Miracle-dz/UniFAR-FTUs huggingface dataset:
A tale of two entries - How Netto and Lidl entered the Swedish Grocery Market

Almost simultaneously two hard discount grocery cha ins entered the Swedish market. The two chains, Netto and Lidl have similar profile s and are so-called hard discounters, which is an expression that generally denotes smaller stores with fewer stock keeping units than soft discounters. However, when it comes to mode of entry and establishment, Lidl and Netto have chosen radic ally different paths. The Danish firm Netto entered through a joint venture with the major chain in the Swedish market, ICA. German Lidl, on the other hand, made a greenfield investment in the market without any form of cooperation with establi shed chains. The differences relating to mode of entry have a number of conseque nces for how they manage logistics internationally regarding storage facilit ies, inventory, transportation, unitization and packaging, and communication. Methodologically, we will use a combination of inte rviews and secondary data to collect the empirical data, aiming to map and analy se the cases from a total supply chain perspective. At this stage secondary data are used to draw an initial picture. This paper compares and contrasts the two empirical cases with the prescriptions from internationalisation theory and SCM theory. The ana lysis illustrates the close interconnectedness between the choices made regardi n SCM, and the resulting opportunities and restrictions in other strategic a spects of operating a grocery retail chain. How logistics influence the internationaliza t on process will also be described and analyzed. We can conclude that Lidl used a global sourcing st rategy wit a higher degree of standardization and own labels and relying on their G man logistics supplier and solutions. Netto on the other had more of a multinational stra tegy enabled by using the ICA network. However, increasing competition with ICA o ver time seems to cause Netto to leave the joint venture and establish themselves on the Swedish market.
Prevalence of Hepatitis C Virus Infection in General Population of Mashhad, Northeastern Iran

Background: Hepatitis C Virus (HCV) infection is one of the major blood-borne infections worldwide. HCV carriers may develop chronic hepatitis leading to liver cirrhosis and hepatocellular carcinoma (HCC). There is no overall estimate of the infection prevalence in the northeast of Iran. We have performed this research in order to determine accurately the prevalence and risk factors of HCV infection among general population in Mashhad. Methods: During 2009, 1678 people between 1 to 90 yr old with the mean age of 29.1±18.5 yr were selected randomly by multistage sampling from different geographical regions of the city proportionate to sex and age distribution of population in 2006 census. ELISA was used to screen for antibodies and RT-PCR tested the positive samples. Results: HCV infection was detected in 7/1654 cases; overall prevalence of the infection was 0.42% (95%CI: 0.17–0.87%), 0.80% and 0.11% among males and females, respectively (P= 0.051). One HCV-infected subject was also positive for hepatitis B surface antigen (HBsAg), however, no cases showed HIV or HTLV seropositivity. Conclusion: In comparison with similar studies, the prevalence of HCV infection in Mashhad is low.
Dynamic Opacity Optimization for Scatter Plots

Scatterplots are an effective and commonly used technique to show the relationship between two variables. However, as the number of data points increases, the chart suffers from "over-plotting" which obscures data points and makes the underlying distribution of the data difficult to discern. Reducing the opacity of the data points is an effective way to address over-plotting, however, setting the individual point opacity is a manual task performed by the chart designer. We present a user-driven model of opacity scaling for scatter plots built from crowd-sourced responses to opacity scaling tasks using several synthetic data distributions, and then test our model on a collection of real-world data sets.
Malaria Journal in Vivo Transcriptional Profiling of P. Falciparum in Vivo Transcriptional Profiling of Plasmodium Falciparum

This peer-reviewed article was published immediately upon acceptance. It can be downloaded, printed and distributed freely for any purposes (see copyright notice below). permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background Both host and pathogen factors contribute to disease outcome in
Improving clinician-carer communication for safer hospital care: a study of the 'TOP 5' strategy in patients with dementia.

