Miracle-dz/UniFAR-FTUs huggingface dataset:
Organized Outpatient Care: Stroke Prevention Clinic Referrals Are Associated With Reduced Mortality After Transient Ischemic Attack and Ischemic Stroke

Background and Purpose— Organized inpatient stroke care decreases mortality and morbidity irrespective of patient age, stroke severity, or stroke subtype. Limited information is available on whether organized outpatient care models such as stroke prevention clinics (SPC) improve outcomes after a transient ischemic attack or ischemic stroke. We compared 1-year mortality and stroke readmission in patients with transient ischemic attack or ischemic stroke referred versus not referred to an SPC. Methods— This was a retrospective cohort study including 16 468 consecutive patients with ischemic stroke or transient ischemic attack who were seen in the emergency department or admitted to a hospital between July 1, 2003 and March 31, 2008 at registry stroke centers (n=12) in the province of Ontario. Cox proportional hazards models and propensity score-matched analyses were used to evaluate 1-year mortality and readmission. Results— One-year mortality rates were lower in those referred to SPCs compared with those not referred, even after adjustment for age, sex, ethnic origin, income, comorbid conditions, stroke symptoms and severity, receipt of thrombolysis, stroke unit care, discharge destination, and functional status at discharge (adjusted hazard ratio [HR], 0.67; 95% CI, 0.60–0.75). Survival analysis after propensity matching showed a 26% reduction in 1-year mortality (HR, 0.74; 95% CI, 0.65–0.84). There were no significant differences in 1-year readmission rates in those referred versus not referred to SPCs. Conclusions— Referral to an SPC is associated with a one-quarter reduction in mortality after ischemic stroke or transient ischemic attack. This supports the argument that outpatient stroke units may prove as effective as their inpatient counterparts.
Photo-induced instability and temperature dependence of amorphous In-Ga-Zn-O thin film transistors

The photo-induced instability of amorphous In-Ga-Zn-O (a-IGZO) thin-film transistors (TFTs) is investigated. Device characteristics under white light illumination with various intensities and its spontaneous recovery at room temperature after illumination are observed. All the observed phenomena, especially the variation of threshold voltage and subthreshold swing, are explained by a simple model based on the photon generated electrons and oxygen vacancy defects in the a-IGZO channel. Further more, the temperature dependence of transfer characteristics for a-IGZO TFTs can be also explained with the same model.
Exxonmobil's social responsibility messaging – 2002–2013 CEO letters

ABSTRACT The purpose of this study was to evaluate ExxonMobil's social responsibility/social responsiveness (SR) communication to determine the company's social responsibility messaging in terms of the Social Responsibility themes of the Triple bottom Line Model (profit, people, planet) and to evaluate if the messaging changed in response to external events such as the 2007 financial crisis and 2010 BP oil spill. ExxonMobil's CEO letters included varying levels of emphasis on SR content related to profit and planet. The analysis showed a reduced focus on profit following the 2007 financial crisis and an increased focus on the planet following the 2010 BP oil spill.
Aflatoxin Biosynthesis Cluster Gene cypA Is Required for G Aflatoxin Formation

ABSTRACT Aspergillus flavus isolates produce only aflatoxins B1 and B2, while Aspergillus parasiticus and Aspergillus nomius produce aflatoxins B1, B2, G1, and G2. Sequence comparison of the aflatoxin biosynthesis pathway gene cluster upstream from the polyketide synthase gene, pksA, revealed that A. flavus isolates are missing portions of genes (cypA and norB) predicted to encode, respectively, a cytochrome P450 monooxygenase and an aryl alcohol dehydrogenase. Insertional disruption of cypA in A. parasiticus yielded transformants that lack the ability to produce G aflatoxins but not B aflatoxins. The enzyme encoded by cypA has highest amino acid identity to Gibberella zeae Tri4 (38%), a P450 monooxygenase previously shown to be involved in trichodiene epoxidation. The substrate for CypA may be an intermediate formed by oxidative cleavage of the A ring of O-methylsterigmatocystin by OrdA, the P450 monooxygenase required for formation of aflatoxins B1 and B2.
Frequency-Dependent Peak-Over-Threshold algorithm for fault detection in the spectral domain

