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.
The Influence of Leadership Style, Spiritual Concepts, and Madrasah Principals’ Performance on the Work Motivation of Teachers at State Madrasah Aliyah Across Banten Province
This research examines how leadership style, spiritual values, and the performance of madrasah principals affect teachers’ work motivation in State Islamic Senior High Schools (Madrasah Aliyah Negeri) across Banten Province. Leadership effectiveness and the integration of spiritual principles are considered important elements in shaping a positive organizational climate that can encourage teachers to enhance their motivation and professional commitment. In the context of Islamic educational institutions, leadership that combines managerial capability with spiritual orientation is expected to foster stronger dedication and responsibility among teachers in carrying out their educational roles. The study applied a quantitative approach using a survey design. The population consisted of 1,252 teachers working in State Madrasah Aliyah throughout Banten Province. From this population, 100 teachers were selected as respondents through proportional sampling techniques. Data were gathered using structured questionnaires and were analyzed with statistical procedures, particularly multiple regression analysis, to identify the relationships among the variables. The findings reveal that leadership style has a positive and significant influence on teachers’ work motivation. The application of spiritual concepts in madrasah leadership also contributes to strengthening teachers’ intrinsic motivation. Furthermore, the performance of madrasah principals plays an important role in encouraging teacher motivation. Collectively, these three factors significantly influence teachers’ motivation to perform their duties effectively, which ultimately supports the improvement of educational quality in Islamic schools.
Development of Report Text Writing Teaching Materials Based on the Local Wisdom of Tangerang Batik in English Learning at Senior High Schools Across Tangerang City
This study aims to develop English teaching materials for writing report texts based on the local wisdom of Batik Tangerang for senior high school students in Tangerang City. The integration of local cultural content into English language learning is considered important to enhance students’ writing skills while simultaneously strengthening their understanding and appreciation of regional cultural heritage. Batik Tangerang, as a representation of local wisdom, provides meaningful contextual learning resources that can make English writing activities more relevant and engaging for students. This research employed a Research and Development (R&D) approach to produce contextualized teaching materials for writing report texts. The development process followed several stages, including needs analysis, product design, material development, expert validation, product revision, and limited trials in selected senior high schools in Tangerang City. Data were collected through questionnaires, interviews, and classroom observations involving English teachers and students. The collected data were analyzed using descriptive and qualitative analysis to evaluate the feasibility and effectiveness of the developed teaching materials. The results of the study indicate that the developed teaching materials based on the local wisdom of Batik Tangerang are valid, practical, and effective for use in English learning, particularly in teaching report text writing. The materials help students understand the structure and language features of report texts while encouraging them to explore local cultural themes in their writing. In addition, the use of culturally relevant materials increases students’ learning motivation, creativity, and participation in the classroom. In conclusion, integrating local wisdom into English teaching materials can enhance both language competence and cultural awareness among students. Therefore, the development of culturally based learning resources is recommended for English teaching in secondary education.
Digital Literacy and Local Culture-Based Interactive Speaking Platform to Improve English Speaking Competence of Vocational High School Students at SMKN 10 and SMK TI YPML Tangerang City
This study aims to develop and implement a digital literacy and local culture-based interactive speaking platform to improve the English-speaking competence of Vocational High School (SMK) students. The background of this research is based on the relatively low level of students' English-speaking ability, which is caused by limited interactive learning media, lack of communication practice, and the suboptimal use of digital technology in the learning process. This research was conducted at SMKN 10 Tangerang City and SMK TI YPML Tangerang City using a Research and Development approach. The research stages include needs analysis, platform design, product development, limited trials, and evaluation of the effectiveness of the platform in the learning process. The developed platform integrates interactive speaking practice features, digital literacy-based materials, and content that incorporates local cultural values as a communication context. The results of the study indicate that the use of the interactive speaking platform increases students’ participation in speaking practice, enriches their vocabulary, and enhances their confidence in communicating in English. Furthermore, the integration of digital literacy and local culture in learning provides a more contextual, engaging, and relevant learning experience for students. Therefore, this platform can serve as an innovative and effective alternative for English language learning to improve the speaking competence of vocational high school students in the digital era.
Digital Transformation in Vietnam and the Issue of Developing Digital Human Resources in State Governance
Digital transformation is becoming a trend for countries; it is not simply about applying technology to the organization and operation of state agencies, but also a comprehensive change in mindset, operating models, and methods of state governance, providing efficient services, creating new value, and promoting sustainable development. This change is reflected in the shift of state governance from a physical to a digital environment, creating a need for the development of digital human resources to perform public duties in the digital environment (developing digital civil servants), and is a necessary solution to achieve the goal of digital transformation in state governance. This study develops a theoretical framework on digital transformation and the development of digital human resources in state governance (digital knowledge and skills of civil servants; digital work capacity of civil servants). The theoretical model consists of two independent scales/variables: “Digital knowledge and skills of civil servants” (KS) and “Digital working capacity of civil servants” (WC), and one independent scale/variable: “State governance in the context of digital transformation” (SG). Based on this theoretical framework and model, this study surveys the opinions of 360 local government leaders at the commune level to collect information and conduct empirical analysis and evaluation of the digital capacity of civil servants and its impact on state governance in the context of digital transformation in Vietnam. Based on the conclusions of this empirical research, the author discusses policy solutions for developing digital human resources in state governance to meet the requirements of digital transformation in Vietnam.
