Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Training Translators for a Better Translation of the Meaning of the Qur’anic Text
No full textThe translation of religious texts, particularly the Holy Qur’an, is one of the most challenging tasks. Despite the experts’ efforts to come out with insightful theories and strategies, the translation of the meanings of the Holy Qur’an is still a difficult task. The present research aimed to reveal the importance of training future translators and experts in the field by offering a 20-hour training program to familiarize a target group with the specificities of Qur’anic Texts and the challenges its translation involves. The study attempted to answer three main research questions related to (i) the specificities of the Qur’anic Text and the main challenges its translation poses; (ii) the quality and value of the training program and its efficiency and (iii) the trainees’ views and evaluations of the training program. The study opted for a mixed approach using a questionnaire and an interview to elicit the trainees’ views about the efficiency of the training program. Eighty participants from three Master’s programs in Islamic studies and translation were involved. The findings revealed that the training was an added value for the trainees who realized that awareness of Arabic rhetoric and Islamic exegetical works is crucial for a faithful translation of the meanings of the Holy Qur’an. Some recommendations were formulated for Master Programs in Islamic studies and translation
Paraneoplastic Syndrome Associated Immune Complications: A Narrative Review of the Literature
No full textParaneoplastic syndromes occur in cancer patients due to an alteration in their immune response. There are several factors that may result in the occurrence of paraneoplastic syndrome, including the presence of abnormal cytokines that cause widespread effects throughout the body. The paraneoplastic syndrome may be present in patients before a diagnosis of cancer, and thus, understanding it is crucial as it will help achieve a timely diagnosis, which may aid in improving the chance of treatment. It is associated with several complications/presentations in patients. In this review, we will discuss several paraneoplastic syndrome associated complications, including hypercoagulable state, venous thromboembolism, arterial thromboembolism, thrombotic microangiopathy, disseminated intravascular coagulation, and malignancy associated non-bacterial thrombotic endocarditis. Although some are rare, it is vital for clinicians to have a knowledge of each to allow time for management
Application of Gibberellins in Melon Cultivation Based on Substrate Hydroponic System with Drip Fertigation
No full textGibberellins are a group of plant hormones that play a role in regulating plant growth and development. This study aimed to determine the timing and concentration of gibberellin administration that is most effective in stimulating melon plants\u27 vegetative and generative growth in a substrate hydroponic system using the drip fertigation method. This research was carried out from May to August 2023 in Kragilan Surakarta. This study was conducted using a one-factor Complete Randomized Design (RAL) that has seven levels based on the method of gibberellin application, namely Level 1: G0 without Gibberellin (Control). Level 2: G1 Gibberellin concentration 60 ppm sprayed on days 5, 10, and 15 hst. Level 3: G2 Gibberellin concentration 60 ppm sprayed on day 20,25,30 hst. Level 4: G3 Gibberellin concentration 80 ppm sprayed on day 20,25,30 hst. Level 5: G4 Gibberellin concentration 80 ppm sprayed on day 30,35,40 hst. Level 6: G5 Gibberellin concentration 100 ppm sprayed on day 30,35,40 hst. Level 7: G6 Gibberellin concentration 100 ppm sprayed on day 30,40,50 hst. The results showed that application of gibberellins with concentrations of 100 ppm at 30,40 and 50 days after planting (HST) resulted in significant differences in chlorophyll content of a+b (total) compared to applications of concentrations of 60 ppm, 80 ppm, and 100 ppm at different times. There was a significant difference in sweetness compared to applying GA3 at concentrations of 100 ppm at different times. The application of GA3 did not significantly affect chlorophyll a, chlorophyll b, fruit diameter, fruit weight, root weight, and crush weight in melon plants based on Hydroponic systems using drip fertigation
Environmental and Socio-Economic Impact Assessment of Renewable Energy Using Machine Learning Models
No full textRenewable energy sources, such as solar, hydro, wind, and geothermal energy, have emerged as key alternatives to fossil fuels in combating climate change and addressing energy security concerns in the USA and ad worldwide. Strategic use of this renewable resource is important not only for carbon emission reduction and improvement of environmental sustainability but also for maintaining future energy supplies. At the same time, such transition raises thorough assessments of environmental and socio-economic impacts. Machine learning (ML) models offer a powerful tool for predicting and analyzing such impacts, allowing for more efficient decision-making and long-term planning. These models are supposed to analyze patterns in energy production, land use, and emissions to make a more dynamic and predictive understanding of how renewable energy adoption influences CO2 levels. The principal aim of this research project was to develop and curate machine learning algorithms for predicting CO2 emissions based on renewable energy data, using the knowledge to better understand how solar, wind, hydro, and geothermal energy systems affect environmental outcomes. The predictive models developed in this research would serve as useful tools for the policymakers and major stakeholders in decision-making on investments in energy infrastructure and characterization of regulatory frameworks. These datasets for this research project were retrieved from several prominent institutions, including governmental agencies, international organizations such as the International Energy Agency-IEA and the World Bank, satellite data repositories, and USA environmental monitoring agencies. For this research project, 3 machine learning algorithms in the experiment were used, namely Logistic Regression, XG-Boost, and Random Forest. Amongst these three, the linear regression model gave the best performance, as it had the least MSE; indicating that its predictive capability was impressive. The comparative analysis of renewable energy projects in Germany, China, and California underlines that effective policy-making plays a very decisive role in the transition toward sustainable energy
