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.
Memory of Self in Robert Lowell’s Poetry
The aim of this article shows empirical data from recent close studies on the effect, during the 21st century pandemic and reflect circumstances on possible etchings based on meditation through technical aspects of scientific improvement in psychological treatment of patients online. This article pursues social factors such as Self-Cognition, how the organs of sense, what Lowell’s poetry gives which techniques in consciousness at present. Thus the rabid development of digitalization in modern society where the mind engages dedicated survivalists to tolerate one’s mind. Though the key elements in thought process of mind which anxiety lingual with blood in justice sensitivity through Neurophysiologic problem of the prophetic mind naturally stress in conscious mind, which hormone leads to mental analysis in self-cognitive through psychology, central nervous system which anxiety techniques in hymn. Though even as a poet’s filling pain which a person co-relates between the difficult perspectives of qualitative and quantitative mental fitness. A Psychiatrist who has brought-up phenomena of consciousness through annoyed minds equated people able to teach actual reality taking-up coated elevating grasp ideas neurophysiology, still finding out electro radiation analysis in mind essentially depends upon knowledge based on consciousness experience for mental agony.
An Empirical Study of Digital Marketing and Brand Value Among Business Enterprises in Tamilnadu
Studying brand value in the Indian digital marketing environment is consequently important for understanding how digital communication, consumer engagement, online experiences, and brand-related perceptions contribute to the long-term competitiveness and sustainability of businesses. For these purpose 160 businessmen selected from Salem District of Tamil Nadu using convenient sampling method. Interview schedule method is used to conduct research and to test the above hypothesis, descriptive analysis, t-test, F-test are used. The results show that human capital factors, particularly education and income, play a more important role in shaping brand value perceptions than business-related characteristics such as business age, entrepreneurial generation, or business sector. These findings imply that improving entrepreneurial knowledge, managerial competencies, and financial capacity may contribute more effectively to enhancing brand value than merely increasing business experience or operating in a particular sector.
Recital Assessment of Chaotic Encipherment Method
The rapid expansion of multimedia communication led to a number of data transfer security problems. Furthermore, there is little data transmission security offered by the network utilized for digital communication. Tens of millions of individuals used the internet during this time for necessary communication and as a tool for business. Therefore, security is a crucial topic to address. Data confidentiality must be safeguarded, and secure connections must be made. As a result, we must acknowledge the various facets of security and their uses. Numerous of these applications cover anything from private chats to secure commerce, password or pin protection, and payment processing. As is well known, cryptography is becoming a crucial component of safe communication. The science of creating secret code using a reliable algorithm and key is known as cryptography. Digital signatures, hashed message authentication codes, and encryption and decryption algorithms are the fundamental elements of cryptography. We are aware that cryptography and encryption are synonymous. In this day and age, various forms of encryption are employed. The kind of encryption that has embraced the idea of chaos is called chaotic encryption. This paper examines the development of cryptography up till chaotic cryptography and evaluates the effectiveness of the chaotic encryption method. The evaluation is carried out in terms of battery power consumption, CPU utilization over time, and encryption speed. The efficiency of the algorithms is described in the experimental findings.
Chaotic Encryption, Chaotic Video Encryption Scheme, Non-Linear Chaotic Algorithm, Escrowed Encryption Scheme, Scalable Encryption Algorithm
The TZ-Multi-Level Inverter Fed Multi-Phase Induction Motor Drive Systems Fault Diagnostics
More and more high-power, safety-critical industries including electric vehicles, aircraft, and renewable energy are using multiphase induction motor drive systems (MPIMDS) because they are more fault-tolerant and have better torque density. The Trans-Z-Source Multi-Level Inverter (TZ-MLI) topology has been added to boost performance even more. It has lower switching losses, larger voltage gain, and better harmonic performance. But the reliability of these systems is greatly affected by possible problems, such as inverter switch failures and stator inter-turn short circuits, which cause current distortion, torque pulsations, and lower efficiency. This study describes a complete fault diagnosis framework for a five-phase induction motor drive system powered by a TZ-MLI. A comprehensive mathematical model of the TZ-MLI and MPIMDS is developed, succeeded by fault analysis under various operating scenarios. Researchers are looking into new diagnostic methods, such as negative-sequence current monitoring and classification based on convolutional neural networks (CNNs). A MATLAB/Simulink model is created to mimic both normal and abnormal situations. The results show that the suggested system keeps total harmonic distortion (THD) below 3 perentage during normal operation. However, it does show clear current and torque problems when there is a failure. The CNN-based diagnostic method gets more than 95 percntgae of the classifications right, which is better than traditional Fast Fourier Transform (FFT) and wavelet-based methods in noisy settings. The suggested method is very reliable, can find faults quickly, and is very strong, which makes it perfect for modern fault-tolerant motor drive applications using a five phase induction motor drive system.
