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.
AI-Powered Secure Network for Intelligent Threat Detection and Automated Mitigation Using Machine Learning
The rapid escalation of cyber threats has exposed critical limitations in traditional security mechanisms, which frequently fail to address evolving and sophisticated attack vectors. This research presents an AI-powered network security framework utilizing supervised machine learning algorithms specifically Random Forest and XGBoost for real-time vulnerability detection and automated threat mitigation. The architecture consists of three modular components: (i) a real-time traffic monitoring and AI-driven classification engine, (ii) a Node.js backend for automated mitigation and secure logging, and (iii) an interactive React.js dashboard for visualization and system control. Emphasizing scalability, modularity, and secure inter-component communication, the framework enables adaptive threat response via rule-based automation. Extensive experimental evaluation on benchmark intrusion detection datasets demonstrates a detection accuracy of 94.6% and a false positive rate below 6%, with robust performance under heavy network loads. Furthermore, the system establishes a foundation for integrating deep learning based anomaly detection, block chain enabled auditability, and cloud-native deployment. The results underscore the potential of AI-driven architectures to deliver proactive, scalable, and resilient cybersecurity solutions for contemporary digital ecosystems.
Experimental analysis of Charge Controllers: Comparative Insights into Conventional, Smart, and IoT-Based Intelligent Designs for Islanded PV Systems
This paper presents the experimental and simulation implementation of an IoT-integrated intelligent MPPT (Maximum Power Point Tracking) charge controller tailored for Islanded mode photovoltaic (PV) systems. Comparatively experimentation with three charge controllers were used for standalone PV system to draw constant power of 250/500Wp from off-grid PV system on charging and discharging on intermittent weather conditions for two weeks respectively Conventional, Smart and Intelligent controllers. Adopting advanced control algorithms, real-time data monitoring, and smart decision-making, the system achieves significant improvements in efficiency, reliability, and energy management is conducted through MATLAB and PVGIS simulations. Main performance parameters such as charging efficiency, load handling, and adaptability under variable solar irradiance are evaluated. The research validates the superiority of intelligent controllers using IoT over traditional designs in islanded mode solar applications.
Long Distance, Multi-device communication with Electrical Systems through RS-485 communication
Remote monitoring of Electrical systems in industry is crucial for safe and efficient operation of the system. If the accurate data is available, time-based analysis can be easily performed and intelligent algorithms can be applied for prediction of faults as well as need for system expansion or even maintenance. The proposed work is about development of a communication system which can help in getting accurate real-time data from multiple sensors using the RS-485 communication protocol. The RS-485 is a multi-node sensor network that can be connected to maximum 32 sensors. The proposed work demonstrates multi-node master slave network of RS-485 by connecting different types of sensors like, temperature -humidity, pyranometer etc. The proposed work contributes towards increasing remote monitoring capabilities and collecting large spatial data for electrical systems like renewable energy system, particularly showing the robustness and adaptability of RS-485 in challenging applications.
Synergistic Utilization of Industrial By-products for Developing Sustainable High-Strength Concrete
The increasing environmental impact of Ordinary Portland Cement (OPC) production necessitates the development of sustainable alternatives in concrete technology. This study investigates the performance of high-strength concrete (HSC) incorporating industrial byproducts, namely fly ash (FA), ground granulated blast furnace slag (GGBS), and silica fume (SF), as supplementary cementitious materials (SCMs). An experimental program was designed using binary and ternary blended mixes with a constant water-to-binder ratio, targeting M60 grade concrete. The results indicate that the inclusion of SCMs significantly enhances mechanical and durability properties. Ternary blends demonstrated superior performance compared to control and binary mixes, with the optimal mix (15% FA, 15% GGBS, and 5% SF) achieving the highest compressive strength and improved tensile and flexural behavior. Durability assessments revealed reduced water absorption, lower chloride ion permeability, and enhanced sulfate resistance, attributed to refined pore structure and increased C–S–H formation. Additionally, partial cement replacement up to 45% contributes to reduced carbon emissions and promotes sustainable waste utilization. The findings establish that ternary blended HSC offers a viable and eco-efficient solution for modern construction applications.
