Search Results (8,086)

This paper documents a fundamental sectoral divergence in capital–employment relationships using UK quarterly data (2014Q1–2024Q4, N = 44). While manufacturing automation studies consistently find negative employment effects, we show that information-intensive service sectors (SIC J: Information and Communication; K: Financial and Insurance; M: Professional/Scientific/Technical) exhibit robust positive co-movement between capital formation and employment. Structural vector autoregression analysis reveals persistent positive employment responses following capital shocks, with effects peaking at 5–6 quarters and remaining significant through 10 quarters. This pattern holds across eight alternative specifications with varying lag structure, variable ordering, and subsample periods. Granger causality tests reveal bidirectional temporal relationships (capital → employment: F = 3.932, p = 0.028; employment → capital: F = 5.659, p = 0.007), indicating joint determination from anticipated demand growth rather than unidirectional technology-driven dynamics. This finding—while complicating causal interpretation—strengthens the contribution by providing honest empirical characterization of coordination mechanisms in information-intensive sectors. Our capital formation proxy measures all investment in AI-intensive sectors (buildings, equipment, conventional IT, emerging AI systems) rather than AI expenditure specifically, creating measurement ambiguity we acknowledge transparently. The sectoral focus (J+K+M sectors with 22–34% AI adoption rates exceeding the 15% economy-wide average) provides indicative evidence that patterns relate to advanced technology deployment, but measurement breadth prevents definitive AI-specific conclusions. The contribution lies not in establishing AI-specific causality—which aggregate time-series methods cannot achieve—but in documenting robust sectoral heterogeneity using methodology comparable to manufacturing displacement studies. The positive association in information-intensive services contrasts sharply with manufacturing’s negative relationship, suggesting technology–employment dynamics vary fundamentally across sectors with different task structures. Three limitations constrain interpretation: (i) recursive identification cannot definitively rule out common demand shocks, (ii) the 44-quarter sample provides limited statistical power for precise magnitude estimation, and (iii) external validity to other countries, time periods, or service sectors remains uncertain. The findings motivate sector-specific rather than economy-wide technology policy approaches, recognizing that extrapolating manufacturing evidence to service-dominated economies may systematically mischaracterize employment dynamics. Full article

In national park sections adjacent to large cities, protected wildlife habitats often intersect with roads, tourism, agriculture, forestry, and other community-use spaces. This overlap complicates the joint prioritization of multi-species conservation and potential human-wildlife conflict governance. Using species trace-point data from the Fourth National Giant Panda Survey, we developed 30 m MaxEnt distribution models for 12 mammal species in the Chengdu section of the Giant Panda National Park and integrated protected-species’ conservation priority with potential wild-boar-related conflict pressure. Test AUC values ranged from 0.702 to 0.897, and elevation was the dominant predictor for 11 species. The Top 15% weighted conservation priority area, based on protection status and rarity, covered 350.1 km². Potential wild boar conflict pressure was defined as wild boar suitability multiplied by human exposure, and the Top 15% risk area covered 348.3 km². Overlaying the two layers identified 61.6 km² of high-conservation-high-conflict areas. Functional-zone statistics showed that the core conservation zone concentrated higher multi-species conservation value, whereas the general control zone carried stronger potential wild boar conflict pressure. This framework provides a spatial basis for coordinating protected mammal monitoring, crop-damage warning, and community co-management. Full article

Underwater RGB–sonar object detection remains challenging due to severe optical degradation, strong sonar noise, and spatial misalignment between heterogeneous modalities. Existing multimodal detectors usually rely on simple feature aggregation or limited structural coupling, which cannot effectively model global cross-modal dependencies or address modality-specific degradation. To address these challenges, we propose Chrs-Net, a YOLOv12-based dual-stream framework for underwater RGB–sonar object detection. The proposed network integrates three key components: a Transformer-based Cross-Modal Communication Fusion module (C-mcf) for global cross-modal interaction and semantic alignment, a Multi-Layer Feature Enhancement module (MLFE) for degraded optical feature enhancement, and a Pinwheel-Shaped Convolution module (PConv) for sonar-side structural feature extraction. In addition, an RGB–sonar object detection dataset is constructed for experimental evaluation by relabeling part of the RGBS benchmark, combining simulator-collected samples, and introducing style-transfer-based augmentation to improve data diversity. Experiments on the constructed dataset yield 94.91% mAP@0.5 and 61.10% mAP@0.5:0.95 on the RGB branch, and 94.00% and 57.13% on the sonar branch, respectively, with an inference speed of 53.6 FPS. Compared with representative single-modality and multimodal detectors, Chrs-Net consistently yields superior detection accuracy and localization performance. These results demonstrate that the combination of global cross-modal communication and modality-specific enhancement is effective for robust underwater RGB–sonar object detection in complex environments. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