OBJECTIVE To examine the impact of implementing a clinician-carer communication tool for hospitalized patients with dementia. DESIGN Surveys were conducted with clinicians and carers about perceptions and experiences. Implementation process and costs were explored through surveys of local staff. Time series analysis was conducted on incident-reported falls, usage of non-regular anti-psychotics and one-to-one nursing. SETTING Twenty-one hospitals in Australia. PARTICIPANTS Surveys were returned by 798 clinicians, 240 carers and 21 local liaison staff involved in implementation. INTERVENTION Implementation of a communication tool over 12 months. MAIN OUTCOME MEASURES The process of implementation was documented. Outcome measures included clinician and carer perceptions, safety indicators (incident-reported falls and usage of non-regular anti-psychotics), resource use and costs. RESULTS Clinicians and carers reported high levels of acceptability and perceived benefits for patients. Clinicians rated confidence in caring for patients with dementia as being significantly higher after the introduction of TOP 5, (M = 2.93, SD = 0.65), than prior to TOP 5 (M = 2.74, SD = 0.75); F(1,712) = 11.21, P < 0.05. When analysed together, there was no change in incident-reported falls across all hospitals. At one hospital with a matched control ward, an average of 6.85 fewer falls incidents per month occurred in the intervention ward compared with the matched control ward (B = -6.85, P < 0.05). CONCLUSIONS Our findings indicate that the use of a simple, low-cost communication strategy for patient care is associated with improvements in clinician and carer experience with potential implications for patient safety. Minimally, TOP 5 represents 'good practice' with a low risk of harm for patients.
Brain-Machine Interactions for Assessing the Dynamics of Neural Systems

A critical advance for brain–machine interfaces is the establishment of bi-directional communications between the nervous system and external devices. However, the signals generated by a population of neurons are expected to depend in a complex way upon poorly understood neural dynamics. We report a new technique for the identification of the dynamics of a neural population engaged in a bi-directional interaction with an external device. We placed in vitro preparations from the lamprey brainstem in a closed-loop interaction with simulated dynamical devices having different numbers of degrees of freedom. We used the observed behaviors of this composite system to assess how many independent parameters − or state variables − determine at each instant the output of the neural system. This information, known as the dynamical dimension of a system, allows predicting future behaviors based on the present state and the future inputs. A relevant novelty in this approach is the possibility to assess a computational property – the dynamical dimension of a neuronal population – through a simple experimental technique based on the bi-directional interaction with simulated dynamical devices. We present a set of results that demonstrate the possibility of obtaining stable and reliable measures of the dynamical dimension of a neural preparation.
Performance of redundancy methods in P2P networks under churn

Peer-to-Peer (P2P) networks organize large amounts of resources distributed across the Internet for user access. However, the availability of these resources is hindered by the participants' transient connectivity. Redundancy can be used to deal with this problem, but the redundancy repair traffic must be controlled to prevent performance degradation. In this paper, we present a comprehensive analytical formulation for erasure code redundancy methods in P2P networks, focused on fragments availability and redundancy repair cost. We use this model to evaluate the effectiveness of a proposed new redundancy scheme (PR) that outperforms other methods. Our results provide important insights for the design and construction of automated content availability mechanisms for P2P overlays under churn. Our evaluation framework allows us to determine at which point substituting a traditional erasure coding method (MDS) with other schemes is viable. For instance, e_MBR network coding performs worst than MDS for average fragment availabilities below 0.52. In addition, our experimental results indicate that adaptive repair strategies with elastic maintenance intervals can produce significant bandwidth savings compared with reactive and periodic redundancy repair strategies. In our experimental evaluation, an adaptive repair strategy consumes up to 49% less bandwidth than its periodic counterpart.
Detecting design flaws via metrics in object-oriented systems

The industry is nowadays confronted with large-scale monolithic and inflexible object-oriented software. Because of their high business value, these legacy systems must be re-engineered. One of the important issues in re-engineering is the detection and location of design flaws, which prevent the efficient maintenance and further development of the system. In this paper, we present a metrics-based approach for detecting design problems, and we describe two concrete techniques for the detection of two well-known design flaws found in the literature. We apply our technique to an industrial case study and discuss the findings. The proposed technique indeed found real flaws in the system and the experiment suggests that, based on the same approach, further detection techniques for other common design flaws could be defined.
Transfer Learning for Emotional Polarity Classification

Emotional Polarity Classification is an important task in Sentiment Analysis area. It is applied in many real problems such as reviews of consumer products and services, financial markets, and forensic analysis. The scientists from the areas of text mining and nature language processing have studied how to solve emotional polarity classification problem. They used a variety of methods, from simple methods (e.g., Lexicon-based categorization) to sophisticate methods (e.g., Statistical models). However, the problem of statistical models does not work well in a new test set whose distribution is different from training set. Therefore, the accuracy of Emotional Polarity Classification problem is still unstable. In this paper, we propose a novel approach formalism to solve this problem by using adaptation transfer learning. The transfer learning utilizes the labelled data available to solve the related but different problems. We also propose a new method that uses this approach to improve performance. The effectiveness of our approach is verified by the experiment results with two synthesis datasets and three real Twitter datasets.
Pharmacologic Boosting of Atazanavir in Maintenance HIV-1 Therapy: The COREYA Propensity-Score Adjusted Study