An original novelty detection algorithm in the Fourier domain, using extreme value theory (EVT) is considered in this article. Periodograms may be considered as frequency-dependent random vari- ables, and this can be taken into account when designing statistical tests. Frequency-Dependent Peak-Over-Threshold (FDPOT) puts special em- phasis on the frequency dependence of extreme value statistics, thanks to Vector Generalized Additive Models (VGAM) estimation. An application is discussed in the field of mechanical vibrations. It is first shown that performance increases compared to POT detection. Then FDPOT is compared to state-of-the-art algorithms such as KPCA.
Latitudinal Gradients in Degradation of Marine Dissolved Organic Carbon

Heterotrophic microbial communities cycle nearly half of net primary productivity in the ocean, and play a particularly important role in transformations of dissolved organic carbon (DOC). The specific means by which these communities mediate the transformations of organic carbon are largely unknown, since the vast majority of marine bacteria have not been isolated in culture, and most measurements of DOC degradation rates have focused on uptake and metabolism of either bulk DOC or of simple model compounds (e.g. specific amino acids or sugars). Genomic investigations provide information about the potential capabilities of organisms and communities but not the extent to which such potential is expressed. We tested directly the capabilities of heterotrophic microbial communities in surface ocean waters at 32 stations spanning latitudes from 76°S to 79°N to hydrolyze a range of high molecular weight organic substrates and thereby initiate organic matter degradation. These data demonstrate the existence of a latitudinal gradient in the range of complex substrates available to heterotrophic microbial communities, paralleling the global gradient in bacterial species richness. As changing climate increasingly affects the marine environment, changes in the spectrum of substrates accessible by microbial communities may lead to shifts in the location and rate at which marine DOC is respired. Since the inventory of DOC in the ocean is comparable in magnitude to the atmospheric CO2 reservoir, such a change could profoundly affect the global carbon cycle.
Spatial Mismatch and Determinants of Foreign and Domestic Information and Communication Technology Firms in Urban China

Studies on foreign direct investment locations in China have been conducted mainly at interregional and interprovincial scales, and little attention has been paid to the intraurban scale where location decisions of foreign firms can differ from domestic firms. This article explores the intraurban locations of information and communication technology (ICT) firms in Suzhou, a city experiencing rapid globalization. We have found that the distribution of ICT firms in Suzhou exhibits distinctive spatial patterns characterized by a geographically based, institutionally created spatial mismatch between foreign and domestic firms. Foreign firms are concentrated in national-level development zones—China–Singapore Suzhou Industrial Park in the east and Suzhou New and Hi-Tech District in the west—whereas domestic firms tend to agglomerate in the inner city and the provincial-level development zone in the north. Poisson and negative binomial analyses further reveal that the locations of foreign firms are strongly correlated to development zones, and the agglomeration economies derived from the stock of foreign investment rather than domestic firms. The case of Suzhou highlights the challenges that Chinese cities face in industrial upgrading and technological development through embedding transnational corporations.
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be

Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.
Fast Correlation Computation Method for Matching Pursuit Algorithms in Compressed Sensing

There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem a.k. a. compressed sensing (CS). In the MPAs, the correlation computation step has a dominant computational complexity. In this letter, we propose a new fast correlation computation method when we use some classes of partial unitary matrices as the sensing matrix. Those partial unitary matrices include partial Fourier matrices and partial Hadamard matrices which are popular sensing matrices. The proposed correlation computation method can be applied to almost all MPAs without causing any degradation of their recovery performance. And, for most practical parameters, the proposed method can reduce the computational complexity of the MPAs substantially.
Instrumen Kenyamanan Lingkungan Belajar Berbasis Ergonomi

