Search results for: negative data

Authors: Jacob Wahl, Jane Zhang

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This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

Procedia PDF Downloads 134

Authors: Khushboo Shah

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Categorical bans on the death penalty and life-without-parole sentences for juvenile offenders in a growing number of countries have established a new era in juvenile jurisprudence. This has been brought about by integration of the growing knowledge in cognitive neuroscience and appreciation of the inherent differences between adults and adolescents over the last ten years. This evolving understanding of being a child in the criminal system can be aptly reflected through policies that incorporate the mitigating traits of youth. First, the presentation will delineate the structures in cognitive neuroscience and in particular, focus on the prefrontal cortex, the amygdala, and the basal ganglia. These key anatomical structures in the brain are linked to three mitigating adolescent traits—an underdeveloped sense of responsibility, an increased vulnerability to negative influences, and transitory personality traits—that establish why juveniles have a lessened culpability. The discussion will delve into the details depicting how an underdeveloped prefrontal cortex results in the heightened emotional angst, high-energy and risky behavior characteristic of the adolescent time period or how the amygdala, the emotional center of the brain, governs different emotional expression resulting in why teens are susceptible to negative influences. Based on this greater understanding, it is incumbent that policies adequately reflect the adolescent physiology and psychology in the criminal system. However, it is important to ensure that these views are appropriately weighted while considering the jurisprudence for the treatment of children in the law. To ensure this balance is appropriately stricken, policies must incorporate the distinctive traits of youth in sentencing and legal considerations and yet refrain from the potential fallacies of absolving a juvenile offender of guilt and culpability. Accordingly, three policies will demonstrate how these results can be achieved: (1) eliminate housing of juvenile offenders in the adult prison system, (2) mandate fitness hearings for all transfers of juveniles to adult criminal court, and (3) use the post-disposition review as a type of rehabilitation method for juvenile offenders. Ultimately, this interdisciplinary approach of science and law allows for a better understanding of adolescent psychological and social functioning and can effectuate better legal outcomes for juveniles tried as adults.

Keywords: criminal law, Juvenile Justice, interdisciplinary, neuroscience

Procedia PDF Downloads 325

Authors: Lauren C. Porter, Andrew Curtis, Eric Jefferis, Susanne Mitchell

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A wealth of research has been generated examining the variation in crime across neighborhoods. However, there is also a striking degree of crime concentration within neighborhoods. A number of studies show that a small percentage of street segments, intersections, or addresses account for a large portion of crime. Not surprisingly, a focus on these crime hot spots can be an effective strategy for reducing community level crime and related ills, such as health problems. However, research is also limited in an important respect. Studies tend to use official data to identify hot spots, such as 911 calls or calls for service. While the use of call data may be more representative of the actual level and distribution of crime than some other official measures (e.g. arrest data), call data still suffer from the 'dark figure of crime.' That is, there is most certainly a degree of error between crimes that occur versus crimes that are reported to the police. In this study, we present an alternative method of identifying crime hot spots, that does not rely on official data. In doing so, we highlight the potential utility of neighborhood-insiders to identify and understand crime dynamics within geographic spaces. Specifically, we use spatial video and geo-narratives to record the crime insights of 36 police, ex-offenders, and residents of a high crime neighborhood in northeast Ohio. Spatial mentions of crime are mapped to identify participant-identified hot spots, and these are juxtaposed with calls for service (CFS) data. While there are bound to be differences between these two sources of data, we find that one location, in particular, a corner store, emerges as a hot spot for all three groups of participants. Yet it does not emerge when we examine CFS data. A closer examination of the space around this corner store and a qualitative analysis of narrative data reveal important clues as to why this store may indeed be a hot spot, but not generate disproportionate calls to the police. In short, our results suggest that researchers who rely solely on official data to study crime hot spots may risk missing some of the most dangerous places.

Keywords: crime, narrative, video, neighborhood

Procedia PDF Downloads 236

Authors: Jacob Hornik, Matti Rachamim, Ori Grossman

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Schadenfreude, a bittersweet phenomenon, is considered atypical and complicated state that might reflect ambivalent types of sentiments -a mixed of both positive and negative reactions towards others misfortunes. This brief note reports a study that examined the association between trait ambivalence, using the Trait Mixed Emotions Scale (TMES), and four different consumer schadenfreude affairs. Results propose that trait ambivalence offers a novel explanation for schadenfreude responses. Showing that trait ambivalence enhances schadenfreude, when consumers encounter misfortune type of information about a disliked or rival marketplace entity.

