OSRAM (United States)

Background: Cardiovascular diseases (CVDs) are the leading cause of global mortality, responsible for approximately 17.9 million deaths annually, with a disproportionately high burden in low- and middle-income countries. Teachers play a critical role in shaping community health behaviors; however, their knowledge, attitudes, and practices (KAP) regarding CVD prevention remain inadequately explored. Objective: To assess the knowledge, attitudes, and practices related to cardiovascular diseases among primary and secondary school teachers. Methods: A descriptive cross-sectional study was conducted among 384 teachers using a structured, pre-tested questionnaire. The tool assessed socio-demographic characteristics and KAP domains. Knowledge scores were categorized as good (>75%), moderate (50–75%), and poor (<50%). Data were analyzed using SPSS version 25. Descriptive statistics and chi-square tests were applied, with p<0.05 considered statistically significant. Results: The mean age of participants was 36.8 ± 8.9 years. Females constituted 53.1% of the sample. Overall, 58.6% of participants demonstrated good knowledge, while 28.6% had moderate knowledge and 12.8% had poor knowledge. Positive attitudes toward CVD prevention were observed in 64.3% of respondents. However, only 41.2% exhibited good preventive practices. Awareness of smoking as a risk factor was highest (82%), while knowledge regarding cholesterol-related risks was lowest (60.5%). Only 42% reported engaging in regular physical activity, and 35.1% monitored blood pressure regularly. A statistically significant association was observed between education level and knowledge (p=0.03). Conclusion: Despite moderate knowledge and favorable attitudes, preventive practices among teachers remain inadequate. There is a need for targeted interventions focusing on behavior change and lifestyle modification.

To assess the effect of insoluble dietary fibre sources and level on growth performance and nutrient utilization, a feeding experiment was conducted on day old broiler chicks (n=270) for 35 days under 2×3 factorial arrangement. The day old chicks were randomly divided in to six groups (T1 to T6) of 45 each. Two sources of dietary fibre (soy hull or rice husk) were used at 0, 2 or 4 % level and each dietary level have the three replicates of 15 chicks each. The T1, T2 and T3 were allocated to soy hull group, wherein T4, T5 and T6 were fed rice husk at 0, 2 and 4% dietary level, respectively. Inclusion of rice husk (4% level) significantly (P<0.05) improved the feed intake and body weight of chicks at 35th d. Although, source of fiber did not affect the feed intake and FCR both source of fiber at 4% level had higher (p<0.05) feed intake. The DM metabolizability was higher (P<0.05) in control group of chicks fed diet with soy hull, whereas metabolizability of EE was significantly (P<0.05) higher in control group of chicks fed rice husk. The metabolizability of EE was significantly (P<0.05) better in rice husk group as compared to soy hull. The energy and Ca retention were similar among the groups, but per cent P retention was higher (P<0.05) in chicks of control group (T1). It may be concluded that the inclusion of 2 and 4% level of coarse insoluble fiber sources such as rice husk (42.68%CF) and soy hull (27.25% CF) in diets had no comparative adverse effect on overall performance of broiler birds. Inclusion of rice husk in broiler finisher diet at 4% level resulted in improved performance of broiler birds.

Intracranial hemorrhage (ICH) is a life-threatening neurological emergency with a high mortality and long-term disability rate that necessitates the following rapid and accurate diagnosis. Nevertheless, the presence of subtle bleeding patterns, non-uniform circumscription of hemorrhage, and structural asymmetries of non-contrast CT images tend to challenge the conventional means of diagnostics. To overcome those constraints, the proposed study suggests combining the hybrid deep learning framework that jointly utilizes VGG-based convolutional neural networks to extract fine-grained local features with Vision Transformers to learn global dependencies. A mechanism of attention-directed feature fusion is used to boost the discriminative feature representation, and a symmetry-aware asymmetry mapping approach is introduced to maintain midline shift information which is one of the key radiological biomarkers of ICH evaluation. The suggested framework works with 2D non-contrast brain CT images taken of publicly accessible benchmark datasets, that is, 731 clinically annotated cases of primary ICH subtypes such as intraparenchymal, intraventricular, subarachnoid, subdural, and epidural hemorrhage. Normal preprocessing and data augmentation methods were performed to enhance robustness and generalization. Experimental evaluation on 6794 CT scans demonstrates classification accuracies of 97.2% for binary detection and 94.6% for multiclass ICH classification, sensitivity, specificity and area under the ROC curve (AUC). These findings support the usefulness of local convolutional feature, global attention transformer-based and symmetry-preserving segmentation to achieve reliable, interpretable, and clinically adaptive detection of intracranial hemorrhage.

Working with high-dimensional data is challenging, and the infrequency of fraudulent transactions complicates the detection of credit card fraud, posing a significant issue in the banking sector. This study introduces a thorough and lucid ensemble-based approach that integrates resampling techniques, feature selection, and interpretability to efficiently detect fraud in extremely imbalanced datasets. This study utilized the Kaggle Credit Card Fraud dataset to evaluate the efficacy of our effort. A total of 284,807 transactions occurred, with merely 0.17 % deemed fraudulent. To address the issue of uneven class distribution, this study employed SMOTE, ADASYN, and SMOTE-ENN. This study employed correlation analysis and mutual information selection to identify the most beneficial features. This study trained and evaluated three models: XGBoost, a Stacking Ensemble, and Random Forest. This study employed accuracy, precision, recall, F1-score, ROCAUC, and confusion matrices for this analysis. The Stacking Ensemble performed exceptionally on all assessments, indicating its efficacy in detecting elusive fraudulent transactions. SHAP-based explainability elucidated the significance of each feature on both global and local scales, hence enhancing the model's comprehensibility. The proposed architecture offers a transparent, precise, and dependable method for detecting credit card fraud within actual financial systems.

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The rapid emergence of tools like ChatGPT and other generative AI (GenAI) applications has transformed higher education by providing new avenues for learning, collaboration, and academic support. This study investigates the perceptions, self-efficacy, and adoption of generative AI tools among undergraduate students at the University of Kelaniya. An integrated theoretical framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT) was employed to examine how performance expectancy, effort expectancy, social influence, self-efficacy, social reinforcement, and facilitating conditions shape students' behavioral intentions to adopt AI tools. The study population comprised 14,500 undergraduate students from the University of Kelaniya, with a sample of 374 respondents. Data was collected using a structured five-point Likert-scale questionnaire. The results indicate that performance expectancy, effort expectancy, social influence, self-efficacy, and social reinforcement significantly predict students' behavioral intentions to use GenAI tools, whereas facilitating conditions were not significant, suggesting that students currently lack adequate support, guidance, and resources to use AI tools effectively for academic work. These findings highlight the need for institutional guidelines, training programs, and support mechanisms to promote responsible and effective use of GenAI tools in higher education, while fostering essential skills such as critical thinking, creativity, and academic integrity.