bluuebunny/crossref_metadata_2025 huggingface dataset:
The Current Outbreak of COVID-19 with Reference to India
Authors: Gupta Harshita
Published: October 2020

This review summarizes the outbreak of viruses causing the fatal disease, which is highly
pathogenic and efficiently transmitted from human to human. This first began in Wuhan, China, and
now turned into an epidemic situation worldwide. A novel coronavirus (2019-nCoV) or severe acute
respiratory syndrome coronavirus(SARS-CoV)-2 belongs to β-coronavirus genera originated in bats due
to highly identical genome which leads to bat coronavirus. This review highlights the study of Indian
Council of Medical Research, India, which determined the detection of pathogenic coronavirus in two
different species of Indian bats. Indian Council of Medical Research, India, has successfully isolated the
COVID-19 virus strain which was the first step towards diagnosis and the development of vaccines in
the country. The outbreaks of coronavirus received worldwide attention for overcoming the challenges
faced during this current pandemic as there is no clinically approved antiviral drug or vaccine available
yet. However, preventive measures and different treatments were taken to cope with this viral outbreak.
In response to this global outbreak, this review tries to explain the Virology, Epidemiology and pathogenesis,
and discusses the diagnosis and treatment strategies of COVID-19. This review emphasizes the
current update of COVID-19.
Smartphone App (2kmFIT-App) for Measuring Cardiorespiratory Fitness: Validity and Reliability Study
Authors: Muntaner-Mas Adria , Martinez-Nicolas Antonio , Quesada Alberto , Cadenas-Sanchez Cristina , Ortega Francisco B
Published: October 2020

Background

There is strong evidence suggesting that higher levels of cardiorespiratory fitness (CRF) are associated with a healthier metabolic profile, and that CRF can serve as a powerful predictor of morbidity and mortality. In this context, a smartphone app based on the 2-km walk test (UKK test) would provide the possibility to assess CRF remotely in individuals geographically distributed around a country or continent, and even between continents, with minimal equipment and low costs.

Objective

The overall aim of this study was to evaluate the validity and reliability of 2kmFIT-App developed for Android and iOS mobile operating systems to estimate maximum oxygen consumption (VO2max) as an indicator of CRF. The specific aims of the study were to determine the validity of 2kmFIT-App to track distance and calculate heart rate (HR).

Methods

Twenty participants were included for field-testing validation and reliability analysis. The participants completed the UKK test twice using 2kmFIT-App. Distance and HR were measured with the app as well as with accurate methods, and VO2max was estimated using the UKK test equation.

Results

The validity results showed the following mean differences (app minus criterion): distance (–70.40, SD 51.47 meters), time (–0.59, SD 0.45 minutes), HR (–16.75, SD 9.96 beats/minute), and VO2max (3.59, SD 2.01 ml/kg/min). There was moderate validity found for HR (intraclass correlation coefficient [ICC] 0.731, 95% CI –0.211 to 0.942) and good validity found for VO2max (ICC 0.878, 95% CI –0.125 to 0.972). The reliability results showed the following mean differences (retest minus test): app distance (25.99, SD 43.21 meters), app time (–0.15, SD 0.94 seconds), pace (–0.18, SD 0.33 min/km), app HR (–4.5, 13.44 beats/minute), and app VO2max (0.92, SD 3.04 ml/kg/min). There was good reliability for app HR (ICC 0.897, 95% CI 0.742-0.959) and excellent validity for app VO2max (ICC 0.932, 95% CI 0.830-0.973). All of these findings were observed when using the app with an Android operating system, whereas validity was poor when the app was used with iOS.

Conclusions

This study shows that 2kmFIT-App is a new, scientifically valid and reliable tool able to objectively and remotely estimate CRF, HR, and distance with an Android but not iOS mobile operating system. However, certain limitations such as the time required by 2kmFIT-App to calculate HR or the temperature environment should be considered when using the app.
Perceived Impact of an Online Community Care Platform for Dutch Older Adults on Local Participation, Informal Caregiving, and Feelings of Connectedness: Pretest-Posttest Observational Study
Authors: Willard Sarah , van Rossum Erik , Spreeuwenberg Marieke , de Witte Luc
Published: October 2020

Background

In a changing ageing society wherein older adults are increasingly expected to take care of themselves instead of relying on health care services, online community care platforms can help older adults to meet these expectations. A considerable number of these online community care platforms have been introduced in several European countries based on their potential. However, their actual impact is unclear.

Objective

The aim of this study was to investigate the self-reported use, expectations, and perceived impact of a Dutch online community care platform called Grubbenvorst-Online among Dutch older adults. The following 2 questions were studied: (1) What is the self-reported use of Grubbenvorst-Online among older adults? (2) What are their expectations and perceived impact of Grubbenvorst-Online regarding local participation, their social network, mutual informal caregiving, and feelings of connectedness?