Digital Transformation In State Governance In Vietnam
Digital technology is developing and being applied in the governance activities of various types of organizations, reflecting a shift in mindset and operating methods through digital technology. For state organizations, in the face of the explosive development of digital technology, the methods of state governance have changed to adapt, shifting from a physical environment to a digital environment, building a digital government to better serve the people. This places demands on the digital capabilities of the entities involved in state governance, including civil servants and citizens. Therefore, developing digital civil servants and digital citizens is a necessary solution to achieve the goal of digital transformation in state governance. This study develops a theoretical framework on digital transformation by analyzing the impact of developing digital civil servants and digital citizens on the goal of digital transformation in state governance. Based on the established theoretical framework, the author surveyed the opinions of 330 local government leaders at the commune level to draw empirical conclusions about the digital capabilities of civil servants and citizens and their impact on digital transformation goals in local government administration. From these research conclusions, the author discusses policy solutions aimed at developing digital human resources in Vietnam now and in the future.
Cyber Threat Evaluation Using Neutrosophic Hyper Soft Rough Matrices: A TOPSIS-Based MCDM Method
Cybersecurity monitoring systems continuously generate heterogeneous and uncertain alerts, making reliable threat prioritization a complex challenge. Traditional decision-making models are limited in their ability to quantify indeterminacy arising from dynamic attack conditions and incomplete security evidence. To address this issue, this paper proposes a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based Multi-criteria decision making (MCDM) method built on the Neutrosophic Hyper Soft Rough Matrix (NHSRM) framework. A MATLAB code is given to demonstrate the computational procedure of the proposed method, involving neutrosophic matrix formation, normalization, weighted aggregation, and closeness coefficient computation. Experimental outcomes indicate that attacks exploiting service disruption and unauthorized internal access pose greater operational impact compared to well-mitigated malware-based threats, reflecting the current maturity of defensive technologies in that domain. Sensitivity analysis highlights that inappropriate weight selection can lead to suboptimal threat ranking; therefore, periodic weight adjustment based on evolving attack intelligence is recommended.
Optimized Design Aspects of Low-Volume Traffic Roads on Expansive Soil Stabilized with Fly Ash
A significant portion of villages in India has been built with water-bound macadam (WBM) or bituminous roads. Rural roads typically experience low traffic volume, usually less than 0.50 MSA, primarily consisting of light transport vehicles with infrequent heavy traffic. If a pavement is constructed over a weak subgrade soil, such as expansive soil, and is unable to sustain the prevailing traffic load, it could lead to various issues and pavement failures. Maintenance of these roads is often neglected due to limited budgetary allocations, leading to deterioration of the road assets. There is an urgent need to examine alternative pavement designs to construct sustainable, enduring rural road infrastructure. In this research, a traffic survey was conducted between Undargaon–Wakav-Londhewadi village road block Madha, district Solapur, Maharashtra, India, to assess the traffic count of three days by an automatic traffic count classifier (ATCC). This research explores the strength characteristics of expansive soil near Undargaon–Wakav-Londhewadi village road block in the Madha, district of Solapur, Maharashtra, India. Fly ash utilized in this research was sourced from coal fired facility operated by National Thermal Power Corporation Ltd. (NTPC) in Solapur, Maharashtra, India. This study assessed the impacts of fly ash on the index properties and strength-related parameters of soil, including an unconfined compressive strength (UCS) and california bearing ratio (CBR). Tests were executed using expansive soil blended with varying proportions of fly ash in increments of 10%, ranging from 0% to 50% by weight. The findings indicate that incorporating 20% fly ash markedly enhanced the engineering behaviour of the expansive soil. UCS and CBR values improved by approximately 2.98 and 3.92 times, respectively, compared with untreated soil. Overall, the inclusion of fly ash produced favourable outcomes by strengthening the soil and allowing a reduction in pavement thickness by about 1.46 times relative to the natural soil condition. The study determines that fly ash as an effective additive is suitable for road construction, particularly in rural areas. The development of predictive systems using linear regression and nomographs based on the study results delivers practical tools for field engineers to guide road construction projects.
Key Insights into Performance of Textured Single Point Cutting Tool towards using Metal Matrix Composite: A Conceptual
Cutting tools play a crucial role in manufacturing industries. Currently, research is being conducted on using textured tools on various metal matrix composites to enhance tool life and improve machining quality. Experimental studies are being carried out by creating textured patterns such as grooves, dimples, concave, linear and convex textures on carbide tips. These studies demonstrate that textured single-point cutting tools exhibit improved tool life, reduced cutting forces, and enhanced lubrication capabilities. Good machining performance has been achieved using textured single-point cutting tools with various parameters. This study will be beneficial in improving tool life and enhancing cutting performance on aluminium metal matrix composite alloys using carbide-tipped tools.
