Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Challenges of Post-Editing in English to Arabic Machine Translation of Technical Texts: A Study of Technological and Linguistic Barriers
No full textThe increasing reliance on machine translation (MT) for English-to-Arabic technical texts presents significant linguistic and technological challenges, necessitating extensive human post-editing. This study examines these challenges by analyzing machine-translated technical texts and assessing the post-editing process undertaken by professional translators. Despite advancements in neural machine translation, English-Arabic translation remains problematic due to syntactic, morphological, and terminological discrepancies between the two languages. The study employs House’s (1997) Translation Quality Assessment (TQA) Model to evaluate machine translation quality and the impact of post-editing interventions. Methodologically, ten technical texts were selected from car and hair dryer manuals and translated using Google Translate. Two professional translators, each holding a PhD in translation, post-edited these texts in a two-stage process, producing a single collaboratively refined version. Semi-structured interviews were then conducted to explore the translators\u27 experiences, the challenges they faced, and their perspectives on the effectiveness of MT tools. The analysis of the interviews revealed key technological and linguistic barriers, including inconsistent terminology, unnatural sentence structures, and difficulties in maintaining semantic and pragmatic accuracy. The findings highlight that MT tools struggle with context-sensitive technical terms, resulting in inaccuracies that demand significant human intervention. Additionally, issues such as word order mismatches, poor handling of Arabic morphology, and ineffective recognition of formal registers contribute to the post-editing workload. The study recommends improvements in MT systems, including enhanced AI-driven context recognition, customizable glossaries, and adaptive learning mechanisms to refine MT accuracy over time. By addressing these gaps, MT tools can better integrate into professional translation workflows, reducing post-editing efforts while improving the quality of English-to-Arabic technical translations
Nunation in Arabic
No full textTraditionally, Nunation is a phenomenon in Arabic that signals the Case of a noun or adjective. It is marked by the inflection of the final letter in a word with a diacritical mark called "tanwin" represented by the letter /n/ in transliteration. The nunation is reminiscent of Semitic constructions, and it is constrained to be strictly attached to the head noun, just like the case in construct state constructions (CSCs) where the embedded genitive NP must be strictly adjacent to the head noun. The fact that they both (nunation-structures and CSCs) are in complementary distribution suggests that they should be treated similarly. Based on observations, Jarrah & Zibin (2016) argue that the nunation suffix, -n, is used to fill the head position in a determiner phrase when the definite article or a personal pronoun does not occupy the latter. This argument raises several questions in both syntax and morphology. In this paper, I suggest a new analysis of the Arabic nunation as a complement attaching to the head and absorbing Case marking. Viewed this way, the nunation affix in Arabic illustrates the complement in the head-complement pattern of grammatical relations
Effect of Individuals Task Conflict on Employees Creativity in Private Medical Hospitals of Nangarhar Province, Afghanistan
No full textAs the concept of individuals’ task conflict was a buzzing topic in field of management sciences and has been considered as an integral part of the organization while recent studies have shown that its crucial for creativity of employees’. Therefore, the aim of this research study is to determine the effect of individual task conflict on employees’ creativity in private hospitals of Nangarhar, Afghanistan. The data has collected from a sample who selected from rest of population with a stratified random sampling technique with adopted questionnaires. For determining the effect of individuals’ task conflict on employees’ creativity the statistical analysis descriptive statistics, correlation matrix and regression analysis have been used and pointed out that; there is strong and positive association between individual task conflict and employees’ creativity. Besides, regression analysis has shown that employees’ creativity is dependent on having a task relevant conflict. On the other hand, can said that; individual task conflict has effect on employees’ creativity. The details based on data has shown that; there is 0.731 or 73.1% association between individual task conflict and employees’ creativity with having 0.01 significant value. Furthermore, the beta value of regression analysis declared that 1% consideration on individual task conflict could positively increase the employees’ creativity 0.738 times with 0.01 significant level. Conclusively, the task conflict is one of key indicators for better employees’ creativity, and The findings of current study were same with past studies conducted by various authors in different times and areas (Lu, Zhou & Leung, 2009; Khan et al., 2020; Pelled, Eisenhardt, & Xin, 1999; Mumford & Gustafson, 1988; De Dreu, 2006; Farh et al., 2010; Xie et al., 2014)
Business Model Canvas: Business Analytics on Gas stations with C-stores in United States
No full textToday’s gas stations with convenience stores in the United States are not inclined to only selling gas and diesel, they are now offering more value propositions to their respective customers following their needs and choices. In the United States, people from every corner are now connected to gas stations and convenience stores for their daily needs. Therefore, any further improvements in this sector would benefit them even more. Gas stations with convenient stores can develop and maintain Business Model Canvas (BMC) for further improvement in their business and create even better value propositions for their customers. BMC will facilitate gas stations with better control of their business and help increase revenue streams. It also helps optimize the cost structure and fair pricing. Therefore, applying BMC model could facilitate gas stations with endless benefits by integrating all the nine segments of the model. In this research work, we designed our plan to explain the BMC model and analyze segments to the most relevant extent. Then we like to apply the model in a typical Gas station with C-store