Search Results (38,231)

Improving the thermal conductivity of polymer/carbonaceous nanocomposites is still an interesting topic and is affected by a number of factors, such as polymer/nanofiller interaction, dispersion quality and agglomerate formation. In the present work, several polymer/nanocomposite types based on two different linear low-density polyethylenes (LLDPEs), as well as polylactic acid (PLA), reinforced with carbon nanotubes (CNTs) or a combination of CNTs/carbon nanofibers (CNFs) with graphene oxide (GO) at various loadings were investigated. The thermal diffusivity results for monofiller and hybrid nanocomposites with varying nanofiller loadings were analyzed. Additionally, existing models were employed to simulate the thermal conductivity using experimental data of both monofiller and hybrid nanocomposites. The model parameter values were utilized to explore the mechanism of the thermal conductivity increase, the role of CNTs and the synergistic effects of hybrid nanocomposites. Full article

Stress urinary incontinence (SUI) is conventionally viewed as a peripheral mechanical disorder, yet its psychological burden suggests that a broader biopsychosocial interpretation may be warranted. This perspective considers whether recurrent leakage, anticipatory anxiety, behavioural avoidance, and social evaluative threat may allow SUI to function as a chronic psychosocial stressor. It integrates clinical observations with established concepts in stress physiology, hypothalamic–pituitary–adrenal (HPA) axis regulation, allostatic load, inflammation, and stress-related epigenetic mechanisms, including FKBP5 and NR3C1. Direct molecular evidence in SUI populations remains limited; therefore, the proposed links are presented as a hypothesis-generating framework rather than as established causal pathways. The framework further emphasises the heterogeneity of patient outcomes. Differences in perceived symptom burden, coping, stress responsivity, prior vulnerability, and resilience may help explain why psychological symptoms improve after treatment in some individuals yet persist in others. These interacting psychosocial and biological processes may reinforce a feedback loop between symptom-related threat, sustained stress activation, and affective vulnerability. By moving beyond a solely mechanical model, this perspective supports a more interdisciplinary approach combining urogynecological and psychological assessment. The proposed stress-epigenetic framework is intended to guide future longitudinal and biomarker-informed research and to encourage more integrated approaches to the prevention and management of psychological burden in SUI. Full article

The polyunsaturated fatty acid (PUFA) content of phytoplankton determines the nutritional quality of aquatic basal food resources and influences food-web energy transfer. Field studies suggest that phytoplankton PUFA content is associated with taxonomic characteristics, with nutrient enrichment promoting cyanobacteria dominance to reduce phytoplankton PUFA content. Nevertheless, laboratory evidence indicates that nutrient enrichment can also alter the fatty acid composition of single algae species. The role of intraspecific physiological alterations has long been overlooked. To partition the relative importance of taxonomic and intraspecific physiological alterations on phytoplankton nutritional quality, nutrient-manipulated mesocosm experiments were conducted in spring and winter using natural plankton communities from a tropical eutrophic reservoir. Fatty acid analysis showed that the nutritional quality of the phytoplankton community declined in mesocosms after nutrient additions. Piecewise structural equation modeling (pSEM) was employed to disentangle direct effects (representing intraspecific physiological alterations) and indirect effects (via the Shannon–Wiener index, representing taxonomic alterations) of phosphorus enrichment on ω-3 PUFA. Within each season, phosphorus enrichment exerted a significant direct negative effect on ω-3 PUFA, regardless of whether cyanobacteria dominated in winter or chlorophytes dominated in spring. In the pooled cross-seasonal model, community diversity showed a significant positive effect on ω-3 PUFA, with a stronger standardized effect than that of phosphorus enrichment, whereas phosphorus enrichment still exerted a significant direct negative effect. Our results indicate that intraspecific physiological alterations in phytoplankton may play a crucial role in the changes in phytoplankton nutritional quality following additional nutrient loading, particularly in tropical, eutrophic waters. However, the underlying biochemical mechanisms remain uncertain because direct molecular or enzymatic evidence was not obtained in this study. Full article

