Tuijin Jishu/Journal of Propulsion Technology
Tuijin Jishu, which translates to "Propulsion Technology" in English, is a prestigious and cutting-edge academic journal dedicated to advancing the field of propulsion engineering and technology. Our mission is to serve as a platform for researchers, engineers, and enthusiasts alike to share their knowledge, discoveries, and innovations in the propulsion domain. At Tuijin Jishu, we believe that propulsion technology is at the forefront of modern scientific and engineering achievements, driving humanity forward in space exploration, aviation, transportation, and beyond. As such, we are committed to promoting the dissemination of high-quality, peer-reviewed research that pushes the boundaries of propulsion systems, fuels, and related technologies. Tuijin Jishu is a peer-reviewed open-access journal, ensuring that all published articles undergo rigorous scrutiny by expert reviewers in the field. We strive to maintain the highest standards of integrity and scientific accuracy, upholding the trust and credibility of our readers and contributors. Our journal covers a broad spectrum of propulsion-related topics, including but not limited to: Rocket Propulsion: Advancements in chemical propulsion, hybrid systems, electric propulsion, and novel propulsion concepts. Aerospace Propulsion: Cutting-edge research on aircraft engines, propulsion systems, and aero-thermodynamics. Space Exploration: Innovations in space propulsion, interplanetary travel, and propulsion challenges in long-duration missions. Green Propulsion: Sustainable and eco-friendly propulsion technologies, biofuels, and reducing environmental impact. Advanced Materials: Research on materials for propulsion systems, thermal management, and structural integrity. Computational Fluid Dynamics (CFD): Modeling and simulation techniques to optimize propulsion performance. Propulsion Control Systems: Developments in autonomous and intelligent control of propulsion systems. Why Tuijin Jishu? Expertise: Our editorial board comprises esteemed professionals and researchers from diverse propulsion-related disciplines, ensuring a balanced and multidisciplinary approach to propulsion technology. Global Community: Tuijin Jishu fosters collaboration among propulsion enthusiasts worldwide, breaking down geographical barriers and promoting international cooperation. Innovation: We celebrate innovation and originality, encouraging authors to present groundbreaking research that contributes to the advancement of propulsion technology. Open Access: Tuijin Jishu follows the open-access model, making all published articles freely available to the global scientific community, accelerating the exchange of knowledge. Ethics and Integrity: We uphold the highest ethical standards in academic publishing, ensuring transparency, fairness, and confidentiality throughout the review process. Become a Contributor: Tuijin Jishu welcomes contributions from researchers, academics, and practitioners involved in propulsion technology. If you have exciting research to share or wish to stay updated with the latest developments in the field, subscribe to our journal or consider submitting your work to us.
Solar Based Wireless EV Charging Station with IoT Enabled EV Battery Monitoring
The global transition toward sustainable transportation is currently hindered by the limitations of traditional, plug-in electric vehicle (EV) charging infrastructure, which is often cumbersome, inefficient, and reliant on fossil-fuel-generated grid power. This project presents an innovative solution: a Solar-Based Wireless EV Charging Station integrated with IoT-enabled real-time battery monitoring. By leveraging Inductive Power Transfer (IPT) technology, the system eliminates physical connectors, providing a safe, weather-resistant, and user-friendly charging experience. The station is powered by a high-efficiency rooftop solar array, which feeds into a smart energy management system to minimize grid dependency. Furthermore, the inclusion of an Internet of Things (IoT) framework allows for the continuous, remote monitoring of critical battery parameters—including State of Charge (SoC), State of Health (SoH), temperature, and discharge cycles—via a cloud-based dashboard. Experimental results demonstrate that this integrated system not only optimizes energy utilization by prioritizing renewable sources but also extends battery longevity through intelligent diagnostic feedback. This research offers a blueprint for a self-sustaining, autonomous EV ecosystem, bridging the gap between renewable energy harvesting and intelligent mobility.
Predictive Drug Inventory Management Using XGBoost, ARIMA, and IoT Technologies
Drug inventory is a critical component of healthcare systems; however, it is frequently managed through manual or basic processes, leading to medicine shortages, overstock, delivery delays, and waste due to expired medications. Recent advancements emphasize machine learning and data-driven techniques for drug inventory management. Methods such as XGBoost, Random Forest, and ARIMA are utilized to enhance demand forecasting and trend analysis, while classification algorithms identify medicines at risk of shortage. Essential features, including real-time monitoring, tracking, and secure access, are recognized as vital for system reliability and efficiency. Despite the availability of numerous methods, most solutions address only isolated aspects and lack a comprehensive approach. Consequently, there is a need for an integrated system that combines prediction, monitoring, and security to improve decision-making, reduce waste, and enhance the overall effectiveness of healthcare inventory management.