Methods

An observational pretest-posttest study was conducted. Participants were recruited via a web-based message on the Grubbenvorst-Online platform and data were collected via postal questionnaires among older users at the start of the study and 4 months later. Data regarding the expectations and the perceived impact of Grubbenvorst-Online were compared and tested.

Results

Forty-seven Grubbenvorst-Online users with an average age of 74 years participated in this study. They were healthy, predominantly “internet-skilled,” and they found the internet important for maintaining social contacts. In general, the use of the online community care platform decreased during the 4-month follow-up period. The perceived impact of Grubbenvorst-Online was significantly lower than that expected regarding information provision (P=.003), seeking help from fellow villagers (P<.001), giving help to fellow villagers (P<.001), and consulting care or welfare services (P<.001).

Conclusions

The findings of this study indicate that online community care platforms perhaps do not provide enough “added value” in their current form. We suggest a new direction in which online community care platforms primarily support existing offline initiatives aimed at stimulating local participation, informal caregiving, and feelings of connectedness.
Relationship Between COVID-19 Infection and Risk Perception, Knowledge, Attitude, and Four Nonpharmaceutical Interventions During the Late Period of the COVID-19 Epidemic in China: Online Cross-Sectional Survey of 8158 Adults
Authors: Xu Hong , Gan Yong , Zheng Daikun , Wu Bo , Zhu Xian , Xu Chang , Liu Chenglu , Tao Zhou , Hu Yaoyue , Chen Min , Li Mingjing , Lu Zuxun , Chen Jack
Published: October 2020

Background

So far, there have been no published population studies on the relationship between a COVID-19 infection and public risk perception, information source, knowledge, attitude, and behaviors during the COVID-19 outbreak in China.

Objective

This study aims to understand the relationships between COVID-19 infection; four personal nonpharmaceutical interventions (NPIs; handwashing, proper coughing habits, social distancing, and mask wearing); and public risk perception, knowledge, attitude, and other social demographic variables.

Methods

An online survey of 8158 Chinese adults between February 22 and March 5, 2020, was conducted. Bivariate associations between categorical variables were examined using Fisher exact test. We also explored the determinants of four NPIs as well as their association with COVID-19 infection using logistic regression.

Results

Of 8158 adults included, 57 (0.73%) were infected with COVID-19. The overwhelming majority of respondents showed a positive attitude (n=8094, 99.2%), positive risk perception (n=8146, 99.9%), and high knowledge levels that were among the strongest predictors of the four adopted NPIs (handwashing: n=7895, 96.8%; proper coughing: 5997/6444, 93.1%; social distancing: n=7104/8158, 87.1%; and mask wearing: 5011/5120, 97.9%). There was an increased risk of COVID-19 infection for those who did not wash their hands (2.28% vs 0.65%; risk ratio [RR] 3.53, 95% CI 1.53-8.15; P=.009), did not practice proper coughing (1.79% vs 0.73%; RR 2.44, 95% CI 1.15-5.15; P=.03), did not practice social distancing (1.52% vs 0.58%; RR 2.63, 95% CI 1.48-4.67; P=.002), and did not wear a mask (7.41% vs 0.6%; RR 12.38, 95% CI 5.81-26.36; P<.001). For those who did practice all other three NPIs, wearing a mask was associated with a significantly reduced risk of infection compared to those who did not wear a mask (0.6% vs 16.7%; P=.04). Similarly, for those who did not practice all or part of the other three NPIs, wearing a mask was also associated with a significantly reduced risk of infection. In a penalized logistic regression model including all four NPIs, wearing a mask was the only significant predictor of COVID-19 infection among the four NPIs (odds ratio 7.20, 95% CI 2.24-23.11; P<.001).

Conclusions

We found high levels of risk perception, positive attitude, desirable knowledge, as well as a high level of adopting the four NPIs. The relevant knowledge, risk perception, and attitude were strong predictors of adapting the four NPIs. Mask wearing, among the four personal NPIs, was the most effective protective measure against COVID-19 infection, with added preventive effect among those who practiced all or part of the other three NPIs.
The Need for Education and Clinical Best Practice Guidelines in the Era of Direct-to-Consumer Genomic Testing
Authors: Myers Madeleine , Bloss Cinnamon
Published: October 2020

Many people share the results of their direct-to-consumer personal genomic testing (DTC-PGT) within the primary care setting, seeking interpretation of and counsel about the results. However, most primary care physicians (PCPs) are not trained to interpret and communicate about DTC-PGT results. New guidelines must be developed to help PCPs maximize the potential of emerging DTC-PGT technologies.
Predictors of Parental Barriers to Reduce Excessive Child Screen Time Among Parents of Under-Five Children in Selangor, Malaysia: Cross-sectional Study (Preprint)
Authors: Mansor Elliza , Ahmad Norliza , Raj Diana , Mohd Zulkefli Nor Afiah , Mohd Shariff Zalilah
Published: October 2020

BACKGROUND

Globally, there is an increasing prevalence of excessive screen time exposure among young children, including in Malaysia. Parents are advised to limit this exposure, but there are barriers for many of them to follow this recommendation. To date, there is a lack of research on the factors that cause these parental barriers.

