Search Results (2,629)

The efficient resolution of complete NP problems, such as the Traveling Salesman Problem (TSP), particularly for large instances, remains a major challenge in operations research and combinatorial optimization, especially for many businesses, particularly in sectors such as logistics, urban planning, and networks, where efforts are made daily to optimize routes and delivery times. Optimization methods inspired by collective behavior, such as Ant Colony Optimization (ACO), offer competitive results for solving these types of problems. The main problem is the size of the instances because, when it becomes large, many existing algorithms fail to converge to a good solution within a reasonable timeframe: the execution time is generally very long, and the solution obtained is generally far from being the optimal solution to the problem. In this article, we propose a new way of approaching the resolution of the TSP through new metaheuristics inspired by artificial intelligence techniques and ant colony theory. To evaluate the effectiveness of our methodology, particularly the Multi-colony Ant Colony Optimization version 2-SK (MACOV2SK) method, simulations were performed on several instances of the TSP, focusing on large-scale instances. The experimental results clearly demonstrate that the proposed approach significantly improves upon several other approaches in the literature in terms of execution time and solution quality, especially for large-scale problems. Full article

Cloud-dependent irrigation platforms can support remote monitoring, but their use in precision fertigation is limited when local decisions must be made quickly and reliably. Network delay, temporary disconnection, and the use of single-point measurements may all reduce the ability of a system to respond to spatial variation in soil moisture and nutrient demand. In this work, an edge-controlled precision fertigation system was developed by combining multi-parameter soil sensing, spatial task scheduling, and a 6-DOF robotic manipulator. The ESP32 controller runs a preemptive FreeRTOS scheduler, allowing sensor acquisition, inverse-kinematics calculation, and pump actuation to be handled as separate tasks. A Kalman filter was used to smooth soil moisture measurements, and a hysteresis-based control strategy was adopted to reduce false triggering and repeated pump switching. To improve fertigation safety, a hardware–software interlock was added so that fertilizer delivery is always accompanied by water delivery. Hardware-in-the-Loop simulation and a 14-day field deployment were used to evaluate the system. The controller achieved an end-to-end latency of less than 38 ms and maintained operation during network interruptions through cached local parameters. After calibration, the robotic end-effector positioning error was reduced to ±2.4 mm. The hysteresis strategy lowered daily pump cycling by 71%. Based on prototype duty-cycle data and seasonal extrapolation, the projected seasonal water use and fertilizer demand were 44% and 38% lower, respectively, than those estimated for a uniform application. These values should be interpreted as model-based projections rather than direct season-long measurements. During 72 h of continuous operation, no Modbus faults were observed, and RTOS heap fragmentation remained stable. Overall, the results suggest that edge-based deterministic control can provide a practical route for precision fertigation where both spatial variability and intermittent connectivity must be considered. Full article

Although chitosan (CS)- and essential oil (EO)-based packaging systems have been widely reviewed, a focused synthesis connecting nano-delivery design with interfacial regulation, film-network evolution, release behavior, and preservation performance in real food systems remains lacking. This review addresses that gap by examining CS-based nano-delivery systems for EOs in active food packaging, with an emphasis on how carrier design and multiscale organization govern functional performance. Major delivery strategies, including nanoemulsions, nanoparticles, nanogels, Pickering emulsions, nanofibrous systems, and nanocomposites, are discussed in relation to EO stabilization, dispersion uniformity, and controlled release. Their effects on film microstructure, mechanical and barrier properties, thermal stability, optical behavior, and antimicrobial and antioxidant activities are further evaluated alongside preservation outcomes in fruits, vegetables, dairy products, meat, and aquatic products. Particular attention is given to structure-function relationships across the carrier, interface, and film-network levels, and to the distinction between established active-packaging functions and emerging smart-packaging applications. Current challenges include EO compositional variability, limited cross-study comparability, sensory constraints, migration and regulatory concerns, and insufficiently scalable fabrication routes. Future work should prioritize mechanism-informed interfacial design, standardized evaluation frameworks, food-specific release-preservation correlations, and scalable green manufacturing. Full article

Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application. Full article

This review shows that lignocellulosic biomass is not merely an abundant feedstock for nanocellulose production but a strategic platform for building the next generation of sustainable, high-performance materials, integrating feedstock diversity, processing logic, characterization, market direction, and translational applications into a single narrative. Comparing woody and non-woody biomass through the lens of processability, recalcitrance, and value creation while showing why agricultural residues are increasingly central to low-cost, circular nanocellulose production beyond the usual acid-hydrolysis-centered discussion by emphasizing enzymatic hydrolysis as a lower-energy, lower-toxicity alternative while still acknowledging the persistent industrial advantages and environmental costs of chemical and mechanical routes. A further strength of this review is its effort to bridge structure and function: it links extraction strategy to morphology, crystallinity, thermal stability, and surface chemistry, then connects these properties to real applications in packaging, drug delivery, electronics, filtration, energy storage, and biomedical systems. Its distinctive contribution lies in showing that the future of nanocellulose depends not only on how it is extracted but also on how intelligently the biomass source, processing route, material performance, and market need are aligned. Full article

Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload. Full article

Medical and pharmaceutical last-mile deliveries are simultaneously expected to be fast, reliable and temperature-safe for patients and to become measurably greener, yet these objectives often pull transport operations in opposite directions. Third-party logistics (3PL) providers are therefore increasingly required not only to report transport CO₂ emissions, but also to learn from them; however, it remains unclear whether the routine operational data they collect are sufficiently informative to enable such emission-based learning in this regulated and service-critical setting. This study examines the predictive limits of machine learning models in estimating CO₂ emissions in medical and pharmaceutical last-mile deliveries performed by a 3PL operator. Using operational data from six customers, we compare global and customer-specific models for the following two dependent variables: total CO₂ emissions per transport operation and CO₂ emissions per pallet. Linear and non-linear models, including linear regression, ElasticNet, Random Forest, HistGradientBoosting and XGBoost, are evaluated using chronological train-test splitting and cross-validation. The results show that global models fail to outperform a naïve benchmark, with negative R² values for both emission measures. Customer-level models reveal substantial heterogeneity as follows: for selected customers, especially those with more regular operational patterns, moderate predictive performance is achieved, while for others, emissions remain largely unpredictable using the available variables. The findings suggest that routine shipment-level data are insufficient for robust emission prediction in 3PL last-mile operations. Emission-based learning requires richer contextual, vehicle, route, traffic and telematics data, as well as customer-sensitive modelling approaches. The study contributes by identifying the data and modelling limits of sustainability intelligence in medical and pharmaceutical last-mile logistics. Full article

With the rapid growth in e-commerce demand, increasing pressure on same-day delivery, and rising last-mile logistics costs, UAV-based logistics systems have emerged as a promising solution for efficient transportation in complex environments. In mountainous regions, however, irregular terrain, limited infrastructure accessibility, and strict flight constraints significantly increase the difficulty of logistics planning. To address these challenges, this study proposes a two-layer collaborative optimization framework for multi-center UAV logistics delivery systems. At the lower level, a multi-center site selection model was developed to determine the optimal distribution center locations and assign task areas. A trajectory cost matrix was constructed by comprehensively considering multiple constraints. The model was solved using a hybrid strategy that combines chaotic initialization and local enhancement based on the elite saDE method to improve the Starfish Optimization Algorithm, called the Mixed-Strategy Improved Starfish Optimization Algorithm (MISFOA), thereby generating feasible three-dimensional flight trajectories between local nodes. At the upper level, an improved Adaptive Large Neighborhood Search (IALNS) algorithm is applied to perform UAV mission assignment and route scheduling within each distribution center, based on the trajectory cost matrix pre-calculated at the lower level. The proposed framework achieves effective information exchange and hierarchical coupling between center selection and scheduling at the distribution level, thereby enabling unified optimization of the multi-center location and coordinated dispatch system. Simulation results demonstrate that the proposed method significantly improves delivery efficiency and solution quality in complex mountainous environments while ensuring trajectory feasibility and operational safety. This model provides a scalable and practical optimization framework for low-altitude logistics network planning under complex constraints. Full article

The transition to electric vehicles (EVs) in urban logistics presents complex operational challenges, driven primarily by limited battery capacities, charging station scheduling, and dynamic traffic congestion. This paper introduces a framework to solve the Capacitated Multi-Depot Electric Vehicle Routing Problem (MD-EVRP). We propose a novel Multi-Depot Rotational Sweep Cluster K-means (MD-RSCK) algorithm to partition large-scale spatial data while strictly adhering to vehicle capacity constraints. To optimize intra-cluster routing, we develop an Ant Colony Optimization (ACO) engine augmented with a Time-Dependent Congestion Model. Furthermore, the framework integrates an Energy-Aware Route Refiner (EARR). This architecture utilizes recursive backtracking to ensure battery-feasible routes, adapting to both symmetric Euclidean approximations and real-world asymmetric traffic networks. The framework is evaluated against standard IEEE EVRP benchmarks and a multi-depot urban case study based on the road network of Shanghai, China. Experimental results demonstrate that this integrated architecture achieves competitive distance and cost metrics within a 2.44% optimality gap of state-of-the-art algorithms while ensuring strictly feasible battery states and preventing cyclic entrapment, providing a scalable operational tool for modern sustainable logistics. Full article

