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Abstract:

This study presents a transient thermo-mechanical finite element framework for high-speed end milling of AISI 4340 steel. The model couples moving heat sources, rate- and temperature-dependent plasticity, and adaptive mesh refinement (AMR) triggered by temperature gradient, plastic strain rate, and contact pressure. It is integrated with a design of experiments/response surface methodology using cutting speed (VC), feed per tooth (fZ), radial depth of cut/width of cut (ae), axial depth of cut (ap), and coolant mode. Responses include peak interface temperature per tooth (Tpeak), predicted surface residual stress (?_xx^"surf" ), and depth of compressive residual stress layer (dcomp). Experiments provide X-ray diffraction-based surface/depth profiles and arithmetic mean surface roughness (Ra). AMR is applied in this study to minimize the cut compute cost by 41-52% and error by 35-45%. Across 12 validation cuts, root mean square errors were 24 °C of Tpeak, 33 MPa of ?_xx^"surf" , 0.07 µm of Ra, and 22 MPa of dcomp. The response surface methodology and analysis of variance identified VC as the main driver of thermal load, while fZ, ae, and ap controlled the sign and depth of the residual field; coolant modified heat partition. Multi-objective desirability optimization with a material removal rate constraint yielded a balanced minimum quantity lubrication. Overall, exit-edge cooling and subsurface plasticity jointly set residual sign and magnitude; AMR is essential to resolve these gradients efficiently. The framework offers a reproducible route for residual stress-aware process planning in fatigue-critical AISI 4340 components while preserving throughput and is readily transferable to allied high-strength steels.

Keywords:

Residual Stress,High-Speed End Milling,Thermo-Mechanical Finite Element,X-Ray Diffraction,Design of Experiments/Response Surface Methodology,Multi-Objective Optimization,

References:

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Abstract:

The squeeze film behaviour of poroelastic bearings with rough surfaces and couple stress fluids is studied using a simplified model in which the action of the couple stress synovial fluid in lubricating the hip joint is examined. The articular cartilage. The layer is modelled as a biphasic poroelastic matrix material. A modified average Reynolds equation is derived, which accounts for the couple stress effects, random surface roughness, and the elastic nature of the cartilage-bearing surface. Two types of one-dimensional random roughness patterns, longitudinal roughness and transverse roughness, are presented using Christensen's stochastic theory. By using a domain transformation, the reduced governing equations can be mapped onto the unit square and solved numerically using the sine-cosine wavelet operational matrix of integration method. Uniqueness, uniform convergence, convergence of the partial sums to the exact function, and commutation of the integration and limit operations are guaranteed by proving some properties of the wavelet approximation. The numerical results indicate that, although couple stresses can improve the performance of the joint as a whole, the effect on the squeeze film performance of surface roughness must be considered, depending on the patterns of surface roughness. The wavelet-based method proposed in this paper is accurate and efficient.

Keywords:

Sine cosine wavelet operational matrix of integration (SCWOMI),Poroelastic Squeeze film,longitudinal roughness,transverse roughness,Finite difference method.,

References:

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Abstract:

Network protection relies on machine learning-based systems that detect intrusions. The detection systems lose their effectiveness because they use multiple duplicate features, and their performance depends on the specific network traffic patterns and system operational requirements, which prevent real-time functioning. The research presents a PSO-DBF intrusion detection framework, which begins with Distributional Boosting Forest (DBF) as its first step to create two groups (C1 and C2) that display similar probabilistic characteristics through network traffic clustering. The research team uses Particle Swarm Optimization (PSO) to process each cluster when they complete their clustering process because the method helps them find the most valuable network attributes, which decrease feature duplication while enhancing the ability to distinguish different features. K-Nearest Neighbors (KNN) provides the best performance when conventional machine learning classifiers use optimized feature subsets for intrusion detection. The proposed framework demonstrated its efficiency through experiments that utilized recognized IDS datasets. PSO removed almost 50% of the initial features while keeping 18 features from NSL-KDD and 21 features from UNSW-NB15, achieving reduction rates of approximately 56 percent and 57 percent. The proposed PSO–DBF with KNN framework achieved 99.36% accuracy on NSL-KDD and 99.89% accuracy on UNSW-NB15, exceeding the performance of Support Vector Machine (SVM), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), deep neural models, and recent hybrid metaheuristic-based IDS frameworks. The main improvement of the proposed method comes from its ability to reduce detection times, which drop from 0.44 milliseconds to 0.29 milliseconds. The DBF-PSO framework achieves its optimal performance for intrusion detection in enterprise cloud and edge-network security environments because of its detection accuracy and energy efficiency.