OBJECTIVE

This study aimed to determine the parental barrier toward the reduction of excessive child screen time and its predictors among parents of children aged younger than 5 years in the Petaling District, Selangor, Malaysia.

METHODS

A cross-sectional study was conducted from April 2019 to June 2020 among 789 parent-child dyads attending child health clinics in the Petaling District. Validated self-administered questionnaires were used to capture information on sociodemographic, parental, child-related, and environmental factors and parental barriers. Stratified sampling with probability proportionate to size was employed. Data were analyzed using SPSS Statistics version 25 (IBM Corp). Descriptive analysis and bivariable analysis were performed before multiple linear regression was used to identify predictors of parental barriers.

RESULTS

The overall mean score of parental barriers was 3.51 (SD 0.83), indicating that the average numbers of barriers experienced by parents were more than 3. The multivariable analysis showed that the predictors of parental barriers included monthly household income (adjusted β=–.03, 95% CI –0.05 to –0.02), parents who worked in public sectors (adjusted β=.18, 95% CI 0.06 to 0.29), positive parental attitude on screens (adjusted β=.68, 95% CI 0.58 to 0.79), low parent self-efficacy to influence child’s physical activity (adjusted β=–.32, 95% CI –0.43 to –0.20), and child screen time (adjusted β=.04, 95% CI 0.02 to 0.06).

CONCLUSIONS

The strongest predictor of parental barriers to reduce excessive child screen time was the positive parental attitude on screen time which could contribute to their abilities to limit child screen time. Thus, future intervention strategies should aim to foster correct parental attitudes toward screen time activities among young children.
Peran Moral Disengagement dan Kepemimpinan Etis terhadap Intensi Korupsi pada Pegawai Negeri Sipil
Authors: Hikmah Asfira Tadwimil
Published: October 2020

Penelitian ini bertujuan untuk mengetahui peranan moral disengagement dan kepemimpinan etis terhadap intensi korupsi pada Pegawai Negeri Sipil (PNS). Sebanyak 100 PNS pada beberapa dinas pemerintahan di Kota Yogyakarta menjadi partisipan dalam penelitian ini. Metode penelitian ini adalah survei menggunakan tiga skala, yaitu Moral Disengagement Scale yang telah diadaptasi oleh Maharani, Ethical Leadership Scale  yang telah diadaptasi oleh Seliamang, dan Skala Intensi Korupsi oleh Nayoan. Analisis regresi linear sederhana menunjukkan bahwa moral disengagement secara signifikan mampu memprediksi intensi korupsi pada PNS sebanyak 13% (F = 14,583; p < 0,05). Artinya, PNS yang memiliki moral disengagement yang tinggi, maka akan memiliki intensi korupsi yang tinggi, dan juga sebaliknya. Namun, kepemimpinan etis tidak mampu memprediksi intensi korupsi pada PNS (p > 0,05). Analisis tambahan menemukan bahwa tidak ada perbedaan intensi korupsi berdasarkan jenis kelamin maupun usia.
Regaining a Bright Future: Psychological Dynamics in Female Adolescent Victims of Sexual Harassment
Authors: Dumpratiwi Ajeng Nova
Published: October 2020

Rampant sexual harassments affect the victims and need to be followed up. Victims who are majority women are likely to feel aggrieved by pregnancy, receive negative stigma from society, drop out of school, and feel unable to achieve well-being in the future. This phenomenon is the basis for research conducted with the aim of knowing the psychological dynamics and aspects that affect the psychological conditions in female adolescent victims of sexual harassment. The phenomenological approach and interview method were used in this research. Three teenage girls aged 13-18 years became informants in this study. The results showed that the psychological condition of victims of sexual harassment was influenced by who the perpetrator is or the close relationship between the victim and the perpetrator. It was also influenced by support from the social environment, especially family as the closest environment. The psychological dynamics of victims of sexual harassment are influenced by four things: 1) the attitude of the environment; 2) self-understanding ability; 3) motivation to achieve self-actualization; and 4) the attitude of comparing oneself with others who are not in a better condition. From the results of this study, it was concluded that the supporting external or environmental factors and internal process in victims of sexual harassment combined really encourage the process recovery to achieve a better future.
Influence of Parents’ Age on Perceptions of the Role of Male Teachers in Preschools in Vihiga Sub-County, Kenya
Authors: Elisheba Avoga , Wambiri Dr. Gladwell , Begi Dr. Nyakwara
Published: October 2020

