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.
Monetary Policy Instruments and Inflation Rate in Nigeria
This research work examined the impact of monetary policy instruments on inflation rate in Nigeria within the epoch of 1981 to 2022. Data was sourced from CBN statistical bulletin. Inflation rate (INF) was utilized as an endogenous variable while the exogenous variables were interest rate (ITR), exchange rate (EXR), Open market operation proxied by treasury bill rate (TBR), reserve requirement ratio (RRR) and money supply (MS) used as controlled variable. Consequently, this research made use of Ex-Post Factor research design in which multiple regression method of analysis was used. Augmented Dickey Fuller (ADF), Philip Perron (PP), Zivot-Andrews (ZA) and Dynamic ARDL were employed as the research techniques, specifically for unit root tests and estimation of the long run and short run relationship of the variable. The outcomes indicated that: interest rate has negative and significant impact on inflation rate in Nigeria by contributing to 2.57% and 2.03% decrease in INF in short and long run periods respectively. On the other hand, Open market operation proxied by treasury bill rate has positive and significant impact on inflation rate in Nigeria by contributing to 0.11% and 0.005% increase in INF in short and long run periods respectively. On the contrary, reserve requirement ratio negatively and insignificantly impacted on inflation rate by leading to 0.26% and 0.04% decrease in INF in the short and long run periods respectively. Similarly, exchange rate had adverse and significant effect on rate of inflation in Nigeria by contributing to 10.9% and 2.6% decrease in INF in both the short and long run periods respectively. Lastly, supply of money has positive and significant effect on inflation rate in Nigeria by contributing to 0.46% and 0.66% increase in INF in the short and long run periods respectively. Consequently, this work recommends that CBN should maintain a contractionary monetary policy with the capacity to restrain too much supply of money, so as to mitigate rate of inflation in the country. This measure will drastically minimize the volume of money circulating outside the banks which is identified as responsible for the rising rate of inflation in Nigeria. Again, CBN should liberalize interest rate such that will reduce prime lending rate and thereby encourage productive investment which will reduce inflation rate in Nigeria.
Dream Emotion Prediction Using Random Forest Algorithm on EEG Signal Data: An Age wise Analysis
Dreams serve as a reflection of both conscious and subconscious emotional states, often influenced by age, psychological development, and cognitive function. This study presents an agewise analysis of dream emotion classification using EEG (Electroencephalogram) signals and a Random Forest algorithm. EEG data collected during REM sleep was used to classify emotions into three categories: Positive, Negative, and Neutral. The study evaluates emotional trends across four distinct age groups 0–16, 17–30, 31–45, and 46–100 years through machine learning and visual analytics, including bar charts, confusion matrices, heatmaps, ROC curves, and pie charts [1]. The Random Forest model achieved a high overall accuracy of 99%, with an AUC score of 0.95 for the 17–30 age groups, indicating superior predictive performance. Results reveal significant variations in emotional expression during dreams across age brackets, with negative emotions predominantly observed in adults. These findings demonstrate the potential of EEG-based emotion prediction for advancing applications in sleep therapy, mental health monitoring, and affective computing.
EEG, Emotion, Dream, Random Forest, Age Analysis, Classification
Prediction of Alzheimer’s Disease Risk Based on Plasma Lipid Profiles and Covid-19 History Via Logistic Regression.
Alzheimer’s disease (AD), a form of dementia is known as a neurodegenerative disorder with gradual memory impairment. Recent research studies have suggested a putative risk of increased susceptibility to AD post COVID-19 infection. This study attempted to clarify this relationship by examining lipid profile data in conjunction with the information available in patient database of COVID-19. Data were analyzed by preprocessing, exploratory data analysis through pivot tables and a logistic regression model for the prediction of risk of AD. The model adjusted for APOE4 genotype, age and COVID-19 status. Model performance metrics such as accuracy, precision, recall and F1 score were evaluated. Although reasonably accurate, there was a high rate of false negatives in the model and more work is required to improve these. These results emphasized the intricate association of lipid profiles, COVID-19 infection and AD risk. Identification of biomarkers and effective risk assessment strategy for AD in the setting of COVID-19 needs more investigations. The timely diagnosis and intervention may lead to focused therapy and preventive interventions that reduce the chronic neurological impact of COVID-19.
