Existing Data Sets Research Articles

SASCAT: Natural language processing approach to the study of economic sanctions

Existing datasets of economic sanctions rely primarily on secondary sources and do not tend to take full advantage of government documents related to economic coercion. Such data may miss sanctions, and do not capture important details in how coercive measures are threatened, imposed and removed. The latter processes often have much to do with the domestic politics in sender countries. Understanding these processes may be necessary in order to fully account for sanctions’ effectiveness. We present a natural language processing (NLP) approach to retrieving sanctions-related government documents. We apply our method to the case of US sanctions. The United States is the world’s pre-eminent user of sanctions. Our method can be applied to other cases. We collect all sanctions events originating in the office of the US president, and all congressional sanctions, for 1988–2016. Our approach has three advantages: (1) by design, it captures all sanctions-related documents; (2) the resulting data are disaggregated by imposing branch of government; (3) the data include the original language of the measures. These features directly shed light on interbranch delegation, domestic (partisan) conflict, and policy priorities. We show that our data record more episodes than most existing sanctions’ data, and have features that other datasets lack. The availability of the original text opens up new avenues for research and analysis.

Investigation of the N14(p,γ)O15 reaction and its impact on the CNO cycle

The CNO cycle is the main energy source in massive stars during their hydrogen burning phase, and, for our sun, it contributes at the $\ensuremath{\approx}1%$ level. As the $^{14}\mathrm{N}(p,\ensuremath{\gamma})^{15}\mathrm{O}$ reaction is the slowest in the cycle, it determines the CNO energy production rate and thus the CNO contribution to the solar neutrino flux. These CNO neutrinos are produced primarily from the $\ensuremath{\beta}$ decay of $^{15}\mathrm{O}$ and, to a lesser extent, from the decay of $^{13}\mathrm{N}$. Solar CNO neutrinos are challenging to detect, but they can provide independent new information on the metallicity of the solar core. Recently, CNO neutrinos from $^{15}\mathrm{O}$ have been identified for the first time with the Borexino neutrino detector at the INFN Gran Sasso underground laboratory. There are, however, still some considerable uncertainties in the $^{14}\mathrm{N}(p,\ensuremath{\gamma})^{15}\mathrm{O}$ reaction rate under solar temperature conditions. The low energy reaction data presented here, measured at the CASPAR underground accelerator, aim at connecting existing measurements at higher energies and attempts to shed light on the discrepancies between the various data sets, while moving towards a better understanding of the $^{14}\mathrm{N}(p,\ensuremath{\gamma})^{15}\mathrm{O}$ reaction cross section. The present measurements span proton energies between 0.27 and 1.07 MeV, closing a critical gap in the existing data. A multichannel $R$-matrix analysis was performed with the entire new and existing data sets and is used to extrapolate the astrophysical $S$ factors of the ground state and the 6.79 MeV transition to low energies. The extrapolations are found to be in agreement with previous work, but find that the discrepancies between measured data and $R$-matrix fits, both past and present, still exist. We examine the possible reasons for these discrepancies and thereby provide recommendations for future studies.

Quality Indicators of Secondary Data Analyses in Special Education Research: A Preregistration Guide

Secondary data analyses occur when new analyses are proposed for existing data. Although they are prevalent in special education research, there is little guidance on how to prepare secondary data analyses studies. Preregistration of secondary data analyses studies provides a nice opportunity and structure for fellow researchers to share innovative questions and analytic approaches to existing data sets as well as increase transparency. In this manuscript, we (a) describe quality indicators of secondary data analyses consistent with open science practices and (b) provide applied examples of these indicators from a sampling of published studies based on two iterations of data from the National Longitudinal Transition Study (NLTS2 and NLTS2012) with the overall goals to provide guidance to authors and peer reviewers and promote collaboration among fellow researchers engaged in secondary analyses for a range of purposes.