Children who are taught by both male and female teachers develop holistically. This means that male teachers are vital in the education of young children. The study was to explore parents’ perceptions of the role of male teachers’ in preschools in Vihiga Sub-county as well as examine the influence of age on parents’ perception of the role the teachers in pre-schools in the sub-county. The study was guided by Albert Bandura’s Social Learning theory. Descriptive research design was used to guide the study. The dependent variable was parents’ perception of role of male teachers’ in pre-schools, while the independent variable was parents’ age. The locale of the study was Vihiga Sub County in Kenya. Questionnaire and interview schedules were used to collect data and analyzed using qualitative and quantitative methods. Results from data analysis indicated that parents’ perception of the role of male teachers’ in pre-schools was negative. It was also clear from the results that the perception was influenced by parents’ age. It was recommended that school management should organize programs to help parents to understand the role played by male teachers in preschools in order to change their attitude towards the teachers.
Colaboração premiada, paternalismo processual e “juízes camaleões”: simplificação e eficiência do procedimento na luta contra a corrupção
Authors: Barbosa Paulo Roberto Fonseca
Published: October 2020

Detectando-se uma espécie de paternalismo processual na atuação dos juízes, busca-se demonstrar que a dupla análise judicial do acordo de colaboração premiada, conforme previsto pela Lei 12.850/2013, é ponto de desestabilização do sistema de justiça penal negociada brasileiro, contribuindo negativamente para uma série de inconsistências processuais, notadamente por transgredir os princípios constitucionais da imparcialidade e do juiz natural, sendo gênese de nulidades processuais. Nesse contexto, pretende-se responder ao seguinte problema: a simplificação do procedimento, envolvendo a confecção do acordo de colaboração premiada, com a extinção da dupla análise judicial do referenciado acordo, contribuiria para potencializar o combate à corrupção sem afetar garantias fundamentais dos acusados? Para resposta do problema, utiliza-se da análise de jurisprudência e doutrina sobre a temática, incluindo estudo comparatístico entre institutos jurídicos estrangeiros congêneres.
A transnacionalização da corrupção e a instrumentalização política do processo penal
Authors: Gloeckner Ricardo Jacobsen , Silveira Felipe Lazzari da
Published: October 2020

O presente artigo problematiza o entrelaçamento das cruzadas contra a corrupção e a racionalidade neoliberal, e também os reflexos produzidos por essa dinâmica sobre o processo penal, sobretudo a sua utilização como instrumento político. Tal esforço teórico justifica-se em razão do que se verificou após a Operação Lava Jato, especialmente o uso de velhos e novos discursos punitivistas visando o recrudescimento do sistema de justiça criminal e a sua influência no cenário político-governamental. Nesse sentido, partindo da premissa de que a corrupção não pode ser reduzida mediante a supressão de direitos e garantias processuais, este escrito tem como objetivo demonstrar que as campanhas anticorrupção, nos moldes conforme disseminadas desde as últimas décadas do século passado, têm servido para alavancar o projeto neoliberal e, no caso brasileiro, para obstaculizar a democratização do campo processual penal. Para tanto, mediante uma revisão bibliográfica e documental, estruturado em quatro tópicos, apresentará uma genealogia da transnacionalização da corrupção, demonstrará o vínculo das campanhas anticorrupção com os intentos neoliberais, e esmiuçará o papel decisivo desempenhado pela cruzada contra a corrupção iniciada no Brasil em 2014 para o fortalecimento dos caracteres autoritários do processo penal, bem como para o seu uso político.
Discursos sobre a corrupção nas votações do Supremo Tribunal Federal (2015-2017)
Authors: Fernandes Daniel Fonseca , Santana Tainan Bulhões
Published: October 2020

Este artigo analisa os discursos sobre a corrupção nas votações do Supremo Tribunal Federal, entre os anos de 2015 e 2017. A pesquisa empírica realizada tem caráter qualitativo e utiliza técnicas de pesquisa bibliográfica e documental, por meio da coleta e análise de decisões colegiadas do Supremo, referentes ao crime de corrupção passiva. A pesquisa se orienta pelo seguinte problema: quais discursos sobre a corrupção foram mobilizados pelos ministros do Supremo Tribunal Federal nos julgamentos colegiados entre os anos de 2015 e 2017? Para posicionar estes discursos, o trabalho reflete sobre categorias centrais do pensamento social brasileiro, como “patrimonialismo”, problematizando seus usos pelos juristas. A atuação do Supremo é contextualizada a partir dos fenômenos do Mensalão e da Lava Jato e as pressões por determinados resultados exercidas sobre a Corte. As relações com a imprensa, as pressões das agências judiciais e as disputas internas são destacadas como fatores importantes para compreender as decisões.
Editorial - Preprint e postprint em publicações científicas e no Direito: discussões e medidas para ciência aberta e divulgação de pesquisas
Authors: Vasconcellos Vinicius Gomes de , De-Lorenzi Felipe
Published: October 2020

