Search Results (5,281)

Magnetizing living cells with superparamagnetic iron oxide nanoparticles (SPIONs) enables their remote manipulation using external magnetic field. This lays the foundation for magnetically assembling tissue precursors within cell-friendly, proliferation-permissive environments and holds considerable promise for biomedical applications, particularly in the development of complex single- and multicellular tissue constructs for bone and organ reconstruction. However, progress in this field is limited by the lack of robust mathematical tools for accurate control of ensembles of magnetic nano- and micro-objects. In practical printing scenarios, collective behavior and unavoidable statistical heterogeneity—such as variations in SPION size and shape or deviations in cell magnetization—render traditional equation-based modeling inadequate. We developed a hybrid modeling framework integrating conventional physics-based simulations with artificial intelligence-driven image analysis. Dynamic parameters were extracted from video recordings of magnetized cells moving within model microfluidic devices exposed to well-defined magnetic fields and gradients. The AI-based analysis enabled quantitative characterization of ensemble behavior under heterogeneous conditions. The proposed framework successfully captured the collective dynamics of magnetized cell ensembles and enabled accurate control of their spatial organization under external magnetic actuation. The integration of simulation and data-driven analysis provided robust parameter identification despite statistical heterogeneity within the system. This integrated modeling approach provides a practical and effective tool for controlling the three-dimensional magnetic assembly of living cells, with strong potential for applications in tissue engineering. Full article

With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous and discrete variables, resulting in high computational complexity that renders them unsuitable for daily real-time scheduling in large-scale power systems. This paper develops a flexible real-time rolling optimization scheduling model that incorporates OTS and proposes a two-stage fast solution framework based on deep neural networks (DNN). In the offline training phase, a multilayer perceptron-based DNN is trained using load and renewable generation data to rapidly and accurately predict the optimal line switching scheme. In the online application phase, the network topology predicted by the DNN transforms the original mixed-integer linear programming problem into a standard linear programming problem, substantially reducing computational complexity and solution time. Case studies on the modified IEEE 118-bus and IEEE 300-bus systems show that the proposed method achieves high prediction accuracy, reduces solution time by up to 117 times, and maintains nearly identical system operating costs to the physics-driven approach in the majority of cases. The results demonstrate that the proposed approach effectively balances computational efficiency and economic performance, verifying the practical value of optimal transmission switching in enhancing large-scale renewable energy accommodation and overall power system flexibility. Full article

The decoupling of physical loading configurations from dynamic temperature control in cold-chain logistics exposes supply chains to severe thermal compliance risks and exponential cost penalties. To address this structural gap, this study formulated the Cold Chain Unitization Loading Optimization Problem (CCULP). We propose a mixed-integer linear programming (MILP) model that integrates continuous-time heat-transfer dynamics—including door-opening impulse disturbances—and Q10-driven quality-decay kinetics as endogenous constraints within the hierarchical assignment of perishable goods to insulated containers, pallets, and vehicles. By treating container thermal resistance as a core decision variable, the model operationalizes a “prevention-first” economic strategy. To solve this NP-hard problem, we developed a Temperature-Aware Heuristic Algorithm (TAHA) that embeds a forward-Euler temperature simulation loop directly into the combinatorial search. Computational experiments on instances up to 100 SKU types demonstrate that TAHA achieves near-optimal solutions (within 0.7% of the MILP proven optimum) while converging 63 times faster than a genetic algorithm benchmark. Moreover, compared with traditional geometry-centric heuristics, TAHA’s proactive container-polarization strategy effectively eliminates the “penalty cliff,” yielding up to a 25.9% reduction in total system cost on Large-scale instances, almost entirely attributable to the elimination of temperature-violation penalties. Sensitivity analyses further confirm TAHA’s robustness under extreme environmental stress (e.g., 40 °C ambient temperatures) and frequent logistical disturbances, offering an integrated framework for proactive risk mitigation and for reducing food loss in sustainable temperature-controlled distribution. Full article

