This study was conducted to produce empirical evidence of validity and reliability of a set of questionnaire. Questionnaire drawn from the results of previous studies and the validity of the tests will determine whether all aspects of the construct domain were represented, thus ensuring the high objectivity level of the questionnaire. In addition, an alternative approach was used to assess the discriminant validity, using heterotrait-monotrait ratio of correlations. The study empirically proves that the questionnaire used is unchanged by culture. This is important because if not, its use will be restricted to a population in which the questionnaire was developed. The proposed method is better in which to enhance and improved the discriminant validity, using heterotrait-monotrait ratio of correlations. The results of the analysis in the measurement model indicated that the questionnaire meets the standards of reliability and construct validity.

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Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than others. Radiological images such as Chest X-ray (CXR) images provide early detection and prompt action, where typical CXR for such a disease is characterized by radiopaque appearance or seemingly solid segment at the affected parts of the lung due to inflammatory exudate formation replacing the air in the alveoli. The early and accurate detection of pneumonia is crucial to avoid fatal ramifications, particularly in children and seniors. In this paper, we propose a novel 50 layers Convolutional Neural Network (CNN)-based architecture that outperforms the state-of-the-art models. The suggested framework is trained using 5852 CXR images and statistically tested using five-fold cross-validation. The model can distinguish between three classes: viz viral, bacterial, and normal; with 99.7% ± 0.2 accuracy, 99.74% ± 0.1 sensitivity, and 0.9812 Area Under the Curve (AUC). The results are promising, and the new architecture can be used to recognize pneumonia early with cost-effectiveness and high accuracy, especially in remote areas that lack proper access to expert radiologists, and therefore, reduces pneumonia-caused mortality rates.

The goal of this research is to investigate the thermal, chemical, and tensile properties of chemical modification of sugarcane bagasse (SCB)-filled polypropylene (PP) and recycled acrylonitrile butadiene rubber (NBRr). The composites with different SCB loading (5, 15, and 30 per hundred resin) were prepared using a heated two-roll mill at temperature of 180°C.Thermal and the tensile properties of the modified SCB composite have shown improvement. The silane-treated composites have higher thermal stability compared to treated NaOH. The degradation temperature at 70% weight ( T 70%) of NaOH and silane composite increase by 6% and 15%, respectively. Meanwhile, the tensile strength and Young’s modulus for the both treatment showed an improvement of 20% and 25% for NaOH and 30% and 32% for silane compared to untreated composites, respectively. The chemical properties were investigated using Fourier transform infrared analysis. The modification SCB fiber has improved the adhesion and interfacial bonding between SCB fiber and PP/NBRr matrices.