Keywords: schadenfreude, consumer behavior, mixed emotions, sentiments, ambivalence

Procedia PDF Downloads 123

Authors: Mo'ath F. Bataineh

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Exercise is highly effective against obesity. Milk contains several components that support exercising and physical performance. However, there is a lack of published studies on the relationship between camel milk and ability to exercise. A pilot study was conducted with the purpose of comparing the impact of milk type (Cow vs Camel) compared with water on physical performance. Seven male obese participants (age: 20.3 ± 1.5 years; BMI: 35.7 ± 2.7 kg/m2; resting heart rate: 92.7 ± 4.7 beats per minute; training frequency: 4.4 ± 0.8 days/week) were recruited for this pilot study. In a randomized counterbalanced crossover design, participants took part in 3 trials that included ingesting 3 different pre workout drinks in a random order. The pre workout drinks were water (W), whole cow milk (CW), and whole camel milk (CM). On each trial day, participants were asked to report to the laboratory after an overnight fasting. Following a 15-minute short recovery period after their arrival to the laboratory, each participant was presented with a 500 ml of the assigned experimental drink and were asked to ingest it in one minute and at least 120 minutes prior to performing the 6-minute walk test. All drinks were presented at room temperature. Trials with different experimental drinks were performed on separate days. Participants were given at least 4 days of washout period between trials. The trial order was randomized to avoid bias due to learning effect. The 6-minute walk test was performed by all participants and immediately at the conclusion of the test, the covered distance in meters and the rating of perceived exertion (RPE) were recorded. All data were analysed using SPSS software (Version 29.0). The repeated measures ANOVA testing of collected data showed a significant main effect for treatment on covered distance in meters, F (2, 8) = 5.794, p=0.028 with a large effect size (partial eta squared (ηp2) =0.592). Also, LSD post hoc pairwise comparison analysis revealed that Camel milk and Cow milk were significantly (p = 0.044 and p = 0.020 respectively) superior to water in improving the covered distance during the test and that Camel milk tended to be better than Cow’s milk. The RPE values were not significantly different between experimental drinks (p>0.05). In conclusion, milk is superior to water as a pre workout drink, and camel milk is comparable to cow’s milk in enhancing ability to support a higher level of performance compared with water, therefore, camel milk could be used to replace cow’s milk as a suitable pre-exercise drink without expecting any negative consequences on physical performance. The fact that these positive results were obtained with obese individuals should encourage using camel milk without the fear of disturbing physical performance in other weight categories.

Keywords: camel milk, cow milk, obesity, physical performance, pre-workout drink

Procedia PDF Downloads 38

Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Amine M. Benmehaia, Amine Oulmane

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The supply response of agricultural commodities to changes in price incentives is an important issue for the success of any policy reform in the agricultural sector. This study aims to quantify the responsiveness of producers of some livestock commodities to price incentives in Algerian context. Time series analysis is used on annual data for a period of 52 years (1966-2018). Both co-integration and vector error correction model (VECM) are used through the Nerlove model of partial adjustment. The study attempts to determine the long-run and short-run relationships along with the magnitudes of disequilibria in the selected commodities. Results show that the short-run price elasticities are low in cow and sheep meat sectors (8.7 and 8% respectively), while their respective long-run elasticities are 16.5 and 10.5, whereas eggs and milk have very high short-run price elasticities (82 and 90% respectively) with long-run elasticities of 40 and 46 respectively. The error correction coefficient, reflecting the speed of adjustment towards the long-run equilibrium, is statistically significant and have the expected negative sign. Its estimates are 12.7 for cow meat, 33.5 for sheep meat, 46.7 for eggs and 8.4 for milk. It seems that cow meat and milk producers have a weak feedback of about 12.7% and 8.4% respectively of the previous year's disequilibrium from the long-run price elasticity, whereas sheep meat and eggs producers adjust to correct long run disequilibrium with a high speed of adjustment (33.5% and 46.7 % respectively). The implication of this is that much more in-depth research is needed to identify those factors that affect agricultural supply and to describe the effect of factors that shift supply in response to price incentives. This could provide valuable information for government in the use of appropriate policy measures.

