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.
Enhancing Financial Crime Detection through Forensic Accounting Techniques in Zimbabwean Commercial Banks: A Critical Review and Integrated Investigative Framework
Purpose Commercial banks remain exposed to diverse forms of financial crime including money laundering, cyber financial crime, inside fraud, procurement fraud, unauthorized electronic money transfers, fraudulent financial statements and the disguise of movement of assets. The swift development of digital banking and economic instability in Zimbabwe has amplified the institutional exposure to financial crime. The research analyses the application of forensic accounting in enhancing financial crime detection and investigating capacity to improve fraud governance in Zimbabwean commercial banks. Design/methodology/approach The research employed a conceptual and empirical literature review approach. Secondary data was gathered through the examination of journal articles, institutional reports, banking fraud articles, governance literature and forensic accounting literature. Thematic analytical synthesis approach was employed in discovering themes, conceptual links and institutional issues in relation to forensic accounting techniques and financial crime detection. Findings Trace of transactions, digital forensic accounting, fraud analysis, forensic statement analysis, forensic interviewing, preservation of evidence, and financial reconstruction appeared to be crucial instruments in enhancing investigations into banking fraud. On the contrary, poor institutionalization of forensic accounting is the key obstacle facing the commercial banks in Zimbabwe. Practical implications The research recommended that commercial banks should institutionalize forensic accounting units, improve fraud analytics mechanisms, enhance digital investigative capability and incorporate forensic evidential mechanisms into governance and compliance mechanisms. Originality/value This article enriches the literature on forensic accounting and banking governance by presenting a new Forensic Accounting Investigative Framework-an Integrated Forensic Accounting Investigative Framework designed for Zimbabwean commercial banks and other emerging economies.
Forensic Accounting, Financial crime detection, fraud investigation, commercial banks. Digital forensics, Governance.
Secure File Transfer Using Hybrid Cryptography-Based Web App
Secure file management has become a key concern in the current digital landscape, where sensitive information is exchanged and stored regularly for personal, academic and sprofessional activities. In addition to holding such sensitive information as business contracts, health records, legal documents and financial information, the potential for confidentiality of these types of information is increasing due to cyber threats from unauthorized access, phishing attacks, ransomware and insider leaks. The above-mentioned data risks demand effective systems to protect files from being tampered with, stolen or misused, while at the same time allowing users to continue access and control of the files they create or manage. Typical file storage solutions have restrictions on how securely they can provide file storage and may use proprietary methods of encryption/security that lack transparency and strong encryption/protective controls, thus exposing confidential information to potential data breaches. The mentioned problems can be solved through cryptography by using systematic methods to convert human-readable information into unreadable but secure formats to ensure the confidentiality, integrity, and authenticity of the information stored.
Image Forgery Detection using Image Denoising and Cascaded 3D UNET Segmentation
Image forgery detection is the identification of changes or modifications in digital images, thereby guaranteeing their integrity and validity. It is very important in the fight against false information, online fraud, and maintaining confidence in visual media. Multi-Noise Elimination Neural Network (MNEN) is introduced as a novel multi-scale nested encoder network. The goal is to reduce noise while maintaining appropriate structural integrity. MNEN successfully captures spatial hierarchies and contextual interactions by using a multi-scale, layered architecture that collects data at several levels. In the encoder pipeline, highly connected convolutional layers with skip connections improve suspicions about fading gradients while still providing considerable feature propagation. Using a layered skip fusion approach to merge low- and high-level semantic data helps to minimize noise while maintaining image quality. Blocks of progressive upsampling and filtering allow the decoder pipeline to recover high-quality denoised outputs. Extensive testing on benchmark datasets reveals that, in terms of PSNR and SSIM, MNEN frequently exceeds state-of-the-art techniques. Results of ablation studies reveal that denoising performance is much improved by layered connections and multi-scale feature integration. Because of its competitive inference times, MNEN is ideal for implementation on edge devices and real-time applications. Studies indicate that MNEN is a potential technique for image restoration in various contexts. The Cascaded 3D U-Net model enhances digital image tampering detection by performing multi-level segmentation on image data
A Comprehensive Study of Material Optimized TFET Biosensors for Low Power and Reliable Sensing Applications
For next-generation biomedical sensing applications, tunnel field-effect transistor (TFET)-based biosensors have drawn a lot of interest because of their steep subthreshold swing, low power consumption, steep subthreshold swing, and superior sensitivity compared with conventional MOSFET-based biosensors. This work presents a comprehensive study of material-optimized TFET biosensors aimed at achieving enhanced sensing performance, improved thermal stability, and reliable device operation for label-free biomolecule detection. The proposed biosensor employs dielectric modulation in the cavity region along with advanced material engineering techniques to strengthen electrostatic control and carrier tunnelling efficiency. Different biomolecules with varying dielectric constants are introduced into the sensing cavity to analyze their impact on important electrical characteristics such as drain current, threshold voltage, transconductance, and sensitivity factor. Furthermore, the influence of high-k gate dielectric materials, heterojunction engineering, and channel optimization on device performance is systematically investigated. Thermal stability analysis is performed over a wide temperature range to evaluate the robustness of the proposed structure under practical operating conditions. In addition, structural reliability is assessed by examining variations in cavity dimensions, oxide thickness, and channel parameters to ensure consistent sensing behavior.The simulation results demonstrate that the optimized TFET biosensor achieves enhanced sensitivity, improved ON-state current, reduced ambipolar conduction, and stable electrical characteristics with minimal temperature-induced degradation. The proposed architecture offers a promising platform for low-power, highly sensitive, and reliable biosensing applications in future biomedical and healthcare systems.
