Search Results (27,337)

To address the problem of degraded positioning accuracy in traditional visual–inertial navigation systems (VINS) due to interference from moving objects in dynamic scenarios, this paper proposes an improved algorithm based on the VINS-Fusion framework, which resolves this issue through a synergistic combination of multi-scale feature optimization and real-time dynamic feature elimination. First, at the feature extraction front-end, the SuperPoint encoder structure is reconstructed. By integrating dual-branch multi-scale feature fusion and 1 × 1 convolutional channel compression, it simultaneously captures shallow texture details and deep semantic information, enhances the discriminative ability of static background features, and reduces mis-elimination near dynamic–static boundaries. Second, in the dynamic processing module, the ASORT (Adaptive Simple Online and Realtime Tracking) algorithm is designed. This algorithm combines an object detection network, adaptive Kalman filter-based trajectory prediction, and a Hungarian algorithm-based matching mechanism to identify moving objects in images in real time, filter out their associated dynamic feature points from the optimized feature point set, and ensure that only reliable static features are input to the backend optimization, thereby minimizing pose estimation errors caused by dynamic interference. Experiments on the KITTI dataset demonstrate that, compared with the original VINS-Fusion algorithm, the proposed method achieves an average improvement of approximately 14.8% in absolute trajectory accuracy, with an average single-frame processing time of 23.9 milliseconds. This validates that the proposed approach provides an efficient and robust solution for visual–inertial navigation in highly dynamic environments. Full article

This work addresses the tracking control problem of nonstrict-feedback nonlinear systems affected by unmodeled dynamics and input delays, which significantly complicate controller design and degrade system performance. To overcome these challenges, a predefined-time adaptive control framework is developed. A command-filtered backstepping scheme is employed to reduce computational complexity, while an error compensation mechanism is introduced to counteract the inaccuracies caused by command filtering. The unknown nonlinear dynamics are approximated using radial basis function-based estimators, and a dynamic auxiliary signal is designed to mitigate the effects of unmodeled dynamics. Input delays are handled by integrating Padé approximation with an intermediate compensating variable. The proposed control strategy guarantees uniform boundedness of all closed-loop signals and ensures that the tracking error converges to a small neighborhood of the desired trajectory within a predefined time. Simulation results and comparative studies are provided to demonstrate the effectiveness and advantages of the proposed method. Full article

Hydrogen (H₂) is a key clean energy carrier for achieving carbon neutrality, featuring both cleanliness and high efficiency. Biomass-to-hydrogen technologies, with the advantages of strong renewability and low emissions, provide a highly promising alternative to fossil fuel-based hydrogen production. This review summarizes the main pathways and latest research progress in catalytic hydrogen production from biomass, focusing on the role of catalysts and optimization directions in the two major processes of thermochemical and biochemical methods. Despite the rapid development in this field, the large-scale application of biomass-to-hydrogen technologies is still limited by issues such as catalyst deactivation, feedstock composition fluctuations, and low energy efficiency. Traditional biomass-to-hydrogen technologies cannot achieve breakthrough progress in large-scale production in the short term; however, through coupled emerging technologies like biomass electrooxidation for hydrogen production and on-site hydrogen production via aqueous ethanol reforming, biomass-based hydrogen production is expected to solve problems such as low energy efficiency and high transportation difficulties, thereby making an important contribution to the construction of a green and low-carbon hydrogen economy system. Future research should focus on the rational design of stable nanocatalysts, artificial intelligence-driven research and development as well as advanced characterization technologies and the application of integrated systems and process innovation, along with diverse feedstocks and high-value-added product systems. Full article

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

Unmanned aerial vehicle (UAV) tracking faces significant challenges due to small targets and background interference. Traditional anchor-based tracking algorithms require designing numerous proposals to capture such tiny targets, which entails unacceptable computational overhead. On the other hand, anchor-free tracking methods struggle to adapt to target scale variations, resulting in suboptimal tracking accuracy in anti-UAV tracking scenarios. To address these limitations, we pioneer the integration of diffusion models into visual tracking, proposing SiamDiff—a scale-adaptive anti-UAV framework. We reformulate the tracking task as a bounding box prediction problem, where a diffusion model is leveraged to generate scale-adaptive proposals. Furthermore, we propose a Learnable Mask Module (LMM) and a Frequency Channel Fusion Module (FCFM) to enhance discriminative feature extraction for small targets. Additionally, we design a Scale-Aware Diffusion Strategy (SADA) to boost robustness to scale variations. Experimental results on the Anti-UAV and Anti-UAV410 benchmarks demonstrate the effectiveness of our approach, achieving a State Accuracy (SA) of 71.90% and 67.03%, respectively, outperforming the baseline and other trackers. Moreover, our method shows superior adaptability to scale variations, confirming its robustness in complex anti-UAV tracking scenarios. Full article

