nils-herrmann/clean_scirepeval huggingface dataset:
Media Effects on Young Women ’ s Body Image Concerns : Theoretical Perspectives and an Agenda for Research

Although there is a voluminous literature on mass media effects on body image concerns of young adult women in the U.S., there has been relatively little theoretically-driven research on processes and effects of social media on young women’s body image and self-perceptions. Yet given the heavy online presence of young adults, particularly women, and their reliance on social media, it is important to appreciate ways that social media can influence perceptions of body image and body image disturbance. Drawing on communication and social psychological theories, the present article articulates a series of ideas and a framework to guide research on social media effects on body image concerns of young adult women. The interactive format and content features of social media, such as the strong peer presence and exchange of a multitude of visual images, suggest that social media, working via negative social comparisons, transportation, and peer normative processes, can significantly influence body image concerns. A model is proposed that emphasizes the impact of predisposing individual vulnerability characteristics, social media uses, and mediating psychological processes on body dissatisfaction and eating disorders. Research-based ideas about social media effects on male body image, intersections with ethnicity, and ameliorative strategies are also discussed.
Genistein, a component of soy, inhibits the expression of the EGF and ErbB2/Neu receptors in the rat dorsolateral prostate.

Epidemiological reports suggest that Asians consuming a diet high in soy have a low incidence of prostate cancer. In animal models, soy and genistein have been demonstrated to suppress the development of prostate cancer. In this study, we investigate the mechanism of action, bioavailability, and potential for toxicity of dietary genistein in a rodent model.



Lobund-Wistar rats were fed a 0.025-1.0-mg genistein/g AIN-76A diet. The dorsolateral prostate was subjected to Western blot analysis for expression of tyrosine-phosphorylated proteins, and of the EGF and ErbB2/Neu receptors. Genistein concentrations were measured from serum and prostate using HPLC-mass spectrometry. Body and prostate weights, and circulating testosterone levels, were measured.



Increasing concentrations of genistein in the diet inhibited tyrosine-phosphorylated proteins with molecular weights of 170,000 and 85,000 in the dorsolateral prostate. Western blot analysis revealed that the 1-mg genistein/g AIN-76A diet inhibited by 50% the expression of the EGF receptor and its phosphorylation. In rats fed this diet, serum-free and total genistein concentrations were 137 and 2,712 pmol/ml, respectively. The free and total genistein IC50 values for the EGF receptor were 150 and 600 pmol/g prostate tissue, respectively. Genistein in the diet also inhibited the ErbB2/Neu receptor. Body and dorsolateral prostate weights, and circulating testosterone concentrations, were not adversely effected from exposure to genistein in the diet for 3 weeks.



We conclude that genistein in the diet can downregulate the EGF and ErbB2/Neu receptors in the rat prostate with no apparent adverse toxicity to the host. The concentration needed to achieve a 50% reduction in EGF receptor expression can be achieved by eating a diet high in soy products or with genistein supplementation. Genistein inhibition of the EGF signaling pathway suggests that this phytoestrogen may be useful in both protecting against and treating prostate cancer.
Validation of a Greek version of PSS-14; a global measure of perceived stress.

To evaluate validity of the Greek version of a global measure of perceived stress PSS-14 (Perceived Stress Scale - 14 item).



The original PSS-14 (theoretical range 0-56) was translated into Greek and then back-translated. One hundred men and women (39 +/- 10 years old, 40 men) participated in the validation process. Firstly, participants completed the Greek PSS-14 and, then they were interviewed by a psychologist specializing in stress management. Cronbach's alpha (a) evaluated internal consistency of the measurement, whereas Kendall's tau-b and Bland & Altman methods assessed consistency with the clinical evaluation. Exploratory and Confirmatory Factor analyses were conducted to reveal hidden factors within the data and to confirm the two-dimensional character of the scale.



Mean (SD) PSS-14 score was 25(7.9). Strong internal consistency (Cronbach's alpha = 0.847) as well as moderate-to-good concordance between clinical assessment and PSS-14 (Kendall's tau-b = 0.43, p < 0.01) were observed. Two factors were extracted. Factor one explained 34.7% of variability and was heavily laden by positive items, and factor two that explained 10.6% of the variability by negative items. Confirmatory factor analysis revealed that the model with 2 factors had chi-square equal to 241.23 (p < 0.001), absolute fix indexes were good (i.e. GFI = 0.733, AGFI = 0.529), and incremental fix indexes were also adequate (i.e. NFI = 0.89 and CFI = 0.92).



