Dhofar University

In recent years, Intelligent Computer-Assisted Language Assessment (ICALA) has emerged as a transformative approach in language education, leveraging technology to enhance student assessment and learning processes. Despite its growing importance, there is a scarcity of research investigating the connections among needs satisfaction, teacher support, L2 learning experience, willingness to communicate, and academic motivation—factors that play a crucial role in students’ academic performance in this context. This study addressed the lack of knowledge by examining the impact of needs satisfaction and teacher support among Omani students enrolled in English as a foreign language (EFL) classes. The structural equation modeling data via SMART PLS revealed a positive correlation between needs satisfaction, L2 learning experience, increased willingness to communicate, and academic motivation in ICALA. Furthermore, teacher support positively impacted willingness to communicate, academic motivation, and L2 learning experience. The results facilitate fostering needs satisfaction and teacher support in ICALA and contribute to our understanding of the connections between these variables. A more in-depth discussion is held regarding the significance of the study for educational practices.

Background Flight attendants have direct contact with customers and are believed to be the face of the company. Therefore, factors causing employee engagement of flight attendants are important to understand. Objectives The purposes of this study are two-fold: (a) to investigate the impact of authentic leadership and group cohesiveness on employee engagement; (b) to examine the moderating role of work-life conflict on the hypothesized relationships. Methods Data was collected from 253 flight attendants from different Pakistani airlines with two separate time frames. This research study applies the hierarchical linear model to analyze the survey data. Results Results confirmed the significant and positive relationship between authentic leadership and group cohesiveness with employee engagement. In addition, work-life conflict was found to be a significant moderator for the relationships between the authentic leadership and flight attendants’ engagement. However, if there is a high level of work-life conflict, flight attendants who perceived a strong group cohesiveness turned out to indicate a high engagement level. Conclusion This study is the first to examine the work-life conflict faced by female flight attendants in association with authentic leadership, group cohesiveness, and employee engagement and the boundary conditions created by work-family conflict.

This study investigates if transformational leadership, green human resource management (GHRM) and employees’ intrinsic motivation are all crucial for employee engagement in GHRM initiatives. It adopts a quantitative approach and a cross-sectional survey which was conducted among 325 permanent faculty members of public and private sector universities in Lahore, Pakistan. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS (3.0) was used for testing the hypotheses. The results empirically support the antecedents of employee engagement in green initiatives (transformational leadership and GHRM) by taking intrinsic motivation as a moderator. The findings highlight that GHRM practices of the organisations and especially of the HEIs through effective leadership need to be designed in a way that not only encourages but also empowers the employees to be environment conscious. In such a way this work offers significant contributions to the existing body of research; shedding light on its present condition and pinpointing prospective avenues for future inquiries in this particular sector. Findings can help policymakers shape the pro-environmental behaviour of current academic staff as well as attract those people who have an environmental mindset.

A flexible financial instrument that is frequently used to assess the fair value of options is the Binomial Option Pricing Model (BOPM). This study examines the innovative use of the BOPM in booking tickets, highlighting how it might improve decision-making in the quickly changing ticketing sector. The option pricing model is used in the study to solve the problem of cancelation fees in ticket purchases. The aim is to provide a solution that enhances the company’s reputation while also being in line with client pleasure. This study probably considers the dynamic nature of ticket prices, variations in demand, and industry uncertainty by using the BOPM. The model’s adaptability is used to customize a response that considers consumer preferences and expectations regarding cancelation fees. The utilization of the BOPM in the ticketing industry highlights the tool’s capacity to enhance decision-making procedures by offering a more sophisticated and customer-focused method for setting prices and developing policies.

The one-phase brushless DC motor (BLDC) has become indispensable in-home appliances due to its high-power density, flexible control, and straightforward driving circuit, outperforming induction and universal motors. Additionally, it ensures higher efficiency across a wide range of speed-torque loads. This paper introduces a pioneering real-time control algorithm based on machine learning to enhance the BLDC motor’s overall performance compared to the traditional fuzzy-PID controller. A dynamic model of the BLDC motor is utilized to determine the EMF (electromotive force) and torque properties through finite element simulations conducted in the ANSYS/Maxwell environment. The targeted BLDC motor is driven by a space vector modulation inverter powered by a DC voltage source. The proposed machine learning-based control algorithm demonstrates superior performance over traditional methods under various load disturbances and reference speed variations, with overshoot/undershoot and settling time improvements of at least 60% and 46%, respectively. The enhanced performance was validated using a comprehensive dynamic model developed in the MATLAB environment and confirmed through an experimental setup.

