Volume 13 | Issue 12 |

 Abstract

This study reviews a new way that viral infections might cause a supersedure, or colony-wide coup, in Apis mellifera honey bees. Researchers found that queens infected with viruses like deformed wing virus B (DWV-B) have impaired reproductive health, as seen by fewer eggs laid and crucially lower levels of the queen pheromone component methyl oleate. Methyl oleate serves as a vital chemical signal to the colony, indicating the queen's fitness and reproductive viability. Worker bees interpret the sharp decline in this pheromone as a signal that the queen is no longer fit to rule, subsequently initiating the process of rearing a new queen from a larva. Methyl oleate acts as a "honest signal" to the worker bees, communicating the queen's reproductive health and overall vigor. High levels of Methyl oleate is a chemical assurance to the colony that the queen is healthy, highly fertile, and laying eggs prolifically.This finding establishes a direct link between viral pathogenesis, specific pheromone chemistry, and the complex social behaviour of supersedure, illustrating how pathogen-induced physiological changes can manipulate key colony functions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Apis mellifera, Deformed Wing Virus B, Queen Supersedure, Methyl Oleate, Pheromone, Social behaviour.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture continues to be the cornerstone of economic growth and food security across the globe, particularly in developing nations. With increasing population pressure, climate change, and the need for sustainable resource management, the integration of Artificial Intelligence (AI) and Machine Learning (ML) presents transformative opportunities for modernizing agriculture. AI and ML technologies enable data-driven decision-making by analyzing vast agricultural datasets to optimize crop production, manage resources efficiently, and predict potential threats such as pests, diseases, and extreme weather conditions. From precision irrigation and soil health monitoring to automated machinery and market forecasting, these technologies are reshaping the entire agricultural value chain. Despite their immense potential, the adoption of AI and ML in agriculture faces significant challenges. High implementation costs, inadequate digital infrastructure in rural areas, and limited technical knowledge among farmers hinder large-scale deployment. Issues related to data privacy, inconsistent datasets for ML model training, and ethical concerns about automation and employment further complicate the adoption process. To bridge these gaps, a multi-stakeholder approach involving governments, research institutions, agritech startups, and farmer communities is essential. This paper explores the applications, opportunities, and challenges of AI and ML in advancing agriculture. It highlights how these technologies can enhance productivity, promote sustainability, and empower smallholder farmers through accessible, low-cost innovations. Ultimately, AI and ML hold the promise of transforming agriculture into a smart, sustainable, and resilient sector, contributing significantly to global food security and rural development.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Machine Learning, Agriculture, Precision Farming, Sustainability, Digital Transformation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) in research data analytics has become a driving force behind innovation, accuracy, and efficiency in modern scientific inquiry. This paper, titled "Artificial Intelligence in Research Data Analytics: Opportunities, Challenges, and Future Trends," examines how AI technologies are reshaping data-driven research through advanced methods of prediction, classification, and pattern recognition. AI enhances research workflows by automating data preprocessing, improving analytical precision, and enabling real-time insights from large and complex datasets. Opportunities include faster data interpretation, improved decision-making, and the discovery of hidden trends that were previously inaccessible through traditional analytical methods. However, the paper also highlights major challenges such as data quality issues, algorithmic bias, lack of transparency in AI decision-making, and ethical concerns related to privacy and accountability. Addressing these challenges requires developing explainable AI systems, establishing strong ethical frameworks, and fostering interdisciplinary collaboration between data scientists and domain experts. The discussion on future trends emphasizes responsible AI development, integration of generative and adaptive AI models, and the growing importance of human-AI partnerships. Ultimately, the paper concludes that the responsible and strategic application of AI in research data analytics will play a pivotal role in shaping the future of scientific discovery and global innovation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Research Data Analytics, Predictive Analytics, Machine Learning, Data Management, Ethical AI, Explainable AI, Human-AI Collaboration, Future Trends, Data Privacy, Research Innovation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Horticultural crops are highly perishable commodities, and post-harvest losses remain a critical constraint across supply chains. Globally, up to 40% of fruits and vegetables are lost due to inadequate storage, transportation, and processing. Recent advances in post-harvest technologies--including cold chain logistics, controlled atmosphere (CA) storage, edible coatings, nanotechnology-based packaging, and digital monitoring--have provided innovative solutions to reduce losses and extend shelf life. At the same time, value addition strategies such as minimal processing, nutraceutical extraction, fermentation, and freeze-drying are transforming horticultural crops into higher-value products with expanded market opportunities. This review synthesizes global research (2018-2025) on post-harvest innovations and value addition, providing two synthesis tables and two conceptual figures to highlight the scope of technological interventions and their market potential.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Advances in Post-Harvest Technology and Value Addition in Horticultural Crops