Next-Generation Edge-AI Architectures for Secure EV Charging Infrastructure
The growth in the use of electric vehicles (EVs) has led to a massive increase in the demand for infrastructure needed for the charging infrastructure, thus putting an immense amount of pressure on the stability of the grid, cybersecurity and efficiency. In this work, we propose a novel Edge AI architecture for threat and security in the EV charging infrastructure powered by integrating the distributed intelligence, application of real-time anomaly detection and adaptive load optimisation. The proposed system employs artificial intelligence models that are based on fuzzy logic and run at the edge of the network, in the monitoring of the grid conditions, the health of the EVs and cyber threats. Secure coordination between EV's, charging stations and central servers is put in place for increased system reliability, congestion and infrastructure resilience. Motivation: Weaknesses in the grid control structures and cybersecurity frameworks have been exposed by the increase in the number of EV charging stations. Uncoordinated charging loads have the potential to destabilise power distribution networks and pose to create complex danger due to cyberattacks and data breaches. Current centralised solutions have latency and scalability issues, which emphasise the need for a decentralised and intelligent framework that can make decisions at the edge level 2 (in real-time) regimes. This paper is motivated by the need to ensure safe, efficient and scalable EV charging operations and protect grid stability and the safety of the critical energy infrastructure. Novelty: The novelty in this study is to combine Edge AI intelligence with a fuzzy logic-based decision mechanism for the security of EV charging infrastructure. As opposed to typical architectures that are developed on the premise of cloud dependency, the proposed architecture shifts computational intelligence to the charging stations to achieve the specific purpose of ensuring low-latency anomaly detection and adaptive load balancing. The framework provides the unique combination of a grid stress analysis, EV battery health monitoring and cyber security threat analysis as a single decision module. This multidimensional and edge-centric design helps to increase the system resilience and improve the scalability, and allow to correct coordination of the involved components (EV, grid and communication networks). Findings: Simulation and analytical exams indicate that the proposed Edge-AI architecture has a very positive impact on improving the reliability of charging operations and security of the charging infrastructure. The system can identify the abnormal charging behaviour and counter the cyber threats, and also dynamically adjust the charging loads to prevent grid overloading. Results show a reduction in response time, increased efficiency in load distribution and better security than unauthorised access. The integrated decision framework provides a higher degree of integration of operational stability as opposed to the traditional centralised systems, and this justifies the effectiveness of implementing distributed approaches using Edge AI to change the performance of the EV charging infrastructure and enhance cybersecurity resilience
Digital Supply Chain Management and Digital Inclusion in Food & Beverage Companies in Nigeria: The Mediating Role of Sustainability
Digital Supply Chain Management (DSCM) is increasingly viewed as a catalyst for organizational transformation, yet its role in advancing digital inclusion remains underexplored, particularly in emerging economies. Prior research has largely focused on the efficiency and technological gains of DSCM, overlooking the extent to which sustainability may serve as a bridge between digitalization and inclusive outcomes. This study addresses that gap by investigating the mediating effects of economic, social, and environmental sustainability on the relationship between DSCM and digital inclusion in Nigeria’s food and beverage sector. Adopting a cross-sectional survey design, data were collected from 256 staff at multiple organizational levels across food and beverage companies in Nigeria. Using PROCESS Macro (Model 4, parallel mediation), both direct and indirect pathways were tested. The findings reveal that DSCM positively influences all three dimensions of sustainability, each of which significantly predicts digital inclusion. Mediation analysis demonstrates that DSCM’s impact on inclusion operates entirely through sustainability, with economic sustainability exerting the strongest influence, followed by social and environmental dimensions. This study concludes that digital transformation on its own is insufficient to foster inclusive outcomes unless complemented by sustainable practices. The study recommends that managers should align digitalization strategies with fair procurement, timely payments, digital literacy programs, and environmentally responsible initiatives. Policymakers are also encouraged to design enabling policies and incentives that support the integration of sustainability within digital supply chain practices.
Determination of Strength Properties of Cement Concrete Utilizing Recycled Concrete Aggregates and Thickness Design of Rigid Pavement for Low Volume Road
Recycled concrete aggregate refers to aggregate obtained from demolished concrete. It can be utilized for cement concrete production to meet rising need of huge quantities of aggregates and reduced problem of disposal of demolished concrete simultaneously. Low volume road carries average daily traffic less than 450 commercial vehicle per day. In laboratory, properties of recycled concrete aggregate was studied and it was mixed with coarse crushed aggregate in different proportions i.e. 100%, 80%, 60%, 40%, 20%, 0% by weight to produce concrete with addition of plasticizer and microsilica. The concrete was casted and cured to perform compressive strength, split tensile strength and flexural strength tests for 7 days and 28 days concrete. Design of thickness of cement concrete pavement was done according to Guideline for Design and construction of cement concrete pavements for low volume road IRC:SP:62-2014 & Design and construction of rigid pavement for low volume roads prepared by Institute of Professional Training and Management Nepal. Cost estimation was done for construction of cement concrete pavement according to Department of Roads norms for Road and Bridge Work. Result showed that M30 grade concrete for low volume road can be produced with coarse recycled concrete aggregate. The compressive strength, split tensile strength, and flexural strength of concrete gets reduced with addition of recycled concrete aggregate upto 6.7%, 6.25% & 8.99% respectively as compared to crushed aggregate concrete. Design thickness of plain jointed cement concrete pavement for low volume road was developed for different value of subgrade CBR 4, 5, 6, 7, 8, 9, 10, 15, 20, 50 traffic volume 50 CVPD, 150 CVPD, 300 CVPD & 450 CVPD, Hilly/Terai region underlying granular sub-base or cementitious sub-base. Cost estimation calculation for M30 grade concrete with crushed aggregate, recycled concrete aggregate and their combination showed upto 8.5% cost saving per cubic meter concrete if recycled aggregate concrete is used.
