Miracle-dz/UniFAR-FTUs huggingface dataset:
Polysaccharide Carriers for Induction and Evaluation of Tissue Regeneration and Drug Delivery

Induction of tissue regeneration has been highly expected as a new field of medical treatment covering or compensating two advanced medical therapies; reconstruction and organ transplantation therapies. The basic idea of tissue regeneration induction is to regenerate or repair the injured or lost tissues and substitute organ functions based on the natural selfhealing potential of patients themselves. With the recent rapid development of cell biology, it has been possible to make use of various progenitor and stem cells with high potentials of proliferation and differentiation for cell-based induction of tissue regeneration. It has been demonstrated that cells themselves have good therapeutic potentials in terms of their inherent targetability to the site injured or biological properties. However, the therapeutic efficacy of cells transplanted is not always as high as expected, which is one of the largest problems in cell therapy. This is because the survival rate of cells transplanted is low, and consequently the biological functions of cells are not always expected in the body. To tackle the problem, it is indispensable to develop materials, technologies, and methodologies to provide the cells a local environment where the survival and biological functions of cells transplanted can be maintained or enhanced. On the other hand, the technologies and methodologies to evaluate the extent and process of tissue regeneration still depend on the conventional diagnostic methods such as histological, biochemical, and morphological examinations. Clinical availability of these examinations is limited in terms of their invasiveness and 1
Non-invasive Cardiac Output of Children in Health and Disease: Respiratory Gas Techniques

Cardiac output (Q) is an important determinant of the cardiovascular system‟s ability to meet the oxygen needs of the body. This dissertation addresses the non-invasive measurement of Q, in healthy children and those with heart and lung disease. 1) The correction factors for collision broadening, downstream difference and end tidal CO2 (PetCO2), used in the CO2 rebreathe (equilibrium) method, were evaluated. In lung disease, one is unable to assume a normal dead space to estimate arterial CO2 (PaCO2), and the use of any of these correction factors alone should be used with caution as they each exert a profound effect on the Q measurement. 2) A new equation to predict PaCO2 from PetCO2 in patients with CF was derived via multiple regression analysis, taking into account disease severity. 3) The validity and reliability of Q measures via the inert gas rebreathing technique (Innocor TM device) were evaluated. The highest intraclass correlation coefficients were attained during exercise (0.70.98), indicating excellent reliability of the device. Comparisons of Q measures from the Innocor TM (QInn) to the AMIS mass spectrometer system (QAmis) were made to assess validity. The bias (QInnQAmis) and limits of agreement (±2SD) were 0.45 ± 1.9 L.min -1 and 0.27 ± 2.1 for children with congenital heart disease and healthy controls respectively, with no systematic differences between the
Four Parental Approaches to Sharenting on Facebook

Abstract This chapter discusses the contested practice of sharing pictures and information of one’s children on social media, newly coined as “sharenting”. Based on a multi-case study of eight Danish first-time parent couples’ uses and experiences of digital media in relation to their new role as parents, the chapter identifies four types of communicative orientation that characterise parents’ approach to Facebook as a social network site (SNS). The four types are expressed through differences in aesthetics, values and attitudes toward sharenting and consist of 1) family-oriented, 2) peer-oriented, 3) oppositional and 4) non-use. On this basis, the chapter discusses the ways in which sharenting poses new challenges and demands for “good parenting”.
Categorical Salience Theory

Monetary lotteries are the overwhelmingly predominant tool for understanding decisions under risk. However, many real-world decisions concern multidimensional outcomes involving dierent goods. Recent studies have tested whether people treat multidimensional risky choices in the same manner as unidimensional monetary lotteries and found that choices over consumer goods are less risk-averse and more consistent with expected utility theory than choices over monetary lotteries. While these puzzling results cannot be explained by any standard model of decision making, we demonstrate that these ndings are predicted by a salience-based model of category-dependent preferences that also explains the classic anomalies for choices under risk. Additionally, we experimentally verify a novel prediction of this Categorical Salience Theory. We further demonstrate that our model can explain empirical puzzles in nancial markets, insurance markets, and principal agent settings, including behavior in a new portfolio choice experiment that is unexplained by expected utility theory or prospect theory.
A REVIEW PAPER ON HEART DISEASE DETECTION USING ECG FEATURES

: Heart disease is crucial health problem in human society. This paper has summarized techniques and available methods for predication of heart disease. Hug amount of patient related data that is ECG signal is stored which can be useful for predicting the occurrence of heart disease. There different types of heart diseases that can be detected through ECG signal and there is need of robust method for detecting such diseases by means of classification process. There is variety of classifiers such artificial neural network, support vector machine, k-nearest neighbor which can be adopted for classification purpose.
Prediction Of Diabetes Using Machine Learning Classification Algorithms