Drug Inventory Management, Artificial Intelligence, XGBoost, ARIMA, Real-Time Tracking, Demand Forecasting, Healthcare Logistics, Expiry Prediction, Face Recognition, OTP Authentication
Government Scheme Prediction and Identification using K-Means Clustering
In an era of digital governance, citizens are often overwhelmed by the sheer volume of welfare schemes, leading to a "discovery gap" where eligible individuals fail to benefit from available resources. This research proposes an intelligent framework designed to bridge this gap by automating the identification and recommendation of government schemes using K-means and Unsupervised Machine Learning. By deploying the K-Means clustering algorithm, we categorize a vast repository of government schemes into distinct thematic clusters based on semantic keyword extraction and feature vectorization. The model processes unstructured scheme descriptions to identify underlying patterns, allowing for precise matching between user-inputted profiles and relevant policy categories. Our experimental results demonstrate that K-Means effectively delineates schemes into actionable domains—such as healthcare, education, agriculture, and financial aid—providing a scalable solution for personalized scheme retrieval. This approach not only enhances administrative transparency but also empowers citizens by transforming complex public policy data into accessible, personalized guidance.
Secure Email Communication Using Key Generation Algorithm
In the modern digital era, email remains the primary medium for professional and personal correspondence. However, the inherent vulnerability of standard SMTP protocols leaves sensitive data exposed to interception, phishing, and unauthorized access. This paper presents a robust framework for secure email communication utilizing advanced key generation algorithms. We propose a hybrid cryptographic approach, integrating asymmetric encryption for secure key exchange and symmetric encryption (AES) for high-speed data transmission. The system incorporates a dynamic key generation module that ensures perfect forward secrecy and resistance against man-in-the-middle (MITM) attacks. Experimental results demonstrate that our implementation provides high-level confidentiality and integrity with minimal latency overhead. By automating the key rotation and management process, this solution bridges the gap between complex cryptographic theory and user-friendly email security, offering a scalable defense mechanism for enterprise communication environments.
Comparative Analysis of Jatropha and Waste Cooking Oil Biodiesel Blends: Fuel Properties, Engine Performance, and Emissions
This study evaluates the fuel properties, engine performance and emission characteristics of biodiesel-diesel blends prepared from Jatropha oil and waste cooking oil (WCO). Three biodiesel feedstock combinations were considered: J50W50, J70W30 and J60W40, where the notation represents the volume percentage of Jatropha biodiesel and WCO biodiesel in the biodiesel fraction. Four biodiesel-diesel blends, B20, B40, B60 and B80, were tested in a single-cylinder, four-stroke, direct-injection diesel engine at 1500 rpm and a fixed-load operating point corresponding to approximately 72% of the rated engine power. Fuel properties, brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE) and exhaust emissions (CO₂, CO, HC and NOₓ) were measured and compared with diesel operation. The results show that biodiesel blends produced lower brake power and higher BSFC than diesel under the selected operating condition, while HC emissions were generally lower. CO and NOₓ trends were blend-dependent. However, the measured viscosity, density and calorific-value data for several high-biodiesel blends fall outside the expected ranges reported for biodiesel fuels and standards; therefore, the fuel-property results are treated as preliminary values requiring instrument calibration verification and repeated measurements. The revised analysis avoids using these questionable fuel-property values to draw strong combustion correlations and identifies J70W30-B60 CO₂ as an outlier requiring re-measurement. The study highlights the potential of Jatropha-WCO biodiesel blends while emphasizing the need for rigorous fuel-property validation before final performance-emission correlations are published.