Patch-Embedded Transformer Model For ECG Noise Removal
Cardiovascular disease ranks as one of the most dangerous medical conditions that exists throughout the globe, which doctors use electrocardiogram (ECG) signals to monitor.The analysis and diagnosis process becomes challenging because different types of noise disrupt ECG signal at various strength levels. The medical field requires proper denoising methods because they help establish better signal-to-noise ratios, which enable accurate cardiovascular patient monitoring. The research presents a deep learning system for ECG signal denoising, which combines one-dimensional convolutional neural networks with Transformer architecture. The 1D convolutional layers extract features from the ECG signal using multiple kernel sizes, creating a multi-scale patch embedding representation. The system processes this embedding through a Transformer-based network, which strengthens contextual learning while enhancing overall denoising results. The proposed model effectively removes noise while preserving important morphological features of the ECG signal.In additional convolutional block is incorporated within the architecture to enhance local feature preservation and improve reconstruction quality. By combining multi-scale local feature extraction global contextual modeling through self-attention, and enhanced reconstruction via the encoder–decoder structure the proposed method achieves effective noise suppression while preserving important morphological features of the ECG waveform
ECG Signal Denoising, One-Dimensional ConvolutionalNeural Network (1D-CNN), Transformer Architecture, Signal-to-Noise Ratio (SNR), Encoder &Decoder Block
A Comparative Analysis of Socio-Economic Status and Livelihood Patterns of Eri and Muga Silk Rearers in the Bodoland Territorial Region of Assam
ericulture is a traditional and important source of income in Assam, especially in the Bodoland Territorial Region (BTR). The farming of Eri and Muga silk creates jobs for rural families. This paper compares the social and economic conditions and living patterns of Eri and Muga silk producers in the five districts of BTR: Kokrajhar, Chirang, Tamulpur, Baksa, and Udalguri. The study examines the demographic and economic characteristics of sericulture producers. It identifies the factors that affect their choice between Eri and Muga rearing. It also evaluates how these activities affect household incomes and women's empowerment. Multistage sampling is used to select households for structured interviews and data collection, along with field observations. Statistical tools like descriptive statistics, t-tests, and chi-square analyses are employed to compare the socio-economic profiles, income patterns, production levels, and seasonal changes among Eri and Muga silk rearers. The study intends to find out that Eri silk rearing is more common because it needs a lower initial investment and a simpler rearing process. In contrast, Muga silk production generates higher economic returns and better market opportunities. Factors like land size, education, and access to extension services strongly affect the choice of silk variety and overall productivity. Female participation is important, especially in rearing and post-cocoon processing. This shows that sericulture is important for women's economic empowerment. The findings highlight the challenges and opportunities that Eri and Muga rearers face. They emphasize the need for focused actions such as skill development, improved market access, and policy support to make sericulture a more sustainable livelihood in BTR. This study helps us understand the social and economic aspects of sericulture. It also offers guidance for promoting sustainable rural development in Assam.
A High-Return Circular Recovery Model for Near-Zero Waste in Hot Aluminum Rolling for an Aluminum Flat Rolled Products (FRP) Industry
The aluminum industry faces significant economic and environmental challenges due to the rapid degradation of rolling lubricants. Traditional "Bleed and Feed" maintenance fails to address the "Viscosity Trap," leading to the disposal of over 1,000 MT of hazardous waste annually. This study evaluates an integrated Circular Resource Recovery Suite consisting of Vertical Pressure Leaf Filtration (VPLF), Thin Film Evaporation (TFE), and Thermal Desorption (TDU). Empirical data demonstrates a 90% Resource Recovery Rate (RRR) for oil and an 85% RRR for adsorbents. The intervention achieved an annual saving of ₹9.52 Crore with a capital payback period of only 52 days. The transition to on-site regeneration results in a 93.6% reduction in OPEX and ensures compliance with the 2023 Indian EPR Amendment Rules, proving that circularity is a self-financing necessity for modern metallurgical operations.
Impact of Digital Technology on the Buying Behaviour of Indian Consumers: A Study of Punjab State
The rapid advancement of digital transformation has significantly transformed the buying behaviour of Indian consumers. Digital innovations including smartphones, digital platforms, social media, electronic payment systems, and artificial intelligence have reshaped the way consumers search for information, evaluate alternatives, and make purchase decisions. This research paper studies that how digital transformation effect on the consumption behaviour of an Indian consumers, with a focus on changes in purchasing patterns, decision-making processes, and post-purchase behaviour. The study focused on graphic research proposal including both primary and secondary data to analyse consumer awareness, preferences, and attitudes toward digital transformation-enabled shopping. The findings reveal that digital transformation has increased consumer convenience, access to information, price transparency, and personalization, thereby influencing impulse buying, brand loyalty, and customer satisfaction. The paper also highlights the increasing role of online interaction platforms influencers, online reviews, and electronic marketing strategies in determining buyers perceptions. The study concludes that digital transformation has become a critical determinant of buying behaviour in India, compelling businesses to adopt innovative digital strategies to remain competitive in a rapidly evolving marketplace.
