Search Results (27,898)

This paper presents the foot design for humanoid robot Mithra, with the goal of biomimetically improving impact behavior, natural power cycling throughout the gait cycle, and balance. For this purpose, an optimization framework was built which evaluates the human-inspired objectives using a dynamic finite element analysis validated by benchtop experiments. Using this framework and through several concept design iterations, a low-cost, compliant foot was optimized, designed, and fabricated. The analyses showed that the optimized foot significantly outperformed the baseline rigid foot in approaching the characteristics of human feet. The proposed framework is not limited to humanoids and can also be applied to the foot design for lower-limb prostheses and exoskeletons. Full article

This study presents the evaluation of the photometric performance and energy-saving potential of light-coloured pavement mixtures (LCPMs) in road lighting applications, along with their effects on surface friction, macrotexture, and specularity. The application of LCPMs in tunnels can enhance road surface illumination, thereby improving driver visibility, increasing road safety and comfort, and reducing energy consumption per kilometre. While such surface treatments enable more efficient and cost-effective lighting, maintaining an optimal balance in surface performance poses many challenges due to the impact on concurrent targets in terms of friction, macrotexture, noise contribution, and specularity. Indeed, issues related to friction performance, macrotexture characteristics, and the concurring energy-saving potential of LCPMs remain insufficiently explored. To this end, investigations were conducted to assess the energy-saving potential of light-coloured surface treatments and to evaluate the photometric, frictional, and macrotexture properties of different densely graded LCPMs. A new method was set up and implemented to compare different surface treatments. The results indicate that light-coloured surface treatments increased the average luminance coefficient (up to 0.2406), with glass-containing mixtures offering greater potential for improved surface texture, friction, and energy-efficient road lighting. Full article

Oxide semiconductor gas sensors are widely used due to their low cost, rapid response, small footprint, and ease of integration. However, in complex gas mixtures their selectivity is often limited by inherent cross-sensitivity. To address this, we developed a reconfigurable sensor-array system that supports up to 12 chemiresistive sensors with four- or six-electrode configurations, independent thermal control, and programmable gas paths. As a representative case study, we designed a customized array for fish-spoilage biomarkers, intentionally leveraging the cross-sensitivity and broad-spectrum responses of metal-oxide sensors. Following principal component analysis (PCA) preprocessing, we evaluated convolutional neural network (CNN), random forest (RF), and particle swarm optimization–tuned support vector machine (PSO-SVM) classifiers. The RF model achieved 94% classification accuracy. Subsequent channel optimization (correlation analysis and feature-importance assessment) reduced the array from 12 to 8 sensors and improved accuracy to 96%, while simplifying the system. These results demonstrate that deliberately leveraging cross-sensitivity within a carefully selected array yields an information-rich odor fingerprint, providing a practical platform for complex gas-mixture identification and food-freshness assessment. Full article

Quasi-two-dimensional (Q2D) perovskite films have garnered significant attention as novel gain media for lasers due to their tunable bandgap, narrow linewidth, and solution processability. Q2D perovskites endowed with intrinsic quantum well structures demonstrate remarkable potential as gain media for cost-effective miniaturized lasers, owing to their superior ambient stability and enhanced photon confinement capabilities. However, the mixed-phase distribution within Q2D films constitutes a critical determinant of their optical properties, exhibiting pronounced sensitivity to specific fabrication protocols and processing parameters, including annealing temperature, duration, antisolvent volume, injection timing, and dosing rate. These factors frequently lead to broad phase distribution in Q2D perovskite films, thereby inducing incomplete exciton energy transfer and multiple emission peaks, while simultaneously making the fabrication processes intricate and reducing reproducibility. Here, we report a novel annealing-free and antisolvent-free method for the preparation of Q2D perovskite films fabricated in ambient atmosphere. By constructing a tailored mixed-solvent vapor atmosphere and systematically investigating its regulatory effects on the nucleation and growth processes of film via in situ photoluminescence spectra, we successfully achieved the fabrication of Q2D perovskite films with large n narrow phase distribution characteristics. Due to the reduced content of small n domains, the incomplete energy transfer from small n to large n phases and the carriers’ accumulation in small n can be greatly suppressed, thereby suppressing the trap-assistant nonradiative recombination and Auger recombination. Ultimately, the Q2D perovskite film showed a single emission peak at 519 nm with the narrow full width at half maximum (FWHM) of 21.5 nm and high photoluminescence quantum yield (PLQY) of 83%. And based on the optimized Q2D film, we achieved an amplified spontaneous emission (ASE) with a low threshold of 29 μJ·cm⁻², which was approximately 60% lower than the 69 μJ·cm⁻² of the control film. Full article

