Search Results (1,059)

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article

In resource-constrained environments, distributed split learning allows for collaborative training; however, the system suffers from high communication overhead and is sensitive to system heterogeneity. Despite advances in IoT data reduction and distributed learning, existing approaches treat heterogeneity, adaptability, and communication efficiency as separate problems. As a result, the Heterogeneity-Aware Dynamic Federated Split Learning with Adaptive Compression (HADFL-AC) framework is proposed, enabling adaptive adjustment of communication payloads to instantaneous bandwidth conditions during training. This approach distinguishes itself by focusing on feature-representation-level adaptation, offering seamless transitions between linear PCA, nonlinear Tiny Autoencoder (TinyAE), and hybrid PCA–AE compression methods without requiring changes to architecture or retraining. Experiments were conducted using the CIFAR10 and CI=NIC datasets with a lightweight ResNet-18 backbone under Dirichlet-based non-IID data partitioning and fluctuating network scenarios. HADFL-AC achieves significant communication reductions of 80.86% on CIFAR-10 and 77.2% on CINIC-10, as well as significant reductions in training time and energy consumption. In addition, the framework achieved these gains while maintaining competitive performance, reaching 79.58% on CIFAR-10 and exhibiting stable convergence on CINIC-10. Consequently, the results demonstrate that leveraging network heterogeneity as an adaptive signal facilitates efficient and scalable distributed learning while effectively balancing communication efficiency and model accuracy. Full article

Existing energy consumption models suffer from accuracy degradation and limited robustness in complex urban environments due to insufficient consideration of the route spatiotemporal characteristics of electric buses. To address this limitation, a Time-Partitioned Dual-Layer LSTM (TP-D-LSTM) framework driven by cloud data and spatiotemporal characteristics is proposed. First, a spatiotemporal characteristics analysis is conducted on urban bus routes to reveal the underlying traffic flow dynamics. Based on these insights, a time-partitioning strategy is developed to classify the continuous operating data into independent periods while preserving the kinematic continuity of individual trips. Subsequently, a Dual-Layer LSTM (D-LSTM) is constructed to precisely capture the distinct energy consumption mechanisms within each partitioned scenario. Experiments based on real-world cloud-logged data demonstrate that the proposed TP-D-LSTM framework is superior to existing baseline models. By alleviating the limitations of global mixed modeling, the TP-D-LSTM significantly reduces the Root Mean Square Error (RMSE) to 6.15, achieving an improvement of over 50% compared to the D-LSTM, and exhibits remarkable stability under highly volatile traffic conditions. Full article

This paper presents a symmetric positive definite (SPD) coupling between the boundary element method (BEM) and the finite element method (FEM) in the framework of the numerical manifold method (NMM) for two-dimensional linear elastic static problems. The BEM subdomain is treated as a single mathematical patch whose local approximation is derived from the displacement boundary integral equation, thereby preserving the nonlocal nature of BEM. The remaining domain is covered by a finite element mesh, with each node defining a patch and the associated shape functions serving as weight functions. Weight functions are defined over the entire mathematical cover, with explicit zero values outside the support of each patch. This global definition ensures that the partition of unity holds everywhere and enables the global displacement approximation to be expressed as a superposition of contributions from all patches. Within this unified framework, the interface between the BEM and FEM subdomains emerges naturally as a transition zone of weight functions, rather than a distinct boundary. Displacement continuity is automatically satisfied through the partition of unity, and traction equilibrium is approximately enforced through the variational formulation. To fully incorporate the coupling formulation into the minimum potential energy framework, the tractions on the BEM patch are eliminated in favor of displacements using the displacement boundary integral equation (BIE). Prescribed tractions on the BEM patch are enforced via a penalty method. The resulting algebraic system is symmetric by construction and remains positive definite when either constant or isoparametric boundary elements are used. This work serves as a proof-of-concept study for the SPD coupling framework with constant elements. Numerical examples demonstrate the accuracy and convergence of the method. The results show that the coupling procedure preserves the intrinsic convergence properties of each subdomain: the BEM part converges at a rate close to unity for displacements and approximately 2.0 for stresses, while the FEM part achieves quadratic convergence for both. The study also reveals that near-singular integrals in the strain BIE can affect the convergence rate when the element size becomes sufficiently small. Full article

