Exploring the Photocatalytic and Cytotoxic Potential of Quassia indica-Derived Bimetallic Silver-Zinc Oxide Nanocomposites
Cytotoxicity; Degradation; Green synthesis; Nanocomposites; Quassia indica; Textile dyes
In response to the escalating need for nanomaterials characterized by enhanced properties and reduced environmental impact, this study addresses critical challenges associated with conventional nanomaterial synthesis methods, particularly focusing on concerns related to environmental toxicity and economic feasibility. In this study, we report the eco-friendly synthesis of silver-zinc oxide nanocomposites using leaf extracts of Quassia indica (QI- Ag: ZnO NC). The synthesized QI- Ag: ZnO nanocomposites were characterized using various techniques including UV-visible spectroscopy, X-ray diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FTIR), Dynamic Light Scattering (DLS), Field-Emission Scanning Electron Microscopy (FE-SEM) with Energy Dispersive X-ray Spectroscopy (EDX), High Resolution Transmission Electron Microscopy (HR-TEM), and Selected Area Electron Diffraction (SAED). The photocatalytic activity of the biosynthesized QI- Ag: ZnO NC was evaluated against several textile dyes. Reactive Blue-220 exhibited the highest percentage of degradation (99.97%), closely followed by Reactive Blue-222 (99.37%), while Reactive Red-120 displayed significant degradation (94.62%). Remarkably, these nanocomposites exhibited significant photocatalytic degradation of the tested dyes, suggesting their potential application in wastewater treatment for dye removal. Furthermore, phytotoxicity studies were conducted to assess the impact of the nanocomposites on plant growth and brine shrimp mortality. To evaluate their cytotoxicity, the nanocomposites synthesized were assessed using the MTT assay on MCF-7 and MDA-MB-231 cancer cells. These findings suggest that QI- Ag: ZnO NCs have promising applications in environmental remediation and cancer therapy, opening avenues for further advancements in the arena of nanomaterial synthesis and utilization. The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Waste and Biomass Valorization, Vol-15, No. 11, pp. 6251-6265.
Springer Science and Business Media B.V.
Scaria S.S., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560029, India; Joseph K.S., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560029, India
Developing a global sustainable electricity use index using the pressure-state-response framework
Electricity; Entropy; PSR framework; Sustainable electricity use
This study analyse and compare the sustainable electricity usage in 60 countries listed on the official websites of World Energy Consumption Statistics and Climate Bond Initiative. The study also analyses the impact of increased usage of sustainable electricity on the economies' dependence on non-renewable energy sources in the evaluation system. We used a standard index system based on the Pressure-State Response (PSR) model to measure global sustainable electricity usage. Model results convey that Norway is the best performer in sustainable electricity usage, while several European countries display commendable scores, confirming their commitment to sustainable electricity practices. On the other hand, despite being the leading economies in terms of GDP, major economies such as the United States, China, Japan, and India have underperformed compared to others in the evaluation system. The study employs regression techniques to explain the relationship between sustainable electricity usage and non-renewable energy dependence. Results confirm a negative relationship between the variables, indicating the role of sustainable energy practices in reducing fossil fuel consumption. It emphasizes the urgency of a balanced approach to economic growth and natural resource usage to support a green future. 2024 Elsevier Ltd
https://doi.org/10.1016/j.jup.2024.101826 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204736103&doi=10.1016%2fj.jup.2024.101826&partnerID=40&md5=85c912b30bc32376b60bf7a5663fee1b
Kappil S.R., Department of Data Science, Christ University, Karnataka, Bangalore, India; Aneja R., Department of Economics, Central University of Haryana, Mahendergarh, India; Verma A., Department of Economics, Central University of Haryana, Mahendergarh, India
Convergence of Health Expenditure and Health Outcomes in Central Europe and the Baltic Region
Baltic countries; convergence; health expenditure; health outcomes
This research work examines the convergence of health expenditure in Central Europe and the Baltic region. The study reveals the absolute convergence in per capita health expenditures, indicating similar health outcomes for all eleven countries of the region. However, there is a divergence in health expenditure and outcomes across the eleven countries. Notably, public health expenditure diverges in Denmark, Estonia, Finland, and Norway, while, private health expenditure converges in Poland, Russia, and Sweden. Despite an overall convergence in life expectancy at birth across the countries, mortality rates due to non-communicable diseases only converge in Estonia. 2024 Taylor & Francis Group, LLC.
