Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Tax Rate in the Context of the Economic Determinants

Corporate taxation is an important tool for an efficient tax system. That is an important source of revenue for the country's budget, also a factor influencing business decisions regarding capital mobility. In decision-making for foreign investors, it is important not only a macro view but also a micro view, that monitors the companies from a managerial field. The aim of this article is to quantify the effect of selected economic determinants of corporate taxation, which significantly affect the effective tax rate and indirectly affect the decision of companies about the location of businesses in the European Union. The linear regression was monitored by enterprises in the EU member states for the period 2008-2016. Two hypotheses were confirmed in the article. The results of the analysis point out the microeconomic indicators are all important determinants for effective corporate tax rate. The leverage effect, the capital intensity and the profitability of the company have a negative impact, and the nominal rate and R & D spending have a positive effect on the effective rate. In the macroeconomic model, the nominal rate and the gross domestic product are statistically significant. An increase of 1% in gross domestic product will result in an increase in the effective rate of 0.4869% and an increase of 1% in unemployment will result in an increase in the effective rate by 6.421%. On the other hand, the tax revenues of corporations have the negative impact. An increase of 1% cause a decline in the effective rate of 13.75%.
Chapter 9 : Food Transportation Issues and Reducing Carbon Footprint

Chapter Summary Transportation is the largest overall contributor to greenhouse gas emissions in many developed countries. Since food products often travel long distances to reach consumers, one might expect transportation to be the major food-related contributor to greenhouse gas emissions. As we show in this chapter, this is rarely the case. Supply chains tend to be long and complex, especially in the food industry. We provide an overview of supply chain basics and the special issues related to transport and storage of food from the point of production to the retail shelf. We include a variety of carbon audits and discuss the lessons learned from several industry case studies. We evaluate several options for packaging and distributing food and beverage products. We also consider interactions and trade-offs associated with these options. In carrying out these analyses, we are often able to identify opportunities for significant energy savings and overall greenhouse gas emissions reductions.
Investigating Purchase-to-Pay process using Process Mining in a multinational corporation

The Business Process Intelligence Challenge 2019 focuses on the compliance analysis of the Purchase-to-Pay process within a multinational company operating from The Netherlands. The participants are provided with a real-life event log data, which captures the information on the activities performed within the process of purchasing. Along with the data, the process owner suggests the specific scope for an analysis, which covers examination of at least 4 process models, throughput times of an invoicing process and spotting potential deviations. To address the challenges the analysis were divided into three main parts exploratory, process focused and predictive analysis, which were performed using various data analytics and process mining techniques including visualization tools, like SQL, PowerBI, Celonis and Python.
Risk factors associated with poor clinical outcome in pyogenic spinal infections: 5-years' intensive care experience.

INTRODUCTION Management of pyogenic spinal infections (PSI) after the development of neurological deficit has not been specifically addressed in the literature. We aimed to describe real-life clinical outcomes of PSI in patients admitted to an intensive care unit with neurological deficit and identify factors associated with good prognosis. METHODOLOGY Consecutive patients admitted to ICU with a possible diagnosis of spinal infection over five years' period were included. Descriptive statistics were performed to examine the demographics and clinical parameters. RESULTS The majority (71%) of patients were male. The mean age was 57.4 years (27-79), and 71% were > 50 years old. At least one underlying risk factor was identified in 68% of the patients; the most common comorbidity was diabetes mellitus (DM). All patients have presented with fever accompanied by a neurological deficit (86%) and back pain (79%). A complete recovery was achieved in 25% of patients. However, the majority of patients had adverse outcomes with 21.4% mortality, and 43% remaining neurological sequelae. Increased age with a cut-off of 65 years and pre-existing DM were identified as being associated with poor outcome. CONCLUSION Mortality among patients admitted to ICU with PSI was significantly higher than reported in the literature. The residual neurological deficit was common, one-third of patients had remaining neurological sequelae, and only one-fourth had complete recovery. Increased age and background DM were the most important determinants of poor clinical outcome. The impact of DM appears to be much more important than currently recognised in this population.
An Experimental Study of the Impact of Pre-Training on the Pruning of a Convolutional Neural Network

In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely hinders their deployment, notably on mobile devices or for real-time applications. Neural networks usually involve a large number of parameters, which correspond to the weights of the network. Such parameters, obtained with the help of a training process, are determinant for the performance of the network. However, they are also highly redundant. The pruning methods notably attempt to reduce the size of the parameter set, by identifying and removing the irrelevant weights. In this paper, we examine the impact of the training strategy on the pruning efficiency. Two training modalities are considered and compared: (1) fine-tuned and (2) from scratch. The experimental results obtained on four datasets (CIFAR10, CIFAR100, SVHN and Caltech101) and for two different CNNs (VGG16 and MobileNet) demonstrate that a network that has been pre-trained on a large corpus (e.g. ImageNet) and then fine-tuned on a particular dataset can be pruned much more efficiently (up to 80% of parameter reduction) than the same network trained from scratch.
A CNN based monitoring system to minimize medication errors during treatment process at home

