Search Results (331)

This paper presents the performance analysis of a 100 Gbps long-reach passive optical network (LR-PON) based on intensity modulation and direct detection (IM-DD). The LR-PON is designed for low-complexity environments that reuse previously deployed infrastructure and extend coverage to rural areas. It features a point-to-multipoint PON topology with a 1:64 split and links up to 100 km long. The paper analyzes the impact of the booster amplifier, preamplifier, and chromatic-dispersion-compensating module on the bit error rate (BER) using OptSim simulations. The results demonstrate that the LR-PON, operating at 100 Gbps over a 100 km link and with losses over 3 dB over a legacy network, maintains acceptable BER levels in the order of ${10}^{-6}$, validating its viability as a scalable, efficient, and economical solution for optical access networks in suburban or rural areas in locations such as Quito city (Ecuador). Full article

Background: The Canada Health Act (CHA), enacted in 1984, guarantees universal access to medically necessary care, yet inequities in hospital use persist. Acute length of stay (ALOS) is a key indicator of hospital efficiency, patient recovery, and healthcare system performance, with prolonged stays linked to higher costs, avoidable infections, and strain on acute care capacity. Understanding patterns in ALOS is critical not only for hospital management but also for public health, as extended stays can limit timely access to care and exacerbate population-level health inequities. Objective: This study examines social, geographic, and clinical gradients in ALOS and investigates whether the effects of admission urgency vary by sex, neighbourhood income, and rural–urban residence within a universal healthcare system. Methods: Using 2024–2025 hospital discharge data from the Canadian Institute for Health Information, this study examined ALOS as a function of comorbidity, sex, socioeconomic status, rural–urban residence (geography), and admission type (urgent versus elective). Interaction effects between admission urgency and key social and geographic variables were evaluated to assess subgroup differences in ALOS. Results: Disparities in ALOS were evident. Older age, male sex, urgent admission, and greater comorbidity were associated with longer stays, whereas higher neighbourhood income and urban residence were linked to shorter stays. Interaction analyses revealed substantial heterogeneity: compared with elective rural admissions, urgent urban admissions had 30.4% longer ALOS. Urgent admissions also amplified socioeconomic and sex-based differences, with male patients experiencing 27.9% longer stays than females. Conclusions: From a public health perspective, these findings highlight how system capacity constraints and social inequities jointly shape hospital use. Reducing avoidable variation in ALOS will require policies that strengthen acute care surge capacity, improve coordination for urgent admissions, and address upstream socioeconomic and geographic barriers to care, thereby promoting more equitable and efficient hospital services. Full article

RGBT tracking holds irreplaceable value in unmanned aerial vehicle (UAV) ground observation missions, effectively supporting scenarios such as nighttime monitoring and low-altitude reconnaissance. However, existing frameworks based on CNNs or Transformers face inherent trade-offs between interaction capabilities and computational efficiency. Furthermore, current methods perform poorly in challenging scenarios involving target scale variations and rapid motion from UAV perspectives. To address these issues, this paper proposes a novel multimodal interaction and fusion Mamba network (MIFMNet), which achieves fundamental innovations relative to existing RGB-T fusion trackers and recent Mamba-based tracking methods. Different from existing RGB-T trackers that rely on CNN’s local convolution or Transformer’s quadratic-complexity self-attention for cross-modal fusion, MIFMNet departs from these architectures and designs modality-adaptive interaction mechanisms based on Mamba, fully leveraging the complementary information while resolving the efficiency-accuracy trade-off. Specifically, this paper designs the scale differential enhanced Mamba (SDEM), which expands the receptive field through multiscale parallel convolutions while amplifying complementary information via differential strategies to enhance feature responses to scale-varying objects. Furthermore, we propose flow-guided multilayer interaction Mamba (FMIM), which integrates inter-frame motion information into scanning prediction. This enables the network to adaptively adjust interaction priorities between shallow texture and high-level semantic features based on motion intensity, mitigating early information forgetting and enhancing robustness in dynamic scenes. Extensive experiments on four major benchmarks demonstrate that MIFMNet achieves state-of-the-art performance on precision and success rate, particularly excelling in UAV scenarios involving occlusion, scale variations, and rapid motion. Simultaneously, it achieves an inference speed of 35.3 FPS, enabling efficient deployment on resource-constrained platforms, thereby providing robust support for UAV applications of RGBT tracking. Full article

