Search Results (71,655)

Efficient and approximate evaluation of urban coverage is important for wireless network planning. While standard statistical propagation models are fast, they do not directly describe the physical environment of a specific urban scene and consequently often fail to accurately capture local blockage and site-specific propagation effects. Ray tracing can model these effects more directly, but becomes costly when testing many tiles, frequencies, and transmitter heights simultaneously. To address this problem, the present study investigates the use of an RT-supervised simulation-to-simulation tile-based learning framework for path loss prediction based on geographic environmental information. This methodology first builds realistic 3D city scenes from geographic data, then uses offline ray tracing to generate supervision labels across multiple carrier frequencies and base-station heights. Each city region is divided into 500 m by 500 m tiles, which are then further discretized into 125 by 125 grids. For each tile, raster priors, such as occupancy, normalized height, and a valid-ground mask, are prepared. During training and inference, the model input is organized as an 8-channel raster tensor together with a 2D condition vector for frequency and transmitter height. The raster tensor combines three stored environment priors and five online-generated transmitter-related feature maps. By utilizing masked supervision, the network learns the excess loss residual exclusively on valid outdoor pixels, and the final path loss map is reconstructed by combining the residual prediction with the FSPL prior. The final model in this work was trained on 134,317 samples and validated on 33,589 samples. In the in-city setting, used as a preliminary verification before subsequent cross-city experiments, it achieved an MAE of 5.0116 dB and an RMSE of 9.3182 dB. On the formal cross-city test with a completely unseen target city, it achieved an MAE of 4.8536 dB and an RMSE of 9.3504 dB. These results demonstrate that the proposed framework can provide a stable tile-level approximation of RT-generated path loss maps under multiple conditions. Because both training labels and evaluation references are generated by RT rather than drive-test measurements, the present study should be understood as a simulation-to-simulation surrogate framework rather than a direct validation of real-world propagation accuracy. Full article

Onion peel represents a valuable food by-product rich in bioactive phenolic compounds. Building on previous phytochemical investigations, an onion peel extract from the Rossadi Tropea variety was developed as a standardized bioactive ingredient (OPI-T), defined by flavonol (quercetin and its glycosylated and oxidized derivatives) and anthocyanin (cyanidin derivatives) markers, ensuring batch-to-batch consistency, and evaluated for its potential against hepatic steatosis. The present study aimed to assess the protective effects of OPI-T against palmitate-induced steatosis and oxidative stress in HepG2 cells, a widely used in vitro model of hepatic lipid accumulation. An onion peel extract derived from the Ramata di Montoro variety was included as a natural negative reference to account for varietal variability. HepG2 cells were co-treated with palmitate (500 µM) and OPI-T (25 or 50 µg/mL). Lipid accumulation was evaluated by Oil Red O and BODIPY staining, while oxidative stress was assessed by the DCF assay. Mitochondrial dynamics and autophagy were investigated through the analysis of key protein markers, including MFN2, DRP1, SQSTM1/p62 and LC3 II/I. OPI-T significantly attenuated palmitate-induced lipid accumulation (−18%) and reduced intracellular ROS production (−75%), while modulating mitochondrial dynamics toward a reduced fission phenotype with a marked increase in the MFN2/DRP1 ratio (1.66) and improving autophagy flux. In contrast, the Ramata di Montoro variety showed weaker or inconsistent effects under the same experimental conditions. Overall, these findings support the functional validation of a standardized onion peel-derived ingredient, highlighting its potential application as a bioactive component for functional food or nutraceutical development targeting hepatic steatosis and oxidative stress. Full article

The Babylonian numeral system, developed more than four thousand years ago, is one of the earliest known positional number systems, employing a sexagesimal (base-60) structure and a limited set of wedge-shaped symbols. Despite their visual simplicity, Babylonian numerals exhibit substantial structural and positional complexity, particularly when multiple symbols are combined to represent larger numerical values. This complexity presents significant challenges for modern computational recognition, especially in handwritten and degraded archaeological contexts. Most existing research has focused on the recognition of isolated Babylonian numeral symbols, which does not adequately reflect real inscriptions where numerals typically appear as composite sequences. To address this limitation, this paper proposes a hybrid deep learning framework capable of identifying, interpreting, and computing the decimal values of multi-symbol handwritten Babylonian numerals. Building on prior work in single-symbol recognition, we construct a synthetic yet realistic dataset of composite numeral images by combining handwritten glyphs into sequences of two to four symbols while incorporating natural variations in spacing, alignment, and handwriting style. The proposed framework integrates a Convolutional Neural Network (CNN) for visual feature extraction with optional structural feature fusion, followed by a Support Vector Machine (SVM) classifier for reliable multi-class discrimination. A rule-based positional decoder is then applied to convert recognized symbol sequences into their corresponding decimal values using Babylonian base-60 logic. By combining visual recognition with positional numerical reasoning, the proposed system enables end-to-end interpretation of handwritten Babylonian numeral sequences. To the best of our knowledge, this work represents one of the first approaches to jointly classify, decode, and compute numerical values from multi-symbol handwritten Babylonian numerals, contributing to digital epigraphy, archaeological text analysis, and cultural heritage preservation. Full article

