S B Jain Institute of Tech. , Mgmt., & Reserch, Nagpur

This paper focuses on the development of required hardware and software for automatic single-point strain analysis in sheet metal forming through image processing. The software was developed in python with a user-friendly GUI. The handheld USB microscope has been modified with side lighting for capturing the image. An image database has been created to validate the proposed system by capturing the filled and unfilled ellipses of different dimensions. These ellipses are printed on Steel, Aluminium, and Copper Sheets by screen-printing technique. The software can detect edges automatically and evaluate the strains with 100% efficiency, irrespective of the sheet material. The maximum absolute percentage error in the strain values were found to be 1.507, 1.009, and 0.945 for copper, steel, and aluminium respectively with average error less than 2%. The proposed system has also been tested on actual formed component. For this, the sheets are printed with open and filled circular grids of 3 mm diameter and deformed into a hemispherical dome. The deformed circles are captured and strain values are evaluated successfully with the proposed strain measurement system. The proposed approach demonstrates a viable technique to compute the sheet metal surface strain with sufficiently high accuracy and less human interference compared to traditional manual methods such as using Mylar tape, toolmaker microscope, and travelling microscope.

Enhancement of concrete strength is critically important for increasing construction materials’ lifespan and sustainability. Traditionally, concrete mixture optimization methods—especially those used for fly ash and steel fiber concretes—normally fail to accurately predict the strength due to the high degree of complexity and non-linearity involved in the interaction of their components. These limitations are overcome in this study, which uses advanced artificial intelligence techniques—The Multilayer Perceptron (MLP) Neural Networks, Gradient Boosting Machines (GBM), and Convolutional Neural Networks (CNN) to optimize concrete mixtures for improved strength. Among these, the MLP neural network was selected for this work because of its ability to model highly complex, nonlinear relationships and hence will be able to capture the intricate interactions among fly ash, steel fibers, and other additives. For this reason, Gradient Boosting Machine was chosen for its robustness against overfitting and high accuracy in handling linearity or nonlinearity in an optimization problem. Traditionally, CNN has been applied to image processing, but in this work, it had been uniquely adapted to include the spatial distribution of concrete mix components, hence giving a new dimension in strength prediction. In this study, every method was used with a comprehensive data set and the input variables were taken as the percentages of fly ash and steel fibers, the water-cement ratio, aggregate size distribution, and curing delays. The accuracies of prediction for the proposed models were improved significantly, with the Mean Absolute Error (MAE) for compressive strength by the MLP model and an R² value of 0.90–0.95 by the GBM model. It is interpreted from CNN that there could be a potential reduction in prediction error by 10–15% compared to traditional methods. The work provides a robust framework for concrete strength optimization with substantial improvements in the reliability and performance of concrete materials used in construction.

Torsional vibrations pose a significant challenge in various industrial sectors, necessitating effective damping solutions to mitigate their adverse consequences. Mitigating vibrations stands as a fundamental approach to extending the operational lifespan of machinery. This paper presents a comprehensive study on the design and implementation of an integrated dual spiral spring with a rotary hydraulic damper, aimed at attenuating the inertia forces generated by an oscillating nozzle arm within a dual action oscillation gearbox utilized in pesticide sprayers. The research encompasses a detailed investigation into the kinematic linkage design and the determination of the system forces within the gearbox to optimize the damping performance. The theoretical design of the spiral spring is carried out to ascertain the maximum moment it must endure to effectively damp torsional vibrations. The integral components of the dual spiral spring system, including the spiral spring base, retraction lever, and bell crank, are meticulously modeled. Subsequently, a comprehensive analysis of these components is conducted using Ansys Workbench-16.0, a widely recognized tool for mechanical analysis. This research contributes valuable insights and practical solutions to the pressing issue of damping torsional vibrations in dual action oscillation gearboxes for pesticide sprayers. The outcomes presented herein have the potential to not only enhance the operational efficiency and longevity of such equipment but also find application in diverse industrial settings grappling with analogous vibration-related challenges.

Power oscillations in modern power grids are inherent phenomena that may threaten system reliability. Therefore, to ensure acceptable system reliability, effective damping of power oscillations is inevitably required. In this context, this article introduces a novel approach to designing fractional power system stabilizer (FPSS) for effective damping of power oscillations. Bidirectional long‐short‐term memory (Bi‐LSTM) approach is adopted to predict the parameters of FPSS. The conventional phase compensation technique is used to train Bi‐LSTM network. To validate the efficacy of FPSS, different test scenarios of contingent operating conditions are simulated for the system. Comparative analysis is carried out with conventional power system stabilizers (PSSs) and optimization‐based PSS techniques. Additionally, a test scenario is performed against existing deep neural network‐based PSS methods to ascertain the robustness of the proposed PSS. Furthermore, the performance of the proposed Bi‐LSTM‐based FPSS is validated in real‐time simulation using an interfaced OPAL‐RT OP5700 hardware device.

