Tuijin Jishu/Journal of Propulsion Technology
Tuijin Jishu, which translates to "Propulsion Technology" in English, is a prestigious and cutting-edge academic journal dedicated to advancing the field of propulsion engineering and technology. Our mission is to serve as a platform for researchers, engineers, and enthusiasts alike to share their knowledge, discoveries, and innovations in the propulsion domain. At Tuijin Jishu, we believe that propulsion technology is at the forefront of modern scientific and engineering achievements, driving humanity forward in space exploration, aviation, transportation, and beyond. As such, we are committed to promoting the dissemination of high-quality, peer-reviewed research that pushes the boundaries of propulsion systems, fuels, and related technologies. Tuijin Jishu is a peer-reviewed open-access journal, ensuring that all published articles undergo rigorous scrutiny by expert reviewers in the field. We strive to maintain the highest standards of integrity and scientific accuracy, upholding the trust and credibility of our readers and contributors. Our journal covers a broad spectrum of propulsion-related topics, including but not limited to: Rocket Propulsion: Advancements in chemical propulsion, hybrid systems, electric propulsion, and novel propulsion concepts. Aerospace Propulsion: Cutting-edge research on aircraft engines, propulsion systems, and aero-thermodynamics. Space Exploration: Innovations in space propulsion, interplanetary travel, and propulsion challenges in long-duration missions. Green Propulsion: Sustainable and eco-friendly propulsion technologies, biofuels, and reducing environmental impact. Advanced Materials: Research on materials for propulsion systems, thermal management, and structural integrity. Computational Fluid Dynamics (CFD): Modeling and simulation techniques to optimize propulsion performance. Propulsion Control Systems: Developments in autonomous and intelligent control of propulsion systems. Why Tuijin Jishu? Expertise: Our editorial board comprises esteemed professionals and researchers from diverse propulsion-related disciplines, ensuring a balanced and multidisciplinary approach to propulsion technology. Global Community: Tuijin Jishu fosters collaboration among propulsion enthusiasts worldwide, breaking down geographical barriers and promoting international cooperation. Innovation: We celebrate innovation and originality, encouraging authors to present groundbreaking research that contributes to the advancement of propulsion technology. Open Access: Tuijin Jishu follows the open-access model, making all published articles freely available to the global scientific community, accelerating the exchange of knowledge. Ethics and Integrity: We uphold the highest ethical standards in academic publishing, ensuring transparency, fairness, and confidentiality throughout the review process. Become a Contributor: Tuijin Jishu welcomes contributions from researchers, academics, and practitioners involved in propulsion technology. If you have exciting research to share or wish to stay updated with the latest developments in the field, subscribe to our journal or consider submitting your work to us.
Ai-Driven Multi-Disease Prediction System Using Federated Learning For Privacy-Preserving Healthcare Analytics
Medical institutions accumulate large volumes of patient data, yet strict privacy legislation and organizational constraints limit the exchange of such data between healthcare providers. These restrictions often prevent the development of robust machine learning models for clinical decision support. To address this challenge, this study introduces a Federated Learning (FL) based multi-disease prediction model that allows multiple hospitals to collaboratively train a global classifier without exposing sensitive patient information. Three major chronic conditions—diabetes, heart disease, and chronic kidney disease (CKD)—were selected to build a unified multi-class prediction system. Each participating institution trains the model locally, and the global model is updated using the Federated Averaging (FedAvg) approach. Classical machine learning algorithms and a deep neural network (DNN) were evaluated under both centralized and federated setups. The federated DNN achieved an accuracy of 92.4%, which is comparable to the centralized accuracy of 93.1%, demonstrating that high performance can be achieved without data centralization. The findings confirm that FL is a viable solution for privacy-aware multi-disease diagnosis and can be deployed in real-time healthcare analytics
Effects of Bhramari Pranayama on Anxiety (GAD-7) and Depression (PHQ-9) among university students over a duration of 8 weeks: A Pilot Study
Background: A yogic breathing technique called bhramari pranayama, which entails humming and exhaling, has been shown to have beneficial effects on anxiety and depression in a variety of groups. Objective: To assess the effect of a structured 8-week Bhramari pranayama intervention on anxiety and depression among university students using the GAD-7 and PHQ-9 scores. Methods: Twenty university students who had at least mild symptoms of anxiety or depression at baseline participated in this pilot trial, which used a pre-post single-group design. The Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9) were used to measure anxiety and depression, respectively. Participants engaged in guided Bhramari pranayama for 10 minutes per day, five days a week, for eight weeks. Paired t-tests were used to compare the scores before and after the intervention. Results: The mean GAD-7 scores reduced from 10.05 ± 3.03 to 6.55 ± 2.26 (p < 0.0001), a 34.8% decrease. PHQ-9 scores dropped from 10.25 ± 3.54 to 7.10 ± 2.63 (p < 0.0001), a 30.7% decrease. Following the session, 70% of students improved from moderate-severe to minimal-mild anxiety and depression. Conclusion: This pilot study demonstrates that Bhramari pranayama may be a realistic and potentially effective additional strategy for reducing anxiety and depression symptoms in university students. Larger randomized controlled trials are needed to validate these findings.
