Search Results (714)

A collectible rotor hybrid aircraft (CRHA) represents a novel type of vertical takeoff and landing (VTOL) unmanned aircraft configuration, combining the typical rotor and transmission systems of helicopters with the wing and propulsion systems of fixed-wing aircraft. Its weight estimation and parameter design during the conceptual design stage cannot directly use existing rotorcraft or fixed-wing methods. This paper presents a rapid key design parameter sizing and maximum takeoff weight (MTOW) estimation approach tailored to CRHA, explicitly scoped to the 5–8-metric-ton (t) MTOW class. Component weight models are first formulated as explicit functions of key design parameters—including rotor disk loading, power loading, and wing loading. Segment-specific fuel weight fractions for VTOL and transition flight are then updated from power calculations, yielding a complete mission fuel model for this weight class. A hybrid optimization framework that minimizes MTOW is constructed by treating the key design parameters as design variables and combining a genetic algorithm (GA) with sequential quadratic programming (SQP). The empty-weight model, fuel-weight model, and optimization framework are validated against compound-helicopter, tilt-rotor, and twin-turboprop benchmarks, and parameter sensitivities are evaluated locally and globally. Results show prediction errors of roughly 10% for empty weight, fuel weight, and MTOW. Sensitivity analysis indicates that at the baseline design point, wing loading exerts the greatest influence on MTOW, followed by power loading and disk loading. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article

The conceptual design phase in architecture establishes the foundation for subsequent design decisions and influences up to 80% of a building’s lifecycle environmental impact. While Building Information Modeling (BIM) demonstrates transformative potential for sustainable design, its application during conceptual design remains constrained by perceived technical complexity and limited support for abstract thinking. This research examines how BIM tools can facilitate conceptual design through diagrammatic reasoning, thereby bridging technical capabilities with creative exploration. A mixed-methods approach was employed to develop and validate a Diagrammatic BIM (D-BIM) framework. It integrates diagrammatic reasoning, parametric modeling, and performance evaluation within BIM environments. The framework defines three core relationships—dissection, articulation, and actualization—which enable transitions from abstract concepts to detailed architectural forms in Revit’s modeling environments. Using Richard Meier’s architectural language as a structured test case, a 14-week quasi-experimental study with 19 third-year architecture students assessed the framework’s effectiveness through pre- and post-surveys, observations, and artifact analysis. Statistical analysis revealed significant improvements (p < 0.05) with moderate to large effect sizes across all measures, including systematic design thinking, diagram utilization, and academic self-efficacy. Students demonstrated enhanced design iteration, abstraction-to-realization transitions, and performance-informed decision-making through quantitative and qualitative assessments during early design stages. However, the study’s limitations include a small, single-institution sample, the absence of a control group, a focus on a single architectural language, and the exploratory integration of environmental analysis tools. Findings indicate that the framework repositions BIM as a cognitive design environment that supports creative ideation while integrating structured design logic and performance analysis. The study advances Education for Sustainable Development (ESD) by embedding critical, systems-based, and problem-solving competencies, demonstrating BIM’s role in sustainability-focused early design. This research provides preliminary evidence that conceptual design and BIM are compatible when supported with diagrammatic reasoning, offering a foundation for integrating competency-based digital pedagogy that bridges creative and technical dimensions of architectural design. Full article

Modern custom machine construction and automation projects face pressure to shorten innovation cycles, reduce durations, and manage growing system complexity. Traditional methods like Waterfall and V-Model have limits where end-to-end data traceability is vital throughout the product life cycle. This study introduces the implementation of a web application that incorporates a model-based design approach to assess its applicability and effectiveness in conceptual design scenarios. At the heart of this approach is the Case-Based Axiomatic Design Assistant (CADA), which utilizes Axiomatic Design principles to break down complex tasks into structured, analyzable sub-concepts. It also incorporates Case-Based Reasoning (CBR) to systematically store and reuse design knowledge. The effectiveness of the visual assistant was evaluated through expert-led assessments across different fields. The results revealed a significant reduction in design effort when utilising prior knowledge, thus validating both the efficiency of CADA as a model and the effectiveness of its implementation within a user-centric application, highlighting its collaborative features. The findings support this approach as a scalable solution for enhancing conceptual design quality, facilitating knowledge reuse, and promoting agile development. Full article

