Search Results (94)

Due to globalization, companies are exposed to a culturally diversified workforce; therefore, great emphasis is placed on identifying the most effective leadership style that would be able to manage such a workforce. Although numerous studies have attempted to identify successful leadership styles in different cultural settings, none have focused on the perceptions of top managers who work in multinational corporations (MNCs) in culturally diversified surroundings. Thus, our research attempts to identify the most preferred leadership style and characteristics from the perspective of top managers in MNCs in the United Arab Emirates (UAE). The 13 leadership characteristics analyzed in this study were generated from the 21 characteristics found by Global Leadership and Organizational Behavior Effectiveness (GLOBE) research. The participants, top managers in MNCs, needed to evaluate leadership styles by considering leadership characteristics. To ensure the objectiveness of the study, we analyzed their answers by applying the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicated that the most preferred leadership characteristics were visionary, inspirational, collaborative team-oriented, and performance-oriented. Moreover, the transformational leadership style emerged as the most preferred leadership style. The study’s findings show that top managers believe that employees in MNCs in the UAE seek a leader with a vision who will inspire, motivate, and help them fulfill their true potential. Full article

Visual imagery (VI) is a mental process in which an individual generates and sustains a mental image of an object without physically seeing it. Recent advancements in assistive technology have enabled the utilization of VI mental tasks as a control paradigm to design brain–computer interfaces (BCIs) capable of generating numerous control signals. This, in turn, enables the design of control systems to assist individuals with locked-in syndrome in communicating and interacting with their environment. This paper presents an electroencephalogram (EEG) dataset captured from 30 healthy native Arabic-speaking subjects (12 females and 18 males; mean age: 20.8 years; age range: 19–23) while they visually imagined the 28 letters of the Arabic alphabet. Each subject conducted 10 trials per letter, resulting in 280 trials per participant and a total of 8400 trials for the entire dataset. The EEG signals were recorded using the EMOTIV Epoc X wireless EEG headset (San Francisco, CA, USA), which is equipped with 14 data electrodes and two reference electrodes arranged according to the 10–20 international system, with a sampling rate of 256 Hz. To the best of our knowledge, this is the first EEG dataset that focuses on visually imagined Arabic letters. Full article

Paraphrasing means expressing the semantic meaning of a text using different words. Paraphrasing has a significant impact on numerous Natural Language Processing (NLP) applications, such as Machine Translation (MT) and Question Answering (QA). Machine Learning (ML) methods are frequently employed to generate new paraphrased text, and the generative method is commonly used for text generation. Generative Pre-trained Transformer (GPT) models have demonstrated effectiveness in various text generation tasks, including summarization, proofreading, and rephrasing of English texts. However, GPT-4’s capabilities in Arabic paraphrase generation have not been extensively studied despite Arabic being one of the most widely spoken languages. In this paper, the researchers evaluate the capabilities of GPT-4 in text paraphrasing for Arabic. Furthermore, the paper presents a comprehensive evaluation method for paraphrase quality and developing a detailed framework for evaluation. The framework comprises Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Lexical Diversity (LD), Jaccard similarity, and word embedding using the Arabic Bi-directional Encoder Representation from Transformers (AraBERT) model with cosine and Euclidean similarity. This paper illustrates that GPT-4 can effectively produce a new paraphrased sentence that is semantically equivalent to the original sentence, and the quality framework efficiently ranks paraphrased pairs according to quality criteria. Full article