Sustainable concrete; High-strength concrete; Fly ash; GGBS; Silica fume; Ternary blend; Durability
Comparative Analysis of VR/AR Platforms in Secondary Education: Integration Challenges and Pedagogical Potential
The present study aims to conduct a comparative evaluation of six educational VR/AR platforms (Virbela, ClassVR, zSpace, Merge EDU, Assemblr EDU, and Quiver) on a five-point scale with regard to interactivity, educational value, applicability in economics education, ease of use, and accessibility. Methodologically, a theoretical-comparative approach is applied, integrating a systematic literature review, qualitative content analysis, and multi-criteria evaluation with the calculation of average scores. The results show that among the VR platforms, ClassVR and zSpace demonstrate the highest values for interactivity and educational value (both 5), with zSpace also having the highest applicability in economics (5), but lower accessibility (2) and more complex use (3); Virbela stands out for its highest accessibility (5) but has moderate applicability in economics (3). Among the AR platforms, Assemblr EDU demonstrates the highest applicability in economics education (4) and maximum ease of use (5), Quiver is most accessible (5) and easy to use (5) but with low economic applicability (2), while Merge EDU combines high interactivity and educational value (4), with moderate accessibility (3). The composite indices rank ClassVR (4.2) as the strongest platform, followed by zSpace and Virbela (each 4.0), Assemblr EDU and Quiver (each 3.8), and Merge EDU (3.6). The conclusions support a differentiated implementation model: VR for highly immersive and simulation-intensive scenarios where robust infrastructure and teacher training are available, and AR for scalable, resource-efficient, and easily integrated solutions.
Integrated Theoretical and CFD Study on the Aerodynamic Behaviour of a Horizontal Axis Wind Turbine
Wind energy is among the most promising renewable resources for sustainable electricity generation, and Horizontal Axis Wind Turbines (HAWTs) are extensively adopted because of their superior aerodynamic efficiency and dependable operation. This work presents a combined theoretical and Computational Fluid Dynamics (CFD) investigation of the aerodynamic performance of a large-scale HAWT with a rotor radius of 40 m. The main aim is to assess key performance indicators, including the power coefficient (Cp), torque, tip speed ratio (TSR), as well as velocity and pressure distributions, while validating analytical predictions through CFD simulations. The theoretical framework is developed using momentum theory and Betz’s limit to estimate the maximum achievable wind power extraction and to determine turbine performance characteristics. These analytical outcomes serve as a reference for comparison with numerical findings. CFD simulations are conducted in ANSYS Fluent, where turbine blades based on the NACA 4412 aerofoil are modelled under steady wind conditions. The SST k–ω turbulence model is applied to effectively capture boundary layer phenomena, pressure gradients, and three-dimensional flow behaviour around the blades. The CFD results provide detailed insights into flow patterns, highlighting increased velocity near the blade tip and clear pressure differences between the suction and pressure surfaces. Overall, strong agreement is obtained between theoretical and CFD predictions, with deviations remaining within acceptable ranges. The estimated power output and torque values further confirm the turbine’s efficient aerodynamic performance. This study underscores that integrating theoretical analysis with CFD simulations offers a robust and comprehensive methodology for wind turbine aerodynamic evaluation, supporting design optimization and performance improvement of large-scale HAWTs.
Operations on Picture Fuzzy Rough Matrices for MCDM Applications
This work presents the picture fuzzy rough matrix, which generalizes the intuitionistic fuzzy rough matrix. We define picture fuzzy rough matrix and explore its structure, including operations such as AND, OR, min-min composition and the complement. We establish several key properties such as commutativity, associativity, distributivity, idempotency and absorption.
Vertex-Magic Labeling And Maximum, Minimum Constant Of Cyclic And Path Graphs
In this article, we discussed about the magic labeling of Vertex Set for cyclic and path graphs. Also, we find the maximum and minimum vertex magic constants for Magic labeling of vertex Set.
Magic Labeling of Vertex Set, Minimum-Vertex-Magic Constant Maximum Vertex-Magic Constant.
An Intelligent Real-Time Breast Cancer Classification in a Cloud Environment Using Optimized ELM
Breast cancer remains a major global health challenge, particularly for women in remote regions where the medical infrastructure is very poor. AI-powered diagnostic systems supported by cloud computing technology offer a practical way to deliver early detection services to these under-served areas. This work proposes an AI-driven framework that combines Gain Ratio–based feature selection, PCA for reducing overfitting, an optimized Extreme Learning Machine (ELM) for breast cancer classification, and a cloud-enabled platform for remote diagnosis. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the cloud-based ELM is compared with leading diagnostic methods. Results show that the proposed system outperforms existing approaches, achieving 98.25 % accuracy, 0.9891 recall, 0.9861 precision, an F1-score of 0.9861, and an AUC of 0.9931.
