Introduction: Nutrition quality has been consistently linked to academic outcomes. Studies across different cultural contexts suggest that adolescents with balanced diets tend to show better well-being and school adjustment. Objective: This study aimed to examine the associations between adolescents’ regular eating habits and their academic performance in the Beni Mellal-Khénifra region of Morocco. Methodology: A descriptive cross-sectional study was conducted with 439 students aged 14 to 20 years. Eating habits were assessed using the Arab Teens Lifestyle Study (ATLS) questionnaire, and academic performance was measured via overall grade point average. Statistical analyses using SPSS explored correlations between dietary patterns and school achievement. Results: The participants’ average age was 15.02 years (43.7% male, 56.3% female). Descriptive analysis showed that fries, fast foods, and sweet drinks were frequently consumed, while fruits, vegetables, and breakfast at home were less common. Spearman’s correlation revealed a positive association between healthy eating (Factor 2) and academic performance (ρ = 0.166, 95% CI [0.08, 0.25], p < 0.001), indicating a small but meaningful effect. Unhealthy food consumption (Factor 1) was not significantly related to GPA. Regression analysis confirmed that higher healthy eating scores predicted better academic performance (B = 0.472, 95% CI [0.214, 0.729], p < 0.001), while unhealthy eating did not show a significant effect, even after controlling for gender, age, education level, and area of residence. Discussion: These findings align with previous research showing links between diet quality and school performance. The small effect size suggests that healthier eating is associated with modest differences in academic outcomes, highlighting that school performance is influenced by multiple factors, including psychosocial and environmental aspects. Conclusions: The study identified significant associations between eating habits and academic performance among adolescents in the Beni Mellal-Khénifra region. Considering dietary patterns in adolescent development research appears valuable. Further longitudinal or experimental studies are recommended to clarify these relationships.
Dans un contexte de transformation numérique accélérée, l'intelligence artificielle (IA) s'impose comme un levier stratégique majeur pour les organisations contemporaines. Cet article examine les mécanismes par lesquels l'adoption de l'IA contribue à l'amélioration de la performance organisationnelle, ainsi que les conditions organisationnelles qui en déterminent l'efficacité. S'appuyant sur une revue systématique de la littérature conduite selon le protocole PRISMA 2020, ayant permis de retenir 60 sources issues de six bases de données académiques (Scopus, Web of Science, ScienceDirect, Emerald Insight, SpringerLink et Google Scholar) sur la période 2015–2025, et mobilisant quatre cadres théoriques complémentaires (Resource-Based View, capacités dynamiques, TAM et UTAUT), l'étude propose un cadre conceptuel intégrateur articulant adoption de l'IA, facteurs organisationnels médiateurs et résultats de performance. Les résultats suggèrent que l'IA pourrait améliorer l'efficacité opérationnelle, stimuler l'innovation et renforcer l'avantage concurrentiel, mais que son impact est conditionné par la culture organisationnelle, le leadership stratégique, la gouvernance technologique, la qualité des données et le niveau de compétences des collaborateurs. Cette recherche contribue à la littérature en management stratégique en offrant une lecture intégrée et structurée du rôle de l'IA dans la création de valeur organisationnelle, et fournit aux décideurs un cadre opérationnel pour orienter leurs stratégies d'intégration de l'intelligence artificielle.
Occupational radiation exposure during fluoroscopy-guided dorsolumbar spine surgery was evaluated using ATOM phantoms and carbon-doped aluminium oxide () nanoDot optically stimulated luminescence (OSL) dosemeters. A controlled 50-acquisition protocol (comprising anteroposterior, oblique, and lateral projections) was used to map organ-specific doses for an operating surgeon at 50 cm from the C-arm isocentre. The expanded measurement uncertainty was 7.6% (). In the unprotected configuration, the highest equivalent doses occurred in the thoracic region, specifically the breasts (0.0430 mSv/procedure), oesophagus (0.0335 mSv/procedure), lungs (0.0230 mSv/procedure), and stomach (0.0280 mSv/procedure). Standard protective equipment (0.5 mm Pb-equivalent) reduced these organ doses by 27.3%-42.9%. However, these reduction factors characterise the complete geometric setup rather than the intrinsic attenuation of individual shielding devices. Based on International Commission on Radiological Protection Publication 103 tissue-weighting factors, the calculated effective dose was 0.152 mSv per simulated procedure. Illustrative workload extrapolations () yielded cumulative annual effective doses of 14.58 mSv yrfor 96 procedures and 36.45 mSv yrfor 240 procedures. These scenario-based projections are highly specific to the evaluated setup, table height, and acquisition parameters. The findings support the utility of local kerma-area product-normalised dose audits, rigorous individual monitoring, and the consideration of conditional Category A classification for high-volume spine surgeons where comparable exposures are verified by local clinical practice.
Pediatric head computed tomography (CT) is widely used for urgent neuroimaging, but children’s higher radiosensitivity makes protocol optimization and quantitative validation essential. Many available phantoms lack patient-specific pediatric anatomy and realistic brain-mimicking attenuation, limiting their relevance for neuro-CT research and benchmarking. To develop and validate a CT-derived, patient-specific pediatric brain phantom that preserves anatomically faithful geometry and reproduces brain-mimicking soft-tissue attenuation with physics-validated energy dependence. A fully anonymized head CT of a 5-year-old child was segmented in 3-D Slicer, exported to standard tessellation language (STL), and partitioned into hemispheres. A polylactic acid (PLA) master was three-dimensional (D)-printed to fabricate a reinforced silicone mold. A homogeneous brain-mimicking soft-tissue surrogate was produced by modifying an epoxy system with acetone. The assembled phantom was scanned at 80, 100, 120, and 140 kilovolt peak (kVp). Region of interest (ROI)-based CT numbers were measured across both hemispheres. Computational attenuation verification was performed using Particle and Heavy Ion Transport code System (PHITS) Monte Carlo simulations with a mono-energetic transmission method over 15-150 kiloelectronvolt (keV) and compared with PhyX–Photon Shielding and Dosimetry (PhyX-PSD) and National Institute of Standards and Technology XCOM database (NIST/XCOM) reference. The fabricated phantom preserved the main bilateral morphology and retained 96.85
Converting Hounsfield units (HU) into relative electron density is an important step in radiotherapy treatment planning. This single-center study aimed to develop a semi-automatic Python tool for HU extraction and CT–electron density calibration curve generation and to assess its agreement with manual ImageJ measurements. A CIRS Model 062 electron density phantom was scanned on a SOMATOM go.Sim CT simulator at 80, 100, and 120 kV. For each tube voltage, 17 paired measurements were compared using Bland–Altman analysis. The ImageJ/Python coefficients of determination were 0.9050/0.8730 at 80 kV, 0.9446/0.9371 at 100 kV, and 0.9620/0.9353 at 120 kV. Mean biases were 24.23, 9.79, and 17.54 HU, with 95% limits of agreement of −125.44 to 173.89 HU, −45.80 to 65.38 HU, and −96.18 to 131.27 HU at 80, 100, and 120 kV, respectively. The dispersion of the differences was lowest at 100 kV. The largest discrepancies between the two methods involved the Dense Bone 800 inserts. These findings provide a preliminary validation of the tool under the investigated conditions but do not demonstrate interchangeability with ImageJ.