The ability of a patient to take correct medicine at right time may be reduced when having visual or auditory impairments. Use of inappropriate drug intake can be dangerous and it is important that the patient takes right drug at schedule time. But it is difficult for the elderly persons and the patients with audio and visual impairments to carry out treatment process independently and correctly. This article presents a Convolutional Neural Network (CNN) based medication monitoring system and this system is a sub component of an intelligent pill reminder system.The goal of the intelligent pill reminder system in general, is to assist patient during treatment process at home and role of the monitoring system in particular, is to minimize medication errors. This system is demonstrated on GUI application with a satisfactory accuracy.
Kernel Normalised Least Mean Squares with Delayed Model Adaptation

Kernel adaptive filters (KAFs) are non-linear filters which can adapt temporally and have the additional benefit of being computationally efficient through use of the “kernel trick”. In a number of real-world applications, such as channel equalisation, the non-linear mapping provides significant improvements over conventional linear techniques such as the least mean squares (LMS) and recursive least squares (RLS) algorithms. Prior works have focused mainly on the theory and accuracy of KAFs, with little research on their implementations. This article proposes several variants of algorithms based on the kernel normalised least mean squares (KNLMS) algorithm which utilise a delayed model update to minimise dependencies. Subsequently, this work proposes corresponding hardware architectures which utilise this delayed model update to achieve high sample rates and low latency while also providing high modelling accuracy. The resultant delayed KNLMS (DKNLMS) algorithms can achieve clock rates up to 12× higher than the standard KNLMS algorithm, with minimal impact on accuracy and stability. A system implementation achieves 250 GOps/s and a throughput of 187.4 MHz on an Ultra96 board with 1.8× higher throughput than previous state of the art.
Small but Powerful

Enabling students to recognize and evaluate the ubiquitous impact of computing technology on society is an internationally proclaimed goal of a K-12 computing education. To that end, students need to actually engage with their computing knowledge in concrete everyday situations. From the perspectives of learning transfer and variation theory, we conducted three iterations of a classroom intervention and qualitatively analyzed students’ learning processes. As a result, we propose a model of four so-called critical aspects of everyday computing technology in that context. We present various classroom situations and learning experiences in relation to those aspects, and discuss what seems to have enabled or prevented meaningful learning. In particular, we found that several students had difficulties in conceiving of computing technology as simultaneously economical and powerful, thus limiting its potential ubiquity. We discuss our findings in the context of contemporary theories of learning transfer and argue that they suggest specific issues that may seriously inhibit students to appropriately engage with their computing knowledge in the context of everyday technologies.
BBB-CFI

Code-reuse attack is a concrete threat to computing systems because it can evade conventional security defenses. Control flow integrity (CFI) is proposed to repel this threat. However, former implementations of CFI suffer from two major drawbacks: complex offline processing on programs and high overheads at runtime. Therefore, it is impractical for performance-constrained devices to adopt the technology, leaving them vulnerable to exploitation. In this article, we develop a cross-layer approach named basic-block-boundary-based control flow integrity (BBB-CFI) to minimize the overheads of both offline analysis and runtime checking. Our approach employs basic block information inside the binary code and read-only data to enforce CFI. We identify a key binary-level property called basic block boundary, and based on it we propose the code-inspired method where short code sequences can endorse a control flow transition. Our solution enables quick application launching because it does not require control flow graph construction at the offline stage. We only demand a lightweight analysis on read-only data and a small amount of code of the application. According to the experiments, our approach incurs a negligible 0.11% runtime performance overhead with a minor processor extension, whereas it achieves an order of magnitude speedup in pre-preprocessing compared to a baseline approach. Without control flow analysis or recompilation, BBB-CFI still effectively reduces 90% of the attack surface in terms of gadget numbers. Besides this, we show that the Turing-completeness in the libc is unsustainable. Our approach also demonstrates high applicability to many programs, and it is capable of protecting striped binaries.
On the Complexity and Topology of Scoring Games: of Pirates and Treasure

