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With the increasing awareness of global warming and the important role of last mile distribution in logistics activities, the purpose of this paper is to build an environmental and effective last mile distribution model considering fuel consumption and greenhouse gas emission, vehicle capacity and two practical delivery service options: home delivery (HD) and pickup site service (PS). This paper calls the problem as the capacitated pollution-routing problem with pickup and delivery (CPRPPD). The goal is to find an optimal route to minimize operational and environmental costs, as well as a set of optimal speeds over each arc, while respecting capacity constraints of vehicles and pickup sites.

To solve this problem, this research proposes a two-phase heuristic algorithm by combining a hybrid ant colony optimization (HACO) in the first stage and a multiple population genetic algorithm in the second stage. First, the HACO is presented to find the minimal route solution and reduce distribution cost based on optimizing the speed over each arc.

To verify the proposed CPRPPD model and algorithm, a real-world instance is conducted. Comparing with the scenario including HD service only, the scenario including both HD and PS option is more economical, which indicates that the CPRPPD model is more efficient. Besides, the results of speed optimization are significantly better than before.

The developed CPRPPD model not only minimizes delivery time and reduces the total emission cost, but also helps logistics enterprises to establish a more complete distribution system and increases customer satisfaction. The model and algorithm of this paper provide optimal support for the actual distribution activities of logistics enterprises in low-carbon environment, and also provide reference for the government to formulate energy-saving and emission reduction policies.

This paper provides a great space for the improvement of carbon emissions in the last mile distribution. The results show that the distribution arrangement including HD and PS services in the last mile adopting speed optimization can significantly reduce the carbon emission. Additionally, an integrated real-world instance is applied in this paper to illustrate the validity of the model and the effectiveness of this method.

The purpose of this paper is to monitor the environmental efficiency of suppliers in the presence of undesirable output and dual-role factors with static and dynamic aspects. Meanwhile, it also aims to explain the main reason for the low efficiency of suppliers.

The authors propose a modified data model considering undesirable output and dual-role factors. The study integrates the modified data envelopment analysis model into the distance function of the Malmquist–Luenberger index. Moreover, this study uses the global benchmark technology to formulate a two-stage model. To verify the validity of this model, a model application is conducted on an automotive spare components company in China.

The results identify the unique status of dual-role factors based on the global optimality of the model and then categorize inefficient suppliers in an individual evaluation cycle. In addition, each supplier is projected on a frontier curve after obtaining the improved data. Furthermore, through the status plot of M-L and its components, this paper concludes that efficiency scale change is the main reason for the gap in ecological performance between different suppliers.

The proposed model considers both undesirable output and dual-role factors; however, variables with different features, such as imprecise, fuzzy and qualitative characteristics, can be embedded into the presented two-stage model.

Evaluating green suppliers through multiple consecutive evaluation cycles will aid a company in effectively managing its key suppliers. Furthermore, the evaluation provides policy guidance for further improvement of suppliers.

Utilising the resource dependency theory, this study investigates the impact of board interlocks (CEOs' interlocks, women board interlocks, independent board interlocks and total board interlocks) on carbon emissions performance in India.

This research applies varieties of regression methods comprising robust least squares, generalised method of moments and Heckman's regression on a final sample of 63 of India's top 200 Bombay Stock Exchange (BSE) listed companies that voluntarily participate in the Carbon Disclosure Project's (CDP) Climate Change Program and disclose their climate change data for years 2013–2020.

We provide strong evidence for a strong negative association between CEOs' interlocks and women board interlocks on carbon emissions performance. Independent and total board interlocks are not found to significantly affect carbon emissions performance.

Our sample is restricted to the proportion of the top 200 BSE firms that voluntarily submit their carbon emissions data to CDP. Also, the study's focus is India, limiting the generalisation of our findings to other emerging economies.

The study's findings provide valuable insight for regulators and corporate board of directors on the important role of CEOs and women board who interlock with other firms in steering the carbon emissions reduction. Specifically, the corporate board of directors should encourage CEOs to build more networks through outside board memberships. The regulators should revisit the Companies Act, 2013 and the Securities Exchange Board of India (SEBI) regulation to increase the number of multiple directorships of CEOs and women board of directors.

This study responds to the dearth of literature on the efficacy of board interlocks on carbon emissions performance in emerging economies.

The purpose of this paper is to understand the natural wind field characteristics of the tunnel entrance section and analyzing the aerodynamic performance of high-speed railway trains (HSRTs) under natural winds.

Three typical tunnel entrance section sites, namely, tunnel–bridge in a dry canyon (TBDC), tunnel–bridge in a river canyon (TBRC) and tunnel–flat ground (TF), are selected to conduct a continuous wind field measurement. Based on the measured wind characteristics, the natural winds of the TBDC and TF sites are reconstituted and imported into the two corresponding full-scale computational fluid dynamics models. The aerodynamic loads of the HSRT running on TBDC and TF with reconstituted winds are simply analyzed.

