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.
Organizational Culture And Employee Work Commitment: A Dialectical Approach Into Construction Companies In Lagos State
The shared system of values, beliefs, assumptions, norms, practices, and behavioral patterns that characterize an organization and guide how its members interact, make decisions, and conduct business has been a pillar that connect the members of the organizational . This study examined the relationship between organizational culture and employee commitment in Nigerian construction organizations. Grounded in social identity theory and organizational identity theory, the research investigated how organizational values and beliefs, organizational norms, shared sense of purpose, recognition and appreciation, and mission and strategy influence employee commitment. Employing a cross-sectional survey design, data were collected from construction professionals using a simple random sampling technique to ensure representativeness and minimize selection bias. A structured questionnaire was administered to respondents across various construction firms in Nigeria, and the collected data were analyzed using appropriate statistical techniques including correlation and regression analyses. The findings revealed that all five dimensions of organizational culture, values and beliefs, norms, shared sense of purpose, recognition and appreciation, and mission and strategy - demonstrated significant positive relationships with employee commitment. These results underscore the critical role of organizational culture in fostering affective, continuance, and normative commitment among construction employees. The study contributes to organizational behavior literature by validating identity theories within the Nigerian construction sector and provides practical insights for industry leaders seeking to enhance workforce commitment through deliberate cultural development initiatives. The research recommends that construction organizations prioritize the cultivation of strong cultural foundations, establish robust recognition systems, and ensure clear communication of mission and strategy to build more committed and productive workforces capable of navigating the unique challenges of Nigeria's construction industry.
Multi-Modal AI Framework for Predicting Lemon Juice Stability Using ML, Deep Learning, and Computer Vision
Lemon juice undergoes noticeable chemical and physical transformations during storage, making its stability and safety a crucial concern in food monitoring. A multi-modal artificial intelligence framework is developed, integrating Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) methods to predict lemon juice stability across different aspects. The approach is divided into three modules: (i) a Light Gradient Boosting Machine (LightGBM) model for classifying drinkability based on physicochemical and observational features, (ii) a Gated Recurrent Unit (GRU) network for forecasting future pH values to capture temporal quality changes, and (iii) an EfficientNet-based Convolutional Neural Network (CNN) for detecting spoilage visually from juice color patches. Experimental results show high classification accuracy, effective pH trend prediction, and robust visual stage identification. The hybrid framework supports early spoilage detection and enhances decision-making for food quality assurance.
Lemon Juice, Stability Prediction, LightGBM, GRU, EfficientNet, Food Quality Monitoring, Computer Vision.
A Deep Neuro-Fuzzy Multi-Objective Framework for Quality Optimization of UV-C Treated Microgreen Juices
Ultraviolet C treatment is increasingly applied as a non-thermal preservation technique to enhance the safety and quality of plant based beverages. Microgreen juices produced from fenugreek, sunflower, and radish are valued for their high concentrations of antioxidants and bioactive compounds but are susceptible to quality degradation during storage. Accurate prediction and optimization of quality parameters under varying processing conditions is critical for ensuring consumer accept- ability and functional efficacy. This study focuses on quality assessment of ultraviolet C treated microgreen juices using a data driven computational framework. A curated dataset comprising one hundred experimental samples was utilized, incorporating ultraviolet C exposure duration and storage time as input variables. Quality attributes evaluated include sensory parameters such as taste and aroma, microbial stability measured by yeast count, and visual quality indicated by browning index. Multi objective optimization techniques were applied to identify balanced processing conditions that maintain desirable quality across domains. Results highlight the potential of advanced computational approaches in supporting decision making for minimally processed functional beverages. The integration of sen- sory, microbial, and physicochemical metrics provides a holistic perspective on quality retention and supports intelligent control in non-thermal juice preservation.