Since the 1960s, theories on the relationship between people and their environment have explored how elements of the built environment may directly or indirectly influence human behavior. In this context, neuroarchitecture is emerging as an interdisciplinary field that integrates neuroscience, architecture, environmental psychology, and cognitive science, with the aim of providing empirical evidence on how architectural spaces affect the human brain. This study investigates the potential of neuroarchitecture to inform environmental design by clarifying its current conceptual framework, examining its practical applications, and identifying the context in which it is being implemented. Beginning with an in-depth analysis of the definition of neuroarchitecture, its theoretical foundations, and the range of interpretations within the academic community, the study then offers a critical review of its practical applications across various design fields. By presenting a comprehensive overview of this emerging discipline, the study also summarizes the measurement techniques commonly employed in related research and critically evaluates design criteria based on observed human responses. Ultimately, neuroarchitecture represents a promising avenue for creating environments that deliberately enhance psychological and physiological well-being, paving the way toward truly human-centered design. Nevertheless, neuroarchitecture is still an emerging experimental field, which entails significant limitations. The experiments conducted are still limited to virtual reality and controlled experimental contexts. In addition, small and heterogeneous population samples have been tested, without considering human variability. Full article

The REPowerEU plan is aimed at a target of 35 bcm of biomethane annually by 2030, up from 4 bcm in 2023, requiring about EUR 37 billion in investment. Food waste is identified as a key feedstock, characterized by discrete homogeneity, although its availability may vary seasonally. In Italy, the Emilia-Romagna region generates approximately 450 kt/y of industrial waste from the food and beverage sector, primarily originating from meat processing (NACE 10.1), fruit and vegetable processing (NACE 10.3), and the manufacture of vegetable and animal oils and fats (NACE 10.4). Of this amount, food and beverage processing waste (EWC 02) accounts for about 302 kt from NACE 10 (food, year 2019) and 14 kt from NACE 11 (beverage, year 2019). This study provides a comprehensive screening of waste streams generated by the local food and beverage industry in Emilia-Romagna, evaluating the number of enterprises, their value added, and recorded waste production. The screening led to the identification of suitable streams for further valorization strategies: a total of ~93 kt/y was selected for the preliminary conceptual design of an integrated process combining anaerobic digestion with hydrothermal treatment, aimed at supporting national biomethane production targets while maximizing material recovery through hydrochar production. Preliminary estimations indicate that the proposed process may achieve a biochemical methane potential of approximately 0.23 Nm³/kg_VS, along with a hydrochar yield of about 130 kg/t_waste. Full article

Diabetes has become a global health challenge, driving the demand for innovative, non-invasive diagnostic technologies to improve glucose monitoring. Urinary glucose concentration, a reliable indicator of metabolic changes, provides a practical alternative for frequent monitoring without the discomfort of invasive methods. In this simulation-based study, we propose a novel asymmetric photonic structure that integrates Tamm plasmon polariton (TPP) modes and a cavity mode for high-precision refractive index sensing, with a conceptual focus on the potential detection of urinary glucose. The structure supports three distinct resonance modes, each with unique field localization. Both the TPP modes, confined at the metallic–dielectric interfaces, serve as stable references whose wavelengths are unaffected by refractive-index variations in human urine, whereas the cavity mode exhibits a redshift with increasing refractive index, enabling high responsiveness to analyte changes. The evaluation of sensing performance employs a sensitivity formulation that leverages either TPP mode as a reference and the cavity mode as a probe, thereby achieving dependable measurement and spectral stability. The optimized design achieves a sensitivity of 693 nm·RIU⁻¹ and a maximum figure of merit of 935 RIU⁻¹, indicating high detection resolution and spectral sharpness. The device allows both reflectance and transmittance measurements to ensure enhanced versatility. Moreover, the coupling between TPP and cavity modes demonstrates hybrid resonance, empowering applications such as polarization-sensitive or angle-dependent filtering. The figure of merit is analyzed further, considering resonance wavelength shifts and spectral sharpness, thus manifesting the structure’s robustness. Although this study does not provide experimental data such as calibration curves, recovery rates, or specificity validation, the proposed structure offers a promising conceptual framework for refractive index-based biosensing in human urine. The findings position the structure as a versatile platform for advanced photonic systems, offering precision, tunability, and multifunctionality beyond the demonstrated optical sensing capabilities. Full article

