Amit Kumar

Even though riverine ecosystems constitute the basis of ecological stability, biodiversity conservation, and provision of critical ecosystem services, there is a growing threat concerning the levels of aquatic pollution caused by industrial effluents, agricultural runoff, municipal waste discharge, and overt urban growth and expansion. Manual sampling and lab analysis based traditional methods of water quality monitoring are commonly slow, spatially limited, and incapable of defining the great dynamism of pollution signatures within flowing river systems. To overcome these shortcomings, this paper suggests a holistic AI integrated remote sensing system based on IoT to operate in real-time, high-resolution, spatio-temporal measures of aquatic pollution and ecosystem well-being from riverine settings. The framework combines these elements in a low-power wireless sensor network (WSNs) of continuous in-situ monitoring, multispectral/hyperspectral satellite data (such as Sentinel-2 and Landsat-8) on a platform, and unmanned aerial vehicle (UAV)-mounted optical and thermal already holds useful information to create a multi-source/ multi-scale environmental dataset. The feature extraction is being performed using the advanced artificial intelligence model such as deep neural networks (DNN), long short-term memory (LSTM) networks, gradient boosting algorithms, and spatio-temporal kriging; the predictive models, anomaly detection, and estimation of key water quality indicators such as pH, dissolved oxygen (DO), turbidity, total dissolved solids (TDS), nitrate concentration, and chlorophyll-a can be done. The unified system also includes analytics in the clouds and geospatial decision support tools to create pollution heatmaps, predict cases of contamination, and an analysis of the index of ecosystem health. As is evident in experimental validation with real world field data, the proposed framework is far more effective than the traditional method of monitoring in terms of prediction accuracy, latency, spatial coverage and also allows the ability to issue early-warnings. In general, the created AI-IoT-enabled remote sensing architecture provides an efficient, intelligent, and scalable framework of managing sustainable river basin, environmental policy control, and data-driven ecosystem security in response to emerging pressures caused by humans.

The rapid proliferation of next-generation wireless systems, such as 5G and beyond, presents unprecedented challenges in managing interference generated by dense heterogeneous deployments, spectrum sharing, and mobile user dynamics. Conventional static or semi-dynamic interference mitigation strategies cannot address real-time network dynamics whose characteristics fluctuate over time of the network. This shortcoming results in suboptimal performance and a degradation of quality of service (quality of service) for the user. This paper presents a comprehensive framework for real-time interference management through intelligent algorithms, utilizing a combination of machine learning-based predictive models, coordinated multipoint (CoMP) transmission, and dynamic spectrum access procedures. The proposed framework utilizes innovative methods to explore and maximize the potential of real-time data analytics and adaptively beamform multiple transceivers to identify and potentially mitigate sources of interference. The simulation results showed improvements in signal-to-interference-plus-noise ratio (SINR), spectral efficiency, and lower latencies compared to conventional interference solutions. The proposed framework also emphasizes cross-layer optimization and the incorporation of edge intelligence to facilitate low latency and scalability. The figure presented in the paper also represents significant advances in research on interference resiliency frameworks, which are essential for the dependability and efficiency of wireless networks in the future.

Financial cloud computing is based on the virtualization technology paradigm, which has recently gained prominence in the information technology (IT) industry. The resource allocation in the existing system is not guaranteed, and convergence problems occasionally cause the process to move slowly. Consequently, there has been a notable decline in the overall efficacy of financial cloud computing. In this work, new methods are presented to improve the financial cloud. The three main stages of the proposed system are resource allocation, load balancing and cost-effective virtual machine (VM) migration. To enhance load balancing, this study employs the Enhanced Weighted Round Robin Algorithm (EWRR) technique. Load balancing is achieved by shifting workloads from overloaded to underloaded nodes. The Differential Evaluation-based Bat Algorithm (DEBA) efficiently chooses more optimal resources to accomplish the optimal resource allocation in the financial cloud. Resolute Support Vector Machine (RSVM) technique is utilized to Provide Cost-effective VM migration. The simulation's findings show that the recommended DEBA-EWRR algorithm performs better than existing techniques due to advancements in throughput, Makes pan, Fitness score and resource utilization.

