Search Results (1,354)

With the promotion of national fitness, the requirements for regulating indoor environments during non-competition periods are low and relatively flexible under the trend of composite sports buildings. To maximize the use of natural ventilation and lighting for energy savings, passive optimization design based on building ontology has emerged as an effective strategy. This paper focuses on the spatial prototype of large- and medium-sized gymnasiums, optimizing key geometric design parameters and envelope structure parameters that influence energy consumption. This optimization employs a combination of orthogonal experiments and performance simulations. This study identifies the degree to which each factor affects energy consumption in the competition hall and determines the optimal low-energy consumption gymnasium prototype. The results reveal that the skylight area ratio is the most significant factor impacting the energy consumption of large- and medium-sized gymnasiums. The optimized gymnasium prototype reduced energy consumption by 5.3%~50.9% compared to all experimental combinations. This study provides valuable references and insights for architects during the initial stages of designing sports buildings to achieve low energy consumption. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

For the past few decades, during each disastrous earthquake, severe damage and poor seismic performance of masonry infilled RC frames, including many newly designed ones, have been reported extensively. Inherent problems related to analysis and design methods for tight-fit infilled frame structures have not yet been solved and are recognized as being far from satisfactory in terms of completeness and reliability. The primary objective of this research was to propose and test an innovative method that can effectively mitigate undesirable interaction damage to masonry infilled RC frame structures. This proposed technical solution consists of connection of the infill panel to the bounding columns with steel reinforcement connections deployed in mortar layers and anchored to the columns. This is practical, cheap and easy to implement without any specific technology, which is especially important for developing countries. A three story, two bay RC building model with the proposed connection implemented on the infill walls was designed and tested on the shake table at IZIIS in Skopje, N. Macedonia. The test results and design guidelines/recommendations from the proposed research are also expected to benefit the infrastructural development in other countries threatened by earthquakes, preferably in the Balkan and the Mediterranean region. Full article

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general named entities. We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. Our method replaces the traditional biaffine classifier with a label attention mechanism that leverages comprehensive natural language descriptions of entity types, encoded using BERT to capture rich semantic relationships between labels and input spans. We conducted comprehensive experiments on four benchmark datasets: GENIA (nested NER), ACE 2004 (nested NER), ACE 2005 (nested NER), and CoNLL 2003 English (flat NER). Performance was evaluated across multiple few-shot scenarios (1-shot, 5-shot, 10-shot, and 20-shot) using F1-measure as the primary metric, with five different random seeds to ensure robust evaluation. We compared our approach against strong baselines including BERT-LSTM-CRF with nested tags, the original Biaffine model, and recent few-shot NER methods (FewNER, FIT, LPNER, SpanNER). Results demonstrate significant improvements across all few-shot scenarios. On GENIA, our LDE model achieves 45.07% F1 in five-shot learning compared to 30.74% for the baseline Biaffine model (46.4% relative improvement). On ACE 2005, we obtain 44.24% vs. 32.38% F1 in five-shot scenarios (36.6% relative improvement). The model shows consistent gains in 10-shot (57.19% vs. 49.50% on ACE 2005) and 20-shot settings (64.50% vs. 58.21% on ACE 2005). Ablation studies confirm that semantic information from label descriptions is the key factor enabling robust few-shot performance. Transfer learning experiments demonstrate the model’s ability to leverage knowledge from related domains. Our findings suggest that incorporating label semantic information can substantially enhance NER models in low-resource settings, opening new possibilities for applying NER in specialized domains or languages with limited annotated data. Full article

