Metallurgical and Materials Engineering (E-Journal)
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Study The Geotechnical Properties Of Expansive Soils Under Variable Moisture Conditions
Get PDFThe purpose of this study is to understand how the moisture content and dry density impact the maximum stress, strain at failure, shear strength, as well as axial stress at failure in swollen soil. This occurs through unconfined compression, direct shear and triaxial shear tests, after which it is determined that the presence of moisture can affect the soil’s strength and less moisture with higher dry density makes the soil better able to resist fatigue and become stronger. From unconfined compression, it is evident that the soil with the lowest moisture had maximum stress and the least stress to fail. Direct shear and triaxial shear also showed that adequate moisture content tends to support the cohesion and friction between particles, but higher moisture causes both of these to decrease. It was found that proper moisture management helps prevent the instability of swelling soil
Optimized Deep Learning Approach For Dermatological Condition Classification Using Efficient Net
Get PDFSkin disorders are becoming more common, but because of the variety of conditions and the complexity of medical imaging, it is still difficult to provide an accurate diagnosis. Generative Adversarial Networks (GANs) are the mainstay of current systems for predicting skin diseases; yet, they suffer from instability and inconsistent training. Furthermore, when the generated images fall short of accurately capturing real-world variability, the dependence on artificial data production frequently leads to less accurate forecasts. The suggested approach uses EfficientNetB0, a deep learning model tailored for medical image processing, to get over these restrictions. EfficientNetB0 uses a hybrid scaling approach that equalizes depth, width, and resolutions, allowing for highly precise extraction of characteristics. It is perfect for classifying skin diseases because of its lightweight construction, which enables faster processing without sacrificing speed. Utilizing EfficientNetB0, the system lowers the risk of misclassification, increases early detection, and improves diagnostic accuracy—all of which contribute to improved patient outcomes in clinical practice
Consumer Preferences And Purchase Intentions – A Connectivity Approach Between Online And Offline Retail Stores
Get PDFIn this study, titled "Consumer Preferences and Purchase Intentions – A Connectivity Approach Between Online and Offline Retail Stores," data from online and offline retail store consumers were collected through convenient sampling. Linear regression and Mediation analysis, led by Andrew F. Hayes, were employed as statistical tools for analysis. Key findings highlighted the pivotal role of in-store product quality in diminishing offline purchase intentions and emphasized the importance of aligning product selections with consumer preferences. The study further revealed that a higher perceived product quality substantially reduced purchase intention, especially in offline contexts. Conversely, discerning consumers with high-quality standards displayed selectivity in online purchases. Importantly, these effects were not solely direct but also mediated through consumer satisfaction. To enhance product quality perception, providing comprehensive product information, high-resolution imagery, and training staff to effectively communicate quality details and respond to consumer inquiries is imperative
Predictive Data Analytics Framework Based On Child And Pregnant Women Health Care Systems
Get PDFA potent technique for enhancing healthcare outcomes is predictive data analytics, especially for vulnerable groups like children and pregnant women. A strong framework that makes use of this technology can greatly improve the efficacy and efficiency of healthcare systems that are devoted to their welfare. The creation and use of such a framework are examined in this article, with an emphasis on how it might enhance resource allocation, preventative care, and general health equity. A predictive data analytics framework has great potential to enhance the healthcare of expectant mothers and children. In order to ensure responsible and successful implementation—which will ultimately result in improved health outcomes including a more equal healthcare system—it is imperative that the related difficulties and ethical issues be addressed. Proactive intervention and precise prediction are critical components of effective treatment. Timely and focused interventions are essential for improving health outcomes and lowering death rates for vulnerable groups, such as children and pregnant women. The