Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms for managing medical institutions in times of crisis

Medical institutions face a lot of problems with insufficient staffing, infrastructure, special materials and equipment during the crisis The health care system depends on government action at the national and regional levels, especially it is about problem-solving strategies and action plans This article is based on a qualitative analysis of the experience of crisis management in connection with the spread of COVID-19 Germany and the Czech Republic have proven the importance of strategic and integrated approaches to good governance It is determined that the centralized management with a possibility of acceptance of local regional decisions by medical establishments is effective in times of crisis It is proved that the crisis causes reorganization of medical institutions, redistribution of functions and responsibilities of medical staff, transformation of the management system and change of strategies Strategic and integrated approaches provide centralized management and involvement of the private sector, institutions and the public in the process of counteracting the consequences of the crisis Management processes and mechanisms are characterized by speed, flexibility and adaptability to changing crisis situations The government is the main regulator in a crisis Education and training, reorganization of medical services and medical institutions, the formation of a digital market, the development of new types of equipment occur as a result of strategic management Strategic management is based on the flexibility and adaptability of the health care system to possible changes The experience of functioning of management mechanisms of medical organizations in the conditions of crisis depends on an initial condition of human resources, physical infrastructure and institutional capacity of authorities at national and regional levels A strategic and comprehensive approach to crisis management in Germany and the Czech Republic proves the effectiveness of counteracting the consequences of the crisis
Heuristics in dynamic decision making: Coping with the time constants of a dynamic task by doing something else

Laboratory studies have shown that people cannot handle the time constants in dynamic tasks. Yet they obviously cope with such tasks with some success outside the laboratory. This study is one in a se ries of studies that examine the hypothesis that people cope by relying on heuristics that allow them to simplify the task. The heuristic studied here was that of relying on frequency differences, i.e., what Reas on (1990) calls frequency gambling. It examines the effects of varyi ng the relative frequency of scenarios that require different responding, and where relying on frequency rather than learning the actual time constants will lead to some success. The results show that the partici n s did not learn the time constants, but the heuristic used did not seem t o be frequency as much as a heuristic that could be called “better sa f than sorry”, i.e., they sent out more than the minimum necessary a sets to fight the fire. A variant of this heuristic involving rapid and massi ve responding has also been identified in earlier studies.
Monitoring of Stream Processing Engines Beyond the Cloud: An Overview

The Internet of Things (IoT) is rapidly growing into a network of billions of interconnected physical devices that constantly stream data. To enable data-driven IoT applications, data management systems like NebulaStream have emerged that manage and process data streams, potentially in combination with data at rest, in a heterogeneous distributed environment of cloud and edge devices. To perform internal optimizations, an IoT data management system requires a monitoring component that collects system metrics of the underlying infrastructure and application metrics of the running processing tasks. In this paper, we explore the applicability of existing cloud-based monitoring solutions for stream processing engines in an IoT environment. To this end, we provide an overview of commonly used approaches, discuss their design, and outline their suitability for the IoT. Furthermore, we experimentally evaluate different monitoring scenarios in an IoT environment and highlight bottlenecks and inefficiencies of existing approaches. Based on our study, we show the need for novel monitoring solutions for the IoT and define a set of requirements.
Antibiotic resistance studies of Escherichia coli and Salmonella species isolated from diarrheal patients

© 2021 Avijit loha et al. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Abstract Antibiotic resistance is a widespread problem. However, limited studies are available on Escherichia coli and Salmonella spp. associated with stool samples of diarrhea patients in Leda Diarrhea Treatment Center (DTC), Teknaf, Cox, Bazar. In this study, 51 stool samples were collected between October and December 2020 from diarrhea patients from a selected DTC. Bacteria isolation and antibiotics susceptibility testing were carried out using standard microbiological procedures. Isolates of bacteria that were resistant to more than three classes of antibiotics were considered Multidrug Resistance (MDR). About 20 bacteria that include E. coli (n = 15) and Salmonella spp. (n = 5) were isolated while the highest prevalence of 73.3% and 60% respectively were observed among the under-5 years, age group. The highest (60%) frequency of isolation of Salmonella spp. was found among illiterate patients. The high resistance to each of amoxicillin (80%), ampicillin (100%), and tetracycline (73.3%) among E. coli isolates was observed while tetracycline was the most frequently used antibiotic for diarrhea treatment among sampled patients. Among Salmonella spp. 100% resistance to each of amoxicillin and ampicillin was observed, while 60% resistance to tetracycline was noticed. Two and one MDR E. coli and Salmonella spp. were recognized respectively. The occurrence of both E. coli and Salmonella among infants and aged adults, attached with MDR characteristics presented by bacteria from this study are of public health importance. Therefore, widespread studies are necessary for the determination of the molecular epidemiology of these resistant bacteria for public health surveillance.
Phoebe: A Learning-based Checkpoint Optimizer

Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes of data. However, as the complexity and scale of analytical jobs increase, they encounter a number of unforeseen problems, hotspots with large intermediate data on temporary storage, longer job recovery time after failures, and worse query optimizer estimates being examples of issues that we are facing at Microsoft. To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer. Given a set of constraints and an objective function at compile-time, Phoebe is able to determine the decomposition of job plans, and the optimal set of checkpoints to preserve their outputs to durable global storage. Phoebe consists of three machine learning predictors and one optimization module. For each stage of a job, Phoebe makes accurate predictions for: (1) the execution time, (2) the output size, and (3) the start/end time taking into account the inter-stage dependencies. Using these predictions, we formulate checkpoint optimization as an integer programming problem and propose a scalable heuristic algorithm that meets the latency requirement of the production environment. We demonstrate the effectiveness of Phoebe in production workloads, and show that we can free the temporary storage on hotspots by more than 70% and restart failed jobs 68% faster on average with minimum performance impact. Phoebe also illustrates that adding multiple sets of checkpoints is not cost-efficient, which dramatically reduces the complexity of the optimization.
Decision-making in the purchase of equipment in agricultural research laboratories: a multiple-criteria approach under partial information

Article history: Received February 1, 2021 Received in revised format: June 1, 2021 Accepted July 27 2021 Available online July 27, 2021 Investments in the agricultural sector represented by innovations and new technologies strongly influence the economic growth in developing countries. In this context, purchasing decisions have become more relevant. Multiple-criteria decision-making techniques are well suited for decision-makers (DMs) who are considering the introduction of new technologies. In this paper, a multi-criteria model is built to help a Colombian agricultural research company make decisions on purchasing different laboratory equipment. A compensatory approach based on trade-offs is used to elicit the preferences of a group of DMs. The high number of answers and cognitive effort required from them during the elicitation process led to using an alternative approach based on partial information, called the FITradeoff (The Flexible and Interactive Tradeoff) method. It showed to be the best fit to solve the company’s purchasing problem and allowed its managers to make decisions that consider criteria other than price, taking account of the DMs’ conflicting viewpoints. The proposed model aimed at contributing to the articulation of the end-user knowledge in decision-making in order to ensure effective articulation of actors and strengthening of science, technology and innovation in agriculture. by the authors; licensee Growing Science, Canada 1 2 20 ©
Mechanized Theory of Event Structures: A Case of Parallel Register Machine

The true concurrency models, and in particular event structures, have been introduced in the 1980s as an alternative to operational interleaving semantics of concurrency, and nowadays they are regaining popularity. Event structures represent the causal dependency and conflict between the individual atomic actions of the system directly. This property leads to a more compact and concise representation of semantics. In this work-in-progress report, we present a theory of event structures mechanized in the COQ proof assistant and demonstrate how it can be applied to define certified executable semantics of a simple parallel register machine with shared memory.
Design and pharmaceutical applications of porous particles

Porous particles (PPs) are a novel kind of carrier with a large surface area, high porosity, stable uniform porous structure, tunable pore sizes with narrow distribution, and well defined surface properties. There are many methods for preparing PPs, mainly including using porogens or templating agents to form pores, self-forming pores by particle matrices without any pore-forming agents, and chemical synthesis. In this review, reports on various PPs are collected, which are applied in various fields of medicine for many purposes, such as improvement of dissolution of poorly soluble drugs, development of novel drug delivery systems, and improvement of the functional properties of drug-loaded particles. In general, these functions of PPs are closely related to their fundamental particle and pore properties, such as morphology, size, surface property, surface area, and porosity. The reasonable application of PPs has markedly facilitated the development of pharmaceutics and the number of published reports has increased progressively from year to year. However, there remains a serious deficiency of summarization and systematic analysis of these reports. Therefore, this review aims to provide a comprehensive and relevant overview on the design and pharmaceutical applications of PPs, which can serve as an informative reference for interested readers.
The Impact of Financial Deepening Indices on CO2 Emissions: New Evidence from European and Sub Saharan African Countries

