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Determinants of Procrastination among young adults in their academics and professional lives
Procrastination is one of the most pervasive issues which exist in contemporary times among students and professionals. This research aims to understand the modern determinants of procrastination, including factors such as Fear of Missing out (FOMO), Social Zapping, and Sensation Seeking with Impulsivity as a mediator. Limited research has talked about these variables connection with one another. The study was conducted by collecting data from 294 young adults using convenience and snowball sampling with scales of the five variables in question. A mediation analysis was performed which concluded that FOMO has a significant effect on procrastination. Additionally, Social Zapping and FOMO showed a significant relationship with impulsivity. This suggests that FOMO is the key factor that leads to students and professionals choosing to procrastinate their academic/work-related activities in favour of other alternatives such as social or recreational activities. 2023 RJ4All. -
Improvement to Recommendation system using Hybrid techniques
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE. -
Fabrication of cobalt oxide@cellulose/nitrogen doped carbon nanotubes decorated metal organic frameworks composite for symmetric supercapacitor applications
The two main issues facing the world's population now are energy storage needs and environmental protection. A lot of work has gone into creating electrochemical energy storage using chemical processes and a variety of possible electrode active materials. Supercapacitors, which are energy storage devices with a unique structure and morphology of cellulose materials for green energy resource. In this regard, solid state hydrothermal process is used to fabricate Co3O4@Cellulose (CE), Co3O4@CE/N-MWCNT, and Co3O4@CE/N-MWCNT/ZIF-67 composite materials. XRD, XPS, BET, and HR-TEM analyses verified the structural, surface, and morphological analysis. The electrochemical studies by a three- and two-electrode fabrication in presence of 1M KOH electrolyte for supercapacitor applications. When 1M KOH electrolyte is present, the fabricated Co3O4@CE/N-MWCNT/ZIF-67composite electrode displayed exceptional cyclic stability and a specific capacitance of ?835 F g?1 at 1 A/g. The constructed composite electrodes of Co3O4, Co3O4@CE, and Co3O4@CE/N-MWCNT have specific capacitances of 263, 406, and 576 F g?1 at 1 A/g, respectively, which improves electrochemical properties using a three-electrode design. The Co3O4@CE-N-MWCNT/ZIF-67//1MKOH/SSC composite is produced using two electrode configurations. The final material showed a capacitance of 258 F g?1 at 1 A/g, a capacitance retention of 84.95 % across 8000 cycles, and an energy density of 30.99 W h kg?1 at a power density of 5409 W kg?1. Hence, the composite electrodes that have been produced have the potential to be used in electrochemical systems. 2025 Elsevier B.V. -
Improving Signal Coverage in Millimeter-Wave Massive MIMO via Efficient Predefined-Time Adaptive Neural NetworkBased Beam Training
This paper proposes an advanced deep learning framework for efficient beam training in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To overcome the limitations of conventional beam training approaches such as high overhead, slow adaptation to dynamic environments, and poor scalability, an Improving Signal Coverage in Millimeter Wave Massive MIMO via Efficient Predefined Time Adaptive Neural Network based Beam Training (ISC-MMIMO-EPTANN-BT) model is proposed. The proposed model used deep neural network (DNN) to learn complicated nonlinearities in channel power leakage (CPL) and used an efficient predefined time adaptive neural network (EPTANN) to provide real-time responsiveness and temporal synchronism in beam training. The parameters of the model are also optimized using fire hawk optimization algorithm (FHOA) to get better convergence speed and signal coverage. The proposed technique is executed in MATLAB. The proposed approach attains better performance under successful rate by significantly less beam training overhead and also increases signal coverage based on simulation results. The proposed ISC-MMIMO-EPTANN-BT method attains 26.15%, 21.08%, and 33.75% higher successful rates and 16.32%, 28.94%, and 20.24% lower normalized mean square error compared with existing methods such as deep learning for beam training in millimeter wave massive MIMO schemes (BT-MMIMO-DNN), deep learning for combined feedback and channel prediction in large-scale MIMO systems (CNN-JCS-MMIMO), and triple-refined hybrid-field beam training in mmWave extremely large-scale MIMO (TR-FBT-MIMO), respectively. The ISC-MMIMO-EPTANN-BT technique reduced beam training overhead, enhanced signal coverage, and identified a promising candidate for successful beam training in mmWave massive MIMO schemes. 2025 John Wiley & Sons Ltd. -
A highly effective curcumin analogue as naked eye colorimetric and fluorescent sensor for sensitive and selective detection of Hg2+ ions and its application on test strips and real sample analysis
