Search Results (839)

With the in-depth development of digital twin technology in modern agriculture, smart pig farm construction is evolving from basic environmental modeling toward refined, bio-behavior-driven approaches. This study addresses the non-standard body configurations and complex behavioral patterns of pig models by proposing a binding method that combines lightweight skeletal design with automated weight allocation strategies. The method optimizes skeletal layout schemes based on pig physiological structures and behavioral patterns, replacing manual painting processes through geometry-driven weight calculation strategies to achieve a balance between efficiency and animation naturalness. The research constructs a motion template library containing common behaviors such as walking and foraging, conducting quantitative testing and comprehensive evaluation in simulation systems. Experimental results demonstrate that the proposed method achieves significant improvements: it demonstrated superior computational efficiency with 95.2% reduction in computation time, memory storage space reduced by 91.7% through weight matrix sparsification (density controlled at 8.3%), and weight smoothness was maintained at 0.955 while cross-region weight leakage reduced from 15.3% to 2.1%. The method effectively supports animation expression of eight typical pig behavioral patterns with key joint angle errors controlled within 2.3 degrees, providing a technically viable and economically feasible pathway for virtual modeling and intelligent interaction in smart agriculture. Full article

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

Fine-grained image recognition is one of the key tasks in the field of computer vision. However, due to subtle inter-class differences and significant intra-class differences, it still faces severe challenges. Conventional approaches often struggle with background interference and feature degradation. To address these issues, we draw inspiration from the human visual system, which adeptly focuses on discriminative regions, to propose a bio-inspired gradient-aware attention mechanism. Our method explicitly models gradient information to guide the attention, mimicking biological edge sensitivity, thereby enhancing the discrimination between global structures and local details. Experiments on the CUB-200-2011, iNaturalist2018, nabbirds and Stanford Cars datasets demonstrated the superiority of our method, achieving Top-1 accuracy rates of 92.9%, 90.5%, 93.1% and 95.1%, respectively. Full article

This review surveys the past five years of research on energy-aware path optimization for both solar-powered and battery-only UAVs. First, the energy constraints of these two platforms are contrasted. Next, advanced computational frameworks—including model predictive control, deep reinforcement learning, and bio-inspired metaheuristics—are examined along with their hardware implementations. Recent studies show that hybrid methods combining neural networks with bio-inspired search can boost net energy efficiency by 15–25% while maintaining real-time feasibility on embedded GPUs or FPGAs. Among the remaining challenges are federated learning at the edge, multi-UAV coordination under partial observability, and field trials on ultra-long-endurance platforms like the Airbus Zephyr HAPS. Addressing these issues will accelerate the deployment of truly persistent and green aerial services. Full article

Across ten months of monitoring (1 October 2024–1 July 2025) at three drilled irrigation wells in the La Yarada Los Palos coastal aquifer, this study evaluates boron phytotoxicity risk and its interaction with salinity and sodicity in a hyper-arid coastal agroecosystem. Groundwater samples (n = 10 per well; n = 30) were analyzed for boron, major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and EC. Salinity–sodicity indices (EC-based classes, SAR, Kelly Index, %Na, Mg/Ca ratio) were computed, and relationships among boron, cations, and EC/TDS were examined using correlation analysis and principal components. Boron concentrations ranged from 1.18 to 2.47 mg/L; all samples exceeded the FAO guideline for sensitive crops (0.7 mg/L), and 56.7% were ≥1.5 mg/L. Southern Border exhibited the highest boron (mean ≈ 2.10 mg/L), Ashlands intermediate (≈1.65 mg/L), and Bio Garden Los Palos the lowest (≈1.35 mg/L). EC remained ≈1–1.5 dS/m at Southern Border and Bio Garden Los Palos but reached ≈3–4 dS/m at Ashlands; all SAR values were <9, indicating low sodicity risk. Spearman correlations revealed weak associations between boron and EC/TDS, but moderate positive correlations with Ca²⁺ and Mg²⁺, highlighting partly decoupled controls on boron and salinity. For tolerant crops such as olive and orange, and more sensitive ones such as oregano and quinoa, these conditions imply risks that require combined management of salinity, boron, and cation balance. A risk-based monitoring scheme centered on EC, SAR, boron, and Ca–Mg–Na ratios is proposed to support irrigation decisions in La Yarada Los Palos and similar hyper-arid coastal agroecosystems. Full article