Background/Objectives: Numerous diseases such as diabetes, Alzheimer’s disease, and cancer have spread in the whole world, especially in the Arab world. Also, various applications of Schiff-base functionalized nanoparticles and copper oxide nanoparticles (CuO-NPs) such as therapeutic applications have been discovered. Thus, the current research highlights (i) the synthesis of copper oxide nanoparticles (CuO-NPs) produced with a Schiff base (SB) serving as a capping agent during their synthesis and (ii) assessment of the in vitro biological activities of Schiff base-synthesized copper oxide nanoparticles (SB-CuO-NPs) and a Schiff base (SB). Methods: SB-CuO-NPs were characterized using ultraviolet-visible (UV-Vis) spectroscopy, zeta potential, DLS analysis, and transmission electron microscope (TEM). It also focuses on assessing the in vitro biological applications and activities, including antioxidant, scavenging, anti-diabetic, anti-Alzheimer, anti-arthritic, anti-inflammatory, cytotoxic activities, and enzymes inhibitory potential, of Schiff base-synthesized copper oxide nanoparticles (SB-CuO-NPs) and a Schiff base (SB) using methods described in the literature. Results: The results of the biological activities of the SB-CuO-NPs were compared with those of the SB. The SB-CuO-NPs demonstrated superior in vitro biological activities when compared to the SB from which they were produced. Conclusions: The results of this investigation concluded that the CuO-NPs, synthesized with the SB serving as an alternative capping agent, exhibited enhanced biological efficacy relative to the original SB. In the future, the biological efficiency of SB-CuO-NPs against diabetes, Alzheimer’s, and cancer diseases will be assessed in experimental animals (in vivo). Full article

Road traffic accidents (RTAs) are a significant public health and safety concern, resulting in numerous injuries and fatalities. The growing number of cases referred to traffic accident rooms in courts has underscored the necessity for an automated solution to determine victim indemnifications, particularly given the limited number of specialized judges and the complexity of cases involving multiple victims. This paper introduces RTACompensator, an artificial intelligence (AI)-driven decision support system designed to automate indemnification calculations for road accident victims. The system comprises two main components: a calculation module that determines initial compensation based on factors such as age, salary, and medical assessments, and a machine learning (ML) model that assigns liability based on police accident reports. The model uses Arabic bidirectional encoder representations from transformer (AraBERT) embeddings to generate contextual vectors from the report, which are then processed by extreme gradient boosting (XGBoost) to determine responsibility. The model was trained on a purpose-built Arabic corpus derived from real-world legal judgments. To expand the dataset, two data augmentation techniques were employed: multilingual bidirectional encoder representations from transformers (BERT) and Gemini, developed by Google DeepMind. Experimental results demonstrate the model’s effectiveness, achieving accuracy scores of 97% for the BERT-augmented corpus and 97.3% for the Gemini-augmented corpus. These results underscore the system’s potential to improve decision-making in road accident indemnifications. Additionally, the constructed corpus provides a valuable resource for further research in this domain, laying the groundwork for future advancements in automating and refining the indemnification process. Full article

Ocimum sanctum Linn (O. sanctum L.), commonly known as Holy Basil or Tulsi, is a fragrant herbaceous plant belonging to the Lamiaceae family. This plant is widely cultivated and found in north-central parts of India, several Arab countries, West Africa and tropical regions of the Eastern World. Tulsi is known to be an adaptogen, aiding the body in adapting to stress by harmonizing various bodily systems. Revered in Ayurveda as the “Elixir of Life”, Tulsi is believed to enhance lifespan and foster longevity. Eugenol, the active ingredient present in Tulsi, is a l-hydroxy-2-methoxy-4-allylbenzene compound with diverse therapeutic applications. As concerns over the adverse effects of conventional antibacterial agents continue to grow, alternative therapies have gained prominence. Essential oils (EOs) containing antioxidants have a long history of utilization in traditional medicine and have gained increasing popularity over time. Numerous in vitro, in vivo and clinical studies have provided compelling evidence supporting the safety and efficacy of antioxidant EOs derived from medicinal plants for promoting health. This comprehensive review aims to highlight the scientific knowledge regarding the therapeutic properties of O. sanctum, focusing on its antibacterial, anti-diabetic, anti-carcinogenic, radioprotective, immunomodulatory, anti-inflammatory, cardioprotective, neurogenesis, anti-depressant and other beneficial characteristics. Also, the extracts of O. sanctum L. have the ability to reduce chronic inflammation linked to neurological disorders such as Parkinson’s and Alzheimer’s disease. The information presented in this review shed light on the multifaceted potential of Tulsi and its derivatives in maintaining and promoting health. This knowledge may pave the way for the development of novel therapeutic interventions and natural remedies that harness the immense therapeutic potential of Tulsi in combating various health conditions, while also providing valuable insights for further research and exploration in this field. Full article

