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This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

Based on complex network theory, a method for critical elements identification of China Railway High-speed 2 (CRH2) train system is introduced in this paper.

First, two methods, reliability theory and complex theory, are introduced, and the advantages and disadvantages for their application in identifying critical elements of high-speed train system are summarized. Second, a multi-layer multi-granularity network model including virtual and actual nodes is proposed, and the corresponding fusion rules for the same nodes in different layers are given.

Finally, taking CRH2 train system as an example, the critical elements are identified by using complex network theory, which provides a reference for train operation and maintenance.

A method of identifying key elements of CRH2 train system based on integrated importance indices is introduced, which is a meaningful extension of the application of complex network theory to identify key components.

This study aims to validate a model for estimating platoon delay due to pedestrian crossing for use in Kuwait City.

The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically. Using video footage to observe the site, several variables were collected, and a model was used to calculate the delays suffered by the vehicles because of pedestrian crossing. The model was validated using the actual footage and manual observation to measure the delays.

The model showed a good match fit to the observed data, as the average delays differed by 22.5% between the two methods. Following the comparison, a sensitivity analysis was made on three variables: the acceleration rate, deceleration rate, as well as the pedestrian walking time. The analysis has shown that deceleration rate has approximately twice the effect on the model than the acceleration rate has. It has also shown that the pedestrian walking time has a major effect on the model, in an almost one-to-one correlation. A 50% change of the pedestrian walking time is associated with approximately 50% change in the model’s output delay.

A model for estimating platoon delay because of pedestrian crossing was validated for use in Kuwait City. The model was modified slightly for the scenario used in Kuwait, in which the presence of raised crosswalk meant that all incoming traffic would slow down automatically.

Climate hazards in the form of cyclones are projected to become more intense under the pressures of future climate change. These changes represent a growing hazard to low lying coastal cities like Beira, Mozambique. In 2019, Beira experienced the devastating impact of Cyclone Idai. One of the many impacts resulting from this Cyclone was disrupted drinking water access. This investigation explores the distribution of Cyclone Idai’s impact on drinking water access via an environmental justice lens, exploring how preexisting water access characteristics may have predisposed households to the impacts of Cyclone Idai in Beria.

Relying on household survey data collected in Beira, the investigation applied a decision tree algorithm to investigate how drinking water disruption was distributed across the household survey sample using these preexisting vulnerabilities.

The investigation found that households that mainly relied upon piped water sources and experienced inconsistent access to water in the year prior to Cyclone Idai were more likely to experience disrupted drinking water access immediately after Cyclone Idai. The results indicate that residents in formal areas of Beira, largely reliant upon piped water supply, experienced higher rates of disrupted drinking water access following Cyclone Idai.

These findings question a commonly held assumption that informal areas are more vulnerable to climate hazards, like cyclones, than formal areas of a city. The findings support the inclusion of informal settlements in the design of climate change adaptation strategies.

The present study has been carried out to assess the effect of constructs of service convenience on customer satisfaction of the Indian online shoppers.

The primary data was collected through a structured questionnaire. Convenience sampling has been used to choose a sample (n = 260) of e-shoppers in India. Factor analyses (both EFA and CFA) have been done to validate different factors and its items. A conceptual model has been proposed to measure the effect of different factors of service convenience on customer satisfaction. Moreover, the perceived difference with respect to study variables has been measured. The path analysis through AMOS 22.0 has been done to test the hypotheses under study.

It can be concluded that the effect of access convenience, search convenience, and order convenience have significant effects on customer satisfaction. However, evaluation convenience and logistics and reverse logistics convenience have an insignificant effect on customer satisfaction. The present study has a unique contribution in the field of service convenience to e-retailing customers. Moreover, the present study indicates that gender does not moderate the effect of convenience on customer satisfaction.

This is one of the few papers that focuses solely on the effect of gender on service convenience and customer satisfaction. The findings will generate value with their originality and significant managerial implications for marketers, as well as future research directions for the researchers.

Transparent and fair price discovery is essential to commodity market participants in the trade value chain for competitive benefit. The purpose of this paper is to investigate the price discovery of Indian cardamom at e-auction, spot and futures markets in addition to the existence of the day of the week effect at e-auction apart from exploring a novel price risk management framework.

This study used Johansen co-integration, vector error correction model, Granger causality and regression with dummy variables to understand a day of the week effect in high-value agri-commodity of cardamom e-auction prices. These price data were based on authenticated sources of Spices Board India and Multi Commodity Exchange of India Ltd.

The statistical results indicate price discovery exists in the e-auction market and it leads to spot and futures prices. cardamom e-auction prices are negatively related to cardamom futures and positively related to spot prices. It also finds the non-existence of the day of the week effect in the high-value cardamom e-auction system in India. The study revealed that a cardamom e-auction is more active in price discovery than a cardamom futures contract.

These results shall facilitate policymakers to explore intervention of online forward market mechanism at the national level to ensure price discovery and market efficiency. However, the study did not explore reasons for the non-equilibrium of a cardamom futures contract with spot and e-auction market.

The results of this study are useful in understanding the price discovery of cardamom e-auction and its role in the spot and futures market. Cardamom price discovery depends upon the e-auction system; any change of auction policy shall be binding on Indian cardamom prices. The introduction of an online forward market mechanism as described in the paper shall facilitate price risk management apart from improving the efficiency of price discovery.

This is the first study considering cardamom e-auction, spot and futures prices in the price discovery process in India. Statistical results of a day of the week effect clearly show no significant volatility of cardamom prices during the week. Besides, this study did not find the role of cardamom futures contracts intended to serve the economic function of price discovery and price risk management. Hence, suggests policy intervention for implementing an online Forward Market mechanism for Indian cardamom to ensure market efficiency and manage price risk.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.