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It is significant for station passenger flow organization, platform equipment layout and train running optimization to master platform passenger distribution characteristics of urban rail transit stations. The purpose of this paper is to master the characteristics of platform passenger flow distribution in urban rail transit stations and optimize the station passenger flow organization, platform equipment layout and train operation adjustment.

The model considers two types of passengers, with and without luggage, as well as their door selection and microscopic movement behavior. In addition, it improves the traditional social force model by incorporating passenger state transitions under same-platform-transfer scenarios, considering the influence of static obstacles and dynamic queues on passenger movement direction.

Based on this model, a dynamic simulation of platform passenger flow distribution was conducted using Beijingxi Railway Station as an example under same-platform-transfer scenarios, verifying the model’s effectiveness.

Complex scenarios, such as carrying luggage and same-platform-transfers, bring difficulties to the analysis of passenger distribution on platforms. Therefore, this paper proposes a simulation model for the dynamic distribution of subway platform passengers, taking into account the scenarios of carrying luggage and same-platform-transfers.

Salt rock from salt lakes can serve as a cost-effective material for subgrade filling, as demonstrated in projects like the Qarhan Salt Lake section of the Qinghai-Tibet Railway and the Qarhan Salt Lake section of the G215 Highway. This state-of-the-art paper aims to summarize the engineering properties of salt rock filling and present the advances of its utilization.

This paper collects and analyzes laboratory and field data of salt rock filling from previous studies to present a comprehensive analysis of the engineering properties and utilization of salt rock fillings.

Salt rock primarily contains minerals such as halite and glauberite, which contribute to its unique phase-changing behavior under varying environmental conditions, impacting its mechanical properties. Salt rock filling shrinks when in contact with vapor or unsaturated brine and expands under cooling or evaporation. Its use is particularly recommended for arid regions, with specific restrictions depending on the structure type. This paper discusses suggested countermeasures to mitigate these issues, as well as key quality acceptance indices for salt rock filling compaction. Moisture content after air-drying is recommended as a crucial parameter for construction quality control.

This review aims to support future research and engineering practices in salt rock subgrade applications.

This study aims to minimize the warpage issue in memory-computing integrated chiplets with 2.5D packaging on a large-scale wafer subjected to multistress through synergistic optimization of key structure paraeters.

In this study, memory-computing integrated chiplet-based 2.5D packaging was designed and the warpage optimization under electro-thermal-vibration coupling was conducted with finite element analysis simulation. Compared studies were also conducted with the imposing condition of single electrical and thermal stress.

The research results indicated that electrical and thermal stress had a significant impact on the packaging warpage while that of vibration on warpage was minimal. For structure parameters, the chiplet thickness had a significant effect on the warpage of the model, while the influence of chiplet size was relatively small. When the chiplet thickness decreased to 100 µm, the warpage was reduced by 8.96%. Under thermal stress loading, the impact of packaging density on the overall warpage of the chiplet-based 2.5D packaging model is relatively small. However, under the loading of electrical stress or electro-thermal coupling, the packaging density has a severe impact on warpage, with the maximum approaching 1.3011 µm for just one chiplet. Compared with vibration alone, electro-thermal-vibration coupling slightly increased the warpage, which is primarily evident in the exacerbation of warpage in stacked chiplets and edge locations. Lower packaging density with 150 µm chiplet thickness contributed to a minimal warpage in stacked chiplet, the edge locations of which exhibited a relatively severe warpage.

The research provides a theoretical basis for warpage optimization of memory-computing integrated chiplets with 2.5D packaging subjected to multistress coupling.

When designing the layout of chips for large-sized high-reliability silicon substrates, this method can be used to control the most severely warped areas at the edges, thus making it easier to optimize warpage.

With the approaching of physical limits dictated by Moore’s Law, advanced packaging technologies, primarily centered around 2.5D packaging and three-dimensional packaging, were attached importance to. It can integrated multifunctional chips, which have become increasingly complex, leading to greater difficulty in design and implementation, and an obvious increase in overall manufacturing costs. In this context, chiplets offered a viable approach for future chip designs. With the high demand for high-performance computing, memory-computing integrated chiplets were designed. The warpage of 2.5D packaging by altering structural parameters under multiphysical field coupling conditions was studied to provide a theoretical basis for warpage optimization of memory-computing integrated chiplets with 2.5D packaging.

In the context of the severe global challenges posed by climate and environmental issues, this paper aims to explore the connection between female Chief Executive Officers (CEOs) and the level of sustainable development in companies. This study aims to investigate the impact of female CEOs on corporate ESG performance and provide a detailed analysis of the underlying mechanisms.

This study uses a sample of listed companies from 2010 to 2021, as reported by Bloomberg. This study uses logit regression models to test hypotheses and conduct robustness tests using the generalized method of moments, propensity score matching and heckman two statge tests.

The research findings indicate that female CEOs can enhance a company’s ESG performance, primarily by elevating the level of green innovation and engaging in more philanthropic activities. When environmental uncertainty is high, the risk-averse attitude of female CEOs may diminish the enhancement of ESG performance. However, granting a higher proportion of equity to female CEOs incentivizes risk-taking, thereby strengthening the improvement of ESG performance. Further analysis reveals that the impact of female CEOs on ESG performance is more significant in non-state-owned enterprises, high-pollution industries, and companies with low financing constraints.