Keywords: Algeria, cointegration, livestock, supply response, vector error correction model

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Authors: Ibrahim A. Adeniran, Rui F. Zhu, Man S. Wong

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Owing to the insufficiency in the spatial representativeness and continuity of in situ temperature measurements from weather stations (WS), the use of temperature measurement from WS for large-range diurnal analysis in heterogenous landscapes has been limited. This has made the accurate estimation of land surface temperature (LST) from remotely sensed data more crucial. Moreover, the study of dynamic interaction between the atmosphere and the physical surface of the Earth could be enhanced at both annual and diurnal scales by using optimal LST data derived from satellite sensors. The tradeoff between the spatial and temporal resolution of LSTs from satellite’s thermal infrared sensors (TIRS) has, however, been a major challenge, especially when high spatiotemporal LST data are recommended. It is well-known from existing literature that polar satellites have the advantage of high spatial resolution, while geostationary satellites have a high temporal resolution. Hence, this study is aimed at designing a framework for the cross-comparison of LST data from polar and geostationary satellites in a heterogeneous landscape. This could help to understand the relationship between the LST estimates from the two satellites and, consequently, their integration in diurnal LST analysis. Landsat-8 satellite data will be used as the representative of the polar satellite due to the availability of its long-term series, while the Himawari-8 satellite will be used as the data source for the geostationary satellite because of its improved TIRS. For the study area, Hong Kong Special Administrative Region (HK SAR) will be selected; this is due to the heterogeneity in the landscape of the region. LST data will be retrieved from both satellites using the Split window algorithm (SWA), and the resulting data will be validated by comparing satellite-derived LST data with temperature data from automatic WS in HK SAR. The LST data from the satellite data will then be separated based on the land use classification in HK SAR using the Global Land Cover by National Mapping Organization version3 (GLCNMO 2013) data. The relationship between LST data from Landsat-8 and Himawari-8 will then be investigated based on the land-use class and over different seasons of the year in order to account for seasonal variation in their relationship. The resulting relationship will be spatially and statistically analyzed and graphically visualized for detailed interpretation. Findings from this study will reveal the relationship between the two satellite data based on the land use classification within the study area and the seasons of the year. While the information provided by this study will help in the optimal combination of LST data from Polar (Landsat-8) and geostationary (Himawari-8) satellites, it will also serve as a roadmap in the annual and diurnal urban heat (UHI) analysis in Hong Kong SAR.

Keywords: automatic weather station, Himawari-8, Landsat-8, land surface temperature, land use classification, split window algorithm, urban heat island

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Authors: Ruchi Yadav, Shivani Pandey, Prachi Srivastava

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Microarrays provide a rich source of data on the molecular working of cells. Each microarray reports on the abundance of tens of thousands of mRNAs. Virtually every human disease is being studied using microarrays with the hope of finding the molecular mechanisms of disease. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation. A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project based on the R programming language. This paper describes specific procedures for conducting quality assessment, visualization and preprocessing of Affymetrix Gene Chip and also details the different bioconductor packages used to analyze affymetrix microarray data and describe the analysis and outcome of each plots.

Keywords: microarray analysis, R language, affymetrix visualization, bioconductor

Procedia PDF Downloads 473

Authors: K. A. Razhiyah

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Disruptive behavior is a problem that is usually associated with those with autism. There is a need to overcome this behavioral problem because the negative impact of this problem does not only effect the social relation of the students but also can cause uneasiness to those around them. This condition will be worse if the techniques used failed to motivate students to change the behaviour. The purpose of this study was to determine the effect of the circus games technique on the disruptive behavior amongst students with autism. The positive results of the intervention that was carried out for three months show the reduction in disruptive behaviour, and also improvement in the turn-taking and focusing ability aspect. Positive changes shown by the students had an encouraging effect and in a way are helping them in the teaching and learning process.

Keywords: autism, desruptive behaviour, circus, effect

Procedia PDF Downloads 241

Authors: Najrullah Khan, Athar Ali Khan

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The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation.