Combined Screening for Chromosomal Aneuploidies in the First Trimester: Results and Risk Assessment in an Indian Tertiary Care Population
Background: For the early detection of chromosomal aneuploidies, first trimester combination screening (FTCS), which combines nuchal translucency (NT) ultrasonography with maternal serum free beta-human chorionic gonadotropin (free beta-hCG) and pregnancy-associated plasma protein-A (PAPP-A), is recommended worldwide. Due to the lack of population-specific data on FTCS performance and risk distribution in India, clinical practice must be guided by institution-level outcome assessments. Objectives: to examine the biochemical marker profiles, risk stratification patterns, and screening outcome distribution in a tertiary care cohort of 1941 pregnant women receiving FTCS. Methods: 1941 singleton pregnancies at 11+0 to 13+6 weeks of gestation were the subject of a retrospective observational study. In addition to maternal age and clinical history, the combined test protocol included NT measurement, free beta-hCG, and PAPP-A levels represented as multiples of the median (MoM). The Fetal Medicine Foundation (FMF) methodology was used for risk categorization. Results: 1841 (94.85%) of the 1941 pregnancies were categorized as low risk. There were 33 (1.70%), 7 (0.36%), and 2 (0.10%) instances with increased risk for Trisomy 21 (T21), T18, and T13, respectively. 124 (6.39%) women were identified as having advanced maternal age (AMA). Biochemical abnormalities included low PAPP-A MoM in 12 (0.62%) instances and excessive free beta-hCG MoM in 118 (6.08%) cases. Eight instances (0.41%) had ultrasonography abnormalities, and ten cases (0.52%) were classified as intermediate risk. Conclusion: With a high percentage of low-risk outcomes, FTCS successfully stratified chromosomal risk in the research cohort. The significance of biochemical and clinical risk factors in Indian obstetric populations is highlighted by the high rates of AMA and elevated free beta-hCG. These results emphasize the necessity of integrated counseling protocols in tertiary care settings and encourage the regular application of FTCS.
Classifying the Small Text into Multiple Sentiment Labels Using Enhaned Features and Machine Learning Technique
Social media and e-commerce is become one of the most popular applications. Almost every person has an account on social media and e-commerce. In both platforms, a small size of text is used for expressing opinions by users on a topic or a product. User opinion is one of the sources to understand how a user interacts with a social media post or a product in the e-commerce platform. In this paper, the e-commerce product reviews have been taken into consideration. Product reviews are also playing a vital role in the orientation of the buyer’s decision toward product purchasing. In this condition, fake or spam reviews may negatively impact the buyer’s decision Thus spam reviews can be a reason for the loss of credibility of the e-commerce platform and also impact buyers. Thus the main aim of the presented work is to identify spam reviews using Machine Learning (ML) technique. The proposed model includes an overview of a modified Term-frequency and inverse document frequency (TF-IDF) to select features from the review text. Then, k-means clustering is used to assign initial class labels to the review text. Finally, for assigning the final classes to the review text a neural network has been used. The experimentation has been conducted on the Amazon product review dataset, which is used for spam review classification tasks. The results were measured and compared, which shows improvement in prediction accuracy as compared to similar spam classification models.
A Hotel Model’s Generalized Ulam-Hyers Stability of System of Additive Functional Equations
The generalized Ulam-hyers stability of the system of additive functional equations in Banach spaces are explored in this work. Additionally, an application of the functional equations are examioned
A Non - Markovian Queue with Two Types of Phases Services, with an Optional Service and Balking
A single server non -Markovian queue with two types of essential services of phase type and with an optional service has been considered. In which the customer May balk. The model in analyzed in study state. Some numerical results are presented
On Cubic Neutrosophic C- Prime and Primary Ideals
The concept of cubic neutrosophic c-prime and primary ideals is newly defined in this paper. Various properties associated with these ideals are investigated. Their behavior in relation to classical ideals is discussed. Further results are developed to strengthen the theory.
Degree Based Topological Indices on Fuzzy Graph and its Applications
The degree-based topological indices of fuzzy graphs, emphasizing their mathematical definitions, properties, and potential applications. Topological indices are numerical values that characterize the structure of a graph and are widely applied in chemistry and network analysis. The fuzzy graphs have emerged as powerful tools for developing novel degree-based topological indices, effectively capturing uncertainty and vagueness in real-world systems. In this study explore the relationships with topological indices, and highlight their applicability in modeling complex networks. The work particularly emphasizes fuzzy graphs based on the Hyper-Zagreb index and contributes to the understanding of fuzzy regularization, with a specific focus on antiviral drugs. This research opens new avenues for both theoretical exploration and applied studies in the domain of fuzzy graph theory.
