Search Results (637)

Biopolymers and bioactive peptides of plant origin represent sustainable resources with high potential for the development of functional ingredients with health benefits. An underutilized plant source of antihypertensive peptides is lima bean protein (Phaseolus lunatus); however, these peptides can be inactivated or degraded during their passage through the gastrointestinal tract. This study evaluated chia (Salvia hispanica) mucilage (CM) combined with sodium alginate (Al) as a hybrid encapsulation matrix for ACE-inhibitory peptides (<10 kDa) from P. lunatus. The ionic gelation technique was used, and encapsulation conditions were optimized using a 2³ factorial design that evaluated CM:Al ratios, calcium concentration, and hardening time. The optimal formulation (30:70 CM:Al; 0.05 M CaCl₂; 20 min of hardening time) achieved approximately 48% encapsulation efficiency and maintained the peptides’ ACE-inhibitory (IC₅₀ mg/mL) activity during simulated gastric digestion with controlled intestinal release. The formed capsules demonstrated good flow properties, thermal stability up to 178 °C, and preserved ACE-I activity (0.1 mg/mL IC₅₀) significantly better than alginate alone after in vitro digestion. These findings suggest that CM:Al blends could produce capsules with the ability to protect bioactive peptides with low molecular weight, warranting further investigation through in vivo bioavailability studies and structural characterization to confirm the proposed matrix-enhancing mechanisms. Full article

During the excavation process of the foundation pit, soil parameters evolve dynamically. In order to improve the accuracy of soil parameter selection in foundation pit engineering and achieve accurate deformation prediction, this paper proposes a displacement inverse analysis method that combines the enhanced starfish optimization algorithm (ESFOA) and the hybrid kernel least squares support vector machine (LSSVM). The ESFOA improves the global search capability and convergence accuracy of the starfish optimization algorithm (SFOA) by optimizing the initial population and introducing a hunting mechanism. On this basis, the ESFOA was used to optimize the RBF kernel function width ($\sigma$), polynomial kernel coefficient ($q$), regularization penalty coefficient ($c$), and kernel function mixing weight ($\lambda$) of the hybrid kernel LSSVM model. Samples were obtained through finite element simulation and orthogonal experiments, and the optimized ESFOA-LSSVM model was used to establish the nonlinear mapping relationship between the horizontal displacement of the foundation pit excavation enclosure and the soil parameters. The horizontal displacement monitoring data of the foundation pit retaining structure is used to invert the soil parameters and predict the deformation of the retaining structure under subsequent conditions. The results show that (1) compared with other algorithms, the ESFOA has good global search capabilities and convergence accuracy; (2) the ESFOA-LSSVM model is tested through test samples, and the model has good accuracy and feasibility; (3) the parameters obtained by the inversion can effectively improve the prediction accuracy of foundation pit deformation, and the prediction results are closer to the actual monitoring values. Full article

Intelligent transportation systems increasingly rely on artificial intelligence and predictive analytics to achieve sustainability. This study presents Adaptive Weighting, Chaos Theory, and Gaussian Mutation-based RIME algorithm-tuned Extreme Gradient Boosting (ACGRIME-XGBoost), an advanced Artificial Intelligence (AI)-driven framework specifically designed for carbon emission prediction in air transport to contribute to the development of sustainable smart infrastructure. The proposed hybrid model integrates XGBoost with ACGRIME, a novel metaheuristic optimization algorithm enhanced with chaos theory, adaptive weighting, and Gaussian mutation mechanisms to overcome limitations in traditional hyperparameter tuning approaches. The framework demonstrates exceptional performance on Congress on Evolutionary Computation (CEC) 2020 benchmark functions, outperforming conventional optimization algorithms in accuracy and robustness. When applied to real-world flight data within a smart transportation monitoring, ACGRIME-XGBoost achieves a 94% R² score for CO₂ emission prediction, significantly surpassing other optimized machine learning models. This research bridges the gap between advanced AI optimization techniques and sustainable transportation infrastructure, offering a scalable decision-support system that can be integrated with IoT sensor networks and mobility platforms in the future. The results demonstrate how metaheuristic-assisted machine learning can enhance environmental monitoring capabilities in smart transportation ecosystems, supporting data-driven policy-making for climate-resilient infrastructure and sustainable aviation management within the broader context. Also, the research contributes to sustainable aviation by enabling high-fidelity CO₂ prediction models that can inform policy-making and be integrated into digital monitoring tools for future smart transport infrastructures. Full article