Office buildings are significant contributors to energy consumption and carbon emissions due to high occupancy density and prolonged operation. To balance decarbonization with indoor environmental quality, this study proposes a simulation-driven multi-strategy optimization framework for a three-story office building in Jinan. This study integrates EnergyPlus 23.2, jEPlus+EA 2.3.2, and the NSGA-II algorithm to co-optimize building performance. We evaluate the synergistic effects of roof photovoltaic coverage ratio, night ventilation turn-on temperature difference, and HVAC control strategies on carbon emissions and thermal comfort, while ensuring that CO₂ concentrations remain within health thresholds. The results indicate that the night ventilation temperature turn-on temperature difference is the most influential parameter. It yields standardized regression coefficients (SRCs) of 0.7456 for carbon emissions and 0.5325 for thermal discomfort. The Pareto-optimal solution achieves a carbon footprint of approximately 477 tCO₂, with only 8.8% indoor discomfort hours. This framework provides a robust, practical approach for the low-carbon and healthy operation of office buildings. Full article

To combat climate change, farmers want to develop sustainable agriculture that enhances food production while strengthening their capacity to cope with extreme weather events and pest and disease pressures. Promoting agroecological farming practices is a promising approach in enhancing sustainability and strengthening the climate-resilient farming systems. Recent research often overlooks to what extent the agroecological farming practices (AFP) provide a measurable advantage over non-AFP methods under increasing environmental challenges. In this regard, this study compares the extent of climate resilience between AFP mango-based farming systems and non-AFP mango-based farming systems in southern Ethiopia. AFP adopters applied ecological principles like intercropping, integrated pest management, agroforestry, canopy management, varietal diversity, and water and soil preservation to enhance biodiversity and soil health, and boost productivity and ecosystem services. The study employed a mixed-method design, drawing on the data from 395 selected households. The resilience of AFP and non-AFP farming systems was assessed by computing the 13 agroecosystem indicators of climate resilience using the Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP+) tool. Households in AFP mango-based farming system demonstrated greater diversification in agricultural production system compared to those in non-AFP mango farming system. The analysis of climate resilience indicators showed that the mango production systems under the AFP were more climate-robust than their conventional systems. Both the compound resilience score and the household resilience index showed that the mango farming systems under AFP substantially enhanced climate resilience. Hence, coordinated supports from the extension services, NGOs, and researchers are needed to scale up these benefits of AFP. Strengthening the AFP mango farming requires addressing the key barriers such as market access, input availability, and crop diversification strategies. This paper identifies important avenues for further AFP research in Sub-Saharan African countries. Full article

This study presents a longitudinal examination of intergroup relations between Arab and Jewish students in an Israeli higher education institution, focusing on their willingness for intergroup closeness and actual intergroup interactions. Building upon previous data collected in 2023, the 2025 findings reveal both persisting views in certain attitudes and behaviors and changes in others. Arab students consistently reported higher levels of willingness for academic, friendship, and acquaintanceship relations, as well as more frequent intergroup interactions. In contrast, Jewish students demonstrated a significant decline in both willingness and engagement, particularly following the 7 October 2023 attacks and ensuing war between Hamas and Israel. The study highlights the gap between expressed willingness and actual behavior, emphasizing the need for structured institutional interventions to foster meaningful intergroup contact. Differences were also observed across gender and friendship status, with Arab students showing increased openness over time. The findings underscore the critical role of higher education institutions in promoting intercultural competence and suggest practical recommendations for enhancing intergroup relations in conflict-affected societies. Full article