In the digital economy era, how to effectively leverage data elements to promote green productivity has become a critical issue. The Big Data Comprehensive Pilot Zone (BDCPZ) serves as an institutional arrangement to promote data circulation, governance, and efficient allocation. Utilizing panel data from Chinese A-share listed firms spanning 2012–2023, this study treats the 2016 establishment of BDCPZ as a quasi-natural experiment and employs a difference-in-differences (DID) model to investigate how improvements in the data institutional environment induced by BDCPZ affect enterprise green total factor productivity (GTFP). Empirical results indicate that the establishment of BDCPZ significantly enhances GTFP, with results remaining robust across specification tests. Heterogeneity analyses demonstrate that these positive effects are more pronounced among non-heavily polluting enterprises, high-technology enterprises, and enterprises in less competitive markets. Mechanism analyses suggest that data-oriented institutional reforms primarily enhance GTFP through innovation incentives, human capital accumulation, and industrial structure upgrading. Furthermore, superior managerial efficiency and stronger managerial equity ownership amplify these positive effects. This study provides firm-level empirical evidence on the relationship between data-oriented institutional reforms and GTFP enhancement, contributing to the literature on data-driven institutional reforms and green productivity, and policy implications for optimizing data element utilization and promoting sustainable development. Full article

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (${\chi }^2=49.42$, $p=4.78\times {10}^{-10}$, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis ($r_s=0.109$, $p=0.386$). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

Tornadoes pose growing threats to both communities and the built environment, yet few studies have quantified how spatial characteristics of the built environment interact with social and economic factors while influencing tornado impacts. This paper introduces an integrated metric that combines tornado risk and exposure to evaluate localized disaster impact. Focusing on Florida’s Panhandle, we examine how housing density and affordability, network connectivity, and urban form efficiency, together with demographic and socioeconomic attributes, shape tornado impacts at the U.S. census block group (CBG) level. To address spatial autocorrelation and non-stationarity, five statistical models were compared, including both global and local spatial regressions. The findings indicate that multiscale geographically weighted regression (MGWR) most effectively captures the spatial heterogeneity of tornado impacts. Built-environment and affordability factors show clear spatial heterogeneity— smart location indexand housing cost burden (h_ami) are positively associated with tornado impact in CBGs near Tallahassee and parts of Pensacola—suggesting amplified impacts in location-efficient urban areas where exposure is concentrated and affordability stress may limit preparedness and recovery. In contrast, network density is negatively associated with the impact of key clusters, consistent with the idea that denser, more redundant road networks can reduce canopy-weighted disruption by providing alternative routes for emergency access and restoration. Overall, these findings can inform our understanding of how the built environment influences tornado exposure, offering critical insights for planners and policymakers seeking to strengthen communities against tornadoes. Full article

This study examined how policy synergy in cleaner production regulation affects firms’ capacity utilization in China. Using firm-level panel data, this study empirically examined the impact of policy synergy in cleaner production regulation—operationalized through the coordination between the incentive and constraint instruments—on the enterprises’ capacity utilization (CU). The results showed that higher levels of policy synergy significantly enhanced the capacity utilization, with stronger effects observed in state-owned enterprises, firms in competitive industries, high-R&D investment firms, and regions with lower public environmental attention. The mechanism analysis indicated that policy synergy improved capacity utilization primarily by enhancing the resource allocation efficiency. Further, the analyst attention positively moderated this relationship, amplifying the effect of coordinated policy instruments. Overall, this study clarifies the mechanisms and boundary conditions through which the policy synergy under a dual-incentive governance framework affects the firms’ capacity utilization, thereby offering theoretical insights into policy coordination and practical guidance for the design of cleaner production regulation. Full article

Under the dual pressures of global warming and high-density urbanization, extreme heatwaves have emerged as a critical ecological risk constraining the sustainable development of Chinese urban agglomerations. Based on multi-source remote sensing, meteorological, and economic data for 19 major urban agglomerations from 2014 to 2023, this study develops an emerging industrial agglomeration–energy activity–thermal environment response framework. Using XGBoost-SHAP interpretable machine learning and GeoSHAPLEY spatial decomposition, the nonlinear and spatially heterogeneous impacts of industrial agglomeration on heatwave characteristics are systematically quantified. Results indicate that the heatwave index increased from 0.619 to 0.637, with the model explaining 80.7 percent and 74.7 percent of variance in duration and frequency, respectively. Moreover, emerging industrial agglomeration ranks among the top contributors to both duration and frequency, explaining over 20 percent of duration variability and surpassing traditional industrial and socioeconomic factors. Heatwave duration and frequency exhibit nonlinear relationships. During early agglomeration, energy efficiency improvements generated marginal cooling of five to eight percent, whereas intensified agglomeration amplifies duration by over ten percent through energy-intensive activities and infrastructure heat islands. Meanwhile, green innovation at high agglomeration levels mitigates six to nine percent of the warming effect. In addition, spatial differentiation of industrial agglomeration, reflected by a Gini increase from 0.685 to 0.728 and inter-regional contribution around 62 percent, underpins heat risk heterogeneity. Furthermore, natural endowments, socioeconomic development, infrastructure, environmental regulation, and technological innovation significantly moderate these effects, with high-tech innovation attenuating heatwave amplification. Consequently, the thermal effects of industrial agglomeration follow a three-stage spatial evolution of warming, stabilization, and counter-regulation. These findings highlight that coordinated optimization of industrial spatial layout and green technological innovation is crucial for enhancing climate resilience and promoting low-carbon transformation in urban agglomerations. Full article