Background Among HIV-1 infected patients who achieved virologic suppression, the use of atazanavir without pharmacologic boosting is debated. We evaluated the efficacy and tolerance of maintenance therapy with unboosted atazanavir in clinical practice. Methods and Results This multicenter retrospective cohort study evaluated the efficacy of switching HIV-1-infected patients controlled on triple therapy to unboosted (ATV0, n = 98) versus ritonavir-boosted atazanavir (ATV/r, n = 254) +2 nucleos(t)ide reverse transcriptase inhibitors. The primary endpoint was time to virologic failure (VF, >200 copies/mL). ATV groups were compared controlling for potential confounding bias by inverse probability weighted Cox analysis and propensity-score matching. Overall and adjusted VF rates were similar for both strategies. Both strategies improved dyslipidemia and creatininemia, with less jaundice in the ATV0 group. Conclusion In previously well-suppressed patients, within an observational cohort setting, ATV0–based triple-therapy appeared as effective as ATV/r- based triple-therapy to maintain virologic suppression, even if co-administered with TDF, but was better tolerated.
The intangible benefits and costs of investments: evidence from financial markets

A model is presented of how the financial market valuation of firms can be used to estimate the intangible costs and benefits of computer capital and several new empirical results based on this model are presented. Using eight years of data for over 1,000 firms in the United States, the study finds that an increase of one dollar in the quantity of computer capital installed by a firm is associated with an increase of up to ten dollars in the financial markets’ valuation of the firm. Other forms of capital do not exhibit these high valuations. The model suggests that intangible assets can provide an explanation for the high market valuation found for computers in this study as well as the excess returns found for computer capital in other studies. Costly investments in software, training, and organizational transformations that accompany computer investments can be regarded as creating intangible assets. These intangible assets do not appear on firms’ conventional balance sheets but they can produce both higher market valuations and “excess” returns. The empirical evidence suggests that up to nine-tenths of the costs and benefits of computer capital are embodied in otherwise unobserved intangible assets. “matter itself is only a deposit, a stratum, which yields only to the most meticulous examination what constitutes the real treasure hidden within” —Walter Benjamin, A Berlin Chronicle (1970) “When I was a kid in the bank, the key economic indicator we looked at was freight-car loadings. Who the hell cares about them now? What we need is a way to measure the knowledge we bring to the work we do. ” —retired Citicorp chairman Walter Wriston
Low Omega-3 Index in Pregnancy Is a Possible Biological Risk Factor for Postpartum Depression

Background Depression is a common disorder affecting 10–15% women in the postpartum period. Postpartum depression can disrupt early mother-infant interaction, and constitutes a risk factor for early child development. Recently, attention has been drawn to the hypothesis that a low intake of seafood in pregnancy can be a risk factor for postpartum depression. Seafood is a unique dietary source of the marine omega-3 fatty acids and is a natural part of a healthy balanced diet that is especially important during pregnancy. Methods In a community based prospective cohort in a municipality in Western Norway, we investigated both nutritional and psychological risk factors for postpartum depression. The source population was all women who were pregnant within the period November 2009 - June 2011. The fatty acid status in red blood cells was assessed in the 28th gestation week and participants were screened for postpartum depression using the Edinburgh Postnatal Depression Scale (EPDS) three months after delivery. The aim of the present study was to investigate if a low omega-3 index in pregnancy is a possible risk factor for postpartum depression. Results In a simple regression model, the omega-3 index was associated with the EPDS score in a nonlinear inverse manner with an R square of 19. Thus, the low omega-3 index explained 19% of the variance in the EPDS score. The DPA content, DHA content, omega-3 index, omega-3/omega-6 ratio, total HUFA score, and the omega-3 HUFA score were all inversely correlated with the EPDS score. The EPDS scores of participants in the lowest omega-3 index quartile were significantly different to the three other omega-3 index quartiles. Conclusion In this study population, a low omega-3 index in late pregnancy was associated with higher depression score three months postpartum.
A case for unlimited watchpoints

Numerous tools have been proposed to help developers fix software errors and inefficiencies. Widely-used techniques such as memory checking suffer from overheads that limit their use to pre-deployment testing, while more advanced systems have such severe performance impacts that they may require special-purpose hardware. Previous works have described hardware that can accelerate individual analyses, but such specialization stymies adoption; generalized mechanisms are more likely to be added to commercial processors. This paper demonstrates that the ability to set an unlimited number of fine-grain data watchpoints can reduce the runtime overheads of numerous dynamic software analysis techniques. We detail the watchpoint capabilities required to accelerate these analyses while remaining general enough to be useful in the future. We describe a hardware design that stores watchpoints in main memory and utilizes two different on-chip caches to accelerate performance. The first is a bitmap lookaside buffer that stores fine-grained watchpoints, while the second is a range cache that can efficiently hold large contiguous regions of watchpoints. As an example of the power of such a system, it is possible to use watchpoints to accelerate read/write set checks in a software data race detector by nearly 9x.
Automatic speech segmentation to improve speech synthesis performance

