Miracle-dz/UniFAR-FTUs huggingface dataset:
ffects of Beta-Blocker herapy on Ventricular Repolarization ocumented by 24-h Electrocardiography n Patients With Type 1 Long-QT Syndrome

OBJECTIVES We tested the hypothesis that in long-QT syndrome (LQT) type 1 (LQT1), beta-blocker therapy may decrease both the diurnal maximal T-wave peak to T-wave end interval (TPE) and the maximal ratio between late and early T-wave peak amplitude (T2/T1 ratio), which are electrocardiographic counterparts of transmural dispersion of repolarization (TDR) and early afterdepolarizations (EA), respectively. BACKGROUND Ventricular repolarization duration and increased TDR and EAs are the three electrophysiological components generating the high risk of ventricular arrhythmias and sudden death in the inherited LQT. In the most prevalent LQT1 form of LQT, treatment with beta-blockers reduces serious arrhythmia events dramatically without a known influence on QT interval duration. In experimental LQT1 models, beta-blockers decrease TDR and prevent EAs. METHODS We reviewed 24-h electrocardiographic recordings obtained before and during the treatment with beta-blockers from 24 genotyped LQT1 patients to record maximal TPE intervals and T2/T1 ratios as well as maximal and rate-adapted QT intervals using a computer-assisted program. RESULTS Treatment with beta-blockers decreased the maximal diurnal T2/T1 amplitude ratio from 3.0 1.0 to 2.2 0.6 (p 0.002). Beta-blockers also decreased both maximal TPE intervals and abrupt maximal QT intervals at heart rates higher than 85 beats/min, whereas QT intervals measured at steady-state conditions remained unchanged. CONCLUSIONS Prevention of abrupt increases of electrocardiographic TDR, EA, and ventricular repolarization duration at elevated heart rates may explain the favorable clinical effects of beta-blockers in LQT1. (J Am Coll Cardiol 2006;48:747–53) © 2006 by the American College of Cardiology ublished by Elsevier Inc. doi:10.1016/j.jacc. 2006.04. 084
Paternity Determination of Captive Southern Pudu (Pudu puda) Using Bovine and Ovine Microsatellite Markers

In the current study, we attempted to determine the paternity of captive southern pudu (Pudu puda) using bovine and ovine microsatellite markers. Of 15 markers tested, nine (60.0%) were successfully amplified and seven were polymorphic, with the number of alleles per locus ranging from three to five. Observed heterozygosity in the seven markers ranged from 0.667 to 1.00. Most of these polymorphic markers could be used to accurately verify the relationship between father and offspring. These bovine and ovine microsatellite markers may be useful for parentage testing and genetic diversity analysis in southern pudu.
EVALUATION STUDY BETWEEN THE TRADITIONAL FINANCIAL ACCOUNTING AND ELECTRONIC FINANCIAL ACCOUNTING AND ITS IMPACT ON RAISING THE EFFICIENCY OF COMPANY'S CAPITAL

The study evaluated the differences between traditional accounting system and Electronic Accounting System (EAS) and their impact on enhancing the public capital efficiency of firms and identified the reasons behind each system’s implementation, as well as each system’s effectiveness in light of work. The study adopted Davis’s TAM to shed insight into the acceptance/rejection of EAS implementation by sample members and the alternative use of traditional accounting system. The study sample comprised of accountants employed in banks, public shareholding firms and financial auditing firms, to which questionnaire copies were administered to, in order to test the study hypotheses. From the findings, majority of the accountants surveyed (96.4%) were using EAS in Jordan, with the top commonly used programs being the e-table/excel (66%) and specialized programs were also being used by some accountants (30.4%). On the other hand, the findings also showed that only few accountants (3.6%) still employed traditional accounting system. As mentioned, Davis’s TAM was used to shed light into the EAS usage among Jordanian accountants and its role in enhancing the public capital efficiency of the firms. The study provided recommendations on how to promote the use of EAS among accountants accordingly.
The Impact of Rehabilitating Strategies on Learning of Inmates: Evidence from Central Prison, Khairpur

Shabana Abdul Jabbar Phulpotoa, Mahnaz Aslamb, Prof. Dr. Muhammad Yousuf Sharjeelc, Noman Ali Samod, Abid Ali Ujjane, Shehzad Haiderf, aSindh Education and Literacy Department, Junior School Teacher, bAssistant Professor university of Turbat, cDepartment of Teacher Education, Federal Urdu University of Arts, Science and Technology, dAssistant Manager Training, The Citizen Foundation (TCF), eSZABIST Social Sciences Scholar, Faculty of Social Sciences, fLecturer-Department of Education, LUAWMS-Lasbela, Email: ateachersp7@gmail.com, bmahnazbaloch1313@gmail.com, cyousufsharjeel@hotmail.com, dnomanalisamo4@gmail.com, eabidaliujjan43@gmail.com, fwarriors.uthal@gmail.com
Intellectual Capital and Firm Performance in the Ghana Stock Exchange