Breast cancer; ELM; Cloud computing; PCA, Real-Time Diagnosis Telehealth etc..
Evaluating the Dynamics of Land Use and Land Cover Changes in Relation to the Land Surface Temperature of Hyderabad City
Land Surface Temperature (LST) is a critical indicator of urban thermal comfort and heat stress. This study investigates the relationship between land use/land cover (LULC) changes and LST dynamics in Hyderabad City, India, over a 30-year period (1989–2019) using decadal Landsat imagery. The satellite data were pre-processed through radiometric and atmospheric correction, and classified into four LULC classes-built-up, barren land, vegetation, and water bodies-using the Support Vector Machine (SVM) algorithm. LST was retrieved using mono-window algorithms, while the Contribution Index (CI) was employed to quantify the thermal impact of each LULC class. Results reveal a significant expansion of built-up areas from 35.8% to 56.5%, alongside a decline in barren land (42.7% to 33.4%), vegetation (19.4% to 8.2%), and water bodies (2.1% to 2.0%). The warming effect of barren land decreased (CI from 0.58 to 0.22), while the cooling influence of vegetation (CI from -0.42 to -0.06) and water bodies (CI from -0.14 to -0.10) also weakened over time. Declining values of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) indicate reductions in vegetation health and water extent. Simultaneously, rising Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) reflect increasing urbanization and land conversion. These findings highlight the shifting surface energy balance driven by LULC changes and provide critical insights for sustainable urban planning and climate adaptation strategies.
Urban Heat Island, Contribution Index, Remote Sensing, Hyderabad City, Support Vector Machine.
Experiences with Non-pharmacological Interventions for Labour Pain Relief Among Midwives' and Women in Tertiary Institutions, Nigeria
Background: Non-pharmacological interventions for labour pain relief are non-invasive, cost-effective alternatives to pharmacological methods, yet their utilization in Nigerian federal hospitals remains underexplored. Understanding both midwives' and women experiences is crucial for optimizing positive childbirth experiences. Methods: This convergent mixed-methods study was conducted across six federal hospitals representing Nigeria's six geopolitical zones. Quantitative data were collected from 160 postpartum mothers and 122 midwives using structured questionnaires, while qualitative data were obtained through semi-structured interviews with 15 midwives. Analysis employed descriptive statistics and Braun and Clarke’s thematic analysis. Results: Commonly used non-pharmacological methods included breathing techniques emotional support massage maternal positioning and warm compresses. Mothers expressed predominantly negative perceptions regarding effectiveness (grand mean=1.00), whereas midwives demonstrated positive perceptions (grand mean=2.50). Conclusion: While midwives employ various non-pharmacological techniques, significant perceptual gaps exist between providers and recipients. Addressing barriers through targeted training, resource provision, and enhanced antenatal education is essential for optimising labour pain management in Nigerian federal hospitals.
Labour pain, non-pharmacological interventions, midwives, maternal experience, Nigeria, mixed-methods.
Reinforcement Learning for Liquidity-Weighted Optimal Trade Execution strategies
Optimal execution of large institutional orders is a critical problem in electronic markets. Traditional algorithms such as TWAP (Time-Weighted Average Price) and VWAP (Volume-Weighted Average Price) fail to adapt to dynamic liquidity fluctuations in modern high-frequency markets. This paper proposes a novel Liquidity-Weighted Reinforcement Learning Execution (LW-RLX) framework that learns optimal order slicing policies based on real-time liquidity signals, order book imbalance, and market impact estimates. The proposed model formulates trade execution as a Markov Decision Process (MDP) and integrates deep reinforcement learning with liquidity-aware state representations. The agent dynamically adjusts order sizes to minimize market impact, slippage, and execution risk. Simulation results using realistic limit-order-book environments demonstrate that the proposed method outperforms classical execution benchmarks such as TWAP, VWAP, and Almgren-Chriss optimal execution models.