Generative artificial intelligence (AI) is increasingly present in medical education, yet its indiscriminate use risks impairing the acquisition of foundational clinical competencies, including clinical reasoning, hypothesis generation, and patient-centered communication, through processes of never-skilling, mis-skilling, and deskilling. This paper presents M3RGE-AI (Responsible, Reliable, and Reflexive use of Generative AI in Medical Education), a conceptual framework for the purposeful integration of AI as a cognitive scaffold in medical training. Drawing on established learning theories, zone of proximal development, deliberate practice, and peer learning, the framework assigns progressively expanding AI functions across training stages, prioritizes Socratic over directive interactions, requires transparent and verifiable sourcing of AI-generated content, and incorporates peer moderation and AI-off assessment checkpoints to mitigate over-reliance. The framework is operationalized through alternating AI-on and AI-off cycles, governance processes, and educator training protocols. Applied within these constraints, AI can shorten feedback loops and broaden clinical exposure while preserving independent reasoning and authentic patient communication. M3RGE-AI offers a theoretically grounded and institutionally implementable model for integrating generative AI into medical curricula without sacrificing the essential human competencies that underpin safe clinical practice. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

The study evaluated the effect of intercropping Paulownia (Paulownia spp.) with spring barley (Hordeum vulgare L., cv. KWS Thalis) on selected components of agroecosystem biodiversity under Polish conditions. A field experiment established in 2019 compared an alley cropping system with barley monoculture during the 2025 growing season. Weed infestation, soil microbial communities, mesofauna abundance, and crop yield were assessed. Weed abundance was lower in the intercropping system than in monoculture, reaching 5.6 vs. 15.6 plants m⁻² at BBCH 21 and 21 and 22.8 vs. 35.6 plants m⁻² at BBCH 75. Bacterial alpha diversity was significantly higher under intercropping conditions, with Shannon index values ranging from 5.12 to 5.25, compared with 4.98–5.09 in monoculture. Fungal diversity showed moderate differences between systems, whereas the abundance of Collembola and Acari was influenced mainly by seasonal variation rather than by cultivation system. No significant reduction in barley yield was observed under intercropping conditions. The results suggest that Paulownia-based alley cropping may reduce weed pressure and support selected soil biological properties without negatively affecting crop productivity. However, the observed responses varied depending on the analyzed parameter and sampling period, indicating the preliminary and context-dependent character of the results. Further long-term studies are required to better understand the ecological mechanisms operating in such agroforestry systems. Full article

As livestock production continues to consolidate into fewer but larger operations, odor complaints from neighboring communities have become a major challenge to industry growth, making the establishment of appropriate odor setback distances essential. This paper reiterates the development procedure of odor footprint tools for setback determination based on AERMOD, a regulatory air dispersion model, using North Dakota as an example. Specifically, we developed North Dakota Odor Footprint Tool (NDOFT), an Excel-based calculator designed to estimate odor setback distances between animal production facilities and surrounding communities. The tool utilizes county-specific meteorological data to predict odor concentrations at various distances and directions relative to an established annoyance threshold of 75 OU m⁻³. Setback distances are determined based on the percentage of time during which modeled odor concentrations remain below this threshold, corresponding to annoyance-free frequencies ranging from 91% to 99%. Facility characteristics, including livestock types, source areas, and odor control measures, are incorporated to enable scenario-based assessments. The influence of complex terrain on setback determination was also evaluated, revealing that no simple correction factors adequately capture terrain effects for valleys and hilltops. Overall, the use of county-specific meteorological inputs substantially improves the accuracy of predicted setback distances compared with area-representative approaches, providing an updated and more robust framework for odor setback planning and environmental evaluation. This work is expected to guide future efforts in developing and refining odor setback tools. Full article