In the current knowledge economy, intellectual capital serves as a strategic asset, yielding competitive advantage. Studies have explored how this asset affects organizational performance. These studies were mostly on the banking industry, at the expense of listed firms, which constitute a different industry. Results on the stock market can be beneficial for a wide range of stakeholders due to the significant role the market plays in the economy. The purpose of this study was to examine the impact of intellectual capital on the performance of listed firms. The study used the Value Added Intellectual Coefficient (VAICTM) model as a proxy for intellectual capital performance, which has human capital efficiency, structural capital efficiency and capital employed efficiency as components. Also, a dataset of 18 companies listed on the Ghana Stock Exchange between 2008 and 2017 was used with return on asset, return on equity and Tobin’s Q as measures of performance. The regression results indicated that VAICTM and all the three components positively and significantly affected profitability. However, market performance measured by Tobin’s Q was not affected by intellectual capital. The results are significant for the corporate world, suggesting that investment in knowledge assets is an essential means of enhancing profitability. However, the market players in the Ghanaian bourse may not be considering the intellectual capital quotient of firms in their interactions in the market. Financial analysts are advised to include intellectual capital ratios as part of predicting models.
The Information Advantage of Industry Common Blockholders and Its Stabilizing Effect on Stock Price Crash Risk

Institutional investors’ common blockholdings within an industry produce an information advantage, allowing them to differentiate between the industry-wide and firm-specific nature of bad news released by peer firms and avoiding selling on false spillover signals (i.e., “panic exit”), which in turn reduces future stock price crash risk. This evidence is in sharp contrast with the evidence that total institutional ownership increases firm-level stock price crash risk. Our study highlights the economic value of common owners in the stock markets: Their information advantage provides a stabilizing effect against contagion.
Tissue Engineering Auricular Cartilage: A Review of Auricular Cartilage Characteristics and Current Techniques for Auricular Reconstruction

Microtia and anotia are congenital auricular anomalies that negatively impact the psychosocial development of those affected. Because auricular cartilage is a type of elastic cartilage that lacks regenerative capacity, any notable defect in its structure requires a surgical approach to reconstructing the auricle. While there are several reconstructive options available between alloplastic and prosthetic implants, autologous rib cartilage grafts remain the most commonly used treatment modality. Still, this widely used technique is accompanied by significant patient discomfort in a young child and carries additional risks secondary to the traumatic process of rib cartilage extraction, such as pneumothorax and chest wall deformities, and the final esthetic results may not be ideal. To circumvent these limitations, tissue engineering approaches have been used to create a realistic-looking ear that mirrors the complex anatomy of the normal ear. This article reviews the biochemical and biomechanical properties of human auricular cartilage as they relate to design criteria. In addition, a variety of cell sources, biocompatible scaffolds, scaffold-free techniques, and mechanical and biological stimuli are discussed. This review aims to identify knowledge gaps in the literature related to auricular cartilage characteristics and make recommendations to drive the field of auricular tissue engineering.
The Impacts of Foreign Portfolio Flows and Monetary Policy Responses on Stock Markets by Considering COVID-19 Pandemic: Evidence from Turkey

Abstract This study researches the impacts of foreign portfolio flows (proxied by foreign investors' retention share) and monetary policy responses (proxied by the repurchase interest rate) on Turkey's stock market index taking the COVID-19 pandemic into consideration. A volatility index, credit default swap spreads, and foreign exchange rates are used as control variables, with a daily dataset between January 2, 2017, and October 20, 2020. After examining the stationarity and nonlinearity characteristics of the variables, we applied a nonlinear autoregressive distributed lag (NARDL) model and then conducted a Markov switching regression (MSR) for a robustness check. The results reveal that both foreign portfolio flows and monetary responses have an important effect on the index, and foreign portfolio flows have a higher effect than monetary responses. Accordingly, the results obtained from the NARDL and MSR models are robust and consistent.
Behavioural Insights for Living within the Earth's Carrying Capacity: An Interdisciplinary Review of the Scholarly Literature