Artificial Intelligence and Machine Learning Implementation in Intelligent Vehicular Coordination
Artificial Intelligence (AI) and Machine Learning (ML) are transforming vehicular networks by enabling autonomous, collaborative, and context-aware decision-making. Modern transportation systems demand ultra-low latency, high reliability, and rapid adaptation, which traditional rule-based or centralized approaches fail to provide. This work explores the role of distributed intelligence—spanning centralized cloud AI, edge AI, federated learning (FL), and multi-agent AI—in ensuring real-time vehicular coordination. Reinforcement Learning (RL) and Multi-Agent RL (MARL) support dynamic tasks such as lane changing, adaptive routing, collision avoidance, and resource allocation in highly variable traffic conditions. Cooperative Perception (CP) enhances situational awareness by enabling vehicles and roadside sensors to share processed features or decisions, significantly improving detection accuracy under occlusions and adverse conditions. Additionally, AI-driven resource allocation optimizes spectrum, power, and computing distribution across 6G-enabled IoV architectures, ensuring efficient QoS management under dense mobility. Performance analyses—via convergence plots, reward evolution, perception trade-off curves, and newly evaluated latency, communication overhead, and PDR graphs—highlight the superiority of AI-driven mechanisms over static or heuristic baselines. Overall, this study demonstrates that AI/ML techniques form the backbone of next-generation intelligent transportation systems, enabling scalable, secure, and cooperative vehicular ecosystems.
Intelligent Vehicular Coordination, Federated and Edge AI, Reinforcement Learning (RL), Cooperative Perception (CP), 6G-Enabled IoV Networks.
Bot Or Not? Analyzing the Strategic Use And Impact of Ai Influencers Vs. Human Influencers in Public Relations Campaigns
This study identifies the critical influencer attributes that drive public relations (PR) outcomes in the emerging hybrid environment where both human and artificial intelligence (AI) influencers operate. The purpose is to identify which influencer attributes (trustworthiness, expertise, attractiveness, similarity, interactivity, trendiness, involvement) significantly influence key PR outcomes (trust in branded posts, brand awareness, and purchase intention). Integrating the Social Media Influencer Value (SMIV) framework with the Stimulus Organism Response (SOR) paradigm, the study employed a cross-sectional Structural Equation Modeling (SEM) analysis using data collected from 397 active social media users in Nigeria. Findings reveal that interaction, involvement, and trendiness are the most significant predictors of brand awareness, while trustworthiness and expertise primarily drive trust in branded posts. Crucially, trust in branded posts emerged as the strongest overall predictor of purchase intention. The study concludes that PR strategists should prioritize designing or selecting influencers regardless of whether they are human or AI based on demonstrated high engagement, consistent credibility, and acute trend sensitivity to maximize communication effectiveness.
Leveraging Sustainability Communication for Crisis Resilience and Leadership Transformation in Emerging Markets
This study investigates how sustainability communication drives crisis resilience and leadership transformation in emerging markets. The quantitative data were analyzed using regression to analyze the relationships among the variables. The regression results reveal that sustainability communication showed a significant positive influence on crisis resilience highlighting its role in translating into effective crisis response and adaptive leadership capacity. Additionally, sustainability communication significantly impacted both Crisis Resilience and Leadership Transformation thereby underscoring the synergistic relationship between leadership evolution, resilience building and long-term organizational stability. The result also showed that sustainability communication variables in the study collectively explained 54.2% of the variance in crisis resilience and 51.6% in leadership transformation. The study provides valuable insights for leaders and policymakers in emerging markets seeking to build crisis ready and sustainability driven organizations.
