With the acceleration of urbanization, environmental degradation is increasingly restricting the improvement of residents’ quality of life, and promoting the transformation of old communities has become a key path for sustainable urban development. However, existing buildings generally face challenges, such as the deterioration of the performance of the envelope structure and the rising energy consumption of the air conditioning system, which pose a serious test for the realization of green renovation. Inspired by the application of bionics in the field of architecture, this study innovatively designed five types of bionic envelope structures for outdoor air conditioning units, namely scales, honeycombs, spider webs, leaves, and bird nests, based on the aerodynamic characteristics of biological prototypes. The ventilation performance of these structures was evaluated at three scales—namely, single building, townhouse, and community—under natural ventilation conditions, using a CFD simulation system. The study shows the following: (1) the spider web structure has the best comprehensive performance among all types of enclosures, which can significantly improve the uniformity of the flow field and effectively eliminate the low-speed stagnation area on the windward side; (2) the structure reorganizes the flow structure of the near-wall area through the cutting and diversion of the porous grid, reduces the wake range, and weakens the negative pressure intensity, making the pressure distribution around the building more balanced; (3) in the height range of 1.5–27 m, the spider web structure performs particularly well at the townhouse and community scales, with an average wind speed increase of 1.1–1.4%; and (4) the design takes into account both the safety of the enclosure and the comfort of the pedestrian area, achieving a synergistic optimization of function and performance. This study provides new ideas for the micro-renewal of buildings, based on bionic principles, and has theoretical and practical value for improving the wind environment quality of old communities and promoting low-carbon urban development.

In the field of industrial fault detection, accurate and timely fault identification is crucial for ensuring production safety and efficiency. Effective feature selection (FS) methods can significantly enhance detection performance in this process. However, the recently proposed Black-winged Kite Algorithm (BKA) tends to suffer from premature convergence and local optima when handling high-dimensional feature spaces. To address these limitations, this paper proposes an improved Black-winged Kite Algorithm (IBKA). This algorithm integrates two novel enhancement mechanisms: First, the Stagnation-Triggered Diversification Mechanism monitors the algorithm’s convergence state and applies mild perturbations to the worst-performing individuals upon detecting stagnation, effectively preventing traps in local optima. Second, the Adaptive Weak Guidance Mechanism employs a conditional elite guidance strategy during the late optimization phase to provide subtle directional guidance to underperforming individuals, thereby improving convergence efficiency. We comprehensively evaluated the proposed IBKA across 26 benchmark functions. Results demonstrate superior performance in solution quality, convergence speed, and robustness compared to the original BKA and other advanced meta-heuristics. Furthermore, fault detection applications on public datasets validate the practical applicability of the binary version of the IBKA (bIBKA), showcasing significant improvements in detection accuracy and reliability. Experimental results confirm that these enhancement mechanisms effectively balance exploration and exploitation capabilities while preserving algorithmic simplicity and computational efficiency.

The Black-winged Kite Algorithm (BKA) is a novel heuristic optimization algorithm proposed in 2024, which has demonstrated superior optimization performance on most CEC benchmark functions and several engineering problems. To further enhance its convergence accuracy and solution quality, this paper proposes a Modified Black-winged Kite Algorithm (MBKA). First, a three-phase attacking strategy is designed to replace the original BKA’s attacking mechanism, thereby enhancing population diversity and improving solution quality. Additionally, a Lévy–Cauchy migration strategy is incorporated to achieve a more effective balance between exploration and exploitation. The effectiveness of MBKA is assessed through extensive experiments on 18 classical benchmark functions, the CEC-2017 and CEC-2022 test suites, and two real-world engineering optimization problems. The results indicate that MBKA consistently outperforms the original BKA and several state-of-the-art algorithms in both convergence accuracy and convergence speed across most test cases.

The extensive integration of intelligent and bionic technologies in the automotive industry has significantly heightened interest in the advancement of smart vehicle cockpits. The growing demand for automobile cockpit functions makes the personalization of intelligent automobile bionic cockpits more challenging. In addition, the evaluation and selection for cockpits considering multiple attributes remains incomplete, which hinders the development of intelligent automobile bionic cockpits. Thus, this paper constructed a multiple criterion model considering the personalization needs of drivers and passengers, which include comfort, security, and spiritual entertainment needs. A novel decision-making approach that merges the entropy measure and the Elimination and Choice Expressing Reality (ELECTRE) method is introduced to address the selection challenges of smart vehicle cockpits. This methodology incorporates the Spherical Fuzzy Set (SFS) to accurately gather and interpret the data within the decision matrix. This study employs a practical application by examining three types of intelligent automobile cockpits to validate the effectiveness of the proposed decision-making method. Through sensitivity analysis and comprehensive validation, the findings substantiate that the research offers a potent instrument for addressing the selection challenges associated with intelligent automobile cockpits, providing valuable insights for designers.