Although the use of adaptive pulleys enhances the motion characteristics of cable-driven parallel robots (CDPRs), it significantly increases the complexity of the kinematics model. Conventional methods often fail to account for the influence of adaptive pulley motion on cable length variation, making it difficult to establish a precise kinematics model. To deal with the problem, this study presents a kinematic modeling and solution method for CDPRs, which incorporates adaptive pulley kinematics. First, the structural design of the CDPR driven by eight cables is analyzed. Then, the generalized kinematics model and the improved kinematics model with adaptive pulley considerations are established. Furthermore, a hybrid Levenberg–Marquardt and Genetic algorithm is proposed to achieve the efficient and high-precision solution of kinematics equations by combining the rapid global search and precise local optimization. Finally, the proposed method is validated through straight path simulation and elliptical path simulation. The simulation results indicate that the tracking accuracy of the end-effector is better than the 1 × 10⁻⁷ level for the proposed method. Full article

This article delves into the fuzzy finite-time adaptive control problem for uncertain nonlinear systems where state measurements are unavailable, nonlinear functions are unknown, and communication is limited. To emulate the unknown nonlinear relationships within the control methodology, we exploit fuzzy logic systems, while also proposing a state observer to address the challenge of unobservable states. To avoid the “complexity explosion” problem intrinsic to conventional backstepping techniques, the controller is developed based on the dynamic surface control methodology, which incorporates first-order filters to successfully alleviate this issue. An event-triggered approach is introduced to alleviate the computational and communication overhead. By leveraging the finite-time control approach, an adaptive finite-time fuzzy control algorithm is constructed using the adaptive backstepping technique. An event-triggered mechanism is designed to reduce communication frequency, while rigorously maintaining closed-loop stability and ensuring a positive minimum inter-event time to avoid Zeno behavior. The proposed finite-time controller achieves finite-time stability of the controlled systems, thereby guaranteeing that all system signals remain bounded within a finite time, despite the presence of unmeasurable states, unknown nonlinear functions, and limited communication constraints. This paper differentiates itself from recent related studies by proposing a co-designed observer–controller framework that rigorously guarantees finite-time stability under an event-triggered communication mechanism, thereby effectively addressing the multiple concurrent challenges of state estimation, rapid convergence, and limited network resources. Simulation examples are conducted to illustrate the effectiveness and feasibility of the derived control algorithm. Full article

This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both $D=10$ and $D=20$. At $D=10$, it ranks first on all functions (average rank $1.00$; $12/12$ wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At $D=20$, the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests ($\alpha =0.05$) against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion. Full article

The current investigation examines pathways linking individual risk factors, namely maladaptive emotion regulation, behavior problems, and drinking motives, with adolescents’ alcohol consumption. In a cross-sectional design, 243 adolescents attending secondary school in Germany completed questionnaires. The Cognitive Emotion Regulation Questionnaire was used to assess maladaptive emotion regulation, the Strengths and Difficulties Questionnaire for assessing internalizing and externalizing behaviors, and the Drinking Motives Questionnaire Revised for assessing the four drinking motives, namely social, enhancement, coping, and conformity motives. Adolescents also reported their daily and problematic alcohol consumption. The path analysis revealed that maladaptive emotion regulation was positively associated with both internalizing and externalizing problems, and all four drinking motives. Externalizing problems were positively associated with adolescents’ enhancement and coping motives, and their alcohol consumption. Internalizing problems were only negatively associated with enhancement motives. Only coping motives were positively associated with alcohol consumption. Moreover, maladaptive emotion regulation had an indirect effect on alcohol consumption, via externalizing problems and coping motives. The findings emphasize the interactions between the risk factors in contributing to adolescent alcohol consumption, underscoring the importance of targeting emotion regulation and coping motives in substance use prevention efforts prior and during adolescence. Full article

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning. Full article

Automated assessment in education has seen rapid growth with the integration of AI, particularly for objective and structured tasks. However, evaluating open-ended design problems such as Entity Relationship (ER) diagrams and relational schemas remains a significant challenge due to the variability in valid representations. This paper proposes an AI-assisted framework using Large Language Models(LLMs) to interpret natural language database scenarios, generate reference ER diagrams and schemas in PlantUML format. and compare student submissions against the system generated solutions to assess correctness. We proposed a novel scoring mechanism for evaluating the semantic and structural similarity of entities, relationships, keys, and table mappings, rather than relying on exact syntax matching. Additionally, manual verification of AI-generated reference outputs enables human oversight and refinement, making the system a supportive tool rather than a replacement for educators. This approach offers scalable, intelligent evaluation for database design tasks, reducing the manual grading effort while ensuring fair and concept-driven assessment. Experimental results demonstrate the system’s effectiveness in accurately evaluating varied student submissions while maintaining adaptability across different design styles. Full article

