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To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure, energy-saving performance of the whole metro system cannot be guaranteed.

A cooperative train control framework is formulated to regulate a novel train operation mode. The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train. An improved brute force (BF) algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.

Case studies on the actual metro line in Guangzhou, China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters. The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.

Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process, which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation. This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea, where energy-efficient train operation can be realised once train running time is determined, thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

The authors’ examination aims to offer a quantitative perspective on the interrelationships between tourist harassment, the destination image, emotions and destination revisit intent. Furthermore, it explores the moderating role of travelers' experiences and tolerance in the link between tourist harassment, the destination image and revisit intentions.

The authors’ examination seeks to fill this research gap by utilizing a combination of qualitative and quantitative methods to test eight hypotheses using AMOS 23 and PROCESS MARCO.

The findings showed that tourist harassment negatively impacts the destination image and revisit intentions. Moreover, it indicated that tourists' experiences and tolerance moderate the link between harassment, the destination image and revisit intentions for travelers with high levels of experience and tolerance compared to those with low levels.

This article contributes to travel research and service failure recovery research on tourist harassment and its consequences. To this end, it developed and validated a new tourist harassment scale. Moreover, it is the first study that examines the moderating role of visitors' experiences and tolerance on the link between tourist harassment, the destination image and revisit intentions. Finally, this article is the first to empirically offer destination harassment reduction techniques.

This paper develops a debt-run model to study the effects of liquidity injections on debt markets in the presence of a renegotiation option. In the model, creditors decide when to withdraw their funding and equityholders can renegotiate the contract terms of debt. We show that when equityholders have a large bargaining power, liquidity injections into distressed firms can rather cause more aggressive runs from their creditors, hurting the debt value. This outcome occurs because equityholders can strategically utilize the renegotiation option as a bankruptcy threat, pushing down the debt value below the potential liquidation value of the firm. In such a scenario, a deterred default resulting from emergency capital injections could be detrimental to creditors.

Freshwater availability is reducing globally, due to increasing demand with population growth and climate change and is disproportionately impacting developing countries. This study aims to investigate the dynamics of water access and consumption across all regions of Thailand with various characteristics and water systems. Understanding the relationship between institutional, economic and climate variables in Thailand’s water management is important for water scarcity planning. Our paper fills a gap in the literature by examining the determinants of water consumption and exploring potential water management policies.

The authors empirically analyze the determinants of water consumption in Thailand, including institutional, economic and climate variables. The authors use data sets from both metropolitan and provincial waterworks authorities (PWA), as well as economic and meteorological macro-level data. The authors also adopt an auto-regressive distributed lag (ARDL) model and a Johansen cointegration test to estimate short- and long-run effects of the variables on water consumption.

The authors confirm a negative relationship between water pricing and consumption and verify a positive relationship between economic growth and water consumption across most regions of Thailand. Furthermore, the authors reveal a clear relationship between climate factors and water consumption and an inverse relationship between income and water consumption in metropolitan area. Findings indicate that authorities, especially PWA, should examine high water use in agriculture and develop regulations to ensure equitable water distribution to sustain economic growth. The authors recommend that water prices are increased within specific income thresholds to prevent impacting low-income families and to secure higher public revenue. In pursuit of environmental sustainability, the authors also recommend increasing public awareness of freshwater scarcity through education programs and investment in water-saving technologies. Differences among regions should be considered when developing water management strategies, which could be monitored through the respective water boards.

This study provides deep insight into the key factors that drive both water prices and water consumption in poor and rich areas. The unique nature of the research indicated that the paper will be of interest to policymakers and the academic community. The findings are relevant for water consumption management in Thailand and other developing countries with similar characteristics.

As the world began to witness an unprecedented rate of environmental destruction, economists and international institutions have been toiling away for decades, making every effort to dissect the dynamics of the relationship between the environment and the economy. Many claims have preached that there is a trade-off between environmental wellbeing and economic prosperity, where economic growth would be hindered by environmental protection. As we continue to neglect nature, will the world be capable of maintaining infinitely growing economies without falling into a deficit of natural resources? The foundation of all forms of economic growth springs from nature. Therefore, this study aims to explore the true impact of environmental protection policies on economic performance, and claims that well-designated environmental policies would only strengthen economies.

