Within the realm of digital communication, evaluating the effectiveness of promoting traditional craftsmanship, such as Huimo production, is treated as a multi-attribute decision-making problem. This study introduced an enhanced grey relational analysis (GRA) model integrated with neutrosophic cubic sets—a structure adept at capturing uncertainty, inconsistency, and conflicting data. Attribute weights were objectively assigned via the Criteria Importance Through Intercriteria Correlation approach to minimize bias. An upgraded neutrosophic cubic number (NCN)-GRA framework was developed along with a detailed stepwise computation process. A practical application investigating the digital promotion of Huimo production validated the model's relevance in contemporary educational settings, integrating diverse criteria aligned with present-day pedagogical goals. Comparative analyses verified that the proposed NCN-GRA technique outperformed existing models, showcasing greater reliability and precision when addressing complex decision-making environments. The research provides valuable insights for improving the assessment of communication approaches related to intangible cultural heritage in the digital age.

In recent years, dynamic portfolio optimization has grown critical in finance, as traditional static models fail to adapt to complex market fluctuations—this is the key scientific problem. Deep reinforcement learning (DRL; learning optimal actions using neural networks and reward feedback) offers strong adaptability for dynamic allocation. Therefore, this study proposes a multi-asset DRL-neural network optimization framework that enables real-time market response via state perception, action selection, and reward feedback. Using authoritative data (e.g., Wind, Yahoo Finance) covering major indices or exchange-traded funds (ETFs; a fund that tracks a basket of assets and trades like stocks), the method tests the model across market cycles (e.g., Hong Kong and United States stocks); analyzes return, Sharpe ratio, and maximum drawdown; and compares performance with the mean-variance and Black-Litterman models. Results show the model outperforms traditional methods in risk control, return, and robustness. The study concludes that DRL enhances return stability, supporting intelligent financial decisions. Future research will integrate transaction cost models to boost real-world use.

The evaluation of development quality in the digital rural agricultural economy, against the background of rural revitalization, is a multiple-attribute decision-making (MADM) problem. This study first presents the fundamental operational rules of interval-valued intuitionistic fuzzy sets (IVIFSs), including the Hamacher sum and Hamacher product operations. On this basis, an induced interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (I-IVIFHIHWA) operator is proposed, which integrates the advantages of the interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging operator and the induced ordered weighted averaging operator. Several desirable properties of the I-IVIFHIHWA operator are also investigated. Subsequently, the proposed operator is applied to solve MADM problems within the IVIFS framework. Finally, a practical case concerning the development quality assessment of the digital rural agricultural economy under rural revitalization is presented to verify the validity of the operator.

Efficiency evaluation is a crucial tool for assessing transportation performance, providing valuable insights for policymakers by examining the outcomes of transportation systems relative to the resources used. This study leverages the experiences of European countries to compare and enhance the efficiency of rail and road passenger transport in Turkey. It evaluates the technical efficiency, pure technical efficiency, and scale efficiency of rail and road from 2013 to 2022 and ranks the efficient Decision Making Units (DMU). Both radial and non-radial Data Envelopment Analysis (DEA) methods, while techniques data analysis MAXDEA ULTRA is applied to examine slacks, listing peer, benchmark projection, and strong efficiency projection. The findings indicate that Turkey is more adept than many European countries at converting its transport indicators into useful outputs. Turkey's road passenger performance demonstrates high efficiency, while its rail passenger performance is found to be inefficient generally outperforming European benchmarks.

The evaluation of quality in higher education management focuses on assessing the effectiveness, efficiency, and overall performance of administrative processes within higher education institutions. This evaluation also identifies areas for improvement, fosters accountability, and supports continuous development to enhance institutional performance and competitiveness in a rapidly changing educational environment. The quality evaluation in higher education management is a Multiple-Attribute Group Decision-Making (MAGDM) problem. In this study, the CoCoSo approach is identified for MAGDM with INSs Interval neutrosophic sets (INSs). Subsequently, the interval neutrosophic number Hausdorff Hamming distance CoCoSo (INN-HHD-CoCoSo) approach in light with interval neutrosophic number Hausdorff Hamming distance (INNHHD), is developed for MAGDM. The entropy approach is utilized to determine weight in light with INNHHD under INNs. Ultimately, numerical example for the quality evaluation in higher education management is provided to illustrate the INN-HHD-CoCoSo approach.

The evaluation of railway cold chain logistics service quality is essential for ensuring the safe, efficient, and reliable transport of temperature-sensitive goods, such as food and pharmaceuticals. It identifies strengths and weaknesses in logistics processes, enabling continuous improvement and optimal resource allocation. As the evaluation of railway cold chain logistics involves multiple-attribute group decision-making (MAGDM), this study employs the TODIM and PROMETHEE approaches to propose a robust framework. To effectively manage fuzzy information during the quality evaluation process, probabilistic linguistic term sets (PLTSs) are utilized. The study manages the probabilistic linguistic TODIM-PROMETHEE (PL-TODIM-PROMETHEE) approach, which is specifically designed to address MAGDM problems within the PLTSs framework. To determine weight values in this context, the MEREC approach is applied. Finally, a numerical example evaluating the service quality of railway cold chain logistics is provided to validate the effectiveness of the PL-TODIM-PROMETHEE approach.

Higher education administration is crucial for future development but less evaluated. Faculty performance in colleges and universities plays a key role in administration. This paper is based on simulated annealing algorithm to optimize the administrative level of colleges and universities. The results show large differences between different groups, especially in the east, middle and west. Reforming the performance structure can reduce the differences. Regional differences are small and related to the economy, which need to be optimized. There are large differences between different disciplines, and improving the performance of science and engineering teachers needs administrators' attention. The simulated annealing algorithm improves the evaluation accuracy and provides a reference for improving the management level of universities.

This paper aims to investigate how past decision-making experiences can improve future decision-making. Nine semi-structured interviews were conducted with profitable professional Poker players. The results point out that it is the knowledge background of the decision-maker that makes him make sense of the situations he experiences. The research findings allowed the identification of three mechanisms that facilitate and make future decisions faster and more appropriate based on past experiences: (1) memory, (2) reflection, and (3) tools and analytical approach. The research contributes by showing evidence that, when supported by analytical tools, decision-makers can improve the quality and speed of the decision-making process. For organizations and supply chains, the paper highlights the importance of recognizing patterns based on the past to make sense of the future. For operations management, in events like COVID-19, companies can take advantage of memory to enact over unprecedented scenarios, prevent disruptions, and recover.