Eastern European Economics, Vol-63, No. 1, pp. 111-124.
Singh S., Symbiosis Law School, Symbiosis International Deemed University, Pune, Noida, India; Jain R.U., Symbiosis Centre for Management Studies, Noida, Symbiosis International Deemed University, Pune, India; Kumar N., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India
Automated lung cancer T-Stage detection and classification using improved U-Net model
Computed tomography; Deep learning; Lung nodule; Segmentation; TNM staging; U-Net
Lung cancer results from the uncontrolled growth of abnormal cells. This research proposes an automated, improved U-Net model for lung cancer detection and tumor staging using the TNM system. A novel mask-generation process using thresholding and morphological operations is developed for the U-Net segmentation process. In the pre-processing stage, an advanced augmentation technique and contrast limited adaptive histogram equalization (CLAHE) are implemented for image enhancement. The improved U-Net model, enhanced with an advanced residual network (ARESNET) and batch normalization, is trained to accurately segment the tumor region from lung computed tomography (CT) images. Geometrical parameters, including perimeter, area, convex area, solidity, roundness, and eccentricity, are used to find precise T-stage of lung cancer. Validation using performance metrics such as accuracy, specificity, sensitivity, precision, and recall shows the proposed hybrid method is more accurate than existing approaches, achieving a staging accuracy of 94%. This model addresses the need for a highly accurate automated technique for lung cancer staging, essential for effective detection and treatment. 2024 Institute of Advanced Engineering and Science. All rights reserved.
International Journal of Electrical and Computer Engineering, Vol-14, No. 6, pp. 6817-6826.
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijece.v14i6.pp6817-6826 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206235686&doi=10.11591%2fijece.v14i6.pp6817-6826&partnerID=40&md5=c5cf2a65179676f42ce821f77351dee3
Sathiyamurthy B.K., Department of Information Science and Engineering, RV Institute of Technology and Management, Bangalore, Affiliate to Visvesvaraya Technological University, Belagavi, India, Department of Computer Science and Engineering, CHRIST University, Bangalore, India; Madhaiyan V.K., Department of Information Science and Engineering, RV Institute of Technology and Management, Bangalore, Affiliate to Visvesvaraya Technological University, Belagavi, India
A high-efficiency poly-input boost DCDC converter for energy storage and electric vehicle applications
Conversion efficiency; Electric vehicle (EV) applications; Energy storage; Poly-input DCDC converter (PIDC); Renewable energy systems
This research paper introduces an avant-garde poly-input DCDC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sourcessolar energy and fuel cellswith an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 8590% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 9195% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDCs adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DCDC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures. The Author(s) 2024.
Singh A.R.; Suresh K.; Parimalasundar E.; Kumar B.H.; Bajaj M.; Tuka M.B.
Singh A.R., Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Hubei, Shiyan, 442000, China; Suresh K., Department of Electrical and Electronics Engineering, Christ Deemed to Be University, Bangalore, India; Parimalasundar E., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Kumar B.H., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Bajaj M., Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan, Graphic Era Hill University, Dehradun, 248002, India; Tuka M.B., Department of Electrical and Computer Engineering, College of Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks
Artificial neural network; DC- approximate dynamic programming; DC-DC and DC-AC; Universal operation
This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability. The Author(s) 2024.
Suresh K.; Parimalasundar E.; Kumar B.H.; Singh A.R.; Bajaj M.; Tuka M.B.
