Search Results (20,930)

Black Modernist architecture offers a powerful yet underexamined pathway for advancing restorative capacity in American cities. This paper argues that Black Modernism functions as a restorative design methodology, addressing social, economic, and ecological harm imposed on Black communities through slavery, racial capitalism, urban renewal, and infrastructural violence. Grounded in the restorative economics framework pioneered by O’Hara, the paper explores the role Black Modernism plays in sustaining sink capacities defined as the social, ecological, and emotional processes that absorb stress, pollution, waste, and trauma. Conventional economic models ignore these capacities, despite their necessity for economic productivity. Black communities, like all marginalized communities, have historically been forced to provide them without compensation. Situating Black Modernist architecture within this framework, the paper demonstrates how Black architects have designed buildings and landscapes that restore dignity, memory, health, and cultural identity, thereby expanding community sink capacities. Drawing on the works of various scholars, the paper examines case studies from Washington, DC, Atlanta, and Chicago, which reveal how Black communities have borne the burden of unremunerated restorative labor while shaping the American built environment. The paper positions Black Modernism as both a design language and a political–economic intervention, challenging architectural value systems that privilege monumental production over community restoration. It concludes by proposing a Restorative Design Framework that integrates Black Modernist principles with restorative economics, offering policy and planning pathways that recognize cultural labor, emotional restoration, and community well-being as essential components of sustainable urban development. Full article

This research investigates the capacity of Artificial Intelligence (AI) to serve as a generative and interpretative framework for revitalizing Al-Qatt Al-Asiri art. By developing sustainable design solutions for temporary exhibitions, the study seeks to reinforce Saudi Arabia’s cultural and touristic identity through a synthesis of heritage and technology. The study adopts a descriptive–analytical and applied methodology to examine the potential of AI to support creative design processes that integrate authenticity and innovation while preserving local heritage and meeting environmental sustainability requirements. Utilizing this descriptive–analytical and applied methodology. the study evaluates the efficacy of AI in augmenting creative design processes. The primary objective is to reconcile cultural authenticity with modern innovation, ensuring the preservation of local heritage while adhering to rigorous environmental sustainability standards. A controlled design experiment was executed for a temporary heritage exhibition, employing AI applications to simulate the complex decorative motifs of Al-Qatt Al-Asiri art. These technologies were used to generate sustainable exhibition units constructed from reusable local materials, bridging the gap between the digital generation and physical sustainability. This study presents a theoretical framework, a review of previous studies, the research methodology, quantitative and qualitative evaluation results, and an expert panel assessment. It involved three expert reviewers who evaluated the proposed design models based on eight sustainability criteria. This study also utilized a structured evaluation tool and AI applications, including ChatGPT-5.2, OpenAI and Gemini 3 Pro—Nano Banana. The results of the exploratory study indicate that the use of AI contributes to achieving a balance between preserving traditional aesthetic identity and promoting sustainable design practices derived from the characteristics of Al-Qatt Al-Asiri art. It also enhances cultural and tourism engagement by integrating AI applications into artistic design processes. The findings also revealed no statistically significant differences among the experts’ evaluations regarding the sustainability criteria of the implemented models. This study recommends integrating AI technologies into art and design education programs at Saudi universities and developing ethical and technical guidelines that ensure the preservation of heritage and cultural identity when applying AI in design practices. Full article

Zonal ship central cooling systems, which are primarily implemented in naval platforms and advanced specialized vessels to ensure high survivability, exhibit complex fluid–thermal interactions and multi-level valve networks, challenging conventional resilience analysis, especially under large-scale fault scenarios and dynamic topology reconfiguration. This paper presents a physics-informed dynamic resilience assessment and reconfiguration optimization method tailored for such systems. To address the high-dimensional reconfiguration search space, a physics-informed pruning mechanism combining topological reachability filtering and nodal continuity-based feasible-flow verification is introduced, eliminating 42.6% of invalid topologies and reducing optimization time by approximately 38%. Additionally, a cumulative thermal severity (CTS) metric is developed to capture transient thermal shock risks, quantitatively assessing deviation from the 50 °C system safety boundary at the most critical node. Simulation results for a main seawater pump failure scenario demonstrate that the proposed reconfiguration strategy, which coordinates cross-zone tie valves and leverages healthy zones’ pressure margins, shortens recovery time by 47%, suppresses peak temperature from 51.5 °C to 50.2 °C, reduces maximum over-temperature from 1.5 °C to 0.2 °C, and decreases CTS from 8.5 °C·s to 0.1 °C·s (a 98.8% reduction). These findings demonstrate that physics-informed pruning substantially reduces the computational burden of high-dimensional reconfiguration, while the proposed CTS metric enables quantitative assessment of transient thermal-shock risk. Together, they offer robust methodological guidance for resilience-oriented decision support and fault-tolerant design in complex shipboard fluid–thermal systems. Full article