Characterisation of Tribological, Mechanical And Microstructurality of Magnesium Based Functionally Gradation AZ31D Incorporated With Titanium Carbide/Carbon Fiber Four Wheeler Steering Yoke Fabricated Through Powder Metallurgy
The research work emphasizes to synthesize an AZ31D magnesium alloy based steering yole composite with reinforcement titanium carbide (TiC) and carbon fiber in weight proportions of 2, 4, 6 and 8 % using automation equipped powder metallurgy technique. The micro structural implications of AZ31D/TiC/CF steering yoke had undergone through mechanical, microstructural and tribological characterization through the use of specified equipments as per ASTM-391 standardization. With the enhancement of titanium carbide there is a appreciable increase of grain size AZ31D Mg matrix FGM (functional graded material). The TiC has also performed as an prominent element in the momentous changes with regards to microstructural behavior of AZ31D FGM (functionally graded materials) steering yoke composites. The prominent increment in the mechanical and tribological properties is been excellently executed with the increase of addition of carbon fiber of particulate size of 65 microns and the micro hardness of 125VHN and wear rate at its maximum amount of 34 microns operating at 15 m/s is achieved. Further the addition of carbon fiber not only improves the surface properties but also increased the thermal conductivity behavior of FGM. The AZ31D composite with 6% TiC reinforcement depicts that the highest compressive strength of 415.86 MPa. The hardness of AZ31D FGM composite is increased by 32% by the addition of 6% TiC / 8% carbon fiber reinforcement particles. The fractured tensile specimen surfaces of the alloy and the FGM composite are investigated with respect to the fracture failure analysis and depicted that they are capable to withstand a cyclic load of 785 KN. The uniform dispersion of refined TiC and carbon particles and the efficient load distribution between the reinforcements and the matrix elements appears to be playing an extravagant role in the high improved mechanical, micro structural and impact properties of AZ31D functionally gradation composite in turn results in the enhancement of properties of steering yoke used in automobile applications.
Finite Element-Based Multi-Parametric Analysis of Residual Stress Evolution in Thin Structural Steel T-Joints
Optimization among welding parameters is important to minimize residual stress, distortion, and mechanical strength in the thin structural steel T-joint plates. In the present work, a finite element–based multi-parametric simulation was established to study the impact of welding current, arc voltage, and welding speed on the thermo-mechanical performance of welded T-joints. A co-developed thermo-mechanical finite element model along with a statistically rigorous Design of Experiments (DOE) using Central Composite Design (CCD) and Response Surface Methodology (RSM) was developed to systematically assess the interaction of parameters and the nonlinear effects with the least computational effort. The response variables include maximum temperature, residual von Mises stress, total deformation, and ultimate tensile strength. Quadratic regression models have been formulated and compared for 95% confidence level at ANOVA. Their predictive accuracy was impressive; all models had coefficient of determination (R²) higher than 0.98. Results indicated that welding current directly affects the residual stress and the tensile strength, and the deformation behavior depends on the welding speed. Interaction and quadratic dependence were significant implying that the welding process is coupled, as well as nonlinear. Heat input was defined as a regulating function of thermal cycles and mechanical events. The combined FEM–DOE approach is an effective and computationally efficient method for predicting and adjusting welding parameters in thin plate T-joint configurations. The approach offers solutions for data based optimization of welding processes and a strong implication for structural and fabrication systems.
Advances in Pipeline Leak Detection: A Review of Computational Fluid Dynamics, Machine Learning, and Hybrid Diagnostic Frameworks
Pipeline leakage is one of the current economically important operational issues in the water distribution systems, petroleum and chemical processing and pharmaceutical fluid procedures networks in the world. Over the past two decades the technology of the leak detection has been transformed fundamentally- beginning with manual scrutiny and primitive pressure assessments to considerably more intricate sensor-based, signal-driven, and the most current concentrating on hybrid-calculation-machine learning diagnostic schemes. The given review critically reviews the current state of the field of pipeline leak detection and particularly how the combination of computational fluid dynamics (CFD) simulation and supervised and unsupervised machine learning can take place. The paper in question categorizes the previous detection methodologies in terms of hardware-based, software-based and hybrid ML-CFD and evaluates the capability, constraints and practicable restrictions of its application with regards to synthesis of over thirty representative publications published in the period between ninth year 1987 and 2025. Much attention is specifically paid to such emergent themes as: Science (i) used physics-consistency of generating synthetic training data via physics simulation through COMSOL Multiphysics and ANSYS based models; (ii) the relative usefulness of classical ML algorithms (Random Forest, Support Vector machine, Ridge Regression, Gradient Boosting) versus deep learning models ( CNN, LSTM, hybrid CNNLSTM); (iii) how pre-electra-steady-state single-audioid models can be applied to the problem of variability; variability (iv) the treatment of This review has highlighted the presence of such unanswered challenges as the pre-eminence of steady-state single-fluid research, lack of experimental validation, and lack of standardized benchmark datasets, and suggests a systematic research agenda to fully autonomous, multi-fluid, real-time pipeline integrity management systems.