Hybrid Grey Butterfly Optimizer-Based Feature Selection for Enhancing Agricultural Commodity Price Prediction Using Machine Learning Classifiers
One of the most important tasks for economic planning and decision-making in the agriculture industry is the prediction of agricultural commodity prices. However, high dimensionality and redundancy are common in this field's datasets, increasing computing cost and decreasing prediction accuracy. To address these problems, this study proposes a novel Hybrid Grey Butterfly Optimizer (HGBO) for feature selection that combines the exploratory nature of the Butterfly Optimization Algorithm (BOA) with the leadership-driven search capabilities of Grey Wolf Optimization (GWO). The proposed hybrid approach enhances the feature selection process by avoiding local optima and preventing premature convergence. The selected feature subsets were assessed using machine learning classifiers including XGBoost, Random Forest, Gradient Boosting, and Support Vector Machines (SVM) to know the efficacy of the HGBO strategy. The performance of the proposed method was assessed in comparison to alternative optimization techniques, utilizing a range of evaluation metrics such as accuracy, precision, recall, and the F1-score. The findings indicate that HGBO demonstrates superior performance by identifying the most pertinent feature subsets, thereby enhancing predictive accuracy. This research underscores the HGBO algorithm's capacity for forecasting agricultural prices, presenting a valuable approach for addressing high-dimensional data challenges within agricultural analytics. .
Comparative Evaluation of Feature Representation Techniques for Hate Speech Detection in Social Media Text
Social media platforms generate an enormous volume of short and highly dynamic textual content, much of which is posted without effective moderation. Although several computational approaches have been proposed for analyzing social media data, reliable hate speech detection remains difficult because model performance is strongly influenced by the quality of feature representation. This study examines the impact of multiple feature extraction techniques on a deep learning model for hate speech classification. Five feature representation methods—bi-gram features, part-of-speech (PoS) features, count vectorization, TF-IDF features, and word embeddings—were implemented and evaluated using a Sequential Convolutional Neural Network (SCNN). The models were assessed on a publicly available hate speech detection dataset through comparative experimental analysis. The findings indicate that count vectorizer and TF-IDF representations achieve superior training and validation accuracy, while changes in feature dimensionality significantly influence classification performance. The results also show that higher-dimensional feature spaces increase computational cost in terms of memory usage and execution time. Overall, the study highlights the importance of word-level textual information in the classification of short social media posts. . Based on these observations, future work will focus on designing a richer feature descriptor to further improve hate speech detection in short-text environments.
The Effect of Formative Assessment Strategy on Students’ Academic Achievement in Social Studies in Nasarawa State Public Junior Secondary Schools. Implication for Counselling.
The study examined the effect of formative assessment strategy on students’ academic achievement in social studies in Nasarawa state public junior secondary schools. The study adopted quasi-experimental research design. The population of the study comprises 46,532 JSS2 students in 370 public junior secondary schools in Nasarawa State, Nigeria. The sample for the study comprised of two intact classes with 128 JSSII Students. The instrument used for data collection was a 40-item Social Studies Achievement Test (SSAT). The instrument was subjected to the scrutiny of experts in social studies as well as Measurement and Evaluation. Scores emanating from the scrutiny of the experts based on its appropriateness and comprehensiveness was used to obtain validity index of 0.73. Furthermore, it was subjected to pilot study in order to ascertain its reliability. The instruments were pilot tested on 30 students. These respondents were part of the population but not part of the sample for this study. The data obtained from the pilot test was used to compute the internal consistency of the instruments using Cronbach’s Alpha reliability method. Results obtained from the exercise were used to obtain a reliability index of 0.77. Mean and standard deviation were used to answer the research questions developed for the study while ANCOVA statistic was used to test the hypotheses at 0.05 level of significance. The findings from the study showed there is a significant effect of formative assessment strategy on students’ academic achievement in social studies in Nasarawa state public junior secondary schools and there is a significant effect of formative -assessment strategy on students’ academic achievement in social studies when segregated according to gender in Nasarawa state public junior secondary schools. The implication is that counselling through the guidance counsellors in those schools should be provided to both the teachers and students on the need to use formative assessment in teaching and learning process for success in students’ academic achievement. The study concluded that .. The study recommended that the use of formative assessment, assessment strategy by teachers should be encouraged in the assessment of assignments, class work tests in order to provide effective feedback on student’s achievement and also efforts should be made to ensure effective assessment of learners using formative assessment strategy irrespective of gender.
