Search Results (94)

Arabic remains underrepresented in many task-specific natural language processing (NLP) resources and benchmarks, particularly for specialized user interface (UI) understanding tasks. In mobile commerce, Arabic UI text may contain persuasive or deceptive design cues known as dark patterns; however, Arabic-language dark pattern detection remains largely unexplored. To the best of our knowledge, this paper presents the first ML-based benchmark for Arabic dark pattern detection. We construct a novel annotated dataset of 223 Arabic UI text strings from nine e-commerce mobile applications operating in Saudi Arabia, labeled across five dark pattern categories and a non-dark-pattern class (Cohen’s kappa κ = 0.89). Using a stratified, leakage-free 70/10/20 split with parent-aware paraphrase augmentation applied only to the training partition, we fine-tune five pretrained transformer models: AraBERTv2, MARBERT, mBERT, BERT-base-uncased, and RoBERTa-base. Our primary evaluation is 5-fold cross-validation on the 223 original, non-augmented instances, separate from the augmented training corpus used for the held-out test comparison. Under this evaluation, MARBERT achieves the strongest performance (mean macro-F1 = 0.4230), numerically ahead of AraBERTv2 (0.2998) by a margin that does not reach statistical significance at five folds (p ≈ 0.064), and ahead of mBERT (0.3234); MARBERT significantly outperforms both English-only baselines, and mBERT is numerically stronger than both, though mBERT was not directly tested against them for significance. This suggests pre-training on dialectal, code-switched Arabic may matter more here than Arabic pre-training alone. Per-class analysis shows every model struggles with several minority categories, indicating Arabic dark pattern detection remains genuinely difficult at current data volumes. The annotated dataset is publicly released to support future low-resource Arabic NLP research. Full article

Wild-food foraging is booming across the Global North, but its meaning—and its value for plant conservation—differs sharply between settings. We compare three case studies: Denmark, an intensively cultivated country with a book- and app-led revival; Southern Scotland, a post-industrial landscape whose foragers are largely incomers with a media-sourced repertoire; and Tel Aviv–Jaffa (Israel), where Nordic-inspired “neo-foraging” coexists with an eroding Arab–Palestinian oral tradition. Pooling three independently collected ethnobotanical datasets (2012–2021; Denmark: questionnaires n = 27 and interviews n = 10; Southern Scotland: questionnaires n = 12 and interviews n = 9; Tel Aviv–Jaffa: interviews only, n = 10), we record 153 wild-food taxa—136 plants (including three macroalgae) and 17 fungi—in 51 plant families. Read through the Latvian (Riga) sociology of foraging, all three cases sit on the delocalised, “lifestyle” side of the forager spectrum. Yet, all three converge on one under-exploited fact: foraging can train ordinary urban people to see, name, track and value wild plants. The apparent contradiction between gathering plants and conserving them, we argue, is resolved by the target flora rather than by the practice itself: harvest risk is concentrated in a small minority of rare, range-edge and habitat-specialist taxa, whereas plant awareness is built through repeated encounters with the commonest species—precisely those whose gathering is demographically trivial. Where foragers anchor their teaching in this ordinary, ubiquitous flora, their role is not merely compatible with plant conservation but quintessential to it. Foragers are, thus, an underused constituency of citizen educators capable of countering plant awareness disparity (also termed “plant unawareness”), provided their enthusiasm is coupled to a stewardship ethic, and we set out how conservation actors might mobilise rather than merely regulate this potential. Full article

This study examined how social and cultural contexts influence coping resources of Druze and Bedouin in the Iron Swords War in Israel. The two Arab minority groups exposed to similar existential threats but with different cultural backgrounds and relations with the Israeli state were studied. A total of 210 adolescents (115 Druze, 95 Bedouin) aged 15–18 years filled out validated questionnaires on sense of coherence, social support, institutional trust, and post-traumatic growth. Findings indicated that compared to Bedouin adolescents, Druze adolescents demonstrated a higher sense of coherence and institutional trust. Family support served as a universal resource for both groups. Bedouin adolescents displayed stronger relationships between external resources and post-traumatic growth, while Druze adolescents seemed to benefit from internal processes of meaning-making. These findings reveal two distinct pathways to post-traumatic growth shaped by structural position: an externally driven pathway for marginalized populations involving resource mobilization against barriers and an internally driven pathway for integrated populations emphasizing meaning-making. The study demonstrates that resilience is not only individual achievement but a social product, requiring both psychological intervention and structural inclusion to support adolescents during wartime. Full article