In this paper, we propose the Adaptive Volcano Support Vector Machine (AVSVM)—a novel classification model inspired by the dynamic behavior of volcanic eruptions—for the purpose of enhancing malware detection. Unlike conventional SVMs that rely on static decision boundaries, AVSVM introduces biologically inspired mechanisms such as pressure estimation, eruption-triggered kernel perturbation, lava flow-based margin refinement, and an exponential cooling schedule. These components work synergistically to enable real-time adjustment of the decision surface, allowing the classifier to escape local optima, mitigate class overlap, and stabilize under high-dimensional, noisy, and imbalanced data conditions commonly found in malware detection tasks. Extensive experiments were conducted on the UNSW-NB15 and KDD Cup 1999 datasets, comparing AVSVM to baseline classifiers including traditional SVM, PSO-SVM, and CNN under identical computational settings. On the UNSW-NB15 dataset, AVSVM achieved an accuracy of 96.7%, recall of 95.4%, precision of 96.1%, F₁-score of 95.75%, and a false positive rate of only 3.1%, outperforming all benchmarks. Similar improvements were observed on the KDD dataset. In addition, AVSVM demonstrated smooth convergence behavior and statistically significant gains (p < 0.05) across all pairwise comparisons. These results validate the effectiveness of incorporating biologically motivated adaptivity into classical margin-based classifiers and position AVSVM as a promising tool for intelligent malware detection systems. Full article

SARS-CoV-2, the virus responsible for COVID-19, disrupts human cellular pathways through complex protein–protein interaction, contributing to disease progression. As the virus has evolved, emerging variants have exhibited differences in transmissibility, immune evasion, and pathogenicity, underscoring the need to investigate their distinct molecular interactions with host proteins. In this study, we constructed a comprehensive SARS–CoV–2–human protein–protein interaction network and analyzed the temporal evolution of pathway perturbations across different variants. We employed computational approaches, including network-based clustering and functional enrichment analysis, using our custom-developed Python (v3.13) pipeline, BioEnrichPy, to identify key host pathways perturbed by each SARS-CoV-2 variant. Our analyses revealed that while the early variants predominantly targeted respiratory and inflammatory pathways, later variants such as Delta and Omicron exerted more extensive systemic effects, notably impacting neurological and cardiovascular systems. Comparative analyses uncovered distinct, variant-specific molecular adaptations, underscoring the dynamic and evolving nature of SARS-CoV-2–host interactions. Furthermore, we identified host proteins and pathways that represent potential therapeutic vulnerabilities, which appear to have co-evolved with viral mutations. Full article

Background and Clinical Significance: Advanced periodontitis with severe vertical bone loss and grade III mobility is usually managed by extraction and implant placement. Digital workflows and modern regenerative techniques have opened the possibility of preserving teeth that would traditionally be considered for extraction. This report describes a digitally guided modified intentional replantation (MIR) protocol applied to a maxillary tooth with severe periodontal involvement and unfavourable prognosis. Case Presentation: A 68-year-old male, non-smoker, with a history of heart transplantation under stable medical control, presented with generalized Stage IV, Grade C periodontitis. Tooth 21 showed >75% vertical bone loss, probing depths ≥ 9 mm, bleeding on probing, and grade III mobility. After non-surgical therapy and periodontal stabilization, a CAD/CAM-assisted MIR procedure was planned. Cone-beam computed tomography (CBCT) and a 3D-printed tooth replica were used to design a surgical guide for a new recipient socket. The tooth was atraumatically extracted, stored in chilled sterile saline, and managed extraorally for approximately 10 min. Apicoectomy and retrograde sealing with Biodentine^® were performed, followed by immediate replantation into the digitally prepared socket, semi-rigid splinting, and guided tissue regeneration using autologous bone chips, xenograft (Bio-Oss^®), enamel matrix derivative (Emdogain^®), and a collagen membrane (Bio-Gide^®). A conventional orthograde root canal treatment was completed within the first month. At 12 months, tooth 21 exhibited grade 0 mobility, probing depths of 3–4 mm without bleeding on probing, and stable soft tissues. Standardized periapical radiographs and CBCT showed radiographic bone fill within the previous defect and a continuous periodontal ligament-like space, with no signs of ankylosis or root resorption. The tooth was fully functional and asymptomatic. Conclusions: In this medically complex patient, digitally guided MIR allowed preservation of a tooth with severe periodontal involvement and poor prognosis, achieving favourable short-term clinical and radiographic outcomes. While long-term data and larger series are needed, MIR may be considered a tooth-preserving option in carefully selected cases as an alternative to immediate extraction and implant placement. Full article