Recentemente, a Revista Brasileira de Direito Processual Penal alterou suas políticas editoriais para, em conformidade com as diretrizes de Scielo e COPE, autorizar e incentivar o depósito de manuscritos em servidores certificados de preprint e postprint, de modo a ampliar a publicidade, a transparência e a celeridade na divulgação dos resultados de pesquisas científicas. Neste editorial, a partir de revisão bibliográfica, busca-se examinar alguns aspectos dessas práticas científicas, como sua origem, definição, benefícios e problemas, com foco na utilização nas ciências jurídicas – em particular, as criminais. Conclui-se que a tendência de ampliação de preprints aporta um cenário de novas perspectivas à produção de conhecimento e à editoração de revistas científicas, contribuindo com a consolidação de medidas para uma ciência aberta. Seus riscos e benefícios devem ser ponderados com cautela, mas os potenciais benefícios se sobressaem, como a maior celeridade na divulgação científica e a possibilidade de maior debate para aprimoramentos na pesquisa até a sua publicação definitiva.
Adaptive Inception Based on Transfer Learning for Effective Visual Recognition
Authors:   Visvesvaraya Technological University, Sreenivasulu Balaji , Pasala Anjaneyulu ,   Infosys Labs, Vasanth Gaikwad ,   Government Engineering College
Published: October 2020

In computer vision, domain adaptation or transfer learning plays an important role because it learns a target classifier characteristics using labeled data from various distribution. The existing researches mostly focused on minimizing the time complexity of neural networks and it effectively worked on low-level features. However, the existing method failed to concentrate on data augmentation time and cost of labeled data. Moreover, machine learning techniques face difficulty to obtain the large amount of distributed label data. In this research study, the pre-trained network called inception layer is fine-tuned with the augmented data. There are two phases present in this study, where the effectiveness of data augmentation for Inception pre-trained networks is investigated in the first phase. The transfer learning approach is used to enhance the results of the first phase and the Support Vector Machine (SVM) is used to learn all the features extracted from inception layers. The experiments are conducted on a publicly available dataset to estimate the effectiveness of proposed method. The results stated that the proposed method achieved 95.23% accuracy, where the existing techniques namely deep neural network and traditional convolutional networks achieved 87.32% and 91.32% accuracy respectively. This validation results proved that the developed method nearly achieved 4-8% improvement in accuracy than existing techniques.
Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing
Authors:   Koneru Lakshmaiah Education Foundation, Kollu Archana , Vadlamudi Sucharita ,   Koneru Lakshmaiah Education Foundation
Published: October 2020

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.
Estimation of Photovoltaic Cells Parameters Using Chaos Embedded Salp Swarm Algorithm
Authors:   University of Ferhat Abbas Setif-1, Nasri Dallel , Mokeddem Diab ,   University of Ferhat Abbas Setif-1, Bourouba Bachir ,   University of Ferhat Abbas Setif-1
Published: October 2020

Solar photovoltaic (PV) systems have recently attracted researcher’s attention as a clean source of energy. Thus, the importance to design appropriately the photovoltaic cells highly raises. The main problems faced in the design process are first, the development of a useful model describing the characteristics of the current vs. voltage able to simulate the real solar cells behaviours and then, the precise estimation of photovoltaic cells parameter values. This paper employs an improved version of Salp Swarm Algorithm called Chaotic Salp Swarm Algorithm (CSSA) for the parameters estimation of solar cells in both single and double diode models. CSSA approach benefits from chaotic maps proprieties, and has the advantage of providing good equilibrium between exploration and exploitation mechanisms as well. Performance of the proposed CSSA is compared to fourteen known algorithms. Experimental results demonstrate that the proposed algorithm has the ability to find the optimal solutions with an accurate estimation of parameters for the courant vs voltage characteristics of real solar cell with high performance.
Outlier Detection Method based on Adaptive Clustering Method and Density Peak
Authors:   Koneru Lakshmaiah Education Foundation, Jayanthi Neelampalli , Babu Burra ,   Koneru Lakshmaiah Education Foundation, Rao Nandam ,   Vardhaman College of Engineering
Published: October 2020