The von Kármán spectrum can be used to describe the wind field at the tunnel entrance section. In the reconstituted natural wind condition, a time-varying feature of wind speed distribution and leeward side vortex around the HSRT caused by the wind speed fluctuation is found. The fluctuating amplitude of aerodynamic loads at the TBDC infrastructure is up to 97.9% larger than that at the TF infrastructure.

The natural wind characteristics at tunnel entrance sections on the high-speed railway are first measured and analyzed. A numerical reconstitution scheme considering the temporal and spatial variation of natural wind speed is proposed and verified based on field measurement results. The aerodynamic performance of an HSRT under reconstituted natural winds is first investigated.

The paper aims to understand the cost-benefits, financial performance and relationships of the key actors in the table grape supply chain by using value chain analysis method.

This study presents the result of analysis on financial performance of key actors in three table grape supply chains and the relationship among them using value chain analysis. The table grape supply chain was analyzed using data collected from a field survey in 5 regions of China. The key actors of these chains were interviewed. The costs, benefits, operational and financial performance of supply chain actors were analyzed.

The results show that, despite receiving the highest proportion of total net profit and making the highest value creation, vinegrowers are facing fluctuant and uncertain commercial returns due to the production and market risks, fluctuation of farm-gate price and the buyer dominant relationship with wholesalers. Moreover, although it is profitable to all key actors, table grape supply chain still faces several challenges including unorganized and dispersed production systems, power asymmetry and lack of information sharing, all of which are the barriers to the improvement of the whole chain performance and the long term sustainability of this important industry.

This paper not only helps to understand the relationship among the key actors and the critical factors impacting on the whole chain performance, but also offer useful guidance for the policy makers and key actors to achieve further improvement of the table grape supply chain performance and provide valuable implications for the future studies on agriculture products.

This study comprehensively reviews the global literature on busy boards and audit committees.

Six eight articles on busy boards and audit committees from prominent accounting journals are reviewed and analyzed under the “reputation” and “busyness” premise.

Most studies advocating the “reputation” hypothesis have the consensus that busy directors have their benefits (knowledge spillovers), particularly regarding sharing their in-depth knowledge, experiences and expertise. This phenomenon is pronounced for younger and IPO firms, which have high advising and financing needs. From the “busyness” perspective, busy directors are too overboard in carrying out their duty effectively and responsibly.

This study identifies future research avenues on busy boards/audit committees and suggests that policymakers and regulators should limit the number of board appointments.

This is the first study to extensively amalgamate research on busy directors and audit committees. It reveals the various proxies used to measure the busyness of board and audit committee members and the consequences of busyness.

Previous research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.

First, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.

The new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.

This study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.

The purpose of this paper is to provide a new method of the fault diagnosis of wireless sensor networks (WSNs) node, which is based on wavelet neural network (WNN).

The approach uses WNN to diagnose the sensor module of the node.

The method based on WNN sensing parts of the WSN nodes in additional fault location is accurate feasible.

The fault of WSNs node protean, it is necessary to establish even more fault model for the training of WNN.

The simulation results provide useful guidelines for the engineers faced with the detection the fault of the WSN node.

The WNN is well‐known. The innovation here is applying this method in order to diagnose the fault of WSNs node.

The purpose of this paper is to introduce an improved method for joint training of low‐ and high‐resolution dictionaries in the single image super resolution. With simulations, the proposed method is thereafter evaluated.

Sparse representations of low‐resolution image patches are used to reconstruct the high‐resolution image patches with high resolution dictionary. By using different factors, the scheme weights the two dictionaries in the high‐ and low‐resolution spaces in the training process. It is reasonable to achieve better reconstructed images with more emphasis on the high‐resolution spaces.

An improved joint training algorithm based on K‐SVD is developed with flexible weight factors on dictionaries in the high‐ and low‐resolution spaces. From the experiment results, the proposed scheme outperforms the classic bicubic interpolation and neighbor‐embedding learning based method.

By using flexible weight factors in joint training of the dictionaries for super resolution, better reconstruction results can be achieved.

This study aims to examine the potential influence of multiple directorships (MDs) on the firm value of listed firms in Jordan.

Using a sample of 1,067 firm-year observations of Jordanian listed companies from 2010 to 2020, this study applies a pooled ordinary least squares regression model to examine the above-stated relationship. This technique was supported by conducting a generalized method of moments estimation to address the possible occurrence of endogeneity concerns.

The results show a significant negative relationship between MDs and firm performance, thereby supporting the “Busyness Hypothesis”, which suggests that directors with MDs are expected to be over-committed, too busy and less vigilant. Thus, their ability to effectively monitor the company management on behalf of the shareholders is quite limited.

To the best of the authors’ knowledge, this is the first study in Jordan, and one of the very rare studies in the Middle Eastern and North African region, to examine the relationship between MDs and firm performance. This study provides important policy and practitioner implications in the field of corporate governance by highlighting the necessity of imposing stricter limits on the number of directorships allowed for board directors. Crucially, the empirical evidence implies that limited directorships ensure that directors are able to fulfil their board responsibilities appropriately, which is significantly associated with the firm value.