Search Results (2,424)

This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) that integrates routing decisions with truck loading schedules at a single depot with constrained capacity. To solve this NP-hard problem, we develop a metaheuristic algorithm based on Ant Colony Optimization (ACO), enhanced with a global memory system and a novel stochastic return rule that allows trucks to return to the depot when additional deliveries are suboptimal. Parameter calibration experiments are conducted to determine optimal values for the return probability and ant population size. The algorithm is tested on a real forestry dispatch scenario over six working days. The results show that an Ant Colony System (ACS–CTLRP) algorithm reduces total distance traveled by 23%, travel time by 22%, and the number of trucks used by 13 units, while increasing fleet utilization from 54% to 83%. These findings demonstrate that the proposed method significantly outperforms current company planning and offers a transferable framework for depot-constrained routing problems in time-sensitive distribution environments. Full article

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

Wireless power transfer (WPT) systems for autonomous underwater vehicles (AUVs) are gaining traction in marine exploration due to their operational convenience, safety, and flexibility. Nevertheless, disturbances from ocean currents and marine organisms frequently induce rotational, axial, and air-gap misalignments, significantly degrading the output power stability. To mitigate this issue, this paper proposes a novel reconfigurable WPT system utilizing coaxial antipodal dual DD (CAD-DD) coils, which strategically switches between a detuned S-LCC topology and a detuned S-S topology at a fixed operating frequency. By characterizing the output power versus the coupling coefficient (P-k) profiles under both reconfiguration modes, a parameter design methodology is developed to ensure stable power delivery across wide coupling variations. Experimental validation using a 1.2 kW AUV charging prototype demonstrates remarkable tolerance to misalignment: ±30° rotation, ±120 mm axial displacement, and 20–50 mm air-gap variation. Within this range, the output power fluctuation is confined to within 5%, while the system efficiency exceeds 85% consistently, peaking at 91.56%. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

Intratumoral injections of macromolecules, such as biologics and immunotherapeutics, show promise in overcoming dose-limiting side effects associated with systemic injections and improve treatment efficacy. However, the retention of injectates in the tumor microenvironment is a major underappreciated challenge. High interstitial pressures and dense tumor architectures create shear forces that rapidly expel low-viscosity solutions post-injection. Injectable hydrogels may address these concerns by providing a viscoelastic delivery vehicle that shields loaded therapies from rapid expulsion from the tumor. A chitosan–glycerol hydrogel was thus developed and characterized with the goal of improving the injection retention of loaded therapeutics. The gelation parameters and mechanical properties of the hydrogel were explored to reveal a shear-thinning gel that is injectable through a 27-gauge needle. Biocompatibility studies demonstrated that the chitosan–glycerol hydrogel was nontoxic. Retention studies revealed significant improvements in the retention of model therapeutics when formulated with the chitosan–glycerol hydrogel compared to less-viscous solutions. Finally, release studies showed that there was a sustained release of model therapeutics of various molecular sizes from the hydrogel. Overall, the chitosan–glycerol hydrogel demonstrated injectability, enhanced retention, biocompatibility, and sustained release of macromolecules, indicating its potential for future clinical use in intratumoral macromolecule delivery. Full article

Circular RNAs (circRNAs) have emerged as a transformative class of RNA therapeutics, distinguished by their closed-loop structure conferring nuclease resistance, reduced immunogenicity, and sustained translational activity. While challenges in pharmacokinetic control and manufacturing standardization require resolution, emerging synergies between computational design tools and modular delivery platforms are accelerating clinical translation. In this review, we synthesize recent advances in circRNA therapeutics, with a focused analysis of their stability and immunogenic properties in vaccine and drug development. Notably, key synthesis strategies, delivery platforms, and AI-driven optimization methods enabling scalable production are discussed. Moreover, we summarize preclinical and emerging clinical studies that underscore the potential of circRNA in vaccine development and protein replacement therapies. As both a promising expression vehicle and programmable regulatory molecule, circRNA represents a versatile platform poised to advance next-generation biologics and precision medicine. Full article

This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios. Full article

The COVID-19 pandemic has highlighted critical vulnerabilities in urban logistics systems, particularly in last-mile delivery. To enhance logistics resilience and efficiency, the Korean government has initiated an innovative project that repurposes idle spaces in subway vehicle bases within the Seoul Metropolitan Area into logistics centers. This study proposes a comprehensive multi-criteria evaluation framework combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the suitability of ten candidate sites. The evaluation criteria span four dimensions, facility, geographical, environmental, and social factors, derived from the literature and expert consultations. AHP results indicate that geographical factors, especially proximity to urban centers and major logistics facilities, hold the highest weight. Based on the integrated analysis using TOPSIS, the most suitable locations identified are Sinnae, Godeok, and Cheonwang. The findings suggest the strategic importance of aligning infrastructure development with spatial accessibility and stakeholder cooperation. Policy implications include the need for targeted investment, public–private collaboration, and sustainable logistics planning. Future research is encouraged to incorporate dynamic data and consider social equity and environmental impact for long-term urban logistics planning. Full article

