Online Profiles Research Articles

Online monitoring of profiles via function‐on‐scalar model with an application to industrial busbar

AbstractIn Statistical Process Control, profile monitoring is used for monitoring whether the process is in‐control or not by checking the relationship stability between the process response and covariates. In the industrial process recently, the functional relationship between the process variables is more and more complex, and the profile data are often time‐dependent and subject to within‐profile autocorrelation. We need to use appropriate models to characterize these complex relationships, and design new profile monitoring schemes to ensure satisfactory process monitoring performance. This paper studies a practical nonparametric profile monitoring problem, in which the functional relationship is in the form of function‐on‐scalar and within‐profile data are autocorrelated. A new online profile monitoring scheme is proposed to monitor process anomalies in real time. In Phase Ⅰ, based on the in‐control historical data, the model for the process is established estimated. Then, using the monitoring statistics based on penalty spline smoothing, the function‐on‐scalar monitoring parameters control scheme with a moving window is presented, and is applied to the anomaly monitoring of online sequential profile data. A large number of numerical simulation results show that in the presence of the function‐on‐scalar relationship, the proposed scheme has a superior performance in various cases. Finally, an application example of industrial busbar running process monitoring is given to illustrate the effectiveness of the scheme.

Striving for gender equity at theMedical Journal of Australia

Striving for gender equity at the<i>Medical Journal of Australia</i>

Digital Skills and Self-employment among Graduates of Technical Higher Education Institutions in Tanzania

Digital skills are the driving forces for innovative, inclusive and sustainable growth through innovations and digitalization which in turn stimulate job creation, alleviate poverty, reducing inequality, facilitating the delivery of goods and services. However, large number of graduates is shown to be weak in digital skills in the industry hence affect their employability including self-employment. Recently, the Technical Higher Education Institutions (THEIs) took some initiatives to instill the digital skills to their respective students. This study therefore assessed the level of competence of digital skills among graduates following the initiatives taken by the THEIs and their influence on self-employment. It specifically identified the digital skills and ranking the digital skills acquired by the graduates when in the THEIs. It likewise established relationship between digital skills and self-employment among the graduates. The data were collected using a questionnaire from 500 graduates sampled through a stratified random sampling technique. The collected data were analyzed using Descriptive Statistics and Chi-square. The findings show that, the surveyed graduates acquired basic digital skills (i.e. creating professional online profiles, word processing, using keyboards and touch-screens, managing privacy settings, emails) to the large extent (53%); intermediate digital skills (i.e. desktop publishing, digital graphic design, digital marketing) to the small extent (52%) and advanced digital skills (i.e. big data, artificial intelligence, digital entrepreneurship, cybersecurity, internet of things, virtual reality) to the very small extent (54%) when in the college/university. Furthermore, intermediate and advanced digital skills were ranked higher as digital skills for self-employment by the graduates. All three digital skills were found positive and significant for self-employment among the graduates in Tanzania. The study recommends that, the colleges, institutes and universities should emphatically inculcate all the digital skills to the students as they are both significant for self-employment of their respective graduates.

Network Pharmacology-Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury.

Astragali Radix (AR) has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. However, little is known about the effects of AR on acetaminophen (APAP)-induced liver injury (ALI). In the current study, a network pharmacology–based approach was applied to characterize the action mechanism of AR on ALI. All compounds of AR were obtained from the corresponding databases, and active compounds were selected according to its oral bioavailability and drug-likeness index. The potential genes of AR were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and PubChem, whereas the potential genes related to ALI were obtained from Online databases (GeneCards and Online Mendelian Inheritance in Man) and Gene Expression Omnibus profiles. The enriched processes, pathways, and target genes of the diseases were analyzed by referring to the Search Tool for the Retrieval of Interacting Genes/Proteins database. A network constructed through Cytoscape software was used to identify the target proteins that connected the compounds in AR with the differential genes of ALI. Subsequently, the potential underlying action mechanisms of AR on ALI predicted by the network pharmacology analyses were experimentally validated in APAP-induced liver injury in mice and HL7702 cells incubated with APAP. The compound-target network included 181 targets, whereas the potential genes related to ALI were 4,621. A total of 49 AR–ALI crossover proteins, corresponding to 49 genes, were filtered into a protein–protein interaction network complex and designated as the potential targets of AR on ALI. Among the genes, the three highest-scoring genes, MYC, MAPK8, and CXCL8 were highly associated with apoptosis in ALI. Then in vitro and in vivo experiments confirmed that AR exhibited its prominent therapeutic effects on ALI mainly via regulating hepatocyte apoptosis related to inhibiting the expressions of MYC (c-Myc), MAPK8 (JNK1), and CXCL8 (IL-8). In conclusion, our study suggested that the combination of network pharmacology prediction with experimental validation might offer a useful tool to characterize the molecular mechanism of AR on ALI.

