University College of Bahrain

This study assessed Jordanian public-school teachers perceived awareness of how integrated STEM pedagogy can support students’ social and emotional learning (SEL) competencies and examined whether this awareness differs by teacher characteristics (gender, specialization, teaching experience, grade level taught, and school location). A researcher-developed 24-item questionnaire aligned to the five CASEL competencies was administered online using a non-probability snowball approach (N = 114 valid responses). Internal consistency was acceptable (Cronbach’s alpha = 0.863). Descriptive statistics indicated uniformly high perceived awareness across items (overall M = 4.96/6), suggesting strong perceived alignment between STEM practices (for example, inquiry, design thinking, collaboration, and reflection) and SEL principles. One-way ANOVA indicated no statistically significant differences by gender, specialization, grade level, or school location; a significant omnibus difference emerged across teaching-experience groups (p = 0.018; η² = 0.087), with the highest mean observed among teachers with less than five years of experience. Given the restricted variance (ceiling tendency) and non-probability sampling, results are interpreted as exploratory evidence of perceived awareness rather than demonstrated effectiveness or student outcomes. Implications are discussed for professional development that explicitly connects STEM instructional design to SEL competencies.

Endocrine-disrupting chemicals (EDCs) perturb hormonal homeostasis, dysregulating multiple biological pathways and subsequently resulting in adverse health outcomes, including impaired reproductive function. Bisphenols represent an important subclass of EDCs with widespread use in polycarbonate plastics, thermal paper formulations, epoxy resins, and various everyday consumer products. Bisphenol A (BPA) was the first bisphenol to be synthesized, with extensive industrial applications. However, the concerns over its potential health risks, most notably reproductive dysfunction, prompted the development and introduction of several structurally related BPA analogues. That said, studies on the potential hormonal effects of these BPA analogues remain limited. Therefore, strengthening the evidence base on their reproductive safety evaluation remains an essential priority for ensuring their safe application, and this study contributes to that broader objective. The study aimed to explore the potential endocrine-disrupting activity of two commonly used BPA analogues, bisphenol F (BPF) and bisphenol S (BPS), on reproductive hormone signalling, contributing to ongoing safety assessment efforts. The molecular interactions of these analogues with the estrogen receptor-α (ERα) and estrogen receptor-β (ERβ) were analyzed through structural binding characterization employing the induced fit docking (IFD) approach using the Schrödinger 2019 suite. The overall results revealed that the two indicated BPA analogues were placed successfully in the ligand-binding pockets of ERα and ERβ. Their binding pattern and molecular interactions showed certain similarities; however, they did not fully replicate the amino acid residue environment of the native ligands of ERα and ERβ, estradiol. Notably, the binding energy estimations revealed that BPF and BPS showed substantially lower values than those calculated for native ligands of ERα and ERβ. In summary, this study suggests that BPF and BPS exhibit lower predicted binding affinity toward ERα and ERβ under the applied molecular docking conditions. However, these computational findings do not establish receptor activation, endocrine potency, or safety outcomes, and the potential involvement of other reproductive signalling pathways warrants further investigation.

The food industry is a significant contributor to emissions of carbon dioxide worldwide because of its energy intensive nature of packaging, processing, refrigeration and extended supply chains. Intense climate, fossil fuel prevents energy, desalinated water, and food imports boost these emissions in dry location like Gulf Cooperation Council (GCC). Carbon dioxide is functional medium for fermentation, refrigeration, carbonation, microbial control, and preservation in food industry, which could be an appropriate platform to absorb carbon and exertion. CO2-reuse paths in the food business: A detailed analysis, with an emphasis on GCC economies. Physical, chemical, and biological pathways of usage are being evaluated based on technological food hygiene, maturity, and fusing potential. The priority is on functional applications such as modified environment packaging, cryogenic freezing, dairy processing, beverage carbonation, and developing carbon to food pathways like microphytes protein production, microbial lipid synthesis, and synthesis carbohydrates. Patent landscape analysis weighs revolution trends and commercial attentiveness, and procedure, coast effective, and social obstacles to large scale adoption are evaluated. CO2 recycle in the food industry is an auspicious decarbonization approach for dry areas with long term promise for food security if governmental frameworks, industrial incorporation, and communal assurance are formed.

The paper retrieves quiescent dispersive solitons in dispersion-flattened optical fibers having nonlinear chromatic dispersion and the Kerr law of self-phase modulation. The platform model is the Schrödinger–Hirota equation. The enhanced direct algebraic method has made this retrieval possible. The intermediary functions are Jacobi’s elliptic function and Weierstrass’ elliptic function. The final results appear with parameter constraints for the existence of such solitons.

Achieving sustainable economic growth by addressing structural and environmental issues has become a top priority for decision-makers worldwide. This study investigates the impacts of renewable energy use, export diversification, and agricultural growth on sustainable economic development among G-20 economies. The analysis employs panel data from 1990 to 2021 with second-generation panel econometric methods, such as the PMG-ARDL test and dynamic panel estimation techniques, to elucidate both short- and long-term relationships. To examine the stationarity of the data series and identify cross-sectional dependence, this study applies some panel unit root tests, including the Cross-Sectionally Augmented Im and Pesaran test, the cross-sectional dependence (CSD) test, the Cross-Sectional Augmented Dickey–Fuller (CADF) test, and the Im, Pesaran, and Shin (IPS) test. The Westerlund co-integration test is employed to explore long-term relationships among the variables. To assess the prolonged impact of these factors, this study adopts the Pooled Mean Group–Autoregressive Distributed Lag (PMG-ARDL) approach. Findings reveal a statistically significant and persistent relationship between the variables, indicating that agricultural growth, renewable energy utilisation, and export diversification negatively affect economic growth. All insights highlight critical implications for G-20 policymakers; countries can develop a more resilient and sustainable economic pathway by addressing obstacles in agriculture, clean energy, and trade diversification.