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Indian agriculture continues to face challenges like low productivity, inefficient resource use, and unpredictable climatic conditions due to its dependence on traditional farming practices. As the nation's economy heavily depends on crop production, strengthening agricultural systems has become increasingly important. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as an effective smart farming strategy to overcome these limitations. AI-based applications, combined with real-time data from sensors, drones, and satellite imaging, aid in agricultural activities like fertilizer selection, water management, crop selection, soil fertility prediction, and much more. Despite its potential, the adoption of smart farming is limited by high initial costs, insufficient rural infrastructure, data management issues and the need for continuous technological upgrades and training. Nevertheless, smart farming offers a sustainable and innovative approach to meeting the growing food demand, improving resource efficiency, and promoting environmental and human health. This review offers a glimpse of how digital-agriculture innovations can transform the sector toward greater productivity and ecological sustainability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart farming, Artificial Intelligence, Internet of Things, ecological sustainability.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The use of Artificial Intelligence (AI) has changed the medical imaging field by increasing the accuracy, efficiency, and outcomes of patient care. This comprehensive review outlines the applications of AI technologies, mainly machine learning and deep learning methods, in various imaging modalities including X-ray, MRI, CT, SPECT, ultrasound, and mammography. Advances related to applications of AI technologies related to image segmentation, disease detection, predictive analytics, and quality improvement are also discussed. After a systematic assessment of 24 research studies, we see that deep learning algorithms detect images with efficiency between 65% and 100% across different diagnostic tasks. Specific advantages seen in the studies were increased accuracy of diagnosis, early diagnosis, individualized treatment care pathways, and improved utilization of healthcare services. Despite these advances, there are still limitations around data quality, transparency of algorithms, and ethical considerations. This paper recommends ongoing development, innovation, and collaboration with radiologists and developers in AI imaging to realize the potential of AI in medical imaging.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Medical Imaging, Deep Learning, Machine Learning, Diagnostic Accuracy, Image Segmentation, Predictive Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Public health is under severe threat due to ingestion of microplastics into human body through various routes. The entry of harmful microplastics into marine environment and ground water increased rate of contamination the plankton growth and food chain. The susceptibility for development of chronic respiratory diseases and deadly cancers is higher and the prediction of their accumulation in human tissues can be helpful to eradicate deadly diseases in the preliminary stages. This, study focuses on human exposure to microplastics, exploring pathways such as ingestion, inhalation, and dermal contact. A novel computational approach is designed to detect and quantify microplastics in biological samples. The primary goal is to advance our understanding of the impact of microplastics on human health and contribute to the development of an automated detection system for comprehensive analysis.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Microplastics, Deep learning, Automated detection, Ingestion pathways

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The functioning of the global economy is being altered by artificial intelligence, which is having an impact on production, trade, and even the organization of jobs. Because it aligns with the United Nations' Sustainable Development Goals (SDGs), this presents a unique opportunity to create growth pathways that are inclusive, sustainable, and resilient. Artificial intelligence has the potential to not only reduce the rate of economic growth but also to solve social and environmental issues. Specifically, it can accomplish these goals by enhancing the efficiency of farming and making it simpler for individuals to transition to green energy. In this article, the economic elements of artificial intelligence's engagement in achieving the Sustainable Development Goals (SDGs) are examined, with a particular emphasis on governance, employment, environmental preservation, and inclusivity. Technology discusses both the positive and negative aspects of artificial intelligence and offers suggestions on how to incorporate technology into long-term economic strategies for emerging nations, particularly India.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Sustainable Development Goals, Economic Growth, Inclusive Development, Green Economy, and Digital Transformation

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming higher education globally, offering new possibilities to enhance teaching, learning, research, and governance (Luckin et al., 2016; Holmes et al., 2021; Lee, 2024). This paper examines AI's potential to develop "Smart Universities" -- digitally integrated, adaptive, and sustainable ecosystems. Building on recent research, it proposes a comprehensive framework integrating AI across academic, research, administrative, and governance domains (Zawacki-Richter et al., 2019; Sposato, 2025). The framework emphasizes ethical implementation, inclusivity, scalability, and institutional readiness, aligning with UNESCO's AI in Education principles and India's National Education Policy 2020 (UNESCO, 2021; Ministry of Education, 2020). Recent studies highlight persistent challenges such as data governance, faculty preparedness, and infrastructure gaps (Dwivedi et al., 2021; Fortier, 2025). This work contributes to the growing body of knowledge by offering a structured model to guide responsible AI adoption and inform future research and policymaking for AI-driven higher education transformation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Higher Education, Smart Universities, Digital Transformation, Framework.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture and plant sciences face unprecedented challenges due to climate change, population growth, and resource scarcity. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, enabling precision farming, early disease detection, yield prediction, and enhanced plant phenotyping. This review synthesizes recent advancements, drawing from diverse applications such as convolutional neural networks (CNNs) for image-based disease identification, predictive analytics for crop optimization, and IoT-integrated systems for real-time monitoring. Key benefits include reduced crop losses, optimized resource use, and improved food security. However, challenges like data privacy, model interpretability, and integration barriers persist. By examining literature from 2023-2025, this paper highlights AI's role in sustainable agriculture, including genomics, stress detection, and robotics. Future directions emphasize interdisciplinary collaboration and robust AI frameworks to address global agricultural demands.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial intelligence, machine learning, precision agriculture, plant sciences, crop disease detection, yield prediction, plant phenotyping, sustainable farming.

  License

Creative Commons Attribution 4.0 and The Open Definition