Miracle-dz/UniFAR-FTUs huggingface dataset:
Parent attitudes about childhood vaccines: a vaccine hesitancy point prevalence study.

Abstract Objectives The aim of this study was to measure the prevalence of vaccine hesitancy using Parent Attitudes about Childhood Vaccines (PACV) survey regarding paediatric vaccines for their children, in a population of parents attending a STEM (Science, Technology, Engineering and Mathematics) outreach event in Cork, Ireland. Study design Cross-sectional survey study. Methods This study was conducted in November 2018 at the Community Festival of Science STEM event. Eligible attendees were invited to read the participant information leaflet, provide written informed consent, and complete the validated Parent Attitudes about Childhood Vaccines (PACV) survey. Each of the 15 PACV survey items was scored. A score [≥]50 identified vaccine hesitant parents. Results A total of 105 parents participated in the study. A small number of participants (6.7%, n = 7) were identified as vaccine hesitant. There were no statistically significant differences between the vaccine hesitant and non-hesitant groups based on age, education, or number of children. Parents had concerns around vaccine side effects (36.2%, n=38) and vaccine safety (20%, n=21). Parents trusted the information they received on vaccines (85.6%, n=90) and 81.9% (n=86) believed that the vaccine schedule was good for their child. Conclusions The findings of this research indicate the presence of vaccine hesitancy in parents in Ireland regarding paediatric vaccines. Parents had concerns regarding vaccine side effects and the number of vaccines on the paediatric immunisation schedule. Further research is necessary to understand how these issues may contribute to vaccine hesitancy, and to develop evidence-based interventions to build on parents existing trust in the vaccination schedule.
Potential health benefits of eliminating traffic emissions in urban areas

Traffic is one of the major contributors to PM2.5 in cities worldwide. Quantifying the role of traffic is an important step towards understanding the impact of transport policies on the possibilities to achieve cleaner air and accompanying health benefits. We carried out a meta-analysis using the World Health Organisation (WHO) database of source apportionment studies of PM2.5 concentrations. Specifically, we used a Bayesian meta-regression approach, modelling both overall and traffic-related (tailpipe and non-tailpipe) concentrations simultaneously. We obtained the distributions of expected PM2.5 concentrations (posterior densities) of different types for 117 cities worldwide. For each city, we calculated the probabilities of exceeding the WHO-recommended concentration of PM2.5 if all of traffic emissions were removed. Using the nonlinear Integrated Exposure Response (IER) function of PM2.5, we estimated percent reduction in different disease endpoints for a scenario with complete removal of traffic emissions. We found that this results in achieving the WHO-recommended concentration of PM2.5 only for a handful of cities that already have low concentrations of pollution. The percentage reduction in prevented mortality for cardiovascular and respiratory diseases increases up to a point (30-40 ug/m3), and above this concentration, it flattens off. For Diabetes-related mortality, the percentage reduction in mortality decreases with increasing concentrations, a trend that is opposite to other outcomes. For cities with high concentrations of pollution, the results highlight the need for multi-sectoral strategies to reduce pollution. The IER functions of PM2.5 result in diminishing returns of health benefits at high concentrations, and in case of Diabetes, there are even negative returns. The results show the significant effect of the shape of IER functions on health benefits. Overall, despite the diminishing results, a significant burden of deaths can be prevented by policies that aim to reduce traffic emissions even at high concentrations of pollution.
Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes using a Novel Machine Learning Approach

There are many small datasets of significant value in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies is of great value as it can indicate leads for innovative drug discovery and development. Here, we report on a machine intelligence-based study that utilized a combination of two small non-small cell lung cancer (NSCLC) datasets consisting of 58 samples of adenocarcinoma (ADC) and squamous cell carcinoma (SCC) and 45 samples from the gene expression analysis of human lung cancer and control samples series (GSE18842). Utilizing a novel machine learning approach, we were able to uncover subpopulations of ADC and SCC while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. An interactive hypothesis-generating interface designed to work with machine learning methods allowed us to explore the hypotheses generated by the unsupervised components of the system. Using these methods, we were able to uncover genes implicated by other methods and accurately discover known subpopulations without being asked, such as different levels of aggressiveness within the SCC and ADC subtypes. Furthermore, PIGX was a novel gene implicated in this study that warrants further study due to its role in breast cancer proliferation. Here we demonstrate the ability to learn from small datasets and reveal well-established properties of NSCLC. These machine learning techniques can reveal the driving factors behind subpopulations of patients altering the approach to drug discovery and development by making precision medicine a reality.
ASSESSMENT OF REGIONAL CEREBRAL BLOOD FLOW USING DYNAMIC CONTRAST-ENHANCED MRI IMAGES

