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.
Censoring-Aware Pseudo-Labeling for Semi-Supervised Survival Prediction using Risk-Order Agreement Filtering
Practical cohorts often have a small labelled fraction with confirmed time-to-event outcomes and a larger covariate pool with missing, delayed, or incomplete endpoints; yet, survival prediction is essential to clinical prognosis modeling. A problem formulation is derived that makes censoring constraints and pseudo-label safety requirements explicit after reviewing survival modeling, assessment under censoring, and semi-supervised learning (SSL) ideas that are relevant to pseudolabeling. Here is a conservative method blueprint called Adaptive Pseudo-Label Selection and Correction (APSC). It uses mediancrossing pseudo events and pseudo-censoring at tmax to generate weak pseudo outcomes from predicted survival curves. A curvegeometry confidence proxy is used to admit pseudo labels. Unstable pseudo labels are filtered using risk-order agree-ment across rounds. Finally, a down-weighted Cox partial-likelihood objective is used to inject pseudo supervision. Where naive selftraining can breach censoring limits and magnify early model bias, the formulation aims at registry-style situations with delayed endpoints and heterogeneous follow-up. In order to provide a supervised backup in the event that pseudo labels are unreliable, the survey condenses these factors into specific design criteria and deployment diagnostics. There are no numerical comparisons reported in the text; instead, the focus is on auditoriented operating methods like validation gating, monitoring of acceptance and correction rates, and tests for coefficient stability
Semi-supervised learning, survival analysis, pseudo-labeling, censoring, Cox proportional hazards, literature survey.
Electric Vehicle Charging Infrastructure in Smart Cities
The rapid adoption of electric vehicles (EVs) has transformed urban transportation and accelerated the demand for intelligent, reliable, and sustainable charging infrastructure. Smart cities aim to integrate advanced information and communication technologies with energy management systems to improve urban mobility while reducing greenhouse gas emissions and dependence on fossil fuels. However, the increasing number of EVs presents several challenges, including uneven charging demand, power grid instability, long waiting times at charging stations, limited renewable energy integration, cybersecurity risks, and inefficient resource utilization. These issues require intelligent charging management strategies capable of optimizing charging schedules, predicting demand, balancing electrical loads, and ensuring secure communication among charging stations, vehicles, and cloud platforms. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud computing, and edge computing have significantly improved the capabilities of smart EV charging infrastructure. Machine learning algorithms enable accurate prediction of charging demand based on historical usage patterns, traffic density, weather conditions, electricity prices, and user behaviour. AI-driven optimization techniques dynamically allocate charging resources, reduce operational costs, minimize peak-hour congestion, and improve energy efficiency. Furthermore, IoT-enabled sensors continuously collect real-time data from charging stations, while cloud platforms process large-scale datasets for predictive analytics and intelligent decision-making. The integration of renewable energy sources and vehicle-to-grid (V2G) technology further enhances energy sustainability by allowing EVs to act as distributed energy storage systems. This study presents a comprehensive review of intelligent EV charging infrastructure within smart cities by examining modern architectural frameworks, AI-driven charging optimization techniques, predictive analytics, renewable energy integration, cybersecurity considerations, and cloud-based management systems. The research also evaluates practical implementation strategies through industrial and metropolitan case studies, highlighting measurable improvements in charging efficiency, energy utilization, service availability, and operational reliability. A machine learning-based prediction model is demonstrated using Python to forecast charging station overload conditions, illustrating the practical application of predictive analytics in smart city environments. The findings indicate that AI-powered charging infrastructure significantly enhances charging efficiency, minimizes waiting time, optimizes electricity consumption, improves grid stability, and supports sustainable urban transportation, making it an essential component of future intelligent smart city ecosystems.
Electric Vehicles, Smart Cities, Machine Learning, Smart Charging Infrastructure, Renewable Energy
Mechanical and Thermal Characterization of Silane-Treated Basalt/Glass Fiber Reinforced Hybrid Polymer Composites
The growing demand for strong and cost-effective construction materials has led to increased research on fiber-reinforced polymer composites. Hybridization of fibers is an effective approach to combine the advantages of different materials. In this study, basalt and glass fibers were used as reinforcements in an epoxy matrix to develop hybrid composites. Basalt fibers provide high stiffness and thermal resistance, while glass fibers contribute to cost reduction and improved toughness. To enhance interfacial bonding between fibers and matrix, a silane coupling agent was applied as a surface treatment. Composite laminates were fabricated using the hand lay-up technique with different stacking sequences of treated fibers. Proper surface preparation was carried out to ensure improved adhesion and minimize internal defects. Mechanical properties of the developed composites were evaluated through tensile, flexural, microhardness, and impact tests. The results indicate that silane treatment significantly improves fiber–matrix bonding, enabling efficient stress transfer and reducing internal weaknesses. Hybrid composites exhibited better strength, stiffness, and toughness compared to single-fiber composites. The combination of basalt and glass fibers, along with chemical surface modification, resulted in enhanced mechanical and thermal performance. This study demonstrates that silane-treated hybrid composites are promising materials for load-bearing structural applications due to their improved performance and cost efficiency.
Iraq's Development Road Project Amid Political Instability, Economic and Security Challenges
Iraq has been involved in battles and wars for years and has become an arena for both regional ambitions and conflicts. The current study aims to investigate Iraq’s internal challenges and their implications for the development road project, which seeks to transform the country into a transit zone for trade between Asia and Europe. For this purpose, descriptive-analytical approaches based on secondary sources were employed to examine the impact of these challenges on the project. In addition, this study utilized Patron-Client Politics theory to analyze how political instability, security and economic challenges, and widespread corruption affect the project’s progress and implementation. The findings provide evidence that rivalry among political elites over resources and power, which undermines the decision-making process regarding the project, the spread of corruption, Iraq’s dependency on oil, and the control of armed militias have significantly hindered the project. Moreover, the study highlights the influence of the armed groups such as; the Popular Mobilization Forces, together with Iraq’s heavy reliance on oil revenues, remain critical obstacles to achieving stability. Further research might explore the positions of regional actors such as Türkiye, the GCC countries, and Iran, and their role in influencing the long-term success and sustainability of the development road project.