Bhramari pranayama, University students, Anxiety, Depression, GAD-7, PHQ-9, Yoga, Pilot study
ESP32-CAM Based Robot for Restricted and Hazardous Area Surveillance
The machine that is based on ESP32-CAM is, without a doubt, a simple and cheap device for remote monitoring and surveillance in dangerous, limited, or even inaccessible areas. It benefits from an ESP32-CAM microcontroller which merges video streaming, Wi-Fi connection, and basic two-directional motor movement into a tiny layout. Besides, the robot can live video feed via Wi-Fi, and, at the same time, it lets the operator control it through a web page which can be accessed by both phones and computers. As a result, the robot is able to roam and keep watch over the area without being spotted, and consequently, it can be of great assistance in chemical factories, tunnels, disaster zones, or even in just ordinary factories. The hardware is really basic and consists solely of an ESP32-CAM, an L298N motor driver, and two DC motors which not only lower the price but also make the robot simpler as compared to the ones made with Raspberry Pi or several Arduino modules. Furthermore, the system utilizes HTTP requests for moving the robot and MJPEG for video streaming. Such kind of a robot can be deployed for various purposes including safety, disaster response, defense, and learning. It is a very affordable and portable solution by the robot to provide operator security and at the same time not to lose any important functions. The present research has described the making of the ESP32-CAM robot along with its usage, pros, and possible future upgrades that could make it even better.
AI-driven IoT system for diagnosing and rectifying issues with solar panels
Solar photovoltaic (PV) technology has come a long way in recent years because to its many advantages, such as being easy to maintain, reliable, renewable, and having little impact on the environment. However, various photovoltaic flaws may nonetheless manifest, leading to degradation, diminished production impact, or even a spike at various levels. This is conditional on the exterior working conditions and the usual weather changes that might damage production, delivery, or setup. Consequently, measuring the efficiency of PVS electricity generation is crucial. The "Internet of Things" (IoT) refers to a set of innovative technologies that are being rigorously studied to improve fault detection including predictive analysis in environmental monitoring including solar power plant operations. This study's results propose a machine learning-based strategy for assessing power data and anticipating problems to facilitate solar power plant management.The provided data provided by solar power facilities include both the plant's electricity output and meteorological information. The data undergoes initial pre-processing before being used for training with AI. The trained model was capable of identifying both significant and minor mistakes, along with anomalies in the provided data. Traditionally, these identifications need an increased volume of effort for detection and maintenance actions. The study's results indicate that the suggested model yielded 8.9 Mean Squares Errors, 2.98 and Root Mean Squares Errors with the present technique. The figures were obtained by model testing.
Linguistic and Discursive classification of Knowledge Assets
This abstract provides a comprehensive overview of the classification and management of knowledge assets, highlighting their importance, challenges, and strategic implications.The importance, difficulties, and strategic ramifications of knowledge asset classification and management are all highlighted in this abstract, which offers a thorough review of the subject. In the knowledge economy, knowledge assets—also known as intellectual capital—are essential for gaining a competitive edge and achieving organizational success. To manage these assets—which include both tangible and intangible components—systematic classification is necessary. The several types of knowledge assets are outlined in this abstract along with their attributes, importance, and management approaches
Knowledge Assets, Human Capital, Structural Capital, Relational Capital, Knowledge Management, Organizational Strategy
An Intelligent AI-Driven Web Application for Semantic Website Understanding, Dynamic UI Generation, and Adaptive Backend Reconfiguration
Modern organizational websites struggle to evolve alongside changing business objectives, user behavior, and long-term functional demands. Traditional static UI/UX design approaches and manual backend reconfiguration introduce scalability limitations, increased maintenance costs, and delayed innovation cycles. This paper proposes an AI-driven web application that semantically analyzes connected organizational websites to understand their purpose, usage trajectory, and long-term operational intent. Based on this analysis, the system dynamically generates advanced, non-static UI styles—including fluid motion-based layouts, wave-inspired transitions, and floating interaction elements—while offering a conversational, prompt-driven design interface inspired by large language model platforms. A key innovation of the proposed system is its user-consented adaptive backend reconfiguration mechanism, which aligns server-side workflows, API connectivity, and data pipelines with approved frontend design changes. The platform further integrates intelligent usage tracking, session-based access limitations, and progressive monetization prompts to encourage premium adoption. Experimental evaluation demonstrates reduced design iteration time, improved UX satisfaction metrics, and seamless frontend–backend synchronization without system downtime. The proposed architecture provides a scalable, enterprise-ready foundation for next-generation AI-assisted website evolution.
A study on a hybrid-composite prepared using Areca Nut Husk Fibre, Glass Fibre Cloth, and Epoxy Resin
The demand for natural fibres has increased in recent years due to their low density, excellent biodegradability, stiffness, and cost-effectiveness. Natural fibre composites are widely used in various engineering applications, including the marine, construction, aerospace, and automotive industries. The present research work highlights the fabrication of natural composites by using areca nut husk and glass fibre dispersed in epoxy resin by the hand lay-up technique. The fabricated composites are then tested to determine the mechanical properties. The examined composites exhibited a hardness of 55 BHN and a porosity of 92.5%. The density of the composite ranges from 0.3808 to 0.4075 g/cm³.