A thiophene appended curcumin-based colorimetric and fluorescent receptor (TAA) for selective recognition of Hg2+ ions was synthesized and characterized using 1H NMR, 13C NMR and LC-MS spectroscopic techniques. TAA facilitates detection of Hg2+ by a naked-eye color change from yellow to colorless in visible light, and fluorescence turn-off in UV light (365 nm). The observed fluorescence quenching is due to the chelation-enhanced fluorescence quenching (CHEQ). TAA exhibited excellent selectivity and sensitivity toward Hg2+ ions, even in the presence of competing cations. The binding constant (Ka) for Hg2+ ions was found to be 3.4 105 M?1, indicating a strong binding affinity. The binding mechanism was elucidated using DFT calculations and supported by LC-MS and FT-IR studies. TAA forms a 1 : 1 complex with Hg2+ ions, as confirmed by Job's plot analysis. Additionally, the colorimetric limit of detection was found to be 0.67 ?M, while the fluorometric limit of detection was found to be 0.24 ?M, which demonstrates the high sensitivity of TAA towards Hg2+. Furthermore, TAA probe exhibited successful detection of Hg2+ ions in real water samples. Also, it can serve as an effective on-site detection tool for mercury ions by a simple test strip method that requires no additional instrumentation. 2025 The Royal Society of Chemistry. -
The Desire to Escape: A Reflection on the Neocolonial Bearings on Trinidadian Literary Landscape
Under waning colonial power, the second half of twentieth century witnessed mass-migration from the Caribbean to the metropolitan centre of Britain. This generation was called the windrush generation, and included prominent writers and authors from the island nation of Trinidad like V.S Naipaul, Samuel Selvon, among others. The article explores the neocolonial factors that drove these literary migrations and its subsequent influence on the literary contributions of these writers. It discusses, in brief, the idiosyncrasies of their circumstances in comparison to that of home-grown authors like Earl Lovelace. The paper concludes with insights into the contemporary status of this development and its implications. This paper has drawn information from primary sources (fictions of the above-mentioned writers) and secondary sources including author interviews, critical essays, newspaper articles, blogs, among others, to validate the study. 2024 selection and editorial matter, Dr. L. Santhosh Kumar, Ms. Minu A., Dr. Barnashree Khasnobis, Dr. Preetha M. and Dr. Merrin R. S.; individual chapters, the contributors. -
Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization
Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for English as a second language (ESL). The following research steps include simplifying system complexity while adding multimodal learning signals including eye monitoring and speech recognition and further developing the model across various educational subject areas. The research works as a promising foundation which propels AI real-time adaptive education systems for students from various backgrounds. (2025), (Science and Information Organization). All Rights Reserved. -
Relationship Between Family Environment, Objectified Body Consciousness and Appearance Self-Esteem Among Urban Indian Young Adults
Extant research has shown that objectification, especially sexual objectification, can encourage the internalization of others perspective on their own bodies and thereby transforming their own self into object of continuous assessment and judgement. Using the objectification theory and theories of identity formation, the present research examines how family environment (FE) and objectified body consciousness (OBC) may have a relation with appearance self esteem (ASE) among urban Indian young adults. Based on previous literature, it was hypothesized that OBC and FE would have a significant association with ASE. To examine the hypotheses, a survey was conducted on young adults (N = 141) of age range from 18 to 25years. Regression analysis was carried out using statistical tools. Multiple-linear regression showed that the model was found to account for a statistically significant amount of variance in ASE. The results point out how OBC has a negative relationship with ASE. This implies that the level to which one objectifies themselves negatively relates to how they value their appearance or looks. The present research discusses the implication of understanding the different factors which may be associated with low appearance-related self-esteem. The research also explains the findings in line with cultural underpinnings. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Improving Financial Audits and Management of Compliance using Artificial Intelligence and Secure Cloud Technology
