Miracle-dz/UniFAR-FTUs huggingface dataset:
Judicious red blood cell transfusion practices in non-cardiac thoracic surgery: more than just matters of the heart

J Thorac Dis 2023;15(7):3505-3508 | https://dx.doi. org/10.21037/jtd-23-802 A mindful approach to intra-operative utilization of red blood cell (RBC) transfusion has become a topic of renewed interest in the last few years. While maintaining adequate tissue perfusion and oxygen carrying capacity is vital to optimizing surgical outcomes, liberal transfusion parameters are also potentially harmful. Transfusion of blood products is associated with increased rates of infection (cytomegalovirus, Epstein-Barr virus, hepatitis B, hepatitis C, chagas, babesiosis, Lyme, etc.), acute transfusion reactions, transfusion related acute lung injury, and hemolysis, among other complications, suggesting that a judicious approach to their use is appropriate (1-5). Initiatives to improve blood utilization may have the most impact in the surgical arena, given large variations in transfusion practices that have been evidenced in both cardiac and non-cardiac surgical subspecialties (6,7). As such, significant efforts have been made to standardize blood utilization in surgical practices. For example, Mazer and colleagues demonstrated that a restrictive approach to intraoperative red cell transfusion in moderate-high risk cardiac surgery patients was non-inferior to a liberal approach in terms of survival, cardiovascular, and renal outcomes (8). In fact, in 2021 the Society for Thoracic Surgery, American Society of Extracorporeal Technology, Society of Cardiovascular Anesthesiologists, and Society for Advancement of Blood Management published an update to their blood management practice guidelines (9). This document incorporated the framework of patient blood management (PBM) in proposing sweeping recommendations in pre-operative, intra-operative, and post-operative care practices to reduce blood loss and transfusion. The tenets of PBM, which are included in Figure 1, provide structure to the protocolized approach that the guidelines espouse. Most of the focus in blood utilization has been on surgical subspecialties with high rates of intra-operative hemorrhage, like orthopedic and cardiovascular surgery (10). Unfortunately, there is little evidence to guide transfusion practice changes in noncardiac thoracic surgery, although opportunity exists in this realm, too. In a recent issue of Journal of Thoracic Disease, Galata and colleagues provide a much-needed update on erythrocyte transfusion practices in non-cardiac thoracic surgery (11). The authors performed a retrospective analysis of 379 consecutive patients that underwent general thoracic surgery at their tertiary referral center in Mainz, Germany in 2021. In their analysis the authors assessed RBC transfusion needs during the first three days of surgery Editorial
How leader member exchange affects effectiveness of performance appraisal system: A chain of reactions model

Abstract The main purpose of the current study was to evaluate the effectiveness of performance appraisal system that highlights the role of performance appraisal reactions (i.e. performance appraisal fairness and performance appraisal satisfaction) in the relationship between leader-member exchange and effectiveness of performance appraisal. Fairness of performance appraisal reactions (i.e. distributive and procedural) mediates at step one and satisfaction with performance appraisal reaction mediates at step two, in a serial mediation model. The study was a cross-sectional quantitative study in which survey approach was used for data collection. The data were collected through a survey questionnaire, from 557 teachers, working in school education department of Punjab, Pakistan, using proportionate random sampling technique. The findings of this study showed the significant effect of the quality of the leader member exchange relationship on effectiveness of performance appraisal system. Further, in a serial mediation model, performance appraisal reactions, i.e. fairness of performance appraisal (distributive and procedural), were found as mediators at step one and satisfaction with performance appraisal as mediator at step two in the aforementioned relationship.
Evaluation of Autochthonous Coagulase—Negative Staphylococci as Starter Cultures for the Production of Pastırma

The aim of the study was to investigate the effects of Staphylococcus xylosus 39, S. equorum 53, or S. vitulinus 75, previously isolated from pastırma, on the quality characteristics of pastırma, a Turkish dry-cured meat product, and to evaluate their potential use as starter cultures. The pastırma production was carried out with a traditional method. The control pastırma groups were manufactured without adding any starter culture. At the end of production, the groups were subjected to microbiological and physico-chemical analyses. The pH was above 5.5, and the aw value was below 0.90 in all groups. The strains used exhibited good adaptation to the pastırma. The S. equorum 53 decreased the thiobarbituric acid reactive substances (TBARS) value in pastırma, while the S. xylosus 39 increased the redness (a*) color value. The autochthonous strains caused a decrease in the palmitic acid (C16:0). However, they had no significant effect on the stearic acid (C18:0) and the oleic acid (C18:1n-9c). A total of 41 volatile compounds were identified in the groups. S. vitulinus 75 increased both benzaldehyde and 2-methyl-3-phenylpropanal levels. In addition, the principal component analysis (PCA) of volatile compounds provided a good separation, and PC1 separated S. xylosus 39 from other groups.
Double-trace spectrum of 
N=4
 supersymmetric Yang-Mills theory at strong coupling

