Miracle-dz/UniFAR-FTUs huggingface dataset:
ff ects of a Dementia Screening Program on Healthcare Utilization in South Korea : A Di ff erence-InDi ff erence Analysis

To determine whether Seoul’s dementia screening program increased the rate of diagnosis and the appropriate use of healthcare services for people with dementia, a retrospective data analysis was conducted based on administrative data from the Health Insurance Review and Assessment Service. Two cohorts were constructed to represent the year before Seoul’s dementia screening program began (2007) (control group) and the year after the implementation of the program (2009) (treatment group). A difference-in-difference analysis was used to compare the diagnosis rates, number of clinic visits, and dementia-related drug prescription rates for 4 districts that implemented dementia screening programs between 2007 and 2009 and 14 areas that did not. After the introduction of the program, there was a 55.4% increase in physician-diagnosed dementia. The “average drug cost per patient” increased by 52.2% (Exp(β) = 1.522, p = 0.0264), the “average outpatient visits per patient” tended to increase by 13.5% (Exp(β) = 1.135, p = 0.1852), and the “average outpatient treatment fees per visit per patient” tended to increase by 24.4% (Exp(β) = 1.244, p = 0.0821). The implementation of dementia screening programs led to an increase in healthcare service utilization. Therefore, this program was found to be an effective strategy for reducing undiagnosed dementia cases and encouraging patients to use adequate healthcare services.
Efficacy of different doses of 12-month Interferon alfa therapy in patients with HBeAg negative chronic hepatitis B: A randomized trial

The best interferon-alfa (IFNα) regimen for HBeAg negative chronic hepatitis B has not been established yet. We evaluated the efficacy of three regimens of IFNα in 75 patients with histologically documented HBeAg negative chronic hepatitis B, who were randomly allocated to receive IFNα-2b thrice weekly in a dose of: 3MU for 12 months (Group A, n=25); 5 MU for 6 months and 3 MU for another 6 months (Group B, n=25); and 3 MU for 6 months and 1 MU for another 6 months (Group C, n=25). Initial biochemical response was observed in 71% of the 75 patients and initial virological response (undetectable serum HBV DNA by bDNA assay) in 38% of the patients with pre-treatment detectable serum HBV DNA. The decline of HBV DNA levels at the end of therapy was significant in group A (P=0.02) or B (P=0.004), but not in group C. Sustained biochemical and virological response rates (at 6 months after the end of therapy) were 35% and 22% respectively, without any significant difference among the three groups. There was a significant improvement in the grading (P<0.001) and no significant change in the staging of the post-treatment liver biopsies at 6 months after the end of therapy compared with the pre-treatment histological findings. In conclusion, a 12-month course of IFNα can induce sustained biochemical response in about one third and sustained virological response in approximately one fifth of patients with HBeAg negative chronic hepatitis B. A standard dose of 3 MU IFNα thrice weekly seems to be the most cost effective therapeutic regimen. Key words: Interferon alfa, chronic hepatitis B, sustained response,HBV DNA
Resonant behavior of a single plasmonic helix : supplementary material

KATJA HÖFLICH1,2,*, THORSTEN FEICHTNER4, ENNO HANSJÜRGEN3, CASPAR HAVERKAMP1,2, HEIKO KOLLMANN3, CHRISTOPH LIENAU3, AND MARTIN SILIES3 1Helmholtz Zentrum für Materialien und Energie Berlin, Hahn-Meitner-Platz 1, D – 14109 Berlin, Germany 2Max Planck Institute for the Science of Light 3Ultrafast Nano-Optics, Institute of Physics and Center of Interface Science, Carl von Ossietzky Universität Oldenburg, D-26129 Oldenburg, Germany 4Nano-Optics & Biophotonics Group, Department of Experimental Physics 5, Röntgen Research Center for Complex Material Research (RCCM), Physics Institute, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany *Corresponding author: katja.hoeflich@helmholtz-berlin.de
Evaluating Geochemistry by Multivariate Analysis of Groundwater in DCM Industrial Area Kota , Rajasthan ( India )

Monsoon Ground water samples of DCM Industrial area locations and adjoining areas were analysed by multivariate studies for quality assessment by estimating physico-chemical parameters, major cations and anions, irrigation quality parameters like SAR & % Na. The studies indicate that alkaline earth elements exceeded alkalis concentration while weak acids exceeded strong acid element. It was found that HCO3 was predominant among anions, while Ca-Mg dominated cations. Classification based on SAR and Salinity Hazard revealed that samples by Wilcox analysis diagram were under excellent (S1, 60 %), good (S2, 33.33 %), (C2, 33.33%) and doubtful (S3, 6.66 %), (C3, 66.66%) categories respectively. Cluster analysis show sampling station S6 & S7 in Group C alone which formed a group with highest Euclidian distance compared to other cluster group’s noticeable spatial variation in the physicochemical parameters.
Adaptive inexact Newton methods for discretizations of nonlinear diffusion PDEs . I . General theory and a posteriori stopping criteria ∗