Suresh K., Department of Electrical and Electronics Engineering, Christ Deemed to Be University, Bangalore, India; Parimalasundar E., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Kumar B.H., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Singh A.R., Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Hubei Shiyan, 442000, China; Bajaj M., Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan, Graphic Era Hill University, Dehradun, 248002, India; Tuka M.B., Department of Electrical and Computer Engineering, College of Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
Synthesis and characterization of biowaste-derived porous carbon supported palladium: a systematic study as a heterogeneous catalyst for the reduction of nitroarenes
Biowaste; Heterogeneous catalysis; Nitroarene reduction; Palladium based catalysts; Porous carbon
In this study, we present a green synthesis approach for the fabrication of porous carbon supported palladium catalysts derived from Caesalpinia pods. The synthesis involves self-activation of Caesalpinia pods in a nitrogen atmosphere at various temperatures (600C, 800C, and 1000C) to produce porous carbon nanoparticles. Among the synthesized carbon materials, the sample CP-CNS/10 synthesized at 1000C exhibited the highest surface area of 793 m2/g with an average pore size diameter of 1.8nm. The resulting porous carbon material served as an efficient support for palladium nanoparticles, with a low metal loading of about 0.2mol% Pd for the reaction. This catalyst demonstrated excellent performance in the reduction of nitroarenes to their corresponding aromatic amines. The successful incorporation of approximately 4.5% Pd during the deposition process highlights the potential of the porous carbon supported palladium catalyst synthesized at 1000C for a sustainable and efficient heterogeneous catalyst for the reduction of nitroarenes. Graphical Abstract: (Figure presented.) Akadiai Kiad Budapest, Hungary 2024.
Reaction Kinetics, Mechanisms and Catalysis, Vol-137, No. 6, pp. 2989-3004.
Shetty A., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India, Centre for Advanced Research and Development (CARD), Christ University, Hosur Road, Bangalore, 560029, India; Hegde G., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India, Centre for Advanced Research and Development (CARD), Christ University, Hosur Road, Bangalore, 560029, India
Mechanical and Wear Behavior of Aluminium Metal Matrix Composites Reinforced Ceramics Materials for Light Structures
Aluminium Alloy 8011; Density; Microhardness; Silicon Nitride; Wear Test
Aluminium Alloy based Metal Matrix Composites (AAMMCs) has widely used in defense, aircraft and automobile applications because of their enhanced engineering properties with light weight metals. Nano sized silicon nitride (80 ?m) is used as a reinforcement in this study, whereas aluminium alloy 8011 is selected as the matrix material. Using the stir casting method, metal matrix composites made of aluminium alloy 8011 with varying weight percentages of Si3N4(0, 4, 8, 12, and 16) are created. The stir casted AL 8011/Si3N4composites further heated under T6 condition. The AL 8011/Si3N4 T6 composites are further subjected to Energy Dispersive X ray Analysis (EDAX) and Scanning Electron Microscope (SEM) to identify by the presence of elements and study the microstructure characterization, respectively. The density, microhardness and wear test are conducted by employing Archimedes principle, Vickers hardness tested and pin on disc equipment, respectively. The wear test is done at different sliding distances like (500, 1000, 1500 and 2000 m), applied load like (10, 20, 30 and 40 N) and kept sliding at a speed of 1 m/s. The increasing weight percentage of silicon nitride expands the increasing of density and Vickers hardness up to 12 wt % of silicon nitride and decreasing by 16 wt % addition. The wear resistances of AL 8011/12 wt % Si3N4T6 composite exhibits higher wear resistance than other Al8011 based composites. 2024, Informatics Publishing Limited. All rights reserved.
Karthick L.; Umashankar M.; Singh A.K.; Bhambere V.L.; Xavier L.F.; Bhandari R.
Journal of Mines, Metals and Fuels, Vol-72, No. 12, pp. 1347-1359.
https://doi.org/10.18311/jmmf/2024/46077 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216504101&doi=10.18311%2fjmmf%2f2024%2f46077&partnerID=40&md5=a7d2f9f0bf0ef703c37a3d322c959497
Karthick L., Department of Mechanical Engineering, Hindusthan College of Engineering and Technology, Tamil Nadu, Coimbatore, 641032, India; Umashankar M., Department of Mechanical Engineering, Sona College of Technology, Tamil Nadu, Salem, 636005, India; Singh A.K., Department of Mechanical Engineering, Aditya College of Engineering and Technology, Andhra Pradesh, Surampalem, 533437, India; Bhambere V.L., Department of Mechanical Engineering, Jagadambha College of Engineering and Technology, Maharashtra, Yavatmal, 445001, India; Xavier L.F., School of Engineering and Technology, Christ (Deemed to Be University), Kengeri Campus, Karnataka, Bangalore, 560074, India; Bhandari R., Department of Mechanical Engineering, Sanaka Educational Trust's Group of Institutions, Malandighi, West Bengal, Durgapur, 713212, India
Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering
Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) pointindicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
S K.; Rao D.D.; Jain A.; Sharma S.; Pandit S.V.; Pandey R.