Dynamic Response of FG-BNNT Reinforced Composite Plate Subjected to Low Velocity Impact Loading
In the world of emerging materials, composites play a major role due to their lightweight and high strength-to-thickness ratio. There are many classes of composites such as Metal matrix composites, Ceramic matrix composites and Polymer matrix composites based on the matrix. And short fibre composites, long-fibre composites and particulate composites based on the reinforcement. The materials used as matrix and reinforcement play a major role in defining the strength and properties of a composite. Of all the different composites available in today’s life, Nanocomposite has become the centre of interest because it exhibits high strength with a small amount of reinforcement. The reinforcement in the nanocomposites can be of different types. They can be arranged in the form of nanotubes, nanoparticles, nanosheets etc. In this research, we focus on the polymer matrix nanocomposite taking reinforcement as the nanotubes. The mechanical properties of the nanocomposite are evaluated using the Halpin-Tsai micromechanical model and the analytical investigation is carried out using the new simple higher-order shear deformation theory and the contact force is calculated using the Hertz contact law and New mark’s technique is used for numerical investigation. The dynamic response of the FG-reinforced nanocomposite is studied and the best nanocomposite from the two different matrices and fibres is discussed. The influence of fibre volume fraction, Fibre orientation and the Fibre arrangement on the dynamic response is also presented and discussed and the comparison of CNT and BNNT with epoxy as matrix is presented and the comparison of Epoxy and HDPE with BNNT as reinforcement is also studied.
BNNT, CNT, New Simple HSDT, Functionally Graded and Halpin-Tsai Micromechanical Model
Improving Stock Price Prediction Accuracy Through Deep Learning Models and Hidden Markov Models: A Comparative Analysis
Stock price prediction is a crucial research area, especially in the financial market, which plays a critical role in the global economy. This research compares the forecasting performance of several deep learning models on the BSE dataset. The models used are Long Short-Term Memory (LSTM), CNN-LSTM, Recurrent Neural Network (RNN), and a hybrid model of CNN-LSTM and Hidden Markov Models (HMM). Standard performance measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), are used to compare the models. The experimental results showed that the hybrid LSTM-HMM model achieved the best predictive performance among the approaches tested. The RNN model performed well in forecasting, while the CNN-LSTM and RNN-HMM models showed relatively weak performance. The results indicate that deep learning can be combined with probabilistic models like Hidden Markov Models to enhance the prediction accuracy and reliability of the stock price. Future research may focus on incorporating advanced optimization algorithms with these hybrid models to further enhance forecasting performance.
Stock Price Prediction, Deep Learning, LSTM, CNN-LSTM, RNN, Hidden Markov Models.
Impact of Artificial Intelligence Tools on Indian Students: Using UTAUT and Machine Learning Model
The rapid advancement of Artificial Intelligence (AI) technologies has led to a transformation in higher education. AI tools provide academic support to students, available anytime, anywhere, to enhance their knowledge and skills. This study focuses on understanding the determinants of AI tools’ acceptance and use for academic support among students, influencing student satisfaction and academic performance in India. This work presents a quantitative investigation into the adoption of AI tools, including Chat-GPT, Google Gemini, Microsoft Co-Pilot, and related platforms, using the Unified Theory of Acceptance and Use of Technology (UTAUT). Data is collected from Kaggle, which contains 3,614 student records drawn from 1,246 institutions across 34 states in India. UTAUT constructs were operationalized as composite proxies from the available survey variables: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions (FC). The Actual Use Behaviour (USE) construct captured daily interaction intensity and contextual application diversity. A multi-layered Machine Learning (ML) model is employed, integrating Decision Tree, Logistic Regression, Random Forest, XGBoost, Gradient Boosting, and an ensemble Stacking Classifier. Class imbalance in the target variable addressed via SMOTE-ENN resampling. The final XGBoost model achieved al classification accuracy of 98.48% and an AUC score of 0.9986 and Cross-validated mean accuracy was 94.63% (SD = 0.0021). SHAP analysis provided post-hoc explainability, identifying USE as the dominant predictive feature in descending order of importance. The study contributes a theory-driven, methodologically transparent framework for analyzing AI adoption in higher education and offers practical implications for policy and institutional strategy, emphasizing the importance of structured support systems, digital competency development, and pedagogically aligned AI implementation. The paper concludes by emphasizing the need for a balanced approach to AI integration in education. Promoting AI literacy, implementing ethical AI policies, encouraging critical thinking exercises, and providing mental health support are recommended. By fostering responsible AI use, Indian educational institutions can maximize benefits while minimizing cognitive and psychological drawbacks.
Neutrosophic Vague Hypersoft Open and Closed Mappings
In this paper, we introduce and formalize the concepts of Interval-Valued Neutrosophic Hypersoft (IVNHS) open mappings and IVNHS-closed mappings within the framework of IVNHS-topological spaces. We establish necessary and sufficient conditions for these mappings using the fundamental topological operators of interior and closure. Furthermore, we extensively investigate the behavior of these mappings under composition. Specifically, we prove that the composition of two IVNHS-open (or closed) mappings preserves their respective properties. We also derive the structural conditions under which the component mappings inherit openness or closedness from their composite forms, utilizing the auxiliary conditions of injectivity, surjectivity, and continuity. These findings enrich the structural theory of interval-valued neutrosophic hypersoft topology and pave the way for deeper geometric and algebraic mappings within multi-attribute decision-making environments.
