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Enhancing the Strength of Geopolymer Composites Synthesized from Iron Ore Tailings and Fly Ash for Use as Subgrade Material in Pavement Construction
As the idea of sustainable pavement becomes more important, an increasing number of industrial waste products and recycled materials are being used in the pavement industry to conserve natural resources. This study evaluates the potential use of iron ore tailings (IOT) blended with fly ash (FA) and activated with NaOHNa?SiO? solutions as a liquid alkaline activator (L) to synthesise IOT-FA geopolymers, which can be used as a sustainable material for the pavement subgrade layer. The influence of FA replacement levels and alkaline activator ratios on the geotechnical and microstructural behaviour of IOT was examined through compaction, unconfined compressive strength (UCS), and California bearing ratio (CBR) tests, supported by Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) analyses. The results show that 20% FA replacement gives the optimal mix, resulting in a 28-day UCS improvement of over four times that of untreated IOT due to enhanced geopolymerization. The liquid alkaline-activated mixes achieved CBR values of ? 8, meeting IRC requirements for subgrade applications. SEM analysis revealed dense gel formation and improved particle bonding, while XRD results indicate the development of geopolymeric reaction products. TCLP results indicate that metal leaching remained within permissible limits, establishing the environmental safety of the developed composite. The thickness of the pavement layer was designed using IITPAVE software based on CBR values and assessed against IRC:372018 criteria. The analysis indicated a reduction in layer thickness for various daily commercial vehicle counts ie, CVPD (450,1000), across all evaluated combinations. Overall, the study demonstrates that alkaline-activated IOTFA mixtures offer a technically viable and sustainable alternative for pavement subgrade construction. Chinese Society of Pavement Engineering 2026. -
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 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 Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach
Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the Natural Language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition. 2024 IEEE. -
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 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 Operational Efficiency of FPOs by Using Predictive Model Building in Machine Learning Through Feature Selection Method
This study employs a machine learningbased feature selection approach to identify key factors that significantly enhance the predictive accuracy of models assessing farmer producer organization (FPO) performance. It focused on an FPO in the Anantapur and Kadapa districts of Andhra Pradesh, India; the research investigated the operational effectiveness of small and marginal farmers who are members by the end of 2022. The study reviewed existing shortcomings and proposed necessary policy amendments to ensure the sustainability of FPOs. The study utilized a survey design method, with the exploratory questionnaire through the field study. A sample of 73 small-scale producer farmers from the region was selected for the analysis. The result indicated that a) the reason for the operational efficiency of FPOs, b) the gradient boosting regressor achieved the highest testing accuracy of 0.78 to predict the best feature for FPO performance, and c) FPO insights for policy guidelines. 2025 Scrivener Publishing LLC. -
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 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 electrochemical performance of rGO-based ternary composite for next generation supercapacitors
This work explores the rational design and synthesis of a high-performance ternary nanocomposite rGO/CeO2/PPy, by incorporating cerium oxide and polypyrrole into the rGO matrix, through a hybrid approach of combining hydrothermal synthesis with in situ oxidative polymerization. Comprehensive structural characterization of the rGO/CeO2/PPy composite confirms the successful integration of components, revealing a hierarchically porous architecture that optimizes both charge transport and ion diffusion kinetics. The ternary composite exhibits exceptional interfacial interactions, including ?-? conjugation between rGO and PPy, coupled with electrostatic stabilization from CeO2, resulting in enhanced mechanical integrity and improved electrolyte accessibility. Electrochemical characterization reveals remarkable performance metrics, with a specific capacitance of 874 F g?1 and outstanding cyclic durability of 94% capacity retention after 5000 charge-discharge cycles at 1 A g?1. The configured rGO/CeO2/PPy//AC system exhibits exceptional energy storage performance, yielding an energy density of 39.6 Wh kg?1 while sustaining a power density of 2859 W kg?1. These outstanding characteristics underscore the material's suitability as a cutting-edge electrode for sophisticated energy storage systems, showcasing the benefits of strategic component integration in hybrid nanocomposite design. 2025 The Royal Society of Chemistry. -
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 digital consumer experience: The role of artificial intelligence
