Search Results (23,863)

Cultural heritage assets in seismic metropolitan regions are increasingly exposed to interacting natural hazards, yet disaster risk assessments for historic buildings often remain limited to single-hazard interpretations. This study addresses this gap by developing a Geographic Information Systems (GIS)-based multi-hazard risk assessment for Ferhatpaşa Mosque, a sixteenth-century Ottoman heritage asset located in Çatalca, Istanbul. Eight spatial parameters were evaluated at the neighborhood scale: slope, elevation, aspect, precipitation, distance to fault lines, distance to hydrological features, land use, and soil capability. The model was developed through Weighted Overlay analysis and interdisciplinary expert-based weighting. Distance to fault lines and precipitation received the highest weights, each accounting for 17.22% of the model, followed by distance to hydrological features and soil capability, each weighted at 13.89%. The final risk map classified 71.99% of the study area as medium risk, 28% as low risk, and 0.02% as high risk. Ferhatpaşa Mosque was located within the medium-risk zone, approximately 29,600 m from active fault lines, 250 m from the nearest dry streambed, 800 m from the nearest stream, and 320 m from the nearest high-risk zone. These findings demonstrate that the mosque’s risk profile is shaped not by seismic proximity alone, but by the cumulative interaction of topography, precipitation, hydrology, soil conditions, and land-use characteristics. The proposed model provides a spatial decision-support framework for integrating cultural heritage conservation into sustainable disaster risk reduction and local risk mitigation planning. Full article

Despite the significant reduction in the overall cultivated area registered in recent decades, poplar still plays an important economic role in the Po Valley–Italy, where many farms involved in the plantation of this species are present, and the leading wood-processing industries are located. This paper describes the current organization of the poplar plywood wood-chain and explores the challenges in introducing new cultivars into the sector. In particular, it analyzes the main physico-mechanical properties of solid wood from five selected poplar clones (‘Dvina’, ‘Lux’, ‘Mella’, ‘Soligo’, ‘Taro’) that are characterized by fast growth, more sustainable agronomic practices, and increased disease resistance. These clones were cultivated in a seven-year-old linear plantation located in Northern Italy. This model, widely used in the past, is being re-proposed as a complement to the traditional system with square planting distances. The peeling yields and some performances of plywood manufactured from their veneers were also investigated. Results indicate that all clones have a much higher (from +30% to +56%) wood basic density than the ‘I-214’, which remains the lighter and preferred reference. These clones appeared also suitable for rotary cutting, but only ‘Lux’ and ‘Soligo’, and to a lesser extent ‘Mella’, provided veneers of the best quality class. Interesting mechanical features were registered for the sample plywood produced, especially in relation to the age of the harvested timber, which reached a diameter adequate for processing in a shorter time compared to the turnover adopted in conventional plantations. Except for ‘Dvina’, for all the clones, bending MOE and MOR were found to be comparable with those of spruce plywood made of similar thickness and the same lay-up. The findings suggest that the availability of new poplar cultivars and that of different cultivation models designed to enhance fast growth, when supported by targeted research and cooperation among multiple stakeholders (including farmers and industrial manufacturers), can lead to new applications where their plywood performances are valued. This, in turn, allows the resulting panels to meet specific needs in previously unexplored sectors, offering additional market opportunities. Full article

Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration. Full article

With increasing urban solid waste generation and the advancement of Zero Waste City initiatives, sanitation-facility siting has become central to urban waste governance but continues to trigger Not-In-My-Backyard (NIMBY) conflicts related to perceived environmental risk, spatial equity, and asset-value concerns. Existing studies often explain NIMBY effects through objective exposure or facility distance, while less attention has been paid to the mismatch between objective risk and residents’ subjective fear and its cost implications. Taking Suzhou, China, as a case study, we develop an integrated framework to assess NIMBY effects associated with current and planned sanitation facilities. An objective risk index is constructed based on facility hazard, exposure, and vulnerability. Resident questionnaires are used to measure subjective fear, and the bias between objective risk and subjective fear is quantified. Probit, hedonic price, and welfare models are then combined to evaluate the effects of this bias on facility support, housing prices, and marginal social welfare losses. The results show that (1) sanitation facilities in Suzhou present clear type differentiation and spatial clustering, with terminal treatment facilities mainly located on the urban periphery, and transfer, sorting, and recovery facilities more embedded in daily living spaces; (2) stronger subjective fear, particularly risk perception, significantly reduces residents’ support for facility expansion, especially under the planned scenario; (3) perception bias is negatively associated with housing prices and generates substantial marginal social welfare losses, especially when the planned expansion of facilities is considered at the system level. This study extends the explanatory framework of environmental NIMBY effects and provides evidence for integrating risk communication, spatial equity compensation, and marginal social welfare loss reduction into Zero Waste City governance. Full article

