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N-tier modelling of robust key management for secure data aggregation in wireless sensor network
Security problems in Wireless Sensor Network (WSN) have been researched from more than a decade. There are various security approaches being evolving towards resisting various forms of attack using different methodologies. After reviewing the existing security approaches, it can be concluded that such security approaches are highly attack-specific and doesnt address various associated issues in WSN. It is essential for security approach to be computationally lightweight. Therefore, this paper presents a novel analytical modelling that is based on n-tier approach with a target to generate an optimized secret key that could ensure higher degree of security during the process of data aggregation in WSN. The study outcome shows that proposed system is computationally lightweight with good performance on reduced delay and reduced energy consumption. It also exhibits enhanced response time and good data delivery performance to balance the need of security and data forwarding performance in WSN. 2019 Institute of Advanced Engineering and Science. -
N-rGO/NiCo2O4 nanocomposite for high performance supercapacitor applications
Spinel structured transition metals oxide GO/NiCo2O4 nanocomposites and nitrogen doped N-rGO/NiCo2O4 nanocomposites were developed. Powder X-ray diffraction investigations confirmed the structure. The bonding vibrations of the produced nanocomposites were confirmed using infrared and Raman spectroscopy. EDX analysis was used to determine the composition and element weights of the nanocomposites. The electrochemical properties of the nanomaterials were measured using 1M KOH electrolyte. At 5mVs?1 scan rates, cyclic voltammetry revealed a specific capacitance (Csp) of 1078.2 Fg?1 for N-rGO/NiCo2O4. The bare and nanocomposites of NiCo2O4, GO/NiCo2O4, and N-rGO/NiCo2O4 specific capacitance, charge-discharge capability, and cyclic stability were investigated. Energy density and power density of the N-rGO/NiCo2O4 nanocomposite were estimated to be 20.4 Wh kg?1 and 1300W kg?1, respectively. N-rGO//N-rGO/NiCo2O4 asymmetric supercapacitor device with Ed of 14.9 Wh kg?1 and Pd of 3500W kg?1 was fabricated. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
N-doped graphene quantum dots incorporated cobalt ferrite/graphitic carbon nitride ternary composite for electrochemical overall water splitting
Multicomponent electrocatalysts containing carbon supports play a crucial role in influencing the hydrogen and oxygen evolution reactions which enhance the total water splitting. Herein, we report a ternary composite with cobalt ferrite, graphitic carbon nitride, and N-doped graphene quantum dots prepared via hydrothermal technique. The purity of the samples is established by carrying out various characterization methods. The intrinsic characteristics of the obtained materials are investigated by employing electrocatalytic processes in an alkaline media toward hydrogen and oxygen evolution reactions. Cobalt ferrite/graphitic carbon nitride/N doped graphene quantum dots electrocatalyst demonstrates a very low overpotential towards hydrogen evolution reaction of 287 mV at a constant 10 mA cm?2 current density in 1.0 M KOH. Tafel slope and Rct values generated are 94 mV dec?1 and 0.86 cm2, respectively. Oxygen evolution reaction studies reveal an overpotential of 445 mV at 10 mA cm?2 with a Tafel slope of 69 mV dec?1. Finally, the cell potential needed for the cobalt ferrite/graphitic carbon nitride/N doped graphene quantum dots electrode to achieve 10 mA cm?2 in total water splitting is only 2.0 V while displaying long-term stability. 2022 Hydrogen Energy Publications LLC -
My Motherhood, My Way: A Sociological Study of Contemporary Employed Mothers in Kolkata
Motherhood in India has been understood primarily by placing mothers in the domestic space. A mother is constructed as a protector and the complete caregiver of her children. But there have been significant changes in the status of Indian women recently. In the 21st century, with suitable qualifications and employment opportunities, women have the choice to be economically independent and career-driven, which has a profound impact on their roles and responsibilities as protectors and caregivers in the home. It is essential to study and document how women in this generation have started to redefine their roles and negotiate what a mothers duties are at home. This study aims to make a systematic inquiry to understand the issues and challenges faced by employed mothers in everyday life and how they balance their career and childcare activities. Researchers investigate this through a qualitative study on mothers employed in different types of professions in the city of Kolkata. Data was collected by conducting in-depth interviews of around twenty-nine urban, upper-middle class employed mothers from different professional backgrounds to have a set of diverse narratives about their experiences and struggles. The key findings of this study provide an insight into the challenges that mothers face and their balancing mechanisms. Such studies have the scope to motivate many employed mothers by presenting some cases of women who have succeeded in breaking the stereotypical ideas of motherhood and are redefining their stories in more humane terms. 2021. Journal of International Womens Studies. -
Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining
Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user's behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIP-BSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIP-BSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods. 2020. All Rights Reserved. -
Museum visit intervention in K-12 education: a scoping review