: Diabetes is a sickness that takes place whilst glucose content in your blood is too excessive. Insulin is a hormone made through the pancreas, facilitates to separate glucose from meals get into your body-cells for power. On this we used category set of rules techniques of the device mastering to are expecting the diabetes. Five machine getting to know algorithms namely SVM, selection Tree, NaiveBayes , Logistic Regression and KNN are used to hit upon diabetes. This may be capable of predict the chance degrees of diabetes and gives the first-class getting to know set of rules with better accuracy comparatively different algorithms.
Public Leaderboard Feedback in Innovation Contests: A Theoretical and Experimental Investigation

We investigate the role of performance feedback, in the form of a public leaderboard, in innovation competition that features sequential search activity and a range of possible innovation qualities. We find that in the subgame perfect equilibrium of contests with a fixed ending date (i.e., finite horizon), providing public performance feedback results in lower equilibrium effort and lower innovation quality. We conduct a controlled laboratory experiment to test the theoretical predictions and find that the experimental results largely support the theory. In addition, we investigate how individual characteristics affect competitive innovation activity. We find that risk aversion is a significant predictor of behavior both with and without leaderboard feedback and that the direction of this effect is consistent with the theoretical predictions. JEL classification: D90, O31, C90, D83
Gold, farms, and forests: Enforcement and alternative livelihoods are unlikely to disincentivize informal gold mining

Informal gold mining (IGM) is a major driver of deforestation and source of global mercury emissions. Policy makers may seek to control the spread of IGM by enforcing rules and/or providing alternative livelihoods. We investigated the dynamics and drivers of IGM in northern Myanmar to shed light on the conditions needed for alternative livelihood and enforcement interventions to succeed. We surveyed 226 respondents who practiced mining and/or farming. We found that mining and agriculture provided complementary livelihoods for many respondents as they met different livelihood needs, and that many of the miners were economic migrants. Livelihood‐based interventions based on agriculture/plantations—as currently planned by the regional government—are thus unlikely to provide true substitutes. The willingness of migrant miners to move under different economic circumstances suggests that livelihood‐based interventions are unlikely to scale well—mining may simply be displaced to other regions, or new migrants might replace old migrants. We estimated that current enforcement efforts were insufficient and that a much higher level of enforcement—either constant presence of enforcement officials at each informal mining site, or confiscating equipment every month—would be required to make informal mining unprofitable. Enforcement effectiveness was further undermined by corruption in the guise of informal payments to local authorities. Our study is the first to estimate costs that enforcement imposes and the level of enforcement required to deter informal gold mining, and adds to the growing body of evidence that enforcement and alternative livelihood approaches alone are unlikely to deter informal gold mining.
The Social Presence Benefits of Synchronous, Interactive Video in Online Classes

What are the benefits of interactive, two-way video and web conferencing in online classes in terms of social presence? This study compared four presentations, two were synchronous and were delivered in real-time and two were pre-recorded and delivered asynchronously. Student volunteers (n = 162) were organized into four treatment groups. The first treatment group participated from an immersive telepresence, video conferencing system, the second group participated via online web conferencing, the third group viewed a recording of the telepresence group’s meeting, and the fourth group viewed a recording of the web conferencing group’s meeting. The use of the telepresence system improved participants’ sense of instructor immediacy and social presence, followed by the perception of the web conferencing group, while the asynchronous viewers reported a lower sense of connectiveness. The data analysis also indicated no significant difference among the treatment groups in terms of extraneous cognitive load. The results indicate the social presence benefits of real-time engagement, active learning, and suggests that instructors and instructional designers should consider the integration of synchronous activities in their online classes.
Financial Valuation of Material Price Adjustment Clauses

Uncertainty in material costs has been a serious challenge for state Departments of Transportation (DOTs) and contractors since the last decade. Uncertainty in the price of critical materials increases the risk of contractors in fixed-price contracts and can lead to price speculation and bid inflation. One of the most common strategies used to address inflated bid prices is price adjustment clauses (PACs) which guarantee an adjustment in payments to contractors based on the size and direction of material price changes. Contrary to the widespread application of PACs by state DOTs, there is little knowledge about the financial value of offering PACs in transportation contracts. This gap in knowledge makes it difficult for contractors and state DOTs to systematically evaluate the impacts of offering PACs on their risk profiles. The research objective of this paper is to develop a model that measures the financial value of PACs in transportation contracts. In this study, we apply real option theory to price the PACs in transportation projects. A market-based option pricing approach called risk-neutral valuation method is devised to determine the financial value of the PACs. The results of this study help state DOTs and contractors develop their economic risk profiles properly.
Efficiency of Intelligent Agents in Anti-Tabnabbing

