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.
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.
Microhardness Assessment of Magnesium Alloy after Powder Mixed Wire Electrical Discharge Machining and Hydroxyapatite Coating
Magnesium (Mg) alloys, which have excellrnt biocompatibility and characteristics similar to bone, are ideal choices for temporary biodegradable othopedic implants. However, because of their low ductility and fast corrosion, their applicability in biological environments is currently limited. Surface properties can be altered by surface treatments such as coating, alloying, and mechanical working to make it application friendly. In this experimental study, ZE41A Mg alloy was processed with powder mixed wire electric discharge machining (PMWEDM) and hydroxyapatite (HA) coating, to analyse their effect on microstructure and micro-hardness. Surface microstructure of parent, PMWEDMed, and HA-coated samples was analyzed by using Field emission scanning electron microscopy (FE-SEM). The microhardness (MH) of these samples was determined using a Vickers micro-hardness tester before and after 14 days of immersion in simulated body fluid (SBF). The results show that PMWEDM and HA coating produced surfaces with higher MH. The post-immersion data show a loss of MH in all three types of Mg samples, which is attributed to sample degradation in SBF.
Mechanical Performance of Biodegradable Magnesium Alloy after Immersion in Simulated Body Fluid
Magnesium (Mg) alloys, with a light weight, favorable strength, biodegradability, and biocompatibility, have the potential to be employed as biomedical implants; however, rapid degradation prevents their usage because it lowers their mechanical properties. In this study, ZE41A Mg alloy was investigated for mechanical properties after processed with powder mixed wire electric discharge machining (PMWEDM) and hydroxyapatite (HA) coating. Zinc metal powder is mixed with dielectric medium for PMWEDM process and electrochemical deposition (ECD) technique is used for preparing HA coating. Field emission scanning electron microscopy (FE-SEM) was employed to study surface morphologies. Bending/flexural and compression testing of polished, PMWEDMed, and HA-coated samples were performed before and post immersion in simulated body fluid (SBF). Results reveal a loss of bending (σb) and compressive (σc) strengths when immersed samples were compared with non-immersed ones. Immersed samples showed a decline in mechanical strengths as a result of SBF degradation. PMWEDMed samples exhibited the maximum loss of strength among all three.
PMWEDM; Hydraoxyapatite; Bending Strength; Compressive Strength; FE-SEM; Simulated Body Fluid.
Probabilistic Seismic Performance Assessment of RC Structures with Material and Geometric Uncertaities
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
“The Effect of Influencer Marketing on Consumer Purchase Behaviour: A Systematic Review of Global Literature (2015–2025)”
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.
Influencer Marketing, Purchase Intention, Social Media, Consumer Behaviour, Systematic Review
Heat Source and Slip Impacts on Porous Medium Flow of Williamson Nanofluid: Numerical Approach via Keller-Box Scheme
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.
Williamson nanofluid, Darcy-Forchheimer model, slip effects, MHD, Mixed convection, Stagnated flow, PST, PHF, Keller-Box scheme.
Succession Planning Practices and their Impact on Organisational Service Performance: Cross-National Evidence from Nigeria and Ghana
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.
Design and Fabrication of a Motorized Screw Jack for Automotive Applications
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.
A Mult-order Non-fourier Fractional Differential Equation for Heat Transmission
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.
Fractional Caputo derivative, Fourier transform, Laplace transform, Contour integral.
Intelligent Criminal Face Recognition Using Deep Feature Refinement and Adaptive Machine Learning for Enhanced Identification Accuracy
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.
Exploring Alternative Funding Models to Enhance the Autonomy of Bangladesh’s Anti-Corruption Commission (ACC): A Survey-Based Quantitative Study
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.