We consider nonlinear algebraic systems resulting from numerical discretizations of nonlinear partial differential equations of diffusion type. To solve these systems, some iterative nonlinear solver, and, on each step of this solver, some iterative linear solver are used. In this first part, we derive adaptive stopping criteria for both iterative solvers. Both criteria are based on an a posteriori error estimate which distinguishes the different error components, namely the discretization error, the linearization error, and the algebraic error. We stop the iterations whenever the corresponding error does no longer affect the overall error significantly. Our estimates also yield a guaranteed upper bound on the overall error at each step of the nonlinear and linear solvers. We prove the (local) efficiency and robustness of the estimates with respect to the size of the nonlinearity owing, in particular, to the error measure involving the dual norm of the residual. Our developments are carried at an abstract level, yielding a general framework. We show how to apply this framework to the Crouzeix–Raviart nonconforming finite element discretization, Newton linearization, and conjugate gradient algebraic solution, and we illustrate on numerical experiments for the p-Laplacian the tight overall error control and important computational savings achieved in our approach. Part II is devoted to the application of our abstract framework to a broad class of discretization methods.
Turbofan Engine Optimization for Low NO x Emissions

More stringent limits for pollutants emissions are imposed to accommodate the experienced growth in the world’s aircraft fleet. The most regulated of these pollutants are the nitrogen oxides, NOx. These represent a health and environmental hazard, when emitted at both low and high altitudes. To reduce emissions without reducing engines performance presents a difficult task for engine design engineering. In order to do so, early-stage design tools are used to compute fast and realistically accurate results. These tools include the formulation of the models under the 0D framework. Following this approach, a 0D model of a two-spool turbofan was formulated and validated by comparing the results with those obtained from commercially available software. A combustion model was also formulated under the 0D framework, and validated by comparing its results with those presented in the literature. Two turbo-emissions models were achieved, one by unifying both turbofan and combustion models, and the second by coupling the turbofan model with a semi-empirical NOx predictions model. Both models were validated by comparing the results with available data from the literature. The second unified model was used under an optimization algorithm in order to compute optimized parameters with the objective of low NOx emissions while maintaining a low specific fuel consumption. To better understand the effects of the optimization in the combustion mechanism, the optimized parameters were computed through the more detailed turbo-combustion model.
Stock market trend prediction with sentiment analysis based on LSTM neural network

This paper aims to analyze influencing factors of stock market trend prediction and propose an innovative neural network approach to achieve stock market trend prediction. With the breakthrough of deep learning recently, there occurred lots of useful techniques for stock trend prediction. This thesis aims to propose a method of feature selection for selecting useful stock indexes and proposes deep learning model to do sentiment analysis of financial news as another influencing factor influencing stock trend. Then it proposes accurate stock trend prediction method using LSTM (Long Short-term Memory). Index: Stock trend prediction, LSTM, Sentiment Analysis, Deep learning, Chinese Stock market, Feature Selection.
Liquidity Fluctuations in Lemon Markets and Policy Interventions∗

This paper studies endogenous liquidity fluctuations in dynamic lemon markets. Agents trade heterogeneous qualities of an asset over the counter and bargain under asymmetry of information. Future liquidity begets current liquidity as current prices increase with the resale value of a lemon. Conversely, current liquidity crowds out future liquidity through an endogenous composition effect in the pool of sellers. When this latter effect dominates, stationary cycles emerge where price and volume oscillate. From a normative point of view, unstable markets call for specific policy interventions to restore liquidity. I show that when buyers can post prices before matching, liquidity and welfare may actually decrease. These results provide new insights on the role of competition and information for market efficiency.
THE IMPACT OF CHINA'S WTO MEMBERSHIP ON ITS TRADE VIA HONG KONG by YongWang

The objective of this study is to discuss the impact of China's World Trade Organization (WTO) accession on its trade via Hong Kong. To this end, this paper analyzes three important aspects: China's WTO accession and increasing Foreign Direct Investment (FDI), China's indirect trade with Taiwan, and changes in Hong Kong 's comparative advantage. Theoretical discussions lead to the conclusion that China's WTO accession will adversely affect Hong Kong's intermediary role. Consequently, Hong Kong will eventually lose its comparative advantage in serving as China's middleman. In addition, this study conducts some empirical investigation regarding Hong Kong's re-export trade with China and its importance to economic growth. Following that, this paper addresses several policy options which, at least in the short-run, may have some positive effects.
CS265/CME309: Randomized Algorithms and Probabilistic Analysis Lecture #6: Balls in Bins and Power-of-Two-Choices

