Tuijin Jishu/Journal of Propulsion Technology
type: channel
description: 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 boar…
publisher: Science Research Society
language: en
publication name: Tuijin Jishu/Journal of Propulsion Technology
issn: 1001-4055
items: Survey Paper on: Leveraging Artificial Intelligence (AI) and Blockchain to Enhance Supply Chain Management for a Ganapati Idol Selling Platform
items: A Study on the Role and Challenges of Food Supply Chain Management Towards Achieving Zero Hunger in Uttar Pradesh
items: Using Technology to Improve Language Learning: A Thorough Analysis
items: Academic English: Motivating students for Academic success
items: Assessment of the Effectiveness of Marma Chikitsa in Treating Avabahuk W.S.R to Frozen Shoulder : A Case Study
items: Talent Management, Talent Retention and Succession Planning in Universities: An Integrated Theoretical Framework for Sustainable Academic Excellence
items: A Pilot Study on Talent Management, Talent Retention and Succession Planning Among Academic Staff in a Malaysian Higher Education Institution
items: Design and Eco-Friendly Synthesis of 4-Oxothiazolidinone-Thiourea Derivative: In Silico Antimicrobial Evaluation and DFT Studies
items: Transforming E-Collaboration with AI and Emerging Digital Technologies
items: The Economic Evaluation of Exports in Nigeria
items: “Holistic Impacts of AICS Assistance: Addressing Clients’ Needs beyond Financial Relief”
items: CNNRMHSA-LGTEN: An Efficient Local Graph Transformer and Convolutional Attention Exchange Network for Drug Target Interaction Prediction
items: Development of a Novel Analytical Framework for Investigating Non-symmetric Deformation Behavior in Strip Rolling
items: Remote and Hybrid Work Policies as Sustainability Drivers: Re-Imagining the Future of IT Through Lessons from Other Industries
items: Bridging Silence and Semantics: A Multimodal Review of Sign Language Recognition, Translation, and Adaptive Learning Systems
items: Emission Control and Sustainability: Evaluating Bharat Stage - 6 (BS6) Technology in Indian Automotive Industry
items: A Hybrid AI-Driven Integral Transform Framework for Nonlinear Differential Equations in Engineering Systems
items: Complexities in Remote Sensing-Based Object Detection and Interpretation
items: Analysis of Diseased One Prey Two Predator Model with Prey Harvesting in Holling type -II Functional Response
items: Design and Optimization of Machine Learning-Enhanced Forward Error Correction Codes for Improving Data Integrity in Global Navigation Satellite Networks
items: Title
items: Probabilistic Seismic Performance Assessment of RC Structures with Material and Geometric Uncertaities
items: “The Effect of Influencer Marketing on Consumer Purchase Behaviour: A Systematic Review of Global Literature (2015–2025)”
items: Heat Source and Slip Impacts on Porous Medium Flow of Williamson Nanofluid: Numerical Approach via Keller-Box Scheme
items: Succession Planning Practices and their Impact on Organisational Service Performance: Cross-National Evidence from Nigeria and Ghana
items: Design and Fabrication of a Motorized Screw Jack for Automotive Applications
items: A Mult-order Non-fourier Fractional Differential Equation for Heat Transmission
items: Intelligent Criminal Face Recognition Using Deep Feature Refinement and Adaptive Machine Learning for Enhanced Identification Accuracy
items: Exploring Alternative Funding Models to Enhance the Autonomy of Bangladesh’s Anti-Corruption Commission (ACC): A Survey-Based Quantitative Study

Survey Paper on: Leveraging Artificial Intelligence (AI) and Blockchain to Enhance Supply Chain Management for a Ganapati Idol Selling Platform
type: item
description: This study introduces a new digital platform that is focused on enhancing customer engagement in the retailing of the Ganesh idol. It integrates artificial intelligence, machine learning, and blockchain into a single online platform. The website would provide an engaging shopping experience for customers. For instance, buyers can interact with AR-based 3D models that can help preview, customize, and evaluate idol designs in detail prior to purchase. The aim is to go beyond mere conventional e-commerce models and create a more personalized and interactive marketplace driven by modern digital technologies. To implement this, we use computer vision, deep learning models, and generative models like Semantic Segmentation. These methods provide clear visualizations and tailored customizations. In contrast, traditional methods may be restricted when processing images or using predictive models such as LSTM, ARIMA, and GRU. These techniques have difficulty with dynamic user edits/changes or detailed artistic features. Semantic Segmentation combined with AR tools can facilitate real-time customization and realistic design previews, while blockchain-based smart contracts preserve the integrity of every transaction in a secure, open, and tamper-proof environment, furthering trust and accountability at every point in the supply chain. Moreover, the study shows that by integrating deep learning methods with AR-enabled 3D visualization, customers can participate in design-related activities. They can personalize features based on their preferences. Utilizing blockchain for safe and traceable exchanges, the system establishes a sound, adaptable, and transparent structure. This renovates not only the supply chain but also upgrades the overall e-commerce.
subject: Articles
  topic: section
creator: Patil Sahil Dinanath
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: 344
ending page: 351