On δ- Open Sets in Picture Fuzzy Topological Spaces
Picture fuzzy sets offer a powerful mathematical tool for capturing uncertainty and imprecision that cannot be adequately handled by classical or intuitionistic fuzzy set theories. Motivated by this framework, the present work explores the concept of -open sets in the context of picture fuzzy topology. Building upon this foundation, the -interior and -closure operators are formulated and their essential characteristics are examined in detail. In addition, four new generalized categories of -open sets are put forward, specifically -preopen sets, -semiopen sets -open sets and -open sets, all of which are thoroughly analyzed under the picture fuzzy topological framework. The mutual relationships between these newly defined classes and the already established picture fuzzy open sets are explored, leading to several significant characterization results. Suitable illustrative examples are also constructed to validate and support the theoretical findings presented in this study.
CAD-Based Structural Analysis and Finite Element Topology Optimisation of a Leaf Spring Bracket for a Light Commercial Vehicle
Topology optimisation is a mathematically rigorous technique for redistributing material within a defined design domain to minimise structural compliance while satisfying prescribed mass and stress constraints. This paper presents a complete design–analysis–optimisation–fabrication–validation workflow applied to a leaf spring mounting bracket of a light commercial diesel truck. The bracket, fabricated from SS 4012A-E34 hot-rolled micro-alloyed structural steel, was originally characterised by a minimum factor of safety of 2.018 and a peak equivalent von Mises stress of 67.9 MPa under a service load of 6,000 N derived from the front axle loading of the vehicle. Three-dimensional parametric modelling was carried out in CREO Parametric 2.0, and both static and fatigue analyses were executed in ANSYS Workbench 19 using SOLID187 tetrahedral elements at a 2 mm global mesh size. Topology optimisation was subsequently performed with a compliance minimisation objective and an 80 percent mass retention constraint, followed by a shape optimisation study comparing square, circular, and triangular cut-out geometries for the vertical plate. The circular cut-out was selected on the basis of minimal corner stress concentration. The re-engineered optimised bracket incorporates a rectangular slot on the horizontal plate and a circular aperture on the vertical plate, reducing component mass by 200 grams (from 1.8 kg to 1.6 kg, an 11.1 percent reduction) while maintaining essentially unchanged peak stress (66.79 MPa versus 67.9 MPa) and preserving infinite fatigue life. Physical fabrication was accomplished through wire-cut electrical discharge machining, milling, and drilling operations. Experimental validation via strain gauge testing on a Universal Testing Machine (UTM) yielded 153.2 microstrain at 6,000 N, against an FEA prediction of 180.7 microstrain, representing a 15.2 percent discrepancy within the accepted tolerance for fabricated metallic components. The study confirms that topology optimisation within a conventional FEA environment provides a systematic and manufacturable pathway for mass reduction in automotive mounting hardware.
CAD-Integrated Finite Element Analysis of Fiber Orientation and Stacking Sequence Effects on Composite Beam Strength
Composite drive shafts fabricated from E-Glass/Epoxy and High Modulus (HM) Carbon/Epoxy laminates were evaluated as lightweight replacements for conventional steel shafts in automotive transmission systems. A four-layered hollow cylindrical shaft configuration was developed and subjected to both static structural and modal analyses using ANSYS 13 finite element software, with SHELL281 elements employed to represent the layered composite architecture. Static analysis investigated the influence of individual ply fiber orientation varied in increments of 10° from 0° to 90° on layer-wise shear stress distribution and total axial deformation, while keeping the remaining three layers fixed at 0°. Modal analysis subsequently extracted the first six natural bending frequencies for each orientation configuration. Results indicate that the composite shaft achieves approximately 50% mass reduction over a geometrically equivalent steel shaft while simultaneously exhibiting markedly lower maximum equivalent stress and substantially higher natural frequencies across all bending modes. Third-order polynomial regression models were developed in MATLAB to correlate fiber angle orientation with the computed mechanical responses, yielding design equations that enable rapid parametric exploration without repeated finite element computations. Comparative evaluation confirms that the carbon/glass-epoxy configuration surpasses structural steel in both torsional performance and dynamic stability under identical boundary conditions. Fiber angle orientation emerges as the dominant design parameter, and the regression framework provides a computationally efficient tool for optimizing lightweight drivetrain components.
Harmonic Reduction in Microgrid Using APF with Fuzzy Controller
This study proposes a method to minimize harmonic distortion in microgrids by using an Active Power Filter (APF) governed by a fuzzy logic controller. The integration of renewable energy sources and nonlinear loads introduces significant power quality challenges, especially harmonics. To overcome these issues, a fuzzy-based control technique is applied to improve the APF performance. The controller adapts effectively to changing system conditions and load variations. It generates appropriate compensating signals to suppress harmonic currents and enhance voltage and current waveforms. The approach aims to keep Total Harmonic Distortion (THD) within recommended limits. The results indicate improved system performance and stability. The proposed solution contributes to efficient and reliable microgrid operation.