S K., Christ University, Karanataka, Banglore, India; Rao D.D., Indian Institute of Technology, Patna, India; Jain A., School of Computer Applications, Manav Rachna International Institute of Research and Studies(MRIIRS), Haryana, Faridabad, India; Sharma S., School of Computer Applications, Manav Rachna International Institute of Research and Studies(MRIIRS), Haryana, Faridabad, India; Pandit S.V., Department of Artificial Intelligence and Data Science, PES Modern College of Engineering, Pune, Shivajinagar, 411005, India; Pandey R., Department of Computer Science & amp; Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India
Investigating MnSe@Y2O3 nanocomposite as an electrode for asymmetric hybrid supercapacitor
Asymmetric hybrid device; Energy density; Gel electrolyte; Metal oxides; Metal selenides
In this research work, manganese selenide (MnSe) and yttrium oxide (Y2O3) nanoparticles have been synthesized by facile melt diffusion and hydrothermal technique which are then composited by ultrasonication. The composite MnSe@Y2O3 has been analyzed as a supercapacitor electrode. The growth structure of the composite was scrutinized systematically by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), high resolution transmission electron microscopy (HRTEM), and selected area diffraction pattern (SAED). The Trasatti and Dunn's plots have been also plotted to calculate the capacitive and diffusive contribution. The device is fabricated with PVA-KOH gel electrolyte. Also, the fabricated device MnSe@Y2O3||AC has exhibited a specific capacity of 48.39 C/g at 1 A/g through the potential window of 01.7 V. The wide potential window is evidence for high energy density. This also provides elevated energy density of 19 Wh/kg, at high power density of 1445 W/kg, and has shown brilliant cyclic stability of 70.16 % even after 5000 charge/discharge cycles. 2024 Elsevier B.V.
Bharathi A.N.; Devi V.K.; Sambasivam S.; Albaqami M.D.; Kumar M.K.; Velsankar K.; Sudhahar S.
Journal of Alloys and Compounds, Vol-1009
https://doi.org/10.1016/j.jallcom.2024.176867 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205898653&doi=10.1016%2fj.jallcom.2024.176867&partnerID=40&md5=0c677a470cef213d3abe622624daaea5
Bharathi A.N., Department of Physics, Alagappa University, Karaikudi, 630 003, India; Devi V.K., Department of Physics, Alagappa University, Karaikudi, 630 003, India; Sambasivam S., National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Albaqami M.D., Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia; Kumar M.K., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, 560 029, India; Velsankar K., Department of Plant Pathology, College of Agriculture, Guizhou University, Guiyang, 550025, China; Sudhahar S., Department of Physics, Alagappa University, Karaikudi, 630 003, India
Optimising lead qualification through machine learning: A customer data-driven approach
cold leads; decision tree; hot leads; logistic regression; machine learning; supervised ML algorithms
Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. Converting bad client leads can cause businesses to burn through cash reserves. As a result of this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (converted to a customer or not). In this study, an attempt is made to evaluate historical data for leads produced by other businesses in order to train and validate a machine learning (ML)/deep learning (DL) model and test it against real-world characteristics to categorise them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low codeno code libraries, such as PyCaret in Python, and can be used to make predictions regarding probable lead creation, propensity to convert generated leads and optimal actions on the leads by communications teams. Supervised ML algorithms such as logistic regression, decision trees, random forests and other models using a Python library were built to score leads for identifying potential conversions. With good and broad lead-scoring models in place, businesses can optimise their CTI actions on the basis of lead prioritisation and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of this study reveals that 52 per cent of the sample of 74,779 leads are cold leads and 48 per cent are hot leads that are sales qualified. The leads are qualified using the lead score matrix. This method can aid digital businesses to remove unqualified leads and manage leads better, and therefore improve the quality of the leads sent to clients. This, in turn, will improve conversion rates for individual customers. These increased conversion rates will enhance the business strategy of digital marketing firms. Henry Stewart Publications.
Helen J.V.L.; Moorthy V.; Sai C.; Joji B.