Predictive Moving Target Defense for Ransomware Using Temporal Transformers and Uncertainty-Aware Risk Estimation
Ransomware has evolved into one of the most disruptive cyber threats, leading to prolonged service outages and irreversible data loss. Conventional static defense mechanisms and signature-based detection methods are no longer sufficient to counter adaptive attack strategies and rapidly evolving ransomware variants. Moving Target Defense (MTD) has attracted considerable interest as a proactive defense paradigm by continuously varying system configurations for increasing attacker uncertainty. However, existing MTD mechanisms are often reactive, rule-based, or weakly synchronized with detection engines, resulting in unnecessary and potentially disruptive reconfigurations, delayed responses, and degraded system performance. To address these limitations, a machine learning–driven, behaviour-aware MTD framework is proposed for ransomware defense. The behavioral traces are processed through sliding-window analysis and hybrid feature encoding that integrates statistical descriptors with categorical embeddings. A time-series Transformer encoder models temporal attack patterns, whereas uncertainty-aware risk estimation, together with Monte Carlo Dropout, facilitates confidence-guided MTD triggering. Defense actions are activated only when predicted risk surpasses predefined thresholds with low uncertainty, minimizing false alarms and redundant system reconfigurations. Moreover, a feedback-driven self-supervised adaptive learning mechanism enables the model to handle behavioural drift over time. The MLRan dataset is used for simulations, which demonstrate high ransomware detection reliability with timely MTD activation, reduced false positives, and improved system resilience, proving the efficacy of the proposed framework for real-time capable ransomware mitigation.
Human-Wildlife Conflict Mobility and Landscape-Level Safeguarding Forests through UAV Applications
Conflict between humans and wildlife that results in the loss of life, property, or resources is referred to as human-wildlife conflict. The natural habitats of the animal kingdom will inevitably be invaded by humans due to the growing human population. Because of this, many countries have integrated human-wildlife conflict mitigation into their national environmental teams. The goal of this endeavor is to better protect human lives and save animals. When interactions between people and wildlife result in detrimental outcomes like the loss of property, livelihoods, or even life, this is known as HWC. Human proximity to wildlife's natural habitats is one of the elements causing conflict between humans and wildlife. The importance of geospatial technology, such as remote sensing (RS) and geographic information systems (GIS), as effective tools for mapping, tracking, and controlling regions affected by conflicts between humans and wildlife is examined in this paper. RS entails gathering data without making direct physical contact. RS is adamant about recognizing important elements that lead to conflicts in the context of HWC, such as resource availability in forest areas and habitat changes. The unification and analysis of spatial data are made possible by GIS. For example, herbivores graze crops, while carnivores graze animals, forcing farmers who depend on both to take drastic steps to save wildlife extinction. Conflicts between humans and wildlife can manifest as crop damage, livestock loss, disease transfer, human injury, and fatalities in the vicinity of protected areas.
The Role of HMG-COA Reductase Inhibitors in the Treatment of Stable Angina Pectoris in Representatives of Different Professional Groups in Society
Prosthetic Rehabilitation of Mandibular Partially Edentulous Arch with Titanium Cast Partial Denture. – A Case Report
The rehabilitation of partially edentulous arches insists on a broad prosthodontic approach that balances the esthetics, comfort, function, and material biocompatibility. The case report presents the prosthodontic restoration of a patient with a partially edentulous arch with missing mandibular teeth with a history of extensive endodontic treatment. The treatment plan involved fabrication of titanium mandibular cast partial denture and over an existing a maxillary metal-ceramic fixed dental prosthesis with attachment prosthesis. The use of titanium as the framework material for the mandibular partial denture provided a lightweight and durable solution, significantly improving patient comfort. The case report highlights the practicality and advantages of incorporating titanium frameworks in complex partially edentulous situations for accomplishing long-term functional and esthetic outcomes.
Hybrid Tumor Classification Using Vision Transformers and PSO-Optimized Feature Selection with XGBoost
To enhance patient outcomes and efficiently direct treatment, it is critical to quickly and precisely classify brain lesions in magnetic resonance imaging (MRI). Our hybrid classification system combines an XGBoost classifier, Principal Component Analysis (PCA)-driven dimensionality reduction, Particle Swarm Optimization (PSO)-based feature selection, and Vision Transformer (ViT)-based feature extraction in a way that complements each other. We evaluate our approach using the Brain Tumor Classification (MRI) dataset from Kaggle, which consists of 3,264 T1-weighted images evenly distributed across four categories: healthy, meningioma, pituitary tumor, and glioma. To preserve 95% of the variance, the ViT encoder compresses the large amount of global context data it receives using PCA. By selecting the most valuable components, PSO further enhances this representation and reduces the feature count by over 70%. The final classification accuracy of 97.8%, 98% precision and recall, and 0.99 area under the ROC curve (AUC) are achieved by XGBoost using the optimized feature subset. With a mean accuracy of 96.5% (±1.0%) and a mean AUC of 0.985 (±0.005), five-fold cross-validation demonstrates the stability of the model. In addition to being more effective and simpler to comprehend, our pipeline performs on par with or better than the top CNN and transformer models. This study demonstrates that there is a powerful, scalable method for automatically diagnosing brain tumors using sophisticated deep learning architectures and evolutionary optimization. Other medical imaging applications might also make use of this technique.