Keywords: Bayesian Inference, JAGS, Laplace Approximation, LaplacesDemon, posterior, R Software, simulation

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Authors: Georges Jabbour

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Background and aims: This two-year follow-up study explores the long-term sustainability of physical activity (PA) levels in young people with type 1 diabetes, focusing on the relationship between PA, hypoglycemia, and behavioral scores. The literature highlights the importance of PA and its health benefits, as well as the barriers to engaging in PA practices. Studies have shown that individuals with high levels of vigorous physical activity have higher fear of hypoglycemia (FOH) scores and more hypoglycemia episodes. Considering that hypoglycemia episodes are a major barrier to physical activity, and many studies reported a negative association between PA and high FOH scores, it cannot be guaranteed that those experiencing hypoglycemia over a long period will remain active. Building on that, the present work assesses whether high PA levels, despite elevated hypoglycemia risk, can be maintained over time. The study tracks PA levels at one and two years, correlating them with hypoglycemia instances and Fear of Hypoglycemia (FOH) scores. Materials and methods: A self-administered questionnaire was completed by 61 youth with T1D, and their PA was assessed. Hypoglycemia episodes, fear of hypoglycemia scores and HbA1C levels were collected. All assessments were realized at baseline (visit 0: V0), one year (V1) and two years later (V2). For the purpose of the present work, we explore the relationships between PA levels, hypoglycemia episodes, and FOH scores at each time point. We used multiple linear regression to model the mean outcomes for each exposure of interest. Results: Findings indicate no changes in total moderate to vigorous PA (MVPA) and VPA levels among visits, and HbA1c (%) was negatively correlated with the total amount of VPA per day in minutes (β= -0.44; p=0.01, β= -0.37; p=0.04, and β= -0.66; p=0.01 for V0, V1, and V2, respectively). Our linear regression model reported a significant negative correlation between VPA and FOH across the visits (β=-0.59, p=0.01; β= -0.44, p=0.01; and β= -0.34, p=0.03 for V0, V1, and V2, respectively), and HbA1c (%) was influenced by both the number of hypoglycemic episodes and FOH score at V2 (β=0.48; p=0.02 and β=0.38; p=0.03, respectively). Conclusion: The sustainability of PA levels and HbA1c (%) in young individuals with type 1 diabetes is influenced by various factors, including fear of hypoglycemia. Understanding these complex interactions is essential for developing effective interventions to promote sustained PA levels in this population. Our results underline the necessity of a multi-strategic approach to promoting active lifestyles among diabetic youths. This approach should synergize PA enhancement with vigilant glucose monitoring and effective FOH management.

Keywords: physical activity, hypoglycemia, fear of hypoglycemia, youth

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Authors: R. Morbidelli, C. Saltalippi, A. Flammini, A. Garcia-Marin, J. L. Ayuso-Munoz

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The climate change, mainly due to greenhouse gas emissions associated to human activities, has been modifying hydrologic processes with a direct effect on air surface temperature that has significantly increased in the last century at global scale. In this context the Mediterranean area is considered to be particularly sensitive to the climate change impacts on temperature indices. An analysis finalized to study the evolution of temperature indices and to check the existence of significant trends in the Umbria Region (Italy) is presented. Temperature data were obtained by seven meteorological stations uniformly distributed in the study area and characterized by very long series of temperature observations (at least 60 years) spanning the 1924-2015 period. A set of 39 temperature indices represented by monthly and annual mean, average maximum and average minimum temperatures, has been derived. The trend analysis was realized by applying the non-parametric Mann-Kendall test, while the non-parametric Pettit test and the parametric Standard Normal Homogeneity test (SNHT) were used to check the presence of breakpoints or in-homogeneities due to environmental changes/anthropic activity or climate change effects. The Umbria region, in agreement with other recent studies exploring the temperature behavior in Italy, shows a general increase in all temperature indices, with the only exception of Gubbio site that exhibits very light negative trends or absence of trend. The presence of break points and in-homogeneity was widely explored through the selected tests and the results were checked on the basis of the well-known metadata of the meteorological stations.