Blood Vessel Segmentation in Retinal Images using PCA and AI
Medical Image Processing plays a vital role in the early diagnosis and treatment of ophthalmic diseases. Retinal blood vessel analysis is one of the most important tasks in retinal image processing because the structure and condition of retinal vessels provide valuable information regarding diseases such as Diabetic Retinopathy, Glaucoma, hypertension, and macular degeneration. Manual examination of retinal images by ophthalmologists is time-consuming, labour-intensive, and subject to human error. Therefore, there is a strong need for an automated and intelligent system that can accurately segment retinal blood vessels and assist in disease prediction This project proposes an advanced framework titled Blood Vessel Segmentation in Retinal Images using PCA and U-Net with AI-Based Disease Prediction. The proposed system integrates Principal Component Analysis, Convolutional Neural Network, and U-Net to achieve efficient and accurate retinal vessel segmentation. The system is designed to improve the quality of retinal image analysis while reducing computational complexity and enhancing diagnostic capability Initially, retinal fundus images are collected from standard datasets and pre-processed using grayscale conversion, resizing, and normalization techniques. These preprocessing steps improve image quality and prepare the images for segmentation. A patch-based PCA technique is then applied to extract important vascular features and reduce unnecessary information from the retinal images. PCA enhances the visibility of blood vessels by preserving significant features while suppressing noise and redundant data. The transformed PCA feature maps are combined with original retinal images to form enhanced multi-channel inputs for deep learning models The proposed framework utilizes two segmentation approaches. The first approach uses a CNN model for basic vessel detection and segmentation. CNN automatically learns hierarchical image features such as edges, textures, and vessel structures. However, standard CNN models may fail to capture thin vessels and fine details. To overcome this limitation, the second approach integrates PCA-enhanced inputs with the U-Net architecture. U-Net consists of encoder and decoder layers connected through skip connections, enabling the model to capture both global contextual information and fine-grained vessel details. This hybrid PCA + U-Net model produces more accurate and refined vessel segmentation results compared to traditional methods After segmentation; post-processing operations such as thresholding, median filtering, and morphological processing are applied to improve the quality of the segmented masks. These operations remove noise, smooth vessel boundaries, and enhance vessel continuity. The segmented retinal vessels are then analyzed to calculate vessel density, which is an important indicator of retinal health. Based on vessel density values and regional irregularity analysis, the system predicts the severity of retinal diseases and classifies conditions into healthy, mild, moderate, or severe categories An additional feature of the proposed system is the integration of an AI-based advisory module that generates explanations, possible causes, and precautionary suggestions for the detected retinal condition. This intelligent module improves system interpretability and helps users better understand diagnostic outcomes. The entire application is implemented through a user-friendly graphical user interface developed using Python Tkinter, allowing users to upload datasets, train models, perform predictions, and visualize outputs interactively
AI-Driven Intrusion Detection Systems For Next-Generation Networks: Techniques, Challenges And Future Directions
The rapid evolution of next-generation networks, including 5G technology, emerging 6G technology, and large-scale Internet of Things ecosystems, has significantly increased the complexity and scale of cybersecurity threats. Traditional intrusion detection systems (IDS), primarily based on signature and rule-based techniques, are increasingly ineffective against sophisticated attacks such as zero-day exploits, polymorphic malware, and encrypted traffic anomalies. In this context, Artificial Intelligence (AI)-driven IDS has emerged as a promising solution for enhancing detection accuracy, adaptability, and real-time response capabilities. This paper presents a comprehensive analysis of AI-based intrusion detection mechanisms tailored for next-generation network environments. It explores the integration of machine learning (ML), deep learning (DL), and hybrid intelligent models for detecting both known and unknown threats. Specifically, techniques such as convolutional neural networks, recurrent neural networks, long short-term memory models, and generative adversarial networks are evaluated for their ability to capture complex traffic patterns and temporal dependencies. The study further examines widely used benchmark datasets, including NSL-KDD, UNSW-NB15, and CICIDS2017, to assess model performance across diverse attack scenarios..
Enhancement and Classification of Coronary Heart Disease using X-ray Angiography Images
Diagnosing Coronary Artery Disease (CAD) detection using angiographic image is challenging due to non-uniform image quality, inconsistent noise, complex vascular structure. This paper proposes an Integrated AI Coronary Artery Disease (AI-CAD) detection framework. The AI-CAD framework combines data integrity validation, image normalisation, vessel enhancement, topology-preserved segmentation, and graph-based classification Initially proposed. Confident Learning with Extended CleanLab is used for dataset reliablity check removes annotation noise from 17.4% to 3.2% and raises the baseline model AUC to 0.947. Next image I processed with propose A Self-Supervised Radiographic Normaliser (SSRN) method improves image quality, results in an average improvement of 13 dB in SNR and 92% decrease in device variance. Further proposed Deep Multi-Scale Vessel Transformer (DMVT) algorithm enhances vessels region. The proposed Graph-Enhanced Annotation Mapper (GEAM) is used for obtaining masks ROIs, which provides an accuracy of bifurcation to 96%. TransUNet++ is used for vessel segmentation, and provides a Dice score of 94.1%. Finally, Graph-Enhanced Coronary Classifier (GECC) is used to classify CAD severity. GECC provides AUC values up to 0.98 and sensitivity of 97.3% for multi-vessel disease.