Causality extraction is an important task in natural language processing, yet it remains underexplored in informal Arabic social media text, particularly in dialectal contexts. This study investigates causal-reason extraction from Saudi Arabic tweets related to sick-leave requests. A gold-standard dataset was annotated for multiple causality-related tasks, including cause-presence detection, cause-span extraction, cause-category classification, causal-marker detection, and causal marker text identification. The study compares two modeling paradigms: fine-tuned BERT-based models, represented by SaudiBERT and AraBERT, and prompting-based large language models (LLMs), represented by GPT-4.1-mini and Gemini-2.5-flash. The descriptive analysis showed strong class imbalance, substantial implicit causality, and uneven cause-category distributions. Results showed that SaudiBERT generally outperformed AraBERT when macro-level and minority-class performance were considered. Among LLMs, Gemini-2.5-flash achieved the strongest overall performance, particularly under natural 10-shot single-tweet prompting, while balanced few-shot prompting improved macro-F1 for cause-category classification. However, step-wise prompting did not consistently improve performance and may have introduced error propagation. Overall, the findings show that causality extraction in informal Saudi Arabic remains challenging, especially for implicit causal expression. The study highlights the complementary strengths of dialect-specific transformers and LLM-based prompting for Arabic causality extraction. Full article

This cross-sectional study examined identity, belonging, perceived discrimination, and psychological well-being among 348 Bedouin young adults in Israel (ages 18–30; M = 20.8, SD = 3.7). The survey assessed identity endorsement and centrality, belonging to the Bedouin community, perceived discrimination, perceived identity threats, and psychological well-being. Latent class analysis identified three descriptive identity profiles: (1) Ambivalent/Moderate Bedouin (mixed identities with moderate endorsement; n = 158), (2) Bedouin-Centered (near-exclusive endorsement of Bedouin identity with limited endorsement of the other measured categories; n = 74), and (3) Integrated Religious-National Bedouin (high Bedouin, Arab, and Muslim endorsement; n = 116). The Integrated profile reported somewhat lower well-being, higher perceived discrimination, and more perceived identity threats in descriptive comparisons. In hierarchical regression, stronger belonging was associated with higher well-being and greater perceived discrimination with lower well-being after adjustment for demographics; identity profiles did not significantly improve prediction once these social experiences were included. The findings underscore that psychological well-being among Indigenous minority young adults is associated not only with identity configuration but also with the social conditions that enable belonging and reduce discrimination. Full article

CD19-directed chimeric antigen receptor (CAR) T-cell therapy has fundamentally transformed the treatment landscape for relapsed and refractory B-cell malignancies, yet antigen escape remains a persistent therapeutic challenge that limits long-term remission durability. While antigen loss is typically considered a somatic event acquired during tumor evolution under therapeutic selective pressure, germline CD19 polymorphisms could theoretically influence CAR-binding kinetics, alter epitope presentation, and modulate therapeutic outcomes in ways that remain largely not characterized. Unfortunately, Middle Eastern populations are underrepresented in pharmacogenomic databases and CAR-T clinical trials, creating a knowledge gap that may perpetuate global health disparities in access to precision immunotherapy. We analyzed publicly available whole-exome sequencing data from 1196 individuals of Arab origin to comprehensively characterize CD19 variants with potential relevance to CAR T-cell immunotherapy. The L174V (rs2904880) variant stood out, and showed the Valine/Valine (V/V) genotype frequency was 65.3%, corresponding to a V174 allelic frequency of 76.6%, while the minor allele, L174, has a frequency of 23.4%. The missense mutation (c.520C > G) responsible for this variant results in a leucine-to-valine (L174V) substitution at position 174 of the CD19 protein, relative to the reference genome. The cohort genotypes (CC, CG, and GG) exhibited a significant deviation from Hardy–Weinberg equilibrium (p < 0.00001). While this deviation is consistent with the high consanguinity rates (25–60%) amongst Arab populations, it remains not fully explained, and may be attributed to population structure, relatedness, or technical factors. We further emphasize that our computational analysis cannot establish any direct clinical or functional impact due to this variant, and therefore we refrain from suggesting any specific actions at the current time. In light of these findings, we hypothesize that the distinctive genetic architecture of consanguineous populations should not be viewed as a confounding variable. Instead, it presents a unique opportunity to investigate the clinical relevance of germline variation in the context of precision oncology, particularly at therapy-relevant loci, pending functional validation. Full article