One of the systems needed to improve vehicle safety is the traction control system (TCS). This control mechanism keeps the wheels from slipping too much when the car accelerates, especially when it moves quickly. Because of the significant nonlinear behavior of the tire during acceleration and the unknown impacts of the road surface, it might be difficult to maintain wheel slip in an acceptable range, especially in bad weather. However, some uncertainties, such as unmodeled dynamics and vehicle parameter uncertainty, should be taken into account when designing the controller. Consequently, TCS requires the existence of a strong nonlinear control law. In this study, an analytical design for a TCS controller is made using the method of nonlinear predictive control. The control system’s resistance is then increased by employing an adaptive radial basis neural network (RFNN) to predict the system’s unknown uncertainties. In this study, the controller was designed using half car and quarter car models, respectively. The behavior of the suggested control system for tracking the reference wheel’s slip in the face of uncertainty for various movements is examined in the simulation results that follow. The resulting results have been compared with the simulation results generated from the nonlinear sliding mode controller response used in valid articles in order to provide a more thorough examination of the suggested control system. The findings demonstrate the effective performance of the suggested control mechanism against nonlinear effects and uncertainties.

Correlated color temperature (CCT) is widely used to describe the chromaticity of white light sources, although chromaticity is only two-dimensional, and the distance from the Planckian locus is typically absent. Herein, a novel single-phase Ca3YAl3B4O15:Tm³⁺,Dy³⁺,Eu³⁺, an emerging white-emitting phosphor with good optical properties and thermal-stability, is produced, and the practical calculation methods to calculate the chromaticity-shift (ΔE) and Duv value for color-quality are also demonstrated, making it a good contender for possible use in LEDs. The incorporation of Eu³⁺ into Ca3YAl3B4O15:0.015Tm³⁺,0.08Dy³⁺ resulted in attractive warm-white light with CCT declining from 4635 K to 3065 K. The Ca3YAl3B4O15:Tm³⁺,Dy³⁺,Eu³⁺ exhibited excellent thermal stability (I@400 K = ∼93%). The Ca3YAl3B4O15:Tm³⁺,Dy³⁺,Eu³⁺-based WLED exhibits satisfactory parameters of high Ra (89.9) and low-CCT (3065 K). Additionally, this article offers useful mathematical strategies for calculating Duv over a wide-range of chromaticity, from 2000 to 6000 K in CCT and from −0.002 to 0.014, which strongly matches the range in an American National Standards Institute (ANSI) standard. For the first time, white light with minimized thermal-quenching, improved CRI, and color quality has been used in near-UV chip-excited WLEDs.

Although AI technologies show great promise for education, their inclusion into assessment systems has generated debates regarding student motivation, anxiety, learning opportunities, and academic results. This study explored the influence of teacher support in AI-assisted exams on L2 learners’ demotivation, anxiety, L2 learning experience, and academic success. Conducted at a large university in Ethiopia, participants included 92 BA Management students from two intact classes, equally distributed by gender and ranging in age from 18 to 23. The two intact classes were randomly divided into an experimental and a control group. Using a quantitative quasi-experimental pretest–posttest control group design, the study administered an Oxford Quick Placement Test, the Academic Motivation Scale, the Foreign Language Classroom Anxiety Scale, the L2 Learning Experience Scale, and a researcher-made test to assess academic success. The AI tools integrated into the exams included automated assessment and feedback systems to enhance learner engagement. Chi-square analyses and independent samples t-tests revealed significant positive effects of teacher support on reducing demotivation and anxiety, enhancing L2 learning experiences, and improving academic success in the experimental group compared to the control group, highlighting the benefits of combining AI tools with teacher support. These findings suggest that teacher support in AI-assisted exams can substantially benefit L2 learners. Additionally, the findings indicate that AI-assisted exams can considerably improve learning outcomes when paired with effective teacher involvement, highlighting implications for various stakeholders in L2 instruction. Implications of the study, limitations, and suggestions for future research are discussed.

Given the multidimensional nature of e-participation, current studies in this field are fragmented, under-theorized, and dispersed. Understanding the primary issues under investigation is challenging, particularly for early career researchers. Despite citizens’ widespread use of e-government services, government agencies still face several challenges, particularly in providing feedback on the quality and effectiveness of these services. The existing challenges related to e-government services and citizens’ e-participation provide avenues for important research opportunities. Accordingly, the purpose of this systematic review is to (i) determine the problems and obstacles faced by citizens’ e-participation as end users of e-government services, (ii) determine the multidisciplinary elements that contribute to the success of e-participation in e-government services, and (iii) propose future research avenues. Following PRISMA 2020 guidelines, this systematic review is conducted by combining two methods: Kitchenham & Charters and Webster & Watson, focusing on studies from the period between 2010 and 2022. The findings show that while existing research models are helpful, their primary focus is on technical and political factors. Hence, a focus on psychological and social aspects is necessary, which could assist in providing a sound understanding of users’ experiences of using the different e-participation tools set by governments. This review provides a comprehensive synthesis of global e-participation challenges and success factors and presents a theoretical basis for future research endeavours.