A 700 m pilot-scale cast iron pipeline reactor was operated for 120 days to investigate corrosion-stage evolution under reclaimed-water conveyance conditions. Sampling points were arranged at 50, 250, 450, and 650 m, and water-quality monitoring, coupon weight-loss tests, scanning electron microscopy (SEM), and high-throughput 16S rRNA sequencing were combined to characterize corrosion-rate variation, corrosion-product morphology, and microbial community succession. During transport, NH₄⁺ generally decreased while NO₃⁻ increased, indicating nitrification-related nitrogen transformation under aerobic conditions; meanwhile, PO₄³⁻ declined and DOC fluctuated, reflecting coupled physicochemical and biological processes. SEM observations showed a transition from loose porous deposits to relatively compact layered corrosion products, followed by local deterioration and renewed porous structures in the later period. The corrosion rate followed an increase–decrease–re-increase pattern rather than a monotonic trend. Therefore, corrosion acceleration (CA = dc/dt) was introduced as an auxiliary diagnostic indicator to identify whether corrosion activity was increasing, decreasing, or temporarily stabilizing. Microbial community analysis showed stage-associated variation in biofilm and nitrogen-transformation-related taxa, supporting the interpretation that corrosion evolution was jointly affected by water-quality change, corrosion-product development, and microbial succession. Overall, the combined interpretation of corrosion rate, CA, water quality, SEM morphology, and microbial succession provides a more informative basis for diagnosing corrosion-stage transitions in reclaimed-water cast iron pipelines. From a sustainability perspective, this diagnostic framework can support long-term operation, maintenance planning, and risk monitoring of urban reclaimed-water distribution infrastructure, thereby improving pipeline durability, reducing leakage and maintenance risks, and enhancing the reliability of reclaimed-water reuse systems. Full article

With the advancement of artificial intelligence (AI) technologies such as large language models and autonomous driving, the data traffic via optical interconnects in data centers has surged significantly. The stability of the optical interconnects relies on intelligent operation and maintenance (O&M). Integrated sensing and communication (ISAC) over fibers enables vibration sensing utilizing existing communication fibers, providing critical support for intelligent O&M in data centers. Compared to sensing in the coherent systems, it is difficult to use phase and state of polarization (SOP) monitoring for vibration detection in intensity-modulation and direct-detection (IM-DD) systems. In this paper, we propose a joint phase-based and SOP-based sensing scheme integrated in IM-DD systems. In the proposed scheme, the received IM-DD communication signals are tapped for sensing with a power ratio of 10%. Then the tapped signals are split for vibration sensing based on SOP and phase, respectively. In the phase-based sensing arm, a circulator, a $3\times 3$ coupler and two Faraday rotating mirrors (FRMs) are used to build an unbalanced Michelson interferometer without phase fading and polarization fading. For the purpose of SOP-based sensing, a polarizer is used to monitor the vibration-induced SOP variations. Experimental results demonstrate that the proposed scheme enables vibration sensing based on both phase and SOP across a frequency range of 200 Hz to 10 kHz. Regarding the communication performance, the integration of the sensing system only induces 0.8 dB received optical power penalty. This vibration-sensing scheme based on both phase and SOP can be integrated into pluggable optical modules, providing an efficient and reliable solution for intelligent optical network O&M. Full article

The spatial distribution and productivity of subsistence resources are central to food security, nutrition, and cultural vitality in circumpolar Indigenous communities. Yet few studies incorporate Indigenous Knowledge in methodology to monitor subsistence plant species. Here, we apply participatory action research to develop a monitoring system for the culturally and nutritionally important Rubus chamaemorus (atsalugpiaq, salmonberry) near the Yup’ik village of Quinhagak in southwest Alaska. With support from community members, two ground-truth surveys assessed berry productivity at nine sites within Quinhagak’s Traditional Land Use Area. Seventeen interviews identified key themes related to subsistence harvest and highlighted winter meteorological factors important for analysis. We compiled a multi-year dataset including PlanetScope eight-band SuperDove imagery (3 m GSD); airborne LiDAR and satellite-derived DEMs; and four meteorological parameters. Linear regression and multiple adaptive regression splines were tested to evaluate relationships among vegetation health, climate, landscape features, and berry productivity. Model outputs identified chlorophyll-related vegetation indices, particularly MTCI, as strong predictors of harvest outcomes, with higher flowering-season MTCI values associated with greater berry abundance. This work establishes a foundational, scalable approach for the long-term monitoring of Arctic subsistence plants in conjunction with Arctic communities and demonstrates the value of multi-layer data integration in regions historically challenging for remote sensing and ground surveys improving outcomes for regional harvest predictions and increased understanding of possible mechanisms controlling berry productivity in Arctic regions. Full article

