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Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

This study addresses the production optimization of a cellular manufacturing system (CMS) in magnetic production enterprises. Magnetic products and raw materials are more critical to transport than general products because the attraction or repulsion between magnetic poles can easily cause traffic jams. This study needs to address a method to promote the scheduling efficiency of the problem.

To address this problem, this study formulated a mixed-integer linear programming (MILP) model to describe the problem and proposed an auction and negotiation-based approach with a local search to solve it. Auction- and negotiation-based approaches can obtain feasible and high-quality solutions. A local search operator was proposed to optimize the feasible solutions using an improved conjunctive graph model.

Verification tests were performed on a series of numerical examples. The results demonstrated that the proposed auction and negotiation-based approach with a local search operator is better than existing solution methods for the problem identified. Statistical analysis of the experiment results using the Statistical Package for the Social Sciences (SPSS) software demonstrated that the proposed approach is efficient, stable and suitable for solving large-scale numerical instances.

An improved auction and negotiation-based approach was proposed; The conjunctive graph model was also improved to describe the problem of CMS with traffic jam constraint and build the local search operator; The authors’ proposed approach can get better solution than the existing algorithms by testing benchmark instances and real-world instances from enterprises.

The purpose of the study is to examine the impact of corporate reputation (hereafter CR) and the degree of economic development on firms’ cost of capital remains unresolved. This study addresses these issues.

Using a global sample across 20 countries, the study investigates the discrete and joint effects of CR and jurisdictional economic development on the cost of equity (COE) and cost of debt (COD) capital. The analysis encompasses a dual data set, comprising 1,308 observations for COE and 1,223 observations for COD, allowing for a comprehensive exploration of these dynamics.

The findings indicate that CR leads to a reduction in the cost of capital for reputable firms. Nevertheless, the extent of this decrease varies per type of capital and firm’s reputation level and is contingent upon the economic development level within the firm’s jurisdiction. Particularly noteworthy is the moderating effect of economic development on CR, which shows that COE capital tends to be lower for reputable firms operating in economically developed jurisdictions. Albeit, this is not the case for COD capital for reputable firms in similarly developed jurisdictions.

This study illustrates that effective CR management, aimed at reducing the cost of capital, necessitates a combination of the firm’s unique competitive advantage and the economic development context of its jurisdiction to truly achieve its intended goal.

To the best of the authors’ knowledge, this is the first global study to explore the impact of CR on both COE and COD capital. Furthermore, this study is primarily towards understanding the moderating role of economic development in the relationship between CR and cost of capital.

The regulatory design of Indian stock market provides us with the opportunity to disaggregate initial returns into two categories, i.e. voluntary premarket underpricing and post market mispricing. This study explores the impact of investor attention on the disaggregated short-run returns and long-run performance of initial public offerings (IPOs).

The study employs regression techniques on the sample of IPOs listed from 2005 to 2019. It measures investor attention with the help of the Google Search Volume Index (GSVI) extracted from Google Trends. Along with GSVI, the subscription rate is used as a proxy to measure investor attention.

The empirical results suggest a positive and significant relationship between initial returns and investor attention, thus validating the attention theory for Indian IPOs. Furthermore, when the returns are analysed for a more extended period using buy-and-hold abnormal returns (BHARs), it was found that price reversal holds in the long run.

This study highlights the importance of information diffusion in the market. It emphasizes the behavioural tendency of the investors in the pre-market, which reduces the market efficiency. Hence, along with fundamentals, investor attention also plays an essential role in deciding the returns for an IPO.

According to the best of the authors’ knowledge, this is one of the first studies that has attempted to explore the influence of investor attention and its interplay with underpricing and long-run performance for IPOs of Indian markets.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

Financial inclusion and digital finance go side by side and help enhance agricultural activities; however, the magnitude of digital financial services varies across countries. In line with this argument, this study aims to examine whether financial inclusion enhances agricultural participation and decompose the significance of the difference in determinants of agricultural participation between financially included – not financially included households and digital finance – no digital finance households.

This study uses Pakistan’s household integrated economic survey 2018/19 to test hypotheses. The logit model is used to examine the effect of financial inclusion on agriculture participation. Moreover, this study employs a nonlinear Fairlie Oaxaca Blinder technique to investigate the difference in determinants of agricultural participation.