Pediatric pain remains a highly prevalent and under-addressed health problem worldwide, largely due to educational gaps, limited humanization of care, and insufficient integration of digital and pedagogical innovations in higher education, and the purpose of this study is to describe and implement an international, higher education–driven model to improve training in humanized pediatric pain management. This multicenter mixed-methods study involves 15 universities from Europe, Africa, and Latin America and includes the development and cross-cultural validation of the HUPEDCARE-Q questionnaire to identify knowledge gaps, the design of an open-access, multilingual digital learning platform (PEDCARE) that integrates learning management and social networking functions, and the implementation of capacity-building workshops based on a training-the-trainers model for students, educators, health professionals, and families. The expected outcomes of the project include the establishment of a standardized instrument for assessing educational needs, the creation of a scalable digital educational environment, and the feasibility of international academic collaboration to strengthen competencies in pediatric pain care. The study suggests that higher education, combined with digital transformation and culturally sensitive approaches, may support the humanization of pediatric pain management and address educational and health inequities, although further research is needed to confirm these potential impacts. Full article

Campus streetscapes are a key part of universities’ everyday public realm, yet the same scene may be perceived positively in one dimension while negatively in another. To diagnose such multi-dimensional perceptual differences and translate them into actionable design evidence, this study develops an interpretable vision analytics framework for adaptive campus design. Using 72,733 Baidu Street View images collected from 41 campuses in mainland China, the study integrates ResNet-50-based perception prediction, spatial element extraction, XGBoost–SHAP-based mechanism interpretation, Kruskal–Wallis H testing, and GIS-based scene mapping. Supported by supplementary in situ validation, six types of multi-dimensional perceptual differences were identified. Sky, buildings, vegetation, hardscape, and terrain were found to be the five most important spatial elements overall, among which sky, buildings, and vegetation repeatedly emerged as the dominant core elements distinguishing different perceptual types. These elements do not act independently or linearly, but jointly shape different types of multi-dimensional perceptual differences through nonlinear threshold effects and interactions. These perceptual difference types were further found to cluster in recognizable campus scenes, including main roads, plazas, lawns, forest belts, and lakeside spaces. Based on these findings, scene-specific piecemeal optimization strategies were derived to support the coordinated enhancement of perceived safety, liveliness, and beauty. Overall, the study shows that campus perception is shaped by holistic spatial configurations rather than the simple accumulation of isolated elements, and provides a quantitative basis for iterative, feedback-oriented adaptive campus design. Full article

Accurate and real-time localization is a fundamental prerequisite for the autonomous navigation of mobile robots. LiDAR–Inertial Odometry (LIO) achieves high-precision state estimation and scene reconstruction in unknown environments by effectively fusing data from LiDAR and Inertial Measurement Units (IMU). However, conventional LIO methods typically rely solely on geometric features during point cloud registration. In complex scenarios, such as outdoor unstructured or dynamic environments, these methods are often susceptible to reduced localization accuracy due to geometric degeneration or mismatches. To address these challenges, we propose SV-LIO, A Probabilistic Adaptive Semantic Voxel Map for LiDAR–Inertial Odometry, which leverages point-wise semantic information from semantic segmentation to enhance registration accuracy and system robustness. Specifically, we construct a probabilistic adaptive semantic voxel map that extracts multi-scale spatial planes attached with semantic information. Building on this representation, we employ a semantic-guided strategy for nearest-neighbor plane association between LiDAR scans and the local map, and construct semantic-weighted point-to-plane residuals to constrain pose estimation. By jointly optimizing the IMU-propagated pose prior and semantic-guided LiDAR observation constraints, SV-LIO realizes high-precision real-time state estimation and semantic scene reconstruction. Extensive experiments on the KITTI dataset demonstrate that SV-LIO achieves significant improvements in both localization accuracy compared to state-of-the-art (SOTA) LIO methods, while also constructing semantic maps capable of providing rich environmental information. Full article

