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Brain tumors are a significant challenge to human health as they impair the proper functioning of the brain and the general quality of life, thus requiring clinical intervention through early and accurate diagnosis. Although current state-of-the-art deep learning methods have achieved remarkable progress, there is still a gap in the representation learning of tumor-specific spatial characteristics and the robustness of the classification model on heterogeneous data. In this paper, we introduce a novel Pyramidal Attention-Based bi-partitioned T Network (PABT-Net) that combines the hierarchical pyramidal attention mechanism and T-block based bi-partitioned feature extraction, and a self-convolutional dilated neural classifier as the final task. Such an architecture increases the discriminability of the space and decreases the false forecasting by adaptively focusing on informative areas in brain MRI images. The model was thoroughly tested on three benchmark datasets, Figshare Brain Tumor Dataset, Sartaj Brain MRI Dataset, and Br35H Brain Tumor Dataset, containing 7023 images labeled in four tumor classes: glioma, meningioma, no tumor, and pituitary tumor. It attained an overall classification accuracy of 99.12%, a mean cross-validation accuracy of 98.77%, a Jaccard similarity index of 0.986, and a Cohen’s Kappa value of 0.987, indicating superb generalization and clinical stability. The model’s effectiveness is also confirmed by tumor-wise classification accuracies: 96.75%, 98.46%, and 99.57% in glioma, meningioma, and pituitary tumors, respectively. Comparative experiments with the state-of-the-art models, including VGG19, MobileNet, and NASNet, were carried out, and ablation studies proved the effectiveness of NASNet incorporation. To capture more prominent spatial-temporal patterns, we investigated hybrid networks, including NASNet with ANN, CNN, LSTM, and CNN-LSTM variants. The framework implements a strict nine-fold cross-validation procedure. It integrates a broad range of measures in its evaluation, including precision, recall, specificity, F1-score, AUC, confusion matrices, and the ROC analysis, consistent across distributions. In general, the PABT-Net model has high potential to be a clinically deployable, interpretable, state-of-the-art automated brain tumor classification model.

The study evaluates the present state of global research on financial inclusion and well-being of female entrepreneurs, including the identifications of key contributors, patterns of collaborations, a thematic map, and the intellectual and social structure that supports this domain. A bibliometric analysis was conducted on a sample of 332 documents pertaining to women entrepreneurship and financial well-being. The analysis was centered on inclusion-exclusion criteria that were established using a specific search technique on the Scopus database, encompassing the time frame from 2010 to 2023. The bibliometrix R and VOSviewer tools were utilized for the research. The findings indicate that the interdisciplinary domain of women’s entrepreneurship and financial well-being has transformed. This research uncovers the conceptual structure, illuminates the intellectual framework, and focuses on the most critical concerns in this sector, namely, how to accomplish sustainable objectives (SDG 8) through integrating female entrepreneurs in the financial ecosystem. This study offers valuable insights for researchers, policymakers, and practitioners, promoting a more nuanced understanding of female entrepreneurs’ financial inclusion. This research highlights the significance of multidisciplinary collaboration and the opportunities given to female entrepreneurs in attaining sustainable development goals and promoting financial inclusion. Plain language summary A bibliometric analysis of global studies on women entrepreneurs’ financial well-being This research assesses the current status of international studies on financial inclusion and the well-being of female entrepreneurs. It also identifies important contributors, examines collaboration patterns, provides a thematic map, and examines the intellectual and social framework supporting this field. A selection of 332 documents about women’s entrepreneurship and financial well-being were subjected to a bibliometric study. The focus of the analysis was inclusion-exclusion criteria that were developed by means of a particular search method on the Scopus database, covering the period from 2010 to 2023. For the investigation, VOSviewer and bibliometrix R were used. The results show a transformation in the interdisciplinary field of women’s financial well-being and entrepreneurship. This study reveals the conceptual framework, sheds light on the intellectual framework, and addresses the most pressing issues in this field, specifically, how to achieve Sustainable Development Goal 8 (SDG 8) by including female entrepreneurs into the financial ecosystem. This study contributes to a more sophisticated knowledge of the financial inclusion of female entrepreneurs by providing insightful information to researchers, policymakers, and practitioners. This study emphasizes the value of interdisciplinary cooperation as well as the chances that female entrepreneurs have to realize sustainable development objectives and advance financial inclusion.

The present work experimentally evaluated the performance of a solar collector comprised evacuated tube heat pipe (ETHP) coupled with a compound parabolic concentrator at different tilt angles. Therefore, experiments have been conducted in the climate conditions of Tamil Nadu (77.07° E,11.04° N), India, from April 15, 2019, to May 20, 2019. The objective of the work is to explore the effect of a tilting angle on the performance of an evacuated tube solar collector with a thermosyphon attached to the compound parabolic concentrator. The CPC is designed with an aperture width of 343 mm, concentration ratio of 2.32, and aperture angle of 25.4°, improving the solar collector efficiency with the help of MATLAB Programming, which gets Coordinate points based on these coordinate points. CPC Profile is fabricated. The Thermosyphon heat pipe is constructed with a Copper tube having a 19 mm diameter with 40% Acetone charged. The experiments were conducted by varying the tilting angles of the solar collector at 15°, 30°, 45°, and 60° horizontal. The heat resistance and instantaneous efficiency of the solar collector are studied in this study. The result reveals a minimum thermal resistance of 0.02 kW−1, and a maximum efficiency of 78% was recorded at a 45° tilting angle.

