Sri Sri University

Liver disease is a critical global health issue that often progresses without noticeable symptoms, making early detection essential for effective treatment and prevention. This study presents a machine learning based approach for the prediction of liver disease using clinical and biochemical parameters. The dataset used includes various attributes such as age, gender, bilirubin levels, liver enzyme measurements, and protein levels,which are relevant indicators of liver function

Agricultural biotechnology has emerged as a critical frontier in addressing the multifaceted challenges of 21st-century food systems, driven by a growing global population and the intensifying impacts of climate change. This review explores the transformative potential of modern biological tools, including genetic engineering, CRISPR-Cas9 genome editing, and multi-omics integration, in enhancing crop resilience and productivity. By synthesizing current research, the paper highlights how these technologies mitigate both biotic stresses, such as viral and fungal pathogens, and abiotic stresses, including drought, salinity, and extreme temperatures. Beyond yield enhancement, biotechnology plays a pivotal role in nutritional biofortification and environmental stewardship, notably through the reduction of synthetic pesticide reliance and the sequestration of carbon emissions. The analysis further discusses the transition toward a circular bio-economy, utilizing agricultural residues for bio-refining and biodegradable packaging. However, the global adoption of these innovations remains uneven, hindered by stringent regulatory frameworks, public safety concerns, and policy lags in developing regions.1 To realize the goal of a sustainable "Evergreen Revolution," the review emphasizes the necessity of harmonized biosafety guidelines and the integration of artificial intelligence with molecular breeding. Ultimately, biotechnology stands as an indispensable pillar for ensuring global food security, offering precise solutions to maintain ecological balance while meeting the demands of a projected population of nine billion by 2030.

ABSTRACT The digital economy has established data as a fundamental asset, shaping industries, governance, and technological innovation while driving competitive advantage across sectors. The proliferation of data‐generating technologies like IoT and social media has transformed data into a strategic resource, particularly benefiting Big Tech companies. This study examines data's dual nature as both asset and commodity, analyzing market structures, monetization challenges, and emerging technology impacts on the data economy. Employing a multivocal literature review (MLR) approach, this research systematically reviewed academic literature and gray sources including white papers and industry reports. Comprehensive searches across five major databases such as ACM digital library, IEEE Xplore, ScienceDirect, SpringerLink, and Wiley Online Library yielded 764 records, with an additional 86 from other sources (i.e., gray‐literature), totaling 850 screened studies. Ultimately, 64 studies addressed five key research questions on data's economic characteristics, market structures, monetization challenges, technological impacts, and public‐commercial balance frameworks. Analysis reveals data's distinctive economic properties like non‐rivalry, non‐excludability, and scalability necessitating novel valuation approaches. Market dynamics are significantly influenced by regulatory frameworks like GDPR, while emerging technologies such as AI and blockchain reshape monetization strategies. Key challenges include accurate valuation, data quality assurance, and privacy compliance. Data's economic essence defies conventional valuation frameworks due to its unique properties. AI and blockchain are transforming data markets through enhanced value extraction and secure transactions. However, robust governance frameworks like data commons and trusts are essential for balancing monetization with ethical use, requiring future research on comprehensive valuation models integrating technological advancements with public interest alignment. This article is categorized under: Application Areas > Business and Industry