Modern financial ecosystem requires highly complex audit trails and more stringent compliance issues therefore require highly advanced secure and intelligent systems. This research outlines a hybrid framework which juxtaposes Artificial Intelligence (AI) and Secure Cloud Technology to improve financial audit process and establish strong compliance management. Taking advantage of the strengths of AI, the strengths in question including Natural Language Processing (NLP), anomaly detection and machine learning classifiers, this system is used to enhance data accuracy, and detect irregularities in real time and automate regulatory reporting. At the same time implementation of Zero Trust Cloud Architectures, along with homomorphic encryption, provides data integrity, privacy, and end to end security. The proposed methodology is centred around the integration of intelligent document processing and blockchain-verified logs in the federated learning framework - where both transparency and decentralization are fostered. In addition, predictive analytics are used for the prediction of possible risks and non-compliance incidents to facilitate proactive decision making. Extensive simulations are used to reveal enhanced performance relative to traditional systems, with increased accuracy of anomaly detections, audit traceability, and validation speed-up of compliance. This integration is not only focused on streamlining audit workflows, but can also cut on operational cost and human error as well. The results emphasize the importance of employing AI-enabled secure cloud infrastructures as a primary strategy for financial institutions in a growing regulated digital economy while trying to sustain compliance. The new system achieves a 96.2% rate of accuracy while auditing and only consumes 91.3% the time in compliance to encourage efficiency. 2025 IEEE. -
Fabrication of cobalt oxide@cellulose/nitrogen doped carbon nanotubes decorated metal organic frameworks composite for symmetric supercapacitor applications
The two main issues facing the world's population now are energy storage needs and environmental protection. A lot of work has gone into creating electrochemical energy storage using chemical processes and a variety of possible electrode active materials. Supercapacitors, which are energy storage devices with a unique structure and morphology of cellulose materials for green energy resource. In this regard, solid state hydrothermal process is used to fabricate Co3O4@Cellulose (CE), Co3O4@CE/N-MWCNT, and Co3O4@CE/N-MWCNT/ZIF-67 composite materials. XRD, XPS, BET, and HR-TEM analyses verified the structural, surface, and morphological analysis. The electrochemical studies by a three- and two-electrode fabrication in presence of 1M KOH electrolyte for supercapacitor applications. When 1M KOH electrolyte is present, the fabricated Co3O4@CE/N-MWCNT/ZIF-67composite electrode displayed exceptional cyclic stability and a specific capacitance of ?835 F g?1 at 1 A/g. The constructed composite electrodes of Co3O4, Co3O4@CE, and Co3O4@CE/N-MWCNT have specific capacitances of 263, 406, and 576 F g?1 at 1 A/g, respectively, which improves electrochemical properties using a three-electrode design. The Co3O4@CE-N-MWCNT/ZIF-67//1MKOH/SSC composite is produced using two electrode configurations. The final material showed a capacitance of 258 F g?1 at 1 A/g, a capacitance retention of 84.95 % across 8000 cycles, and an energy density of 30.99 W h kg?1 at a power density of 5409 W kg?1. Hence, the composite electrodes that have been produced have the potential to be used in electrochemical systems. 2025 Elsevier B.V. -
Nanoscale synthesis of nickel oxide@carboxy methyl cellulose@nitrogen doped carbon nanotubes supported metal organic frameworks ternary composite for use symmetric supercapacitor
Metal-organic frameworks (MOFs) are a novel class of porous materials that combine organic linkers and inorganic metal ions. Supercapacitors use a large specific surface area, adjustable architecture, and tunable porosity and pore diameters to improve the electrochemical performances with metal sulfides. The main goal of this study was to make a nickel oxide ternary composite using a hydrothermal method with urea as a catalyst for electrochemical uses. We characterized these fabricated composite materials using analytical and morphological characterization for their confirmation. These results show that the composite electrode had a great specific capacitance of 464 F/g at 0.5 A/g in a 1 M KOH electrolyte when set up with three electrodes. The symmetric two-electrode system showed 52.83 F/g at 0.5 A/g with an excellent energy density of 13.14 Whkg?1 and a power density of 616 Wkg?1 via 1 M KOH electrolyte. The fabricated ternary composite electrode demonstrated cyclic stability, with an excellent retention rate of 89 % after 7000 cycles. Therefore, the fabricated ternary composite electrode materials have enormous potential for electrochemical storage properties. 2025 Elsevier B.V. -
Effect of the Process Parameters on Machining of GFRP Composites for Different Conditions of Abrasive Water Suspension Jet Machining
