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The purpose of this paper is to investigate the performance of natural Jordanian zeolite tuff to remove ammonia from aqueous solutions using a laboratory batch method and fixed-bed column apparatus. Equilibrium data were fitted to Langmuir and Freundlich models.

Column experiments were conducted in packed bed column. The used apparatus consisted of a bench-mounted glass column of 2.5 cm inside diameter and 100 cm height (column volume = 490 cm3). The column was packed with a certain amount of zeolite to give the desired bed height. The feeding solution was supplied from a 30 liter plastic container at the beginning of each experiment and fed to the column down-flow through a glass flow meter having a working range of 10-280ml/min.

Ammonium ion exchange by natural Jordanian zeolite data were fitted by Langmuir and Freundlich isotherms. Continuous sorption of ammonium ions by natural Jordanian zeolite tuff has proven to be effective in decreasing concentrations ranging from 15-50 mg NH₄-N/L down to levels below 1 mg/l. Breakthrough time increased by increasing the bed depth as well as decreasing zeolite particle size, solution flow-rate, initial NH₄+ concentration and pH. Sorption of ammonium by the zeolite under the tested conditions gave the sorption capacity of 28 mg NH₄-N/L at 20°C, and 32 mg NH₄-N/L at 30°C.

This research investigates the performance of natural Jordanian zeolite tuff to remove ammonia from aqueous solutions using a laboratory batch method and fixed-bed column apparatus. The equilibrium data of the sorption of Ammonia were plotted by using the Langmuir and Freundlich isotherms, then the experimental data were compared to the predictions of the above equilibrium isotherm models. It is clear that the NH₄+ ion exchange data fitted better with Langmuir isotherm than with Freundlich model and gave an adequate correlation coefficient value.

Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.

This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.

The results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.

The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.

This study explores the factors influencing the investment intentions of potential home buyers among paraprofessionals in sustainable housing within urban construction. By combining the technology acceptance model (TAM) and the extended model of goal-directed behavior (EMGB), the research seeks to understand how technological perceptions, personal motivations and behavioral intentions intersect to shape these investment decisions.

A quantitative, cross-sectional and descriptive research design was employed in this study. The study surveyed 641 paraprofessionals as potential home buyers in various Indian cities using a multi-stage stratified sampling technique. It incorporated variables from the TAM and EMGB, modifying some aspects to include financial self-efficacy, hedonic motivation and anticipated regret, alongside adding location as a new factor to examine its impact. For data analysis, partial least squares structural equation modeling was utilized. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess these relationships’ significance and magnitude.

The study found that perceived usefulness and ease of use significantly enhance attitudes toward sustainable homes, while subjective norms have a minimal effect on such investments in India, emphasizing personal rather than societal influences. Financial self-efficacy, anticipated regret and hedonic motivation are key drivers, indicating that economic capacity and the pursuit of a satisfying lifestyle are crucial for investment intentions. Additionally, the importance of location is highlighted, with infrastructural aspects notably affecting sustainable housing appeal. These insights reveal unique dynamics in India’s sustainable housing sector, diverging from trends in developed countries.

The study lies in its unique fusion of the TAM and EMGB specifically tailored to the Indian urban construction context. It introduces financial self-efficacy, hedonic motivation and anticipated regret as novel variables within these frameworks, alongside emphasizing the significant role of location in sustainable housing decisions. This approach offers new insights into the psychological and socioeconomic factors driving sustainable housing investments in developing countries.

The purpose of this paper is to examine mediating role of individual performance between the association of organizational politics and organizational performance in Jordanian public sector.

The partial least squares–structural equation modeling was conducted to test the suggested hypotheses.

The findings revealed that organizational performance is significantly and negatively influenced by organizational politics and positively influenced by individual performance. In addition, the results also revealed that individual performance is significantly and negatively influenced by organizational politics. Finally, the results show that individual performance has mediated the association between organizational politics and organizational performance, and hence last hypothesis was accepted.

The current study provides several recommendations to the decision-makers in the Jordanian public sector, including enhancing transparency and publishing policies and regulations in a general and easily accessible manner.

The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor.

A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these relationships, respectively.

One of the key findings of this study was the establishment of significant positive relationships between awareness of consequences, ascription of responsibility and personal norms with behavioral intentions. This underscored the importance of personal ethical considerations in shaping intentions. Perceived usefulness and ease of use were found to significantly influence attitudes positively, highlighting the relevance of these factors in forming favorable attitudes toward behaviors. Attitude, subjective norms, financial self-efficacy and location played significant positive roles. However, negative anticipated emotions decreased desire. This illustrated the complex role emotions play in motivational processes. The study also revealed that subjective norms did not significantly contribute to shaping personal norms. This indicated a potential decoupling of societal expectations from personal ethical obligations in the decision-making process.

This study offers actionable insights for both policymakers and real estate developers. For policymakers, the findings highlight the need to craft initiatives that go beyond mere awareness, instead fostering a deep sense of personal responsibility and environmental stewardship among potential homebuyers. For real estate developers, the emphasis on financial self-efficacy and location suggests a strategy shift toward designing sustainable homes that not only meet environmental standards but also align with buyers’ financial confidence and geographic preferences. Together, these strategies can drive a more widespread adoption of sustainable housing, making sustainability a tangible and appealing choice for millennials.

To the best of the authors’ knowledge, this empirical research study was one of the first studies that contributed to the literature by integrating the model of goal-directed behavior, technology acceptance model and norm activation model. This study thus offered a nuanced understanding of the interplay between normative influences, usability perceptions, ethical considerations and emotions in the context of behavioral intentions.

The present research work is intended to investigate the work passion and job satisfaction relationship of social workers in Delhi/NCR and examine the influence of belongingness and psychological empowerment on the association. The study has used self-determination theory to support the relationship.

The study used the standardized instruments to assess the relationship. Statistical tools such as SEM, convergent and discriminant validity, reliability, and moderated regression analysis were used to analyze the data.

The study found that psychological empowerment and belonging moderated the association between passion and job satisfaction.

HR managers and practitioners should promote a culture of openness, empowerment, collectivism, and meaningful work to ensure the fulfillment of psychological needs of the social workers.

The fulfillment of psychological needs can become a significant motivator for the social workers as due to political and administrative constraints, giving financial incentives or introducing variable financial pay would not be possible.

The authors were not able to locate any paper exploring the relationship between work passion and job satisfaction of social workers. The present research work proposes that there are certain psychological needs, which can be fulfilled other than monetary needs in order to motivate social workers for their work.

Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.

An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.

The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.

A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.

The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.

The proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

After 15 years of research, this paper aims to present a review of the academic literature on the ISO/IEC 27001, the most renowned standard for information security and the third most widespread ISO certification. Emerging issues are reframed through the lenses of social systems thinking, deriving a theory-based research agenda to inspire interdisciplinary studies in the field.

The study is structured as a systematic literature review.

Research themes and sub-themes are identified on five broad research foci: relation with other standards, motivations, issues in the implementation, possible outcomes and contextual factors.

The study presents a structured overview of the academic body of knowledge on ISO/IEC 27001, providing solid foundations for future research on the topic. A set of research opportunities is outlined, with the aim to inspire future interdisciplinary studies at the crossroad between information security and quality management. Managers interested in the implementation of the standard and policymakers can find an overview of academic knowledge useful to inform their decisions related to implementation and regulatory activities.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.