Search Results (2,006)

In the context of rapid development within the logistics sector and widespread advocacy for sustainable development, this paper proposes enhancements to the task scheduling and path planning components of four-way shuttle systems. The focus lies on refining and innovating modeling approaches and algorithms to address issues in complex environments such as uneven task distribution, poor adaptability to dynamic conditions, and high rates of idle vehicle operation. These improvements aim to enhance system performance, reduce energy consumption, and achieve sustainable development. Therefore, this paper presents an energy-saving and efficiency-enhancing optimization study for a four-way shuttle-based high-density automated warehouse system, utilizing deep reinforcement learning. In terms of task scheduling, a collaborative scheduling algorithm based on an Improved Genetic Algorithm (IGA) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) has been designed. In terms of path planning, this paper provides the A*-DQN method, which integrates the A* algorithm(A*) with Deep Q-Networks (DQN). Through combining multiple layout scenarios and adjusting various parameters, simulation experiments verified that the system error is within 5% or less. Compared to existing methods, the total task duration, path planning length, and energy consumption per order decreased by approximately 12.84%, 9.05%, and 16.68%, respectively. The four-way shuttle vehicle can complete order tasks with virtually no conflicts. The conclusions of this paper have been validated through simulation experiments. Full article

Public transit subsidization often suffers from a double (or bilateral) moral hazard problem, where both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts. Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge. This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators are active co-producers of service quality whose own unobservable efforts—distinct from the subsidy itself—are critical to outcomes.The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery. Full article

Azaleas (Ericaceae) are among the most diverse ornamental plants, celebrated for their cultural and economic significance. R. simsii has been extensively utilized in horticulture as a parent species for both “pot azalea” cultivars and various cultivars grown in the warmer regions of China. From 2021 to 2023, approximately 15% of R. simsii in nurseries situated in the Xuanwu District, Nanjing, exhibited symptoms of wilting and chlorosis. Investigations revealed that these symptoms were caused by a pathogen responsible for crown and root rot. Strains were isolated from the roots of affected plants. The morphology of the colonies was predominantly radial to stellate, characterized by intercalary and terminal hyphal swelling. The sporangia appeared spherical, pyriform, or ovoid with a single papillae. For accurate identification, the 28S rDNA gene (Large subunit, LSU), cytochrome oxidase subunit I (COXI), and cytochrome oxidase subunit II (COXII) genes were amplified through PCR and then sequenced. The species was identified as P. vexans after completing the phylogenetic analysis. Healthy R. simsii plants were infected with zoospores and developed symptoms similar to those of natural infection. Furthermore, the morphological characteristics of the isolates from the experimentally infected plants were similar to those of the original inoculated strains. This study identified P. vexans as the pathogen causing root rot in R. simsii. During the sampling process, several strains were isolated from the rhizosphere soil of healthy rhododendron plants. Based on this, research was immediately initiated to explore whether there are specific bacterial species in the soil that have the potential to inhibit the occurrence of root rot. Additionally, an endophytic bacterial strain BL1 was isolated from rhizosphere soil and subjected to Whole-Genome Shotgun (WGS) sequencing, thus constructing a bacterial genome framework for this isolate. The strain BL1 was identified as Bacillus licheniformis. To our knowledge, this is the first report of the occurrence of P. vexans causing crown and root rot of R. simsii in China. In this study, we also focused on exploring the potential of biological control agents against P. vexans. The isolation and identification of the endophytic bacterial strain BL1 (Bacillus licheniformis) from the rhizosphere soil of healthy soil show strong in vitro antagonism, identifying it as a promising candidate for future biological control studies of root rot in R. simsii. The genomic component analysis and coding gene annotation of BL1 provide insights into its genetic makeup and potential mechanisms of action against pathogens. However, these findings are based on in vitro assays. Therefore, further research, including in planta experiments, is essential to confirm the efficacy of BL1 in controlling P. vexans infections in R. simsii and to evaluate its potential for practical application. Full article

