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Miniaturized Band Stop Frequency Selective Surface for Stable Resonance Characteristics
In this paper, miniaturized 7.45 GHz resonant frequency band stop frequency selective surface (FSS) is designed. The unit cell dimensions of designed FSS is only about 0.1?0 at the 7.45 GHz. Proposed design involves a crossed dipole metallic element together with meander shape on the substrate. Simulation results provide about 800 MHz bandwidth (7.1 GHz-7.9 GHz) with-20 dB insertion loss. The FSS properties are studied on a unit cell using electromagnetic (EM) solver to observe the characteristics. Proposed FSS demonstrates a stable resonance frequency behavior for the arbitrary angle of incidences in both the polarizations such as TM and TE modes. Thus, the design holds a polarization independent characteristic for all incident angles and polarizations. Finally, the FSS properties are validated by a fabricated array of 311 mm2. 2018 IEEE. -
Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. 2023 by the authors. -
Minimizing the waste management effort by using machine learning applications
Waste management is a process of collecting, transporting, disposing, and monitoring waste materials generated by human activities. It is an essential part of maintaining public health, hygiene, and environmental sustainability. Waste management systems can be designed to handle different types of waste, such as household waste, industrial waste, hazardous waste, and medical waste. The increasing amount of waste has become a major issue for the development of sustainable communities. Machine learning can help solve this problem by allowing scientists to analyze and reduce waste. This chapter aims to provide a comprehensive overview of the various aspects of waste management using machine learning. The chapter covers the various aspects of waste disposal, generation, transportation, and collection. It also explores machine learning's potential in this area, such as data analysis and prediction. It additionally compiles case studies about how machine learning has been utilized in this field. 2024, IGI Global. -
Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches
The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community's overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA's infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD. 2013 IEEE. -
Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix
Early detection of any sort of cancer, particularly lung cancer, which is one of the worlds most lethal illnesses, can save many lives. Life expectancy can be improved and the degree of mortality reduced by adopting the early forecast. While there are different methods like X-ray and CT scans to detect lung cancer cells, CT images resulted as more favored. The 2D images are used for more accurate medical results, such as CT scans. The proposed approach here will address how to interpret the CT images for the Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix. This research will explore how the image conversion can be achieved through different methods of image processing to obtain better results from CT images. The Confusion Matrix helps to estimate inequality in a picture pattern. After the evaluation of the processed images by Confusion Matrix, a final accuracy with a result of 93% is obtained. 2023 Scrivener Publishing LLC. -
Mining the web data for classifying and predicting users' requests
Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client?s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands. Copyright 2018 Institute of Advanced Engineering and Science. All rights reserved. -
Minority Stress and Mental Health of Indian Non-binary Individuals
This study investigated how Indian non-binary individuals experience minority stress and its impact on mental health, with a focus on the role of social support and coping mechanisms. Semi-structured interviews with eight non-binary participants aged 1823 from Bengaluru revealed four main themes: societal treatment, self-identity, minority stress and mental health, and social support. Findings indicate that experiences with discrimination, misgendering, gender dysphoria, and identity concealment contribute to negative mental health outcomes. However, social support and effective coping strategies were found to positively influence mental health by affirming identity. These results suggest potential avenues for developing targeted interventions and support systems to improve mental health among non-binary individuals. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Mirabijalones S-W, rotenoids from rhizomes of white Mirabilis jalapa Linn. and their cell proliferative studies