Internal and civil wars affect the lives of religious and ethnic minorities the most. For Syrian citizens of Armenian origin, the Republic of Armenia represented one of the most accessible and meaningful destinations to relocate to, shaped by shared ethnicity, collective memory, and historical ties. When the Syrian war erupted in 2011, thousands opted to resettle in Armenia, yet they and host institutions struggled to categorize them as immigrants, refugees, or repatriates. This ambiguous status has received little scholarly attention. To explore these complexities, the study employed a survey-based research design involving 124 participants, supplemented by an open-ended question intended to capture personal narratives and nuanced identity negotiations. The manuscript examines how the labels immigrant, refugee, and repatriate carry distinct legal, social, and emotional implications, especially against the backdrop of the 1915 Armenian Genocide’s enduring memory and the particularly negative connotations of “immigrant” and “refugee” in Western Armenian and Arabic languages. Within this contested semantic and policy terrain, repatriation appears not merely as a bureaucratic category but as a culturally resonant and sometimes preferred pathway for some Diaspora Armenians, informed by lifelong exposure to repatriation narratives through formal education (language textbooks) and informal communal practices. The case sheds light on the broader conception of stakeholders, including how they self-identify, how they understand their status in Armenia, and the factors shaping their choices, particularly in the context of contemporary geopolitics and the role of education in influencing external perceptions of them. Full article

Background: Dental caries is a chronic disease mediated by biofilm, which is caused by Streptococcus mutans, and enamel genetics modulates susceptibility. The variants of ENAM might alter the adhesion of enamel and bacteria. One important anti-viral target is sortase A (SrtA), which restricts colonization but does not have an impact on bacterial survival. Aim: The aim of this study was to find out the relationship between ENAM rs3796704 and dental caries vulnerability among adult Iraqi Arab females and to assess the antibiofilm capacity of eugenol and cinnamic acid against S. mutans SrtA using molecular docking, ADMET prediction, and molecular dynamics modeling. Methods: A case–control study was done on 240 women (aged 25–30 years; 120 caries, 120 controls). HRM real-time PCR was done to genotype ENAM rs3796704. An analysis of allelic and genotypic distributions was done using chi-square tests and odds ratios (p < 0.05). An in silico docking analysis aimed at SrtA (PDB: 4TQX) was performed in AutoDock Vina, and this was followed by ADMET profiling and a 50 ns molecular dynamics simulation (OPLS4/TIP3P, NPT 300 K/1 atm). Results: The level of the G allele was found to be lower in the cases than in the controls (60% vs. 70; OR = 0.6429; p = 0.02), but the level of the A allele was found to be higher in the cases (40% vs. 30; OR = 1.5556; p = 0.02). Docking showed a minor difference in binding affinities with eugenol (−4.961 kcal/mol) and cinnamic acid (−4.939 kcal/mol) as compared with chlorhexidine (−4.692 kcal/mol). Both compounds showed stable binding for more than 50 ns as well as desirable predicted pharmacokinetics. Conclusions: The caries vulnerability in this sample was associated with ENAM rs3796704. Eugenol and cinnamic acid undergo stable dissociative interactions with SrtA and were found to have favorable safety profiles in silico. Therefore, they may be considered as adjunctive anti-virulence agents in the prevention of caries. Full article

This study examines the perceptions of Palestinian Arab students enrolled in teacher training colleges regarding their religious, ethnic, and educational identities and investigates the associations between these identities and life orientation, self-mastery, and self-esteem. In addition, the study evaluates a hypothesized model in which life orientation is posited to mediate the relationship between religious identity, ethnic identity, self-esteem, self-mastery, and educational identity. The research includes a sample of 512 Arab Palestinian Israeli students studying in Israeli teacher training colleges who filled out an online questionnaire. The findings show that participants reported clear and coherent perceptions of their religious, ethnic, and educational identities, along with a generally positive life orientation and moderate levels of self-esteem and self-mastery. Significant correlations were found between the variables. Furthermore, religious and ethnic identity, self-esteem, self-mastery, and life orientation were all directly associated with educational identity. Bias-corrected bootstrap analyses confirmed significant indirect effects through positive life orientation, supporting the hypothesized mediation model. These findings help to illustrate the extent to which the religious and ethnic identities of indigenous multi-religious and multicultural minorities contribute to the growth and development of individuals and advocate for their strengthening, contrary to claims that belittle these identities or call for ignoring and suppressing them. Furthermore, the study underscores the potential role of identity awareness in fostering adaptive psychosocial adjustment and reducing social polarization. Full article