This study stems from the need to investigate how GenAI tools, particularly ChatGPT-4o, can support the professional development of mathematics teachers. It explores how Blum’s modeling cycle can serve as a conceptual and operational framework for mathematics teachers’ instructional design when supported by ChatGPT-4o. Drawing on a qualitative case study within a teacher professional development program, the research analyzes how two upper secondary school teachers engaged with ChatGPT-4o to redesign a mathematical task involving probability and real-world contexts. Data include responses to three modeling-related tasks, teachers’ prompts and interactions with ChatGPT-4o, and the final mathematical activity they designed. These materials were analyzed qualitatively according to the modeling cycle and its sub-competencies. The results indicate that the modeling cycle provided teachers with a cognitive and methodological scaffold to guide their interaction with ChatGPT-4o, allowing them to structure, validate, and refine AI-generated ideas through all stages of modeling—from understanding and mathematizing to interpreting and validating. These findings suggest that the modeling cycle can be reinterpreted as a design-oriented framework for integrating ChatGPT-4o in mathematics teacher education. Implications for teacher professional development and future research directions are discussed. Full article

This article explores how fashion, as a culture-intensive industry, can act as a testbed for ecosystem-centred sustainability transitions. Building on debates on the Triple Transition (green, digital, resilience) and the four pillars of sustainability (environmental, social, economic, cultural), the study addresses a theoretical and methodological gap: while transition agendas and sustainability frameworks are well developed at policy and conceptual levels, there is limited empirical integration of these frameworks into design-oriented methods capable of guiding situated organisational decisions in fashion and cultural and creative industries. It proposes a design- and futures-driven methodology that combines intuitive-logics scenario building, horizon scanning and a customised three-axis Polar Map. The Polar Map translates the Triple Transition into three composite orientations: Bios, Techné and Resilience, used to structure four narrative scenarios applied to the fashion ecosystem: Trailblazing Agency, Other-than-Human Agency, Constructive Agency and Normative Agency. Each scenario assembles concepts, weak signals and case examples into plausible configurations of the fashion value chain and its ecosystem. The results show how these scenarios act as meta-narratives, orienting devices and boundary objects that support futures literacy, make the cultural and intangible consequences of design decisions explicit and reveal interdependencies across value chains. Conceptually, the work operationalises combined transitions and the four pillars of sustainability in a flagship CCI; methodologically, it advances a design-oriented adaptation of scenario practices; and practically, it offers organisations narrative tools to rehearse ecosystem-centred innovation pathways. The conclusion reflects on structural constraints and methodological directions for further hybridisation within foresight methods. Full article

Aims: To conduct an exploratory analysis the psychometric properties of the Spanish version of the Soft Skills Questionnaire for Nurses (SSQN) and examine its conceptual coherence and its preliminary empirical behavior among nursing professionals and students. The aim is to critically assess the instrument’s suitability as a tool for exploring perceptions and self-reported soft skills rather than to establish its psychometric validity. Design: Exploratory methodological study focused on analyzing the empirical performance and conceptual adequacy of the SSQN within a Spanish sample, with particular attention to the internal patterns of responses and the coherence between the instrument’s items and its proposed dimensions. Methods: The process included the translation of the questionnaire and an empirical application in a sample of nursing professionals and students. Exploratory analyses were performed, including exploratory factor analysis and reliability assessment (Cronbach’s alpha and McDonald’s omega), using Jeffreys’s Amazing Statistics Program (JASP) (version 1.18.3), in order to examine the structural performance of the instrument and detect possible conceptual and methodological limitations. Results: The SSQN showed notable inconsistencies in its empirical structure, with dimensions that did not display clear or theoretically coherent patterns. Factor inconsistencies and low internal consistency suggest that the instrument does not adequately capture the multidimensionality of interpersonal skills, reflecting weaknesses inherent in its original formulation rather than in the adaptation process. Conclusions: The Spanish version of the SSQN cannot be considered valid or reliable in its current form. The results underscore the need for a thorough revision of the questionnaire and a conceptual rethinking to develop more robust tools for assessing soft skills. Impact: This study highlights the need for a solid methodological evaluation before introducing instruments designed to measure complex and subjective competencies in the healthcare field. Full article