Background/Objectives: Human papillomavirus (HPV) is the main causative agent of cervical cancer and contributes to a significant proportion of other anogenital and oropharyngeal malignancies. The need for better biomarkers and therapeutic approaches in HPV-associated cancers has drawn attention to exosomes, small extracellular vesicles known for their stability, biomolecule transport capabilities, and role in cell-to-cell communication. Methods: This review comprehensively evaluates recent literature on the diagnostic, prognostic, and therapeutic applications of small extracellular vesicles, particularly exosomes, in HPV-related cancers. It analyzes findings on exosomal nucleic acids, proteins, and long non-coding RNAs, as well as engineered exosome-based therapies. Results: Exosomal miRNAs (e.g., miR-204-5p, miR-99a-5p, miR-21), proteins (e.g., glycolytic enzymes, HSP90), and lncRNAs (e.g., HOTAIR, DLEU1) have emerged as promising biomarkers for disease detection and monitoring. Exosomal cargo actively participates in HPV-related tumor progression. For example, miRNAs such as miR-21 and miR-146a modulate immune cell polarization and inflammatory signaling, while lncRNAs like HOTAIR promote oncogenic transcriptional programs. Exosomal proteins including HSP90 and ANXA1 facilitate extracellular matrix remodeling and immune evasion, thereby influencing tumor growth and metastasis. In HPV-positive head and neck and cervical cancers, exosomal cargo reflects HPV status, tumor progression, and treatment response. Therapeutic studies demonstrate the utility of exosomes in vaccine delivery, immune modulation, and drug delivery systems, including the use of PROTACs. However, clinical translation faces barriers including isolation protocol standardization, biomarker validation, and scalable production. Conclusions: Exosomes hold great promise for integration into diagnostic and therapeutic workflows for HPV-related cancers. Future research should focus on resolving standardization issues, validating biomarkers in diverse cohorts, and optimizing engineered exosome platforms for targeted therapy. Full article

As the scale of distributed resources continues to expand, decentralization and multi-agent characteristics bring significant challenges to low-carbon dispatching and market participation of power grids. To this end, this paper proposes a collaborative optimization scheduling framework with distributed resource aggregators (DRAs) as the main body, innovatively coupling carbon Emission trading (CET) with electric vehicle carbon quota participation, and the renewable energy quota (RPS) with tradable green certificate (TGC) transaction as the carrier, as well as constructing the connection path between the two to realize the integrated utilization of environmental rights and interests. Based on the ε-constraint method, a bi-objective optimization model of economic cost minimization and carbon emission minimization is established, and a multi-dimensional evaluation system, covering the internal and overall operation performance of the aggregator, is designed. The example shows that, under the proposed CET-RPS coupling mechanism, the total cost of DRA is about 23.4% lower than that of the existing mechanism. When the carbon emission constraint is relaxed from 2700 t to 3000 t, the total cost decreases from CNY 2537.32 to CNY 2487.74, indicating that the carbon constraint has a significant impact on the marginal cost. This study provides a feasible path for the large-scale participation of distributed resources in low-carbon power systems. Full article