The detection of biomolecules has advanced quickly as a result of the biotechnology revolution. One way to effectively utilise these developments is through nanotechnology, which can be used to create continuous environmental monitoring systems. Although it has long been recognised that bacteria play a significant role in regulating a wide range of environmental processes, genomic research has only recently made it feasible to comprehend biological diversity in environmental systems. It will be essential to identify biological species through genetic analysis and evaluate the proteins that are being expressed in order to completely comprehend the interactions of biological systems in the environment. The capacity to perform biological analysis at incredibly low concentrations, beyond the detection limits of current biological sensors, will be necessary for this. Furthermore, it will be essential to measure a huge number of chemical and biological species at the same time and correlate these results across a wide range of length scales, from sub-micrometer to hundreds of kilometres, in order to completely comprehend environmental systems. Studies of the effects of combustion-generated nanoscale particles in ambient air on human cardiovascular health from a toxicological, epidemiological, and human health perspective are inconclusive when the bulk chemical composition of the particles is correlated with physiological response or health effects (health endpoints). As a result, measuring methods that differentiate the bulk particle interior from the chemical makeup and structure of particle surface layers are required. Targeting a particular molecule or chemical is the foundation of the majority of biological measurement methods currently in use. Measurement and sensing devices that can evaluate biological variety in the environment are necessary because the majority of microbial species are unknown.

This study examines the impact of food culture aspects such as taste, display, and preparation process on visitor satisfaction and motivation during food festivals. Data from 150 participants were analyzed utilizing SPSS 25 for data analysis. The study uses Spearman's Rank Correlation, Confirmatory Factor Analysis (CFA), and Chi-Square testing to investigate how these characteristics influence festival experiences. Significant positive correlations were found using Spearman's Rank Correlation, taste had a strong correlation with visitor satisfaction (ρ = 0.65,p = 0.01) and motivation (ρ = 0.60,p = 0.02), while display also had a moderately positive correlation with both (ρ = 0.55,p = 0.05) satisfaction and motivation (ρ = 0.50,p = 0.04). The association for the preparation process, however, was not statistically significant and was much weaker. It was established that CFA had high dependability with estimates of 0.78 for taste, 0.85 for display, and 0.80 for the preparation procedure, all of which met high CR and AVE values. The relationships between aspects of food culture and visitor outcomes were evaluated using the Chi-Square Test. Taste was found to have significant correlations with motivation (χ² = 10.45, p = 0.05) and satisfaction. While the display was only significantly connected with satisfaction (χ² = 7.89, p = 0.10), the preparation process was strongly associated with satisfaction (χ² = 12.34, p = 0.03). These results emphasize how important taste is to improving visitor experiences and show how particular characteristics of food cultures can affect festivalgoers' motivation and level of satisfaction. The study found that taste has a substantial impact on both satisfaction and motivation, emphasizing its importance for good event planning and increased attendee involvement.

This study offers a thorough comparative analysis of the life cycle assessment of three significant energy storage technologies—Lithium-Ion Batteries, Flow Batteries, and Pumped Hydro—evaluating their environmental, economic, and social aspects in a complete manner. When considering the environmental effect, it is evident that Lithium-Ion Batteries surpass Flow Batteries and Pumped Hydro in terms of carbon footprint, water use, and land utilization. Flow Batteries and Pumped Hydro exhibit significant decreases, ranging from 40% to 60%, showcasing their potential as ecologically sound alternatives with heightened sustainability advantages. From an economic standpoint, it can be confidently said that Flow Batteries and Pumped Hydro surpass Lithium-Ion Batteries in terms of both capital and operational expenses, resulting in a decreased Levelized Cost of Storage (LCOS). The examination of percentage change showcases significant decreases, ranging from 20% to 50%, underscoring the economic competitiveness of Flow Batteries and Pumped Hydro. Regarding societal consequences, Flow Batteries and Pumped Hydro exhibit a propensity for heightened job production, augmented community acceptability, and enhanced health and safety records in contrast to Lithium-Ion Batteries. The assessments of percentage change further underscore the societal benefits of Flow Batteries and Pumped Hydro, demonstrating a substantial increase of 40% to 100% in job creation, a notable rise of 6.25% to 12.5% in community acceptability, and a significant decrease of 50% to 75% in health and safety problems. These results jointly emphasize the comprehensive benefits of Flow Batteries and Pumped Hydro, indicating their potential as sustainable, cost-effective, and socially responsible energy storage options. Given the ongoing evolution of the energy landscape, the findings obtained from this research greatly enhance the ability of stakeholders and policymakers to make well-informed decisions in their efforts to design a more environmentally friendly and robust energy future. The study emphasizes the significance of taking into account not only the technological efficacy, but also the wider environmental, economic, and social circumstances when implementing energy storage technology.