The combinatorial game Pirates and Treasure is played between two players, Left and Right, on a finite, simple, undirected weighted graph. The vertices of the graph correspond to islands, and a weight function on the vertices indicates the amount of treasure the island has. Left player has n ships, Right player has m ships, in pre-defined vertices. Each turn, the current player moves one of his ships into an adjacent, non-visited vertex, and the weight of the vertex is added to the current points of the player. If a player in his turn cannot move, the game ends. Left moves first. The player who collects more treasures (points) than his opponent wins the game. It is shown that it is PSPACE-complete to decide whether or not Left has a winning strategy in the Pirates and Treasure game. Moreover, it is also PSPACEcomplete to decide whether or not Left has a winning strategy when we assume that the two players are moving in di↵erent components of the graph. For a fixed graph G, topological and convexity properties of weightings are analyzed. Among other things it is shown that the winning space of Left is connected, but not always convex. INTEGERS: 17 (2017) 2
La valeur d’un indice composite pour décrire les effets des tendances de résistance antimicrobienne à l’Escherichia coli

BACKGROUND: Drug resistance indexes (DRIs) quantify the cumulative impact of antimicrobial resistance on the likelihood that a given pathogen will be susceptible to antimicrobial therapy. OBJECTIVE: To derive a DRI for community urinary tract infections caused by Escherichia coli in British Columbia for the years 2007 to 2010, and to examine trends over time and across patient characteristics. METHODS: Indication-specific utilization data were obtained from BC PharmaNet for outpatient antimicrobial prescriptions linked to diagnostic information from physician payment files. Resistance data for E coli urinary isolates were obtained from BC Biomedical Laboratories (now part of LifeLabs Medical Laboratory Services). DRIs were derived by multiplying the rate of resistance to a specific antimicrobial by the proportional rate of utilization for that drug class and aggregating across drug classes. Higher index values indicate more resistance. RESULTS: Adaptive-use DRIs remained stable over time at approximately 18% (95% CI 17% to 18%) among adults ≥15 years of age and approximately 28% (95% CI 26% to 31%) among children <15 years of age. Similar results were observed when proportional drug use was restricted to the baseline year (ie, a static-use model). Trends according to age group suggest a U-shaped distribution, with the highest DRIs occurring among children <10 years of age and adults ≥65 years of age. Males had consistently higher DRIs than females for all age groups. CONCLUSIONS: The stable trend in adaptive-use DRIs over time suggests that clinicians are adapting their prescribing practices for urinary tract infections to local resistance patterns. Results according to age group reveal a higher probability of resistance to initial therapy among young children and elderly individuals.
Spurious sodium results (2) – pseudohypernatremia

In this second article we consider pseudohypernatremia, i.e. falsely increased plasma sodium due to decreased plasma protein or lipid concentration. The major focus of this second article is recent research that has revealed that pseudohypernatremia is a greater problem than previously recognized, particularly for critically ill patients. The case will be made for emerging expert opinion borne out of this research that, for critically ill patients at least, plasma sodium should only be estimated using direct ISE techniques because they are unaffected by abnormal protein or lipid concentration.
Interaction Tarot: A Card-Based Design of Knowledge Construction for Brainstorming in HCI

Many studies focused on how to construct a card-based inspiration tool to support designers in the early stage of their design process. However, most of these studies are very purposespecific for informing/ supporting designers with homogeneous stimuli for well-defined problems and rarely discussed the fundamental concepts underlying interaction design for comprehensive exploration in the unknown design space. To address this need, the present study proposes an approach that demonstrates how we relate archetypal images of Tarot to interaction design issues to develop an isomorphic structure, which is broad enough to cover overall interaction design notions. Based on the structure as an underlying bearer of intermediate-level knowledge, we designed an ideation tool, Interaction Tarot as design provocations not only to make academic knowledge accessible for interaction designers but also to stimulate their divergent imaginations in the ideation process where wild exploration should be embraced. Two expert reviews provided us with design considerations for its revision and indicated that Interaction Tarot could span the ideation horizons, become physical anchors to focus the idea, and open continuous possibilities to stimulate designer’s imagination. Interaction design; Design ideation; Brainstorming; Card-based design, Creative process There has been a growing interest in forming knowledge of interaction design for brainstorming in the early design stage (Bekker & Antle, 2011; Belman, Flanagan, & Nissenbaum, 2009; Brandt & Messeter, 2004; Deng, Antle, & Neustaedter, 2014;
How Information Technology Matters in Societal Change: An Affordance-Based Institutional Perspective

While there has been much work on the relationship between Information Technology (IT) and organizational change, there has been limited research that theorizes the relationship between IT and societal change. This paper draws on institutional theory, in particular institutional logics, to develop a model of IT and societal change, which we argue is critical in an era of large scale digital transformation. Our approach is based on a view of society as an inter-institutional system, reflecting the multiplicity of logics at the societal level. We conceptualize societal change as shifts in the multiplicity of logics, with a focus on changes in the levels of centrality and compatibility. Our model relates these changes to the materiality of technology through the concept of IT affordances. We propose three mechanisms, namely sensegiving, translating, and decoupling, through which IT affordances become elements of societal change. We identify three corresponding carriers through which IT affordances gain scale and stability, namely objects, networks, and platforms. We discuss implications of our theoretical developments for future research on IT and societal change.
Equity risk premium and Sovereign debt