Implementation of a Facial Recognition Attendance System using Raspberry Pi
Manual attendance systems, such as manual sign-in sheets in academic institutions, are often error-prone, time-consuming, and can be easily compromised. Fingerprint biometric systems can pose hygiene risks due to shared contact surfaces. This motivated the development of an automated, contactless facial recognition-based attendance system to enhance accuracy, efficiency, and hygiene in educational settings. This project aimed to design and implement a system that uses facial recognition technology to accurately log student attendance in real time, while leveraging the use of the Raspberry Pi. The system integrates a Raspberry Pi Camera Module for capturing facial images, a 16x2 I2C LCD for displaying recognized names and matric numbers, and a buzzer for auditory confirmation. Using Python, the system was built with key libraries such as face recognition, dlib, and OpenCV. A dataset comprising 60 students, each with about 15 facial images, was used for training. Facial features were extracted using 128-dimensional embedding from dlib and classified with a Support Vector Machine (SVM). The system was tested under different lighting and angles, which resulted in producing an accuracy range of 83–96%, an average recognition time of 6 seconds, a false acceptance rate (FAR) of 15%, and a false rejection rate (FRR) of 10%. This paper is subdivided into 6 sections. Section 1 is the introduction, Section 2 is the literature review, Section 3 is Methodology, Section 4 results and discussion, Section 5 recommendations, Section 6 conclusion.
Mental Health Outcomes Among the Indian General Population Exposed to COVID-19 Lockdown Compared to Pre-Lockdown Conditions
The COVID-19 pandemic, declared a Public Health Emergency of International Concern in January 2020 and a global pandemic in March 2020, necessitated stringent public health containment measures worldwide. In India, a nationwide lockdown was implemented from March 24, 2020, to mitigate community transmission. While effective in infection control, prolonged restrictions on mobility, social interaction, and economic activities raised concerns regarding mental health and psychosocial well-being. Public health emergencies of this magnitude are associated with adverse psychological outcomes, including stress, anxiety, depression, and functional disruptions. Objectives: This study aimed to assess the impact of the COVID-19 lockdown on mental health status and daily lifestyle among the general population in India, with specific assessment of stress, anxiety, depression, mood disturbances, and coping strategies conceptualized through the optimism (persisting) and adaptation (shifting) framework. Demographic determinants of these outcomes were also examined. Methods: A descriptive cross-sectional online survey was conducted during the complete lockdown period (March–May 2020). A structured self-administered questionnaire was developed using validated constructs from the Perceived Stress Questionnaire, Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), Mood Disorder Questionnaire, and the Shift-and-Persist model. The survey was disseminated via digital platforms, yielding 90 completed responses. Statistical analysis was performed using SPSS version 25. Stepwise multiple regression was applied to identify demographic predictors, including age, gender, education, occupation, marital status, and housing characteristics. Results: Disruption of daily routines was reported by 94% of participants, whereas 82% experienced improved family engagement. Financial strain was prevalent. Common psychological manifestations included sleep disturbances (46%), irritability (38%), anticipatory fear (>50%), and mental fatigue (~35%), with higher symptomatology among females. Higher educational attainment significantly predicted lower stress and anxiety levels and greater adaptive coping, while marital status predicted mood variability. Conclusions: The lockdown exerted dual psychosocial effects, characterized by enhanced familial cohesion alongside substantial psychological distress, highlighting the necessity for targeted mental health strategies during large-scale public health emergencies.
Public Health, Lockdown, Pandemic, Covid-19, Mental Health, Stress, Anxiety, Depression, Mood Swings, Shift, Persist, Optimism
Multi-Domain Medical Image Classification Using EfficientNet-B3
Medical image analysis has seen considerable progress with the integration of deep learning approaches, especially convolutional neural networks (CNNs), which are known to be successful in extracting relevant spatial information from complex biomedical data. However, most existing models are generally developed for a particular application and are not flexible enough to be used on multiple disparate datasets in a single system. In real-world medical applications, ECE images for cardiac analysis, EEG-based graphical representations for seizure analysis, and facial images for emotion analysis are quite disparate in terms of structural information and feature distributions, which make it quite challenging for existing CNN models to generalize across domains without resorting to multiple models or repeating training. These application-specific models are quite computationally intensive and less efficient in terms of deployment. To overcome these hurdles, this study proposes a unified classification platform based on the EfficienyNet-B3 model, which is capable of handling multiple biomedical imaging applications using a single backbone model. The proposed platform incorporates a dynamic key-driven selection module that enables runtime switching between applications without requiring the loading of multiple independent models, thus overcoming redundancy while preserving excellent predictive accuracy and scalability for real-world applications.