Purpose The purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector. Methodology The study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation. Results The results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption. Implication The implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation. Contribution This study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance.

There has been a surge in employing artificial intelligence (AI) in all areas of language pedagogy, not the least among them language testing and assessment. This study investigated the effects of AI-powered tools on English as a Foreign Language (EFL) learners’ speaking skills, psychological well-being, autonomy, and academic buoyancy. Using a concurrent mixed-methods design, the study included 28 upper-intermediate EFL students from an Ethiopian university. We gave the Michigan Language Proficiency Test to evaluate degrees of proficiency before the TOEFL iBT speaking section, which used ChatGPT for scoring and feedback. Speaking abilities were assessed using pretests, immediate posttests, and delayed posttests. Furthermore, we evaluated the impacts on psychological well-being, autonomy, and academic buoyancy using narrative frames. We used one-way repeated measurements to examine the quantitative data and thematically evaluated the qualitative data. According to the results, speaking abilities, psychological well-being, learner autonomy, and academic buoyancy showed notable increases. The results suggest that by improving skill development, offering individualized feedback, and meeting students' emotional and psychological needs, AI systems like ChatGPT have the capacity to transform language assessment and pedagogy. Encouraging the incorporation of AI technologies to enhance educational outcomes and provide a more flexible and adaptable learning environment, the study presents important implications for various stakeholders.

The current study aims to examine how toxic management styles can lead to both psychological and physical withdrawal of employees in the healthcare sector. The quantitative approach was used in this research. Preliminary data was collected through online questionnaires from 413 employees working in private and public hospitals and health centers in France. Structural equation modeling was used to test the research hypotheses in the SmartPLS program. The research results indicate a direct positive effect of two styles of toxic leadership (unpredictability and authoritarian leadership) on physical withdrawal behaviors. The results also showed that self-promotion and unpredictability positively affect psychological withdrawal behaviors in hospitals and health centers. The results of the research can be useful for managing health centers to remove the behaviors of toxic leaders from the work environment and protect and support staff so that they can continue carrying out their duties.

This study investigates the impact of artificial intelligence (AI)-assisted assessment on young L2 learners’ vocabulary knowledge, immunity, and resilience, considering parental and teacher support roles. Sixty junior high school students in Afghanistan, aged 13 to 14, participated in the study. They were divided into an experimental group receiving AI-assisted assessment and a control group with traditional instruction. The research employed a pretest–posttest control group design, using teacher-made vocabulary tests validated for reliability and instruments measuring immunity and resilience. The findings revealed that AI-assisted assessment significantly improved vocabulary knowledge and emotional resilience compared to the control group. While parental support showed a positive trend toward vocabulary enhancement, teacher support did not significantly impact the outcomes. The study highlights the potential of AI in language education, emphasizing the need for collaborative efforts among educators, parents, materials developers, syllabus designers, and policymakers to maximize the benefits of AI tools. These findings underscore the importance of integrating advanced technologies into educational frameworks to support cognitive and emotional development in learners.

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC-3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing's sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

In recent years, language practitioners have paid increasing attention to artificial intelligence (AI)’s role in language programs. This study investigated the impact of AI-assisted language assessment on L2 learners’ foreign language anxiety (FLA), attitudes, motivation, and writing skills. The study adopted a sequential exploratory mixed-methods design. Divided between an experimental group (receiving AI-assisted assessment) and a control group (receiving paper-format assessment), the participants were 70 intermediate English learners from two intact university classes in Bangladesh. The TOEFL iBT writing section measured writing skills, while the study also investigated perceptions and experiences of FLA, attitudes, and motivation using narrative frames. Thematic analysis of the narrative data showed that AI-assisted assessment greatly raised learners’ motivation, improved attitudes about language acquisition, and lowered FLA. According to quantitative analysis, the pretest writing abilities across groups showed no appreciable variation. Even though the difference was not statistically significant on the posttest, the experimental group exceeded the control group. The results of this study imply that AI-assisted assessments can generate a helpful learning environment, lower anxiety, improve attitudes, and increase motivation, thereby delivering useful information. Future studies should investigate long-term consequences, and further improvements to AI tools should optimize educational advantages—attitudes, motivation, and writing skills.