Namespotting: Username toxicity and actual toxic behavior on Reddit

Without relying on any user reports, we use algorithmic detection and Bayesian statistical methods to analyse two large data streams (329 k users) of Reddit content to study the correlations between username toxicity (of various types, such as offensive or sexually explicit) and their online toxic behavior (personal attacks, sexual harassment among others).As it turns out, username toxicity (type) is a useful predictor in online profiling. Users with toxic usernames produce more toxic content than their neutral counterparts, with the difference in predicted mean increasing with activity (predicted 1.9 vs. 1.4 toxic comments a week for users with regular activity, and 5.6 vs. 4 for top 5% active users). More users with toxic usernames engage in toxic behavior than among neutral usernames (around 40% vs. 30%). They are also around 2.2 times more likely to have their account suspended by moderators (3.2% vs. 1.5% probability of suspension for regular and 4.5% vs. 2% for top 5% users)—detailed results vary depending on the username toxicity type and toxic behavior type. Thus, username toxicity can be used in the efforts of online communities to predict toxic behavior and to provide more safety to their users.

Generalized Stochastic Matching

In this paper, we generalize the recently studied stochastic matching problem to more accurately model a significant medical process, kidney exchange, and several other applications. Up until now the stochastic matching problem that has been studied was as follows: given a graph G= (V,E), each edge is included in the realized sub-graph of G independently with probability pe, and the goal is to find a degree-bounded sub-graph Q of G that has an expected maximum matching that approximates the expected maximum matching of G. This model does not account for possibilities of vertex dropouts, which can be found in several applications, e.g. in kidney exchange when donors or patients opt out of the exchange process as well as in online freelancing and online dating when online profiles are found to be faked. Thus, we will study a more generalized model of stochastic matching in which vertices and edges are both realized independently with some probabilities pv, pe, respectively, which more accurately fits important applications than the previously studied model. We will discuss the first algorithms and analysis for this generalization of the stochastic matching model and prove that they achieve good approximation ratios. In particular, we show that the approximation factor of a natural algorithm for this problem is at least 0.6568 in unweighted graphs, and 1/2+ε in weighted graphs for some constant ε &gt;0. We further improve our result for unweighted graphs to 2/3 using edge degree constrained sub-graphs (EDCS).

Pet analytics: Predicting adoption speed of pets from their online profiles

Many animals are put up for adoption. In turn, having many available animals can overburden shelters if this number rises above a shelter’s maximum capacity. Pet adoption websites need to identify which characteristics or general information about the animals are most salient to potential pet adopters to increase adoption speed and avoid overcrowding. To solve this problem, we have applied several data mining techniques including regression, decision tree, random forest, gradient boosting, and neural network to find which technique is best at predicting adoption speed (a.k.a. length of stay - LOS), and which variable(s) are considered significant. We applied topic modeling techniques to discover latent features using information extracted from description scripts and fed them into our predictive models. Our ensemble model produced an impressive average squared error (ASE) of 1.15584 weeks. While the use of textual data helped improve the performance of regression and gradient boosting models, other models did not receive any additional benefit from textual data. Our results indicated that pet characteristics such as age, breed, quantity, and sterilization status are key factors that influence adoption speed. This study could further help shelters around the world be able to optimize their adoption process.

Ageism-Induced Anxiety of Job Seekers Aged 50–83: Preliminary Findings from A Phenomenological Case Study Problem of Practice Dissertation

Ageism in today’s job market has a range of detrimental emotional, psychological, and economic impacts on older job seekers aged 50-83. Even as such job seekers “de-age” their professional documents and online profiles, they still navigate misperceptions about older workers that disadvantage them in the recruitment and hiring process. While anecdotal evidence raises concerns about older job seekers’ equitable access to employment, empirical evidence documenting the impacts of ageism on this population remains limited, thus impeding the efficacy of their support systems. This article reports on progress from the qualitative case study that I designed for my CPED-informed Problem of Practice Dissertation that captured data from 30 job seekers, aged 50-83, through one-on-one interviews, focus groups, and questionnaires. This article offers preliminary findings from this research, documenting the negative financial, emotional, psychological, and physical effects of navigating persistent ageism for older job seekers.

