Search Results (3,041)

Artificial Intelligence (AI) has emerged as a transformative force in the field of architectural design. This study aims to systematically analyze the influence mechanisms of Artificial Intelligence-based Architectural Design (AIAD) by constructing a comprehensive hybrid model that integrates the Analytic Hierarchy Process (AHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). Based on the previous quantitative literature review, 6 primary categories and 18 secondary influencing factors were identified. Data were collected from a panel of fifteen experts representing the architecture industry, academia, and computer science. Through weighting analysis, causal mapping, hierarchical structuring, and driving–dependence classification, the study clarifies the complex interrelationships among influencing factors and reveals the underlying drivers that accelerate or constrain AI adoption in architectural design. By quantifying the hierarchical and causal influence of factors, this research provides theoretical findings and practical insights for design firms undergoing digital transformation. The results extend previous meta-analytical studies, offering a decision-support system that bridges academic research and real-world applications, thereby guiding stakeholders toward informed adoption of artificial intelligence for future cultural tourism development and regional spatial innovation. Full article

Scientific assessment of sustainable development potential (SDP) and analysis of spatial heterogeneity mechanisms of traditional villages are crucial for promoting the synergy between cultural heritage conservation and rural revitalization strategies. With an emphasis on traditional villages in the Cantonese region, this study develops a thorough evaluation methodology that combines spatial analysis and multi-criteria decision-making. It aims to (1) systematically reveal the spatial differentiation characteristics of sustainable development potential; (2) develop and validate a combined weighting method that effectively integrates both subjective and objective weights; and (3) identify key driving factors and their interaction mechanisms influencing the formation of this potential. To achieve these objectives, the research sequentially conducted the following steps: First, an evaluation indicator system encompassing socioeconomic, cultural, ecological, and infrastructural dimensions was developed. Second, the Analytic Hierarchy Process and the Entropy Weight Method were employed to calculate subjective and objective weights, respectively, followed by integration of these weights using a combined weighting model. Subsequently, the potential assessment results were incorporated into a Geographic Information System, and spatial autocorrelation analysis was applied to identify agglomeration patterns. Finally, the Geographical Detector model was utilized to quantitatively analyze the explanatory power of various influencing factors and their interactions on the spatial heterogeneity of potential. The main findings are as follows: First, the sustainable development potential of traditional Cantonese villages exhibits a significant “core–periphery” spatial structure, forming a high-potential corridor in the Zhongshan–Jiangmen–Foshan border area, while peripheral areas generally display “low–low” agglomeration characteristics. Second, the combined weighting model effectively reconciled 81.0% of case discrepancies, significantly improving assessment consistency (Kappa coefficient above 0.85). Third, we identified economic income (q = 0.661) and ecological baseline (q = 0.616) were identified as key driving factors. Interaction detection revealed that the interaction between economic income and transportation accessibility had the strongest explanatory power (q = 0.742), followed by the synergistic effect between ecological baseline and architectural heritage (q = 0.716), highlighting the characteristic of multi-factor synergistic driving. The quantitative and spatially explicit evaluation framework established in this study not only provides methodological innovation for research on the sustainable development of traditional villages but also offers a scientific basis for formulating regionally differentiated revitalization strategies. The research findings hold significant theoretical and practical importance for achieving a positive interaction between the conservation and development of traditional villages. Full article