A Study on the Role and Challenges of Food Supply Chain Management Towards Achieving Zero Hunger in Uttar Pradesh
type: item
description: Aim: The study’s aim is to explore the role of and challenges faced by the food supply chain management of Uttar Pradesh towards achievement of Zero Hunger in the state. Methods: The secondary qualitative analysis was employed in this research to identify both the optimistic aspects and barriers of the food system and supply chain management. Results: The review through thematic analysis implicate food supply chain management to be highly important, hence given the obstructions in food supply chain and food security indicate challenges faced by the state on agricultural, infrastructural, and demographic reach level. Conclusion / Recommendation: Alongside this, the study also included several tangible recommendations, that can help the researchers to meet the study goals to achieving the Zero Hunger in Uttar Pradesh.
subject: Articles
  topic: section
creator: Mohd. Basim
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 330
ending page: 343

Using Technology to Improve Language Learning: A Thorough Analysis
type: item
description: Technology in language learning engage some form of technology to create an artificial learning environment. In a traditional language classroom course, students program brings technology into the classroom, can generate blended learning to catch student’s attention. When technology is integrated in the classrooms, teachers are able to initiate more effective teaching. The communication and collaboration between teacher and students improve. In other words, technology can advance students learning and help teachers at school. Teachers can create interactive classrooms and can provide examples of new linguistic features in online communication, lastly teachers can provide authentic materials such as newspapers gamification and podcasts. In the globalized world of today, multilingualism is becoming more and more desirable. Technology has become a potent instrument in language learning, providing creative answers to age-old problems. This thorough analysis looks at the various ways that technology can help with language learning. In addition to looking at the various digital tools and platforms available, pedagogical approaches, adaptive learning systems, social and collaborative learning environments, gamification, assessment techniques, challenges, and future directions, it also looks at the historical development of technology in language education and the theoretical frameworks that guide its integration. This presents a thorough grasp of the function of technology in aiding language acquisition and offers insights into its prospective uses and implications by synthesizing the existing literature. The digital age we live in has brought about a change in education in many areas, including language learning. It also looks at the historical development of technology in language education and the theoretical frameworks that guide its implementation. This study offers insights into the transformative potential of technology in increasing language learning and presents d…
subject: Articles
  topic: section
creator: Madhumita Banerjee
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 319
ending page: 329

Academic English: Motivating students for Academic success
type: item
description: Students want to pursue English for academic purposes; this has emerged as a critical element of language instruction. This prepares students for being successful in their higher education by offering a very thorough analysis of the program's guiding concepts, objectives, methods, difficulties, and future directions. A particular segment of the English language program, English for Academic Purposes, has tried to give the students the language proficiency required for academic studies in English-speaking contexts. Academic success requires fluency in English, as there is a lot of demand for international research and education
subject: Articles
  topic: section
creator: Madhumita Banerjee
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 311
ending page: 318

Assessment of the Effectiveness of Marma Chikitsa in Treating Avabahuk W.S.R to Frozen Shoulder : A Case Study
type: item
description: Avabahuk, classified as a Vata Vyadhi in ancient Samhitas, is a condition that primarily affects the Sira (vessels) of the Amsa Pradesh, manifesting with symptoms such as stiffness (Stambhata), limited range of motion (Alpakriyata), inflammation (Shotha), and shoulder pain (Shoola). In today's era, characterized by sedentary lifestyles, Avabahuk often stems from the aggravation of Vata Dosha or a combination of Vata and Kapha Doshas. This condition can be likened to the modern clinical syndrome known as frozen shoulder, characterized by painful restrictions in both passive and active shoulder movements
subject: Articles
  topic: section
creator: Vikas Chauhan
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 304
ending page: 310

Talent Management, Talent Retention and Succession Planning in Universities: An Integrated Theoretical Framework for Sustainable Academic Excellence
type: item
description: This paper examines the interrelationships between talent management (TM), talent retention (TR) and succession planning (SP) in the context of Malaysian higher education, with particular reference to UTMSPACE as a representative institutional context. Drawing on the Talent Management in Education Model by Davies and Davies, Das’s Talent Retention Criteria Model and Rothwell’s Succession Planning Performance Model, the study develops an integrated theoretical framework that positions TM as primary strategic driver, TR as a critical mediating mechanism and SP as the key organisational outcome for ensuring leadership continuity in universities. Within this framework, TM is conceptualised as a holistic and systematic process encompassing talent identification, performance appraisal, targeted development initiatives, and the cultivation of a supportive talent culture aimed at building a strong pool of high-potential academics. TR is articulated through multiple interelated dimensions, including compensation, rewards and recognition, promotion and growth opportunities, participation in decision making, work–life balance, work environment, training and development, leadership and job security, highlighting the combined influence of financial and non-financial factors on academics’ intention toremain in the institution. SP is framed as a systematic, long-term organisational process that identifies key positions, assesses current and future role requirements, evaluates individual performance and potential, addresses development gaps through targeted leadership development, and incorporates continous program evaluation. By synthesising these three established bodies of literature, the paper proposes an integrated TM, TR and SP framework tailored to the for university context, arguing that strategic and coherent TM practices, when reinforced by effective retention strategies, are critical to sustaining robust SP pipelines and long-term academic leadership continuity. The pape…
subject: Articles
  topic: section
creator: Yusmady Md Junus
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 290
ending page: 303

