Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in the treatment of ST Elevation Myocardial Infarction in the UK

The treatment of acute coronary occlusion with Primary PCI has been a major factor in improving outcomes of patients suffering STEMI in the last 15 years, and is the standard treatment for patients suffering STEMI in the UK. Treatment is beneficial for patients presenting within 12 hours of the onset of symptoms, with the goal being opening of the occluded artery within 150 min of the call for help. Opening of the occluded artery is typically completed with a drug-eluting stent followed by administration of antiplatelet medications for 12 months. Procedures are performed using the radial artery which is associated with improved outcomes compared to vascular access via the femoral artery. Evidence is growing to support full revascularisation including the treatment of severe narrowing in other blood vessels as well as the culprit vessel.
UM ESTUDO PARA A APLICAÇÃO DE GRÁFICOS DE CONTROLE ESTATÍSTICO DE PROCESSO EM INDICADORES DE QUALIDADE DA ÁGUA POTÁVEL A STUDY FOR THE APPLICATION OF STATISTICAL PROCESS CONTROL CHARTS IN DRINKING WATER QUALITY INDICATORS

The water supply in terms of quantity and quality is a growing concern. Water quality has been compromised since the spring, because of the discharge of effluents, which requires investment in treatment plants to ensure the quality of the water supplied. The water leaving treatment plants to the distribution network must achieve the specification for some parameters set in legislation. But in addition to meeting these limits, to maintain the quality of the water supply it is also important to assess the variability. In this sense, statistical control charts play an important role. The main objective of this paper is to analyze the application of statistical control charts for turbidity and residual chlorine, two indicators of the quality of drinking water. The data, both turbidity and residual chlorine, were autocorrelated, and it was necessary to apply specific techniques to solve this problem. Shewhart control charts for individual measures with extended limits extended and applied to residuals from an ARIMA model, were analyzed in this study. These proposals were adequate to monitor these parameters.
Effect of Crude Oil on Biomass Production , Polysaccharide , and Polyphenol Content of Leucocoprinus cretaceus ( Bull . ) Locq . a white rot fungus

There is need to establish the best lignocellulosic wastes with bio-stimulatory effects on mushrooms been investigated for myco-remediation purposes. Solid state fermentation of four substrates (sawdust of Anthostema aubryanum Baill, Mansonia altissima (A. Chev.) A. Chev. , Nauclea diderrichii (De Eild & T. Durand) Merr and Malt Extract Agar) contaminated with various levels of crude oil contamination by Leucocoprinus cretaceus (Bull.) Locq. , was studied. The effect of crude oil on mycelial biomass production, polysaccharide, and polyphenol contents of L. cretaceus during solid state fermentation of lignocellulosic wastes was determined. The polysaccharide and polyphenol content of the mycelia was determined with the Phenol-Sulphuric acid and FolinCiocalteu methods, respectively. Solid state fermentation of crude contaminated substrates improved biomass and polysaccharide content of L. cretaceus. The fungus is a good bioremediation agent degrading crude oil with increasing levels of crude oil contamination (28.00% in 1.00% crude oil contamination to 81% in 10.00% crude oil contamination). Radial mycelial extension increased with increasing levels of crude oil contamination. Crude oil did not cause increased polyphenol concentration and therefore not a stress factor. There was increase in polysaccharide content indicating metabolization of crude oil for metabolic build up. Supplementing crude oil contaminated substrates with the sawdust of M. altissima resulted in the highest level of crude oil degradation by the test fungus. L. cretaceus is a potential strong myco-remediation agent. This study records the first time the fungus is used for degradation of crude oil contaminated substrates. The mushroom has the potential to completely mineralize petroleum hydrocarbons. DOI:https://dx.doi. org/10.4314/jasem.v25i11. 5 Copyright: Copyright © 2021 Sanni and Adongbede. This is an open access article distributed under the Creative Commons Attribution License (CCL), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dates: Received: 22 August 2021; Revised: 17 September 2021; Accepted: 06 October 2021
DB121050 2230..2239

