In production/inventory control systems, the goal of the controller is to generate sophisticated decisions by controlling the order rate and inventory level. This paper aims at modeling a dynamic-stochastic production/inventory control system under two sources of variability (uncertainty) including uncertainties on demand rate and frustrating rate. The study deals with obtaining a robust optimal design of a Proportional-Integral-Derivative (PID) controller in in the stochastic control system. For this purpose, a new robust simulation-optimization method in the class of computational intelligence is proposed. To cope with the unknown distribution of uncertainty, the crossing weighted uncertainty scenarios are combined with the proposed method. Within this study, a new sequential robust efficient global optimization is proposed to make a trade-off between optimal and robustness terms in final optimization results. Finally, a numerical case with simulation experiments is conducted to demonstrate the advantages of the proposed policy in terms of optimal result, robustness, and computational cost.

Customer satisfaction is the bedrock of e-commerce. This study identifies the factors and consequences of the perceived flow of consumers with shopping websites. The research scrutinizes the association linking purchase intentions arising from customer satisfaction, website quality and perceived flow by incorporating the model of Stimulus Organism Response. The data were collected from online shoppers using purposive sampling targeted through social media. The data from 270 respondents have been analyzed using SmartPLS incorporating the Partial Least Square-Structural Equation Modeling. The assessment showed an important beneficial impact of website usability, their security, functionality, and privacy features on the perceived flow of customers. Furthermore, perceived flow has been found to affect customers’ purchase intentions as well as satisfaction, and lastly, purchase intention is induced by how satisfied customers are with their shopping experience. The findings identify how important perceived flow functions in enhancing purchase intentions and customer satisfaction online. The findings can be noteworthy for online businesses and banks to improve online shopping experience and transactions.

Agriculture products are one of the main income sources of developing countries. This study analyzes the road transport system (network) to reveal the network’s structural properties and identifies hubs that consolidate agricultural products to export to China via the China-Laos railway. Analysis of 20 provinces in the Northeast of Thailand has found that the network has 340 districts connected by 1,015 transport routes. The network is sparse, in which all districts cannot be connected to each other. The network has low connectivity efficiency but has high intra-connectivity among districts in the same province. In addition, the network has a modularity structure that can develop the communities. Hubs consolidating agricultural products of the region are Na Khu, Kuchinaria, Mueng Chiyaphum, Nam Phong, Na Wa, Mueang Mukdahan, Prasat and Rasi Salai. The findings of the study draw implications for the government, sector and exporters to design and improve their operations and services.

The purpose of this study is to explore how various online marketing tools, including Content Marketing, Email Marketing, Social Media Marketing, and SEO, affect customers' decisions to purchase Internet services. The study utilized a survey distributed through simple random sampling, with 410 questionnaires given to Zain Telecom customers in Zarqa city. After excluding incomplete questionnaires, 385 were analyzed using multiple regressions, descriptive analysis, and reliability tests. Results showed that all independent variables had a significant impact on customers' purchasing decisions of Internet services, with Email Marketing having the strongest relationship. The study recommends further investment in Email Marketing strategies to improve customer retention and acquisition.

The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.

This study explores the factors influencing personal brand identity and their impact on successful personal branding. It examines the positive influence of social media usage, self-expression, professionalism, and self-disclosure on personal brand identity, while also considering the inconsistent findings regarding the role of self-efficacy. The study finds that social media usage, self-expression, professionalism, and self-disclosure play crucial roles in developing a strong personal brand identity, which is essential for achieving successful personal branding. However, the findings on self-efficacy's influence on personal brand identity are inconsistent with previous research. This study contributes to the understanding of personal brand identity and provides valuable insights for individuals and organizations seeking to develop and maintain strong personal brands.

The main objective of the present study is to investigate the risk factors influencing on construction firm’s project success in Pakistan. The study uses the partial least square structural equation modelling technique to analyse the data collected from 61 construction firms. The study finds positive impacts of design risk, financial risk, technical risk and labour risk on the success of the project. Alternatively, the current study finds no relationship between external risk factors and the success of the project. Furthermore, the current study suggests government and construction firms to consider the risk factors during the decision making for project implementation.

Correct application of the classical factorial F-test depends on normality and homogeneity of variance assumptions. If these assumptions are violated the type I error rate will be inflated and power of the test will be decreased. Therefore nonparametric statistical tests have been proposed to analyze the interaction effects in factorial designs. A simulation was conducted to investigate the effect of non-normality on type I error rate and power of the test of the classical factorial F-test and five nonparametric tests namely rank transformation (FR), Winsorized mean (FW), modifies mean (FM), adjusted rank transform (ART) and adjusted median transform (AMT) using program SAS 9.4 with 1,000 replications. The study used 2×2 factorial design with replications of 3, 4 and 6 making sample sizes of 12, 16, and 24, respectively and 3×3 factorial designs with replication of 3 making a sample size of 27 studied at 0.05 level of significance. As a results, when the normality of assumption is satisfied all six statistical tests have the ability to control type I error in all situations. The ART test cannot control type I error rate for 3×3 factorial design when sample size is 27 when normality assumption is violated. For power of the test, the F-test provided the highest test power when the normality of assumption is met. The ART and AMT tests provided approximately the same test power. The AMT and ART tests can be effectively used to analyse the interaction effect between factors A and B in 2×2 factorial design when the sample size is 12 and 16 or 24 respectively and the normality of assumption is not met. Moreover, the results showed that when sample sizes increased, all six statistical tests tended to increase the power of the test.

This paper proposes a new distribution named the negative binomial-weighted Lindley. The study uses the maximum likelihood estimation to estimate the parameters of the proposed distribution and compares the performance of the new method with other distributions. The study finds that the negative binomial-weighted Lindley distribution, obtained by the mixing the negative binomial distribution with the weighted Lindley distribution is another mixed negative binomial distribution and may provide an appropriate fit for data estimation with overdispersion. Some characteristics of the proposed distribution, such as mean and variance are also derived.

This study analyzes the tourism network and destinations after the COVID-19 pandemic using social network analysis (SNA). Analysis of 789 destinations in Thailand has found that the destinations are connected by 1,1175 tourism routes. The network is a sparse network with a low network density. It seems to have a scale-free property that reflects that most destinations have low connectivity and a small number of destinations have high connectivity. The network has a large average path length and low clustering coefficient. Different roles of destinations are identified based on degree, betweenness and closeness centrality. The findings draw implications for vitalizing the sector.