Search Results (1,562)

The Thailand 4.0 agenda elevates entrepreneurship education (EE) as a lever to escape the middle-income, inequality, and imbalance traps, yet EE remains weakly embedded in basic education—especially in Thai language. We designed and piloted a community-economy context-based learning model integrating EE (CEC-EE) for Grade 12 Thai via a two-cycle R&D process: needs analysis (surveys and focus groups with teachers and students) and prototype development. The model operationalizes six instructional steps (6Cs: connect, comprehend, clarify, construct, carry over, and conclude) anchored in Mae Chan’s community economy and targets entrepreneurial language skills (ELSs) consisting of analytical reading and creative writing. In a one-group pretest–posttest with Grade 12 students (n = 32), academic achievement and ELSs—analytical reading and creative writing—improved markedly. Posttest means exceeded pretests with very large effect. Experts rated the model appropriate, feasible, and useful; teachers and students reported high perceived value alongside concerns about implementation cost, support capacity, and student readiness. The CEC-EE model offers a context-responsive pathway for embedding EE in Thai-language instruction; future work should employ comparative designs, multi-site samples, and cost-effectiveness analyses to assess scalability and sustained impact. Full article

The traditional mantis search algorithm (MSA) suffers from limitations such as slow convergence and a high likelihood of converging to local optima in complex optimization scenarios. This paper proposes an improved mantis search algorithm (IMSA) to overcome these issues. An adaptive probability conversion factor is designed, which adaptively controls the proportion of individuals entering the search phase and the attack phase so that the algorithm can smoothly transition from large-scale global exploration to local fine search. In the search phase, a probability update strategy based on both subspace and full space is designed, significantly improving the adaptability of the algorithm to complex problems by dynamically adjusting the search range. The elite population screening mechanism, based on Euclidean distance and fitness double criteria, is introduced to provide dual guidance for the evolution direction of the algorithm. In the attack stage, the base vector adaptive probability selection mechanism is designed, and the algorithm’s pertinence in different optimization stages is enhanced by dynamically adjusting the base vector selection strategy. Finally, in the stage of sexual cannibalism, the directed random disturbance update method of inferior individuals is adopted, and the population is directly introduced through the non-greedy replacement strategy, which effectively overcomes the loss of population diversity. The experimental results of 29 test functions on the CEC2017 test set demonstrate that the IMSA exhibits significant advantages in convergence speed, calculation accuracy, and stability compared to the original MSA and the five best meta-heuristic algorithms. Full article

Complex interactions in soil carbon and nitrogen (C-N) synchronisation in tropical perennial orchards are highly responsive to fertiliser chemistry. However, the intensity and stage-specific dynamics of these interactions are not well quantified. Six nitrogen regimes, namely, urea (URT), ammonium (AMT), nitrate (NT), slow-release fertiliser (SRT), bio-organic fertiliser (BFT), and an unfertilised control, were assessed at the vegetative, flowering, fruit-set, and maturity stages of durian cultivated on highly weathered tropical soils. A two-way ANOVA indicated high to very high treatment × phenology interactions for almost all soil properties (p < 0.001), indicating that nutrient responses were highly stage-dependent. The highest soil organic carbon (SOC) and cation exchange capacity (CEC) values were consistently obtained with the BFT, which was often associated with significant differences compared with synthetic treatments. In contrast, the SRT showed the most consistent nutrient release behaviour, especially in flowering. On the other hand, soil pH did not differ significantly among the treatments during the vegetative and maturity stages. A significant decrease in pH was observed for the URT and NT treatments during the flowering stage, indicating temporary acidification at this stage and steep increases in nitrate nitrogen (NO₃^—N), indicating strong nitrification and attenuated carbon (C) stabilisation. Leaf nutrient responses were increased in phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) by 23% in response to the SRT and BFT. The NT and URT tended to enhance leaf nitrogen (N) primarily, and PCA (59–69% variance explained) clearly displayed clustering of the fertiliser effects, with the maximum difference at flowering, the peak period of nutrient demand in the crop. In general, fertiliser chemistry and phenophase jointly controlled the C-N partitioning, soil chemical paths, and nutrient yield correlations. The BFT and SRT showed the greatest significant gains in soil fertility and nutrient retention, making them the best high-performance alternatives in sustainable durian production in tropical systems. Full article

