With the advancement of intelligent transformation in the manufacturing industry, the building steel structure industry is also facing an urgent need to enhance its intelligent manufacturing level. This article takes the building steel structure manufacturing factory of China Construction Steel Structure Guangdong Co., Ltd. as the research object and deeply explores its management practice of intelligent upgrading. The traditional discrete intelligent manufacturing model of steel structures has many problems, such as low production efficiency, insufficient flexibility of the production line, and backward manufacturing technology. To address these issues, the factory has innovated in multiple key processes. In terms of equipment upgrading, a high-power laser cutting machine is introduced in the blanking process, significantly improving cutting quality and speed; advanced 3D visual recognition technology and cutting robots are adopted in the bevel cutting process, enhancing precision and efficiency; the welding process is optimized with laser rust removal and the application of new technologies to expand the use of welding robots; the grinding process utilizes large-format laser rust removal equipment to improve the rust removal effect of small steel structure components; at the same time, the processes of steel structure turning and transportation are optimized to ensure safety and improve efficiency. Based on the standardized intelligent units of each process, the factory actively constructs intelligent production lines, including intelligent H-shaped steel production lines and intelligent box-shaped production lines, realizing the interconnection and interoperability of equipment and integrated analysis of data, optimizing resource allocation and quality control. In addition, the factory uses technologies such as industrial PON networks to build intelligent manufacturing systems and big data platforms, improving the digital management level. Through these intelligent upgrading measures, the production capacity of the factory's equipment is fully released, the manufacturing efficiency and product quality of steel structures are improved, costs are reduced, benefits are increased, and the employee structure is optimized. This practice not only provides valuable experience for the steel structure industry but also has important reference value for the transformation and upgrading of the entire discrete manufacturing industry in dealing with complex order production modes, helping enterprises closely follow the development trend of intelligent manufacturing and enhance market competitiveness.

Specific grades of high-entropy alloys (HEAs) can provide opportunities for optimizing properties toward high-temperature applications. In this work, the Co-based HEA with a chemical composition of Co47.5Cr30Fe7.5Mn7.5Ni7.5 (at%) was chosen. The refractory metallic elements hafnium (Hf) and molybdenum (Mo) were added in small amounts (1.5at%) because of their well-known positive effects on high-temperature properties. Inclusion characteristics were comprehensively explored by using a two-dimensional cross-sectional method and extracted by using a three-dimensional electrolytic extraction method. The results revealed that the addition of Hf can reduce Al2O3 inclusions and lead to the formation of more stable Hf-rich inclusions as the main phase. Mo addition cannot influence the inclusion type but could influence the inclusion characteristics by affecting the physical parameters of the HEA melt. The calculated coagulation coefficient and collision rate of Al2O3 inclusions were higher than those of HfO2 inclusions, but the inclusion amount played a larger role in the agglomeration behavior of HfO2 and Al2O3 inclusions. The impurity level and active elements in HEAs were the crucial factors affecting inclusion formation.

In the context of developing a new era, the pharmaceutical supply chain market has gradually transformed from a seller’s market to a buyer’s market. The closer the consumers are, the greater the market pricing power, so the pharmaceutical market power of manufacturers and retailers has also changed. This study considers the effect of service on the pricing strategy of the pharmaceutical platform supply chain. The study aimed to coordinate optimization, and the coordination strategy of the pharmaceutical platform supply chain of complementary products is discussed mainly by researching the price and service factors. Various situations are studied by hypothesis and model solving. This study uses Stackelberg game decision-making. Manufacturers are at the forefront of platform supply chain decisions. The research found that the price was lower under centralized decision-making than under decentralized decision-making. Coordination between price and service levels needs attention in the pharmaceutical platform supply chain of complementary products, and the service level should be controlled within a certain range. Only by improving the service level can enterprises maximize profits, providing a theoretical basis for pharmaceutical supply chain pricing strategy research. Supply chain members must strive to improve service levels to improve medical supply consumers’ (patients) psychological satisfaction level. Service levels do not fully mitigate channel conflict. Therefore, pharmaceutical complements have become a way to alleviate the conflicts in the pharmaceutical platform supply chain.

With the continuous improvement of people's living standards, especially under the influence of shopping festivals such as '618', 'Double 11,' and 'Double 12', the competition in the express delivery industry is becoming increasingly fierce. Since there is no express cabinet in many areas, it is necessary to go to the express station to pick up the parcel in person. Therefore, pick-up efficiency affects the customer's satisfaction with the express station. To improve customer satisfaction, we must first improve the efficiency of pick-up. Many express companies now adopt a combination of self-service and manual methods to solve the problem of too long waiting times in picking up parcels. However, it is still inevitable that there will be too long waiting times in shopping festivals due to the explosion of express delivery. In order to improve the pick-up efficiency, this paper uses Arena to establish a simulation model to simulate the pick-up waiting situation and allocate the number of self-service pick-up machines and manual services to solve the problem of excessively long waiting times during pick-up.

Aiming at the problem of low carbon emission reduction in the sustainable development of the logistics industry, this study adopts the research method combining game theory and dynamic system analysis. A tripartite evolutionary game model including the logistics company, the manufacturer, and the government is constructed, and the strategy choice and mutual influence of each participant in the process of low-carbon emission reduction are discussed. At last, MATLAB software is used to simulate the model. By adjusting the values of different parameters, the process and results of the dynamic evolution of the system are displayed intuitively. It was found that the government's sound penalty and subsidy policy plays a key role in promoting the evolution of the manufacturer and the logistics company to adopt low-carbon strategies. Specifically, higher subsidies and moderate penalties can encourage enterprises to take low-carbon measures and promote the system's evolution to a low-carbon and stable equilibrium point. These conclusions help the logistics company and the manufacturer to identify the key factors and paths of low-carbon development, promote growth in the direction of low-carbon, green, and sustainable development, and provide theoretical support and empirical basis for the government to formulate and improve relevant policies on carbon emission reduction, and improve the effectiveness and pertinency of policies.