The selection of parameters for abrasive water suspension jet (AWSJ) machining of GFRP composites is a major aspect to be considered for optimizing the process. Generally, machining of plastics, polymer matrix composites are accomplished by the AWSJ machining carried out in the presence of atmospheric air; however, the existence of air around the AWSJ may lead to expansion of jet which results in increase in the kerf width and surface roughness; thus to overcome this drawback, an effort has been made in the current work to compare the effect of different process parameters on kerf width and surface roughness while using AWSJ techniques for machining glass fibre-reinforced plastic composite submerged in water. The exploratory outcomes have herewith validated the fact that the surface roughness and kerf width diminishes in under water machining when contrasted with that of free air machining; this is majorly attributed to the fact that the jet diameter reduces in under water AWSJ machining, thereby reducing the kerf width and surface roughness for optimized values of the parameters of speed, feed and standoff distance. Further, the experimental trials have clearly shown that the AWSJ machining used with an optimized set of parameters yields better machining capabilities as compared to abrasive water jet machining. 2019, King Fahd University of Petroleum & Minerals. -
Corrosion Characterization of Friction Stir Weld Dissimilar Aluminium Alloy Joints
The course of contact mix welding is quick acquiring conspicuousness in aviation, marine and car industry because of its benefits as far as mechanical strength, effect and hardness characteristics. There is as yet a requirement for sure fire consideration from the exploration local area to erosion in grating mix welding zones, hence the work introduced here centres around the consumption portrayal of the grinding mix weld divergent aluminium composite. This study looks into friction stir welding under various parametric settings and shows how corrosion happens in a sodium chloride electrolytic media under potentio-dynamic conditions. The friction stir weld joints of dissimilar alloys aluminium are constructed using three sets of parameters. Straight cylinder, taper cylinder, and straight triangular tool profiles; tool rotational speeds of 800, 1000, and 1200 rpm; tool feed rates of 100, 120, and 140 mm/min; and tool offsets of 0.5, 0 mm, and-1.5 mm. The corrosion current (Icorr) reduces as tool rotating speed increases up to 1200 rpm, after which it slightly increases due to the creation of ridges all around the periphery of the friction stir weld area. 2022, Books and Journals Private Ltd.. All rights reserved. -
Friction Stir Welding of Dissimilar Aluminium Alloys for Vehicle Structures
Welding process in vehicle structures has gained importance, especially for better strength and mechanical properties. Hence, there is vast research going on in the domain of newer welding techniques. Friction Stir Welding (FSW) is one of them. FSW is used in this research to join two different grades of aluminium alloys by varying the process parameters. The process parameters are optimized based on the Design of Experiments (DoE) and the Taguchi techniques. From the experimental findings for different process parameters, the optimized set of conditions involving the normal, transverse forces and the torque are determined. Further, the process methodology is validated. 2022, MechAero Found. for Techn. Res. and Educ. Excellence. All rights reserved. -
Influence of heat treatment on the tensile and hardness characteristics of friction stir weld joints of dissimilar aluminium alloys
Friction stir welding (FSW) is a solid-state low energy input welding technique. Most capable of joining very high strength alloys, which are finding wide range of applications in automobile and aerospace components. The current research focuses on the influence of post weld heat treatment on mechanical properties of friction stir weld joints of AA 7075 and AA 5052 dissimilar aluminum alloys. The trial experiments have been carried out using design of experiments (L16 Orthogonal Array) and the optimized process parameters have been selected based on the maximum hardness and the corresponding ultimate tensile strength (UTS). Further, the friction stir welding is accomplished with optimized process parameters (L9 Experimental trial) viz., the feed rate of 100?mm/min, tool rotational speed of 1200?rpm, tool offset of (-) 0.5?mm and using a cylindrical taper pin tool profile. The post heat treatment has been carried out on the friction stir weld joints obtained using the optimized parameters and the mechanical properties of the L9 Heat Treated (L9 - HT) and L9 - Non Heat Treated (L9 - NHT) specimens have been compared. The results shows that the post heat treated weld joints have higher micro hardness and tensile strength compared to the non-heat-treated weld joints. This is majorly attributed to recrystallization and elimination of voids due to the change in the microstructure of the weld joint. 2022 Author(s). -
Design and optimization of the process parameters for friction stir welding of dissimilar aluminium alloys
