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Enhancing the biodegradability and environmental impact of microplastics utilizing Eisenia fetida earthworms with treated low-density polyethylene for sustainable plastic management
Low-density polyethylene (LDPE) is widely used in food packaging and agricultural mulching, but its disposal generates macro, meso and microplastics that infiltrate the food chain and carry harmful substances. The present study aimed to improve remediation strategies for soils contaminated with LDPE and enhance the survivability of Eisenia fetida. The study dissolved LDPE in trichloroethylene and treated it with starch, hydrogen peroxide, nitric acid and acetic acid, initiating thermo-oxidative reactions. The treatment decreased LDPE's crystallinity index from 48.48% to 44.06% (single treatment), 44.06% to 40.02% (double treat-ment) and 40.02% to 32.98% (triple treatment), achieving a 15.5% reduction in crystallinity. LDPE microplastics with 40.02% crystallinity showed lower mortality rates in Eisenia fetida earthworms compared to those with 44.06% and 32.98% crystallinity and untreated LDPE. When introduced to E. fetida, microbiota in the earthworm casts included unidentified species from Pseu-domonas and Zoopagomycota, known polyethylene degraders. Microbial analysis of treated LDPE microplastics showed changes in gut microbiota, including potential degraders from Aeromonas and Malassezia restricta. XRD (X-ray diffraction techniques analyses) and FTIR(Fourier Transform Infrared Spectroscopy) analyses provided insights into distinct LDPE degradation patterns, identifying hydroxyl and carboxylic groups as functional groups. The study also investigated the ability of altered mi-croflora with treated microplastics to degrade LDPE, favouring decreased earthworm mortality rates. The crystallinity index of treated polyethylene further reduced from 40.02% to 23.58% after 21 days of exposure to E. fetida. This research advances the understanding of oxidised plastics' ecological impacts and will help to develop environmentally sustainable and biodegradable LDPE. Author (s). -
Enhancing the efficiency of parallel genetic algorithms for medical image processing with Hadoop /
International Journal of Computer Applications, Vol.108, Issue 17, pp.92-97, ISSN No: 0975-8887. -
Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network
A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods. 2022 Northeastern University, China. -
Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm
In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment. Copyright 2023 Inderscience Enterprises Ltd. -
Enhancing the performance in education by implementing gamification
The gaming industry is growing rapidly in the present generation along with the advancement of technology. Gaming has captured all the young minds with its high and realistic graphics. What makes the gaming industry so attractive is that the players have complete freedom in the game. Freedom to fail, they can try until they succeed another feature is that game is user-centric. Consequently, a lot of research is been in the field of education to increase student's engagement towards studies. The main aim of this paper is to combine these game elements with learning to see if it yields better results. A quantitative approach is used to analyze the student's performance and interest in learning. Using these game elements in education will encourage the students to learn as well as have the flexibility to complete the course at their own pace. Copyright 2019 American Scientific Publishers All rights reserved. -
Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design
The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems. 2023 Elsevier Ltd -
Enhancing the stability of DSSC by Co-activation of microwave synthesized TiO2 with biomass derived carbon dots
Dye-sensitized solar cells (DSSCs) that utilize natural dyes have garnered interest due to their low cost, eco-friendly manufacturing process, and competitive photovoltaic performance. However, their efficiency and stability issues have hindered their widespread implementation. To enhance their performance, this paper proposes a novel approach of modifying the photoanode with carbon dots (CDs) to align the band gap for easier carrier collection. The material properties were thoroughly characterized by examining their structural, morphological, optical, and electrical properties. In this study, titanium dioxide (TiO2) was synthesized using the microwave-assisted solvothermal method, while nitrogen-doped CDs derived from Citrus medica fruit juice were prepared using a simple hydrothermal treatment. Three sets of Natural Dye Sensitized Solar Cells (NDSSC) devices were created using co-activated photoanode (CD/TiO2) and unmodified photoanode (TiO2) with Platisol T/sp coated ITO serving as the counter electrode. Hibiscus (Hibiscus rosa-sinensis) and Onion (Allium cepa) peel extracts were utilized as sensitizers and Iodolyte HI-30 as the electrolyte. The most efficient device attained an efficiency of 3.5 % with Voc = 0.81 V and Jsc = 6.57 mA/cm2. This marks the highest efficiency reported using Hibiscus as a sensitizer with the current configuration, accompanied by prolonged device stability. This study showcases the potential of Citrus medica-derived nitrogen-doped CDs in achieving durable device stability. 2024 Elsevier B.V. -
