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.
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
Comparative Analysis of Laparoscopic and Open Surgical Techniques in Common Abdominal Procedures
Background Surgical practices have undergone remarkable transformation over recent decades with the advent of minimally invasive techniques, particularly laparoscopic surgery. While open surgery has historically been the cornerstone of operative management, the adoption of laparoscopy has redefined standards in patient recovery, postoperative complications, cosmetic results, and hospital efficiency. Objective To perform a comprehensive comparative analysis between laparoscopic and open surgical approaches for three commonly performed abdominal procedures cholecystectomy, appendectomy, and inguinal hernia repair focusing on clinical, operative, and patient-centered outcomes. Methods A review of more than 30 peer-reviewed studies was conducted to evaluate key surgical parameters, including operative duration, postoperative complications, infection rates, hospital stay, cost-effectiveness, and overall patient satisfaction. Both laparoscopic and open techniques were compared across multiple metrics and clinical scenarios, including uncomplicated and complex cases. Results Findings indicate that laparoscopic surgery, characterized by small incisions, trocar use, and camera-guided visualization, offers distinct advantages such as reduced postoperative pain, shorter hospitalization, faster recovery, and better cosmetic outcomes. Open surgery, though more invasive, remains indispensable in complex or emergency cases and in resource-limited settings Conclusion Laparoscopic surgery demonstrates superior outcomes for uncomplicated abdominal procedures, yet open surgery continues to hold relevance when patient complexity or limited resources constrain the use of laparoscopy. With ongoing advancements in surgical education, technology, and accessibility, laparoscopy is poised to become the preferred approach for most abdominal operations. This comparative analysis provides evidence-based recommendations to guide clinical decision-making and optimize patient care.
Laparoscopic surgery, open surgery, abdominal surgery, cholecystectomy, appendectomy, hernia repair, minimally invasive surgery, postoperative outcomes.
Synergistic Impact of Dual Split Injection on the Combustion, and Emission Characteristics of Crdi Diesel Engine Fueled with Carbon Black Nanoparticle-Enriched Dairy Scum Biodiesel B20 Blend
This study investigates the combined influence of dual split injection and carbon-black nanoparticle enrichment on the performance, combustion, and emission characteristics of a single-cylinder CRDI diesel engine fueled with dairy-scum biodiesel (DSOBD) blends. Experiments were conducted at varying injection timings, pilot–main mass fractions, and loads, comparing single injection with optimized dual split injection. Results show that the optimized split injection strategy—main injection at 14° BTDC and pilot injection at 7° BTDC with a 90:10 mass fraction—achieved up to 8.6% higher brake thermal efficiency (BTE) and 6–7% lower brake specific fuel consumption (BSFC) than single injection. Carbon-black nanoparticle addition shortened ignition delay (ID) by 18%, reduced combustion duration (CD) by 12%, and increased peak pressure (PP) by 4–5%, indicating faster and more efficient combustion. At full load, the optimized split strategy reduced smoke opacity by 23.1%, HC emissions by 15–17%, CO emissions by 12–14%, and NOx emissions by 8–10% compared to single-injection biodiesel operation. These improvements are attributed to enhanced atomization, better air–fuel mixing, and controlled combustion phasing. The findings confirm that integrating carbon-black nanoparticles with dairy-scum biodiesel under optimized dual split injection delivers a balanced improvement in efficiency and emissions, offering a viable pathway for sustainable CRDI diesel engine operation.
