International Journal of Scientific Research in Science and Technology
International Journal of Scientific Research in Science and Technology (IJSRST) is peer-reviewed, online international journal published Bimonthly by Technoscience Academy. IJSRST is a highly-selective journal, covering topics that appeal to a broad readership in various branches of Science and Technology and related fields. The journal has many benefits, all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRST Articles section have been subjected to a rigorous review process. This includes blind reviews by one or more members of the international editorial review board, followed by a detailed review by the IJSRST editors. Email addresses for submitting articles are editorijsrst@gmail.com, editor@ijsrst.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Science and Technology Frequency : Bimonthly Online ISSN : 2395-602X | Print ISSN : 2395-6011 Publisher : Technoscience Academy [ https://technoscienceacademy.com ] Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2014 Subject : Science and Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRST ) Email Id : editor@ijsrst.com, editorijsrst@gmail.com Mobile No. : +91-8160745806 (WhatsApp support only) Website : https://ijsrst.com
Causal ML in Education: Enhancing Teacher pedagogical Strategies and Decision-Making
The present paper examines shifts in preservice students understandings about teaching and learning because of developing of a student. This review paper explores how causal ML techniques can support teachers in evaluating and refining pedagogical interventions using observational educational data. Pedagogical decision made by teacher educators construct curriculum development. This explores the application of Causal ML in education, highlighting its potential to enhance teacher decision-making, personalize instruction, and optimize resource allocation. Research indicates that ideas prioritized by teacher educators are presented more explicitly within the curriculum. This report explores how teacher educators shape curriculum through pedagogical decisions, influenced by their purposes and contexts, as evidenced by collaborative self-studies in physical education teacher education. Furthermore, it proposes an educational artificial intelligence framework that integrates neural collaborative filtering with causal reasoning. causal inference foundations, modern machine learning–based causal estimators, and recent applications in educational data mining. It also discusses the role of counterfactual explanations in making AI-driven recommendations interpretable and actionable for teachers. By utilizing a hybrid architecture of generalized matrix factorization and multi-layer perceptron, alongside double machine learning for quantifying individual treatment effects, the framework achieves counterfactual decision optimization. The integration of these technologies enhances long-term learning efficiency while maintaining ethical regulation. Experimental validation demonstrates that this framework significantly enhances the explainability and long-term learning efficiency of teaching decisions while maintaining large-scale exhortation efficiency, providing a solution robust in both technology and ethical regulation. The main goal is to build teacher-centered, fair, and trustworthy causal decision- pedagogical support systems that empower educators to make evidence-based decisions that are technically robust and philosophically aligned with core pedagogical values. This paper reviews the foundations of causal inference and recent machine learning–based causal models used in educational data mining to analyze observational data from learning management systems, online assessments, and intelligent tutoring platforms.
Copyright (c) 2026 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
A Review of Artificial Intelligence Techniques for Cotton Leaf Disease Identification
Cotton is one of the most important cash crops worldwide, and its productivity is severely affected by leaf diseases that reduce yield and fiber quality. Traditional disease identification methods rely on expert knowledge and manual inspection, which are time-consuming, subjective, and often impractical for large-scale agricultural monitoring. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled automated, accurate, and scalable cotton leaf disease identification using image-based analysis. This review comprehensively analyzes state-of-the-art AI techniques employed for cotton leaf disease detection and classification. It covers conventional image processing approaches, handcrafted feature-based machine learning models, and modern deep learning architecture such as convolutional neural networks, transformers, ensemble learning, and explainable AI frameworks. Additionally, the role of publicly available datasets, data augmentation, lightweight models, and resource-efficient architectures is discussed. By synthesizing findings from recent literature, this review highlights key research trends, performance improvements, and practical limitations of existing approaches. The paper also identifies critical challenges and future research directions to support the development of robust, interpretable, and deployable AI-based systems for precision cotton agriculture.
A Comprehensive Review on Accurate Mesothelioma Classification Using Medical Imaging and AI Techniques
Mesothelioma is a rare and aggressive malignancy primarily associated with asbestos exposure, and its early diagnosis remains challenging due to subtle visual differences and overlapping clinical features. Medical imaging modalities, particularly computed tomography (CT), play a vital role in identifying pleural thickening, effusions, and tumor spread, while histopathological images provide cellular-level evidence essential for definitive diagnosis. In recent years, artificial intelligence (AI) techniques have demonstrated significant potential in enhancing the accuracy and consistency of mesothelioma classification using both CT and histopathological data. This comprehensive review analyzes existing research on mesothelioma classification employing medical imaging and AI-based approaches. It covers conventional image processing methods, machine learning techniques, and advanced deep learning models applied to CT scans and histopathological images. The review highlights the importance of feature extraction, multimodal data fusion, and robust preprocessing strategies for improving diagnostic performance. Additionally, the role of explainable AI is discussed to address clinical interpretability and trust in automated systems. Comparative performance analysis, commonly used datasets, and evaluation metrics are summarized to identify current trends, limitations, and research gaps. Finally, challenges such as data scarcity, class imbalance, and real-world clinical deployment are discussed, providing directions for future research toward reliable AI-assisted mesothelioma diagnosis.