With the rapid development of global aquaculture, the frequent occurrence of fish diseases has had a serious impact on the efficiency of aquaculture and the ecological environment. Antimicrobial peptides, as a kind of natural immune active substance existing in organisms, participate in innate immunity and adaptive immunity. Due to their extensive antibacterial properties and low toxicity, they have gradually become a hot topic in scientific research. This article reviews the classification, tissue distribution, mechanism of action, extraction, and synthesis techniques of antimicrobial peptides (AMPs) derived from fish, as well as their applications in disease prevention in aquaculture, product preservation, and antibiotic substitution. Although antimicrobial peptides are expected to become alternatives to antibiotics, challenges such as environmental stability, production costs, and regulatory frameworks remain to be addressed. This article holds that antimicrobial peptides derived from fish are a feasible strategy for sustainable aquaculture. The future development direction lies in biotechnology-driven optimization, carrier innovation, and combined application with traditional antibiotics. Full article

Rising temperatures, driven by global climate change, are profoundly altering high-latitude ecosystems, influencing vegetation phenology and productivity. However, understanding the long-term, nuanced responses of these ecosystems remains a critical challenge. Soil warming experiments have served as useful tools for understanding these shifts. However, many of these studies have relied on a single measure, predominantly the Normalized Difference Vegetation (NDVI), measured at a single level of warming. This approach often fails to separate structural greening from underlying physiological responses. To address these gaps, this study provided a comprehensive snapshot assessment of growing season vegetation dynamics in a subarctic grassland ecosystem in Iceland that had been exposed to continuous geothermal soil warming for over 60 years. Using high-resolution multispectral drone imagery, twelve different vegetation indices (VIs) were derived to assess not only greenness but also physiological stress and photosynthetic efficiency across a range of mean annual soil temperatures (MATs). Using linear regression and redundancy analysis (RDA), the responses of these indices to warming and their relationships with other environmental drivers, such as standing biomass and plant nutrient concentrations (nitrogen and phosphorus), were analyzed. The results revealed significant positive linear relationships between most of the indices and MATs across the 5 to 11 °C range. This indicated that higher MATs led to increased biomass and structural growth, without revealing any significant thresholds or tipping points in vegetation response within the observed warming range. However, the Photochemical Reflectance (PRI) showed a significant negative relationship with warming, suggesting a decoupling between structural greening and photosynthetic light-use efficiency. Furthermore, RDA results indicated that, while most of the VIs were primarily driven by biomass, the decline in PRI was likely a compounding effect of physical canopy self-shading and plant phosphorus constraints. Ultimately, this study demonstrated that, while these subarctic grasslands exhibited local evidence of “Arctic greening” under further warming, multispectral drone remote sensing could detect underlying physiological adjustments and nutrient constraints that traditional greenness indices might overlook, providing a more nuanced understanding of ecosystem response. Full article

Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority ($R^2=0.802$) over the traditional Spatial Error Model ($R^2=0.437$). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically quantifying non-linear dynamics such as coastal high-floor premiums, severe mountainous altitude penalties, and latent urban reconstruction premiums. Ultimately, this research bridges the gap between predictive computational power and spatial economic interpretability, offering a robust informatics framework for equitable urban planning. Full article

Education is very important for a society’s economic, social, and cultural growth. However, educational systems still have structural problems that make it hard for students to stay in school and achieve academic success. One of the most significant problems in education is student dropout. This situation is especially impactful in high school, where its effects extend beyond the school and into long-term social and economic outcomes. This paper proposes an approach to identify data-driven indicators of dropout risk by using supervised learning and optimization methods. Our proposal consists of a supervised feature-weighted metric learning strategy that improves class separability in distance-based classifiers by reweighting features based on label information. To achieve the best possible k-nearest neighbors classification accuracy, we formulate metric learning as an optimization problem. Moreover, to optimize our proposal, we considered population-based, gradient-free metaheuristics. Furthermore, our proposed method preserves the original feature space to improve neighborhood relationships in contrast to traditional preprocessing or dimensionality reduction methods, which are important for educational outcomes. Actual school records from a high school in Ciudad Madero, Tamaulipas, Mexico, were used to conduct the experimentation to assess our proposal. Based on the experimental results, we observe an improvement in classification performance, with accuracy increasing from about 0.87 to 0.98. For statistical support, we applied a nonparametric Friedman test, which showed that these improvements are statistically significant. Hence, our proposal could be a useful and scalable method for educational data and support strategies for early identification of students at risk of dropout. Full article