AI-Driven Pain Assessment and Departmental Triage in Healthcare Systems
Accurate pain assessment and timely triage are essential in high-volume clinical care, yet traditional methods relying on self-reporting and manual judgment are prone to delays and inconsistency. This study evaluates an AI-based system integrating facial expression analysis, physiological biosignals, and natural language processing for pain detection and automated triage. Thirty patients were assessed, with AI-derived scores showing strong correlation with Visual Analog Scale ratings (r = 0.87) and triage accuracy of 90%. Decision-making time was reduced to 19 seconds per case versus 6.4 minutes manually. Findings demonstrate the feasibility of AI-assisted pain management, improving efficiency, accuracy, and consistency. Recommendations include scaling, integration into hospital systems, and ethical deployment, highlighting AI’s transformative potential in healthcare delivery.
Hybrid Deep Learning and Machine Learning Approach for ECG-based Arrhythmia Classification and Multi-Class Disease Identification
This Study shows a thorough hybrid technique for the classification of arrhythmias utilizing ECG signals, incorporating both Deep Learning (DL) and Machine Learning (ML) methods. We assess a pasture of ML classifiers to decide the most efficient model for arrhythmia discovery, with Adaboost discovered as the dominant classifier, attaining a precision of 96.9%. In the deep learning section, we exploit VGG16 for robotic feature extraction, succeeded by classification of these features applying a Support Vector Machine (SVM), which outcome in an accuracy of 96.6%. To develop an expansion of our study, we concoct a multi-class disease classification approach predicate on a hybrid CNN-LSTM framework, which attains an impressive accuracy of 99.53%. These experiment outcomes feature the conclusiveness and strength of our hybrid approach in enhancing ECG-based arrhythmia identification and multi- class CVD classification..
Hybrid Feature–Noise Adaptive Preprocessing for Liver Disease Prediction Using Statistical Learning Models
Liver diseases pose a global health challenge, requiring accurate and reliable prediction methods for early diagnosis. This study introduces a Hybrid Feature–Noise Adaptive Preprocessing (HFNAP) algorithm that enhances data quality through adaptive imputation, dynamic outlier rejection, and correlation-based feature pruning. Using the UCI Hepatitis C Virus dataset, HFNAP statistically stabilizes data distributions and mitigates class imbalance while maintaining computational efficiency. Comparative evaluation with two contemporary methods—LPDS and Poly-SHAP—demonstrates that HFNAP achieves superior reduction in data skewness, feature redundancy, and imbalance ratio. The approach offers a transparent, statistically grounded preprocessing framework suitable for improving the performance and reliability of downstream classification models. Overall, HFNAP establishes a reproducible foundation for medical data preparation, bridging the gap between efficiency and interpretability in healthcare machine learning systems.
Liver Disease Prediction, Adaptive Preprocessing, Feature Selection, Statistical Learning, Medical Data Analysis
Blockchain based Agri food Supply chain using AI Smart Contracts
Blockchain technology offers a revolutionary way to improve the agricultural food supply chain's traceability, security, and transparency. As global food production and consumption continue to increase, systems that can efficiently track and verify food products from farm to table are becoming more and more important. Food safety and quality are a concern because current supply chain models frequently have problems with data integrity, fraud, and transparency. This paper proposes a comprehensive framework that incorporates a multi-layer architecture, including a data layer, blockchain layer, AI layer, traceability chain, and off-chain databases, to address these problems. The data layer collects and processes relevant data from all stakeholders in the agricultural supply chain to guarantee accurate and consistent data flow.Trade and delivery events are permanently and explicitly recorded by the blockchain layer, which uses smart contracts to automate transactions and enforce adherence to predefined standards.The suggested method improves data security and privacy by combining Artificial Intelligence (AI) with Multi-Party Computation (MPC) to give intelligent, privacy-preserving analytics.This link allows for collaborative verification and forecast findings without revealing personal participant information.Additionally, a Trusted Execution Environment (TEE) ensures tamper-proof operations by securely isolating critical calculations behind a hardware enclave. Participants may confirm that all data stored or off-chain is genuine and unaltered because of hash functions, which also offer data immutability and integrity. The proposed blockchain-based smart contract solution works effectively with transaction quantities between 25,000 and 28,500 Gwei across various Agri-Food Supply Chain operations. The average transaction time, which ranges from 0.001 to 0.0025 ms, demonstrates low latency and excellent responsiveness
A Hybrid Machine Learning Framework for Student Performance Prediction Integrating Academic, Demographic, Behavioural, Technology Access, and Psychological Indicators in Higher Education
This study presents an explainable hybrid Random Forest (RF) and K-Nearest Neighbor (KNN) model for predicting student performance in higher education. The model integrates academic, demographic, behavioral, psychological, and technological access attributes to enable holistic analysis. Using a dataset of 1,500 students, the hybrid RF–KNN model achieved 96.3% accuracy, outperforming individual algorithms such as Decision Tree (J48) and Naïve Bayes. Explainable AI (XAI) techniques, including SHAP and LIME, were employed to interpret the feature contributions and provide actionable insights for educators. Results demonstrate that non-academic factors—particularly psychological and technological access—significantly enhance predictive performance and interpretability. This work aligns with Outcome-Based Education (OBE) principles by supporting early identification of at-risk learners.
