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This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

The use of mobile medical apps in clinical settings has recently received considerable attention. While some practitioners are using this technology to optimize decision making, others, on the other hand, are indifferent about its usage. Therefore, this study has utilized a modified UTAUT2 model to determine factors that influence the acceptance and use of mobile medical apps among professional nurses in the Ghanaian setting. A web-based data collection tool (Google Forms) was used to solicit data from 216 health professionals. Of the 216 respondents, 126 (58.33%) of them were Public Health Nurses, with 85 (39.35%) being General Nurses, and the remaining 5 (2.32%) were midwives, resulting in an average age of 31.57±4.14 years. The study used a previously validated self-administered questionnaire (UTAUT 2) to find out factors that informed respondents to adopt and use mobile medical apps. The findings established that professional qualification, gender, and the number of years that the individual had used smartphones were key determinants in accepting and using mobile medical apps. That is why this study makes the case that using technology in clinical practice has enormous advantages. It is, however, important to understand the factors that will influence the intention to adopt such technologies and their possible use.

The main drivers of value creation in a ‘brand community’ are social networking, community engagement, impression management, and brand use. Marketers are therefore interested in determining which factors affect the value creation practices. This study examines the impact of the Interactivity of Electronic Word of Mouth (EWOM) systems on value creation practices in a brand community, which in turn influences the loyalty of the customers. In this regard, a conceptual model was developed and tested by the researchers of the current study. The results indicate that perceptions of the users regarding the interactivity of EWOM systems, highly impact only three of the four value creation practices including community engagement practices, impression management practices, and brand use practices. Furthermore, the researchers found that collective value creation practices could significantly and directly enhance brand loyalty. Several theoretical contributions and managerial implications were also discussed

Corporate Social Responsibility (CSR) is a general concept where companies voluntarily decide to adopt social, economic and environmental responsibilities for a better society and environment. Researchers in the management field have created various CSR models and theories. Moreover, enterprises are working out to strength its competitiveness performance though adopting CSR approaches. For that, decision makers in an enterprise have to target the most appropriate CSR approach that fit with the environment and the nature of their enterprise. The Analytical Hierarchy Process (AHP) has been presented through the article in order to help an organization take decision toward the most convenient CSR method according to many characteristics and criteria.

The study of path loss propagation is of high importance in telecommunication, for optimizing the efficiency of wireless communication networks. In this paper, four path loss propagation models; free space path loss (FSPL) model, Okumura model, Okumura-Hata model, and COST 231-Hata model were compared. The models were compared using measurement data from Choba, Port Harcourt, Nigeria. The data is gotten from a 4G walk test using two mobile applications. The first mobile application Network, Signal Info (version 5.68.07), was used to acquire the received signal strength in and the distance between the BTS antenna and the mobile antenna. While the second mobile application, G-NetTrack Lite (version 14.8), was used to map out the track path followed during the test. Choba, the study area, is a coastal suburban area with map coordinates 4.8941° N, 6.9263° E. The measured distance range was from 0.09 km to 0.45 km, and a path loss range of 69dB to 81dB. While the BTS and mobile antenna heights were 32 m and 1m respectively, with a carrier frequency of 800MHz. BTS transmission power of 23 dBm was assumed, based on 3GPP eNodeB recommendations. A graph comparison of the studied models showed that Okumura-Hata model and COST 231-Hata model had the closest predictions to the measured path loss. The need for better path loss models for 4G and 5G propagations was observed