Functional MRI (fMRl) of the human brain is based on a change in the NMR signal on T2* weighted images due to local changes in the concentration of deoxyhemoglobin during neural activity associated with the performance of a task. This is known as blood oxygenation level dependent (BOLD) contrast. If we wish to quantify these functional changes in image intensity we may use a scale of magnitude of signal change on a voxel by voxel basis. An alternative approach is to measure the magnitude of signal change and the variance in the data during the experiment and use these to generate a statistical image. The advantage of this approach is that the degree of physiological fluctuation in the data is taken into account when assessing the difference in signal level between two different functional states. The method of statistical parametric mapping (SPM) as developed by Friston and colleagues in our dept. is an advanced approach to generating functional brain images using PET or fMRl.
The Case for a Positive Euro Area Inflation Target: Evidence from France, Germany and Italy

Using micro price data underlying the Harmonized Index of Consumer Prices in France, Germany and Italy, we estimate relative price trends over the product life cycle and show that minimizing price and mark-up distortions in the presence of these trends requires targeting a significantly positive inflation target. Relative price trends shift the optimal inflation target up from a level of zero percent, as suggested by the standard sticky price literature, to a range of 1.1%- 2.1% in France, 1.2%-2.0% in Germany, 0.8%-1.0% in Italy, and 1.1-1.7% in the Euro Area (three country average). Differences across countries emerge due to systematic differences in the strength of relative price trends. Other considerations not taken into account in the present paper may push up the optimal inflation targets further. The welfare costs associated with targeting zero inflation turn out to be substantial and range between 2.1% and 4.5% of consumption in present-value terms. JEL Classification: E31, E52
Network Intrusion Detection using Optimized Machine Learning Algorithms

Network intrusion detection mechanism is a primary requirement in the current fast-growing network systems. Data mining and machine learning approaches are widely used for network anomaly detection during past few years. Machine learning based intrusive activity detector is becoming more popular. The most commonly used machine learning algorithms for Intrusion Detection System (IDS) are K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). However, the performance of these methods is reliant upon the selection of the proper parameter values. This research focuses its aim to build an IDS model based on the most effective algorithms. The machine learning algorithms are used in this research are KNN, SVM and RF. To improve these algorithms classification accuracy, some parameters of the algorithms are optimized using Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization techniques, while other parameters are used with default values. The result of this experiment shows that optimized KNN, SVM and RF perform better than these algorithms with their default parameter values. Furthermore, the results the experiment shows that KNN is the most suitable algorithm for network anomaly detection regarding detection of known network attacks and unknown network attacks. NSL-KDD standard dataset is used for the experiments of this research. It has been proven that our proposed model performs better than what is provided in the state-of-arts models.
Developing Answers to Scientiﬁc Questions with BERT Category

In this paper we show that BERT model ﬁne-tuned on SQuAD for Question Answering (QA) tasks can be successfully extended to help address emerging COVID-19 questions. By further ﬁne-tuning on COVID data and utilizing BERT with biomedical text understanding capabilities, we are able to improve Exact Match by 49% and F1 score by 30% for COVID QA tasks. As more COVID or biomedical QA datasets are accumulating, the model will continue to advance to ﬁnd the right answer for more scientiﬁc questions
FISH DETECTION AND SPECIES CLASSIFICATION USING MASK RCNN

The goal of study is to create a "Fish detection and species categorization using Mask RCNN" model. Faster R-CNN has a variant-called Mask R-CNN. For object identification tasks, the faster R-CNN is commonly used. It returns the class name and bounding box coordinates of each object in the image. Mask R-CNN is simple to set up, and it only adds a little amount of overhead to Faster R-CNN. Mask R-CNN is divided into two phases. The First phase, it generates a premise about the possible locations for an object based on the image. Second phase, based on the primary state proposition, it predicts the object’s class, refines the bounding box, and creates a mask at a pixel level of the object dependent on the primary stage proposition. The system proposes the detection and species classification of the fishes AlbacoreTuna, BigEyeTuna, YellowFinTuna, MoonFish, DolphinFish, Shark. The system provides accuracy of 93%.
Experimental Validation and Multi-objective optimization of Batch cooling crystallization of sulfanilamide

This research aims to estimate the kinetics of sulfanilamide crystallization from batch crystallizer data obtained from a linear cooling profile and thereby extended to find the optimum cooling profile for the desired combination of conflicting objectives. Real coded genetic algorithm (RCGA) is applied to estimate the rate parameters from the experimental batch cooling crystallization data of liquid phase concentration. The validated model is used to find the different optimal temperature profiles for the combination of conflicting objectives (i.e. maximization of number mean size (NMS) and minimization of coefficient of variance (CV)). This is done by using real coded non-dominated sorting genetic algorithm (NSGA II). In this effort, together with finding the optimum temperature trajectories, the effect of target choice on the final achievable Pareto front is analyzed using the above two conflicting objective functions.
Biomechanical Analysis of Human Gait for Bipedal Robotic Applications