Assessing Geopolitical Risks and Their Economic Impact on the USA Using Data Analytics
No full textUnderstanding geopolitical risks is a paramount aspect of examining the stability and resilience of national economies, specifically in today’s rapidly evolving global surroundings. Advanced analytics in the big data era open unparalleled avenues toward the quantification and comprehension of geopolitical risks on the performance of the economy of the United States. The prime objective of this study was to analyze the impact of geopolitical events on the U.S. economy, to identify key risk factors and their economic implications as well as propose strategies for mitigating adverse effects. Datasets used in this exploration were collected from different reliable sources to assess sources of geopolitical risk data and their economic impact on the U.S. First, data on geopolitical risk were collated from a combination of real-time news reports, government databases, and international organizations involved in monitoring geopolitical events. Key sources for this included GDELT news and sentiment data, official reports from U.S. government agencies such as the Department of State and the Department of Defense about foreign policy, conflict, and security, while major financial news outlets like Bloomberg and Reuters provided moment-by-moment coverage of events in the geopolitical sphere. We applied the Geo-Risk-Regressor model, a form of multimodal design to predict geopolitical threats arising from economic indicators, real-time news sentiment, and government reports on geopolitical events. The Geo-Risk-Regression Model is an integrated set of machine learning algorithms, from time-series and NLP to econometric regression, on structured and unstructured data comprising economic indicators, real-time news sentiment, and government reports on geopolitical events. A rigorous structured procedure was followed in implementing the Geo-Risk-Regressor to analyze the economic impact of geopolitical risks in the U.S. To assess and evaluate the performance of the algorithms, two key performance evaluation metrics were utilized MSE & R-squared. Among all the models, the best performance was that of XG-Boost; it had the lowest MSE and highest R². Thus, XG-Boost is the best model fitted for the prediction of GPRD_THREAT, probably because of its robust optimization and also its capability to capture a lot of complicated patterns in data. The geopolitical threat level perceived using the proposed models will enable business organizations in the USA to identify and manage risks that may affect the operations of the business organizations. Companies can, therefore, understand factors that contribute to risk and develop contingency plans, enabling them to take proactive measures to mitigate negative impacts from geopolitical events. Predictive models will help businesses in America estimate the potential risks to their supply chains and create strategies for mitigating any disruptions that might come through geopolitical events
Investigating Students\u27 Attitudes Towards the Use of ICT in Learning: The Specialized Institute of Applied Hotel Technology and Tourism in Ouarzazate-Morocco- as a Case Study
No full textThis study seeks to examine the students’ attitudes towards the use of information communication technology (ICT) in learning based on the Technology Acceptance Model (TAM) developed by (Davis, 1989) among tourism students at the Specilaized Institute of Applied Hotel Technology and Tourism in Ouarzazate, Morocco. In the current research, a questionnaire in Google form is administered to 238 students enrolled in the Institute to easily get responses. The study espouses a mixed-methods research design that uses both qualitative and quantitative techniques to collect and analyze data. To unknown reasons, only sixty-four student-respondents managed to fill out the questionnaire entitlted : Investigating Students\u27 Attitudes Towards the Use of ICT in Learning: The Specialized Institute of Applied Hotel Technology and Tourism in Ouarzazate-Morocco- as a Case Study. The findings reveal that most respondents hold a positive attitude towards the use of ICT in their learning. Furthermore, the results indicate that respondents perceive ICT as valuable and easy to use, enhancing engagement, interaction, and achievement in learning. On the other hand, technical issues, internet connectivity, ICT competency, and hardware issues are the major challenges faced by students while espousing ICT in their learning process
Forecasting Electric Vehicle Adoption in the USA Using Machine Learning Models
No full textElectric vehicles (Electric Vehicles) are at the vanguard of the global dispensation to sustainable transportation, depicting a pivotal step toward diminishing greenhouse gas emissions and reliance on fossil fuels. Notwithstanding, the adoption of Electric Vehicles has been growing in the USA, but their future remains at a crossroads. The objective of the research is to design and execute machine learning models capable of providing accurate predictions of future trends in electric vehicle adoption in the USA. The dataset gathered for analyzing EV adoption in the USA comprises data across three primary categories: environmental data, economic indicators, and policy-related data. The economic indicators include household income, fuel prices, electricity rates, and lithium battery costs that affect EV purchasing power obtained from the U.S. Census Bureau and the U.S. Energy Information Administration (EIA). Environmental data include greenhouse gas emissions and air quality indices from the EPA, providing information on regional environmental conditions that might affect EV attractiveness. Other policy data included federal and state incentives such as tax credits, rebates, and EV infrastructure data, collected from