Adaptive User Profiling in E-Commerce and Administration of Public Services

The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called ‘user profiling‘. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our online profiles could be beneficial both to customers and stores. When shopping at a traditional physical store, real time targeted “personalized” advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits.

International Students’ Perspectives on Online Interfaces, Identity, and Environment in a U.S. Writing Center

This article investigates the perspectives of international students in the United States who use writing centers to support their identities and to meet their writing needs and goals. Using survey and focus group data to look closely at how students use one writing center’s online Profile and more broadly at the accessibility and responsiveness of writing center services, this study found that students have much to say about their place within writing centers and how writing consultants can best support them. Listening to these international student voices reveals how writing centers and other student support services must take the initiative in opening up conversations with and among our students to create the conditions for their success.

Use of Machine Learning and Detect Fake Profiles in a Social Media Network

The social network has become an important part of our lives today which is prone to online impersonation as well as fake accounts. These online profiles are used by intruders to cause harm in online social networks (OSNs) in order to perform malicious activities in form of privacy intrusion like theft identity. So it becomes necessary in OSN, to identify the account whether it is real or fake. In this article we have proposed several algorithms for classification like deep neural network and SVM (Support Vector Machine) algorithm. It also studies comparisons of these methods for classification based on the spam account dataset that is used to select the best.

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

Image manipulation can cause many ethical, economical, and political issues for everyone. It can be used to create fake information, fake ids, fake online profiles, fake news. A proper technique for image detection and verification is thus, a hot research area, which helps the affected people to overcome the forgery attacks. Due to the increased interaction among people through social networking sites, image forgery is prevalent and it is an essential activity to detect any forged image. This paper’s main objective is to identify fraud or tampered images effectively. By using the methods of copy move forgery detection, the obtained results are more accurate in a minimum time as compared to previous algorithms.

Put on your sunglasses and smile: The secret of Airbnb hosts’ profile photos?

On Airbnb, profile photos play a crucial role in decision-making. This paper examines how hosts’ profile photos—specifically, gender, facial expression and the presence of sunglasses—affect guests’ intentions to trust and book. An experiment was conducted to seek both close- and open-ended responses (N = 524), the former analyzed statistically and the latter thematically. According to the quantitative results, female hosts were preferred to males. Positive facial expressions outperformed neutral ones. A significant interaction effect emerged such that the positive effect of a positive facial expression was stronger when sunglasses were present (vs. absent). Moreover, a mediated moderation was identified: The interaction between facial expression and the use of sunglasses on intention to book was mediated by intention to trust. Themes from the qualitative analysis complement and extend the quantitative results. Overall, the paper adds to the literature on online profile photos in the context of peer-to-peer tourism and hospitality platforms.

On-line motion profile optimization for reciprocating mechanisms

Reciprocating mechanisms are widely used in industry because a complex movement is achieved by a simple rotation of the driven axis. Given the tendency to evolve towards more energy-efficient machines and flexible production, motion profile optimization offers a cost-effective solution as it results in large energy savings without any hardware adaptions. However, the existing optimizers are used off-line because the position-dependent parameters such as load torque and inertia of the system model must be known in advance. When the actual machine differs from the model, or when parameters change during operation due to process flexibility, the off-line determined motion profile is no longer optimal and results in unnecessary energy consumption.This paper therefore presents an on-line approach in which the varying inertia is estimated on the actual machine and used for updating the motion profile. The sliding discrete Fourier transform is proposed for real-time estimation and a gradient-based algorithm combined with Chebyshev polynomials is proposed for on-line optimization. Experimental validation on an industrial pick-and-place unit proves that the presented method leads to similar energy savings as off-line optimizers, but without prior knowledge of the parameters, and is moreover capable of handling mass changes during operation.

Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities.