Applications of behavioural insights (BI) are increasingly being used in social and economic contexts by both public and private actors, and particularly in highly developed countries like Canada. These applications aim to use an understanding of human decision-making informed by key findings in behavioural science in recent decades, one that challenges conventional assumptions about rationality. BI applications are based on systematic and predictable errors in individual decision-making, the biases and shortcuts individuals use routinely. Proponents of BI foreground their low cost of implementation and their ability to produce benefits without regulation or incentives, preserving individual choice. In the last decade, numerous governments have established dedicated units to further pilot and implement BI in public policy, including the UK and Canada. <br><br>Our Synthesis Paper assesses the underlying scholarship of BI, by examining the research testing its significance for sustainability goals. We conducted a comprehensive literature review using an interdisciplinary approach and methods (from psychology, economics and public policy). Our findings indicate that this field is still cohering and contains considerable promise for reducing adverse environmental impacts. However, we conclude that the state of the research does not yet support many general policy applications. <br>
The Effect of CO and H Chemisorption on the Chemical Ordering of Bimetallic Clusters

The effect of alloying on ligand adsorption energies and how this can modify the segregation patterns of selected binary nanoalloys is studied via first-principles total energy calculations. A model setup is considered, in which high-symmetry 38-atom truncated-octahedral (TO) clusters with compositions A6B32 and B6A32 are used as substrates to bind a single CO molecule or H atom in centroid sites for the following (A,B) pairs: (Au,Pd), (Pd,Pt), and (Cu,Pt), and the relative changes in the energetics of the systems upon ligand coordination are analyzed. We find qualitative similarities but quantitative differences between the CO and H cases (as examples of reducing agents), and a wide variety of behavior for the three (A,B) pairs. In AuPd, Pd−CO bonding is not strongly affected by neighboring Au atoms but the PdcoreAusurface segregation pattern (favored for bare particles) is expected to be inverted in the presence of CO coordinating species. At the other extreme, in CuPt, both Pt−CO and Pt−H bonding is st.
Investigating the Economic Impacts of COVID-19 on the Mental Health of Different Demographics

With increases in unemployment and individuals experiencing mental health symptoms, the effects of the COVID-19 pandemic have become a prevalent issue in society. The purpose of this paper is to educate communities about the current effects of COVID-19 on the economy as well as mental health. We aim to establish connections between economic hardships and patterns of depression and anxiety across different demographic groups within the U.S. labor force participation pool aged 18 or older. We analyzed existing literature on past recessions, past pandemics, and the current pandemic’s impacts. The findings from past literature suggest that the pandemic has caused various economic impacts which in turn affect mental health, particularlyanxiety and depression rates. We used data from the U.S. Census Bureau Household Pulse Survey (HPS) to conduct chi-squared tests of independence on employment income loss and symptoms of anxiety and depression over 12 weeks of the pandemic. For each demographic variable (race, income level, gender, and age), we found an association between each categoryand experienced employment income loss. The tests yielded the same conclusions for the data of the symptoms of anxiety and depression.
When to Broadcast? Inventory Disclosure Policies for Online Sales of Limited Inventory

Online sales of limited inventory such as lightning and flash deals have become popular among e-commerce platforms including Amazon and eBay. Motivated by empirical studies of the impact of inventory information on demand in flash sales (Cui et al. 2019 and Calvo et al. 2020), we study the platform’s best timing of disclosing inventory information to maximize the expected sales in a finite horizon. We analyze the following common policies in practice: “always disclose,” “never disclose,” and the fixed threshold policy. The fixed threshold policy requires the platform to broadcast once the inventory level drops below a preset level. We also propose a time-dependent threshold policy, which requires a comparison between the current time and a time-threshold associated with the current inventory level, and show that the proposed policy is the optimal policy under certain assumptions that are consistent with the empirical findings of the extant literature. For both threshold policies, we devise efficient algorithms to optimize the policy parameters, and we compare them through a numerical study. We find that both threshold policies can significantly outperform the two simple policies of always or never disclose. In particular, when the observational learning (herding) effect dominates the scarcity effect on demand, the fixed threshold policy is near optimal. However, when the scarcity effect dominates, employment of a fixed threshold policy may backfire as the customers may interpret not disclosing as a signal of slow sales. Furthermore, when both effects are not profound, the proposed time-dependent threshold policy can significantly improve the fixed threshold policy, so the platform should employ the proposed policy instead of the fixed-threshold policy. Therefore, our study provides not only effective and efficient algorithms for policy optimization but also guidelines for policy selection.
Supporting the technical management of residential buildings in the process of their exploitation