A field-oriented, real-time implementation of the previously developed vision-based bridge weigh-in-motion (V-BWIM) framework is presented for vehicle-load monitoring under variable-speed and mixed-traffic conditions. Vehicle and wheel positions are obtained through YOLOv5-based object detection, binocular measurement, and coordinate transformation, whereas bridge responses are measured by strain gauges and synchronized with the vision-derived axle trajectories. A field-image dataset constructed from full-scale bridge experiments is used to compare object-detection performance, inference efficiency, and model complexity, and YOLOv5s is selected for field implementation. The calibrated bridge influence line is then combined with synchronized axle positions and structural responses for axle-load identification. Controlled field tests on a simply supported bridge quantitatively evaluate vehicle positioning and load identification under constant-speed, variable-speed, and two-vehicle car-following conditions. A further continuous-beam bridge experiment demonstrates vehicle-information estimation under random traffic. Because independent reference weights were unavailable for the randomly passing vehicles, the continuous-bridge results are interpreted as a field demonstration rather than an independent validation of load-identification accuracy. Full article

The growing demand for large, lightweight, heat-resistant, and multifunctional aerospace structures has raised the requirements for metal matrix composites in terms of defect minimization, performance enhancement, and near-net-shape manufacturing. Additive friction stir deposition of composites enables feedstock delivery, reinforcement mixing, and layer-by-layer consolidation in a thermoplastic state below the melting point of the matrix, thereby mitigating porosity, hot cracking, elemental segregation, reinforcement degradation, and excessive interfacial reactions commonly encountered in fusion-based additive manufacturing. This review summarizes recent advances in the application of this technology to the fabrication of metal matrix composites, elucidates the mechanisms of material flow, interlayer bonding, microstructural evolution, and defect formation during deposition, and discusses the effects of tool design, process parameters, and reinforcement characteristics on interfacial bonding, microstructure control, and mechanical properties. Remaining challenges include the uniform delivery and quantitative control of reinforcements, characterization of interfacial bonding and load transfer, forming stability of complex components, and evaluation of in-service performance; accordingly, thermo-mechanical-flow multiphysics models, multisensor closed-loop control systems, and unified quality-assessment methods should be developed to promote the engineering application of large-scale, multimaterial graded aerospace components. Full article

High penetration of renewable energy imposes greater demands on the scheduling flexibility of demand-side resources in virtual power plant (VPP) dispatch. Nevertheless, heterogeneous resources exhibit remarkable differences in response reliability, and electric vehicles (EVs) in particular show distinct execution performance between orderly charging and vehicle-to-grid (V2G) modes. To tackle this issue, this paper proposes a two-stage optimal scheduling strategy for multi-agent VPPs incorporating scheduling success probability. A quantitative model for the effective dispatch contribution coefficient is constructed from two dimensions, i.e., relative capacity weight and dispatch execution reliability, with differentiated parameters tailored for EV charging and V2G modes. The two-stage leader–follower game problem is decoupled via backward induction, and the optimal dispatch price is rigorously derived through Karush–Kuhn–Tucker conditions. A 24 h case study covering wind power, photovoltaics, energy storage, EVs, and air-conditioning loads validates the proposed method. Results indicate that the strategy boosts total VPP revenue by 7.43% compared with independent operation, lifts the renewable energy accommodation rate from 88.3% to 94.6%, and reduces the average operating cost by 19 CNY/MWh. Through dual-mode differentiated scheduling, EVs achieve 5.10% revenue growth and serve as a key flexible resource for VPP economic operation. Full article

Andes hantavirus (ANDV) is a rodent-borne orthohantavirus associated with hantavirus cardiopulmonary syndrome in southern South America. Its maintenance and spillover risk depend on the ecology of its principal reservoir, Oligoryzomys longicaudatus, and on environmental changes that alter habitat availability, host abundance, and human–rodent interfaces. We developed a mechanistic eco-epidemiological model that couples effective habitat cover to an SIR framework for ANDV transmission in O. longicaudatus. Habitat degradation and compensatory restoration modify rodent carrying capacity, natality, and the force of infection, which depends on infected host load relative to instantaneous ecological capacity. We derived the habitat equilibrium, the basic reproduction number ${\mathcal{R}}_0$, and the time-dependent effective reproduction number ${\mathcal{R}}_e\left(t\right)$ and evaluated infection-burden and threshold indicators across degradation-restoration scenarios. The analysis shows that ${\mathcal{R}}_0$ is independent of equilibrium habitat cover because susceptible abundance scales with carrying capacity at the disease-free equilibrium. In contrast, ${\mathcal{R}}_e\left(t\right)$, cumulative incidence, and infected load depend on transient habitat-mediated crowding. Restoration increases reservoir abundance and absolute infection burden, whereas degradation can reduce abundance while increasing crowding-driven transmission pressure and prolonging supercritical windows. These results identify ecological conditions under which habitat change may intensify reservoir infection pressure and guide One Health surveillance at human–rodent interfaces. Full article

