended antiresorptive or osteoanabolic therapy. Analyses included T-scores, 25(OH)D, calcium, handgrip strength, Chair Rise Test (CRT), tandem stance (TS), pain parameters, alkaline phosphatase (AP), HbA1c, fractures, comorbidities, and body mass index (BMI). Time-dependent changes were evaluated using linear mixed-effects models. Results: Bone mineral density improved highly significantly (ΔT-score ≈ +0.45 SD; p < 0.001), with no differences between therapy groups (antiresorptive vs. osteoanabolic) or BMI categories. Serum 25(OH)D levels increased markedly (Δ ≈ +20 nmol/L; p < 0.001), while calcium levels showed a small but highly significant decrease (Δ ≈ −0.047 mmol/L; p < 0.001), particularly under antiresorptive treatment. Dominant (Δ ≈ −1.95 kg; p < 0.001) and non-dominant handgrip strength (Δ ≈ −0.83 kg; p = 0.046) decreased significantly. In contrast, functional performance improved significantly: CRT time decreased by ~1 s (p = 0.004), and TS time increased by ~1 s (p = 0.007). Back pain decreased highly significantly (Δ ≈ −1.5 NRS; p < 0.001), while pain-free walking time (Δ ≈ +38 min; p = 0.031) and pain-free standing time (Δ ≈ +31 min; p = 0.038) both increased significantly. AP levels decreased significantly (p = 0.003), particularly among normal-weight patients. HbA1c changes were not significant. Overall, 73% of patients had at least one major osteoporotic fracture. Conclusions: In this real-life cohort, guideline-based specific pharmacological osteoporosis therapy was associated with significant improvements in bone mineral density, vitamin D status, functional performance, and pain-related outcomes. Despite a moderate decline in handgrip strength, balance- and mobility-related functional parameters improved, suggesting preserved or even enhanced functional capacity in daily life. These findings provide real-world evidence on the associations between SPOT, laboratory parameters, functional performance, and pain outcomes in a very elderly and multimorbid population. Full article

In this contribution, we propose and study long-time behaviors of a new class of N-dimensional delayed Kirchhoff–Carrier-type problems with variable transfer coefficients involving a logarithmic nonlinearity. We take into account the dependence of diffusion and damping coefficients on the position and direction, as well as the presence of different types of delays. This class of nonlocal anisotropic and nonlinear wave-type equations with multiple time-varying mixed delays and dampings, of a fairly general form, containing several arbitrary functions and free parameters, is of the following form: ${\displaystyle \frac{{\partial }^{2}u}{\partial t^2}-{div(\mathcal{K}(\Vert \sigma \nabla u\Vert }_{L^2(\Omega )}^2)A_{\sigma }{(\mathbf{x})\nabla u)+\mathcal{M}(\Vert u\Vert }_{L^2(\Omega )}^2)u-div(\zeta (t)A_{\sigma }(\mathbf{x})\nabla \frac{\partial u}{\partial t})+d_0(t)\frac{\partial u}{\partial t}+{\mathbb{D}}_r(\mathbf{x},t;\frac{\partial u}{\partial t})=\mathbb{G}(u),}$ where $u(\mathbf{x},t)$ is the state function, $\mathcal{M}$ and $\mathcal{K}$ are the nonlocal Kirchhoff operators and the nonlinear operator $\mathbb{G}(u)$ corresponds to a logarithmic source term. The symmetric tensor $A_{\sigma }$ describes the anisotropic behavior and processes of the system, and the operator ${\mathbb{D}}_r$ represents the multiple time-varying mixed delays related to velocity ${\displaystyle \frac{\partial u}{\partial t}}$. Our problem, which encompasses numerous equations already studied in the literature, is relevant to a wide range of practical and concrete applications. It not only considers anisotropy in diffusion, but it also assumes that the strong damping can be totally anisotropic (a phenomenon that has received very little mathematical attention in the literature). We begin with the reformulation of the problem into a nonlinear system coupling a nonlocal wave-type equation with ordinary differential equations, with the help of auxiliary functions. Afterward, we study the local existence and some necessary regularity results of the solutions by using the Faedo–Galerkin approximation, combining some energy estimates and the logarithmic Sobolev inequality. Next, by virtue of the potential well method combined with the Nehari manifold, conditions for global in-time existence are given. Finally, subject to certain conditions, the exponential decay of global solutions is established by applying a perturbed energy method. Many of the obtained results can be extended to the case of other nonlinear source terms. Full article