Anti-Corruption Commission, Financial Independence, Asset Recovery, Mixed Funding Model, Operational Autonomy, Bangladesh
Modeling the Interaction of Fiber Bundles with Airflow and Aerodynamic Correlations in Fiber Cleaners
The modernization of process control methods, the development of automated methods for designing equipment and technologies, and the development of automated control systems for the cotton processing process. Currently, due to the improvement of machines for cleaning cotton fiber, which are developed according to the technology of primary processing of cotton, one of the urgent issues is to ensure the production of high-quality fiber. Taking this into account, it is necessary to conduct a constructive analysis of the fiber cleaning equipment and develop an effective design. In view of this, this study was aimed at improving the aerodynamic parameters of a fiber cleaner on the basis of theoretical and applied research by creating and experimenting new saw blade construction. In process of the research, methods are used optimization by theoretical statistics, evaluation and target electronic programs, theoretical and applied mechanics, higher mathematics and vibration theory. Aerodynamic equilibrium of a freely rotating saw cylinder due to the presence of friction forces arising between the saw blades and air and atmospheric pressure cannot be overcome by centrifugal forces acting on the air mass during rotation. This justification makes it possible to explain the reason for the industry's refusal of the cleaning rate at 1000 min-1 and its transition to 1500 min-1. For the normal operation of the fiber cleaning machine, it is necessary that the saw cylinder, taking air on the cleaning arc. The conducted studies show that the dependence of the reduced velocity of air particles depends on the polar angle practically depends on a linear law, and an increase in air velocity in the initial arc of entry in the zone of the inter-core space also leads to an increase in the velocity when particles exit space, as well as the absolute velocity of air particles in the inter-core space non-linearly depends on the polar angle
fiber cleaner, saw cylinder, saw blade, fire grate, air flow, aerodynamic parameters
The Current State of Readiness for Family Life Development
This article describes the psychological features of preparing students for family life and its components, as well as the choice of complex methods for the empirical analysis of this problem and their psychological correlation analysis.
Preparation for family life, value, emotional-interpersonal, cognitive-behavioral, component, family life, family
Improving The Organization of Traffic When The Movement of Vehicles on the Roads is Restricted by the Internal Affairs Bodies
In this article, the issues of traffic organization when the movement of vehicles is restricted on the roads by the internal affairs bodies in accordance with Article 23 of the Traffic Law were studied from a social, economic, and ecological point of view.
traffic organization, temporary restrictions, administrative restrictions, traffic jams, motorcade of protected persons, bypass roads, traffic flow, safe and smooth road, Tashkent traffic jams, road safety
Multi-Objective Optimization and Mechanism-Driven Microstructural Evolution of AA5083-H32 During Cold Rolling Using Hybrid Design of Experiments
The mechanical and surface characteristics of cold-rolled AA5083-H32 aluminum alloy strips are examined in this study in relation to rolling speed, reduction percentage, and lubrication condition. Experiments were carried out at two speeds (44 rpm and 54 rpm), three reduction levels (40%, 45%, and 50%), and three lubrication states (SAE10, SAE20, and none) using an L18 orthogonal array design. In cold-rolled samples, mechanical characterization, such as tensile testing and Rockwell hardness measurement, showed increased tensile strength and hardness along with decreased elongation and yield strength, indicating a trade-off between strength and ductility. Surface roughness analysis showed the lubrication is the vital parameter for enhancing high surface quality and drastically reducing rolling power consumption. SEM micro structural analysis revealed that surface deformation bands, second-phase particle alignment, and deformation-induced grain elongation and enrichment. Rolling speed was found to be the most important factor effecting surface hardness, surface quality structure and energy efficiency through statistical analyses such as ANOVA and Grey Relational Analysis by applying the optimisation technique. These results enable the optimization of cold rolling conditions to enhance the performance of AA5083-H32 alloy for domestic to industrial applications by offering a thorough understanding of the interaction between process parameters and material response methodology.
Muscle And Joint Involvement In Selected Standing Yoga Asanas: A Review And Applied Aspects
Background: Yoga has been practiced for many years and shown to have holistic health benefits. This scoping review investigates how some standing yoga poses entail joint and muscle motions. Objectives: To recognize and chart the nerve supply, joint motions, and muscle activities used in the practice of Parivrtta Parshvakonasana, Trikonasana, Garudasana and joints. Charting Methods: For each asana, information about the muscles, joints, movements, and nerve supply was retrieved. Data on range of motion (ROM) was included in accordance with AMA and AAOS guidelines. Results: Particular joint movements across the shoulder, elbow, hip, knee, ankle, wrist, and neck were involved for each of the three asanas, along with matching muscle actions and innervations. Significant neural controls and ROM values were noted. Conclusions: Muscular strength and joint mobility are greatly enhanced by the asanas under examination. Results might provide credence to the use of yoga in clinical and therapeutic application.