Chia (Salvia hispanica) seeds are oilseeds, often known as pseudo-cereals, which contain a variety of nutrients, including macro and micronutrients, as well as health aids; consequently, they could be classified as a nutraceuticals food. The seeds are a wonderful source of phenolic compounds like rosmarinic acid, caffeic acid, protocatechuic acids, quercetin, and myricetin. According to studies, chia seeds have a high nutritious content of protein (18-24%), fiber (30-34%), and a variety of fatty acids. Chia seeds also have a variety of minerals and vitamins and shown to have beneficial effects in the treatment of hypertension, diabetes, and dyslipidaemia, as well as acting as an antioxidant, anti-anxiety, laxative, anti-depressant, analgesic, and strengthen the immune system. Due to its presence of minerals, lipids (omega-3), fibers, proteins, and antioxidants in chia seed and its health benefits, it has now grabbed the attention of many food industries and educators. The present review article highlights the nutritional composition, phytochemical profile, and therapeutic potentials like cardio-protective, diabetes-controlling, immune boosting, and antioxidant action in detail.

Entrepreneurs have an essential role to play in bringing positive change and growth to the world's economy. Entrepreneurship is a necessary aspect of economic growth because of its contribution to people's welfare through employment opportunities. Likewise, institutions of higher learning offer compulsory entrepreneurship courses for students with the support of government policies to encourage students towards entrepreneurship. Therefore, this study aimed to determine the factors influencing the students' intentions to become green entrepreneurs. The study uses the extended theory of planned behaviour model (TPB) and entrepreneurial education to develop a theoretical framework. The model has been examined on 350 tourism university students using structural equation modelling. The key findings indicate that Ajzen's TPB theory of planned behaviour and entrepreneurial education can be extensively expanded to determine sustainable entrepreneurial intentions in developing economies such as India. Attitude, subjective norms, perceived behavioural control, and entrepreneurial education are antecedents of entrepreneurial intent. Our results have valuable implications for aspiring entrepreneurs, policymakers, and scholars.

In the rapidly evolving gut and gastroenterological research field, postbiotics, a fermentation byproduct, are emerging as a cutting-edge alternative in the nutraceutical and pharmaceutical environment. These functional bioactive components, which include muropeptides, extracellular polymeric substances (EPS), teichoic acids, polysaccharides, cell-free supernatants, short-chain fatty acids, enzymes, bactericidal antibiotics, and vitamins, play an important role in immune system development. This review explores the intricate pathways through which gut microorganisms produce metabolites that contribute to the health-promoting properties of postbiotics. Postbiotics have a variety of appealing qualities, including anticancer, antibacterial, and immunological actions. Their multiple effects include influencing immunological responses, decreasing cancer cell growth, and preventing bacterial infections. Their presence in dairy and plant-based meals is noteworthy, as it provides a perfect matrix for fermentation and a varied range of antibacterial chemicals. This article delves into enhancing understanding of the concept of therapeutic properties of postbiotics in promoting human health.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.

The purpose of this study was to examine the factors that influence career aspirations in students, including the role of university, gender, and parental education. The research methodology involved analyzing the attitudes of students at four universities in India using a closed-ended questionnaire and factor analysis, MANOVA, and Structural-Equation-Modeling (SEM) analysis. The results showed significant differences in the attitudes of students at private and government universities, and a significant effect of parental education, but no significant impact of gender. However, the study had a limitation of data collected from only four specific universities and may not be representative of the entire national university population. The findings have practical implications for universities to improve the positive attitudes of their students and increase their motivation levels. This study is original in its focus on the impact of parental education and socio-demographic factors on career aspirations in adolescent students in Delhi NCR, India, the first of its kind in this population in the Delhi NCR region of India.

This research aimed to explore natural, cost-effective alternatives to conventional antibiotics, focusing on Citrus sinensis peel and Moringa oleifera leaf extracts. The objectives were to extract, characterize, and evaluate the antimicrobial potential of these extracts against common poultry pathogens. The proximate analysis of M. oleifera leaf powder revealed 22% protein, 10% fat, 10% ash, and 10% fiber. Phytochemical screening of both aqueous and ethanol extracts of M. oleifera and orange peel indicated the presence of alkaloids, flavonoids, saponins, tannins, and total phenolics. These extracts exhibited significant antimicrobial activity against bacterial strains like E. coli, S. gallinarum, P. aeruginosa, B. cereus, Campylobacter, S. aureus and M. gallisepticum. In a study involving 30 broilers, those treated with M. oleifera extract showed superior antibacterial effects compared to other treatment groups. These findings suggest that M. oleifera leaf extract could serve as an effective, affordable alternative to antibiotics in broiler diets, potentially improving disease management in the poultry industry.