Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.

Low-cost, vanadium-based mixed metal oxides mostly have a layered crystal structure with excellent kinetics for lithium-ion batteries, providing high energy density. The existence of multiple oxidation states and the coordination chemistry of vanadium require cost-effective, robust techniques to synthesize the scaling up of their morphology and surface properties. Hydrothermal synthesis is one of the most suitable techniques to achieve pure phase and multiple morphologies under various conditions of temperature and pressure. We attained a simple one-step hydrothermal approach to synthesize the reduced graphene oxide coated Nickel Vanadate (rGO@Ni3V2O8) composite with interconnected hollow microspheres. The self-assembly route produced microspheres, which were interconnected under hydrothermal treatment. Cyclic performance determined the initial discharge/charge capacities of 1209.76/839.85 mAh g−1 at the current density of 200 mA g−1 with a columbic efficiency of 69.42%, which improved to 99.64% after 100 cycles. High electrochemical performance was observed due to high surface area, the porous nature of the interconnected hollow microspheres, and rGO induction. These properties increased the contact area between electrode and electrolyte, the active surface of the electrodes, and enhanced electrolyte penetration, which improved Li-ion diffusivity and electronic conductivity.

Family motivation as a mediating mechanism is a novel and under-researched area in the field of positive organizational scholarship. Drawing on Social Exchange Theory (SET), this study empirically validates family motivation as a mediator between family support and work engagement. The process by Hayes (2013) was used to analyze time-lagged data collected from 356 employees of the education sector. Results confirm the mediating role of family motivation in the relationship between family support and work engagement and the moderating role of calling in the relationship between family support and family motivation. This study adds to the literature of family-work enrichment accounts by validating family support as a novel antecedent for family motivation and positive attitudes. The implications of the study are discussed.

Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS.

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