The outlier detection technique is widely used in the data analysis for the clustering of data. Many techniques have been applied in the outlier detection to increase the efficiency of the data analysis. The Local Projection based Outlier Detection (LPOD) method effectively identifies neighbouring values of data, but this has the drawback of random selection of the cluster centre that affects the overall clustering performance of the system. In this study, the Adaptive Clustering by Fast Search and Find of Density Peak (ACFSFDP) is proposed to select the clustering centre and density peak. This ACFSFDP method is implemented with the min-max algorithm to find the number of categories that measured the local density and distance information. The density and distance are used to select the cluster centre, but density is not calculated on the existing distance based clustering techniques. The ACFSFDP method calculates cluster centre based on the density and distance during the clustering process, whereas the existing techniques randomly select the data centre. The results indicated that the ACFSFDP method is provided effective outlier detection compared with existing Clustering by Fast Search and Find of Density Peak (CFSFDP) methods. The ACFSFDP is tested on two datasets Pen-digits and waveform datasets. The experiment results proved that Area Under Curve (AUC) of the ACFSFDP is 99.08% on the Pen-Digit dataset, while the existing distance classifier method k-Nearest Neighbour has achieved 68.7% of AUC.
Implementing and Evaluating Health as a Service in Fog and Cloud Computing using Raspberry Pi
Authors:   University of Mumbai, Kanani Pratik , Padole Mamta ,   The Maharaja Sayajirao University of Baroda
Published: October 2020

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.
Detection of Breast Cancer on Magnetic Resonance Imaging Using Hybrid Feature Extraction and Deep Neural Network Techniques
Authors:   Annamalai University, Pullaiah Nagaraja , Venkatasekhar Dorai ,   Annamalai University, Venkatramana Padarthi ,   Sree Vidyanikethan Engineering College, Sudhakar Balaraj ,   Annamalai University
Published: October 2020

Breast cancer is one of the most occurring cancers in women due to the uncontrolled growth of abnormal cells in the lobules or milk ducts. The treatment for the breast cancer at an early stage is important using Magnetic Resonance Imaging (MRI) which effectively measures the size of the cancer and also checks tumors in the opposite breast. The deposition of calcium components on the breast tissue is known as micro-calcifications. The calcium salts deposited in the breast are involved with the cancer and were not diagnosed accurately due to the low effectiveness of existing imaging technique namely Haralick feature extraction technique. The MRI breast cancer diagnosis creates problems during classification of breast image and leads to misclassifications, such as unidentified calcium deposits in the existing K-Nearest Neighbour (KNN) classifier. The misclassification issues are overcome by an accurate classification and identification of calcium salts and checks whether deposited salt on breast tissue is involved with cancer or not. Initially, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to remove the unwanted noise in the MRI and Morphological, Multilevel Otsu’s Thresholding and region growing techniques perform segmentation to mask unwanted breast tissues. The proposed Hybrid LOOP Haralick feature extraction technique is developed by combining the both Local Optimal Oriented Pattern (LOOP) and Haralick texture feature and the hybrid parameters are applied to the Stacked Auto Encoder based (SAE) to classify the breast MRI image as a Malignant or Benign. The performance of the proposed hybrid LOOP Haralick feature extraction shows significant accuracy improvement of 3.83% when compared to the Haralick feature extraction technique.
Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition
Authors:   University of Babylon, Kamil Anas , Nasr Mahmoud ,   University of Babylon, Alwash Shamam ,   University of Babylon
Published: October 2020

The maximum power point tracking (MPPT) is an essential key to ensure that the photovoltaic (PV) system is operated at the highest possible power generation. This paper presents an efficient MPPT method for the PV system based on an enhanced particle swarm optimization algorithm to track the location of the global maximum power point, whatever its location changes in the search space under all environmental conditions, including the partial shading on strings. In this paper, the formulation of the conventional particle swarm optimization algorithm is enhanced to decrease the searching time and the oscillation of the generated output power as well as the power losses in the online tracking process. This enhancement can be achieved by utilizing a special time-varying weighting coefficient and removing the effect of some other coefficients in the conventional particle swarm optimization algorithm (PSO) that cause winding of the particles during the online tracking process. Test results verified the accuracy of the proposed method to track the global maximum power point with considering the effect of partial shading condition. The proposed method was also compared with other MPPT methods to verify the superiority of the proposed work. The obtained results reveal that the proposed method is effective to improve the tracking efficiency and reduce the tracking time and the number of iterations for the different irradiances and load conditions. The maximum number of iterations was 11 iteration and the highest tracking time was 0.273s with tracking efficiency of about 99.98%.
Private Browsing Forensic Analysis: A Case Study of Privacy Preservation in the Brave Browser
Authors:   Prince Sultan University, Mahlous Ahmed , Mahlous Houssam ,   King’s College London University
Published: October 2020