Gait is the traversing of human leg from one point to another for the purpose of walking while expending least possible energy during the process. The human gait cycle consists of 8 different phases in which movement of the legs occur. The optimum profile of the foot trajectory for the bipedal application is selected to be cycloidal and a gait generation model is used for the estimation of co-ordinates of points across the pattern. The joint angles can be determined using the method of Inverse Kinematics. Finally, a concept model of a human like Biped robot is created and it has been demonstrated that the human gait can be successfully translated to robotic applications. Keywords—Gait; Biomechanics; Robotics; Inverse Kinematics; Human structure
Paid Family Leave, Inventor Mobility, and Firm Innovation

We exploit the adoption of US state-level Paid Family Leave (PFL) laws to test whether family-friendly policies affect firm innovation. We find that PFL policies increase the innovation outputs of firms whose employees are more exposed to these laws. The stronger attraction and the higher retention of female inventors contribute to the output gains. Tests at the state level suggest that younger inventors move into states after PFL adoption, and the move-in inventors are generally more productive than the move-out inventors at the personal level. Further tests at inventor level show that females are less likely to drop out from the inventor career after PFL adoption. Overall, the behavior change in inventor career choices is the main channel through which PFLs affect firm innovation.
Credentials Matter, but Only for Men: Evidence from the S&P 500

We study gender differences in the value of credentials in managerial labor markets. Exploiting quasi-random variation in S&P 500 index membership, we examine the careers of managers whose firms were “just included” on the index and compare them to the careers of managers of similar firms that were not included. We use within-firm variation in S&P 500 status for marginal firms and managers who joined a marginal firm before its addition to the index to obtain near-random assignment of S&P 500 experience. Men with experience at an S&P 500 firm obtain more subsequent independent directorships and executive roles at other S&P 500 firms, but not at non-S&P 500 firms. The increase is 12-42% relative to the average. Strikingly, we observe no such relationship for women. Our results suggest that receiving less credit for similar credentials poses an obstacle for women in the managerial labor market.
Waterfall Illusion in Risky Choice – Exposure to Outcome-Irrelevant Gambles Affects Subsequent Valuation of Risky Gambles

Abstract Based on recent discoveries in economics, neuroscience, and psychology, we hypothesize that pure exposure to high-payoff or low-payoff gambles can change people's subsequent reported valuations of gambles and confirm this hypothesis in a laboratory experiment. In particular, the same participants within the same experimental session provide higher valuations for the same gambles after they have been exposed to low-payoff gambles compared to after they have been exposed to high-payoff gambles. These results are consistent with the current understanding of how the nervous system encodes payoffs and imply that even brief experiences that do not change wealth can impact an individual's reported valuations of risky options.
Emphasizing Inter-Nation Equity in the New Digital Economys Taxing Rights Allocation Scheme

Efforts to quell the challenges of taxing the digital economy have shown significant progress. The phase has narrowed down to agreeing on broader allocation of taxing rights to market jurisdictions. However, various groups still have differing views regarding the scheme of new taxing rights allocation (Amount A) contained in the proposal OECD/G20 Pillar One. The issue of fairness mounts mainly in the formula for reallocating profits to the eligible jurisdictions. This study aims to examine areas in the proposal related to the principle of inter-nation equity. We apply a qualitative study by conducting literature analysis and document review. The result shows that several areas need to improve inter-nation equity, particularly in the mechanism for reallocating MNE group profits to jurisdictions entitled to Amount A. We identify the stages in the mechanism where the principle of equity amidst countries becomes essential as a point of consideration. These are residual profit terms, profitability threshold, and the percentage of reallocation. Consensus needs to consider the favored percentage formula to improve inter-nation equity before the scheme is fully implemented.
II. Goproporphyrin I Metabolism and Hematopoietic Activity