The integration of rapidly renewable biomass into insect production systems has been proposed as a strategy to improve resource-use efficiency in insect production. This study evaluated the graded inclusion levels of dried watermeal (Wolffia globosa) in diets of two-spotted crickets (Gryllus bimaculatus) and assessed voluntary nutrient regulation under free-choice feeding. Four fixed-inclusion diets (0%, 25%, 35%, and 45% watermeal) and one self-selection treatment were tested over 28 days. Growth performance, feed conversion ratio (FCR), survival rate (Surv), production index (PI), and whole-body composition were determined. Repeated-measures analysis using linear mixed-effects models indicated that treatment, week, and their interaction were statistically significant (p ≤ 0.024). However, partial R² analysis showed that the independent contributions of treatment and week were negligible, whereas the treatment × week interaction explained measurable variance, indicating that dietary effects were primarily expressed through time-dependent responses. Segmented regression identified a breakpoint at 35% watermeal inclusion (95% CI: 24.93–45.07), indicating that PI was the highest within a moderate supplementation range under the present fixed-diet conditions rather than at a precise single optimum. Inclusion levels beyond this threshold reduced performance. Under free-choice conditions, crickets progressively increased watermeal intake with age and maintained stable nitrogen-free extract (NFE):crude protein (CP) and gross energy (GE):CP intake ratios, selecting an average of 25–35% watermeal over the experimental period. This supplementation range improved feed efficiency and protein deposition while limiting lipid accumulation, suggesting improved energy–protein balance and nutrient partitioning. The self-selection result is interpreted as evidence of behavioral intake regulation under choice conditions and not as direct validation of the segmented-regression breakpoint. Collectively, these findings provide complementary statistical and behavioral evidence supporting a biologically relevant moderate inclusion range (approximately 30–35%) of dried watermeal. Full article

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

Industrial symbiosis (IS) research has documented many successful ecosystems but still lacks an empirically grounded typology linking resource flow configurations to environmental outcomes across diverse contexts. This study develops such a typology and tests whether distinct configurations achieve comparable environmental performance through different pathways—the configurational principle of equifinality. Drawing on 68 documented IS ecosystems across 48 countries, we apply k-means clustering to five flow-intensity dimensions—material, energy, water, logistics, and knowledge—and characterise the resulting partition using one-way ANOVA, Tukey HSD post hoc tests, multinomial logistic regression, and a Cox proportional-hazards model. Four configurations emerge: a dominant low-flow group (n = 34) and three coordinated configurations—energy–knowledge (n = 11), material-dominant (n = 16), and water-oriented (n = 7). The three coordinated configurations all significantly outperform the low-flow group on environmental performance (F(3, 57) = 11.60, p < 0.001), with effect sizes very similar and no significant differences among them, providing direct empirical evidence for equifinality. Economic performance does not differ significantly across configurations, and the multinomial model of contextual predictors is jointly insignificant—a pattern we read as consistent with equifinal contextual pathways rather than as a methodological flaw. Robustness checks across alternative clustering algorithms, operationalisations, and sub-samples support the typology’s stability. This study contributes an empirically grounded framework for circular economy practice that moves beyond one-size-fits-all prescriptions and offers a configurational lens for the design of sustainable industrial ecosystems. Full article

Bangia fuscopurpurea is a marine alga with significant commercial value. Although a high-light adapted species, the productivity of its commercial cultivation is frequently limited by environmental light attenuation, resulting in the algae operating under energy-limiting, sub-saturating conditions. This study investigated its physiological responses and antioxidant defense mechanisms across a sub-saturating light gradient (20, 40, and 80 µmol photons m⁻² s⁻¹). We employed exogenous ascorbic acid (AsA) supplementation to evaluate the dynamic response of the ascorbate-glutathione (AsA-GSH) cycle. Without AsA supplementation, the 40 µmol photons m⁻² s⁻¹ condition supported redox homeostasis and the highest soluble protein accumulation. In contrast, the lowest irradiance (20 µmol photons m⁻² s⁻¹) restricted physiological performance. At 80 µmol photons m⁻² s⁻¹, which remained below the light saturation point, the algae experienced oxidative stress, indicated by elevated lipid peroxidation and hydrogen peroxide levels. The efficacy of exogenous AsA depended on these energy states. Under the highest tested irradiance (80 µmol photons m⁻² s⁻¹), AsA reduced malondialdehyde (MDA) and maintained electron transport capacity, but these effects were accompanied by a significant degradation of photosynthetic pigments. These findings imply an altered partitioning of cellular reducing power, where the demand for AsA regeneration might limit the resources available for biosynthetic pathways. The study highlights that antioxidant efficacy is constrained by the cellular energy availability, which limits simultaneous stress mitigation and growth in light-limited aquaculture environments. Full article

