Search Results (2,958)

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

This study examines the evolution and integration of Buddhist architecture in East Asia and emphasizes the preservation of indigenous building traditions by adapting pre-Buddhist architectural typologies, vernacular construction techniques, and localized worship practices. In addition, this study highlights the adaptive transformation of Indian Buddhist structures as they incorporate regional architectural forms, resulting in distinct monumental styles that had a profound symbolic significance. By introducing the concept of a cosmopolitan attitude, it underscores the dynamic coexistence and reciprocal influence of universalized and vernacular architectural traditions. The findings highlight the interplay between cultural universality and particularity, illustrating how architectural meaning and intention define the uniqueness of structures beyond their stylistic similarities. This study demonstrates that even when architectural forms appear similar, their function and underlying intent must be considered to fully comprehend their historical and cultural significance. Full article

In 1961, Los Angeles experienced the disastrous Bel Air fire, which swept through an affluent neighborhood situated in a hilly, WUI (wildland–urban interface) location. In January 2025, the city was devastated again by a nearly-simultaneous series of wildfires, the most severe of which took place close to the 1961 fire location. Disastrous WUI fires are, unfortunately, an anticipatable occurrence in many U.S. cities. A number of issues identified earlier remained the same. Some were largely solved, while other new ones have emerged. The paper examines the Palisades Fire of January, 2025 in this context. In the intervening decades, the population of the city grew substantially. But firefighting resources did not keep pace. Very likely, the single-most-important factor in causing the 2025 disasters is that the Los Angeles Fire Department operational vehicle count shrank to 1/5 of what it was in 1961 (per capita). This is likely why critical delays were experienced in the initial attack on the Palisades Fire, leading to a runaway conflagration. Two other crucial issues were the management of vegetation and the adequacy of water supplies. On both these issues, the Palisades Fire revealed serious problems. A problem which arose after 1961 involves the unintended consequences of environmental legislation. Communities will continue to be devastated by wildfires unless adequate vegetation management is accomplished. Yet, environmental regulations are focused on maintaining the status quo, often making vegetation management difficult or ineffective. House survival during a wildfire is strongly affected by whether good vegetation management practices and good building practices (“ignition-resistant” construction features) have been implemented. The latter have not been mandatory for housing built prior to 2008, and the vast majority of houses in the area predated such building code requirements. California has also suffered from a highly counterproductive stance on insurance regulation. This has resulted in some residents not having property insurance, due to the inhospitable operating conditions for insurance firms in the state. Because of the historical precedent, the details in this paper focus on the Palisades Fire; however, many of the lessons learned apply to managing fires in all WUI areas. Policy recommendations are offered, which could help to reduce the potential for future conflagrations. Full article

This paper presents a comprehensive review of research on Historic Building Information Modeling (HBIM), focusing on its role as a tool for managing knowledge and supporting conservation practices of Architectural Heritage. While previous review articles and most research works have predominantly addressed geometric modeling—given its significant challenges in the context of historic buildings—this study places greater emphasis on the integration of non-geometric data within the BIM environment. A systematic search was conducted in the Scopus database to extract the 451 relevant publications analyzed in this review, covering the period from 2008 to mid-2024. A bibliometric analysis was first performed to identify trends in publication types, geographic distribution, research focuses, and software usage. The main body of the review then explores three core themes in the development of the information system: the definition of model entities, both semantic and geometric; the data enrichment phase, incorporating historical, diagnostic, monitoring and conservation-related information; and finally, data use and sharing, including on-site applications and interoperability. For each topic, the review highlights and discusses the principal approaches documented in the literature, critically evaluating the advantages and limitations of different information management methods with respect to the distinctive features of the building under analysis and the specific objectives of the information model. Full article

With the increasing scale of low Earth orbit (LEO) satellite networks, leveraging non−terrestrial networks (NTNs) to complement terrestrial networks (TNs) has become a critical issue. In this paper, we investigate the issue of handover satellite selection between multiple terrestrial terminal groups (TTGs). To support effective handover decision-making, we propose a long short-term memory (LSTM)-network-based traffic prediction mechanism based on historical traffic data. Building on these predictions, we formulate the handover strategy as a Markov Decision Process (MDP) and propose an attention-enhanced rainbow-DQN-based joint traffic prediction and handover design framework (ARTHF) by jointly considering the satellite switching frequency, communication quality, and satellite load. Simulation results demonstrate that our approach significantly outperforms existing methods in terms of the handover efficiency, service quality, and load balancing across satellites. Full article