The spectrum of IIB supergravity on ${\mathrm{AdS}}_{5}\ifmmode\times\else\texttimes\fi{}{S}^{5}$ contains a number of bound states described by long double-trace multiplets in $\mathcal{N}=4$ super Yang-Mills theory at large `t Hooft coupling. At large $N$ these states are degenerate and to obtain their anomalous dimensions as expansions in $\frac{1}{{N}^{2}}$ one has to solve a mixing problem. We conjecture a formula for the leading anomalous dimensions of all long double-trace operators which exhibits a large residual degeneracy whose structure we describe. Our formula can be related to conformal Casimir operators which arise in the structure of leading discontinuities of supergravity loop corrections to four-point correlators of half-BPS operators.
Uniqueness results for equations of the p(x)-Laplacian type

We study uniqueness of weak solutions of elliptic equations of the type - div (a(x,u,∇u))+b(x,u)=f(x) in a bounded domain Ω⊂ℝ n with Lipschitz boundary γ=∂Ω. We consider in particular mixed boundary conditions - i.e. Dirichlet condition on one part of the boundary and Neumann condition on the other part. A model equation could be - div (a(x,u)|∇u|p(x)-2∇u)+b(x,u)=f(x),x∈Ω· We establish uniqueness results for these equations. We also indicate conditions which guarantee existence of solution.
Magnetic Susceptibility Study of Hole-Doped Organic Metal κ-(ET)4Hg3-δCl8, δ=22% and κ-(ET)4Hg3-δBr8, δ=11%

The Non-Fermi-Liquid (NFL) state has been one of central issues in the strongly correlated electrons systems. This deviates from conventional Fermi Liquid (FL) behavior. In the hole-doped triangular lattice organic metal κ-(ET)4Hg3-δBr8, δ = 11% (κ-HgBr), the NFL state is observed as a linear temperature dependence of the resistivity which changed to the temperature square dependence behavior by pressure. The spin susceptibility dependence of the muon Knight shift, K(c), is not linear in the region below 50 K, unlike in other k-type organic superconductors. Furthermore, 13C-NMR study under pressure concluded that strong antiferromagnetic spin fluctuations (AFSF) contribute to the origin of NFL. To understand the underlying correlation of the enhanced AFSF and NFL state in k-HgBr, the K(c) plot study in the sister compound κ-(ET)4Hg3-δCl8, δ=22% (κ-HgCl) which shows metal to insulator transition at TMI ~ 20 K is desirable. In this study, we report a precise susceptibility measurement in both k-HgBr and k-HgCl. Furthermore, we summarize the c(T) data of k-HgBr and k-HgCl and discuss them from the viewpoint of the triangular and square lattice models.
A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure

The increasing prevalence of machine learning (ML) and automated machine learning (AutoML) applications across diverse industries necessitates rigorous comparative evaluations of their predictive accuracies under various computational environments. The purpose of this research was to compare and analyze the predictive accuracy of several machine learning algorithms, including RNNs, LSTMs, GRUs, XGBoost, and LightGBM, when implemented on different platforms such as Google Colab Pro, AWS SageMaker, GCP Vertex AI, and MS Azure. The predictive performance of each model within its respective environment was assessed using performance metrics such as accuracy, precision, recall, F1-score, and log loss. All algorithms were trained on the same dataset and implemented on their specified platforms to ensure consistent comparisons. The dataset used in this study comprised fitness images, encompassing 41 exercise types and totaling 6 million samples. These images were acquired from AI-hub, and joint coordinate values (x, y, z) were extracted utilizing the Mediapipe library. The extracted values were then stored in a CSV format. Among the ML algorithms, LSTM demonstrated the highest performance, achieving an accuracy of 73.75%, precision of 74.55%, recall of 73.68%, F1-score of 73.11%, and a log loss of 0.71. Conversely, among the AutoML algorithms, XGBoost performed exceptionally well on AWS SageMaker, boasting an accuracy of 99.6%, precision of 99.8%, recall of 99.2%, F1-score of 99.5%, and a log loss of 0.014. On the other hand, LightGBM exhibited the poorest performance on MS Azure, achieving an accuracy of 84.2%, precision of 82.2%, recall of 81.8%, F1-score of 81.5%, and a log loss of 1.176. The unnamed algorithm implemented on GCP Vertex AI showcased relatively favorable results, with an accuracy of 89.9%, precision of 94.2%, recall of 88.4%, F1-score of 91.2%, and a log loss of 0.268. Despite LightGBM’s lackluster performance on MS Azure, the GRU implemented in Google Colab Pro displayed encouraging results, yielding an accuracy of 88.2%, precision of 88.5%, recall of 88.1%, F1-score of 88.4%, and a log loss of 0.44. Overall, this study revealed significant variations in performance across different algorithms and platforms. Particularly, AWS SageMaker’s implementation of XGBoost outperformed other configurations, highlighting the importance of carefully considering the choice of algorithm and computational environment in predictive tasks. To gain a comprehensive understanding of the factors contributing to these performance discrepancies, further investigations are recommended.
Primary healthcare antibiotic therapy in Poland: a patient perspective – preliminary survey study

