Search Results (15,239)

An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However, little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, here the influence of experimental test pulse characteristics on the parameterized ECM accuracy was systematically studied. The test pulse duration was varied in a wide range from 9 s to about 2.5 min. The portion of the relaxation phase data used by the parameter optimization algorithm was also varied in an even wider range. Total 168 ECM parameter sets were obtained. Each parameter set was validated using nine diverse current profiles representing different battery operation conditions, including one based on Urban Dynamometer Driving Schedule (UDDS). The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point that it can overshadow the use of a higher-order Thevenin model. By choosing the optimal parameter set, the simulated voltage root mean square error (RMSE) was reduced to as low as 3.0 mV and 1.2 mV for first- and second-order ECM, respectively, while the second-order model based on arbitrary chosen test pulse on average yields RMSE value above 5 mV. Full article

To promote the transition to a cleaner energy structure and support the achievement of the “carbon peak and carbon neutrality” goals, concentrated solar power (CSP) technology has attracted increasing attention. The molten salt globe valve, as a key control component in CSP systems, faces significant challenges related to low-temperature salt crystallization and thermal stress control. This study proposes an active electromagnetic induction heating method based on a triangular double-helix cross-section coil to address issues such as molten salt blockage in the seal bellows and excessive thermal stress during heating. First, electromagnetic simulation comparisons show that the ohmic loss of the proposed coil is approximately 3.5 times and 1.8 times higher than that of conventional circular and rectangular coils, respectively, demonstrating superior heating uniformity and energy efficiency. Second, transient electromagnetic-thermal-fluid-structure multiphysics coupling analysis reveals that during heating, the temperature in the bellows seal region stabilizes above 543.15 K, exceeding the solidification point of the molten salt, while the whole valve reaches thermal stability within about 1000 s, effectively preventing local solidification. Finally, thermal stress analysis indicates that under a preheating condition of 473.15 K, the transient thermal shock stress on the valve body and bellows is reduced by 266.84% and 253.91%, respectively, compared with the non-preheating case, with peak stresses remaining below the allowable stress limit of the material, thereby significantly extending the service life of the valve. This research provides an effective solution for ensuring reliable operation of molten salt valves and improving the overall performance of CSP systems. Full article

Thermal comfort and energy efficiency are two main goals of heating, ventilation, and air conditioning (HVAC) systems, which use about 40% of the total energy in buildings. This paper aims to predict optimal room temperature, enhance comfort, and reduce energy consumption while avoiding extra energy use from overheating or overcooling. Six Machine Learning (ML) models were tested to predict the optimal temperature in the classroom based on the occupancy characteristic detected by a Deep Learning (DL) model, You Only Look Once (YOLO). The decision tree achieved the highest accuracy at 97.36%, demonstrating its effectiveness in predicting the preferred temperature. To measure energy savings, the study used RETScreen software version 9.4 to compare intelligent temperature control with traditional operation of HVAC. Genetic algorithm (GA) was further employed to optimize HVAC energy consumption while keeping the thermal comfort level by adjusting set-points based on real-time occupancy. The GA showed how to balance comfort and efficiency, leading to better system performance. The results show that adjusting from default HVAC settings to preferred thermal comfort levels as well controlling the HVAC to work only if the room is occupied can reduce energy consumption and costs by approximately 76%, highlighting the substantial impact of even simple operational adjustments. Further improvements achieved through GA-optimized temperature settings provide additional savings of around 7% relative to preferred comfort levels, demonstrating the value of computational optimization techniques in fine-tuning building performance. These results show that intelligent, data-driven HVAC control can improve comfort, save energy, lower costs, and support sustainability in buildings. Full article

As global agriculture seeks to reconcile the dual imperatives of food security and environmental sustainability, this study examines the role of Internet access in promoting green agricultural production, specifically by reducing fertilizer and pesticide use. Using a panel dataset from 16 rural fixed observation points in Henan Province from 2009 to 2022, we find that Internet access significantly lowers per-unit farmland expenditures on fertilizers and pesticides by 6.0% and 7.3%, respectively. Mechanism analysis reveals that these positive effects operate through three main channels: improved information accessibility delivers timely agricultural data and guides input decisions; enhanced technical learning efficiency reduces barriers to adopting green technologies; and stronger market connectivity via e-commerce platforms shortens supply chains and provides price incentives. Heterogeneity analysis further identifies more pronounced effects among farmers with higher human capital (higher education, better health, younger age), higher production capital (greater mechanization, larger farmland, stronger decision-making capacity), lower livelihood capital (lower income, lower consumption, less communication expenditure), and higher spatial capital (residing in urban suburbs, poverty registration villages, and traditional villages). This study provides micro evidence for digital technology to empower sustainable agricultural development and provides policy implications for building a sustainable agri-food system. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