Drought is a major problem affecting upland rice growth worldwide, including in northeast Thailand, with insufficient irrigation, where drought stress leads to reduced yields and may affect the functional compound content of rice grains. This research aimed to study the efficacy of arbuscular mycorrhizal fungi (AMF) Rhizophagus variabilis KS-02 and endophytic fungi (EPF) Trichoderma zelobreve PBMP16 on promoting the growth and accumulation of functional substances in upland black rice under drought conditions. Factorial experiments in a randomized complete block design (RCBD) were conducted by cultivating rice inoculated with AMF and EPF as well as co-inoculated with AMF+EPF under three watering conditions: 100% field capacity (FC), 66% FC, and 33% FC. The results show that both AMF, EPF improved some plant growth parameters and physiological performance under both well-watered and water-limited conditions. Inoculating plants with fungi increased the production of enzymes APX, CAT, and GR, as well as proline, which helps plants tolerate water deficit stress. Functional grain quality, including phenolic compounds, anthocyanins, and antioxidant activity, was also increased by fungal inoculation. While co-inoculation provided advantages for certain parameters, particularly antioxidant activity and biomass, single inoculation with AMF or EPF was equally effective or superior for specific traits depending on the level of water stress. Overall, this report shows that both AMF and EPF contribute to improving the productivity and functional quality of upland black rice under drought conditions, with treatment effects varying according to fungal type and water availability. Full article

This paper addresses the trajectory tracking problem for a six-degree-of-freedom (6-DOF) underwater manipulator subject to complex disturbances and input saturation. It proposes a fixed-time preset performance sliding mode control method considering input saturation (FT-PP-SMC-IS), aiming to achieve rapid and stable tracking performance under these constraints. Firstly, to improve modeling accuracy, the Newton–Euler method and Morison’s equation are integrated to establish a more precise dynamic model of the underwater manipulator. Secondly, to balance dynamic and steady-state performance, a preset performance function is designed to constrain the tracking error boundaries. Based on dual-limit homogeneous theory, a fixed-time sliding mode surface is constructed, significantly enhancing the convergence speed and fixed-time stability. Furthermore, to suppress the effects of input saturation, a fixed-time auxiliary system is designed to compensate in real-time for deviations caused by actuator saturation. By separately constructing the sliding mode reaching law and equivalent control law, global fixed-time convergence of the system states is ensured. Based on Lyapunov stability theory, the fixed-time stability of the closed-loop system is rigorously proven. Finally, comparative simulation experiments verify the effectiveness and superiority of the proposed method. Full article

Compared with the finite element method (FEM), the meshfree smoothed point interpolation method (SPIM) has a more accurate stiffness and is not sensitive to mesh distortion, which has high potential in solving engineering problems. In this study, an effective simulation program based on the face-based SPIM was developed and was applied to solve geomechanical problems. To enhance the reliability of the SPIM program when dealing with large-scale and nonlinear problems, the line search algorithm, the adaptive sub-step method, and the OpenMP parallel design were adopted to enhance the convergence, stability, and computational efficiency. The test results of the slope stability analysis show that the SPIM program is correct when compared with the Bishop method. Moreover, the SPIM program has an asymptotic quadratic convergence and satisfactory stability, even when the slope is in a critical state. In addition, for large-scale examples, the speedup ratio of the OpenMP parallel program can achieve a speedup ratio of 6~8 on a computing platform with 20 CPU cores, and the maximum speedup ratio for a single load step can reach 14.50. Finally, future work on the developing face-based SPIM simulation program is discussed. Full article

In response to the growing complexity and uncertainty in real-world decision-making, this study introduces a novel framework based on the polytopic fuzzy tensor (PFT) model, which unifies the geometric structure of polytopes with the representational power of fuzzy tensors. The PFT framework is specifically designed to handle high-dimensional, imprecise, and ambiguous information commonly encountered in multi-criteria group decision-making scenarios. To support this framework, we define a suite of algebraic operations, aggregation mechanisms, and theoretical properties tailored to the PFT environment, with comprehensive mathematical formulations and illustrative validations. The effectiveness of the proposed method is demonstrated through a real-world application involving the evaluation of six pancreatic cancer treatment strategies. These alternatives are assessed against five key criteria: quality of life, side effects, treatment accessibility, cost, and duration. Our results reveal that the PFT-based approach outperforms traditional fuzzy decision-making techniques by delivering more consistent, interpretable, and reliable outcomes under uncertainty. Moreover, comparative analysis confirms the model’s superior ability to handle multidimensional expert evaluations and integrate conflicting information. This research contributes a significant advancement in the field of fuzzy decision science by offering a flexible, theoretically sound, and practically applicable tool for complex decision problems. Future work will focus on improving computational performance, adapting the model for real-time data, and exploring broader interdisciplinary applications. Full article