Ultrasonic and Acoustic Investigation of Binary Mixtures of Ethyl Benzoate and 2 Methyl 1 Propanol at Different Temperatures
Ultrasonic velocity (U), density (ρ) and viscosity (η) of the binary liquid mixture consisting of ethyl benzoate (X₁) and 2 methyl 1 propanol (X₂) were measured over the entire mole fraction range at temperatures 303.15, 308.15, 313.15 and 318.15 K. From the experimental data, several acoustical and thermodynamic parameters such as molar volume (V), adiabatic compressibility (β_ad), intermolecular free length (L_f), internal pressure (π), acoustic impedance (Z) and enthalpy (H) were evaluated. The variation of these parameters with composition and temperature has been used to understand the nature and strength of molecular interactions in the mixture. The results indicate the presence of specific interactions, mainly hydrogen bonding and dipole–dipole interactions, between unlike molecules.
Phishing URL Detection Using Machine Learning
Phishing is a common cyber-attack where attackers create fake websites to trick users into revealing sensitive information such as passwords, credit card numbers, and personal details. Detecting phishing URLs is important for protecting users and reducing the risk of identity theft and fraud. This project focuses on the use of machine learning techniques to help identify phishing URLs by analysing different characteristics of the URLs. The aim is to understand how these methods can learn from data and recognize patterns that distinguish phishing URLs from legitimate ones. The study involves gathering datasets of URLs, extracting features that describe their structure and content, and building models that can classify URLs accordingly. This research does not assume any particular machine learning approach but explores various possibilities to find effective solutions. The goal is to investigate how machine learning can contribute to automated and efficient phishing detection, which could be useful in improving online security tools such as browsers and email filters.
A Panoramic View on Neurodegeneration, Its Connection with Microbiota and Its Therapeutics
Neurodegenerative diseases are becoming one of the leading causes of death globally and silently affecting the society. The major cause of neurodegeneration is accumulation of free radicals and reactive oxygen species that can induces cellular events of mitochondrial damage, activation of other necrotic cascades that results into neuronal death. One of the underlying causes of this accumulation is aging and reduced antioxidants in the body as well as the novel finding suggests that imbalance in Gut Brain Axis (GBA) is the major cause. Microbial gut flora plays an important role in balancing these free radicals as well as neural homeostasis. Due to aging and other causes this axis is damaged resulting into failure in repair mechanism against the neuronal death. Current therapy for neurodegeneration fails to treat the condition and only managing the symptoms because the therapy is intended for symptomatic cure only. Recent advances in therapy of neurodegeneration involves the repairing of system from root cause, by the means of gene therapy, immunomodulation, anti-oxidants therapy, balancing gut microbiota which can balance the Gut Brain Axis and thus can helpful to control several conditions, use of novel drug delivery system to improve the effectiveness of available treatments like nano-gel, nano-suspension, hydrogels, etc.
Role of Activated Charcoal and Polyvinylpyrrolidone (PVP) in Enhancing Callus Quality in Recalcitrant Plants
Recalcitrance in plant tissue culture remains a major constraint to efficient callus induction, regeneration, and downstream biotechnological applications, particularly in phenolic-rich and woody plant species. Excessive phenolic exudation, oxidative stress, and accumulation of inhibitory metabolites often compromise callus quality, leading to tissue browning, necrosis, and poor regenerative competence. This study evaluated the roles of activated charcoal and polyvinylpyrrolidone as media additives for improving callus quality in recalcitrant plant species. Using a factorial experimental design, the individual and combined effects of these additives on callus induction, growth, morphology, and physiological stability were systematically assessed. Activated charcoal enhanced callus induction frequency and biomass accumulation by adsorbing inhibitory metabolites and stabilizing the culture medium during prolonged culture. Polyvinylpyrrolidone effectively mitigated oxidative stress during early culture stages, reducing phenolic oxidation and improving callus color, friability, and viability. Notably, the combined application of activated charcoal and polyvinylpyrrolidone produced synergistic effects, resulting in the highest callus quality, lowest browning indices, and improved survival across subculture cycles. Enhanced callus quality translated into improved regeneration potential, with positive implications for somatic embryogenesis, organogenesis, and genetic transformation efficiency. The findings demonstrate that integrated use of adsorptive and antioxidant additives offers a robust strategy for overcoming in vitro recalcitrance and provide a practical framework for optimizing tissue culture protocols for difficult-to-culture plant species.