Arabic diglossia, whereby Standard Arabic (SA) exists alongside numerous vernaculars, often leads to diglossic style-shifting, based on context or topic changes and marked in the vernacular by shifting to standard linguistic features. While this phenomenon has been widely studied in the speech of educated adults, research on diglossic style-shifting by children and adolescents has been rather limited. This paper investigates how it operates amongst 3–17-year-olds from a Bedouin speech community of Palestinian refugees in Syria. It examines context effects on realizations of the variables (θ) and (ð), which overlap with local realizations and (q), which has a standard realization ([q]) that is independent of dialectal variation in the community. Participants were recorded during sociolinguistic interviews and a picture-naming task, the latter being expected to evoke a school setting and prompt the use of more standard realizations, signaling diglossic style-shifting in their speech. Style-shifting was influenced by age, context, and the linguistic variables under examination. While picture-naming prompted greater use of standard realizations of all variables, shifting to [q] also appeared during the interview in lexical borrowings from SA, revealing topic effects on diglossic style-shifting. Children aged 6–14 exhibited more style-shifting in picture-naming, likely reflecting the central role of school in their lives, while the speech of 15–17-year-olds contained more lexical borrowing with [q]. This likely reflects their larger linguistic repertoires and longer exposure to SA than their younger peers. These findings indicate that SA plays a key role in participants’ linguistic practices and reflect their awareness of how to employ it appropriately in their speech. Full article

Fake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. While numerous attempts have been made by academia and the industry to develop fake news detection systems, research on Arabic content remains limited. This study investigates transformer-based language models for Arabic FND. While transformer-based models have shown promising performance in various natural language processing tasks, they often struggle with tasks involving complex linguistic patterns and cultural contexts, resulting in unreliable performance and misclassification problems. To overcome these challenges, we investigated an ensemble of transformer-based models. We experimented with five Arabic transformer models: AraBERT, MARBERT, AraELECTRA, AraGPT2, and ARBERT. Various ensemble approaches, including a weighted-average ensemble, hard voting, and soft voting, were evaluated to determine the most effective techniques for boosting learning models and improving prediction accuracies. The results of this study demonstrate the effectiveness of ensemble models in significantly boosting the baseline model performance. An important finding is that ensemble models achieved excellent performance on the Arabic Multisource Fake News Detection (AMFND) dataset, reaching an F1 score of 94% using weighted averages. Moreover, changing the number of models in the ensemble has a slight effect on the performance. These key findings contribute to the advancement of fake news detection in Arabic, offering valuable insights for both academia and the industry Full article

Yarrow (Achillea millefolium L., AM) is a widely used medicinal plant, with its essential oil highly valued in the cosmetic industry. In view of the numerous biological effects, however, microencapsulation, due to its ability to protect sensitive constituents, transform liquids into solid-state material, and provide modification of release kinetics, might open up new possibilities for the biomedical utilization of yarrow essential oil (AMO). In the current work, yarrow plantation was established by its propagation from spontaneous flora. Following the steam distillation of aerial parts, the chemical composition of the essential oil was determined by GC-MS analysis and compared with two commercial samples. This study concludes that Achillea millefolium L. from this region, given the environmental conditions, produces high-azulene-content essential oil. Furthermore, microencapsulation of AMO was successfully performed by complex coacervation into gelatin (GE) and gum arabic (GA) based core–shell microcapsules (MCs). According to the optical microscopic investigation, the particle sizes of the formed polynucleated microcapsules ranged from 14 to 132 µm, with an average of 47 µm. The assessment of morphology by SEM analysis of the freeze-dried form revealed a sponge-like character with embedded circular structures. The microencapsulation was confirmed by FT-IR spectroscopy and differential scanning calorimetry (DSC), while an encapsulation efficiency of 87.6% was determined by UV spectroscopy. GC-MS analysis revealed that microencapsulation preserves the key components of the essential oil. It was concluded that AMO can be effectively processed by complex coacervation followed by freeze-drying into solid-state material for new applications. Full article