EfficientNet-B3, Biomedical Image Classification, Heart Disease Detection, Epileptic Seizure Detection, Facial Emotion Recognition
Comparative Analysis of Homeopathic Treatment Approaches in Cancer: A Research Perspective
Current data on cancer incidence and mortality underscore the urgent need to strengthen existing treatment infrastructure, which remains inadequate to address the increasing demands. This situation has prompted research into the efficacy of various homeopathic approaches in cancer treatment. Single blind, prospective, observational clinical study and the sample size was fixed at 40 cases using systematic purposive sampling. The cancer diagnostics, we used their approaches such as Kent, German New Medicine (GNM), and Boenninghausen-Boger (BB). Results of the cancer diagnostics among the treatment approaches, the Kent method demonstrated a statistically significant effect, with an odds ratio (OR) of 3.947 (95% CI: 1.305–51.411, p < 0.05), indicating that moderate improvement was approximately four times more likely compared to mild improvement, using the Organopathic approach as the reference. The German New Medicine GNM approach showed an OR of 2.399 (95% CI: 0.603–46.649, p = 0.05), suggesting moderate improvement was about twice as likely compared to mild improvement; however, this effect was statistically insignificant relative to the Organopathic approach. The BB approach (OR = 1.621; 95% CI: 0.261–35.427, p > 0.05) showed no significant effect on the predicted results. Regarding sex, with female as the reference category, the odds ratio was 0.028 (95% CI: 0.002–0.376, p < 0.05), indicating a statistically significant impact of sex on the outcome. The Kent approach was moderately more effective than the other methods and reached statistical significance..
Cancer, Homoeopathic treatment, Kent approach, German New Medicine (GNM), Boenninghausen-Boger (BB)
Decision-support for Rocket Propulsion Technology evaluation
The evaluation of rocket propulsion technologies involves complex trade-offs among performance, reliability, technological maturity, cost, environmental impact, and mission adaptability. These criteria are frequently hierarchical, interdependent, and characterized by uncertainty or incomplete information, making traditional additive or fully compensatory decision models inadequate. This paper proposes a hierarchical interval outranking model to support preferential classification of rocket propulsion technologies under imperfect knowledge. The proposed framework allows criteria to be decomposed into coherent sub-criteria across multiple levels (e.g., thrust performance, specific impulse, structural complexity, scalability, lifecycle cost, and Technology Readiness Level). Interaction effects such as synergy, redundancy, and antagonism among criteria are explicitly incorporated through interaction weights. Uncertainty in performance data, weights, veto thresholds, and majority thresholds is modeled using interval numbers. The method generates credibility indices for outranking relations at each hierarchical level and supports ordinal classification into preferentially ordered categories. This enables decision-makers to conduct both global and dimension-specific assessments while preserving essential structural properties such as monotonicity and consistency. The applicability of the model is demonstrated through a comparative assessment of selected rocket propulsion technologies. Results illustrate how the proposed approach captures complex performance trade-offs and supports transparent, structured, and robust technology evaluation in aerospace decision-making contexts.
Hybrid AI–Polynomial Compensation Framework for Multi-Parameter Error Mitigation in Fiber Bragg Grating Sensors
FBG networks fit much into a small space. They are strain gauges, temperature gauges, pressure gauges, and even refractive index gauges, and they do it repeatedly and reliably even in harsh conditions. However, when you play them on the field, it gets dirty. It is contaminated with systematic errors, cross-sensitivities, thermomechanical effects, interrogator hysteresis, nonlinearity of wavelength, and packaging effects. These are not such trifles. They pose a danger to accuracy particularly where measurements count. The real challenge? Paying makes the readings credible once more. In this case, the extra techniques are needed. Learn data using advanced techniques and physics-based polynomial models, and you can learn quickly with interpretable compensation even on hardware with limited resources. Such is the concept of the hybrid frame presented here. Deeper down is an AI regressor constructed using radial-basis expansions that have a ridge regularization. This regressor addresses the multi-parameter corrections which are complex. After that, an elementary poly block intervenes, forcing up the structure you would get as an application of the Bragg condition and known cross-terms. This arrangement specifically addresses such problems as temperature-strain coupling, drift of the interrogator, and sluggish hysteresis of tunable filters. It maintains the calibration even when the ambient conditions vary. In this research, the experiments are conducted with the use of a simulated dataset that is built on the statistic properties of publicly available FBG datasets such as the PHM Society 2015 FBG Data Challenge dataset, the Kaggle FBG Spectrum Dataset, and the IEEE Dataport FBG Time-Series Dataset. So, how does it stack up? Pitting this hybrid approach against an approach that simply uses a plain polynomial compensator you find obvious improvements in the form of lower root-mean-square error (RMSE) and reduced bias in the event of drift and narrower confidence bands around the reconstructed values. Practically, the paper has achieved three items, namely (i) a concise hybrid AI-polynomial design that makes the residual separation explicit; (ii) a production that can produce seven diagnostic plots that can be audited with ease; and (iii) a well- Benchmarked workflow. The bottom line: the hybrid method reduces RMSE, when compared to conventional methods of polynomies, by 25-45 % across all channels, and it also provides transparent coefficients which can be used to recalibrate fields and verify quality assurance.
