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The utilisation of renewable energy sources for generating electricity and potable water is one of the most sustainable approaches in the current scenario. Therefore, the current research aims to design and develop a novel co-generation system to address the electricity and potable water needs of rural areas.

The cogeneration system mainly consists of a solar parabolic dish concentrator (SPDC) system with a concentrated photo-voltaic module at the receiver for electricity generation. It is further integrated with a low-temperature thermal desalination (LTTD) system for generating potable water. Also, a novel corn cob filtration system is introduced for the pre-treatment to reduce the salt content in seawater before circulating it into the receiver of the SPDC system. The designed novel co-generation system has been numerically and experimentally tested to analyse the performance at Karaikal, U.T. of Puducherry, India.

Because of the pre-treatment with a corn cob, the scale formation in the pipes of the SPDC system is significantly reduced, which enhances the efficiency of the system. It is observed that the conductivity, pH and TDS of seawater are reduced significantly after the pre-treatment by the corncob filtration system. Also, the integrated system is capable of generating 6–8 litres of potable water per day.

The integration of the corncob filtration system reduced the scaling formation compared to the general circulation of water in the hoses. Also, the integrated SPDC and LTTD systems are comparatively economical to generate higher yields of clean water than solar stills.

The purpose of this paper is to investigate the distribution characteristics of airflow in mine ventilation suits with different pipeline structures when the human body is bent at various angles. On this basis, the stress points are extracted to investigate the pressure variation of a ventilation suit under different ventilation rates and pipeline structures.

Based on the three-dimensional human body scanner, portable pressure test and other instruments, a human experiment was conducted in an artificial cabin. The study analyzed and compared the distribution characteristics of clearance under three different pipeline structures, as well as the pressure variation of the ventilation suit.

The study found that the clearance in front of two pipeline structures gradually increased in size as the degree of bending increased, and there was minimal clearance in the chest and back. The longitudinal structure exhibits a significant decrease in clearance compared to the spiral structure. The pressure value of the spiral pipeline structure with the same ventilation volume is low, followed by the transverse structure, while the longitudinal structure has the highest pressure value. The increase in clothing pressure value of a spiral pipeline structured ventilation suit with varying ventilation volumes is minimal.

The ventilation suit has a promising future as a type of personal protective equipment for mitigating heat damage in mines. It is of great value to study the pipeline structure of the ventilation suit for human comfort.

Given the prevalence of protecting geoheritage tourism sites, Ladakh has enormous potential. However, Ladakh’s potential and developments in its growth as a geotourism destination have received scant consideration. As such, this paper aims to explore Ladakh’s key drivers as a potential geotourism destination through cognitive dissonance and protection motivation theories.

This paper conducted in-depth interviews with 86 geoscientists and tourism professionals to assess Ladakh’s potential as a geotourism destination. This paper used a qualitative approach to congregate four constructs based on the image difference of the consensus map.

Because of the interviewees’ heterogeneity and uniqueness, meaningful constructs in the consensus were included for each figure and cloud words. This paper concluded that four drivers are the main forces behind geotourism development.

This paper explores the drivers – geotourism services, legislative requirements, experiential geotourism and SPARC (scientific, preservation, aesthetic, recreational, cultural) values – affecting geotourism in Ladakh. It is feasible to lessen the negative effects of overtourism and make sure that Ladakh’s distinctive nature is preserved for future generations by implementing sustainable tourism practices.

This study aims to investigate whether the extent of corporate disclosure, proxied by COVID-19-related disclosure, affects the dividend policy of listed firms.

The study uses a multinomial logistic regression model to examine the relation between corporate disclosure and the dividend policy of the 100 largest market-cap firms in Vietnam in 2021. The COVID-19 pandemic, with its unique impact on business operations, serves as the backdrop for this analysis.

The findings indicate that firms with more extensive COVID-19-related disclosure are more inclined to distribute dividends in the form of stocks or cash instead of omitting them.