Economic dispatch in wind-integrated power systems is a critical challenge, yet many recent metaheuristics suffer from premature convergence, heavy parameter tuning, and limited ability to escape local optima in non-smooth valve-point landscapes. This study proposes a new hybrid optimization framework, the Fractional Grasshopper Optimization algorithm (FGOA), which integrates fractional-order calculus into the standard Grasshopper Optimization algorithm (GOA) to enhance its search efficiency. The FGOA method is applied to the economic load dispatch (ELD) problem, a nonlinear and nonconvex task that aims to minimize fuel and wind-generation costs while satisfying practical constraints such as valve-point loading effects (VPLEs), generator operating limits, and the stochastic behavior of renewable energy sources. Owing to the increasing role of wind energy, stochastic wind power is modeled through the incomplete gamma function (IGF). To further improve computational accuracy, FGOA is hybridized with Sequential Quadratic Programming (SQP), where FGOA provides global exploration and SQP performs local refinement. The proposed FGOA-SQP approach is validated on systems with 3, 13, and 40 generating units, including mixed thermal and wind sources. Comparative evaluations against recent metaheuristic algorithms demonstrate that FGOA-SQP achieves more accurate and reliable dispatch outcomes. Specifically, the proposed approach achieves fuel cost reductions ranging from 0.047% to 0.71% for the 3-unit system, 0.31% to 27.25% for the 13-unit system, and 0.69% to 12.55% for the 40-unit system when compared with state-of-the-art methods. Statistical results, particularly minimum fitness values, further confirm the superior performance of the FGOA-SQP framework in addressing the ELD problem under wind power uncertainty. Full article

Environmental pesticide exposure has been linked to adverse health effects, and residential proximity to agricultural land is commonly used as a proxy for exposure; however, the contribution of non-agricultural biomes remains insufficiently explored. This study examined whether the proximity and area of different biomes are associated with the detection of selected pesticides in human urine in Latvia. Urine samples were collected from 202 participants (101 adults and 101 children) within the Human Biomonitoring for Europe (HBM4EU) study during the winter and summer seasons of 2020. A suspect screening approach using liquid chromatography–high-resolution mass spectrometry (LC-HRMS) was applied and 23 pesticides were detected (8 insecticides, 12 fungicides, 2 herbicides and triclosan, an antimicrobial ingredient used in cleaning agents). Geospatial data were analysed in Quantum Geographic Information System (QGIS) to derive biome proximity and area within a 1000 m residential buffer; associations were assessed using generalized linear mixed-effects models. Agricultural land was present within 1000 m of 93.1% of residences, yet neither its distance nor area was consistently associated with pesticide detection. Boscalid was detected in 18.4% of samples and was positively associated with wetland area across seasons (p < 0.001), while fludioxonil (14.7%) showed weak and heterogeneous spatial associations and pirimiphos-methyl (10.2%) showed no significant patterns. Overall, pesticide exposure was substance-specific and influenced by landscape characteristics beyond agricultural proximity, highlighting the need to integrate non-agricultural biomes into human biomonitoring in low-intensity pesticide-use settings. Full article

Background and Objectives: Asymmetries in body composition and movement patterns are common in professional basketball due to the sport’s repetitive and unilateral demands. While both structural and functional asymmetries have been independently studied, little is known about their interaction under real training conditions. The aim of this study was to compare structural asymmetries, obtained from bioelectrical impedance analysis, with functional asymmetries, measured through inertial devices in professional basketball players. Methods: Twenty-five male professional basketball players from two Spanish teams were monitored over a two-month period. Structural asymmetries were assessed via the TANITA MC-780MA multi-frequency analyzer, while functional asymmetries were quantified using WIMU Pro™ inertial units during 43 training sessions. Descriptive, correlational, and cluster analyses were performed, followed by linear mixed-effects models adjusted for individual random effects, with statistical significance set at p < 0.05. Results: Descriptive results revealed low overall fat mass and no relevant group-level asymmetries in muscle mass or functional variables, although fat mass asymmetry showed greater variability across players. Correlation analyses indicated weak and non-significant relationships between structural and functional asymmetries. Cluster analysis grouped muscle mass and functional asymmetries together, while fat mass asymmetry formed a distinct cluster. Linear mixed-effects models confirmed significant differences for muscle mass asymmetry and demonstrated high inter-individual variability. Conclusions: Structural and functional asymmetries behave independently, with muscle mass asymmetry showing greater variability and functional relevance. These findings highlight the need for individualized monitoring approaches integrating morphological and functional assessments to optimize performance and reduce injury risk in elite basketball players. Full article