Search Results (111)

The rapid expansion of e-commerce and on-demand logistics has intensified the need for cost-effective and reliable urban distribution systems. This paper investigates an energy-aware routing problem for electric vehicle fleets operating from multiple depots under time-varying traffic conditions. We propose a novel multi-depot vehicle routing model that jointly incorporates time-dependent travel speeds, simultaneous pickup and delivery operations, and time window constraints. The model explicitly captures key operational realities, including battery capacity limitations, load- and speed-dependent energy consumption, synchronized pickup-delivery requirements, and soft time windows. The objective is to minimize total operational cost by simultaneously optimizing depot assignments, vehicle routes, and service schedules. Given the NP-hard nature of the problem, we develop a two-stage heuristic solution framework. In the first stage, a spatio-temporal clustering strategy is employed to assign customers to depots efficiently. In the second stage, route construction and improvement are performed using an enhanced Adaptive Large Neighborhood Search (ALNS) algorithm equipped with problem-specific destroy and repair operators. Computational experiments on adapted benchmark instances demonstrate that the proposed approach consistently produces high-quality solutions and exhibits robust convergence behavior. In addition, sensitivity analyses provide managerial insights, revealing an optimal range of vehicle energy capacity and an economically efficient speed band that balances travel time and energy consumption. Full article

Adipose tissue serves as a critical metabolic and endocrine organ, essential for maintaining systemic energy homeostasis and inter-organ communication. During the aging process, it undergoes significant structural remodeling and functional decline, characterized by dysregulated lipid metabolism, chronic low-grade inflammation, reduced insulin sensitivity, and adipokine imbalance. These alterations not only compromise the physiological integrity of adipose tissue but also contribute to the progression of various age-associated metabolic disorders, including type 2 diabetes, atherosclerosis, and nonalcoholic fatty liver disease. In recent years, natural products have emerged as a focal point in anti-aging research, owing to their broad accessibility, high biological safety, and capacity for multi-target regulation. Polyphenolic and polysaccharide, in particular, have demonstrated robust antioxidant, anti-inflammatory, autophagy-modulating, and mitochondrial-protective effects in cellular and animal models, indicating their promise in attenuating adipose tissue aging. Although the anti-aging effects of these natural compounds are well documented in the neural, hepatic, and cardiovascular systems, their specific mechanisms in adipose depots—especially differential regulatory patterns between white and brown adipose tissues, which may inform depot-specific therapies—and the development of targeted delivery approaches remain inadequately explored. This review, grounded in the three primary hallmarks of adipose tissue aging (oxidative stress, chronic inflammation, and dysregulated lipid metabolism), systematically elucidates the molecular mechanisms and recent advancements in the application of polyphenols and polysaccharides as natural modulators. This review establishes a cohesive theoretical foundation and delivers innovative perspectives to guide the advancement of natural product-based nutritional and therapeutic strategies for combating adipose tissue aging. Full article

Embryogenesis is a critical process for which nutritional and metabolic signals act as informational cues that shape adipose tissue development and establish long-lasting metabolic health. Emerging evidence indicates that adipose tissue is not a passive energy storage but a developmentally and metabolically dynamic organ. Cellular composition, functional capacity, and plasticity of adipose are programmed early through coordinated transcriptional, epigenetics, and proteomics processes. Maternal environments in nutritional challenge, including overnutrition and malnutrition, influence adipocyte lineage commitment, depot-specific expansion, and metabolic functionality, predisposing offspring to divergent risks of obesity and metabolic disease. The future of perinatal adipose biology and genomics relies on integrating multi-omics approaches with an artificial intelligence (AI)-driven analytical perspective to resolve complex developmental processes and predict long-lasting metabolic health. Furthermore, the incorporation of sex-specific models is important, which will be essential for capturing biological heterogeneity and ensuring translational relevance. Together, these advance perspectives are predisposed to shift the field from descriptive associations toward predictive and preventive paradigms, reinterpreting metabolic disease risk as a modifiable consequence of early-life adipose programming rather than an inevitable outcome of later-life exposures. Full article