The Arctic Puffin Optimization (APO) Algorithm is a recently proposed metaheuristic algorithm that has been widely applied to solve optimization problems in continuous spaces. However, it cannot be directly used to solve combinatorial optimization problems in discrete spaces. To address these limitations, a Binary Arctic Puffin Optimization (BAPO) Algorithm is proposed, focusing on developing transfer functions to convert the algorithm’s continuous solutions into discrete binary solutions. Two primary transfer function types, S-shaped and V-shaped, are commonly employed. Experimental analysis identifies optimal functions for different algorithmic stages. These are then integrated with a conversion factor to propose a hybrid transfer function for the binarization of the Puffin Optimization Algorithm. To address the issue of slow particle convergence in the later stages of the exploration phase and the tendency to overlook high-quality solutions during the exploitation phase in the binary algorithm, logarithmic inertia weight and the golden sine strategy are incorporated, respectively, for improvement. Simulation experiments were conducted to solve both single-dimensional and multidimensional 0–1 knapsack problems. Experimental data and convergence curves, including mean values and standard deviations, were analyzed. The results demonstrate that the binary Arctic puffin optimization algorithm exhibits excellent convergence, stability, and fast search speed. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

Reasonable adjustment of construction parameters is of great value to reduce surface settlement and ensure the safety of shield construction. A novel hybrid intelligent optimization framework based on combination weighting and gray correlation analysis methods (CWG), a long short-term memory (LSTM) model and a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (CPSOS) algorithm was proposed. The CWG method was employed to screen key construction parameters and determine the weights of various influencing factors of surface settlement, thereby constructing a CWG-LSTM prediction model for surface settlement. On this basis, the prediction model served as the objective function for optimizing construction parameters, and the CPSOS algorithm was used for the optimization of shield construction parameters. Based on the Qingdao Metro Line 4 in China, sample sets were collected to verify the performance of the developed framework. The CWG-LSTM model achieved coefficients of determination (R²) of 0.92 and 0.91 on the train and test sets, respectively, along with root mean square errors (RMSE) of 1.29 and 1.03, and mean absolute percentage errors (MAPE) of 15.60% and 17.18%. Its prediction ability surpasses that of other comparison models, such as the Gated Recurrent Unit, Random Forest, Transformer, and Multiple Linear Regression, demonstrating higher accuracy. Optimized construction parameters derived from the CWG-LSTM-CPSOS facilitated shield tunneling in the unconstructed section. All surface settlement monitoring results recorded during excavation fell within the safety threshold, demonstrating that the proposed hybrid intelligent optimization framework effectively manages surface settlement during shield tunneling and serves as a reliable optimization approach for construction parameters. Full article