Areca nut husk, glass fibres, epoxy resins, hardness, natural composites.
Reform of Strategy in Ensuring International Peace and Religious Tolerance in Uzbekistan
This article analyzes the views on the strategy of reforms in ensuring interethnic harmony and religious tolerance in Uzbekistan. The article also contains opinions on changes in the global geopolitical arena, the growing process of globalization and the constant awareness of the world community about the maintenance of peace and stability. In such difficult conditions, maintaining peace, stability and strengthening independence remains one of the priorities of each country. Today, an effective fight against internal and external threats and dangers in the field of spirituality, strengthening ideological immunity in society, strengthening the atmosphere of mutual respect, friendship and harmony between representatives of different nationalities and religions in our country. Particular emphasis is placed on the priority task of public policy.
Interethnic harmony, religious tolerance, spirituality, peace, globalization, peace, reform, security, stability
Some Features of the Implementation of the Duties of the Internal Affairs and National Guard Bodies to Restrict Human Rights and Freedoms under the State of Emergency.
This manuscript is dedicated to discussing some features of implementing the duties of internal affairs and the National Guard bodies to restrict human rights and freedom. In particular, the manuscript articulates existing legal norms regarding the restriction of human rights during the state of emergency situations, opinions and comments of legal scholars in this regard, ensuring the safe life of people in society and public safety, and the basic rights and freedoms. The nature of the special legal regime and the norms of national legislation, proposals, and recommendations for eliminating existing deficiencies in them are described
state of emergency, internal affairs bodies, national guard, human rights and freedoms, national legislation.
Survey Paper on: Leveraging Artificial Intelligence (AI) and Blockchain to Enhance Supply Chain Management for a Ganapati Idol Selling Platform
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.
A Study on the Role and Challenges of Food Supply Chain Management Towards Achieving Zero Hunger in Uttar Pradesh
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.
Using Technology to Improve Language Learning: A Thorough Analysis
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 directions for future research and practice by integrating the existing knowledge.
Academic English: Motivating students for Academic success
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
Assessment of the Effectiveness of Marma Chikitsa in Treating Avabahuk W.S.R to Frozen Shoulder : A Case Study
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
Talent Management, Talent Retention and Succession Planning in Universities: An Integrated Theoretical Framework for Sustainable Academic Excellence
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 paper contributes conceptually by offering a theoretically grounded model that can guide future empirical research and inform institutional policy and leadership development strategies in an increasingly competitive higher education environmentand ensuring academic leadership continuity in an increasingly competitive higher education environment
A Pilot Study on Talent Management, Talent Retention and Succession Planning Among Academic Staff in a Malaysian Higher Education Institution
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.
Design and Eco-Friendly Synthesis of 4-Oxothiazolidinone-Thiourea Derivative: In Silico Antimicrobial Evaluation and DFT Studies
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.
Transforming E-Collaboration with AI and Emerging Digital Technologies
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
The Economic Evaluation of Exports in Nigeria
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.
“Holistic Impacts of AICS Assistance: Addressing Clients’ Needs beyond Financial Relief”
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.
CNNRMHSA-LGTEN: An Efficient Local Graph Transformer and Convolutional Attention Exchange Network for Drug Target Interaction Prediction
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.
Drug Target Interaction prediction, Local Subgraph, Graph Neural Networks, Multi-head SelfAttention, Graph Attention Encoder, Gated Fusion, Deep Learning.
Development of a Novel Analytical Framework for Investigating Non-symmetric Deformation Behavior in Strip Rolling
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 front tension emerges as the second most influential parameter, accounting for 8.64%, and plays a supportive role in enhancing process stability and surface integrity. The analytical predictions of the proposed model are rigorously validated through confirmation experiments, demonstrating strong agreement with theoretical outcomes. Based on the GRA-derived optimal parameter set, the study recommends specific operating conditions that effectively minimize roller surface damage while simultaneously improving the surface quality of the rolled product
Remote and Hybrid Work Policies as Sustainability Drivers: Re-Imagining the Future of IT Through Lessons from Other Industries
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.
Bridging Silence and Semantics: A Multimodal Review of Sign Language Recognition, Translation, and Adaptive Learning Systems
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.
Sign Language Recognition, Neural Machine Translation, Emotion Detection, Adaptive Learning Platforms, Trans- former Models, Multimodal Deep Learning.
Emission Control and Sustainability: Evaluating Bharat Stage - 6 (BS6) Technology in Indian Automotive Industry
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.
A Hybrid AI-Driven Integral Transform Framework for Nonlinear Differential Equations in Engineering Systems
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
Complexities in Remote Sensing-Based Object Detection and Interpretation
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.
Analysis of Diseased One Prey Two Predator Model with Prey Harvesting in Holling type -II Functional Response
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.
Design and Optimization of Machine Learning-Enhanced Forward Error Correction Codes for Improving Data Integrity in Global Navigation Satellite Networks
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.