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I²C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation. Full article

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models. Full article

A targeted modification approach involving the synthesis of Ce/C co-doped TiO₂ aerogels (CeCTi) via a sol–gel method combined with supercritical CO₂ drying and subsequent heat treatment is employed to enhance the photocatalytic CO₂ reduction performance of cost-effective and stable TiO₂ aerogels. The results demonstrate that the CeCTi exhibits a pearl-like porous network structure, an optical band gap of 2.90 eV, and a maximum specific surface area of 188.81 m²/g. The black aerogel sample shows an enhanced light absorption capability resulting from the Ce/C co-doping, which is attributed to the formation of oxygen vacancies. Under simulated sunlight irradiation, the production rates of CH₄ and CO reach 27.06 and 97.11 μmol g⁻¹ h⁻¹ without any co-catalysts or sacrificial agents, respectively, which are 82.0 and 5.7 times higher than those of the pristine TiO₂ aerogel. DFT reveals that C-doping facilitates the formation of oxygen vacancies, which introduces defect states within the calculational band gap of TiO₂. The proposed photocatalytic mechanism involves the light-induced excitation of electrons from the valence band to the conduction band, their trapping by oxygen vacancies to prolong the charge carrier lifetime, and their subsequent transfer to adsorbed CO₂ molecules, thereby enabling efficient CO₂ reduction, which is experimentally supported by photoluminescence measurements. Full article

In the complex urban path planning of unmanned ground vehicles (UGVs), the dung beetle optimization (DBO) algorithm is widely used due to its simple structure and fast convergence speed. However, it still has the disadvantages of poor convergence accuracy and is easy to fall into a local optimum. To solve these problems, this paper proposes a multi-strategy enhanced DBO algorithm (ESDBO). Firstly, sine mapping is introduced in the population initialization stage to enhance solution diversity. Secondly, an adaptive information volatilization mutation strategy is proposed, which dynamically balances the convergence and global search ability. Finally, a multi-mechanism co-evolution strategy is designed, which significantly improves the local search ability and stability. Through ablation experiments and CEC2017 benchmark tests, the optimization ability of the proposed strategy and the convergence accuracy and stability of ESDBO are verified. Further path planning experiments are carried out on the public Random MAPF benchmark map. The results show that ESDBO can generate global optimal paths with short path length, few turns, and high safety margin on different obstacle densities and map scales. The algorithm provides an efficient and reliable solution for autonomous navigation in complex urban environments. Full article

The Superb Fairy-wren Optimization Algorithm (SFOA) is a meta-heuristic algorithm inspired by the behavior of the superb fairy-wren. However, the conventional SFOA tends to converge to local optima and exhibits limited convergence accuracy when addressing complex optimization problems. To overcome these drawbacks, this study proposes an Improved Superb Fairy-wren Optimization Algorithm (ISFOA). The ISFOA incorporates four strategies—Chebyshev chaotic mapping, an adaptive weighting factor, Cauchy–Gaussian mutation, and t-distribution perturbation—to enhance the algorithm’s ability to balance global exploration and local exploitation. An ablation study using the CEC 2021 test suite was performed to evaluate the individual contribution of each strategy. Moreover, to comprehensively assess the performance of ISFOA, a comparative analysis was carried out against eight other meta-heuristic algorithms on both the CEC2005 and CEC2021 benchmark function sets. Additionally, the practical applicability of ISFOA was examined by comparing it with eight other optimization algorithms across seven engineering design problems. The comprehensive experimental results indicate that ISFOA outperforms the original SFOA and other compared algorithms in terms of robustness and convergence accuracy, thereby offering an efficient and reliable approach for solving complex optimization problems. Full article