Energy efficiency has become a primary bottleneck in hardware platforms supporting machine learning workloads, particularly as modern inference and training tasks demand sustained high-throughput computation. This challenge is further amplified in energy-harvesting and intermittently powered systems, where the available energy budget varies over time. This work introduces a run-time configurable multiply–accumulate (MAC) architecture that dynamically adjusts arithmetic precision to match instantaneous energy availability. The proposed design relies on an internally adaptive multiplier based on bit-level logic compression, enabling controlled modulation of power consumption while preserving numerical robustness. Crucially, the MAC maintains a fixed external operand interface, allowing for seamless precision adaptation without operand reformulation or datapath disruption. The architecture is implemented in System Verilog and evaluated using both ASIC synthesis in a 90 nm CMOS technology and FPGA deployment. Experimental results demonstrate approximately a fourfold improvement in power–delay product (PDP) relative to full-precision operation, with only limited degradation in inference accuracy. Full article

The governance of the rural water environment is essential for improving the quality of life of rural residents and advancing the construction of ecological civilization. However, the current governance system faces issues such as fragmented governance entities and low collaborative efficiency. Therefore, in this study, we focus on the intergovernmental collaborative governance mechanism for rural water environments. Drawing on complex network theory and evolutionary game theory, we employ complex network analysis and construct a complex network evolutionary game model among government departments, and we further conduct numerical simulations to examine the evolutionary dynamics of intergovernmental collaboration in rural water environmental governance. The findings show the following: (1) The reward and punishment mechanism, collaborative gain coefficient, and loss intensification trend coefficient all positively influence the participation rates of local governments. When these parameters exceed certain thresholds, they can rapidly and stably increase the proportion of participating nodes. (2) Nodes with stronger environmental preferences respond more directly to the collaborative gain coefficient, while the loss intensification trend coefficient promotes cooperation by amplifying the cost of non-cooperation. (3) The heterogeneity in economic preferences of local governments affects the stability of cooperation. Governments with stronger environmental priorities are more inclined to form the core of cooperation, whereas those driven by stronger economic priorities are more vulnerable to parameter fluctuations, leading to instability in overall participation levels. Reducing or eliminating this heterogeneity can improve both participation rates and the stability of cooperation. These findings offer theoretical support for designing intergovernmental collaborative governance mechanisms for rural water environments and provide practical guidance for calibrating reward–punishment schemes, identifying key coordinating departments, and stabilizing cross-departmental participation, thereby facilitating an efficient transition in rural water environmental governance models. Full article

In recent decades, the reliability and safety of large engineering structures have become a critical issue due to failures caused by undetected micro-level deformations. Fiber-optic strain sensors, especially Fiber Bragg Grating (FBG) and interferometric systems, are widely used in structural health monitoring (SHM); however, their standard sensitivity is often insufficient for early detection of nano-strain level damage. This paper presents a comparative analysis and system-level optimization of the main sensitivity enhancement methods, including mechanical amplification, functional coatings and composite embedding, interferometric schemes, and advanced spectral signal processing. Analytical modeling and numerical simulations were performed. It is shown that flexure-beam amplifiers provide a stable sensitivity gain of 2.1–4.8, whereas lever-type mechanisms achieve higher amplification (5.6–9.3) at the cost of dynamic degradation. Functional coatings increase the strain transfer coefficient from 0.62 to 0.68 to 0.91–0.97, but introduce temperature-induced errors up to 1.5–2.0 µε. Interferometric systems can detect strains at the 10⁻⁸ level but exhibit high temperature cross-sensitivity. Advanced spectral processing reduces the Bragg wavelength estimation error by 8–15 times, improving the equivalent strain resolution to (2–5) × 10⁻⁸. Based on these results, an optimized integrated approach combining moderate mechanical amplification (2.5–3.5), improved strain transfer (η ≈ 0.85–0.92), and efficient spectral processing is proposed. This improves the equivalent strain resolution from 1 × 10⁻⁶ to (1.5–3.0) × 10⁻⁸ while keeping temperature-induced errors within 15–25% and limiting the computational load increase to 2–3 times. The proposed solution is suitable for long-term monitoring of large engineering structures. Full article