in the United States of America to see how a gas station can develop BMC in their business. In a typical gas station with C-store, we see, the customer segment composed of local consumers, commuters, travelers, and late-night shoppers. Gas stations are striving to extend their value propositions to attract more consumers and satisfy their growing needs. Value propositions include uninterrupted flow of gas/diesel supply, fresh produce, foods, merchandise items, friendly customer service, a wide range of products available in-store, ATM service, EV (Electric Vehicle) charging facility, car wash facility and more. Procurements of merchandise from various suppliers, building credit-worthy relationships with them, managing inventory/stocks are the key activities for this type of business. Key partners like banks, suppliers, delivery partners, and professional IT partners. A typical gas station with C-store may have multiple revenue streams. The cost structure of a typical gas station is classified as fixed costs and variable costs including suppliers’ pay, utilities pay, payroll, monthly rents, and maintenance costs
Retaining Values and Interchanging Values within Organizational Contexts
No full textThis literature review examines two concepts: ‘retaining values’ versus ‘interchanging values’, and their impact on organizational dynamics. Retaining values are the non-negotiable principles fundamental to individual identity, while interchanging values can be adapted for harmony and respect in diverse settings. ‘Retaining values’ can include standards such as honesty or fairness that individuals in an organization uphold at all times. ‘Interchanging values’ refer to the cultural adaptation necessary to work with people from diverse backgrounds. The concepts are assessed across three contextual situations: organizational changes and leadership transitions, international business interactions, and scenarios challenging cultural and religious norms. This review addresses the recurring importance of values clarification for individuals in complex organizational contexts, cross-cultural interactions, and potential conflicts between personal and professional value systems in today’s globalized business environment. To explore ‘retaining values’ and ‘interchanging values’ a literature review and analysis were conducted. Over 30 peer-reviewed articles from reputable scholarly databases, spanning organizational behavior, cross-cultural management, and ethical decision-making, were accessed. The analysis focused on identifying key themes, emerging concepts, theoretical frameworks, and empirical findings related to value dynamics in contexts such as organizational change, international business, as well as cultural and religious norms. This synthesis also informed the development of the proposed ‘values verification approach’. The findings show that retaining values are critical for maintaining professionals’ psychological stability and resilience, even in unfamiliar or challenging environments. Leaders who remain committed to foundational values, such as fairness, integrity, and respect, are considered more trustworthy, particularly during transitions. Similarly, managers who embody empathy, transparency, and accountability build team loyalty, especially in high-pressure environments like crisis management. Studies by Mokline and Ben Abdallah (2021) and Guillemin and Nicholas (2022) support this postulation by arguing that value retention correlates with higher employee commitment and leadership credibility. ‘Interchanging values’ are equally important as they enable cooperation and adaptability in diverse environments. The contemporary organizational environment has people from diverse backgrounds and differing cultural views. Individuals who wish to succeed in this area must, therefore, be willing to embrace different values and norms. Lipscomb (2024) and Saaida (2023) highlight how individuals who distinguish between core and peripheral values are better equipped to promote collaboration without compromising identity. For example, professionals working internationally must adapt to foreign norms yet retain essential ethical standards. The results suggest that an intentional approach to values clarification enhances personal integrity and interpersonal harmony. In other words, identifying which values are core and which are negotiable is necessary for psychological and professional satisfaction. The findings have far-reaching implications for leadership training and professional development. Change management is challenging for organizations, regardless of size and specialization. Delineating values before embarking on change can eliminate unforeseen problems and allow firms to undergo the change process successfully. Organizations can also benefit from integrating value-based assessments into hiring decisions to ensure alignment with organizational culture and global expectations. Future research should explore actionable pathways for implementing values clarification in professional settings
Determinants of Stock Pledge Risk, ESG and Firm-specific Financial Factors. A novel model to measure Risk associated with Stock Pledges
No full textThe study aims to present a theoretical model indicating the factors influencing stock pledge risk and the different aspects constituting each factor. For this purpose, it collects recently published articles investigating stock pledge risk and employs qualitative content analysis techniques. The authors run different queries like “word tree”, “word frequency map”, and “word tag”. The study observes that ESG and firm-specific variables influence the stock pledge risk. The study also finds determinants of ESG, stock pledge risk and firm-related financial factors. Finally, the study offers a theoretical model and recommends that future researchers use the model to assess the stock pledge risk under the umbrella of ESG and firm-specific financial factors. Moreover, the study named the model as Fin-ESG-SPR model of Li et al. (2024)
AI-Driven Fraud Detections in Financial Institutions: A Comprehensive Study