AI Based Learning Model Management Framework for Private Cloud Computing

<p>Artificial Intelligence (AI) systems are computational simulations that are “educated” using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.</p> <p> </p>

A Mechanistic Sea Spray Generation Function Based on the Sea State and the Physics of Bubble Bursting

AbstractBubbles bursting at the ocean surface are an important source of ocean‐spray aerosol, with implications on radiative and cloud processes. Yet, very large uncertainties exist on the role of key physical controlling parameters, including wind speed, sea state and water temperature. We propose a mechanistic sea spray generation function that is based on the physics of bubble bursting. The number and mean droplet radius of jet and film drops is described by scaling laws derived from individual bubble bursting laboratory and numerical experiments, as a function of the bubble radius and the water physico‐chemical properties (viscosity, density and surface tension, all functions of temperature), with drops radii at production from 0.1 to 500 µm. Next, we integrate over the bubble size distribution entrained by breaking waves. Finally, the sea spray generation function is obtained by considering the volume flux of entrained bubbles due to breaking waves in the field constrained by the third moment of the breaking distribution (akin to the whitecap coverage). This mechanistic approach naturally integrates the role of wind and waves via the breaking distribution and entrained air flux, and a sensitivity to temperature via individual bubble bursting mechanisms. The resulting sea spray generation function has not been tuned or adjusted to match any existing data sets, in terms of magnitude of sea salt emissions and recently observed temperature dependencies. The remarkable coherence between the model and observations of sea salt emissions therefore strongly supports the mechanistic approach and the resulting sea spray generation function.

Networking to reduce microbial risk in foods

Networking to reduce microbial risk in foods

How Online Gaming Could Enhance Your Career Prospects

Background Online gaming motivations are differently associated with career interests. However, very little is known about online gaming behaviour based on the actual games played and how career interests are reflected in what people play. Hence, we investigated the actual gaming behaviour of individuals from an extensive secondary data set to further support gamers’ future career planning and professional training. Methods The study comprised 16,033 participants playing a different number of games on Steam. Our study was based on the 800 most played games only and included participants where we had access to gender and job details. We employed a secondary data analysis approach by using an existing data set (O’Neill et al., 2016), looking into the actual gaming behaviour of Steam users and additional administrative data (i.e., job details and gender) provided by Game Academy Limited. We used logistic regression on the participants’ top ten games, allowing us to investigate any possible associations between different professions, gender, and the games played. Results We found that IT professionals and engineers played puzzle-platform games, allowing for enhanced spatial skills. Managers showed an interest in action roleplay games where organisational and planning skills can be improved. Finally, engineers were associated with strategy games that required problem-solving and spatial skills. There were apparent gender differences too: females preferred playing single-player games, whereas males played shooting games. Conclusion Our study found that online gaming behaviour varied between different job categories, allowing the participants to gain different soft skills. The soft skills gained could assist gamers with training that leads to a particular career path. The reasons for these findings and suggestions for future research will be discussed.

Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage.

Several short forms of the Stroke Impact Scale Version 3.0 (SIS 3.0) have been proposed in order to decrease its administration time of about 20 min. However, none of the short-form scores are comparable to those of the original measure. To develop a short-form SIS 3.0 using a machine learning algorithm (ML-SIS). We developed the ML-SIS in three stages. First, we calculated the frequencies of items having the highest contribution to predicting the original domain scores across 50 deep neural networks. Second, we iteratively selected the items showing the highest frequency until the coefficient of determination (R2) of each domain was ≥.90. Third, we examined the comparability and concurrent and convergent validity of the ML-SIS. Hospitals. We extracted complete data for 1,010 patients from an existing data set. Twenty-eight items were selected for the ML-SIS. High average R2s (.90-.96) and small average residuals (mean absolute errors and root-mean-square errors = 0.49-2.84) indicate good comparability. High correlations (rs = .95-.98) between the eight domain scores of the ML-SIS and the SIS 3.0 indicate sufficient concurrent validity. Similar interdomain correlations between the two measures indicate satisfactory convergent validity. The ML-SIS uses about half of the items in the SIS 3.0, has an estimated administration time of 10 min, and provides valid scores comparable to those of the original measure. Thus, the ML-SIS may be an efficient alternative to the SIS 3.0. What This Article Adds: The ML-SIS, a short form of the SIS 3.0 developed using a machine learning algorithm, shows good potential to be an efficient and informative measure for clinical settings, providing scores that are valid and comparable to those of the original measure.