Detecting Sarcasm Using Commonsense Reasoning Capabilities of Comet and Representation Power of Bert
Sarcasm poses significant challenges for natural language processing systems due to its reliance on implicit meaning and contextual cues. While models like BERT have advanced sarcasm detection, they still face difficulties with non-literal interpretations. This paper introduces a hybrid model that combines BERT's contextual representations with COMET's commonsense reasoning. The approach involved fine-tuning BERT on sarcasm datasets and integrating commonsense inferences via a feature integration layer. Evaluated on three key datasets, the BERT–COMET model demonstrated superior performance in recognizing subtle sarcastic expressions and contradictions, highlighting that incorporating commonsense knowledge can enhance detection accuracy and reduce errors.
Molecular Docking and Biological Evaluation of Synthesized Compounds for Prospective Biomedical Applications
A novel Schiff base ligand was obtained by the reaction of p-anisidine (4-methoxy aniline) and 3-ethoxy salicylaldehyde (3-ethoxy-2-hydroxy benzaldehyde) and their complexes with the metal Ni ++ and Zn ++ were obtained. Characterization of the ligand and metal complexes were done by elemental analysis, molar conductance and magnetic susceptibility measurements and spectroscopies methods. Furthermore, the in vitro & in silico activity of the ligand and its metal complexes was assessed by different biological assays. The results of the study show that the binding energies are (-96.9 -110.09 kcal/mol) in trypsin activity. Among them, 5(c) compound had the least energy values, which suggests the most stable binding. In the process of pepsin docking, the binding energies are -67.9 to -73.4 kcal/mol, 5(b) having the best binding ability. In the case of lipase, the binding energy of compounds is -77.5 to -99.115 kcal/mol, where 5(c) exhibited the best affinity. The docking results shows good binding energies together with the establishment of strong intermolecular interactions, such as hydrogen bonding and hydrophobic contacts, which comprise the main features of enzyme ligand binding. The results of these computations study (Insilco) show a good correlation with the in-vitro laboratory results.
Schiff base, Ligands-Metal Complexes, Anti enzymatic, Characterization Molecular docking.
Did Restricting Index Derivatives Calm the Spot Market? Evidence from India's 2024 SEBI Reform
In October 2024 the Securities and Exchange Board of India (SEBI) imposed the most restrictive set of index-derivatives rules in that market's history: minimum contract value tripled, weekly expiries withdrawn from all but one benchmark index per exchange, option premium collected upfront, and an additional 2% Extreme Loss Margin levied on short option positions on expiry day. The measures cut index-derivatives turnover sharply. The regulator's own market-stability rationale — and a chorus of contemporaneous commentary — predicted that curbing speculative derivatives activity would dampen volatility in the underlying spot indices. We test that prediction and find no support for it. Identification exploits an asymmetry in the reform that has gone unremarked: it did not treat all indices equally. The National Stock Exchange retained weekly expiry for the Nifty 50 while withdrawing it from Nifty Bank, Nifty Financial Services and Nifty Next 50, and eleven sectoral indices carry no index derivatives at all. This yields a dose gradient across fifteen indices that share one market, one trading calendar and one set of macroeconomic shocks — converting a question that a single-index study could not identify into a difference-in-differences design with a control group the reform built itself. The difference-in-differences estimate is −4.6% on 21-day realised volatility (β = −0.047, SE = 0.103), with an exact randomisation-inference p-value of 0.591 obtained by enumerating all 364 estimable treatment assignments. The estimate is stable in sign and insignificant across every alternative sample, volatility measure and event window we examine. Chow tests find no break at the implementation date (Nifty 50: F = 0.04, p = 0.96); estimated endogenously, no treated index selects a break near the reform — the Nifty 50 selects April 2020, the month of the COVID crash. An expiry-day triple difference, aimed squarely at the sessions three of the six measures targeted, is likewise indistinguishable from zero (β3 = 0.004, p = 0.945 on the cleanest sample). Machine-learning models trained on pre-reform data do not degrade after the reform; on treated indices they improve, landing below the 10th percentile of a 200-draw placebo distribution. The one index where forecast accuracy significantly deteriorates is Nifty IT — an untreated control. We are explicit about what a null of this design can and cannot support. The minimum detectable effect at 80% power is a 25% reduction in volatility: we can reject reductions of that magnitude but cannot separate smaller changes from zero. Within that resolution, a reform that removed a large share of India's index-derivatives turnover left spot volatility where it found it. The investor-protection case for the reform is untouched by this finding; the market-stability case is not supported.