To address the challenges of complex composition, high chemical oxygen demand (COD) content, and the difficulty of treating organic wastewater from brewery wastewater, as well as the limitations of traditional Fenton technology, including low catalytic activity and high material costs, this study proposes the use of biochemical sludge as a raw material. Coupled with iron salt activation and mechanical ball milling technology, a low-cost, high-performance iron-doped mesoporous nano-sludge biochar material is prepared. This material was employed as a particle electrode to construct a three-dimensional electro-Fenton system for the degradation of organic wastewater from sauce-flavor liquor brewing. The results demonstrate that the sludge-based biochar produced through this approach possesses a mesoporous structure, with an average particle size of 187 nm, a specific surface area of 386.28 m²/g, and an average pore size of 4.635 nm. Iron is present in the material as multivalent iron ions, which provide more electrochemical reaction sites. Utilizing response surface methodology, the optimized treatment process achieves a maximum COD degradation rate of 71.12%. Compared to the control sample, the average particle size decreases from 287 μm to 187 nm, the specific surface area increases from 44.89 m²/g to 386.28 m²/g, and the COD degradation rate improves by 61.1%. Preliminary investigations suggest that the iron valence cycle (Fe²⁺/Fe³⁺) and the mass transfer enhancement effect of the mesoporous nano-structure are keys to efficient degradation. The Fe-O-Si structure enhances material stability, with a degradation capacity retention rate of 88.74% after 30 cycles of use. When used as a particle electrode to construct a three-dimensional electro-Fenton system, this material demonstrates highly efficiency in organic matter degradation and shows promising potential for application in the treatment of organic wastewater from sauce-flavor liquor brewing. Full article

The A* algorithm is a cornerstone in mobile robot navigation. However, the traditional A* suffers from key limitations such as poor path smoothness, lack of adaptability to dynamic environments, and high computational costs in large-scale maps. This review presents a comprehensive analysis of 20 recent studies (2020–2025) on improved A* variants and their hybrid integrations with complementary algorithms. The improvements are categorized into two core strategies: (i) geometric and structural optimization, heuristic weighting and adaptive search schemes in A* algorithm, and (ii) hybrid models combining A* with local planners such as Dynamic Window Approach (DWA), Artificial Potential Field (APF), and Particle Swarm Optimization (PSO). For each group, the mathematical formulations of evaluation functions, smoothing techniques, and constraint handling mechanisms are detailed. Notably, hybrid frameworks demonstrate improved robustness in dynamic or partially known environments by leveraging A* for global optimality and local planners for real-time adaptability. Case studies with simulated grid maps and benchmark scenarios show that even marginal improvements in path length can coincide with substantial gains in safety and directional stability. This review not only synthesizes the state of the art in A*-based planning but also outlines design principles for building intelligent, adaptive, and computationally efficient navigation systems. Full article

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI. Full article

Industrial wastewater can serve as a low-cost nutritional source for sustainable microalgal biomass production. This study investigated the biomass of Chlorella vulgaris var. vulgaris TISTR 8261 grown in untreated wastewater collected from four food industry factories in Phra Nakhon Sri Ayutthaya Province, Thailand. Among them, wastewater from a processed food production plant (PFPP) supported the highest algal growth. Supplementation with 17.4 mM sodium acetate significantly improved algal biomass yield. Further optimization with 3.7 mM NH₄Cl, 1.0 mM KH₂PO₄, 0.2 mM MgSO₄, and a moderate concentration of trace minerals enhanced the specific growth rate and chlorophyll concentration. Scaled-up cultivation in 3.5 L culture bottles in optimized PFPP yielded a maximum biomass yield of 8.436 ± 0.378 g L⁻¹, comparable to 6.498 ± 0.436 g L⁻¹ in standard TAP medium. Biomass composition analysis after 15 days of cultivation revealed 42.70 ± 1.40% protein, 17.10 ± 1.60% carbohydrate, and 1.90 ± 0.10% lipid on a dry weight basis. These findings demonstrate that optimized PFPP wastewater can effectively support high-density cultivation of C. vulgaris var. vulgaris TISTR 8261, yielding nutritionally rich biomass, and offering a cost-effective and environmentally sustainable strategy for industrial-scale microalgal production. Full article