Microgreen juices, UV-C treatment, Non ther- mal preservation, Juice quality prediction, Sensory and microbial assessment, Multi objective optimization, Functional beverages
Identity in Flux: Tourism and the Semiotics of Hmong Cultural Symbols in Sa Pa
This paper explores how tourism reconfigures the semiotic meanings of Hmong cultural symbols in Sa Pa, northern Vietnam. Drawing on selective qualitative data from interviews and field observations, it analyzes the transformations of dresses, the khèn (bamboo flute), and community festivals as signs—shifting from indexes of lived practice to icons of visual consumption and, ultimately, to symbols redefined by local actors. Using a heritage semiotics framework (Bendix, 1997; Smith, 2006; Waterton & Smith, 2010), the study traces three semiotic states—Seen, Staged, and Redefined—and argues that Hmong youth and artisans actively reclaim semiotic agency by reinterpreting these symbols. The paper contributes a typology of semiotic shifts in living heritage under tourism influence and highlights implications for semiotic literacy in sustainable cultural interpretation..
Investigaton on Fuzzy g ̈ - Irresolute Functions
A fuzzy g¨- irresolute function is a concept from fuzzy topology using fuzzy sets. Fuzzy topological space is a set of with collection of fuzzy subsets. In this work, fuzzy g¨- irresolute functions from a fuzzy topological space X to a fuzzy topological space Y was introduced and obtained some properties of fuzzy g¨- irresolute functions which are more helpful for solving many Engineering problems
Gender Dimensions of Financial Literacy on Personal Financial Planning: A Study of Rural and Urban Populations in Telangana.
Financial literacy serves as a cornerstone of sound financial decision-making and long-term economic well-being. Despite significant progress in financial inclusion initiatives across India, gender disparities persist in access to financial knowledge and participation in personal financial planning. This study investigates the gender dimensions of financial literacy and its impact on personal financial planning among rural and urban populations in Telangana. Using descriptive and analytical research design, data were collected from 400 respondents through structured questionnaires. The analysis employed descriptive statistics, t-tests, and regression models to examine relationships between financial literacy and financial behavior. Findings reveal that men exhibit higher financial literacy levels than women, with the disparity being more pronounced in rural areas. Financial literacy was found to significantly influence financial planning behavior across both genders and regions. The study concludes with recommendations for targeted financial literacy programs to bridge gender and geographical divides, thus promoting inclusive financial empowerment in Telangana.
Financial Literacy, Gender, Personal Financial Planning, Rural–Urban Divide, Telangana, Financial Inclusion.
A Review on Integration of nanotechnology in Herbal medicine
From the ancient times animal kingdom remained dependent upon plant kingdom for it’s abundant medicinal values. Herbal medicines gained popularity due to it is abundant sources as well as low cost which may solve the problems of unavailability. Different parts of plants contain different secondary metabolites which are the main components for the therapeutic effects of plants.The main disadvantage of herbal medicines was patient incompatibility.In some cases the solubility of active constituents of plants remains higher, however due to their high molecular weight they can’t be absorbed in the lipid membrane of cells in sufficient quantity. Due to this, they show very limited bioavailability and rapid excretion. So to get the therapeutic benefits, it become essential to it’s repeated administration with a large dose which deviates the patient compliance most times.One such use of nanotechnologyis to increase bioavailability and decrease toxicity. If it is possible to delivery herbal drugs in nanoscale, then improvements in solubility, bioavailability, pharmacological action and stability of novel herbal formulations are capable enhancing the distribution of macrophages in tissues as well as extended delivery, and prohibit the chemical and physical degradation. “Novel Drug Delivery Systems” is a renowned technology was found to solve the noncompliance of herbal medicines. This technology is nothing but Nanotechnology.It studied that when herbal medicines and nanotechnology are fused, then the noncompliance of herbal medicine is successfully diminished.
