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"Sharma, Deepak"
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Green computing in network security : energy efficient solutions for business and home
\"This book focuses on green computing-based network security techniques and addresses the challenges involved in practical implementation. It also explores the idea of energy-efficient computing for network and data security and covers the security threats involved in social networks, data centers, IoT, and biomedical applications. Green Computing in Network Security: Energy Efficient Solutions for Business and Home includes analysis of green-security mechanisms and explores the role of green computing for secured modern internet applications. It discusses green computing-based distributed learning approaches for security and emphasizes the development of green computing-based security systems for IoT devices. Written with researchers, academic libraries, and professionals in mind so they can get up to speed on network security, the challenges, and implementation processes\"-- Provided by publisher.
Book
3D printed gyroid scaffolds enabling strong and thermally insulating mycelium-bound composites for greener infrastructures
Mycelium-bound composites (MBCs) grown from fungi onto solid lignocellulosic substrates offer a sustainable alternative to petroleum-based materials. However, their limited mechanical strength and durability are often insufficient for practical applications. In this work, we report a method for designing and developing strong and thermally insulating MBCs. The method grows mycelium onto 3D-printed stiff wood-Polylactic Acid (PLA) porous gyroid scaffolds, enhancing the strength of the scaffold while imparting other functional properties like thermal insulation, fire resistance, hydrophobicity, and durability. The extent of improvement in MBCs’ performance is directly dependent on the mycelium growth, and the best growth is observed at 90% porosity. We observe yield strength (σ
y
) of 7.29 ± 0.65 MPa for 50% porosity MBC, and thermal conductivity (K
t
) of 0.012 W/mK for 90% porosity MBC. Maximum improvement in σ
y
(50.4–77.7%) between before and after mycelium growth is observed at medium (70%)–high (90%) porosity. The MBCs also exhibit design-dependent improved fire-resistance and durability compared to the base wood-PLA scaffold, further enhancing their suitability for practical applications. Our findings show that integration of 3D printing, design, and biomaterials enables the development of sustainable bio-based composites to replace pollution-causing materials from the construction industry.
Mycelium-bound composites grown from fungi onto solid lignocellulosic substrates show limited mechanical strength and durability for practical applications. Here, the authors report the growing of mycelium onto 3D-printed stiff wood-polylactic acid porous gyroid scaffolds as a method to produce strong and thermally insulating mycelium-bound composites.
Journal Article
Robotic technologies in biomedical and healthcare engineering
\"This book aims at exhibiting the latest research achievements, findings, and ideas in the field of robotics in biomedical and healthcare engineering, primarily focusing on the walking assistive robot, telerobotic surgery, upper/lower limb rehabilitation, and radiosurgery, etc\"-- Provided by publisher.
Book
Intrauterine Growth Restriction: Antenatal and Postnatal Aspects
Intrauterine growth restriction (IUGR), a condition that occurs due to various reasons, is an important cause of fetal and neonatal morbidity and mortality. It has been defined as a rate of fetal growth that is less than normal in light of the growth potential of that specific infant. Usually, IUGR and small for gestational age (SGA) are used interchangeably in literature, even though there exist minute differences between them. SGA has been defined as having birth weight less than two standard deviations below the mean or less than the 10th percentile of a population-specific birth weight for specific gestational age. These infants have many acute neonatal problems that include perinatal asphyxia, hypothermia, hypoglycemia, and polycythemia. The likely long-term complications that are prone to develop when IUGR infants grow up includes growth retardation, major and subtle neurodevelopmental handicaps, and developmental origin of health and disease. In this review, we have covered various antenatal and postnatal aspects of IUGR.
Journal Article
A Comprehensive Review on Exploring the Impact of Telemedicine on Healthcare Accessibility
Telemedicine has emerged as a transformative force in healthcare delivery, particularly in improving healthcare accessibility. This comprehensive review examines the impact of telemedicine on healthcare accessibility, exploring its ability to overcome geographical, financial, sociocultural, and infrastructural barriers to healthcare access. Through remote consultations, monitoring, and diagnosis facilitated by technology, telemedicine extends healthcare reach to remote and underserved areas while enhancing temporal accessibility with round-the-clock availability. By streamlining healthcare delivery systems, telemedicine reduces costs and promotes efficiency, ultimately fostering health equity and improving health outcomes. However, technological barriers, regulatory hurdles, and patient acceptance remain. To realize telemedicine's full potential, collaboration among stakeholders in the healthcare and technology sectors is imperative. Policymakers must enact supportive regulations, healthcare providers must integrate telemedicine into their practices, and technology companies must innovate to develop user-friendly platforms. Through concerted efforts, telemedicine can catalyze advancing healthcare accessibility and enhance the health and well-being of individuals worldwide.