: Internet phishing has become a continual threat that keeps growing day by day. Phishing takes advantage of the user’s trust and use social engineering techniques to deceive users. Despite having several anti-phishing strategies, the threat of phishing is not mitigated as modern types of attack keeps coming to the fore. Tabnabbing is a sophisticated phishing attack which tricks users to submit their personal information and credentials by leveraging the facilities tabs offer to web browsers. Existing anti-phishing techniques are still struggling for the detection and prevention of this dominant phishing attack as the attack takes action in inactive browser tabs. This paper explains the effectiveness of software agents in detecting Tabnabbing attack by making a comparison with existing methods
Effectiveness of stratified sampling to model payment card acceptance and usage

In our paper we analysed payment card acceptance and payment card use decisions in retail situations comparing logit models estimated on the comprehensive Online Cashier Register database and random stratified subsamples. We compare county, industry and store size strata with the true population estimates to simulate the usual stratification criteria for merchant surveys. In our model we control for several other factors relevant in payment instrument adoption and use but we primarily focus on the performance of these three types of stratification to estimate the exact county, industry and size effects for the entire population. In our comparison we create random stratified subsamples of 1 percent of the merchant database and random stratified subsamples of 0.01 percent of the transaction database. Based on our analysis in card acceptance models the store size stratification provides good estimates, however the same stratification cannot be used to effectively estimate variable effects in a card usage model. Journal of Economic Literature (JEL) codes: C44, D22; D12, G02, G20
Archean to Paleoproterozoic seawater halogen ratios recorded by fluid inclusions in chert and hydrothermal quartz

Abstract Past changes in the halogen composition of seawater are anticipated based on the differing behavior of chlorine and bromine that are strongly partitioned into seawater, relative to iodine, which is extremely depleted in modern seawater and enriched in marine sediments due to biological uptake. Here we assess the use of chert, a chemical sediment that precipitated throughout the Precambrian, as a proxy for halide ratios in ancient seawater. We determine a set of criteria that can be used to assess the primary nature of halogens and show that ancient seawater Br/Cl and I/Cl ratios can be resolved in chert samples from the 2.5 Ga Dales Gorge Member of the Brockman Banded Iron Formation, Hamersley Group, Western Australia. The values determined of Br/Cl ~2 × 10-3 M and I/Cl ~30 × 10-6 M are comparable to fluid inclusions in hydrothermal quartz from the 3.5 Ga North Pole area, Pilbara Craton, Western Australia, that were the subject of previous reconstructions of ancient ocean salinity and atmospheric isotopic composition. While the similar Br/Cl and I/Cl values indicate no substantial change in the ocean halide system over the interval 2.5–3.5Ga, compared to modern seawater, the ancient ocean was enriched in Br and I relative to Cl. The I/Cl value is intermediate between bulk Earth (assumed chondritic) and the modern seawater ratio, which can be explained by a smaller organic reservoir because this is the major control on marine iodine at the present day. Br/Cl ratios are about 30% higher than both modern seawater and contemporary seafloor hydrothermal systems, perhaps indicating a stronger mantle buffering of seawater halogens during the Archean.
Determinants of customer loyalty among GSM users in the south-eastern Nigeria

This study focuses on the determinants of customer loyalty among GSM users in the South-Eastern Nigeria. Most of the studies on customer loyalty in the Nigerian GSM market are alien to South-Eastern Nigeria. The main objective of this study is to empirically inquire the factors that influence customer loyalty in the Nigerian GSM market. The study adopted survey research design and the population of study is the mobile phone users in Ebonyi State. Quota sampling method was employed while the sample size was determined using survey systems formula. Questionnaire was employed as the research instrument and the reliability test was achieved using split-half method. Factor analysis was used for data reduction and multiple regressions were employed to test the hypotheses. The findings revealed that trust, customer satisfaction, corporate image, service quality, network coverage have significant effect on customer loyalty with customer satisfaction as the strongest predictor of customer loyalty in the Nigerian GSM market. In addition, prompt customer service was seen to have no significant effect on customer loyalty. It was recommended that GSM service providers should focus more on customer satisfaction, trust, corporate image, service quality, and network coverage in order to build sustainable relationship with their customers as to become very competitive. Also, attention should be paid on providing quality customer care service elements.
Paper Title (use style: paper title)