A Pilot Study on Talent Management, Talent Retention and Succession Planning Among Academic Staff in a Malaysian Higher Education Institution
type: item
description: Talent Management (TM), Talent Retention (TR), and Succession Planning (SP) are increasingly recognised as an integrated strategic triad for sustaining academic talent and ensuring leadership continuity in higher education institutions (HEIs). In the Malaysian higher education context, particularly in institutions such as UTMSPACE and comparable HEIs, intensifying competition and evolving stakeholder expectations necessitate systematic approaches to attracting, developing, retaining, and preparing academic staff for future leadership roles. This paper reports the findings of a pilot study conducted to validate a structured survey instrument measuring TM, TR, and SP among academic’s staff. The instrument was developed based on extensive review of literature and s prior surveys and administered in a pilot study involving 17 academics in UTMSPACE. The pilot study aimed to assess the clarity, reliability, and preliminary validity of the instrument. The results demonstrate very high internal consistency across all main constructs, with Cronbach’s Alpha values exceeding 0.96, while most sub-dimensions demonstrate good to very good reliability. Nomological validity was supported through significant positive correlations among the key variables, confirming that higher perceptions of one construct are associated with higher perceptions of the others. Overall, these results suggest that the instrument is psychometrically robustand suitable for use in the main study to further examine the interrelationships between TM, TR, and SP in Malaysian higher education settings.
subject: Articles
  topic: section
creator: Yusmady Md Junus
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 279
ending page: 289

Design and Eco-Friendly Synthesis of 4-Oxothiazolidinone-Thiourea Derivative: In Silico Antimicrobial Evaluation and DFT Studies
type: item
description: A novel 1-(2-(4-methylphenyl)-4-oxothiazolidin-3-yl)thiourea derivative is synthesized through a tetrabutylammonium bromide catalyzed multicomponent condensation involving 4-methyl substituted aldehydes, amines, and thioglycolic acid. The molecular structures of the obtained 4CTA derivatives are confirmed using FT-IR, 1H NMR, and 13C NMR spectroscopic techniques. In silico studies target Orientia tsutsugamushi, the causative agent of scrub typhus, to evaluate the potential biological activity and to correlate computational results with experimental findings. The developed synthetic protocol demonstrates environmental sustainability and operational simplicity, affording excellent yields (>95%), reduced reaction times, and a broad substrate scope, which highlights its suitability for the rapid synthesis of biologically relevant thiazolidinone frameworks. ADMET and ProTox computational evaluations are carried out to assess pharmacological relevance, indicating favorable pharmacokinetic properties and low predicted toxicity. Density Functional Theory (DFT) calculations are employed to investigate the electronic properties and reactivity profiles of the active compounds, providing insight into their stability and possible mechanisms of action.
subject: Articles
  topic: section
creator: Karpagam Ezhilarasan
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 268
ending page: 278

Transforming E-Collaboration with AI and Emerging Digital Technologies
type: item
description: This chapter highlights how Artificial Intelligence (AI) integrated with health informatics and e-collaborative platforms is reshaping public health, particularly by narrowing the persistent divide between rural and urban healthcare systems. The findings demonstrate that AI-driven tools significantly enhance disease surveillance, predictive analytics, clinical decision-making, and equitable access to health information and services. While urban areas adopt AI more rapidly due to stronger infrastructure and digital literacy, rural communities also show substantial readiness, especially where mHealth initiatives, community participation, and government support converge. Association and correlation analyses reveal that socioeconomic factors including income, digital literacy, and trust in digital systems strongly influence AI adoption, emphasizing the need for targeted capacity-building and awareness programs The study further underscores the socio-technical nature of AI deployment, highlighting the importance of collaborative governance among policymakers, healthcare providers, technologists, NGOs, and local communities. Ethical considerations such as data privacy, transparency, and algorithmic fairness remain critical, particularly in low-resource settings. The chapter concludes that future AI integration should prioritize context-specific, hybrid implementation models backed by robust policy frameworks, interoperability standards, and continuous evaluation. When supported by inclusive and ethically grounded strategies, AI has the potential to drive sustainable, equitable, and transformative healthcare delivery across diverse populations
subject: Articles
  topic: section
creator: T Chandrapushpam
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 244
ending page: 267

The Economic Evaluation of Exports in Nigeria
type: item
description: In this research, the economic evaluation of exports in Nigeria is carried out within the period of 1985-2022. Hence, econometric technique adopted to achieve the objectives were unit root, co-integration and Vector Error Correction Model (VECM), in which oil exports (OEXP), non-oil exports (NOEX), foreign direct investment (FDI) and exchange rate (EXR) were regressed on gross domestic product (GDP), making use of yearly data from statistical report in CBN. The outcome of unit root test revealed that every variable used was stationary at first deviation and long run association amongst the variables was also found. The results of the VECM model showed that oil exports had affirmative and important impact on the expansion of the Nigerian’s economy, while non-oil exports had negative as well crucial impact on economic expansion in Nigeria. Based on the findings above, the researchers advised that administarion in the country should develop meaty economic policies that can resuscitating the non-oil sector, mostly the agricultural sector, so as to stimulate the non-oil sector products.
subject: Articles
  topic: section
creator: Atuma Emeka
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 236
ending page: 243