This study investigates the impact absorption performance of bamboo-inspired 3D-printed circular tubes in terms of infill type (grid, gyroid, honeycomb, Archimedean chords), infill ratio (10%, 20%, 30%, and 40%), wall thickness (0.8, 1.2, 1.6, and 2.0 mm), and inner diameter parameters. The structures designed using Taguchi L16 orthogonal design are printed with PLA material using FDM technology and evaluated by quasi-static compression tests. Peak crushing force (PCF), energy absorption (EA), and specific energy absorption (SEA) criteria are used to analyse crashworthiness performance. The experimental results showed that EA improves as the infill rate increases, but the gain decreases as the infill rate approaches 40% (SEA at 30% is better than that at 40%). By visualising the PCF and EA data relative to the utopia point (the lowest PCF and highest EA), GRID_T16F30D24, HCOMB_T08F30D22, and GRID_T12F20D22 are found to be the best-performing tubes. The grid and honeycomb infill types showed superiority at 20–30% infill rates and similar wall thicknesses. The Archimedean chords type performed poorly due to its tendency to fracture.

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement.

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R2 to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing.

The increased consumption of apparel has resulted in a corresponding increase in the volume of waste textiles, with polyester–cotton blended textiles accounting for as much as 80% of the total. However, extant recycling methodologies are beset by challenges, including high cost and difficulty in separation. Mycelium has been shown to possess the ability to degrade complex components in culture substrates. The present study explores the feasibility of using polyester–cotton yarn as a substrate for mycelium composite materials, thus offering an innovative approach to the treatment of waste blended textiles. Five mycelium composite materials with varying polyester–cotton ratios were prepared and tested for mechanical strength, moisture resistance, and biodegradability. ANOVA analysis confirmed that all properties of the mycelium composites were significantly influenced by the polyester–cotton matrix ratio, with partial eta-squared (ηp2) exceeding 84% across all properties. The most significant effect was observed in compressive strength (ηp2 > 99%). Experiments identified a 65:35 polyester–cotton ratio as yielding optimal comprehensive properties: namely, a compressive strength of 0.221 MPa and flexural strength of 0.791 MPa, coupled with excellent moisture resistance and biodegradability. This provides data support for the development of textile-based mycelium composite products. In light of the aforementioned performance studies and material characteristics, the development of three series of experiential home products was undertaken. Design evaluations were conducted to explore the potential application of mycelium composites, which could have significant implications for promoting sustainable development in the textile and apparel industry and advancing innovative designs for mycelium composite materials.

Lattice structures produced by additive manufacturing are increasingly used in lightweight, load-bearing applications, yet their mechanical performance is strongly influenced by geometry, process parameters, and boundary conditions. This study investigates the compressive behavior of body-centered cubic (BCC) 316L stainless steel lattices fabricated by laser powder bed fusion (LPBF). Four relative densities (20%, 40%, 60%, and 80%) were achieved by varying the strut diameter, and specimens were built in both vertical and horizontal orientations. Quasi-static compression tests characterized the elastic modulus, yield strength, energy absorption, and mean force, while finite element simulations reproduced the deformation and hardening behavior. The experimental results showed a direct correlation between density and mechanical properties, with vertically built specimens performing slightly better due to reduced processing defects. Simulations quantified the effect of strut–joint rounding and the need for multi-cell configurations to closely match the experimental curves. Regardless of the boundary conditions, for a density of 20%, simulating a single cell underestimated stiffness because of unconstrained strut buckling. For higher densities and thicker struts, this sensitivity to boundary conditions strongly decreased, indicating the possibility of using a single cell for shorter simulations—a point rarely discussed in the literature. Both experiments and simulations confirmed Gibson–Ashby scaling for elastic modulus and yield strength, while the tangent modulus was highly sensitive to boundary conditions. The combined experimental and numerical results provide a framework for the reliable modeling and design of metallic lattices for energy absorption, biomedical, and lightweight structural applications.

Purpose: This study aimed to evaluate the effect of different coronal restoration methods on stresses in immature central incisors with regenerative endodontic treatment and excessive loss of coronal structure. Methods: A three-dimensional (3D) Finite Element Analysis (FEA) model of a maxillary central incisor treated with a 3 mm MTA coronal plug after regenerative endodontic treatment was created. Six different models were simulated: (1) intact immature tooth (control), (2) direct composite resin build-up, (3) fibre-reinforced composite build-up, (4) hybrid ceramic endocrown, (5) LiSi ceramic endocrown, and (6) endocore and ceramic crown restoration. Analyses were performed with SolidWorks/CosmosWorks, and a 150 N load was applied at a 135° angle. Results: Maximum tensile stresses were concentrated in the cervical region (4.577 MPa). Direct composite and fibre-reinforced restorations showed high stress in root dentin (3.891 and 3.841 MPa, respectively). The endocore/ceramic crown restoration (1.578 MPa) provided the closest stress distribution to the natural tooth (1.322 MPa). Conclusions: The biomechanical performance of the restoration–tooth complex depends on both the restorative material and the restoration design. In immature teeth undergoing regenerative endodontic treatment, the most biomechanically favourable restoration option was an endocore/ceramic crown.