Sentiment analysis has become increasingly important across various domains, particularly in business intelligence, where it is crucial for improving the performance of companies by identifying the sentiments and emotions expressed in customer feedback on products and services. Despite its growing relevance, sentiment analysis still faces several challenges, including class imbalance in datasets, limitations in feature extraction techniques, and the selection of appropriate classification models. Effectively addressing these challenges requires the integration of robust representation methods, reliable data balancing strategies, and efficient classification frameworks. In this study, we propose a novel sentiment analysis approach that combines SBERT for contextual feature extraction, WGAN-based synthetic data generation for addressing class imbalance, and a soft voting ensemble classifier for improved prediction. The proposed approach is evaluated on five datasets, including two English datasets and three Arabic datasets, in order to assess its performance in a multilingual setting. We compare the effectiveness of the proposed model with several baseline machine learning classifiers, as well as with commonly used data balancing techniques such as the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic (ADASYN). The evaluation is conducted using multiple performance metrics, including accuracy, precision, recall, F1-score, MCC, ROC–AUC and training and inference time, along with different validation strategies including fixed train–test splits and k-fold cross-validation. The experimental results demonstrate the effectiveness and stability of the proposed approach. In particular, they highlight the importance of capturing sentence-level contextual representations and generating realistic synthetic samples to address class imbalance. Full article

This study validates Danielson Framework for Teaching (DFfT) instruments’ structure, dependability, and contextual appropriateness within the multicultural, standards-driven education system of the United Arab Emirates (UAE) in accordance with Vision 2021 and national teacher competency frameworks. Quantitative data were collected from 629 UAE schoolteachers through administering a questionnaire-based survey. Principal Component Analysis and Confirmatory Factor Analysis yielded discriminant, convergent, and construct validity in addition to internal consistency using the Composite Reliability Index and Average Variance Extracted for all scales. Four DFfT domains were shown to have a stable structure based on Principal Component Analysis results: planning and preparation (six factors, α = 0.92–0.99), learning environment (five factors, α = 0.98–0.99), learning experiences (five factors, α = 0.96–0.99), and principled teaching (six factors, α = 0.69–0.99). Notably, all constructs had excellent model fit with substantial factor loadings and inter-item as confirmed by the results of the Confirmatory Factor Analysis. With the exception of one minor subscale (α = 0.69), all dependability coefficients exceeded recommended benchmarks. The first-order full DFfT structural model of the four main domains validation demonstrated a reliable framework (CFI = 0.917, TLI = 0.902, IFI = 0.919, χ²/df = 1.635, and RMSEA = 0.078) for professional development, instructional improvement, and policy alignment with potential relevance beyond the UAE context, as well as psychometric soundness and contextual adaptability for teachers’ professional growth and evaluation in UAE schools. The study’s findings are significant, as they are the first to empirically validate the psychometric properties of the Danielson framework of teaching instruments in the UAE. Full article

The rapid growth of Arabic social media content requires the development of accurate and efficient methods for sentiment analysis. We propose a resource-efficient multi-task learning (MTL) framework for modern standard Arabic (MSA). The model uses a shared AraBERT encoder to jointly predict emotion, polarity, and intention. We integrate knowledge distillation (KD) from a large teacher model, self-distillation (SD) using model self-ensembling, and adversarial training (AT) as a regularization strategy. Experiments conducted on an annotated corpus of MSA tweets demonstrate that all distilled models outperform a fine-tuned multi-task baseline, and the combined KD+SD+AT configuration achieves competitive results. For instance, KD alone raised Macro F1 for emotion from 0.83 to 0.88 and for intention from 0.67 to 0.72. KD+SD+AT achieved the best intention F1 (0.76) and the highest polarity F1 (0.90). Notably, F1-scores for several minority classes show consistent improvement, particularly under KD and combined configurations. Paired t-tests confirm that several improvements, especially those obtained with KD and KD+SD+AT, are statistically significant ($p<0.05$). Our results indicate that distillation, combined with adversarial regularization, enables the development of smaller and more efficient Arabic sentiment models while maintaining competitive accuracy. These findings address a gap in Arabic multi-task sentiment analysis and provide a scalable, resource-efficient framework, along with empirical insights for distillation in Arabic language models. Full article