“Holistic Impacts of AICS Assistance: Addressing Clients’ Needs beyond Financial Relief”
type: item
description: This study investigates the holistic impacts of the Assistance to Individuals in Crisis Situations (AICS) program in Zamboanga City, examining how social welfare interventions address client needs beyond immediate financial relief. Grounded in Psychosocial Theory, which emphasizes the interaction between individual psychological functioning and the social environment, the study explores how comprehensive support fosters resilience, adaptive coping, and household stability among beneficiaries. A mixed-methods design was employed, with quantitative data collected on financial aid distribution, utilization, and beneficiary satisfaction, and qualitative insights obtained through in-depth interviews with both beneficiaries and social workers. Findings indicate that while monetary assistance provides essential short-term relief, the program’s broader effects—including psychosocial support, referrals to health services, engagement with livelihood and skills programs, and follow-up counseling—significantly enhance overall well-being. Beneficiaries reported reduced stress, improved coping strategies, strengthened family cohesion, and increased participation in community activities, highlighting the multidimensional nature of program impacts. The study underscores the importance of integrating economic, social, and psychosocial interventions in social welfare program design to achieve sustainable outcomes. In practice, social workers play a pivotal role in facilitating access to community resources, providing counseling, and ensuring follow-up support, thereby enhancing resilience at both individual and household levels. These findings propose that the AICS program can be optimized through a holistic, psychosocial approach, ensuring that vulnerable populations receive comprehensive, long-term support that addresses both immediate crises and ongoing adaptive needs.
subject: Articles
  topic: section
creator: Munib A. Kahal, DPAl
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 224
ending page: 235

CNNRMHSA-LGTEN: An Efficient Local Graph Transformer and Convolutional Attention Exchange Network for Drug Target Interaction Prediction
type: item
description: Drug Target Interaction (DTI) prediction plays a crucial role in drug discovery by reducing experimental cost and accelerating candidate screening. Traditional computational approaches, including similarity-based and network-based methods, often fail to capture complex nonlinear relationships between molecular structures and protein sequences. Recent deep learning models improve prediction accuracy but still struggle to jointly model global molecular topology and fine-grained local chemical substructures that drive binding specificity. In this work, we propose a CNNRMHSA-LGTEN: An Efficient Local Graph Transformer and Convolutional Attention Exchange Network for Drug Target Interaction Prediction for accurate DTI prediction. The proposed framework explicitly constructs khop local concept subgraphs from drug molecular graphs to preserve functional chemical motifs, while a relative multi-head self-attention mechanism encodes both structural and attribute-level dependencies. Protein sequences are modeled using stacked one-dimensional Convolutional Neural Networks to extract hierarchical residue patterns. A gated attention-based fusion module integrates drug and protein representations, followed by a fully connected prediction head. Experimental results demonstrate that the proposed model consistently outperforms state-of-the-art baselines across multiple evaluation metrics, highlighting the effectiveness of combining local subgraph awareness with attention-driven graph representation learning for DTI prediction.
subject: Articles
  topic: section
subject: Drug Target Interaction prediction, Local Subgraph, Graph Neural Networks, Multi-head SelfAttention, Graph Attention Encoder, Gated Fusion, Deep Learning.
  topic: keywords
creator: A. Jane
rights: Copyright (c) 2026
date: 2025-07-16
publication date: 2025-07-16
starting page: 1459
ending page: 1478
doi: 10.52783/tjjpt.v46.i03.10313

Development of a Novel Analytical Framework for Investigating Non-symmetric Deformation Behavior in Strip Rolling
type: item
description: In recent years, the asymmetrical rolling process has attracted considerable research attention due to its ability to induce non-uniform deformation characteristics within metallic workpieces. In this context, the present study introduces a novel analytical framework for asymmetrical cold rolling based on an enhanced slab method, specifically designed to overcome the inherent limitations of existing analytical models when applied to a wide range of asymmetric rolling conditions. A newly developed mathematical formulation for the asymmetric slab rolling process is proposed, offering a more comprehensive representation of the deformation mechanics. Unlike conventional approaches, the modified model identifies the emergence of three distinct deformation regions within the roll bite: the backward slip zone (BSZ), the forward slip zone (FSZ), and a newly characterized cross-shear zone (CSZ). This zonal classification provides deeper insight into the complex material flow behavior unique to asymmetric rolling. The study systematically investigates the influence of critical rolling parameters—including thickness reduction, roll speed ratio, applied tensions, and friction coefficient—on the configuration and evolution of these deformation zones. It is demonstrated that at specific critical roll speed ratios and tension levels, distinct deformation zone configurations are established, validating the adaptability of the proposed model under varying process conditions. Furthermore, the interdependence between deformation zone configurations and rolling parameters is elucidated through comprehensive process maps, which illustrate the variation of critical speed ratios and critical tensions across diverse rolling scenarios. To quantify the relative influence of process variables, Grey Relational Analysis (GRA) is employed, revealing that the roll velocity ratio is the most dominant factor governing rolling responses, contributing 80% to overall performance optimization. The fron…
subject: Articles
  topic: section
creator: P Gopalakrishnaiah
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 203
ending page: 223