Influence of MSW landfill site on Seismic Response of RC Buildings considering Soil Structure Interаction under far field and near field Earthquake
The kind of chаllenging soil conditions аdopted for this аnаlysis is Landfill Site (LFS) condition to evaluate the seismic response of RC buildings considering soil structure interaction. The results of the pile cаp, storey lаterаl displаcement, аnd inter-story drift, story accelaration need to be obtаined for soil-structure interаction on Landfill Site supported structure аnd compаred with the rigid bаse structurаl response. The impаct of LFS on the seismic response of multistorey buildings is а criticаl considerаtion in modern Civil Engineering. This study focuses on investigаting the seismic behаvior of multistorey buildings constructed on Landfill Site (LFS), with а pаrticulаr emphаsis on soil-structure interаction (SSI). Аdvаnced tools such аs ETАBS, CSI Detаiling, аnd АBАQUS CАE аre employed for the design, detаiling, аnd SSI аnаlysis, respectively. The low rise (G+4), mid-rise (G+14) and high rise (G+24) storey building situаted on Landfill Site (LFS) deposites is аdopted for this reseаrch. The аnаlysis includes а detаiled compаrison between buildings constructed on nаturаl soil versus those on challenging soil site like landfill sites, exаmining the influence of vаrying soil properties on the seismic response. А key аspect of the reseаrch is the vаlidаtion of results using experimentаl dаtа аnd compаrisons with findings from previous studies, ensuring the reliаbility of the conclusions drаwn.
Comparison of Advanced MPPT Techniques & Introduction of Incremental Conductance MPPT Controller Based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) for PV system
Efficient usage of renewable energy systems like photovoltaic (PV) and wind energy systems greatly benefit from Maximum Power Point Tracking (MPPT) techniques. The primary objectives of these advanced MPPT algorithms are addressing slow response times, inefficiency in partial shading, and other issues such as undulated behavior around the Maximum Power Point (MPP). This paper provides a comparative analysis of various advanced MPPT techniques, namely P&O with adaptive step size, fuzzy logic based Incremental Conductance (INC), Artificial Neural Networks (AAN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Sliding Mode Control (SMC) techniques. Key metrics considered for evaluating the techniques included tracking speed, overall efficiency, the complexity of the algorithms, and performance under varying conditions. The study showed that higher utilization of fuzzy logic in conjunction with hybrid integrated intelligent control mechanisms was more effective. The proposed method will demonstrate that the strategy performs noticeably better than conventional techniques in terms of responsiveness, stability, and efficiency.
Optimal Control of Brushless DC Motor Using Soft Computing Optimization Techniques: A Review
Brushless Direct Current (BLDC) motors are widely utilized in various applications due to their high efficiency, low maintenance, and robust performance. Achieving optimal control of BLDC motors, however, is a complex task due to their nonlinear dynamics, parameter uncertainties, and operating conditions. Traditional control strategies like PID often fail to deliver the desired performance across all operating conditions. Soft computing optimization techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Ant Colony Optimization (ACO), have been proposed to address these challenges. This paper presents a comprehensive review of the application of soft computing techniques for the optimal control of BLDC motors. We provide a detailed analysis of various optimization methods, their advantages, limitations, and specific applications in BLDC motor control. The review highlights the significant improvements in performance achieved through these methods and offers insight into future research directions.
