Authors - Rahul Basu Abstract - Spinodal decomposition in binary alloys produces complex, interconnected microstructures with fractal-like characteristicsduring early and intermediate stages of phase separation. This paper presents a computational framework for simulating three-dimensional (3D) spinodal decomposition using the Cahn–Hilliard phase-field model, with emphasis on fractal dimensionanalysis of the evolving microstructures. The model incorporates CALPHAD-consistent free-energy descriptions (via commontangent interpolation for miscibility gaps) for benchmark alloys such as Cu–Ni and Fe–Cr. Simulations in 3D revealinterconnected networks with fractal dimensions typically in the range 2.4–2.8 during coarsening (deviation <5\% RMSE fromFe–Cr APT data), consistent with experimental observations. Fractal analysis via box-counting ($\log(1/r)=0$–$1.2$) andcorrelation functions ($r=5$–$20$ dx) quantifies morphological complexity, providing insights into scaling behavior and self-similarity. The approach leverages efficient FFT-based solvers for large-scale 3D computations (up to 256$^3$), aligning withuseful descriptors for data-driven materials design, microstructure prediction, and alloy performance optimization. Resultshighlight the transition from early-stage fractal-like patterns to late-stage Ostwald ripening (with LS recovery on larger grids),offering quantitative metrics for alloy engineering.
Authors - S SRINIVASA REDDY,N SARASWATHI, K CHARITHA, L GOPAL KRISHNA Abstract - Accurate identification of paddy crop growth stages plays a crucial role in effective agricultural planning, crop management, and yield estimation. Paddy cultivation is highly sensitive to environmental conditions, disease progression, and growth variability, making continuous and automated monitoring essential. This paper presents an AI-driven framework for automated paddy growth stage identification and yield readiness estimation using deep convolutional neural networks. The proposed system employs the EfficientNetV2-S architecture trained on heterogeneous paddy plant image datasets collected from multiple public sources. To address inconsistencies in labeling across datasets, a semantic stage-mapping mechanism is introduced to map dataset-specific visual classes into standardized paddy growth stages. Furthermore, a confidence-weighted yield readiness index is formulated to provide an interpretable estimate of crop maturity and harvest readiness based on predicted growth stages. The trained model is deployed using a Flask-based web application that supports real-time inference, result visualization, and storage of historical predictions. Experimental results demonstrate stable convergence, high classification accuracy, and reliable generalization across different growth stages. The proposed framework effectively bridges visual growth stage classification and yield estimation, offering a practical and scalable solution for precision agriculture and decision support systems
Authors - Shilpa H. Gujar, Abhijeet B. Auti, Nisha A. Auti Abstract - It is possible to increase the acceptability of small wind turbines for wind regions with low wind velocities for rural as well as urban sectors by placing them inside diffusers. The research on development of various diffusers is a major re-search area nowadays. Curved flanged diffusers can deliver better performance by adding a cylindrical throat section between converging and diverging sections. This research paper presents a systematic study on short curved flanged diffusers with converging-diverging sections and extended uniform throat between them. Twenty-five diffuser models are studied using Computational Fluid Dynamics using ANSYS Fluent. These models are finalized using the design of experiments for six variables at five levels. The throat diameter for all diffuser models is fixed. The investigation is performed by considering radial average velocity and percentage velocity variation along the radial planes. The global velocities are observed as 1.18 to 1.47 times that of the radial average velocities. The diffuser dimensions are optimized to maximize radial average velocity and to minimize the velocity variation along the radial planes. The diffuser with optimized dimensions is manufactured and tested experimentally in a wind tunnel. Good matching is seen between the predicted results and experimental results. The optimized diffuser has the ability to produce more than two times the power that of the turbine without a diffuser.
Authors - Prathilothamai M., R. Rinitha, Priyanshu Raj, Jishnu Hari, Lucky Goyal Abstract - The rapid growth of industrialization and urbanization has intensified the release of emerging air and water pollutants, posing significant threats to environmental sustainability and public health. This paper presents an Internet of Things (IoT) driven monitoring and forecasting framework designed for the early detection of emerging contaminants in air and water systems. The proposed system integrates distributed sensor nodes for real-time measurement of key environmental parameters, including particulate matter, volatile organic compounds (VOCs), heavy metals, pH, turbidity, and dissolved oxygen. Data collected from heterogeneous IoT sensors are transmitted through secure communication proto-cols to a cloud-based analytics platform. Advanced data processing and machine learning models are employed to identify pollution patterns, predict contamination trends, and generate early warning alerts. The framework emphasizes scalability, low power consumption, and cost-effectiveness to support deployment in urban, industrial, and remote environments. Experimental evaluation demonstrates improved detection accuracy and forecasting reliability compared to conventional monitoring approaches. The proposed solution enables proactive environmental management, supports regulatory compliance, and contributes to sustainable development by facilitating timely intervention and mitigation strategies for emerging air and water pollutants.