Emerging growth of information and communication technologies has influenced the research trends to focus on speech technologies. Pre-processing of speech signal serves various purposes in any speech processing application. It includes noise removal, endpoint detection, pre-emphasis, framing, windowing, echo canceling etc. Out of these, automatic word/sentence boundary detection is the fundamental step for applications like speech recognition and speech synthesis. This paper expose the problem of automatic words and sentences boundary detection in the silent and noisy situations. This study proposes an algorithm for automatic segmentation of Indian languages voiced speech. A modified data based scheme of finding the entropy of the speech data is placed for better performance. The entropy based method expose good performance features than the energy-based methods. To determine the candidates speech segments, adaptive threshold is used which are related to words and sentences. Simulation results expose that this algorithm will provide better performance than energy based algorithms.
Evaluation and Improving Hypergraph Based Learning Algorithm over Data Integration Problem for Cancer Related Genes

Reliable predictive model build using semi supervised learning utilising classification algorithm has evolved rapidly in successful cancer treatment. In order to optimise the data integration problem, such as hypergraph based learning to integrate microarray gene expressions and protein interactions for predicting cancer outcome, novice optimization techniques are employed. The need of the hour is to have a good optimisation technique that would converge within acceptable amount of time in predicting promising result. So we propose an optimisation technique that is used in the first step of two step iterative method that alternatively optimises the labelling of samples for the hypergraph based learning. This learning method incorporates gene interactions as prior knowledge in protein interaction network. This optimisation technique can be imposed in various learning algorithm which utilises the principle iteration for optimisation. The proposed solution using Gauss-Seidel method converges faster and has better time complexity.
A low profile antenna with shaped beams for indoor applications of WLAN systems

A novel low profile antenna composed of a shorted conical monopole, a top-loaded circular patch and a probe feed is presented. The antenna can operate in two different modes: the monopole antenna mode and the microstrip antenna mode, which leads to down-tilt radiation patterns when the antenna is mounted on a wall. An antenna prototype with dimension of 5.5 mm in height for WLAN operation in the 2.4-GHz band is demonstrated. Simulated and measured results show that the antenna has an impedance bandwidth of 560 MHz (2.17-2.73 GHz) and shaped-beam radiation patterns.
Characteristics of Function-Anatomy Mismatch in Patients with Coronary Artery Disease

Background and Objectives Coronary lesions with mismatched functional and anatomical significance are not uncommon. We assessed the accuracy and predictors of mismatch between fractional flow reserve (FFR) and quantitative coronary angiography (QCA) analyses in patients with coronary lesions. Subjects and Methods A total of 643 lesions with pre-interventional FFR and QCA measurements were consecutively enrolled and divided into four groups using FFR ≤0.80 and percent diameter stenosis (%DS) ≥50% as cutoffs for functional and anatomical significance, respectively. Accordingly, FFR >0.80 and DS ≥50%, and FFR ≤0.80 and DS <50% defined false-positive (FP) and false-negative (FN) lesions, respectively. Results Overall, 40.4% (260/643) of the lesions were mismatched, and 51.7% (218/414) and 18.3% (42/229) were FP and FN lesions, respectively. In a multivariate analysis, independent predictors of FP were non-left anterior descending artery location {odds ratio (OR), 0.36; 95% confidence interval (CI), 0.28-0.56; p<0.001}, shorter lesion length (OR, 0.96; 95% CI, 0.95-0.98; p<0.001), multi-vessel disease (OR, 0.47; 95% CI, 0.30-0.75; p=0.001), and larger minimal lumen diameter by QCA (OR, 2.88; 95% CI,1.65-5.00; p<0.001). Independent predictors of FN were multi-vessel disease (OR, 1.82; 95% CI, 1.24-5.27; p=0.048), aging (OR, 0.96; 95% CI, 0.93-0.99; p=0.034), smoking (OR, 0.36; 95% CI, 0.14-0.93; p=0.034), and smaller reference vessel diameter by QCA (OR, 0.30; 95% CI, 0.10-0.87; p=0.026). Conclusion A mismatch between FFR and angiographic lesion severity is not rare in patients with coronary artery disease; therefore, an angiography-guided evaluation could under- or over-estimate lesion severity in specific lesion subsets.