When solving complex Markov Decision Processes (MDPs), the state value function can be represented by a parameterized function or a lookup table. Parameterized functions are space-efficient but computationally complex, while lookup tables are efficient but struggle with large state spaces, leading to memory waste or high query complexity. This study proposes a lookup table design with O(1) query complexity and no memory waste for complex MDPs commonly found in dynamic vehicle routing problems. We define a state key as a numerical triple and design two 3D arrays. Though initially memory-wasteful, we apply operations like mirroring, rotation, and translation to remove unused memory while maintaining O(1) query complexity. Our contributions are: (1) making state aggregation optional in certain MDPs, and (2) efficiently managing memory for querying complex data. An illustrative example is used throughout to enhance clarity.

This paper addresses the problem of document-level event extraction as a critical step toward automated knowledge acquisition for intelligent systems. Extracting structured event knowledge from unstructured text is essential for downstream knowledge-based applications such as knowledge graph construction, decision support, and intelligent question answering. However, existing approaches struggle to effectively capture long-range dependencies and complex inter-event relationships within documents while maintaining computational efficiency. To address these challenges, we propose a novel knowledge-aware framework, the Graph Convolutional Network with Pseudo-trigger Combination Recognition(GCN-PCR). The proposed model explicitly represents entities, mentions, and sentences as interconnected nodes in a relational graph, enabling structured modeling of semantic dependencies and co-reference information across document contexts. Furthermore, the pseudo-trigger combination strategy enhances the identification of event structures by improving the robustness of event boundary detection and argument association. Extensive experiments on two public datasets demonstrate that the proposed approach achieves superior performance compared to state-of-the-art methods. More importantly, our framework provides a scalable and effective solution for transforming unstructured textual data into structured event knowledge, facilitating its integration into knowledge-based systems.

Traditional journal ranking metrics like the Journal Impact Factor (JIF) depend on raw citation counts, often overlooking citation context, interdisciplinary influence, and temporal dynamics, which restricts their ability to accurately evaluate scholarly impact. This paper introduces an edgeconditioned Graph Attention Networks (GAT) to refine JIF-based assessment by incorporating citation types weighted through adjustable schemes tailored to evaluation priorities. Trained on 11,054 journals from the Journal Citation Reports database, the model achieves strong predictive accuracy for JIF and produces rankings that closely align with JIF, SCImago Journal Rank, and Article Influence metrics. It uses an enhanced GAT variant by leveraging citation and publication counts, refining journals in rankings, reflecting their broader influence. In contrast to JIF's rigid, static methodology, this approach provides a configurable framework that adapts to diverse scholarly assessment needs, offering improvement over conventional citation-based metrics. By predicting JIF with high precision, this work bridges the gap between traditional bibliometric indicators and modern networkbased methods. It advances journal evaluation by combining predictive power with a deeper understanding of citation counts and their types with publication volume in one framework.

In today's big data era, where a significant volume of business data is generated daily, managing conflicting information within business process networks is crucial for maintaining operational efficiency. This paper addresses this challenge by proposing an efficiency model for business process networks tailored to handle conflict information, drawing on queuing theory and evidence theory. Firstly, we introduce a novel approach for measuring conflict information based on evidence theory and Pignistic probability transformation theory. Next, we tailor efficiency models for the four fundamental structures found in business process networks: sequential, selective, parallel, and loop structures, using queuing theory to manage conflict information effectively in each scenario. We further extend this approach by conceptualizing virtual business activities, allowing us to view the entire business process network as a sequential structure of virtual business activities, facilitating efficiency measurement across the network. Utilizing these measurements, we formulate the queuing service of the business process network as a nonlinear programming problem aimed at minimizing time, thus determining the optimal service rate for business process activities. Finally, we demonstrate the applicability and effectiveness of our proposed model through an experimental analysis focused on the railway intermodal transportation business process. The experimental results indicate that our model significantly reduces the impact of conflicting information, leading to a measurable improvement in the efficiency of the business process network. Specifically, the model achieves a notable enhancement in the coordination and execution of intermodal transportation activities, thereby streamlining operations and reducing decision-making uncertainties. This structured approach not only addresses the challenge of managing conflicting information within business process networks but also provides a clear framework for understanding and optimizing network efficiency.

Progress in Chinese Named Entity Recognition (CNER) has highlighted lexicon-based methods that use word information to boost performance. However, these methods neglect two crucial aspects: the regularity of word boundary and the characteristics of the NER task. To address these shortcomings, we introduce BADA-LAT, a Transformer architecture that incorporates word boundary information and introduce local attention computation. It can enhance entity boundary recognition and concentrate the attention weight of the target token on adjacent characters and matched words. Furthermore, to mitigate the issue of class imbalance, we augment the original training data using large language model (LLM). Our method outperforms other lexicon-based ones, as shown in experiments on four Chinese datasets.