This paper presents a simplified near state PWM algorithm (NSPWM) for the reduction of common mode voltage (CMV) in direct torque controlled induction motor drives. In the proposed PWM algorithm instead of using zero voltage vectors, active voltage vectors are utilized for composing the reference voltage vector, So that the CMV changes from +Vdc/6 or -Vdc/6 due to application of active voltage vectors. As the proposed algorithm is 120 0 bus clamping PWM algorithm, it reduces the switching frequency and switching losses of the inverter. To validate the proposed algorithm, simulation studies have been carried out using MATLAB-Simulink and results have been presented Keywords-c Common mode voltage, induction motor drives, PWM inverter, Space vector PWM.
Online Suspect Verification System

—At present crime investigating departments use some of the old outdated file systems to view and analyze the crime details. There are many situations in which data about criminals should be shared with other police stations. When one police station requires any criminal information during their investigations, they need to call at that police station and get it, which is time consuming and require manpower. Also, citizens cannot get any information about criminals and the status of any crime investigation case. Our web based application provides an easy to use platform for the investigating departments concerned with the case in finding the prime suspect. It allows individual investigation teams in different locations to keep track and coordinate on cases. Also, it provides an easy to use interface for general public to submit evidences with respect to specific cases and check the status of any ongoing investigations. It extracts information from huge volume of data entered by the officers/general public in the social networking websites such as Twitter as well as our system. The data extracted is unstructured data and is converted into structured data which is analyzed and frequency of negative words with respect to each suspect is generated. Based on this, the probability of suspects in a crime is generated and the suspect who is having highest frequency of negative words is predicted as the prime suspect.
Statistical learning and causal inference for oil and gas production analysis and forecasting

The forecast of oil and gas production decline, at well and field levels, is an issue of major importance. Traditionally this is done by exponential decline curve fitting. More recently a random forest based statistical learning approach was used to analyze well production. The results, though promising, did not give entire satisfaction. Uncertainty was not accurately quantified and in particular the extreme forecasts were badly estimated–when compared to field data they were underestimated. In addition, influent variables were incorrectly identified. Production modeling depends on a large number of parameters and variables. Standard statistical inference approaches, and even machine learning (as seen above), cannot distinguish between association (correlation) and cause. Thus, in a multivariate context, it is very difficult to make predictions that are reliable and whose uncertainties can be quantified. The optimal production strategy for exploiting wells and fields could have extensive economic and environmental benefits. Today, companies rely on a trial-and-error approach to find an optimal completion practice, modifying one or two design characteristics at a time and comparing production with previously drilled and competitors’ wells. This approach is inefficient because it does not account for other impacting effects. Competitors’ wells may be drilled in slightly different geology. Older wells may have been drilled with different strategies. Service companies may vary between operators, etc. However, large quantities of field data are available (more than 700 wells in one field) that include production data time-series, exploitation conditions and the associated geological data. This thesis will thus develop a data-driven approach, coupling causality with machine learning, to study causal inference for production analysis and forecasting.
Deep Leaning based Breast Cancer Tumor Detection and pN-Stage Prediction

This paper presents an automatic method of tumor segmentation and slides classification. It also predicts the pN-stage at the patient level based on the slide’s classification. The pN-stage for every patient is determined by the number of positive lymph nodes that consists of 5 categories. We used convolutional neural networks for slide level segmentation of tumor cells. The pN-stage evaluation has been done as post processing stage for detected positive regions by using blob analysis and trained XGboost. This method is trained and validated on Camelyon17 challenge datasets. The method consists of two parts: tumor region detection using CNN and cancer metastasis stage prediction using XGboost .
Rituximab® and High Dose Methylprednisolone (HDMP) for the Treatment of Patients with Relapsed/Refractory Chronic Lymphocytic Leukemia.

High-dose gluocorticoids and the anti-CD20 mAb Rituximab each can effect partial responses in patients with chronic lymphocytic leukemia (CLL), although complete and durable responses to treatment with either of these agents have not been reported. We examined whether patients with relapsed and/or refractory CLL could respond to these agents when used in combination in a pilot clinical trial. Fourteen patients with progressive, symptomatic, and relapsed/refractory disease were treated with three four-week cycles of high-dose Methylprednisolone (HDMP) at 1gr/m2 daily for 5 days and weekly Rituximab® at 375mg/m2 for four weeks. The median age of the patients was 60 years, the male to female ratio was 4:1, the ECOG performance status was