Five undescribed (2-6) rotenoid derivatives along with three known rotenoids (1, 7 and 8) were isolated from the rhizomes of white colored variety of Mirabilis jalapa Linn. The structures of these undescribed compounds were elucidated based on UV, IR, HR-MS (ESI), 1D and 2D NMR spectroscopic techniques. Selected compounds were evaluated for their cell viability and proliferation in two cancer cell lines namely, cervical (HeLa), breast (SKBR-3) and normal lung fibroblast (WI-38). Among them, the compounds Boeravinone C (1), Mirabijalone S (2), Mirabijalone T (3) and 4, 6, 11-trihydroxy-9-methoxy-10-methylchromeno [3, 4-b] chromen-12(6H)-one (8) showed moderate cytotoxicity against HeLa cells with IC50 values in the 8.40 ? 12.9 ?M range, and compound 8 exhibited cytotoxicity against SKBR-3 cells with IC50 value of 17.6 ?M. Molecular docking studies of isolated compounds were performed with three apoptosis proteins, 3H11, 2AR9 and 1X0X. These results revealed that the isolated compounds were found to interact with Caspase 8 and 9 along with the anti-apoptotic protein Survivin. Since these compounds exhibit cytotoxic effects against SKBR3 and HeLa cells, they are expected to show apoptosis and may be further utilized for wet lab apoptotic studies. 2021 Phytochemical Society of Europe -
MIST-based Tuning of Cyber-Physical Systems Towards Holistic Healthcare Informatics
The entire world seems shaken and disrupted since the strike of Covid-19 ever since its outbreak towards the end of 2019 and its continued perils. During this unprecedented event of the century, people's health emerged as the most vulnerable and affected area either directly or indirectly by the coronavirus and its new variants. Disrupting almost all spheres of life, patients' health and care systems required timely support from healthcare professionals to provide the needed medical advice on one hand and a prescriptive mechanism to avoid another impending casualty. Similarly, a proactive approach became desirable from the health ministry, pharmaceutical firms, medical insurance companies, and other stakeholders in fine-tuning their offerings to the patients as per the recommender systems. The devices to measure the vitals of a person, became more efficient and ergonomically sound with the advent of wearable gadgets. These devices monitored the physical activities of the user and transferred the vital signals wirelessly to any base computing device and cloud-based repositories. This mechanism, however, was reported to fail in addressing the issues with non-communicating or stand-alone devices that were used by the masses in developing countries including India. If the real-time data could be used from these devices, the healthcare diagnosis and analysis of a patient's medical condition could have taken a progressive dimension with the addition of missing data points. This research thus aims to fill the information gap and proposes a transforming approach towards existing non-communicating devices used to measure the vitals like blood pressure, oxygen level, blood sugar, etc. The proposed MIST-based Cyber-Physical System shall create extensive scalability towards the retrieval of the vital details from the devices which were otherwise captured offline previously and were unused at multiple critical points of healthcare processes. 2022 IEEE. -
Misuse of Internet Among School Children: Risk Factors and Preventative Measures
The Internet has been one of the most transformative and rapidly growing technologies. In recent years, it has improved the quality of life in areas such as communication, education, recreation. On the contrary, there are growing concerns about the use of the Internet that have created adverse consequences in the areas of social life, interpersonal relationships, family environment, and school activities. School-going children were vulnerable to such unhealthy outcomes due to readily available high-speed Internet and ease of access to different Internet platforms, which resulted in risky behaviours, decreased academic performance, poor nutrition, decreased sleep quality, and a high incidence of inter-social conflicts. While the majority of the research has focused on the adolescent population in terms of problematic Internet use, only a few studies have identified the vulnerabilities of school-going children in the same context. The research also confirmed that the risk factors for problematic Internet use start as early as middle childhood. Heightened risky use of the Internet was observed in children with neurodevelopmental concerns. This study explores risk factors associated with problematic Internet use among school-going children, identifying relevant warning signs followed with preventative measures. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Mitigating Abiotic Stress Using Nanoparticles and Involved Cellular Processes Studied by Multiple Omics
