Search Results (245)

Building placement strongly conditions performance, experience, and meaning in architecture and urban planning, yet siting rationales in design studio work are rarely made explicit or examined systematically. This post hoc, observational study analyzes 22 student proposals for a paddle school on a defended coastal headland in Cascais, Portugal, to reveal siting patterns and test convergence toward an expert recommendation. Each project is mapped onto a common grid and encoded as building mass and external paths, and a site-specific expert prior is formalized as a polygon that follows the defended wall and upper terrace, combining edge protection, elevation, and ocean prospect. Alignment with this prior is assessed using exact permutation tests under uniform and elevation-stratified random siting, and each proposal is summarized by three descriptors that capture where mass concentrates, how far it extends, and how broadly it uses the site. Results show a pronounced nucleus along the upper terrace, a contour-parallel circulation spine, and extensive underused areas elsewhere, with alignment to the expert prior significantly above chance. Clustering projects by the three descriptors differentiates siting families, from edge-anchored schemes to prospect-led variants and a small set of deliberate counterexamples. The framework turns studio designs into auditable evidence of how cohorts occupy a site and makes siting heuristics explicit and testable, supporting more transparent discussion of site strategies in architectural education and informing practice-oriented design guidance. Full article

Numerous models exist for estimating the specific energy yield of wind turbines, typically relying on meteorological wind speed data and turbine characteristics. However, the applicability and accuracy of these models must be validated against real-world data, particularly concerning the conditions specific to Poland. The primary objective of this study was to verify the accuracy of existing wind energy yield models for onshore wind turbine installations in Polish conditions. The study was conducted in two parts. First, the compliance of wind speed data derived from two global reanalysis databases (ERA5 and MERRA) was analyzed against actual hourly measurements. These measurements were collected from nacelle-mounted sensors at the hub height of six operational turbines (two 3 MW and four 0.8 MW units) at a wind farm site over the course of 2019. Second, a computational model for the specific energy yield was verified using the same on-site measurements, incorporating data on turbine configuration, location, and the ERA5/MERRA inputs. A significant discrepancy was observed: wind speeds measured directly on the higher-capacity turbines (3 MW) were consistently higher than those reported in the ERA5 and MERRA databases. This difference is attributed to the fact that the coarse grid resolution of global databases does not capture the precise, optimized placement of turbines at sites specifically selected for high wind potential, often considering local topography. Despite this initial wind speed variance, the subsequent verification of the energy yield model showed satisfactory agreement with real production data. The relative mean bias error (rMBE) was found to be below 8% for the ERA5 input paired with the 3 MW turbine data and below 12% for the MERRA input paired with the 0.8 MW turbine data. The findings confirm that while global reanalysis databases may underestimate local wind speeds due to generalized grid resolution, the tested energy yield model provides satisfactory results for wind turbine planning in Poland. Full article

Fatigue cracks in highly stressed regions of marine structures, caused primarily due to wave loading, are critical life-limiting factors that can lead to structural failure. Structural Health Monitoring (SHM) systems offer the ability to remotely monitor damage progression during its initial phases, enabling failure prevention. One diagnostic approach utilizes the strain redistribution in the vicinity of the crack tip, captured by sensor readings, to inversely calculate the corresponding crack length. This work addresses the challenge of accurately calculating the crack length under variable sources of uncertainty by employing the statistical framework of Maximum Likelihood Estimation (MLE). The method is demonstrated on a simplified test geometry using simulated strain data, registered at locations where structural response sensors may be placed. This approach enables the integration of multiple strain features at modest computational cost, facilitating the assessment of different sensor placement strategies under realistic noise conditions. Full article

Agrivoltaic systems (AVS) are gaining global attention as an innovative solution to simultaneously address food, water, and energy security challenges. However, the effective design and management of these dual-use systems hinge on a comprehensive understanding of their microclimatic impacts. This systematic review critically analyzes the current literature on AVS microclimates, focusing on key atmospheric (air temperature, relative humidity, wind speed), radiation (Photosynthetically Active Radiation—PAR, global radiation, shading rate), and soil parameters (temperature, moisture). Results indicate that while reduced soil temperature and enhanced moisture retention are consistent and agronomically significant benefits, the effects on air temperature are highly variable. These often demonstrate site-specific warming or pronounced vertical thermal stratification. Furthermore, AVS significantly alters light availability, with PAR reduction ranging from 5% to 94%, emphasizing the system’s inherent spatial and temporal heterogeneity. A major gap identified is the lack of standardized measurement methodologies, limiting data comparability. To address this, we propose a “Minimum Viable Monitoring” (MVM) framework, advocating for multi-zone and multi-height sensor placement to accurately capture microclimatic variability. These findings highlight that the observed heterogeneity, rather than a limitation, presents a unique opportunity for precision agriculture and zoned management strategies. Full article