This scoping review aims to provide an overview of empirical studies on worldwide museum visit intervention in K-12 education. The study employed Mendeley citation software to identify the articles in the database. A metaanalysis PRISMA statement is used for reporting the items. Out of 135 possibly rich articles, the present study reviewed 18 studies that met the inclusion criteria and were subjected to descriptive and content analyses published between 2017 and 2021. Most of the studies are experimental and from primary school contexts. It is revealed that science is the subject matter context majority of the studies, but philosophy, disaster management, language, and environmental science are also represented. The content analysis resulted in the following learning and social outcomes. It states that social outcome is explored chiefly, followed by learning outcome. The findings indicate that museum visit intervention positively impacts students learning and social outcome. The review also identifies the need for further research on museum visit intervention in the Asia Pacific region. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Musculoskeletal Disorders and Psychological Well-being among Indian Nurses: A Narrative Review of Impacts and Interventions (2024)
Background: A prevalent occupational health issue that may have a detrimental effect on nurses' mental health and general well-being is musculoskeletal problems. This narrative review aimed to explore the social, economic, and personal implications of Musculoskeletal Disorder on nurses in India, and examine support, and intervention strategies available for them. Material & Methods: A comprehensive literature search was conducted in electronic databases, including PubMed, Scopus, and Google Scholar, using relevant keywords related to Musculoskeletal Disorder, mental health, nurses, social, personal, support, and intervention. The inclusion criteria were articles published in English and focused the nursing workforce in India. Results: A total of 15 articles were selected for review synthesis. According to the summary, nurses in India who suffer from musculoskeletal disorders deal with serious social and personal repercussions that impact their everyday life and general well-being. Musculoskeletal Disorder can lead to decreased social connections, reduced job satisfaction, and physical and emotional distress. However, limited interventions are available that address Musculoskeletal Disorder and the mental health of nurses in India. Conclusion: There is a significant effect of Musculoskeletal Disorder on the mental health, quality of life, and economic well-being of nurses in India. However, limited scientific research exists exploring the prevalence and psychosocial implications of Musculoskeletal Disorder in the Indian nursing population. Consequently, additional research is essential to comprehend the scope and ramifications of this occupational health concern. To create interventions and support systems that are effective in the unique cultural and occupational context of nursing in India, it is imperative to engage in interdisciplinary collaboration. 2024 The Author(s); Published by Rafsanjan University of Medical Sciences. -
Murraya koenigii extract blended nanocellulose-polyethylene glycol thin films for the sustainable synthesis of antibacterial food packaging
Non-biodegradable plastics are a worldwide problem that have a negative impact on all living things, including humans. Nanocellulose, an excellent biopolymer is known for their increasing uses in food, healthcare, cosmetics, and various other fields. Nanocellulose is readily biodegradable, bioderived, and useful for creating innovative bioplastics that are employed in the production of food packaging and wound dressing. Curry leaves (Murraya koenigii) belongs to the rutaceae family and has many health benefits. Synthesis of Murraya koenigii incorporated nanocellulose thin films, and its characterisation using FT-IR, and XRD is discussed in detail. The source of nanocellulose in this study is sugar cane bagasse, an easily available agricultural residue in Kerala. Also, a biocompatible plasticizer is utilised to produce antibacterial packaging for food. The synthesised nanocomposites showed non-toxicity against THP1-derived macrophage cells and significant antibacterial activity against gram positive and gram-negative bacteria suggesting the possible application as a viable alternative for food packaging materials. 2023 Elsevier B.V. -
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved. -
Multiwavelength spectral modelling of the candidate neutrino blazar PKS 0735+178
The BL Lac object PKS 0735+178 was in its historic ?-ray brightness state during 2021 December. This period also coincides with the detection of a neutrino event IC 211208A, which was localized close to the vicinity of PKS 0735+178. We carried out detailed ?-ray timing and spectral analysis of the source in three epochs: (a) quiescent state (E1), (b) moderate-activity state (E2), and (c) high-activity state (E3) coincident with the epoch of neutrino detection. During the epoch of neutrino detection (E3), we found the largest variability amplitude of 95 per cent. The ?-ray spectra corresponding to these three epochs are well fit by the power-law model and the source is found to show spectral variations with a softer when brighter trend. In epoch E3, we found the shortest flux doubling/halving time of 5.75 h. Even though the spectral energy distribution in the moderate-activity state and in the high-activity state could be modelled by the one-zone leptonic emission model, the spectral energy distribution in the quiescent state required an additional component of radiation over and above the leptonic component. Here, we show that a photomeson process was needed to explain the excess ?-ray emission in the hundreds of GeV that could not be accounted for by the synchrotron self-Compton process. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multitask EfficientNet affective computing for student engagement detection
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multiplier-free Realization of High throughout Transpose Form FIR Filter
This paper presents a multiplier-free realization of the block finite impulse response (FIR) filter in transpose form configuration using binary constant shifts method (BCSM). The proposed architecture is synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with the existing works in the literature. The comparison highlights the advantages of the proposed architecture in terms of power, hardware complexity and throughput for realizing reconfigurable high throughput block FIR filters. 2020 IEEE. -
Multiple solutions and stability analysis in MHD non-Newtonian nanofluid slip flow with convective and passive boundary condition: Heat transfer optimization using RSM-CCD