Background/Objectives: Systemic sclerosis (SSc) is a heterogeneous connective tissue disease characterized by immune dysregulation, vasculopathy, and fibrosis. Objectives: To evaluate the genetic architecture and autoantibody profile in a Kazakh cohort of patients with SSc. Methods: A total of 26 Kazakh patients with diffuse SSc were examined for disease activity and organ impairment using EScSG and the modified Rodnan skin score (mRSS). Eighteen healthy volunteers were enrolled in the control group. Antinuclear factor (ANF) was estimated on HEp-2 cells, while antibodies to Scl-70, CENP-B, U1-snRNP, SS-A/Ro52, SS-A/Ro60, Sm/RNP, Sm, SS-B, Rib-P0, and nucleosomes were determined by immunoblotting. The level of IL-6 cytokine was detected using ELISA. To investigate the genetic basis of SSc in Kazakh patients, a custom AmpliSeq panel including targeting immune/fibrosis pathways and 120 genes was used on the Ion Proton sequencer. The statistical analysis of categorical variables was conducted using Fisher’s exact test and Chi-square (χ²) test. Results: The examination of SSc patients (mRSS 16 ± 7.2; EScSG 3.54 ± 2.18) revealed a broad range of antibodies to Scl-70, CENP-B, SS-A/Ro60, SS-A/Ro52, U1-snRNP, and RNP/Sm, which were undetectable in the control group. Genetic analysis identified multiple variants across immune regulatory genes, including likely pathogenic changes in SAMD9L, REL, IL6ST, TNFAIP3, ITGA2, ABCC2, AIRE, IL6R, AFF3, and TREX1. Variants of uncertain clinical significance were detected in LY96, IRAK1, RBPJ, IL6ST, ITGA2, AIRE, IL6R, JAZF1, IKZF3, IL18, IL12B, PRKCQ, PXK, and DNASE1L3. Novel variants at the following genomic coordinates were identified and have not been previously reported in association with SSc: LY96 (chr8:74922341 CT/C), PTPN22 (chr1:114381166 CT/C), IRAK1 (indels at chrX:153278833), and SAMD9L (chr7:92761606 GT/G; chr7:92764981 T/TT). Conclusions: The first immunogenetic investigation of SSc in Kazakhstan revealed a polygenic architecture involving immune signalling pathways that partially overlap with international cohorts while exhibiting region-specific variation. Although the limited sample size and lack of functional validation constrain the interpretability of the findings, the results provide a framework for larger research to confirm the pathogenic mechanisms and establish clinical relevance. Full article

Buildings in Indonesia’s tropical climate face significant barriers to energy efficiency due to high cooling loads and electricity intensity. Previous studies have primarily addressed technical optimization or policy frameworks, but few have provided an integrated and data-driven evaluation model for tropical conditions. This study develops an Internet of Things (IoT) and EDGE-based hybrid framework to support the transition toward Net-Zero-Energy Buildings (NZEBs) while maintaining occupant comfort. The research combines real-time IoT monitoring at the LLDIKTI Region XIII Office Building in Banda Aceh with simulation-based assessment using Excellence in Design for Greater Efficiencies (EDGE). Baseline energy performance was established from architectural data, historical electricity use, and live monitoring of HVAC systems, lighting, temperature, humidity, and CO₂ concentration. Intervention scenarios—including building envelope enhancement, lighting optimization, and adaptive HVAC control—were simulated and validated against empirical data. Results demonstrate that integrating IoT-driven control with passive design measures achieves up to 31.49% reduction in energy use intensity, along with 24.7% improvement in water efficiency and 22.3% material resource savings. These findings enhance indoor environmental quality and enable adaptive responses to user behavior. The study concludes that the proposed IoT–EDGE framework offers a replicable and context-sensitive pathway for achieving net-zero energy operations in tropical office buildings, with quantifiable environmental benefits that support sustainable public facility management in Indonesia. Full article

Accurate and reliable detection of landslides plays a crucial role in disaster prevention and mitigation efforts. However, due to unfavorable environmental conditions, uneven surface structures, and other disturbances similar to those of landslides, traditional methods often fail to achieve the desired results. To address these challenges, this study introduces a novel multi-scale cross-layer feature interaction network, specifically designed for landslide segmentation in remote sensing images. In the MCFI-Net framework, we adopt the encoder–decoder as the foundational architecture, and integrate cross-layer feature information to capture fine-grained local textures and broader contextual patterns. Then, we introduce the receptive field block (RFB) into the skip connections to effectively aggregate multi-scale contextual information. Additionally, we design the multi-branch dynamic convolution block (MDCB), which possesses both dynamic perception ability and multi-scale feature representation capability. The comprehensive evaluation conducted on both the Landslide4Sense and Bijie datasets demonstrates the superior performance of MCFI-Net in landslide segmentation tasks. Specifically, on the Landslide4Sense dataset, MCFI-Net achieved a Dice score of 0.7254, a Matthews correlation coefficient (Mcc) of 0.7138, and a Jaccard score of 0.5699. Similarly, on the Bijie dataset, MCFI-Net maintained high accuracy with a Dice score of 0.8201, an Mcc of 0.8004, and a Jaccard score of 0.6951. Furthermore, when evaluated on the optical remote sensing dataset EORSSD, MCFI-Net obtained a Dice score of 0.7770, an Mcc of 0.7732, and a Jaccard score of 0.6571. Finally, ablation experiments carried out on the Landslide4Sense dataset further validated the effectiveness of each proposed module. These results affirm MCFI-Net’s capability in accurately identifying landslide regions from complex remote sensing imagery, and it provides great potential for the analysis of geological disasters in the real world. Full article