Financial volatility increasingly challenges firms to maintain operational sustainability; yet the mechanisms through which cash flow uncertainty (CFU) shapes environmental practices remain unclear. Based on an international unbalanced panel of 14,798 firm-year observations (2010–2021), this study analyzes how CFU affects waste generation and recycling. Panel regression models are employed, complemented by robustness checks using generalized method of moments (GMM) estimations to mitigate endogeneity concerns. The findings suggest that higher CFU is associated with lower waste generation at the source due to more disciplined resource allocation, alongside higher recycling levels, reflecting a strategic response to operational risk and stakeholder expectations. Moreover, these effects are amplified in contexts characterized by stricter environmental policy stringency, the existence of corporate social responsibility committees, and sustainable supply chain management, underscoring the importance of institutional and organizational settings in shaping environmental operational outcomes. Overall, the results indicate that financial uncertainty can act both as a constraint and a catalyst, encouraging more efficient and circular practices. This study offers novel empirical evidence on the operational implications of CFU, providing valuable insights for managers and policymakers aiming to align financial management with sustainable and resilient production strategies. Full article

Reinforcement learning for robotic manipulation is often limited by poor sample efficiency and unstable training dynamics, challenges that are further amplified in federated settings due to data privacy constraints and task heterogeneity. To address these issues, we propose F-DRL, a federated dynamics-aware representation learning framework that enables multiple robotic tasks to collaboratively learn structured latent representations without sharing raw trajectories or policy parameters. The framework combines robotics priors with an action-conditioned latent dynamics model to learn low-dimensional state and state–action embeddings that explicitly capture task-relevant geometric and transition structure. Representation learning is performed locally at each client, while a central server aggregates encoder parameters using a similarity-weighted scheme based on second-order latent geometry. The learned representations are then used as frozen auxiliary inputs for downstream model-free reinforcement learning. We evaluate F-DRL on seven heterogeneous robotic manipulation tasks from the MetaWorld benchmark. While achieving performance comparable to centralized training and standard federated baseline, F-DRL substantially improves training stability relative to FedAvg on heterogeneous manipulation tasks with partially shared dynamics (e.g., Drawer-Open and Window-Open), reducing the mean across-seed standard deviation and the AUC of this deviation by over 60%. The method remains neutral on simple tasks and performs less consistently on contact-rich manipulation tasks with task-specific dynamics, indicating both the benefits and the practical limits of representation-level knowledge sharing in federated robotic learning. Full article

Sea-level rise (SLR) and regional precipitation pattern change cause island subsurface freshwater, typically shaped like a thin lens, to be at higher risk of contamination from seawater intrusion (SWI). Installing a cutoff wall is considered a feasible strategy for protecting coastal fresh groundwater from SWI. However, the performance of the cutoff wall in managing freshwater lens (FWL) development and mitigating SWI into island aquifers under SLR and aquifer recharge (RCH) fluctuations remains inadequately quantified. This study investigates how water table elevation (WTE), FWL depth, thickness, and SWI extent, measured by aquifer salt mass and freshwater volume, in an island aquifer equipped with cutoff walls, respond to SLR and RCH fluctuations. It focuses on a two-dimensional, variable-density island groundwater simulation model based on hydrogeological conditions of San Salvador Island, Bahamas. The results demonstrate that RCH critically influences cutoff wall effectiveness for FWL development and SWI mitigation, with higher RCH amplifying gains in WTE, FWL metrics, freshwater storage, and aquifer salt removal, but this influence diminishes with wall depth increasing. SLR elevates WTE in a stable manner associated with its magnitude but negligibly affects the cutoff wall performance in FWL enhancement and SWI mitigation. Under simultaneous SLR and RCH fluctuations, SLR can offset the WTE reduction caused by reduced RCH, but the joint effects of SLR and RCH on FWL metrics, freshwater storage and aquifer salt removal align with their individual impacts. Moreover, cutoff walls are more efficient in low-RCH settings, yielding greater relative improvements in FWL development and SWI mitigation per unit wall depth increase. Full article

Wireless power transfer (WPT) systems are generally sensitive to variations in separation distance and coil alignment, which result in reduced power transfer efficiency and delivered power. Various approaches based on control system and active matching circuits have resulted in more complex implementations. This work, by contrast, presents a full DC–DC inductively coupled WPT system employing coupled nonlinear resonators to automatically adapt the system for variations in transfer coil separation and orientation, maintaining high transfer efficiency at a constant output power level. With entirely passive circuit components, the nonlinear resonators suppress the frequency-splitting phenomenon typical of WPT systems that leads to efficiency degradation. A class-EF power amplifier used in the transmitter experiences an approximately constant impedance, providing a constant output power while maintaining high efficiency. On the receive side, a class-E rectifier operates at a constant input power, achieving high overall efficiency without active control. An experimental demonstration delivers 5 W with a 6.12% power variation over a 1 to 9 cm distance variation and achieves a peak DC–DC efficiency of 71.6%. The response of the system to changes in coil separation is compared with a conventional linear WPT circuit, showing a constant-power and high-efficiency operation. Full article