The developed Greek version of PSS-14 seems to be a valid instrument for the assessment of perceived stress in the Greek adult population living in urban areas; a finding that supports its local use in research settings as an evaluation tool measuring perceived stress, mainly as a risk factor but without diagnostic properties.
Stories the ocean tells us: the making of "Moana"

Telling the story of "Moana" became one of the most ambitious things we've ever done at the Walt Disney Animation Studios. We felt a huge responsibility to properly celebrate the culture and mythology of the Pacific Islands, in an epic tale involving demigods, monsters, vast ocean voyages, beautiful lush islands, and a sweeping musical visit to the village and people of Motunui. Join us as we discuss our partnership with our Pacific Islands consultants, known as our "Oceanic Story Trust," the research and development we pursued, and the tremendous efforts of our team of engineers, artists and storytellers who brought the world of "Moana" to life.
Design of Secure and Lightweight Authentication Protocol for Wearable Devices Environment

Wearable devices are used in various applications to collect information including step information, sleeping cycles, workout statistics, and health-related information. Due to the nature and richness of the data collected by such devices, it is important to ensure the security of the collected data. This paper presents a new lightweight authentication scheme suitable for wearable device deployment. The scheme allows a user to mutually authenticate his/her wearable device(s) and the mobile terminal (e.g., Android and iOS device) and establish a session key among these devices (worn and carried by the same user) for secure communication between the wearable device and the mobile terminal. The security of the proposed scheme is then demonstrated through the broadly accepted real-or-random model, as well as using the popular formal security verification tool, known as the Automated validation of Internet security protocols and applications. Finally, we present a comparative summary of the proposed scheme in terms of the overheads such as computation and communication costs, security and functionality features of the proposed scheme and related schemes, and also the evaluation findings from the NS2 simulation.
KnoVA: a reference architecture for knowledge-based visual analytics

The increase in storage capacity and the progress in information technology today lead to a rapid growth in the amount of stored data. In increasing amounts of data, gaining insight becomes rapidly more difficult. Existing automatic analysis approaches are not sufficient for the analysis of the data. The problem that the amount of stored data increases faster than the computing power to analyse the data is called information overload phenomenon. Visual analytics is an approach to overcome this problem. It combines the strengths of computers to quickly identify re-occurring patterns and to process large amounts of data with human strengths such as flexibility, intuition, and contextual knowledge. In the process of visual analytics knowledge is applied by expert users to conduct the analysis. In many settings the expert users will apply the similar knowledge continuously in several iterations or across various comparable analytical tasks. This approach is time consuming, costly and possibly frustrating for the expert users. Therefore a demand for concepts and methods to prevent repetitive analysis steps can be identified. This thesis presents a reference architecture for knowledge-based visual analytics systems, the KnoVA RA, that provides concepts and methods to represent, extract and reapply knowledge in visual analytic systems. The basic idea of the reference architecture is to extract knowledge that was applied in the analysis process in order to enhance or to derive automated analysis steps. The objective is to reduce the work-load of the experts and to enhance the traceability and reproducibility of results. The KnoVA RA consist of four parts: a model of the analysis process, the KnoVA process model, a meta data model for knowledge-based visual analytics systems, the KnoVA meta model, concepts and algorithms for the extraction of knowledge and concepts and algorithms for the reapplication of knowledge. With these concepts, the reference architecture servers as a blueprint for knowledge-based visual analytics systems. To create the reference architecture, in this thesis, two real-world scenarios from different application domains (automotive and healthcare) are introduced. These scenarios provide requirements that lead to implications for the design of the reference architecture. On the example of the motivating scenarios the KnovA RA is implemented in two visual analytics applications: TOAD, for the analysis of message traces of in-car bus communication networks and CARELIS, for the aggregation of medical records on an interactive visual interface. These systems illustrate the applicability of the KnoVA RA across different analytical challenges and problem classes.
Understanding the Sharing Economy: The Role of Response to Negative Reviews in the Peer-to-peer Accommodation Sharing Network

Recognizing the potentially ruinous effect of negative reviews on the reputation of the hosts as well as a subjective nature of the travel experience judgements, peer-to-peer accommodation sharing platforms, like Airbnb, have readily embraced the “response” option, empowering hosts with the voice to challenge, deny or at least apologize for the subject of critique. However, the effects of different response strategies on trusting beliefs towards the host remain unclear. To fill this gap, this study focuses on understanding the impact of different response strategies and review negativity on trusting beliefs towards the host in peer-to-peer accommodation sharing setting utilizing experimental methods. Examination of two different contexts, varying in the controllability of the subject of complaint, reveals that when the subject of complaint is controllable by a host, such strategies as confession / apology and denial can improve trusting beliefs towards the host. However, when the subject of criticism is beyond the control of the host, denial of the issue does not yield guest’s confidence in the host, whereas confession and excuse have positive influence on trusting beliefs.
Nuclear Medicine Imaging of Neuroendocrine Tumors.