ABSTRACT Orexin A and orexin B are neuropeptides that play an important role in regulating physiological functions such as stress response, reward, energy, and arousal. Orexin receptor antagonists (ORAs), such as nivasorexant, are a new class of medications targeting the orexin system. Nivasorexant is a selective orexin-1 receptor antagonist (OX1R) that was developed to avoid dual receptor antagonists. We aimed to conduct this systematic review to evaluate the safety and efficacy of nivasorexant and to explore the potential clinical uses in psychiatry. Relevant databases were searched to identify studies focused on nivasorexant’s pharmacokinetics, pharmacodynamics, safety, efficacy, and clinical uses in humans. Out of 34 articles identified, five articles were included. Preclinical evidence suggested a promising role for nivasorexant in modulating compulsive behaviors due to its high selectivity for OX1R and lack of significant sedation. Clinical investigations have provided insights into its pharmacokinetic profile and generally favorable safety. While initial efficacy findings for a specific psychiatric indication were not definitively met, exploratory analyses suggest that patient characteristics might influence treatment outcomes. Furthermore, nivasorexant’s metabolic profile indicates potential for drug interactions, requiring careful consideration in clinical practice. Nivasorexant’s potential for addressing disorders is characterized by dysregulated reward processing, where maintaining alertness is desirable, based on its selective mechanism and tolerability. However, this review is limited by the current volume of clinical data, the scope of studies conducted, and the generalizability of findings across diverse patient populations. Further rigorous clinical validation is needed to fully ascertain its therapeutic potential and optimal application in psychiatry.

India, the world’s largest democracy, derives its legitimacy from free, fair, and transparent elections. However, over decades, persistent challenges — such as electoral malpractices, money and muscle power influence, weak regulatory frameworks, and systemic delays — have diluted the democratic ethos. This paper examines the necessity, evolution, and impact of electoral reforms in India and assesses whether existing measures are sufficient to uphold democratic integrity. The study analyzes legislative changes, institutional mechanisms like the Election Commission of India (ECI), and reform proposals from civil society and political stakeholders. Using qualitative methodologies, including document analysis and expert commentary, the research identifies key issues and offers actionable recommendations for strengthening India’s electoral processes. The findings suggest that while significant reform efforts have been made, deeper structural reforms geared toward transparency, accountability, and inclusivity are required to truly safeguard India’s democratic foundations.

The fast growing digital culture has attracted people’s attention and motivated them to interact with fascinating multimedia materials, including images, videos, voice notes and messages. Video is the most often used digital material format globally; it is used for marketing for companies, education, proof of concern, memorial ceremonies, and amusement among other purposes. The primary goal of video/image captioning is to represent the dynamics of a video clip using simple and understandable conversational language. The motivation for captioning comes from its ability to enhance video accessibility for those who are deaf or hard-of-hearing, improve concentration and memory of information and allow viewing in sound-sensitive environments. In this proposed framework, we demonstrate how to create captions for videos using Gated Recurrent Unit (GRU) along with a Sentence Vector Branch (SVB) and skip connections employing two word embedding methods: Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Model (ELMO). The encoder section of the model uses skip connections to address the issue of vanishing gradients and the sentence vector branch is used to improve the efficiency of the encoder-decoder architecture. The decoder generates a suitable description using the sentence vector branch together with past generated words. All of our experiments make use of the Standard MSVD databases. BLEU@4, METEOR, ROUGE and CIDEr measures have been applied for the performance assessment. Comparative analysis of BERT and ELMO word embeddings reveals their respective strengths. Features are extracted from pre-trained CNNs NASNet-Large, VGG16, Inceptionv3, DenseNet-169 and ResNet50. In the MSVD dataset, BERT beats ELMO in the case of the GRU-based model, whereas ELMO beats BERT in the case of the LSTM-based model; among LSTM and GRU, LSTM outperforms GRU. DenseNet-169 beats every feature extraction method used in the MSVD dataset.

Real-time evacuation guidance is a critical safety function for intelligent Cyber-Physical Systems (CPS). The core challenge lies in executing complex, multi-objective path finding algorithms like Iterative Dynamic Programming (IDP) under stringent real-time constraints. This paper presents a scalable, accelerated framework designed around “EnMod-DP,” a novel suite of hybrid algorithms that integrate Dynamic Programming with Reinforcement Learning (RL) heuristics. We evaluate 20 algorithms across varying grid sizes from 5×5 to 100×100. Our results demonstrate that Parallel EnMod-DP solvers achieve significant acceleration on large-scale grids, with the Parallel Hybrid DP-RL solver reducing execution time by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 45 {\%}$</tex> compared to serial baselines. The primary novelty of this work lies in its engineering implementation: bridging the gap between theoretical optimality and real-time viability through a robust, parallelized architecture deployable on edge systems.