index derivatives, market regulation, realised volatility, GARCH, difference-in-differences, expiry-day effects, natural experiment, India
SAFE-Loss: Specialized Aligned Fusion of Experts for First-Trimester Pregnancy Loss Prediction
Background. Multimodal clinical prediction is conventionally approached by engineering domain-specific features, assigning specialist encoders to different views of the patient, and combining those encoders with a learned per-patient gate. The premise — that modality-specific specialisation recovers signal a single joint model would miss — is widely adopted and rarely tested against its own counterfactual. Objective. To build such a system for first-trimester pregnancy-loss prediction, to evaluate it beyond discrimination alone, and to determine by controlled ablation where its accuracy actually comes from. Methods. We developed CAFE (Clinically-Aligned Fusion of Experts): 43 engineered features in five clinically motivated families — temporal trajectory, growth velocity, inter-marker discordance, maternal risk profile and cross-modal interaction, all built on gestational-age normalisation — tree experts over a flat view and a cross-modal view, an optional 12,585-parameter temporal-attention encoder over the raw scan sequence, and a per-patient softmax gate over expert logits. Evaluation used a synthetic dual-etiology cohort (n = 3000, prevalence 20.3 %) under 5-fold cross-validation repeated twice. Every arm, hybrid and baseline alike, received an identical 66-feature union view, so no result can be attributed to an information advantage. Reported measures were AUROC, AUPRC, Brier score, Cox calibration slope and intercept, expected calibration error, decision-curve analysis, and a screening operating point selected on one repeat and scored on the other. A difference was declared real only if it exceeded twice the pooled fold standard deviation and agreed in sign on at least 8 of 10 folds. Results. CAFE reached AUROC 0.852, the highest point estimate we recorded on this cohort and inside the 0.840–0.855 band predicted for it in advance. It was also the best-calibrated arm we measured (slope 0.946, expected calibration error 0.030, no post-hoc correction), returned the highest mean net benefit across risk thresholds 0.05–0.50, and trained in 43 s against 269 s for the deep temporal architecture it replaces. Ablation. Its accuracy does not originate in its architecture. Replacing the gated multi-view ensemble with a single gradient-boosted tree on the same features moved AUROC by 0.001. The +0.030 total improvement over logistic regression was dominated by two of its four steps: +0.021 for the choice of boosting library and +0.015 for the engineered features — the latter only for a learner unable to represent interactions natively, since the same features were worth +0.002 to the deployed learner. The gated fusion contributed +0.001 and the sequence encoder −0.005; the gate independently learned to discount that encoder, assigning it 0.195 against an equal share of 0.333. Interpretability. Feature importance and feature contribution pointed in opposite directions: out-of-fold exact TreeSHAP assigned the engineered block 66.8 % of total attribution mass, while ablation valued that block at 0.002 AUROC for the deployed model. The gate proved confident, patient-specific and clinically legible yet inconsequential — near-bimodal weights (mean 0.557, SD 0.402, spanning 0–1) routing abnormal yolk-sac measurements to the cross-modal expert and prominent maternal risk to the flat expert, for +0.001. Conclusions. The arms separated in exactly one dimension, and it was not one the architecture gave any reason to examine: calibration. The XGBoost-derived arms had calibration slopes of 0.430–0.480 against 0.923–0.946 for the CatBoost-derived arms, with non-overlapping per-fold distributions, while their AUROCs differed by 0.021 and their Brier scores tied. The 90 %-sensitivity thresholds consequently spanned 0.004 to 0.081 across arms, so any workflow with a hard-coded risk threshold is silently coupled to the calibration of one particular model. CAFE is worth deploying; the explanation for why it performs as it does is not the one its architecture suggests.
Machine Learning-Based Intelligent Fault Diagnosis and Signal Forecasting for Photovoltaic Systems Using Performance Indicators
Reliable fault diagnosis and predictive monitoring are essential for improving the performance, reliability, and operational safety of photovoltaic (PV) systems. This paper presents an intelligent framework for fault diagnosis and signal prediction based on performance indicators and machine learning techniques. First, a set of representative electrical and performance indicators is extracted from the PV system under normal and faulty operating conditions. These indicators are then used to identify different fault scenarios and to predict the evolution of key electrical signals, enabling early detection of abnormal behavior and supporting predictive maintenance. Several machine learning algorithms are investigated and evaluated according to their diagnostic accuracy, prediction capability, and computational efficiency. The proposed approach demonstrates high fault classification accuracy and reliable signal prediction under varying environmental conditions, while reducing false alarms and improving the robustness of the monitoring process. The results confirm that the integration of performance indicators with machine learning provides an effective solution for intelligent condition monitoring, fault diagnosis, and predictive maintenance of photovoltaic systems.
“A Study to Assess the Effectiveness of Information Booklet on Knowledge Regarding Vitamin D Deficiency on Health Among Working People in Selected Area”.
Background: Vitamin D deficiency is a widespread public health concern that affects individuals across all age groups, particularly working people who spend long hours indoors with limited exposure to sunlight. Deficiency of Vitamin D can lead to various health problems, including weakened bones, muscle weakness, fatigue, and an increased risk of chronic diseases. Objective: The study aimed to assess the effectiveness of an information booklet on knowledge regarding Vitamin D deficiency and its impact on health among working people in a selected area. Methods: A quantitative research approach was adopted for the study. Two group Pre Test Post Test design was used. 200 samples were selected using convenient sampling technique. Baseline knowledge regarding Vitamin D deficiency was assessed using a structured questionnaire. Following the pre-test, participants received an information booklet containing details about the causes, risk factors, symptoms, complications, prevention, dietary sources, and importance of sunlight exposure for maintaining adequate Vitamin D levels. A post-test was conducted after the intervention to evaluate changes in knowledge. Descriptive and inferential statistics were used to analyze the data. Result: The findings demonstrated a significant improvement in participants' knowledge scores after exposure to the information booklet. The mean post-test knowledge score was higher than the mean pre-test score, indicating that the educational intervention was effective. Conclusion: The information booklet proved to be an effective educational tool for improving knowledge regarding Vitamin D deficiency among working people. Regular health education programs and awareness campaigns can help promote preventive behaviors, encourage healthy lifestyle practices, and reduce the burden of Vitamin D deficiency in the working population.
Assess, Effectiveness, Infromation Booklet, Vitamin D Deficiency, Working People.
A Study to Assess the Effect of Health Teaching on Knowledge Regarding Prevention of Torch Infection During Pregnancy among Antenatal Mothers in Selected Rural Area.