Many phenomenon can be modeled via the process of tossing n balls, uniformly at random, into one of m different “bins”, and then examining certain properties of the resulting allocation of balls to bins. For example, the coupon collector problem can be reformulated in this framework as the following question: what is the minimum value of n, as a function of m, such that we expect there to be zero empty bins after tossing the n balls? Similarly, nearly all questions regarding the construction or analysis of hashing schemes (including Bloom filters that you may have seen in other courses) can trivially be restated as a balls-in-bins problem.
DBSCAN-Based No-Load Road Detection Algorithm

Taxi plays an important role in the urban passenger transport system. Good taxi service quality will help increase the happiness of urban residents. However, the issue of no-load taxis has always been one of the most concerned issues for urban managers. This paper uses the improved DBSCAN method to carry on the cluster analysis to the taxi trajectory, prompts the taxi driver to avoid the no-load area, and achieves the purpose that to raise the taxi utilization factor. The improved DBSCAN method introduces two distance metrics, segment feature distance and dynamic space-time distortion distance. The feature distances of the segment include the vertical distance, horizontal distance, and angular distance, which can effectively measure the distance between trajectory segments. The dynamic space-time distortion distance introduces spatial factors on the basis of the dynamic distortion distance, and successfully overcomes the similarity error problem caused by different trajectory lengths. At the same time, using the GPS data set of Nanjing taxi to carry out experiments, the results show that the algorithm has higher accuracy and stability than the algorithms in the literature.
Effect of Ovariohysterectomy on Some Oxidative Stress Markers in the Rat

The purpose of the present study was to evaluate the effect of ovariohysterectomy (OHE) on oxidative stress in rats. A total of 12 female Sprague Dawley rats weighing 200-250 g were included in the study. Six rats underwent OHE (group I) and the other 6 rats sham operation (group II) under xylazine (5 mg/kg, i.m.) and ketamine (45 mg/kg, i.m.) anesthesia. In both groups, blood samples were collected preoperatively, day 1 and 10th days after OHE. Serum concentration of malondialdehyde (MDA) and activities of superoxide dismutase (SOD) and glutathione peroxidase (GPx) were measured by spectrophotometric methods. The concentration of MDA increased while activities of SOD and GPx decreased in group I compared to group II on day 1 after the surgery. Similarly, the concentration of MDA was higher while activities of SOD and GPx appeared to be lower in group I than that of group II, although the differences were not statistically significant on the 10th day after surgery. In conclusion, our findings indicated that ovariohysterectomy is related with oxidative stress in the rat.
The timing of glucocorticoid-induced osteocytic cell necrosis under hypoxia

Background: Osteocytic cell necrosis is induced when dexamethasone (Dex) administration and hypoxia are superimposed. We investigated the time needed for osteocytic cell necrosis to develop in cultured osteocytic cells. Materials and Methods: Cultured murine MLO-Y4 osteocytic cells were used. After the addition of 1 μM Dex they were cultured in 1% O2 (hypoxia) in an incubator for 6 h (6 h group), 9 h (9 h group), 12 h (12 h group), or 24 h (24 h group) respectively, and then cultured under normoxia (20% O2) until the respective total culture time reached a total of 24 h. Viability assays were then performed using an Apoptotic/Necrotic Cells Detection Kit according to the manufacturer’s instructions, and the percentages of apoptotic and necrotic cells relative to the total cell number were determined. As a Control group cultured osteocytic cells to which Dex was added and cultured for 24 h under normoxia were used. Results: The Control group, 6 h group, 9 h group, and 12 h group showed only slight increases in apoptosis, with no significant differences found between any of them. In contrast, in the 24 h group apoptosis was significantly increased, while necrosis, which did not show any increase in any of the groups up to the 12 h one, was significantly increased in the 24 h group. Discussion: In the presence of Dex and hypoxia the development of osteocytic cell necrosis appears to require a period of at least 12-24 h. This suggests that the development of osteocytic cell necrosis might be interrupted if hypoxia was suppressed or blocked within the critical time limit of 12 h.
SES-Dedup: a Case for Low-Cost ECC-based SSD Deduplication