Domestic economy of the emerging markets and international sovereign debt market, such as those of Latin America and Asia, are affected by changes in the U.S. monetary policy. We present a model, which uses a pricing kernel to evaluate risk and return in financial markets of a lending country while embedding it in a model of sovereign default of a borrowing country. Thus, we bring together two separate strands of literature and present a holistic model of international finance, which evaluates how pricing of risk affects the price of debt, spreads and behavior of the borrowing country, i.e., its ability to borrow and smooth consumption. This helps in matching high spreads with low default probabilities. We find that variation of sovereign spread is driven by business cycles of the lending country. Boom in lending country leads to cheaper debt contracts in international lending market. Moreover, higher debt is issued during booms in the borrowing or the lending country. ∗I am extremely grateful to Professor Andrés Erosa for his constant guidance and support. I would also like to thank Professor Hernán Seoane and Professor Matthias Kredler for their valuable comments. †Adam Smith Business School, University of Glasgow. Email: Lovleen.Kushwah@glasgow.ac. uk
Mesenchymal Stem Cells Attenuate Diabetic Lung Fibrosis via Adjusting Sirt3-Mediated Stress Responses in Rats

Diabetes affects a variety of organs such as the kidneys, eyes, and liver, and there is increasing evidence that the lung is also one of the target organs of diabetes and imbalance of Sirt3-mediated stress responses such as inflammation, oxidative stress, apoptosis, autophagy, and ER stress may contribute to diabetic lung fibrosis. Although previous studies have reported that mesenchymal stem cells (MSCs) have beneficial effects on various diabetic complications, the effect and mechanisms of MSCs on diabetes-induced lung injury are not clear. In this study, the STZ-induced diabetes model was constructed in rats, and the effect and potential mechanisms of bone marrow MSCs on diabetic lung fibrosis were investigated. The results revealed that fibrotic changes in the lung were successfully induced in the diabetic rats, while MSCs significantly inhibited or even reversed the changes. Specifically, MSCs upregulated the expression levels of Sirt3 and SOD2 and then activated the Nrf2/ARE signaling pathway, thereby controlling MDA, GSH content, and iNOS and NADPH oxidase subunit p22phox expression levels in the lung tissue. Meanwhile, high levels of Sirt3 and SOD2 induced by MSCs reduced the expression levels of IL-1β, TNF-α, ICAM-1, and MMP9 by suppressing the NF-κB/HMGB1/NLRP3/caspase-1 signaling pathway, as well as regulating the expression levels of cleaved caspasese-3, Bax, and Bcl2 by upregulating the expression level of P-Akt, thereby inhibiting the apoptosis of the lung tissue. In addition, MSCs also regulated the expression levels of LC3, P62, BiP, Chop, and PERK, thereby enhancing autophagy and attenuating endoplasmic reticulum stress. Taken together, our results suggest that MSCs effectively attenuate diabetic lung fibrosis via adjusting Sirt3-mediated responses, including inflammation, oxidative stress, apoptosis, autophagy, and endoplasmic reticulum stress, providing a theoretical foundation for further exploration of MSC-based diabetic therapeutics.
Fossil evidence for alleged apemen— Part 2: non-Homo hominids

The supposed apemen belong to a category of fossils known as hominids, a group that includes living humans, their ancestors and any other ape-like creature believed to be more closely related to humans than to chimpanzees. Some evolutionists now refer to this group as hominins.1 The term hominid is nonsensical from a creationist point of view, because the hominids were either human or apes, not anything in-between. The term is used here only to categorize these fossils, and does not imply a belief that such creatures really existed. According to evolutionists, the defining moment for determining a hominid is the hypothesized split between humans and chimpanzees. In the past the human and chimpanzee calibration point has been determined by a molecular clock to be 5 million years ago (Ma), using as calibration point the supposed split between Old World monkeys and apes 30 Ma, as determined by conventional radiometric dating of fossils.2 The split is now thought, by evolutionists, to have occurred about 5–7 Ma. Radiometric dating3 and molecular clocks4 are based on unproven and problematic assumptions. When the evolutionary ages of fossils are given in this article, it is for the purpose of putting them in an evolutionary context, and it in no way implies that these age dates are valid. The australopithecines refer to the members of the genus Australopithecus, and in earlier times this was the only hominid genus apart from Homo. However, in recent years many hominids have been assigned to new taxa outside Australopithecus, such as the genus Ardipithecus, Sahelanthropus, Orrorin and Kenyanthropus. From a creation point of view, it should be remembered that a species is not equivalent to a biblical kind.5 Sahelanthropus tchadensis