the U.S. Led by the U.S. Department of Energy\u27s Alternative Fuels Data Center and the Energy Laboratory, additional EV sales trends were pulled from databases of the automotive industry. In this research project, credible and proven machine learning models were employed, most notably, Linear Regression, Random Forest, and XG-Boost. The performance of the models was tested for EV adoption prediction by considering a few important metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). From the model performance metrics presented, the Gradient Boosting Regressor and Random Forest models performed far better by a big margin than Linear Regression. The prediction models, particularly, the Random Forest regressors and Gradient Boosting regressors demonstrated incredible forecasting of electric vehicle adoption. The model works excellently on the premise that historical data with relevant features can be utilized to gain some valuable insight into future trends. Policy-makers interested in stimulating the wider use of electric vehicles can ensure that targeted policies address both current barriers and future demands. Results of this analysis suggest incentives, such as tax credits, rebates, and subsidies, are some of the most common actions to reduce the upfront cost of an EV, a key circumventing factor in the choice that many consumers face
Enhancing Efficiency and Accuracy of Optimization Techniques in Time Series Data Prediction Using Machine Learning: A Systematic Literature Review
No full textUndoubtedly, time series data prediction stands as a primary focus of computational intelligence among researchers in both academia and industry, owing to its wide-ranging applications and significant impact. The efficacy of prediction models heavily relies on the optimization techniques employed to enhance both efficiency and accuracy. Within the realm of Machine Learning (ML), researchers have developed numerous optimization techniques and models, leading to a plethora of studies. Consequently, there exists a considerable body of literature comprising reviews of ML-based time series prediction. Recently, Deep Learning (DL) models have emerged in this domain, demonstrating performance levels that notably surpass those of traditional ML methods. Despite the burgeoning interest in advancing time series prediction models, there remains a noticeable absence of systematic review papers dedicated solely to enhancing the efficiency and accuracy of optimization techniques. Therefore, this paper is motivated by the need to present a systematic review of the efficiency and accuracy of optimizers studies concerning time series prediction implementations. We not only classify these studies based on their targeted optimization techniques, prediction applications—such as crop yield prediction, weather forecasting for farming, and pest detection and management—but also categorize them according to the types of optimization techniques and models employed, including Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM) networks. Additionally, this study endeavor to provide insights into the future of the field by highlighting the challenges and potential future research opportunities, thereby offering guidance to interested researchers
Effects of Assisted Tools and Learning Conditions on L2 Vocabulary Learning: A Study based on Large Language Model
No full textIn this study, we investigated the effects of assisted input, namely gloss and dictionary, on L2 incidental vocabulary learning. Furthermore, intentional and incidental learning conditions were also compared while merely utilizing a dictionary. Additionally, the gloss material was provided by a large language model (LLM) ChatGPT through prompt engineering. Besides, learning gains were measured not only solely from knowledge breadth (form-meaning connection) but also from more dimensions regarding knowledge depth (synonym discrimination, derivation production, collocation production). Sixty-four English learners of grade 2 from a senior high school were divided into three treatment groups and one control group. Those two kinds of comparisons were made respectively between every three groups. Results indicated that the gloss provided by LLM showed efficiency in collocation retention while the dictionary brought better effects in derivations and synonym discrimination. Furthermore, intentional learning may exert a good role in the long-term retention of knowledge depth and enhanced synonym discrimination effectively. The results are discussed along with students’ feedback from the questionnaire
A New Lexicon for the Anthropocene: The Words of the Pandemic
No full textThe term “Anthropocene”, introduced by Crutzen and Stoermer in 2000, describes the massive impact of human activities able to influence geological processes: humans are a force of nature in a geological sense. The recognition of a close interdependence between man and nature has been consciously elaborated only since the late 1960s and early 1970s, thanks to publications in scientific environmentalism. It is indeed valuable to note that literary texts discuss these issues much earlier. Henry David Thoreau (1817–1862), in his essay Walking back in 1861 introduces the idea of preserving nature when it was unknown and unpredictable. A few years later, another author, John Muir (1838–1914), supports the interpretation of nature as worthy of intrinsic value and contributes to the creation of the Yosemite Park in California. What these texts have in common is that they belong to the genre “nature writing”, which is capable of putting itself at the service of the natural environment and to which Spillover: Animal Infections and the Next Human Pandemic (2012) by American nature writer, David Quammen, also belongs. Spillover fits perfectly into this literary tradition, embodying the main characteristics of the genre. Moreover, it shows some optimism towards the future, offering the possibility of redemption to our species. The redemption of literature in the context of environmental narration is solidified not only through nature writing. Spillover proves to be prophetic like its classical ancestors, also through the introduction of a new terminology that contributes to developing a new lexicon, that of the Anthropocene