We address surrogate-assisted multi-objective optimization for computationally expensive structural designs. The testbed is an axisymmetric laminated composite shell whose geometry, ply angles, and plywise materials are optimized to simultaneously (i) maximize separation of selected natural frequencies from a known excitation and (ii) minimize material cost. To reduce high-fidelity (HF) finite element evaluations, we develop a deep neural network surrogate framework with three variants: an HF-only baseline; a multi-fidelity (MF) pipeline using an auxiliary refinement network to convert abundant low-fidelity (LF) data into pseudo-HF labels for a single-fidelity evaluator; and a cascaded ensemble that emulates HF responses and then maps them to pseudo-experimental targets. During optimization, only surrogates are queried—no FEM calls—while final designs are verified by FEM. Pareto-front quality is quantified primarily by a normalized relative hypervolume indicator computed against an envelope approximation of the True Pareto Front, complemented where appropriate by standard indicators. A controlled training protocol and common validation regime isolate the effect of fidelity strategy from architectural choices. Results show that MF variants markedly reduce HF data requirements and improve Pareto-front quality over the HF-only baseline, offering a practical route to scalable, accurate design under strict computational budgets. Full article

This study focuses on the mathematical method developed by integrating the surrogate model as constraints for wall stability into the heuristic optimization algorithm to gain the optimum cost and ${\mathrm{CO}}_2$ emission value of the gabion retaining wall (GRW). This study also includes the comparison of optimum GRW results with optimum cantilever retaining wall (CRW) designs for different design cases. The Harmony Search Algorithm (HSA), which efficiently explores the design space and robustly reaches the optimum result in solving optimization problems, was used as the heuristic optimization algorithm. The primary construction scenario was considered as an optimization problem, which involved excavating the slope, constructing the wall, and compacting the backfill soil to minimize the cost and CO${}_2$ emissions for separate objective functions of GRW and CRW designs. Comparative results show that GRWs outperform CRWs in terms of sustainability and cost-efficiency, achieving 55% lower cost and 78% lower CO${}_2$ emissions on average, while the HSA–surrogate model provides a fast and accurate solution for geotechnical design problems. The surrogate models for sliding, overturning, and slope stability safety factors of GRW exhibited exceptional accuracy, characterized by minimal error values (MSE, RMSE, MAE, MAPE) and robust determination coefficients ($R^2\approx 0.99$), hence affirming their dependability in safety factor assessment. By integrating the surrogate model based on the statistical method into the optimization algorithm, a quick examination of the wall’s stability was performed, reducing the required computational power. Full article

The integration of unmanned aerial vehicles (UAVs) and deep learning (DL) has significantly advanced crop disease detection by enabling scalable, high-resolution, and near real-time monitoring within precision agriculture. This systematic review analyzes peer-reviewed literature indexed in the Web of Science Core Collection as articles or proceeding papers through 2024. The main selection criterion was combining “unmanned aerial vehicle*” OR “UAV” OR “drone” with “deep learning”, “agriculture” and “leaf disease” OR “crop disease”. Results show a marked surge in publications after 2019, with China, the United States, and India leading research contributions. Multirotor UAVs equipped with RGB sensors are predominantly used due to their affordability and spatial resolution, while hyperspectral imaging is gaining traction for its enhanced spectral diagnostic capability. Convolutional neural networks (CNNs), along with emerging transformer-based and hybrid models, demonstrate high detection performance, often achieving F1-scores above 95%. However, critical challenges persist, including limited annotated datasets for rare diseases, high computational costs of hyperspectral data processing, and the absence of standardized evaluation frameworks. Addressing these issues will require the development of lightweight DL architectures optimized for edge computing, improved multimodal data fusion techniques, and the creation of publicly available, annotated benchmark datasets. Advancements in these areas are vital for translating current research into practical, scalable solutions that support sustainable and data-driven agricultural practices worldwide. Full article