The Influence Of Organizational Justice On Job Satisfaction And Employee Engagement Among Consulting Services Workforce In Lagos State
Purpose The perception of justice in the work environment has become pronounced among sensitive employees globally today. This study investigates the importance of organisational justice on employee job satisfaction and, by extension, its significance on employee engagement in HR management consulting service organisations in Lagos State, Nigeria. Design/methodology/Approach This study uses a cross-sectional research design to survey employees of HR management consulting companies in Lagos, Nigeria, using a simple random sampling technique to administer the research instrument. A battery of scales of measurement from several authors was adapted to form a single research questionnaire on 385 participants. A partial least squares (PLS) structural equation model (SEM) was employed to further analyse the collected data from the employees of the chosen HR management consulting service organisations. Findings The results of this study demonstrated that organisational justice was pronounced and equitable in those organisations. The findings of the study underscored that procedural justice (how processes of decision-making are reached), and distributive justice (allocation of meaningful resources, reward systems, compensation structure, and recognition), as well as informational justice (provision of adequate data and information alongside clear pattern of communication), and interactional justice (sense of fair treatment among the employees, and interpersonal relationship between management and the employees) have positive significant impact on the job satisfaction of the entire workforce. The outcome of this study established that a satisfied employee will likely be engaged with the workplace in the face of any challenging experience. Originality The study contributes novel components to the organisational behaviour and human resource management literature, especially in the context of HR management consulting services. The originality lies in its comprehensive investigation of all four dimensions of organisational justice within a single integrated model, particularly targeting the consulting services employees, a knowledge-intensive industry that received low attention in justice research compared to conventional manufacturing or service industries.
Efficient 3D Artifact Modeling in the Field: A Simplified Photogrammetry Approach
Photogrammetry is a 3D scanning technique that uses a series of 2D photos to create a virtual 3D model of an object. This technique involves measuring and analyzing features from multiple photographs, with photogrammetry software combining these to generate a detailed 3D model. The photogrammetry process is intricate and demands a certain level of expertise to accurately scan an object in 3D. It typically requires manual photography from various angles, with the object needing proper lighting to ensure quality results. While the process can be executed with multiple cameras, this approach can be costly. To simplify and automate photogrammetry, a specialized device called a photogrammetry rig has been developed. This rig automates the photogrammetry process by placing the object on a turntable. The device rotates the object at predefined angles while a camera captures a series of images. After completing one full rotation, the camera shifts to a new angle and the process continues. The captured images are then uploaded to software, which processes them into a high-quality 3D model. By automating the photogrammetry process, this rig makes 3D scanning more accessible and affordable, allowing users to obtain high-quality scans of various objects without requiring specialized knowledge. This technology has broad applications across numerous fields globally.
Digital Agility, Strategic Alignment, and Learning Orientation: Drivers of Organizational Resilience and Effectiveness
Organizational resilience and efficacy have become pivotal elements of long-term sustainability in the present-day unstable and dynamic business environment. This paper explores the relationship between digital agility, strategic alignment, and learning orientation as key building blocks for resilient and high-performing organizations. The capability to flexibly adapt digital capabilities in a very quick way is digital agility, which serves as a stimulus to organizational flexibility. Strategic alignment makes sure that there is consistency between what the organization aims at and what it does, whereas learning orientation makes sure that there is constant innovation and adaptation. This study aims to know how the three constructs interact (combine) to determine their effect on resilience and organizational effectiveness. The emergence of digital disruption, uncertainty in the market, and global crises has increased the importance of organizations changing their resilience strategies. Older forms of strategy and ability-making can no longer cope with new challenges. This paper considers and empirically works with the association between digital agility, strategic alignment, and learning orientation regarding organizational resilience and effectiveness. The study constructs a conceptual model through a comprehensive analysis of previous literature and validates it using Structural Equation Modeling (SEM). A structured questionnaire was used to gather data on 346 mid and senior-level managers from the employees of OPTCL. The constructs were measured with the help of the developed scales. Before carrying out SEM, convergent and discriminant validity, and reliability were tested. The findings suggest that there are strong direct and mediating correlations between the variables to support the hypotheses. The implications of the findings are on the digital transformation strategy, leadership practices, and knowledge management systems. Through a more humanistic approach to agility and alignment, and learning, organizations will be able to manage uncertainty more easily and increase their overall effectiveness.