Electrochemical CO₂ reduction to ethanol is a promising route for circular carbon fuel and chemical production, but practical implementation remains limited by coupled membrane, catalyst, transport, and system integration constraints. This Communication reassesses anion-exchange membranes (AEMs) and bipolar membranes (BPMs) for CO₂-to-ethanol electroreduction by integrating recent 2024–2026 advances with foundational membrane and CO₂RR literature. The central argument is that membrane selection is not a passive separation choice; instead, it actively controls local pH, charge carriers, CO₂ availability, carbonate formation, water activity, proton/cation delivery, product crossover, and downstream techno-economic assessment (TEA) and life-cycle assessment (LCA) burdens. AEM operation can create alkaline cathodic microenvironments that favor C–C coupling, but bicarbonate/carbonate formation imposes carbon-loss, salt-management, and CO₂-recovery penalties. BPM operation can improve pH separation and carbon management through water dissociation and bicarbonate acidification, but its viability depends on water-dissociation efficiency, co-ion exclusion, junction stability, hydration management, and voltage control. Recent ethanol-selective catalyst studies further show that copper oxidation state, grain boundaries, subsurface dopants, ionomers, interfacial wettability, and dynamic operation interact strongly with membrane-imposed microenvironments. This Communication proposes a membrane-centered decision framework linking AEM/BPM selection with ethanol selectivity, single-pass carbon utilization, energy efficiency, durability, TEA/LCA boundaries, and future reactor design. Full article

This paper investigates the path-planning problem of unmanned aerial vehicles (UAVs) for logistics delivery in urban environments. The impact of real-time obstacle avoidance and path smoothness on the flyability of UAVs remains a challenge in existing research. To address the issue that the path generated by the traditional Rapidly exploring Random Tree (RRT) algorithm exhibits a sudden slope change at connection points, which makes the UAV non-flyable, this paper proposes an improved algorithm that combines the traditional RRT algorithm with Bézier curves. The proposed real-time path generation strategy consists of two stages. The first stage constructs the environment model. The second stage integrates the RRT algorithm with Bézier curves, enabling the generated route to achieve real-time obstacle avoidance while being smooth and free of curvature discontinuities. Simulation experiments compare the improved algorithm with the traditional RRT algorithm and global path optimization methods. The experimental results show that the improved algorithm has the advantage of real-time obstacle avoidance, and the generated route is smooth at connection points with no curvature discontinuities, thereby ensuring good flyability. Full article

Chemotherapy has been used in cancer treatment for decades; however, standard chemotherapy treatments still have significant weaknesses, including collateral damage to healthy tissue, rapid development of drug resistance, and dose-limiting toxicity that limits therapeutic value. There is now an alternative approach using polymer materials that are responsive to biological stimuli that will allow for improved treatment of cancer while avoiding the limitations. Responsive polymer materials are designed to be inert during circulation until they reach their site of action; then, they will respond to specific triggers. These smart carriers respond to stimuli present in the tumor microenvironment (e.g., low pH, high glutathione levels, and increased proteolytic activity) or external stimuli applied at the bedside (e.g., localized heat, light, ultrasound, and applied magnetic fields). In both cases, there is a consistent pattern where the drug is released exactly where/when it is needed, with minimal drug release occurring outside that location and timeframe. Therefore, it is theorized that the use of polymeric-based delivery systems with stimuli-regulated drug release will significantly increase the concentration of drug delivered intratumorally, decrease the drug toxicity, and provide a potential mechanism to overcome the development of multidrug resistance from a variety of cancer treatments. To date, various types of responsive polymers have been developed and could be combined to give rise to a wide variety of different vehicle systems (e.g., micelles, nanogels, hydrogels, and hybrid delivery systems), with many of these carriers designed to respond to multiple stimuli simultaneously. Nonetheless, significant challenges remain in the clinical application of these materials due to tumor heterogeneity, immune system interactions, reproducibility issues, polymer chemistry advances, surface chemistry, and other interaction mechanisms. As a result of all of these evolving regulatory systems, as well as some of the emerging areas of polymer chemistry and surface engineering, theranostic integration will allow for new routes to provide therapy for patients with cancer. Additionally, because of these scientific advances, there will also be more opportunities to provide targeted, controllable, and on-demand treatments to patients using stimuli-responsive polymers. Full article