Background: Cancer remains a leading cause of morbidity and mortality worldwide, and current therapies are limited by toxicity, cost, and resistance. Inhibition of the MDM2–p53 interaction is a promising anticancer strategy, as this pathway is frequently dysregulated across tumors. Spiro-isatin-thiazolidinone derivatives have shown diverse biological activities, including anticancer effects, but require optimization to improve potency and selectivity. The aims were to design, synthesize, and evaluate novel spiro-isatin-thiazolidinone hybrids with enhanced cytotoxicity against cancer cells and reduced toxicity toward normal cells. Methods: Derivatives were designed using molecular docking against MDM2, followed by structural optimization. Cytotoxic activity was evaluated in vitro by MTT assays on human and murine cancer cell lines and pseudo-normal cells. Docking and 100 ns molecular dynamics simulations assessed binding stability, while ADMET properties were predicted in silico. Results: Several derivatives exhibited micromolar cytotoxicity, with compound 18 emerging as the most potent and selective candidate (IC₅₀ 6.67–8.37 µM across most cancer lines; >100 µM in HaCaT). Docking showed a strong affinity for MDM2 (−10.16 kcal/mol), comparable to the reference ligand, and stable interactions in simulations. ADMET predictions confirmed good oral bioavailability and moderate acute toxicity, fully compliant with Lipinski’s Rule of Five. Overall, the newly synthesized spiro-isatin-thiazolidinone hybrids, particularly compound 18, demonstrated potent and selective anticancer activity, favorable pharmacokinetic properties and a good toxicity profile. Full article

Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion is proposed for energy-efficient path planning. First, the improved algorithm introduces heuristic path information to guide the sampling process, enhancing the quality of sampled nodes. By defining a heuristic boundary, the search space is constrained to goal-relevant regions, thereby improving path planning efficiency. Secondly, focused sampling and reconnection strategies are adopted to significantly enhance path quality while ensuring the global convergence of the algorithm. Combined with line segment sampling and probability control strategies, the algorithm balances global exploration and local refinement, further optimizing path selection. Finally, Bezier curves are applied to smooth the generated path, markedly improving path smoothness and feasibility. Comparative experiments conducted on a constructed three-dimensional simulation platform demonstrate that, compared to other algorithms, the proposed algorithm achieves significant optimization in planning time, path cost, number of path nodes, and number of random tree nodes, while generating smoother paths. Notably, under different operational modes, this study provides a quantitative evaluation of operational efficiency and energy consumption based on energy efficiency trade-offs, offering an effective technical solution for the intelligent operation of bridge cranes. Full article

Stricter requirements on nutrient removal in wastewater treatment are being imposed by rapid urbanization and tightening water-quality standards. Despite their excellent solid–liquid separation and effective biological treatment, MBRs in conventional operation remain hindered by membrane fouling, limited robustness to influent variability, and elevated energy consumption. In recent years, precise process control and resource-oriented operation have been enabled by the integration of artificial intelligence (AI) with MBRs. Advances in four areas are synthesized in this review: optimization of MBR control architectures, intelligent adaptation to multi-source wastewater, regulation of membrane operating parameters, and enhancement of nitrogen and phosphorus removal. According to reported studies, increases in total nitrogen and total phosphorus removal have been achieved by AI-driven strategies while energy use and operating costs have been reduced; under heterogeneous influent and dynamic operating conditions, stronger generalization and more effective real-time regulation have been demonstrated relative to traditional approaches. For large-scale deployment, key challenges are identified as improvements in model interpretability and applicability, the overcoming of data silos, and the realization of multi-objective collaborative optimization. Addressing these challenges is regarded as central to the realization of robust, scalable, and low-carbon intelligent wastewater treatment. Full article

Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar tracking with a spray-cooling and cleaning subsystem and an active wind-protection strategy that automatically flattens the array when wind speed exceeds 8.0 m/s. Temperature, wind speed, and irradiance sensors are coordinated by an Arduino-based supervisor to optimize tracking, thermal management, and tilt control. A 10 W floating module and a fixed-tilt reference were fabricated and tested outdoors in Penghu, Taiwan. The FPV achieved a 25.17% energy gain on a sunny day and a 40.29% gain under overcast and windy conditions, while module temperature remained below 45 °C through on-demand spraying, reducing thermal losses. In addition, a hybrid energy storage system (HESS), integrating a 12 V/10 Ah lithium-ion battery and a 12 V/24 Ah lead-acid battery, was validated using a priority charging strategy. During testing, the lithium-ion unit was first charged to stabilize the control circuits, after which excess solar energy was redirected to the lead-acid battery for long-term storage. This hierarchical design ensured both immediate power stability and extended endurance under cloudy or low-irradiance conditions. The results demonstrate a practical, low-cost, and modular pathway to couple FPV with hybrid storage for coastal energy resilience, improving yield and maintaining safe operation during adverse weather, and enabling scalable deployment across cage-aquaculture facilities. Full article