Ants are widely used as bioindicators because of their sensitivity to environmental change and their functional roles in ecosystems. This study presents the first comparative analysis of ant communities in two habitats, an agricultural system and a semi-natural forest, within the Natural Park of Montesinho (northeastern Portugal). From May to October 2022, four plots were sampled per habitat: (i) semi-natural oak forest and (ii) chestnut orchard under human management, using five pitfall traps in each plot. A total of 1969 ants were captured, representing 32 species and 15 genera. Traditional chestnut orchards supported more exclusive species and greater functional diversity, dominated by generalist and thermophilic taxa. In contrast, oak forests hosted more specialist and cold-adapted species, which may reflect a higher structural stability. Seasonal variation was more pronounced in chestnut orchards, consistent with disturbance-driven dynamics. The functional composition also differed: chestnut orchards favoured granivores and scavengers, while oak forests supported predators and mutualists. These findings highlight the value of ant communities as sensitive indicators of land use and ecosystem condition in Mediterranean mountain systems. Full article

In recent years, the deployment of robots in human-centric environments has necessitated the development of artificial skins that integrate safety and durability. Traditional damage detection often relies on raw signal thresholds, lacking the functional integration of touch, pain, and damage found in biological systems. This study proposes a bio-inspired artificial skin model that separately evaluates these three states through a spatio-temporal anomaly detection framework. We developed an unsupervised model combining a Convolutional Autoencoder (CAE) and Convolutional LSTM (ConvLSTM) to learn the latent representations of tactile maps from intact skin. By quantifying spatial reconstruction and temporal prediction errors, the system generates individual scores for touch, pain, and damage. Pain is defined as an abstract signal of instantaneous abnormality, while damage is identified as a persistent structural deviation. We implemented a dynamic thresholding mechanism mimicking biological sensitization and recovery, with damage detection gated by a pain-flag constraint to minimize false positives. Experimental results across various conditions—including incisions (3–6 cm) and abrasions (10–30 times)—demonstrate that the model can distinguish between momentary noxious stimuli and sustained structural degradation. Quantitative evaluation shows that the proposed model achieves an Area Under the Curve (AUC) of 0.653, outperforming a threshold-based baseline and maintaining zero false positives under strong, non-damaging contact. Specifically, the system successfully mimics biological aftereffects and the pain-gating mechanism, where damage is only assessed in the presence of a pain-related trigger. This research provides a scalable, software-driven foundation for robot self-protection that overcomes the implementation constraints of hardware-dependent neuromorphic systems. Full article

Understanding how sustainable urban competitiveness evolves within megaregions has become a central concern in urban and regional studies, particularly under the pressures of carbon neutrality, spatial inequality, and network-driven urbanization. This study develops a multidimensional framework to assess the sustainable competitiveness of cities in the Beijing–Tianjin–Hebei “2 + 36” urban agglomeration and examines its spatio-temporal evolution and relational structure. Using a 30-indicator system grounded in factor foundations, economic performance, innovation capacity, openness, and environmental livability, we construct a composite competitiveness index through principal component analysis (PCA). Kernel density estimation reveals a pattern of overall improvement accompanied by widening disparities, characterized by selective agglomeration and the emergence of a pronounced high-value tail. Spatial autocorrelation consistently indicates significant spatial dependence, while LISA analysis identifies persistent low–low clusters and limited spillover absorption around core cities. A modified gravity model further uncovers a transition from a linear, corridor-based linkage structure to a more polycentric and networked competitiveness system, albeit with enduring peripheral weak nodes. The study contributes theoretically by conceptualizing sustainable urban competitiveness as a multidimensional nexus shaped jointly by territorial attributes and relational network structures. It demonstrates that competitiveness dynamics in megaregions emerge from the interplay of hierarchical consolidation, spatial divergence, and network reconfiguration—challenging the traditional assumption of simple core-to-periphery diffusion. The findings offer broader global implications, showing that the Beijing–Tianjin–Hebei case mirrors worldwide megaregional patterns, where proximity alone is insufficient to ensure functional integration, and where coordinated governance, network embeddedness and sustainability transitions increasingly determine regional competitiveness. This research provides a comprehensive analytical foundation for understanding and governing megaregional competitiveness in the era of sustainable development. Full article