Designing thermal–fluid devices that reduce peak temperature while limiting pressure loss is challenging because high-fidelity (HF) Navier–Stokes–convection simulations make direct HF-driven topology optimization computationally expensive. This study presents a two-dimensional, steady, laminar multifidelity topology design framework for thermal–fluid devices operating in a low-to-moderate Reynolds number regime. A computationally efficient low-fidelity (LF) Darcy–convection model is used for topology optimization, where SEMDOT decouples geometric smoothness from the analysis field to produce CAD-ready boundaries. The LF optimization minimizes a P-norm aggregated temperature subject to a prescribed volume fraction constraint; the inlet–outlet pressure difference and the P-norm parameter are varied to generate a diverse candidate set. All candidates are then evaluated using a steady incompressible HF Navier–Stokes–convection model in COMSOL 6.3 under a consistent operating condition (fixed flow; pressure drop reported as an output). In representative single- and multi-channel case studies, SEMDOT designs reduce the HF peak temperature (e.g., ~337 K to ~323 K) while also reducing the pressure drop (e.g., ~18.7 Pa to ~12.6 Pa) relative to conventional straight-channel layouts under the same operating point. Compared with a conventional RAMP-based pipeline under the tested settings, the proposed approach yields a more favorable Pareto distribution (normalized hypervolume 1.000 vs. 0.923). Full article

Nanoparticle–surfactant composite flooding systems significantly enhance oil recovery through synergistic effects. When the optimal ratio of SiO₂ nanoparticles to nonionic surfactant alkylphenol polyoxyethylene ether (OP-10) in the composite system is 3:2, the oil–water interfacial tension (IFT) decreases to 0.005 mN/m, and the contact angle changes from the original 128° to 42°, achieving effective wettability alteration. Core displacement experiments demonstrate that the recovery rate using nanoparticles alone is 46.8%, and using surfactant alone is 52.3%, while the composite system achieves 71.5%, representing a 39.2 percentage point improvement over water flooding. The composite system operates through multiple mechanisms including interfacial tension reduction, wettability alteration, stable emulsion formation, and enhanced sweep efficiency. The wedging effect of nanoparticles at pore throats and the interfacial activity of surfactants form significant synergistic enhancement, providing a new technical pathway for efficient development of low-permeability reservoirs. Full article

This study systematically investigates the kinematic characteristics and static stability of a spatial under-constrained four-cable-driven parallel mechanism, specifically designed for supporting aircraft models in wind tunnel tests. Addressing the inherent strong coupling between kinematics and statics in such systems, an integrated solution framework is proposed. Firstly, a hybrid intelligent algorithm integrating genetic algorithm, chaos optimization, and particle swarm optimization is introduced to efficiently solve the direct and inverse geometric-statics problems, ensuring the identification of physically feasible equilibrium configurations under constraints such as cable tension limits and mechanical interference. Subsequently, a stability evaluation method based on the eigenvalue analysis of the system’s total stiffness matrix is employed, establishing a criterion (minimum eigenvalue λ_min > 0) to identify statically stable equilibrium points. Finally, the static feasible workspace and the static stable workspace are systematically analyzed and quantified, providing practical operational limits for mechanism design and trajectory planning. The effectiveness of the proposed solution framework is validated through numerical computations, simulations, and experimental tests, demonstrating its superiority over benchmark methods. This study provides theoretical support for the design, analysis, and control of under-constrained four-cable-driven parallel mechanisms. Full article

Rotation-invariant (RI) point cloud models aim to reduce sensitivity to viewpoint changes, but their performance still drops noticeably in real-world settings when local geometry is degraded by noise, occlusion, and uneven sampling. Once these disturbances propagate through deeper layers, they can lead to significant robustness degradation, especially for high-capacity RI backbones. To address this problem, we propose AGSM-CPA (Adaptive Geometric Signal Modulation with Cross-Perturbation Alignment), a lightweight and plug-and-play framework that enhances the robustness of RI models without altering their core convolutional operators. It integrates two complementary modules: the Geometric Signal-to-Noise Ratio (G-SNR) modulation mechanism, which adaptively suppresses unreliable neighborhoods based on local coordinate variance, and the Cross-Perturbation Semantic Consistency Alignment (CP-SCL) module, which enforces prediction consistency between weakly augmented inputs and strongly corrupted ones. We evaluate AGSM-CPA on ModelNet40, ScanObjectNN, and ShapeNetPart. Across standard corruption protocols, AGSM-CPA consistently improves robustness while maintaining competitive clean accuracy with negligible computational overhead. These results indicate that AGSM-CPA offers a practical, reliability-aware adapter for robust rotation-invariant point cloud learning. Full article

