Search Results (6,164)

Ceratitis capitata (Diptera: Tephritidae), or medfly, is an invasive pest widespread in Argentina, where standardized management methods, such as cultural and chemical controls, are commonly implemented. The success in controlling medfly populations depends on implementing preventive, sustainable, long-term, and eco-friendly eradication/control strategies across all invaded environments. One strategy may involve augmentative biological control using parasitoids adapted to local conditions, such as Ganaspis pelleranoi (Brèthes) (Hymenoptera: Figitidae), a Neotropical-native parasitoid that mostly forages on tephritid larvae in a broad range of fallen fruit. Two hypotheses were tested in the current study: (1) G. pelleranoi females are more efficient in controlling medfly larvae infesting different fruits as the density of released females progressively increases, and (2) such parasitoid-induced host mortality capacity remains when host density is increased. Parasitism (reproductive effects) and additional host mortality (non-reproductive effects) were the indicator variables of parasitoid-induced host ability. Trials were performed in field cages (semi-field conditions) using two medfly-multiplier host fruit species, namely sour orange and peach, and with variations in both parasitoid release and host larval densities. Three major findings were highlighted: (1) G. pelleranoi females successfully parasitized host larvae on peach and sour orange, regardless of their strongly differing physical features, although medfly larvae in peaches were significantly more susceptible to the parasitoid; (2) medfly mortality significantly increased in both peach and sour orange relative to the gradual increase in released G. pelleranoi females, regardless of the increase in host density offered to parasitoids; and (3) G. pelleranoi females induced a substantially high host die-off rate when the additional mortality was added to the analysis, which was not revealed when parasitism alone was regarded as a medfly mortality variable. Such outcomes may provide relevant information for implementing an augmentative biological control against medfly using indigenous parasitoid species within an eco-friendly fruit fly pest management approach. Full article

Virtual coupling control of trains is a promising technology for improving railway capacity and operational efficiency. However, existing multi-agent reinforcement learning (MARL) approaches struggle to capture long-sequence temporal dependencies among train states in complex multi-train interaction scenarios, resulting in limited robustness and coordination stability. To address this issue, this paper proposes a Predictive Mamba-based Multi-Agent Soft Actor–Critic (PM-MASAC) framework. A Mamba-based state prediction module is embedded into the centralized Critic network to model historical state sequences and generate predictive state representations, thereby enhancing value estimation accuracy. In addition, a multi-agent aggregated prioritized experience replay (PER) mechanism is introduced to improve the utilization of critical cooperative samples and stabilize training. A hierarchical local–global reward structure is further designed to ensure individual tracking performance while promoting overall formation coordination. Experimental results under realistic railway operating conditions demonstrate that PM-MASAC achieves superior robustness compared with baseline MARL methods. Velocity and spacing tracking errors are maintained within 3% and 1%, respectively, and the steady-state formation success rate exceeds 95.7% in the training environment. Full article

Political polarization and policy uncertainty have become increasingly consequential for regional economic adjustment, yet their joint role in shaping socioeconomic resilience remains underdeveloped in the literature. This study advances the debate by conceptualizing regional resilience as the outcome of a multi-layer socioeconomic system in which external policy disturbances, institutional fragmentation, and structural adaptive capacity interact over time. Using balanced panel data for 16 Korean regions from 2004 to 2023, the analysis develops an integrated empirical framework that combines panel local projections, threshold estimation, structural moderation tests, dynamic robustness checks, and forward-looking machine-learning prediction. The results show that policy uncertainty is associated with lower regional socioeconomic resilience and that this effect persists over time. More importantly, political polarization does not simply accompany weaker resilience; it amplifies the transmission of uncertainty shocks, especially once institutional fragmentation crosses a critical threshold. Structural conditions further shape this process. Digital transformation, industrial diversification, and financial depth reduce vulnerability, whereas trade exposure intensifies it. These findings indicate that resilience is not determined by economic structure alone, nor by institutional instability in isolation. It emerges from the interaction between disturbance, amplification, and adaptive capacity within a regional system. The predictive analysis reinforces this interpretation. Variables identified as central in the econometric models also carry forward-looking information about future vulnerability states. This study therefore contributes not only by combining methods, but by linking explanation and prediction within a single systems-oriented account of regional resilience. The Korean case shows how institutional coherence and structural adaptability jointly condition resilience under uncertainty. Full article