Recent studies have revealed that the central nervous system, particularly the hypothalamus, is critical for regulating insulin sensitivity in peripheral tissues. The aim of our current study is to investigate the possible involvement of hypothalamic activating transcription factor 4 (ATF4) in the regulation of insulin sensitivity in the liver. Here, we show that overexpression of ATF4 in the hypothalamus resulting from intracerebroventricular injection of adenovirus expressing ATF4 induces hepatic insulin resistance in mice and that inhibition of hypothalamic ATF4 by intracerebroventricular adenovirus expressing a dominant-negative ATF4 variant has the opposite effect. We also show that hypothalamic ATF4-induced insulin resistance is significantly blocked by selective hepatic vagotomy or by inhibiting activity of the mammalian target of rapamycin (mTOR) downstream target S6K1. Finally, we show that inhibition of hypothalamic ATF4 reverses hepatic insulin resistance induced by acute brain endoplasmic reticulum (ER) stress. Taken together, our study describes a novel central pathway regulating hepatic insulin sensitivity that is mediated by hypothalamic ATF4/ mTOR/S6K1 signaling and the vagus nerve and demonstrates an important role for hypothalamic ATF4 in brain ER stress–induced hepatic insulin resistance. These results may lead to the identification of novel therapeutic targets for treating insulin resistance and associated metabolic diseases. Diabetes 62:2230–2239, 2013
Elastic and Inelastic Cross Sections for Low-Energy Electron Collisions with ClF Molecule Using the R-Matrix Method

The ClF molecule belongs to an interhalogen family and is important in laser physics and condensed phase molecular dynamics. The elastic and excitation scattering cross sections are obtained in a fixed nuclei approximation using the UKRmol+ codes based on R-matrix formalism. The scattering calculations were performed in the static-exchange (SE), static-exchange-plus-polarisation (SEP), and close-coupling (CC) models. Three CC models with different target states were employed, namely, the 1-state, 5-states, and 12-states. In the CC model, the target states were represented by configuration interaction (CI) wavefunctions. A good agreement of dipole and quadrupole moments of the ground state was obtained with the experimental values, which indicates a good representation of the target modelling. The study predicted the existence of a shape resonance in the SE, SEP, and 5-states CC models. This resonance vanished in the 12-states CC model. The excitation cross sections from ground to the lowest two excited states were also reported. The elastic differential and momentum transfer cross sections were obtained in the 12-states CC models. The contribution of long-range interactions to elastic scattering was included via Born closure approach. The quantities like collision frequencies and rate coefficients were also presented over a wide range of electron temperatures. The ionization cross sections were computed using the binary-encounter-Bethe (BEB) model. The results were reported in C2v point group representation.
Optimal Operation of Energy Storage Using Linear Programming Technique

The utility deregulation allows a small power producing facility to operate in parallel with a local electric grid, thus making it possible to buy and sell energies under the spot prices to the power grid. This paper deals with the optimal operation of an energy storage unit when it is installed in a small power producing facility. The assumption of the modified Gaussian distribution for the electric load and a binomial distribution for the on-site generation and the power grid are used in determining the optimal operation of energy storage unit and the optimal initial storage energy level.
Author Gender Classiﬁcation from Blogs: A Comparative Analysis of N-Grams-Based Author Attribution Techniques

This thesis explores n-grams-based gender classiﬁcation analyses using various n-grams types, sizes, and feature sets. This study expanded on previous research by including a non-binary gender category. First, a state-of-the-art n-grams analysis using a simple dissimilarity measure was replicated, and peak accuracy reached 71%. Seeking to improve this result, a formal feature selection and extraction process was performed. This secondary analysis yielded lower peak accuracy of 61% overall, but non-binary and female-speciﬁc accuracy reached 99–100%. Both results are comparable to ﬁndings from previous research.
Diversity-Driven Automated Formal Verification

Formally verified correctness is one of the most desirable properties of software systems. But despite great progress made via interactive theorem provers, such as Coq, writing proof scripts for verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs or proof scripts. For example, CoqHammer can prove 26.6% of theorems completely automatically by reasoning using precomputed facts, while TacTok and ASTactic, which use machine learning to model proof scripts and then perform biased search through the proof-script space, can prove 12.9% and 12.3% of the theorems, respectively. Further, these three tools are highly complementary; together, they can prove 30.4% of the theorems fully automatically. Our key insight is that control over the learning process can produce a diverse set of models, and that, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof's correctness), this diversity can significantly improve these tools' proving power. Accordingly, we develop Diva, which uses a diverse set of models with TacTok's and ASTactic's search mech-anism to prove 21.7% of the theorems. That is, Diva proves 68% more theorems than TacTok and 77% more than ASTactic. Complementary to CoqHammer, Diva proves 781 theorems (27% added value) that CoqHammer does not, and 364 theorems no existing tool has proved automatically. Together with CoqHammer, Diva proves 33.8% of the theorems, the largest fraction to date. We explore nine dimensions for learning diverse models, and identify which dimensions lead to the most useful diversity. Further, we develop an optimization to speed up Diva's execution by 40×. Our study introduces a completely new idea for using diversity in machine learning to improve the power of state-of-the-art proof-script synthesis techniques, and empirically demonstrates that the improvement is significant on a dataset of 68K theorems from 122 open-source software projects.
A Model of Online Misinformation