This study explores the effect of Williamson nanofluid in the presence of radiation and chemical reaction caused by stretching or shrinking a surface with convective boundary conditions. After implementing two-component model and Lie group theory, the transformed ODEs are solved using the RungeKutta DormandPrince (RKDP) shooting approach technique. The dual solutions are predicted for certain range of physical nanofluid parameters, such as Williamson parameter ((Formula presented.)), stretching/shrinking parameter ((Formula presented.)), and suction parameter ((Formula presented.)) with different slip (Formula presented.) and magnetic M parameters. Contour plots are generated for the stable branch of the Nusselt number ((Formula presented.)) for different combinations, providing insights into the heat transfer characteristics. The eigenvalue problem is solved in order to predict flow stability. The optimization of heat transfer in nanoliquid is conducted by RSM-CCD. The resulting quadratic correlation enables the prediction of the optimal Nusselt number for (Formula presented.), (Formula presented.), and (Formula presented.). This investigation is motivated by various applications including manufacturing processes, thermal management systems, energy conversion devices, and other engineering systems where efficient heat transfer iscrucial. 2023 Wiley-VCH GmbH. -
Multiple slip effects on MHD non-Newtonian nanofluid flow over a nonlinear permeable elongated sheet: Numerical and statistical analysis
Purpose: The purpose of this paper is to examine the interaction effects of a transverse magnetic field and slip effects of Casson fluid with suspended nanoparticles over a nonlinear stretching surface. Mathematical modeling for the law of conservation of mass, momentum, heat and concentration of nanoparticles is executed. Design/methodology/approach: Governing nonlinear partial differential equations are reduced into nonlinear ordinary differential equations and then shooting method is employed for its solution. The slope of the linear regression line of the data points is calculated to measure the rate of increase/decrease in the reduced Nusselt number. Findings: The effects of magnetic parameter (0=M=4), Casson parameter (0.1=?<8), nonlinear stretching parameter (0=n=3) and porosity parameter (0=P=6) on axial velocity are shown graphically. Numerical results were compared with another numerical approach and an excellent agreement was observed. This study reveals the fact that the Brownian motion parameter and boundary layer thickness have a direct relationship with temperature. Also, Brownian motion and thermophoresis contribute to an increase in the thermal boundary layer thickness. Originality/value: Despite the immense significance and repeated employment of non-Newtonian fluids in industry and science, no attempt has been made up till now to inspect the Casson nanofluid flow with a permeable nonlinear stretching surface. 2019, Emerald Publishing Limited. -
Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE. -
Multiple Approaches in Retail Analytics to Augment Revenues
Knowledge is power. The retail sector has been revolutionized around the clock by the plentiful product knowledge available to customers. Today, customers can use the knowledge available online at any time to study, compare and purchase products from anywhere. Retail companies can stay ahead of shopper trends by using retail information analytics to discover and analyze online and in-store shopper patterns. A product recommender will suggest products from a wide selection that would otherwise be very difficult to locate for the customer. The algorithm would recommend various products, increase the sales of items that would otherwise be difficult to sell. Market basket analysis is a common use scenario for the search for frequent patterns, which involves analyzing the transactional data of a retail store to decide which items are bought together. To do so data from online resource has been taken, which is analyzed and several conclusions were made. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach
Recent years have seen a significant increase in attention in multimodal biometric systems for personal identification especially in unconstrained environments. This paper presents a multimodal recognition system by combining feature level fusion of ear and profile face images. Multimodal biometric systems by combining face and ear can be used in an extensive range of applications because we can capture both the biometrics in a non-intrusive manner. Local texture feature descriptor, BSIF is used to extract discriminative features from biometric templates. Feature level and score level fusion is experimented to improve the performance of the system. Experimental results on different public datasets like GTAV, FEI, etc., show that the proposed method gives better performance in recognition results than individual modality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Multimodal emotional analysis through hierarchical video summarization and face tracking
The era of video data has fascinated users into creating, processing, and manipulating videos for various applications. Voluminous video data requires higher computation power and processing time. In this work, a model is developed that can precisely acquire keyframes through hierarchical summarization and use the keyframes to detect faces and assess the emotional intent of the user. The key-frames are used to detect faces using recursive Viola-Jones algorithm and an emotional analysis for the faces extracted is conducted using an underlying architecture developed based on Deep Neural Networks (DNN). This work has significantly contributed in improving the accuracy of face detection and emotional analysis in non-redundant frames. The number of frames selected after summarization was less than 30% using the local minima extraction. The recursive routine introduced for face detection reduced false positives in all the video frames to lesser than 2%. The accuracy of emotional prediction on the faces acquired through the summarized frames, on Indian faces achieved a 90%. The computational requirement scaled down to 40% due to the hierarchical summarization that removed redundant frames and recursive face detection removed false localization of faces. The proposed model intends to emphasize the importance of keyframe detection and use them for facial emotional recognition. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Multimodal Emotion Recognition Using Deep Learning Techniques
Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today's world. 2022 IEEE.