Environmental-monitoring systems often bundle sensing, communication, storage, visualization, and control into one application, making later changes difficult. We designed a service-oriented platform that separates these functions through defined interfaces. It combines a Raspberry Pi gateway, a dedicated motor-control microcontroller, five environmental sensor modules, Node-RED middleware, a database, and a web interface. Deterministic code alone evaluates threshold and composite rules and controls safety-relevant alerts; an optional large language model (LLM) turns pre-computed statistics and rule outcomes into narrative reports. We examined data acquisition and rule processing during two short indoor campaigns. In the residential campaign, the SCD41 yielded 78 valid three-minute bins (234 min of recorded data) across four sessions between 09:18 and 17:12 local time; binned CO₂ concentrations ranged from 679 to 1471 parts per million (ppm). Using the initial campaign for development and the residential campaign as a temporal holdout, the persistence model produced a 15 min forecast mean absolute error of 58.3 ppm and a root mean square error of 78.8 ppm. A separate controlled experiment generated 270 reports from nine deterministic synthetic scenarios. Every reporter preserved all deterministic alert identifiers, while the fixed template and seven of the nine locally hosted LLMs achieved 100% numerical fidelity. Qwen 3.5 9B was the only LLM that returned all required measured content without automated claim-review flags and produced identical outputs across repetitions for every scenario. These results confirm integration and functional separation under the tested conditions, but they do not demonstrate week-scale reliability, longer-horizon forecasting accuracy, robotic mobility performance, or load scalability. Full article

Accurate estimation of Behind-the-meter Photovoltaic (BTM PV) generation is essential for load forecasting and grid planning. Most distributed PV systems are installed behind customer meters, making their output unobservable. Disaggregating PV output from net load is therefore critical for improving distribution network observability. However, existing deep-learning-based disaggregation methods require large labeled datasets, and obtaining such data is costly. Under limited budgets, only a few users can be labeled, which constrains model performance. This paper proposes a user-level BTM PV disaggregation method based on active learning with adaptive weight updates, aiming to maximize model performance with minimal labeling cost. We design a multi-dimensional user value assessment system incorporating epistemic uncertainty, aleatoric uncertainty, and representativeness. In each iteration, the most informative users are selected for sub-meter installation. To dynamically optimize the selection strategy, we propose an adaptive weight update mechanism that adjusts the weights for the next round based on performance improvement gradients across dimensions. This closed-loop feedback enables the strategy to capture evolving model needs and prioritize users that yield the greatest performance gains. The proposed method is validated on the public Ausgrid dataset, and experimental results demonstrate its effectiveness under limited budgets. Full article

Modern distribution networks increasingly face reverse power flow, heavy loading or overloading, and voltage violations as distributed generation and flexible demand introduce large spatiotemporal variations in active-power injections and withdrawals. This paper proposes a planning-operation consistent DC-AC time-series optimal power flow (OPF) method for flexible-resource planning and operation optimization to mitigate these problems. The DC module optimizes investment decisions with embedded DG curtailment and flexible-load regulation to improve the operational relevance. The AC module further considers resource reactive-power flexibility and optimizes their operation under voltage constraints. The consistent design of the two modules in objective structure, operating constraints, and flexible-resource representation allows the planning results to be parsed as the initial schedule for AC operation refinement, improving operation-optimization efficiency. Furthermore, the model introduces discrete type-and-number BESS planning, endogenous initial state of charge (SOC) optimization, and a unified flexible-load model to improve operability and economic relevance. The method is implemented in a CloudPSS-based DSLab environment and tested on a real distribution feeder and the IEEE 123-node benchmark. The real-feeder case demonstrates coordinated mitigation of reverse-power export, branch overloads, and voltage violations. In the IEEE 123-node benchmark, the 8760 h AC operation case converges in 376.31 s, confirming tractability for long-horizon time-series optimization. Full article