Friction Stir Welding (FSW) is one of the unique solid state welding technique that is fast gaining importance because of its ability to produce strong joints. The friction stir welding technique is effectively used in this research to join 5 mm thick dissimilar aluminium alloys of AA 7075-O and AA 5052-O grade. The effect of tool pin profile and tool rotational speed on the mechanical properties like micro-hardness and tensile strength are studied by the optimized Design of Experiments (DOE). The experiments are designed based on L16 orthogonal array considering TAGUCHI techniques for four design parameters and four parametric levels. The outcomes of experimental techniques are tabulated and TAGUCHI analysis, Analysis of Variance (ANOVA) are carried out in Minitab software. From the experimental results and statistical techniques, the methodology is validated and the outcomes of the experiments are found to be in close agreement with the statistical results with the error less than 5% of the mean difference value. The optimized process parameters for better micro hardness are as follows: tool rotational speed of 1200 rpm, feed of 120 mm/min, tool offset of 1 mm, and cylindrical tapered pin tool profile; while the optimized design of process parameters for better tensile strength are as follows: tool rotational speed of 1400 rpm, feed of 120 mm/min, tool offset of 1 mm and cylindrical tapered pin profile. The design and optimization of the process parameters for friction stir welding of dissimilar aluminium alloys is necessary for high strength weld joints. 2021, Paulus Editora. All rights reserved. -
Optimization of Friction Stir Welding Parameters Using Taguchi Method for Aerospace Applications
The current research work investigated the optimization of the input parameters for the friction stir welding of AA3103 and AA7075 aluminum alloys for its applications in aerospace components. Friction stir welding is rapidly growing welding process which is being widely used in aerospace industries due to the added advantage of strong strengths without any residual stresses and minimal weld defects, in addition to its flexibility with respect to the position and direction of welding. Thus, the demand for this type of welding is very high; however, the welding of aluminum alloys is a key aspect for its use in aircraft components, particularly with respect to bracket mounting frames, braces and wing components. Henceforth in the current work, research is focused on optimization of welding of aluminum alloys, viz. AA 3103 and AA 7075; AA 3103 is a non-heat treatable alloy which is having good weldability, while AA 7075 is having higher strength. Therefore, the welding of these aluminum alloys will produce superior mechanical properties. The optimization of input parameters was accomplished in this work based on L9 orthogonal array designed in accordance with Taguchi methodusing which the friction stir welding experiment was conducted. There were nine experimental runs in total after formulating the L9 orthogonal array table in Minitab software. The input parameters which were selected for optimization weretool rotation speed, feed rate, tool pin profile. The output parameters which were optimized were hardness, tensile strength and impact strength. In addition, the microstructure of the fractured surfaces of the friction stir welded joint was analyzed. It was found from the optimization of the process parameters that strong friction stir welded joints for aerospace applications can be produced at an optimized set of parameters of tool rotational speed of 1100rpm, traverse speed of 15mm/min with a FSW tool of triangular pin profile of H13 tool steel material. 2020, Springer Nature Singapore Pte Ltd. -
ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites
This study investigates the effectiveness of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. The focus was on analyzing key process parametersabrasive size, feed rate, and standoff distance (SOD)under submerged cutting conditions and their impact on material removal rate (MRR), kerf width, and surface roughness. Experimental trials were conducted, and advanced computational techniques, including Response Surface Methodology (RSM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were used for parameter optimization and predictive analysis. The results showed that submerged cutting significantly improved machining quality by reducing surface roughness and ensuring uniform kerf widths. Increasing the jet diameter in underwater conditions stabilized the nozzle, leading to smoother and more precise cuts. Among the predictive models, XGBoost demonstrated the highest accuracy and efficiency in forecasting MRR, while Random Forest and ANN provided competitive performance. The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. These findings are particularly relevant for industries like aerospace and automotive, where machining efficiency and precision are crucial. The Author(s) 2025. -
Opinion mining on newspaper headlines using SVM and NLP
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.