Background: During food processing and storage, traditional protein-based delivery systems encounter significant challenges in maintaining the structural and functional integrity of bioactive compounds, primarily due to their temporal instability. Methods: In this study, a nanocomposite hydrogel was prepared through the co-assembly of a self-assembling peptide, 9-Fluorenylmethoxycarbonyl-phenylalanine-arginine-glycine-aspartic acid-phenylalanine (Fmoc-FRGDF), and hyaluronic acid (HA). The stability of this hydrogel as a quercetin (Que) delivery carrier was systematically investigated. Furthermore, the impact of Que co-assembly on the microstructural evolution and physicochemical properties of the hydrogel was characterized. Concurrently, the encapsulation efficiency (EE%) and controlled release kinetics of Que were quantitatively evaluated. Results: The findings indicated that HA significantly reduced the storage modulus (G′) from 256.5 Pa for Fmoc-FRGDF to 21.1 Pa with the addition of 0.1 mg/mL HA. Despite this reduction, HA effectively slowed degradation rates; specifically, residue rates of 5.5% were observed for Fmoc-FRGDF alone compared to 14.1% with 0.5 mg/mL HA present. Notably, Que enhanced G′ within the ternary complex, increasing it from 256.5 Pa in Fmoc-FRGDF to an impressive 7527.0 Pa in the Que/HA/Fmoc-FRGDF hydrogel containing 0.1 mg/mL HA. The interactions among Que, HA, and Fmoc-FRGDF involved hydrogen bonding, electrostatic forces, and hydrophobic interactions; furthermore, the co-assembly process strengthened the β-sheet structure while significantly promoting supramolecular ordering. Interestingly, the release profile of Que adhered to the Korsmeyer–Peppas pharmacokinetic equations. Conclusions: Overall, this study examines the impact of polyphenol on the rheological properties, microstructural features, secondary structure conformation, and supramolecular ordering within peptide–polysaccharide–polyphenol ternary complexes, and the Fmoc-FRGDF/HA hydrogel system demonstrates a superior performance as a delivery vehicle for maintaining quercetin’s bioactivity, thereby establishing a multifunctional platform for bioactive agent encapsulation and controlled release. Full article

Bacteriophages (phages), the most abundant biological entities on Earth, have long served as both model systems and therapeutic tools. Recent advances in synthetic biology and genetic engineering have revolutionized the capacity to tailor phages with enhanced functionality beyond their natural capabilities. This review outlines the current landscape of synthetic and functional engineering of phages, encompassing both in-vivo and in-vitro strategies. We describe in-vivo approaches such as phage recombineering systems, CRISPR-Cas-assisted editing, and bacterial retron-based methods, as well as synthetic assembly platforms including yeast-based artificial chromosomes, Gibson, Golden Gate, and iPac assemblies. In addition, we explore in-vitro rebooting using TXTL (transcription–translation) systems, which offer a flexible alternative to cell-based rebooting but are less effective for large genomes or structurally complex phages. Special focus is given to the design of customized phages for targeted applications, including host range expansion via receptor-binding protein modifications, delivery of antimicrobial proteins or CRISPR payloads, and the construction of biocontained, non-replicative capsid systems for safe clinical use. Through illustrative examples, we highlight how these technologies enable the transformation of phages into programmable bactericidal agents, precision diagnostic tools, and drug delivery vehicles. Together, these advances establish a powerful foundation for next-generation antimicrobial platforms and synthetic microbiology. Full article

The increasing adoption of electric vehicles (EVs) requires efficient management of charging infrastructure, particularly in optimizing the allocation of limited charging resources. This paper addresses the preemptive electric vehicle charging scheduling problem (EVCSP), where charging sessions can be interrupted to maximize the number of satisfied demands. The existing mathematical formulations often struggle with scalability and computational efficiency for even small problem instances. As a result, we propose an enhanced mathematical programming model, which is further refined to reduce decision variable complexity and improve computational performance. In addition, a constraint programming (CP) approach is explored as an alternative method for solving the EVCSP due to its strength in handling complex scheduling constraints. The experimental results demonstrate that the developed methods significantly outperform the existing models in the literature, providing scalable and efficient solutions for optimizing EV charging infrastructure. Full article

Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

Neuropathic pain is a significant global clinical issue that poses substantial challenges to both public health and the economy due to its complex underlying mechanisms. It has emerged as a serious health concern worldwide. Recent studies involving dorsal root ganglion (DRG) stimulation have provided strong evidence supporting its effectiveness in alleviating chronic pain and its potential for sustaining long-term pain relief. In addition to that, there has been ongoing research with clinical evidence relating to the role of small non-coding ribonucleic acids known as microRNAs in regulating gene expressions affecting pain signals. The signal pathway involves alterations in neuronal excitation, synaptic transmission, dysregulated signaling, and subsequent pro-inflammatory response activation and pain development. When microRNAs are dysregulated in the dorsal root ganglia neurons, they polarize macrophages from anti-inflammatory M2 to inflammatory M1 macrophages causing pain signal generation. By reversing this polarization, a therapeutic activity can be induced. However, the direct delivery of these nucleotides has been challenging due to limitations such as rapid clearance, degradation, and reduction in half-life. Therefore, safe and efficient carrier vehicles are fundamental for microRNA delivery. Here, we present a comprehensive analysis of miRNA-based nano-systems for chronic neuropathic pain, focusing on their impact in dorsal root ganglia. This review provides a critical evaluation of various delivery platforms, including viral, polymeric, lipid-based, and inorganic nanocarriers, emphasizing their therapeutic potential as well as their limitations in the treatment of chronic neuropathic pain. Innovative strategies such as hybrid nanocarriers and stimulus-responsive systems are also proposed to enhance the prospects for clinical translation. Serving as a roadmap for future research, this review aims to guide the development and optimization of miRNA-based therapies for effective and sustained neuropathic pain management. Full article