Dairy-scum biodiesel, Carbon-black nanoparticles, Dual split injection, CRDI diesel engine, Sustainable fuels, Nanofuel blends
Crop Yield Prediction Using a Hybrid Convolutional and Recurrent Neural Network Model
Precise forecasting of crop yields signifies a critical authority for addressing global food security challenges, optimising supply chain logistics, and informing evidence-based agricultural policy formulation. Conventional yield prediction methodologies frequently demonstrate limitations in capturing the intricate, non-linear interdependencies among meteorological conditions, edaphic properties, and cultivation practices that collectively determine agricultural productivity. This research proposes CLDNet, a novel deep learning framework that architecturally integrates Convolutional Neural Networks with Long Short-Term Memory networks to predict soybean yield across the United States. The CNN module specialises in discerning spatially-distributed patterns from multidimensional environmental inputs, while the LSTM component effectively models temporal sequences and dependencies throughout the crop development cycle. Our framework was developed and validated using an extensive public dataset from Kaggle, incorporating historical yield records, meteorological measurements, and pedological characteristics. Empirical findings indicate that our integrated CLDNET paradigm surpasses the predictive performance of conventional approaches, including Random Forest, Support Vector Regression (SVR), and isolated LSTM configurations, manifesting superior performance through reduced Root Mean Square Error (RMSE) and enhanced coefficient of determination (R²) metrics. The model was optimised using adaptive moment estimation (Adam), culminating in a root mean square error of 3.78 bushels/acre and R² score of 0.897 on independent test data. This research substantiates the transformative potential of deep learning systems in decoding complex agro-climatic relationships and establishes a scalable, data-centric paradigm for agricultural yield projection.
Convolutional Neural Networks, Long Short-Term Memory, Precision Agriculture, Adaptive Moment Estimation, Agro-informatics
Optimal Inventory Management with Time-Varying Exponential Demand, Weibull Decay, Partial Shortages, and Permissible Payment Delays
This study presents a comprehensive inventory model that integrates exponential demand, gradual deterioration following a Weibull distribution, and partial backlogging, under a trade credit policy framework. The primary objective is to develop a price and lot-sizing model suitable for retail environments where suppliers permit a delay in payments—a realistic and common trade credit scenario. The model assumes that demand increases exponentially over time, while items deteriorate gradually rather than instantaneously. Additionally, the possibility of shortages is allowed, with partial backlogging occurring only during the shortage period for the remaining unmet demand. To determine the optimal ordering policy, the first and second order conditions are established, leading to the development of a structured algorithm aimed at minimizing the total cost. The algebraic approach, combined with cost minimization techniques, enables the identification of multiple optimal values across different inventory scenarios. The model addresses practical conditions often faced in retail inventory systems, such as payment flexibility, time-sensitive demand, and limited backordering capabilities. This framework provides a robust and realistic measurement model for inventory control, allowing businesses to strategically balance ordering decisions, holding costs, shortage implications, and the benefits of deferred payments. The findings contribute to the advancement of inventory theory by offering a refined decision-making tool that accommodates both market dynamics and supplier-buyer financial arrangements.
Inventory model, Exponential demand , Weibull deterioration, Trade credit , Partial backlogging , Cost minimization , Perfect Measurement
Digital Marketing and Customer Satisfaction Experience: Empirical Insight from Nigeria's Listed Construction and Transportation Industries
Digital marketing is now essential for customer satisfaction and loyalty, yet its impact in Nigeria’s construction and transportation sectors remains underexplored. This study examined how digital marketing practices affect satisfaction among listed firms, guided by Expectation-Confirmation Theory (ECT) and Uses and Gratifications Theory (UGT). Five dimensions were analyzed: content value, social media engagement, online visibility, relationship management, and service experience. Data from 712 managerial staff were analyzed using descriptive statistics and Covariance-Based Structural Equation Modeling (CB-SEM). Results showed relationship management had the strongest effect on satisfaction (β = 0.31, p < 0.001), followed by content value (β = 0.28, p < 0.001), social media engagement (β = 0.24, p < 0.001), service experience (β = 0.22, p < 0.01), and visibility (β = 0.19, p < 0.05).Findings highlight that strong online relationships and valuable content most effectively enhance customer experiences. Firms should invest in relationship systems, quality content, and responsive services to sustain satisfaction. Despite its cross-sectional design, the study advances emerging-market scholarship by quantifying multidimensional digital marketing effects and offering actionable insights, showing managers can personalize interactions and strengthen loyalty through value-driven engagement.
Digital marketing, customer satisfaction, digital content value, social media engagement, online visibility, Nigeria, emerging markets, digital customer experience
Statistical Learning Models for Performance Optimization-AI Driven Wireless Communication Networks
The rapid expansion of 5G and emerging 6G networks has increased data traffic, user demands, and system complexity. Artificial Intelligence (AI) and statistical learning models are critical for optimizing latency, throughput, spectrum efficiency, and energy consumption. This chapter examines integrating regression models, Bayesian inference, SVMs, ensemble learning, and deep learning into AI-driven wireless systems. Performance optimization strategies include resource allocation, traffic prediction, QoS enhancement, and dynamic spectrum management, while addressing challenges such as scalability, interpretability, and real-time adaptability. Future research directions focus on AI-statistical learning convergence for next-generation networks.