This paper presents a measurement-driven study of end-to-end latency in an indoor private standalone 5G network deployed at TU Wien IFT TEC-Lab. The testbed combines pico radio units, edge computing resources, and a local 5G core to form a campus-scale private network architecture designed for low-latency communication. To characterize network performance, TCP throughput and UDP one-way latency measurements were collected in a single-user, constant-bit-rate downlink setting at seven predefined indoor locations using iPerf-based tests at six traffic levels (1–500 Mbps), with five repetitions per condition. Based on these measurements, a compact parametric model was calibrated for this deployment to describe latency as a function of achieved bandwidth and location-dependent effects. The results show that latency remained low and relatively stable at low and medium traffic levels, generally staying below 20 ms between 1 and 200 Mbps, but increased more strongly as the operating point approached the practical throughput limit of the setup. The fitted model captured the overall latency trend with an in-sample MAE of 2.52 ms, an RMSE of 3.13 ms, and an $R^2$ of 0.842, while retaining comparable predictive performance under a trial-based test split ($R^2=0.835$) and leave-one-location-out validation ($R^2=0.818$). The compact model also achieved lower out-of-sample errors than the minimal M/M/1-type and polynomial-regression baselines under both validation schemes. Overall, the findings indicate that traffic load was the main driver of latency growth in the studied environment, while spatial effects remained measurable but secondary. The resulting formulation should be understood as an interpretable empirical model of end-to-end latency for one indoor private standalone 5G deployment under single-user, constant-bit-rate downlink conditions, rather than as a general latency model for private 5G networks. Within that scope, the model provides an interpretable description of the measured latency behavior and a basis for preliminary capacity assessment in this deployment. Its applicability to another private 5G network has not been established and would require a new measurement campaign, complete parameter re-estimation, and independent validation. Full article

To investigate the effects of end-plate thickness on the seismic performance and damage evolution of K-shaped eccentrically braced frames with end-plate connections (EPEBFs), two scaled specimens with different end-plate thicknesses were tested under low-cycle reversed loading. The influence of end-plate thickness on mechanical behavior and seismic response was evaluated from failure modes, hysteretic response, skeleton curves, ductility, energy dissipation, and stiffness degradation, together with damage assessment using different models. Refined finite element models were then established for parametric analysis. The results show that EPEBFs exhibit a well-defined plastic development path. Damage in the reference specimen was mainly concentrated in the link and its end-plate connection region, thereby protecting the frame columns, beams, and braces. For the two thicknesses tested, the specimen with thicker end plates showed higher lateral resistance, greater ultimate deformation capacity, and better late-stage resistance retention, but lower displacement ductility, indicating enhanced absolute deformation capacity but reduced post-yield deformation reserve. Thicker end plates also altered stress transfer and plastic development in the connection regions, causing damage to extend toward the frame-beam ends. Among the damage models considered, the elastic–plastic energy dissipation ratio model better captured the accumulation of energy dissipation and plastic deformation. The finite element results agreed well with the tests. Within the investigated range, increasing end-plate thickness mainly improved ultimate deformation capacity and late-stage resistance retention, without a clear monotonic effect on lateral resistance. The findings provide a basis for prefabricated design, damage control, and post-earthquake replacement and repair of EPEBFs. Full article