Smart Restroom Environmental Monitoring and Alert System
Maintaining hygiene in public restrooms is essential for ensuring public health and user safety. Traditional cleaning systems rely on fixed schedules, which do not reflect real-time conditions and often lead to inefficient maintenance. This paper presents an Internet of Things (IoT)-based smart restroom hygiene monitoring system that enables real-time assessment using environmental and gas sensors. The proposed system integrates ammonia (NH₃) and carbon monoxide (CO) gas detection, indoor air quality (IAQ) monitoring, and occupancy tracking to provide a comprehensive evaluation of restroom conditions. Sensor data is transmitted using the MQTT protocol to a cloud database and visualized through a dashboard. Alerts are generated when predefined thresholds are exceeded, enabling timely cleaning and safety measures. The inclusion of CO monitoring enhances the system’s capability to detect toxic gases, improving overall safety. Experimental results demonstrate improved efficiency, reduced unnecessary cleaning, and enhanced hygiene management.
Intelligent Medical Diagnosis Based on Fuzzy Neutrosophic Turiyam Matrices
The process of diagnosing diseases contains numerous uncertainties, approximations, and incompleteness in the light of overlapping symptoms, varying clinical observations, and the personal opinion of specialized experts. The present mathematical models, as well as the fuzzy approaches, are not accurate enough to present the complete uncertainties for the process of diagnosing diseases. To address the above-presented difficulty, a novel mathematical model, termed the Fuzzy Neutrosophic Turiyam Matrices (FNTM), based on the Turiyam conception, has been developed within the current paper. The developed mathematical model improves the present mathematical conception of the mathematical model of Fuzzy Neutrosophic Matrix with the Turiyam conception included. The Turiyam conception investigates the hidden uncertainties of the fourth dimension rather than the truths, the indeterminations, and the falsity described in the other three-dimensional conception models. The association of the diseases with the symptoms has been presented within the mathematical formulation based on the conception of the mathematical matrix, the elements of the matrices of which have been defined with the four independent values. The present mathematical paper has utilized the necessary mathematical operations of the matrices in order to perform effective mathematical deductions. It has been demonstrated in the present mathematical paper that the methodology of generating the diagnosis introduced within the developed mathematical conception of the FNTM offers a generalized and powerful mathematical deduction process in comparison with the rest mathematical models.
Error Estimation of Functions Belonging to the Lipschitz Class by Borel-Euler Summability Means of the Conjugate Series of Fourier Series
In this paper, we have estimated the error of functions belonging to the Lipschitz class by Borel-Euler product means.
Error estimation of function, Lipschitz class, conjugate Fourier series, Borel-Euler product means, Borel mean, Euler mean.
Evaluating Social Media Platform Effectiveness In Influencing Consumer Buying Behaviour: Evidence From Chennai City
Social media has emerged as a powerful digital marketing tool that significantly influences consumer buying behaviour. This study compares the effectiveness of six major social media platforms—WhatsApp, Instagram, YouTube, Facebook, Telegram, and X in influencing consumer buying behaviour among digital consumers in Chennai City. A descriptive and analytical research design was adopted, and primary data were collected from 150 respondents using a structured questionnaire through convenience sampling. The data were analysed using descriptive statistics, mean score analysis, one-way ANOVA, and Tukey HSD post hoc test with the help of SPSS. The findings revealed that YouTube was the most effective platform, followed by WhatsApp and Instagram, while Facebook, Telegram, and X showed comparatively lower effectiveness. The ANOVA results indicated a significant difference in the effectiveness of the selected social media platforms (F = 18.546, p < 0.001). The post hoc analysis further confirmed that YouTube, WhatsApp, and Instagram significantly outperformed Facebook, Telegram, and X in influencing consumer buying behaviour. The study concludes that businesses should prioritize high-performing social media platforms to improve consumer engagement and enhance marketing effectiveness. The findings provide useful insights for marketers in designing platform-specific digital marketing strategies.
Multimodal Stress Detection Using Deep Learning: Integrating Physiological and Behavioral Signals
The Stress is a big deal nowadays, really taking a toll on how people feel mentally and physically. Traditionally, figuring out someone's stress levels uses questionnaires, but those can be pretty off since they depend on the person's memory and mood at the time. This paper suggests something new model that hooks up physiological signals like heart rate, sweat stuff, and brain waves with how you look and sound to tell if you're stressed. This model use Convolutional Neural Networks and LSTM networks together to spot patterns in your body’s responses, both in the moment and overall. The evaluation results obtained clearly highlights that the proposed model worked better than other traditional methods that only focus on one kind of signal. This Proposed model can consistently check for stress in real-time, keep track of health, helps to boost work performance, and set up modified relaxation platforms. Furthermore, the results obtained showed that combining multiple types of signals like behavioral and physiological improves accuracy and stability. This proposed hybrid deep learning technique out performs traditional techniques in terms of all performance evaluation metrics like accuracy, precision, recall, f1-score.