Fiber Bragg grating (FBG), error compensation, cross-sensitivity, hysteresis, drift, machine learning, polynomial modelling, ridge regression, radial basis features, metrology
Phishing URL Detection Using Machine Learning
Phishing attacks are one of the most significant cybersecurity risks in the online era because they may deceive users into providing sensitive data via fraud websites, which mimic reliable sources. The rule-based and blacklist-based methods of detection are usually ineffective at detecting new phishing URLs because these methods are static. To overcome this constraint, this paper proposes a machine learning model of phishing URL recognizer with real-time detection. The system proposed here will use a trained supervised Gradient Boosting Classifier to run on a labeled body of URLs. The feature extraction is done exhaustively to examine lexical, domain based, and webpage characteristics such as URL length, use of special characters, use of HTTPS, presence of IP address, age of the domain, redirection, and HTML based features. Depending on these characteristics, the model labels URLs as a legitimate or phishing one and produces a threat score that is based on confidence. The system is implemented on a web-based interface that displays risk levels in a visual manner and shows the features presented in explanations to make it more transparent and comprehensible to the user. Experimental analysis proves that the proposed system has a high detection rate, which proves its efficiency as a practically applicable, explainable, and deployable phishing URL detection system.
Determinants to Prefer Solar Products by The Customers and Problems Faced by Them.
This study primary focuses on understanding problems and factors influencing the solar energy in Erode district. Solar energy one of the most promising forms of renewable energy can be harnessed in two principal ways. Majority of Indians still use traditional fuels such as dried cow dung, agricultural waste and fire wood as cooking fuel. These forms of fuels are inefficient sources of energy, as its burning releases high levels of smokes, particulate matter. It is obvious that solar products are the products which can carry out the energy demand accomplishment aim. It has also been understood that solar products need awareness among the consumers but how should it be promoted, how should it be made available for all the consumers or for consumers at mass level, how should solar products be advertised, how should it get attention in the market in comparison to others products? These are few questions which have significant impact on solar products. The answers to all of these questions can be met by one of the marketing management’s core concept i.e. “Marketing Strategy”. Marketing strategy is defined as, “A business' overall game plan for reaching people and turning them into customers of the product or service that the business provides”.
Role of Tribal Women in Biodiversity Conservation in North East India with Special Reference to Assam: An Eco-Feminist Approach
This paper attempts to explore the critical yet often overlooked role of tribal women in biodiversity conservation in North East India, with a specific focus on Assam. Drawing upon an eco-feminist framework, the study highlights how the intersection of gender, ecology, and indigenous identity shapes environmental practices in tribal communities such as the Mishing, Bodo, and Karbi. These women are deeply embedded in the stewardship of local ecosystems, engaging in sustainable practices such as wetland management, agroforestry, seed preservation, and the protection of sacred groves. Their knowledge is transmitted through oral traditions, songs, rituals, and hands-on labor, forming a living body of ecological wisdom. Despite their significant contributions, tribal women remain largely invisible in state-led conservation efforts and environmental policy-making. Modern development, deforestation, displacement, and climate change continue to threaten both biodiversity and the socio-cultural systems that sustain it. Additionally, the erosion of traditional ecological knowledge among younger generations poses a serious challenge to long-term conservation. By using secondary sources, ethnographic literature, and a thematic analysis grounded in eco-feminist theory, this study argues for the urgent need to recognize and integrate the roles of tribal women into formal conservation frameworks. It advocates for community-led, gender-inclusive, and culturally sensitive environmental governance models. Ultimately, this research positions tribal women not merely as resource users but as custodians of biodiversity and agents of ecological resilience in Assam’s diverse landscapes.
Eco-feminism, Tribal women, Biodiversity conservation, Indigenous knowledge, Assam
Statistical Evaluation of Diagnostic Radiation Exposure Across Imaging Modalities and Patient Demographics
The increasing reliance on diagnostic imaging techniques that utilize ionizing radiation has raised important concerns regarding patient radiation exposure and associated health risks. This study presents a statistical analysis of diagnostic radiation doses received by patients undergoing common medical imaging procedures, including X-ray and computed tomography (CT) examinations. The objective of the study is to evaluate dose distribution patterns, identify factors influencing radiation exposure, and assess variability across different patient demographics and examination types. Radiation dose data were collected along with relevant patient and procedural variables such as age, gender, type of scan, and anatomical region examined. Descriptive statistical methods were used to summarize exposure levels, while inferential statistical techniques, including regression analysis and hypothesis testing, were applied to determine significant relationships between radiation dose and influencing parameters. The results demonstrate notable variations in radiation exposure based on imaging modality and body part, with higher doses observed in advanced imaging procedures. The findings highlight the importance of continuous monitoring and statistical evaluation of diagnostic radiation practices. By identifying trends and potential sources of unnecessary exposure, this study supports optimization strategies in accordance with radiation protection principles, particularly the ALARA concept. The statistical approach adopted in this study provides valuable insights for improving diagnostic protocols, enhancing patient safety, and promoting evidence-based decision-making in radiological practice.