A Review on Emerging Technologies of Telepresence Robot Applications, System Design and Engineering Challenges
A telepresence robot is a type of mobile robot that supports a human to achieve a sense of physical presence in a remote environment. A real time audio visual communication, mobility and interactive control is integrated within the robot. Early telepresence robot systems is based on manual teleoperation, through recent advances in communication and mobile robot technologies that increasingly merge adaptive motion and intelligent navigation improved system safety, usability and operational efficiency. This review is intended to study emerging telepresence specific studies in robots and challenges faced with mobile robot technologies. This review aims to cover concepts and developments to favour students and researchers to explore future research directions. The study is majorly done with scopus indexed journal articles and is also complemented with selected conference papers, technical studies and emerging work that address experimental platforms and application based developments relevant to telepresence robots and adaptive mobile robotics. This review is studied based on advances in navigation strategies, autonomy, perception, human robot interaction and system architectures. The paper explores current research trends, compares common existing approaches and highlights critical challenges such as autonomy and user balance, robustness in crowded environments and integration of intelligent decision making. This review also highlights the research gaps and future directions for the development of future telepresence robots with improved autonomy level and navigation capabilities.
Association Between Tobacco Consumption and Caloric intake Among Adolescents: A Cross-sectional study from urban slums of Jaipur.
Background: Adolescents in socioeconomically disadvantaged urban settings are at heightened risk of both undernutrition and early initiation of tobacco use. Nicotine’s appetite-suppressing effects may adversely influence dietary intake, potentially exacerbating nutritional deficits in resource-constrained environments. However, empirical evidence examining this relationship in urban slum populations remains limited. Objectives: To assess the association between tobacco consumption and dietary intake among adolescents, and to identify predictors of caloric intake below the Recommended Dietary Allowance (RDA). Methods: A cross-sectional analytical study was conducted among 900 adolescents (10–19 years) residing in selected urban slum clusters of Jaipur, India. Data were collected using a structured questionnaire, anthropometric measurements, and a 7-day dietary logbook. Nutritional status was assessed using Body Mass Index (BMI), and daily caloric intake was compared against ICMR (2010) RDA guidelines. Associations were examined using Chi-square tests and correlation analysis. Multivariate logistic regression was performed to identify independent predictors of caloric intake below RDA. Results: Overall, 58.0% of adolescents had caloric intake below RDA. Underweight prevalence was 33.5%, while 60.4% had normal BMI and 6.0% were overweight. Tobacco use was significantly associated with inadequate caloric intake, with 75.6% of users consuming below RDA compared to 40.4% of non-users (χ² = 113.9, p < 0.001). A significant association was also observed between tobacco use and BMI category (χ² = 6.13, p = 0.047). Smokeless tobacco, particularly gutkha, was the predominant form, with a mean initiation age of 14.3 years. In multivariate analysis, tobacco consumption emerged as an independent predictor of caloric intake below RDA. Conclusion: Tobacco consumption is significantly associated with inadequate caloric intake and increased risk of undernutrition among adolescents in urban slums. Integrated interventions targeting both tobacco use and nutritional improvement are warranted in this vulnerable population.
An Introduction to δ-open sets in a Pentapartitioned Neutrosophic Topological Spaces
In this work , we present a stronger form of – open sets and – closed sets in pentapartitioned neutrosophic topological spaces. We examine some of their fundamantal characteristics and provide examples in pentapartitioned neutrosiphic topological spaces.
Enhanced Power Quality in PMBLDC Motor Drives Using Bridgeless Buck–Boost PFC Converters with Intelligent Controller
This study presents a comparative analysis of various controllers employed in power factor correction (PFC) converters used for Permanent Magnet Brushless DC (PMBLDC) motor drives. A Bridgeless Buck–Boost converter-based BLDC drive is extensively examined with respect to power factor correction capability and Total Harmonic Distortion (THD), using both Proportional–Integral (PI) and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers. The outcomes of this investigation serve as a guide for selecting the most suitable converter topology for PMBLDC drives, based on performance indices such as input power factor, harmonic distortion, power density, system size, and cost. Simulation studies are conducted to illustrate the performance differences among the controllers in terms of power quality parameters, particularly input power factor and THD. In addition, this work outlines a simplified closed-loop speed-control strategy for PMBLDC motors. The speed regulation characteristics of the PFC-fed PMBLDC drive are evaluated under varying load torques using PI and ANFIS controllers. MATLAB/Simulink is employed to provide a reliable and flexible platform for modelling, analysis, and performance validation. The proposed control method effectively suppresses torque oscillations and demonstrates high accuracy and robustness. A bridgeless PFC buck–boost rectifier is proposed to mitigate harmonic currents in the BLDC motor drive and to enhance the overall input power factor. In comparison with conventional bridged PFC rectifiers, the bridgeless topology significantly reduces conduction losses associated with the input diode bridge of the typical Voltage Source Inverter (VSI)-fed BLDC motor drive. The use of a bridgeless boost configuration consequently leads to improved conversion efficiency. Simulation results further confirm the improvement in input power factor and reduction in harmonic content achieved by the bridgeless PFC buck–boost converter. This work presents the digital simulation and performance evaluation of a PFC buck–boost converter supplying a VSI-fed PMBLDC motor drive, in which the VSI functions as an electronic commutator. The findings highlight the effectiveness and practicality of using bridgeless PFC topologies and advanced control strategies such as ANFIS for enhancing the dynamic and steady-state performance of PMBLDC drive systems.