The Internet and its users are in continual growth. With it grows the number of organized crimes on the Internet and the potential for individuals to carry out illegal activities. These criminals have gained more awareness of private browsing facilities, and many have found a haven in privacy designed browsers that cover up their tracks and shield their nefarious actions. The development of these privacy features has proven to be a challenge for digital forensic investigators. They strive to perform a thorough analysis of web browsers to collect artefacts relating to illegal activity to be presented as evidence to the court of law and used to convict criminals. “Brave” browser is one of the most recent and fastest-growing private browsers that, up to this point, has not been studied in-depth, and its privacy preservation functionality remains unclear. In this paper, we studied Brave’s private browsing mode, examined its privacy-preserving and forensic data acquisition, and outlined the location and type of evidence available through live and post-mortem state analysis. The unique approach taken included a set of experiments that unveiled how the browser functions and showed the appropriate tools that could be utilized to extract leftover artefacts. Analysis of our results showed that despite Brave leaving no traces of browsing activity on the Hard Disk, visited URLs, images, keyword searches, and even cached videos were retrievable from the RAM, which shows that Brave is not entirely private.
Carrier-based PWM Method for Indirect Matrix Converters based on Space Vector Analysis
Authors:   Industrial University of Hochiminh City, Nguyen Hoai , Nguyen Dinh ,   Hochiminh City University of Technology, Chau Minh ,   Industrial University of Hochiminh City
Published: October 2020

In this paper, a novel approach to realize the carrier-based pulse width modulation (CBPWM) method for indirect matrix converter (IMC) is presented. The advantage of the proposed CBPWM method is that only one triangular carrier signal with constant gradient falling and rising edges is used to generate the PWM to the power switches in both the rectifier and the inverter stages of IMC. The analysis of the proposed CBPWM method is based on the space vector approach and the relationship between CBPWM and space vector pulse width modulation (SVPWM) methods is provided. In this paper, four CBPWM methods called: sinusoidal PWM, third harmonic injection PWM, symmetrical PWM and discontinuous PWM are presented. These different CBPWM schemes are established according to the distribution of zero vectors and the switching pattern of the SVPWM method. The comparison of output voltage performance with different CBPWM schemes are investigated. Compared to the conventional method the calculation time of the proposed method is reduced around 50%. Concretely, the calculation time of the SVPWM method is 45 μs, while the calculation time of the proposed method is 22 μs. The experiments have been carried out to show the effectiveness of the proposed method.
An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory
Authors:   Amrita School of Engineering, Murugesan Priyatharishini , Manickam Nirmala ,   Amrita School of Engineering
Published: October 2020

In modern electronic design, hardware Trojan has emerged as a major threat in the hardware security. To detect the hardware Trojan is a major problem in testing process because of their inherent concealed nature. In this work, we propose a deep learning-based Trojan classification approach, which extracts the optimal feature to indicate the nets affected by the Trojan module. In this approach, a handcrafted algorithm along with the structural report is also analyzed for extracting further features of the gate level netlist, which stamp out the requirement of golden chip. This detection technique is also validated using game theoretical approach, which is modelled as zero-sum game between the attacker and the defender. The Simulation is employed on ISCAS’85, ISCAS’89 and Trust-HUB circuits and the deep learning algorithm performs the best in detection and classification of Trojan type with an average True positive rate of 96.69% and an accuracy of 96.25%.
Blind Digital Images Tampering Detection Based on Singular Value Decomposition
Authors:   University of Kufa, Abbadi Nidhal , Mohsin Alyaa ,   University of Kufa
Published: October 2020

The growing use of digital images in a wide range of applications, and growing the availability of many editing photo software, cause to emerge a challenge to discover the images tampering. In this paper, we proposed a method to detect the most important type of forgery image (copy and move). We suggested many steps to classify the image as forgery or non-forgery image, started with preprocessing (included, convert image to gray image, de-noising, and image resize). Then, the image will be divided into several overlapping blocks. For each block, feature extracted (used it as a matching feature) by using the singular value decomposition (SVD) transformation. According to these features, the pixels were collected in many main groups, and then these groups clustered to many subgroups. The weight for each main group can be determined by comparing the subgroups with each other according to suggested conditions. The number of subgroups and weights are used to classify images to forgery or non-forgery images. The accuracy of detection and classified the forgery images were up to 97%. The suggested method is robust for tampered object rotation, scaling, and change of illumination.
MLO: Multi Leader Optimizer
Authors:   Shiraz University of Technology, Dehghani Mohammad , Montazeri Zeinab ,   Shiraz University of Technology, Dehghani Ali ,   Islamic Azad Universities of Estahban Estahban, Ramirez-Mendoza Ricardo ,   Tecnológico de Monterrey, Samet Haidar ,   Shiraz University, Guerrero Josep ,   Aalborg University, Dhiman Gaurav ,   Government Bikram College of Commerce
Published: October 2020