In the era of rapid technological advancement, the integration of artificial intelligence (AI) and digital consumerism has created new trends in business. Consumers are able to communicate with brands in new ways owing to developments in digital media. AI-powered recommendation systems, chatbots and virtual assistants are the main drivers of this change. It allows businesses to offer product recommendations, customer support, and personalized content to increase user engagement and satisfaction. This chapter provides real-world examples of AI applications across industries, highlighting success stories of companies using AI to create better value and customer satisfaction. Finally, the integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. 2024, IGI Global. All rights reserved. -
Enhancing the confidentiality of text embedding using image steganography in spatial domain
Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components. 2021 American Institute of Physics Inc.. All rights reserved. -
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 Teacher-Student Engagement: The Role of Intellectual Humility
The book chapter explores the significant role of intellectual humility in cultivating strong teacher-student engagement within the landscape of education. It proposes that teachers modelling intellectual humility by admitting their mistakes and uncertainties signal students to take intellectual risks by asking questions or expressing their perspectives. Furthermore, the chapter also highlights that intellectually humble students are more open towards diverse viewpoints, are eager to learn from new information and expand their cognitive capacity, which are pivotal for active participation. Lastly, the chapter suggests various strategies for fostering intellectual humility in both teachers and students as well as for enhancing the advancements in the educational environments. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network
Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The models performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future. 2023 by the authors. -
Enhancing student engagement in blended learning through personalization strategies on EdTech platforms: An MCDM-based approach
The rapid growth of educational technology highlights the essential need for personalized learning in blended environments. This study uses Multi-Criteria Decision Making (MCDM) methodologies, including the Analytic Hierarchy Process (AHP), Fuzzy AHP, and Analytic Network Process (ANP), to evaluate and prioritize personalization strategies in EdTech platforms. The research identifies data-driven adaptive learning as the most critical strategy (36.46%), followed by AI-powered content recommendations (15.46%) and personalized learning paths (15.11%). It reveals that personalization strategies are interconnected, creating dynamic feedback loops that reinforce one another, enabling continuous learning optimization. The study provides a holistic framework for educational technologists, policymakers, and designers. This approach bridges technological innovation with pedagogy, emphasizing adaptive, datainformed systems that respond dynamically to learner needs, ensuring the balance between innovation and educational quality. 2025, IGI Global Scientific Publishing. -
Enhancing student engagement and learning experience through augmented reality: A study on the integration of assemblr studio into hybrid classrooms
The impact of technology, initiated during the fourth industrial revolution with concepts like AI, AR, and VR, has significantly influenced education. Integrating Augmented Reality (AR) in education enhances student engagement and learning. AR enables students to visualize complex theories through real actions, making learning interactive and fun. This paper explores student experiences with Assemblr Studio, a 3D AR visualization tool, in hybrid learning at Christ University, Bangalore. The study examines Assemblr Studio's educational impact, user experience, and classroom effectiveness using questionnaires and focus group discussions. Results show that AR via Assemblr Studio fosters innovative and effective learning, positively altering traditional educational strategies. 2025, IGI Global Scientific Publishing. All rights reserved. -
Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE. -
Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis
This paper proposes a multi-model strategy that would improve the predictive power of stock prices by combining time-series analytics with external market indicators. The system allows five different base prediction methods; Long Short-Term Memory (LSTM), Enhanced Bidirectional LSTM (XLSTM), Support Vector Machine (SVM) which may use radial basis function (rbf), linear or polynomial (poly) kernels, Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average (SARIMA). A stacking procedure which uses linear regression as a meta-model together with a voting ensemble method is then employed to link these base models. The feature engineering is thorough, as it provides for general price and volume data, a battery of technical indicators (SMA10, SMA20, EMA 12, EMA 26, MACD elements, and RSI14) and a general sentiment indicator (summarised financial news). Sentiment analysis is performed by a pipeline that is trained using RoBERTa and yields discrete numerical values (0 negative, 1 neutral, 2 positive). The model's capability is very rigorously gauged by the conventional metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). The real-world results demonstrate that the ensemble method is very efficient where the stacking arrangement leads to the lowest total MAPE of 0.6027 % MSFT and the highest directional Accuracy of 75.86 % GOOGL, thus, providing a strong evidence for the effectiveness of the thorough integration of heterogeneous machine-learning, statistical, and sentiment- analysis methods to produce the most accurate financial forecasts. 2026 IEEE.