Objectives: Anticipated telehealth use is an important determinant of whether telehealth can function as a durable component of hybrid care models. However, there are limited practical tools to identify patients at risk of discontinuing telehealth. We aim to (1) identify factors associated with anticipated telehealth use; (2) develop a risk stratification tool (CONTEST); (3) compare its performance with machine learning (ML) models; and (4) evaluate model fairness across sex and race/ethnicity. Methods: We conducted a retrospective cross-sectional analysis of the 2024 Health Information National Trends Survey 7 (HINTS 7), including U.S. adults with ≥1 telehealth visit in the prior 12 months. The primary outcome was anticipated telehealth use. Survey-weighted multivariable logistic regression informed a Framingham-style point score (CONTEST). ML models (XGBoost, random forest, logistic regression) were trained and evaluated using the area under the receiver operating characteristic curve (AUROC), precision, and recall. Global interpretation used SHAP values. Fairness was assessed using group metrics (Disparate Impact, Equal Opportunity) and individual counterfactual-flip rates (CFR). Results: Approximately one-third of adults reported at least one telehealth visit in the prior year. Among these users, nearly one in ten expressed an unwillingness to continue using telehealth in the future. Four telehealth experience factors were independently associated with unwillingness to continue: lower perceived convenience, technical problems, lower perceived quality compared to in-person care, and unwillingness to recommend telehealth. CONTEST demonstrated strong discrimination for identifying individuals with lower anticipated telehealth use (AUROC 0.876; 95% CI, 0.843–0.908). XGBoost performed best among the ML models (AUROC 0.902 with all features). With the same four top features, an ML-informed point score achieved an AUROC of 0.872 (95% CI, 0.839–0.904), and a four-feature XGBoost model yielded an AUROC of 0.893 (95% CI, 0.821–0.948, p > 0.05). Group fairness metrics revealed disparities across sex and race/ethnicity, whereas individual counterfactual analyses indicated low flip rates (sex CFR: 0.024; race/ethnicity CFR: 0.013). Conclusions: A parsimonious, interpretable score (CONTEST) and feature-matched ML models provide comparable discrimination for stratifying risk of lower anticipated telehealth use. Sustained engagement hinges on convenience, technical reliability, perceived quality, and patient advocacy. Implementation should pair prediction with operational support and routine fairness monitoring to mitigate subgroup disparities. Full article

The increasing penetration of distributed renewable energy resources and the emergence of prosumers are reshaping the operational landscape of distribution grids. This work proposes a comprehensive prosumer-centric control and coordination framework integrated into the IEEE 33-bus radial distribution feeder. Selected buses are modeled as aggregated prosumer nodes equipped with photovoltaic (PV) generation, wind turbines, oncentrated solar power (CSP), a hybrid energy storage system (HESS) including redox flow batteries (RFBs), superconducting magnetic energy storage (SMES), and fuel cells (FCs), as well as electric vehicle (EV) fleets. A hierarchical power management strategy is developed, combining a decentralized fuzzy logic controller for real-time dispatch with a Particle Swarm Optimization (PSO) layer that tunes membership functions and rule weights to enhance system stability and renewable utilization. Time-series simulations are conducted to evaluate the impact of prosumer integration on network performance. The results show a significant improvement in the voltage profile across all buses, particularly at downstream nodes, highlighting the effectiveness of distributed renewable injections and coordinated storage management. The proposed framework illustrates the potential of clustered prosumers to support voltage stability, improve grid operation and enable high-renewable penetration in distribution networks. Full article

Additive manufacturing (AM) has reshaped production methodologies by enabling the fabrication of complex geometries for high-performance applications. As a leading AM technique, Fused Deposition Modeling (FDM) is widely used for its versatility. However, the structural reliability of FDM-printed parts is fundamentally dictated by their mechanical performance, where impact toughness functions as a critical benchmark across demanding industrial environments. Polylactic acid (PLA) has distinguished itself as a premier biodegradable polymer, favored for its superior stiffness and processability. Nevertheless, the inherent brittleness and anisotropic behavior of FDM-printed PLA pose significant challenges, necessitating investigation of their fracture mechanics. This study firstly evaluates the impact toughness of FDM-processed PLA Izod specimens using impact tests, structured within a Taguchi design of experiments (DoE) methodology. An L27 orthogonal array was employed to investigate the influence of manufacturing parameters on impact behavior and fracture energy. Then, to achieve high-fidelity predictions from experimental data, the parametric effects were systematically investigated through an advanced machine learning framework. In the first stage, optimal prediction models were identified by evaluating five mathematical formulations hybridized with five nature-inspired optimization algorithms (GWO, SMA, GSA, FPA, and KH) across nine dataset combinations. In the second stage, these best-performing models were integrated into a metaheuristic ensemble using the GWO to perform a weighted aggregation. This hybrid ensemble methodology significantly enhanced predictive accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 5.0847%, which represents a 37.3% relative improvement over the best individual base model. Full article