The automation of wind turbine maintenance processes is aimed at improving the operational efficiency of wind farms through timely diagnosis of technical condition, predictive identification of potential failures, and optimization of the distribution of repair and restoration procedures. In this context, the main objective of the study is to improve the reliability and efficiency of wind energy infrastructure by developing an intelligent decision support system for wind turbine maintenance. The proposed architecture includes a module for optimizing the routes of robotic agents, which implements a hybrid method based on a combination of the A* algorithm and a modified ant algorithm with dynamic pheromone updating and B-spline trajectory smoothing, as well as a module for detecting based on a modified YOLOv3 model with integrated adaptive feature fusion and bio-inspired anchor frame optimization. The choice of the YOLOv3 architecture is due to the optimal balance between accuracy and inference speed on embedded platforms of robotic autonomous agents, which ensures the functioning of the detection module in real time with limited computing resources. The results of the computational experiment confirmed a 15–20% reduction in route length and energy consumption, as well as a 41% increase in the $F1$ detection metric relative to the baseline implementation of YOLOv3 while maintaining a performance of 42 frames per second. The set of results obtained confirms the practical feasibility and integration potential of the developed architecture into the predictive maintenance and life cycle management of wind energy infrastructure. Full article

STEM (Science, Technology, Engineering, and Mathematics) is essential for the development of 21st-century skills, particularly in a world driven by technological innovation. However, in vulnerable school contexts, access to meaningful STEM experiences remains limited. This study addresses this issue through the design and implementation of a didactic strategy in a public high school in Bogotá, Colombia, based on two educational resources: the BioSen electronic board, which is compatible with Arduino technology and designed to acquire physiological signals such as electrocardiography (ECG), electromyography (EMG), electrooculography (EOG), and body temperature; and the Space Exploration instructional guide, which is organized around contextualized learning missions. This study employed a quasi-experimental mixed-methods design that combined pre–post perception questionnaires, unstructured classroom observations, and a contextualized knowledge test administered to three student groups. Findings demonstrate that after eight weeks of implementation with upper secondary students, the strategy had a positive impact on the development of 21st-century skills, such as creativity, computational thinking, and critical thinking. These skills were assessed through a mixed quasi-experimental design combining perception questionnaires, qualitative observations, and knowledge evaluations. Unlike the control groups, students who participated in the intervention adjusted their self-perceptions when facing real-world challenges and showed progress in the application of key competencies. Overall, the results support the effectiveness of integrating low-cost biomedical tools with gamified STEM instruction to enhance higher-order thinking skills and student engagement in vulnerable educational contexts. Full article

The hierarchical structure and multifunctional properties of bio-based cellular materials, particularly cellulose, hemicellulose, and lignin, have attracted increasing attention and interest due to their sustainability and versatility. Recent advances in computational modeling and machine learning strategies have provided transformative insights into the molecular, mechanical, thermal, and electronic behaviors of these biopolymers. This review categorizes the conducted studies based on key material properties and discusses the computational methods utilized, including quantum mechanical approaches, atomistic and coarse-grained molecular dynamics, finite element modeling, and machine learning techniques. For each property, such as structural, mechanical, thermal, and electronic, we have analyzed the progress made in understanding inter- and intra-molecular interactions, deformation mechanisms, phase behavior, and functional performance. For instance, atomistic simulations have shown that cellulose nanocrystals exhibit a highly anisotropic elastic response, with axial elastic moduli ranging from approximately 100 to 200 GPa. Similarly, thermal transport studies have shown that the thermal conductivity along the chain axis (≈5.7 W m⁻¹ K⁻¹) is nearly an order of magnitude higher than that in the transverse direction (≈0.7 W m⁻¹ K⁻¹). In recent years, this research area has also experienced rapid advancement in data-driven methodologies, with the number of machine learning applications for biopolymer systems increasing more than fourfold over the past five years. By bridging multiscale modeling and data-driven approaches, this review aims to illustrate how these techniques can be integrated into a unified framework to accelerate the design and discovery of high-performance bioinspired materials. Eventually, we have discussed emerging opportunities in multiscale modeling and data-driven discovery to outline future directions for the design and application of high-performance bioinspired materials. This review aims to bridge the gap between molecular-level understanding and macroscopic functionality, thereby supporting the rational design of next-generation sustainable materials. Full article