Influence of Welding Current on Nugget Formation and Mechanical Performance of Dissimilar Steel Joints: A Multiphysics Approach
The current requirement of lightweight and crash-resistant automotive construction methods and the application of multi-material groupings have promoted the extensive application of this material groupings making solid joining of different grades of steel desired. The proposed research investigates the Resistance Spot Welding (RSW) of three dissimilar mixtures: Mild Steel (MS) to Stainless Steel (SS), SS to HSLA 355 Steel (HSLA 355 metal), MS to HSLA 355 metal. The following sequential electro-thermal-mechanical numerical model was established using ANSYS 19.2 to predict nugget assembly and stress distribution. A wide-field experiment which was evaluated was conducted in the welding range 8–12 kA with a constant sheet thickness of 3 mm. Results illustrate an interplay of performance, where the SS-HSS joint displayed the best maximum Tensile Shear Strength at 11 kA of 9.85 kN with the highest TSS: 18.6% enhancement over MS-SS from a baseline standard. The numerical model presented was shown very closely, with nugget diameter and TSS predictions also in accordance with the experimental data by only 6%, being close to experimental results. Analysis of stress showed that maximum concentrations occurred at the nugget periphery to 1350 MPa in high-strength concomitant welds, which eased the evolution from interfacial to nugget pull-out failure modes. Final word: A good welding current of 11 kA was defined to allow joint strength without any expulsion of metal. With this knowledge, a validated framework for predictive prediction of mechanical integrity of dissimilar automotive welds could emerge that will mitigate the need to rely on experimentation.
“The Identity Activation Loop: A Conceptual Framework for Sustained Digital Engagement”
Digital platforms increasingly rely on personalization, gamification, and social feedback to shape user behaviour, particularly in health and wellness contexts. Despite these advances, many digital interventions struggle to sustain engagement over time, with participation often declining once novelty or external incentives weaken. Existing engagement models largely emphasize motivation, rewards, or habit formation, yet these approaches offer limited explanation for why some behaviours persist while others relapse under similar conditions. This paper develops the Identity Activation Loop (IAL), a conceptual framework that explains sustained digital engagement by positioning identity activation as a dynamic mediating mechanism between digital communication and long-term behaviour. Rather than treating identity as a fixed individual trait or a distant outcome of behaviour, the framework conceptualizes identity as a continuously activated process that is shaped and reinforced within digital environments. The Identity Activation Loop consists of five interdependent stages: stimulus design, where digital cues are framed to signal identity relevance; identity internalization, through which individuals interpret behaviour as self-defining; social reinforcement, where validation and visibility strengthen identity salience; habitual action, in which identity-consistent behaviour becomes increasingly automatic; and feedback amplification, where system feedback reinforces identity alignment and reinitiates the cycle. Together, these stages form a self-reinforcing loop that explains how digital systems can stabilize behaviour beyond short-term motivation or incentives. The framework contributes to engagement and health communication theory by clarifying why reward-based and motivation-centric designs often fail to produce durable outcomes. It further offers a structured basis for empirical testing and provides practical guidance for designing ethically responsible digital interventions that support sustainable behaviour rather than compulsive engagement.
An Interpretable Deep Learning Framework for Diabetic Retinopathy Detection Using Patch-Level Variational Autoencoders and Graph Attention Networks
Diabetic Retinopathy (DR) is a sight-threatening complication of diabetes that affects millions worldwide. Early detection and accurate grading of DR severity are critical for timely treatment. In this work, we propose an interpretable and modular pipeline that combines patch-level Variational Autoencoders (VAE) for compact feature encoding, spatial graph construction to capture anatomical context, and Graph Attention Networks (GAT) for classification with region-level interpretability. The approach emphasizes clinical relevance by producing attention heatmaps that align with lesion locations. We evaluate the method on the EyePACS dataset and report competitive performance across five DR grades while offering improved interpretability over conventional end-to-end CNNs.
Explainable and Secure Cyber Attack Detection in Railway Industrial Control Systems using Temporal CNN
In this paper we have proposed a layered framework for Industrial Railway Signal Control System defence against the cyber threats. Using the temporal CNN data and SCADA signal control system real time data we applied various machine learning techniques for knowledge pattern extraction. The adaptive framework also enables us to enforce various industrial standards and policies along with ML methods. Applied several metrics and features for better classification of cyber threats in this work. All the classification algorithms performance evaluation done. In this work we made to explore Cyber Security levels over industrial railway control systems towards security perspective.
Crop Yield Prediction Based on Indian Agriculture Using Machine Learning
This paper develops a machine-learning framework to estimate crop yield per hectare in different Indian districts based on open-agricultural data. The system combines data preprocessing, correlation-based and LASSO feature selection methods, and the performance evaluation of six regression algorithms: Ridge, Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The CatBoost model was able to perform well with an R2 close to 0.93 and a low RMSE, demonstrating accurate yield prediction can be achieved using agro-economic and spatial features without explicit soil and weather data. Furthermore, the model is made available through a FastAPI backend along with a React-based multilingual web interface, which collectively offers live, easy-to-reach, and data-driven insights for farmers and policymakers.