Correctional Legal Politics in the Indonesian Sentencing System
Criminal law is positioned within society with the primary objective of regulating social life in order to provide security and protection for individuals and groups within the framework of state life. This objective is intended to ensure that society may live peacefully and free from fear of threats arising from acts committed by individuals or certain groups that may cause harm to a person’s life, dignity, and property.
Competence Levels and Encountered Challenges among Public School Teachers in Research Design and Development
The study assessed the competence level and challenges encountered by public school teachers in conducting research in rural communities in Leyte Philippines. It examined factors such as age, sex, civil status, educational attainment, designation, field of specialization, length of service, and research trainings attended. The respondents were licensed teachers in education representing various fields of specialization. The study employed quantitative design, with data collected through a validated questionnaire administered to 80 teacher respondents. Findings revealed that the majority of the respondents were female and had obtained units toward advance degree aligned to their field of specialization in education. Furthermore, the results indicated that the respondents are moderately competent (2.65) in research design and development. Challenges related to research construction were moderately felt by the teachers, suggesting a need for institutional support to strengthen teachers’ research capabilities. This suggests that schools may provide targeted research training and seminars to help enhance teachers’ skills in research construction. Moreover, most demographic variables had no significant association with research competence; however, the field of specialization showed a significant relationship, implying that certain disciplines may be more conducive to or aligned with research engagement. To address these gaps, school administrators are encouraged to provide institutional support by fostering a research-oriented environment through mentorship programs, professional development workshops, and collaborative initiatives. Finally, future researchers may explore intervention strategies that bridge the gap between research engagement and practice, particularly by examining the role of school leadership, institutional support, and incentive mechanisms in promoting research culture among public school teachers.
competence level; competency needs; institutional support; research-based education; research engagement;
Coordinated EV Charging and Discharging Strategy Using a Mamdani MIMO Fuzzy Controller: Application to the Onitsha 11-kV Network
Modern electricity distribution networks are facing serious operating difficulties as a result of the explosive proliferation of electric vehicles (EVs), especially in areas like Nigeria's Onitsha distribution network that have little grid flexibility. Severe voltage imbalance, frequency aberrations, and higher power losses can result from uncoordinated grid-to-vehicle (G2V) charging and vehicle-to-grid (V2G) discharging. In order to effectively coordinate EV charging and discharging while maintaining grid stability, this work creates a Multi-Input Multi-Output (MIMO) Mamdani fuzzy logic controller. The controller generates two outputs: charging rate and a quantitative grid stability index by integrating four real-time indicators: State of Charge (SOC), Time Remaining (TR), grid frequency, and grid voltage. To capture expert operating logic, eighty-one fuzzy rules were created, allowing for the simultaneous optimization of charging performance and stability support. The Onitsha 11 kV network model, comprising 29 load centers and two injection substations, was used to validate the controller’s performance across five critical operating scenarios ranging from charging-priority to grid-critical and rapid-discharging conditions. Comparative evaluation with a conventional PI controller and a DISO fuzzy controller showed that the MIMO controller consistently achieved superior grid stability indices (0.826– 0.846 in normal conditions) and provided effective protective behavior during grid-critical states by entering safe idle mode. Frequency deviation was also reduced by up to 22% compared to baseline controllers. Statistical significance tests further confirmed the superiority of the MIMO controller in specific pairwise comparisons. Overall, the proposed MIMO Mamdani controller presents a robust, adaptive, and intelligent solution for coordinated EV integration in weak distribution networks.
MIMO fuzzy controller, Onitsha distribution network, EV charging, vehicle-to-grid, fast charging
A Conic Curve Digital Signature with Blockchain Mechanism for Securing EHR Data in IOT-Fog-Cloud Environments
The use of Internet of Things (IoT) technology and cloud computing in healthcare, outsourcing Electronic Health Records (EHRs) created by IoT devices becomes an important issue. In a real-time scenario, since EHRs might be gathered by multiple divisions within a hospital and sent over intranet or public networks, the situation raises risks for privacy breaches. Many existing studies use cloud-based access control and encryption methods to safeguard outsourced EHRs. Nonetheless, in an IoT cloud data-sharing environment where data comes from a variety of devices and user permission status changes often, there is still a gap in achieving the complete and systematic integration of secure IoT data transmission and aggregation. Furthermore, advances in attack tactics and resources may eventually compromise any cryptographic system based on a single mathematical flaw. By designing methods that rely on a variety of challenging factors, the system is safeguarded even if one of them is compromised. Therefore, this manuscript presents a new Conic Curve Digital Signature with Blockchain (CCDSB-Chain) system for securing EHR data in IoT-Fog-Cloud environments. In this system, Conic Curve-Based Encryption with Digital Signature (CCBE-DS) is proposed, which uses blockchain and fog-cloud computing to safely manage EHR data. This system collects data from IoT devices, processes and encrypts it at fog nodes, and then securely stores it on the blockchain. Access to critical information is meticulously regulated by preset attribute sets, guaranteeing that only authorized individuals may access the data. Furthermore, the experimental results show that the proposed CCDSB-Chain enhances EHR data security, reduces computational costs, and maximizes access efficiency.