Enhancing the stability of electrochemical asymmetric supercapacitor by incorporating thiophene-pyrrole copolymer with nickel sulfide/nickel hydroxide composite
The practical application of a supercapacitor predominantly relies on its sustained cyclic stability. Hence it is essential to develop materials with high stability for the efficient supercapacitor applications. Herein, we demonstrate the integration of a copolymer of poly thiophene-pyrrole (cPPyTh) to surpass the limited cyclic stability of the nickel sulfide/nickel hydroxide (NSH) composite. Though the lower electronegativity of sulfur in coexistence with hydroxide achieves a superior capacity for NSH, it lacks extended cyclic stability. By incorporating cPPyTh into the layers of NSH, the stability of the resultant composite (NCP) could be enhanced by preventing the aggregation of layered NSH during longer runs. NCP electrode provides a specific capacity of 87 C/g at a current density of 1 A/g in a three-electrode system. An energy density of 25.47 Wh/kg and power density of 8.65 kW/kg is obtained for the asymmetric supercapacitor fabricated with NCP as positive and modified activated carbon (MAC) as negative electrode. The NCP demonstrates a superior cyclic stability of over 94% for 10,000 cycles in comparison to NSH with stability ? 73% over 5,000 cycles for the asymmetric supercapacitor. 2021 -
Enhancing Transparency and Trust in Agrifood Supply Chains through Novel Blockchain-based Architecture
At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demandsupply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains. Copyright 2024 KSII. -
Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0
Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models. 2024 -
Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake Using Remote Sensing Data
The summer season in India is marked by a severe shortage of water, which poses significant challenges for daily usage and agricultural practices. With unpredictable weather patterns and irregular rainfall, it is crucial to monitor and maintain water bodies such as domestic ponds and lakes in urban areas to ensure they provide clean and safe water for regular use, free from industrial pollutants. In this research paper, we propose an innovative ensemble deep learning approach (e-DLA) that leverages deep learning models to predict the turbidity of Dooskal Lake, located in Telangana, India, using remote sensing data. The proposed approach utilizes various deep learning models, including bagging, boosting, and stacking, to analyze the complex relationships between remote sensing data and turbidity levels in the lake. The study aims to provide accurate and efficient predictions of turbidity levels, which can aid in the management and conservation of water resources in the region. Hyperparameter tuning is employed, and dynamic climatic features are extracted and integrated with the ensemble learning global protective intelligent algorithm to reveal the complex relationship between in situ and measured values of turbidity during the measuring timeline. The proposed approach provides accurate predictions of turbidity levels, enabling the implementation of effective control measures to maintain water quality standards. Experimental results demonstrate that the proposed approach significantly reduces prediction errors compared to existing deep learning models. Overall, this research highlights the potential of machine learning techniques in monitoring and maintaining water resources, particularly in urban areas, to support sustainable water management and usage, and addresses an urgent and pressing issue in India and around the world. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Enterococcus species and their probiotic potential: Current status and future prospects