Background: The maternal infections that are transmissible in utero at several stages of the pregnancy and it can be caused by many organism of which the members of the TORCH complex, namely Toxoplasma Gondi, Rubella, Cytomegalovirus (CMV), the Herpes simplex virus (HSV) 2 occupy prominent position. Objectives: To assess the effect of health teaching on knowledge regarding prevention of torch infection during pregnancy among antenatal mothers in selected rural area. Method: Quantitative Research Approach was used. One group pre-test post-test pre-experimental research design was adopted. Independent variable: health teaching program regarding prevention of TORCH infection Dependent variable: knowledge regarding prevention of TORCH infection. The setting of the study was selected rural area. The population of the study was antenatal mothers residing in selected rural area. Sample size of the study 100 antenatal mothers residing in selected rural area. Non-probability purposive sampling technique was adopted for this study. Tool and technique of data collection was divided into two sections. Section A: Structured questionnaire on demographic variables such as Age, Educational status, occupation, gravida (No. of pregnancy), gestational age (in weeks), source of information about TORCH infection, previous history. Section B: Structured knowledge questionnaire to assess the level of knowledge regarding prevention of TORCH infection among antenatal mothers in selected rural area. Result: The result of the study showed that the in pre-test 29 % of the Antenatal mothers had poor level of knowledge score, 54 % had average level of knowledge score. In post-test 51 % of the Antenatal mothers had good level of knowledge score and 45 % had excellent level of knowledge score. Conclusion: The knowledge score of the Antenatal mother’s shows marked improvement after giving Health teaching.
Assess, Effect, Health Teaching, Prevention of TORCH Infection, Pregnancy, Antenatal Mothers.
Environmental Fate and Corrosion Inhibition Performance of Benzotriazole-Based Compounds: A Sustainability Perspective
Benzotriazole-based compounds are among the most widely used corrosion inhibitors in industrial systems because of their remarkable efficiency in protecting copper, steel, and mixed-metal surfaces. However, increasing environmental concerns regarding persistence, toxicity, and bioaccumulation have raised questions about their long-term sustainability. This research paper examines the corrosion inhibition performance, environmental occurrence, degradation pathways, ecotoxicological effects, and sustainable alternatives of benzotriazole derivatives. The study integrates findings from recent environmental and electrochemical investigations and evaluates the balance between corrosion protection efficiency and ecological safety. Results indicate that benzotriazole compounds exhibit inhibition efficiencies exceeding 90% under optimized conditions, but their persistence in aquatic systems and resistance to biodegradation present environmental risks. Sustainable strategies including biodegradable inhibitors, nanostructured delivery systems, and advanced oxidation treatment technologies are discussed as future directions for green corrosion science.
Benzotriazole; Corrosion inhibition; Environmental fate; Sustainability; Ecotoxicology; Biodegradation; Copper corrosion; Green corrosion inhibitors; Advanced oxidation processes; Nanostructured inhibitors; Aquatic toxicity; Surface adsorption; Industrial corrosion protection; Environmental pollution; Sustainable chemistry.
Performance Analysis of Discrete – Time 〖Geo〗^x/ G/1 Retrial Queue – Two Vacation Types with Impatient Customers
A Stochastic system where bulk (batch) of customers arrives according to a Geometric distribution retrial time follows a general distribution. We examine a retrial queueing system featuring two vacation types. This paper investigates a discrete – time 1 retrial queue under urgent and normal vacations with impatient customers. We attain some efficiency characteristics of the probability generating function and orbit size of the system.
Batch arrival, Retrial queue, impatient customers, single server, Vacations
“A Study to Assess the Effect of Health Teaching on Knowledge Regarding Health Hazard of Soft Beverages among High School Students in Selected Schools.”
Background: Consumption of two or more soda drinks daily was related to an increased risk of chronic kidney diseases due to high levels of orthophosphoric acid within the body. Objectives: To assess the effectiveness of a structured teaching programme on knowledge regarding the harmful effects of soft drink consumption among high school students in selected schools Method: In this study quantitative approach is used. Simple Random sampling technique was used to select the samples. One group pre-test post-test pre-experimental design was adapted to this study. The setting of the study was selected high school Pune city. The samples were all high school children of selected school Pune city. Sample Size was 100 high school children. Non-Probability Convenient Sampling Technique was used. Population of the study were Age group of 13-16. Independent variable was Health teaching regarding health hazards of soft beverages among high school students. Dependent variable was Knowledge regarding health hazards of soft beverages among high school students. The tool of the study divided into two sections. Section A: It consists of semi-structured questionnaire to collect demographic data, Section B: Self-Structured Knowledge Questionnaires Result: In the pre-test 12 % of the Students had poor level of knowledge score, 77 % had average level of knowledge score. In post-test 10 % of the Students had good level of knowledge score and 90 % had excellent level of knowledge score. The difference between pre-test and post-test level of knowledge score was found to be statistically significant (ﭏ2-value= 30.93). Conclusion: The study concludes that the knowledge score of the Students shows marked improvement after giving Structured Teaching Program. This indicates that Structured Teaching Program was effective in improving the knowledge of the Students regarding health hazards of soft drinks.
Assess Effect, Health Teaching, Health Hazard of Soft Beverages, High School Students
Apply Distance Measures to Derive Similarity Measure for Interval Valued Nth Power Root Fuzzy Soft Set and to Solve Mcdm Problem
The notion of interval-valued Nth power root fuzzy soft set is an advanced level of cubic root fuzzy soft set. In this article, various operations are defined, along with some basic results and properties. Additionally, solving MCDM problem using by Hamming distance, Normalized Hamming distance, Euclidean distance, Normalized Euclidean distance and Similarity measure with algorithm steps is proposed. Lastly, a suitable illustrative example for IVNPRFSS is provided.
CRFSS,IVNPRFSS, Basic operators, HD, normalized HD, ED, normalized ED,Similarity MCDM.