Real-Time Driver Drowsiness Detection System Using Eye Blink Analysis and WhatsApp Alert Integration
Driver drowsiness is a common yet under-reported contributing factor to road traffic accidents, since diminished alertness reduces attention to primary tasks, in turn affecting response time and the decision making process. This paper proposes a computer vision and artificial intelligence-based smart real-time model to detect driver drowsiness. The use of a webcam captures a view of the driver, and OpenCV's implementation of HaarCascade Classifier detects and tracks eye movement. Early fatigue can be detected with the blink count and duration. When the driver has their eyes closed for more than 2 seconds, an automated alert is sent to the driver, vehicle owner, and emergency contacts using the Twilio WhatsApp API. While solutions such as Drowsy Driving Detection already exist, the proposed technique is vision-based, low-cost, and could be deployed in any vehicle. It also continuously records blink activity for behavioural experiments, and it shows effectiveness and consistency in a variety of light and face orientations. This shows how AI-driven computer vision combined with real-time communication can directly make road safety-improving interventions, particularly to reduce accident risk due to fatigue.
Knowledge, Attitude and Practice (KAP) on Breast Self-Examination (BSE) amongst Young Female Students and Adult Females in Pune, Maharashtra - A Comparative Study
Introduction: Breast cancer is the commonest cancer in Indian females, and its mortality rate is relatively high. Early detection is crucial for better prognosis, and breast self-examination (BSE) is a simple tool that can aid in early detection of any abnormality. Although there has been an increasing trend in its incidence, awareness and practice of BSE are still lacking in various age groups. Methods: This analysis-based cross-sectional study shall be carried out among 200 subjects, comprising 100 young female students between 17 and 23 years old from Dr. D. Y. Patil University, Pimpri, Pune and 100 adult women between 24 and 60 years old from local neighborhoods. Participants will be recruited through convenient sampling. KAP information for BSE will be obtained using a pretested and semi-structured questionnaire. The gathered data will be analyzed descriptively and inferentially, employing the use of the Chi-square test, independent t-test, and logistic regression techniques. Results: It is anticipated that there will be significant differences between the two groups in terms of KAP. Young female students will exhibit superior knowledge, yet poor practice of BSE, whereas adult women will display an optimistic attitude toward the practice, with different degrees of practice. The socio-demographic characteristics of the respondents will significantly influence KAP. Conclusion: The results of this study will be useful in highlighting significant gaps in knowledge and practices regarding BSE among various socio-demographic groups. The results will be helpful in designing effective interventions that may assist in early detection of breast cancer cases.
Numerical Analysis of SEIR-Type Mathematical Models for African Swine Fever Transmission Using Finite Difference Methods: A Cross-Disciplinary Approach
African swine fever (ASF) is a highly contagious viral disease affecting domestic and wild pigs, with mortality rates approaching 100%. This paper presents a comprehensive numerical analysis of modified SEIR (Susceptible-Exposed-Infectious-Recovered) epidemiological models for ASF transmission. We employ finite difference methods (FDM) to discretize the governing non-linear ordinary differential equations and analyze stability, convergence, and error properties of the numerical scheme. Our results demonstrate that the proposed numerical approach accurately captures critical epidemiological thresholds, including the basic reproduction number R_0, and provides reliable predictions for disease intervention strategies. We extend the model to include spatial dynamics using reaction-diffusion PDEs discretized with finite element methods, stochastic modeling via stochastic differential equations, and optimal control analysis using Pontryagin's maximum principle. Sensitivity analysis reveals that transmission rate and incubation period are the most influential parameters. Comparative analysis of numerical schemes (Euler, RK4, and fractional Adams-Bashforth-Moulton) demonstrates first-order convergence for Euler and fourth-order for RK4. These findings underscore the importance of robust numerical methods in epidemiological modeling and offer practical insights for ASF control policies.
In human being tooth loss is a very common problem which may be due to various reasons like diseases and trauma. For solving this problem we use Dental implants which provide support for replacement of missing teeth and then we place crown above it. Research on dental implant designs, materials and techniques is continuously increasing and it depends upon the position of teeth. There is still a lot of work involved in the use of better biomaterials, implant design, surface modification and functionalization of surfaces to improve the long-term benefits of implant treatment. This paper provides a brief history of dental implants, its material and it’s properties and about its parts. This research paper work describe the new designs and conventional design of dental implant and stress distribution using Finite Element Analysis method on the surface of the whole system under various loading conditions. This also describe how to remove complexities which is associated with tooth abutment implants such as its motion, complex design of screw, high cost etc.
