Search Results (14,943)

The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained and calibrated using 6400 image fractions with a resolution of $120\times 90$ pixels. Different architectures are developed with a focus on two particular aspects. Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). Moreover, a hybrid model is employed, essentially a conjunction of a CNN with a multi-layer perceptron (MLP). The neural network’s input constitutes combined numerical and visual data, and its architecture primarily consists of seven dense layers and eight convolutional layers. By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. Secondly, the role of the pre-training process is investigated. The obtained CNN-MLP model can inherit the learning from a pre-trained multi-label CNN, initially developed for distinct features such as localization and number of passes. It is demonstrated that the pre-training effectively accelerates the learning process for the target feature. Therefore, it is concluded that appropriate architecture design and pre-training are essential for applying machine learning techniques to realistic problems. Full article

As global climate change and energy crises intensify, the pursuit of low-carbon integrated energy systems (IESs) has become increasingly important. This paper proposes an improved genetic algorithm (IGA) designed to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA incorporates circular crossover and polynomial mutation techniques, which not only preserve advantageous traits from the parent population but also enhance genetic diversity, enabling comprehensive exploration of potential solutions. Additionally, the algorithm selects parent populations based on individual fitness and dominance, retaining successful chromosomes and eliminating those that violate constraints. This process ensures that subsequent generations inherit superior genetic traits while minimizing constraint violations, thereby enhancing the feasibility of the solutions. To evaluate the effectiveness of the proposed algorithm, we tested it on three different IES scenarios. The results demonstrate that the IGA successfully reduces equality constraint violations to below 0.3 kW, representing less than 0.2% deviation from the IES’s power demand in each time slot. We compared its performance against a multi-objective genetic algorithm, a multi-objective particle swarm algorithm, and a single-objective genetic algorithm. Compared to conventional genetic algorithms, the IGA achieved maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm, demonstrating its superior performance in multi-objective optimization for low-carbon IESs. These outcomes underscore the algorithm’s reliability and practical applicability. Full article

Objectives: Metabolic dysfunction-associated steatohepatitis (MASH) lacks effective therapies. This study aimed to evaluate the therapeutic potential of compound 3d, a novel elafibranor derivative, focusing on its dual mechanisms of PPAR pathway activation and p38 MAPK signaling inhibition. Methods: Integrated in vitro and in vivo approaches were employed. In vitro, free fatty acid (FFA)-induced lipid accumulation in L02 hepatocytes and lipopolysaccharides (LPSs)-stimulated inflammatory responses in RAW264.7 macrophages were used to evaluate lipid metabolism and anti-inflammatory effects. In vivo, a high-fat diet (HFD)-induced MASH model in C57BL/6 mice assessed serum biochemical parameters (triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate transaminase (AST), tumor necrosis factor-α (TNF-α), nitric oxide (NO), and interleukin-6 (IL-6)), liver histopathology (H&E, Oil Red O, Masson staining), and proteomic profiling. Gut microbiota composition was analyzed via 16S rRNA sequencing. Western blotting quantified PPAR isoforms (γ/δ), downstream targets (Acox1, EHHADH, Acaa1), and p38 MAPK pathway proteins (p-p38, caspase-8, Bcl-2). Results: In vitro, 3d significantly reduced lipid accumulation (reduction in TG, p < 0.01) and inflammation (decrease in ALT activity, p < 0.05) in hepatocytes, while suppressing LPSs-induced TNF-α (63% reduction), NO (51% decrease), and IL-6 (48% reduction) in macrophages (p < 0.01). In vivo, 3d (30 mg/kg) lowered serum TG (39% decrease), TC (32% reduction), LDL-C (45% decline), and TNF-α (57% reduction) in HFD-fed mice (p < 0.05 vs. model), normalized AST/ALT levels, and ameliorated hepatic steatosis, ballooning, and fibrosis. Proteomics demonstrated PPARγ/δ activation (2.3–3.1-fold upregulation of Acox1, EHHADH, Acaa1; p < 0.001) and p38 MAPK pathway inhibition (54% reduction in p-p38, 61% decrease in caspase-8; 1.8-fold increase in Bcl-2; p < 0.01). Gut microbiota analysis revealed enrichment of beneficial taxa (Lactobacillus: 2.7-fold increase; Bifidobacterium: 1.9-fold rise) and reduced pathogenic Proteobacteria (68% decrease, p < 0.05). Conclusions: Compound 3d alleviates MASH via PPAR-mediated lipid metabolism enhancement and p38 MAPK-driven inflammation/apoptosis suppression, with additional gut microbiota modulation. These findings highlight 3d as a multi-target therapeutic candidate for MASH. Full article