We present a model of online content sharing where agents sequentially observe an article and decide whether to share it with others. This content may or may not contain misinformation. Each agent starts with an ideological bias and gains utility from positive social media interactions but does not want to be called out for propagating misinformation. We characterize the (Bayesian-Nash) equilibria of this social media game and establish that it exhibits strategic complementarities. Under this framework, we study how a platform interested in maximizing engagement would design its algorithm. Our main result establishes that when the relevant articles have low-reliability and are thus likely to contain misinformation, the engagement-maximizing algorithm takes the form of a “filter bubble”— creating an echo chamber of like-minded users. Moreover, filter bubbles become more likely when there is greater polarization in society and content is more divisive. Finally, we discuss various regulatory solutions to such platform-manufactured misinformation.
A FULL POTENTIAL EQUATION SOLVER BASED ON THE ALGEBRAIC MULTIGRID METHOD: ELEMENTARY APPLICATIONS

This article reports the development of an e cient, and robust full potential equation (FPE) solver for transonic ow problems, which is based on the algebraic multigrid (AMG) method. AMG method solves algebraic systems based on multigrid principles but in a way that it is independent on the problem's geometry. The mathematical di culties of the problem are associated with the fact that the governing equation changes its type from elliptic (subsonic ow) to hyperbolic (supersonic ow). The ow solver is based of the bodytted structured grid approach in complex geometries. We demonstrate the AMG performance on various model problems with di erent ow speed from subsonic to transonic conditions. The computational method was demonstrated to be capable of predicting the shock formation and achieving residual reduction of roughly an order of magnitude per cycle, both for elliptic and hyperbolic problems.
Students’ Demotivating Factors in English Language Learning: A Case Study

Demotivation in English language learning was discussed in this article based on a case study at one Islamic Junior High School in Palembang, Indonesia. Most of the previous studies focus on positive motivation in learning, but in fact students may also be negatively affected by several factors that can diminish their motivation and may lead to their failure in learning. Therefore, this study was aimed at exploring the factors causing demotivation among students in English language learning. The participants were the second-grade students being demotivated in learning. The data were gained by using document reviews and face-to-face interviews and were analyzed by using thematic analysis. Member checking was used to measure credibility and control of biases in this study. The findings revealed there were five students‟ demotivating factors, namely insufficient school facilities, lack of teachers' competence and teaching styles, inappropriate learning materials and contents, uncomfortable classroom condition and negative classmates' attitudes, and lack of self-confidence. School and teachers must take these factors into account in their English instruction as they have several impacts on the students‟ overall learning. Some improvements must be taken accordingly to minimize the possible causes of student demotivation in English language learning.
Nessie: Automatically Testing JavaScript APIs with Asynchronous Callbacks

Previous algorithms for feedback-directed unit test generation iteratively create sequences of API calls by executing partial tests and by adding new API calls at the end of the test. These algorithms are challenged by a popular class of APIs: higher-order functions that receive callback arguments, which often are invoked asyn-chronously. Existing test generators cannot effectively test such APIs because they only sequence API calls, but do not nest one call into the callback function of another. This paper presents Nessie, the first feedback-directed unit test generator that supports nesting of API calls and that tests asynchronous callbacks. Nesting API calls enables a test to use values produced by an API that are available only once a callback has been invoked, and is often necessary to ensure that methods are invoked in a specific order. The core contributions of our approach are a tree-based representation of unit tests with callbacks and a novel algorithm to iteratively generate such tests in a feedback-directed manner. We evaluate our approach on ten popular JavaScript libraries with both asynchronous and synchronous callbacks. The results show that, in a comparison with LambdaTester, a state of the art test generation technique that only considers sequencing of method calls, Nessie finds more behavioral differences and achieves slightly higher coverage. Notably, Nessie needs to generate significantly fewer tests to achieve and exceed the coverage achieved by the state of the art.
PHYSIOLOGICAL RESPONSES OF GROWING CALIFORNIA RABBITS TO GUAVA BY-PRODUCTS SUPPLEMENTATION