Investigating The Impact of Physics Curriculum Design on Students’ Mental Health and Academic Performance Implication for Counselling
The study investigated the impact of physics curriculum design on students’ mental health and academic performance: Implication for counselling. To achieve this, a quantitative research design was adopted for the study. The population for the study comprised of 17,729 SS2 physics students in public senior secondary schools in the FCT, Abuja. A Sample size of 380 SS2 students were selected for the study through Research Advisors Table (2006). A 21-item instrument tagged ‘Questionnaire on Impact of Physics Curriculum Design on Mental Health and Students Academic Performance’ (QPMA) was developed for the study. In addition, a 40-item Physics Achievement Test was developed for the study for the purpose of assessing students’ academic achievement in physics. To ensure validity of the instruments used, they were given to experts in educational research and Measurement and Evaluation. Scores from the appraisal of experts were used to obtain consensus logical validity indices of 0.80 and 0.72 respectively. The instruments were later pilot tested on 30 students; the 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 instrument using Cronbach’s Alpha reliability method. and indices of 0.78 and 0.74 were obtained. Mean and standard deviation was used to answer the research questions while regression statistics was used to test the hypotheses at 0.05 level of significance. Findings on the study showed there is a significant the impact of physics curriculum design on students’ mental health and academic performance. The study recommended that physics curriculum be restructured to help enhance students’ mental health and academic performance
Perception as A Predictor of Undergraduates’ Readiness to Use Artificial Intelligence Tools for Their Learning And Enhanced Achievement in North-Central Nigeria
Artificial Intelligence (AI) as one of the major technologies in the fourth educational revolution, it is an innovative technology used by undergraduates’ for learning. Despite the importance of artificial intelligence (AI) tools in learning and achievement , undergraduate need to be repositioned in their use AI tools for their learning and achievement in North-Central Nigeria. Hence, this study investigated predictors of undergraduates' readiness to use AI tools for learning and enhancing achievement in North-Central Nigeria. This study adopted a correlation research approach comprising of quantitative and qualitative research design using an explanatory sequential research design. The population for the study were all undergraduate students in North Central Nigeria. The target population consisted of all undergraduate students in the selected universities with an estimated population of 500,000 undergraduate students. Three states were purposively selected because of their large population size. Multistage sampling technique was used in the study comprising of purposive sampling, simple random sampling and proportional sampling techniques to select 1,065 undergraduate students from the population and 15 participants for interview. A researcher-designed questionnaire wand semi-structured interviews were used for data collection. The instruments were validated by six experts and a pilot test was conducted on 40 undergraduate students, the data obtained were analyzed using Cronbach alpha and a reliability index of 0.80 and 0.85 were obtained for perception and readiness respectively. Mean, standard deviation and scatter plot were used to answer the research questions. Research hypotheses were analyzed using linear regression at 0.05 level of significance. The qualitative data were organized, transcribed and analyzed. Findings of the study revealed: r (1,972) = 5817.061, p (0.000) < 0.05 implying that Undergraduate perception is significantly predictive of readiness to use artificial intelligence tools for learning and enhanced achievement in North-Central Nigeria. Based on the findings of this study, it was recommended among others that conferences, seminars and workshops should be organized for undergraduate to increase their level of awareness on the available AI tools that can enhance their learning.