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.
Integration of Group Method Analysis and Rough Sets Theory to Investigate Heat and Mass Transfer of the Flow of a Non-Newtonian Nanofluid towards a Vertical Stretching Surface
Authors:   Prince Sattam bin Abdulaziz University, Nabwey Hossam
Published: October 2020

This work reports how to utilize rough set theory and group method analysis for generating a set of rules to investigate heat and mass transfer of mixed convection stagnation point flow of a non-Newtonian nanofluid towards a vertical stretching surface. By utilizing group method analysis, the main partial differential equations which describe the flow are rehabilitated to nonlinear Ordinary Differential Equations (ODEs). Then, the resultant nonlinear ODEs are solved numerically by applying the implicit finite-difference scheme. The numerical values thus obtained are depicted in tabular form and the basic principles of rough sets are applied to get all reducts and finally a group of generalized rules are extracted to predict the value of local Nusselt number and local skin-friction coefficient. The outcomes demonstrate the novelty of the current work which can be summarized as hybridization of group method analysis and rough set theory to use in the field of fluid dynamics effectively. The resultant set of generalized classification rules which performed with basic logic functions can be considered as knowledge base with high accuracy and may be valuable in many engineering applications like power production, thermal extrusion systems and microelectronics.
PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes
Authors:   Brawijaya University, Ratnawati Dian , Marjono Marjono ,   Brawijaya University, Widodo Widodo ,   Brawijaya University, Anam Syaiful ,   Brawijaya University
Published: October 2020

Classification of active compounds is necessary to trivialize their properties and activities and can be carried out by identifying functional groups and their structures based on the simplified molecular input line entry system (SMILES) code. This study classified active compounds based on the SMILES structure using a combination of the machine learning techniques, extreme learning machine (ELM), and particle swarm optimization (PSO). ELM facilitates learning very quickly and at with high predictive accuracy. The PSO covers the limitations of the ELM, i.e., randomly determining the weight and bias, trial, and error in determining the number of hidden neurons. PSO optimizes the weight, bias, and number of hidden neurons. This study’s results indicate that the classification of active compounds’ function based on the SMILES code can be done by the ELM and PSO-ELM with high accuracy. The PSO-ELM can improve the accuracy performance of the ELM classification by optimizing the weight, bias, and number of hidden neurons automatically. Moreover, the proposed PSO-ELM is better than the PSO-ELM for average accuracy, computation time, and standard deviation. The proposed algorithm has the highest average accuracy compared to the ELM by increasing the accuracy to 2.54%, 6.43%, and 3.85% for 2, 3, and 4 classes, respectively. A comparison with other machine algorithms shows that the proposed is superior.
HR i det 21. århundrede: Udviklingstrends og nye roller
Authors: B. Andersen Brian , S. Andersen Joakim , Brøns Kringelum Louise
Published: October 2020

Flere HR-afdelinger er i en transformationsproces for at gøre den værdi, de medvirker til at skabe i organisationen mere eksplicit. Samtidig har den teknologiske udvikling medvirket til en omfattende automatisering af de klassiske HR-opgaver, som skaber nye præmisser for, hvordan HR-afdelinger kan medvirke til at skabe vækst og organisatorisk succes.

I indeværende artikel præsenteres de udfordringer, som HR-afdelinger står over i det 21. århundrede gennem et litteraturreview af den seneste forskning inden for HR-feltet. Gennem interviews med HR-ansvarlige i tre større danske organisationer diskuteres de fremtidige udfordringer i en dansk kontekst. Derved sættes fokus på HR’s rolle, værdiskabelse og relevans for organisationers udvikling i det 21. århundrede.
Walking with Soldiers: How I learned to stop worrying and love cadets
Authors: Susanna Hast
Published: October 2020

“Walking with Soldiers” examines an auto-ethnographic moment of marching across the city of Helsinki with first-year cadets of the Finnish National Defence University. In a reparative reading, the walk dismantles boundaries of bodies, critiques, and affects. Through a walking methodology and autoethnography, the present exposition demonstrates how the author began orienteering within military structures through an affective investment. The exposition is a researcher’s journey across subjectivities and difference in a female civilian body. Epistemologically, it brings theory closer to the skin; and empirically, it offers insight into the affective world of belonging. “Walking with Soldiers” is multimodal and polyphonic: it consists of a text for reading, three audio tracks for listening and co-walking, as well as illustrations created by Julia Järvelä based on photographs taken by the author. The provided materials can be selectively attended to. The artistic technique used in the exposition is seduction: the reader/listener is invited into an experience. The exposition is a conversation between critical military studies and artistic research: it gives artistic attention to a military march and places importance on the acoustic and vibrating qualities of academic research. The writing itself subverts the practice of authoritative scholarly writing by presenting descriptive work as theoretical work, and by using citations as companions from the outside.