Background/Objectives: Glioblastoma (GBM) is a fatal tumor in the central nervous system (CNS) with a poor prognosis. Preventing tumors from post-surgical recurrence is a significant clinical challenge, since current methods deliver chemotherapeutic agents in a rapid manner and are not effective against the residual tumor cells. To address these limitations, we develop a blue light-crosslinking hydrogel which can be rapidly gelled in situ and tightly adhere on the tissues for controlled chemotherapy, radiotherapy, and enhanced laser interstitial thermal therapy (LITT) to inhibit residual tumor cells from post-surgical recurrence. Methods: We utilize gelatin-MA based hydrogel with crosslinker VA-086 as hydrogel scaffold to encapsulate small-molecule drugs (Epirubicin and Cisplatin) and LITT agent polypyrrole-coated graphine oxide (PPy@GO). The mixture can form into hydrogel in situ by blue light irradiation and performed chemo-LITT and radio therapy simultaneously. Then we determine the prevailing factors that affect efficient encapsulation of therapeutic agents within hydrogels, efficiency of gelation, LITT enhancement, and drug release. Then evaluate efficiency in human cancer cells and an in vivo tumor model. Results: Our results demonstrate that 18 wt% Gelatin MA formulation achieved >95% gelation within 2 min, with drug-loaded gels forming within 5 min. The gelation can perform both in vitro and in vivo without affect the drug efficiency. This multi-treatment system can effectively prevent tumor recurrence and significantly prolong the medium survival of glioma-bearing (MBR-614 or U87-MGFL) mice to above 65 days compared with the control group (36 days). Conclusions: The results demonstrated promising effect of this system as a multi-therapeutic platform which combined chemo-LITT and RT. This synergistic strategy presents a new approach to the development of a local drug delivery system for the prevention of brain tumor recurrence. Full article

Brucea javanica (BJ), a key representative of traditional Chinese herbal medicine, is derived from the dried mature fruit of Brucea javanica (L.) Merr., a plant in the Simaroubaceae family. Its pharmacological activity is largely attributed to diverse chemical constituents. To date, approximately 200 distinct chemical constituents have been isolated and identified, mainly comprising quassinoids, triterpenes, alkaloids, steroids, phenylpropanoids, and flavonoids. Contemporary pharmacological studies have demonstrated the significant activities of BJ in various areas, including anti-tumor, anti-inflammatory, and anti-parasitic effects. Notably, its oil form (Brucea javanica oil) has been extensively utilized in treating various cancer types. This review aims to systematically summarize the antitumor components, mechanisms of action, and clinical applications in cancer therapy, with the goal of providing theoretical support for further antitumor research and the development of new BJ-based drugs, highlighting its potential as an antitumor agent. Full article

Prefabricated building projects represent industrialized and intelligent construction through factory production, standardized design, and mechanized assembly. This study presents a multi-agent simulation approach to model the prefabricated construction process, allowing for the concurrent optimization of the prefabricated component (PC) splitting design and the construction organization plan through iterative simulation. (1) Employing a questionnaire survey, it identifies critical factors affecting schedule and cost from a design–construction coordination perspective. (2) Based on these findings, an agent-based model was developed incorporating PC installation, crane operations, and storage yard spatial constraints, along with interaction rules governing these agents. (3) Data interoperability was achieved among Revit, NetLogo3D and Navisworks. This integrated environment offers project managers digital management of design and construction plans, simulation support, and visualization tools. Simulation results confirm that a hybrid resource allocation strategy utilizing both tower cranes and mobile cranes enhances resource leveling, accelerates schedule performance, and improves cost efficiency. 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

Ferulic acid (FA), together with its polymers and derivatives, has been attracting growing attention as a building block for advanced sustainable polymeric materials due to its renewable origin, intrinsic antioxidant activity, and potential for biodegradability. This review aims to provide a comprehensive overview of recent progress in the synthesis and functionalization of FA-based polymers, covering polymerization strategies, enzymatic modifications, and grafting approaches. The physicochemical characteristics of these materials are discussed, with particular emphasis on thermal stability, antioxidant performance, controlled release of active agents, and their impact on the mechanical and barrier properties of polymer matrices. Furthermore, key application domains—including biomedicine, food packaging, and environmental engineering—are examined, highlighting both the advantages and current limitations associated with FA utilization. Finally, perspectives are outlined regarding the necessity for further research to enhance bioavailability, stability, and synthetic efficiency, as well as the potential of FA-derived polymers in the development of next-generation, functional, and environmentally sustainable materials. Full article