The bonding between cabin sections and exterior shells represents a critical manufacturing operation in shell assembly, directly determining the reliability and structural performance of the assembled structure. However, traditional manual scraping suffers from low efficiency, poor consistency, and heavy reliance on manual operation, while conventional teach-and-repeat robotic automation fails to adapt to significant manufacturing tolerances and complex surface curvatures common in large-scale shell components. To address these challenges, this paper proposes a vision-guided robotic scraping method that generates adaptive trajectories on irregular cabin sections. The method achieves full pipeline integration and is particularly suited for production lines where various models share similar macro-geometries but possess subtle geometric variations. A system integrating a laser profile sensor is developed to perceive surface geometry and local normal vectors. By establishing a unified coordinate transformation chain and a scan–mesh–spline workflow, the sensed geometric information is directly mapped to the robot end-effector pose. A trajectory generation algorithm based on point cloud meshing and B-spline interpolation is employed to construct continuous, smooth scraping paths that accommodate geometric deviations without relying on complex fixtures. Unlike RGB-D correction-based methods that require pre-programmed initial trajectories, or CAD-driven offline programming that cannot adapt to manufacturing deviations, the proposed approach directly generates conformal scraping paths from measured geometry. Experimental results on a typical cabin section demonstrate that the generated trajectories accurately follow the surface normals, achieving a low standard deviation of 36 μm in adhesive layer thickness, indicating excellent thickness consistency and uniformity. Furthermore, the automated process reduced the total operation time to approximately 40 min, improving production efficiency by more than two times compared to manual operations, thereby validating the robustness and suitability of the method for high-precision batch manufacturing. Full article

Uplift modeling optimizes intervention-based campaigns by identifying customers whose behavior changes exclusively due to specific treatments, moving beyond standard baseline risk predictions. However, in real-world deployments, algorithms that maximize traditional causal ranking metrics (e.g., the Qini coefficient) often fail to be optimal in practice. The inherent variance of Conditional Average Treatment Effect (CATE) estimators exposes critical trade-offs between expected economic value, algorithmic stability, and policy interpretability. To address this gap, this study proposes a stability-aware, value-driven computational framework for selecting an uplift policy. The pipeline evaluates multiple causal and non-causal algorithmic families, including traditional baselines, multimodel approaches, and transformed-outcome variants, within a repeated-run validation protocol. Candidate policies are assessed primarily through incremental revenue and target-set stability, whereas a post hoc surrogate tree distillation step is used to translate the selected policy into interpretable rule-based customer segments. An empirical evaluation of the publicly available Telco Customer Churn dataset under two distinct regimes (a causally controlled semisynthetic scenario and an observational proxy scenario) reveals that the highest-yielding causal policy frequently suffers from severe targeting instability, inducing a clear risk–return trade-off. Furthermore, uplift models outperform traditional baselines in the causally controlled regime, whereas traditional baselines remain economically superior in the confounded proxy settings. Overall, this study establishes that jointly assessing economic utility, algorithmic stability, and transparent segmentation is essential for deploying robust and defensible causal machine learning in production environments. Full article