Crop yield is greatly impacted by abiotic stress, necessitating creative methods to increase plant resilience. In order to reduce these stressors, this chapter focusses on the possibilities of integrating multi-omics technology with nanoparticle applications. Studies suggest that nanoparticles improve plant tolerance by improving antioxidant activity and minimizing oxidative damage. In addition, multi-omics techniques (genomics, transcriptomics, proteomics, and metabolomics) offer a comprehensive understanding of the molecular processes that stress initiates. The creation of nanoparticles designed to specifically target particular stress-response pathways is made possible by these findings. The chapter's case studies demonstrate how tailored nanoparticles made with multi-omics data might improve crop performance in difficult conditions. The chapter highlights a promising avenue for developing targeted, sustainable solutions to increase crop resilience under abiotic stress conditions by combining nanotechnology with omics techniques, providing long-term sustainability. CAB International 2025. All rights reserved. -
Mitigating Mental Health Burden of Youth During COVID-19 Through Resilience and Hope: Evidences from India and Germany
In the global crisis caused by the COVID-19 pandemic, young professionals and graduating students experience considerable psychological adversity due to the uncertainty surrounding their futures. Given the positive psychological outcomes and the potential to alleviate stress, we examine the role of resilience and hope in causing a substantial variance in the stress response to anticipation of crisis among Indians living in India and Germany. Resilience, hope, crisis apprehension, and the psychological response to the COVID-19 pandemic were measured among participants from India and Germany (n = 650) via an online survey using non-probability convenient sampling. Parallel mediation and conditional indirect effects showcase the differential roles of resilience and hope among socio-culturally similar but geographically divergent groups. Hope mediates the effect of pandemic-led crisis apprehension on perceived stress among those residing in India; resilience operates to mitigate stress among those from Germany. Findings highlight the contradistinctive role of resilience and hope in reducing stress and imply an urgent need for promotion of ameliorative practices. Resilience effectively mitigates the psychological burden of the COVID-19 crisis and can be promoted to reskill individuals; however, elevating hope in a crisis obligates prudence. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem. 2024 Author(s). -
Mitigating Subjectivity and Annotation Inconsistencies in Sentiment Analysis via an SVM-RoBERTa Ensemble
This research addresses a main limitation in the Natural Language Processing that is the impact of subjectivity and annotation inconsistencies on the accuracy of the sentiment classification. We did a systematic comparison of two fundamentally different architectures. A traditional feature based Support Vector Machine and a deep contextual fine tuned RoBERTa transformer using a challenging, noisy, real-world Twitter dataset. This corpus retains ambiguity and sarcasm on purpose and serve the crucible for testing model robustness. We developed a soft voting ensemble method that combines the probability scores from both models to obtain the best predictive capabilities. The results showed a clear technological hierarchy. The RoBERTa model with its deep semantic grasp outperformed the SVM by a substantial margin achieving 90% accuracy against 83.5% accuracy. But the hybrid ensemble model attained the highest overall accuracy of 91.35% and showed better reliability across all the sentiment classes. These findings shows that a hybrid approach fusing a transformer's nuanced understanding with the stabilization provided by ensemble learning is the most effective and robust method for mitigating data imperfections in modern sentiment analysis. 2025 IEEE. -
Mitigation of harmonics for five level multilevel inverter with fuzzy logic controller
Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry. M.S. Sujatha, S. Sreelakshmi, E. Parimalasundar, K. Suresh. -
Mixed convection 3D radiating flow and mass transfer of eyring-powell nanofluid with convective boundary condition
Three-dimensional mixed convection flow, heat and mass transfer of Eyring-powell fluid over a convectively heated stretched sheet is inspected in this paper. The encouragement of Brownian motion, thermophoresis, convective condition and thermal radiations are accounted. Appropriate transformations are used to reduce the principal PDE's into set of coupled highly nonlinear ODE's which are then solved numerically using RKF fourth-fifth order method. The consequence of several parameters on flow, heat and mass transfer characteristics are deliberated with the help of graphs and tables. It is observed that the temperature and concentration profiles diminish for higher values mixed convection parameter. Further, the temperature and its related boundary layer thickness is increases with increasing the Biot number and thermal radiation effects. 2018 Trans Tech Publications, Switzerland. -