Optimizing Hand Hygiene and Infection Control in Chest Drain Care: Essential Guidelines for Nurses
Background Chest drain management is a vital nursing intervention for patients requiring the drainage of air, fluid, or pus from the pleural cavity. This procedure is essential for conditions such as empyema, pneumothorax, pleural effusion, and other thoracic disorders. Nurses are responsible for handling, monitoring, and supervising patients with chest tubes. However, research indicates that nurses often rely on informal sources such as peer interactions and personal experience rather than standardized resources like guidelines, seminars, or conferences. While informal knowledge exchange plays a role in practice, its inconsistency may impact patient outcomes negatively. The initial step in nursing care—assessment—plays a crucial role in identifying risks and complications related to chest tube placement. A structured approach that incorporates evidence-based guidelines, ongoing education, and individualized treatment plans can enhance patient outcomes and mitigate complications. Aim This study aims to: Examine current nursing practices in chest drain management. Evaluate the effectiveness of formal and informal educational resources for chest drain care. Assess the impact of nursing assessment and critical thinking on improving patient outcomes related to chest tube placement and maintenance. Conclusion This study highlights the importance of an evidence-based approach in chest drain nursing care. While informal knowledge-sharing remains prevalent, structured education, training, and adherence to research-based guidelines are essential. Providing access to up-to-date resources through in-service education, seminars, and university courses can significantly reduce complications such as infection, dislodgement, and bleeding. By integrating critical thinking and a patient-centered approach throughout the nursing process, nurses can enhance patient care and reduce adverse events.
Chest drain management, Nursing care, Evidence-based practice, Critical thinking in nursing, Nursing assessment.
International Law: Its Enhanced Scopes of Anti-Corruption and Anti Money Laundering Platform in Context to Bangladesh
This study examined how international law contributes to Bangladesh’s efforts in fighting against corruption and money laundering. The objectives are to explore the influence of global legal tools like the UNCAC and FATF Recommendations, assess challenges in local application and suggest reforms for stronger alignment. A qualitative approach was used. Data were collected from 60 participants including legal experts, institutional representatives, policymakers and civil society actors. Structured questions were based on specific clauses of UNCAC, FATF standards, and national laws. Findings showed that Bangladesh has adopted many international standards. Yet, its enforcement is weak. The Anti-Corruption Commission and the Financial Intelligence Unit has a lacking in full capacity. Political will is limited, asset recovery remains slow and inconsistent. Respondents highlighted that although the legal framework is present, its use in practice is low. The study recommends reforms to strengthen institutional powers, promote political neutrality and ensure consistent application of global standards. Cross-border cooperation should be improved. Capacity building and legal training are needed. In conclusion, Bangladesh must not only adopt international norms but also focus on their proper enforcement. Legal improvements, institutional reform and strong political backing are necessary for reducing corruption and financial crimes.
International Law, Anti-Corruption, Money Laundering, Bangladesh, UNCAC, FATF
Centric Student Assessment Framework Integrating Co-Po And Pso Performance Through An Nlp-Enhanced Obe Approach.
This study presents an Outcome-Based Education (OBE) framework integrated with Artificial Intelligence (AI) and Natural Language Processing (NLP) to assess Course Outcomes (CO), Program Outcomes (PO), and Program-Specific Outcomes (PSO). The proposed system leverages data-driven techniques to evaluate student performance holistically, emphasizing cognitive, psychomotor, and affective learning dimensions. A hybrid AI model combining K-Nearest Neighbors (KNN), Decision Trees, and K-Means clustering was used to classify and predict student readiness for industry and entrepreneurial potential. The results demonstrate that integrating AI into OBE-based assessment enhances accuracy, interpretability, and the relevance of academic outcomes to real-world applications.
A Study on the Effect of Sic and Ticn Reinforcements on Mechanical and Microstructural Properties of Friction Stirred Al 6061 Alloy
The friction stir processing (FSP), its pass counts, and the inclusion of hardened particles (SiC and TiCN) were studied for their effects on the micro hardness, wear characteristics, and tensile performance of the Al6061 aluminium alloy. To examine the microstructure of the alloy, FSPed composite samples, optical microscopy were used. After a single FSP pass, microscopic examinations showed that the SiC and TiCN particles were not evenly distributed throughout the matrix. However, a significant improvement in the distribution of the SiC and TiCN particles was seen when the pass number was increased. The use of FSP caused a general softening in comparison to the base metal. The coarsening of Mg2Si was likely to blame for the Al6061 alloy softening. The FSPed samples' tensile strength was significantly lower than that of the basic metal. As the addition of TiCN particles increases the wear behaviour and hardness of composite compared to the inclusion of SiC particles, applying a higher pass number of the process and also adding the toughened particles increased the micro hardness and wear behavior of the samples.