Remote and Hybrid Work Policies as Sustainability Drivers: Re-Imagining the Future of IT Through Lessons from Other Industries
type: item
description: The transition to remote and hybrid work models in response to the COVID-19 pandemic has offered organizations renewed perspectives and innovative pathways toward achieving sustainability. Many companies have recognized that redesigning workplace strategies can be instrumental in aligning business objectives with broader environmental goals. While industries such as automotive, manufacturing, and logistics have long advanced in integrating environmental measures within their operational structures, the IT industry has lagged in comprehending the sustainability dimensions of workplace architecture. This study conducts a structured review employing bibliometric, content, and secondary data analyses to examine how flexible work models can serve as levers to mitigate environmental impacts. Drawing insights from established practices across other industries, the paper introduces the Workplace Sustainability Model (WSM)—a conceptual framework linking employee configuration, human resource strategies, and environmental performance outcomes. The findings suggest that hybrid work arrangements can simultaneously reduce emissions, enhance ESG ratings, and improve both employee well-being and operational sustainability. The paper concludes by emphasizing that corporate and policy leadership must realign workplace configurations as integral components of climate strategies consistent with the United Nations Sustainable Development Goal (SDG) 13: Climate Action.
subject: Articles
  topic: section
creator: Sumit Das
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: 193
ending page: 202

Bridging Silence and Semantics: A Multimodal Review of Sign Language Recognition, Translation, and Adaptive Learning Systems
type: item
description: Bridging communication gaps for the deaf and mute community remains an open AI challenge, demanding systems that go beyond static sign recognition toward adaptive, emotion-aware interaction. While existing research has advanced isolated gesture recognition, few works address dynamic sentence translation, con- textual understanding, and learner adaptability in real-world envi- ronments. This review analyzes recent developments in multimodal learning—integrating vision, text, and speech—to enable seamless bidirectional communication and personalized education. It highlights the evolution from CNN-based recognition to transformer-driven sign language understanding and avatar-based delivery. This review synthesizes emerging multimodal approaches that blend recognition, translation, and emotion-aware adaptation into a unified assistive learning framework.
subject: Articles
  topic: section
subject: Sign Language Recognition, Neural Machine Translation, Emotion Detection, Adaptive Learning Platforms, Trans- former Models, Multimodal Deep Learning.
  topic: keywords
creator: Antony Jacob
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 185
ending page: 192

Emission Control and Sustainability: Evaluating Bharat Stage - 6 (BS6) Technology in Indian Automotive Industry
type: item
description: India’s Bharat Stage 6 (BS6) standards represent a step-change in vehicular emission control, mandating ultra-low-sulphur fuels and advanced after-treatment (DPF, SCR) to curb NOx and particulate emissions. This paper evaluates BS6 through four lenses such as vehicle performance and fuel efficiency, cost and pricing implications, consumer perception, and environmental outcomes, framed by Diffusion of Innovations. We conducted a structured survey (N=50) comprising five binary items to gauge awareness and perceived impacts; responses were summarized using proportions with confidence intervals. Results indicate broad consumer recognition of BS6 benefits: 70% report improved fuel efficiency, 90% perceive performance gains over BS4, and 90% believe BS6 contributes to cleaner air. At the same time, 80% observe higher vehicle prices and 70% acknowledge costlier, more sophisticated components, pointing to manufacturer and lifecycle-cost pressures. Synthesis with prior literature highlights enabling factors (fuel quality, after-treatment reliability) and implementation challenges (service capability, real-world emissions monitoring, and legacy vehicle fleets). We discuss policy and industry implications, including incremental ICE efficiency, hybridization as a bridge to electrification, and robust compliance/diagnostics. The study provides a concise, consumer-level view of BS6 adoption and outlines practical pathways to strengthen environmental performance while managing affordability and operability.
subject: Articles
  topic: section
creator: Puneet Bansal
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 174
ending page: 184

A Hybrid AI-Driven Integral Transform Framework for Nonlinear Differential Equations in Engineering Systems
type: item
description: Integral transforms such as the Laplace, Fourier, and Mellin transforms have traditionally helped engineers simplify complex differential equations. However, when systems become nonlinear or involve irregular physical conditions, these classical tools lose their accuracy and stability. To bridge this gap, this paper presents an AI-assisted hybrid integral transform framework designed to enhance the stability and accuracy of nonlinear engineering models. The framework automatically adjusts transform kernel parameters, improves convergence, and stabilizes the inverse process. Through case studies involving nonlinear heat transfer, viscous fluid flow, and thermoelastic wave propagation, the proposed model demonstrates 35–40% improvement in accuracy. The analysis confirms that AI‑driven kernel adaptation significantly enhances the practical usability of integral transforms for advanced engineering problems
subject: Articles
  topic: section
creator: Amarender Reddy Kommula
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: 168
ending page: 173