Driven by the commercialization of intelligent ships, the increasingly complex mixed maritime traffic environment presents significant challenges for collision avoidance between multiple ships due to cognitive and behavioral differences between intelligent and traditional ships. Therefore, it is essential to develop a human-like collision avoidance strategy that incorporates traditional navigational experience and handling practices, enhancing explainability and autonomy. By addressing the actual decision-making needs for predicting other ships’ intentions and considering potential risk impacts, a hierarchical strategy is designed that first seeks course direction adjustment and then determines the magnitude of adjustment. A direction adjustment intention estimation model is proposed, accounting for risk membership and COLREGS, to predict other ships’ collision avoidance intentions. Additionally, an intention influence model and a state influence model are introduced to design decision-making objectives, forming an optimization function based on angle range and maneuvering time constraints to determine the appropriate adjustment magnitude. The results demonstrate the strategy’s effectiveness across various scenarios. Specifically, the distance between ships increased by nearly 25% during the process, significantly enhancing safety. It is worth mentioning that the model has the potential to enhance intelligent ships’ capabilities in complex situational handling and intention understanding. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Douglas fir (Pseudotsuga menziesii [Mirb.] Franco) is a valuable timber species native to western North America that was introduced to Europe in the 19th century. The objective of this study was to select the most valuable and stable Douglas fir provenances in Romania by combining growth and quality traits, using two indices recently used in forest tree species: the multi-trait genotype–ideotype distance index (MGIDI) and the multi-trait stability index (MTSI). The study was conducted across three common garden experiments in Romania, established in 1977, evaluating 61 provenances from the United States, Canada, Germany, France, and Romania. The analyzed traits were diameter at breast height (DBH), total height (TH), and pruned height (PH). Significant genotype–environment interactions were observed, with the Douglas fir showing superior growth performance in one of the testing sites in western Romania (Aleșd). The MGIDI and MTSI identified high-performing provenances from diverse geographic origins, including the Pacific Northwest, Europe, and Canada. Selection differentials ranged from 2.8% to 10.9% for individual traits, highlighting the potential for genetic improvement. The selected provenances represent valuable genetic resources of Douglas fir that are adapted to environmental conditions in the Carpathian region, contributing to the development of climate-adaptive breeding strategies and sustainable forest management. Full article

Developing spacecraft for efficient aerocapture missions demands managing extreme aerothermal environments, precise controls, and atmospheric uncertainties. Successful designs must integrate vehicle airframe considerations with trajectory planning, adhering to launcher dimension constraints and ensuring robustness against atmospheric and insertion uncertainties. To advance robust multi-objective optimization in this field, a new framework is presented, designed to rapidly analyze and optimize non-thrusting, fixed angle-of-attack aerocapture-capable spacecraft and their trajectories. The framework employs a three-degree-of-freedom atmospheric flight dynamics model incorporating planet-specific characteristics. Aerothermal effects are approximated using established Sutton–Graves, Tauber–Sutton, and Stefan–Boltzmann relations. The framework computes the resulting post-atmospheric pass orbit using an orbital element determination algorithm to estimate fuel requirements for orbital corrective maneuvers. A novel algorithm that consolidates multiple objective functions into a unified cost function is presented and demonstrated to achieve superior optima with computational efficiency compared to traditional multi-objective optimization approaches. Numerical examples demonstrate the methodology’s effectiveness and computational cost at optimizing terrestrial and Martian aerocapture maneuvers for minimum fuel, heat loads, peak heat transfers, and an overall optimal trajectory, including volumetric considerations. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