Application of Zooplankton Indicators for Assessing Ecological Functioning Across an Urbanization Gradient
Urbanization is one of the most pervasive drivers of freshwater ecosystem degradation, altering hydrology, nutrient loading, and habitat structure in ponds, lakes, and reservoirs embedded within growing metropolitan landscapes. Zooplankton communities, owing to their short generation times, sensitivity to water-quality change, and central position in pelagic food webs, are increasingly recognized as effective bioindicators of ecological functioning. This study examined zooplankton assemblages across eight standing-water sites distributed along a rural-to-highly-urban gradient, quantified by percentage impervious surface cover in the surrounding catchment (4-81%). Zooplankton density, taxa richness, Shannon-Wiener diversity (H'), Pielou evenness (J'), and the rotifer-to-crustacean abundance ratio were used as indicators of ecological condition, alongside supporting water-quality variables (total phosphorus and chlorophyll-a). Diversity and richness declined sharply and significantly with increasing imperviousness (r = -0.94 and -0.97, respectively; p < 0.001), while the rotifer-to-crustacean ratio increased more than five-fold from rural to highly urban sites (r = 0.96, p < 0.001), reflecting a shift toward small, tolerant taxa under nutrient enrichment and hydrological instability. Zooplankton density exhibited a unimodal response, peaking at intermediate urbanization before declining in the most impervious catchments, consistent with a subsidy-stress model in which moderate nutrient enrichment initially increases secondary production before habitat degradation suppresses it. These results support the use of zooplankton community metrics, and in particular the rotifer:crustacean ratio and Shannon diversity index, as sensitive and cost-effective indicators for urban water-body monitoring programs and management decision-making.
zooplankton, bioindicators, urbanization gradient, freshwater ecology, Shannon diversity, rotifer-crustacean ratio, urban stream syndrome
Advancements in Understanding Histone Modification Regulation Mechanisms and Their Applications in Breeding for Plant Responses to Abiotic Stress
Abiotic stresses such as drought, salinity, extreme temperature, and heavy-metal toxicity are among the principal constraints on global crop productivity, and their impact is intensifying under climate change. Beyond changes in DNA sequence, plants rely heavily on reversible chromatin modifications to rapidly and flexibly adjust gene expression in response to fluctuating environments. Among these, histone acetylation, methylation, phosphorylation, and ubiquitination act as an interconnected regulatory layer, often described as the histone code that governs whether stress-responsive genes are activated, repressed, or kept in a poised state for rapid future induction. This review synthesizes current understanding of the molecular writers, erasers, and readers that establish and interpret histone marks in plants, examines how these marks are dynamically remodeled during drought, salinity, temperature extremes, and heavy-metal exposure, and considers the phenomenon of chromatin-based stress memory mediated by bivalent H3K4me3–H3K27me3 domains. We further evaluate how this mechanistic knowledge is being translated into breeding practice, including epiallele-based selection, genome-wide epigenomic profiling, and CRISPR/dCas9-based epigenome editing that directly installs or removes histone marks at target loci without altering the underlying DNA sequence. Representative case studies in Arabidopsis, rice, wheat, cotton, poplar, soybean, and common bean illustrate measurable gains in stress tolerance achieved through manipulation of histone-modifying enzymes such as HDA6, HDA9, HD2C/D, OsHDT701, GCN5/ADA2, and HUB2. We conclude by outlining remaining challenges, including incomplete understanding of crosstalk between histone marks and other epigenetic layers, the stability and heritability of engineered epialleles, and regulatory pathways for non-transgenic epigenome-edited crops, and we propose priority directions for integrating chromatin biology into climate-resilient breeding pipelines.
histone modification; chromatin regulation; abiotic stress; epigenetics; histone acetylation; histone methylation; CRISPR/dCas9 epigenome editing; stress memory; crop breeding
Physicochemical Frameworks for Polymer Nanocomposite Dental Materials in Prosthodontics: A Cross-Disciplinary Review
Background: Optimization of polymer nanocomposite denture base materials has traditionally relied on empirical concentration screening, which involves testing various filler loadings and identifying the best-performing formulation after experimentation. Although this method has produced a substantial body data, it remains fundamentally limited in its ability to generate mechanistic insight, predict optimal formulations for untested conditions, or offer transferable design principles for novel material systems. Objective: To systematically evaluate the applicability of seven established phytochemical and mathematical theoretical frameworks in predicting, designing, and optimizing the properties and performance of polymer nanocomposite denture base materials. Methods: A structured narrative review was conducted, integrating foundational physics and chemistry literature with prosthodontic dental materials research. For each therotical framework, the original physical and chemical basis is presented, the mathematical formalism is described, prior applications in polymer composite and dental materials research are reviewed and a concrete illustration of applicability to PMMS-ZnO-nPLA-type ternary nanocomposite system is provided. Conclusion: Despite the presence of these theoretical frameworks within the field of physics, chemistry, and engineering over many decades, they have not yet been integrated into the design of prosthodontic nanocomposites. Their combination provides a general theoretical framework that transforms the design of dental nanocomposites into a more scientific endeavor rather than an empirical one. This literature review highlights specific validation papers in the field of physics and chemistry that confirm the usefulness of these theoretical frameworks. It then shows how the gap in literature within prosthodontics is addressed by applying these theoretical frameworks.