The energy consumption of digital communication infrastructures is increasingly recognized as a component of operational building energy use. In multi-story public buildings, Wireless Local Area Networks (WLANs) are typically deployed under static, always-on configurations, leading to avoidable energy overhead caused by spatial interference and inefficient access point placement. This study proposes an energy-aware WLAN deployment framework that integrates user-weighted spatial placement with deterministic three-dimensional vertical interference coordination. The framework is evaluated using 50 independent Monte Carlo simulations on a representative three-story public building model. Results indicate a reduction in annual operational energy consumption from 1892.71 kWh to 1333.71 kWh (-29.5%), with a proportional decrease in carbon emissions, while maintaining a 97% coverage requirement. Furthermore, worst-case signal quality improves, with Signal-to-Interference-plus-Noise Ratio (SINR) P10 increasing from 17.66 dB to 25.53 dB and median network capacity rising by 30.6%. These findings suggest that interference-aware spatial coordination can function as an effective energy optimization layer within building-integrated digital infrastructures. Full article

Mental health interventions in rural areas often face systemic and governance barriers that limit their implementation. This study analysed how governance dimensions at the municipal, state, and federal levels influence the perceived feasibility of implementing the Primary Care and Psychiatry Model (MAP-PSI), an early intervention strategy targeting adolescent depression in rural Mexico. A descriptive–interpretative qualitative design was employed, using semi-structured interviews and hybrid (deductive–inductive) content analysis. Participants were purposively selected institutional stakeholders involved in MAP-PSI implementation, including local health managers, state and federal decision-makers, and community-based actors. The coding process was collaboratively developed and validated through consensus and critical reflection among researchers. Five interrelated governance dimensions were identified: local leadership, intersectoral coordination, resource mobilisation, community participation, and institutional adaptability. These dimensions converge in a multilevel governance framework that illustrates how governance capacities across levels can enable or constrain community-based mental health interventions. The findings provide an empirically grounded framework to inform the design, adaptation, and future evaluation of community-based mental health strategies in underserved rural contexts. Full article

Protected areas are increasingly expected to reconcile biodiversity conservation with socio-economic sustainability, yet operational tools for assessing local sustainability are limited. This study develops a replicable viability index as an operationalization of socio-economic sustainability at the settlement scale, focusing on the capacity of rural communities to maintain demographic balance and housing dynamics over time. The framework was applied to the West Estonian Archipelago Biosphere Reserve (WEABR), an inhabited UNESCO “Man and the Biosphere” site. Using harmonized census data from 1979 to 2021, the index combines three village-level binary indicators: population dynamics, residential construction activity, and demographic balance. Binary scoring reduces statistical volatility in small settlements and enables comparison across time. Approximately 60% of rural settlements remained viable over four decades, while highly viable settlements declined from 14% to 7%. Population stabilization increased, but ageing intensified and new construction decreased. Viability concentrates near urban centres, ports, transportation corridors, and coastal areas, while inland peripheral villages stagnate. Compared with mainland rural Estonia, WEABR shows a relatively resilient middle tier of viable settlements. The framework provides a transferable tool for monitoring settlement level socio-economic sustainability in inhabited protected areas. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Neural scaling laws describe how model performance improves as a power law with size, but existing work has focused almost entirely on models above 100 M parameters. The regime below 20 million parameters, where TinyML and edge AI systems operate, remains largely unexamined. We train 90 models spanning 22 K to 19.8 M parameters across two architecture families (a plain ConvNet and MobileNetV2) on CIFAR-100, varying width while holding depth and training protocol fixed. Both architectures follow approximate power laws, with exponents of $\alpha =0.156$ (ScaleCNN) and $\alpha =0.106$ (MobileNetV2). However, the power law does not hold uniformly: local exponents decay with scale, and MobileNetV2 saturates at 19.8 M parameters (${\alpha }_{\mathrm{local}}=0.006$), hitting a data wall. The structure of errors also changes with scale. The Jaccard overlap between error sets of the smallest and largest ScaleCNN models is only 0.35; compression changes which inputs are misclassified, not merely how many. Small models develop a triage strategy, concentrating capacity on easy classes (Gini of per-class accuracy: 0.26 at 22 K params vs. 0.09 at 4.7 M) while effectively abandoning the hardest ones (bottom-5 class accuracy: 10% vs. 53%). The smallest models achieve the lowest ECE values (0.013 vs. peak 0.110 at mid-size), reversing the typical overconfidence–capacity relationship, though this partly reflects a global-mean matching artifact rather than well-calibrated per-bin confidence. On CIFAR-100, aggregate accuracy alone is therefore a misleading basis for edge deployment decisions; validation must happen at the target model size. All findings in this study are based on CIFAR-100 (32 × 32, 100 classes); their generalizability to other datasets, resolutions, and architectures remains to be verified. Full article