The increasing use of rooftop photovoltaic (PV) systems in distribution networks can lead to operational challenges, including feeder overloading, reverse power flow, fluctuating net-load characteristics, and future hosting-capacity constraints. This research presents an integrated data-driven approach for the holistic evaluation of the impacts of rooftop PV on the Sohar Grid Station (GSS) distribution network in Oman. The proposed framework integrates spatial feeder-loading analysis, PV adoption assessment, net-load and duck-curve evaluation, reverse-power-flow detection, PV performance analysis under dust conditions, machine learning-based net-load forecasting, hosting-capacity screening, and SHAP-based explainability. The framework is based on operational feeder data, Sahim PV installation and export data, meteorological factors, irradiance, and dust-related metrics. The findings demonstrate that the present network is already under operational stress, with 10 out of 35 feeders exceeding the 100% loading reference limit, including two feeders that reached loading levels of 192.8% and 183.3%, respectively. The present PV penetration of the Sahim system causes a modest decrease in net daytime demand and localised reverse-power-flow effects on feeders with higher PV-to-load ratios. The hosting-capacity study identified a PV accommodation potential of 27.11 MWp DC, compared with an existing installed capacity of 2.037 MWp DC, indicating substantial screening-level potential for additional PV deployment, although the available capacity varies across feeders and should not be interpreted as a definitive interconnection limit. Among the evaluated forecasting methods, the combined convolutional neural network and long short-term memory (CNN-LSTM) model achieved the lowest root mean square error (0.063 MW) and the highest coefficient of determination (0.984). In addition, SHAP analysis of the Random Forest model showed that recent and weekly historical net-load values were the dominant predictors. The proposed framework provides a useful decision-support tool for reliable rooftop PV integration and distribution network planning. Full article

Thermostatically controlled loads (TCLs) can respond rapidly to frequency disturbances, but fast modulation alone does not establish firm primary-frequency reserve. This study develops a readiness framework that links evidence synthesis, probabilistic reserve sizing, post-event recovery and feeder-level compatibility. A focused review of 74 journal studies informed a two-stage simulation framework. Stage A combined deterministic stress testing, 300-realization Monte Carlo sizing and an independent capacity-hold verification, while Stage B embedded this independently simulated profile in an 8500-node benchmark distribution feeder. The realistic aggregation remained frequency-responsive, but operational constraints produced substantial nominal-to-firm derating. For the modeled upward-reserve case, under the assumed population distributions and operating conditions, an empirical declaration of 0.885 MW achieved approximately 95% joint compliance, while only 56.35% of event-state nominal upward flexibility was firm in the independent verification. Recovery remained distinct from delivery readiness, and feeder analysis showed that an aggregate-compliant profile could still create local voltage and loading violations. The results show that credible TCL reserve requires reliability-conditioned derating, explicit recovery assessment and feeder-specific qualification rather than reliance on nominal flexibility or response speed alone. Full article

Accurate modeling of the tooth friction coefficient is central to analyses of efficiency, vibration, and durability in enclosed gear drives. Physics-based models describe these contacts using a large set of coupled thermal, contact, and lubrication laws whose individual parameters have uncertainties that are difficult to propagate. This study proposes a compact alternative: the HAF model, a three-parameter phenomenological description of the friction coefficient as a function of the slide-to-roll ratio (SRR). The three parameters have distinct tribological interpretations: h (hysteresis) governs the steepness of the sigmoidal transition, a (attrition) governs the slope of the plateau, and ν (friction level) governs the overall magnitude. The model is calibrated using nonlinear regression with fourteen two-disc traction curves acquired with a one-factor-at-a-time (star) design around a reference operating point (1.6 GPa, 80 °C, and 20 m/s): the contact pressure (1.2, 1.6, and 1.9 GPa), the injection temperature (40, 80, and 100 °C), and the mean speed (10, 20, and 30 m/s) are each varied in turn, for both smooth and rough discs. Parameter uncertainty is quantified using the residual-resampling bootstrap (BIG) established in a companion paper and applied over 10⁵ iterations; it yields near-Gaussian, weakly correlated parameter distributions. The three HAF parameters are then expressed as linear functions of load, temperature, speed, and roughness; least-squares inference across the fourteen conditions shows that eleven of the fifteen regression coefficients differ significantly from zero at the 5% level, with roughness having the strongest effect on the friction level (t = 7.98). Substituting these laws into the HAF equation and reoptimizing the resulting expression globally yields a single closed-form model that reproduces the measured friction coefficient with a residual spread of σ ≈ 0.001 in friction-coefficient units (R² ≈ 0.996) and approximately Gaussian, zero-mean, and homoscedastic residuals with no evident systematic structure. Being differentiable and equipped with bootstrap confidence intervals, the model predicts friction throughout the tested operating envelope—across which the maximum friction coefficient varies by a factor of eight, from 0.0049 to 0.0388—and supports gradient-based optimization of low-friction operating conditions. The contribution of this study is a compact, interpretable, and statistically characterized predictive law for tooth friction, expressed in closed form as a function of the operating conditions and of the surface state. Full article