percolation threshold; DLVO theory; interfacial thermodynamics; Flory-Huggins theory; Halpin-Tsai model; Hill equation; Pareto optimisation; PMMA denture base; nanocomposite design; dental materials science; ZnO nanoparticles; polylactic acid
Review: Transition metal oxides (RuO2, Co3O4, MnO2, and NiO) for pivotal electrode materials for high performance supercapacitors
Because they offer quick energy storage, load-leveling, and grid stabilization, supercapacitors are essential to renewable energy applications. This paper highlights the substantial potential of supercapacitors in renewable energy applications and discusses their important advancements. Supercapacitors can benefit from high surface area, low cost, and high theoretical specific capacitance (3560 Fg−1) of cobalt oxide (Co3O4) electrode materials. RuO2 can perform fast Faraday redox reactions and has a high theoretical capacitance of 2000 Fg−1. Through quick surface - redox processes, cobalt (II,III) oxide (CoO₄) stores energy and frequently produces high specific capacitances more than 1100 Fg−1. Transition metal oxide cobalt oxide has two direct optical band gaps (1.48 eV and 2.19 eV), making it an intrinsic p-type semiconductor. Because of its low cost, high theoretical capacitance (1387 Fg−1), and environmental friendliness, manganese dioxide (MnO2) is thought to be a perfect material for supercapacitors. A popular pseudocapacitive material for supercapacitors, nickel oxide (NiO) has a huge theoretical specific capacitance of about 2584 Fg⁻¹. With its strong redox activity, high theoretical capacitance, and affordability, nickel oxide (NiO) is a promising electrode material for energy storage. Pseudocapacitive electrode materials bridge the gap between high-power electric double-layer capacitors and high-energy batteries by storing energy through quick, reversible Faradaic surface redox processes.
Metal oxides, supercapacitor, specific capacitance, current density
Fabrication of Air Purifier Using Ultraviolet Germicidal Irradiation (Uvgi) Light
The project titled “Fabrication of Air Purifier Using Ultraviolet Germicidal Irradiation (UVGI) Light” presents the development of a smart air purification and environmental monitoring system integrated with intelligent sensing technologies. The proposed system combines HEPA filtration and UVGI sterilization to remove airborne particulate matter and deactivate harmful microorganisms such as bacteria, viruses, and fungi. An ESP32 microcontroller is used for real-time monitoring and control of environmental parameters including air quality, temperature, humidity, and operational status. A significant feature of the project is the integration of the LDT0-028K Piezoelectric Vibration Sensor for vibration monitoring and predictive maintenance analysis. The sensor continuously detects vibration generated by the BLDC motor and airflow system to identify abnormal operating conditions such as fan imbalance and structural instability. The monitored data is displayed using a 20×4 LCD display for real-time operational analysis. Furthermore, the developed system demonstrates the application of Advanced Manufacturing System (AMS) principles through the integration of intelligent sensing and embedded monitoring. The implementation of the ESP32-based monitoring platform and piezoelectric vibration sensing enables continuous condition monitoring of system components, which is a key requirement in modern smart manufacturing environments. By incorporating automated data collection, operational diagnostics, and predictive maintenance capabilities, the project aligns with the objectives of Advanced Manufacturing Systems, which emphasize enhanced operational efficiency, reliability, process optimization, and intelligent decision-making. The proposed architecture therefore serves as a small-scale demonstration of how smart manufacturing technologies can be applied to environmental management systems
Indoor Air Quality, Ultraviolet Germicidal Irradiation (UVGI), HEPA Filtration, Clean Air Delivery Rate (CADR), Airborne Pathogens..
Information Technology Infrastructure: Digital-Based Attendance Applications and Quality of Information Technology Services on Employee Performance Levels: Case Study APATOKI Application Soppeng of Indonesia
Digital transformation in information technology infrastructure has brought significant changes in various aspects, including human resource management. One of the innovations implemented is a digital-based attendance application that aims to increase the efficiency and effectiveness of employee performance. This study aims to analyze the influence of the use of the APATOKI digital attendance application and the quality of information technology services on the level of employee performance in Soppeng Regency, South Sulawesi Province. The research method used is a quantitative approach with data collection techniques through surveys to employees who use the APATOMKI application. Data analysis was carried out using descriptive and inferential statistical methods to test the relationship between independent variables, namely the use of digital attendance applications and the quality of information technology services, to the dependent variable, namely employee performance using SmartPLS 4. The results of the study show that the use of digital attendance applications has a positive and significant influence on increasing employee discipline and productivity. In addition, the good quality of information technology services also contributes to increasing job satisfaction and operational effectiveness of employees. Simultaneously, the use of digital-based attendance applications and the quality of Information Technology services have a significant effect on Employee Performance with an R-Suare Value of 0.699 which means 69% of the influence contributed and the remaining 31% is influenced by other aspects. Thus, the implementation of digital attendance applications supported by reliable information technology infrastructure can be a key factor in optimizing employee performance. This study provides recommendations for government agencies and other organizations to continue to improve the quality of information technology services to support a more effective and efficient digital transformation
Experimental Investigation of the Thermo-Mechanical Performance of High-Strength Self-Compacting Concrete Incorporating Bagasse ash
The environmental impact of Portland cement production has intensified the need for sustainable supplementary cementitious materials in high-performance concrete. While bagasse ash has been explored in conventional concrete, experimental data on the thermo-mechanical performance of high-strength self-compacting concrete incorporating bagasse ash under elevated temperature conditions remain limited. This study experimentally investigates the thermo-mechanical behaviour of HSSCC with bagasse ash as a partial replacement for Portland cement. Bagasse ash was calcined at 550 °C, finely ground, and incorporated at replacement levels of 0–10% by mass, while maintaining a constant binder content of 600 kg/m³ and a water-to-binder ratio of 0.29. Fresh properties were evaluated using slump flow, V-funnel, and L-box tests, while compressive strength was measured up to 42 days. Thermal performance was assessed through controlled furnace exposure, and microstructural characteristics were examined using SEM–EDS. All mixes satisfied self-compacting concrete workability requirements. Elevated temperature exposure reduced compressive strength due to dehydration and thermal microcracking; however, increasing bagasse ash content significantly enhanced residual strength. The 10% replacement level achieved the highest residual compressive strength of 14.00 N/mm² at 42 days. The findings demonstrate that bagasse ash enhances the thermo-mechanical performance and fire resistance of high-strength self-compacting concrete while reducing cement consumption, supporting its application in sustainable and fire-resilient structural concrete.