IoT-Fog-Cloud network, E-health data, Blockchain, Conic curve encryption, Digital signature
A Comprehensive Survey of Machine Learning and Deep Learning Methods for Clinical Decision Support Systems
A Clinical Decision Support System (CDSS) is a type of health information technology that helps doctors and nurses make clinical decisions. Health information technology (HIT), especially clinical decision support systems (CDSSs) that work with electronic health records (EHRs), can help people make more consistent judgments based on evidence. There have been many published examples of CDSS success stories in the last ten years or more. CDSS has evolved as an essential component in contemporary healthcare, thereby altering the method in which medical professionals make decisions, with the goals of enhancing clinical decision-making, improving patient outcomes, and optimizing healthcare delivery. CDSSs can help in diagnosis, treatment, patient management, and prevention, which can lead to better patient outcomes, fewer medical errors, and better quality. Clinicians generally use modern CDSS solutions at the point of care, where the system gives them data-driven insights and suggestions that help them improve their skills. Priorities include investing in strong IT infrastructure, extensive training programs, and public awareness campaigns, as well as making CDSSs work better with EHR platforms. India can use CDSSs to improve patient care and outcomes if government agencies, healthcare institutions, and technology suppliers work together to remove these impediments. Overall, CDSS is a big step forward for smart healthcare systems. It makes clinical workflow more efficient and helps patients get better results. Finally, it is evaluated with two datasets from the UCI repository, and its performance is high in deep learning when compared with the other techniques in terms of accuracy, sensitivity, and specificity.
Clinical Decision Support Systems, Electronic Health Records, Public Health, Patient Safety, and Health Information Technology
From Speed to Stagnation: Litigation- Induced Delays in CIRP and Their Legal Implications
This paper examines the emerging problem of litigation-induced delays in the Corporate Insolvency Resolution Process (CIRP) under the Insolvency and Bankruptcy Code,2016. Initially, the code was enacted with one of the objectives of ensuring a time-bound resolution within 180 to 330 days; however, in practice, this timeline is often exceeded due to increasing judicial intervention and complexity with procedures. The research highlights the gap between the statutory framework and the actual timelines observed during proceedings. The paper adopts a doctrinal research methodology, relying on analysis of statutory provisions, judicial precedents, and secondary sources such as reports and articles to examine the issue in depth. The paper further examines the cause of delays, including frequent appeals, strategic litigation by stakeholders, and infrastructural limitations of the adjudicating authority. It also examines how the litigation-induced delay in CIRP leads to value erosion of assets, reduced creditor confidence, and how it deviates it from one of the main objectives of the Code. In addition, the research identifies emerging litigation trends that have contributed to the shift from a resolution-oriented process to a litigation-driven mechanism. Finally, the paper proposes a practical and modern solution to address these challenges, aiming to restore the efficiency and effectiveness of CIRP.
Design, Analysis and Retrofitting of an Internal Combustion Engine Mini-Truck into an Electric Vehicle
The growing concerns over vehicular emissions and rising fuel consumption have accelerated the need for sustainable transportation solutions. Electric vehicle (EV) retrofitting has emerged as a cost-effective and practical alternative to the adoption of new EVs, particularly for small commercial vehicles widely used in urban transport. This study focuses on the conversion of a conventional internal combustion engine (ICE)-based mini-truck into a fully electric vehicle. The power and torque requirements under various driving conditions, including acceleration, steady-state motion, and inclined travel, are evaluated using analytical and numerical approaches. Standard vehicle dynamics equations are employed for theoretical analysis, while MATLAB Simulink is utilized to simulate realistic drive cycles and obtain dynamic performance characteristics. Based on the obtained results, suitable motor and battery specifications are identified, leading to the selection of a Permanent Magnet Synchronous Motor (PMSM) due to its high efficiency and superior performance. Furthermore, key mechanical components such as the motor mounting clamp, mounting plate, coupler, and fastening system are designed and modelled using CAD tools. Structural analysis is conducted using ANSYS to assess stress distribution, strain, and deformation under operational loading conditions. The results demonstrate that all designed components are structurally reliable, exhibiting minimal deformation and stress values well within permissible material limits.The study validates the feasibility of EV retrofitting as an efficient and sustainable solution for reducing emissions in urban transportation.
Retrofitting, Permanent Magnet Synchronous Motor (PMSM), ANSYS Structural Analysis, MATLAB Simulink Simulation
Efficient Brain Tumor Segmentation and Classification Using a Hybrid Qcq-Cnn Model with Quantum Neural Networks
Accurate detection and segmentation of brain tumors from magnetic resonance imaging (MRI) remain critical challenges in intelligent medical diagnosis due to the complexity of tumor structures and variability in imaging conditions. This study presents a novel hybrid quantum-classical deep learning framework, termed QCQ-CNN, for enhanced brain tumor segmentation and classification. The proposed approach integrates Gray Level Co-occurrence Matrix (GLCM)-based texture feature extraction with a convolutional neural network (CNN) to capture spatial and textural representations, followed by a U-Net architecture for precise tumor segmentation. To further improve classification performance, a Quantum Neural Network (QNN) is employed as a decision-making module, leveraging parameterized quantum circuits to model complex nonlinear feature relationships. The framework is evaluated on the BRISC 2025 MRI dataset, which includes multi-class tumor categories and expert-annotated segmentation masks. Performance is assessed using standard metrics such as accuracy, precision, recall, F1-score, and Intersection-over-Union (IoU). Experimental results demonstrate that the proposed model achieves superior segmentation accuracy and classification robustness compared to conventional deep learning methods. The integration of quantum computing enhances the model’s generalization capability, particularly in limited and noisy data scenarios. Overall, the proposed hybrid framework provides an efficient and scalable solution for next-generation medical image analysis and intelligent diagnostic systems.