This research addresses two specific knowledge gaps. The first regards the influence of domestic low-carbon technology (LCT) installation approaches and occupier status on user acceptance. The second is to demonstrate the role of machine learning techniques in producing an enhanced model-based understanding of domestic LCT acceptance. Together, these two approaches provide new insights into LCT acceptance through the theory of planned behaviour and demonstrate the value of machine learning for modelling such acceptance. Our aim is therefore to contribute to model-based knowledge about the acceptance of domestic LCTs. Specifically, we contribute new knowledge of the acceptance of LCTs according to the theory of planned behaviour and of the value of machine-learning techniques for modelling this acceptance. Through empirical research using an online quasi-experiment with 3813 English residents, we developed a model of low-carbon technology adoption and evaluated machine learning for model analysis. The design factors were the installation approach and occupier status, with main outcomes including adoption intention, willingness to accept, willingness to pay, attitude, subjective norm, and perceived behavioural control. To examine residents’ technology acceptance, we created two virtual reality models of technology implementation, differing in installation approach. For machine learning analysis, we employed nine techniques for model validation and predictor selection: linear regression, LASSO regression, ridge regression, support vector regression, regression tree (decision tree regression), random forest, XGBoost, k-NN, and neural network. LASSO regression emerged as the best technique in terms of predictor selection, with (near-)optimal model fit (R² and MSE). We found that attitude, subjective norm, and perceived behavioural control significantly predicted the intention to adopt low-carbon technologies. The installation approach influenced willingness to accept, with higher intention for new-build installations than retrofits. Homeownership positively predicted perceived behavioural control, while age negatively predicted several outcomes. This study concludes with implications for policy and future research, a specific emphasis upon contemporary UK policy towards Future Homes Standards, and public information campaigns targeted to specific demographic user groups. This research demonstrates the value of an extended theory of planned behaviour model to study the acceptance of LCTs and the value of machine learning analysis in acceptance modelling. Full article

This article analyzes the role of parish churches, erected after 1945, in shaping the urban and social structures of the Szczecin housing estates, examining their importance and impact on the surrounding space. This research focused on three groups of churches as spatial landmarks and symbols of young heritage, analyzing their location, form, and social significance. The objective of the present research was to determine how contemporary churches perform a dominant function in space and how they affect the social identity of residents, to determine whether these churches can be considered objects of young cultural heritage. This work used field research, spatial and photographic analysis, and typological classification. Six selected churches were subjected to comparative analysis. The results indicate a clear impact of sacred architecture on public spaces and the need to formulate new monument protection strategies for contemporary sacred objects as elements of urban cultural heritage. This article fits into the current discussions on the creation of criteria for evaluating post-war architecture and its place in the structure of a developing city. We noticed the lack of tools and directions for the protection of young heritage, and suggested directions for the sustainable protection of contemporary buildings. Full article

Point clouds acquired by mobile laser scanners (MLS) are widely used for generating as-built building information models (BIM), particularly in indoor construction environments and existing buildings. While MLS offers fast and efficient scanning through SLAM technology, its accuracy and precision remains lower than that of terrestrial laser scanners (TLS). This study investigates the potential to improve MLS-based as-built BIM accuracy by analyzing and utilizing error distribution patterns inherent in MLS point clouds. Based on the assumption that each MLS device exhibits consistent and unique error distribution patterns, an experiment was conducted using three MLS devices and TLS-derived reference data. The analysis employed iterative closest point (ICP) registration and cloud-to-mesh (C2M) distance measurements on mock-ups with closed shapes. The results revealed that error patterns were stable across scans and could be leveraged as correction factors. In other words, the results indicate that when using MLS for as-built BIM generation, robust fitting methods have limitations in obtaining realistic object dimensions, as they do not account for the unique error patterns present in MLS point clouds. The proposed method provides a simple and repeatable approach for enhancing MLS accuracy, contributing to improved dimensional reliability in MLS-driven BIM applications. Full article

Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. Fourier Transform Infrared (FTIR) spectroscopy, due to its high sensitivity, non-destructive nature, and potential for online monitoring, has emerged as a key technique in gas detection. However, the complex underground environment often causes baseline drift in IR spectra. Furthermore, the variety of gas species and uneven distribution of concentrations make it difficult to achieve precise and reliable online analysis using existing quantitative methods. This paper aims to perform a quantitative analysis of coal mine gases by FTIR. It utilized the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of coal mine gases, they could be classified into gases with mutually distinct absorption peaks and gases with overlapping absorption peaks. For gases with distinct absorption peaks, three spectral lines, including the absorption peak and its adjacent troughs, were selected for quantitative analysis. Spline fitting, polynomial fitting, and other curve fitting methods are used to establish a functional relationship between characteristic parameters and gas concentration. For gases with overlapping absorption peaks, a wavelength selection method bassed on the impact values of variables and population analysis was applied to select variables from the spectral data. The selected variables were then used as input features for building a model with a backpropagation (BP) neural network. Finally, the proposed method was validated using standard gases. Experimental results show detection limits of 0.5 ppm for CH₄, 1 ppm for C₂H₆, 0.5 ppm for C₃H₈, 0.5 ppm for n-C₄H₁₀, 0.5 ppm for i-C₄H₁₀, 0.5 ppm for C₂H₄, 0.2 ppm for C₂H₂, 0.5 ppm for C₃H₆, 1 ppm for CO, 0.5 ppm for CO₂, and 0.1 ppm for SF₆, with quantification limits below 10 ppm for all gases. Experimental results show that the absolute error is less than 0.3% of the full scale (F.S.) and the relative error is within 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance. Full article

Throughout history, communities have struggled to build homes in places actively hostile to their presence, a challenge long faced by African descendants in the American diaspora. In cities across the U.S., including Washington, D.C., efforts have often been made to erase Black cultural identity. D.C., once a hub of Black culture, saw its urban fabric devastated during the 1968 riots following Dr. Martin Luther King Jr.’s assassination. Since then, redevelopment has been slow and, more recently, marked by gentrification, which has further displaced Black communities. Amid this context, Black architects such as Michael Marshall, FAIA, and Sean Pichon, AIA, have emerged as visionary leaders. Their work exemplifies Value-Inclusive Design and aligns with Roberto Verganti’s Design-Driven Innovation by embedding cultural relevance and community needs into development projects. These architects propose an intentional approach that centers Black identity and brings culturally meaningful businesses into urban redevelopment, shifting the paradigm of design practice in D.C. This collective case study (methodology) argues that their work represents a distinct architectural style, Black Modernism, characterized by cultural preservation, community engagement, and spatial justice. This research examines two central questions: Where does Black Modernism begin, and where does it end? How does it fit within and expand beyond the broader American Modernist architectural movement? It explores the consequences of the destruction of Black communities, the lived experiences of Black architects, and how those experiences are reflected in their designs. Additionally, the research suggests that the work of Black architects aligns with heutagogical pedagogy, which views community stakeholders not just as beneficiaries, but as educators and knowledge-holders in architectural preservation. Findings reveal that Black Modernism, therefore, is not only a design style but a method of reclaiming identity, telling untold histories, and building more inclusive cities. Full article

Developing multicultural leadership in youth is crucial for fostering social harmony, emphasizing cross-cultural communication, adaptability, creative problem solving, and ethical leadership, particularly in Thailand’s three southern border provinces. This study aimed to analyze the confirmatory factors and assess the validity of the measurement model for multicultural leadership among youth in Thailand’s three southern border provinces. The study sample comprised 640 participants, and the data were analyzed using second-order confirmatory factor analysis. The findings revealed that multicultural leadership among youth in the region consists of the following six key components: (1) awareness and acceptance of cultural diversity, (2) intercultural communication skills, (3) flexibility and adaptability in multicultural contexts, (4) creative problem solving in a multicultural context, (5) building intercultural collaboration networks, and (6) developing culturally relevant morality and ethics. The measurement model demonstrated a good fit with the empirical data. Considering the Chi-square value of 411.81, p-value of 0.07, the relative Chi-square (χ²/df) was 1.11, the Goodness-of-Fitness Index (GFI) was 0.96, the Adjusted Goodness-of-Fitness Index (AGFI) was 0.94, and the Root Mean Square Residuals Index (SRMR) was 0.03. These findings provide valuable insights for formulating effective policies and concrete strategies to enhance and develop multicultural leadership among youth in diverse sociocultural contexts. Full article