The multi-depot vehicle routing problem with soft time windows (MDVRPSTW) has long been a focus in both academic and industrial circles. This paper proposes a deep reinforcement learning framework designed to enhance the efficiency and quality of MDVRPSTW solutions, addressing the limitations of traditional heuristic algorithms in large-scale complex scenarios. The framework first transforms the mathematical model into a sequential decision-making problem through a Markov decision process, then extracts path selection strategies using an encoder–decoder architecture based on attention mechanisms and graph neural networks, and employs unsupervised reinforcement learning for model training. Test results on the Solomon benchmark dataset demonstrate that for small-scale problems (N = 20), our method reduces solving time by over 96% compared to comparative algorithms, with the objective value difference from the generalized variable neighborhood search (GVNS) being less than 9%. For medium-to-large scale problems (N = 50/100), our method achieves a 27.7 to 96.3 percent improvement over GVNS, maintaining stable solution times within 3 to 10 s. Compared to exact algorithms and meta-heuristic methods, our approach reduces computational costs by 2–3 orders of magnitude while demonstrating strong adaptability to variations in the number of depots and vehicles. In summary, this method significantly outperforms baseline models in both solution quality and computational efficiency, providing an efficient end-to-end solution for MDVRPSTW in complex scenarios. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

Peptide-based biomaterials have emerged as versatile tools for pharmaceutical drug delivery due to their biocompatibility and tunable sequences, yet a comprehensive overview of their categories, mechanisms, and optimization strategies remains lacking to guide clinical translation. This review systematically collates advances in peptide-based biomaterials, covering peptide excipients (cell penetrating peptides, tight junction modulating peptides, and peptide surfactants/stabilizers), self-assembling peptides (peptide-based nanospheres, cyclic peptide nanotubes, nanovesicles and micelles, peptide-based hydrogels and depots), and peptide linkers (for antibody drug-conjugates, peptide drug-conjugates, and prodrugs). We also dissect sequence-based optimization strategies, including rational design and biophysical optimization (cyclization, stapling, D-amino acid incorporation), functional motif integration, and combinatorial discovery with AI assistance, with examples spanning marketed drugs and research-stage candidates. The review reveals that cell-penetrating peptides enable efficient intracellular payload delivery via direct penetration or endocytosis; self-assembling peptides form diverse nanostructures for controlled release; and peptide linkers achieve site-specific drug release by responding to tumor-associated enzymes or pH cues, while sequence optimization enhances stability and targeting. Peptide-based biomaterials offer precise, biocompatible and tunable solutions for drug delivery, future advancements relying on AI-driven design and multi-functional modification will accelerate their transition from basic research to clinical application. Full article

In this paper, we take the longitudinal two-stage and two-yard EMU (Electric Multiple Unit) depot as an example and discusses the optimization challenges of the first-level maintenance shunting operation plan under the background of limited maintenance capacity. A multi-objective programming is constructed, which adopts the lexicographic ordering method and aims to minimize the occupancy time of key line areas and the number of train storage times. In order to enhance the flexibility and solution efficiency of the shunting operation plan, we design an efficient three-stage strategy algorithm. Specifically, in the first stage, the genetic and mutation rules are integrated, and the fast iterative advantage of the genetic algorithm is utilized to solve the time decision variables in the optimization problem. In the second stage, the allocation of track occupancy variables is further solved. The third stage focuses on the optimized allocation of maintenance team variables to ensure the scientific scheduling of maintenance resources. Finally, a validation experiment was conducted using the maintenance tasks of 19 EMU sets as the test scenario. The results indicate that when the number of maintenance teams is set to 4, an optimal balance between maintenance efficiency and operational cost is achieved, the occupancy duration of key line zones reaches 3034 min (the theoretical optimum), the number of maintenance teams is reduced by 33.33% compared to the initial 6 teams, and the number of storage operations is optimized to 27 times. Additionally, the algorithm’s solution time remains under 50 s, demonstrating significantly improved computational efficiency. Comparative experiments with baseline algorithms show that the proposed method reduces the occupancy duration of key line zones by up to 0.49%, decreases the number of storage operations by 14 times, and advances the maximum completion time by 20 min. In summary, the proposed method provides solid theoretical support for the formulation of maintenance plans and shunting schedules in EMU depots. Particularly in complex scenarios with limited maintenance capacity, it offers innovative and robust decision-making foundations, demonstrating significant practical guidance value. Full article