Self-compacting concrete, Bagasse cement, Thermo-mechanical performance, Elevated temperature, Sustainable building.
Hybrid Neuro-Fuzzy Systems in Artificial Intelligence: Design, Challenges, and Emerging Applications
Hybrid Neuro-Fuzzy Systems represent an advanced area of Artificial Intelligence that combines the adaptive learning capability of Artificial Neural Networks with the uncertainty-handling and reasoning ability of Fuzzy Logic Systems. The present study titled “Hybrid Neuro-Fuzzy Systems in Artificial Intelligence: Design, Challenges, and Emerging Applications” examines the theoretical foundations, architectural design, computational behavior, and emerging applications of hybrid neuro-fuzzy models. The study adopts a hypothetical and analytical research methodology integrating conceptual analysis, theoretical validation, and application-oriented evaluation. The research explores important components such as adaptive learning, fuzzy inference mechanisms, intelligent decision-making, rule optimization, and computational efficiency. The study further incorporates mathematical and logical structures including definitions, lemmas, theorems, corollaries, and proofs to strengthen the scientific foundation of the proposed framework. The findings indicate that hybrid neuro-fuzzy systems provide improved adaptability, prediction accuracy, and uncertainty management compared to conventional AI approaches. The research also identifies major challenges including computational complexity, scalability limitations, and interpretability issues. Furthermore, the study highlights the growing applications of neuro-fuzzy systems in healthcare, robotics, cybersecurity, smart agriculture, finance, and intelligent automation. Overall, the research concludes that hybrid neuro-fuzzy architectures have significant potential in the future development of intelligent, explainable, and adaptive Artificial Intelligence systems.
Hybrid Neuro-Fuzzy Systems, Artificial Intelligence, Neural Networks, Fuzzy Logic, Intelligent Systems, Adaptive Learning, Computational Intelligence, Soft Computing, Machine Learning, Explainable AI.
Predictive Oncology Deep Learning-Based Multi-Cancer Detection
Predictive Oncology using deep learning enables automated multi-cancer detection by extracting complex patterns from high-dimensional clinical and imaging data. Traditional cancer diagnostics are limited by manual feature extraction and variability in interpretation. Deep learning techniques such as convolutional neural networks (CNNs) and transformer models have shown remarkable performance in single-cancer detection tasks. However, existing approaches are generally constrained to a specific cancer type, such as skin cancer, creating a need for scalable multi-cancer frameworks. This research proposes an integrated deep learning model for simultaneous detection of liver, skin, and breast cancers. The model incorporates multi-modal data fusion to enhance generalization across varying cancer presentations. Extensive experiments demonstrate improved accuracy, sensitivity, and robustness compared to traditional methods. The proposed framework supports early detection, aiding clinical decision-making and personalized treatment planning. These results highlight the potential of deep learning-based predictive oncology for broad clinical application and real-world deployment.
Automated Medical Image Analysis for Brain Tumour Detection Using DL
Brain tumors are among the most critical neurological disorders, requiring timely and accurate diagnosis to improve treatment outcomes and patient survival. Conventional diagnostic procedures primarily rely on manual interpretation of Magnetic Resonance Imaging (MRI) scans by radiologists, which can be time-consuming and susceptible to inter-observer variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have enabled the development of intelligent computer-aided diagnosis systems capable of assisting medical professionals in detecting brain abnormalities with high precision. This paper presents a deep learning-based framework for automated brain tumor detection and classification using MRI images. The proposed system integrates image preprocessing, data augmentation, convolutional feature extraction, and deep neural network classification to identify the presence of brain tumors and categorize them into clinically relevant classes. Image enhancement and normalization techniques are employed to improve the quality of MRI scans before feature learning, enabling the model to capture complex tumor characteristics while minimizing the effects of noise and intensity variations. The trained model is deployed through a secure web-based application that allows users to upload MRI images and receive prediction results with confidence scores in real time. Experimental evaluation demonstrates that the proposed framework achieves high classification accuracy, robustness, and computational efficiency while reducing diagnostic time and supporting reliable clinical decision-making. The proposed approach provides an intelligent, scalable, and user-friendly solution for early brain tumor diagnosis, offering significant potential for integration into modern healthcare systems and computer-aided radiology applications.
Corporate Economic Sustainability and Competitiveness of Selected Banks in Nigeria: The Role of Female Leadership
Corporate sustainability has become a central driver of long-term competitiveness, yet the role of female leadership in shaping sustainable outcomes within developing economies remains insufficiently explored. Prior research has emphasized the environmental, social, and economic dimensions of sustainability but has often overlooked how gender diversity in leadership may strengthen these sustainability and competitiveness linkages. This study addresses this gap by examining the mediating role of corporate sustainability, measured through economic, social, and environmental dimensions, in the relationship between female leadership and competitiveness among selected Nigerian banks. Using a cross-sectional survey design, data were collected from 247 respondents across top, middle, and lower management levels in five leading banks. Employing PROCESS Macro (Model 4, parallel mediation), both direct and indirect relationships were analyzed. The findings reveal that female leadership positively influences all dimensions of corporate sustainability, each of which significantly enhances bank competitiveness. Mediation results indicate that female leadership partially transmits the effect of corporate sustainability on competitiveness, with social sustainability exerting the strongest mediating influence. The study concludes that while female leadership directly contributes to competitiveness, its full potential is realized when integrated with sustainability-driven strategies. Practically, the study underscores the need for gender-inclusive governance and sustainability-oriented leadership development in Nigeria’s banking sector.