An important role is reserved for nuclear imaging techniques in the imaging of neuroendocrine tumors (NETs). Somatostatin receptor scintigraphy (SRS) with (111)In-DTPA-octreotide is currently the most important tracer in the diagnosis, staging and selection for peptide receptor radionuclide therapy (PRRT). In the past decade, different positron-emitting tomography (PET) tracers have been developed. The largest group is the (68)Gallium-labeled somatostatin analogs ((68)Ga-SSA). Several studies have demonstrated their superiority compared to SRS in sensitivity and specificity. Furthermore, patient comfort and effective dose are favorable for (68)Ga-SSA. Other PET targets like β-[(11)C]-5-hydroxy-L-tryptophan ((11)C-5-HTP) and 6-(18)F-L-3,4-dihydroxyphenylalanine ((18)F-DOPA) were developed recently. For insulinomas, glucagon-like peptide-1 receptor imaging is a promising new technique. The evaluation of response after PRRT and other therapies is a challenge. Currently, the official follow-up is performed with radiological imaging techniques. The role of nuclear medicine may increase with the newest tracers for PET. In this review, the different nuclear imaging techniques and tracers for the imaging of NETs will be discussed.
Predicting User-Topic Opinions in Twitter with Social and Topical Context

With popular microblogging services like Twitter, users are able to online share their real-time feelings in a more convenient way. The user generated data in Twitter is thus regarded as a resource providing individuals' spontaneous emotional information, and has attracted much attention of researchers. Prior work has measured the emotional expressions in users' tweets and then performed various analysis and learning. However, how to utilize those learned knowledge from the observed tweets and the context information to predict users' opinions toward specific topics they had not directly given yet, is a novel problem presenting both challenges and opportunities. In this paper, we mainly focus on solving this problem with a Social context and Topical context incorporated Matrix Factorization (ScTcMF) framework. The experimental results on a real-world Twitter data set show that this framework outperforms the state-of-the-art collaborative filtering methods, and demonstrate that both social context and topical context are effective in improving the user-topic opinion prediction performance.
Using ISO/IEC 12207 to Analyze Open Source Software Development Processes: An E-Learning Case Study

To date, there is no comprehensive study of open source software development process (OSSDP) carried out for open source (OS) e-learning systems. This paper presents the work which objectively analyzes the open source software development (OSSD) practices carried out by e-learning systems development communities and their results are represented using DEMO models. These results are compared using ISO/IEC 12207:2008. The comparison of DEMO models with ISO/IEC 12207 is a useful contribution; as it provides deeper understanding to-wards the OS e-learning system development.
Activities and mortality in the elderly: the Leisure World cohort study.

Although physical activity has substantial health benefits and reduces mortality, few studies have examined its impact on survival beyond age 75.



Using the population-based Leisure World Cohort Study, we explored the association of activity on all-cause mortality in older adults (median age at baseline = 74 years). We followed 8,371 women and 4,828 men for 28 years or until death (median = 13 years) and calculated relative risks for various measures of activity at baseline using Cox regression analysis for four age groups (<70, 70-74, 75-79, and 80+ years) in men and women separately.



Time spent in active activities, even ½ hour/day, resulted in significantly lower (15-35%) mortality risks compared with no time in active activities. This reduction was evident in all sex-age groups except the youngest men. Participants who reported spending 6 or more hours/day in other less physically demanding activities also had significantly reduced risks of death of 15-30%. The beneficial effect of activities was observed in both those who did and those who did not cut down their activities due to illness or injury. Neither adjustment for potential confounders, exclusion of the first 5 years of follow-up, nor exclusion of individuals with histories of chronic disease substantially changed the findings.



Participation in leisure-time activities is an important health promoter in aging populations. The association of less physically demanding activities as well as traditional physical activities involving moderate exertion with reduced mortality suggests that the protective effect of engagement in activities is a robust one.
How emotional auditory stimuli modulate time perception.