Conflict Resolution Mechanisms in Multicultural Aerospace Work Environments in India
The aerospace industry in India has entered a transformative phase marked by accelerated technological growth, expanded international collaboration, and an increasingly diverse workforce drawn from various regions of India as well as several global aerospace hubs. As large organizations such as Hindustan Aeronautics Limited (HAL), the Indian Space Research Organisation (ISRO), and Defence Research and Development Organisation (DRDO), along with multinational firms such as Airbus–Tata and Boeing India, scale their operations; the multicultural composition of engineering, design, and project teams has intensified. This shift has increased the frequency and complexity of conflicts related not only to technical decisions but also to communication styles, cultural expectations, leadership perceptions, and hierarchical norms. Given the safety-critical nature of aerospace work, where conflict mismanagement can jeopardize mission timelines, product reliability, and team cohesion, it is essential to understand the mechanisms through which conflicts arise and are resolved in multicultural environments. This study examines conflict resolution mechanisms in multicultural aerospace workplaces in India using a mixed-methods approach consisting of surveys, structured interviews, and organizational observations. Drawing on cultural frameworks and empirical research on conflict dynamics, the study identifies major conflict type’s technical disagreements, interpersonal frictions, and cross-cultural miscommunications as well as cultural factors that influence conflict frequency and intensity, including power distance, linguistic diversity, differing communication norms, and culturally shaped expectations of leadership. Quantitative findings highlight systematic variations in conflict patterns across roles, experience levels, and cultural groups, while qualitative analyses reveal gaps in organizational preparedness, limited cultural sensitivity training, and heavy reliance on informal, hierarchy-based conflict resolution strategies. The anticipated findings suggest that although Indian aerospace organizations possess strong technical leadership, they often lack institutionalized and culturally adaptive conflict management frameworks. This deficiency manifests in delayed decision-making, reduced psychological safety, and communication breakdowns within high-stakes engineering teams. The study proposes a multilayered conflict resolution model that integrates cultural competence development, structured mediation protocols, cross-cultural communication guidelines, and hybrid leadership strategies that balance technical authority with collaborative team management. The implications are substantial for human resource development, engineering management, and organizational policy. Strengthening conflict resolution systems can enhance team performance, reduce production delays, improve innovation outcomes, and foster a more inclusive, psychologically safe aerospace workplace capable of supporting India’s expanding role in the global aerospace ecosystem.
Conflict resolution, multicultural teams, aerospace industry, India, communication, leadership, organizational behaviour, high-reliability organizations.
Using Electronic Learning Environments to Develop Digital Competence of Future Teachers
To develop project-based competencies in future teachers, students must first acquire practical knowledge about how to solve problems and achieve the intended result, and secondly, form such cognitive skills as how to develop, monitor and evaluate the set goal, which is a constant main task in today's globalization environment. The problem of developing educational outcomes is solved by organizing the development of new, including project-based, experienced teacher competencies and the preparation of future teachers for the implementation of project-based differentiated teaching. To this end, the widespread introduction of digital technology tools into the continuous education system, as a result of which the level of quality and efficiency of their purposeful use in teaching, the formation of design skills, the rapid development of the use of electronic learning environments, platforms and artificial intelligence systems in the development of creative abilities, cognitive thinking and digital competence of future teachers is interpreted as one of the important components of education in today's globalization process. This article examines the methods of forming the ability of future teachers to create content-rich, interactive electronic educational resources and developing competence in the products of the educational process, using the capabilities of digital technology tools effectively.
Information, education, digital technology, artificial intelligence, resource, design, virtual, ability, cognitive thinking, competence, motivation, assessment.
Studying The Efficiency of a Differentiated Instruction in the Development of Physical Qualities of Students of Non-Sports Faculty
The article presents the results of research on changes in body mass index indicators when applying the differentiated approach developed by the authors. This approach is based on a comprehensive individualization and considers the individual characteristics, interests, and motivations for engaging in systematic physical education activities among students with different constitutional types, determined by the R.N. Dorokhov algorithm, from non-sporting faculties of a pedagogical university during their education. The results obtained confirmed the effectiveness of using an individually differentiated approach, with exercises organized around circuit training to improve physical qualities in physical education for students at a pedagogical university. As a result of applying the innovative method developed by the authors, which provides a practical and health-oriented focus on improving the physical development and overall physical fitness of participants, the average relative increase in the results of the arithmetic means of all six physical fitness indicators for the control group of retarded-type students in the pedagogical experiment was 5.58%. In the experimental group of the same development type, this indicator was 9.66%. For students of the normal-type development in both the control and experimental groups, these values were 6.33% and 11.15%, respectively. For students of the accelerated-type development, the average relative increase in the results of the arithmetic means of all six physical fitness indicators in the pedagogical experiment was 7.20% for the control group, while in the experimental group, this value was 13.38%. The overall average increase in physical fitness indicators across three different constitutional types of students during the pedagogical experiment for all studied indicators was 6.37% for the control group and 11.40% for the experimental group.
differentiated instruction, accelerant, retardant, statistical reliability, type of development, physical qualities.