This study investigates the optimal service life of urban Electric Multiple Units (EMUs) by integrating two complementary evaluation methods: economic service life and maintenance limit life. Using a comprehensive dataset from Seoul Metro—including 498 trainsets and 3554 overhaul records—this research examines the relationship between long-term maintenance costs, depreciation, and residual values. The economic service life is derived by minimizing the average equivalent annual cost (AEC), while maintenance limit life is assessed based on government guidelines that define cost-inefficiency thresholds. The analysis finds that the average economic service life for EMUs on Lines 1–4 is approximately 39 years—substantially exceeding the traditional 25-year benchmark used in past replacement policies. Maintenance limit life, based on permissible cost ratio thresholds, extends up to 47 years in some cases. Sensitivity analysis indicates that maintenance cost variations exert a greater influence on optimal service life than discount rate assumptions, highlighting the importance of strategic maintenance management. The proposed dual-framework approach demonstrates the limitations of rigid, statutory-based replacement planning and supports a transition toward data-driven, line-specific decision-making. The findings provide actionable insights for transit authorities and policymakers seeking to improve capital investment efficiency and optimize lifecycle management of urban rail assets. Beyond economic efficiency, the study contributes to sustainability by supporting resource-efficient asset utilization, reducing premature disposal of serviceable rolling stock, and lowering lifecycle carbon emissions associated with manufacturing new vehicles. The proposed framework thus offers a practical basis for integrating economic and environmental considerations in sustainable urban rail asset management. Full article

Although secondary control of direct current (DC) microgrids has been widely studied, traditional static current sharing may still cause severe voltage sag under large-signal constant power load (CPL) steps, and many distributed schemes rely on global topology information while showing limited transient disturbance rejection. To address these issues, this paper proposes an observer-assisted, stability-margin-driven prescribed-time distributed secondary control strategy for islanded DC microgrids. A dynamic CPL risk evaluation function updates current-sharing ratios according to converter operating margins, while a distributed prescribed-time observer estimates disturbance envelopes and alleviates high-frequency chattering. Local adaptive gains remove the explicit dependence of controller tuning on global Laplacian eigenvalue information. MATLAB R2024a-based numerical studies show that, under a 6000 W CPL stress scenario, the proposed method limits the maximum voltage drop to 3.37 V, compared with 24.60 V for the conventional virtual current derivative (VCD) method. Under heterogeneous line impedances and a non-ideal digital benchmark, the proposed method yields a normalized current-sharing error of 0.72%, whereas the VCD method exhibits milder voltage transients. These results support the algorithmic effectiveness and numerical robustness of the proposed strategy within the adopted validation environment. Full article

Traditional activated clay (AC) bleaching usually shows limited adsorption selectivity, leading to micronutrient loss during pigment removal, and also suffers from high residual oil retention and poor regenerability. Developing mild bleaching materials with both high adsorption efficiency and selectivity is therefore important for oil refining. Mesoporous Al-MCM-41 (AM) adsorbents with different Si/Al ratios were prepared and characterized in pore structure and acidity, and the bleaching performance against AC in terms of pigment removal and the retention of micronutrients in rapeseed oil and the bleaching mechanism were studied. The results showed that AM25 (Si/Al = 25) exhibited the best overall performance among the AM samples under the tested conditions (70 °C, 20 min). It achieved a bleaching efficiency of 92.3% and removed 94.56% of chlorophyll, 92.94% of lutein, and 84.09% of β-carotene. In addition, AM25 reduced the peroxide value from 2.52 to 0.58 mmol/kg. High retentions of tocopherols (93.89%), phytosterols (98.73%), and squalene (96.32%) were also observed. Meanwhile, the adsorption rates of α-tocopherol, brassicasterol, and α-linolenic acid showed the highest values in their relative homologues of tocopherols, phytosterols, and free fatty acids (FFAs), respectively, due to differences in the methyl amount of tocopherols, the side-chain unsaturation of phytosterols, and the fatty acid chain unsaturation of fatty acids. Furthermore, the kinetic and isotherm data for chlorophyll and carotenoids were better described by the pseudo-second-order and Freundlich models, respectively. Combined with thermodynamic analysis, they indicated that adsorption was a spontaneous, endothermic, entropy-driven, heterogeneous multilayer process dominated by physical adsorption. Further, pigment adsorption was mainly governed by uniform mesopores and Si–OH/Si–OH–Al sites in AM. Among them, carotenoid removal depended primarily on the dispersion effect of moderately strong acid sites within pore-confined regions, whereas chlorophyll removal was more sensitive to the number of acidic sites in AM. AM25 still maintained 83.31% bleaching efficiency after five regeneration cycles. These performances of AM25 are significantly superior to that of AC. Full article