Tramdol is one of most popular opioids used for postoperative analgesia worldwide. Among Arabic countries, there are reports that its dosage is not appropriate due to cultural background. To provide theoretical background of the proper usage of tramadol, this study analyzed the association between several genetic polymorphisms (CYP2D6/OPRM1) and the effect of tramadol. A total of 39 patients who took tramadol for postoperative analgesia were recruited, samples were obtained, and their DNA was extracted for polymerase chain reaction products analysis followed by allelic variations of CYP2D6 and OPRM A118G determination. Numerical pain scales were measured before and 1 h after taking tramadol. The effect of tramadol was defined by the difference between these scales. We concluded that CYP2D6 and OPRM1 A118G single nucleotide polymorphisms may serve as crucial determinants in predicting tramadol efficacy and susceptibility to post-surgical pain. Further validation of personalized prescription practices based on these genetic polymorphisms could provide valuable insights for the development of clinical guidelines tailored to post-surgical tramadol use in the Arabic population. Full article

Firms in the construction industry are under increasing pressure from regulatory bodies, clients, and the public to integrate sustainability into their business strategies. However, they encounter numerous barriers that hinder the implementation of environmental sustainability practices in construction project management. This study aimed to examine these barriers within the context of the United Arab Emirates’ (UAE’s) construction industry. To achieve this, the research employed a mixed-method approach. Initially, interviews were conducted to identify the prevalent barriers, resulting in the identification of twelve key barriers. Subsequently, a structured questionnaire was distributed to project managers from 90 firms, both ISO 14001-certified and non-certified, to rank these barriers and assess their significance. The findings revealed that “economic benefits placed above meeting environmental sustainability requirements” was the most critical barrier. Through factor analysis, three latent factors were extracted: (1) organizational and policy barriers, (2) compliance and resource efficiency barriers, and (3) sustainable design implementation barriers. Notably, significant differences were observed between ISO 14001-certified and non-certified firms, particularly regarding the importance of “economic benefits placed above meeting environmental sustainability requirements” and “insufficient consultation with stakeholders”. This study highlights the critical barriers to implementing environmental sustainability practices in the UAE’s construction industry and provides actionable suggestions for policymakers and decision-makers to overcome these challenges, with implications for similar environments worldwide. Full article

This study examines the semantic variation in fruit and vegetable classifier usage in Amman, Jordan, employing a cognitive sociolinguistic approach. The semantic variation revolves around using idiomatic classifiers, such as raːs basˤal (“head of onion”), in contrast to neutral classifiers, i.e., ħabbet basˤal (“a piece of onion”) or numerals, such as basˤalteːn (“two onions”). This study focuses on classifiers used with fruits and vegetables, which are particularly relevant due to their physical shapes often prompting metaphorical classifiers and their tendency to take Arabic collective forms that are grammatically singular but semantically plural, complicating the issue of counting and potentially leading to the innovation of novel classifiers. The sample comprised 50 individuals from Amman, stratified based on their gender, age, and education. Data were collected through semi-structured interviews. The findings reveal a statistically significant inclination among older, male, and less formally educated speakers towards favoring idiomatic classifiers over the neutral options. This preference suggests that the choice between idiomatic and neutral classifiers may be influenced by social factors. We categorized the metaphors underlying the idiomatic classifiers as entrenched, conventionalized, and transparent, based on Müller (2009). The context of conventional metaphors demonstrates that the source domains of these metaphors could be active for a speaker at a specific moment but may not be active for another speaker at another moment, proposing that metaphoricity is not only a property of a linguistic item but also the cognitive achievement of a certain speaker. The preference for idiomatic classifiers, we argue, may be associated with notions of lower refinement, traditionalism, or reduced prestige. Full article