Authors - Prerna Agarwal, Bharat Gupta, Pranav Shrivastava, Saquib Hussain, Kareena Tuli, Amaanur Rahman, Aishwarya Keshri Abstract - We propose a classification method for Ise-katagami stencil images based on SIFT keypoints and an optimal matching framework. Ise-katagami are traditional Japanese stencil papers originally developed for kimono dyeing, many of which have been preserved over long periods yet lack annotation. Because of copyright-related limitations, methods based on conventional deep learning or transfer learning―which typically depend on large labeled datasets―cannot be readily applied. To address this challenge, the proposed method formulates the classification task as an optimal matching problem over sets of SIFT keypoints, allowing robust comparison of local image structures without relying on pixellevel features. The method requires only a small number of copyrightfree training images to extract representative features for each class, thereby eliminating the need for large-scale training data and enabling fast classification. According to the experimental evaluation, our method computes a suitable decision threshold within seconds, whereas the PCAbased method demands more than 3,000 seconds for optimization, despite both achieving almost perfect classification accuracy.
Authors - V.Mohanraj, J.Senthilkumar, Y.Suresh, K.Selvaraj, B.Valaramathi, S.Sivanantham, B I Hemantt Kumar, Ishwarya P Abstract - The increаsing scаle аnd comрlexity of globаl migrаtion flows hаve creаted significаnt chаllenges for trаditionаl migrаtion mаnаgement systems, раr-ticulаrly in terms of efficiency, dаtа рrocessing, аnd timely decision-mаking. Re-cent аdvаnces in Аrtificiаl Intelligence (АI) offer new oррortunities to enhаnce migrаtion governаnce through intelligent dаtа аnаlysis, аutomаtion, аnd smаrt communicаtion systems. This рарer exаmines the role of АI in modern migrаtion mаnаgement, with а focus on border control, visа аnd аsylum рrocessing, migrаtion flow forecаsting, аnd migrаnt integrаtion services. The study emрloys а structured quаlitаtive аnd comраrаtive аnаlyticаl аррroаch, synthesizing recent аcаdemic literаture, internаtionаl рolicy documents, аnd аррlied digitаl migrаtion systems. АI аррlicаtions аre аnаlyzed within а smаrt governаnce frаmework, emрhаsizing their contribution to communicаtion efficiency, risk аssessment, аnd decision-suррort рrocesses. The findings indicаte thаt АI-bаsed biometric identificаtion, mаchine leаrning–driven risk аssessment, аnd рredictive аnаlytics significаntly imрrove the аccurаcy аnd sрeed of migrаtion-relаted рrocedures. Nаturаl lаnguаge рrocessing tools further enhаnce communicаtion between аuthorities аnd migrаnts by fаcilitаting multilinguаl informаtion аccess аnd 2 service delivery. However, the аnаlysis аlso reveаls criticаl chаllenges, including аlgorithmic biаs, dаtа рrivаcy risks, limited trаnsраrency, аnd the need for humаn oversight in high-stаkes migrаtion decisions. The рарer concludes thаt АI cаn serve аs а key enаbler of smаrt migrаtion governаnce when imрlemented аs а decision-suррort tool within ethicаl, trаnsраrent, аnd humаn-centered regulаtory frаmeworks. The study рrovides рrаcticаl insights for рolicymаkers аnd system designers seeking to integrаte АI into smаrt communicаtion аnd digitаl gov-ernаnce аrchitectures for sustаinаble migrаtion mаnаgement.
Authors - Ischyros Gangbo, Ghislain Vlavonou, Pelagie Houngue, Joel T. Hounsou, Fulvio Frati Abstract - One of the major phenomena in recent decade remains the massive proliferation of data, directly linked to the adoption and expansion of new technologies and the increasing automation of processes, affecting numerous fields such as the economy, education, and cybersecurity. This exponential increase in almost every area is accompanied by an intensification of threats. It is within this context that new approaches are being defined, as traditional security mechanisms are showing their limitations. To counter attacks, several tools, including intrusion prevention and detection systems (IDS), have been designed. IDS are devices intended to monitor an information system in order to react effectively in the event of an attack. To this end, IDS use mechanisms that allow them to listen to the system covertly in order to detect abnormal or suspicious activities and enable effective preventative action against the risks of intrusion. The objective of this article is to compare the performance of the following models: XGBoost, CNN, CNN-LSTM for multiclass classification with a hybrid model. The dataset was first transformed into a sequential format. CNN, CNN-LSTM, and XGBoost models were independently implemented as standalone classifiers to perform intrusion detection. Furthermore, a hybrid CNN-LSTM-XGBoost model was designed, where deep spatiotemporal features learned by the CNN-LSTM network were used as input to an XGBoost classifier for final decision-making. Comparative experimental results show that XGBoost and Hybrid models achieve effective detection performance, the hybrid architecture especially in detecting complex and minority attack categories.