The Impact of Poverty and Social Inequality on Spiritual Stability: Modern Approaches
This article analyzes the phenomena of poverty and income inequality from economic and philosophical perspectives. Information is provided on the essence of poverty and income inequality, their interconnections, the factors influencing them, and their relationship with spiritual stability. Issues of the fairness of resource distribution in society, social equality, and human well-being are discussed. Furthermore, the causes of the growing inequality in modern economic systems and its socio-spiritual consequences are highlighted.
Poverty, economics, income inequality, Gini coefficient, solutions, social justice, sustainable development, prosperity, economic development, philosophical approach, policies.
Raxmonova Nigina Tolibovna, 2Mirzayeva Moxira Kurbanovna, 3Raxmatova Nargiza Akbar qizi, 4Yаzdоnоvа Sахibа Kurbаnоvnа,5 Yazdonov Ulugbek Tashmurotovich, 6Qarshi davlat universiteti 7Ne’matova Rayxona Jasurbek qizi,8 Nargiza Abdivaliyevna Askarova.
Improving The Methodology of Teaching Russian Language to Medical University Students on the Basis of an Integrative Approach
This article addresses the problem of improving the methodology of teaching the Russian language to students of medical higher education institutions through the application of an integrative approach. The relevance of the study is determined by the growing need for future physicians to master professionally oriented Russian language communication skills that combine linguistic competence with medical content knowledge, while existing teaching practices frequently treat language instruction and professional medical training as separate, disconnected processes. The purpose of the research is to develop and experimentally verify a methodological model for teaching Russian language in medical universities that integrates linguistic, communicative, and professional-medical components within a unified instructional framework. The article follows the IMRAD structure: it presents the theoretical background and problem statement, describes the materials and methods used, including the pedagogical experiment design, diagnostic instruments, and participant sample, reports the quantitative and qualitative results obtained before and after the implementation of the integrative model, and discusses these findings in relation to existing research on language for specific purposes and integrative pedagogy. The results demonstrate that students taught according to the integrative model achieved statistically significant improvement in professional-terminological competence, communicative fluency in clinical contexts, and motivation toward language learning compared with students taught through the traditional, linguistically isolated methodology. The article concludes that an integrative approach, which combines Russian language instruction with authentic medical content, clinical communication scenarios, and interdisciplinary coordination between language and medical faculty, offers a more effective pathway for developing the professional language competence required of contemporary medical specialists.
Alternative Justice: Modern Trend in Criminal Development Justice
In this study, the author analyzed the origins and essential properties of an alternative form of criminal proceedings, the current state and development trends in the practice of forming and improving alternative forms to the general and basic procedural orders and rules of justice. The author, based on the legislative practice of democratic countries, examines the ways and directions of development of procedural legal relations, this problem is especially relevant not only for the practice of justice in developing countries, but also for states in which the informatization of public relations, due to the intensification of these relations, plays an important role. Alternative justice, which, according to the author, arose in the bosom of the social-integrative theory of the last century, has become a pressing problem in the implementation of independent, fair and swift justice, aimed, first of all, at ensuring the rights and interests of the accused, the defendant. It is alternative justice in the current state of development of societies that has the highest chances of subsequently gradually replacing the conservative order of justice of the “old world”. In this regard, this study of the forms and methods of implementing alternative procedures in criminal proceedings is very attractive for developing countries, both Central Asian countries and developed countries, in the context of transforming criminal justice to the requirements of intensively developing social relations. The purpose of the study, is to analyze the features of the development of alternative justice, identify its socio-legal significance, through the study of various classical and modern procedural forms of alternative justice, to reveal the essence and properties, the main directions of development of criminal justice in the Republic of Uzbekistan and other countries in the region. Research methods consists of designing and integrating problems, deductive and inductive data analysis, knowledge of the structural and functional features of a phenomenon and revealing their essence, methods of synthesis and historical analysis of facts. Results and main conclusions: identification of the main trends in changes in socio-legal relations in the field of criminal justice, outlines of ways and methods for the development of alternative forms of implementation of the criminal process, comparative analysis of the procedural features of alternative justice in countries that have favorable practices in this socio-legal relationship. Results of the study: Wider use of the methodological foundations of modern criminological theories in the implementation of scientic research, a deeper understanding of the content of policies to combat crime, expansion of aspects of scientic and theoretical research, as well as the formation of skills in using the dings of the most advanced scientist and criminological theories in improving criminal justice. Annotation: This article discusses the basics of the development of modern trends in the resolution of social conflicts in the field of justice. The article also analyzes the important socio-legal properties of alternative case management, which arose in the 70s of the 20th century as an alternative to the main procedure of legal proceedings. The author believes that the wider application of the alternative procedure of judicial proceedings, the expansion of its procedures and types, will inevitably contribute to solving the problem of the intensification of justice, which, in turn, will help eliminate the socio-legal conflicts caused by the crime by offending the injury inflicted on the victim, as well as solve the problem of the resocialization of the person who committed the crime.