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Synthesis and Structural Characterization of 3-thio-4-acetyl- α-D-glucosyl-5-arylimino-1,2,4-dithiazolidines
The synthesis of numerous 3-Thio-4-acetyl–α-D-glucosyl-5-arylimino-1,2,4-dithiazolidine (III) was achieved by the reaction of N-Sugar-S-chloro isothiocarbamoyl chloride (I) with a variety of Ammonium aryl dithiocarbamate (II). Using conventional chemical transformations, infrared spectroscopy, nuclear magnetic resonance with 1H, and mass spectral analysis, the identities of these newly discovered N-glucosides have been determined. These synthesized molecules applied as a anti-bacterial and antifungal agents which gives excellent results as an anti-microbial agents.
Isolation and Characterization of a Heavy Metal Resistant Bacterial Strains for Biological Treatment of Lead and Zinc Contaminated Water
Heavy metal pollution has emerged as a critical global environmental issue due to its persistence, toxicity, and ability to bio accumulate in living organisms. Industrial activities, mining, waste discharge, agricultural inputs, and atmospheric deposition contribute significantly to the rising concentrations of metals such as cadmium, lead, copper, chromium, and nickel in soil, water, and air. Elevated levels of these metals disrupt essential biological processes in microorganisms, plants, aquatic organisms, and humans. In the present study five different bacteria were isolated from heavy metal polluted soil sample. The isolates were subjected for morphological and biochemical characterization. The bacterial isolates were screened for studying their resistance towards selected heavy metals by agar diffusion test. The heavy metals used for the study were Mercuric chloride, Lead, Cadmium, Zinc, and Copper at the concentrating of 50mMand 100mM. The strain SNGCV1 showed resistance against the tested heavy metals Lead and Zinc. The bacterial strain SNGC V1 was used for the treatment of metal polluted water sample. The organism was able to reduce the concentration of the metals lead and Zinc in the water sample. The organism may be used as key agents in sustainable remediation of heavy metals.
Catalytical and Antibacterial Potentials of Bio-Mediated Silver Nanoparticles Synthesized From Seeds Extract of Corymbiaficifolia
Metal nanoparticles have gained remarkable attention due to their numerous applications in various fields ranging from catalysis to medicine. The present study reports synthesis of silver nanoparticles (AgNPs) using aqueous seeds extract of Corymbiaficifolia. The nanoparticles were characterized by UV-visible and FTIR, SEM-EDX and XRD. The UV-visible spectral study reveals that characteristics peak observed at 370 nm is due to excitation of the surface plasmon resonance (SPR). In FTIR spectrum the peaks identified at 1028 and 1286 cm−1 correspond to the presence C-O functional group, and peaks 1687 and 1834 cm-1 correspond to the presence C=O, whereas peaks appear at 1172 and 2920 cm−1 presence of C-N and C-H function groups of the phytochemical presence in extract which may act as capping and reducing agent for formation of nanoparticles. Additionally, peaks appeared at 530 and 453 cm−1 indicating the presence Ag−O binding in AgNPs. SEM image shows that the spherical and nano nature of the particles and EDX spectrum shows existence of elemental silver and other signals corresponding to C, N, O, and Cl suggested that the phyto constituents of the extract were bound to the surface of AgNPs. The XRD study shows that the crystalline nature of AgNPs. The catalytic role of AgNPs for reduction of 4-nitrophenol (4-NP) by NaBH4 was investigated. The reduction process follows a pseudo first-order kinetic model. The solvent effect study on 4-NP reduction demonstrated that solvent polarity play an important role on reduction. The antibacterial activity of AgNPs on Escherichia coli, a Gram-negative bacterium was also investigated. The formation of inhibition zone of the cell growth of Escherichia coli was found to occur in 18 h corresponding to AgNPs concentration of 50 µg.mL-1 i.e considered as the minimum inhibitory concentration (MIC).
Catalytic activity towards 4-NP reduction
Presumptive Ulcerative Disease in Channa striata: A Comprehensive Case Report from a Cement Tank Culture System
Ulcerative diseases are widespread in tropical freshwater aquaculture and represent one of the most economically devastating health problems affecting a wide range of species. Channa striata, despite being hardy and highly valued, is frequently susceptible to a range of dermal diseases under stress. This study presents a detailed and comprehensive case documentation of a presumptive ulcerative outbreak in C. striata cultured in an outdoor cement tank for a two month feeding experiment involving dried blood worms and formulated commercial pellets. Over the study period, progressive clinical signs including surfacing behaviour, lethargy, anorexia, mucous loss, epidermal peeling, haemorrhagic ulceration, necrotic patches, and caudal fin detachment were recorded. Mortality reached 30%. Although diagnostic confirmation was not performed, lesion morphology strongly aligned with previously documented forms of Epizootic Ulcerative Syndrome (EUS) and secondary bacterial infections. The paper integrates nutritional, environmental, and pathological interpretations to offer a holistic understanding of disease progression under small scale cement tank culture. This report underscores the importance of preventive health management, nutritional balance, and water quality regulation to prevent ulcer outbreaks.