Theoretical investigations on the liquid-phase molecular separation in isolation and purification of pharmaceutical molecules from aqueous solutions via polymeric membranes

We have developed ANFIS (adaptive neuro-fuzzy inference system) modeling strategy for investigations on molecular separation using membrane systems. The results can be useful for purification and isolation of pharmaceutical molecules during manufacturing, and also for purification of water effluents. The developed methodology is based on combination of machine learning model which is ANFIS in this case and also computational fluid dynamics (CFD). The integration between two models is built through prediction of drug concentration in the system via both method in which the CFD results are considered as inputs to the ANFIS model. In order to analyze the existing data set, which has 2 inputs r and z and one output C , we used the optimized ANFIS model. A total of over 8 thousand data rows are included in this data set which are produced using the CFD simulation of membrane separation. The ANFIS model has been optimized using three population-based methods, namely Genetic Algorithm (GA), Firefly Algorithm (FA), and Bat Algorithm (BA), in order to achieve desired results. The optimization of hyper-parameters with all three techniques has had significant results and all have shown an R 2 score higher than 0.99. In terms of MSE, the error rate decreased to 3.907 and the RMSE optimized to 1.02E+01. • Computational simulation of drug separation using membranes. • ANFIS simulation of molecular separation via polymeric membranes. • Optimization of ANFIS parameters using three optimization methods. • Combination of CFD and ANFIS in membrane simulation.

Mass Media Analysis and Exposure of Bangga Kencana Program towards Youth in North Sumatera Province, Indonesia

The use of mass media as a form of public perception of a program must be a concern so that the success of a program can be measured properly. North Sumatra Province is a province that is divided into three regional segmentations, namely the coast, the central mountains, and the archipelago which are divided into 33 districts/ cities. Analysis of 2019 SKAP BKKBN data. The number of adolescents from the 2019 SKAP data was 1834 people who were the total sample of adolescents in this pellet. This research is descriptive analytic in the form of a secondary data study or analysis, which is to further analyze existing data sets or those that have been collected from the 2019 North Sumatra Province Accountability Program Performance Survey (SKAP). The analysis uses chi-square for bivariate and multiple multiple regressions for multivariate analysis. The results of the study consisted of descriptive and analytic. The description of this research The condition of the IEC media used in informing teenagers in North Sumatra on the pride issues of kencana still rely on print media, electronic media, outdoor media and online media. Its effectiveness is also influenced by the level of education, gender, area of ​​residence, and also the age of adolescence. Electronic media is still popular as a medium that informs youthful issues of good fortune, followed by online media which is a percentage more acceptable to teenagers.

PREDICTION OF THE ENGLISH PREMIER LEAGUE CHAMPION TEAM FOR THE 2021/2022 SEASON USING THE NAÏVE BAYES METHOD

The English Premier League is the top division league in England, the most popular football league for football fans. The competition date for the 2021/2022 season was announced on March 25, 2021 and the schedule was released on June 16, 2021. Manchester City won last season (2020/2021). The purpose of this study is to find out the results of Naive Bayes' accuracy in determining which team deserves to win in the English Premier League 2021/2022 season and which team does not deserve to win in the final standings. The method used for prediction is Naïve Bayes, which is to predict an opportunity in the future based on events in the past which is then called the Bayes theorem. Combined with Naïve's theorem, it can be said that a condition between attributes can be independent. Naïve Bayes is an arrangement of a system that calculates a collection of opportunities that are summed at a frequency and a combination of values ​​from the existing data set. Based on the research conducted, it was generated from predictions to determine the winning team quickly and accurately and from the test results it was necessary to process training data and test data with the Naïve Bayes method, it was obtained an accuracy of 32.7% for the chances of Manchester City winning and Liverpool with 42.06% accuracy to be able to win the English Premier League season 2021/2022.

Measurements of the 27Al(α,n) Thick Target Yield near Threshold

We present results from direct measurements of the thick-target yield from laboratory incident energies 3 to 5 MeV, performed with the 3HeBF3 Giant Barrel (HeBGB) neutron detector at the Edwards Accelerator Laboratory. Our measurements have a small energy cadence in order to address discrepancies and sparseness of thick-target-yield data sets existing for this energy region. We find general agreement with existing data sets, including yields derived from cross-section data, while resolving a discrepancy between existing thick-target-yield data sets for MeV. However, for MeV, our results are substantially lower than previous thick-target-yield data and somewhat larger than yields calculated from existing cross-section data. Our data complete the energy range needed for estimates of the contribution to neutrino and dark matter detector backgrounds and result in increased viability of as a plasma diagnostic tool at fusion facilities such as the National Ignition Facility.