Long-distance pulling of submarine cables inside horizontal directional drilling (HDD) steel casings is often limited by high interfacial resistance, rapid pulling-force accumulation, and poor adaptability to muddy-sandy environments. In addition, conventional outward-extending roller devices are relatively bulky, which increases the required casing diameter and reaming size. To address these issues, this study proposes a compact ball-frame and tensioned steel cable series drag-reduction device. An 18 m local full-scale pulling test system was established using an actual engineering submarine cable, a practical-scale steel casing, and full-scale drag-reduction devices. The effects of pipe curvature, device spacing, terminal reaction force, and in-casing medium conditions on the equivalent friction coefficient were investigated. The results show that the equivalent friction coefficient of the submarine cable–steel casing system is maintained at 0.25–0.36 under most test conditions, which is significantly lower than the commonly adopted value of 0.55 for direct contact. Based on the experimentally identified parameters, a simplified assessment model for ultimate pulling length was established for construction scheme comparison and preliminary capacity estimation. The results indicate that, with the implementation of the tensioned steel cable series system, the ultimate pulling length increases from 431/696 m for direct pulling to 954/1424 m. These results provide valuable technical references for drag-reduction scheme selection and preliminary construction-capacity assessment in HDD landfall sections. Full article

Enhancing urban energy efficiency is central to low-carbon transition and broader urban sustainability. However, whether innovation-oriented urban policy can generate such gains, through which channels it operates, and whether its effects extend beyond pilot cities remain insufficiently understood. Focusing on China’s Innovative City Pilot (ICP) program, this study uses panel data for 274 Chinese cities from 2006 to 2022 and treats the staggered implementation of the program as a quasi-natural experiment. A multi-period difference-in-differences model is employed to examine the impact of the ICP program on urban energy efficiency. The results show that the ICP program significantly improves urban energy efficiency, and this conclusion remains robust across a series of robustness checks. Mechanism analysis further suggests that the policy effect operates through lower per capita carbon emissions and stronger green technological innovation. Heterogeneity analysis shows that the effect is more pronounced in larger cities, economically more developed cities, and cities with stronger pre-existing innovation capacity. Spatial analysis indicates that the program generates not only significant local benefits but also positive spillover effects on neighboring cities. Overall, these findings suggest that innovation-oriented urban policies can promote energy-efficient, low-carbon, and more sustainable urban development, while highlighting the importance of regional coordination and local innovation capacity in shaping policy effectiveness. Full article

COVID-19 was a prolonged public-health shock that disrupted mobility, access to services, and household spending. Although the official U.S. poverty rate declined to 11.1%, the Supplemental Poverty Measure rose to 12.9%, suggesting that material hardship persisted unevenly across places. This study asks whether pre-existing livelihood vulnerability and local epidemic burden translated into geographically concentrated consumption losses during 2020–2022. Because sustained consumption loss can erode households’ health-related spending, tracking where spending declines concentrate helps connect local social and environmental conditions to how communities withstand a health crisis. We analyze consumer expenditure, unlike prior research relying on aggregate retail sales, to capture fine-grained economic strains as a proxy for shock-absorption capacity. A Livelihood Vulnerability Index (LVI) was calculated for each U.S. county using 16 socio-economic variables, and counties were classified as high- or low-risk. A multilevel model then examined how socio-economic and COVID-19 factors at county and census tract levels shaped consumption changes. Higher-risk communities experienced greater consumption reductions. At the census tract level, the non-White ratio, vacancy rate, built year, per capita income, education level, and housing value were significant. At the county level, COVID-19 cases and deaths, crowding, public transportation use, and vehicle availability mattered most. These findings support place-targeted strategies that combine public-health response with socio-environmental interventions to reduce disparities rooted in pre-existing vulnerability. Full article