Applied Marketing Analytics, Vol-10, No. 3, pp. 255-270.
https://doi.org/10.69554/ETXF7251 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213383861&doi=10.69554%2fETXF7251&partnerID=40&md5=049aa95eff0da5dc89531c56444bf6cb
Helen J.V.L., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India; Moorthy V., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India; Sai C., Enterprise Solution Architect, Airtel Business, India; Joji B., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India
Death-worlds, Necropolitics and Decoloniality Colonial Negotiations in Mah
The boundaries of sovereignty are mostly relegated to modern and late modern political thoughts that focus on biopolitical and democratic theories. This paper marks a shift of sovereign subjectivity to the interstitial spaces of life and death of the colonial subjects. Through the study of the necropolitics of colonial control in the erstwhile French colony of Mah as narrated in the novel On the Banks of the Mayyazhi, this paper argues that colonial subjectivity and the idea of sovereignty have decentred itself from the traditional notions of political control and violence to newer avenues of life and death. The perusal of the decolonial approach to necropolitics will examine how colonial logic has shaped the idea of sovereignty. 2024 Economic and Political Weekly. All rights reserved.
Economic and Political Weekly, Vol-59, No. 52, pp. 52-57.
Sivasankaran A., Department of English, CHRIST (Deemed to be University), Bengaluru, India; Vijay S., Department of English, CHRIST (Deemed to be University), Bengaluru, India
Improving crop production using an agro-deep learning framework in precision agriculture
Agricultural technology; Agro deep learning framework; Artificial intelligence in agriculture; Crop monitoring; Crop yield prediction; Data analysis; Deep learning; Neural networks; Precision agriculture; Smart farming
Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments. The Author(s) 2024.
Logeshwaran J.; Srivastava D.; Kumar K.S.; Rex M.J.; Al-Rasheed A.; Getahun M.; Soufiene B.O.
ISSN: 14712105; PubMed ID: 39487390; CODEN: BBMIC
Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Srivastava D., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Kumar K.S., Department of Computing Technologies, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Rex M.J., Department of Computing Technologies, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Al-Rasheed A., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Getahun M., Department of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar University, Kebri Dehar, Ethiopia; Soufiene B.O., PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia
Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems
DC-DC converter; Electric vehicle power systems; Energy-efficient electric vehicles; High-efficiency power conversion; Modular converter design; Multi-load power distribution; Multi-source energy management; Solar energy integration
This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy sourcespecifically, solar energyto enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100W prototype demonstrated the converters ability to deliver stable 24V and 48V outputs from a 12V input, with output voltage deviations kept within 1%, significantly improving upon the 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications. The Author(s) 2024.
https://doi.org/10.1038/s41598-024-77349-y https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208081718&doi=10.1038%2fs41598-024-77349-y&partnerID=40&md5=4325e91ea2336ecacd521be23359aebc
Singh A.R., Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Hubei, Shiyan, 442000, China; Suresh K., Department of Electrical and Electronics Engineering, Christ Deemed to be University, Bangalore, India; Parimalasundar E., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Kumar B.H., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Bajaj M., Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan, College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia; Tuka M.B., Department of Electrical and Computer Engineering, College of Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
Spectroscopic Study of Late-type Emission-line Stars Using the Data from LAMOST DR6
Line: identification; Stars: activity; Stars: late-type
Low-mass emission-line stars belong to various evolutionary stages, from pre-main-sequence young stars to evolved stars. In this work, we present a catalog of late-type (F0 to M9) emission-line stars from the LAMOST Data Release 6. Using the scipy package, we created a Python code that finds the emission peak at H? in all late-type stellar spectra. A data set of 38,152 late-type emission-line stars was obtained after a rigorous examination of the photometric quality flags and the signal-to-noise ratio of the spectra. Adopting well-known photometric and spectroscopic methods, we classified our sample into 438 infrared (IR) excess sources, 4669 post-main-sequence candidates, 9718 Fe/Ge/Ke sources, and 23,264 dMe sources. From a crossmatch with known databases, we found that 29,222 sources, comprising 65 IR excess sources, 7899 Fe/Ge/Ke stars, 17,533 dMe stars, and 3725 PtMS candidates, are new detections. We measured the equivalent width of the major emission lines observed in the spectra of our sample of emission-line stars. Furthermore, the trend observed in the line strengths of major emission lines over the entire late-type spectral range is analyzed. We further classified the sample into four groups based on the presence of hydrogen and calcium emission lines. This work presents a large data set of late-type emission-line stars, which can be used to study active phenomena in late-type stars. 2024 National Astronomical Observatories, CAS and IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Edwin D.; Mathew B.; Shridharan B.; Valsan V.; Nidhi S.; Bhattacharyya S.; Kartha S.S.; Robin T.