Stable operating conditions in electrolyzers are crucial for preserving system durability, ensuring highly pure hydrogen production, and enabling the sustainable utilization of surplus renewable electricity. However, in active distribution networks, the output uncertainty of distributed energy resources, such as renewable energy sources (RES) on the generation side and load demand side, can lead to voltage fluctuations that threaten the operational stability of electrolyzers and limit their contribution to a low-carbon energy transition. This paper proposes a novel framework for optimal electrolyzer placement, tailored to their operational requirements and to the planning of sustainable renewable-integrated distribution systems. First, probabilistic scenario generation is carried out for RES and load to capture the characteristics of their inherent uncertainties. Second, based on these scenarios, continuous power-flow-based P–V (power–voltage) curve analysis is conducted to evaluate voltage stability and identify the loadability and load margin for each bus. Finally, the optimal siting of electrolyzers is determined by analyzing the load margins obtained from the voltage stability assessment and deriving a probabilistic electrolyzer hosting capacity. A case study under various uncertainty scenarios examines how applying this method influences the ability to maintain acceptable voltage levels at each bus in the grid. The results indicate that the method can significantly improve the likelihood of stable electrolyzer operation, support the reliable integration of green hydrogen production into distribution networks, and contribute to the sustainable planning of other voltage-sensitive equipment. Full article

In the context of pavement management systems (PMSs), overloaded trucks impose severe economic and environmental burdens by accelerating pavement deterioration and increasing greenhouse gas (GHG) emissions. Existing research on Weigh-in-Motion (WIM) placement has rarely incorporated environmental impacts, particularly greenhouse gas (GHG) emissions, into the decision-making process. Instead, most studies have focused on infrastructure damage and have paid limited attention to how enforcement interacts with driver evasion behavior and schedule-related constraints. To address this gap, this study develops a bi-level optimization framework that simultaneously minimizes PMS costs, travel costs, and environmental (GHG) costs. The upper-level problem represents the total social cost minimization, while the lower-level problem models drivers’ routes and demand shift. The framework endogenously captures utility-based demand shifts, allowing overloaded drivers to switch to legal operations when enforcement and schedule-related constraints outweigh overloading benefits. A numerical study using the Sioux Falls network demonstrates that dual WIM installations significantly outperform single configurations, achieving network-wide cost reductions of up to 1.5% compared to 0.4%. Notably, PMS costs for overloaded trucks decreased by nearly 60%, confirming the effectiveness of strategic enforcement. Ultimately, this study contributes a unified decision-support tool that reframes WIM enforcement from a passive control measure into a proactive strategy for sustainable freight management. Full article

Soil-dwelling beetles, including native and invasive species such as Popilia japonica Newman (Coleoptera: Scarabaeidae), are persistent and damaging agricultural pests worldwide. Mass trapping, using pheromone-, light-, or food-based lures to attract and remove adults, is being developed as an environmentally sustainable alternative within integrated pest management (IPM). Scarab beetles respond positively to attractant-based traps, and large-scale programs against P. japonica in North America provide valuable insights for global applications. The efficacy of mass trapping depends on species biology, trap density, environmental conditions and landscape structure. Capturing adults does not always immediately reduce larval populations, as underground stages persist in soil for multiple years. Light traps are effective but often attract many non-target insects, whereas pheromone traps are more selective but require careful optimization of lure composition, release rate and placement. To achieve reliable suppression, mass trapping should be integrated with complementary strategies such as biological control agents (Beauveria spp., Metarhizium spp.), crop rotation, tolerant crop varieties and soil management. Future research should focus on refining lure design, optimizing deployment, testing predictive models and evaluating multi-bait systems. Overall, mass trapping represents a promising and environmentally sustainable tool that, when incorporated into integrated approaches, can enhance the management of soil-dwelling scarab beetles across diverse agroecosystems worldwide. Full article