An analysis of Albanian Muslims in Italy provides a compelling case study of communities perceived as marginal. Studies of Muslims in Italy tend to focus on the majority and chronologically older groups within the country’s Islamic landscape, particularly those from Asia and Africa. In addition to providing a better understanding of Islam in Italy, a study of the identity and community-building issues of the Albanian community of origin offers many insights into that community’s complexity. Albanians in Italy have a very specific historical and religious heritage; so, analyzing their roots and community-building processes helps us to better understand the development of Islam on the margins of large national organizations and majority groups. This article presents the results of the first national study of Albanian Muslims in Italy. Online interviews and field observations were conducted in 2024 within the Union of Muslim Albanians in Italy (Unione degli Albanesi Musulmani in Italia—UAMI), using the ethnographic method. The Association has fewer members compared with national level organizations. It was founded in 2009 to address specific issues related to the management of Muslim Albanian religious identity. The Association has sought to address the fragmentation of religion and Albanian nationalism, a consequence of a long period of state atheism, and to counter the literalist and radical tendencies in the interpretation of religion that have emerged in Albania since the collapse of the communist regime. In addition to these challenges, the Association has also tackled issues related to the Islamic religion in its local and global dimensions. The analysis of these challenges and the ways to deal with them offers a new framework in the Italian Islamic panorama, despite its marginality. The results of this research point to the emergence of new forms of rooting and belonging characterized by spirituality over orthopraxis. These forms adopt a religious approach open to diversity and pluralism. Full article

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

Discourses on breastfeeding extend beyond simple scientific evidence, framed within a dialogue between diverse sociocultural perspectives throughout history. Building on this premise, this article aims to explore, from the perspective of the sociology of deviance, the maternal experiences of women who choose not to breastfeed or cease breastfeeding within the hospital setting. To this end, this qualitative and phenomenological study was conducted, using semi-structured interviews with mothers in the municipality of Lorca who had decided not to breastfeed or had discontinued breastfeeding as a data collection tool. The results indicate that breastfeeding is influenced by the repercussions of delivery room routines and a challenging learning process where complications often arise, contradicting the prevailing innatist discourse of maternal instinct. Within this framework, mothers feel pressured by professionals to breastfeed, and their identities are undermined by symbolic stigma when they express their decision not to breastfeed. We conclude that it is essential to propose clinical approaches and support models that genuinely consider the sociocultural, historical, and experiential factors influencing breastfeeding, moving beyond an exclusive focus on its biological or nutritional aspects. Full article

Structural cracks significantly threaten the safety and longevity of historical buildings, which are essential parts of cultural heritage. Conventional inspection techniques, which depend heavily on manual visual evaluations, tend to be inefficient and subjective. This research introduces an automated framework for crack and damage detection using advanced YOLO (You Only Look Once) models, aiming to improve both the accuracy and efficiency of monitoring heritage structures. A dataset comprising 2500 high-resolution images was gathered from historical buildings and categorized into four levels of damage: no damage, minor, moderate, and severe. Following preprocessing and data augmentation, a total of 5000 labeled images were utilized to train and evaluate four YOLO variants: YOLOv5, YOLOv8, YOLOv10, and YOLOv11. The models’ performances were measured using metrics such as precision, recall, mAP@50, mAP@50–95, as well as losses related to bounding box regression, classification, and distribution. Experimental findings reveal that YOLOv10 surpasses other models in multi-target detection and identifying minor damage, achieving higher localization accuracy and faster inference speeds. YOLOv8 and YOLOv11 demonstrate consistent performance and strong adaptability, whereas YOLOv5 converges rapidly but shows weaker validation results. Further testing confirms YOLOv10’s effectiveness across different structural components, including walls, beams, and ceilings. This study highlights the practicality of deep learning-based crack detection methods for preserving building heritage. Future advancements could include combining semantic segmentation networks (e.g., U-Net) with attention mechanisms to further refine detection accuracy in complex scenarios. Full article

Sustainable building design is significantly impacted by the local climate response knowledge ingrained in traditional architecture. However, its integration and dissemination with contemporary green technologies are limited by the absence of a comprehensive quantitative analysis of the regulation of its humid and temperature environment. The Ganzhou Wei family compound from China’s wind–heat environmental regulation systems are examined in this study. We statistically evaluate the synergy between spatial morphology, material qualities, and microclimate using field data with Thsware and Ecotect software in a multiscale simulation framework. The findings indicate that the compound’s special design greatly controls the thermal and wind conditions. Cold alleyways and courtyards work together to maximize thermal environment regulation and encourage natural ventilation. According to quantitative studies, courtyards with particular depths (1–4 m) and height-to-width ratios (e.g., 1:1) reduce wind speed loss. A cool alley (5:1 height–width ratio) creates a dynamic wind–speed–temperature–humidity balance by lowering summer daytime temperatures by 2.5 °C. It also serves as a “cold source area” that moderates temperatures in the surrounding area by up to 2.1 °C. This study suggests a quantitative correlation model based on “spatial morphology–material performance–microclimate response,” which offers a technical route for historic building conservation renovation and green renewal, as well as a scientific foundation for traditional buildings to maintain thermal comfort under low energy consumption. Although based on a specific geographical case, the innovative analytical methods and strategies of this study are of great theoretical and practical significance for promoting the modernization and transformation of traditional architecture, low-carbon city construction, and sustainable building design. Full article