Adaptive Cloud Security Framework Based On Deep Reinforcement Learning For Cyber Threat Mitigation
The rapid adoption of cloud computing has transformed modern information technology infrastructures by offering scalable, flexible, and cost-effective services. However, this widespread adoption has also introduced significant security challenges, including data breaches, unauthorized access, and sophisticated cyberattacks. Traditional security mechanisms often struggle to cope with the dynamic and large-scale nature of cloud environments, creating a need for intelligent and adaptive defense systems. In this context, techniques from Artificial Intelligence and deep Learning have emerged as powerful tools for enhancing cloud security. To address these challenges, this paper proposes an intelligent cyber defence framework (ICDF) that leverages techniques from Artificial Intelligence and deep reinforcement learning (DRL) for enhanced cloud security. The proposed approach integrates deep reinforcement learning algorithms to enable autonomous and adaptive decision-making in threat detection and mitigation. Unlike conventional machine learning methods, reinforcement learning allows the system to learn optimal security policies through continuous interaction with the cloud environment. By modeling cybersecurity as a sequential decision-making problem, the system dynamically identifies vulnerabilities, detects anomalies, and responds to cyber threats in real time. In this framework, the deep reinforcement learning agent analyzes network traffic patterns, user behaviors, and system activities to detect malicious actions such as distributed denial-of-service (DDoS) attacks, unauthorized access, and data breaches. The agent continuously improves its performance by receiving feedback in the form of rewards and penalties, thereby optimizing defense strategies over time. This adaptive learning capability enhances the system’s ability to respond to evolving and previously unseen cyber threats. Experimental results demonstrate that the proposed AI-driven cyber defence model significantly improves detection accuracy, reduces response time, and enhances resilience against complex attacks compared to traditional security approaches. Overall, this research highlights the effectiveness of combining artificial intelligence with deep reinforcement learning to develop robust, scalable, and self-adaptive cloud security solutions.
DDoS attacks, Internet of things, Deep learning, Cybersecurity, Explainable AI and deep reinforcement learning.
Exploring the Role of Topological Descriptors to Predict Physicochemical Properties of Curcumin Compounds using Supervised Machine Learning Algorithms
Curcumin and its derivatives have attracted significant attention due to their diverse pharmacological activities and potential applications in medicinal chemistry, drug design, and material science. Accurate prediction of their physicochemical properties is essential for understanding molecular behavior, optimizing bioavailability, and accelerating compound development. In this study, we explore the effectiveness of topological descriptors in predicting key physicochemical properties of curcumin compounds using supervised machine learning algorithms. A dataset comprising structurally diverse curcumin analogs was curated, and multiple graph-theoretic topological descriptors were computed to capture molecular connectivity and structural characteristics. Supervised learning models, including Linear Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Gradient Boosting methods, were implemented and evaluated for predictive performance. Model assessment was carried out using statistical metrics such as coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrate that topological descriptors provide significant predictive capability for physicochemical parameters, with ensemble learning techniques outperforming conventional regression approaches in terms of accuracy and robustness. Feature importance analysis further revealed that specific molecular topology indices strongly influence the predicted properties of curcumin derivatives. The findings highlight the potential of combining cheminformatics-based descriptors with machine learning techniques as an efficient and cost-effective framework for physicochemical property prediction and rational molecular design.
An Energy Framework For Spherical Fuzzy Rough Matrices and Its Use in Multi-Criteria Decision-Making
This study proposes the basic idea underlying rough set serves as a technique for man aging information in relational databases. It is a distinctive field of uncertainity mathematics, strongly associated with fuzzy set theory. The integration of set theory in rough form along with spherical fuzzy set leads to the development of spherical fuzzy rough matrices, a powerful tool for managing uncertainity and vagueness in complex data environments. While spherical fuzzy sets enhance the descriptive power of conventional fuzzy models by representing the degrees of belonging, non-belonging and indeterminacy within a spherical framework, rough sets provide boundary-based approximations to handle indiscernibility in data. The combination of these two frameworks allows spherical fuzzy rough matrices (SFRM) to represent uncertain, imprecise and incomplete information in a more robust manner. This study demonstrates that the spherical fuzzy rough matrix acts as a crucial tool in strategic selection processes. The determinant and adjoint of the proposed matrix are formulated, and a ranking function based on its energy is derived. The study introduces an MCDM-based framework for the evaluation and ranking of different alternatives. To validate the effectiveness of the approach, numerical examples are presented, showing the practical applicability of the SFRM and its energy in addressing MCDM problems.
Rough matrix, Spherical fuzzy matrix, Spherical fuzzy rough matrix.