An Integrated Classical–Transformer Framework Combining Weighted Support Vector Machines and Multi-Phase BERT for Large-Scale Twitter/X Sentiment Analysis
Micro blogging platforms such as Twitter/X generate hundreds of millions of short, informal posts every day, forming a valuable but noisy signal of public opinion that is exploited in brand monitoring, political analytics, public-health surveillance, and financial forecasting. Two broad families of solutions have emerged to convert this raw stream into reliable sentiment labels: computationally lean classical machine-learning pipelines built around engineered lexical features, and heavyweight transformer-based deep networks that learn contextual representations directly from text. This paper brings both families together in a single, directly comparable study. The first track re-engineers a Support Vector Machine pipeline around bigram-aware TF-IDF weighting, chi-square statistical feature pruning, and instance-level class-balancing, producing a Weighted SVM (WSVM) classifier evaluated on the 1.6-million-tweet Sentiment140 corpus. The second track introduces a three-stage deep-learning pipeline built on a fine-tuned bert-base-uncased backbone: a Noise-Adaptive Lexical Filter (NALF) that scores and routes each tweet through a proportionate cleaning routine, an Attention-Weighted Sentiment Pooling (AWSP) module that learns to emphasize opinion-bearing token positions and fuses them with TF-IDF context, and an Adaptive Threshold Classifier (ATC) that calibrates per-class decision boundaries from batch statistics rather than relying on a fixed softmax cut-off. The WSVM track reaches 88.5% accuracy and an 87.6% F1-score against a 320,000-tweet held-out partition, improving on baseline SVM, Naive Bayes, logistic regression, and random-forest classifiers by margins of up to eleven points. The transformer track, evaluated on a class-balanced three-way partition of the same underlying tweet population (relabeled Sentiment160), reaches 93.8% accuracy, a 92.6% macro F1-score, and an AUC-ROC of 0.971, with statistically significant, independently verified contributions from each of the three novel components (p < 0.001, McNamara’s test). Beyond reporting these headline numbers, this extended treatment provides full architectural flowcharts for both pipelines, closed-form derivations and worked numerical examples of every governing equation, computational-complexity analysis, an itemized error analysis over both tracks, and a discussion of threats to validity. Taken together, the two tracks illustrate a practical accuracy-versus-cost frontier: the WSVM pipeline delivers competitive, fully interpretable, CPU-trainable sentiment classification, while the three-phase BERT framework pushes state-of-the-art accuracy at a materially higher computational and latency cost. The paper closes by discussing when each track is the more appropriate deployment choice and by outlining shared directions for future work, including knowledge distillation, multilingual extension, and multimodal fusion
Enhanced Intrusion Detection in Wireless Sensor Networks Using PSO-Optimised Machine Learning and Deep Learning Models on Protocol-Specific Datasets
The effectiveness of Machine Learning (ML) and Deep Learning (DL) models for intrusion detection in Wireless Sensor Networks (WSNs) is highly dependent on the dataset used for training and evaluation. This paper presents a comprehensive comparative study using two distinct datasets: the benchmark Wireless Sensor Network Dataset (WSN-DS) and a custom-simulated dataset generated via OMNeT++ incorporating multiple routing protocols. We propose and evaluate a suite of models hyperparameter optimized with Particle Swarm Optimization (PSO), including PSO-MLP, against their standard counterparts (KNN, RF, NB, DT, and MLP). Our results demonstrate that while all models perform well on the generalized benchmark, the PSO-MLP model consistently achieves superior performance (up to 98.1% accuracy) on the complex, protocol-specific simulated dataset. This study highlights the critical importance of dataset selection and the significant performance gains achieved through metaheuristic optimization, providing a robust framework for developing intrusion detection systems (IDS) tailored to specific WSN architectures.
Wireless Sensor Networks, Intrusion Detection, Particle Swarm Optimization, WSN-DS, OMNeT++, Machine Learning, Deep Learning, Comparative Analysis
Censoring-Aware Pseudo-Labeling for Semi-Supervised Survival Prediction using Risk-Order Agreement Filtering
Practical cohorts often have a small labelled fraction with confirmed time-to-event outcomes and a larger covariate pool with missing, delayed, or incomplete endpoints; yet, survival prediction is essential to clinical prognosis modeling. A problem formulation is derived that makes censoring constraints and pseudo-label safety requirements explicit after reviewing survival modeling, assessment under censoring, and semi-supervised learning (SSL) ideas that are relevant to pseudolabeling. Here is a conservative method blueprint called Adaptive Pseudo-Label Selection and Correction (APSC). It uses mediancrossing pseudo events and pseudo-censoring at tmax to generate weak pseudo outcomes from predicted survival curves. A curvegeometry confidence proxy is used to admit pseudo labels. Unstable pseudo labels are filtered using risk-order agree-ment across rounds. Finally, a down-weighted Cox partial-likelihood objective is used to inject pseudo supervision. Where naive selftraining can breach censoring limits and magnify early model bias, the formulation aims at registry-style situations with delayed endpoints and heterogeneous follow-up. In order to provide a supervised backup in the event that pseudo labels are unreliable, the survey condenses these factors into specific design criteria and deployment diagnostics. There are no numerical comparisons reported in the text; instead, the focus is on auditoriented operating methods like validation gating, monitoring of acceptance and correction rates, and tests for coefficient stability
Semi-supervised learning, survival analysis, pseudo-labeling, censoring, Cox proportional hazards, literature survey.