Hybrid CNN–SVM Framework for Skin Cancer Detection and Stage Classification Using Simulated Hyperspectral Imaging
Skin cancer remains one of the fastest-growing malignancies worldwide, where early detection plays a critical role in improving patient outcomes. Conventional diagnostic methods such as dermoscopy and histopathology often face challenges of subjectivity, inter-observer variability, and limited reproducibility. To address these limitations, this study proposes a hybrid Convolutional Neural Network (CNN)–Support Vector Machine (SVM) framework for skin cancer detection and stage classification using simulated hyperspectral imaging derived from dermoscopic RGB images. The methodology integrates hyperspectral simulation, preprocessing, lesion segmentation, handcrafted feature extraction based on the ABCDE rule with entropy, and CNN-based deep feature learning, followed by feature fusion and classification using SVM. Experimental evaluation on benchmark skin cancer datasets demonstrates that the hybrid CNN–SVM model achieves superior accuracy (92%), sensitivity (90%), and ROC-AUC (0.95) compared to conventional machine learning and deep learning baselines. These findings highlight the potential of the proposed framework for reliable, interpretable, and clinically relevant skin cancer diagnosis and staging.
Efficient Hybrid Clustering Framework for Medical CT Image Segmentation: Comparative Evaluation with Traditional Methods
Medical image segmentation is a critical step in computer-aided diagnosis and treatment planning. Clustering methods are widely adopted due to their unsupervised nature and ability to partition image pixels into meaningful regions without prior annotation. In this study, we conduct a comparative analysis of four traditional clustering algorithms—K-Means, Hierarchical, Gaussian Mixture Model (GMM), and Spectral Clustering—along with a proposed Hybrid method that integrates K-Means and Spectral features. Unlike most prior works that vary the number of clusters, we fix the cluster size to k = 6 across all methods to ensure consistency and highlight algorithmic differences. To improve robustness, the small dataset of five CT images obtained from a publicly available Kaggle repository was expanded using augmentation techniques, including CLAHE, flipping, gamma correction, Gaussian noise, and small-angle rotations. Performance was evaluated using standard cluster validation indices, namely Silhouette score, Calinski–Harabasz index, Davies–Bouldin index, as well as runtime, memory usage, and accuracy. The results demonstrate that while traditional methods achieve reasonable segmentation quality, the Hybrid clustering consistently outperformed others, achieving the highest accuracy (85.9%), lowest runtime (0.04 s), and smallest memory footprint (800 KB). This work highlights the potential of hybrid clustering frameworks as reliable alternatives for medical CT image segmentation.
Research on E-Government and Digital Capacity of Vietnamese Civil Servants and Citizens
Current state administration is strongly influenced by the trends of digital social development after the fourth industrial revolution (revolution 4.0) was initiated. Accordingly, state agencies at all levels have changed their management methods based on the application of digital technology, forming and developing e-government in countries. The basic characteristics of e-government are operations in the digital environment, based on digital technology and digital data, posing digital capacity requirements for civil servants and citizens when participating in state administration processes: Civil servants perform public duties and interact with citizens in the digital environment; citizens participate in giving opinions and criticism of policies, requesting administrative records and interacting with the government in the digital environment. When civil servants and citizens have basic and necessary digital capacity, transactions, interactions and performance of public duties in the digital environment will be effective, helping to save time and costs for both the government and the people. This study focuses on analyzing the characteristics of e-government and the requirements for digital capacity of civil servants and citizens to identify and explain the significance of developing digital civil servants and digital citizens to meet the requirements of e-government development. Based on the theoretical framework, the author surveyed 200 commune-level civil servants of 20 selected communes in Da Nang city. The survey results show that the digital capacity of citizens is assessed at a low level; in addition, the digital capacity of civil servants still needs to be improved to operate e-government effectively. From the conclusion of that study, the author discusses appropriate policy solutions to develop digital civil servants and population in the context of Vietnam.
Multimodal Deep Learning Framework for Cross-Cultural Dance Fusion Recognition: Case Study on Indian Classical and Western Motion Patterns
A multimodal deep learning framework for the automated recognition and classification of movement patterns in cross-cultural dance fusion, specifically between Indian classical and Western styles. The model integrates three distinct data modalities: skeletal motion trajectories, rhythmic audio features, and semantic gesture annotations. By employing spatio-temporal analysis, the framework learns to disentangle and identify characteristic motion signatures from each tradition. To ensure interpretability, the architecture incorporates attention mechanisms that visualize the model's focus on diagnostically significant movements and rhythmic cues. Evaluated on a curated dataset, the proposed method demonstrates robust performance in detecting choreographic fusion, quantified through a novel fusion index. This work contributes to the fields of dance informatics and digital heritage by providing a scalable, analytical tool for understanding cultural-artistic synthesis in performing arts.