The aim of the study was to identify doctoral and postdoctoral dissertations that were created between 2003 and 2023 and based on tourism research, and the promotion procedures were conducted within the discipline of ‘Physical Culture Sciences’ (PCS). An attempt was made to identify the connections between such theses and other fields/disciplines of science and the methodological approaches used in them. The conducted research was empirical in nature, and its result is the opinions of the authors of 119 doctoral theses and 42 postdoctoral dissertations addressing tourism issues on the scientific disciplines within which these works were located. An attempt was also made to estimate the contribution that PCS had in their creation. The research results revealed strong connections between ‘tourism’ Ph.D. and postdoctoral theses completed in the PCS discipline, especially with the fields of ‘Social Sciences’ and ‘Humanities’. The results also allowed for determining and performing multi-aspect analyses regarding the methodological profiles of the examined works, visualising such profiles in the form of radar charts, which included information on their 16 most important methodological features. In the research, it was shown that doctoral and postdoctoral dissertations devoted to tourism issues completed within the discipline of PCS are characterised by great diversity concerning the applied methodological approaches. They are largely multi-/inter-disciplinary in nature, and the doctoral theses are dominated by empirical methods focused on cultural research. At the same time, these profiles are strongly diversified depending on the other field of science to which the works formally assigned to the PCS are related. The research results presented in this article suggest that typical bibliometric analyses regarding the disciplinary structure of advance tourism research fail to capture the diversity and methodological specificity of research conducted within various scientific disciplines. This necessitates further research, particularly empirical studies identifying their methodological profiles and demonstrating their differences. These studies can be a valuable source of information not only for methodological refinement and improving the quality of tourism research, but may also provide a basis for discussion on the placement of PCS in the classification of sciences and the role that tourism research should play within this discipline. Full article

Construction quality failures impose substantial costs on the industry, with traditional quality assurance (QA) methods operating reactively by detecting problems after they occur rather than preventing them during planning and design phases. Limited research exists on the systematic integration of immersive technologies (IMTs) for proactive quality failure prevention across construction project lifecycles. This study investigates how IMTs can systematically prevent specific quality failure categories through enhanced spatial visualization and virtual verification processes. A qualitative approach was employed, combining scoping literature review, two purposively selected case studies, and autoethnographic analysis to capture both performance metrics and implementation insights. Case Study 1 achieved 8% improvement in solar panel placement efficiency (optimizing from 82 to 90 modules) and 1.7% increase in useful energy production (85.8% vs. 84.1%) through BIM-Unreal Engine integration for shadow analysis and spatial optimization. Case Study 2 demonstrated virtual site mobilization using the Revit–Twinmotion workflow, eliminating spatial conflicts and safety clearance violations during pre-construction planning. Findings revealed that IMT applications systematically address quality failure root causes by preventing design coordination errors, measurement mistakes, and regulatory non-compliance through virtual verification before physical implementation. This paper establishes IMTs as transformative QA platforms that fundamentally shift construction quality management from reactive detection to proactive prevention, offering measurable improvements in project delivery efficiency and quality outcomes. Full article

The increasing scale and complexity of graph-structured data necessitate efficient parallel training strategies for graph neural networks (GNNs). The effectiveness of these strategies hinges on the quality of graph feature representation. To this end, we propose a Frequency-Adaptive Dual-Pooling Graph Transformer (FADP-GT) model to enhance feature learning for computational graphs. We propose a Frequency-Adaptive Dual-Pooling Graph Transformer (FADP-GT) model, which incorporates two modules: a Frequency-Adaptive Graph Attention (FAGA) module and a Dual-Pooling Feature Refinement (DPFR) module. The FAGA module adaptively filters frequency components in the spectral domain to dynamically adjust the contribution of high- and low-frequency information in attention computation, thereby enhancing the model’s ability to capture structural information and mitigating the over-smoothing problem in multi-layer network propagation. On the other hand, the DPFR module refines graph features through dual-pooling operations—Global Average Pooling (GAP) and Global Max Pooling (GMP)—along the node dimension, which captures both global feature distributions and salient local patterns to enrich multi-scale representations. By improving graph feature representation, our FADP-GT model indirectly supports the development of efficient device placement strategies, as enhanced feature extraction enables the more accurate modeling of node dependencies in computational graphs. The experimental results demonstrate that FADP-GT outperforms existing methods, reducing the average computation time for device placement by 96.52% and the execution time by 9.06% to 26.48%. Full article

In response to the imperative to mitigate methane—one of the most potent greenhouse gases—this study proposes and tests an integrated workflow for designing methane drainage boreholes targeting post-mining areas in an active underground coal mine (Pniówek, Poland). The workflow combines the following: (1) forecasting methane emissions from goafs and active longwalls for 2024–2040; (2) 3D geological characterization (structural and lithofacies models); (3) selection and sealing of goaf zones; and (4) optimization of well placement, drilling, and performance evaluation of drainage boreholes, including an assessment of energy use from the recovered gas. Applying the method delineated priority capture zones and estimated recoverable rates under multiple scenarios. Preliminary field data from a borehole near seam 362/1 indicate stable methane inflow to the drainage system and a concomitant reduction in methane load within the ventilation network. The integrated design improves targeting efficiency and provides a quantitative basis for scheduling, risk management, and sizing of surface-to-underground infrastructure. The results suggest that systematic drainage of post-mining voids can enhance safety, limit fugitive emissions, and create opportunities for on-site power generation. The approach is transferable to other active mines with legacy workings, provided site-specific calibration and monitoring are implemented. Full article