This study reports that financial inclusion positively influences agricultural participation, meaning households may have access to financial services and participate in agricultural activities. The results suggest that the likelihood of participating in agriculture in households with mobiles and smartphones is higher. Moreover, household size, income, age, gender, education, urban, remittances from abroad, fertilizer, pesticides, wheat, cotton, sugarcane, fruits and vegetables are the significant determinants of agricultural participation. To distinguish the financially included – not financially included households’ gap, this study employs a nonlinear Fairlie Oaxaca Blinder decomposition and finds that differences in fertilizer explain the substantial gap in agricultural participation. Likewise, this study tests the digital finance – no digital finance gap and finds that the difference in fertilizer is a significant contributor, describing a considerable gap in agricultural participation.

Empirically identified that various factors cause agricultural participation including financial inclusion and digital finance. Regarding the research limitation, this study only considers a developing country to analyze the findings. However, for future research, scholars may consider some other countries to compare the results and identify their differences.

The accessibility of fertilizer can reduce the agricultural participation gap. However, increased income level, education and cotton and sugar production can also overcome the differences in agriculture participation between digital finance and no digital finance households.

This is the first study to decompose the difference in determinants of agricultural participation between financially and not financially included households.

The aim of this scoping review was to identify key characteristics related to strategies and approaches for delivering sustainable training and professional development (PD) of graduate teaching assistants (GTAs), teaching assistants (TAs), and tutors. While the continuous, coherent and responsive programmes for such training and PD may address needs that are congruent with the needs of other sessional teachers, the literature has not focussed on GTA training and PD that support the longer-term retention of GTAs as sessional teachers.

In this scoping review, we devised a search strategy to identify literature relating to the key characteristics of strategies and approaches for delivering sustainable GTA training and professional development in higher education settings. We were guided by the frameworks for such reviews developed by Arksey and O’Malley (2005), Levac et al. (2010) and Westphaln et al. (2021). We used PRISMA guidelines to guide our reporting processes, and used thematic analysis practice (Braun and Clarke, 2022) as our analytical approach in order to identify and discuss the key themes.

We identified that strategies and approaches for delivering sustainable GTA training and PD frame GTAs as future academics and leaders in teaching; provide institutional support and investment in teaching; deliver departmental training; facilitate peer support; provide pedagogical training; implement training strategies; and support the teacher identity of GTAs.

These findings add to the body of research that explores how strategies and approaches for delivering sustainable GTA training and PD address and meet the needs common to all sessional teachers constrained by the precarity of the part-time faculty/academia. While our findings indicate such training and PD enhance the quality of teaching available to university students, this effect is dependent on institutional support and facilitation of peer and faculty networks.

This study has aimed to analyse the role of innovation-sharing collaboration in the large-scale manufacturing of Covid-19 vaccination across the globe and its impact on the mortality rate of the countries where the pharmaceutical manufacturers received such innovation.

The authors have relied upon the difference-in-difference (DID) approach by utilizing the data available on public platforms such as World Health Organization (WHO) databank, organization for economic co-operation and development (OECD) data bank, istat, Indian bureau of statistics and European centre for disease prevention and control (ecdc) from 2020 to 2021 to establish the empirical inference of the analysis.

This study’s results present that after the invention and commercialization of the vaccine, the Covid-19 impact was still intact and people were dying continuously. However, it was impossible to fulfil the demand of the 7 billion population in a short time. In the light of these facts, the WHO encouraged sharing vaccine innovation with other countries to enhance production capacity. The authors found that after vaccine innovation sharing, Covid-19’s devastation slowed: the fatality rate was marginally reduced, and economic conditions started their recovery journey.

This study’s findings present that the Covid-19 vaccine played a pivotal role in tackling the Covid-19’s devastating impact on the entire world. It emphasizes the role of innovation-sharing collaborations in curtailing hazardous consequences, including the mortality rate during a crisis, and such collaborations’ impact on the countries where institutions involved in them reside.

This study aims to investigate the impact of organizational external environmental factors on strategic management accounting (SMA) usage in an emerging economy.

The study collected data from 79 public limited companies listed with the Dhaka Stock Exchange (Bangladesh) through a questionnaire survey. Multiple regression analysis is employed to test the impact of external environmental variables such as perceived environmental uncertainty and intensity of competition on SMA usage.

The study finds a significant positive impact of environmental uncertainty (fluctuation in the external environmental factors) and intensity of competition (domination by few companies) on SMA usage. However, the direction and magnitude of this impact vary considerably for specific groups of SMA practices such as costing, competitor accounting, customer accounting and planning and performance measurement techniques.

This study shows the impact of several facets of environmental uncertainty (i.e. unpredictability, fluctuation, ambiguity, lack of information and uncertainty of the outcome of decision) and intensity of competition (i.e. stressfulness and domination) in the empirical-based SMA research.