Electric Vehicle Charging Infrastructure in Smart Cities
The rapid adoption of electric vehicles (EVs) has transformed urban transportation and accelerated the demand for intelligent, reliable, and sustainable charging infrastructure. Smart cities aim to integrate advanced information and communication technologies with energy management systems to improve urban mobility while reducing greenhouse gas emissions and dependence on fossil fuels. However, the increasing number of EVs presents several challenges, including uneven charging demand, power grid instability, long waiting times at charging stations, limited renewable energy integration, cybersecurity risks, and inefficient resource utilization. These issues require intelligent charging management strategies capable of optimizing charging schedules, predicting demand, balancing electrical loads, and ensuring secure communication among charging stations, vehicles, and cloud platforms. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud computing, and edge computing have significantly improved the capabilities of smart EV charging infrastructure. Machine learning algorithms enable accurate prediction of charging demand based on historical usage patterns, traffic density, weather conditions, electricity prices, and user behaviour. AI-driven optimization techniques dynamically allocate charging resources, reduce operational costs, minimize peak-hour congestion, and improve energy efficiency. Furthermore, IoT-enabled sensors continuously collect real-time data from charging stations, while cloud platforms process large-scale datasets for predictive analytics and intelligent decision-making. The integration of renewable energy sources and vehicle-to-grid (V2G) technology further enhances energy sustainability by allowing EVs to act as distributed energy storage systems. This study presents a comprehensive review of intelligent EV charging infrastructure within smart cities by examining modern architectural frameworks, AI-driven charging optimization techniques, predictive analytics, renewable energy integration, cybersecurity considerations, and cloud-based management systems. The research also evaluates practical implementation strategies through industrial and metropolitan case studies, highlighting measurable improvements in charging efficiency, energy utilization, service availability, and operational reliability. A machine learning-based prediction model is demonstrated using Python to forecast charging station overload conditions, illustrating the practical application of predictive analytics in smart city environments. The findings indicate that AI-powered charging infrastructure significantly enhances charging efficiency, minimizes waiting time, optimizes electricity consumption, improves grid stability, and supports sustainable urban transportation, making it an essential component of future intelligent smart city ecosystems.
Electric Vehicles, Smart Cities, Machine Learning, Smart Charging Infrastructure, Renewable Energy
Mechanical and Thermal Characterization of Silane-Treated Basalt/Glass Fiber Reinforced Hybrid Polymer Composites
The growing demand for strong and cost-effective construction materials has led to increased research on fiber-reinforced polymer composites. Hybridization of fibers is an effective approach to combine the advantages of different materials. In this study, basalt and glass fibers were used as reinforcements in an epoxy matrix to develop hybrid composites. Basalt fibers provide high stiffness and thermal resistance, while glass fibers contribute to cost reduction and improved toughness. To enhance interfacial bonding between fibers and matrix, a silane coupling agent was applied as a surface treatment. Composite laminates were fabricated using the hand lay-up technique with different stacking sequences of treated fibers. Proper surface preparation was carried out to ensure improved adhesion and minimize internal defects. Mechanical properties of the developed composites were evaluated through tensile, flexural, microhardness, and impact tests. The results indicate that silane treatment significantly improves fiber–matrix bonding, enabling efficient stress transfer and reducing internal weaknesses. Hybrid composites exhibited better strength, stiffness, and toughness compared to single-fiber composites. The combination of basalt and glass fibers, along with chemical surface modification, resulted in enhanced mechanical and thermal performance. This study demonstrates that silane-treated hybrid composites are promising materials for load-bearing structural applications due to their improved performance and cost efficiency.
Iraq's Development Road Project Amid Political Instability, Economic and Security Challenges
Iraq has been involved in battles and wars for years and has become an arena for both regional ambitions and conflicts. The current study aims to investigate Iraq’s internal challenges and their implications for the development road project, which seeks to transform the country into a transit zone for trade between Asia and Europe. For this purpose, descriptive-analytical approaches based on secondary sources were employed to examine the impact of these challenges on the project. In addition, this study utilized Patron-Client Politics theory to analyze how political instability, security and economic challenges, and widespread corruption affect the project’s progress and implementation. The findings provide evidence that rivalry among political elites over resources and power, which undermines the decision-making process regarding the project, the spread of corruption, Iraq’s dependency on oil, and the control of armed militias have significantly hindered the project. Moreover, the study highlights the influence of the armed groups such as; the Popular Mobilization Forces, together with Iraq’s heavy reliance on oil revenues, remain critical obstacles to achieving stability. Further research might explore the positions of regional actors such as Türkiye, the GCC countries, and Iran, and their role in influencing the long-term success and sustainability of the development road project.