creation and use of a predictive data analytics framework aimed at improving the efficacy and efficiency of healthcare systems catering to these populations is examined in this article. The framework forecasts possible dangers and optimizes resource allocation by utilizing easily accessible data. A potent tool for enhancing the health of expectant mothers and their unborn children is provided by this predictive data analytics system. The framework facilitates proactive risk assessment, tailored treatments, and efficient resource allocation by utilizing widely available data and cutting-edge machine learning techniques. In order to increase accuracy and generalizability across a range of populations, future research will concentrate on developing the prediction models and broadening the framework to include more data sources. The ultimate objective is to help lower rates of maternal and pediatric morbidity and mortality in order to create healthier communities
Enhanced Chicken Swarm Optimization And Improved Convolutional Neural Network Algorithm For Attack Detection Over Iot Based Wireless Sensor Network
Get PDFA useful, adaptable, and interoperable network of electronics, gadgets, and objects has been developed named as the Internet of Things (IoT). IoT has emerged from its early days and is regarded as the most significant technology in changing the Internet into an entirely connected future Internet. Recent developments in computing, networking, communications, software, and hardware technology are the primary factors influencing it Utilizing the potential of IoT in useful applications and services, IoT employs Wireless Sensor Networks (WSN) to remotely gather, exchange, and distribute data. But, the existing system has issues with various and serious security attacks. Also, it has problem with Attack Detection (AD)accuracy for the given dataset. To overcome the abovementioned problems in this research, Enhanced Chicken Swarm Optimization and Improved Convolutional (NN) Neural Network (ECSO-ICNN) algorithm is suggested. Some of the primary stages in this study are the system model (SM), NSL-KDD Data Collection (DC), Cluster Head (CH), Node Selection (NS), data pre-processing, and AD. The amount of Sensor Nodes (SN), sensor devices, SN, destinations, and Multipoint Relays (MPRs) with neighbor and CH nodes that are one-hop and two-hop are all included in the system model. Next, the ECSO method is employed for selecting the CH node. It generates best Fitness Values (FV) by means of higher accuracy and lower Energy Consumption (EC) for the given IoT based WSN. With 42 features and class labels, the NSL-KDD dataset is regarded as a class of attacks. Then, data pre-processing is done by using filtering and Feature Selection (FS) process which is used to handle duplication and redundant features effectively for the given NSL-KDD dataset. The ICNN algorithm, which effectively detects attacks, is the last method utilized for AD. In terms of f-measure, accuracy, recall, and precision, the simulation results show that the suggested ECSO-ICNN strategy performs better than the existing approaches for AD
A Critical Analysis Of Scope Of Bail Under POCSO Act
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Abstract
As the world rapidly deteriorates, so too are instances of sexual abuse and small-scale rapes against people. The scope of sexual abuse in the current situation has expanded to include minor children under the age of eighteen, in addition to being restricted and prevented to adults of any age or gender. Since the IPC considers sexual abuse and rape of minors to be crimes, the "POCSO (Protection of Children from Sexual Offences) Act" was passed in 2012 because previous laws had not been sufficient in identifying and combating other sexual offenses.
Sections 5 and 6 of the POCSO law, which deals with serious penetrative sexual assaults, generally have limited circumstances under which bail may be granted. This is especially true if the investigations are still underway and the accused is not supported by any evidence. The court would consider many factors, such as the severity of the offense, the victim's age, the evidence that could be obtained, the likelihood that the accused would tamper with the evidence or influence the witness, and the likelihood that the accused would flee if released on bond.
Although it is gender neutral, there have been certain negative aspects that haven't been seen in a while, which is why the latest POCSO revisions are crucial. S.42 of the POCSO Act and S.376 of the IPC (Now section 64 in Bharatiya Nyaya Sanhita, 2023) were modified by the Ordinance. Furthermore, this study has attempted to investigate the genesis and evolution of events subsequent to the implementation of the POCSO Act.