Journal Article
An Overview of Multi-Criteria Decision-Making Methods in Dealing with Sustainable Energy Development Issues
The measurement of sustainability is actively used today as one of the main preventative instruments in order to reduce the decline of the environment. Sustainable decision-making in solving energy issues can be supported and contradictory effects can be evaluated by scientific achievements of multi-criteria decision-making (MCDM) techniques. The main goal of this paper is to overview the application of decision-making methods in dealing with sustainable energy development issues. In this study, 105 published papers from the Web of Science Core Collection (WSCC) database are selected and reviewed, from 2004 to 2017, related to energy sustainability issues and MCDM methods. All the selected papers were categorized into 9 fields by the application area and into 10 fields by the used method. After the categorization of the scientific articles and detailed analysis, SWOT analysis of MCDM approaches in dealing with sustainable energy development issues is provided. The widespread application and use of MCDM methods confirm that MCDM methods can help decision-makers in solving energy sustainability problems and are highly popular and used in practice.
Journal Article
Application of Fuel Cells in Energy Storage
Based on a technology that separates power conversion and energy storage, fuel cell energy storage enables each function to be separately tuned for performance, cost, or other key variables. This capacity to tune every component of an energy storage system might provide considerable advantages for many uses. Here, different fuel cell-based energy storage systems are discovered that use hydrogen as the energy storage medium. Electrolyzes are fully regenerative fuel cell systems that are relevant for Polymer Electrolyte Membrane (PEM) fuel cells. The technological and product development status of these systems and the state of various hydrogen storage technology choices will be discussed.
Journal Article
Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selecting appropriate medications. Human Activity Recognition, abbreviated as ‘HAR’, is the prediction of common human measurements, which consist of movements such as walking, running, drinking, cooking, etc. It is extremely advantageous for services in the sphere of medical care, such as fitness trackers, senior care, and archiving patient information for future use. The two types of data that can be fed to the HAR system as input are, first, video sequences or images of human activities, and second, time-series data of physical movements during different activities recorded through sensors such as accelerometers, gyroscopes, etc., that are present in smart gadgets. In this paper, we have decided to work with time-series kind of data as the input. Here, we propose an ensemble of four deep learning-based classification models, namely, ‘CNN-net’, ‘CNNLSTM-net’, ‘ConvLSTM-net’, and ‘StackedLSTM-net’, which is termed as ‘Ensem-HAR’. Each of the classification models used in the ensemble is based on a typical 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network; however, they differ in terms of their architectural variations. Prediction through the proposed Ensem-HAR is carried out by stacking predictions from each of the four mentioned classification models, then training a Blender or Meta-learner on the stacked prediction, which provides the final prediction on test data. Our proposed model was evaluated over three benchmark datasets, WISDM, PAMAP2, and UCI-HAR; the proposed Ensem-HAR model for biomedical measurement achieved 98.70%, 97.45%, and 95.05% accuracy, respectively, on the mentioned datasets. The results from the experiments reveal that the suggested model performs better than the other multiple generated measurements to which it was compared.
Journal Article
Lightweight Proof of Game (LPoG): A Proof of Work (PoW)’s Extended Lightweight Consensus Algorithm for Wearable Kidneys
In healthcare, interoperability is widely adopted in the case of cross-departmental or specialization cases. As the human body demands multiple specialized and cross-disciplined medical experiments, interoperability of business entities like different departments, different specializations, the involvement of legal and government monitoring issues etc. are not sufficient to reduce the active medical cases. A patient-centric system with high capability to collect, retrieve, store or exchange data is the demand for present and future times. Such data-centric health processes would bring automated patient medication, or patient self-driven trusted and high satisfaction capabilities. However, data-centric processes are having a huge set of challenges such as security, technology, governance, adoption, deployment, integration etc. This work has explored the feasibility to integrate resource-constrained devices-based wearable kidney systems in the Industry 4.0 network and facilitates data collection, liquidity, storage, retrieval and exchange systems. Thereafter, a Healthcare 4.0 processes-based wearable kidney system is proposed that is having the blockchain technology advantages. Further, game theory-based consensus algorithms are proposed for resource-constrained devices in the kidney system. The overall system design would bring an example for the transition from the specialization or departmental-centric approach to data and patient-centric approach that would bring more transparency, trust and healthy practices in the healthcare sector. Results show a variation of 0.10 million GH/s to 0.18 million GH/s hash rate for the proposed approach. The chances of a majority attack in the proposed scheme are statistically proved to be minimum. Further Average Packet Delivery Rate (ADPR) lies between 95% to 97%, approximately, without the presence of outliers. In the presence of outliers, network performance decreases below 80% APDR (to a minimum of 41.3%) and this indicates that there are outliers present in the network. Simulation results show that the Average Throughput (AT) value lies between 120 Kbps to 250 Kbps.
Journal Article