Complexities in Remote Sensing-Based Object Detection and Interpretation
type: item
description: Detection of object is a fundamental part of processing images from remote sensing. Object analysis in aerial images generally covers the process of automatically identifying and localizing specific objects in satellite or aerial images, and addresses the challenges specific to remote sensing data, such as different scales, complex backgrounds, and spectral information, as well as state-of-the-art techniques. It primarily employs the approaches of deep learning like convolutional neural networks (CNN’s) to classify and detect objects of interest accurately. The exponential growth of deep convolutional neural networks has significantly improved the detection of object in remote sensing images. However, it has a substantial amount of impact on the effectiveness of detector, when the scenario is complex and the dimensions of objects changes drastically. The techniques of deep learning for detecting objects in aerial images has many uses, including pavement extraction, urban building detection, and forest fire monitoring. It also offers robust assistance for agricultural management, environmental monitoring and urban development. We examine the difficulties that both deep learning and conventional approaches encounter. The complexity of various methods is also evaluated and examined with generic datasets of remote sensing aerial images.
subject: Articles
  topic: section
creator: Shalini L
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: 161
ending page: 167

Analysis of Diseased One Prey Two Predator Model with Prey Harvesting in Holling type -II Functional Response
type: item
description: The article develops and studies two possible diseases that can impact the predator population in an ecological model.SIS spread horizontallywhen an infected predator came into touch with a suspicious predator. Moreover, the second (SI illeness) is vertically transferred from environmental influence due toan external source. Diseases cannot be spread from preadtor to victim through touch or predation. Holling type II and linerafunctional response are utilized to show how healthy and suspectable predators feedon each other, while linearincidence is utilized for demonstrationg the evolution of disorders. All possible equilibrium locations were examined for this model. The model’s local and global dynamics are examined by numerical simulation, as wellas the parameter sensitivity analysis.
subject: Articles
  topic: section
creator: K. Akshaya
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 147
ending page: 160

Design and Optimization of Machine Learning-Enhanced Forward Error Correction Codes for Improving Data Integrity in Global Navigation Satellite Networks
type: item
description: A robust error correction method is essential to address the growing demand for accurate data transmission in global Navigation satellite network reliably. Turbo Code Decoder (TCD) and Viterbi Decoder (VD) convolutional coding systems are in wide use due to their capability in improving the data integrity. These methods have limitation to handle high bit error rate and to adapt to continuous changing signal condition found in urban environment. This work presents machine learning based improved Forward Error Correction (ML-FEC) System. This system combines Convolutional Neural Network with conventional Error rectification process. The proposed ML-FEC improves reduction in complexity of computation, Error detection and correction capabilities. This is achieved by incorporating a novel approach of combining Low Density Parity Check algorithm with machine learning model that can adjust to transmission conditions in real time. The error correction efficiency has been improved by adopting new methods like reinforcement learning based parameters and adaptive learning rate adjustments. The proposed system has achieved 0.20% reduction in Bit Error Rate and an improvement of 0.15% in throughput of complete data in comparison to conventional systems. In addition, the load of computation is decreased by 0.25% which addresses the main drawbacks of increased complexity in existing systems. From all these observations, ML-FEC offers to be a viable solution for present systems, by offering increases performance and reliability in complex transmission environments.
subject: Articles
  topic: section
creator: Chaitra S
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 134
ending page: 146

Title
type: item
description: Abstract
subject: Articles
  topic: section
creator: author1
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 123
ending page: 133

Title
type: item
description: Abstract
subject: Articles
  topic: section
creator: author1
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 113
ending page: 122

Probabilistic Seismic Performance Assessment of RC Structures with Material and Geometric Uncertaities
type: item
description: This study presents a probabilistic framework to evaluate the seismic risk of reinforced concrete (RC) buildings in the Koyna–Warna region by incorporating uncertainties in material properties, structural configuration, and seismic loading. Probabilistic collapse curves were developed to compare the influence of key parameters, and a detailed sensitivity analysis revealed that peak ground acceleration (PGA) is the most influential factor affecting collapse probability. Variations of ±20% in PGA resulted in a +45% to −40% change in collapse probability, followed by uncertainties in concrete strength and steel yield stress. Irregular buildings (B3) exhibited higher vulnerability to geometric uncertainties and showed lower median PGA values across all damage states compared to regular buildings (B1 and B2).Model validation against experimental drift ratios demonstrated good agreement, confirming the reliability of the probabilistic approach. Compared to deterministic methods, the probabilistic model provided a realistic range of outcomes and associated probabilities, enabling a more comprehensive assessment of seismic risk. Case studies further revealed that regular buildings perform better under seismic loading, while irregular configurations experience greater damage, especially at higher PGA levels (e.g., 0.5 g). Risk indices also indicated higher annual probabilities of exceedance for B3 relative to B1 and B2.Overall, the findings highlight the necessity of incorporating uncertainties into seismic design and assessment of RC buildings, particularly in seismically active regions such as Koyna–Warna. The proposed probabilistic methodology offers a robust tool for evaluating structural performance, guiding design decisions, retrofitting strategies, and supporting resilient urban infrastructure planning
subject: Articles
  topic: section
creator: Adnan Ahmeda
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 99
ending page: 112