Arabic handwriting recognition and conversion are crucial for financial operations, particularly for processing handwritten amounts on cheques and financial documents. Compared to other languages, research in this area is relatively limited, especially concerning Arabic. This study introduces an innovative AI-driven method for simultaneously recognizing and converting Arabic handwritten legal amounts into numerical courtesy forms. The framework consists of four key stages. First, a new dataset of Arabic legal amounts in handwritten form (“.png” image format) is collected and labeled by natives. Second, a YOLO-based AI detector extracts individual legal amount words from the entire input sentence images. Third, a robust hybrid classification model is developed, sequentially combining ensemble Convolutional Neural Networks (CNNs) with a Vision Transformer (ViT) to improve the prediction accuracy of single Arabic words. Finally, a novel conversion algorithm transforms the predicted Arabic legal amounts into digital courtesy forms. The framework’s performance is fine-tuned and assessed using 5-fold cross-validation tests on the proposed novel dataset, achieving a word level detection accuracy of 98.6% and a recognition accuracy of 99.02% at the classification stage. The conversion process yields an overall accuracy of 90%, with an inference time of 4.5 s per sentence image. These results demonstrate promising potential for practical implementation in diverse Arabic financial systems. Full article

This study investigates the processes leading to karst development in the southeastern part of Riyadh city extending up to Al Kharj. Numerous solution features such as sinkholes, collapsed dolines, and solution caverns are common in the area. The role of water in the development of the karst features was investigated using an integrated geological and hydrochemical approach. Geological investigations included the petrographic analysis of rock samples collected from zones of intense karstification with special emphasis on mineral dissolution. The study showed that the Sulaiy Formation is commonly fractured, brecciated, foliated, and contains numerous cavities, vugs, and openings. These features have formed by mineral dissolution by circulating groundwater, which has removed anhydrite beds from the underlying Arab–Hith sequence. Karstification likely started from the tectonically weak zones when there was more groundwater recharge. Studies show that during the early to mid-Holocene period, the climate in the Arabian Peninsula was humid, promoting groundwater recharge and subsequent mineral dissolution, though the process of karstification must have started much earlier. Hydrochemical findings reveal that mineral dissolution (halite and calcium sulfate) is the main process affecting groundwater chemistry. The Piper plot revealed two main hydrochemical facies: the (Ca²⁺ + Mg²⁺)–(Cl+ SO₄²⁻) Type (Type A) and the (Na⁺ + K⁺)–(SO₄²⁻ + Cl) Type (Type B). Most of the samples belong to Type B, typical of groundwater facies affected by dissolution of halite and anhydrite mineral. The absence of the (Ca²⁺ + Mg²⁺)–(CO₃²⁻ + HCO₃⁻) type of groundwater facies indicates a lack of recent groundwater recharge and the removal of carbonate minerals from the system through precipitation, as evidenced by the saturation indices. Plots of the major ionic pairs (cations vs. anions) in groundwater indicate strong halite and gypsum/anhydrite dissolution. Of the three carbonate minerals, calcite has the highest average saturation index followed by aragonite and dolomite. This suggests significant past rock–water interaction leading to carbonate dissolution. Presently, any additional calcium or carbonate ions introduced into the water lead to calcite precipitation. The study indicates that the process of karst development may not be active today. Currently, groundwater chemistry is mainly influenced by rock–water interaction leading to gypsum/anhydrite dissolution, which has resulted in a high concentration of Na⁺, Ca²⁺, Cl⁻ and SO₄²⁻ ions in groundwater. The dissolution of gypsum and halite from the Hith Formation weakens the structural integrity of the overlying Sulaiy Formation, creating large underground cavities. These cavities increase the risk of roof collapse, leading to cover-collapse sinkholes as the roof becomes too thin to support the weight above. Full article

Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation. Full article