India is a leader when it comes to agriculture. A significant part of the country’s population depends on agriculture for livelihood. However, many of them face challenges due to using unreliable farming techniques. Sometimes the challenges increase to the extent that they commit suicide. Besides, India is highly populated, and its population is steadily increasing, requiring its government to grow its GDP and increase its energy supply proportionately. This paper reviews integrating solar farming with agriculture, known as Agrivoltaics, as a Climate-Smart Agriculture (CSA) option for Indian farmers. This study is further supported by the Strength, Weaknesses, Opportunities, and Threats (SWOT) analysis of agrivoltaics. Using the SWOT analysis, this article presents how agrivoltaics can make agriculture sustainable and reliable. This paper identifies rural electrification, water conservation, yield improvement, sustainable income generation, and reduction in the usage of pesticides as the strengths of agrivoltaics. Similarly, the paper presents weaknesses, opportunities, and threats to agrivoltaics in India. The research concludes with the findings that agrivoltaics have the potential of meeting multiple objectives such as meeting global commitments, offering employment, providing economic stability, increasing clean energy production capacity, conserving natural resources, and succeeding in several others. The paper also includes a discussion about the findings, suggestions, and implications of adopting agrivoltaics on a large scale in India.

Power is an undisputed lifeline in today’s high-tech world. Mass awareness drives have created significant cognizance toward sustainable and green technologies in the past quarter-century in response to the depletion of fossil fuels and volatility of energy prices. Photovoltaics (PV)-based, sustainable, and green-technology-based power sources are in high demand due to their ability to meet industrial, commercial, and residential demands. Government incentives available to consumers for PV-based renewable energy systems have further fueled their acceptance. Due to the consistently increasing popularity of PV-based energy, many advancements have happened that enable improving its performance and reducing its cost. Recent advancements in silicon processing have increased the efficiency of single-crystalline silicon-based PV cells by 28%.¹ Also, many advanced maximum power point tracking algorithms are now available that enable harvesting higher amounts of energy from a PV panel.² Despite these improvements in solar energy harvesting, PV modules still use series-parallel (SP)-based fixed topology. As a result, a PV module remains vulnerable to such issues as partial shading,^3,4 irradiance fluctuation,^{5 – 8} mismatch,^{9 – 11} and faulty conditions,^{12 – 15} which are known to severely affect its power efficiency and performance. In response, most of the PV modules are equipped with a bypass diode to minimize the effects of such issues.^{15 – 17}

Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. However, it is labor-intensive, subjective, and often affected by inter-observer variability. Therefore, this study explores a deep learning-based, multi-class classification framework for distinguishing breast cancer subtypes using convolutional neural networks (CNNs). Unlike previous work using the popular BreaKHis dataset, where binary classification models were applied, in this work, we differentiate eight histopathological subtypes: four benign (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) and four malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma). This work leverages transfer learning with ImageNet-pretrained ResNet architectures (ResNet-18, ResNet-34, and ResNet-50) and extensive data augmentation to enhance classification accuracy and robustness across magnifications. Among the ResNet models, ResNet-50 achieved the best performance, attaining a maximum accuracy of 92.42%, an AUC-ROC of 99.86%, and an average specificity of 98.61%. These findings validate the combined effectiveness of CNNs and transfer learning in capturing fine-grained histopathological features required for accurate breast cancer subtype classification.

Alzheimer’s disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.