Keywords: reception theory, reading, literary translation, horizons of expectation, reader

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

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Authors: Vladimir Simovic, Matija Varga, Predrag Oreski

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XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

Keywords: XHTML, XBRL, XML, JavaScript, AJAX technology, data exchange

Procedia PDF Downloads 392

Authors: Aymen Sekhri, Emmanuel Kwabena Frimpong, Bolaji Mubarak Ayeyemi, Aleksi Hirvonen, Matias Hirvonen, Tedros Tesfay Andemichael

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Fixation identification, which involves isolating and identifying fixations and saccades in eye-tracking protocols, is an important aspect of eye-movement data processing that can have a big impact on higher-level analyses. However, fixation identification techniques are frequently discussed informally and rarely compared in any meaningful way. With two state-of-the-art algorithms, we will implement fixation detection and analysis in this work. The velocity threshold fixation algorithm is the first algorithm, and it identifies fixation based on a threshold value. For eye movement detection, the second approach is U'n' Eye, a deep neural network algorithm. The goal of this project is to analyze and visualize eye-tracking data from an eye gaze dataset that has been provided. The data was collected in a scenario in which individuals were shown photos and asked whether or not they recognized them. The results of the two-fixation detection approach are contrasted and visualized in this paper.

Keywords: human-computer interaction, eye-tracking, CNN, fixations, saccades

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Authors: Sourour Ramzi, Claudia Sarrico

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Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

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Authors: Park Jin Hee

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The purpose of this research is to analyze the meaning and essence of stress experiences school counselors experience while providing counseling. For this research, interviews were conducted with ten school counselors and analyze the interview findings by using phenomenological method of Colaizzi. As for the conclusion in this research, several of types of emotions and mindset of school counselors were examined, and the positive and negative effects of such emotions and mindset on counseling process were shown.

Keywords: counseling stress experience, meaning, school counselors, phenomenological study

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Authors: Bangwon Seo

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In this paper, we consider multicast device-to-device (D2D) direct communication systems in cellular networks. In multicast D2D communications, nearby mobile devices exchanges, their data directly without going through a base station and a D2D transmitter send its data to multiple D2D receivers that compose of D2D multicast group. We consider wiretap channel where there is an eavesdropper that attempts to overhear the transmitted data of the D2D transmitter. In this paper, we propose a secure transmission scheme in D2D multicast communications in cellular networks. In order to prevent the eavesdropper from overhearing the transmitted data of the D2D transmitter, a precoding vector is employed at the D2D transmitter in the proposed scheme. We perform computer simulations to evaluate the performance of the proposed scheme. Through the simulation, we show that the secrecy rate performance can be improved by selecting an appropriate precoding vector.

Keywords: device-to-device communications, wiretap channel, secure transmission, precoding

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Authors: Andrzej Poszewiecki

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The presented paper contributes to the experimental current of research on privacy. The question of privacy is being discussed at length at present, primarily among lawyers and politicians. However, the matter of privacy has been of interest for economists for some time as well. The valuation of privacy by people is of great importance now. This article is about how people valuate their privacy. An experimental method has been utilised in the conducted research – the survey was carried out among customers of an online store, and the studied issue was whether their readiness to sell their data (WTA) was different from the willingness to buy data back (WTP). The basic aim of this article is to analyse whether people shopping on the Internet differentiate their privacy depending on whether they protect or sell it. The achieved results indicate the presence of major differences in this respect, which do not always come up with the original expectations. The obtained results have supported the hypothesis that people are more willing to sell their data than to repurchase them. However, the hypothesis that the value of proposed remuneration affects the willingness to sell/buy back personal data (one’s privacy) has not been supported.

Keywords: privacy, experimental economics, behavioural economics, internet

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Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

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The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: big data, COVID-19, health, indexing, NoSQL, sharding, scalability, well being

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Authors: B. Nazari, M. A. Mohammadifar, S. Shojaee-Aliabadi, L. Mirmoghtadaie

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Millets as a new source of plant protein were not used in food applications due to its poor functional properties. In this study, the effect of high intensity ultrasound (frequency: 20 kHz, with contentious flow) (US) in 100% amplitude for varying times (5, 12.5, and 20 min) on solubility, emulsifying activity index (EAI), emulsion stability (ES), foaming capacity (FC), and foaming stability (FS) of millet protein concentrate (MPC) were evaluated. In addition, the structural properties of best treatments such as molecular weight and surface charge were compared with the control sample to prove the US effect. The US treatments significantly (P<0.05) increased the solubility of the native MPC (65.8±0.6%) at all sonicated times with the maximum solubility that is recorded at 12.5 min treatment (96.9±0.82 %). The FC of MPC was also significantly affected by the US treatment. Increase in sonicated time up to 12.5 min significantly increased the FC of native MPC (271.03±4.51 ml), but higher increase reduced it significantly. Minimal improvements were observed in the FS of all sonicated MPC compared to the native MPC. Sonicated time for 12.5 min affected the EAI and ES of the native MPC more markedly than 5 and 20 min that may be attributed to higher increase in proteins tendency to adsorption at the oil and water interfaces after the US treatment at this time. SDS-PAGE analysis showed changes in the molecular weight of MPC that attributed to shearing forces created by cavitation phenomenon. Also, this phenomenon caused an increase in the exposure of more amino acids with negative charge in the surface of US treated MPC, that was demonstrated by Zetasizer data. High intensity ultrasound, as a green technology, can significantly increase the functional properties of MPC and can make this usable for food applications.