In a previous communication concerning the porphyrin excretion in normal individuals 1 we have briefly outlined a concept of the dualism of the porphyrins, stating that in certain pathological conditions increased or decreased Type III porphyrin construction associated with hematopoietic activity may lead to a proportionally increased or decreased Type I porphyrin construction and excretion. This communication presents data which bear out this concept. A classical case of hemolytic jaundice with 1,600,000 R.B.C., 4.5 gm. of hemoglobin per 100 cc, 21,000 nucleated R.B.C., 5,800 W.B.C., 10% reticulocytes, icterus index 30, Van den Berg (indirect) 3.1 mg. %, and markedly increased R.B.C. fragility was studied. The patient was maintained throughout the period of observation on a bland diet containing small amounts of meat. After 3 blood transfusions splenectomy was performed, following which R.B.C. increased to 2,500,000, hemoglobin to 9.0 gm. per 100 cc. , the reticulocytes decreased to 2%, and the icterus index decreased to 10. Porphyrin and stercobilin excretion were followed quantitatively before and after operation. In the 7 days previous to operation the average daily value for urinary and fecal coproporphyrin was 718 micrograms. The stercobilin averaged 407 mg. during the same period. During the first 6 post-operative days the total coproporphyrin I excretion averaged 648 micrograms per diem. In the same period the stercobilin values fell rapidly to 157 mg. per day. The average coproporphyrin excretion for the next 6 days was 371 micrograms and the stercobilin 73 mg. , both within normal range. These data strongly indicate that physiologically increased hematopoietic activity is accompanied by increased coproporphyrin I excretion. The excretion reverts to normal coincident with decreased hematopoietic activity. A second patient with hemolytic jaundice, followed for 20 days and not operated upon, showed a daily average total coproporphyrin I excretion of 652 micrograms.
Diffusion bonding of TiAl alloy to eutectoid steel and its interfacial self-destruction behavior

Interfacial microstructures and bonding strength of TiAl alloy/eutectoid steel joints have been investigated from the viewpoint of diffusion barrier effect of carbide layer formed at the interface. Diffusion bonding was performed at 1073 to 1273 K for 3.6 ks in a vacuum, and then several specimens were heated at 1073 to 1273 K for 10.8 to 86.4 ks in evacuated quartz tube to examine the growth behavior of reaction layer. The joint has essentially four kinds of the reaction layers. They are composed of two layers with Ti, Al and Fe elements, TiC layer containing Fe-Al compounds and ferrite layer. This indicates that the joint is difficult to have stable diffusion barrier layer consisting only of the TiC due to contribution of Al element to the interfacial reaction. Although the joint bonded at 1073 K shows high bonding strength of 160 MPa, the strength dramatically decreases with increasing bonding temperature. In addition, self-destruction phenomenon is recognized at the interface in the joints heat-treated at 1173 and 1273 K, and the origin is also discussed.
Knowledge Management System Adoption to Improve Decision-Making Process in Higher Learning Institutions in the Developing Countries: A Conceptual Framework

Currently, higher learning institutions (HLIs) are facing their most challenging problem in inefficient information management. The knowledge management system (KMS) application calls for providing several benefits to lecturers and students, producing daily information, documenting records for evidence of a transaction, and eventually improving the decision-making process. Knowledge management can be coupled with fuzzy logic to deal with imprecision and uncertainty of data in a KMS. The ICT dynamic development has shifted the HLI operations from manual to electronic-based handling of related information. KMS is one of the systems that are of significant consideration in this regard. Nevertheless, such a system has not been extensively adopted as expected due to users’ rejection of its use. In the present paper, the factors affecting the decision to adopt/reject KMS are highlighted. The study is qualitative and entails a critical review of the related literature concerning the topic, backed by interviews. KMS experts working with highly reputable HLI were interviewed. A total of 11 factors were focused on in light of their effect on the decision to adopt/reject KMS, as argued by the technological adoption theories and literature review. All the factors were validated and placed in ranks by the experts. From the results, a novel conceptual framework of KMS adoption was developed for Libyan HLIs to bring about technology adoption and improved decision-making.
Cooperative Adaptive Cruise Control(CACC)

Cooperative Adaptive Cruise Controller is the next generation of Adaptive Cruise Controller(ACC). Through communication of vehicles CACC performs control maneuvers. The most important feature of CACC is its ability to enhance the capability of drivers and on-board devices(e.g.,radar or sensors), and thus, improving road traffic safety and efficiency. Inter-Vehicle Communication(IVC) and Roadside-to-Vehicle Communication(RVC) are the basic requirements of CACC. This reprt aims at describing the implementation of IVC/RVC and its advantages. The implementation of MAC layer protocol and Network layer protocol for IVC/RVC communication is described.
HRL Laboratories: Thinking Outside the Box

The Web is evolving from a global information space to a collaborative problem solving environment in which services (resources) are dynamically discovered and composed into workflows for problem solving, and later disbanded. This gives rise to an increasing demand for provenance, which enables users to trace how a particular result has been arrived at by identifying the resources, configurations and execution settings. In this paper we analyse the nature of service-oriented computing and define a new conception called augmented provenance. Augmented provenance enhances conventional provenance data with extensive metadata and semantics, thus enabling large scale resource sharing and deep reuse. A Semantic Web Service (SWS) based, hybrid approach is proposed for the creation and management of augmented provenance in which semantic annotation is used to generate semantic provenance data and the database management system is used for execution data management. We present a general architecture for the approach and discuss mechanisms for modeling, capturing, recording and querying augmented provenance data. The approach has been applied to a real world application in which tools and GUIs are developed to facilitate provenance management and exploitation.