Synthesis of New Cobalt complexes of ((2-chloroquinolin-3-yl)methylene)-3-methoxyaniline
A novel and efficient synthetic strategy was developed for the preparation of cobalt complexes derived from the Schiff base ligand ((2-chloroquinolin-3-yl)methylene)-3-methoxyaniline. The ligand was synthesized via a condensation reaction between equimolar amounts of 2-chloroquinolin-3-carbaldehyde and 3-methoxyaniline under mild conditions, affording the desired product in quantitative yield. The resulting Schiff base was subsequently reacted with cobalt(II) salts to form stable coordination complexes. The formation and purity of the synthesized ligand and its cobalt complexes were confirmed through various spectroscopic techniques. Proton nuclear magnetic resonance (¹H NMR) spectroscopy provided insights into the chemical environment of the hydrogen atoms, supporting the formation of the imine (–CH=N–) linkage. Infrared (IR) spectroscopy revealed characteristic vibrational bands corresponding to the imine group and metal-ligand coordination, further verifying complexation. Mass spectrometry was employed to determine the molecular weight and confirm the molecular structure of both the ligand and the cobalt complexes. These results collectively confirm the successful synthesis and characterization of the cobalt complexes, highlighting the method's efficiency, reproducibility, and potential applicability in coordination chemistry and related fields.
Fe (III) Metal Complex Synthesis, Characterization and Biological Study Derived From (E)-4-Hydroxy-N'-(1-(4-Hydroxy-1-Methyl-2-Oxo-1, 2-Dihydroquinolin-3yl) Ethylidene) Benzohydrazide
The present study emphasizes heterocyclic hydrazone Schiff base ligand of 3-acetyl-4-hydroxy-1- methyl-2(1H)-quinolone (AHQ) and its Fe(III) metal complex. The metal complex synthesized using Schiff base hydrazone synthesized from 4-hydroxybenzhydrazide and 3-acetyl-4-hydroxy-1- methyl-2(1H)-quinolone. Hydrazone complex were subjected to elemental analysis, UV visible, magnetic susceptibility, FTIR,1H NMR spectra, mass spectra, thermal analysis, and antimicrobial activity. IR spectra indicate that free ligand exist in the hydrazo-ketone form in solid states. The ligand behaves as a neutral tridentate with coordination involving the carbonyl of 2(1H) quinolone, hydrazone nitrogen and keto oxygen of amide group that is ONO donor sites. The magnetic and spectral data indicate octahedral geometry for complex. The molar conductivity values indicating non electrolytic nature. The mass spectra and analytical data the stiochitometry of metal complex has been found to be the 2:1 ligand to metal ratio. Ea, ΔS, ΔH and ΔG parameters were evaluated for thermal decomposition stages of complex using Horowitz-Metzer and Coats-Redferm methods. The ligand and their metal chelets have been screened for their antibacterial activity using the Agar cup method at fixed concentration of 1% against microbial strains, E.coli, S. typhi, S. aureus, B. subtilis. The antibacterial screening of ligand and Fe(III) complex illustrates that ligand and Fe(III) complex seems to be inert towards the gram positive bacteria E. coli and S. typhi The complex show higher activity than ligand against gram negative bacteria S. aureus and B. subtilis. Antifungal activity of ligand and complex were tested in vitro against fungi that is A. niger, P. chrysogenum, f. moniliforme, A. flavus by poison plate method using potato dextrose agar medium at fixed (1%) concentration. Ligand does not show fungal activity but Fe(III) complex show better activity. It suggests that the incorporation of metal ion in the ligand results in increasing the antimicrobial activity.
Synthesis, Characterization, and Resolution of Enantiomeric Tris (Ethylenediamine) Cobalt (III) Chloride
This study is aims to synthesis, characterization, and resolution of enantiomeric tris (Ethylenediamine) Cobalt (III) Chloride by precipitation method. The obtained product is an optically active, water-soluble, yellow-orange, or dark-red to brown crystalline solid. It features a stable octahedral cation with specific optical rotation +90o and is commonly used in studies of chiral coordination chemistry.
Tris-ethylenediamine cobalt (III) chloride
Scalable and Secure Infrastructure-as-Code Governance through Multi-Tenant Terraform Cloud Architectures
As organizations expand cloud adoption across multiple teams, environments, and regulatory domains, Infrastructure-as-Code (IaC) implementations increasingly encounter limitations related to scalability, access control, and governance. Traditional monolithic IaC models struggle to enforce isolation, ensure auditability, and support parallel infrastructure operations at enterprise scale. This article presents a multi-tenant Terraform Cloud architecture designed to address these challenges through workspace isolation, role-based access control, and policy-driven automation. The proposed architecture introduces structured separation of infrastructure state, automated governance enforcement, and integrated CI/CD workflows to enable secure and scalable DevOps operations. By combining hierarchical workspace design, least-privilege identity management, and automated compliance evidence generation, the system supports infrastructure provisioning across multiple cloud accounts while maintaining centralized oversight. Empirical evaluation across enterprise environments demonstrates a 47% reduction in average deployment time, a 62% decrease in infrastructure-related errors, and a 73% reduction in compliance audit preparation effort compared to traditional IaC implementations. Additional improvements include faster security incident response and measurable infrastructure cost optimization. These results indicate that multi-tenant IaC governance architectures can significantly enhance operational efficiency, security posture, and regulatory readiness in large-scale cloud environments.