Background: Healthcare systems globally face complex challenges including rising costs, increasing chronic disease burden, and fragmentation of care. Systems-based models represent promising approaches to healthcare transformation, yet their implementation remains incompletely understood. Objective: To critically analyze the Saudi model of Care (MoC) as a case study of systems-based healthcare transformation, examining its conceptual framework, implementation strategies, and projected health outcomes. Methods: We conducted a narrative review synthesizing publicly available official documents on the Saudi MoC, primarily the 2017 overview and 2025 revision, identified through targeted searches of Ministry of Health websites and grey literature portals (no date restrictions); formal quality appraisal was not applied as sources were official policy documents, with bias mitigated through cross-verification and critical analysis. Results: The Saudi MoC exemplifies systems-based transformation through its multi-layered framework organized around six patient-centered systems of care spanning the lifecycle. Key innovations include: (1) an architectural approach integrating activated individuals, healthy communities, virtual care, and traditional clinical settings; (2) a comprehensive intervention taxonomy with 42 specific initiatives; (3) explicit contextual adaptations for diverse settings; and (4) a phased implementation approach with detailed performance metrics. National indicators improved during the reform period, including life expectancy and maternal and child health. These are national trends observed during the period of health reforms. Causal attribution to the Model of Care requires a counterfactual evaluation. Conclusions: This analysis of the Saudi MoC contributes to the literature on systems-based healthcare transformation by illuminating how theoretical principles can be operationalized at national scale. The model’s patient-centered design, comprehensive intervention taxonomy, and attention to implementation factors offer valuable insights for other healthcare systems pursuing transformation. Further research should examine actual implementation outcomes as the model matures. Full article

Straw return is essential for improving soil fertility, recycling organic matter, and sustaining productivity in rice–wheat systems. This study focuses on the conceptual design and systematic analysis of the spatial and temporal variability of straw return methods and their classification. We proposed and analyzed 36 technical models for straw return by integrating spatial distribution (depth and horizontal placement) with temporal variability (decomposition period managed through mulching or decomposers). The models of straw return were categorized into five classes: mixed burial, even spreading, strip mulching, deep burial, and ditch burial. Field experiments were conducted in Babaiqiao Town, Nanjing, China, using clay loam soils typical of intensive rice–wheat rotation. Soil properties (bulk density, porosity, and moisture content) and straw characteristics (length and density) were evaluated to determine their influence on decomposition efficiency and nutrient release. Results showed that shallow incorporation (0–5 cm) accelerated straw breakdown and microbial activity, while deeper incorporation (15–20 cm) enhanced long-term organic matter accumulation. Temporal control using mulching films and decomposer agents further improved moisture retention, aeration, and nutrient availability. For the rice–wheat system study area, four typical straw return modes were selected based on spatial distribution and soil physical parameters: straw even spreading, rotary plowing, conventional tillage with mulching, and straw plowing with burying. This study added to the growing body of literature on straw return by providing a systematic analysis of the parameters influencing straw decomposition and the incorporation. The results have significant implications for sustainable agricultural practices, offering practical recommendations for optimizing straw return strategies to improve soil health. Full article