Research in Astronomy and Astrophysics, Vol-24, No. 12
https://doi.org/10.1088/1674-4527/ad8f9b https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211070102&doi=10.1088%2f1674-4527%2fad8f9b&partnerID=40&md5=be6d900bd3d42d47afbb3fda2b91a4db
Edwin D., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Shridharan B., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India, Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Mumbai, 400005, India; Valsan V., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Nidhi S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India, The Oxford College of Science, 17th, 32, 19th Main road, Sector 4, HSR Layout, Karnataka, Bengaluru, 560102, India; Bhattacharyya S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India; Robin T., Department of Physics and Electronics, CHRIST (Deemed to be University, Hosur Main Road, Bangalore, 560029, India
Assessment of ML techniques and suitability to predict the compressive strength of high-performance concrete (HPC)
Extended deep neural network (EDNN); High-performance concrete (HPC); Multi-objective jellyfish optimization (MOJO); Strength characteristic
Using industrial soil waste or secondary materials for making cement and concrete has encouraged the construction industry because it uses fewer natural resources. High-performance concrete (HPC) is recognized for its exceptional strength and sturdiness compared to conventional concrete. Accurate prediction of the compressive concentration of HPC is vital for optimizing the concrete mix design and ensuring structural integrity. Machine learning (ML) techniques have shown promise in predicting concrete properties, including compressive strength. This research focuses on various ML techniques for their suitability in predicting the compressive dilution of HPC. In this research, the Extended Deep Neural Network (EDNN) technique is used to analyze the strengths, limitations, and performance of different ML algorithms and identify the most effective methods for this specific prediction task. However, there is a problem with accuracy. Therefore, our research approach is the EDNN-centred strength characteristics prediction of HPC. In the suggested approach, data is initially acquired. Afterward, the data is pre-processed through normalization and removing missing data. Thus, the data are fed into the EDNN algorithm, which forecasts the strength characteristics of the particular mixed input designs. With the Multi-Objective Jellyfish Optimization (MOJO) technique, the value of weight is initialized in the EDNN. The activation function is the Gaussian radial function. In the experimental analysis, the implementation of the suggested EDNN is evaluated to the performance of the prevailing algorithms. When compared to current research methodologies, the proposed method performs better in this regard. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Gupta M.; Upreti K.; Yadav S.; Verma M.; Mageswari M.; Tiwari A.
Asian Journal of Civil Engineering, Vol-25, No. 8, pp. 5741-5752.
Gupta M., Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, India; Upreti K., CHRIST University, Delhi NCR Campus, Ghaziabad, India; Yadav S., Jaypee Business School, JIIT, Sector-62, Noida, India; Verma M., GLA University, Mathura, India; Mageswari M., Panimalar Engineering College, Chennai, India; Tiwari A., CHRIST University, Delhi NCR Campus, Ghaziabad, India
Dual drug co-encapsulation of bevacizumab and pemetrexed clocked polymeric nanoparticles improves antiproliferative activity and apoptosis induction in liver cancer cells
biomedical applications; drug delivery systems; nanoparticles, nanowires and nanocrystals
Nanoparticle (NP) enabled approaches have been employed for chemotherapeutic administration due to their capacity to regulate drug release and reduce side effects. Additionally, these methods can use several drugs concurrently and impede the proliferation of cancer cells that have developed resistance. Bevacizumab (BVZ) and pemetrexed (PEM) have demonstrated encouraging outcomes in the treatment and management of cancer. This work investigates the combined antiproliferative efficacy of BVZ and PEM co-loaded PLGA-PEG NPs (BVZ/PEM@PLGA-PEG NPs) against HepG2 liver cancerous cells. The BVZ/PEM@PLGA-PEG exhibited a sphere-shaped and consistent nanosized distribution. In addition, we evaluated the potential mechanisms for inhibiting cell growth and inducing apoptosis using DAPI staining and cell cycle study. The beneficial combined antiproliferative activity and the apoptosis pathway were detected in the HepG2 cells exposed to BVZ/PEM@PLGA-PEG NPs. Our study determined that the combinational drug treatment of BVZ/PEM@PLGA-PEG NPs has a significant effect on promoting the effectiveness of liver cancer treatment. 2024 Wiley Periodicals LLC.