This study aims to investigate the impact of environmental protection policies on gross domestic product (GDP) growth utilizing a selected sample of 18 OECD countries. Fixed effects panel regression was conducted for the sample from 1998 to 2015.

Findings suggest that an increase in the environmental protection stringency is associated with an increase in GDP in the long-run. Whereas in the short run, more stringent environmental policies have been shown to have a questionable impact on GDP, brought to light by the mixed results portrayed in the short-run data.

While it is true that this study has utilized data from the The Organisation for Economic Co-operation and Development (OECD), the findings could be applicable to countries of the MENA region. This is due to the fact that GDP levels of OECD countries and Middle East and North African (MENA) countries have been converging over the past few decades. The convergence suggests that both regions seem to be following similar trends since the year 1990, with an increasing similarity in trend over the years.

This paper empirically proves that the protection of nature is necessary for the sustenance of long-term economic growth. This study also provides an approximate time range of when the economic gains of environmental protection would be realized, specifically in the beginning of a green growth transition. This makes the study findings accurately relevant to Arab countries, where providing a time range is necessary to alleviate some the uncertainty of policymakers in the MENA region towards environmental policies.

This study aims to investigate the impact of environmental responsibility (ER) on consumers' green customization intention (GCI), as well as the separate and joint moderating effects of consumers' negative perceptions and environmental claim type (ECT) on the relationship between ER and GCI.

In this study, two online experimental questionnaires and one between-subject lab experiment were conducted to test all hypotheses.

This study found that high-ER consumers are more likely to choose environmental options in the product customization process (versus low ER). The effect of ER on consumers' GCI is moderated by consumers' negative perceptions of green consumption (e.g. green skepticism and perceived environmental premium (PEP)). Additionally, ECT's joint moderating effect and consumers' negative perception of green customization consumption can be decreased by a substantive (versus associative) claim type in message framing.

The findings provide new insights into the factors affecting consumers' GCIs and have significant practical implications. First, consumers' different ER levels should not be neglected when examining the value of green customization. Second, consumers' green skepticism and PEP could reduce consumers' GCI. Third, the use of substantive environmental claims may also be particularly helpful for companies' communication strategies regarding consumers' low-level negative perceptions of green options.

While previous studies state “environmental responsibility” as a key characteristic of general green consumption, the authors extend that to a green customization process and connect that with consumers' negative perceptions and ECT. In addition, the separate and joint moderating effects of consumers' negative perceptions and ECT were examined. Thus, these findings refine the existing understanding of the relationship between ER and green customization.

The purpose of this paper is to examine key macroeconomic determinants on Cameroon's economic growth from 1970 to 2018.

Data were obtained from the World Development Indicators and applied on time series data econometric techniques. The auto-regressive distributed lag (ARDL) bounds model analyzed the data since the variables had different order of integration.

The results showed long and short runs’ positive and significant connection between economic growth in Cameroon and government expenditure; trade openness, gross capital formation and exchange rate. Human capital development, foreign aid, money supply, inflation and foreign direct investment negatively and significantly affected economic growth in the short and long-runs. Hence, the macroeconomic indicators are not death.

The present research paper has tried to capture the impact of nine macroeconomic determinants on economic growth such as the government expenditure (LNGOVEXP), human capital development (LNHCD), foreign aids (AID), trade openness (LNTOP), foreign direct investment (LNFDI), gross capital formation (INVEST), broad money (LNM2), official exchange rate (LNEXHRATE) and Inflation (LNINFLA). However, these variables have the tendency to affect each other in a unidirectional or bidirectional manner. Further, the present research paper is unable to capture the impact of other macroeconomic variable due to the unavailability of data.

The study recommends that Cameroon should use proper planning and strategic policy interventions to achieve higher sustainable economic growth with human capital development, foreign aid, money supply, foreign direct investment and moderate inflation.

Macroeconomic indicators, if managed well, increase economic growth.

This paper to the best of the researcher's knowledge presents new background information to both policymakers and researchers on the main macroeconomic determinants using econometric analysis.

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

The paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction sites.

The paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).

The paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction sites.

The paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.