Keywords:

Network Security Intrusion Detection System,Particle Swarm Optimization,Distributional Boosting Forest,Machine Learning,Cyberattack Detection,Real-Time Threat Monitoring,

References:

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Abstract:

The present study examines the impact of hydrogen induction on the performance and emission attributes of a Reactivity Controlled Compression Ignition (RCCI) Engine operating on a microalgae biodiesel–diesel blend (B20D80) with a constant injection timing of 23° BTDC and an injection pressure of 200 bar. Experiments were conducted with hydrogen induction at flow rates of 3, 6, and 9 lpm, referred to as B20D80 + H? 3 lpm, B20D80 + H? 6 lpm, and B20D80 + H? 9 lpm, respectively. Among the tested fuel combinations, the B20D80 + H? 9 lpm blend showed improved performance under these fixed injection conditions, achieving a 20.8% enhancement in brake thermal efficiency and a 28.1% decrease in brake specific fuel consumption compared to conventional diesel operation. Emission analysis indicated that hydrogen enrichment led to substantial reductions in major pollutants, carbon monoxide, and smoke opacity, decreasing by 24% and 25%, while carbon dioxide and hydrocarbon emissions were reduced by around 8.6% and 35%, owing to the carbon-free nature of hydrogen and the oxygenated structure of biodiesel. However, nitrogen oxide emissions increased moderately by 22.8%, which is due to higher in-cylinder temperatures resulting from enhanced combustion. Overall, the results demonstrate that hydrogen-assisted microalgae biodiesel operation significantly improves combustion efficiency while effectively reducing most exhaust emissions, highlighting its viability as a cleaner, more efficient dual-fuel strategy for RCCI engines.

Keywords:

RCCI Engine,Performance,Hydrogen,Microalgae Biodiesel,Dual-Fuel,

References:

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XVII. Yasin, N. H. M., Aziz, N. N. C., Azmai, M. B. A., & Hanapi, M. F. M. (2023). Transesterification method of microalgae biomass to produce fatty acid methyl esters. Journal of Chemical Technology & Biotechnology, 98(11), 2774–2783. 10.1002/jctb.7338.
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XIX. E. P.V, “Graphene Oxide Nanoparticle Blended Tamanu Methyl Ester as a Promising Alternative Fuel for Unmodified Compression Ignition Engine,” Int. Res. J. Multidiscip. Technovation, pp. 151–164, Jan. 2025, 10.54392/irjmt25111.

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Abstract:

The persistent trend of global urbanisation presents a dual challenge: accommodating population growth while addressing significant environmental and social pressures. In response, the 'smart city' concept, underpinned by the Internet of Things (IoT), has become a leading vision for future urban development. This literature review systematically synthesises and critically assesses the existing academic discourse on the relationship between IoT-enabled smart city solutions and urban sustainability. It begins by establishing the conceptual foundations, exploring the evolution of urban sustainability and smart city paradigms, and positioning IoT as a vital enabler of infrastructure. The review then thematically examines the application of IoT across key urban sectors, such as energy, water, mobility, waste, and the built environment, analysing contributions towards sustainability goals, including resource efficiency and emissions reduction. Moving forward, the review also scrutinises a broad body of critical literature, highlighting ongoing challenges related to techno-solutionism, data governance, social equity, and barriers to implementation. Through this synthesis, a notable research gap emerges: a deficiency of integrated, socio-technical frameworks that guide the deployment of IoT solutions to ensure they achieve verifiable sustainability outcomes in an equitable way. The review concludes by emphasising the necessity for future research to shift focus from technological potential assessments to empirical studies of real-world implementation processes and comprehensive impact evaluations.

Keywords:

Urban Sustainability,Smart City,Internet of Things (IoT),Sustainable Development Goals (SDGs),Socio-technical Systems,Urban Governance,

References:

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Abstract:

New developments in the Brain-Computer Interface (BCI) technology have increased the rate at which research has been done on precise and quick electroencephalography (EEG)-based signal classification models. This review analyses new trends, procedures, problems, and gaps in research on EEG signal classification in three large cognitive paradigms: Steady-State Visual Evoked Potential (SSVEP), detection of the attention focus, and motor imagery (MI). These paradigms form the focus of real-time BCI applications, e.g., assistive technologies, neurorehabilitation, adaptive learning, and augmented interaction systems. The analysis presented in the paper on the development of the traditional machine learning (ML) and the modern deep learning (DL) models of the EEG interpretation systematically reviews the progression of the original ideas in the EEG interpretation field. Power spectral density analysis, Common Spatial Patterns (CSP), wavelet transform, and empirical mode decomposition (EMD) techniques of feature extraction, and Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) techniques are critically examined. Some of the performance evaluation metrics that are widely employed in the literature are also addressed. Special attention is paid to the real-life issues that accompany real-world EEG data, such as low signal-to-noise ratio, artifact contamination, inter-subject variability, limited diversity of datasets, and bad model interpretability. It is believed that such public benchmark datasets as BCI Competition datasets, PhysioNet, and other multi-subject repositories can be used to support comparative analysis. Additional requirements of unified evaluation frameworks, real-time system-aware assessment, hybrid models, multimodal fusion strategies, transfer learning, and explainable AI have been identified in the review in an attempt to enhance the accuracy, robustness, and trustworthiness of EEG-based cognitive systems. On the whole, the given study can be used as a consolidated basis for the creation of future-generation EEG-based BCI frameworks.