The increasing deployment of islanded microgrids in disaster-prone and infrastructure-constrained regions has elevated the importance of resilient energy storage systems capable of supporting autonomous operation. Grid-forming energy storage (GFES) units—designed to provide frequency reference, voltage regulation, and black-start capabilities—are emerging as critical assets for maintaining both energy adequacy and dynamic stability in isolated environments. However, conventional storage planning models fail to capture the interplay between uncertain renewable generation, time-coupled operational constraints, and control-oriented performance metrics such as virtual inertia and voltage ride-through. To address this gap, this paper proposes a novel distributionally robust optimization (DRO) framework that jointly optimizes the siting and sizing of GFES under renewable and load uncertainty. The model is grounded in Wasserstein-metric DRO, allowing worst-case expectation minimization over an ambiguity set constructed from empirical historical data. A multi-period convex formulation is developed that incorporates energy balance, degradation cost, state-of-charge dynamics, black-start reserve margins, and stability-aware constraints. Frequency sensitivity and voltage compliance metrics are explicitly embedded into the optimization, enabling control-aware dispatch and resilience-informed placement of storage assets. A tractable reformulation is achieved using strong duality and solved via a nested column-and-constraint generation algorithm. The framework is validated on a modified IEEE 33-bus distribution network with high PV penetration and heterogeneous demand profiles. Case study results demonstrate that the proposed model reduces worst-case blackout duration by 17.4%, improves voltage recovery speed by 12.9%, and achieves 22.3% higher SoC utilization efficiency compared to deterministic and stochastic baselines. Furthermore, sensitivity analyses reveal that GFES deployment naturally concentrates at nodes with high dynamic control leverage, confirming the effectiveness of the control-informed robust design. This work provides a scalable, data-driven planning tool for resilient microgrid development in the face of deep temporal and structural uncertainty. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Resource-constrained IoT and IIoT systems require detection architectures that balance accuracy with energy efficiency, scalability, and contextual awareness. This paper presents a conceptual framework informed by a systematic review of energy-aware detection systems (XDS), unifying intrusion and anomaly detection systems (IDS and ADS) within a single framework. The proposed taxonomy captures six key dimensions: energy-awareness, adaptivity, modularity, offloading support, domain scope, and attack coverage. Applying this framework to the recent literature reveals recurring limitations, including static architectures, limited runtime coordination, and narrow evaluation settings. To address these challenges, we introduce a utility-based decision model for multi-layer task placement, guided by operational metrics such as energy cost, latency, and detection complexity. Unlike review-only studies, this work contributes both a synthesis of current limitations and the design of a novel six-dimensional taxonomy and utility-based layered architecture. The study concludes with future directions that support the development of adaptable, sustainable, and context-aware XDS architectures for heterogeneous environments. Full article

Background: Patent ductus arteriosus (PDA) in dogs causes persistent left-to-right shunting, leading to pulmonary overcirculation, left heart volume overload, and potential congestive heart failure. Accurate assessment of fluid imbalance is essential but challenging with conventional echocardiography or biomarkers. Phase angle (PhA), derived from bioelectrical impedance analysis (BIA), may serve as a non-invasive marker of extracellular fluid distribution and cellular integrity. Objectives: This study aimed to evaluate PhA as an indicator of thoracic fluid imbalance in dogs with PDAby analyzing its correlation with pulmonary velocity (PV) and end-diastolic volume (eV), as well as its responsiveness to surgical correction. In addition, we assessed the relationships between PhA and echocardiographic structural indices (LA/Ao, TDI Sep E/Em, TDI Lat E/Em) and examined the influence of the measurement region. Methods: PhA was measured at 5, 50, and 250 kHz in 30 PDA-affected and 15 healthy dogs, with electrode placement across thorax, trunk, and abdomen. Echocardiography evaluated PV, eV, and PDA-specific structural parameters. Results: Thoracic PhA at 5 kHz was significantly reduced in PDAdogs, strongly correlated with PV and moderately with eV. Postoperative measurements showed progressive PhA recovery. Only TDI Lat E/Em correlated with mid-frequency PhA, while other structural indices showed minimal association. Thoracic PhA was lower than trunk or abdominal values, indicating that thoracic measurements may better capture localized extracellular fluid changes in PDAcompared with other regions. Conclusion: Thoracic PhA at 5 kHz effectively reflects extracellular fluid changes in PDA, complements structural echocardiography, and tracks postoperative fluid normalization. Its non-invasive nature supports clinical utility for monitoring hemodynamic burden and therapeutic response. Full article