Medicine occupies an important position in people's lives, and its safety and effectiveness are directly related to people's lives and health. In recent years, with the frequent occurrence of unqualified vaccines, quality control in drug transportation has been paid more and more attention. Therefore, this paper constructs a tripartite evolutionary game model composed of government, pharmaceutical enterprises, and logistics enterprises and models behavioral strategies. The interaction between the two and the influence of different parameters on the evolution are simulated and analyzed. The results show that implementing a government punishment mechanism, inter-firm revenue sharing and drug failure rate will have different impacts on the outcome of the evolutionary stability strategy of pharmaceutical enterprises and logistics enterprises. These conclusions not only enrich the theoretical literature on factors affecting the decision-making factors of pharmaceutical enterprises and logistics enterprises to ensure drug quality but also provide useful references for improving government policies.

In response to the evolving pharmaceutical supply chain market, which is shifting from a seller's to a buyer's market, this study introduces an innovative decision-making model that addresses the volatility in output and demand. Our primary innovation lies in the development of a "revenue-sharing plus margin" collaborative decision-making optimization model, designed to enhance supply chain coordination amidst uncertainty. By establishing a revenue function, we propose a coordinated approach that leverages both revenue-sharing coefficients and profit margins to achieve Pareto improvements for supply chain partners. Through numerical analysis, we identify an optimal value range for the model's parameters, demonstrating the model's efficacy in balancing the expected returns for both suppliers and retailers. The study's contributions are threefold: (1) the introduction of a novel decision-making model tailored to the pharmaceutical supply chain, (2) the provision of a clear framework for comparing collaborative versus decentralized decision-making outcomes, and (3) the empirical validation of the model through case studies, showcasing its superiority in overall supply chain efficiency. This research significantly extends the current literature by providing a nuanced understanding of how supply chain coordination can be optimized under conditions of uncertainty. The findings underscore the importance of collaborative decision-making in achieving supply chain stability and offer practical insights for supply chain management in the pharmaceutical industry.

This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics. Building on this analysis, a collaborative path planning method is proposed, establishing a path optimization model and designing an enhanced simulated annealing algorithm. The effectiveness of this approach is validated through simulation experiments, which reveal that the collaborative delivery system for trucks and drones can significantly boost efficiency, lower costs, and improve service quality. In conclusion, the research findings and potential future research directions are discussed to offer theoretical insights and practical guidance for further innovations in rural logistics technology.

In the global pharmaceutical industry, dual-channel competition has become a common business model, where the synergy and competition between online and offline channels profoundly impact corporate strategic decision-making. This study constructs a dual-channel competition model for pharmaceutical regulatory products to explore blockchain adoption strategies and analyze their impact on corporate profits. Four adoption scenarios are defined: both online and offline channels do not adopt blockchain (NN), offline adopts while online does not (BN), online adopts while offline does not (NB), and both online and offline channels adopt blockchain (BB). The methodology involves developing mathematical models for each scenario using parameters such as pricing, demand, and profit, followed by deriving equilibrium decisions for each model. Optimization techniques are used to solve for the optimal pricing and demand under the defined scenarios. Additionally, numerical analysis is conducted using hypothetical parameter values to evaluate the influence of factors like market potential demand, price sensitivity, and the negative impact of non-adoption. The findings reveal that the BB model consistently achieves the highest profit growth and resilience, demonstrating that full adoption of blockchain technology across both channels provides maximum benefits. Conversely, partial adoption (BN or NB) shows moderate improvement, while non-adoption (NN) leads to significant profit losses. This study highlights that adopting blockchain technology in dual-channel supply chains not only improves transparency, safety, and consumer trust but also optimizes profitability and risk resistance. For resource-constrained firms, a phased adoption strategy-starting with the more profitable channel-is recommended to gradually enhance operational efficiency and market competitiveness.

This work aims to provide a fundamental study of inclusion characteristics in the single bcc-phase high entropy alloy (HEA), Mn-rich and Al-contained multicomponent system (Al-Cr-Mn-Fe-Co-Ni) was selected as the prototype alloy in this study. According to the differential thermal analysis (DTA) measurements, the solidus (TS) and liquidus (TL) temperatures of this alloy were in the range of 1225–1228 °C (1226 ± 2 °C) and 1268–1271 °C (1270 ± 2 °C), respectively. Non-metallic inclusions were investigated in a two-dimensional (2D) cross-section method as well as extracted by a three-dimensional (3D) electrolytic extraction method. It was found that AlN was the dominant inclusion phase, also a small amount of Al2O3 inclusions were observed. They formed in the liquid alloy and mostly presented as Al2O3-AlN agglomerates, where the size range of the AlN inclusions was larger than that of Al2O3. The theoretical calculation showed that AlN inclusion has a higher coagulation coefficient and collision rate than those of Al2O3 inclusions, which agrees well with the experimental observations. The inclusion characteristics of Al2O3 and AlN were closely related to the relative contents of O and N in the presence of high Al content in the alloy. The impurity elements of N and O were the key issues in controling the stable inclusion phase in high entropic alloy.