Integrating the data deduplication function into Solid State Drives (SSDs) helps avoid writing duplicate contents to NAND flash chips, which will not only effectively reduce the number of Program/Erase (P/E) operations to extend the device's lifespan but also proportionally enlarge the effective capacity of SSD to improve the performance of its behind-the-scenes maintenance tasks such as wear-leveling (WL) and garbage-collection (GC). However, these benefits of deduplication come at a non-trivial computational cost incurred by the embedded SSD controller to compute cryptographic hashes. To address this overhead problem, some researchers have suggested replacing cryptographic hashes with error correction codes (ECCs) already embedded in the SSD chips to detect the duplicate contents. However, all existing attempts have ignored the impact of the data randomization (scrambler) module that is widely used in modern SSDs, thus making it impractical to directly integrate ECC-based deduplication into commercial SSDs. In this work, we revisit SSD's internal structure and propose the first deduplicatable SSD that can bypass the data scrambler module to enable the low-cost ECC-based data deduplication. Specifically, we propose two design solutions, one on the host side and the other on the device side, to enable ECC-based deduplication. Based on our approach, we can effectively exploit SSD's built-in ECC module to calculate the hash values of stored data for data deduplication. We have evaluated our SES-Dedup approach by replaying data traces in an SSD simulator and found that it can remove up to 30.8% redundant data with up to 17.0% write performance improvement over the baseline SSD.
Removing Race: How Context and Colorblindness Influence Conceptualizations of Equity in a Third Grade Rural Classroom

The ways teachers both perceive and design supports for her/his/their students are likely influenced by a variety of factors. In this qualitative study, I analyze the ways context and praxis, defined as a teacher’s morally informed beliefs about teaching, influenced supports developed for marginalized students in a rural school setting. Over two years of interviews and one year of observations, patterns emerged related to connections between the teacher's beliefs regarding colorblindness, individuality, and the development of instructional and emotional supports for students. I end by discussing recommendations for educational researchers to understand connections between teachers’ praxes and practice related to developing equitable supports across classroom contexts.
Experimental and Numerical Study of Blood Flow in μ-vessels : Influence of the Fahraeus – Lindqvist E ff ect

The study of hemodynamics is particularly important in medicine and biomedical engineering as it is crucial for the design of new implantable devices and for understanding the mechanism of various diseases related to blood flow. In this study, we experimentally identify the cell free layer (CFL) width, which is the result of the Fahraeus–Lindqvist effect, as well as the axial velocity distribution of blood flow in microvessels. The CFL extent was determined using microscopic photography, while the blood velocity was measured by micro-particle image velocimetry (μ-PIV). Based on the experimental results, we formulated a correlation for the prediction of the CFL width in small caliber (D < 300 μm) vessels as a function of a modified Reynolds number (Re∞) and the hematocrit (Hct). This correlation along with the lateral distribution of blood viscosity were used as input to a “two-regions” computational model. The reliability of the code was checked by comparing the experimentally obtained axial velocity profiles with those calculated by the computational fluid dynamics (CFD) simulations. We propose a methodology for calculating the friction loses during blood flow inμ-vessels, where the Fahraeus–Lindqvist effect plays a prominent role, and show that the pressure drop may be overestimated by 80% to 150% if the CFL is neglected.
Benefits of the WaveNet-Based Speech Intelligibility Enhancement for Normal and Hearing Impaired Listeners

Speech perception becomes challenging in adverse listening conditions, hence increasing mental effort. Modification of recorded clean speech with the goal of increasing its intelligibility is one approach for improving listening experience. Recently, we suggested a data-driven WaveNet-like speech intelligibility enhancement method which is based on the Spectral Shaping and Dynamic Range Compressions (SSDRC) approach. Both approaches achieve intelligibility gains by relocating energy from high sonorant to less sonorant portions of speech. In this study we have assessed the performance of the WaveNet intelligibility enhancer for both normal and hearing impaired participants using formal listening tests, in terms of intelligibility and sound quality. Intelligibility was measured as a percentage of correct words recalled (CWR) and quality was assessed via a comparative mean opinion score (CMOS). Furthermore, we compared performance among native and nonnative listeners using English and Greek stimuli. We observed that modified speech was significantly more intelligible than plain across all listener groups. Hearing impaired candidates rated modified speech 20% higher on average (on a CMOS scale) than normal hearing subjects. The positive impact for the hearing impaired listeners might be attributed to the reallocation of energy to perceptually relevant frequency bands in which the hearing impaired are less sensitive.
The SOSA / SSN Ontology : A Joint W 3 C and OGC Standard Specifying the Semantics of Sensors , Observations , Actuation , and Sampling

The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3. org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3. org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [10] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.