Abstract Service continuity (SC) plays an important role in service industry. In this paper, a daily home parcel pick‐up scheduling and routing problem is studied with SC consideration. In this context, a fixed group of couriers is assigned to provide services exclusively within a specific region. Each day, customers within this region either have pre‐scheduled services in advance or stochastically make service requests during the couriers' shifts. Pre‐scheduled service request can be serviced by any courier serving the region, while stochastic service request can only be serviced by the courier most familiar with the corresponding customer. Consequently, the study problem is divided into two distinct sub‐problems: (i) the allocation of pre‐scheduled requests to couriers before their tours start and (ii) routing each courier independently to service both its allocated customers and its potential stochastic customers. The sub‐problem (ii) is formulated as a Markov decision process and an existing dynamic programming method is applied to solve it exactly. For the sub‐problem (i), Greedy Randomized Adaptive Search Procedure‐based meta‐heuristics is proposed. Finally, these meta‐heuristics are evaluated through computational experiments, yielding valuable application insights.

With the annual growth of social logistics volume, the business volume at the warehouse end continues to increase and management becomes more complex. In order to measure and provide feedback on the effectiveness of the warehouse management system to optimize the warehousing business process, it is necessary to simulate it. The article develops a warehouse business process simulation system that integrates workflow engines based on process interaction strategy. An experiment is designed using the goods warehousing process as an example, and the results verify the effectiveness of the simulation system. The system simulates time through a random number generator. Each link of the process assigns tasks to different workstations for concurrent execution through the listener. The simulation process is created in the central warehouse and uniformly scheduled by the scheduler. The system also has the function of equipment random failure to simulate its impact on warehouse efficiency. Finally, the simulation results can be output for statistical analysis of the data. The results show that the system can calculate and analyze the entire process of warehouse business processes, provide useful guidance for the work of shipping personnel and warehouse management personnel, and achieve refined management of warehouse resources.

随着领域知识图谱价值得到广泛认可,各领域专家就本领域知识图谱的构建展开了广泛研究,并成功将其应用于知识问答、知识推荐等服务中.仓储领域中存在着大量隐性知识,但难以直接为业务人员利用.基于知识情境构建仓储领域知识图谱,将仓储隐性知识显性化可以为仓储业务人员提供更好的知识服务.鉴于此,提出了一种基于知识情境的仓储知识表示方法.提出以数据模型和实例模型为核心的仓储情境知识图谱总体框架,分别给出了"二阶"数据模型构建方法及不同结构数据的实例模型构建方法,运用本体、数据集成及实体关系抽取技术完成了仓储情境知识图谱的构建并将其运用于智能问答系统中.通过仓储情境知识图谱构建案例分析可知,仓储情境知识图谱总体框架及数据/实例模型构建方法可行,构建的仓储情境知识图谱能够满足仓储人员对情境知识的需求.

This paper provides a analysis of the challenges faced in routing in wireless mesh networks and how deep learning can be used to improve performance. Wireless mesh networks (WMNs) are a particular kind of wireless network that use mesh routers and mesh clients that talk to one another to provide wireless coverage over a wide area. The paper discusses the limitations of traditional routing methods in WMNs and highlights the potential of deep learning-based routing approaches to revolutionize WMNs. The survey evaluates the literature focusing on the various routing approaches, performances, and research methodologies. The study aims to provide several potential areas for further investigation in deep learning-based routing approaches for WMNs and discusses several research challenges and opportunities in the field. The identification of trends and challenges, and recommendations for future research directions provide valuable insights for researchers and practitioners in the field of wireless mesh networks.

Colleges and universities have always been the main export base of social talents. However, with the rapid development of the society, the demand for compound talents continues to grow, and the current college curriculum design lags behind the social demand, resulting in the contradiction between the current social demand for intelligent warehousing talents and the lack of practical operation ability of college graduates. To solve this problem, this paper designs an intelligent course of “theory + practice + simulation” based on the experimental teaching platform and the hardware and management system of automatic warehouse equipment. The course is composed of WMS (warehouse management system) based on workflow with a unified data model and simulation module. The unified data model can provide data guarantee for simulation modeling, and the experimental teaching platform is available for students to learn the course knowledge independently. Finally, the combination of theory and practice will be achieved to deepen students’ understanding of automated warehouse and their cognition of intelligent warehouse technology and management, so as to meet the social demand for compound intelligent warehouse management talents.