An Analysis of Consumer Buying Behaviour for Luxury Products
The luxury goods market around the world has gotten big in the few years. This is because people have money to spend, they like to live a certain way, and they are connected to the rest of the world. The internet and social media also play a role in this. We want to know why people buy luxury goods. We are looking at what's going on in their minds, how their friends and family affect their decisions and how much money they have to spend. Luxury goods are special because they are good quality, not many people have them, they are expensive and they have a strong reputation. The research shows that things like how people think of a brand and how good they think are important when people decide what to buy. People also care about how exclusive a brand's what it says about their social status. What other people think matters too, like when friends make you feel like you need something or when you compare yourself to others and want to keep up. This can really affect what people buy. The study also found out that social media and famous people talking about luxury brands make them more appealing to younger people. Overall, the study finds that luxury consumption is a thing. It is influenced by factors and personal drivers. Marketers need to understand these factors. This understanding helps them to create plans. They can meet the changing needs of luxury consumers. Luxury consumption has sides. External social factors play a role. Internal psychological drivers also matter. Marketers must consider these when making strategies. They should focus on the luxury segment. Consumer expectations, in this segment keep changing
Static Structural Performance Evaluation of a Workshop Hoist Using ANSYS Mechanical
Material handling equipment is crucial in manufacturing industries, automobile workshops, maintenance facilities, warehouses, and assembly units.The advent of Computer Aided Engineering (CAE) tools has significantly enhanced mechanical design and analysis. Finite Element Analysis (FEA) allows engineers to predict structural behavior under various loads before manufacturing, reducing development costs, minimizing design errors, and improving reliability. In this study, a detailed modeling and structural analysis of a workshop hoist were performed using ANSYS Workbench 19.2.The three-dimensional hoist model, imported in IGES format, consists of 27 structural components made of structural steel. The hoist has approximate dimensions of 1887.7 mm in length, 1651 mm in height, and 940.21 mm in width, with a total mass of 214.72 kg. Material properties assigned included a Young’s modulus of 200 GPa, Poisson’s ratio of 0.3, yield strength of 250 MPa, and ultimate tensile strength of 460 MPa. For structural analysis, fixed support boundary conditions were applied to the base frame, while a downward force of 4500 N was applied at the lifting point to simulate operational conditions. Static structural analysis determined total deformation, equivalent elastic strain, maximum principal strain, maximum shear strain, equivalent von-Mises stress, and maximum shear stress. The maximum deformation recorded was 16.278 mm, with maximum equivalent elastic strain and principal strain of 1.8636 × 10⁻³ mm/mm and 1.8528 × 10⁻³ mm/mm, respectively. The maximum shear strain was 2.5653 × 10⁻³ mm/mm.Stress analysis revealed a maximum von-Mises stress of 371.05 MPa and maximum shear stress of 197.33 MPa. The highest stress concentrations occurred near the boom linkage and load-carrying joint regions, where bending moments and load transfer forces are greatest, highlighting critical areas for design optimization.
Lanzhou Indices of First Transformation Graph
In molecular science, one of the major challenges is the precise modeling and prediction of a molecule’s chemical properties. To overcome this difficulty, researchers have proposed several theoretical methodologies, among which topological indices have gained considerable importance. These indices are numerical parameters derived from the structural graph of a molecule and are widely applied to estimate its chemical and physical properties. In particular, degree-based topological indices constitute an important category within chemical graph theory and significantly contribute to such predictive analyses. In the present study, we introduce a new degree-based topological index and compute its values for some kind of transformation graphs
Chemical graph theory, degree-based topological index, Transformation graph.
Pythagorean Neutrosophic Super HyperSoft Topology
In this paper, we provide Pythagorean Neutrosophic SuperHypersoft topological spaces, which are defined over a specified starting universe. Fundamental characteristics of Pythagorean Neutrosophic SuperHypersoft closed sets and Pythagorean Neutrosophic SuperHypersoft open sets are examined. The ideas of internal Pythagorean Neutrosophic SuperHypersoft border and Pythagorean Neutrosophic SuperHypersoft closure are finally introduced, and their relationship is examined.
Pythagorean Neutrosophic SuperHypersoft sets; Pythagorean Neutrosophic SuperHypersoft topology; Pythagorean Neutrosophic SuperHypersoft open sets; Pythagorean Neutrosophic SuperHypersoft closed sets
Comparative Analysis of 2025 Flagship Smartphone SoCs Architecture, AI Acceleration, Benchmark Performance, and Energy
Smartphone SoCs lead the way in mobile computing with AI inference, real time graphics, and power efficient processing. Flagship System-on-Chip (SoC) designs from Apple (A19 Pro), Qualcomm (Snapdragon 8 Elite), MediaTek (Dimensity 9500), Samsung (Exynos 2400), Huawei (Kirin 9000S), Google (Tensor G4) launched in 2025 brought game-changing improvements such as 2nm and 3nm process, AI accelerators with more than 45 TOPS, and 6G-capable modems. This article provides a detailed comparison of the six SoCs in the categories of architectural design, AI capability, benchmark performance (AnTuTu, Geekbench, AI Benchmark), connectivity, multimedia solutions and performance-per-watt [1]. The findings reveal that Apple beats in single-core compute and power efficiency, Qualcomm has the best AI performance and connectivity, and Google is also focused on an AI-first era of computing with the integration of Gemini Nano. MediaTek’s thermal performance is balanced, Samsung’s GPU rendering performance far exceeds Huawei, which is due to AMD RDNA3, and it turns out that Huawei remains subject to the bottleneck of 7nm process. The study reveals emerging trends for AI focused design, SW-HW co-optimization and secure on-device computing, providing valuable strategic guidance for mobile processor innovation. Methods: Benchmarking remains a cornerstone methodology in the evaluation of smartphone processors. It provides quantifiable metrics that reflect real-world performance across various domains such as general-purpose computation, gaming graphics, and AI task execution. In this section, we present a systematic performance analysis of the six flagship smartphone SoCs launched in 2025 using industry-standard benchmarking tools. The results are discussed in relation to CPU-GPU architecture, AI acceleration efficiency, and thermal dynamics under sustained workloads. Result:The findings from the architectural, performance, and feature-based comparisons of the 2025 flagship smartphone SoCs [8]. Beyond benchmarking numbers, the discussion highlights the competitive positioning, the engineering trade-offs, and the importance of co-design between hardware and software in achieving end-user value. Conclusion :This study presented a comprehensive and comparative analysis of the leading flagship smartphone processors of 2025—Apple A19 Pro, Snapdragon 8 Elite, MediaTek Dimensity 9500, Samsung Exynos 2400, Huawei Kirin 9000S, and Google Tensor G4. The evaluation encompassed architectural innovations, benchmark performance, AI capabilities, multimedia integration, connectivity, and thermal efficiency. This section consolidates the key insights and outlines directions for future academic and industrial research in the domain of mobile SoC design.