Exploring School-Related Violence (SRV) impact on the Adolescents: The role of Guidance and counselling
The study explored the impact of School-related violence on the adolescents (12-18) in FCT, Abuja, Nigeria and the role of guidance and counselling interventions in curbing the SRV. 300 students obtained from 5 senior secondary schools 1 (SS1) in 5 Area councils of Abuja and 120 guidance counsellors purposefully selected were sampled for the study. The descriptive survey design study examined the background of school-related violence and impact on the adolescents. It provided conceptual clarifications of guidance and counselling, theoretical review of Social Cognitive theory’s relevance to the study and reviewed some related empirical studies. Three research questions were formulated to guide the study while two hypotheses were tested. The study found a grand mean of 3.80 and 3.76 on the impact of SRV on the adolescents’ academic performance and mental health respectively. It also found the impact of the role of guidance and counselling intervention at 3.16 grand mean, indicating the potentials of guidance and counselling intervention in curbing the SRV. Based on the findings it was recommended that Schools should develop evidence-based guidance and counselling programmes that focus on the school-related violence intervention and prevention.
Star–In–Coloring of Friendship and Double Wheel Graphs
This paper discusses Star–in–Coloring of a graph and gives the exact Star–in–Chromatic number of some variants friendship graphs. We show that the path union cycle and open star cycle have Star–in–Coloring properties. We also obtain bounds on the Star–in–Chromatic number of double wheel.
Bulk Queueing Model With Bernoulli Vacation, Different Service And Breakdown Disciplines And State Dependent Arrival Rates
The basic model of this article is a bulk arrival, batch service non-Markovian queue. The customers arrive in batches of variable size follows compound Poisson process. If the service is not immediate, the customer waits in a waiting line of infinite capacity, while waiting in the line, the first in first out queue discipline is applied for service. The services are given in batches of fixed size. Two types of services are given by the server for each group. The first type is of essential kind of service with two modes, based on Bernoulli schedule. The second type is of optional kind of service. The second type is based on Bernoulli schedule. After completion of each service, the server takes vacation of Bernoulli type. During the busy mode of the server, the server may breakdown, immediately send for repair. There are two types of repairs, called essential and optional. The optional repairs follows Bernoulli schedule. The service periods, vacation period and repair periods, all are random time periods follows different general distributions. The number of breakdowns follows Poisson process. In addition, the arrival rates are state dependent, based on the server state. This model is analysed in time independent domain, using supplementary variable technique by obtaining probability generating functions at various server states. Some performance measures and particular models are derived. A numerical study is also carried out.
Automated Visual Inspection in the Era of Pervasive AI: A Systematic Review of PCB Defect Detection Methodologies (2020– 2026)
As the global electronics industry transitions toward the Industry 4.0 framework, the miniaturization of Printed Circuit Board (PCB) components has outpaced the capabilities of manual inspection, necessitating a shift toward Pervasive Automated Visual Inspection (AVI) systems. This systematic review rigorously analyzes 40 seminal research works, documenting a critical architectural metamorphosis from traditional referential image processing to advanced global-context architectures. We categorize these methodologies into a multi-tiered taxonomy, evaluating the evolution of one-stage detectors from YOLOv5 to the high-resolution, deformable-convolution-enabled YOLO11. A focal point is the transition toward the new frontier of industrial AI: the replacement of quadratic-complexity Transformers with linear-scaling Mamba State Space Models (SSMs). Furthermore, the review bridges the gap between algorithmic theory and physical deployment by analyzing hardware-software co-design on FPGA and NVIDIA Jetson platforms. By providing a comparative meta-analysis of mAP against real-time FPS, this paper establishes SOTA benchmarks and offers a strategic roadmap for future research in few-shot learning and synthetic defect generation for resilient manufacturing environments.
Analysis of Various Types of Lung Image Segmentation Techniques
This paper presents a detailed study on lung image segmentation with special focus on the proposed ResUNet++ framework. Automated segmentation plays a key role in computer-aided diagnosis, as manual methods are often slow, subjective, and inconsistent. Traditional techniques struggle with low-contrast or irregular lung regions, whereas deep learning models such as U-Net, UNet++, ResUNet, Attention U-Net, PSPNet, DeepLabV3+, HRNet, and transformer-based networks have brought major improvements. Among these, our experiments show that ResUNet++ offers the best balance of accuracy, stability, and efficiency. It achieved the highest Dice, Precision, Recall, and IoU scores across datasets and showed strong adaptability to different domains. The ablation studies confirm that modules like squeeze-and-excitation and atrous spatial pyramid pooling play a vital role in handling complex pathologies. Visual results further support its ability to generate smooth and anatomically correct lung boundaries. Overall, ResUNet++ advances the state of the art in lung image segmentation and provides a reliable base for clinical tasks such as disease detection, lesion measurement, and treatment monitoring.