The Influence of Organizational Justice on Job Satisfaction and Employee Engagement among Consulting Services Workforce in Lagos State
Advances in Municipal Solid Waste Valorization and Energy Recovery: A Comprehensive Review of Thermochemical, Biochemical, and Integrated Biorefinery Approaches
Municipal solid waste (MSW) management is emerging as one of the key global sustainability challenges. The increasing waste generation rate—driven by rapid urbanization, industrialization, and population growth—poses severe environmental, economic, and social pressures. Conventional landfilling and open dumping practices result in greenhouse-gas (GHG) emissions, groundwater contamination, and loss of valuable resources. Waste-to-energy (WtE) conversion provides a viable pathway for transforming waste into useful forms of energy and materials, aligning with circular-economy and net-zero objectives. This review synthesizes progress in thermochemical (incineration, gasification, and pyrolysis) and biochemical (anaerobic digestion and composting) technologies and highlights recent developments in integrated MSW bio refineries. Comparative analyses of energy efficiency, environmental performance, and techno-economic viability are presented. The study identifies research priorities such as hybrid reactor design, advanced catalysts, digital optimization, and policy instruments that promote sustainable resource recovery.
The Impact of Digital Technology on Heritage Tourism Development: from Vietnam Practice
Heritage is the core of national identity only by preserving its soul can tourism have a sustainable future. This article synthesizes practical evidence in Vietnam to build a framework of factors affecting heritage tourism development (Heritage Tourism Development HTD), including: (1) institutions governance; (2) heritage integrity and capacity; (3) infrastructure access; (4) product experience innovation; (5) community participation benefit sharing; (6) market visa brand; (7) digital transformation–interpretation; (8) human capacity; (9) environment climate safety. The article uses a systematic literature review, policy analysis, and case study vignettes (Ha Long-Cat Ba, Trang An, Hoi An, Hue, Yen Tu, Vinh Nghiem Con Son, Kiep Bac cluster) to contrast growth targets with conservation requirements. The results propose a set of HTD measurement indicators, a multi-level policy recommendation model (Central local management board enterprise community) and a roadmap for implementation “sustainability first, growth later” honest, without embellishment.
Evaluating the Performance of a Direct Injection Diesel Engine Using Corn Oil Biodiesel Blended with Graphene Oxide Nanoparticles and Hydrogen
Global environment and emission policies are mandating vehicle manufacturers to control diesel engine emissions. In this work, the performance of Corn oil biodiesel doped with Graphene oxide (GO) nanoparticles and enriched with hydrogen is studied with a focus on combustion efficiency and emissions. Experiments were conducted on diesel and biofuel blends with varying biofuel vol/vol content ranging from 0% to 25%. Initial results indicate that the B20 blend optimizes combustion, with a notable Brake Thermal Efficiency increase. Later B20 is doped with GO nanoparticles at 100 ppm and experiments were conducted with incremental hydrogen flow rates ranging from 2 to 10 lpm. Further, the integration of GO nanoparticles and hydrogen into B20 significantly reduced emissions, with B2H10 achieving the most significant reduction in both hydrocarbon (93.9%) and carbon monoxide (73%) emissions. Smoke opacity also significantly decreased, particularly with B2H10, which demonstrated a 34.4% reduction at full load. Finally, a Deep Long Short-Term Memory (DLSTM) regression model is developed to predict the emissions characteristics of the fuel blends that showed an averaged RMSE and MAE of 0.498 and 0.413, respectively.