Multimodal Deep Learning, Skeletal Motion Analysis, Cross-Cultural Dance Recognition, Cultural Heritage Informatics, Explainable AI (XAI)
Draining the Lifeblood from Nigeria’s Healthcare System an Investigation of Critical Drivers of Brain Drain in Lagos’ Public and Private Hospitals
High rates of healthcare workforce emigration (“brain drain”) pose a critical challenge in Nigeria, depleting the health system of skilled professionals. This study investigates critical workplace factors driving healthcare professionals’ intentions to emigrate from Lagos State, Nigeria. A cross-sectional survey of 860 physicians, nurses, and other health workers from public and private hospitals was analysed using structural equation modelling to test the effects of five quality-of-work-life factors: workload, workplace safety, professional autonomy, perceived organisational support, and non-wage benefits on migration intentions. The structural model revealed that workplace safety was the most potent driver of brain-drain intentions, followed by workload and professional autonomy. Unsafe working conditions and excessive workloads significantly heightened intentions to migrate, while limited decision-making autonomy also contributed to higher migration propensity. In contrast, greater perceived organisational support and more robust non-wage benefits were associated with reduced migration intentions, although their mitigating effects were comparatively modest. These findings underscore that beyond low salaries, poor working conditions and lack of support drive healthcare brain drain. Policy interventions focusing on improving workplace safety, balancing workloads, and enhancing professional autonomy, along with strengthening support and non-monetary incentives, are critical to retaining healthcare talent. The study further contributes to the literature by demonstrating the decisive role of quality-of-work-life factors in driving health workers’ migration decisions.
brain drain; non-wage benefits; perceived organisational support; professional autonomy; workload; workplace safety.
Design and Analysis of a Fault-Tolerant Modular Multilevel Converter for the Protection and Enhanced Reliability of Offshore PMSG Wind Farms
The growing adoption of offshore permanent magnet synchronous generator (PMSG) wind farms is necessitating strategic and fault tolerant power conversion platforms that would provide constant power under the strenuous marine environment. This paper reports the design and study of the fault-tolerant Modular Multilevel Converter (MMC) which was customized to offshore PMSG applications. In the proposed MMC, the redundancy of sub modules, fault redundancy through adaptive reconfiguration control to take control of short circuit of sub modules, and open circuit is put in proposal. The simulation of a 201 level, 5 MW, 66 kV MMC prototype is tested to confirm the operation. The converter had been tested under single and double conductor submodule faults to stable operation less than 2 % total harmonic distortion (THD) and an error on balance of the voltage noted at, less than 1.5%. A comparison of the results shows that the proposed fault-tolerant scheme increases the availability of converters by up to 18% and system efficiency by up to 3.7% as compared to the traditional MMCs. Moreover, in grid-side disturbances, the design manages dynamic stability of voltage in the range of ±5% of nominal values so that wind power transmission is made continuous. As revealed in the analysis, the proposed fault-tolerant MMC will allow improving significantly the reliability, protection, and the overall energy delivery performance of large-scale offshore PMSG wind farms.
Modular Multilevel Converter, fault tolerance, offshore wind farm, Permanent Magnet Synchronous Generator, harmonic distortion, high-voltage direct current
Performance Evaluation of RC Beams Retrofitted with GFRP and CFRP Strengthening Schemes for Sustainable Structural Rehabilitation
The durability and structural safety of reinforced concrete (RC) beams are often compromised due to environmental exposure, mechanical damage, and aging-related deterioration. Retrofitting with fiber-reinforced polymers (FRP), particularly glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP), has emerged as a sustainable solution for enhancing flexural strength, stiffness, and serviceability of deficient RC members. This study evaluates the effect of different strengthening schemes using GFRP and CFRP systems on the structural performance of RC beams. The review and synthesis of experimental and analytical investigations reveal that externally bonded FRP significantly improves load-carrying capacity, delays crack initiation, and reduces deflection under service loads. Comparative assessment shows CFRP offers higher stiffness and strength efficiency, while GFRP remains a cost-effective alternative with reasonable improvements. The findings highlight the importance of selecting appropriate strengthening schemes based on performance requirements and economic feasibility, contributing toward sustainable structural rehabilitation practices.