Enhanced by mobility? Effect of users’ mobility on information diffusion in coupled online social networks

Online social networks have gradually become crucial tools for users to exchange information. Two characteristics of online social networks should be underlined: first, individuals can freely leave or stay; second, information diffusion is more common on coupled online social platforms. However, very few research combines the above two aspects when studying the information diffusion in online social networks. Therefore, we establish an in-out-unacquired2-acquired 2-rejected2 (IO-S2P2R2) information diffusion model which investigates the time evolution of three types of users considering users’ mobility in coupled online social networks using mean-field theory. We analyze the theoretical threshold of information diffusion via linear analysis and find that the information diffusion threshold is related to the maximum eigenvalue of the super adjacency matrix. Moreover, by comparing the mean-field method with the Monte Carlo method, the consistency of the simulation results is obtained, which verifies the scientificity of our model. The results demonstrate the consistency of linear analysis and simulation experiments. Besides, based on the existing data sets, information diffusion rates and users’ mobility rates are positively correlated with the range of information diffusion, while information decline rates are negatively correlated with the range of information diffusion through the sensitivity analysis of parameters.

Geo-Locations and System Data of Renewable Energy Installations in Germany

Information on geo-locations of renewable energy installations is very useful to investigate spatial, social or environmental questions on their impact at local and national level. However, existing data sets do not provide a sufficiently accurate representation of these installations in Germany over space and time. This work provides a valid approach on how a data set of wind power plants, photovoltaic field systems, bioenergy plants and hydropower plants can be created for Germany based on a data extract from the Core Energy Market Data Register (CEMDR) and publicly available data. Established methods were used (e.g., random forest, image recognition), but new techniques were also developed to fill data gaps or locate misplaced renewable energy installations. In this way, a substantial part of the CEMDR data could be corrected and processed in such a way that it can be freely used in a GIS software by any scientific and non-scientific discipline.

SPARKESX: Single-dish PARKES data sets for finding the uneXpected – a data challenge

ABSTRACT New classes of astronomical objects are often discovered serendipitously. The enormous data volumes produced by recent high-time resolution, radio-telescope surveys imply that efficient algorithms are required for a discovery. Such algorithms are usually tuned to detect specific, known sources. Existing data sets therefore likely contain unknown astronomical sources, which will remain undetected unless algorithms are developed that can detect a more diverse range of signals. We present the Single-dish PARKES data sets for finding the uneXpected (SPARKESX), a compilation of real and simulated high-time resolution observations. SPARKESX comprises three mock surveys from the Parkes ‘Murriyang’ radio telescope. A broad selection of simulated and injected expected signals (such as pulsars and fast radio bursts), poorly characterized signals (plausible flare star signatures), and ‘unknown unknowns’ are generated for each survey. The goal of this challenge is to aid in the development of new algorithms that can detect a wide range of source types. We show how successful a typical pipeline based on the standard pulsar search software, presto, is at finding the injected signals. The data set is publicly available at https://doi.org/10.25919/fd4f-0g20 (Yong et al. 2022).

Detecting and learning city intersection traffic contexts for autonomous vehicles

According to Business Wire, the global market for Autonomous Vehicles estimated at 6.1 thousand Units fpsin the year 2020, is projected to reach a revised size of 110.1 thousand Units by 2026, growing at a CAGR of 60.6% over the analysis period. This strong demand brings more research interests in autonomous vehicle systems. One of the hot topics is autonomous machine vision system and intelligent solutions. Most existing papers on autonomous vehicle machine vision systems apply machine learning models to address automatic object detection and classification issues to support automatic street traffic object detection and classification for vehicles, people/animal, traffic road signs, and signals. However, there is a lack of research results addressing automatic detection and classification of road contexts and transportation intersections under diverse weather conditions. In this work, we present an integrated machine learning model to address the issue and need in street intersection detection and classification based on road contexts and weather conditions. This paper reports our efforts in data collection, processing, and training based on existing data sets (such as BDD100k and COCO), and add a new training data set on street contexts and intersections (13 classes) and weather conditions (6 classes). This paper proposes a 2-stage integrated model to support the detection and classification of different types of traffic contexts and transportation intersection. In the first stage, two deep learning models (Yolo4 and Mask CNN) with transfer learning technique are used to detect the traffic signs, traffic lights, crosswalk and road direction targets on the road in addition to mobile objects (such as people and cars). Later, the generated results from the first stage are used as inputs to a decision tree model to detect and classify the different types of underlying transportation intersections. According to presented experimental results, the proposed 2-stage model receives a high accuracy, so it has strong potential application in computer vision technology of autonomous driving.

RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern

As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks.

Media Coverage as Mirror or Molder? An Inference-Based Framework

Many communication theories in the context of political communication are based on the premise that humans are social beings affected by their perception of what others think, do, or say. For example, the spiral of silence theory predicts that individuals publically speaking their mind on certain issues is dependent on whether they perceive their opinion to be that of the majority or minority, and that the media is a core source for gauging public opinion. Yet, communication research has produced contradictory findings regarding the relationship between media coverage, perceived public opinion, personal opinion, and behavior. We argue that these contradictory findings can be explained by different inference hypotheses that people apply when inferring the opinion and behaviors of others from media coverage. There are two competing inference hypotheses discussed in the literature: While the reflection hypothesis assumes that the audience sees media content as a mirror of what the public thinks, persuasive press inference postulates that individuals perceive media as an influence on public opinion. Drawing on different research strands such as the spiral of silence theory, hostile media, persuasive press inference, and corrective action, several propositions are put forward that link these inference hypotheses to the media coverage and its effects on individual outcomes, and potential drivers are discussed. The propositions are then put to an initial test using an existing data set.

Mixed-integer linear programming models for the type-II resource-constrained assembly line balancing problem

PurposeResource-constrained assembly lines are widely found in industries that manufacture complex products. In such lines, tasks may require specific resources to be processed. Therefore, decisions on which tasks and resources will be assigned to each station must be made. When the number of available stations is fixed, the problem’s main goal becomes the minimisation of cycle time (type-II version). This paper aims to explore this variant of the problem that lacks investigation in the literature.Design/methodology/approachIn this paper, the authors propose mixed-integer linear programming (MILP) models to minimise cycle time in resource-constrained assembly lines, given a limited number of stations and resources. Dedicated and alternative resource types for tasks are considered in different scenarios.FindingsBesides, past modelling decisions and assumptions are questioned. The authors discuss how they were leading to suboptimal solutions and offer a rectification.Practical implicationsThe proposed models and data set fulfil more practical concerns by taking into account characteristics found in real-world assembly lines.Originality/valueThe proposed MILP models are applied to an existing data set, results are compared against a constraint programming model, and new optimal solutions are obtained. Moreover, a data set extension is proposed due to the simplicity of the current one and instances up to 70 tasks are optimally solved.

Calling situated: a survey among medical students supplemented by a qualitative study and a comparison with a surveyed sample of physicians

BackgroundCalling within the medical context receives growing academic attention and empirical research has started to demonstrate its beneficial effects. The purpose of this study is to investigate what motivates students to enter medical school and what role calling may play (i), to evaluate if calling influences the way in which they experience their studies (ii), and to compare medical students’ experience of calling with those of physicians.MethodsA questionnaire survey was distributed among medical students (N = 1048; response rate above 60%) of the University of Lausanne in Switzerland. It was supplemented by a group discussion between bachelor medical students (N = 8) and senior physicians (N = 4), focusing on different facets of calling. An existing data set of a survey among physicians, addressing calling with the same questionnaire, was used to compare students’ and physicians’ attitudes towards calling. Survey data were analyzed with the habitual statistical procedures for categorical and continuous variables. The group discussion was analyzed with thematic analysis.ResultsThe survey showed that experiencing calling is a motivational factor for study choice and influences positively choice consistency. Students experiencing calling differed from those who did not: they attributed different definitions to calling, indicated more often prosocial motivational factors for entering medical school and perceived the learning context as less burdensome. The analysis of the group discussion revealed that the concept of calling has a fluid definition. It was conceived as having the characteristics of a double-edged sword and as originating from within or outside or from a dialectic interplay between the inner and outer world. Finally, calling is experienced less often by physicians than by medical students, with a decreasing prevalence as the immersion in the clinical years of the study of medicine progresses.ConclusionsCalling plays an important role in study choice and consistency of medical students. Given its relevance for medical students and its ramifications with the learning context, calling should become a topic of the reflexive parts of the medical curriculum. We critically discuss the role played by calling for medical students and provide some perspectives on how calling could be integrated in the reflection and teaching on physicianhood.