Sensitivity-Guided Optimization of PID and Fuzzy Controllers in Hybrid PV/Wind Systems Using Adaptive Particle Swarm Intelligence
The need to combine photovoltaic (PV) and wind energy to form hybrid renewable energy systems (HRESs) to manage inherent variability and intermittency of renewable sources has led to development of this solution. Nevertheless, robust and efficient energy management is still challenging to optimize controller parameter for, as the performance of the system is highly sensitive to parameter variations. This gap is closed in this research through systematically conducting a sensitivity analysis on tuning parameters of hybrid intelligent controllers, including Proportional-Integral-Derivative (PID) and Adaptive Particle Swarm Optimization (APSO) controllers. The influence of some critical parameters, such as PID gains, APSO coefficients and fuzzy logic membership configurations on some critical performance indicators like Rise Time, overshoot, settling time and steady state error, were assessed by way of comprehensive simulations using MATLAB Simulink. Results showed that the APSO optimization considerably outperformed the traditional Genetic Algorithm (GA) methods, achieving great reductions in overshoot (66.67%), settling time (60%), rise time (50%) and steady state error (58.33%). Based on these, an adaptive real time tuning mechanism was developed that improves system robustness as well as responsiveness under dynamic operation conditions. The findings are important methodological contributions for design of efficient and reliable hybrid renewable energy systems from the theoretical advances to real systems in this field.
The Economic Evaluation of Agricultural Sector in Nigeria: A Disaggregated Analysis using Dynamic ARDL Model
Before the oil boom in 1970s, agriculture exhibited the pillar of Nigeria’s thriftiness, providing natural resources for industries, generating job chances up to 70%, and food security to the citizens, providing 40% of budgetary income, 60% of external exchange and accounting for 47% of the output expansion in the nation. But due to oil roar in 1970s, the accentuation of government moved from agricultural to oil segment. This shift led to disregarding the agricultural segment as the oil became the chief means of income generation for the government, thereby accounting up to 60% of the nation’s external revenue. It is in view of this that this research evaluated the economic impact of agricultural sector in Nigeria: A dis-aggregated analysis within the period of 1986 to 2023. Data used was sourced from CBN statistical bulletin. GDP, proxied for economic expansion served as an endogenous variable, while the exogenous variables for disaggregated agricultural sector outputs were Crop Production (CRP), Live Stock Production (LSP), Fishery Production (FSP), Forestry Production (FRP) and Inflation Rate (INF). Consequently, this research work made use of Ex-Post Factor research plan in which multiple regression was utilized. Dynamic ARDL was employed as the research technique and the results showed that crop production significantly and positively impacted economic growth by 0.42%; while live stock production significantly promoted economic growth by 0.43%. However, fishery production negatively and insignificantly impacted on economic growth by 0.10%; while forestry production positively impacted on economic growth in Nigeria by leading to 0.29% increase in GDP, but statistically not significant. Based on the results, the research recommends that government should efficiently provide a safety and stimulating surroundings for commercialized agrarian activities which will stimulate internal and external investors to commit in different components of agrarian activities in the nation.
Legal Status of Internal Affairs Bodies : Reforms, Results and Prospects
In the article legal status of internal affairs bodies Based on the analysis of the theoretical approaches to the concept, the legal documents adopted on the reforms implemented in the system and the results achieved in practice, the current state of the legal status of the internal affairs bodies is highlighted, as well as theoretical and practical conclusions, suggestions and recommendations on the topic are formed
legal status, internal affairs bodies, task, activity, right, obligation, law, decree, decision, reform, improvement.
Stability-Oriented Power Management for Grid-Tied Hybrid Microgrid Using Fuzzy Logic Control
A Hybrid Microgrid is characterized by a collection of loads and distributed generation units that function collectively as a singularly manageable entity. As a cohesive energy distribution network, a microgrid possesses the capability to operate either in conjunction with or independently from the primary electrical grid. In the present work PV generating unit (PVU) , wind energy system unit and energy storage unit (ESU) are used for the development of microgrid. The distributed generating (DG) units in microgrid are usually parallel connected which create bus voltage maintenance issues and load sharing among the units. Here fuzzy logic-based control strategy is implemented for power management between source and load with grid-tied operation. The power management strategy manages the power generated from DG, ESU, and Grid and same time control the voltage and frequency of the microgrid the control strategies are verified in the MATLAB/SIMULINK environment.