A Three-phase Thirteen-level Voltage Source Inverter with Inherent Gain
Switched Capacitor Multilevel Inverters (SCMLIs) provide advantages such as integrated voltage boosting capability; and easy voltage balancing of capacitors. However, many existing SCMLI designs are primarily intended for single-phase applications, necessitating three separate DC sources for three-phase applications. This work introduces a novel SCMLI capable of generating seven voltage levels at each pole, including positive, negative, and zero levels, and thirteen levels in the line voltage. Significantly, the proposed inverter requires only a single input DC source for three-phase operation. In order to validate the proposed SCMLI topology, both simulation and experimental results are presented.
An Experimental Study on Tensile and Hardness Properties of Fsw 2024 Aluminium Alloy Hybrid Composites Reinforced with Sic and Mos₂
This study investigates the tribological and mechanical behavior of hybrid composites fabricated from friction stir welded (FSW) 2024 aluminum alloy reinforced with varying proportions of silicon carbide (SiC) and molybdenum disulfide (MoS₂) particles. The composites were prepared using the stir casting method, incorporating SiC (up to 10 wt%) for its superior hardness and MoS₂ (up to 6 wt%) for its self-lubricating properties. FSW was performed under optimized parameters—1000 rpm rotational speed and 70 mm/min travel speed—to ensure defect-free joints and uniform particle dispersion in the weld zone. The addition of SiC contributed to grain refinement and enhanced load-bearing capacity, while MoS₂ facilitated a reduction in friction due to its layered structure, enabling easy shear along basal planes. Tensile testing, conducted as per ASTM E8 standards, revealed an optimal balance between tensile strength and ductility at specific reinforcement levels, attributed to synergistic interactions between SiC-induced strengthening and MoS₂-mediated friction reduction. However, excessive particle addition caused agglomeration, leading to stress concentrations and microstructural anomalies that diminished mechanical properties. Hardness tests on the weld zone, performed using the Rockwell scale, indicated a peak hardness of 148.9 HRN at 8 wt% SiC and 4 wt% MoS₂, highlighting the critical role of reinforcement in enhancing surface resistance to deformation. This research underscores the interplay between microstructural evolution and tribological mechanisms, providing valuable insights for designing advanced materials with superior wear resistance and mechanical performance for aerospace and automotive applications.
A probabilistic inventory model without trade credits with linear random component demand
This paper develops a probabilistic inventory model without trade credits in which demand follows a linear trend with a random component. The holding cost is assumed to be time dependent, and shortages are permitted to meet unexpected demand fluctuations. The model is formulated to minimize the expected total cost, which consists of ordering cost, holding cost, and shortage cost. A numerical example is provided to demonstrate the applicability of the proposed model. The results show that the optimal order quantity is obtained as the sum of expected demand and a safety stock component determined by the variability in demand. Sensitivity analysis reveals that demand variability has the greatest impact on total cost, followed by holding cost, service level, and ordering cost. The findings highlight the importance of incorporating demand uncertainty into inventory policies, especially in situations where immediate payment is required and trade credits are not available. This research provides valuable insights for decision-makers in designing inventory strategies under uncertainty
Probabilistic inventory model, Inventory control, Stochastic demand, Without trade credit, Deteriorating items.
Strategic Leadership Practices and Organizational Sustainability of Agribusiness in Ogun State, Nigeria.