Latency is a determining factor in an IoT-enabled Intelligent Transportation System. To solve the latency issue in an edge computing system connected to the cloud, where the primary challenge is the distance between the end device and the cloud server, an implementation in a real urban environment is presented to illustrate the architecture of Intelligent Edge Computing. The IEC design is scalable through a communication system that incorporates latency and distance measurements in the transmission of a detection signal using deep learning at the edge node. This enabled the transmission of 2-byte detection signals to the fog node, where the received information was processed to count vehicles on up to three streets near the intersection. The vehicle detection signal is transmitted between two different embedded platforms. This architecture enabled an average transmission latency of 15.45 ms and a total end-to-end latency of 47.9087 ms over a distance of 600 m in a real-world urban environment. The IEC system leverages this low latency and offers intelligent processing closer to the data source and, therefore, to the user. Full article

Background: We examined whether higher access to parks and greenspace is independently associated with an increase in physical activity and lower rates of obesity when neighbourhood walkability is accounted for and whether neighbourhood walkability and park access have synergistic effects on these outcomes. Materials and Methods: We used cross-sectional data from the Canadian Community Health Survey between 2007 and 2014 for adults aged 20 to 74 in Ontario, Canada. Neighbourhood-level park access exposures included size of parks and number of parks within 800 m of residential areas, and neighbourhood walkability was based on a validated index. The main outcomes were physical activity during leisure time (LPA), both leisure and transportation physical activity (LTPA), and obesity. Descriptive and multivariate logistic regression analyses were conducted, stratified by age groups, accounting for sex, income, ethnicity, and season. Results: Among 41,945 respondents, park access was associated with higher LPA and LTPA, with effects modified by neighbourhood walkability (p < 0.001). Physical activity was highest in neighbourhoods with high walkability and park access and lowest in low walkability areas without parks. In highly walkable neighbourhoods, ≥1 small- or medium-sized park was associated with 29% higher odds of LPA (OR: 1.29, 95%CI: 1.21–1.37) and 48% higher odds of LTPA (OR: 1.48, 95%CI: 1.38–1.57) than low walkability/no park access. In contrast, associations were modest in low-walkability neighbourhoods (4–7%). High walkability was also associated with lower obesity and marked reductions when combined with very high access to large parks (OR: 0.72, 95%CI: 0.55–0.94). Findings were consistent across age groups. Conclusions: High neighbourhood walkability was the strongest predictor of physical activity and lower obesity risk, with park access providing additional benefits primarily in already walkable environments. These findings suggest that population health interventions targeting urban design need to consider the combined benefits of neighbourhood walkability and park access on health. Full article

Soil depth and habitat degradation can reshape fungal communities in salt-affected wetlands, but their effects on fungal ecological processes remain insufficiently understood. This study examined soil fungi in the Halahai Provincial Nature Reserve and adjacent converted farmland in the western Songnen Plain, Northeast China, where salt-affected meadow soils correspond mainly to Solonetz. Four habitat types—reed wetland, meadow steppe, degraded Suaeda saline patch, and converted farmland—were sampled at 0–20 cm and 20–40 cm soil depths. Soil properties, fungal diversity, taxonomic composition, environmental associations, niche breadth, assembly processes, and FUNGuild-based trophic modes were analyzed using ITS sequencing. Degraded Suaeda soils showed the strongest salinity–alkalinity stress, with pH values of 10.34–10.30 and electrical conductivity of 1.70–1.75 dS·m⁻¹. Fungal richness was highest in surface-converted farmland, with a Sobs value of 423.33, and lowest in deeper degraded Suaeda soil, with a Sobs value of 86.00. Ascomycota dominated most groups, especially degraded Suaeda soils, where its relative abundance reached 75.29–76.80%. ANOSIM confirmed significant community dissimilarity among habitat-depth groups (R = 0.56878, p = 0.001). Specialists accounted for 68.07% of fungal taxa, and stochastic processes, especially drift and dispersal limitation, contributed substantially to assembly. These results indicate that soil depth, salinity–alkalinity, and habitat conversion jointly regulate fungal community structure and ecological processes in degraded soda saline–alkali wetlands. Full article