Introduction and Objective. Growing resistance to commonly used antibiotics has challenged 21st-century medicine. To date, antibiotic policy has induced bacterial resistance and isolation of multi-drug resistant superbugs insensitive to known antibiotics. The aim of the study was to investigate the knowledge of primary healthcare patients about antibiotic usage, and to identify incorrect patient habits. Materials and method. The research was based on a proprietary, anonymous Internet survey consisting of 21 questions, in which only adults could participate. The survey selected nine questions to assess the respondents’ knowledge. Results. 201 patients participated in the study. About 50% of participants based their knowledge about antibiotics on information obtained during a medical visit, 16.4% on consultations with a pharmacist, 24.9% on the Internet, and 11.4% exchanged experiences with friends. Every third respondent was not satisfied with the information obtained from a doctor. People with higher education reported this issue more often (p<0.05). Nearly 35% of people living in the countryside (p<0.05) admitted to using the part of a drug left after previous antibiotic therapy, and 10% administered antibiotics obtained from friends or relatives. Every fourth patient believed that antibiotics were effective against colds and flu. The average knowledge test result was 5.49 out of 9 points, and a positive score of 6 or more was obtained by nearly 60% of the respondents. Conclusions. The results of this preliminary study indicate that more focus should be given to improving patients’ education and compliance with treatment recommendations.
Predicting Short-Term Variations in End-to-End Cloud Data Transfer Throughput Using Neural Networks

Predicting the data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task. The parameters that affect the throughput can be categorized into three different areas: end-system characteristics (e.g., disk I/O bandwidth, CPU utilization), network characteristics (e.g., network bandwidth, latency, background traffic, bandwidth shaping mechanisms), and dataset characteristics (e.g., average file size, dataset size). Although there are promising results in the literature using neural networks, the datasets are collected from network layer devices and memory-to-memory data transfers where end-system and dataset characteristics are not considered as part of the problem. Also, very few studies use multivariate time series data collected from cloud networks, and the variables differ from study to study. In this project, we collected multivariate time series data from Amazon Web Services (AWS) by conducting intra- and inter-region transfers between storage systems and compute resources using monitoring services. This dataset is unique in the sense that end-system metrics in addition to network throughput are collected from both source and destination systems. Different average file size, instance type, and regionality parameters provide various settings, making the dataset applicable to various types of prediction models. Our multivariate neural network models predict the one-step-ahead network throughput with ~3.7% and disk throughput with ~6.1% error rate, outperforming multivariate models with least-correlated variables and univariate models empowered by transfer learning.
Antecedents of big data adoption in financial institutions

Big data (BD) adoption is still relatively slow despite the numerous business opportunities that it embodies. Within the context of financial institutions in South Africa, the specific drivers of big data adoption remain largely indeterminate. Consequently, this study explores the possible relationships between perceived usefulness and perceived ease of use and actual big data adoption, while being duly cognisant of the role that behavioural intention to adopt big data could play. The study employed a survey research approach and relied on data collected from a purposive sample of 195 management level employees of financial institutions. Regression analysis to test the hypothesised relationships in the study revealed that perceived usefulness and perceived ease of use were statistically significant predictors of big data adoption. Instructively, this relationship was subject to the extent of behavioural intention of respondents. For respondents with a low behavioural intention to adopt big data, perceived usefulness and perceived ease of use displayed a statistically insignificant relationship with big data adoption. Conversely, the regression model for the group of respondents with a high behavioural intention to adopt big data is statistically significant. These findings enrich literature related to big data from a developing country context while concurrently identifying veritable antecedents of big data adoption in financial institutions.
The Boundedness of Solutions to Nonlinear Third Order Differential Equations

In this paper, we establish some new sufficient conditions under which all solutions of nonlinear third order differential equations of the form x''' + ψ (x, x') x" + f (x, x') = p(t, x, x', x") are bounded. For illustrations, an example is also given on the bounded solutions.
Education and Economic Growth: Evidence from the EUROMED Countries