Ganesan S.; Upadhye V.J.; Aulakh D.; Bishnoi S.; Chinnathambi A.; Alahmadi T.A.; Abudoleh S.M.; Arulselvan P.; Thangavelu I.
Journal of Applied Polymer Science, Vol-141, No. 47
https://doi.org/10.1002/app.56262 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205075818&doi=10.1002%2fapp.56262&partnerID=40&md5=f8492e708fc42fba7a0032a2598d0bd7
Ganesan S., Department of Chemistry and Biochemistry, JAIN (Deemed-to-be University), Karnataka, Bangalore, India; Upadhye V.J., Parul Institute of Applied Sciences, Parul University, Gujarat, Vadodara, India; Aulakh D., Centre of Research Impact and Outcome, Chitkara University, Punjab, Rajpura, India; Bishnoi S., Department of Sciences, Vivekananda Global University, Jaipur, India; Chinnathambi A., Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia; Alahmadi T.A., Department of Pediatrics, College of Medicine and King Khalid University Hospital, King Saud University, Medical City, Riyadh, Saudi Arabia; Abudoleh S.M., Faculty of Pharmacy, Middle East University, Amman, Jordan; Arulselvan P., Department of Chemistry, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India; Thangavelu I., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Strategic design of MXene/CoFe2O4/g-C3N4 electrode for high-energy asymmetric supercapacitors
Electrode; Energy; MXene; Supercapacitor; Ultrasonication
MXenes are emerging as the next-generation materials for energy storage due to their substantial surface area, exceptional conductivity, and abundant surface-terminating groups. However, the tortuous path for ion transfer within the restacked layers significantly limits the electrochemical performance of multilayered MXenes. To overcome this, interlayer spacers have been introduced. These spacers help mitigate ion diffusion barriers and enhance the accessibility of active sites, thereby improving the overall efficiency and longevity of MXene-based supercapacitors and related devices. In this study, a rational material is designed by incorporating CoFe2O4 and g-C3N4 into the layers of MXene through ultrasonication for supercapacitor application. The physicochemical properties of the synthesized materials have been comprehensively characterized using diverse techniques, revealing that MXene/CoFe2O4/g-C3N4 has successfully evolved into a multilayered structure possessing enhanced surface area, low restacking tendency, high pore diameter, and excellent pore volume. Leveraging these properties, it performs as a viable material for fabricating the working electrode with a specific capacitance (Csp) of 1506.2 F g?1 at a current density of 5 A g?1 in 3 M KOH. It shows good stability with 89 % capacitance retention over 7000 cycles. An asymmetric supercapacitor (ASC) constructed with MXene/CoFe2O4/g-C3N4 as positive electrode and activated carbon as negative electrode exhibits an energy density of 79.8 Wh Kg?1 and power density of 1343.3 W Kg?1. Furthermore, it shows a capacitive retention of 91 % over 10,000 cycles. This MXene based composite, with excellent capacitance and outstanding stability, offers an appreciable performance in the field of sustainable energy storage. 2024 Elsevier B.V.
Mathew S.; Devi K.R. S.; Saravanakumar B.; Pinheiro D.