No full textThe financial sector encounters growing security challenges due to highly advanced fraud systems that demand next-generation protective solutions. The banking industry has discovered Artificial Intelligence as an essential instrument to find and combat fraudulent conduct at institutions. Research analyzes how Artificial Intelligence technologies specifically machine learning applications function for fraud detection while demonstrating their superior capabilities beyond simple rule-based systems. The study examines supervised and unsupervised learning together with deep learning and anomaly detection through practical analysis about their functional capabilities. Fraud detection capabilities benefit greatly from advanced techniques which process original data as well as analytical tools. The evaluation shows that financial institutions gain major advantages through advanced AI-based methods which deliver enhanced precision combined with adaptable capabilities at faster processing speeds than conventional strategies. The implementation of AI-based fraud detection faces critical difficulties although it offers substantial advantages. Several challenges like algorithm bias alongside data distribution disparities and privacy risks as well as compliance hurdles receive analysis. The research addresses ethical principles of transparency accountability and fairness while looking at responsible ways to implement AI. The study demonstrates that AI presents an avenue to build a safer financial system while resolving existing system limitations. The study presents solutions to these obstacles so AI-driven fraud detection systems can continue their developmental path. The increasing adoption of AI technologies by financial institutions will lead to substantial improvements in fraud detection abilities which builds a future foundation of trusted secure financial interactions
Predicting Energy Consumption in Hospitals Using Machine Learning: A Data-Driven Approach to Energy Efficiency in the USA
No full textIn the USA, hospitals are confronted with significant challenges regarding energy consumption, which not only impacts operational costs but also contributes to environmental concerns. The primary objective of this research was to develop and evaluate machine learning models that are capable of accurately predicting energy consumption in U.S. hospitals. This study will be focused on United States hospital energy consumption data, recognizing the unique difficulties and opportunities present in the U.S. healthcare setting. The data used for this hospital energy consumption analysis has been carefully gathered from multiple credible sources, including the U.S. Department of Energy\u27s Energy Star program, whole-building hospital energy audits, and information from local utility providers. This variety in sourcing guarantees a strong and complete dataset that accurately represents real-world energy dynamics in healthcare buildings. In the model selection phase, three powerful algorithms were employed: the Random Forest Classifier, XG-Boost, and Artificial Neural Network (ANN). XG-Boost outperformed other models after tuning, achieving an 81.8% accuracy on the test set. Random Forest showed a decent improvement post-tuning but still lagged behind XG-Boost. Hospital managers can utilize machine learning (ML)--based predictions to achieve substantial cost savings in operational expenditures related to energy usage. With predictive analytics, hospitals can anticipate energy needs based on several parameters, such as patient occupancy rates, time of day, and seasonality. Integration of AI-driven energy prediction in hospital sustainability plans has significant policy implications for the U.S. healthcare sector. The integration of machine learning models and the Internet of Things (IoT)-)-)-enabled energy management systems is a breakthrough step in embracing smart hospital initiatives
Developing Al-Powered Chatbots for Mental Health Support in Rural America
No full textThe Substance Abuse and Mental Health Services Administration [SAMHSA] (2021) reports that rural America faces three major challenges when dealing with mental health needs because of geographic separation combined with insufficient mental health professionals and widespread stigma against mental health. The integration of Artificial Intelligence (AI)-powered chatbots establishes a revolutionary designment for accessible cost-efficient scalable mental health assistance. The chatbots deliver cognitive-behavioral therapy techniques together with real-time crisis intervention and customized guidance that adapts to the specific needs of each rural population (Fitzpatrick et al., 2017). This research examines culturally appropriate AI-powered chatbot design methods together with ethical practices alongside protective measures for user privacy and data security (Reddy et al., 2019). Through technological innovation rural communities can overcome mental health service gaps which create new possibilities for both mental healthcare intervention and better mental well-being results. AI chatbot research shows their effectiveness for reshaping mental health services in rural locations while both protecting patient well-being and building resilience among local populations
Role-based Prompting Technique in Generative AI-Assisted Learning: A Student-Centered Quasi-Experimental Study
No full textThe education landscape has known remarkable transformations with the emergence of AI which has shown great potential in optimizing different educational processes provided its solutions are utilized effectively. Hence, it has become a necessity to open eyes to practices promoting worldwide efficient use. To this aim, the present paper investigates role-based prompting, its importance in enhancing the output quality of GPT-4 model in AI-assisted learning, and students’ satisfaction with the overall performance with and without using role-based prompts. To achieve the required results, the present study adopts a quasi-experimental pretest-posttest research design with a student-centered approach. The sample of this study includes (N=43) education bachelor’s students of the Higher School of Teachers – Moulay Ismail University, whose ratings were measured before and after the researchers’ intervention. For data analysis, following the ordinal non-normally distributed nature of data, the study adopts Wilcoxon Signed-Rank as a non-parametric test for paired samples conducted using both SPSS 25 and Python codes executed on Google Colab coding space for robust statistical transparency and evidence. Results revealed a strong statistically significant difference between output quality before and after using role-based prompting technique. Additionally, results demonstrated that role-based prompts optimize the output quality in terms of clarity, depth, professionalism, insightfulness, innovativeness, relevance, and generosity. It was also found that students’ satisfaction with the output quality significantly increases with the use of role-based prompts. Furthermore, the paper at hand sheds light on limitations and recommendations to guide future research projects in the field or in fields that relate to it