This study examined the theoretical relationship between strategic leadership practices and organizational sustainability of firms in agribusiness in Ogun state, Nigeria. The results indicate that strategic leadership practices such as strategic adaptability to change, stakeholders’ collaboration and ethical leadership enhance organizational sustainability outcomes. Using a qualitative approach, the research relies on secondary data from peer-reviewed journals, policy documents, and industry reports. Thematic analysis was applied to examine how strategic adaptability to change, collaborative stakeholder engagement, and ethical leadership practices influence economic, social, and environmental sustainability. Findings suggest that adaptability enhances responsiveness to policy reforms, climate variability, and technological disruptions; stakeholder collaboration strengthens value chains and trust-building; while ethical leadership fosters transparency, accountability, and long-term legitimacy. These findings align with Stakeholder Theory and the Resource-Based View, underscoring the need for both internal leadership capabilities and external partnerships in achieving sustainability. The paper contributes to leadership literature in Nigeria by connecting strategic leadership to sustainability outcomes and recommend further empirical testing of the variables to obtain more evidences that will allow for statistical validation of conceptual model of this study.
Strategic Leadership, Sustainability, Agribusiness, Adaptability, Collaboration, Ethics
Efficient Physical Design Techniques for High-Speed, Area-Optimized 12x12 Multiplier Using CSA
Designing a high-speed, area-efficient 12-bit by 12-bit multiplier architecture tailored for performance-driven digital systems. Traditional multiplication schemes often encounter challenges in minimizing computational delay and silicon footprint simultaneously. The proposed design adopts a carry save addition (CSA)-based array multiplier structure, which effectively reduces carry propagation delay by deferring carry processing to the final computation stage. The implementation utilizes CMOS-compatible components, including dynamic flip-flops and optimized logic cells, to achieve a compact layout. Furthermore, hierarchical design strategies and advanced physical layout techniques are employed to improve signal integrity and manufacturability. The resulting multiplier demonstrates notable enhancements in speed and area efficiency, making it a strong candidate for integration into high-performance digital processing architectures.
Exploring The Debt Laffer Curve: Evidence From Nigeria’s Public Debt And Sustainable Economic Growth Nexus (1970-2022)
This study explores the debt Laffer curve hypothesis in the relationship between public debt and sustainable economic growth over the study period 1970 to 2022 in Nigeria. The estimation methods employed are quadratic autoregressive distributed lag (ARDL) and structural quadratic autoregressive distributed lag. The ARDL results revealed that debt Laffer curve hypothesis prevailed in the nexus between total, internal and external between public debt and sustainable economic growth in the long run respectively, whereas the structural ARDL results showed that debt Laffer curve prevailed in the nexus between external public debt and long run sustainable economic growth during 1999-2022 only in Nigeria. Going forward, Government should discourage public debt on short-run but cautiously seek public debt in the long run. Furthermore, Government should consolidate on massive human capital development and capital formation investment as well as effective utilization of petroleum earnings to achieve a sustainable economic growth at short-run and long run in Nigeria.
A Holistic Examination of Employee Retention using Machine Learing
Employee retention remains a critical challenge for organizations seeking to maintain stability, enhance productivity, and reduce turnover costs in a highly competitive business environment. This study synthesizes insights from existing literature and empirical research to examine the multifaceted factors influencing retention, including corporate culture, employee empowerment, organizational learning, talent development practices, entrepreneurship education, cultural intelligence, and technological interventions such as artificial intelligence and machine learning. Various predictive modelling approaches, including Logistic Regression, Random Forest, deep learning, and Stacking-Based Transfer Learning, demonstrate the capacity to identify key drivers of attrition, forecast turnover, and inform proactive retention strategies. Empirical studies from diverse organizational contexts, including banking, startups, and multinational workplaces, highlight the mediating and moderating roles of protean career attitudes, work engagement, socialization tactics, and organizational size in shaping retention outcomes. The findings underscore the importance of integrating human-centric strategies with data-driven analytics, offering actionable insights for HR practitioners to design targeted interventions, enhance employee satisfaction, and sustain long-term workforce stability in dynamic and multicultural work environments.