To tackle the navigation challenge in dynamic and complex environments, this study designs a fusion planning framework that synergistically integrates enhanced A* algorithm with improved DWA, inspired by the biological dual-layer navigation mechanism of global path planning and local real-time obstacle avoidance. Firstly, the original global path from the conventional A* algorithm is smoothed and length-reduced through a three-stage optimization strategy involving redundant node removal and forward and reverse path relaxation, mimicking the behavioral logic of honeybees and desert ants that eliminate redundant routes to complete foraging and homing with minimal energy consumption. Secondly, an evaluation function integrating dynamic obstacle perception and adaptive weight adjustment is designed for the DWA to enhance the intelligence of local planning, drawing on the adaptive strategy of animals such as antelopes that adjust behavioral priorities according to environmental complexity to balance safety and efficiency. To comprehensively verify the performance of the proposed algorithm, simulation evaluations are performed in various scenarios, including 20 × 20 and 30 × 30 grid maps, with single and dual dynamic obstacles. Results demonstrate that our algorithm outperforms conventional methods in path length, smoothness, and safety. Further physical verification is carried out on a LiDAR-equipped mobile robot (Shenzhen Yuanchuangxing Technology Co., Ltd., Shenzhen, China) based on the ROS platform, confirming that the algorithm can stably achieve static path tracking and real-time obstacle avoidance in real indoor environments. Consequently, the developed hybrid algorithm delivers a viable and robust solution for autonomous mobile robots to navigate safely and efficiently in unpredictable and complex environments. Full article

This paper develops a generalized Laplace transform framework on weighted function spaces $C_{{\delta }_{\psi },\gamma }^n[a,b]$, establishing a symmetry between integer-order ${\delta }_{\psi }$ operators and fractional $\psi$-Hilfer derivatives at the level of transform representations. Explicit transformation formulas are derived for the nth-order ${\delta }_{\psi }$-derivative and the $\psi$-Hilfer fractional derivative of order $\alpha \in (m-1,m)$, with $m\le n$. These results form an analytical basis for the treatment of higher-order hybrid fractional Cauchy problems that systematically couple integer-order and fractional operators subject to mixed initial conditions. The general solution is expressed in closed form using a bivariate Mittag–Leffler function. To illustrate the utility of the approach, a representative second-order hybrid model is studied and compared numerically with its classical integer-order counterpart. The simulations reveal significant differences in the dynamical response, including variations in amplitude, damping behavior, and long-term evolution. Full article

In complex natural scenarios, facial expression recognition often encounters partial occlusions caused by glasses, hand gestures, and hairstyles, making it difficult for models to extract effective features and thereby reducing recognition accuracy. Existing methods often employ attention mechanisms to enhance expression-related features, but they fail to adequately address the issue where high-frequency responses in occluded regions can disperse attention weights (e.g., incorrectly focus on occluded areas), making it challenging to effectively utilize local cues around the occlusions and limiting performance improvement. To address this, this paper proposes a network based on an adaptive attention mechanism (Adaptive Attention Vision Transformer, AA-ViT). First, an Adaptive Attention module (ADA) is designed to dynamically adjust attention scores in occluded regions, enhancing the effective information in features. Next, a Dual-Branch Multi-Layer Perceptron (DB-MLP) replaces the single linear layer to improve feature representation and model classification capability. Additionally, a Random Erasure (RE) strategy is introduced to enhance model robustness. Finally, to address the issue of model training instability caused by class imbalance in the training dataset, a hybrid loss function combining Focal Loss and Cross-Entropy Loss is adopted to ensure training stability. Experimental results show that AA-ViT achieves expression recognition accuracies of 90.66% and 90.01% on the RAF-DB and FERPlus datasets, respectively, representing improvements of 4.58 and 18.9 percentage points over the baseline ViT model, with only a 24.3% increase in parameter count. Compared to existing methods, the proposed approach demonstrates superior performance in occluded facial expression recognition tasks. Full article