Contribution of Condensation to Dampness on External Surfaces of Walls : A Case Study of Borno and Yobe States in North Eastern Nigeria
Series of efforts were made from various quotas over the decades to curtail dampness on external surfaces of walls and yet the menace has continued unabated, particularly in Nigeria. This is because ‘underground moisture’ from rain and other sources of water is erroneously believed to be the only cause of the menace. This study provides a lasting solution to the problem by acknowledging ‘persistent condensation’ as another cause of dampness on external surfaces of walls. It investigates where the problem lies in the context of ‘persistent condensation’: Is it with the type of coating used on external surfaces of walls or with construction professionals? Structured questionnaires were used in sourcing field data and Chi-Square was the statistical tool utilised in analysis. Results in the study indicate that, the use of tex-cote coating and tiles can alleviate dampness on external surfaces of walls. Further analysis however, has exonerated construction professionals from possessing insufficient knowledge on the general causes of dampness in buildings, but it failed to spare them from possessing sufficient knowledge on the mechanism of ‘persistent condensation’. Recommendations include among others, the general use of tex-cote coating and tiles on surfaces prone to condensation and keeping construction professionals abreast on the sources and causes of condensation on external surfaces of walls by organizing periodic workshops and seminars.
Boko Haram Insurgency in Borno State, Nigeria: Assessment of Destroyed Public Buildings and Appraisal of Causes of Escalation
Armed conflict is one of the major challenges of today and in most instances, the construction sector is the second most affected area after human lives. Nonetheless, economic impact of losses incurred in the sector are always mirrored in the perspective of fixing back the wreckages in their original forms, rather than fixing back their improved and more secured versions. After all, the need for every community, particularly in Nigeria to be accorded the privilege of modern and secured public facilities cannot be overemphasized. In fulfillment of this need therefore, the study enumerated all public buildings destroyed during Boko Haram Insurgency in Borno state, redesigned and evaluated their improved and more secured versions for reconstruction. Additionally, to curtail the magnitude of destruction of public buildings, the reasons behind the bizarre escalation of the insurgency were investigated and remedies established. Structured and self-administered questionnaires were used for data collection and Chi Square is the statistical tool utilised. The study revealed that, public buildings destroyed during Boko Haram insurgency in Borno State require the sum of N3, 614,653,400,000.00 to be replaced with their improved and more secured versions. Identified factors responsible for escalation of Boko Haram Insurgency are Security Forces and Boko Haram Group itself, with no significant difference in their levels of contribution to escalation of the insurgency. It is therefore concluded that the security forces only succeeded in worsening Boko Haram Insurgency. Hence, complete overhaul of the Nigerian security apparatus on civil disorder is recommended among others.
A Comprehensive Analysis of Forest Canopy Cover Dynamics Using MODIS Imagery and Advanced Geospatial Techniques
This study presents a comprehensive analysis of forest canopy cover dynamics using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. We developed an integrated pipeline for preprocessing, computing Normalized Difference Vegetation Index (NDVI), classifying Vegetation Canopy Cover (VCC), and analysing temporal trends. The methodology includes atmospheric correction, reprojection to WGS84, NDVI computation, VCC classification, and statistical trend analysis. Results demonstrate significant changes in forest cover over the study period, with detailed statistical insights and visualizations of spatial patterns. The findings provide valuable information for environmental monitoring and policymaking, highlighting areas of forest gain and loss through difference maps and time-series analyses.
Nonlinear Soil Moisture Retrieval from Sentinel-1 SAR Using Ensemble Machine Learning
Accurate soil moisture estimation plays a vital role in applications such as agricultural management, hydrological modeling, and climate analysis. Synthetic Aperture Radar (SAR) observations provide a reliable source of information for soil moisture retrieval due to their high spatial resolution and capability to operate independently of weather and day conditions. This study investigates the performance of two gradient boosting based machine learning algorithms, Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), for soil moisture prediction using SAR-derived and auxiliary environmental features. Sentinel-1 SAR imagery is employed as the primary data source, while reference soil moisture measurements are obtained from the NASA Soil Moisture Active Passive (SMAP) mission, together with auxiliary variables derived using Google Earth Engine. Feature engineering and regularization strategies are applied to enhance model robustness and reduce overfitting. Model performance is evaluated using the Pearson correlation coefficient (r), Root Mean Square Error (RMSE), and coefficient of determination (R²). The results indicate that both models achieve reliable predictive accuracy; however, XGBoost exhibits stronger fitting capability during training, whereas CatBoost demonstrates improved generalization performance, reduced bias, and enhanced robustness on unseen data, highlighting its suitability for operational SAR-based soil moisture estimation.