The Essence of British Feminist Criminology Theory and its Prospects
The article examines the formation and development of British feminist criminology as a revolutionary direction in criminological science that emerged in the context of second-wave feminism of the 1960s-1970s. It provides detailed analysis of fundamental theoretical concepts by Carol Smart on the patriarchal nature of law and processes of secondary victimization, Frances Heidensohn's theory of multilevel social control explaining gender disproportions in crime, and Pat Carlen's concept of "gender and class deals" revealing mechanisms of economic determination of female criminality. The methodological contribution of these researchers to developing qualitative methods of criminological analysis and their critique of traditional masculinized theories are examined. Based on analysis of the contemporary criminological situation in Uzbekistan, characterized by high levels of domestic violence and specific cultural features of women's victimization, the necessity of creative adaptation of feminist approaches to the national context is substantiated. Specific directions for integration are proposed: establishing specialized research structures at the Institute of Criminology, developing culturally-sensitive methodologies, introducing gender perspective into law school educational programs, and overcoming cultural barriers by demonstrating compatibility of women's protection with traditional values.
feminist criminology, gender violence, women's victimization, Uzbekistan, Smart, Heidensohn, Carlen, social control, patriarchy, criminological integration
Real-Time Traffic Signal Detection and Navigation in Raspberry Pi Autonomous Cars- A Low-Cost Approach to Self-Driving Technology
This paper presents the design and implementation of a simple self-driving car system inspired by recent progress in autonomous vehicle technology. The vehicle is capable of detecting road signals and performing accurate turning actions. The system connects the car’s frame to a computing system via Wi-Fi, allowing real-time video frame processing. For real-world use, the analysis unit can be embedded on board the vehicle. The system effectively delivers precise and dependable decision-making for navigation.
Differential Evolution Based Particle Swarm Optimization Algorithm for Protein Ligand Docking
Molecular Docking is a computerized tool used in discovering a drug. Molecular Docking (MD) which helps to predict the small molecule ligand is able to bind to disease target protein. In MD a configuration molecular structure is generated by the conformational search algorithm and then the position is evaluated based on fitness function. Here fitness value is least binding energy during the interaction of protein and Ligand. The more the least binding energy, the more the ligand is stable in the complex. In this research a differential evolution-based particle swarm optimization algorithm is proposed as a search algorithm for conformational space in protein ligand docking. Using a dataset of 1089 bimolecular complexes from PDBbind the lowest binding energy and time efficiency were tested. The proposed algorithm demonstrates superior when tested with six other existing algorithms.
Integrated approach of computational modal analysis and experimental modal analysis to thorough examination of the dynamic characteristics of aircraft structures
This study investigates aircraft structural integrity using Experimental Modal Analysis (EMA) and integrates it with computational methods. The methodology combines advanced tools, including CatiaV5 for design, Hyper-Mesh for meshing, Nastran for computational analysis, and Sim Center 3D for precise accelerometer placement. This integrated approach thoroughly examines the dynamic characteristics of aircraft structures, focusing on natural frequencies, mode shapes, and damping ratios. Computational modal analysis of components like wings, fuselage, and vertical tails revealed critical modal parameters, which were validated through experimental modal analysis using impact hammers and accelerometers. The experimental results closely aligned with computational predictions, highlighting the accuracy of the approach. This study is an advancement in the aircraft structural analysis, reinforcing the reliability of modal analysis in aerospace engineering and offering a robust methodology for designing safer and more efficient aircraft.
Predictive Analytics and Algorithmic Framework for Social Media Influencer Engagement and Conversion
In the current study, a predictive analytics and algorithm approach is presented, in effort to improve the efficiency of influencer marketing on social media, in the context of the Yamaha Music Waves Montage 8 Synthesizer campaign. The framework utilizes the audience segments that demonstrated the best promise of conversion based on their demographic and behavioral characteristics, leveraging supervision machine learning classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), to help identify valuable audience for promotion. The results indicate that the optimized KNN and SVM models achieve an impressive accuracy (about 93 percent) and high precision and recall metrics in the prediction of purchasing behavior. The analysis also uncovers notable gender differences in purchasing conversion rates, which can help inform marketers marketing and advertising strategy with, and to improve conversion efforts. As the study showed predictive analytics are a practical and concrete method for increasing influencer and their engagement, beyond the application with this focus groups. Through the use of predictive analytics data-driven marketing has a robust tool for decision making, which would be helpful in smart algorithmic practices to optimize influencer marketing decisions.