Nutrient-sensing nuclear receptors (NSNRs), including PPARs, FXR, LXRs, RAR/RXR, VDR, and related orphan receptors, integrate a molecular interface that allows diet to communicate directly with the genome. By binding fatty acids, bile acids, sterols, vitamins, polyphenols, and other food-derived metabolites, NSNRs translate qualitative and quantitative features of the diet into coordinated transcriptional programmes across metabolically active organs. This ligand-dependent signalling network integrates dietary information to orchestrate inter-organ lipid and glucose metabolism, mitochondrial function, thermogenesis, and immune response, thereby enabling the organism to adapt dynamically to fasting–feeding cycles. In this review, we synthesise current evidence on the integrated roles of major NSNRs in the liver, skeletal muscle, white and brown adipose tissue, and kidney, emphasising how receptor networks within and between metabolic organs collectively govern energy expenditure, substrate partitioning, and systemic metabolic flexibility. We propose a conceptual framework in which diet functions as an “external endocrine organ”, acting as the primary source of chemically diverse NSNR ligands, while metabolic tissues serve as secondary signal amplifiers and integrators. Through circulating lipid species, bile acids, oxysterols, and other metabolites, these organs engage in continuous bidirectional communication that reprograms NSNR activity across tissues. We then examine how the global shift from minimally processed, nutrient-rich foods to nutrient-poor, energy-dense ultra-processed diets leads to a reduction in NSNR ligand diversity, promoting hepatic steatosis, muscle metabolic inflexibility, adipose tissue dysfunction, renal lipotoxicity, and chronic low-grade inflammation, ultimately causing obesity, type 2 diabetes, and cardiometabolic disease. Finally, we explore strategies to restore NSNR function, including Mediterranean and plant-based dietary patterns, as well as diets enriched with ω-3 polyunsaturated fatty acids, monounsaturated fats, and polyphenols. By integrating molecular, physiological, and clinical evidence, this review aims to clarify how NSNR networks translate dietary cues into coordinated inter-organ metabolism and how nutrient-poor diets lead to metabolic diseases trough a loss of metabolic information, rather than merely by energy excess. This framework supports a paradigm shift from calorie-centred nutrition to diet quality as the main therapeutic target for preventing metabolic diseases and promoting health. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

Sulfamethoxazole (SMX) is one of the most frequently detected antibiotics in aquatic environments and is difficult to remove by conventional biological treatment because of its persistence, potential toxicity to microbial communities, and associated risk of antibiotic resistance selection. Aerobic granular sludge membrane bioreactors (AGMBRs), which combine the compact and stratified structure of aerobic granular sludge with membrane-based solid–liquid separation, have emerged as a promising platform for SMX-contaminated wastewater treatment because they provide high biomass retention, decoupled sludge retention time (SRT) and hydraulic retention time (HRT), and stable effluent quality. This review systematically summarizes recent advances in AGMBRs for SMX removal, with emphasis on how operating parameters (e.g., dissolved oxygen, hydraulic retention time, organic loading rate, C/N ratio, and sludge retention time) and membrane-related factors (e.g., membrane flux, aeration-induced shear, membrane type, and pore size) affect treatment performance and process stability. The main SMX attenuation pathways in AGMBRs are discussed from three perspectives: sorption and partitioning within granules and extracellular polymeric substances (EPSs), microbial biodegradation and co-metabolism, and membrane retention that prolongs effective contact time and shapes microbial ecology. Particular attention is given to the dual role of EPS and soluble microbial products (SMPs), which contribute to granule stability and SMX tolerance but also accelerate membrane fouling through cake-layer formation, pore blocking, and transmembrane pressure increase. Current challenges include incomplete understanding of transformation products, ARG- and MGE-related risks, long-term fouling–biodegradation interactions, and the lack of pilot-scale validation. Future research should therefore focus on mechanism clarification, integrated control of removal and fouling, energy-efficient operation, and scale-up of AGMBRs for practical antibiotic wastewater treatment. Full article

Against the backdrop of accelerated terrestrial hydrological cycling and the increasing concurrence of drought-heatwave compound extremes under global warming, regional land-atmosphere coupling has emerged as a central mechanism shaping climate feedbacks and trajectories of ecosystem carbon uptake. However, prior studies spanning climatic regimes, observational scales, and data sources have often yielded contradictory conclusions. Here, we challenge these fragmented perspectives by constructing an integrated LST-SM-ET-GPP chain that jointly represents land surface temperature, soil moisture, evapotranspiration, and gross primary productivity, thereby linking water availability, surface energy balance, and plant physiological processes within a unified framework. We synthesize a conceptual diagnostic roadmap for interpreting land-atmosphere coupling across observations and models. When ecosystems operate in humid, energy-limited environments, radiative and advective controls should be prioritized to diagnose system forcing. By contrast, as the system becomes water-depleted, attribution must shift to a nonlinear regime transition framework governed by a critical soil moisture threshold. This threshold mechanism implies that, once the system enters the moisture-limited regime, even modest declines in soil moisture can trigger a rapid weakening of evaporative cooling, substantially amplifying LST anomalies and strongly suppressing GPP. The competitive regulation of stomatal conductance by atmospheric demand (vapor pressure deficit, VPD) and terrestrial supply (rootzone soil moisture) further explains why the “dominant” controlling factor can dynamically reverse across hydrothermal states, timescales, and stages of extreme-event evolution. Notably, the steady-state coupling assumption may break down under flux “flooring” during extreme drought, or when structural buffering such as deep root water uptake is present, delineating strict applicability bounds for existing diagnostic frameworks. Finally, current assessments remain constrained by multiple uncertainties, particularly the lack of ET partitioning constraints, representativeness biases arising from clear-sky observations and sampling-depth limitations, and systematic errors in Earth system model simulations during the warm season. Full article