Purpose: The purpose of the research was to understand the cross media electronic marketing process. In fact, with the emergence of virtual organizations, social network media marketing has become a basic need for the possibility of movement and continued developmentMethod: this research conducted using Grounded theory analysis. Snowball or targeted sampling is utilized to gather the required data. The qualitative part of this study was conducted based on the viewpoints of 24 university professors, marketing activists, experts, elites, managers, and employees of insurance companies. Collected interviews were analyzed in MAXQDA software. To achieve this goal, the collected interviews are evaluated using different coding methods as the main process. The interviews were entered into the MAXQDA software. Coding in data theory methodology is a process of conceptual abstraction by assigning general concepts (codes) to individual occurrences in the data.Findings: Based on the analysis, 104 primary codes, 18 core categories have been identified in 6 categories of the foundation's data. With the emergence of electronic commerce, organizations are changing their business. The proposed model shows that the digital era has changed the expectations of customers from communication with brands. Before social networks, mass media communication channels such as television and radio to brand managers. It allowed them to communicate with consumersConclusion: In the present research, based on the participants' viewpoints, the categories of management factors challenge, financial potential, communication potential, customer characteristics and welfare and comfort, finally, marketing techniques have been identified and it has been related to another broader category called causal conditions. Information technology and targeted marketing were identified as background conditions. Environmental factors and psychological factors of the clients were identified as intervening conditions. Research strategies include business improvement, knowledge and education, and finally innovation and creativity. Outcomes include customer loyalty, sustainability in customer relationships with the product and social media, beneficial word-of-mouth and viral advertising, customer satisfaction, and purchase intent.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.

This paper focuses on the contribution of digital government (DGOV) to Whistleblowing (WB). While considerable efforts have been devoted to DGOV and WB separately, research work at the intersection of these two domains is very scarce; hence and a systematic DGOV for WB (DGOV4WB) research framework has yet to emerge. This paper aims to identify the potential issues in whistleblowing and explore how digital government has been used to address these issues. To this end, this paper uses explanatory case study research methodology and analyses four case studies of existing DGOV initiatives with explicit WB dimensions. The result of the cross-case analysis shows that DGOV4WB initiatives contribute to address goals of the different dimensions of whistleblowing. The most common WB problems addressed are: easily accessible reporting and response whistleblowing channels (Whistleblowing Procedure), easy and timely communication channel with top management (Whistleblowing Organizational culture), and anonymous and confidential communication platforms (Whistleblower Protection). In addition, based on the analysis of the case studies, all the initiatives are classified as either engagement or contextualization stage of the digital government evolution model.

As mobile data traffic increases, and the number of services provided by the mobile network increases, service load flows as well, which requires changing in the principles, models, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of network slices will be cost effective for slice managing and giving the required functionality. So, the aim is to improve the efficiency of mobile network by forming an ideal slice in a multi-service communication network. In this paper, we propose a model to demonstrate network resource allocation system that forms devoted network slices to serve particular types of services independently on shared infrastructure. This model solves the problem of creating a strategy to form multi-service core mobile communication network slices, which allow the providing of a wide scope of services with certain quality indicators according to the effective dynamic configuration of the system. A resource management system model is created, to provide a method that considers costs related with excessive resource allocation, and also reduces the number of network recalculations, allowing for a reasonable proportion of management costs and Qualities of Service.

Substitution-boxes (S-boxes) are very important nonlinear components used for achieving strong confusion for enhancing cryptographic security in most of the block ciphers. Designing cryptographically strong S-boxes has been a major research domain for the designers of symmetric crypto systems. In the proposed research work, Bat Algorithm based swarm technique is proposed to design strong S-boxes.  Cryptographic strong S-boxes are obtained by the developed swarm technique. Authors analyze cryptographic strength of the obtained S-box by evaluating properties like Bijectivity, Nonlinearity, Bit-Independence Criterion, Linear Probability and Differential Uniformity. The obtained performance parameters for the designed new S-box by the swarm technique are compared with some recently reported S-boxes in the literature. The designed S-box has good cryptographic strength. The designed S-box has good cryptographic strength like nonlinearity = 110.75 and average Strict Avalanche Criterion (SAC) value = 0.506. For the constructed S-box, most of the Differential uniformity components are 4 and shows uniform distribution approximately. The proposed new S-box is also free from the fixed points.