Smart textiles are emerging as transformative modern textiles in which sensing, actuation, and communication are directly embedded into textiles, extending their role far beyond passive wearables. This review presents a comprehensive analysis of the convergence between smart textiles, the Internet of Things (IoT), and human-centric design, with sustainability as a guiding principle. We examine recent advances in conductive fibers, textile-based sensors, and communication protocols, while emphasizing user comfort, unobtrusiveness, and ecological responsibility. Key breakthroughs, such as silk fibroin ionic touch screens (SFITS), illustrate the potential of biodegradable and high-performance interfaces that reduce electronic waste and enable seamless human–computer interaction. The paper highlights cross-sector applications ranging from healthcare and sports to defense, fashion, and robotics, where IoT-enabled textiles deliver real-time monitoring, predictive analytics, and adaptive feedback. The review also focuses on sustainability challenges, including energy-intensive manufacturing and e-waste generation, and reviews ongoing strategies such as biodegradable polymers, modular architectures, and design-for-disassembly approaches. Furthermore, to identify future research priorities in AI-integrated “textile brains,” self-healing materials, bio-integrated systems, and standardized safety and ethical frameworks are also visited. Taken together, this review emphasizes the pivotal role of smart textiles as a cornerstone of next-generation wearable technology, with the potential to enhance human well-being while advancing global sustainability goals. Full article

This Special Issue presents recent advances in the production, modelling, processing, and characterization of advanced industrial materials, highlighting the diversity and sophistication of contemporary research discussing metallic, polymeric, composite, and nano-structured systems. The collected contributions address key challenges in materials science, ranging from surface quality control, the development of novel machining and fabrication tools, and optimization of thermoplastic composite consolidation, to provide fundamental insights into additive manufacturing, rheology, and constitutive modelling. The showcased studies introduce innovative approaches to metrology, including advanced optical, fluorescence, and X-ray scattering techniques for characterizing nano-particles, microstructures, and thermal properties. The presented research also features investigations into the welding of dissimilar steels, binder jetting of stainless steel, and the influence of heat treatment on functional steel performance, alongside environmentally oriented research on natural-fibre energy devices and bio-based polymer composites. Further research topics include defect structures in doped crystals, low-temperature synthesis of oxide films, and mechanical behaviour of steels under extreme conditions. Collectively, these articles demonstrate the strong synergy between experimental methods, computational modelling, and industrial applications, underscoring the continued progress in materials reliability, surface engineering, and advanced manufacturing technologies. This Special Issue therefore provides a comprehensive overview of current trends and emerging directions, offering valuable methodological and conceptual insights in the field. Full article

Marginal microleakage compromises the longevity and biological seal of indirect restorations. Despite the growing adoption of computer-aided design and manufacturing (CAD/CAM) and three-dimensional (3D) printing technologies, limited evidence compares the marginal integrity of these materials under combined mechanical and thermal stresses. This study evaluated and compared the marginal microleakage of overlay restorations fabricated from five contemporary restorative materials, IPS e.max^® ZirCAD Prime, BioHPP^®, G-CAM, VarseoSmile Crown^Plus, and IPS e.max^® CAD, after sequential compressive loading and thermocycling. A total of 125 extracted human molars were prepared for standardized 1.5 mm-thick CAD/CAM overlay restorations and assigned to three experimental conditions: control, sequential compressive loading (3 × 500 N), and thermocycling (6000 cycles between 5 °C and 55 °C) followed by loading. Microleakage was assessed using 2% methylene blue dye and stereomicroscopy. Data were analyzed using Fisher’s exact test and Fleiss’ Kappa (α = 0.05). G-CAM and IPS e.max^® ZirCAD Prime exhibited the lowest microleakage across all testing conditions, while BioHPP^® showed the highest values. Both sequential compressive loadings and thermocycling significantly increased microleakage in all materials (p < 0.001). The results indicate that material type significantly influences marginal sealing, with G-CAM and IPS e.max^® ZirCAD Prime maintaining superior marginal integrity compared with other materials tested. Full article

This paper presents an in-depth study on the structural integrity enhancement and machining process optimization of Anti-lock Braking System (ABS) hydraulic valve blocks, focusing on the transition from the MK60 to the MK100 design. The research combines finite element analysis (FEA), topology optimization, fixture redesign, and coolant technology improvements to achieve significant performance, productivity, and sustainability gains. The MK100 exhibits a mass reduction of 31.6%, an increase in tensile strength by 29.2%, and a fatigue life extension of 35% compared to the MK60. Pressure losses have been reduced by 38.8%, improving braking system responsiveness. On the manufacturing side, fixture redesign increased production capacity from 240 to 480 parts per shift while reducing cycle time from 16 min to 8 min per lot. The transition from a semi-synthetic emulsion coolant (AquaCut EM-X45) to a bio-based oil (BioLube AL-2200) extended coolant replacement intervals from six months to two years, reduced tooling costs, and increased tool life by 25%. These findings demonstrate the feasibility of integrating computational design methods with advanced machining strategies to achieve measurable mechanical and economic benefits in the automotive industry. Full article