Cracking occurred in the surface coating of a screw rotor during shale gas well operations. To determine whether the coating cracks could contribute to the failure of the 42CrMo substrate, the microstructure and morphology of surface cracks and local corrosion pits were examined and analyzed using a metallographic microscope, an SEM, and an EDS. To investigate the cross-sectional morphology and elemental distribution of corrosion pits, EDS mapping was performed. The composition of the corrosion products was characterized using Raman spectroscopy and XPS. In addition, four-point bend stress corrosion tests were conducted on screw rotor specimens under simulated service conditions. The results indicate that the P and S contents in the screw rotor substrate exceeded the specified limits, whereas its tensile and impact strengths satisfied the standard requirements. The microstructure consisted of tempered sorbite and ferrite, along with a small amount of sulfide inclusions. The corrosion products on the fracture surface were primarily identified as FeOOH, Fe₃O₄, and Cr(OH)₃. All specimens failed during the four-point bend tests. The chlorine (Cl) content in the corroded regions reached up to 8.05%. These findings demonstrate that the crack resistance of the 42CrMo screw rotor was markedly reduced under the simulated service conditions of 130 °C in a saturated, oxygenated 25% CaCl₂ solution. The study concludes that stress concentration induced by sulfide inclusions in the screw rotor, together with the combined effects of chloride ions, dissolved oxygen, and applied load, promotes the initiation and propagation of stress corrosion cracking. Therefore, it is recommended to strictly control the chemical composition and inclusion content of the screw rotor material and to reduce the oxygen content of the drilling fluid, thereby mitigating the risk of corrosion-induced cracking of the rotor. Full article

To address the current reliance on manual labor in traditional logistics sorting operations, which leads to low sorting efficiency and high operational costs, this study presents the design of an unmanned logistics vehicle based on the Robot Operating System (ROS). To overcome bounding-box loss issues commonly encountered by mainstream video-stream image segmentation algorithms under complex conditions, the novel SMITE video image segmentation algorithm is employed to accurately extract key regions of mail items while eliminating interference. Extracted logistics information is mapped to corresponding grid points within a map constructed using Simultaneous Localization and Mapping (SLAM). The system performs global path planning with the A* heuristic graph search algorithm to determine the optimal route, autonomously navigates to the target location, and completes the sorting task via a robotic arm, while local path planning is managed using the Dijkstra algorithm. Experimental results demonstrate that the SMITE video image segmentation algorithm maintains stable and accurate segmentation under complex conditions, including object appearance variations, illumination changes, and viewpoint shifts. The PaddleOCR text recognition algorithm achieves an average recognition accuracy exceeding 98.5%, significantly outperforming traditional methods. Through the analysis of existing technologies and the design of a novel parcel-grasping control system, the feasibility of the proposed system is validated in real-world environments. Full article

Within the scope of the study, the parameters of friction pendulum-type (FPS) isolators used or planned to be used in different projects were evaluated specifically for the project and its location. The evaluations were conducted within a performance-based seismic design framework using displacement, re-centering, and force-based operability criteria, as implemented through the Türkiye Building Earthquake Code (TBDY) 2018. The friction coefficient and radius of curvature were evaluated, along with the lower and upper limit specifications determined according to TBDY 2018. The planned control points were the period of the isolator system, the isolator re-centering control, and the ratio of the base shear force to the structure weight. Within the scope of the study, isolator groups with different axial load values and different spectra were evaluated. A dataset was prepared by using the parameters obtained from the re-centering, period, and shear force analyses to determine the conditions in which the isolator continued to operate and those in which conditions prevented its operation. Machine learning models were developed to identify FPS isolator configurations that do not satisfy the code-based operability criteria, based on isolator properties, spectral acceleration coefficients corresponding to different earthquake levels, mean dead and live loads, and the number of isolators. The resulting Bagging model predicted an isolator’s operability with a high degree of accuracy, reaching 96%. Full article