Joist floor systems are usually employed in buildings of the Middle Eastern regions. These systems usually have reinforced concrete (RC) wide beams, which in many cases have planted columns in non-seismic regions due to architectural requirements. Changes in building use can increase the loads on these columns, which may increase the shear demand of beams to a level that may exceed their capacity. Consequently, upgrading of such wide beams against shear is crucial. This study investigates two strengthening techniques to enhance the shear performance of RC wide beams with planted columns through experimental testing and analytical evaluation. Four half-scale specimens were tested: two unstrengthened beams (one code-compliant and one shear-deficient) and two strengthened beams, using either externally bonded steel plates or a combination of CFRP U-wraps with planted steel U-stirrups. The performance of the retrofitting schemes was assessed based on failure modes and load-deflection responses. The second strengthening scheme improved the shear resistance of wide beams by 82% compared to the control specimen. Additionally, the shear capacity of the tested beams was analytically predicted, and the results were compared with the test findings, providing insights into the effectiveness of both strengthening methods. Full article

Milk protein content represents a key economic trait in dairy production, yet the genetic architecture underlying this trait remains unexplored in Romanian dual-purpose cattle breeds. This study conducted a genome-wide association analysis for milk protein content in 313 Romanian Simmental (n = 271) and Romanian Brown (n = 42) cows belonging to the Research and Development Station for Bovine Arad, Romania. Following quality control, 33,531 SNPs were tested for association with protein percentage adjusted for other effects (breed, days in milk, season, year, parity) using linear regression with the first five principal components as covariates to control population stratification. Although no SNP reached genome-wide significance (p < 5 × 10⁻⁸), one SNP achieved significance (p < 2.98 × 10⁻⁵) and seven additional SNPs exceeded the nominal threshold (p < 1 × 10⁻⁴) across six chromosomes. The strongest association (p = 9.56 × 10⁻⁶) mapped to chromosome 25 near C7orf61. Biologically relevant candidate genes included KLF6 on chromosome 13, previously associated with milk traits in Chinese Holstein, and AHCYL1 on chromosome 3, involved in calcium homeostasis. These findings provide initial insights into genomic regions influencing milk protein content in Romanian dual-purpose cattle, though validation in larger cohorts needs to be carried out. Full article

Piecewise modeling of power amplifiers (PAs) typically involves assembling different polynomials to capture nonlinear behavior across different operating regions. However, recombining these sub-models can introduce discontinuities at segment boundaries, degrading prediction accuracy and potentially impacting digital predistortion (DPD) performance. This work addresses this issue by introducing a statistical framework to detect discontinuities through localized variations in the conditional mean and variance of amplitude and phase responses. Using the Vector-Switched Generalized Memory Polynomial (VS-GMP) as a case study, we propose a low-complexity post-processing smoothing technique based on a raised cosine weighting function applied at model transition regions. Unlike structural approaches, the method requires no retraining and integrates seamlessly into existing workflows as a post-processing tool. Experimental validation across two PA architectures (Doherty and Single-Stage) and multiple 5G/LTE signals (20–200 MHz bandwidth, up to 11 dB PAPR, including carrier aggregation) demonstrates consistent improvements: up to a 3 dB NMSE reduction and notable spectral error suppression. Full article