Optimizing Revenue Potentials in States’ Internal Revenue Services: Challenges, Opportunities, and Strategies in Light of the Nigerian 2025 Tax Reform Acts
This study examines the revenue mobilization and administration in Nigeria, with a focus on the challenges and opportunities facing internal revenue services across the country. The study employed a survey research design, with a questionnaire administered to 132 staff selected from the state internal revenue services of the 36 states of Nigeria and the Federal capital Territory (FCT), Abuja. Data was analyzed by using both descriptive and inferential statistics. The results show that the revenue generated by various revenue boards have not been sufficient to finance the various states’ budgets. The study further reveals that despite increasing revenue generations across various states and the FCT, the services face significant challenges and that corruption, tax evasion, and bureaucratic bottlenecks are major challenges facing the various revenue mobilization and administration boards. The study also identifies opportunities for improvement, including the use of electronic tax collection methods, public awareness and education campaigns, and streamlining of tax laws and regulations. The study recommends training and capacity building for tax personnel, adoption of electronic tax collection methods, public awareness and education campaigns, reduction of political interference, and streamlining of tax laws and regulations. This study contributes to the existing body of knowledge on revenue mobilization and administration in Nigeria, and provides valuable insights for policymakers and stakeholders to enhance revenue generation nationwide.
Unlocking Nigeria’s Oil and Gas Potentials: How Hydrocarbon Tax Policies Influence Multinational Investment Decisions
This study investigates the influence of Nigerian hydrocarbon tax regimes on investment decisions made by multinational corporations (MNCs) operating in the Nigerian oil and gas sector. The research aims to assess the impact of taxes, including the Petroleum Profits Tax (PPT), royalties and the hydrocarbon tax burden introduced by the Petroleum Industry Act (PIA) of 2021, on the investment decisions of MNCs. Through a quantitative analysis, this research examines how the hydrocarbon tax burden shapes the investment strategies of oil companies such as Shell, ExxonMobil, Total Energies, and Eni, taking into account external factors such as global oil prices and fiscal policy risks. The study also investigates the correlation between the hydrocarbon tax burden and investment trends in the oil and gas industry. The findings highlights a complex relationship between tax regimes and investment behavior, providing insights for policymakers and industry stakeholders. The study concludes with policy recommendations for optimizing Nigeria’s fiscal tax regime to balance government revenue generation with attracting foreign investments in the oil and gas sector.
Bridging the Healthcare Workforce Gap and Improving Patient Outcomes
The global shortage of healthcare workers continues to threaten the delivery of quality care and equitable patient outcomes. Addressing workforce gaps requires systemic strategies involving recruitment, training, policy reform, and technology integration. This article explores the causes of workforce shortages, their impact on patient care, and practical solutions to enhance workforce capacity and patient outcomes, supported by current research and global health perspectives.
Hybrid Wind-Diesel Power Load Forecasting Using Advanced Data Preprocessing and Pso-Optimized Deai-Lstm Model
Accurate load forecasting in hybrid wind-diesel energy systems is crucial to ensuring energy reliability, cost efficiency, and environmental sustainability. In this paper, a strong prediction model is suggested based on the PSO-optimized DeAI-LSTM framework that incorporated complex preprocessing and deep learning. The paper starts with preprocessing of the data which involves the imputation of missing values, removal of outliers, normalization, and dimensionality reduction through the Principal Component Analysis (PCA). A deep autoencoder (DeAI) provides feature transformation in a time-cognisant and denoised fashion. The data transformed is fed to a Long Short-Term Memory (LSTM) model where the hyperparameters are optimized using Particle Swarm Optimization (PSO). Benchmark testing against multivariate time-series data indicates better performance in prediction of the proposed model compared to the traditional models including vanilla LSTM, GRU and ARIMA. DeAI-LSTM model has the best RMSE and MAE since it was stable and efficient when it comes to dealing with intermittent renewable inputs and predicting a diesel generation. The paper discusses the applicability of the model in microgrid and hybrid power operational planning and suggests extensions of the model which can be based on real-time deployment, edge computing, and fusion of ensemble models in the future.