This paper presents the development and evaluation of an autonomous solar dryer designed to enhance the drying efficiency of bee bread granules. In contrast to natural open-air drying, the proposed system utilizes solar energy in an oscillating operational mode to achieve a controlled and accelerated drying process. The dryer comprises a solar collector integrated into the base of the drying chamber, which facilitates convective heating of the drying agent (air). The system is further equipped with a photovoltaic panel to generate electricity for powering and controlling the operation of air extraction fans. The methodology combines numerical modeling with experimental studies, structured by an experimental design framework. The modeling component simulates variations in temperature (288–315 K) and relative humidity within a layer of bee bread granules subjected to a convective air flow. The numerical simulation enabled the determination of the following: the time required to achieve a stationary operating mode in the dryer chamber (20 min); and the rate of change in moisture content within the granule layer during conventional drying (18 h) and solar drying treatment (6 h). The experimental investigations focused on determining the effects of granule mass, air flow rate, and drying time on the moisture content and temperature of the granular layer of Bee Bread. A statistically grounded analysis, based on the design of experiments (DoE), demonstrated a reduction in moisture content from an initial 16.2–18.26% to a final 11.1–12.1% under optimized conditions. Linear regression models were developed to describe the dependencies for both natural and forced convection drying. A comparative evaluation using enthalpy–humidity (I-d) diagrams revealed a notable improvement in the drying efficiency of the proposed method compared to natural drying. This enhanced performance is attributed to the system’s intermittent operational mode and its ability to actively remove moist air. The results confirm the potential of the developed system for sustainable and energy-efficient drying of bee bread granules in remote areas with limited access to a conventional power grid. Full article

As an important supplement to ground computing, edge computing can effectively alleviate the computational burden on ground systems. In the context of integrating edge computing with low-Earth-orbit satellite networks, this paper proposes an edge computing task offloading algorithm based on distributed multi-agent deep reinforcement learning (DMADRL) to address the challenges of task offloading, including low transmission rates, low task completion rates, and high latency. Firstly, a Ground–UAV–LEO (GUL) three-layer architecture is constructed to improve offloading transmission rate. Secondly, the task offloading problem is decomposed into two sub-problems: offloading decisions and resource allocation. The former is addressed using a distributed multi-agent deep Q-network, where the problem is formulated as a Markov decision process. The Q-value estimation is iteratively optimized through the online and target networks, enabling the agent to make autonomous decisions based on ground and satellite load conditions, utilize the experience replay buffer to store samples, and achieve global optimization via global reward feedback. The latter employs the gradient descent method to dynamically update the allocation strategy based on the accumulated task data volume and the remaining resources, while adjusting the allocation through iterative convergence error feedback. Simulation results demonstrate that the proposed algorithm increases the average transmission rate by 21.7%, enhances the average task completion rate by at least 22.63% compared with benchmark algorithms, and reduces the average task processing latency by at least 11.32%, thereby significantly improving overall system performance. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems are extensively utilized in industrial metrology, autonomous navigation, and geospatial sensing due to their high precision and resilience to interference. However, the intrinsic nonlinear dynamics of laser systems introduce significant distortion, adversely affecting measurement accuracy. Although conventional iterative pre-distortion correction methods can effectively mitigate nonlinearities, their long-term reliability is compromised by factors such as temperature-induced drift and component aging, necessitating periodic recalibration. In light of recent advances in artificial intelligence, deep reinforcement learning (DRL) has emerged as a promising approach to adaptive nonlinear compensation. By continuously interacting with the environment, DRL agents can dynamically modify correction strategies to accommodate evolving system behaviors. Nonetheless, existing DRL-based methods often exhibit limited adaptability in rapidly changing nonlinear contexts and are constrained by inefficient uniform experience replay mechanisms that fail to emphasize critical learning samples. To address these limitations, this study proposes an enhanced Soft Actor-Critic (SAC) algorithm incorporating a hybrid prioritized experience replay framework. The prioritization mechanism integrates modulation frequency (MF) error and temporal difference (TD) error, enabling the algorithm to dynamically reconcile short-term nonlinear perturbations with long-term optimization goals. Furthermore, a time-varying delayed experience (TDE) injection strategy is introduced, which adaptively modulates data storage intervals based on the rate of change in modulation frequency error, thereby improving data relevance, enhancing sample diversity, and increasing training efficiency. Experimental validation demonstrates that the proposed method achieves superior convergence speed and stability in nonlinear correction tasks for FMCW LiDAR systems. The residual nonlinearity of the upward and downward frequency sweeps was reduced to $1.869\times {10}^{-5}$ and $1.9411\times {10}^{-5}$, respectively, with a spatial resolution of 0.0203m. These results underscore the effectiveness of the proposed approach in advancing intelligent calibration methodologies for LiDAR systems and highlight its potential for broad application in high-precision measurement domains. Full article

