This research examines customer relationship management (CRM) systems using multi-criteria decision-making (MCDM) methods, with the intention of selecting the most suitable solution for small companies. The main goal of this research is to make a decision when choosing a CRM system by applying an objective approach. For this purpose, objective criteria were used, and an objective evaluation of the observed CRM systems was conducted. By using the MEREC (MEthod based on the Removal Effects of Criteria) method, the importance of the criteria was determined, while the CORASO (COmpromise Ranking from Alternative SOlutions) method was applied to rank the CRM systems. These methods were combined using a methodology into a hybrid approach. The results of this approach indicate that CRM systems with a higher degree of integration and automation achieved a higher rank, while systems with limited functionalities and longer implementation times received a lower ranking. This analysis confirms the importance of CRM systems in optimizing business processes, improving customer satisfaction, and enhancing marketing activities in companies. The results of the research can assist small companies in making decisions when selecting a CRM system. The contribution of this research is to provide efficient decision-making in the selection of a CRM system, thereby strengthening the companies' operations and improving their performance.

In recent years, e-commerce has emerged as a dominant sales channel, with an increasing number of large-scale companies exclusively operating online. The substantial growth of e-commerce has been paralleled by the growing importance of efficient logistics, as the flow of goods in international trade demands sophisticated planning and execution. Following the purchase stage, logistics plays a pivotal role in ensuring timely delivery to end customers, with final distribution being one of the most critical aspects. The optimization of the distribution process is particularly challenging due to the complexities involved in the selection of transport modes, optimal routing, and the appropriate types of vehicles. This study investigates the parcel distribution process in the Serbian logistics sector, providing a comprehensive analysis of e-commerce flows during the initial stages of goods movement. A decision-making model based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods is proposed to optimize vehicle selection for parcel distribution. The model evaluates ten vehicle alternatives across nine distinct criteria: delivery volume ($\mathrm{C}_1$), average number of parcels per delivery ($\mathrm{C}_2$), vehicle fleet size ($\mathrm{C}_3$), payload capacity ($\mathrm{C}_4$), number of customer complaints ($\mathrm{C}_5$), cargo volume ($\mathrm{C}_6$), incidence of damaged shipments ($\mathrm{C}_7$), loss of shipments ($\mathrm{C}_8$), and vehicle height limitations ($\mathrm{C}_9$). Sensitivity analysis is conducted to test the robustness and stability of the proposed model, ensuring that the selected vehicle configurations are resilient under varying operational conditions. The findings contribute to the broader understanding of logistics optimization in e-commerce, offering insights into the effective selection of transport vehicles that can enhance the efficiency and reliability of the final distribution phase.

Digital ink Chinese character recognition (DICCR) systems have predominantly been developed using datasets composed of native language writers. However, the handwriting of foreign students, who possess distinct writing habits and often make errors or deviations from standard forms, poses a unique challenge to recognition systems. To address this issue, a robust and adaptable approach is proposed, utilizing a residual network augmented with multi-scale dilated convolutions. The proposed architecture incorporates convolutional kernels of varying scales, which facilitate the extraction of contextual information from different receptive fields. Additionally, the use of dilated convolutions with varying dilation rates allows the model to capture long-range dependencies and short-range features concurrently. This strategy mitigates the gridding effect commonly associated with dilated convolutions, thereby enhancing feature extraction. Experiments conducted on a dataset of digital ink Chinese characters (DICCs) written by foreign students demonstrate the efficacy of the proposed method in improving recognition accuracy. The results indicate that the network is capable of more effectively handling the non-standard writing styles often encountered in such datasets. This approach offers significant potential for the error extraction and automatic evaluation of Chinese character writing, especially in the context of non-native learners.

Multi-Criteria Decision-Making (MCDM) represents a critical area of research, particularly in artificial intelligence, through the modeling of real-world decision-making scenarios. Numerous methods have been developed to address the challenges of integrating non-quantitative, incomplete, and imprecise information under conditions of uncertainty. This paper presents the enhancement of the Defining Interrelationships Between Ranked Criteria II (DIBR II) method by incorporating interval grey numbers, in accordance with the principles of Grey theory, its arithmetic operations, and the DIBR II methodology. The enhancement includes the introduction of a conviction degree to reflect decision-makers' or experts' confidence in their assertions. The application of this enhanced method is demonstrated through an illustrative example, following the procedural steps. Additionally, its efficacy is validated in a real-world scenario involving the selection of Lean organization system management techniques, utilizing the Rough Multi-Attributive Border Approximation Area Comparison (Rough MABAC) method. The results indicate that the enhanced DIBR II method is effective in determining criteria weight coefficients, offering a more nuanced distribution compared to traditional crisp methods. Furthermore, when implemented in a multi-criteria model, it yields a more refined ranking of alternatives, contingent on the degree of confidence in the given claims.

Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of “data silo”. Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study.