The research analysed the space-use and habitat-preference characteristics of red deer (Cervus elaphus) in the Sopron Mountains, Hungary, utilising high-resolution Global Positioning System (GPS) telemetry data and two distinct land-cover databases. Hourly location data from 10 individuals were processed using the minimum convex polygon (MCP) and kernel home range (KHR) methods. Additionally, a relative stability index (RSI) was developed to describe seasonal shifts in area use. Significant sexual dimorphism was identified in the extent of annual home ranges: the mean space use of stags (3381 ha) significantly exceeded that of hinds (1391 ha). Geomatical analyses highlighted the seasonality of space use: the smallest extent was recorded in June, and shifts in home ranges within a single year were significant, while the winter period exhibited the least seasonal variation. Regarding habitat selection, significant seasonality was observed in hinds, reflecting temporal changes in resource availability, whereas this pattern was not observed in stags. The study concluded that the applied methods are appropriate for gathering baseline information; however, integrating high-precision databases is essential for accurate modelling of deer–forest interactions. Full article

Water erosion is a critical degradation process that reduces fertility and agricultural sustainability, especially in semi-arid regions. The Universal Soil Loss Equation (USLE) allows for the quantification of this phenomenon using factors such as rainfall erosivity (R) and topography (length-slope, LS). In this study, both factors were estimated and analyzed in the Cañitas sub-basin, located in the semi-desert area of the state of Zacatecas, Mexico, characterized by irregular precipitation and limited data availability. The objective of this study is to estimate and analyze the R factor and LS factor to evaluate their influence on soil water erosion processes. Records from five meteorological stations (1986–2022) were used, along with the Modified Fournier Index (MFI) and Geographic Information Systems (GIS) tools, generating spatial maps of rainfall erosivity and topography. An average R factor of 81.69 MJ∙mm/ha∙h∙year was estimated, consistent with the values obtained using the MFI. The LS factor shows that the northwestern area of the study zone has the most extensive and steepest slopes (up to 20). This study analyzes the R and LS factors to identify areas vulnerable to water erosion and to understand the influence of climate and topography in a semi-arid region, which can serve as a reference for planning conservation actions and managing watersheds in semi-arid areas with high climatic variability. Full article

The representativeness of point measurements in the atmospheric boundary layer is a fundamental challenge for interpreting observations and evaluating numerical models. In this study, we quantify the representativeness of near-surface wind measurements using a dense network of 13 meteorological towers from the Army Research Laboratory’s Meteorological Sensor Array. These towers are distributed over an approximately 3 × 3 km domain at the U.S. Department of Agriculture Jornada Experimental Range in southern New Mexico. The analyzed domain consists of relatively flat terrain within a broader region of more complex topography. Representativeness is assessed using pairwise differences between towers and deviations from the array mean. Spatial variability decreases with temporal averaging, with the largest reductions occurring between 1 and 10 min and diminishing improvements beyond 10–30 min. Wind measurements become progressively less similar with increasing separation distance, particularly at separations approaching 1 km. Representativeness errors are larger under unstable conditions due to enhanced turbulence and spatial variability, while stronger winds increase wind speed variability but enhance directional coherence. Deviations from domain-averaged conditions are comparable among towers, indicating that no single location is uniquely representative. These results quantify the extent to which temporal averaging, spatial separation, and atmospheric conditions influence representativeness, providing practical estimates of the associated spatial scales and residual errors. The results are useful for interpreting observations, evaluating models, and designing sampling strategies using fixed and mobile platforms, including Uncrewed Aircraft Systems. Full article

The article presents a comprehensive methodology for optimizing ground impact zones of spent rocket stages based on the integration of geoinformation analysis, remote sensing of Earth, ballistic modeling, and ecosystem sustainability assessment. An information and analytical system (IAS) has been developed and tested, providing automated selection of environmentally sustainable landing points within acceptable dispersion zones. The methodology includes the use of the NDVI, digital terrain models, soil quality assessments, fire hazard assessments, and environmental damage calculations. For the first time, a system for classifying operational-territorial units according to their level of resilience to man-made impacts has been formed. The results suggest the potential for the reduction of the dangerous impact zone under modeled conditions. The system architecture is designed to be scalable and applicable to other spaceports located in continental regions. The presented methodology contributes to the development of an environmentally oriented approach to aerospace infrastructure management. Full article