Quantitative Assessment of Salt Coverage in Seismic Images Using Connected Component Labeling
Quantifying subsurface salt structures plays a critical role in petroleum exploration and seismic interpretation, as salt bodies influence hydrocarbon migration, trap formation, and seismic wave propagation. While most existing research has focused on semantic segmentation of salt regions using deep learning architectures such as U-Net, Attention U-Net, and PINN-U-Net, limited work has addressed the quantitative measurement of salt coverage and distribution across large seismic datasets. In this work, we present a detailed quantitative salt coverage analysis using Connected Component Labeling (CCL) applied to ground-truth masks from the TGS Salt Identification Challenge dataset. The algorithm systematically identifies distinct salt regions and measures their individual and cumulative areas. From seismic masks, an average salt coverage of 24.79% was observed, with a standard deviation of 31.83%, reflecting high variability in salt distribution. This approach not only quantifies salt extent but also establishes a framework for linking salt morphology with depth, paving the way for data-driven geophysical interpretation.
Seismic Image Analysis, Connected Component Labeling, Quantification, Morphological Analysis, TGS Salt Dataset, Salt Coverage Estimation.
Comparison of Solvent Mixing in the Extraction Process of Active Compounds in Dates using GCMS
In a previous study we used three solvents, each one separately: methanol, chloroform and hexane. The results showed that each solvent showed different results, whether in the compounds or in the ratio of the compounds if they were similar. Therefore, in this study we mixed the three solvents in a ratio of mixture of three solvents (33.3 %: 33.3 %: 33.3 %) for each one. The results showed different products that included all the compounds that were extracted previously when we used each compound separately.
A Survey Paper on Malware Analysis Technique
Nowadays, information will become insecure due to illegal transaction over an internet. To steal an information from sender to receiver, in between an information is attacked by a third-party tool or by a person with knowingly or unknowingly. Malignant knowledge is often attacked by the third party person through any form of topology within the computer network. Among different forms of malware analysis there are three types of information evaluative methods identified that involve hybrid millet alongside static malware and dynamic malware. There are various approaches to identify the malicious system in a network. This paper deals with signature-based approach and heuristic based approaches to identify the threat inside the network.
Static malware, dynamic malware, hybrid malware, signature-based, heuristic-based, machine learning based.
Mapping of Literature on Financing of Start-ups in the Last Thirty Years, a Bibliometric Analysis
Research on Start-up financing has observed a significant development in the past few years, and shown boost in the interest & number of publication. This research reviewed the literature which are published on the Start-up financing with the help of bibliometric tools. This research studied the recent development in this field and highlight the important gaps in the present published literature on Start-up financing. Found 1344 research papers in the Scopus data house by searching keywords. Methods of performance analysis used which supported in exploring of significant sources i.e. journals, significant authors, major contributor countries and affiliations to add on the literature on Start-up financing. This systematic process of different tools has presuming significant understanding on the theoretical & cognitive frame of the area. Ultimately, the finding suggested in the research paper will give a significant direction for further research on the subject.
Capital financing decision, entrepreneurship and finance, funding start-ups, Start-up financing.
Optimized Inception-Based Architecture for the Detection of Macular Degeneration
This paper introduces Fusionception, a hybrid deep learning model for the classification of retinal Optical Coherence Tomography (OCT) images [22]. The paper starts with a comparative analysis of three convolutional neural network (CNN) models- InceptionV1, InceptionV3 and InceptionResNetV2 on [42] three publicly available datasets- OCTID, Retinal OCT Images (Kermany et al.), and the Age-related Macular Degeneration OCT dataset. Among these models, InceptionV3 and InceptionResNetV2 resulted in better accuracy and generalization. Based on these, Fusionception combines the strengths of both models by extracting the deep features of each model. These features are then combined using global average pooling and concatenation followed by a custom-designed dense classification layer [23]. We applied fine tuning and data augmentation to ensure its robust performance across different datasets. Fusionception demonstrates potential to be an effective tool in Ophthalmology with a powerful ability to recognize complex patterns and give reliable classifications across different OCT images.