Probiotics are described as live microbes that, once consumed in sufficient quantities, provide a health advantage to the host. A rising number of research works have verified the health benefits of probiotics. Enterococci are common bacteria that may be found almost anywhere. For their opportunistic pathogenicity, Enterococci have been associated with numerous nosocomial infections resulting from resistance to antibiotics and the existence of other virulence factors, notably the development of vancomycin-resistant Enterococci. However, some Enterococcal strains such as E. faecium and E. faecalis strains are being utilized as probiotics and are widely marketed, usually in the form of pharmaceutical solutions. Enterococcus spp. based probiotics are used to treat irritable bowel syndrome, infectious diarrhea, and antibiotic-associated diarrhea, along with decreasing cholesterol levels and enhancing host immunity. To be used as probiotics in the future, Enterococcal strains must be properly defined and thoroughly evaluated in terms of safety and can be beneficial. Here, in this work, we have reviewed various aspects of Enterococcus spp. pertaining to its possibility of being utilized as a probiotic strain. 2023 Krishna, et al. -
Enterpreneurial orientation and the management grid: A roadmap for the enterpreneurial journey /
Asian Journal Of Management, Vol.7, Issue 4, ISSN: 0976-495X (Print), 2321-5763 (Online). -
Entomotoxic proteins of Beauveria bassiana Bals. (Vuil.) and their virulence against two cotton insect pests
Entomopathogenic fungi are widely used as biocontrol agents against several agricultural pests. Among them, Beauveria bassiana is considered the important one against insect and other arthropod pests. The entomotoxic proteins of B. bassiana were extracted by Sephadex G-25 column, and fractionated using HPLC (BBI, BBII and BBIII) and tested against two hemipteran insect pests i.e., Dysdercus cingulatus Fab. and Phenacoccus solenopsis Tinsely (Hemiptera: Pseudococcidae). Results indicated that protein content was higher in fraction BBII than BBI and BBIII. The vibration frequency in FT-IR obtained with a range of 1650 to 1580 cm?1. Bioassays of fractions (I, II and III) reveal that BBII was highly virulent against third nymphal instar of D. cingulatus (LC50 = 800.2 ppm) and adults of P. solenopsis adult (LC50 = 713.3 ppm). Considering the high virulence of BBII subjected to SDS-PAGE, HPLC and MALDI-TOF analyses. Analyses reveals the presence of 174 kDa and designated as BBF2. These results concluded that the entomotoxic protein of B. bassiana can be utilized for management of these investigated hemipreran pests. Further investigations are necessary for the field application of this entomotoxin against these pests or other insect pests. These results also could be helpful for establishing novel biotechnological uses for this fungus. 2021 The Authors -
Entrepreneurial Attitude and Entrepreneurial Intentions of Female Engineering Students: Mediating Roles of Passion and Creativity
Entrepreneurship holds a crucial function in addressing societal and economic issues like joblessness and inequalities between different regions. Acknowledging its significance, government officials and educational institutions exert considerable energy towards nurturing individuals into entrepreneurs. Multiple elements influence a person's path to becoming an entrepreneur. This research seeks to examine how one's entrepreneurial attitude (EA) impacts one's drive to become an entrepreneur, with passion and creativity serving as an intermediary in this connection. The research is explanatory and employs a survey-based approach. The findings convey that entrepreneurial attitude significantly influences the determination of female engineering students to pursue entrepreneurship. The study highlights the mediating roles of passion and creativity in the relationship between entrepreneurial attitude and intentions. While passion positively mediated the relationship, creativity had a negative mediating effect. 2024, Institute of Economic Sciences. All rights reserved. -
Entrepreneurial challenges of transgender entrepreneurs in India
Social exclusion has impeded transgender individuals to enter mainstream society and curbing them to start a business venture. Sporadic transgender individuals have paved their way to start the business venture. This study aims to explore the entrepreneurial challenges faced by transgender entrepreneurs. Twenty transgender entrepreneurs who have relinquished begging and commercial sex work were interviewed. The grounded theory analysis has revealed six significant categories: financial resources, competitors, human resources, marketing issues, natural calamities, and transphobia. The participants expressed that transphobia, and financial resources were highly challenging to start a business venture. These findings extend our understanding of their challenges beyond the current knowledge of cisgender entrepreneurs. Finally, the limitation of the study is enunciated. Copyright 2025 Inderscience Enterprises Ltd. -
Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image
Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients. 2022, International Journal of Computing. All Rights Reserved. -
Entropy generation analysis of magneto-nanoliquids embedded with aluminium and titanium alloy nanoparticles in microchannel with partial slips and convective conditions