Revolutionizing LCP Fixation - The Role of AI and Augmented Reality in Orthopedic Surgery
Background Advancements in Artificial Intelligence (AI) and Augmented Reality (AR) have introduced new possibilities in orthopedic trauma surgery, particularly for Locking Compression Plate (LCP) fixation of supracondylar femur fractures. Conventional fixation techniques rely heavily on fluoroscopy and surgeon expertise, often resulting in variable accuracy and increased radiation exposure. Objective To evaluate the clinical and surgical outcomes of AI- and AR-assisted LCP fixation compared to traditional fixation techniques in supracondylar femur fractures. Methods A prospective comparative study was conducted on 120 patients divided into two groups: traditional LCP fixation (n=60) and AI & AR-assisted LCP fixation (n=60). Primary parameters included surgical time, implant positioning accuracy, radiation exposure, complication rate, and functional outcome (Knee Society Score). Data were analyzed using independent t-tests and chi-square tests, with p<0.05 considered significant. Results The AI & AR-assisted group demonstrated significantly better outcomes, including reduced surgical time (65.25±8.3 vs. 88.45±10.2 min), improved implant accuracy (2.08±0.8 vs. 3.50±1.0 mm deviation), lower radiation exposure (40.27±10.1 vs. 76.38±15.4 sec), and fewer complications (7.62% vs. 13.87%). Functional recovery was superior in the AI & AR group, with a higher Knee Society Score (84.72±4.1 vs. 75.84±5.2). Conclusion AI-assisted planning and AR-guided navigation significantly enhance surgical precision, efficiency, and postoperative recovery in LCP fixation. Broader clinical validation and cost-effective integration are needed for widespread adoption in orthopedic practice.
Proposed A New Model for Risk Management Software for Construction Projects
A construction project involves a wide range of activities and transformations during its planning and execution stages, which can pose several risks. An essential function of risk management is to create awareness of uncertainty, qualify risks, manage controllable risks, and curtail uncontrollable risks. The evidence demonstrates that effective implementation of digital risk management software can achieve significant improvements in safety performance, regulatory compliance, project delivery times, and cost control. This study aimed to propose a new model for Risk Management software for Construction Projects based on a Review of the literature. We conducted a literature review according to the PRISMA guidelines. In our search, we focused on construction and civil engineering projects(P) and Risk Management Software (I). The comparison of interest (C) was Traditional/Manual Methods. In terms of outcomes (O), it was postulated that reported outcomes could be categorized as the “identification of specific outcomes”. We set a timeframe (T) for research published since 2010, which encompassed the last 16 years. A literature search was conducted on 23 September 2025, and a final search was conducted on 22 October 2025 via Web of Science, Scopus, Wiley, and Science Direct databases. The search keywords were “Risk Management, software, Construction Projects” using AND/OR operators. We searched all combinations of terms from each category to find the target studies. We included six articles and two websites. The developed risk management software systems demonstrated a broad application scope, having been designed for use in various countries, including Iraq, China, Korea, and the United Kingdom. These systems were successfully deployed across diverse projects such as hospitals, railways, metro lines, bridge construction, coal chemical enterprises, and small and medium-sized enterprises (SMEs) in the construction sector. Crucially, the majority of the custom-designed software solutions were BIM-based, underscoring the trend towards integrating advanced information modeling into risk management practices across the global engineering sector. We presented the results section in three distinct stages: 1. A review of the literature, 2. the synthesis of the study findings, and 3. the proposal of a new model.BIM is an intelligent 3D model-based tool that gives users a digital depiction of the functional and physical aspects of a facility. Increased understanding of the planning, design, construction, and management of the space is made possible by this type of representation. When properly applied, building information models can address communication issues in the construction industry and offer a shared knowledge base that can serve as a trustworthy foundation for decisions made throughout a building's lifecycle.
Turbulence Modelling Approaches For Predicting Shockwave Boundary Layer Interaction In Hypersonic Conditions
Shockwave boundary layer interaction (SWBLI) remains a critical challenge in hypersonic aerothermodynamics due to its strong influence on separation, heat flux amplification, and vehicle stability. This study evaluates the predictive capability of widely used turbulence models Spalart Allmaras (SA), k–ω SST, and Delayed Detached Eddy Simulation (DDES) for simulating SWBLI under Mach 7 conditions. A 2-D compression ramp configuration is modelled using a high-fidelity CFD solver. Results show that DDES captures separation length and shock-foot oscillations more accurately than RANS models, while k–ω SST performs moderately well with reduced computational cost. SA underpredicts separation but resolves attached boundary layers effectively. These findings highlight the importance of hybrid turbulence modelling in hypersonic applications