A Comparative Study of Processing Speed by Gender in Learning Disabled and Non-Learning Disabled Student Populations
Processing speed is a core cognitive function that refers to the efficiency and rate at which individuals perceive, understand, and respond to information. Learning disability is a neurodevelopmental condition characterized by persistent difficulties in acquiring and using academic skills despite adequate intelligence and educational opportunities. The present study aimed to compare processing speed by gender among learning disabled (LD) and non-learning disabled (NLD) student populations. The study was conducted among school-going children aged 8–12 years from Kerala, India. A total sample of 300 students was selected using purposive sampling, comprising 150 students with learning disabilities (75 males and 75 females) and 150 students without learning disabilities (75 males and 75 females). Processing speed was assessed using the Processing Speed Index subtests—Coding and Symbol Search—of the Wechsler Intelligence Scale for Children (WISC). The collected data were analyzed using Statistical Package for the Social Sciences (SPSS) version 28. The hypothesis stated that there would be a significant gender difference in processing speed among children with and without learning disabilities. Appropriate inferential statistical tests were employed to examine group and gender differences. The results revealed a statistically significant gender difference in processing speed across both LD and NLD groups, thereby supporting the stated hypothesis. The findings highlight the role of gender in processing speed performance and underscore the importance of considering cognitive processing differences in educational assessment and intervention planning for children with learning disabilities.
Gender Differences in Achievement Motivation: A Comparative Analysis across Learning Disabled and Non-Learning Disabled Student Populations
Achievement motivation refers to an individual’s inner drive to strive for success, attain excellence, and perform tasks to a standard of quality. Learning disability (LD) is a neurodevelopmental condition characterized by persistent difficulties in acquiring and using academic skills such as reading, writing, or mathematics, despite adequate intelligence and educational opportunities. The present study aimed to examine gender variation in achievement motivation among children with and without learning disabilities. The study was conducted among 300 school-going children aged 8–12 years from Kerala, comprising students with learning disabilities and those without learning disabilities. Each group consisted of 75 males and 75 females, selected using a purposive sampling method. Achievement motivation was assessed using Ray’s Achievement Motivation Scale. The data were statistically analyzed using SPSS (Version 28). To test the stated hypothesis, that is, there will be a significant gender difference in achievement motivation among children with learning disabilities and there will be a significant gender difference in achievement motivation among children without learning disabilities, an independent samples t-test was employed. The results revealed that there is a significant gender difference in achievement motivation among children without learning disability and there is no significant difference in achievement motivation among children with learning disabilities, thereby rejecting the first hypothesis and supporting the second hypothesis of the study. The findings highlight that the impact of gender on achievement motivation is not universal but is significantly altered by the presence of a learning disability. The study demonstrates that while gender-based motivational gaps are prominent in children without disabilities, they are non-existent among those with learning disabilities. Consequently, educational strategies must move beyond ‘one size fits all’ models to prioritize inclusive, status specific interventions that address the unique motivational profiles of both groups.
Role of Academic Procrastination and Academic Intelligence in Shaping Student Behaviour and Cognitive Performance
The present study investigates among secondary school students. Academic procrastination, understood as the deliberate postponement of academic tasks despite awareness of negative consequences (Steel, 2007), has increasingly been recognised as a behavioural impediment to effective learning and emotional self-regulation among adolescents. Research suggests that chronic procrastination is closely associated with behavioural difficulties, poor time management, academic stress, avoidance tendencies, and reduced classroom engagement (Sirois, Melia-Gordon & Pychyl, 2013). In contrast, academic intelligence, conceptualized as students’ cognitive capacity to process information, reason, problem-solve, and apply knowledge (Gottfredson, 1997), plays a central role in shaping cognitive performance and academic success. Studies indicate that intelligence contributes to cognitive efficiency, decision-making, and adaptive behavioural outcomes; however, it interacts significantly with motivational, emotional, and behavioural variables (Duckworth & Seligman, 2005; Chamorro-Premuzic & Furnham, 2014). This study aims to explore how procrastination behaviours and academic intelligence jointly influence patterns of student behaviour and cognitive performance. A descriptive correlational research design was employed to examine the relationship between procrastination, cognitive ability, and behavioural outcomes. Findings reveal that higher levels of procrastination are associated with maladaptive learning behaviours, decreased academic persistence, and lower levels of cognitive performance, whereas higher academic intelligence corresponds to improved cognitive functioning and more constructive classroom behaviour. However, the results also highlight that intelligence alone does not guarantee positive academic or behavioural outcomes without adequate regulation of procrastinatory tendencies. The implications of the study emphasize the need for educational interventions that integrate cognitive development strategies with behavioural self-regulation practices to enhance academic well-being and performance. Strengthening self-management skills, structured learning routines, and cognitive training frameworks may significantly improve both behavioural responses and cognitive outcomes among students.