“The Effect of Influencer Marketing on Consumer Purchase Behaviour: A Systematic Review of Global Literature (2015–2025)”
type: item
description: In recent years, influencer marketing has emerged as a powerful tool for brand communication and in digital marketing, driven by the exponential growth of social media platforms where influencers significantly shape purchase behaviour. As brands strive to maintain long-term consumer relationships, understanding the role of influencer marketing in shaping brand loyalty has become increasingly important. This systematic review aims to synthesize existing global literature from 2015 to 2025 to explore how influencer marketing impacts brand loyalty across sectors. Following the PRISMA guidelines, a comprehensive search of peer-reviewed articles was conducted using the Google Scholar, Research Gate and other open- access platforms. The review process involved screening over 3409 articles, of which met 65 the inclusion criteria. Key themes that emerged include the persuasion, social network influence and psychological engagement. The review also identifies frequently used independent variables (e.g., trustworthiness, attractiveness, influencer-product congruence) and dependent variables (e.g., purchase intention, brand attitude). An integrative framework is proposed, highlighting the mediators and moderators in influencer marketing dynamics. The study concludes by offering future research directions and practical implications for marketers aiming to enhance consumer engagement via influencers. This review contributes to the existing body of knowledge by consolidating fragmented research on the topic and highlighting underexplored areas such as longitudinal loyalty outcomes, cross-platform influence, and cultural differences in consumer responses. The findings offer valuable implications for brand managers, digital strategists, and future researchers aiming to design impactful influencer-driven loyalty programs and to better communicate with consumers.
subject: Articles
  topic: section
subject: Influencer Marketing, Purchase Intention, Social Media, Consumer Behaviour, Systematic Review
  topic: keywords
creator: Noopur Bhargava,
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 83
ending page: 98

Heat Source and Slip Impacts on Porous Medium Flow of Williamson Nanofluid: Numerical Approach via Keller-Box Scheme
type: item
description: This study presents a mathematical model to analyze the combined effects of prescribed heat sources, velocity slip, and thermal slip on the stagnation point boundary layer flow of a non-Newtonian Williamson nanofluid. The investigation incorporates a time-varying magnetic field and considers two thermal boundary conditions—prescribed surface temperature and prescribed heat flux—within the framework of the Buongiorno nanofluid model. To account for fluid movement through porous media, the Darcy–Forchheimer model is employed. Using similarity transformations, the governing partial differential equations are reduced to a set of nonlinear ordinary differential equations. These equations are then numerically solved using the Keller-box method, an implicit finite difference approach well-suited for handling nonlinear boundary value problems. Numerical results for velocity, temperature, and concentration profiles are illustrated through 2D plots, while 3D visualizations highlight the influence of various parameters on the skin friction coefficient, Nusselt number, and Sherwood number. The findings contribute to a deeper understanding of Williamson nanofluid dynamics in porous environments and hold potential applications in the optimization of nanofluid-based thermal systems.
subject: Articles
  topic: section
subject: Williamson nanofluid, Darcy-Forchheimer model, slip effects, MHD, Mixed convection, Stagnated flow, PST, PHF, Keller-Box scheme.
  topic: keywords
creator: Archie Thakur
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 51
ending page: 82

Succession Planning Practices and their Impact on Organisational Service Performance: Cross-National Evidence from Nigeria and Ghana
type: item
description: Many public tertiary hospitals in sub-Saharan Africa face growing leadership gaps, frequent brain drain, and declining service quality. These challenges underscore the urgent need for structured succession planning to sustain organisational performance. Guided by the Resource-Based View (RBV), Human Capital Theory, and Institutional Theory, this study examined how Succession Planning Practices (SPP), through Talent Identification (TA), Mentoring (ME), Leadership Development (LD), and Career Pathing (CP), influence Organisational Service Performance (OSP) among public tertiary hospitals in Ghana and Nigeria. A cross-sectional survey design was adopted, and data were collected from 731 clinical/medical, support/ancillary/technical and management/administrative staff across all the regions of the two countries. The data were analysed using Covariance Based Structural Equation Modelling (CB-SEM) in AMOS. Findings showed that all four dimensions of SPP had positive and significant effects on service performance: TA (β = .24, p < .001), ME (β = .36, p < .001), LD (β = .29, p < .001), and CP (β = .21, p < .01). The overall SPP-OSP relationship was also positive and significant (β = .62, p < .001). The study concludes that effective succession planning enhances leadership continuity, staff development, and institutional performance. It recommends that public hospital administrators embed structured mentoring and leadership development programmes into HR policy and succession systems. Although limited by cross-sectional design and self-report data, future studies should adopt longitudinal or mixed methods approach to validate these results across other emerging economies in sub–Saharan Africa.
subject: Articles
  topic: section
creator: Oladapo Ololade Abe
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 18
ending page: 50