This research also attempted to look at the POCSO Act's effects and breadth on Indian society, offering helpful recommendations for its correction. The POCSO Act's provisions pertaining to bail was another goal of the research. Critical analysis was also done on the administrative hazards, judiciary shortcomings, and justice delivery issues. To do this, we must investigate whether India's criminal codes are enough to handle cases of sexual offenses and rapes against kids, especially in the wake of self-governing legislation like the POCSO Act, 2012.The main focus of this study is on the implications of the findings and debates for future research, as well as on recommendations for consistent, effective implementation tactics and an analysis of some contentious elements of the aforementioned legislation
The Effect of Argon and Liquified Petroleum Gas Ratio on The Properties of The Diamond-Like Carbon on The Surface of 316L Stainless Steel
Get PDF316L Stainless steel (SS 316L) is a metal material that has superior corrosion resistance properties, so this material is widely used in medical, manufacturing, nuclear, food, etc. However, this metal can still be damaged or scratched due to weaknesses in hardness and wear resistance. This research was carried out to overcome these shortcomings by using Diamond Like Carbon (DLC) coating which can improve the hardness and wear resistance of SS 316L. DLC coating was done using the glow discharge plasma technique at temperature of 400°C and pressure of 1.6 mbar for 4 hours with variety of argon (Ar) and LPG gas mixtures. The results of DLC coating optimum conditions were obtained at the gas mixture ratio of 80% Ar and 20% LPG with the increase of hardness from 157.9 VHN to 329.84 VHN, and wear resistance decreased from 1.0176×10-3 mm3/kg.mm to 0.7×10-3 mm3/kg.mm. The improvement in the properties of this treatment was proven by SEM EDS which showed the formation of 69% graphite phase (G) and 31% diamond phase (D), as well as from Raman spectroscopy analysis, sp3 and sp2 bonds were obtained
Surface Engineering of MXene-Based Materials for Next-Generation Rechargeable Batteries
Get PDFNext-generation rechargeable batteries are being developed to address challenges such as low cost, high stability, high energy density, and safe energy storage materials. MXene-based mate-rials have attracted wide attention due to their unique properties, large surface area, high electrical conductivity, and easy dispersion in solvents compared to graphene. MXene derived from carbide and nitrides of transition metals (Ti3C2TX) have unique properties compared to other two-dimensional materials (2D) for use in rechargeable batteries. MXene electrodes delivered excellent performance and cyclic stability in various rechargeable secondary batteries. This re-view highlights the role of MXene in next-generation rechargeable batteries of lithium ion bat-teries (LIBs), lithium sulfur batteries (LISBs), sodium ion batteries (SIBs), zinc ion batteries (ZIBs), aluminium ion batteries (AlIBs), potassium ion batteries (PIBs) and magnesium ion bat-teries (MIBs). Moreover, in this review, we discussed the current research developments to im-prove the efficiency of energy storage devices and present the future research direction to im-prove the scalability, stability, and overall performance of MXene-coated electrodes in recharge-able batteries to overcome the energy storage challenges.  
Electroluminescence Imaging For Defect Analysis In Polycrystalline Solar Cells
Get PDFSolar energy offers a vast range of applications across industrial and daily contexts, driven by its potential as a clean, sustainable alternative to conventional fuels. However, inherent defects may arise during the manufacturing, transportation, and installation of solar cells, leading to reduced power generation efficiency. To address this challenge, this study presents the application of Electroluminescence (EL) imaging as a non-destructive technique for assessing solar cells, focusing on the identification of defects and performance variations. EL imaging is employed to detect microcracks and other flaws in both flexible and rigid polycrystalline solar cells.
This research details the use of LabVIEW and MATLAB-based image analysis methods, showcasing their effectiveness in detecting and quantifying various defects that affect solar cell reliability and efficiency, including electrical losses, microcracks, and fractures. The LabVIEW approach highlights its robust capabilities in analysing electroluminescence images, while the MATLAB-based method underscores its utility in detailed image processing for defect identification and quantification. The study encompasses the essential tools, image processing techniques, and foundational physical principles required to extract meaningful information from EL images. By providing a comprehensive overview of EL imaging and diagnostic techniques for solar cells, this research contributes to advancements in solar energy conversion technologies and enhances the understanding of solar cell performance assessment
A Novel Intelligent Controller With Dvr Based Hybrid Renewable Fed Grid Tied To Mitigate Voltage Sag
Get PDFPower quality has a significant impact on loads and stability when power equipment is working. Unstable conditions can cause the power system to lose stability due to a variety of defects, fluctuations, and even damage to sensitive loads. Dynamic voltage restorers, which have demonstrated their ability to compensate for distribution side faults and fluctuations notwithstanding the effects of the grid-side system, are a dependable way to counteract these intermediate power occurrences. Fuzzy logic controllers are the controller technology used in DVR simulations, which Favor the new trend of renewable energy sources with hybrid PV and wind systems. In order for the DVR to function as efficiently as possible under ideal circumstances during a voltage sag disturbance, the controller dynamically evaluates the different sag compensation parameters. Matlab is used to systematically model the project's activities while taking into account different situations