By analyzing the individual stages of the building's life cycle, it can be easily concluded that the building's exploitation process is the longest and at the same time it is the justification for the construction project related to the construction of this building. In the course of the building's exploitation, various phenomena occur that affect its condition and thus the possibility of unlimited use. These are natural phenomena, as well as phenomena derived from external influences, which often lead to deterioration of the building's condition, or even its degradation. In response to these phenomena, maintenance, renovation and modernization activities are undertaken. Technical management is related to the identification of these phenomena, programming of adequate measures and their implementation. The conducted analysis of the results of the survey in the group of property managers allows to state categorically that the process of technical management is relatively little supported by IT tools and is still based on individual analysis and often intuitive actions. The article presents the possibilities of applying an innovative approach in the acquisition and collection of information about the technical condition of buildings, indicating the legitimacy of standardizing information forms and using them in building a database of cases of the CBR (case based reasoning) inference system .
A Real-time Engagement Assessment in Online Learning Process Using Convolutional Neural Network

This paper proposes a framework of the practical use of a real-time engagement estimation to assess learner’s engagement state during an online learning activity such as reading, writing, watching video tutorials, online exams and online class. The framework depicts the whole picture of how to implement an engagement estimation tool into an online learning management system (LMS) in a web-based environment, where the input is the real-time images of the learners from a webcam. We built a face recognition and engagement classification model to analyse learners’ facial feature and adopt a convolutional neural network to classify them into one of the three engagement classes, namely, very engaged, normally engaged, or not engaged. The deep learning model is experimented on open Dataset for Affective States in E-Environments (DAiSEE) with hard labeling modification. Extracting images from every 10 seconds snippet video is done to prepare the dataset then to be fed into the convolutional neural network (CNN). The engagement states are recorded into a file to evaluate the learner’s engagement states during any online learning activities.
Temporal Regular Path Queries: Syntax, Semantics, and Complexity

In the last decade, substantial progress has been made towards standardizing the syntax of graph query languages, and towards understanding their semantics and complexity of evaluation. In this paper, we consider temporal property graphs (TPGs) and pro-pose temporal regular path queries (TRPQ) that incorporate time into TPG navigation. Starting with design principles, we propose a natural syntactic extension of the MATCH clause of popular graph query languages. We then formally present the semantics of TR-PQs, and study the complexity of their evaluation. We show that TRPQs can be evaluatedin polynomialtime if TPGs are time-stamped with time points. We also identify fragments of the TRPQ language thatadmit eﬃcient evaluation over amore succinct interval-annotated representation. Our work on the syntax, and the positive complexity results, pave the way to implementations of TRPQs that are both usable and practical.
G + MAo 9 t 7 v ’ Yz = ~ ~ How the K ~ Approach Undermines Groundwater Cleanup

Environmental scientists have long appreciated that the distribution coefficient (the “Kd” or “constant KJ’) approach predicts the partitioning of heavy metals between sediment and groundwater inaccurately; nonetheless, transport models appIied to probIems of environmental protection and groundwater remediation aImost invariably employ this technique. To examine the consequences of this practice, we consider transport in one dimension of Pb and other heavy metals through an aquifer containing hydrous ferric oxide, onto which heavy metals sorb strongly. We compare the predictions of models calculated using the K~ approach to those given by surface complexation theory, which is more realistic physically and chemically. The two modeling techniques give qualitatively differing resuM.s that that lead to divergent water flushing is cleanup strategies. The resuIts for surface complexation theory show ineffective at displacing significant amounts of Pb from the sorbing surface. The effluent from such treatment contains a “tail” of small but significant levels of contamination that persists indefinitely. Subsurface zones of Pb contamination, furthermore, are largely immobile in flowing groundwater. These results stand in sharp contrast to the predictions of models constructed using the& approach, yet are consistent with experience in the laboratory and field.
Urban Tree Species Classification Using Aerial Imagery

Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's aerial images and then investigates how state of the art deep Convolutional Neural Network models such as VGG and ResNet handle the classification problem of urban tree aerial images under different parameters. Experimental results show our best model achieves an average accuracy of 60% over 6 tree species.
Survey of Self-Play in Reinforcement Learning

In reinforcement learning (RL), the term self-play describes a kind of multi-agent learning (MAL) that deploys an algorithm against copies of itself to test compatibility in various stochastic environments. As is typical in MAL, the literature draws heavily from well-established concepts in classical game theory and so this survey quickly reviews some fundamental concepts. In what follows, we present a brief survey of self-play literature, its major themes, criteria, and techniques, and then conclude with an assessment of current shortfalls of the literature as well as suggestions for future directions.
Graph Neural Network Based Access Point Selection for Cell-Free Massive MIMO Systems

A graph neural network (GNN) based access point (AP) selection algorithm for cell-free mas-sive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embed-dings which are then used to predict the links between the nodes. The numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts the potential APs with more accuracy. Compared to the large scale fading coefficient based AP selection algorithms, the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.