The authors have shown that two key ways in which female CEOs enhance a company’s ESG performance are by increasing the level of green innovation and assuming more social responsibility. Nonetheless, this remains a shortcoming of this work, opening a door for future research to examine and enrich. There may be other possible mechanisms explaining the influence of female CEOs on corporate ESG performance. More research is warranted about the CEO’s additional traits, which were not considered in this study but may have an impact on a company’s ESG performance. Finally, while the analysis has delved into the moderating effects of external factors such as environmental uncertainty and CEO ownership on the influence of female CEOs on corporate ESG performance, there is room for exploring whether other factors also play a moderating role in future studies.

First, the findings of this study highlight the beneficial societal and economic effects of choosing female CEOs. The inclination to take on social responsibility and care for the environment are both higher among female CEOs. Furthermore, the authors have also discovered that female CEOs possess unique advantages in promoting corporate sustainability and enhancing ESG standards. This can contribute to breaking down stereotypes about gender roles in the workplace. Finally, this research shows that organizational heterogeneity and market risks have an impact on female CEOs’ capacity to improve company ESG performance.

A significant innovation of this paper lies in its unique focus on the connection between female CEOs and corporate ESG performance, along with the underlying mechanisms. Against the backdrop of sustainable development, the paper integrates social gender theory, upper echelon theory and agency theory into a comprehensive framework, shedding light on the influence of female CEOs on ESG performance and the associated mechanisms.

Most existing wall-climbing robots use electromagnetic adhesion, which faces energy consumption challenges. Therefore, this study aims to design a low-power quadruped robot based on permanent magnetic adhesion to reduce energy consumption.

First, the structural rationality and motion feasibility of the robot were analyzed from a kinematic perspective. Then, dynamic theory was applied to simulate the robot model, verifying the feasibility of the designed V-shaped gait. In addition, three static models were established to determine the minimum magnetic adhesion force required for stable robot motion.

A physical prototype was built for performance verification, and the motion performance tests of the self-made prototype further demonstrated the effectiveness of the V-shaped gait and the feasibility of the low-power permanent magnetic adhesion system.

A low-power permanent magnetic adhesion system was designed while retaining the advantages of electromagnetic adhesion. In addition, a novel V-shaped gait was proposed to address the issue of jamming during the support phase in fully rigidly connected robots when the foot is subjected to strong constraint forces.

The multivariable grey model, a type of multi-output grey model, offers a unified representation of variables from a systemic perspective, carrying significant theoretical implications. However, traditional grey modeling methods generate errors, particularly the jump error from a difference equation to a differential equation. This paper aims to propose an unbiased multivariable grey model to eliminate these inherent errors.

This paper begins by analyzing the sources of errors in the multivariate grey model and subsequently optimizes its parameters to achieve an unbiased outcome. The properties of the unbiased multivariable model are discussed and mathematically proven. The model’s unbiased nature is further validated using data. Finally, the unbiased multivariable grey model is applied to two case studies.

Results indicate the unbiased model aligns completely with simulations and predictions of curves generated by the prediction formula of the multivariable grey model, eliminating its inherent bias. Numerical examples show that the proposed unbiased modeling method enhances the accuracy of the multivariable grey model.

A novel unbiased multivariable grey model is introduced, supported by rigorous mathematical proofs of its properties. Additionally, two case studies compare this model with GM(1,1) and four other multivariable grey models.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

This study investigated how green innovation (GINO), using practices like green marketing orientation (GMO), manufacturing (GMfP), investment (GINV) and HRM, improves environmental performance (EnP) in Ethiopian leather, textile and garment businesses.

The study used a cross-sectional, quantitative design. It surveyed 201 participants, selected via stratified random sampling. Researchers examined the hypothesis using PLS-SEM.

Our findings showed that green practices, especially GMP, GMO, GHRM, GINV and GINO, greatly boost firms’ environmental performance. GMP, GMO and GINV had a strong synergy on green innovation. GHRM had no significant effect. Except for GHRM, results showed that green innovation helps most green practices. It links them to better environmental performance.

For managers and policymakers working in the leather, textile and apparel industries in Ethiopia, this report provides insightful information. Strategic planning that supports and prioritizes the adoption of green practices is shown to be essential. Such activities are expected to improve corporate competitiveness and green innovation, ultimately resulting in ecologically sustainable performance.

Unique findings illuminate emerging nations’ leather, textile and apparel industries. This pioneering study reveals critical links that were previously unexplored. Its relevance extends across developing economies, offering fresh insights into these interconnected sectors.

Enhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity in the context of digital transformation.

Using the data from 536 Chinese listed manufacturing enterprises from 2018 to 2021, this research divides digital transformation into two dimensions (i.e. digital transformation breadth and digital transformation depth) and examines their impacts on total factor productivity as well as the mediation effects of innovation capability and reconfiguration capacity.

It is found that digital transformation breadth, digital transformation depth and their interaction can positively affect manufacturing enterprises’ total factor productivity. The innovation capability and reconfiguration capacity of manufacturing enterprises act as mediators between digital transformation breadth and total factor productivity, as well as between digital transformation depth and total factor productivity.

This study is one of the first attempts to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity from the perspective of digital transformation breadth and depth.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.