In this study, an enhanced variant of the Secretary Bird Optimization Algorithm (SBOA), named MSVSBOA, is proposed to address the limitations of the SBOA in global optimization and UAV 3D path-planning. The proposed MSVSBOA integrates three complementary strategies to achieve a balanced exploration and exploitation trade-off. First, a Levy-based Directed Exploration mechanism is introduced to enrich the global search capability and prevent premature convergence. Second, a spiral movement mechanism is incorporated to strengthen the local exploitation behavior and improve convergence accuracy. Third, a Differential Evolution-inspired refinement strategy (DE-Refinement) is employed to accelerate fine-grained exploitation during the later stages of optimization. The performance of the MSVSBOA is extensively evaluated on the CEC 2014 and CEC 2022 benchmark suites. Experimental results demonstrate that the MSVSBOA achieves superior accuracy, faster convergence, and improved robustness compared to the SBOA and other multi-strategy variants. Furthermore, the MSVSBOA is applied to a challenging UAV 3D path planning problem, where it successfully generates safe, smooth, and collision-free trajectories while outperforming competing algorithms. These findings confirm the effectiveness of the proposed MSVSBOA for both global optimization problems and real-world UAV applications. Full article

Background/Objectives: Folk dance represents an educational and cultural practice that is capable of promoting psychological well-being, social cohesion, and identity formation. However, few studies have integrated students’ voices regarding their lived experiences in these practices. This study aimed to analyze the perceptions of children and adolescents about their participation in school folk dances, exploring their impact on psychological well-being, self-confidence, and body awareness. Methods: A qualitative study with an exploratory and descriptive design was conducted with a purposive sample of 76 elementary and secondary school students who participated in the School Folk Dance Encounter “Heartbeats of My Land”, organized by the Metropolitan University of Educational Sciences (Chile). Semi-structured interviews were applied, and a thematic analysis was performed on 285 statements, organized into two dimensions: Psychological Well-being and Self-Confidence (PWS) and Body Awareness, Expression, and Communication (CEC). Results: The analysis revealed a predominance of the (PWS) dimension (85.3%), focused on positive emotions, self-confidence, and emotional regulation. Students’ testimonies highlighted dance as a means of release, self-esteem, and joy. To a lesser extent (14.7%), the (CEC) dimension reflected the perception of the body as a vehicle for communication and symbolic expression. Conclusions: Folk dance emerges as an integral pedagogical space that enhances emotional well-being, self-confidence, and cultural identity. Its systematic inclusion in Physical Education is proposed as a strategy to foster meaningful learning, mental health, and social cohesion. Full article

To address the complex multi-objective optimization problem of “cost–risk–recovery–dilution” in sublevel caving without bottom pillars under uncertainty, this study develops an operational GOOSE-based framework (OGOOSE) integrated with robust ε-constraint modeling. Methodologically, OGOOSE adopts three synergistic mechanisms: Opposition-Based Learning (OBL) for enhanced initial solution quality and spatial coverage symmetry, an Adaptive Inertia Weight (AIW) mechanism to maintain a symmetrical balance between exploration and exploitation, and a Boundary Reflection Mechanism (BRM) to ensure engineering feasibility. For modeling, an “ellipsoid-plane” geometric surrogate is employed, where the ellipsoid’s structural symmetry serves as the ideal baseline, while the Mean-CVaR criterion quantifies the asymmetry of operational risk (negative tail) under uncertainty. Taking robust cost (C) as the primary objective, the four-objective problem is decomposed via the ϵ-constraint method to enforce a balanced Pareto trade-off. Results demonstrate that OGOOSE significantly outperforms GOOSE, WOA, and HHO on CEC2017 benchmarks, achieving the lowest Friedman rank. In the engineering case study, it attains an average dilution rate of 28.95% (the lowest among comparators) without increasing unit cost or compromising recovery, demonstrating stable operational symmetry across economic and quality indicators. Sensitivity analysis of the ε-thresholds identifies an optimal “knee-point” that establishes a manageable balance between risk control (εR) and dilution limits (εP). OGOOSE effectively balances accuracy, stability, and interpretability, providing a robust tool for stabilizing complex mining systems against inherent operational asymmetry. Full article