Advancing hydropower development is crucial for supporting China’s “Dual Carbon” strategy and ensuring energy security. A key safety challenge in this endeavor is the stability of arch dam abutments under the combined action of high in situ stress and reservoir water loads. This study addresses this issue by proposing an integrated methodology that links detailed geological characterization, in situ stress quantification, and mechanical stability analysis. Using the Guxue high-arch dam as a case study, we first established a three-dimensional geological model to identify controlling discontinuities and delineate potential sliding blocks. A finite difference model was then developed to simulate the in situ geo-stress field and operational water pressures. Through stress tensor transformation, the stress state on potential slip surfaces was accurately determined, and safety factors were calculated based on the Mohr–Coulomb strength criterion. The results show that the critical left and right abutment rock blocks exhibit safety factors of 1.30 and 1.24, respectively, meeting design specifications while indicating a relatively lower safety margin on the right bank. The proposed approach, grounded in precise stress analysis, provides a reliable framework for assessing abutment stability under complex loading conditions, offering practical support for the safety evaluation and targeted reinforcement of high-arch dam projects in similar geological settings. Full article

Smart Design methodologies represent a powerful approach for tackling optimization problems and exploring design spaces that would be unmanageable with traditional methods. By integrating computational approaches, optimization strategies and machine learning, it enables the systematic investigation of multiple configurations and the identification of optimal solutions with reduced computational effort. In the present work, Smart Design algorithms are implemented to investigate the influence of geometric parameters on a lattice–honeycomb–square structure. Results coming from finite element analysis and Life Cycle Assessment are exploited to train Random Forest and XGBoost machine learning models in order to find the lattice parameter set that ensures the optimal balance between mechanical performance and sustainability requirements. Full article

The aim of this study was to optimize the parameters of ultrasonic extraction of polyphenolic compounds from peanut shells and to evaluate the feasibility of using the obtained extract in the development of functional yogurt. The extraction factors considered were the ethanol concentration, particle size of peanut shells, and extraction time. Process optimization was performed using response surface methodology based on a second-order central composite design. Extraction yield and total polyphenol content were selected as the optimization criteria. The optimal ultrasonic extraction conditions were determined as an ethanol concentration of approximately 70% ethanol, 300 μm particle size, and 53 min. Under these conditions, the predicted extraction yield was 9.05% and the total polyphenol content reached 95.15 mg GAE/g of dry extract. The extract obtained under the optimal conditions was used to fortify yogurt at concentrations of 0.25%, 0.5%, and 0.75%. Physicochemical analysis showed that the addition of peanut shell polyphenol extract increased the water-holding capacity and reduced syneresis of yogurt during storage compared with the control sample. Changes in pH and titratable acidity remained within the typical ranges for fermented dairy products. The results confirm the potential of peanut shells as a promising source of polyphenolic compounds and demonstrate the feasibility of using the optimized extract in the development of functional fermented dairy products. Full article

Environmental awareness is playing an increasingly important role in all segments of the world, with sustainability and recycling being key elements. The aim of the research is to examine the challenges companies face in terms of sustainability when implementing procurement and supply chain management processes related to interior design. The research focused on four main questions: how procurement and supply chain management are reflected in construction processes, what challenges these processes face, and how they can influence the sustainable use of materials in architectural supply chains. The literature review was based on a systematic literature review using the PRISMA screening process and the PEO framework, utilizing the SCOPUS database and processing 70 scientific articles following the selection process. During the research, I also used the Critical Incident Technique (CIT), in which I asked interior designers about their positive and negative experiences with the procurement of sustainable materials and supply chain management processes. The methodology thus provided deeper insight into the decision-making processes of professionals, where sustainability conflicts with economic and operational realities. The qualitative research was supplemented by a questionnaire survey, which aimed to assess sustainability, its prevalence, and professional obstacles. The results of the research show that this topic is a research gap, but the openness of professionals shows a positive trend. Companies face numerous challenges related to new technologies and environmental awareness in order to create or transform well-functioning supply chain management processes. Full article