This study examines how mothers raising children with disabilities in American Samoa experience the processes of seeking diagnosis, navigating special education, and advocating for services within an insular rural context. American Samoa, an unincorporated U.S. territory located 2600 miles from Hawaiʻi with a population under 50,000, represents a case of what we term insular rurality—a condition in which the structural disadvantages of rurality are intensified by oceanic isolation, territorial governance, and colonial history. Data were collected through three focus groups with fifteen mothers whose children hold a range of disability diagnoses, with a card sort activity at the outset of each session serving as an idiographic anchor to protect individual voice within the group format. Analysis followed Interpretative Phenomenological Analysis adapted for focus groups (IPA-FG), proceeding from line-by-line exploratory noting through Personal Experiential Themes and Group Experiential Themes within each focus group case to cross-case convergence and divergence analysis, interpreted through the Fonofale model of Pacific wellness. Findings reveal two overarching themes: systemic invalidation, in which mothers encountered deficit-based assumptions, stagnant educational goals, and institutional disengagement; and parent peer support as the primary infrastructure, in which mothers became de facto experts, built community-driven solutions, and envisioned more inclusive futures. Technology emerged as a contradictory force—valuable for parent learning but largely ineffective for children’s remote therapy. These findings suggest how workforce shortages and geographic isolation create conditions in which maternal advocacy becomes a systems-level necessity rather than a personal choice. Implications for rural education policy, IDEA implementation in U.S. territories, and culturally grounded family support are discussed. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Aeolian sand bodies unrelated either to coastal barrier systems of Holocene or earlier age or to modern beaches have been identified along the central New South Wales coast of southeast Australia. Some of these deposits cap headlands or are located above high sea-cliffs. Others lie below modern sea-levels, whilst one substantial dune field extends 12 km inland. Many of these have previously been interpreted as Early Holocene cliff-top dunes, the product of the migration of beach sands up aeolian sand ramps at the foot of the sea-cliffs of the region and onto the cliff tops. The rising sea-levels of the Middle Holocene eroded the ramps and cut off the supply of sand to the dunes, allowing them to stabilise. But re-investigation shows that these dune fields accumulated at times of low Quaternary sea-levels, with a particle-size distribution suggestive of an inland rather than a coastal origin. We therefore propose an alternative model for the accumulation of these features. At times of low sea-level, sediments exposed on the inner shelf were reworked onto the adjacent coast by onshore winds, where they accumulated in locations unconnected to the modern or the earlier Holocene coastal aeolian sedimentary regime. This model challenges the conventional story that the dominant glacial maximum winds across southeastern Australia were from the west (and thus offshore). This pattern of sediment accumulation and its associated wind regime may have been more stable (continuing for over 30 000 years) and more long-lived (repeated through at least the last two glacial cycles) than has previously been believed. Although the cliff-top dune model has been widely applied, we question its suitability in its type location and suggest a more cautious approach to its application elsewhere. We argue that the products of the landward aeolian reworking of sediment exposed on the continental shelf have been overlooked, despite their potential for the preservation of long-term environmental records. Full article

Precise fault localization for high-speed railway continuous transmission lines is indispensable for sustaining power supply reliability and mitigating power outages. This study presents a novel fault localization approach that uses low-voltage information obtained from box-type substations distributed along continuous transmission lines. The proposed scheme relies on the distribution features of the positive-to-negative sequence voltage ratio ($r_{PNV}$) measured at the low-voltage terminals of box-type substations. Results reveal that the magnitude of $r_{PNV}$ gradually declines from the main substation to the fault location in the fault upstream area, while it stays nearly unchanged in the downstream section. Based on this feature, the faulted section is initially determined by means of the nearest neighbor clustering method. Subsequently, the precise fault location is calculated by solving equations that combine the sequence voltage ratio at the fault point with the measurements obtained from the main substation and box-type substations downstream of the fault. The proposed method requires only asynchronous low-voltage measurements, eliminates the need for fault impedance modeling, and is applicable to various asymmetric faults. Simulation tests under different fault types, fault resistances (up to 2000 Ω), noise conditions, and neutral grounding modes demonstrate that the method achieves high accuracy and robustness. Full article

