Mu’tah University (Arabic: جامعة مؤتة, Jāmi‘atu Mu'tah) is a public university in the Jordanian town of Mu'tah which was founded on 22 March 1981 by the Royal Decree to be a national institution for military and civilian higher education.
Background Smartphone addiction is a behavioral health issue prevalent worldwide, especially among adolescents. Although the Smartphone Addiction Inventory (SPAI) is commonly used, its psychometric characteristics have not been determined among Arab adolescents. This study examined the psychometric characteristics of the SPAI among Jordanian adolescents, particularly its internal consistency, construct validity, and factor structure. Methods A cross-sectional survey involved 960 high school students aged 13–17years. Respondents were randomly assigned to two subsamples. Latent structure was tested using exploratory factor analysis (EFA, n=480) and model fit using confirmatory factor analysis (CFA, n=480). Internal consistency was evaluated using Cronbach's alpha and McDonald's omega. Convergent and discriminant validity were tested through average variance extracted (AVE), composite reliability (CR), and heterotrait-monotrait ratios (HTMT). Results EFA supported a four-factor structure comprising tolerance, withdrawal, compulsive behavior, and functional impairment. CFA indicated acceptable model fit, χ2(146)=288.73, CFI=0.943, TLI=0.933, RMSEA=0.045, and SRMR=0.041. AVE values were below the conventional threshold of 0.50 across all four domains, while HTMT ratios showed good discriminant validity. Conclusion The findings support the multidimensional structure of the SPAI-A and provide evidence of its psychometric properties among Jordanian adolescents. The SPAI-A may be useful for assessing problematic smartphone use; however, further research is needed to establish its clinical screening performance and applicability in pediatric nursing practice.
Context: AI systems increasingly support requirements, coding, testing, security review, release, and project coordination. Existing AI-governance guidance is useful, but often too general to specify how much authority AI should have in concrete software engineering decisions. Objective: This study develops and evaluates SE-AIGov, a governance matrix that links SE decisions to AI autonomy, human approval, audit needs, escalation triggers, reversibility, and risk. Method: We derived a 35-row candidate matrix from AI-governance, software-engineering, secure- development, DevOps/change-governance, human–AI oversight, and LLM-security sources. It was refined through a survey of 73 respondents, blind scenario review by eight role-specialized experts, a neutral re-check of domain-sensitive scenarios, and evaluation by six additional domain-aware experts. Results: The process produced an 18-row matrix and a domain-sensitive escalation overlay for healthcare/ safety-critical software, regulated finance, education/minor data, public-sector accessibility and language inclusion, critical infrastructure, and legal/procurement commitments. Expert judgments showed strongest agreement on risk, reversibility, approval, and audit requirements. Autonomy required more conditional wording, especially at the transition from review and recommendation to human-authorized execution. Conclusions: The evidence supports SE-AIGov as an understandable artifact for formative review and structured governance discussion. Respondents and experts could apply the matrix and identify cases needing stricter rules. The study does not establish that its autonomy levels are objectively correct or that SE-AIGov improves organizational outcomes. Field evaluation is still needed.
Despite the widespread adoption of green supply chain management (GSCM) practices, existing research offers conflicting evidence regarding their impact on firm performance, highlighting the importance of investigating the underlying mechanisms that allow firms to fully capture the value of such investments. Moreover, prior studies have largely overlooked the influence of GSCM practices on employee work engagement, which may serve as critical mechanisms through which these practices translate into improved firm performance. To address this, this study examines the relationships between internal (i.e., internal environment management, eco-design, and investment recovery) and external (i.e., green purchasing and customer collaboration) GSCM practices and firm operational performance and the mediating role of employees' work engagement. To this end, we employed a multi-informant data collection design in which GSCM practices and firm operational performance were assessed by managers, while work engagement was captured directly from employees from the 121 surveyed manufacturing firms, thereby enhancing measurement accuracy and the overall roundness of the results. The analysis revealed that internal GSCM practices have a positive impact on firm operational performance, while external GSCM practices do not. Unexpectedly, we found that internal GSCM practices do not increase employees' work engagement, whilst external GSCM practices do. However, employees' work engagement fully mediates the relationship between external GSCM practices and firm operational performance. This study advances the literature by exploring the impact of GSCM on employee-level outcome and explaining how it influences performance through driving employees' work engagement.
In this study, Cs2NaInI6 a cesium-based double perovskite, is investigated as a novel absorber material owing to its favorable optoelectronic properties, stability, and eco-friendliness. In this study, we carried out a comprehensive computational investigation of Cs2NaInI6 by combining Density Functional Theory (DFT) calculations with device-level simulations using SCAPS-1D. Electronic-structure analysis using the TB-mBJ potential revealed a direct band gap of 1.702 eV. Using SCAPS-1D simulation, we systematically optimized device performance by analyzing optimum left metal contact (LMC) selection from (Cu, Fe, C, Au, W, Ni, Pd, Pt, Se), hole transport layer (HTL) from (CBTS, CuI, MoS2, P3HT, GaAs, CdTe, and CFTS), and electron-transport layer (ETL) from (WS2, ZnO, TiO2, and PCBM). The optimal cell, FTO/WS2/Cs2NaInI6/CBTS/Ni, achieved the highest power conversion efficiency (PCE) of 27.23
Methotrexate (MTX) is frequently used to treat a variety of autoimmune diseases and malignancies, but its use is restricted due to a number of side effects, including lung damage. For the first time, this study attempts to assess the potential protective advantages of atomoxetine (ATOM) against MTX-induced lung damage in rats. MTX was used to cause lung damage. A total of 24 male Wistar albino rats were used in this study. Animals were randomly allocated to four experimental groups of six rats each: (Ⅰ) Control group, (Ⅱ) ATOM group, (Ⅲ) MTX group, and (Ⅳ) MTX+ATOM group. Malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) levels in the lungs were measured. ELISA was used to measure the levels of lung IL-10, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), Bcl-2-associated X protein (Bax), and B-cell lymphoma 2 (Bcl-2). NF-κB p65 and caspase-3 were evaluated using immunohistochemistry. Toll-like receptor 4 (TLR4) and myeloid differentiation primary response 88 (MYD88) protein expression levels were assessed using the Western blot technique. A histopathological study of lung tissues was performed. Lung MDA, IL-6, TNF-α, and Bax levels were significantly increased by MTX, while GSH, SOD, IL-10, and Bcl-2 levels were significantly decreased. Additionally, this led to the overexpression of the proteins TLR4 and MYD88. Additionally, the MTX group had higher immunopositivity for both NF-κB p65 and caspase-3. All of the aforementioned biochemical and histological abnormalities were greatly improved with ATOM. ATOM significantly improved MTX-induced pulmonary injury by suppressing TLR4/MYD88/NF-κB p65 and caspase-3-mediated apoptotic signaling pathways.