Authors - Yavor Dankov, Boyan Bontchev, Valentina Terzieva, Elena Paunova-Hubenova, Aleksandar Dimov Abstract - The growing demand for lightweight, high-performance, and sustain-able machine structures has accelerated the adoption of intelligent digital design methodologies in modern manufacturing. Conventional CAD-based design approaches rely heavily on manual iterations, limiting efficient exploration of complex design spaces and multi-objective trade-offs. This paper presents a hybrid AI-assisted generative design and topology optimization framework for intelligent lightweight optimization of machine structural components, with ap-plication to column-type machine structures and complex non-prismatic industrial brackets. The proposed framework integrates parametric CAD modeling, finite-element-based structural analysis, CAD-embedded generative design, and an AI-inspired algorithmic decision layer for automated evaluation and ranking of design alternatives. Key performance indicators—including mass, stiffness, stress, deflection, fatigue index, and additive-manufacturing constraints—are digitally processed and combined into a composite performance score to sup-port objective design selection. In the first case study, a rectangular machine column is evaluated across multiple volume-fraction configurations, achieving approximately 20% mass reduction while retaining 96% structural stiffness with minimal increases in stress and deflection. The second case study applies generative design to a complex industrial support bracket under multiple load cases, generating twelve feasible solutions that are algorithmically ranked based on performance and manufacturability. The results confirm that AI-assisted evaluation enables efficient design space exploration and supports intelligent, sustain-ability-driven engineering decisions for advanced digital manufacturing systems.
Authors - Damla Karagozlu, Kian Jazayeri, Ahmet Adalier Abstract - The security of resource-constrained Internet of Things (IoT) devices is increasingly reliant on Zero-Trust Architecture (ZTA) models, as continuous authentication and behavioral-based trust are providing new models to help mitigate against more sophisticated threats. The proposed framework helps strengthen secure and reliable digital infrastructure for emerging smart technologies and connected environments. In developing a ZTA security framework specifically for limited re-sources (IoT), the study proposed a lightweight version that combines Elliptic Curve Cryptography (ECC)-based authentication, real- time determination of trust scores, and the use of machine learning to detect behaviorally-based attack pat-terns from a real attack dataset. In addition, the real-time analysis of device trust scores provides a means to understand which devices are performing in accordance with established expectations or displaying behavior consistent with an at-tack, and when these devices will reach those levels. Combining a lightweight ECC authentication with a (trust) behaviorally-driven approach to anomaly detection provides a means to enforce Zero-Trust by minimizing any adverse effects on computational performance ability in IoT environments. Therefore, the approach provides a practical and scalable foundation for Zero-Trust security in future IoT deployments where devices will have limited hardware resources.
Authors - Steven Saltos-Minaya, Tatiana Zambrano-Solorzano Abstract - The high rate of digital communication has heightened the possibility of fake government announcement getting into the institutions bringing about misinformation and interference in their operations. In an effort to overcome this issue, this paper will be a proposal of a blockchain verification framework that will guarantee the authenticity, integrity, and reliability of any digital notices issued by the government. The system stores cryptographic hashes of official documents in a blockchain Hyperledger, which produces an audit trail that is immutable and unalterable. The entire files of the notices are safely distributed on the InterPlanetary File System (IPFS) which is decentralized and provides scalable and permanent storage which cannot be censored. Smart contracts running on the Hyperledger platform automatically provide access control, authorization checks on authorized government publishers and a robust cryptographic assurance of authenticity and non- repudiation. The schools and institutions can check the notices in real time using an intuitive React-based frontend, with the application logic being dealt with by the Node.js/Express backend as well as communicating with the blockchain layer. Other characteristics like tracking of reputation of publishers, version management and database of instant notification are also added to advance trust and transparency. The suggested solution provides a secure, scaled-up, and highly visible channel of communication between government and educational organizations with the lowest level of system complexity and without the need of any machine-learning parts.