The new crossed cable-truss spoke structure (CCTSS) significantly improves the lateral stiffness and integral stability of the ordinary spoke cable-truss structure, but it still has the shortcomings of general tensile structures, like low redundancy and weak collapse resistance. Its collapse resistance is still unclear. In the paper, the structural characteristics of CCTSS are introduced. Secondly, the influence of initial prestresses on the collapse performance of CCTSS is studied. Then the collapse response features and collapse mechanism of the members and joints of CCTSS are revealed under the actions of no loads, full-span loads and half-span loads. Finally, a calculation method of the dynamic force amplification coefficient is proposed based on the collapse results of CCTSS, and a calculation method of the importance of members and joints is further proposed based on the dynamic internal force amplification coefficient, which indirectly evaluates structural collapse resistance. The results show that CCTSS has good local collapse resistance, but the failure of ring cables and joints at the ring cables will cause the structure to lose its integral bearing capacity. Meanwhile, the proposed calculation method of the importance of components and joints has a simple calculation process and is convenient to utilize, which has good engineering application value. The research content provides a theoretical basis and analysis method for structural safety design. Full article

Chronic skin inflammatory diseases including psoriasis and atopic dermatitis affect millions worldwide, imposing substantial physical, psychological, and economic burdens. Despite advances in topical therapies, conventional formulations suffer from poor skin penetration, rapid clearance, local and systemic side effects, and suboptimal patient adherence. Polymeric nanogels, internally crosslinked three-dimensional polymer networks with dimensions of 10–200 nm, emerged as promising platforms to overcome these limitations. Their unique properties including high water content, tunable porosity, biocompatibility, deformability, and stimulus-responsive behavior enhance skin penetration allowing for targeted therapeutic action. This review examines nanogel synthesis methods optimized for targeting skin inflammatory diseases, including biopolymer-based approaches utilizing chitosan and hyaluronic acid, offering insights into how different methods and advanced architecture provide multifunctional capacities and bioactivities. Translation challenges including manufacturing scalability, long-term safety assessment, and regulatory compliance are critically discussed alongside emerging opportunities in personalized medicine and smart microneedle integrated systems for adaptive therapy. Full article

While polyurethane acoustic foam is widely used in entertainment settings for sound absorption, it poses a considerable fire risk when exposed to sparks from pyrotechnic devices. Even though fountain-type pyrotechnic devices are often perceived as producing “cold sparks”, the ignition potential of a single incandescent particle remains insufficiently quantified. This study experimentally investigates the ignition capacity of a fountain-type pyrotechnic article on pyramidal polyurethane acoustic foam under controlled conditions. Three dedicated experimental configurations were developed: (i) ignition probability tests at various distances, (ii) scaled configuration tests reproducing realistic installation geometry, and (iii) high-speed visualization of single incandescent particle interaction with the foam surface. For the first two configurations, ignition probabilities of 20% and 22.2% were obtained. High-speed recordings showed two distinct interaction mechanisms: particle fragmentation and ricochet, which did not result in ignition; partial penetration with localized melting, volatile release, and gas-phase ignition when residual thermal energy (about 0.5–1 J) was retained. The results demonstrate that even isolated single incandescent particles generated under realistic conditions can initiate the combustion of polyurethane acoustic foam. These findings challenge the “cold spark” safety perception and provide quantitative evidence that particle–induced ignition represents a significant fire hazard in enclosed environments where combustible acoustic materials and pyrotechnic effects coexist. The findings in this paper have direct implications for safety regulations in entertainment venues. Full article

The rapid integration of renewable energy is increasing the need for fast and sustained load-side frequency regulation, and public electric vehicle (EV) charging networks are promising providers. Their participation, however, is constrained by the volatile charging demand and strict service requirements, which make it difficult to balance regulation revenue with charging quality. This paper proposes a three-layer coordinated framework for multi-site charging networks participating in secondary frequency regulation, comprising market bidding, rolling planning, and fast-response tracking. At the market layer, baseline charging schedules are co-optimized with symmetric regulation capacity bids. At the planning layer, completion margin and progress protection constraints are introduced as tractable service safeguards that preserve charging continuity and deadline compliance. At the execution layer, coordinator-assisted distributed station-level tracking and charger-level urgency-aware allocation track automatic generation control (AGC) commands while correcting the charging progress in real time. The station-level problem is decomposed into local box-constrained subproblems coordinated by a scalar dual signal, enabling real-time AGC tracking with limited inter-station information exchange. Case studies on a reproducible simulated network with 20 stations and 600 chargers show that the proposed method improves ancillary service benefits while maintaining strong tracking performance and markedly improving the charging continuity, deadline compliance, and spatial load balance. Full article