Multi-sensor fusion object detection is an advanced method that improves object recognition and tracking accuracy by integrating data from different types of sensors. As it can overcome the limitations of a single sensor in complex environments, the method has been widely applied in fields such as autonomous driving, intelligent monitoring, robot navigation, drone flight and so on. In the field of autonomous driving, multi-sensor fusion object detection has become a hot research topic. To further explore the future development trends of multi-sensor fusion object detection, we introduce the mainstream framework Transformer model of the multi-sensor fusion object detection algorithm, and we also provide a comprehensive summary of the feature fusion algorithms used in multi-sensor fusion object detection, specifically focusing on the fusion of camera and LiDAR data. This article provides an overview of feature fusion’s development into feature-level fusion and proposal-level fusion, and it specifically reviews multiple related algorithms. We discuss the application of current multi-sensor object detection algorithms. In the future, with the continuous advancement of sensor technology and the development of artificial intelligence algorithms, multi-sensor fusion object detection will show great potential in more fields. Full article

Background/Objectives: The present study aimed to investigate letter processing in children with dyslexia and typically developing readers as a function of the type of orthographic context. Methods and Results: In Experiment 1A, children performed a two-alternative forced choice task (Reicher–Wheeler paradigm) using as probes either high-frequency words, pronounceable pseudo-words, or unpronounceable non-words. The group differences in letter recognition were clearly distinguished from those present in typical word and pseudo-word reading conditions (Experiment 1B), as a global factor was present only in the latter case. In Experiment 2, the two-alternative forced choice task required the child to search for the target letter in the subsequent multi-letter string (i.e., words, pseudo-words, or non-words), thus reducing the memory load. Detecting the target letter was more difficult in a word than in a pseudo-word or non-word array, indicating that the word form’s lexical activation interfered with the target’s analysis in both groups of children. In Experiment 3, children performed the two-alternative forced choice task with symbols (Greek letters) either in the Reicher–Wheeler mode of presentation (Experiment 3A) or in the search condition (Experiment 3B). Children with dyslexia performed identically to typically developing readers in keeping with the selectivity of their orthographic difficulties. Conclusions: The present data indicate that children with dyslexia suffer from an early deficit in making perceptual operations that require the conjunction analysis of a set of letters. Still, this deficit is not due to an inability to scan the letter string. The deficit is confined to orthographic stimuli and does not extend to other types of visual targets. Full article

Road–stream crossings (RSCs) are vital for the sustainability of both stream ecosystems and transportation networks, yet many are aging, undersized, or failing. Limited funding and lack of stakeholder coordination hinder effective RSC management. This study develops a multi-objective optimization (MOO) framework utilizing the non-dominated sorting genetic algorithm (NSGA-II) to maximize and balance diverse stakeholder interests (i.e., environmental and transportation agencies) while minimizing management costs. MOO was used to identify optimal RSC management scenarios at a watershed scale, using the Piscataqua–Salmon Falls watershed, New Hampshire, as a testbed. It was found that MOO consistently outperformed the currently used scoring and ranking method by the environmental and transportation agencies, improving the environmental and transportation objectives by at least 19.56% and 37.68%, respectively, across all evaluated budget limits. These improvements translate to a maximum cost saving of USD 19.87 million under a USD 50 million budget limit. Structural conditions emerged as the most influential factor, with a Pearson coefficient of 0.60. This research highlights the potential benefits of a data-driven, optimization-based approach to sustainable RSC management. Full article

Background/Objectives: Insulin resistance during pregnancy is a key factor underlying gestational diabetes mellitus (GDM) and other adverse perinatal outcomes (APOs). While traditional markers, such as HOMA-IR, are used to evaluate insulin resistance, they may be inaccessible in resource-limited settings. The triglyceride–glucose (TyG) index has emerged as a practical alternative. This study aimed to assess whether or not a TyG index > 8.6 during the first trimester of pregnancy is associated with an increased risk of APOs, including GDM, preeclampsia, and other maternal and neonatal complications. Methods: A prospective cohort study was conducted involving 333 pregnant women in Mexico City, divided into two groups: Group 1 (TyG index > 8.6, n = 153) and Group 2 (TyG index ≤ 8.6, n = 180). Primary outcomes included gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy, preeclampsia, preterm birth, cesarean section, and large-for-gestational-age (LGA) and small-for-gestational-age (SGA) neonates. Logistic regression models were used to calculate the adjusted relative risk (aRR) and 95% confidence intervals (CIs), adjusting for maternal age, pregestational weight, and body mass index (BMI). Results: Women with a TyG index > 8.6 had a significantly higher pregestational weight and BMI than those with a TyG index ≤ 8.6. Group 1 demonstrated a higher risk of GDM (RR 2.05; 95% CI: 1.23–3.41) and preeclampsia (RR 2.15; 95% CI: 1.10–4.21). After adjusting for maternal age, pregestational weight, and BMI, these associations remained significant: GDM (aRR 1.87; 95% CI: 1.0–2.5) and preeclampsia (aRR 2.18; 95% CI: 1.1–5.0). No significant associations were found between an elevated TyG index and other APOs, including LGA, SGA, preterm birth, or cesarean delivery. Conclusions: A first-trimester TyG index > 8.6 is significantly associated with an increased risk of GDM and preeclampsia, highlighting its potential as a predictive marker for adverse perinatal outcomes. These findings underscore the utility of the TyG index as a practical, cost-effective tool for early risk stratification, particularly in resource-limited settings. Further multi-center research is needed to validate these results and refine population-specific thresholds. Full article