Emotional and neutral sounds rated for valence and arousal were used to investigate the influence of emotions on timing in reproduction and verbal estimation tasks with durations from 2 s to 6 s. Results revealed an effect of emotion on temporal judgment, with emotional stimuli judged to be longer than neutral ones for a similar arousal level. Within scalar expectancy theory ( Church, & W. Meck, 1984), this suggests that emotion-induced activation generates an increase in pacemaker rate, leading to a longer perceived duration. A further exploration of self-assessed emotional dimensions showed an effect of valence and arousal. Negative sounds were judged to be longer than positive ones, indicating that negative stimuli generate a greater increase of activation. High-arousing stimuli were perceived to be shorter than low-arousing ones. Consistent with attentional models of timing, this seems to reflect a decrease of attention devoted to time, leading to a shorter perceived duration. These effects, robust across the 2 tasks, are limited to short intervals and overall suggest that both activation and attentional processes modulate the timing of emotional events.
Improved Bayesian Anti-Spam Filter Implementation and Analysis on Independent Spam Corpuses

Spam emails are causing major resource wastage by unnecessarily flooding the network links. Though many anti-spam solutions have been implemented, the Bayesian spam score approach looks quite promising. A proposal for spam detection algorithm is presented and its implementation using Java is discussed, along with its performance test results on two independent spam corpuses – Ling-spam and Enron-spam. We use the Bayesian calculation for single keyword sets and multiple keywords sets, along with its keyword contexts to improve the spam detection and thus to get good accuracy.
An empirical comparison of Big Graph frameworks in the context of network analysis

Complex networks are heterogeneous relational data sets with nontrivial substructures and statistical properties. They are typically represented as graphs consisting of vertices and edges. The analysis of their intricate structure is relevant to many areas of science and commerce, and data sets may reach sizes that require distributed storage and processing. We describe and compare programming models for distributed computing with a focus on graph algorithms for large-scale complex network analysis. Four frameworks-GraphLab, Apache Giraph, Giraph++ and Apache Flink—are used to implement algorithms for the representative problems connected components, community detection, PageRank and clustering coefficients. The implementations are executed on a computer cluster to evaluate the frameworks’ suitability in practice and to compare their performance to that of the single-machine, shared-memory parallel network analysis package NetworKit. Out of the distributed frameworks, GraphLab and Apache Giraph generally show the best performance. In our experiments a cluster of eight computers running Apache Giraph enables the analysis of a network with ca. 2 billion edges, which is too large for a single machine of the same type. However, for networks that fit into memory of one machine, the performance of the shared-memory parallel implementation is usually far better than the distributed ones. The study provides experimental evidence for selecting the appropriate framework depending on the task and data volume.
DCal: A custom integrated circuit for calorimetry at the International Linear Collider

A research and development collaboration has been started with the goal of producing a prototype hadron calorimeter section for the purpose of proving the Particle Flow Algorithm concept for the International Linear Collider. Given the unique requirements of a Particle Flow Algorithm calorimeter, custom readout electronics must be developed to service these detectors. This paper introduces the DCal or Digital Calorimetry Chip, a custom integrated circuit developed in a 0.25um CMOS process specifically for this International Linear Collider project. The DCal is capable of handling 64 channels, producing a 1-bit Digital-to-Analog conversion of the input (i.e. hit/no hit). It maintains a 24-bit timestamp and is capable of operating either in an externally triggered mode or in a self-triggered mode. Moreover, it is capable of operating either with or without a pipeline delay. Finally, in order to permit the testing of different calorimeter technologies, its analog front end is capable of servicing Particle Flow Algorithm calorimeters made from either Resistive Plate Chambers or Gaseous Electron Multipliers.
Recent advancement in biosensors technology for animal and livestock health management.

The term biosensors encompasses devices that have the potential to quantify physiological, immunological and behavioural responses of livestock and multiple animal species. Novel biosensing methodologies offer highly specialised monitoring devices for the specific measurement of individual and multiple parameters covering an animal's physiology as well as monitoring of an animal's environment. These devices are not only highly specific and sensitive for the parameters being analysed, but they are also reliable and easy to use, and can accelerate the monitoring process. Novel biosensors in livestock management provide significant benefits and applications in disease detection and isolation, health monitoring and detection of reproductive cycles, as well as monitoring physiological wellbeing of the animal via analysis of the animal's environment. With the development of integrated systems and the Internet of Things, the continuously monitoring devices are expected to become affordable. The data generated from integrated livestock monitoring is anticipated to assist farmers and the agricultural industry to improve animal productivity in the future. The data is expected to reduce the impact of the livestock industry on the environment, while at the same time driving the new wave towards the improvements of viable farming techniques. This review focusses on the emerging technological advancements in monitoring of livestock health for detailed, precise information on productivity, as well as physiology and well-being. Biosensors will contribute to the 4th revolution in agriculture by incorporating innovative technologies into cost-effective diagnostic methods that can mitigate the potentially catastrophic effects of infectious outbreaks in farmed animals.