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Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

In the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.

This study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.

Findings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.

This study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.

Political Science in the United States has focused too much on variable-oriented, quantitative methods and thus lost its ability to ask “big questions.” Stein Rokkan (d. 1979) was an eminent comparativist who asked big questions and provided such qualitative tools as conceptual maps, grids, and clustered comparisons. Ibn Khaldun (d. 1406), arguably the first social scientist, also asked big questions and provided a universal explanation about the dialectical relationship between nomads and sedentary people. This article analyzes to what extent Ibn Khaldun's concepts of asabiyya and sedentary culture help understand the rise and fall of the Muslim civilization. It also explores my alternative, class-based perspective in Islam, Authoritarianism, and Underdevelopment. Moreover, the article explores how Rokkan's analysis of cultural, geographical, economic, and religio-political variations within Western European states can provide insights to the examination of such variations in the Muslim world.

The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.

The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.

Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.

The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.

In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.

The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.

It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.

It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.

In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target.

The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)¹² model for forecasting were determined based on model identification, estimation and diagnostics.

SARIMA(1, 0, 1) (1, 1, 0)¹² was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target.

This study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna.

The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry.

This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

This study aims to examine the determinants of Foreign Corrupt Practices Act (FCPA) violations and consequences of FCPA enforcements.

This paper uses publicly available data from Compustat, I/B/E/S and Thomson Reuters databases, combined with Securities and Exchange Commission (SEC) and Department of Justice (DOJ) cases, to extract insights on FCPA violations and enforcements using econometric approaches.

The main determinants of FCPA violations appear to be firm size, multinational structure, country corruption and Sarbanes-Oxley Act control weaknesses. Traditional misreporting risks (F-score and M-score) do not predict FCPA violations. This study discovers significant differences between FCPA violations by motivation, as in, sale generation, rent extraction or cost evasion. Bribes motivated by sale generation or rent extraction are partially driven by the extent of the firm’s global operations, whereas bribes motivated by cost evasion relate to internal influences. This study also finds that enforcement is more salient for criminal violations (DOJ enforcement), compared to civil violations (SEC enforcement).

This research provides new insights into the determinants of FCPA violations while underscoring the need for effective measures to combat bribery and promote ethical business practices. This research contributes to the ongoing efforts to curtail bribery, offering valuable insights into the characteristics of firms more likely to engage in bribery and contexts in which these activities occur. It provides critical implications for regulatory bodies, highlighting the differential responses of firms to varying types of enforcement, namely, criminal versus civil, as the authors observe greater decreases in internal control weaknesses following DOJ enforcement compared to SEC enforcement.

For enforcement agencies, the findings underscore the importance of rigorous criminal enforcement against FCPA violations, highlighting the improved control environments prompted by DOJ actions. Managers will find this research relevant, as it demonstrates that a firm’s entry into international markets substantially elevates the risk of its representatives engaging in bribery with foreign officials. In addition, the results are of interest to regulators, revealing that the underlying motivations driving a firm’s activities can significantly alter the factors to consider that might lead to an FCPA violation.

This paper is the original work of the authors and explores the determinants and consequences of FCPA violations and enforcement actions since 2002. To the best of the authors’ knowledge, it is the first to explore bribe determinants by their motive and documents industry-wide benefits arising from criminal enforcement.

The aim of this Real Estate Insight is to comment upon commercial real estate research. Much of the current research on commercial real estate sits in academic silos, constrained by disciplinary boundaries and rejecting insights from other areas. This can lead to an impoverished understanding of the processes and practices that drive market behaviour.

This Real Estate Insight, through the lens of history, draws on insights from a century earlier and, in particular, from the work of Frank Ramsey; the paper argues that market behaviour is shaped by the role of key actors and persistent beliefs which need to be accounted for in our models of market practice.

The paper argues that current research paradigms need to accommodate agency explicitly into existing models and that real estate research will benefit immensely if researcher were more open in seeking ideas from outside the real estate field and to be more open to external ideas and concepts.

The paper suggests that property research needs to be more embracing of other academic disciplines to develop a full understanding of the numerous and various drivers within commercial real estate markets.

This is a review of how beliefs impact upon commercial real estate markets. As with many things, history can help researchers today get a broader and more appropriate perspective on market drivers and how they affect decision-making.