Taking into account the complicated and multidimensional nature of financial development, this study aims to investigate the impact of overall financial markets development, institutions development, and their sub-indices on CO2 emissions. To advance knowledge about the nexus between financial development and CO2 emissions, four financial markets indices (overall financial markets development, FM-access, FM-depth and FM-efficiency), and four financial institutions indices (overall financial institutions development, FI-access, FI-depth and FI-efficiency) are used. The study uses two-stage System-GMM and panel data of 41 EU and 43 SSA countries over the period 2000-2018. The empirical results reveal that the overall financial markets development and its sub-indices (FM-access, FM-depth and FM-efficiency) reduce CO2 emissions in the EU countries. However, for SSA countries, the degrading effect is found. The results further show that the overall financial institutions development and its sub-indices such as FI-access, FI-depth and FI-efficiency foster the environment quality in the EU region while overall financial institutions development and FI-access impede in the SSA region. The estimated effect of FI-depth and FI-efficiency is not significant in the case of SSA countries. The usage of renewable energy is found a viable solution to mitigate the CO2 emissions in both group of countries. The current paper also suggest important sustainable development policies.
Emerging Markets Currency Factors and US High-Frequency Macroeconomic Shocks

Relying on the structural vector autoregression developed by Cieslak and Pang, the authors identify four shocks to the US economy based on the US Treasury yield curve and the stock market: two fundamental news shocks (growth and money) and two risk-premium shocks (common and hedging). They find that these shocks explain over 40% of the time-series variation of emerging markets currency (EMFX) returns. Additionally, EMFX returns increase significantly with positive growth shocks and decrease with monetary tightening and risk-premium shocks. The authors show that growth and common shocks are priced in the cross section of EMFX, with a positive and negative risk premiums, respectively. They then build long–short currency portfolios based on several academically researched style factors and test their performance and relative exposure to the macroeconomic shocks affecting the US economy. They find that only carry and macro momentum long–short portfolios generate positive and significant alphas and excess returns over their sample. However, all single-factor portfolios have sizable exposure to the four shocks. The authors show that a simple multifactor approach to investing in EMFXs eliminates the exposure of excess returns to all macroeconomic shocks.
First use of a neutral cluster and air ion spectrometer in Australia

Aerosol particles form from molecular clusters at a size of about 1.6 nm. Conventional aerosol measuring instruments such as the scanning mobility particle sizer (SMPS) cannot monitor particles smaller than about 5 nm. The neutral cluster and air ion spectrometer (NAIS) is a recently developed instrument that is designed to measure airborne ions and particles down to a size of 0.8 nm, which is smaller than the size boundary between clusters and particles. It is, therefore, able to observe and measure aerosols at the very sizes that they are formed, permitting more reliable identification and characterisation of new particle formation (NPF) events in the atmosphere than has been possible so far. The Queensland University of Technology purchased a NAIS in 2011. Although, it had been used in several locations around the world, at the time it was the very first NAIS in the Southern Hemisphere and is still the only such instrument in Australia and New Zealand. Since then, the instrument has provided a wealth of data in Australia. For example, for the very first time, it has enabled us to monitor charged particle concentrations near busy roads and overhead powerlines and to reliably identify NPF and determine formation and growth rates of particles in various environments. In this article, we will provide a summary of our findings from the various projects that were conducted over the last five years and discuss the future directions of this research.
Public works programs and agricultural risk: Evidence from India

The agricultural sectors in many low- and middle-income countries remain highly vulnerable to weather risk, a vulnerability that will only intensify under climate change. Public works programs, which are growing in popularity globally, have the potential to impact weather-related agricultural risk. I explore the impact of India’s National Rural Employment Guarantee Act (NREGA) on weather-related agricultural risk. My empirical strategy exploits the staggered rollout of NREGA and random weather fluctuations. Using a nationwide panel of data, I find that NREGA makes crop yields more sensitive to low rainfall shocks. I posit that these results are consistent with a labor market channel, by which NREGA increases non-farm labor supply in low rainfall years, and an income channel, by which NREGA leads to riskier agricultural practices. These results highlight the importance of understanding how social protection programs shape agricultural risk.
Unsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of Superpixels

Corresponding Author: Rasha Shoitan Department of Computer and Systems, Electronic Research Institute, Cairo, Egypt Email: rasha.shoitan@eri.sci.eg Abstract: Segmenting out the foreground from a single image remains a challenge in computer vision. Image co-segmentation has been used recently to alleviate the single image segmentation by exploiting the information of the common object to be segmented from a group of images having the same object. This research proposes an unsupervised co-segmentation technique based on saliency detection and optimized features of the histogram of Hue, Saturation and Value (HSV) of the superpixels. The proposed method is formulated as the conventional Markov Random Field (MRF) segmentation model with an added co segmentation constraint. First, an initial segmentation is extracted based on a saliency technique. Afterward, a Particle Swarm algorithm (PSO) is utilized to select, iteratively, some superpixels on the inner and outer boundary of the initial segmentation to be a foreground or a background according to the optimized energy function. PSO is guided by the HSV dominant colors of the image class and the superpixels around the foreground to decide if a certain superpixel is related to the foreground or the background. The proposed method is evaluated by two datasets: iCoseg and MSRC, along with comparisons to the results of using ten conventional methods based on the Intersection over Union (IoU) metric. The experimental results demonstrate that the proposed method can segment the object successfully and accurately more than the traditional cosegmentation methods, even with a cluttered background.
Reliability of ultrasound hepatorenal index and magnetic resonance imaging proton density fat fraction techniques in the diagnosis of hepatic steatosis, with magnetic resonance spectroscopy as the reference standard