In the field of study of fluid–structure interaction (FSI), sloshing dynamics play a crucial role in various engineering applications, from aerospace to civil infrastructure. Finite Volume (FV)-based Computational Fluid Dynamics (CFD) methods for modeling free surface flows like sloshing are computationally expensive, particularly because high-resolution dynamic transient simulations are required. Moreover, FSI effects are usually considered by coupling different solvers for the fluid and the structural domain, respectively, thus adding to the computational burden due to the various steps of data transfer, interpolation, and mesh adaptation needed to obtain accurate results. On the other hand, reduced-order models of sloshing effects are usually obtained by tuning equivalent mechanical models, which often neglect more complex geometries and imperfections. To address this challenge, the use of acoustic finite elements, as an alternative approach for modeling free surface flows interacting with flexible structures, has been proposed previously. Such elements are defined with the sole dynamic pressure as the nodal degree of freedom; therefore, such methods can significantly accelerate simulations to predict sloshing-induced forces and pressure distribution, taking into account the actual geometry of the structure. Due to the reduced computational time, FSI analysis with acoustic elements can serve as a viable tool for control systems and design optimization. Potential applications of this approach include structural analysis of anti-sloshing devices in rocket propellant tanks, control systems for enhanced launch stability, and seismic safety assessment of liquid storage tanks, as well as slosh-induced wall load evaluation in the fuel and water reservoir, transportation, and energy systems. Validation of FSI effects is conducted against results from partitioned two-way coupled fluid–structural simulations. The simplified frequency-prediction model was reliable for practical flexibility ranges. Overall, this work deepens our understanding of how baffle characteristics influence slosh mitigation, offering valuable guidance for anti-sloshing device engineering. Full article

Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpretability, and preserve adaptability as operating conditions evolve. To address this issue, we propose a multimodal short-term photovoltaic (PV) forecasting method that integrates weather-mode partitioning using the Clustering Using Representatives (CURE) algorithm with a self-updating Batch-Lasso model. First, the meteorological-PV dataset is partitioned along two dimensions by combining seasonal grouping with CURE clustering within each season, producing representative weather modes and enhancing the fidelity of weather pattern classification. Second, to extract informative predictors from high-dimensional meteorological inputs while maintaining interpretability, we formulate per-mode Lasso regression and adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to efficiently solve for the sparse regression coefficients. Third, we introduce a batch-based self-update and correction mechanism with rollback verification, enabling the mode-specific models to be refreshed as new historical data become available while preventing performance degradation. Compared with representative machine learning baselines, the proposed method maintains competitive accuracy with substantially lower computational and storage overhead, enabling high-frequency and energy-efficient inference on resource-constrained terminals, thereby reducing operational burdens and computational energy costs and better meeting the deployment needs of sustainable energy systems under heterogeneous weather conditions. Full article

Cosmological filaments, galaxy clusters, and galaxies are magnetized reservoirs of cosmic rays (CRs). The exchange of CRs across these structures is usually modeled assuming that they remain charged and magnetically confined. At high energies, hadronic interactions can convert CR protons to neutrons. This physics is routinely included in air-shower and ultra-high-energy (UHE) CR propagation Monte Carlo simulations used for composition studies but is rarely treated explicitly in propagation models of CR transport and exchange between magnetized reservoirs. CR neutrons are not affected by magnetic fields and can propagate ballistically over kpc-Mpc distances before decaying back into protons, with relativistic time dilation extending their effective decay length. We show how such charged–neutral switching modifies CR confinement and escape in four representative environments: a Milky Way-like galaxy, a starburst galaxy, a galaxy cluster, and a cosmological filament. By solving the transport of a confined CR proton population in each structure using a diffusion/streaming propagation approach with hadronic pp and p$\gamma$ interactions, and treating neutron production and decay as a stochastic Poisson “jump” process, we find that neutron-mediated steps can allow additional CR escape from large-scale cosmological structures at energies where charged-particle transport alone would predict strong CR confinement and attenuation in ambient radiation fields. These effects imply a qualitative shift in how ultra-high-energy CRs are transferred from embedded sources into filaments and voids once intermediate neutron propagation is considered, with consequences for the partitioning of CRs across the large-scale structure of the Universe. Full article