Advancing drug delivery to articular cartilage:From single to multiple strategies

Articular cartilage(AC) injuries often lead to cartilage degeneration and may ultimately result in osteoarthritis(OA) due to the limited self-repair ability. To date, numerous intra-articular delivery systems carrying various therapeutic agents have been developed to improve therapeutic localization and retention, optimize controlled drug release profiles and target different pathological processes. Due to the complex and multifactorial characteristics of cartilage injury pathology and heterogeneity of the cartilage structure deposited within a dense matrix, delivery systems loaded with a single therapeutic agent are hindered from reaching multiple targets in a spatiotemporal matched manner and thus fail to mimic the natural processes of biosynthesis, compromising the goal of full cartilage regeneration. Emerging evidence highlights the importance of sequential delivery strategies targeting multiple pathological processes. In this review, we first summarize the current status and progress achieved in single-drug delivery strategies for the treatment of AC diseases. Subsequently, we focus mainly on advances in multiple drug delivery applications, including sequential release formulations targeting various pathological processes, synergistic targeting of the same pathological process, the spatial distribution in multiple tissues, and heterogeneous regeneration. We hope that this review will inspire the rational design of intraarticular drug delivery systems(DDSs) in the future.

Quality control of a four-echelon agri-food supply chain with multiple strategies

Current research into the supply chain coordination problem of product quality focuses mainly on the two-echelon supply chain,and most coordination models neglect the demand relationship between quantity and quality.Even in a direct sale pattern,an agrifood supply chain involves at least four actors,and the contract between different actors may be different.Here,we present a four-echelon agri-food supply chain that consists of one agricultural producer,one processing enterprise,one distributor,and many consumers.We also analyze the quality decision of each actor based on Stackelberg game theory and develop a combined multiple strategy(profit sharing,quality commitment,and risk sharing)for coordinating quality control in the agri-food supply chain.The results show that these strategies affect quality control at the level of the processing enterprise;however,only the profit-sharing strategy and a quality commitment to consumers affect the product quality from agricultural producers.Product quality is not associated with the profit-sharing contract between the processing enterprise and the distributor.Thus,the quality commitment to consumers is the only way for a processing enterprise to control the food market price.Furthermore,enforcing and enhancing this quality commitment to consumers by the processing enterprise will improve the quality of primary agricultural products and food.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.

Application of Improved Compact Particle Swarm Optimization to Large Ontology Alignment Task

Ontology occupies an important position in artificial intelligence,computer linguistics and knowledge management.However,when different ontologies are constructed to represent the same information in a domain,the so-called heterogeneity problem arises.In order to address this problem,a key task is to discover the semantic relationship of entities between given two ontologies,called ontology alignment.Recently,the meta-heuristic algorithms have already been regarded as an effective approach for solving ontology alignment problem.However,firstly,as the ontologies become increasingly large,meta-heuristic algorithms may be easier to find local optimal alignment in large search spaces.Secondly,many existing approaches exploit the population-based meta-heuristic algorithms so that the massive calculation is required.In this paper,an improved compact particle swarm algorithm by using a local search strategy is proposed,called LSCPSOA,to improve the performance of finding more correct correspondences.In LSCPSOA,two update strategies with local search capability are employed to avoid falling into a local optimal alignment.The proposed algorithm has been evaluated on several large ontology data sets and compared with existing ontology alignment methods.The experimental results show that the proposed algorithm can find more correct correspondences and improves the time performance compared with other meta-heuristic algorithms.