A total of 50 California male rabbits at 6 weeks of age were used in this study. They were randomly classified into five groups, 10 rabbits each. The 1 group, was fed on the basal diet, and considered as the control group, while the other groups [2, 3, 4, and 5] were the treatment groups, which were fed on the basal diet supplemented with different levels of guava by-product meal (GU) 10,20,30 and 30% +1.0gm/kg/diet of kemzyme respectively as replacement of colver hay, Daily weight gain, feed consumption, and feed conversion ratio were calculated. At the end of experiment (14 weeks), four males from each group were individually weighed and slaughtered. Plasma samples were collected and stored frozen at-20°C until biochemical analysis for plasma total protein and lipids fractions, plasma minerals (Ca, P, Fe); plasma enzymes (ALT, AST); glucose level, Hemoglobin and hematocrit values as well as thyroid's hormones concentration. Growth performance and carcass traits were also evaluated. Results obtained could be summarized as follow: 1Body weight, feed consumption, and feed conversion ratio were not significantly affected by GU supplementation. However, feed conversion was improved in animals that fed guava plus enzyme supplementation, (-4.7: -8.4%). 2Liver percentage was significantly affected especially in rabbits that fed on 30% GU plus enzyme (36.6%) than the other groups, but kidney, heart, small and Large intestine, and cecum were not significantly affected. 3Plasma glucose, Ca, phosphorus, total protein and globulin were not significantly affected, however plasma albumin level was increased significantly for rabbits that fed guava and enzyme than control. 4Fe level was slightly increased especially in rabbits fed 30% GU than that of the control. 5Cholesterol and total lipids were decreased in rabbits that fed on 20 and 30% GU than the other groups. 6Thyroid hormones (T3, T4) concentrations, N/L ratio, Hemoglobin, and Hematocrit (PVC) were not significantly affected by treatments as compared to the control. 7ALT activity was not significantly affected, but AST activity decreased in rabbits that fed 30% GU as compared with the control, (-44.9%). 8Total unsaturated fatty acids increased significantly especially in rabbits that fed 20% and 30% plus enzyme than the other groups. 9Economical efficiency, performance index as well as production efficiency factor percentages were significantly higher in rabbits fed on GU especially those fed 20 and 30%. From the achieved results the use of guava by-product as a new inexpensive, safe and available natural waste is recommended product to replace the same percentages of clover hay, in rabbit's diets. Ali, Nematallah G.M.
A New Interpretation of Knowledge Tracing Models' Predictive Performance in Terms of the Cold Start Problem

Previous studies on the accuracy of knowledge tracing models have typically considered the performance of all student actions. However, this practice ignores the difference between students’ initial and later attempts on the same skill. To be effective for uses such as mastery learning, a knowledge tracing model should be able to infer student knowledge and performance on a skill after the student has practiced that skill a few times. However, a model’s initial performance prediction – on the first attempt at a new skill – has a different meaning. It indicates how successful a model is at inferring student performance on a skill from both their performance on other skills and from the difficulty and other properties of the first item the student encounters. As such, it may be relevant to differentiate prediction in these two contexts when evaluating a knowledge tracing model. In this paper, we describe model performance at a more granular level and examine the consistency of model performance across the number of student instances on a given skill. Results from our research show that much of the difference in performance between classic algorithms such as BKT (Bayesian Knowledge Tracing) and PFA (Performance Factors Analysis), as compared to a modern algorithm such as DKVMN (Dynamic Key-Value Memory Networks), comes down to the first attempts of a skill. Model performance is much more comparable by the time the student reaches their third attempt at a skill. Thus, while there are many benefits to using contemporary knowledge tracing algorithms, they may not be as different as previously thought in terms of mastery learning.
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction

With the development of temporal networks such as E-commerce networks and social networks, the issue of temporal link prediction has attracted increasing attention in recent years. The Temporal Link Prediction task of WSDM Cup 2022 expects a single model that can work well on two kinds of temporal graphs simultaneously, which have quite different characteristics and data properties, to predict whether a link of a given type will occur between two given nodes within a given time span. Our team, named as nothing here, regards this task as a link prediction task in heterogeneous temporal networks and proposes a generic model, i.e., Heterogeneous Temporal Graph Network (HTGN), to solve such temporal link prediction task with the unfixed time intervals and the diverse link types. That is, HTGN can adapt to the heterogeneity of links and the prediction with unfixed time intervals within an arbitrary given time period. To train the model, we design a Bi-Time-Window training strategy (BTW) which has two kinds of mini-batches from two kinds of time windows. As a result, for the final test, we achieved an AUC of 0.662482 on dataset A, an AUC of 0.906923 on dataset B, and won 2nd place with an Average T-scores of 0.628942.
Generating Realistic Emotional Pictures with Matched Visual and Emotional Attributes

Extracting emotional values in the natural scene is a fundamental aspect human visual perception. Emotional stimuli are prioritized in human visual processing, and it has been argued that humans selectively attend more to emotional pictures than non-emotional pictures. However, it is still unclear how humans recognize emotional information from the scene in a fast and efficient manner. It has been suggested that emotion perception is mediated by low-level natural statistics (color, contrast, luminance, spatial, and phase information) of the emotional eliciting content [4, 6]. For example, fear inducing images generally contains more high-spatial frequency component than happy or neutral emotion inducing images [2]. Therefore, simple low-level features of the image such as 2D Fourier spectra can be indicative of the emotional sentiment of the scene [1].