Background: Accurate quantification of visceral (VAT) and subcutaneous adipose tissue (SAT) is critical for understanding the cardiometabolic consequences of obstructive sleep apnea (OSA) and other chronic diseases. This study validates a customization framework using pre-trained networks for the development of automated VAT/SAT segmentation models using hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) data from OSA patients. While the widespread adoption of deep learning models continues to accelerate the automation of repetitive tasks, establishing a customization framework is essential for developing models tailored to specific research questions. Methods: A UNet-ResNet50 model, pre-trained on RadImageNet, was iteratively trained on 59, 157, and 328 annotated scans within a closed-loop system on the Discovery Viewer platform. Model performance was evaluated against manual expert annotations in 10 independent test cases (with 80–100 MR slices per scan) using Dice similarity coefficients, segmentation time, intraclass correlation coefficients (ICC) for volumetric and metabolic agreement (VAT/SAT volume and standardized uptake values [SUVmean]), and Bland–Altman analysis to evaluate the bias. Results: The proposed deep learning pipeline substantially improved segmentation efficiency. Average annotation time per scan was 121.8 min (manual segmentation), 31.8 min (AI-assisted segmentation), and only 1.2 min (fully automated AI segmentation). Segmentation performance, assessed on 10 independent scans, demonstrated high Dice similarity coefficients for masks (0.98 for VAT and SAT), though lower for contours/boundary delineation (0.43 and 0.54). Agreement between AI-derived and manual volumetric and metabolic VAT/SAT measures was excellent, with all ICCs exceeding 0.98 for the best model and with minimal bias. Conclusions: This scalable and accurate pipeline enables efficient abdominal fat quantification using hybrid PET/MRI for simultaneous volumetric and metabolic fat analysis. Our framework streamlines research workflows and supports clinical studies in obesity, OSA, and cardiometabolic diseases through multi-modal imaging integration and AI-based segmentation. This facilitates the quantification of depot-specific adipose metrics that may strongly influence clinical outcomes. Full article

Adipose tissue and skeletal muscle are the foremost energy depots and locomotor organs; they orchestrate metabolic homeostasis through the secretion of cytokines via autocrine, paracrine, and endocrine pathways. This intricate interplay is pivotal in the pathogenesis of numerous metabolic disorders, encompassing obesity and muscle atrophy, as well as influencing meat quality in animal production. Despite its significance, unraveling the molecular mechanisms underlying muscle–adipose crosstalk remains a major challenge. Recent advancements in multi-omics technologies have facilitated the identification of a multitude of cytokines derived from adipose tissue and muscle, including adipokines, lipokines, myokines, and myogenic exosomes and adipose-derived exosomes containing various biomolecules. The functional roles of these cytokines have been elucidated through meticulous studies employing trans-well cultures and recombinant proteins. In this comprehensive review, we summarize the bidirectional roles of adipokines and myokines in key biological processes—such as muscle satellite cell differentiation, mitochondrial thermogenesis, insulin sensitivity, and lipid metabolism. By synthesizing these findings, we aim to provide novel insights into the treatment of metabolic diseases and the improvement of animal production. Full article

Lipid metabolism and lipid-derived signaling together ensure cellular and systemic homeostasis. Their dysregulation causes obesity, type 2 diabetes, cardiovascular disease, NAFLD/MASH, and neurodegeneration throughout life. This review integrates central pathways, such as ACC–FASN-mediated de novo lipogenesis, lipid-droplet lipolysis, and mitochondrial and peroxisomal β-oxidation, and their regulation by insulin–PI3K–Akt, glucagon–cAMP–PKA, SREBPs, PPARs, and AMPK. We emphasize the mechanisms by which bioactive lipids like diacylglycerols, ceramides, eicosanoids, and endocannabinoids serve as second messengers linking nutrient state to insulin signaling, inflammation, and stress response; pathologic accumulation of these species enhances insulin resistance and lipotoxicity. Aging disrupts these axes via diminished catecholamine-stimulated lipolysis, defective fatty-acid oxidation, mitochondrial failure, and adipose depot redistribution, facilitating ectopic fat and postprandial dyslipidemia. We suggest a pathway-to-phenotype paradigm that connects lipid species and tissue environment to clinical phenotypes, allowing for mechanism-to-intervention alignment. Therapeutic avenues range from lipid lowering for atherogenic risk to novel agents targeting ACLY, ACC, FASN, CPT1, and nuclear receptors, with precision lifestyle intervention in diet and exercise. Translation is still heterogeneous because of isoform-dependent effects, safety trade-offs, and inconsistent adherence. We prioritize harmonization of lipidomics with multi-omics for stratifying patients, enriching responders, and bridging gaps between mechanistic understanding and clinical outcome, with focus on age-sensitive prevention and treatment for lipid-mediated metabolic disease. Full article