Enhancing Home Economics and Textiles Education in Malawian Secondary Schools: Tackling Challenges and Exploring Innovative Solutions
This paper explored how students gained knowledge and skills in Home Economics (HEC) and Clothing and Textiles (CT) within the context of the disintegrated curriculum in Malawian secondary schools. It was based on the interpretive paradigm and constructivist theory, using a mixed-methods research design that leaned toward qualitative approaches. The main research question investigated how the teaching and learning environment, along with educational policies, influence the development of knowledge and skills in these subjects. The study specifically examined the knowledge and skills taught in HEC and CT, the teaching methods used, and the factors affecting the selection of these methods. Data collection involved five techniques: document analysis, questionnaires, classroom observations, face-to-face interviews, and focus group discussions, with a total of 163 participants from two educational divisions and six secondary schools. Results showed that students have limited knowledge and skills in HEC and CT, with instruction mainly theoretical and little practical assessment. Both subjects face significant challenges, including insufficient resources, a lack of innovative teaching methods, and a reliance on traditional strategies. Teachers tend to strictly follow curriculum documents, leading to less engaging activities, while the use of information and communication technology (ICT) remains uncommon. The study found issues such as inadequate time allocation, a shortage of qualified teachers, and structural problems within the curriculum. To improve HEC and CT instruction, the study recommends integrating ICT, adopting innovative teaching strategies, providing refresher courses for teachers, building well-equipped labs, and enhancing financial and administrative support for schools.
Performance Analysis of Cloud Server Selection Based on Fuzzy Logic
Cloud computing allows the use of Internet access to the centralized network of optimized computing resources, viz., application servers, network, hardware, software, and storage for clients. It provides cloud-based applications, including email, file storage and sharing, voice calling /video conferencing, and social media, to its clients. Network transmission quality is affected by bandwidth and propagation delay between client(s) and server(s), and between server(s) and client(s). The proposed protocol determines the impact of bandwidth and propagation delay of the cloud application server system using fuzzy logic decision-making techniques. The proposed protocol evaluates the quality of a cloud application server system for Email, FTP, and HTTP. The simulation is performed using OPNET. Four network scenarios, defined by combinations of bandwidth and propagation delay, are simulated and compared based on the overall transmission quality of the cloud application system. The simulation shows the performance of transmission quality rating as throughput as a function of bandwidth and propagation delay. Throughput is directly proportional to bandwidth and inversely proportional to propagation delay.
Interactive Fuzzy Goal Programming Approach For Multi-Objective Economic Emission Load Dispatch Optimization In Power Systems
The growing incidence of thermal power generation plants and strict environmental laws have constituted the Economic Emission Load Dispatch (EELD) issue into a challenging problem in contemporary power systems operation. This issue comprises the strategy of reducing the cost of fuel and pollutant emission at the same time and meeting the power balance within the system and the operating limits of the generator as well as the transmission losses. Due to the nonlinearity and conflict of these aims, the traditional single-objective optimization methods do not give flexible and environmentally sustainable solutions. An Interactive Fuzzy Goal Programming (IFGP) methodology is suggested in this work to be used to solve the multi-objective EELD problem. The technique converts the economic and emission targets to fuzzy target in terms of linear membership functions based on the ideal and anti-ideal solutions. This is an interactive decision-making mechanism, which enables the levels of aspiration to be updated stochastically until a satisfying compromise solution has been reached. The approach suggested is verified on three generators thermal power system where the transmission losses are modeled using B-coefficients. Extensive graphical explanations such as cost and emission change with demand of load, trade-off properties, and relative comparison with classical Economic Load Dispatch (ELD), are given. The findings indicate a high level of emission reduction by the IFGP approach at a small cost addition to fuel as opposed to traditional ELD. The suggested framework has increased flexibility, clear sightedness, and decision support and is thus a great fit to sustainable and viable power system planning and operation.