Purpose: Outstanding features such as superior electrical conductivity and thermal conductivity of alloy nanoparticles with working fluids make them ideal materials to be used as coolants in microelectromechanical systems (MEMSs). This paper aims to investigate the effects of different alloy nanoparticles such as AA7075 and Ti6Al4V on microchannel flow of magneto-nanoliquids with partial slip and convective boundary conditions. Flow features are explored with the effects of magnetism and nanoparticle shape. Heat transport of fluid includes radiative heat, internal heat source/sink, viscous and Joule heating phenomena. Design/methodology/approach: Suitable dimensionless variables are used to reduce dimensional governing equations into dimensionless ordinary differential equations. The relevant dimensionless ordinary differential systems are computed numerically by using RungeKuttaFehlberg-based shooting approach. Pertinent results of velocity, temperature, entropy number and Bejan number for assorted values of physical parameters are comprehensively discussed. Also, a closed-form solution is obtained for momentum equation for a particular case. Analytical results agree perfectly with numerical results. Findings: It is established that the entropy production can be improved with radiative heat, Joule heating, convective heating and viscous dissipation aspects. The entropy production is higher in the case of Ti6Al4V-H2O nanofluid than AA7075-H2O. Further, the inequality Ns(?)Sphere > Ns(?)Hexahedran > Ns(?)Tetrahydran > Ns(?)Column > Ns(?)Lamina holds true. Originality/value: Effects of aluminium and titanium alloy nanoparticles in microchannel flows by using viscous dissipation and Joule heating are investigated for the first time. Flow features are explored with the effects of magnetism and nanoparticle shape. The results for different alloy nanoparticles such as AA7075 and Ti6Al4V have been compared. 2019, Emerald Publishing Limited. -
Entropy generation analysis of radiative heat transfer in Williamson fluid flowing in a microchannel with nonlinear mixed convection and Joule heating
In this article, the spectral quasi-linearization (SQLM) method is implemented to solve the complicated differential equations governing the nonlinear mixed convective heat transfer of a Williamson fluid through a vertical microchannel. Unlike the conventional Boussinesq approximation, the quadratic Boussinesq approximation is taken into account in the formulation. The effects of Rosseland thermal radiation, Joule heating, and viscous dissipation are described in the thermal analysis subjected to the boundary conditions of convective thermal heating. The analysis of entropy production is also performed. The importance of various parameters governing velocity, Bejan number, temperature, and entropy generation was explored using graphic illustrations. It was found that the nonlinear density change with a temperature significantly affects the heat transport in the microchannel and thus increases the magnitude of the Bejan number and the production of entropy. Entropy production occurs maximum due to the boundary conditions of convection heating at the walls of the microchannel. Furthermore, due to a stronger viscous heating mechanism, the magnitude of the Bejan number is reduced, while the production of entropy increases significantly. As a limiting case of the problem, a comparison was made with results previously published in the literature and excellent agreement was established. The calculations provide a solid reference point for future CFD models and are relevant to the dynamics of polymers in microfluidic devices and the polymer industries. IMechE 2022. -
Entropy generation analysis of radiative Williamson fluid flow in an inclined microchannel with multiple slip and convective heating boundary effects
The main theme of the current work is to investigate the flow and heat transport characteristics of non-Newtonian Williamson fluid in an inclined micro-channel along with entropy generation analysis. The significance of the thermal radiation, convective boundary condition, and multiple slip effects is explored. The entropy generation of the system has been analyzed by adopting the 2nd law of thermodynamics. The rheological expressions of the Williamson fluid model are also taken into account. The nonlinear system is tackled by using the finite element method. An appropriate comparison has been made with previously published results in the literature as a limiting case of the considered problem. The comparison confirmed an excellent agreement. Detailed discussion of the significance of effective parameters on Bejan number, entropy generation rate, temperature and velocity is presented through graphs. The numerical results portray that the entropy generation and Bejan number have escalating behavior to the higher value of angle of inclination. Furthermore, the Bejan number changing its behavior at two points for different values of Reynolds number. IMechE 2021.