Non-invasive drug delivery for ocular diseases remains a significant challenge in ophthalmology, as conventional eye drops offer less than 5% bioavailability due to pre-corneal barriers and the corneal epithelium. This review explores the intranasal (IN) route as a promising strategy for targeting both the anterior and posterior segments of the eye. The IN route leverages several distinct pathways: the nasolacrimal reflex for remote physiological stimulation; the “neural bridge” through the cribriform plate, allowing direct perineural and vascular transport via the olfactory and trigeminal nerves to bypass the blood–retinal barrier; and systemic absorption that avoids hepatic first-pass metabolism. Pre-clinical evidence indicates that IN administration of agents such as erythropoietin, nerve growth factor, and insulin achieves superior retinal concentrations compared to topical or systemic dosing, offering neuroprotection in models of retinal degeneration and glaucoma. Clinically, varenicline nasal spray is already FDA-approved for dry eye disease, while intranasal steroids demonstrate a favorable ocular safety profile without significantly increasing intraocular pressure. Although limited by mucociliary clearance and small delivery volumes, the IN route offers a painless, non-invasive alternative to intraocular injections, potentially enhancing patient compliance. Future advancements in mucoadhesive nanocarriers are essential to optimize drug residence time and realize the full potential of nose-to-eye delivery in chronic ophthalmic care. Full article

Mesoporous silica nanoparticles (MSNs) are promising nanomaterials for many applications, including water remediation, owing to their high surface area, tunable mesoporosity, and modifiable silanol-rich surfaces. However, their conventional synthesis often relies on costly tetraethyl orthosilicate (TEOS), cationic surfactants, organic solvents, and energy-intensive hydrothermal processing. Herein, a facile sustainable room-temperature sol–gel route is reported using inexpensive sodium silicate as the silica source, L-ascorbic acid as a mild biodegradable acid catalyst, and a binary nonionic surfactant system, Triton X-100/polysorbate 80, as the structure-directing template. The method replaces alkoxysilanes and hazardous cationic templates and eliminates external heating. It enables the production of uniform spherical MSNs with a locally ordered mesoporous structure, high specific surface area up to 551.5 m² g⁻¹, and large pore volume up to 1.98 cm³ g⁻¹. The adsorption capability of the optimized MSNs as nano-adsorbents was demonstrated using amoxicillin (AMX) as a model pharmaceutical contaminant. The optimized sample showed maximum AMX uptake at pH 5.0, followed pseudo-second-order kinetics, and fitted the Langmuir isotherm with a monolayer capacity of 91.3 mg g⁻¹. In spiked water matrices, the optimized MSNs recovered 88.5% and 84.4% of AMX from tap water spiked at 10 and 50 mg L⁻¹, respectively, and 83.5% and 81.0% from synthetic municipal wastewater spiked at the same concentrations, with RSD values below 5%. The adsorbent further retained 94% of its initial capacity after five adsorption–desorption cycles. This work establishes a scalable green route for producing high-quality MSNs and demonstrates the feasibility of the resulting silanol-rich mesoporous nano-adsorbents for pharmaceutical micropollutant removal, while also indicating their potential suitability as carrier platforms for drug-delivery applications. Full article

Therapeutic proteins have emerged as a cornerstone of modern medicine due to their high specificity and strong biological effects. However, delivering these proteins poses significant challenges due to their instability, susceptibility to enzymatic breakdown, low permeability, and reliance on invasive parenteral routes. Buccal drug delivery is a promising non-invasive alternative, offering quick systemic absorption while avoiding gastrointestinal degradation and hepatic first-pass metabolism. Three-dimensional (3D) printing as a fabrication method has further enhanced the potential of buccal delivery, enabling precise dosage control, multilayer structures, and patient-specific customization. This review focuses on the current state of the traditional and 3D-printed buccal film platforms using different printing methods for protein delivery, and critically analyzes protein stability challenges, and formulation strategies. The discussion further highlights emerging proof-of-concept studies. Full article