In contemporary semiconductor manufacturing, wafer-handling robots are essential for achieving high-speed and high-precision wafer transportation. However, the demand for rapid motion and lightweight design introduces flexible transmission components that are prone to residual vibrations, which degrade positioning accuracy and system stability. To address this challenge, this paper proposes a vibration-suppression trajectory planning method based on the Gray Goose Optimization (GGO) algorithm. The proposed algorithm integrates grouped global search with local optimization capabilities, making it well suited for solving multi-objective optimization problems. Comparative tests conducted on eight randomly selected multimodal benchmark functions from the CEC2013 test suite verify the effectiveness and robustness of the GGO algorithm. Establishing a multi-objective function that considers both motion time and vibration energy enables the GGO algorithm to determine the switching time points of an S-shaped velocity profile, thereby generating smooth trajectories with continuous velocity and acceleration. By varying different initial conditions, the trade-off between motion time and vibration energy is systematically analyzed with respect to angular displacement, initial acceleration, and time-weighting factors. Simulation results indicate that the planned trajectories exhibit negligible displacement variation under zero-mean disturbances. The velocity error remains within 0.1 deg·s⁻¹, and the acceleration error is confined within 0.2 deg·s⁻². Consequently, Pareto-optimal solutions are successfully obtained with respect to both motion time and residual vibration energy. Full article

The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature convergence and an imbalance between exploration and exploitation when applied to complex engineering optimization problems. To overcome these limitations, this study proposes a modified Aquila Optimizer (m-AO) incorporating three enhancement strategies: an adaptive chaotic reverse learning mechanism to improve population diversity, an elite alternative pooling strategy to balance global exploration and local exploitation, and a shifted distribution estimation strategy to accelerate convergence toward promising regions of the search space. The performance of the proposed m-AO is evaluated using 23 classical benchmark functions, IEEE CEC 2022 benchmark problems, and a practical plate–fin heat exchanger (PFHE) design optimization problem. Numerical simulations demonstrate that m-AO achieves faster convergence, higher solution accuracy, and improved robustness compared with the original AO and several state-of-the-art metaheuristic algorithms. In the PFHE application, the proposed method yields a significant improvement in thermal performance, accompanied by a reduction in entropy generation and pressure drop under prescribed design constraints. Statistical analyses further confirm the superiority and stability of the proposed approach. These results indicate that the modified Aquila Optimizer is an effective and reliable tool for solving complex thermal system design optimization problems. Full article

This study evaluates the bioadsorption efficiency of sugarcane bagasse (SCB) for removing pharmaceuticals and illicit drugs—such as acetaminophen, atenolol, caffeine, carbamazepine, diclofenac, orphenadrine, losartan, enalapril, citalopram, cocaine, and benzoylecgonine—from wastewater effluents. In Brazil, where 46% of the population lacks access to sewage systems, and over 5.3 billion pharmaceutical packages are consumed annually, untreated discharges contribute significantly to aquatic contamination. Results show that applying SCB biochar at a 1% (m/v) ratio removes up to 99.8% of these compounds at total concentrations of 140 ng mL⁻¹, reducing the ecological risk from high to low for caffeine and losartan. SCB offers several advantages as a bioadsorbent: it is abundant, non-toxic, inexpensive, easy to handle, and exhibits high adsorption capacity and rapid kinetics across a wide range of chemical polarities. These findings highlight SCB’s potential as a sustainable and efficient material for wastewater treatment applications. Full article

In this study, the nonlinear and non-monotonic behavior of amperometric bienzyme biosensors employing an enzymatic trigger reaction is investigated analytically and computationally using a two-compartment model comprising an enzymatic layer and an outer diffusion layer. The trigger enzymatic reaction is coupled with a cyclic electrochemical–enzymatic conversion (CEC) process. The model is formulated as a system of reaction–diffusion equations incorporating nonlinear Michaelis–Menten kinetics and interlayer partitioning effects. Exact steady-state analytical solutions for substrate and product concentrations, as well as for the output current, are obtained for specific cases of first- and zero-order reaction kinetics. At the transition conditions, biosensor performance is further analyzed numerically using the finite difference method. The CEC biosensor exhibits the highest signal gain when the first enzyme has low activity and the second enzyme has high activity; however, under these conditions, the response time is the longest. When the first enzyme possesses a higher substrate affinity (lower Michaelis constant) than the second, the biosensor demonstrates severalfold higher current and gain compared to the reverse configuration under identical diffusion limitations. Furthermore, increasing external mass transport resistance or interfacial partitioning can enhance the apparent signal gain. Full article