Dynamic Response of FG-BNNT Reinforced Composite Plate Subjected to Low Velocity Impact Loading
In the world of emerging materials, composites play a major role due to their lightweight and high strength-to-thickness ratio. There are many classes of composites such as Metal matrix composites, Ceramic matrix composites and Polymer matrix composites based on the matrix. And short fibre composites, long-fibre composites and particulate composites based on the reinforcement. The materials used as matrix and reinforcement play a major role in defining the strength and properties of a composite. Of all the different composites available in today’s life, Nanocomposite has become the centre of interest because it exhibits high strength with a small amount of reinforcement. The reinforcement in the nanocomposites can be of different types. They can be arranged in the form of nanotubes, nanoparticles, nanosheets etc. In this research, we focus on the polymer matrix nanocomposite taking reinforcement as the nanotubes. The mechanical properties of the nanocomposite are evaluated using the Halpin-Tsai micromechanical model and the analytical investigation is carried out using the new simple higher-order shear deformation theory and the contact force is calculated using the Hertz contact law and New mark’s technique is used for numerical investigation. The dynamic response of the FG-reinforced nanocomposite is studied and the best nanocomposite from the two different matrices and fibres is discussed. The influence of fibre volume fraction, Fibre orientation and the Fibre arrangement on the dynamic response is also presented and discussed and the comparison of CNT and BNNT with epoxy as matrix is presented and the comparison of Epoxy and HDPE with BNNT as reinforcement is also studied.
BNNT, CNT, New Simple HSDT, Functionally Graded and Halpin-Tsai Micromechanical Model
Improving Stock Price Prediction Accuracy Through Deep Learning Models and Hidden Markov Models: A Comparative Analysis
Stock price prediction is a crucial research area, especially in the financial market, which plays a critical role in the global economy. This research compares the forecasting performance of several deep learning models on the BSE dataset. The models used are Long Short-Term Memory (LSTM), CNN-LSTM, Recurrent Neural Network (RNN), and a hybrid model of CNN-LSTM and Hidden Markov Models (HMM). Standard performance measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), are used to compare the models. The experimental results showed that the hybrid LSTM-HMM model achieved the best predictive performance among the approaches tested. The RNN model performed well in forecasting, while the CNN-LSTM and RNN-HMM models showed relatively weak performance. The results indicate that deep learning can be combined with probabilistic models like Hidden Markov Models to enhance the prediction accuracy and reliability of the stock price. Future research may focus on incorporating advanced optimization algorithms with these hybrid models to further enhance forecasting performance.
Stock Price Prediction, Deep Learning, LSTM, CNN-LSTM, RNN, Hidden Markov Models.
Impact of Artificial Intelligence Tools on Indian Students: Using UTAUT and Machine Learning Model
The rapid advancement of Artificial Intelligence (AI) technologies has led to a transformation in higher education. AI tools provide academic support to students, available anytime, anywhere, to enhance their knowledge and skills. This study focuses on understanding the determinants of AI tools’ acceptance and use for academic support among students, influencing student satisfaction and academic performance in India. This work presents a quantitative investigation into the adoption of AI tools, including Chat-GPT, Google Gemini, Microsoft Co-Pilot, and related platforms, using the Unified Theory of Acceptance and Use of Technology (UTAUT). Data is collected from Kaggle, which contains 3,614 student records drawn from 1,246 institutions across 34 states in India. UTAUT constructs were operationalized as composite proxies from the available survey variables: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions (FC). The Actual Use Behaviour (USE) construct captured daily interaction intensity and contextual application diversity. A multi-layered Machine Learning (ML) model is employed, integrating Decision Tree, Logistic Regression, Random Forest, XGBoost, Gradient Boosting, and an ensemble Stacking Classifier. Class imbalance in the target variable addressed via SMOTE-ENN resampling. The final XGBoost model achieved al classification accuracy of 98.48% and an AUC score of 0.9986 and Cross-validated mean accuracy was 94.63% (SD = 0.0021). SHAP analysis provided post-hoc explainability, identifying USE as the dominant predictive feature in descending order of importance. The study contributes a theory-driven, methodologically transparent framework for analyzing AI adoption in higher education and offers practical implications for policy and institutional strategy, emphasizing the importance of structured support systems, digital competency development, and pedagogically aligned AI implementation. The paper concludes by emphasizing the need for a balanced approach to AI integration in education. Promoting AI literacy, implementing ethical AI policies, encouraging critical thinking exercises, and providing mental health support are recommended. By fostering responsible AI use, Indian educational institutions can maximize benefits while minimizing cognitive and psychological drawbacks.