Impact of Self-Regulated Learning and Academic Intelligence on Student Behaviour and Cognitive Development
The study investigates the interconnected roles of metacognitive strategies and intellectual capacities in shaping student outcomes. In contemporary educational contexts, learning is no longer perceived as a passive reception of knowledge but as an active, self-directed process where students take ownership of their academic journey. Self-regulated learning, defined as the ability to plan, monitor, and evaluate one’s own learning activities, fosters autonomy, persistence, and adaptability, enabling students to overcome challenges through strategic effort. Academic intelligence, encompassing cognitive abilities such as reasoning, memory, comprehension, and problem-solving, provides the foundational capacities that support learning. The integration of these constructs demonstrates that intelligence alone does not guarantee academic success; rather, it is the effective regulation of intellectual resources through self-monitoring and self-discipline that leads to meaningful educational achievement. This study highlights that the impact of self-regulated learning and academic intelligence extends beyond academic performance into the domain of behaviour and personality development. Students who demonstrate strong self-regulatory capacities tend to cultivate constructive behavioural patterns such as responsibility, resilience, and collaborative engagement, while also avoiding maladaptive tendencies like procrastination and academic disengagement. Similarly, the cognitive development associated with academic intelligence—enhanced critical thinking, metacognitive awareness, and analytical reasoning—enables learners to internalize knowledge more effectively and apply it across contexts. The study underscores that the synergy between self-regulated learning and intelligence produces a holistic form of growth that integrates academic achievement with behavioural maturity.
Review on Design and Implementation of Solar Arduino Uno Based System for Microgrids
The increasing need for sustainable and decentralized power generation has accelerated the development of microgrid technologies capable of integrating renewable resources. This study presents the design and implementation of a solar energy–based microgrid monitoring and control system using an Arduino Uno platform. The proposed system utilizes photovoltaic (PV) modules as the primary power source and incorporates sensors for real-time measurement of voltage, current, temperature, and battery state-of-charge (SOC). An MPPT-enabled charge controller ensures efficient energy harvesting, while the Arduino Uno performs data acquisition, energy management, and load-switching operations. The system communicates key operational parameters through an LCD interface and optional IoT connectivity for remote supervision. Prototype results demonstrate stable power delivery, improved energy utilization, and effective load management for small-scale microgrid environments. This cost-effective and modular architecture provides a scalable solution for rural electrification, smart energy distribution, and renewable-integrated microgrid applications, highlighting its potential for wider deployment in sustainable energy systems
Ultrasonic Velocity Measurements and Adiabatic Compressibility Analysis in Ethanol, Propan-2-ol, and Diethylene Glycol: A Comparative Study Using Interferometric Technique
Background: The ultrasonic interferometry gives a precise non-destructive method for investigating molecular interactions and thermodynamic properties of liquids through acoustic parameter measurements. Objective: Our study aimed to determine ultrasonic velocity and adiabatic compressibility in three industrially and scientifically important liquid 1. Ethanol, 2. Propan-2-ol (isopropanol), 3. Diethylene glycol with a high-frequency ultrasonic interferometer operating at 2 MHz. Methods: As our measurements conducted at room temperature (34-37°C) using a Mittal-type ultrasonic interferometer with a measuring cell of 0.01 mm least count. We have taken multiple readings for each liquid to ensure statistical reliability. We have calculated ultrasonic velocity from wavelength measurements, and also adiabatic compressibility was derived using the Laplace-Newton equation. Results: The mean ultrasonic velocities obtained were: ethanol (1125.12 ± 2.71 m/s), propan-2-ol (1111.58 ± 9.11 m/s), and diethylene glycol (1584.79 ± 31.30 m/s). Corresponding adiabatic compressibilities were: ethanol (1.0012 ± 0.0048 × 10⁻⁹ m²/N), propan-2-ol (1.0298 ± 0.0169 × 10⁻⁹ m²/N), and diethylene glycol (0.3564 ± 0.0139 × 10⁻⁹ m²/N). Conclusions: As diethylene glycol exhibited significantly higher ultrasonic velocity and lower compressibility compared to the alcohols, attributed to stronger intermolecular hydrogen bonding and higher molecular weight. While the inverse relationship between ultrasonic velocity and adiabatic compressibility was confirmed. So these findings contribute to understanding molecular interactions in organic liquids and have implications for industrial applications in fuel quality assessment, pharmaceutical formulations, and material characterization.