The development of a green hydrogen industry is a strategic priority for Russia’s energy transition, yet the dynamics of scaling up this nascent sector remain poorly understood. This study uses agent-based modeling (ABM) to simulate the co-evolution of Russia’s electricity, hydrogen, and electrolyzer sectors over 2024–2050. The model incorporates three types of heterogeneous agents (power producers, hydrogen producers, and electrolyzer manufacturers) operating under bounded rationality. Four scenarios are examined across 50 Monte Carlo runs each, varying the electrolyzer learning rate (10–25%), willingness to pay for green hydrogen (2–6 $/kg), and government support intensity. The results reveal an endogenous three-phase development pattern: Phase I (2024–2028) dominated by renewable capacity build-up reaching ~30 GW; Phase II (2029–2040) characterized by rapid electrolyzer deployment scaling to 14.5 GW; and Phase III (2041–2050) marked by stabilization at approximately 30 GW producing 1.12 Mt/year at 3.1 $/kg. Two critical thresholds are identified: renewable capacity exceeding 30–38 GW and low-cost electricity above 4–7 TWh/year. The electrolyzer learning rate emerges as the most influential parameter, while the pessimistic scenario confirms market failure without a green premium (WTP < 2 $/kg). Strategic investment losses of 2–6 billion USD are necessary catalysts for industry emergence. Russia’s 2030 production target (0.55 Mt) is found structurally infeasible under all scenarios. Full article

Standard 16000-8 of the International Organization for Standardization (ISO 16000-8) specifies the assessment of ventilation performance using age-of-air concepts and tracer gas techniques. Since its publication in 2007, ventilation systems and assessment practices have evolved considerably, driven by increased use of mixed-mode and decentralized ventilation and advances in modeling and measurement technologies. This review examines how ISO 16000-8 can be modernized to harmonize with adjacent ventilation and indoor air quality standards while remaining applicable to contemporary systems and emerging approaches. A structured literature search of Web of Science and Google Scholar identified 76 studies (2007–2026) that engage with ISO 16000-8, age-of-air metrics, or tracer gas-based assessment. The literature was synthesized qualitatively using the framework of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), classifying studies into performance assessment, measurement–simulation convergence, and standardization discourse. The synthesis shows that while the conceptual foundations of ISO 16000-8 remain valid, assumptions of homogeneous mixing and steady-state conditions are often violated in real buildings, leading to inconsistent application of age-of-air indicators. Field and laboratory studies under point-source conditions demonstrate reduced ventilation effectiveness of 0.73–0.82 in classrooms and 0.5–1.4 in various indoor environments, instead of ≈1 for perfect mixing. Spatial heterogeneity is also observed in mixed-mode systems, with an efficiency around 0.5. In decentralized and façade-integrated systems, air exchange effectiveness deviates from theoretical expectations, indicating inhomogeneous air renewal and short-circuiting. Field measurements show configuration-dependent discrepancies in air exchange rates (e.g., carbon dioxide vs. perfluorocarbon tracer methods under varying door positions), while wind induces time-varying infiltration. Collectively, the literature demonstrates systematic violations of well-mixed and steady-state assumptions underpinning ISO 16000-8. Fragmentation between ventilation performance standards and indoor air quality regulation limits practical uptake. Emerging experimental, numerical, and data-driven methods complement ISO 16000-8, provided applicability domains and uncertainties are addressed. The review concludes that ISO 16000-8 should be modernized toward a harmonized, performance-based framework integrating diverse ventilation systems and assessment technologies. Full article

Building retrofit optimization has gained increasing attention as a pathway to improve energy performance and support sustainability. This review examines 162 studies and synthesizes simulation-based (SBMOO) and metamodel-based (MBMOO) multi-objective optimization techniques for building retrofit across climatic conditions. The review also analyzes passive, active, and combined retrofit strategies and evaluates how climatic context influences their suitability and performance. Passive strategies typically involve envelope- or material-related upgrades, whereas active strategies focus on building systems. Energy efficiency, comfort, cost-effectiveness, and environmental impact are identified as the major performance metrics for retrofit evaluation. Sustainability metric such as life cycle assessment (LCA) has yet to be used adequately to evaluate retrofit measures, while social objectives are also less explored. SBMOO provides robust optimization but can be computationally intensive, whereas MBMOO improves computational efficiency through surrogate modeling but depends strongly on dataset quality, sampling strategy, and surrogate model selection. In contrast to earlier reviews that usually emphasize either optimization techniques or retrofit measures independently, this study integrates optimization pathway comparison with climate-based analysis of retrofit strategies. The review also finds that most studies are highly case-specific, limiting transferability across climates, building types, and retrofit contexts. Therefore, this work proposes a synthesized framework to support structured selection of baseline modeling and optimization pathways for future retrofit studies. Overall, the review identifies current methodological trends, key research gaps, and future directions for more consistent and climate responsive retrofit decision-making. Full article