To address the challenges of excessive control pins and inefficient high-throughput droplet manipulation in conventional digital microfluidic chips, this study developed a parallel-motion digital microfluidic system integrated with an image acquisition device. The system employs an enhanced YOLOv8 object detection model for droplet recognition. By enabling parallel droplet transportation and processing, it significantly improves operational efficiency and detection accuracy. For droplet recognition, the YOLOv8 model was optimized through the integration of GAM_Attention and EMA mechanisms, which strengthen feature extraction capabilities and detection performance. Experimental results demonstrated that the optimized model achieves remarkable accuracy and robustness in droplet detection tasks, with mAP50 increasing from 96.5% to 98.7% and mAP50–90 improving from 65.8% to 68.5%. The system exhibits enhanced detection precision and real-time responsiveness, maintaining an error rate below 0.53%. Furthermore, a host computer interface was implemented for multi-droplet path planning and feedback, establishing a closed-loop control system. This work provides an efficient and reliable solution for high-throughput operations in microfluidic chip applications. Full article

Compressed natural gas (CNG) refueling stations operate under highly dynamic thermodynamic conditions, requiring accurate modeling and optimization to ensure efficient performance. In this study, a dynamic simulation model of a CNG station was developed using MATLAB-SIMULINK, including detailed subsystems for multi-stage compression, cascade storage, and vehicle tank filling. Real gas effects were incorporated to improve prediction accuracy of the pressure, temperature, and mass flow rate variations during fast filling. The model was validated against experimental data, showing good agreement in both pressure rise and flow rate evolution. A two-stage multi-objective optimization approach was applied using Taguchi experimental design and gray relational analysis (GRA). In the first stage, storage pressures were optimized to maximize the number of vehicles filled and gas mass delivered, while minimizing compressor-specific work. The second stage focused on optimizing the volume distribution among the low, medium, and high-pressure tanks. The combined optimization led to a 12.33% reduction in compressor-specific energy consumption with minimal change in refueling throughput. These results highlight the critical influence of pressure levels and volume ratios in cascade storage systems on station performance. The presented methodology provides a systematic framework for the analysis and optimization of transient operating conditions in CNG infrastructure. Full article

Assessing the visual quality of rural landscapes is pivotal for quantifying ecological services and preserving cultural heritage; however, conventional ecological indicators neglect emotional and cognitive dimensions. To address this gap, the present study proposes a novel visual quality assessment method for rural landscapes that integrates multimodal sentiment classification models to strengthen sustainability metrics. Four landscape types were selected from three representative villages in Dalian City, China, and the physiological signals (EEG, EOG) and subjective evaluations (Beauty Assessment and SAM Scales) of students and teachers were recorded. Binary, ternary, and five-category emotion classification models were then developed. Results indicate that the binary and ternary models achieve superior accuracy in emotional valence and arousal, whereas the five-category model performs least effectively. Furthermore, an ensemble learning approach outperforms individual classifiers in both binary and ternary tasks, yielding a 16.54% increase in mean accuracy. Integrating subjective and objective data further enhances ternary classification accuracy by 7.7% compared to existing studies, confirming the value of multi-source features. These findings demonstrate that a multi-source sentiment computing framework can serve as a robust quantitative tool for evaluating emotional quality in rural landscapes and promoting their sustainable development. Full article