Product Edge-Antimagic Vertex Labeling of Graphs
A (p, q)-graph G, is defined to be product antimagic if there is a labeling from E(G) onto {1, 2, . . . , q} such that, at each vertex u, the product of the labels on the edges incident with u are distinct. Similarly, a (p, q)-graph G is defined to be product edge-antimagic if there is a labeling f from onto {1, 2, . . . , p + q} with the property that the value , for any edge are distinct. In this paper, we introduce the product edge-antimagic vertex (PEAV) labeling as a bijection f from V(G) to {1, 2, . . . , p} such that, for any edge , the product are distinct. Also we have proved the existence of PEAV labeling for paths and cycles.
Implementing Underwater OpticalWireless Communication Using Visble Light LEDs: A Practical Prototype Approach
This paper presents the design and experimental demonstration of a prototype underwater optical wireless communication (UOWC) system using visible light in the blue and green spectra. The transmitter employs an Arduino to encode and modulate data via serial communication, driving high- intensity blue and green LEDs through TIP122 transistor circuits. Optical signals propagate through a water channel and are detected by a light-dependent resistor (LDR) module coupled with an LM393 comparator circuit. The analog received signal is digitized using an MCP3208 analog-to-digital converter (ADC), and a Raspberry Pi processes the data stream for decoding and display. A 16x2 LCD screen shows decoded information, while real-time visualization of signal strength and data is displayed on a connected monitor for performance analysis. This study compares the underwater communication capabilities of blue and green LEDs, highlighting that blue wavelengths offer deeper pen- etration in water, whereas green wavelengths provide more stable intensity at shorter distances. The system achieves reliable short- range data transmission with cost-effective and scalable hardware components. The results demonstrate the feasibility of using such a UOWC system for applications in marine research, underwater robotic control, and environmental monitoring, addressing the challenges of underwater signal attenuation and bandwidth limitations with practical circuitry and software solutions.
Multiphysics Stress and Vibration Analysis of Pressure Vessels under Low Pressure and Medium Temperature Conditions
The work focus on FE analysis and investigations of thermo mechanical constraints on the pressure vessel. The thermal stresses, deformation developed in the finite element analysis (FEA) for the pressure vessel are compared with analytical results. The vessel is designed using ASME section VIII Division 2 and the required thickness of the head, cylindrical shell etc. are to be validated using the FEA tool. The static and thermal analysis (multi-physics analysis, combined loading) will be carried out using ANSYS tool. Also, natural frequency of pressure vessel will found out using modal analysis. Study focuses on two different constraints conditions of the right-hand saddle and performed for constant internal design temperature and internal design pressure. It is found that the pressure vessel is in a safe zone when the right hand saddle is free along axial direction
Fortification Of Vitamin C In Synbiotic Drink Using Dragon Fruit
In today's health-conscious era, consumers are increasingly drawn to traditional and functional foods that offer improved nutrition and well-being. Functional foods, at their core, revolve around the integration of probiotics, which play a pivotal role in enhancing immune function, cognitive well-being, and overall health. This study delves into the exciting realm of crafting synbiotic beverages using a blend of ragi and dragon fruit. It embarks on a comprehensive exploration of the nutritional, physicochemical, and microbiological attributes of these synbiotic concoctions, employing a diverse array of analytical techniques. Intriguingly, the sensory evaluation conducted unveiled a remarkable consumer preference, earning a commendable 7 on a 9-point hedonic scale. The principal component analysis showed significant variations in the synbiotic drink based on the sensory properties. A standardized technique study illuminated that the zenith of microbial growth materialized within 72 hours, with the onset of organism disintegration taking place after 96 hours. Collectively, the holistic analysis firmly substantiates the suitability of the recommended synbiotic beverage featuring ragi and dragon fruit for fostering gut health, marking a promising development in the realm of functional foods.