The Mediterranean Strategy on Education for Sustainable Development (MSESD) Report reveals that the Mediterranean is an exceptional eco-region not only due to its geographical characteristics, but also its heterogeneous economic structure, in which education is regarded as a key driver of sustainable development. Given this importance, this study attempts to investigate the impact of education as a channel for human capital accumulation on two different growth concepts in a panel group of eight Euro-Mediterranean Partnership (EUROMED) countries. We utilize an endogenous growth model using annual observations spanning from 2000 to 2017 by employing the Augmented Mean Group (AMG) estimator. Empirical results obtained from the Augmented Mean Group estimator indicate a positive impact of education, regardless of the growth proxy. In addition, empirical findings reveal that the impact of education on inclusive growth is five times higher than that on output growth. We also find that capital is the main driver of growth while inclusive growth is primarily triggered by education. One of the main policy implications raised in this study is that education reforms that attempt to improve educational quality will assist policymakers to achieve sustainable development goals. Extensive policy discussions related to the experiential findings are also provided.
Investigation of an Atmospheric Gas Turbine Model Combustor with Large-Eddy Simulation Using Finite-Rate Chemistry

ABSTRACT A large-eddy simulation (LES) of an atmospheric confined jet flame test case is presented, which represents a model gas turbine combustor and for which detailed experimental data is available. A genetic algorithm approach is used to develop a new, cost-effective reduced mechanism for lifted lean premixed methane-air flames at atmospheric conditions. For the mechanism development, auto-ignition-delay time of mixtures of reactants and cooled down combustion products has been introduced as an optimization criterion. The new mechanism consists of 11 species and 12 reactions. The developed mechanism is validated by comparing the results of zero-dimensional (0D) reactor and one-dimensional (1D) flame simulations against results from the well-known reaction mechanism, GRI-3.0 and Lu19. The three-dimensional (3D) LES are carried out using a finite-rate chemistry (FRC) approach combined with the dynamic thickened flame (DTF) model. In the simulations, the developed mechanism is compared against the Lu19 mechanism and experimental data. Simulations using Lu19 show that the flame is predicted accurately. The new mechanism predicts the flame liftoff and position well, while slightly underpredicting the flame length and showing deviation in quenching behavior but achieves a very significant speedup factor of approximately 2.9 for the entire 3D simulation. For the DTF model, two flame sensor functions are compared, the first based on the progress variable and the second on the local heat release rate. The heat release based formulation is found to be preferable, as it detects the reaction region well, as opposed to the progress variable based formulation which additionally senses zones where reaction products are mixed with reactants, i.e. zones where no classical premixed flame propagation is observed.
Heat Stroke-Induced Takotsubo Cardiomyopathy With a Concomitant Transient Brugada-Like Electrocardiogram

A 79-year-old man presented with impaired consciousness, fever with a body temperature of 41.1°C, and an electrocardiogram showing significant ST segment elevation in leads V1-6, coved-type ST elevation in leads V1-3, and partial right bundle branch block. Echocardiography revealed notable left ventricle dysfunction with apex-based akinesis. Coronary angiography confirmed severe obstructive lesions but left ventriculography displayed the distinct apical ballooning of takotsubo cardiomyopathy (TC). This case highlights the presence of atypical electrocardiographic patterns in TC influenced by comorbidities, supporting the diagnosis of TC despite concurrent obstructive coronary artery disease.
Hope, optimism, and pessimism as predictors of positive and negative psychological changes related to the COVID-19 pandemic in Slovak adults

Background and objectives Positive and negative changes in outlook represent psychological changes that are the results of the cognitive processing of stressful and traumatic events by an individual. The objectives of the study were (1) to determine the level of occurrence and types of positive and negative changes in connection with the COVID-19 pandemic among adults in Slovakia and (2) to study the role of personality factors such as hope (dispositional and perceived) and life orientation (optimism and pessimism) in the prediction of positive and negative changes in adults during the fourth pandemic wave. Methods A Short Form of the Changes in Outlook Questionnaire (CiOQ-S), the Dispositional Hope Scale (DHS), the Perceived Hope Scale (PHS), and the Life Orientation Test (LOT-R) were administered. The research sample consisted of 102 participants, whose ages ranged from 20 to 65 years (Mage = 38.90, SD = 14.28). The research design was quantitative, exploratory, and confirmatory. Results In total, 95% of participants reported positive changes related to COVID-19. Concurrently, up to 70% of these participants also reported negative changes from the impact of the pandemic. Only 25% of participants reported positive changes without noticing any negative perception of the consequences of the pandemic. Overall, 68% of participants reported negative changes related to COVID-19. Only 29% of participants reported negative changes without noticing any positive perception of the consequences of the pandemic. In total, up to 86% of participants agreed with experienced psychological changes (positive or negative) as a result of the COVID-19 pandemic. The high prevalence of positive changes along with the relatively high prevalence of negative changes related to the COVID-19 pandemic outline the question of whether reported positive changes represent real or illusory growth. Optimism and pessimism were found to be significant independent predictors of positive changes related to the COVID-19 pandemic. Hope was identified as a significant independent predictor of negative changes related to the COVID-19 pandemic.