Keywords:

EEG signal classification,Steady-State Visual Evoked Potential,attention focus detection,motor imagery,Brain-Computer Interface,machine learning,deep learning,

References:

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XXXV. Zhu, Hao, Dylan Forenzo, and Bin He. "On the deep learning models for EEG-based brain- computer interface using motor imagery." IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022): 2283-2291.

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Abstract:

Hyperparameter tuning remains a major computational challenge in the field of machine learning. Traditional methods (grid search, random search, Bayesian optimization) are constrained by high dimensionality and complex parameter dependencies. This article explores constraint programming (CP) as a promising alternative, leveraging its ability to handle complex constraints and efficiently reduce the search space. We systematically compare CP methods to standard methods across different data types and learning algorithms. Performance metrics include accuracy, computational efficiency, convergence time, and the number of required evaluations. The results highlight the superior advantages of CP for complex hyperparameter dependencies and constrained search spaces, while also identifying scenarios where traditional methods remain preferable. This study contributes to the field of Automated Machine Learning (AutoML) and provides concrete recommendations for hyperparameter tuning.

Keywords:

Hyperparameter tuning; Constraint programming; Machine learning optimization; AutoML; Bayesian optimization; Computational efficiency,

References:

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II. Bergstra, James, and Yoshua Bengio. “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, vol. 13, 2012, pp. 281–305. https://jmlr.org/papers/v13/bergstra12a.html
III. Bischl, Bernd, et al. “Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges.” arXiv, 2021. https://arxiv.org/abs/2107.05847
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VII. Falkner, Stefan, Aaron Klein, and Frank Hutter. “BOHB: Robust and Efficient Hyperparameter Optimization at Scale.” Proceedings of the 35th International Conference on Machine Learning (ICML), 2018, pp. 1437–1446. https://proceedings.mlr.press/v80/falkner18a.html
VIII. Feurer, Matthias, and Frank Hutter. “Hyperparameter Optimization.” Automated Machine Learning, Springer, 2019, pp. 3–33. 10.1007/978-3-030-05318-5_1
IX. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. https://www.deeplearningbook.org
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XIII. Letham, Benjamin, et al. “Constrained Bayesian Optimization with Noisy Experiments.” Bayesian Analysis, vol. 14, no. 2, 2019, pp. 495–519. 10.1214/18-BA1110
XIV. Swersky, Kevin, Jasper Snoek, and Ryan P. Adams. “Multi-Task Bayesian Optimization.” Advances in Neural Information Processing Systems (NeurIPS), 2013, pp. 2004–2012. 10.5555/2999792.2999836
XV. Ungredda, Jonathan, and Jürgen Branke. “Bayesian Optimisation for Constrained Problems.” arXiv, 2021. https://arxiv.org/abs/2105.13245

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Abstract:

This study develops a broad extension of logarithmic series and presents exact formulas for their sums. By reformulating the series through suitable integral and functional representations, the work uncovers direct links between these generalized series and polylogarithmic functions. The approach yields several transformation identities that streamline the evaluation of such series and reveal a unified structure underlying many classical logarithmic and alternating forms. Illustrative special cases and numerical checks highlight the accuracy and versatility of the derived results, demonstrating their usefulness in analytic methods and computational applications

Keywords:

Logarithmic series,Alternating series,Generalization,Summability,

References:

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Abstract:

Career Quest is an AI-enabled career assistance platform designed to enhance resume building and interview preparation through the integration of large language models (LLMs) and multimodal analytics. The system processes resumes using automated workflows and evaluates them using GPT-based models to generate ATS scores, semantic feedback, and job recommendations. For interview preparation, the platform incorporates multi-modal inputs, including text, speech, and facial expressions. Responses are analyzed using speech recognition, linguistic evaluation, and emotion detection models to assess technical accuracy, communication clarity, and behavioral traits. To improve reliability, the proposed framework introduces uncertainty estimation at each processing stage, enabling confidence-aware predictions rather than deterministic outputs. Additionally, a probabilistic fusion mechanism is incorporated to combine multi-modal signals, ensuring consistency across modalities. Experimental evaluation demonstrates strong performance in emotion detection (97.35%), speech hesitation detection (85%), and response evaluation. The system provides interpretable feedback along with reliability scores, making it a saleable and robust solution for career assessment and interview training.

Keywords:

Multimodal Learning,Large Language Models,Uncertainty Estimation,Career Assessment,Mock Interviews,Deep Learning,ATS Scoring,

References:

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