How physicians can get better ratings and more page views in online healthcare communities is an important issue. Based on 38,457 physicians' profiles from a popular online healthcare community in China, we used Latent Dirichlet Allocation model, which is a common topic model, to analyze the non-English text to obtain more doctor's latent characteristics. We found five of the most frequently mentioned topics. In addition to the first topic (doctor's academic rank and practice name), “research ability,” “foreign experience,” “committee position,” and “clinical experience” were included as unstructured descriptions in the doctor's profile. Inferences about physician ratings and page views could be improved if these themes were set as characteristics of physicians. Specifically, in our findings, Physicians' mentions of their “research ability” and “foreign experience” had a significant positive impact on physician ratings. Surprisingly, physicians mentioning more “clinical experience” had a significant negative impact on physician ratings. Moreover, while descriptions about “foreign experience” and “committee position” had a significant positive impact on page views, physician mentions of “research ability” had a significant negative impact on page views. These results provide new insights into the ways in which online healthcare community managers or physicians create their personal online profiles.

CrossFit\xae open performance is affected by the nature of past competition experiences

PurposeTo examine the relationships between past competition performances and 2020 CrossFit® Open (CFO) performance.MethodsA random selection from the top one thousand athletes (n = 220, 28.5 ± 4.4 years, 178 ± 7 cm, 87.5 ± 10.2 kg) were selected for this study. Overall and weekly performances (including ranks and scores) of the 2020 CFO, as well as overall ranks from all previous CFO, regional, and Games™ competitions in which they competed, were recorded from their publicly available online profile. The highest, lowest, average, and standard deviation (SD) of past rankings, as well as participation statistics (i.e., years since first appearance, total and consecutive appearances, and participation rate), were calculated for each competition stage. Relationships were then assessed between 2020 CFO performance and all past competition experience variables by calculating Kendall’s tau (τ) correlation coefficients and Bayes factors (BF10).ResultsOverall and weekly ranking of the 2020 CFO was extremely favored (p < 0.001, BF10 > 100) to be related to the athlete’s highest previous CFO rank (τ = 0.26–0.39) and individual regional appearances (τ = − 0.26 to − 0.34), as well as individual Games™ appearances (overall and for weeks 1, 3, and 4; τ = − 0.20 to − 0.22, p < 0.001, BF10 > 100). Evidence for all other significant relationships ranged from moderate to very strong (p < 0.05, BF10 = 3–100) and varied among specific 2020 CFO workouts. Few associations were noted for team competition experience, and these were generally limited to Games™ appearances (τ = − 0.12 to − 0.18, p < 0.05, BF10 = 3.3–100).ConclusionsAlthough specific relationships were found between 2020 CFO performance and individual appearances at regional and Games™ competitions, the most consistent relationships were seen with participation and ranking in past CFO competitions.

UBUMonitor: An Open-Source Desktop Application for Visual E-Learning Analysis with Moodle

An inherent requirement of teaching using online learning platforms is that the teacher must analyze student activity and performance in relation to course learning objectives. Therefore, all e-learning environments implement a module to collect such information. Nevertheless, these raw data must be processed to perform e-learning analysis and to help teachers arrive at relevant decisions for the teaching process. In this paper, UBUMonitor is presented, an open-source desktop application that downloads Moodle (Modular Object-Oriented Dynamic Learning Environment) platform data, so that student activity and performance can be monitored. The application organizes and summarizes these data in various customizable charts for visual analysis. The general features and uses of UBUMonitor are described, as are some approaches to e-teaching improvements, through real case studies. These include the analysis of accesses per e-learning object, statistical analysis of grading e-activities, detection of e-learning object configuration errors, checking of teacher activity, and comparisons between online and blended learning profiles. As an open-source application, UBUMonitor was institutionally adopted as an official tool and validated with several groups of teachers at the Teacher Training Institute of the University of Burgos.

How can I signal my quality to emerge from the crowd? A study in the crowdsourcing context

• This study aims at understanding how solvers can signal their quality attributes. • The study draws on signaling theory. • By crafting profiles rich in personal and professional information and by participating actively within the discussion blog solvers can influence their success in crowdsourcing contests. • To empirically test the developed hypotheses, an ad-hoc dataset based on a sample of 2479 solvers within the community of the 99designs crowdsourcing platform was built. Crowdsourcing contests are characterized by an intensive level of competition because of the extensive number of solvers that voluntarily self-select to participate in such contests. This study aims at understanding how solvers can signal their quality attributes through the community functionalities, i.e. the online profile and the discussion blog of the crowdsourcing platform, to improve their chances of winning crowdsourcing contests. Drawing on signaling theory, we hypothesize that signaling their skills and capabilities by crafting profiles rich in personal and professional information and by participating actively within the discussion blog by posting and commenting, solvers can influence their success in crowdsourcing contests. To empirically test the developed hypotheses, we built an ad-hoc dataset based on a sample of 2479 solvers within the community of the 99designs crowdsourcing platform. We found that crafting a detailed and informative profile has a positive impact on the success of solvers. Moreover, our results highlight a curvilinear relationship between the solvers posting and commenting activity within the discussion blog and their success. The results of this study offer important contributions to previous crowdsourcing literature and provide critical implications for solvers, seekers and contest organizers of crowdsourcing contests.