Design and Fabrication of a Motorized Screw Jack for Automotive Applications
type: item
description: A screw jack is a mechanical device used for lifting the heavy-duty vehicles. Screw jack is employed by the screw threads for lifting purpose. To operate the screw the handle is used and when the handle is operated clockwise then it lifts the work similarly when the handle is operated in anti-clockwise then it lowers the work. In case of tire flat or changing the tire, the screw jack is very important and useful device. The screw jack is operated manually and when the ancient people when they want to change the tire it is not so easy. So, to overcome the situation it can be employed automatically so that the time is reduced and they can conveniently replace the tire. The motorized screw jack features user-friendly controls, robust safety systems like automatic locks and emergency shut-off functions, and is constructed from durable materials to ensure long-term reliability. It operates via a 12V power supply, typically sourced from a vehicle’s cigarette lighter socket. The project demonstrates that the motorized screw jack can lift a vehicle safely and efficiently, making it a valuable tool for both professional mechanics and everyday vehicle owners. The prototype's performance in load tests confirms its capability to handle substantial weights with improved lifting speed and minimal manual effort.
subject: Articles
  topic: section
creator: Pendyala Sathish
rights: Copyright (c) 2026
date: 2026-01-04
publication date: 2026-01-04
starting page: 10
ending page: 17

A Mult-order Non-fourier Fractional Differential Equation for Heat Transmission
type: item
description: A multi-order fractional differential model was proposed with two fractional Cattaneo derivatives with respect to time and a Caputo symmetrized fractional temperature gradient with respect to space. A unique solution in the form of integral was developed for the proposed heat transfer model using contour integral methods. Numerical examples in graphical forms are provided using the solutions established. The effects of varying parameters of the model are studied from the numerical examples given.
subject: Articles
  topic: section
subject: Fractional Caputo derivative, Fourier transform, Laplace transform, Contour integral.
  topic: keywords
creator: Cheyuo Boniface Domonaamwin
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: 01
ending page: 09

Intelligent Criminal Face Recognition Using Deep Feature Refinement and Adaptive Machine Learning for Enhanced Identification Accuracy
type: item
description: The identification of individuals involved in criminal activities through facial recognition technology presents considerable difficulties stemming from inconsistencies in illumination, head orientation, obstructions, and changes in facial demeanour. This study introduces an advanced system designed to overcome these obstacles, utilizing convolution neural networks (CNNs) for the extraction of complex facial attributes and employing adaptable learning methods for precise subject verification. Initially, incoming images undergo automated preparation, involving techniques such as histogram manipulation, landmark-guided orientation, and signal enhancement, to promote feature uniformity. The CNN-based feature extraction mechanism captures both overarching and localized facial characteristics, while a feature enhancement component, incorporating principal component analysis (PCA) and emphasis-driven prioritization, amplifies discriminatory capability. An adaptable verification algorithm modifies similarity criteria based on variability within subject categories to ensure dependable identification across diverse data collections. Assessment using publicly accessible repositories of criminal facial images indicates that the presented methodology achieves enhanced identification precision, exhibiting advancements of up to 12% compared to conventional CNN-based recognition systems, dependable function in the presence of obstructions, and diminished incorrect positive identifications. These findings substantiate that the integration of sophisticated feature enhancement with adaptable machine learning provides a trustworthy and effective resolution for practical criminal identification and monitoring endeavours.
subject: Articles
  topic: section
creator: M. Kirubakaran
rights: Copyright (c) 2025
date: 2025-10-03
publication date: 2025-10-03
starting page: 1801
ending page: 1813

Exploring Alternative Funding Models to Enhance the Autonomy of Bangladesh’s Anti-Corruption Commission (ACC): A Survey-Based Quantitative Study
type: item
description: This study examines the financial independence of the Anti-Corruption Commission (ACC) of Bangladesh. Currently, the ACC depends entirely on government funding, which limits its operational autonomy. The study’s aim is to assess public and expert opinions on alternative funding sources, including asset recovery and fines, to enhance the ACC’s independence. The research used a survey method to collect opinions from 100 respondents, including public members and experts. The data analysis focused on support levels for mixed funding models and legal reforms to allow the ACC to retain a portion of recovered funds. The study also reviewed international practices in anti-corruption agency funding for comparison. Findings show strong public support for increasing the ACC’s financial independence. About 84% of respondents believe that financial autonomy will reduce corruption within the Commission. A similar percentage supports legal reforms for the ACC to use recovered assets and fines. These findings are aligned with international examples, where agencies like Hong Kong’s ICAC and Indonesia’s KPK have diversified funding sources to strengthen independence and effectiveness. The study recommends that Bangladesh should consider a mixed funding model for the ACC. This model should combine government allocations with income from asset recovery and fines. Clear legal frameworks are essential to regulate this funding and ensure transparency. Capacity building for financial management and cooperation with international anti-corruption bodies will also support these efforts. In conclusion, financial independence is critical for the ACC’s operational freedom and success. Adopting a mixed funding approach can help reduce political influence and enhance the ACC’s ability to fight corruption effectively. This will also improve public trust in the Commission.
subject: Articles
  topic: section
subject: Anti-Corruption Commission, Financial Independence, Asset Recovery, Mixed Funding Model, Operational Autonomy, Bangladesh
  topic: keywords
creator: Shazzad Hussain
rights: Copyright (c) 2025
date: 2025-10-03
publication date: 2025-10-03
starting page: 1782
ending page: 1800
doi: 10.52783/tjjpt.v46.i04.10262