“Stock Price Prediction Using Machine Learning and Data Mining Techniques: A Comparative Study on Market Forecasting Models”
Predicting stock market trends is a challenging task due to its dynamic, nonlinear, and volatile nature. In this study, various machine learning and data mining algorithms are employed to forecast stock prices based on historical and technical indicators. The research explores the performance of supervised models such as Support Vector Machines (SVM), Random Forests (RF), and Gradient Boosting, along with deep learning models like Long Short-Term Memory (LSTM) networks. Data preprocessing techniques including normalization, feature extraction, and correlation-based feature selection are applied to improve model accuracy. The proposed framework also incorporates sentiment analysis from financial news to enhance prediction reliability. Experimental results conducted on benchmark datasets (NSE and NASDAQ) demonstrate that hybrid LSTM models outperform traditional approaches with an accuracy improvement of 10–15%. The study concludes that integrating data mining with advanced machine learning provides a robust solution for real-time financial forecasting.
Increasing the Quality of the Side Surfaces of the Teeth of Saw Cylinder Discs Using Abrasive Blast Treatment
The article presents materials on the effective use of abrasive blasting of the side surfaces of the teeth of saw blades in order to activate them during lightering. The scraping action and cutting of short fibers from seeds is enhanced by microroughnesses of the formed micro profile after impact interaction of an abrasive particle with a metal surface. Using atomic force microscopy, we obtained the height and step parameters of the roughness of the machined tooth surface, which are compared with the transverse dimensions of the cotton fibers, and thus a model for the intensification of the littering process is proposed. It is shown that when modeling a single interaction of an abrasive particle with the surface of a part in the process of collision under air (liquid) pressure, it is necessary to take into account the specifics of impact-abrasive wear, when the separation of a wear particle is preceded by metal destruction. In the process of schematization of the contact interaction during abrasive blasting, some commonality with the shot blasting process and a number of assumptions typical for grinding, when micro cutting with a single abrasive grain is considered, are taken into account. Abrasive blasting of the side surfaces of saw blade teeth with a cotton-processing machine particle made of black silicon carbide is proposed. The achieved required processing quality makes it possible to achieve microrelief on the treated surface.
Developing Digital Human Resources to Meet the Requirements of Digital Government in Vietnam
Digital government is a developing trend in many countries, based on digital technology, digital data, and operating in a digital environment. With the emergence of digital government, the activities of state agencies are shifting from the physical to the digital environment, creating a demand for the development of digital human resources to operate the digital government. Digital human resources here refer to the entities participating in state governance processes in the digital environment, including both civil servants and citizens. Therefore, developing digital human resources to meet the requirements of digital government means developing digital capabilities for civil servants and citizens to form digital civil servants and digital citizens. This study constructs a theoretical framework to analyze the impact of the development of digital civil servants and digital citizens on digital government. The author surveyed 270 local leaders at the commune level from three provinces in three regions of Vietnam: Bac Ninh province (Northern), Quang Tri province (Central), and Tay Ninh province (Southern). The survey results provide further evidence that civil servants possess basic digital knowledge and skills necessary for working in a digital environment, but these still need improvement; citizens are disseminated and updated with digital knowledge and skills to conduct transactions in the digital environment when participating in state administration, but their digital competence is assessed as low, requiring appropriate solutions to develop digital human resources to meet the requirements of digital government in Vietnam.
Digital government; Digital human resources; Digital civil servant; Digital citizens; Vietnam.
Loan Scope: Predicting Real Time CIBIL Impact and Recovery
This project aims to develop an intelligent system to predict an individual's future CIBIL score using machine learning models. It leverages a Random Forest Regressor trained on historical financial data, including variables such as current CIBIL scores, loan amounts, credit card usage, and repayment histories, to accurately predict changes in credit behavior. The model dynamically analyzes how new loans, late payments, and other credit activities influence the future score, making it adaptable to a wide range of user profiles. The system's architecture includes a Flask-based backend that handles user registration, login, and the fetching of user financial data based on PAN numbers from a pre-existing database. By collecting user data through a CSV file, the model uses this input to generate a prediction for future CIBIL scores. It adjusts predictions based on factors such as new loans, penalties for late payments, and rewards for timely payments, providing users with insights into how their actions could impact their financial standing. The project is designed to offer real-time insights and accurate forecasts oncredit scores, empowering users to manage their credit health proactively. This machine learning-driven approach enhances the traditional CIBIL score analysis with a data driven framework
Thermodynamics Guided Machine Learning Models for CO₂ desublimation temperature prediction
Accurate prediction of the carbon dioxide desublimation temperature (CDDT)—the threshold below which CO₂ transitions directly from vapor to solid phase—is critical for cryogenic carbon-capture systems, natural-gas processing, and pipeline safety. This paper presents a unified, three-tier study covering: a classical thermodynamic model based on the Peng–Robinson equation of state (PR EoS) solved via Lagrange’s analytically stable cubic solver; machine learning (ML) approaches including Decision Tree (DT), Gaussian Process Method (GPM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Genetic Programming (GP) applied to a consolidated dataset of 430 experimental measurements; and a physics-informed hybrid machine learning (PI-HML) framework that embeds fugacity equilibrium, Clausius–Clapeyron consistency, and monotonicity constraints directly into the learning objective. Together, these frameworks provide a complete, validated toolkit for managing CO₂ solidification in industrial cryogenic operations.
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