In light of the ambitious “dual carbon” targets, the refined oil supply chain faces challenges in balancing economic viability with environmental sustainability. Traditional resource allocation methods predominantly prioritize cost minimization, often overlooking significant environmental impacts and leading to carbon-intensive transportation practices. This paper proposes a multi-objective optimization model to simultaneously minimize total logistics costs and carbon emissions across the entire refined oil supply chain. The model encompasses key stages, including refinery production, external procurement, multimodal transport operations, and inventory management. The proposed framework integrates practical con straints such as sending and receiving capacities, inventory balance, and supply and demand requirements. The ε-constraint method is employed for model solution to generate a set of Pareto optimal solutions, highlighting the inherent trade-offs between economic and environmental objectives. A case study is carried out, involving a refined oil logistics system in Central China, which comprises five refineries, 31 depots, and two external purchasing nodes. Compared to a purely economic optimization, a balanced scenario (e.g., with an ε-constraint of 9000 tons/season for carbon emissions) achieves a substantial 10–15% reduction in emissions with only a marginal 1–2% increase in logistics costs. Furthermore, the optimization significantly reconfigures the transport structure, increasing pipeline utilization from 27.3% to 35% and leading to a 26.1% reduction in waterway-related carbon emissions. This study can offer an efficient decision-making tool that facilitates the green transformation of the refined oil supply chain, bridging the gap between corporate logistics cost efficiency and ambitious carbon neutrality targets. Full article

Optimizing the siting and servicing of urban facilities is a core operations research problem that must reconcile heterogeneous demand, spatial constraints, and network-realistic travel. We present GD-ARISE, a GIS-integrated and data analytics pipeline that maintains a pedestrian–road network metric from demand inference through siting to routing. The workflow has three modules: (i) GIS integration that unifies spatial layers on one network and distance metric; (ii) data analytics that builds multi-criteria suitability via the Analytic Hierarchy Process (AHP) and maps scores to adaptive service radii; (iii) optimal location-and-routing that selects nonoverlapping sites with a transparent greedy rule (SCASS) and computes depot-to-depot routes via simulated annealing on the same metric. A case study in Seoul’s Gangnam District yields a high-coverage portfolio and feasible collection routes. We add a theoretical framework that casts SCASS as a conflict-graph problem, document the AHP elicitation with consistency checks, and report robustness analyses including sensitivity to AHP weights and to radius bounds. Results indicate that core hotspots remain stable to weighting, whereas mid-range corridors shift as criteria priorities or spatial parameters change. Full article

We present the ML-CALMO framework, which integrates machine learning with queueing theory for last-mile delivery optimization under dynamic conditions. The system combines Long Short-Term Memory (LSTM) demand forecasting, Convolutional Neural Network (CNN) traffic prediction, and Deep Q-Network (DQN)-based routing with theoretical stability guarantees. Evaluation on modern benchmarks, including the 2022 Multi-Depot Dynamic VRP with Stochastic Road Capacity (MDDVRPSRC) dataset and real-world compatible data from OSMnx-based spatial extraction, demonstrates measurable improvements: 18.5% reduction in delivery time and +8.9 pp (≈12.2% relative) gain in service efficiency compared to current state-of-the-art methods, with statistical significance (p < 0.01). Critical limitations include (1) computational requirements that necessitate mid-range GPU hardware, (2) performance degradation under rapid parameter changes (drift rate > 0.5/min), and (3) validation limited to simulation environments. The framework provides a foundation for integrating predictive machine learning with operational guarantees, though field deployment requires addressing identified scalability and robustness constraints. All code, data, and experimental configurations are publicly available for reproducibility. Full article

Multi-agent coordination with task allocation, routing, and scheduling presents critical challenges when deploying heterogeneous robotic systems in constrained agricultural environments. These systems involve real-time sensing during their operations with various sensors, and having quick updates on coordination based on sensed data is critical. This paper addresses the specific requirements of harvesting automation through three heuristic approaches: (1) primal–dual workload balancing inspired by combinatorial optimization techniques, (2) greedy task assignment with iterative local optimization, and (3) LLM-based constraint processing through prompt engineering. Our agricultural application scenario incorporates robot size constraints for navigating narrow crop rows while optimizing task completion time. The greedy heuristic employs rapid initial task allocation based on proximity and capability matching, followed by iterative route refinement. The primal–dual approach adapts combinatorial optimization principles from recent multi-depot routing solutions, dynamically redistributing workloads between robots through dual variable adjustments to minimize maximum completion time. The LLM-based method utilizes structured prompt engineering to encode spatial constraints and robot capabilities, generating feasible solutions through successive refinement cycles. We implemented and compared these approaches through extensive simulations. Preliminary results demonstrate that all three approaches produce feasible solutions with reasonable quality. The results demonstrate the potential of the methods for real-world applications that can be quickly adopted into variations of the problem to offer valuable insights into solving complex coordination problems with heterogeneous multi-robot systems. Full article