This research contributes to the understanding of how corporate disclosure practices influence a firm’s financial decisions, particularly in the context of the COVID-19 pandemic. The findings hold implications for corporate financial decision-making during times of macroeconomic shock.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The purpose of the current research is to examine the crucial role of employees' perception of an incivility norm in predicting supervisors' incivility behaviors, which in turn, results in employees enacting incivility toward their coworkers and employees' emotional exhaustion.

In Study 1, an experience sampling method (a daily-diary approach) in which 143 male participants from several construction sites completed a total of 1,144 questionnaires was used . In Study 2, cross-sectional data from 156 male employees working in a manufacturing organization was collected. In Study 3, a quasi-experiment was conducted in which 33 and 36 employees were assigned to the intervention and control groups, respectively.

In Studies 1 and 2, it was revealed that employees are likely to experience their supervisor’s incivility behaviors when perceiving such incivility behaviors are more acceptable within the organization (incivility norm). Further, once employees experience incivility from their supervisor, they are more likely to enact incivility toward their coworkers and experience emotional exhaustion. In Study 3, changing organizational policies via implementing grievance procedures was effective in improving the study’s outcome variables.

Incivility norms predict some negative work outcomes such as incivility behaviors as both a victim and instigator, and emotional exhaustion. Further, reducing an adverse organizational norm (i.e. incivility norm) via instituting grievance procedures was effective in reducing incivility behaviors and emotional exhaustion.

To develop a fuzzy causal model of enterprise flexibility dimensions in a case study of Indian pharmaceutical industry.

The eight dimensions of enterprise flexibility were identified based on literature review. Fermatean fuzzy decision-making trail and evaluation laboratory (FF-DEMATEL) technique is applied to develop the cause-and-effect interrelationship model among various enterprise flexibility dimensions.

The information technology flexibility, supply chain flexibility, technical flexibility and marketing flexibility are found to be causing/influencing other flexibilities and contributing to overall enterprise flexibilities. Therefore, more attention needs to be paid to develop and sustain them for competitive advantage.

Fermatean fuzzy sets offer more flexibility and more accurate handling complex uncertain group decision making. FF-DEMATEL is a more accurate method to develop inter-dependencies and causal model than ISM, TISM. Ratings from the limited number of decision experts (DEs) from few pharmaceutical firms were done. Future study should take bigger sample of firms and more number of DEs to generalize the findings.

The model will help managers in pharmaceutical industry to prioritize the dimensions of enterprise flexibility to achieve agility, responsiveness, resilience and competitive advantage.

To the best knowledge of the authors, causal modeling enterprise flexibility dimensions using FF-DEMATEL has been studied for the first time in a developing economy context.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

This study explores the impacts of Covid-19 on the performance of firms operating in different industries, and further discovers suspected impacting channels through which Covid-19 is significantly associated with firm performance.

A dataset of 402 listed firms from 2017Q1 to 2021Q4 is proceeded with high dimensional fixed effect (firm-quarter fixed effects) models and difference-in-difference models supported by propensity score matching. A thorough robustness testing procedure with a falsification test with a hypothetical event is applied.

The study asserts that the pandemic has remarkably hurt the businesses in industries that are more vulnerable to the coronavirus and governmental response policies. Adding to the confirmation of sales and expense channels, new channels – competition and short-term receivables –through which the negative impact of the pandemic is passed on firms is also examined.

First, this study is to be the first comprehensively investigate and affirm the varying impact of Covid-19 on the business performance of listed firms from different industries in Vietnam, providing additional insight into this research field in Vietnam and emerging economies. Second, the authors examine possible channels paving the way for the impact of Covid-19 on firms' performance and especially explore new channels associated with competition and short receivables. Third, the findings help to form the recommendations for Vietnamese firms, and the study could be replicated for other emerging countries under other similar infectious diseases-driven crises.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0072