Keywords: functional properties, high intensity ultrasound, millet protein concentrate, structural properties

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Authors: Andreas Aceranti, Simonetta Vernocchi, Marco Colorato, Massimo Briamo, Giovanni Abalsamo

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Most of the benefits of sport come from related physical activity, however, there are secondary psychological positive effects. There are also obvious disadvantages, high tensions related to failure, injuries, eating disorders and burnout. Depressive symptoms and illnesses related to anxiety or stress can be preventable or even simply alleviated through regular activity and exercise. It has been shown that the practice of a sport brings physical benefits, but can also have psychological and spiritual benefits. Reduced performance in male individuals has been linked to sexual activity before competitions in the past. The long-standing debate about the impact of sexual activity on sports performance has been controversial in the mainstream media in recent decades. This salacious topic has generated extensive discussion, although its high-quality data has been limited. Literature has, so far, mainly included subjective assessments from surveys. However, such surveys can be skewed as these assessments are based on individual beliefs, perceptions, and memory. There has been a long discussion over the years but even there objective data has been lacking. One reason behind coaches' bans on sexual activity before sporting events may be the belief that abstinence increases frustration, which in turn is shifted into aggressive behavior toward competitors. However, this assumption is not always valid. In fact, depriving an athlete of a normal activity can cause feelings of guilt and loss of concentration. Sexual activity during training can promote relaxation and positively influence performance. The author concludes that, although there is a need for scientific research in this area, it seems that sexual intercourse does not decrease performance unless it is accompanied by late night socialization, loss of sleep or drinking. Although the effects of sexual engagement on aerobic and strength athletic performance have not been definitively established, most research seems to rule out a direct impact. In order to analyze, as much as possible without bias, whether sexual activity significantly affects an athletic performance or not, we sampled 5 amateur athletes, between 22 and 25 years old and all male. The study was based on the timing of 4 running races of 5 champions. We asked participants to respect guidelines to avoid sexual activity (sex or masturbation) 12 hours before 2 of the 4 competitions, and to practice before the remaining 2 races.In doing so, we were able to compare and analyze the impact of activity and abstinence on performance results. We have come to the conclusion that sexual behavior on athletic performance needs to be better understood, more randomized trials and high-quality controls are strongly needed but available information suggests that sexual activity the day before a race has no negative effects on performance.

Keywords: sex, masturbation, male performance, soccer

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Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Humphrey Atwijukiire, Gladys Nakidde, Anne Tweheyo Otwine, Jane Kabami

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Experiences of HIV Positive Serostatus Disclosure to Sexual Partner Among Individuals in Discordant Couples in Mbarara City, Southwestern Uganda Introduction: Disclosure of HIV status is key in HIV management. Despite many studies on serostatus disclosure, there is a gap in experiences regarding HIV status disclosure among discordant couples. This study explored the lived experiences of serostatus disclosure among discordant couples in Mbarara City, South Western Uganda. Methods: We conducted 12 in-depth interviews using translated interview guide, and audio recorders. Participants were purposively enrolled in the study. The study was conducted at three public health facilities in Mbarara City. Data was analyzed using thematic content analysis. Approval for this research was obtained from Mbarara University Research Ethics Committee and administrative clearance from city clerk of Mbarara City. Results: The mean age of participants was 38 years. An equal number of males (six) and females participated. Most of them had at least secondary level education, only three had primary education. Experienced benefits of HIV serostatus disclosure included: social support and care; decisions regarding health, fertility, and child bearing; sharing information on HIV prevention and protection; positive living; and, ease of HIV disclosure. The challenges included: misunderstandings in the families. Conclusion: Socially, psychologically and financially PLWHIV have benefited from their negative partners. Health wise, they have been supported, and cared for, but some have faced challenges, such as family misunderstandings. Couple HIV counseling and testing by a trained health worker is beneficial in HIV care and could mitigate the challenges related HIV serostatus disclosure.