Sustainability and the Sustainable Development Goals (SDGs) are garnering significant attention due to growing global challenges, including poverty, inequality, environmental degradation, and climate change, with the latter addressed specifically through SDG 13. This study examined the level of self-reported awareness of six science-related SDGs—SDG 3 (Good Health and Well-Being), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land)—among science teachers in the Arab sector in Israel as a function of background variables: gender, seniority, degree type, academic institution, school type, area of specialization, and the integration of these SDGs into the science curriculum. The study employed a mixed-methods approach: in the quantitative component, 204 science teachers responded to a Likert-scale questionnaire; the qualitative component consisted of semi-structured interviews with 30 middle school science teachers from the Arab sector. The findings indicated a moderate level of self-assessed awareness regarding SDGs. Significant differences in awareness were found according to teaching subject: environmental studies teachers demonstrated the highest awareness, followed by general science, biology, and physics teachers, with chemistry teachers ranking lowest. No significant differences were found for the remaining variables (p > 0.05). Qualitative findings indicated that while teachers perceived SDG-related content as implicitly present in the curriculum, explicit and systematic integration of the SDG framework is largely absent. Overall, the findings suggest that teachers are not adequately exposed to the SDGs. Therefore, it is recommended to incorporate these topics into teacher-training courses and professional development programs and to further integrate them into curricula. This study contributes to the growing body of research on SDG integration in science education, particularly within underexplored minority educational contexts. Full article

This qualitative study explores the induction experiences of Palestinian Arab novice teachers in Jewish-majority schools in Israel during a period of intense national tension (2023–2025). Amid ongoing teacher shortages in the Jewish sector and a surplus of qualified teachers in the Arab sector, Boundary-Crossing Teaching (BCT) has become a notable phenomenon. Using semi-structured interviews and reflective journals of 23 beginning teachers and eight mentors, the study investigates how minority educators navigate cultural and political divides in a conflict-affected society. The findings reveal that during periods of heightened tension, teachers’ professional identity is often overshadowed by ethnic suspicion, leading to a “dual burden” of professional and national representation. The data shows that teachers navigate national ceremonies through “strategic ambiguity”—performing outward compliance (e.g., standing for the siren) while maintaining internal identity boundaries. Furthermore, the study identifies a paradox in language dynamics: while Palestinian Arabic is often “securitized” and viewed with suspicion in staffrooms, teachers successfully leverage their linguistic background as “intercultural capital” to build empathy with students. The research finds that shared-identity mentors provide an essential “third space” for processing experiences of racism that are otherwise silenced within the school hierarchy. These empirical results demonstrate that teacher retention in conflict zones requires active institutional protection to prevent professional status from collapsing into national categorization. Full article

Background/Objectives: Adolescents may experience psychosocial consequences from minor dentofacial variations. The relationship between objectively rated smile esthetics and self-reported psychosocial impact remains under-studied in Saudi adolescents. This study aimed to investigate the relationship between the objectively measured smile esthetics with the subjectively reported psychosocial impact of perceived smile esthetics. Methods: Cross-sectional, multistage cluster-stratified sample technique was used to study adolescents aged 15–20 years (n = 344) from Al-Ahsa schools. Standardized extra-/intraoral photography supported Dental Esthetic Screening Index (DESI) scoring and psychosocial impact using Arabic Psychosocial Impact of Dental Aesthetics Questionnaire (PIDAQ) were applied. Reliability was assessed through two-way mixed intraclass correlation coefficient (ICC), Bland–Altman analysis, standard error of measurement (SEM), and minimal detectable change at the 95% confidence level (MDC95). Associations were examined using correlations and regression models. Results: The distribution of DESI categories was excellent (6.4%), good (29.7%), satisfactory (42.2%), insufficient (18.9%), and poor (2.9%). The distribution of PIDAQ impact levels was minimal (37.8%), slight (41.6%), moderate (18.0%), and significant (2.6%) (age p = 0.052; sex p = 0.417). DESI and total PIDAQ were weakly correlated (Spearman ρ = 0.248, 95% CI 0.143–0.347; p < 0.001). In a multivariable linear regression model with continuous PIDAQ total score as the outcome (R² = 0.525; adjusted R² = 0.516; p < 0.001), self-perceived smile dissatisfaction (B = 7.789; β = 0.478; p < 0.001) and tooth-color dissatisfaction (B = 4.099; β = 0.306; p < 0.001) showed the strongest associations with higher PIDAQ scores, while DESI total score showed a smaller association (B = 0.310; β = 0.120; p = 0.002). Age and sex were not significant predictors after adjustment. Conclusions: Objective smile esthetics were modestly associated with psychosocial impact, whereas adolescents’ self-perceived smile and tooth-color dissatisfaction were strongly associated with worse psychosocial outcomes. Although the smile esthetics may be clinically acceptable, adolescents can still experience reduced oral health-related quality of life due to the psychosocial impact of perceived dental esthetics. These findings support incorporating brief subjective questions on smile and tooth-color perception alongside objective assessment during routine adolescent dental care. Full article