107 Composition and Gender Distribution of Editorial Boards for Top Neurosurgical Journals

INTRODUCTION: Women are underrepresented in academic medicine, most notably in surgical subspecialties. Despite increasing numbers of female neurosurgery residents, female neurosurgeons are not obtaining competitive national positions at comparable rates. Serving as a journal editor is one measure of academic success. METHODS: This is a cross-sectional study of editorial boards for the top 10 English-language neurosurgical journals (Journal of Neurosurgery, Neurosurgery, JNS: Spine, World Neurosurgery, Neurosurgical Focus, JNS: Pediatrics, Acta Neurochirurgica, Surgical Neurology International, Neurosurgical Review, and Neurosurgical Clinics). Board members were identified from journal websites. Data was obtained from journal websites, program faculty lists, or physician online profiles. Editors with medical degrees (MD or equivalent) were included. Gender differences were compared using Student’s t-test and chi-squared analysis. RESULTS: 722 board members were examined (female n=65; male n=650; unidentified n=7). Overall, women had fewer years in practice (p=0.002) while men were more likely to have additional degrees (p=0.009) and to have higher h-indices (p&lt;0.0001). Within the editorial boards of Neurosurgery and Acta Neurochirurgica, men were more likely to have been in practice longer (p=0.014;p=0.023), and to have higher h-indices (p=0.003;p=0.025). Male editors for JNS: Spine and World Neurosurgery were more likely to have higher h-indices (p=0.007;p=0.018). CONCLUSION: Women constitute ∼8% of the editorial boards for top neurosurgical journals. While this is comparable to the amount of practicing female academic neurosurgeons, much needs to be done to achieve gender parity in competitive, high-ranking positions. The differences in years in practice and h-index suggest that this discrepancy may improve with time as more women enter practice and produce scientific literature. Efforts to recruit and retain women in neurosurgery should be pursued to rectify this discrepancy.

Towards a semi-automated analysis of fish plasma by 1H NMR metabolomics - applications to aquaculture

Aquafeed is one of the main costs in aquaculture and major efforts are being made to find sustainable ingredients that could improve production sustainability and efficiency. Our research focused on the inclusion of glycerol in the feed of European seabass (Dicentrarchus labrax), as it is a cheap carbon substrate expected to enter metabolism through the glycolytic pathway. The Nuclear Magnetic Resonance (NMR) spectroscopy provides accurate and high-throughput metabolic profiles, allowing quantification and evaluation of the effects of experimental diets in the context of fish nutrition. The aim of the present study was to assess the applicability of the automated online profiling routine Bayesil to fish blood plasma and compare with the manual targeted metabolite profiling approach.After a 60-day feeding trial (0%, 2.5%, 5.0% glycerol supplemented diets), fish were sacrificed at 6 h and 24 h after last meal and blood was sampled. Filtered plasma samples were analysed by NMR, followed by two metabolite-profiling approaches: i) an automated spectral profiling provided by the Bayesil platform, and ii) a manual metabolite profiling.Spectra were analysed with the automated Bayesil routine, identifying and quantifying 51 metabolites, in a reproducible, fast and user-independent method. On the other hand, the manual approach only managed to quantify 20 metabolites. However, under supraphysiological concentrations of glycerol, namely in the 5.0% diet, the Bayesil algorithm was not able to properly integrate the glycerol and glycine signals. The Bland-Altman and PLS-DA analysis revealed differences; nonetheless, with the exception of acetic acid, creatinine, formate, glutamine and threonine, all other metabolites presented a low bias and/or a strong correlation coefficient. Overall, it is expected a reasonable tradeoff between the accuracy in metabolite quantification and the increase of variables.The automated Bayesil database may be applied as a useful tool for systematic monitoring of fish plasma profile in aquaculture research and industrial quality control analysis, to generate robust variance-based multivariate statistical models. To our knowledge, this is the first time a NMR-based high-throughput automated routine is evaluated for aquaculture research.