The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically interpretable components: long-term, seasonal, and short-term variations, forming an expanded multi-scale feature space. A central innovation of this framework lies in training a single unified model on the decomposed feature set to predict the original target variable, thereby enabling the direct learning of scale-specific driver–response relationships. We present the first comprehensive benchmarking of this architecture, demonstrating that it consistently enhances the performance of both regularized linear models (Ridge and Lasso) and tree-based ensemble methods (Random Forest and XGBoost). Under rigorous walk-forward validation, the framework substantially outperforms conventional, non-decomposed approaches—for example, XGBoost improves the coefficient of determination ($R^2$) from 0.80 to 0.91. Furthermore, temporal decomposition enhances interpretability by enabling Ridge and Lasso models to achieve performance levels comparable to complex ensembles. Despite these promising results, we acknowledge several limitations: the analysis is restricted to a single geographic location and time span, and short-term components remain challenging to predict due to their stochastic nature and the weaker relevance of predictors. Additionally, the framework’s effectiveness may depend on the optimal selection of KZ parameters and the availability of sufficiently long historical datasets for stable walk-forward validation. Future research could extend this approach to multiple geographic regions, longer time series, adaptive KZ tuning, and specialized short-term modeling strategies. Overall, the proposed framework demonstrates that temporal decomposition of predictors offers a powerful inductive bias, establishing a robust and interpretable paradigm for surface air temperature forecasting. Full article

South Asia’s worsening air pollution crisis represents one of the most urgent public health and environmental challenges of the 21st century. Nearly two billion people—over one-quarter of the global population—reside in this region, where air quality levels routinely exceed World Health Organization (WHO) guidelines by factors of 10 to 15. This has translated into an unprecedented health burden, with approximately two million premature deaths annually, widespread chronic respiratory and cardiovascular disease, and rising economic losses. According to recent World Bank estimates, welfare losses amount to over 5% of regional GDP, a figure far exceeding the projected costs of coordinated mitigation. Despite this, South Asia continues to lack a binding regional framework capable of addressing its shared airshed. Existing cooperative efforts—such as the Malé Declaration on Control and Prevention of Air Pollution (1998)—have provided a useful platform for dialog and pilot monitoring, but they remain voluntary, under-resourced, and insufficient to manage the transboundary nature of the crisis. National-level programs, including India’s National Clean Air Programme (NCAP), Bangladesh’s National Air Quality Management Plan (NAQMP), and Nepal’s National Air Quality Management Action Plan (AQMAP), demonstrate domestic commitment but are constrained by fragmentation, limited financing, and lack of regional integration. This gap represents the central knowledge and governance challenge that prompted the present commentary. To address it, we propose a dual-track architecture designed to institutionalize binding regional cooperation. Track A would establish a United Nations-anchored South Asian Transboundary Air Pollution Protocol, under the auspices of the United Nations Environment Programme, the World Health Organization (WHO), and the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). This protocol would codify legally enforceable emission standards, compliance committees, financial mechanisms, and harmonized monitoring. Track B would establish a South Asian Association for Regional Cooperation (SAARC) Prime Ministers’ Council on Air Quality (SPMCAQ) to provide political leadership, align domestic implementation, and authorize rapid responses to cross-border haze events. Lessons from the Indian Ocean Experiment, the ASEAN Agreement on Transboundary Haze Pollution, and Europe’s Convention on Long-Range Transboundary Air Pollution demonstrate that legally binding agreements combined with high-level political ownership can achieve durable reductions in pollution despite geopolitical tensions. By situating South Asia within these global precedents, the proposed framework provides a pragmatic, enforceable, and politically resilient pathway to protect health, reduce economic losses, and deliver cleaner air for nearly one-quarter of humanity. Full article

Building archetypes are essential for advancing architectural theory and energy modeling, providing a foundation for scalable assessments of building performance and sustainability worldwide. In Saudi Arabia, educational buildings, especially those in public universities, are predominantly constructed using standardized and repetitive design templates, such as courtyard and prototype models, which have significant implications for energy efficiency, indoor environmental quality, and sustainability outcomes. Despite their prevalence, there is a notable lack of systematic research on the classification and distribution of these archetypes within the Saudi context, particularly regarding their impact on energy consumption and sustainable campus planning. This study addresses this gap by systematically collecting and analyzing data from 29 public universities across Saudi Arabia, employing GIS mapping to document building characteristics including age, region, urban context, masterplan typology, and architectural design. A cumulative weighting factor was applied to quantify the representativeness of archetypes, while chi-square tests and effect size metrics assessed the statistical concentration and significance of observed patterns. The results reveal a pronounced dominance of a small number of archetypes, especially standardized courtyard and identical design models, across the national stock, with the top 10% of archetype ranks accounting for the majority of buildings. This high degree of standardization enables efficient modeling, benchmarking, and targeted energy interventions, while also highlighting the need for greater contextual adaptation in future campus planning. While this study does not directly simulate building energy performance, it establishes a national-scale typological foundation that can support future simulation, benchmarking, and policy design. The developed GIS-based framework primarily serves managerial and planning objectives, offering a standardized reference for facility management, retrofitting prioritization, and strategic energy-efficiency planning in Saudi public universities. Full article