Job satisfaction in sub-Saharan Africa is crucial as it directly impacts employee productivity, retention, and overall economic growth, fostering a motivated workforce that drives regional development. In sub–Saharan Africa, poor remuneration, limited professional development opportunities, and inadequate working conditions impact satisfaction. This study examined job-satisfaction predictors among health workers in primary healthcare settings across eight countries: Ethiopia, Kenya, Malawi, Senegal, South Sudan, Tanzania, Uganda, and Zambia. A cross-sectional study surveyed 1711 health workers, assessing five dimensions: employer–2employee relationships, remuneration and recognition, professional development, physical work environment, and supportive supervision. The study was conducted from October 2023 to March 2024. The job-satisfaction assessment tool was adopted from a validated tool originally developed for use in low-income healthcare settings. The tool was reviewed by staff from all the country offices to ensure contextual relevance and organization alignment. The responses were measured on a five-point Likert scale: 0: Not applicable, 1: Very dissatisfied, 2: Dissatisfied, 3: Neutral, 4: Satisfied, and 5: Very satisfied. The analysis employed descriptive and multivariable regression methods. Job satisfaction varied significantly by country. Satisfaction with the employer–employee relationship was highest in Zambia (80%) and lowest in Tanzania (16%). Remuneration satisfaction was highest in Senegal (63%) and Zambia (49%), while it was very low in Malawi (9.8%) and Ethiopia (2.3%). Overall, 44% of respondents were satisfied with their professional development, with Uganda leading (62%) and Ethiopia having the lowest satisfaction level (29%). Satisfaction with the physical environment was at 27%, with Uganda at 40% and Kenya at 12%. Satisfaction with supervisory support stood at 62%, with Zambia at 73% and Ethiopia at 30%. Key predictors of job satisfaction included a strong employer–employee relationships (OR = 2.20, p < 0.001), fair remuneration (OR = 1.59, p = 0.002), conducive work environments (OR = 1.71, p < 0.001), and supervisory support (OR = 3.58, p < 0.001. Improving the job satisfaction, retention, and performance of health workers in sub-Saharan Africa requires targeted interventions in employer–employee relationships, fair compensation, supportive supervision, and working conditions. Strategies must be tailored to each country’s unique challenges, as one-size-fits-all solutions may not be effective. Policymakers should prioritize these factors to build a motivated, resilient workforce, with ongoing research and monitoring essential to ensure sustained progress and improved healthcare delivery. Full article

To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal. Full article

A major breakthrough in environmentally friendly building materials is the development of sustainable unfired clay bricks including alumina waste produced during liquid nitrogen generation. Though used extensively, conventional fired clay bricks require energy-intensive manufacturing techniques that produce significant amounts of CO₂ and aggravate environmental damage. By removing the need for high-temperature firing and allowing for the valorization of industrial byproducts including alumina waste and lateritic soil, unfired clay bricks offer a reasonable low-carbon alternative. High silica and alumina contents define the alumina waste, which shows pozzolanic reactivity, thus improving the physicomechanical performance of the bricks. With alumina waste substituting 0–8.57% of the cement content, seven different formulations showed improvements in compressive strength, reduced water absorption, and optimal thermal conductivity. Especially, the mechanical performance was much enhanced with alumina waste inclusion up to 30%, without sacrificing thermal insulation capacity or moisture resistance. Further supporting the environmental and financial sustainability of the suggested brick compositions is the economic viability of using industrial waste and regionally derived soils. A comparative analysis of the conventional fired bricks shows that the unfired substitutes have a much lower environmental impact and show better mechanical properties, including greater compressive strength and modulus of rupture. These results support the more general goals of circular economy systems and low-carbon urban development by highlighting the feasibility of including alumina waste and lateritic soil into sustainable building materials. Using such waste-derived inputs in building fits world initiatives to lower resource consumption, lower greenhouse gas emissions, and build strong infrastructure systems. Full article