Pixelwise dense representations are more prevalent in the field of iris recognition, also known as iris templates or IrisCodes. Almost all previous works of this kind are deterministic. To be specific, pixel-level representations are exclusively derived from certain point-by-point modeling, including filter responses, phase correlations, and ordinal relations. Moreover, the binary mask indicating valid iris regions is solely determined by a fixed threshold or the output of standalone segmentation and localization algorithms. Uncertainty in acquisition factors in the process of iris imagery formation is not considered. In this paper, we propose a simple yet effective plug-and-play building block termed dual dense uncertainty embedding (D²UE), which can be seamlessly incorporated into deep learning (DL) frameworks that extract dense representations for iris recognition. D²UE has two pathways wherein both take dense feature maps of the backbone network as input. One pathway of D²UE predicts a variance-scaling map (VSM) and then applies it to an adaptive threshold-masking operation on the iris image. The dynamic threshold for each pixel in this manner is dependent on not only the intensity distribution of the iris image but also each pixel’s low-level uncertainty. The other pathway of D²UE adopts an over-parameterization technique and extracts uncertainty-embedded dense representations (UEDRs) by modeling each pixel’s contextual uncertainty. Extensive experiments on several iris datasets demonstrate that recognition performance under both within-database and cross-database settings can be significantly improved by incorporating D²UE into the baseline method. By integrating D²UE into various deep learning frameworks and evaluating their performance across multiple datasets, the results demonstrate that D²UE can be seamlessly incorporated into diverse architectures and can significantly enhance their recognition capabilities. D²UE only incurs slight computational overhead while surpassing a few SOTA methods with a large backbone network and much more training budget. Full article

As the proportion of renewable energy generation in the power grid continues to rise, the operational state of the power system changes frequently with fluctuations in renewable power output. However, the traditional fixed-weight multi-objective reactive power optimization method lacks the necessary flexibility and adaptability, as it is unable to dynamically adjust the priority levels of different objectives based on real-time operating conditions (such as load fluctuations and changes in network structure). As a result, its optimization decisions may deviate from the system’s most urgent economic or security needs. To address this issue, this paper proposes an adaptive multi-objective reactive power optimization control method. The proposed approach formulates the objective function as the weighted sum of system active power loss and voltage deviation at the grid connection point, with weight coefficients adaptively adjusted based on the voltage deviation at the grid connection point. First, the relationship between voltage fluctuations at the offshore wind farm grid connection point and active/reactive power output is analyzed, and a corresponding reactive power allocation model is established. Second, taking into account the input–output characteristics of wind turbine generators and static var compensators, a reactive power control model is constructed. Third, considering offshore operational constraints such as power and voltage limits, a weighted variation particle swarm optimization algorithm (WVPSO) is developed to solve for the reactive power control strategy. Finally, the proposed method is validated through tests using a practical offshore wind farm as a case study. The test results demonstrate that, compared with the traditional fixed-weight multi-objective reactive power optimization approach, the proposed method can rapidly adjust the priority of each optimization objective according to the real-time grid conditions, achieving effective coordinated optimization of both active power loss and voltage at the grid connection point, and the voltage deviation is kept within 5%, even with power system fluctuations. In addition, compared with the traditional PSO algorithm, for various test situations, WVPSO exhibits above 15% improvement in solution speed and enhanced solution accuracy. Full article

Background/Objectives: Digital transformation is increasingly essential for healthcare organizations to improve operational efficiency and service quality. However, in developing countries such as Indonesia, many lower-middle-class hospitals lag due to limited financial, human, and infrastructural resources. This study examines how environmental dynamism—comprising regulatory changes, market pressures, and technological shifts—affects the digital capabilities of these hospitals. Methods: A quantitative, cross-sectional survey was conducted in Class C and D hospitals across Indonesia. Respondents included hospital directors, deputy directors, and IT heads. Data were collected through structured questionnaires measuring environmental dynamism and digital capability using a six-point Likert scale. Reliability testing yielded Cronbach’s alpha values above 0.96 for both constructs. Correlation analysis was performed to examine the relationship between environmental dynamism and digital capability. Results: Findings reveal a weak positive correlation (r = 0.1816) between environmental dynamism and digital capability. Although external factors such as policy regulations and technological competition encourage digital adoption, hospitals with limited internal resources struggle to translate these pressures into sustainable transformation. Key challenges include low ICT budgets, inconsistent staff training, and insufficient infrastructure. Conclusions: The results suggest that environmental change alone cannot drive digital readiness without internal capacity development. To foster resilient digital healthcare ecosystems, policy interventions should integrate regulatory frameworks with practical support programs that strengthen resources, leadership, and human capital in lower-middle-class hospitals. Full article