Economic emission load dispatch, Interactive fuzzy goal programming, Multi-objective optimization, Power system, Compromise solution.
The crowdfunding application revolutionizes the traditional fundraising landscape by seamlessly integrating advanced digital marketing tools. Unlike conventional platforms, it empowers project creators to both raise funds and actively promote their campaigns. By leveraging social media integration, email marketing, and SEO tools, creators can easily expand their reach and attract a broader audience. The app allows campaign creators to connect their projects to social media platforms, sharing updates and promotional content to increase visibility. It also supports targeted ads for more precise audience engagement. The built-in email marketing tool helps manage contact lists and send personalized updates, keeping backers informed and engaged throughout the campaign. SEO optimization is another key feature, guiding creators in improving their search engine rankings and attracting organic traffic. With these tools, campaigns can reach potential backers who may not have been directly targeted through traditional methods. A real-time analytics dashboard provides detailed insights into campaign performance, tracking key metrics like visitor numbers, conversion rates, and social media engagement. This data allows creators to fine-tune their strategies and make informed adjustments for better results. Overall, the platform combines crowdfunding and digital marketing into one cohesive experience, streamlining promotion and fundraising efforts. By providing project creators with powerful tools for engagement and performance tracking, the app maximizes campaign success and sets a new standard for crowdfunding in the digital age.
Resilient Security Frameworks for 6g Vehicular Iot: Integrating Blockchain, Ai-Driven Ids and Ethical Governance
The fast development of 6G-enabled vehicular Internet of Things (IoT) brings new opportunities of autonomous mobility, cooperative perception, and ultra-reliable communication, but also subjects vehicles to multi-layered, multi-faceted cyber threats. This paper is a cohesive investigation of security, privacy, and trust issues of next-generation vehicular networks and how blockchain, artificial intelligence (AI), and distributed edge architectures can work together to improve resilience. It initially presents the categorization of vehicular threats in terms of authentication, data integrity, availability, privacy, and physical tier, using real-life examples, including the Jeep Cherokee hack and GPS spoofing, to explain how severe the attacks can be practical. It next discusses blockchain-based trust models, which play roles in the decentralized management of identities, the sharing of data without any tampering, and the use of smart contracts to enable automation. Moreover, it examines the AI-driven intrusion detection systems that are based on machine learning, deep learning, federated learning, and reinforcement learning to identify the changing attacks in real-time. The combination of blockchain and AI is discussed as the synergistic model that can be used to secure trust, maintain integrity, and enhance adaptive threat identification. To make sure of responsible deployment, ethical and regulatory factors, such as privacy protection, liability, fairness, transparency, and global standardization, are addressed. This article presents a comprehensive background to the creation of a secure, trustful, and future-resistant vehicular ecosystem during the 6G era with its insights on architecture, case studies, and a pseudo-coded IDS framework.
6G Vehicular IoT, Blockchain Security, AI-driven Intrusion Detection, Vehicular Trust Management, Edge-Distributed Intelligence
An Intelligent UAV-Based System for Real- Time Victim Detection and Geolocation in Disaster Scenarios
Disasters, both natural and man-made, create serious challenges for search and rescue (SAR) operations, where time is crucial for survival. Traditional response methods often struggle due to hard-to-reach areas, dangerous conditions, and a lack of real-time information. This paper discusses the design, development, and evaluation of an intelligent system based on an Unmanned Aerial Vehicle (UAV) that aims to overcome these challenges. The system offers a complete solution for real-time detection and accurate location of victims in disaster-affected areas. It uses a UAV with a high- resolution camera that streams live video to a ground control station. A state-of-the-art deep learning model, You Only Look Once version 8 (YOLOv8), processes this video feed in real-time to quickly and accurately identify human presence. When detection occurs, the system automatically captures the GPS coordinates, logs a timestamp with an image, and shows the victim's location on an interactive dashboard. This gives first responders actionable intelligence right away, which significantly cuts down search times and improves operational efficiency. The paper also outlines a theoretical framework for expanding the system to a multi-UAV swarm, using Swarm Optimization Algorithms for coordinated search patterns and Delay-Tolerant Networks (DTN) to maintain communication in areas without infrastructure. Performance evaluation, which used the VisDrone aerial imagery dataset, shows the system's high detection accuracy and low latency, confirming its potential as a game-changing tool for modern disaster management
Unmanned Aerial Vehicles (UAV), Disaster Management, Object Detection, YOLOv8, GPS Geolocation, Swarm Intelligence, Delay-Tolerant Networks (DTN).