Collagen, as the predominant structural protein in vertebrates, represents a promising biomimetic material for scaffold development. Fibre-based scaffolds produced through textile technologies enable precise modulation of structural characteristics to closely mimic the extracellular matrix architecture using wet-spun collagen fibres. However, this in vitro fibre formation lacks natural crosslinking, resulting in collagen fibres with compromised mechanical strength, enzymatic resistance, and thermal stability compared to their native counterparts, thus restricting their biomedical applicability. Post-fabrication crosslinking is therefore imperative to enhance the durability and functional performance of collagen fibre-based scaffolds. Although traditional crosslinkers like glutaraldehyde effectively improve mechanical strength and stability, their clinical utility is hindered by cytotoxicity and associated adverse biological responses. Alternative synthetic crosslinking agents, such as hexamethylene diisocyanate, 1-Ethyl-3-(3’-dimethyl amino propyl) carbodiimide, and 1,4-Butanediol diglycidyl ether, have demonstrated superior cytocompatibility while effectively improving collagen fibre properties. Nonetheless, synthetic compounds may induce more pronounced foreign body reaction than natural agents, necessitating further investigation into their cytocompatibility across varying concentrations. In contrast, plant-based crosslinking offers a promising, cytocompatible alternative, significantly enhancing the thermal and mechanical stability of collagen fibres, provided that potential fibre discolouration is acceptable for intended biomedical applications. Full article

This paper proposes a coalition-based framework for the coordinated operation of multi-regional power systems subject to extreme uncertainty in demand surges, renewable variability, and resource mobilization delays. Methodologically, we integrate Bayesian learning with distributionally robust optimization (DRO), embedding dynamically updated scenario posteriors into a Wasserstein ambiguity set. This construction captures both stochastic variability from renewable and load realizations and epistemic uncertainty from incomplete knowledge of probability distributions. To align individual incentives with system-level efficiency, we design a risk-adjusted utility mechanism that combines VCG transfers, Shapley allocations, and nucleolus refinements. These mechanisms explicitly consider agent heterogeneity, risk aversion, and coalition stability, ensuring that cooperation remains both efficient and sustainable. The optimization model maximizes expected social welfare while incorporating constraints on transmission corridor capacities, mobilization logistics, demand–response rebound effects, and mobile energy storage operations. A hierarchical decomposition algorithm integrates the Bayesian-DRO dispatch layer with cooperative game-theoretic allocations to maintain tractability and robustness at large scale. A case study on a six-province interconnected system with 14–26 GW peak demand, 10.2 GW solar, 8.6 GW wind, 14 GW peaking units, and 6.8 GW mobile storage demonstrates the effectiveness of the approach. Results indicate that the proposed framework raises expected welfare by nearly 10% relative to a non-cooperative baseline, reduces the probability of unserved energy exceeding 1.5% from almost 2% to negligible levels, and narrows payment disparities across provinces to strengthen coalition stability. Demand response peaks at 250–300 MW with rebound averaging 25%, while mobile BESS units cycle frequently to enhance local reliability. Overall, the findings highlight a robust and incentive-compatible pathway for resilient inter-provincial operation, providing both methodological advances and policy-relevant insights for multi-regional energy governance. Full article