Additive manufacturing technologies enable the design of complex lightweight structures for electric powertrain applications. This study evaluates the topology optimization and conceptual additive manufacturing of a real electric vehicle gearbox housing, aiming to reduce weight while maintaining structural stiffness. Based on an existing industrial component, a topology-optimized design featuring an X-shaped rib structure was developed. The manufacturing concept combines Laser Metal Deposition (LMD) with a pre-machined turned part. A comparative material study was carried out using finite element simulations to assess aluminum, magnesium, titanium, and stainless steel in terms of weight, deformation, and natural frequency. The results indicate that aluminum alloys offer the best balance of stiffness and weight due to their high specific modulus and favorable processability. The optimized design achieved a simulated weight reduction of approximately 21% with only a minor increase in rotational deformation. A cost analysis of different manufacturing methods suggests that, while conventional casting remains more economical at higher volumes, additive processes are becoming increasingly viable for small series. The study provides a theoretical foundation for future development of lightweight functionally integrated gearbox housings in electric mobility. Full article

The growing adoption of modular robotic systems presents new challenges in ensuring ease of assembly, deployment, and reconfiguration, especially for end-users with varying technical expertise. This study proposes and validates an Assembly Complexity Index (ACI) framework, combining subjective workload (NASA Task Load Index) and task complexity (Task Complexity Index) into a unified metric to quantify assembly difficulty. Twelve participants performed modular manipulator assembly tasks under supervised and unsupervised conditions, enabling evaluation of learning effects and assembly complexity dynamics. Statistical analyses, including Cronbach’s alpha, correlation studies, and paired t-tests, demonstrated the framework’s internal consistency, sensitivity to user learning, and ability to capture workload-performance trade-offs. Additionally, we propose an augmented reality (AR) and virtual reality (VR) integration workflow to further mitigate assembly complexity, offering real-time guidance and adaptive assistance. The proposed framework not only supports design iteration and operator training but also provides a human-centered evaluation methodology applicable to modular robotics deployment in Industry 4.0 environments. The AR/VR-assisted workflow presented here is proposed as a conceptual extension and will be validated in future work. Full article

The construction industry in Pakistan faces persistent challenges due to uncertainties such as behavioral intention, risk identification, and stakeholder perception, which often lead to significant losses in construction activities and human resources. This study aims to quantitatively evaluate these critical factors within the theoretical framework of Building Information Modeling (BIM) and the Technology Acceptance Model (TAM). Specifically, key constructs—Behavioral Intention (BI), Hazard Identification (HI), and Stakeholder Perception (SP)—are analyzed to assess their influence on construction safety management practices. A structured questionnaire was distributed electronically to construction professionals across various ongoing projects in Pakistan. The questionnaire items were based on a five-point Likert scale, and reliability was confirmed with high Cronbach’s alpha values for BI (0.82), HI (0.92), and SP (0.91). To evaluate the relationships between constructs, descriptive statistics and multiple regression analysis were employed. The regression results showed strong model fit for BI and HI (R² = 0.945), and near-perfect fit for SP (R² = 0.998), demonstrating robust predictive power. Significant correlations were found among independent variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU), and others. This study further identifies Trust (TR) and Organizational Culture (OC) as critical predictors of stakeholder perception in the BIM context. A conceptual framework was developed incorporating statistical parameters (e.g., p-values, R², t-stats) to categorize the effectiveness of BIM and TAM theoretical integration for safety risk management. This approach is novel in its use of TAM-based constructs to evaluate BIM-related safety outcomes in the Pakistani construction sector—a context where such empirical evidence is limited. The findings provide predictive insights into how behavioral, perceptual, and organizational variables influence construction safety performance, offering practical implications for BIM adoption and safety policy design. Full article