This paper explores the behavioural change process initiated within the Gdańsk Coastal City Living Lab (CCLL)—a site-based effort, initiated under the H2020 SCORE project and significantly deepened through the Horizon Europe PRO-CLIMATE project—through the lens of transforming human–nature relationships for sustainable urban biodiversity conservation. While SCORE established the technical baseline for Nature-based Solutions (NbSs), PRO-CLIMATE provides the critical behavioural framework to ensure these solutions are socially adopted and sustained. Located in a flood-prone coastal city, the Gdańsk CCLL addresses the critical need for nature-based solutions (NbSs) in minimizing the negative impacts of climate change, particularly pluvial flooding. At the heart of this initiative is a participatory change process facilitated by local Change Agents in collaboration with key stakeholders across water management, local government, academia, and civil society. Drawing on interdisciplinary insights from social science, the paper uses the Nature Futures Framework to analyse how conservation actions are influenced by the relational, intrinsic, and instrumental values that stakeholders and residents attach to nature. The paper situates these values in the Gdańsk context and examines how they shape motivations and willingness to engage in urban NbS, such as green roofs, retention parks, and rainwater gardens. The study presents qualitative findings from stakeholder engagement workshops, Change Agents’ reflections, and support mechanisms from behavioural change experts. It evaluates how behavioural change was facilitated through shared vision building, feedback loops, and trust-based relationships, and how barriers were negotiated. A key contribution of the paper is the exploration of how bottom-up and top-down processes intersect in urban adaptation strategies and how behavioural change frameworks can be designed to institutionalise sustainable human–nature interactions in urban governance. The Gdańsk case offers transferable insights for other cities facing climate vulnerabilities while striving to embed biodiversity conservation into everyday practice. Full article

Quantifying moisture transport through building envelope materials is vital for durability, energy efficiency, and healthy indoor environments. Water vapour diffusion resistivity (µ-value) is a key parameter for hygrothermal modelling, moisture control, and mould risk assessment. Globally, data for solid wood species are scarce, and in Australia—despite the rising use of plantation-grown timber—critical hygrothermal properties remain undocumented. To close this gap, this study experimentally evaluated Eucalyptus nitens, a plantation-grown hardwood widely used in Australian construction. Solid-wood specimens prepared from industry-sourced boards were tested at 23 °C and 50% RH using both the wet-cup and dry-cup methods of the gravimetric technique. For wet-cup tests, µ-values ranged from 24 to 33; for dry-cup tests, µ-values ranged from 179 to 273, showing clear variability linked to differences in relative humidity. Experimental issues included surface cupping, sealing integrity, and extended equilibration time during dry-cup testing. These findings provide the first empirical µ-value dataset for E. Nitens under moderate-humidity conditions, delivering essential input parameters for hygrothermal models and supporting moisture-safe, energy-efficient design strategies for the broader construction sector. Full article

Background/Objectives: Mitochondria are dynamic organelles that continuously undergo balanced cycles of fusion and division to maintain optimal function. Mitochondrial division is mediated by Dynamin-Related Protein 1 (DRP1), a cytosolic large GTPase whose phosphorylation at serine 616 (DRP1-S616Ⓟ) promotes its translocation to the outer mitochondrial membrane and organelle division. Dysregulated mitochondrial division disrupts cellular homeostasis and contributes to disease pathogenesis, including cancer. Our prior work demonstrated that the oncogene-induced mitogen-activated protein kinase (MAPK) pathway constitutively phosphorylates DRP1 at serine 616, which is essential to cellular transformation and correlates with oncogene status in patient tissues. Similarly, DRP1-S616Ⓟ is subject to pharmacologic control by targeted therapies against oncogenic MAPK signaling. Methods: Building upon this foundation, we developed and characterized a recombinant murine monoclonal antibody (referred to as 3G11) with high specificity for human DRP1-S616Ⓟ, raised against a peptide derived from the human DRP1 sequence. Results: Using diverse experimental platforms, we demonstrate the robust utility of 3G11 to detect DRP1-S616Ⓟ in melanoma cell extracts and isolated organelles. Immunofluorescence revealed that pharmacologic inhibition of oncogenic MAPK signaling reduces DRP1-S616Ⓟ levels, which correlates with mitochondrial hyperfusion, while immunohistochemistry showed that elevated DRP1-S616Ⓟ expression in human tissues correlates with BRAF^V600E disease. Conclusions: 3G11 is a new recombinant antibody for detecting DRP1-S616Ⓟ and supports studies of mitochondrial division in cancer. Together, these findings establish 3G11 as a specific, versatile, renewable, and cost-effective tool for studying mitochondrial division, with strong potential for clinical applications. Full article