A Review of EEG-Based Epileptic Seizure Detection Techniques Using Signal Processing, Neural Networks and Optimization Algorithms
Epileptic seizure detection has become an increasingly important research area due to the rising number of epilepsy cases and the need for reliable, early diagnosis. Electroencephalography (EEG) remains the most widely used tool for identifying epileptic activity because of its non-invasive nature and ability to capture rapid changes in brain electrical signals. Over the years, numerous computational approaches—ranging from classical signal-processing techniques to advanced deep-learning and optimization-driven models—have been developed to improve automatic seizure-detection accuracy. This review presents a comprehensive analysis of 40 peer-reviewed studies published between 2015 and 2024 focusing on EEG-based seizure detection. The examined methods include time- and frequency-domain signal processing, classical machine-learning classifiers, deep-learning architectures such as CNNs, LSTMs, and Transformers, graph neural networks for connectivity modeling, and hybrid approaches enhanced through evolutionary optimization algorithms. The analysis highlights their strengths, limitations, and performance trends across different datasets. The findings indicate that while deep-learning models offer significant improvements in accuracy and automated feature extraction, they still face challenges such as the need for large labeled datasets, high computational requirements, and limited interpretability. Traditional signal-processing and machine-learning methods provide interpretability but lack generalization and scalability. Graph-based models show promise in capturing spatial relationships between EEG channels but require further refinement for real-time use. The review identifies major research gaps, including poor cross-dataset generalization, limited robustness to noise, absence of explainable AI frameworks, lack of multimodal data fusion, and insufficient clinical validation. Based on these gaps, several future research directions are proposed, including lightweight real-time models, patient-specific adaptive systems, multimodal integration, explainable AI, dynamic graph modeling, and cloud/IoT-enabled monitoring platforms. Overall, this review consolidates current trends, challenges, and emerging opportunities in EEG-based seizure detection, supporting the development of next-generation systems that are accurate, reliable, interpretable, and clinically deployable.
Roadmap for Forensic Tool Development for Orphaned File Fragment Carving
Digital forensic experts encounter a sequence of raw bytes (a fragment of a file) in the absence of both the remaining fragments of the file and file system metadata. A tool must be developed to interpret these orphaned fragments and to reveal any evidence of crime, if such evidence exists. In this paper, we present a roadmap for developing such a challenging tool. The tool development requires a group of computer science researchers and software developers. This paper aims to provide guidance to the team responsible for developing this tool. Two major problems are addressed by authors of this paper in the direction of developing orphaned file fragment carver tool. They are (a) Development of a tool for creating an emulating storage media for use in research lab environment to mimic suspected storage media in forensic labs. (b) Development of an orphaned file fragment carver for an uncompressed image data type. In this paper, we will present the outcomes of these tools and roadmap on how to extend the tools for creating a tool of orphaned file fragment carver as an end product which can be used in digital forensic labs.
Analytic Technique for Determining Fundamental Solutions in Static Thermoelasticity
Static thermoelasticity, with an internal heat source involves analyzing the equilibrium state of a solid body in which both mechanical deformations and temperature distributions are influenced by an internal heat generation mechanism. The governing equations couple the static (time-independent) balance of momentum with the steady-state heat conduction equation, incorporating the effects of thermal expansion and source terms. This paper presents a rigorous derivation and analytical discussion of fundamental solutions for a line heat source in static coupled thermoelastic systems. The problem formulation considers an isotropic and homogeneous thermoelastic solid having an internal line heat source. Using Green’s function techniques and potential theory, the study formulates fundamental solutions. The general representation of displacements, stress tensors, and temperature fields is obtained from the governing partial differential equations. The findings offer new insights into heat–stress coupling for advanced materials, including semiconductors, porous composites, and micro-structured solids, where localized heating and steady deformation coexist. The study concludes that accurate fundamental representation offers predictive efficiency for coupled-field simulations across theoretical and engineering domains.
Impact of School Teachers Inclusive Teaching Practices on the Learning Outcomes of Children with Special Needs: Lecture note approach
Inclusive education plays an important role in today school system. Teachers support children with special needs in regular classrooms. This paper studies the impact of inclusive teaching practices on learning outcomes of children with special needs. Teachers use strategies like differentiated instruction peer learning and supportive classroom environment. These practices help improve academic performance social skills and confidence of CWSN. The study shows that effective inclusive practices lead to positive learning outcomes.
Children with special needs Learning outcomes
Potentiostatically Deposited Iron Oxide Thin Films for Supercapacitive Study: Effect of Precursors
In this work we report electodeposition of iron oxide thin films using potentiostatic mode. The iron oxide films were electrodeposited with three different precursors like Ferrous Sulphate, Ferrous Nitrate and Ferric Chloride. Flexible SS strips are deposited in a 30 ml 0.1 M aqueous solutions of FeSO4, FeCl3, Fe(NO3)3 that were used as precursors solutions, which were set up in an ultra-pure double-distilled water bath. Electrodes so obtained from sulphate, chloride and nitrate precursors are abbreviated as FeS, FeC and FeN, respectively. The mixed phases of iron oxides are observed from X-ray diffraction study. These electrodes are studied for Supercapacitive study. The electrochemical data obtained through cyclic voltametry (CV) curves for different iron oxide samples are studied at a scan rate of 2 mV/SCE within a potential window of −1.6 to 0.2 V/SCE in a 1M KOH aqueous electrolyte. The maximum values of supercapacitance at 2 mV/s scan rate are found to be 398 F/g, 331 F/g and 144 F/g for (a) FeC, (b) FeN and (c) FeS, respectively.