Background: Smart healthcare is increasingly promoted to strengthen primary care services; however, adoption challenges are amplified on remote islands due to geographic isolation and resource constraints. Objectives: This study aimed to identify and prioritize key success factors (KSFs) for smart healthcare adoption in remote-island primary care clinics and to examine whether priorities differ across physician subgroups. Methods: A hybrid framework combining the Modified Delphi Method (MDM) and the Analytic Hierarchy Process (AHP) was applied. MDM (two rounds) refined a literature-based indicator pool to five dimensions and 20 criteria. AHP pairwise comparisons were collected from 21 physicians in Kinmen to derive weights and rankings. Results: System Quality (0.308) was the most critical dimension, followed by Organization (0.221), System Functionality (0.212), Environment (0.165), and Resource Investment (0.094). At the criterion level, Competitive Advantage and Security and Privacy were the two highest-ranked factors, followed by Accuracy and Data Integrity. Subgroup profiles varied across medical specialties and age groups. Conclusions: For remote-island primary care, adoption strategies should prioritize system quality and information assurance, while implementation support and resource considerations should be tailored to specialty- and cohort-specific needs. Full article

Precise tumor segmentation in multimodal MRI is crucial for glioma diagnosis and treatment planning; yet, deep learning models still struggle with irregular boundaries and severe class imbalance under computational constraints. An Enhanced 3D DenseNet with CDC architecture was proposed, integrating Central Difference Convolution, attention gates, and Atrous Spatial Pyramid Pooling for brain tumor segmentation on the BraTS 2023-GLI dataset. CDC layers enhance boundary sensitivity by combining intensity-level semantics and gradient-level features. Attention gates selectively emphasize relevant encoder features during skip connections, whereas the ASPP captures the multi-scale context with dilation rates. A hybrid loss function spanning three levels was introduced, consisting of a region-based Dice loss for volumetric overlap, a GPU-native 3D Sobel boundary loss for edge precision, and a class-weighted focal loss for handling class imbalance. The proposed model achieved a mean Dice score of 91.30% (ET: 87.84%, TC: 92.73%, WT: 93.34%) on the test set. Notably, these results were achieved with approximately 3.7 million parameters, representing a 17–76x reduction compared to the 50–200 million parameters required by transformer-based approaches. Enhanced 3D DenseNet with CDC architecture demonstrates that the integration of gradient-sensitive convolutions, attention mechanisms, multi-scale feature extraction, and multi-level loss optimization achieves competitive segmentation performance with significantly reduced computational requirements. Full article

To address the challenges of characterizing subway passenger flow fluctuations and overcoming the slow convergence and significant errors of existing intelligent optimization algorithms in tuning deep learning parameters for flow prediction, this study proposes a novel subway passenger flow fluctuation interval prediction model based on a Symmetry-Enhanced FIG-ICPO-XGBoost model. The core innovation is an Improved Cheetah Optimization Algorithm (ICPO), which incorporates enhancements including Circle mapping for population initialization, a hybrid strategy of dimension-by-dimension pinhole imaging opposition-based learning and Cauchy mutation to escape local optima, and adaptive variable spiral search with inertia weight to balance exploration and exploitation. The construction of this methodology embodies the concept of symmetry in algorithm design. For instance, Circle mapping achieves uniformity and ergodicity in the initial distribution of the population within the solution space, reflecting the symmetric principle of spatial coverage. Dimension-by-dimension pinhole imaging opposition-based learning generates opposite solutions through the principle of mirror symmetry, effectively expanding the search space. The adaptive variable spiral search strategy dynamically adjusts the spiral shape, simulating the symmetric relationship of dynamic balance between exploration and exploitation. Utilizing fuzzy-granulated passenger flow data (LOW, R, UP) from Harbin, the ICPO was employed to optimize XGBoost hyperparameters. Experimental results demonstrate the superior performance of the ICPO on 12 benchmark functions. The ICPO-XGBoost model achieves mean MAE, RMSE, and MAPE values of 10,291, 10,612, and 5.8%, respectively, for the predictions of the LOW, R, and UP datasets. Compared to existing models such as CPO-XGBoost, PSO-BiLSTM, GA-BP, and CNN-LSTM, these values represent improvements ranging from 4541 to 13,161 for MAE, 5258 to 14,613 for RMSE, and 2.6% to 7.2% for MAPE. The proposed model provides a reliable theoretical and data-driven foundation for optimizing subway train schedules and station passenger flow management. Full article