In this paper, we argue that what is sometimes at fault for the poor attendance and lack of engagement in schools observed from historically marginalized families is a missed opportunity to increase understanding or cultural relevance on the part of schools. In this paper, we use the construct of “counter stories” which has the potential to change the script on the instrumentalist demands of quantity versus quality in parent engagement. By providing examples of what we consider “quality” engagement techniques via the staff’s interpretation of their roles within one demographically rich early learning center, the strategies used to engage parents are documented. Counter-stories of practice show family-centrism as interpreted by school leaders. By describing one community context and its practices of building relationships with newcomer families, relationally driven parent engagement techniques are revealed. The authors highlight how inquiry-based methods surpass the generic approaches described in policy. When parent engagement “arises” from within parents’ motivations and informs authentic knowing (by teachers and school leaders), community systems are elevated. Professionals’ decisions about children and community groups that are informed by families’ knowledge are consequently meaningful and authentic. Full article

Heritage Building Information Modeling (HBIM) has emerged as a key tool in advancing heritage conservation and sustainable management. Preceding reviews had typically concentrated on specific technical aspects but did not provide sufficient bibliometric analysis. This study aims to integrate existing HBIM research to identify key research patterns, emerging trends, and forecast future directions. A total of 1516 documents were initially retrieved from the Web of Science Core Collection using targeted search terms. Following a relevance screening, 1175 documents were related to the topic. CiteSpace 6.4.R1, VOSviewer 1.6.20, and Bibliometrix 4.1, three bibliometric tools, were employed to conduct both quantitative and qualitative assessments. The results show three historical phases of HBIM, identify core journals, influential authors, and leading regions, and extract six major keyword clusters: risk assessment, data acquisition, semantic annotation, digital twins, and energy and equipment management. Nine co-citation clusters further outline the foundational literature in the field. The results highlight growing scholarly interest in workflow integration and digital twin applications. Future projections emphasize the transformative potential of artificial intelligence in HBIM, while also recognizing critical implementation barriers, particularly in developing countries and resource-constrained contexts. This study provides a comprehensive and systematic framework for HBIM research, offering valuable insights for scholars, practitioners, and policymakers involved in heritage preservation and digital management. Full article

Falkland Islands English (FIE) began its development in the first half of the 19th century. In part, as a consequence of its youth, FIE is an understudied variety. It shares some morphosyntactic features with other anglophone countries in the Southern Hemisphere, but it also shares lexical features with regional varieties of Spanish, including Rioplatense Spanish. Che is one of many South American words that have entered FIE through Spanish, with its spelling ranging from “chay” and “chey” to “ché”. The word has received some marginal attention in terms of its meaning. It is said to be used in a similar way to the British dear or love and the Australian mate, and it has been compared to chum or pal, and is taken as an equivalent of the River Plate, hey!, hi!, or I say!. In this work, we explore the hypothesis that che entered FIE through historical contact with Rioplatense Spanish, drawing on both linguistic and sociohistorical evidence, and presenting survey, corpus, and ethnographic data that illustrate its current vitality, usage, and social meanings among FIE speakers. In situ observations, fieldwork, and an online survey were used to look into the vitality of che. Concomitantly, by crawling social media and the local press, enough data was gathered to build a small corpus to further study its vitality. A thorough literature review was conducted to hypothesise about the borrowing process involving its entry into FIE. The findings confirm that the word is primarily a vocative, it is commonly used, and it is indicative of a sense of belonging to the Falklands community. Although there is no consensus on the origin of che in the River Plate region, it seems to be the case that it entered FIE during the intense Spanish–English contact that took place during the second half of the 19th century. Full article

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the community level is a key tool in this effort. Traditionally, engineering-based methods grounded in thermodynamic principles have been employed, offering high accuracy under controlled conditions. However, their reliance on exhaustive building-level data and high computational costs limits their scalability in dynamic REC settings. In contrast, Artificial Intelligence (AI)-driven methods provide flexible and scalable alternatives by learning patterns from historical consumption and environmental data. This study investigates three Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), and CatBoost, and one Deep Learning (DL) model, Convolutional Neural Network (CNN), to forecast community electricity consumption using real smart meter data and local meteorological variables. The study focuses on a REC in Loureiro, Portugal, consisting of 172 residential users from whom 16 months of 15 min interval electricity consumption data were collected. Temporal features (hour of the day, day of the week, month) were combined with lag-based usage patterns, including features representing energy consumption at the corresponding time in the previous hour and on the previous day, to enhance model accuracy by leveraging short-term dependencies and daily repetition in usage behavior. Models were evaluated using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination $R^2$. Among all models, CatBoost achieved the best performance, with an MSE of 0.1262, MAPE of 4.77%, and an $R^2$ of 0.9018. These results highlight the potential of ensemble learning approaches for improving energy demand forecasting in RECs, supporting smarter energy management and contributing to energy and environmental performance. Full article