Environmental policies and intermittent renewable energy (RE) drive large-scale hydrogen production towards hybrid supply configurations, combining collocated RE units and the electricity market (EM). This links the power and hydrogen sectors through EM/hydrogen prices, dispatch, and hydrogen demand profiles. In a hybrid configuration, the strategic role of RE in the EM enhances these links by creating profit opportunities. This work develops a bi-level model, optimizing electrolyzer size and location, operational decisions and RES bidding strategies, while explicitly modeling EM clearing. In the upper-level, an EM player, owning strategically bidding RE assets, evaluates expanding into the use of electrolyzers that act as price-takers. The lower-level problem clears the EM. The proposed framework is applied to an IEEE 24-node test system. The results show how EM conditions determine investments for different hydrogen price cases. It is revealed that differentiated electricity sourcing across electrolyzers and efficiency-preserving dispatch impact operational decisions, leading to revenue improvements. Moreover, renewable capacity withholding is used to avoid zero EM prices and mitigate the economic impact of unmet hydrogen demand when RE availability is limited and electrolyzer participation in the EM is restricted. Time-window-constrained hydrogen demand mitigates unutilized RE by 39% compared to that for hourly demand. Full article

The feasibility of employing the dynamic substructure approach for seismic response analysis of complex structures has been widely recognized. However, the analytical accuracy of this method is affected by several factors, including the element type, the structural configuration, and the analysis method. To address these issues, four types of reticulated shell structures were designed and analyzed using the mode superposition response spectrum method (MSRSM) and the time history analysis method (THAM). The displacements of the key nodes and all member stresses were extracted to compare the simplified finite element models with the original models. The relative errors of nodal displacements calculated by the models with reduced degree of freedom (DOF) were within 1.62%. For the member stresses of the single-layer reticulated shells, the relative errors of the simplified models were within 14.35%. In the simplified models of double-layer reticulated shells, several members exhibited a relative error greater than 30%; however, these members were mainly located near the substructure boundaries and accounted for less than 0.62% of the entire structure. Three strategies are proposed to mitigate the influence of the member stress errors on the structural analysis conclusions for double-layer reticulated shell structures. In addition, the dynamic substructure method was extended to the coupled system of large-span spatial structures and point-supported glass facades. The seismic response results confirmed that this method effectively reduces computational costs while maintaining satisfactory accuracy, indicating that it is a useful tool for simplifying large-span spatial structures in extensive numerical analyses. Full article

Introduction: A major health issue in individuals living at high-altitude regions is an increase in the number of red blood cells (RBCs). This condition generates a series of physiological alterations including the nervous system, where damage can occur due to increased blood viscosity. This increased viscosity, in turn, could compromise oxygen uptake, potentially linked to a degree of cognitive impairment. Objective: To determine the association between exposure to chronic hypoxia and sleep quality with the degree of cognitive impairment in a young adult population residing at different altitude levels. Methodology: A cross-sectional study was conducted with 200 apparently healthy subjects (aged 21–26 years) permanently residing in four Peruvian cities: Lima (154 m), Arequipa (2335 m), Puno (3820 m), and La Rinconada (5100 m) (n = 50 per location). Physiological profiles (SpO₂, blood pressure, heart rate, hemoglobin, and hematocrit) were measured. Cognitive impairment and sleep quality were evaluated using the Montreal Cognitive Assessment (MoCA) and the Pittsburgh Sleep Quality Index (PSQI). Sex-stratified hierarchical multiple linear regression models with bootstrapping were utilized for independent correlation analysis. Results: Hemoglobin levels gradually increased with altitude, peaking at 19.47 ± 3.01 g/dL in La Rinconada, while SpO₂ decreased to 81.64%. Moderate-to-severe cognitive impairment was exclusively restricted to the extreme altitude population of La Rinconada, where only 10% of subjects remained unaffected. In the sex-stratified multivariate regression, residency in La Rinconada initially served as a robust negative predictor of MoCA scores among women (β = −5.52, p < 0.001); however, this geographical effect lost statistical significance after adjusting for biological variables in Model 2 (β = −4.72, p = 0.178). In the fully adjusted models, neither individual hemoglobin levels nor SpO₂ fluctuations displayed an independent linear association with cognitive performance in either sex (p > 0.05). Sleep quality was poor across cohorts but showed no significant association with cognitive impairment (p = 0.174). Conclusions: Chronic exposure to severe hypoxia (>5000 m) is associated with a greater presence of cognitive impairment, which is largely accounted for by individual physiological adaptations rather than isolated, linear effects of independent hematological or subjective sleep parameters. Full article