The executive time of workflow network has an important effect on the efficiency of business process. In this paper, the activity executive time is divided into the service time and the waiting time, then the lead time can be extracted from the waiting time. The executive time formulas of the three basic structures in workflow network are deduced based on the activity lead time. Taken the process of e-commerce logistics as an example, insert appropriate lead time for key activities by using Petri net and the executive time optimization model is built to minimize the waiting time with the time-cost constraints. Then the solution program-using VC++6.0 is compiled to get the optimal solution, which reduces the waiting time of key activities in the workflow, and verifies the role of lead time in the timeliness of e-commerce logistics.

随"互联网+物流"产生的智能托盘,为带板运输提供新的发展机遇,而目前对智能托盘应用价值的研究较为缺乏.本文以三级供应链中托盘共用为背景,构建智能托盘与传统托盘混合共用的闭环循环流通系统,将循环周期分为八个阶段,建立系统总成本和效益测算模型,分别以传统托盘返还率、损坏率和智能托盘损坏率为随机变量测算系统总成本与效益.测算结果表明,相较于传统托盘的在途丢失和周转效率,使用智能托盘能更有效地降低系统总成本并提升效益,传统托盘的适量损坏并不会提高系统总成本,而智能托盘的损坏与系统总成本呈正相关关系,因此智能托盘的使用将提升托盘共用系统的稳定性.

随着电子商务和第三方物流的快速发展,汽车零配件供应商在传统直销渠道的基础上正在通过直销渠道侵入零售市场.为了研究不对称信息下的供应商侵入问题,针对由一个供应商和一个零售商组成的供应链模型,考虑零售渠道与直销渠道的差异,并将不对称信息下的产品竞争类型从现有的数量竞争扩展为伯川德竞争.研究表明:不对称信息且伯川德竞争下,供应商侵入使供应商、零售商的收益情况好转还是恶化,将取决于直销成本和产品替代系数的取值;在一定范围内信息的不对称性可以使供应商和零售商获取比相应对称信息下更多的收益.

Information fusion is increasingly used in workflow. Information fusion can be used to design, validate, execute and visualize scientific workflows and their results. Recently, there are many workflow systems based on information fusion, which can be divided into activity-oriented workflow information fusion, process-oriented workflow information fusion and topic-oriented workflow information fusion from different perspectives. They are also surrounded by information fusion and workflow in various fields of society. This paper mainly discusses various kinds of work. Different modes of information fusion between streams.

The transfer of activity information plays an important role in running process of workflow management system. It not only affects the self-adaption efficiency of workflow, but also can bring about resource consumption problem, which caused by the judgement of transfer of complex activity information. Aiming at this, based on belief rule-base inference methodology using the evidential reasoning approach (RIMER), it is proposed that a novel workflow network model to reduce complexity of the transfer process. After an activity node rule with belief degrees is defined, the activity node rule base is built by using activity history tables in workflow log data. Moreover, calculating the similarity between input activities and rules of activity node and judging the status of activity nodes are carried out to obtain activation rule set. The evidential reasoning (ER) approach is adopted for rule inference on the basis of calculating the activation degree of rule in activation rule set. Subsequently, comprehensive rules are constructed so as to achieve the transfer of activity information. And workflow network model is also successfully developed. Besides, this model can be further applied to improve the benefit and value of business process management.

介绍了基于Flask框架的数字核信号处理平台设计.该平台对单个采集任务上传的数字核信号进行实时滤波,获取能谱和脉冲计数,任务完成后原始核信号数据以数据包保存于服务端.该平台还对数据包里的原始数据使用平台提供的滤波、去堆积、获取能谱和脉冲计数等算法程序进行综合分析.同时也开发了一套基于网页技术的人机交互界面,可以让用户与服务端友好地交互.

无车承运人试点为我国现代物流的发展带来了新的机遇,本文基于多Agent建模技术,对无车承运人参与下的公铁联运物流服务建立了仿真模型.在Anylogic平台上模拟了非协同和协同两种情况下的参与主体行为规则和相互关系,通过对比分析给出了公铁联运物流服务协同形成的机理.研究结果表明:公铁联运物流服务的需求弹性大,其替代品的市场竞争力强;物流服务协同中无车承运人起到主导作用,且有利于提升各参与主体的效益,更好地满足社会物流需求.

Contradiction separation based deduction is a key generalized inference rule of binary resolution, and has unique ability for automated theorem proving. Different inference rules need different proof searching strategies. This paper proposes two methods to avoid the repeated deductions and redundant clauses generated in the contradiction separation based deduction, that is, an improved clauses and literals weighted based method, and a redundant clauses prejudging and backtracking strategy. This work provides fundamental methods for contradiction separation based deduction on practical implementation.