Purpose To evaluate the reliability of ultrasound hepatorenal index (US-HRI) and magnetic resonance imaging proton density fat fraction (MRI-PDFF) techniques in the diagnosis of hepatic steatosis, with magnetic resonance spectroscopy proton density fat fraction (MRS-PDFF) as the reference standard. Materials and methods Fifty-two adult volunteers (30 men, 22 women; age, 31.5 ± 6.5 years) who had no history of kidney disease or viral/alcoholic hepatitis were recruited to undergo abdominal US, MRI, and MRS examinations. US-HRI was calculated from the average of three pairs of regions of interest (ROIs) measurements placed in the liver parenchyma and right renal cortex. On MRI, the six-point Dixon technique was employed for calculating proton density fat fraction (MRI-PDFF). An MRS sequence with a typical voxel size of 27 ml was chosen to estimate MRS-PDFF as the gold standard. The data were evaluated using Pearson’s correlation coefficient and receiver operating characteristic (ROC) curves. Results The Pearson correlation coefficients of US-HRI and MRI-PDFF with MRS-PDFF were 0.38 (p = 0.005) and 0.95 (p<0.001), respectively. If MRS-PDFF ≥5.56% was defined as the gold standard of fatty liver disease, the areas under the curve (AUCs), cut-off values, sensitivities and specificities of US-HRI and MRI-PDFF were 0.74, 1.54, 50%, 91.7% and 0.99, 2.75%, 100%, 88.9%, respectively. The intraclass correlation coefficients (ICCs) of US-HRI and MRI-PDFF were 0.70 and 0.85. Conclusion MRI-PDFF was more reliable than US-HRI in diagnosing hepatic steatosis.
The Effects of Leaders’ Prosocial Orientation on Employees’ Organizational Citizenship Behavior – The Roles of Affective Commitment and Workplace Ostracism

Purpose According to leadership trait theory, leaders’ personality traits are stable factors in organizational situations and exert significant effects on employees’ organizational behaviors. However, studies related to this topic are very limited. In this study, from the leadership trait perspective and based on social identity theory and social exchange theory, the influencing mechanisms of leaders’ prosocial tendencies on affiliation-oriented and challenge-oriented organizational citizenship behaviors are investigated. Specifically, leadership prosocial tendency, affective commitment and workplace ostracism are selected as the independent variable, mediating variable and moderating variable, respectively. Methods The data collection is conducted in two stages in which the leader–employee pairing method is adopted. Ultimately, 347 valid questionnaires are collected from 73 teams. Later, the hierarchical regression analysis and bootstrap methods are used to test the study’s hypotheses. Results Leadership prosocial tendencies have significant positive effects on affective commitment (β = 0.282, p < 0.001), affiliation-oriented (β = 0.648, p < 0.001) and challenge-oriented organizational citizenship behaviors (β = 0.521, p < 0.001). There is a significant positive effect of affective commitment on affiliation-oriented (β = 0.103, p < 0.05) and challenge-oriented organizational citizenship behaviors (β = 0.122, p < 0.05). At the same time, the influence of leadership prosocial tendencies on affiliation-oriented (β = 0.619, p < 0.001) and challenge-oriented organizational citizenship behaviors (β = 0.487, p < 0.001) remains significant. In other words, affective commitment partially mediates the relationships between leaders’ prosocial tendencies and affiliation-oriented, challenge-oriented organizational citizenship behaviors. Workplace ostracism plays a negative moderating role between leaders’ prosocial tendencies and affective commitment (β = −0.098, p < 0.05). Furthermore, workplace ostracism can also mediate the mediating role of affective commitment with 95% bias-corrected confidence intervals [−0.146, −0.017] and [−0.114, −0.003]. Conclusion The results show that leaders’ prosocial tendencies have significant positive effects on both affiliation-oriented and challenge-oriented organizational citizenship behaviors. Affective commitment partially mediates the relationships between leaders’ prosocial tendencies and affiliation-oriented and challenge-oriented organizational citizenship behaviors. Workplace ostracism significantly negatively moderates the relationship between leaders’ prosocial tendencies and affective commitment. Moreover, the study verifies that the mediating effect of workplace ostracism on affective commitment has a significant moderating effect.