As heritage cognition evolves, aligning conceptual understanding with conservation strategies becomes essential for effective practice. This study develops the Heritage Cognition–Conservation Paradigm Correspondence Framework, a methodological tool designed to evaluate the alignment between heritage cognition and conservation paradigms. Methodologically, the framework is constructed through document analysis, conceptual classification, and framing co-construction. Building on a critical review of the development trajectory of heritage conservation, it integrates four cognitive phases and three conservation paradigms into a dual-axis matrix, operationalized through six analytical dimensions for heritage cognition and four for conservation paradigms. The framework is subsequently applied through a case study of Traditional Chinese Villages, demonstrating its diagnostic capacity and analytical utility. The case study reveals a significant misalignment: while official discourse reflects pluralistic heritage thinking (within the most advanced, fourth cognitive phase), conservation practice remains rooted in value-based logics and material-based approaches (within the initial paradigms). This misalignment stems from fragmented object recognition, form-focused objectives, and top–down governance structures that marginalize local agency and overlook cultural processes as the heritage nature of those villages. By establishing and operationalizing the correspondence framework, this study provides a transferable tool for diagnosing cognition–practice disjunctions across heritage contexts. Beyond its empirical findings, the study advances a methodological contribution for heritage conservation and advocates a strategic shift toward process-oriented, community-embedded approaches that emphasize cultural continuity, reframed objectives, and participatory governance. Full article

This study aims to address the challenges junior high school students often encounter when learning abstract spatial vector concepts. By developing and implementing a virtual robotic system, this research intends to improve students’ spatial reasoning, deepen their conceptual understanding, and increase engagement through an interactive, visual, and experiential learning environment that remedies the shortcomings of traditional teaching methods. The system was developed with the Unity Game Engine to deliver 3D visualization, interactive manipulation, and real-time feedback, thereby enhancing conceptual learning. In addition, the instructional design employed the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) to enhance students’ understanding of spatial vector concepts. A quasi-experimental design was conducted involving 60 eighth-grade students divided evenly into experimental and control groups. Pre- and post-tests—including achievement assessments, learning attitude questionnaires, and cognitive load scales—were administered to evaluate learning outcomes. The main findings are as follows: (1) The experimental group demonstrated significantly higher learning achievement compared to the control group. (2) Both groups showed improvements in mathematics learning attitudes, with the experimental group exhibiting greater gains in practicality and confidence. (3) Although the experimental group experienced a slightly higher cognitive load, this difference was not statistically significant. (4) The experimental group reported high satisfaction with the system, especially in perceived usefulness. This study demonstrates that integrating virtual reality with the ADDIE model can substantially enhance learners’ conceptual understanding and motivation. Full article

Recycled aggregate concrete refers to concrete made by using recycled aggregates produced from construction waste to replace natural aggregates. The performance of recycled aggregate concrete is extremely unstable. Internal factors such as water–cement ratio, porosity, and the properties of recycled aggregates, as well as external factors like temperature, humidity, environmental erosion, and the addition of improvement materials, may all have an impact on its mechanical properties. The response surface analysis method was employed to investigate the impact of three key factors—the number of dry–wet cycles, the content of stainless steel fibers, and the concentration of Na₂SO₄—on the mechanical properties of stainless steel fiber recycled aggregate concrete (SSFRAC) under dry–wet cycling conditions in the study. By incorporating stainless steel fibers into the cementitious gel network, SSFRAC is conceptualized as a composite material where the metal fibers are integrated into the gel matrix, forming a hybrid system akin to metallogels. The response models for compressive strength durability coefficient S_c and flexural strength durability coefficient S_f are established using Design-Expert software to evaluate the significance of these factors and their interactions. The version of Design-Expert used in this study is Design Expert 13.0. The results demonstrated that both S_c and S_f models exhibit high fitting accuracy, effectively capturing the relationships among the factors. The number of dry–wet cycles exhibit the highest significance, followed by Na₂SO₄ concentration and stainless steel fiber content. The interaction between dry–wet cycle number and Na₂SO₄ concentration has a particularly significant impact on S_c. For S_f, stainless steel fiber content is the most significant factor, followed by dry–wet cycle number and Na₂SO₄ concentration, with the interaction between fiber content and Na₂SO₄ concentration exerting a notably strong influence. This study highlights the potential of cement-based gels as raw materials for synthesizing functional composite materials, where the incorporation of metal fibers enhances mechanical performance and durability under aggressive environmental conditions. The findings provide insights into the design and optimization of hybrid gel–metal systems for advanced construction applications. Full article