Keywords: discordant couples, disclosure, experiences, HIV

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Authors: Sara Bonetti

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The use of quantitative data to support policies and justify investments has become imperative in many fields including the field of education. However, the topic of data literacy has only marginally touched the early care and education (ECE) field. In California, within the ECE workforce, there is a group of professionals working in policy and advocacy that use quantitative data regularly and whose educational and professional experiences have been neglected by existing research. This study aimed at analyzing these experiences in accessing, using, and producing quantitative data. This study utilized semi-structured interviews to capture the differences in educational and professional backgrounds, policy contexts, and power relations. The participants were three key professionals from county-level organizations and one working at a State Department to allow for a broader perspective at systems level. The study followed Núñez’s multilevel model of intersectionality. The key in Núñez’s model is the intersection of multiple levels of analysis and influence, from the individual to the system level, and the identification of institutional power dynamics that perpetuate the marginalization of certain groups within society. In a similar manner, this study looked at the dynamic interaction of different influences at individual, organizational, and system levels that might intersect and affect ECE professionals’ experiences with quantitative data. At the individual level, an important element identified was the participants’ educational background, as it was possible to observe a relationship between that and their positionality, both with respect to working with data and also with respect to their power within an organization and at the policy table. For example, those with a background in child development were aware of how their formal education failed to train them in the skills that are necessary to work in policy and advocacy, and especially to work with quantitative data, compared to those with a background in administration and/or business. At the organizational level, the interviews showed a connection between the participants’ position within the organization and their organization’s position with respect to others and their degree of access to quantitative data. This in turn affected their sense of empowerment and agency in dealing with data, such as shaping what data is collected and available. These differences reflected on the interviewees’ perceptions and expectations for the ECE workforce. For example, one of the interviewees pointed out that many ECE professionals happen to use data out of the necessity of the moment. This lack of intentionality is a cause for, and at the same time translates into missed training opportunities. Another interviewee pointed out issues related to the professionalism of the ECE workforce by remarking the inadequacy of ECE students’ training in working with data. In conclusion, Núñez’s model helped understand the different elements that affect ECE professionals’ experiences with quantitative data. In particular, what was clear is that these professionals are not being provided with the necessary support and that we are not being intentional in creating data literacy skills for them, despite what is asked of them and their work.

Keywords: data literacy, early childhood professionals, intersectionality, quantitative data

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Pui-Lai To, Chechen Liao, Hen-Yi Huang

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Twenty years after Internet development, scholars have started to identify the negative impacts brought by the Internet. Overuse of Internet could develop Internet dependency and in turn cause addiction behavior. Therefore understanding the phenomenon of Internet addiction is important. With the joint efforts of experts and scholars, Internet addiction has been officially listed as a symptom that affects public health, and the diagnosis, causes and treatment of the symptom have also been explored. On the other hand, in the area of smartphone Internet usage, most studies are still focusing on the motivation factors of smartphone usage. Not much research has been done on smartphone Internet addiction. In view of the increasing adoption of smartphones, this paper is intended to find out whether smartphone Internet addiction exists in modern society or not. This study adopted the research methodology of online survey targeting users with smartphone Internet experience. A total of 434 effective samples were recovered. In terms of data analysis, Partial Least Square (PLS) in Structural Equation Modeling (SEM) is used for sample analysis and research model testing. Software chosen for statistical analysis is SPSS 20.0 for windows and SmartPLS 2.0. The research result successfully proved that smartphone users who access Internet service via smartphone could also develop smartphone Internet addiction. Factors including flow experience, depression, virtual social support, smartphone Internet affinity and maladaptive cognition all have significant and positive influence on smartphone Internet addiction. In the scenario of smartphone Internet use, descriptive norm has a positive and significant influence on perceived playfulness, while perceived playfulness also has a significant and positive influence on flow experience. Depression, on the other hand, is negatively influenced by actual social support and positive influenced by the virtual social support.

Keywords: internet addiction, smartphone usage, social support, perceived playfulness

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