A Comprehensive Study of Material-Optimized TFET Biosensors for Low-Power and Reliable Sensing Applications
A Comprehensive Preprocessing and Feature Extraction Methodology for Traffic Sign Detection on Indian Roads Using the IRTSD-Datasetv1
Traffic sign detection is a fundamental component of intelligent transportation systems (ITS) and autonomous driving technologies. The accuracy of detection models is heavily dependent on the quality of input data and the effectiveness of preprocessing and feature extraction pipelines. Indian road environments present unique challenges, including diverse sign designs, variable illumination, weather-induced degradation, partial occlusions, and cluttered backgrounds. This paper presents a comprehensive preprocessing and feature extraction framework specifically designed for the Indian Road Traffic Sign Detection Dataset (IRTSD-Datasetv1), which comprises 5,141 images spanning 37 traffic sign classes collected from over 90 cities across India. The proposed framework integrates a multi-stage preprocessing pipeline encompassing image resizing and normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Gaussian noise filtering, and morphological operations, followed by an advanced hybrid feature extraction methodology combining Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), colour histogram analysis in HSV space, and Gabor filter-based texture features. The pre-processed dataset is further enriched through a systematic data augmentation strategy incorporating geometric transformations, photometric distortions, and GAN-oriented synthetic sample generation to address class imbalance. Experimental evaluation demonstrates that the proposed preprocessing pipeline achieves a 14.7% improvement in feature discriminability measured through Fisher’s Discriminant Ratio, a 23.2% enhancement in Signal-to-Noise Ratio (SNR), and a 97.8% structural similarity index (SSIM) preservation. When integrated with a baseline Convolutional Neural Network (CNN), the pre-processed features yield an accuracy improvement from 89.3% to 96.7%, confirming the efficacy of the proposed framework. The findings establish a robust foundation for subsequent GAN-based traffic sign detection model development.
"Exploring the Soul of Care: A Cross-Sectional Study on Spirituality, Spiritual Care, and Perceived Spiritual Well-Being Among Nurses in Selected Hospitals of the UAE"
Introduction Spiritual care is an essential yet often overlooked aspect of holistic healthcare. Addressing patients’ spiritual needs enhances emotional well-being, coping, and overall quality of care. Nurses play a key role in integrating spirituality into practice, making it important to understand their spirituality, perspectives on spiritual care, and spiritual well-being. Objectives The present study aimed to assess the levels of spirituality, perception of spiritual care, and perceived spiritual well-being among nurses in selected hospitals in the UAE; to determine the association between socio-demographic variables and spirituality and spiritual care perspectives; and to identify significant predictors influencing nurses’ perception of spiritual care. Method A quantitative descriptive cross-sectional research design was adopted for the study. The research was conducted among 120 nurses working in selected hospitals in the UAE. Participants were selected using a convenience sampling technique. Data was collected using a structured questionnaire consisting of socio-demographic variables, the Spiritual Well-Being Assessment (SWBA), and the Nurse Spiritual Care Perspective Scale (NSCPS). Results A quantitative descriptive cross-sectional study was conducted among 120 nurses using the Spiritual Well-Being Assessment (SWBA) and Nurse Spiritual Care Perspective Scale (NSCPS). Most participants were female (82.5%), aged between 31 to 40 years, with more than 10 years of professional experience. The findings revealed that 81% of nurses had moderate to high levels of spirituality, while 91% demonstrated above-average spiritual well-being. The mean scores for Spirituality, Spiritual Care, and Spiritual Well-being were 83.6, 38.7, and 62.68, respectively. A statistically significant positive correlation was found between spirituality and perception of spiritual care (p = 0.008). Conclusion The study highlights the importance of integrating spirituality into nursing practice to enhance holistic patient care. Higher levels of spirituality among nurses are associated with better perceptions of spiritual care. Promoting spiritual awareness and support within healthcare settings may strengthen emotional resilience among nurses and contribute to improved patient outcomes.