Mixed convection in the stagnation-point flow over a vertical stretching sheet in the presence of thermal radiation
An unsteady two-dimensional stagnation-point mixed convection flow of a viscous, incompressible dusty fluid towards a vertical stretching sheet has been examined. The stretching velocity and the free stream velocity are assumed to vary linearly with the distance from the stagnation point. The problem is analyzed using similarity solutions. The similarity ordinary differential equations were then solved numerical by using the RKF-45 method. The effects of various physical parameters on the velocity profile and skin-friction coefficient are also discussed in this paper. Some important findings reported in this work reveal that the effect of radiation has a significant impact on controlling the rate of heat transfer in the boundary layer region. -
Mixed CoO/Co3O4 phase nanoparticles encapsulated in a carbon shell derived from Co-MOF as a bifunctional electrocatalyst for scalable hydrogen production
Designing high-performance and long-lasting electrocatalysts is essential to enable large-scale green hydrogen generation through water electrolysis. Herein, we present a bifunctional electrocatalyst, C@CoxOy-B/P-700, obtained by pyrolyzing a Co-MOF under N2 atmosphere, followed by phospho-boronization. Extensive characterization revealed the formation of nanoparticles composed of combined Co3O4 and CoO phases encapsulated by a thin carbon shell. This unique architecture provided electron transport and active site accessibility, while the co-incorporation of boron and phosphorus induced abundant oxygen vacancies, enhancing intrinsic kinetics. In alkaline media, low overpotentials of 220 and 79 mV are required at 10 mA/cm2 for OER and HER, along with excellent durability, maintaining performance over 10,000 cycles and 100 h of continuous operation. When implemented in a symmetric two-electrode configuration, it achieves a current density of 10 mA/cm2 at just ?1.57 V, rivalling noble-metal-based systems. Furthermore, scale-up in a zero-gap alkaline electrolyzer confirms industrial applicability, requiring ?1.72 V to achieve a 500 mA/cm2 at 60 C with a negligible degradation rate. These results emphasize the promise of C@CoxOy-B/P-700 as a sustainable and scalable solution for next-generation hydrogen production. 2025 Elsevier Ltd. -
Mixed radiated magneto Casson fluid flow with Arrhenius activation energy and Newtonian heating effects: Flow and sensitivity analysis
The characteristics of Stefan blowing effects in a magneto-hydrodynamic flow of a Casson fluid past a stretching sheet are investigated. The effects of radiation, heat source/sink, Newtonian heating, Arrhenius activation energy and binary chemical reaction are considered for heat and mass transfer analysis. The homotopy analysis method (HAM) was utilised to solve the transformed non-dimensionalized equations analytically. The impact of various physical parameters affecting the flow are investigated. Further, the relationship of various parameters on the skin friction and rate of heat and mass transfer was explored using correlation and probable error. A sensitivity analysis was carried out based on the Response Surface Methodology to analyse the effect of Stefan blowing parameter, magnetic parameter and stretching/shrinking parameter on the reduced Nusselt number and reduced Sherwood number. A constant positive sensitivity for the reduced Nusselt number towards the Stefan blowing parameter for all levels of magnetic parameter and stretching/shrinking parameter was found. Further, the reduced Sherwood number indicated a negative sensitivity towards the Stefan blowing parameter. 2020 Faculty of Engineering, Alexandria University -
ML Algorithms and Their Approach on COVID-19 Data Analysis
This chapter begins with characterizing Supervised Learning and Unsupervised learning and investigates Machine Learning algorithms in every one of the sub domains of Regression, Classification, Clustering, and so forth. It also talks about the engineering of calculations like Linear Regression, Logistic Regression, K-Means, K Nearest Neighbors, Hierarchical, DB Scan, Decision Tree, Random Forest Regression, and Random Forest classifier. Utilization of every algorithm to investigate the dataset will be displayed by carrying out it on renowned dataset model, and output of each piece of code is displayed with their preview. This section likewise takes care of the issue of predicting the future number of COVID-19 cases and the precision behind each model or algorithm is shown and investigated utilizing different measurements dependent on situation or issue articulation, for example, either issue is on forecast or order. This chapter does not focus on the solution of COVID-19 data analysis or expectation, rather it will be followed and will task different models dependent on need with conclusive target being clear comprehension of the Machine Learning algorithms and its execution in Python. 2023 Scrivener Publishing LLC.