In the semantic segmentation tasks of polarimetric SAR images, deep learning has become an important end-to-end method that uses convolutional neural networks (CNNs) and other advanced network architectures to extract features and classify the target region pixel by pixel. However, applying original networks used to optical images for PolSAR image segmentation directly will result in the loss of rich phase information in PolSAR data, which leads to unsatisfactory classification results. In order to make full use of polarization information, the complex-valued BiSeNetV2 with a bilateral-segmentation structure is studied and expanded in this work. Then, considering further improving the ability to extract semantic features in the complex domain and alleviating the imbalance of polarization channel response, the complex-valued BiSeNetV2 with a lightweight attention module (LAM-CV-BiSeNetV2) is proposed for the semantic segmentation of PolSAR images. LAM-CV-BiSeNetV2 supports complex-valued operations, and a lightweight attention module (LAM) is designed and introduced at the end of the Semantic Branch to enhance the extraction of detailed features. Compared with the original BiSeNetV2, the LAM-CV-BiSeNetV2 can not only more fully extract the phase information from polarimetric SAR data, but also has stronger semantic feature extraction capabilities. The experimental results on the Flevoland and San Francisco datasets demonstrate that the proposed LAM has better and more stable performance than other commonly used attention modules, and the proposed network can always obtain better classification results than BiSeNetV2 and other known real-valued networks. Full article

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

The field of underwater robotics is experiencing rapid growth, wherein accurate object detection constitutes a fundamental component. Given the prevalence of false alarms and omission errors caused by intricate subaquatic conditions and substantial image noise, this study introduces an enhanced detection framework that combines the YOLOv8 architecture with multimodal visual fusion methodology. To solve the problem of degraded detection performance of the model in complex environments like those with low illumination, features from Visible Light Image are fused with the Thermal Distribution Features exhibited by Infrared Image, thereby yielding more comprehensive image information. Furthermore, to precisely focus on crucial target regions and information, a Multi-Scale Cross-Axis Attention Mechanism (MSCA) is introduced, which significantly enhances Detection Accuracy. Finally, to meet the lightweight requirement of the model, an Efficient Shared Convolution Head (ESC_Head) is designed. The experimental findings reveal that the YOLOv8-FUSED framework attains a mean average precision (mAP) of 82.1%, marking an 8.7% enhancement compared to the baseline YOLOv8 architecture. The proposed approach also exhibits superior detection capabilities relative to existing techniques while simultaneously satisfying the critical requirement for real-time underwater object detection. Moreover, the proposed system successfully meets the essential criteria for real-time detection of underwater objects. Full article

With rapid urbanization and growing population inflows into metropolitan areas, crowd gatherings have become increasingly frequent and dense, posing significant challenges to public safety management. Although existing crowd gathering detection methods have achieved notable progress, they still face major limitations: most rely heavily on local texture or density features and lack the capacity to model contextual information, making them ineffective under severe occlusions and complex backgrounds. Additionally, fixed-scale feature extraction strategies struggle to adapt to crowd regions with varying densities and scales, and insufficient attention to densely populated areas hinders the capture of critical local features. To overcome these challenges, we propose a point-supervised framework named Multi-Scale Convolutional Attention Network (MSCANet). MSCANet adopts a context-aware architecture and integrates multi-scale feature extraction modules and convolutional attention mechanisms, enabling it to dynamically adapt to varying crowd densities while focusing on key regions. This enhances feature representation in complex scenes and improves detection performance. Extensive experiments on public datasets demonstrate that MSCANet achieves high counting accuracy and robustness, particularly in dense and occluded environments, showing strong potential for real-world deployment. Full article