Background Neonatal jaundice poses significant risks to newborn health, necessitating early detection and management. Machine learning (ML) offers promising avenues for improving classification and monitoring, potentially revolutionizing neonatal care. Materials and methods A comparative analysis was conducted using various ML algorithms to classify neonatal bilirubin levels. Data were collected from neonatal images, and algorithms were trained and tested using standard methodologies. Performance metrics, including accuracy, precision, and recall, were evaluated to assess algorithm effectiveness. Results The Nu-Support Vector Classification (NuSVC) model emerged as the most effective, achieving a testing accuracy of 62.50%, with precision and recall rates of 61.90% and 56.52%, respectively. While variability existed among algorithms, these results highlight NuSVC's potential for clinical application in neonatal jaundice screening. Conclusion ML holds promise for improving neonatal jaundice detection and management. The findings suggest that the NuSVC algorithm can enhance screening accuracy, potentially mitigating risks associated with untreated neonatal jaundice. Future research should focus on refining models for broader clinical applicability and integrating ML into decision support systems to improve neonatal care globally.

This article reviews the oxidation behaviour of the two most significant high entropy alloy systems (HEAs) namely Cantor‐based and refractory HEAs. HEAs have been extensively researched and show potential applications in various industries, including marine engine manufacturing, chemical industry, furnaces, ducting, heat exchangers, jet engines, steam turbines, nuclear reactors and electronic devices, among others. Effect of the presence of elements such as aluminium, manganese, chromium, silicon, tantalum, vanadium etc. is studied for catalysing the oxidation of HEAs. Aluminium, chromium, and silicon are reportedly found to considerably impact the oxidation kinetics and enhance the oxidation resistance. However, silicon can positively or adversely affect the oxidation resistance depending on its concentration and alloy composition. Other elements like manganese tend to adversely impact the oxidation resistance of FeCoNi‐based HEAs. Refractory elements are typically found to be not suitable for oxidation studies due to the formation of non‐protective oxide layers. However, refractory HEAs offer interesting trends both in terms of enhancing or reducing oxidation resistance depending on the alloying elements. Similarly, findings related to other elements are also presented and elaborated.

The Value Chain Optimizer is a PHP-based web application designed for dairy shop management. Functionalities include category and company management, product handling, search, invoice generation, and report creation. The project aims to streamline dairy shop operations, enhance sales tracking, and facilitate efficient product management. This research paper will delve into the development, implementation, and impact of the Value Chain Optimization in Dairy Product Management, exploring its features, usability, and potential contributions to dairy shop administration. Our system is a game-changer in terms of boosting efficiency and promoting inclusivity among different farming scales and levels of technology. Based on the results, economic costs and environmental impacts decreased by 18.5% and 25%, respectively with user-friendly interfaces and adaptable functionalities, this system remains relevant and valuable in all types of dairy farming environments.

Unmanned Aerial Vehicles (UAVs) are advanced technologies that are initially utilized for military apps like border monitoring and reconnaissance in opposed territories. Internet of Things (IoTs) assisted UAV networks suggest the combination of IoT technology with UAVs to generate a networked system that improves the abilities and utility of UAVs for several apps. UAVs’ inherent features namely quick deployment, high dynamicity, low deployment and operational costs, and line of sight communication motivated researchers in the IoT field to assume UAV’s combination into IoT systems near the concept of UAV-assisted IoT systems. However, security concerns with UAVs are evolving as UAV nodes are suitable attractive targets for cyber threats because of extremely developing volumes and poor and weak inbuilt security. Therefore, this paper presents a Modified Marine Predators Algorithm with a Deep Learning-Driven intrusion detection (MMPADL-ID) approach for IoT Assisted UAV Networks. The presented MMPADL-ID technique proposes to identify and classify the presence of intrusions in accomplishing security in IoT-assisted UAV networks. In the MMPADL-ID technique, the feature selection process is performed by the design of MMPA. In addition, the MMPADL-ID technique incorporates the Elman neural network (ENN) model for the recognition and classification of the intrusions. Furthermore, the honey badger algorithm (HBA) can be applied for the hyperparameter tuning of the ENN model and results in improved performance. The simulation value of the MMPADL-ID technique can be tested on benchmark datasets. An extensive comparative outcome reported the better solution of the MMPADL-ID algorithm with existing approaches for various aspects.

Link prediction in the Social Network is most important and an essential part now a days. The continued growth and evolution of this field will lead to new and improved methods for analyzing and understanding social networks. Link prediction is also helpful in various network applications in both academic and real-world contexts. For better understanding of prediction of links in a network graph through the use of different algorithms and information of prediction of missing link between network that all of the clear information is discuss in this paper. This paper presents the study of different types of algorithms which are better informative to understand the connection prediction, in a methodical manner. For this study, the similarity approaches are concentrated with its types of algorithms which are used to forecast the presence of missing links in social networks. This paper addresses the various link prediction approaches considering the structure of the network to reduce uncertainty. Evaluation measures for link prediction and their practical applications are also covered in this work. Lastly, it discusses the difficulties and provides plans for the development of link prediction methods in the future. This discussion may help researchers to choose the proper network structure for predicting the links.

In our research, we tackle the challenge faced by families in finding reliable household workers like caretakers and gardeners. Many struggles with trust issues when hiring help for their homes. To address this, we've developed a secure web application. Users can connect with background- checked workers, ensuring the safety of their homes. The application consists of modules for different users - Admin, Worker, and User - each serving a specific purpose. What makes our app unique is the integration of Deep learning. The DL verifies uploaded images to ensure they are of human faces, enhancing security. Additionally, it checks the similarity of images of workers to maintain consistency and reliability in their profiles. By implementing these features, our web application aims to provide a trustworthy platform for households seeking reliable help, contributing to a safer and more secure hiring process.