Journal of Alloys and Compounds, Vol-1008
https://doi.org/10.1016/j.jallcom.2024.176488 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204349724&doi=10.1016%2fj.jallcom.2024.176488&partnerID=40&md5=fdba43bc97dd8b86b237b7be329c6667
Mathew S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Devi K.R. S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Saravanakumar B., Department of Physics, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India
STOCHASTIC BEHAVIOUR OF AN ELECTRONIC SYSTEM SUBJECT TO MACHINE AND OPERATOR FAILURE
Electronic System; Operator Failure; Regenerative Point Technique; Reliability Measures; semi-Markov Process; Stochastic Analysis
A stochastic model is developed by assuming the human (operator) redundancy in cold standby. For constructing this model, one unit is taken as electronic system which consists of hardware and software components and another unit is operator (human being). The system can be failed due to hardware failure, software failure and human failure. The failed hardware component goes under repair immediately and software goes for upgradation. The operator is subjected to failure during the manual operation. There are two separate service facilities in which one repairs/upgrades the hardware/software component of the electronic system and other gives the treatment to operator. The failure rates of components and operator are considered as constant. The repair rates of hardware/software components and human treatment rate follow arbitrary distributions with different pdfs. The state transition diagram and transition probabilities of the model are constructed by using the concepts of semi-Markov process (SMP) and regenerative point technique (RPT). These same concepts have been used for deriving the expressions (in steady state) for reliability measures or indices. The behavior of some important measures has been shown graphically by taking the particular values of the parameters. 2024, Gnedenko Forum. All rights reserved.
Reliability: Theory and Applications, Vol-19, No. 4, pp. 353-363.
Malik S., Department of Mathematics, Baba Mastnath University, Rohtak, India; Komal, Department of Mathematics, Baba Mastnath University, Rohtak, India; Yadav R.K., School of Sciences, Christ University, Bengaluru, India; Anju, Department of Mathematics, Baba Mastnath University, Rohtak, India
Mn2(CO)10 catalyzed visible-light-promoted synthesis of 1H-pyrazole-4-carboxamides; A sustainable multi-component statergy with antibacterial and cytotoxic evaluations
1H-pyrazole-4-carboxamides; Antimicrobial activity; Cytotoxicity; E. coli; P. aeruginosa
Multicomponent reactions play a pivotal role in synthesizing 1H-pyrazole-4-carboxamides, underscoring its significance in sustainable organic synthesis. These compounds, valued for their diverse biological activities, have garnered substantial attention in pharmaceutical research. A facile, rapid one-pot strategy to access an extensive array of 1H-pyrazole-4-carboxamide derivatives, utilizing substituted aldehydes, cyanoacetamide, and hydrazine hydrate as substrates and a readily accessible Mn2(CO)10 as photocatalyst in EL: H2O (1:1). Among the synthesized series, products 4b, 4 g, 4k showed remarkable antibacterial activity against E coli, P aeruginosa, S. aureus in agar medium and excellent cytotoxicity with Human colorectal carcinoma (HCT-116), Liver cancer cells (Hep-G2) and breast adenocarcinoma (MCF-7) cell lines. The current method is characterized by its affordability, non-toxicity, easy access to starting materials, and notably with minimal waste generation. Additionally, remarkable aspects include its mild operating conditions, environmentally friendly nature, and the ability to accommodate a wide range of both electron-donating and electron-withdrawing groups. 2024 The Author(s)
Govindaraju S.; Ramaraj S.G.; Servesh A.; Rajprasad J.; Kumar N.; Sadaiyandi V.; Tabassum S.
Journal of Organometallic Chemistry, Vol-1022
All Open Access; Hybrid Gold Open Access
https://doi.org/10.1016/j.jorganchem.2024.123384 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204361538&doi=10.1016%2fj.jorganchem.2024.123384&partnerID=40&md5=8622fe571b78b52e8356e14a984176ed
Govindaraju S., Department of Sciences and Humanities, Christ University, School of Engineering and Technology, Kumbalagodu, Mysore Road, Bengaluru, 560074, India; Ramaraj S.G., Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Japan, Department of Materials Physics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMTS), Thandalam, Tamilnadu, Chennai, 602105, India; Servesh A., Department of Chemistry, Christ University, Hosur Road, Bengaluru, 560029, India; Rajprasad J., Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Tamilnadu, Kattankulathur, 603203, India; Kumar N., Department of Electronics & Communication Engineering, Graphic Era Deemed to be University, Uttarakhand, Dehradun, 248002, India; Sadaiyandi V., Department of physics and nanotechnology, SRM Institute of Science and Technology kattankulathur, Tamilnadu, Chengalpattu, India; Tabassum S., Department of Chemistry, Surana College - Autonomous, South End Road, Basavanagudi, Bengaluru, 560004, India