Retracted
cup-to-disc ratio, vertical cup-to-disc ratio, YOLO, and Glaucoma
Flood Sense: Machine Learning-Based Flood Risk Prediction and Optimal Land Selection Using Environmental Data Analysis
Floods are among the most devastating natural disasters, causing significant damage to life and property. This paper presents an advanced flood prediction model using machine learning techniques to assess flood risks and suggest optimal land selection for development. The system integrates Random Forest for structured environmental data analysis and a Convolutional Neural Network using VGG16 for satellite image classification. A web-based interface was developed to make predictions accessible to users. The combination of numerical and image-based flood assessment provides a comprehensive and reliable method for proactive disaster management and urban planning.
Flood prediction, Machine Learning, Random Forest, Convolutional Neural Networks, Urban Planning.
The Influence of Food Bloggers’ Ewom Credibility on Promoting Authentic Saudi Cuisine, a Cross-Sectional Web-Based Survey
The study aims to investigate the influence of food bloggers’ eWOM credibility on promoting authentic Saudi cuisine. After investigating the previous similar studies, Stimulus–Organism–Response (SOR) Model and Electronic Word of Mouth (eWOM) theory were adopted to explain the relationships between study’s variables. A sample of 385 Saudi participants was used to response a self-administrated questionnaire which was distributed through different social media platforms. After that, Smart-PLS software was used to analuze these responses. The study’s findings show that the credibility of food bloggers’ electronic word of mouth positively influence authentic food taste awareness and consumer attitude towards authentic cuisine. The results also showed that authentic food taste awareness positively mediates the relationship between credibility of food bloggers’ electronic word of mouth and consumer attitude towards authentic cuisine.
eWOM, Food Blogger, Authentic Cuisine, Saudi Arabia, Saudi Vision 2030
A Robust Machine Learning-Driven Framework for Efficient Spectrum Sensing in Next-Generation Vehicular Networks
Cognitive Radio-enabled Vehicular Ad Hoc Networks (CR-VANETs) are rapidly emerging as a transformative solution to overcome spectrum scarcity in intelligent transportation systems. As vehicular networks evolve to support real-time, high-bandwidth applications across Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communications, the limitations of static spectrum allocation under the Dedicated Short-Range Communication (DSRC) framework have become evident. Cognitive Radio (CR) offers dynamic spectrum access, allowing unlicensed vehicular nodes to opportunistically utilize underused licensed spectrum without interfering with primary users (PUs). However, effective spectrum sensing in VANETs remains a formidable challenge due to high mobility, fluctuating channel conditions, varying Quality of Service (QoS) requirements, and security threats such as Primary User Emulation Attacks (PUEA) and Spectrum Sensing Data Falsification (SSDF). This paper presents an intelligent and adaptive spectrum sensing framework that integrates traditional signal processing with machine learning (ML) models including logistic regression, SVM, decision trees, random forests, and K-nearest neighbors. The model leverages both centralized and decentralized cooperative sensing while dynamically adjusting sensing parameters based on vehicular mobility, SNR conditions, and PU behavior. Security is enhanced through trust-aware Q-learning, which assigns reliability scores to secondary users to mitigate malicious behavior. A novel segmentation technique using KMeans clustering reduces latency and supports localized sensing decisions. Simulation results show significant improvements in detection accuracy, reduced false alarm rates, enhanced throughput, and strong resilience against adversarial attacks. The proposed framework demonstrates how ML can optimize sensing performance and spectrum utilization under realistic vehicular scenarios. By addressing key research gaps—such as the lack of mobility-aware PU models, adaptive QoS provisioning, and secure cooperative sensing—this work contributes a robust and scalable solution to spectrum management in CR-VANETs, paving the way for more efficient, secure, and intelligent vehicular communication networks.