BACKGROUND: Burnout is an increasingly common problem in modern work settings that significantly affects people’s health and well-being. Several studies have emphasized the impact of career burnout on employees’ performance and efficiency. It is unknown whether career burnout mediated by personal burnout may affect employees’ perception of their workplace physical environment attributes. OBJECTIVES: This research aims to understand if personal and career burnout can affect employers’ acoustic environment evaluation of their workplace. METHOD: Considering commonly experienced or highly experienced personal and career burnout among working women, the study targeted female university faculty members. The research involved stratified sampling and employed data from 272 individuals across five public and private universities in Tehran. Collected data were analyzed using SmartPLS (latest release 4.1). RESULTS: The results revealed a significant link between personal and career burnout and the subjective evaluation of workplace acoustic environment. Career burnout mediated the relationship between personal burnout and negative evaluation of the workplace acoustic environment. CONCLUSION: This study provides compelling evidence that experiences of burnout, whether related to personal or career aspects, substantially impact the subjective assessment of the acoustic environment within the workplace. The results underscore the complex interplay between an individual’s degree of burnout and their subjective perception of the acoustic dimensions of their work environment. The findings extend our understanding of how psychological factors might shape our interpretation of the physical workplace.

English as a medium of instruction (EMI) has been increasingly used in Higher Education institutions in countries where English is spoken as a second or foreign language (ESL/EFL). Research over the last decade has predominantly focused on EMI implementation, perceptions, and attitudes of stakeholders towards EMI as well as the challenges experienced by both teachers and students. However, little research, thus far, has focused on the coping strategies used by students to overcome challenges and manage their EMI study. The present study duly aims to fill this gap and contribute to the advancement of EMI research and pedagogy in this under-researched area. The study particularly attempts to explore group work and translanguaging as coping strategies that students employ to overcome content learning challenges and succeed in their EMI study. The study adopted a qualitative methodology and semi-structured interviews were used as the main method of data collection. Twenty participants voluntarily took part in the study. The data were analysed thematically and inductively. Results showed that students utilised several coping strategies to overcome EMI learning challenges and described these strategies as being helpful for them to understand EMI subject content. The pedagogical implications are discussed.

Understanding the complex relationship between crude oil futures and exchange rates is essential due to its profound implications for global economies and for making policy decisions worldwide. Previous studies have employed various methodologies to explore this dynamic, yet gaps in understanding persist. In this study, we address this gap by paying special attention to countries like Iran, Singapore, UAE, Venezuela, Iraq, Kazakhstan, Azerbaijan, Angola, Algeria, Pakistan, and Bangladesh. For this purpose, we use several methodologies on monthly dataset from January 1998 to February 2024. Our findings reveal that the exchange rates of Singapore and UAE are notably affected by net fluctuations, while results across other countries exhibit inconsistency. Furthermore, our analysis uncovers evidence of time-dependent and bilateral transmission of shocks between the oil and foreign exchange markets. These findings underscore the intricate interaction between crude oil futures and exchange rates, offering premium insights for policymakers and stockholders alike.

The building and construction sector has focused on sustainable cement alternatives due to the rise in carbon dioxide emissions caused by rising cement usage. Blended Cement Concrete (BCC) enhanced with Supplementary Cementitious Materials (SCMs) such as Corncob Ash (CCA) and Calcite Powder (CP) offers a sustainable substitute. Thus, this study utilized Deep Neural Networks (DNNs) to predict the Compressive Strength (CS), Flexural Strength (FS), and Split Tensile Strength (STS) (output variables) of BCC with respect to the mixed design proportions as input variables using concrete classes 25 and 30 MPa after 3–120 curing days. The DNNs were chosen for their ability to learn complex patterns and relationships in the data, making them suitable for predicting the mechanical strengths of the BCC. The models were trained with an experimental data set consisting of 100 values for each strength feature. A set of 8 raw experimental values were used to verify the accuracy of the developed model. The 8–20-20–20-1 network structures exhibited the best performance metrics for training, validating, and testing the input and output variables compared to other architectures. For CS, FS, and STS, the correlation coefficient (R) values were 99.97, 98.69, and 97.24%. There was a strong correlation with 98.82, 99.61, and 99.54% R² for CS, FS, and STS when the developed models were validated using raw experimental datasets. The standard of BCC integrating SCMs would be improved by using this approach.