Fuzzy Stability Analysis of a Quartic- Additive Mixed TypeFunctional Equation
In this research proposal, we exhibit the generalized Hyers-Ulam-Rassias stability of a quartic- additve mixed type functional equation in fuzzy normed space using the popular Hyers direct and fixed point methods.
An NLP-Based Approach for Automated Task Identification in Unstructured Chat Conversations
The task of identifying relevant actions from short workplace messages is difficult because of the lack of contextual information, use of informal language, and code-mixing. Although transformer models have demonstrated remarkable success in minimal-context settings, their performance in such settings has not been adequately explored. This paper presents the application of binary task identification in a single-utterance classification paradigm, where messages are assessed independently without any conversational context. Three different methods are employed for the task: a traditional lexical approach using TF-IDF with logistic regression, a multilingual transformer model with XLM-RoBERTa-base, and an optimized transformer model. Empirical evaluation on 1,190 real-world workplace messages with class imbalance and Hinglish code-mixing demonstrates that lexical approaches are robust baselines, and optimized transformer models perform best. The results show that increased model complexity does not necessarily translate to improved performance and the need for empirical analysis in designing task identification systems for minimal-context workplace communication
Comparison of Part and Full load Emissions of CI and HCCI Engine with Exhaust Gas Recirculation
The present study is experimentation, not only on CI engine but also conducting experiment on CI engine running on HCCI mode operation without using exhaust gas recirculation and with using exhaust gas recirculation and comparing the emissions of both such as the Oxide of Nitrogen (NOX), Oxides of carbon (CO), carbon dioxide (CO2), Un-burnt hydro-carbons (HC)etc., These values are measured and compared under various conditions and plotted for easy understanding of effect of EGR on engine emissions. This paper focused on part load and full load emission comparisons for CI and HCCI mode operations with and without using exhaust gas recirculation. So separate experiments conducted and readings are recorded.
Optimization of Resistance Spot Welding Parameters for HSLA 340 Steel Using Response Surface Methodology and Finite Element Simulation
Resistance spot welding (RSW) is used widely in the manufacturing of automotive body-in-white steels, in which high strength low alloy materials (HSLA) require the welding properties to be effectively established for integrity and reliability of joints. This study investigated the resistance spot welding process of HSLA 340 automotive steel sheets with dimensions of 100 × 25 × 3 mm by combined experimental, statistical and numerical approaches. To obtain a perfect welding current ratio, weld time and electrode force were identified as process parameters and optimized for RSM with a central composite design. Analysis of variance (ANOVA) indicated that welding current was the most important variable affecting the quality of the welds, then weld time and electrode force, and an interaction effect of process parameters was also statistically significant. Ideal welding parameters were established as 8.2 kA welding current, 13 cycles (0.26 s) weld time, and 3.2 kN electrode force. Due to the predicted dimensions, in particular the weld nugget diameter and maximum temperature and residual stress levels predicted in these scenarios were 6.25 mm, 1480 °C and 310 MPa. The proposed coupled electro-thermal-mechanical finite element model was synthesized in ANSYS to simulate the growth of the nuggets, the temperature distribution and the residual stress evolution during welding. The simulated findings exhibited significant agreement with RSM predictions, with deviations less than 3%. The experimental-numerical combined framework is proposed in the present work and it proves to be an efficient method to develop better resistance spot welding parameters of HSLA 340 steel as well as to enhance weld quality and process reliability in automotive applications.