The research conducted in this paper is a practical example of the Design of Experiments methodology. In accordance with the assumptions of the experimental design, the authors drew attention to the problem: how should the spark plasma sintering process be planned to obtain the maximum amount of information needed to optimise the consolidation of the WC-6Co composite at the lowest possible cost? The DOE methodology—a powerful technique for investigating new processes and gaining knowledge about existing ones in order to optimise them for high performance—was employed in the study. The aim of the research was to optimise the consolidation of the spark-plasma sintering process of the WC-6Co composite using the DoE (Design of Experiments) methodology. Four sintering factors were selected for the study: sintering temperature (factor A, 1300–1400 °C); heating rate (factor B, 100–300 °C/min); sintering time (factor C, 150–600 s); and pressure (factor D, 40–50 MPa). Each consolidation factor was designed to cover three levels. The L9 orthogonal array was used. It was found that sintering temperature and heating rate had the greatest impact on apparent density. To validate the statistical model, sintering tests were performed at a temperature of 1380 °C, a heating rate of 100 °C/min, a sintering time of 150 s and a pressing pressure of 45 MPa. Validation analysis of the statistical model demonstrated consistency with the experimental results. The WC-6Co composite achieved an apparent density of 14.85 g/cm³, corresponding to 97.42% of the theoretical density, with a hardness of 1809 HV30 and total porosity of 2.583%. X-ray diffraction studies revealed the presence of tungsten carbide and cobalt in the structure. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Early and reliable fault detection in Brushless Direct Current (BLDC) motors is essential for improving system reliability and reducing unplanned industrial downtime. This study presents a controlled experimental investigation of data-driven machine learning approaches for the classification of multiple common BLDC motor faults. Four representative fault-related indicators were obtained under systematically designed operating conditions, and a consistent feature extraction procedure was applied prior to model development. A comparative evaluation was conducted using Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), k-Nearest Neighbour (kNN), and decision tree-based classifiers. All models were trained and tested on the same dataset using an identical validation protocol to ensure methodological fairness and reproducibility. Performance was assessed through standard classification metrics, enabling a transparent comparison of predictive capability and stability. The results show that the MLP model achieved the highest overall classification accuracy (91.6%), closely followed by SVM (91.4%) and kNN (90.2%). Although the performance differences are moderate, the neural network demonstrated more consistent behaviour in scenarios where fault signatures exhibited overlapping characteristics. These findings suggest that non-linear feature interactions play a significant role in BLDC fault discrimination and can be effectively captured by multi-layer architectures. The study provides a reproducible experimental framework and a balanced performance assessment that may support both academic research and the practical development of intelligent condition monitoring systems for BLDC-driven applications. Full article

The research focuses on the development of chemically bonded phosphate ceramics using potassium hydrophosphate and steel slag (EAF) as raw materials. The objective is to scale up the laboratory results to design a ceramic paste suitable for architectural monolithic products, promoting the recycling of EAF steel slag. The methodology includes field visits, grinding and sieving of raw materials, and the fabrication of specimens following ASTM standards. The laboratory results from existing studies on multiphase phosphate cements from steel slags indicate that exothermic reactions and the increase in reactants can affect process scaling. Furthermore, shaping methods such as casting and pressing are evaluated, where pressing proves to be the most suitable for this type of phosphate cement as it increases the material’s mechanical properties (compressive strength), reduces porosity, and generates a greater utilization of the EAF steel slag residue. Taking into account Colombian technical standards regarding the minimum compressive strength that a monolithic architectural object must withstand for structural and non-structural use, the results obtained in this research allow us to conclude that this material can indeed be used for architectural purposes. Full article

The educational process of developing web design competence remains a persistent challenge for many students and educators, particularly in developing countries where conventional teaching methodologies and assessment models often fall short in promoting higher-order thinking and problem-solving. In this study, we respond to the call for innovative assessment approaches by examining the impacts of assessment models on a web design and development course and students’ cognitive load when adopting the AI-assisted assessment model (AAAM) compared to the traditional assessment model (TAM). We employed a mixed-methods research approach, incorporating a quasi-experimental, non-equivalent pretest–posttest control group design and a qualitative component, involving 63 undergraduate students enrolled in CRE 625. The intervention lasted approximately 10 weeks and focused on web design and development across two universities in a developing country. Consistent with quasi-experimental principles, students were assigned to treatment groups based on pre-existing institutional class structures, thereby controlling allocation using criteria rather than randomization. Two validated instruments were used to assess students’ web design and development competence (WDDC) and cognitive load (CL), and the data were analyzed using ANCOVA to evaluate performance gains and the interaction effect with gender. Full article