Roger Ulrich's Supportive Design Theory (SDT), initially developed for healthcare settings, focuses on three main principles: perceived control, positive distractions, and social support, which aim to alleviate stress and improve well-being. This paper explores the potential implementation of SDT in higher education buildings, highlighting their effectiveness in educational environments. While healthcare design has conventionally emphasised environments that reduce stress to benefit patient outcomes, the impact of physical environments on students’ well-being and academic success is often underestimated. By reviewing current research, the paper demonstrates how design elements—including controlling the surroundings (perceived control), engaging aesthetic features (positive distractions), and designated areas for interaction (social support)—can alleviate student stress and improve well-being (Scherer & Leshner, 2021). By drawing parallels between students and patients, this study identifies common stressors, such as sensory overload and feelings of isolation, which SDT principles can address in both scenarios. For instance, strategies used in healthcare settings, which focus on spatial autonomy and sensory comfort, can be adapted to the educational sphere by creating versatile learning spaces and quieter environments (Pereira, Catharine and Marcella Savioli Deliberador, 2018). However, the unique challenges of academic life, which often require balancing individual focus with collaborative efforts, may necessitate specific adjustments to the SDT approach. This discussion supports the relevance of SDT by integrating insights from environmental psychology and educational design, suggesting that thoughtfully designed campuses can not only improve academic performance but also support students' mental health.

This paper examines the regressive nature of the value-added tax and proposes an integrated framework combining a uniform value-added tax rate with progressive, digitally administered rebates. The model was performed using household- and firm-level microsimulations with Monte Carlo methods. It demonstrates that equity can be reached without revenue neutrality being undermined. Simulation results for a calibrated 2024–2025 economy show the proposed rebate structure reduces the effective tax burden on the lowest income quintile from 13.5% to 5.4% of income, delivering a net cash benefit of USD 786.88 and a welfare gain of 6.10%. The policy is projected to generate a robust average VAT revenue of USD 17.44 million, with a 97.8% probability of a positive fiscal impact, while reducing the poverty rate by 2.6% and lowering inequality (Gini coefficient of utility to 0.199). The outcomes present a welfare gain for the poor, a small firm-level effect, and a decrease in poverty and inequality. The results suggest a feasible policy route towards a more equitable tax system, thus promoting indirectly to the United Nations Sustainable Development Goals (SDGs), specifically SDG 8 (decent work and economic growth) and SDG 10 (reduced inequalities).

Conflicting media frames of Hassan Nasrallah reshape both regional risk calculations and collective memory. This article examines how he is discursively constructed across international (etic) and regional (emic) English-language news reports before and after his death. At the heart of this study is the question: How do emic and etic outlets differently authorize Nasrallah's voice before and after his death? Using a multi-layered framework that integrates Critical Discourse Analysis, Systemic Functional Grammar, quotation studies, framing theory, and corpus-assisted methods, the analysis covers thirteen articles from Reuters, AP, AFP, Al Jazeera English, and Al Mayadeen English. These articles were selected for their comprehensive representation of media perspectives and their influence within their respective media ecosystems, ensuring methodological rigor and sampling validity. Findings show that etic and emic outlets operate within distinct epistemic regimes that allocate agency, legitimacy, and moral meaning in contrasting ways. Pre-death, etic reports strategically amplify Nasrallah through frames of security, threat, and deterrence, while emic outlets emphasize political identity, historical memory, and resistance. Post-death, a discursive rupture occurs: etic outlets enact epistemic erasure, depicting the killing as a technical operational event, whereas emic outlets reassert Nasrallah as a symbolic and moral figure through sacralized naming and communal voice. Ultimately, the study argues that this emic/etic divide is not merely stylistic but political, exposing how Global North media reproduce a ‘coloniality of knowledge’ that silences resistance figures, while regional narratives function as sites of epistemic counter-memory. The insights from this study could inform newsroom strategies by guiding journalists to navigate these epistemic divides more critically and by helping policymakers recognize the broader impacts of media narratives on international relations and socio-political stability.

ABSTRACT Agriculture plays a fundamental role in the global economy, contributing about 6% to global gross domestic product, while in many low‐income nations, it accounts for up to 25%–40% of gross domestic product. Plants provide around 75% of the food consumed by humans and are the primary nutritional source for animals, making plant health crucial to global food security. However, plant diseases represent a serious threat to agricultural productivity, causing 10%–30% loss in global crop yields annually. The early detection and diagnosis of such diseases are thus vital to sustaining agricultural productivity. While deep learning techniques—particularly deep clustering—have shown promise in image‐based disease detection, they often face limitations when dealing with complex and multi‐modal data. To overcome these limitations, we propose an enhanced Dynamic Autoencoder method based on a Gaussian Mixture Model for plant disease detection. Unlike conventional methods that rely heavily on labeled data, our approach leverages probabilistic clustering and adaptive feature learning to distinguish between healthy and diseased plants more effectively. We evaluate our method using the PlantVillage dataset and demonstrate that our approach outperforms traditional deep learning and clustering approaches. The experimental results highlight the model's superior accuracy and normalized mutual information, underscoring its potential for scalable and intelligent plant disease monitoring in smart agriculture.