Resistance Spot Welding, HSLA 340 Steel, Response Surface Methodology, Weld Nugget Formation
A Robust and Reproducible Machine Learning Pipeline for Diabetes Prediction Using Feature Engineering and Ensemble Learning
Diabetes mellitus remains a major global health challenge, requiring accurate and early prediction to reduce the risk of severe complications and improve patient outcomes. Although machine learning techniques have been widely applied to diabetes prediction, existing studies often address key challenges such as class imbalance, feature representation, and model optimization in isolation, leading to suboptimal and non-reproducible predictive performance. This study addresses this gap by proposing a comprehensive and integrated machine learning pipeline for diabetes prediction using the Pima Indians Diabetes Dataset. The proposed framework systematically combines data preprocessing, feature engineering, dimensionality reduction, class imbalance handling, and ensemble learning within a unified pipeline. Missing values are handled using median imputation, while outliers are treated through interquartile range (IQR)-based winsorization. Three domain-informed features Glucose_per_BMI, Age_BMI, and HighPreg are introduced to capture nonlinear relationships, followed by feature selection and Principal Component Analysis (PCA). To address class imbalance, SMOTE-Tomek resampling is applied, improving minority class representation. Multiple models, including Logistic Regression, Support Vector Machines, Random Forest, XGBoost, LightGBM, and a stacked ensemble, are evaluated using cross-validation and a hold-out test set. Experimental results demonstrate that the proposed pipeline significantly improves predictive performance, with accuracy increasing from 74.03% to 82.11%, F1-score from 53.33% to 71.11%, and balanced accuracy from 0.6893 to 0.7780. Notably, recall improved substantially across models, enhancing sensitivity to diabetic cases. Additionally, recall improved substantially, with Logistic Regression increasing from 0.6267 to 0.8000 and SVM from 0.5467 to 0.7467. LightGBM and the stacked ensemble achieved the best overall performance. These findings highlight the effectiveness of integrating preprocessing, feature engineering, and ensemble learning into a unified framework for robust and reliable diabetes prediction.
Diabetes Prediction, Machine Learning Pipeline, Feature Engineering, Ensemble learning
Enhancing Adversarial Attack Detection: Insights from Ensemble Learning, Deep Dyna-Q, and VARMAx Models
Machine learning systems are increasingly crucial for various applications but are also vulnerable to hostile attacks. Current methods are often inflexible and imprecise. This paper presents a sophisticated ensemble model that combines Deep Dyna Q Learning and VARMAx procedures with Active Machine Learning Adversarial Attack Detection frameworks. The model uses a dynamic and responsive methodology, combining data pretreatment methods like tokenization and numerical conversions. Techniques like CNN, SVM, Random Forest, XGBoost, and Logistic Regression are used to improve detection capabilities. The model incorporates uncertainty sampling and query-by-committee for adaptability to new adversarial tactics. VARMAx operations improve prediction accuracy, while Deep Dyna Q Learning anticipates attack vectors. The system's defensive mechanisms are strengthened with GridCAM++ for explainability and GAN-based sample generation. The model outperforms current approaches in precision, accuracy, recall, and AUC, and reduces detection delays
Machine Learning, Adversarial Attacks, Ensemble Methods, Deep Learning, Cybersecurity.
Intelligent Medication Dispensing System with Mobile Communication and Email Notifications
This paper presents the design and implementation of an intelligent pillbox system engineered to enhance medication adherence and patient safety. Traditional pill dispensers, while functional, often lack dynamic oversight and proactive communication capabilities, leading to medication errors and missed doses. Our proposed system integrates a smart pillbox with a Global System for Mobile Communications (GSM) module and an email notification service. The pillbox is equipped with individual compartments, each capable of detecting the presence and dispensing of medication. Upon scheduled times or missed doses, the system triggers a GSM transmission to pre-registered mobile numbers, alerting caregivers or patients. Simultaneously, it dispatches a detailed email report to designated medical professionals or family members, providing a comprehensive log of medication intake. This dual communication strategy aims to create a robust support network, reducing the burden on individuals with complex medication regimens and mitigating the risks associated with non-adherence. The system's modular design and user-friendly interface are discussed, along with potential applications in home healthcare, assisted living facilities, and remote patient monitoring. The system was designed to enhance patient compliance, provide real-time monitoring for caregivers, and promote independent living for individuals managing complex medication regimens. Preliminary trials demonstrate significant improvements in medication adherence rates and caregiver peace of mind, underscoring the potential of this technological intervention.
Skin Lesion Detection: A Survey of Recent Breakthrough
Melanoma, a highly aggressive form of skin cancer, continues to rise in prevalence worldwide. Early identification of skin lesions is crucial for enhancing patient prognosis and minimizing medical expenses. Automated detection systems have emerged as essential tools in dermatology, improving diagnostic precision and efficiency. This paper explores recent advancements in skin lesion analysis, covering both conventional machine learning techniques and state-of-the-art deep learning architectures. Additionally, it discusses existing obstacles and potential future research directions in this field.
Skin lesion detection, deep learning, machine learning, convolutional neural networks, AI in healthcare.