Pengembangan instrumen kenyamanan lingkungan belajar berupa seperangkat simulator lingkungan belajar. Instrumen digunakan untuk mengukur kenyamanan yang dirasakan pebelajar dalam lingkungan belajar. Penelitian pengembangan dalam rangka memecahkan masalah lingkungan belajar berdasarkan teori ergonomi dan teori sosial kognitif. Lingkungan belajar secara ergonomi merupakan tempat terjadinya hubungan antara manusia, pekerjaan, dan lingkungan sebagai tempat aktivitas mental dan fisik berlangsung. Kategori pengukuran adalah kondisi ergonomi dalam lingkungan belajar, berupa indikator-indikator dalam lingkungan belajar yang dapat dirasakan indrawi pebelajar. Perangkat simulator yang dikembangkan secara signifikan dapat mengukur apa yang dirasakan indrawi pebelajar dalam lingkungan belajar.
Evolutionary multi-objective optimization to attain practically desirable solutions

This work investigates two methods to search practically desirable solutions expanding the objective space with additional fitness functions associated to particular decision variables. The aim is to find solutions around preferred values of the chosen variables while searching for optimal solutions in the original objective space. Solutions to be practically desirable are constrained to be within a certain distance from the present non-dominated solutions set computed in the original objective space. The proposed methods are compared with an algorithm that simply restricts the range of decision variables around the preferred values and an algorithm that expands the space without constraining the distance from optimality. Our results show that the proposed methods can effectively find practically desirable solutions.
Intervention Strategies for Increasing Engagement in Crowdsourcing: Platform, Predictions, and Experiments

Volunteer-based crowdsourcing depend critically on maintaining the engagement of participants. We explore a methodology for extending engagement in citizen science by combining machine learning with intervention design. We first present a platform for using real-time predictions about forthcoming disengagement to guide interventions. Then we discuss a set of experiments with delivering different messages to users based on the proximity to the predicted time of disengagement. The messages address motivational factors that were found in prior studies to influence users' engagements. We evaluate this approach on Galaxy Zoo, one of the largest citizen science application on the web, where we traced the behavior and contributions of thousands of users who received intervention messages over a period of a few months. We found sensitivity of the amount of user contributions to both the timing and nature of the message. Specifically, we found that a message emphasizing the helpfulness of individual users significantly increased users' contributions when delivered according to predicted times of disengagement, but not when delivered at random times. The influence of the message on users' contributions was more pronounced as additional user data was collected and made available to the classifier.
Traffic offloading with rate-based cell range expansion offsets in heterogeneous networks

A heterogeneous network (HetNet) with macro base stations (BSs) together with low power small cells, such as pico BSs and femto BSs, is a promising solution to enhance network capacity. In HetNet, the transmission range of pico BSs are limited due to the large transmission power difference between macro and pico BSs, and thus pico BSs are typically underutilized. Cell Range Expansion (CRE) is a way to increase the opportunity of the user equipment (UE) associations to pico BSs so that more traffic from the macro cell can be offloaded to the pico cells. However, the expansion region of pico UEs with unbalance downlink and uplink signal qualities suffer from bad downlink quality, and the macro UEs near the expansion region may also experience low uplink rates. Therefore, we propose a new scheme with rate-based CRE offsets in HetNet so that UEs can decide to their associations based on their traffic demands. We formulate this problem as a mixed integer programming problem and prove that it is NP-hard. We then propose a polynomial time heuristic algorithm to solve this problem. The simulation results show that our scheme indeed achieves good traffic offloading gain for HetNet.
R & D and the productivity growth of investment goods

From the 1960s in the US, the relative price of investment goods in terms of consumption goods has declined, partly due to higher productivity growth in the investment sector. From the mid-1970s, the differences between the two sectors have become even more apparent. New data on the ratio of R&D expenditure to GDP show R&D spending to be much higher in the investment sector. Furthermore, statistical tests indicate a break in R&D expenditure in the 1970s, particularly for the investment sector. The paper developes a model with R&D in the investment and consumption sector, where research intensity in each sector is determined endogenously. The balanced growth path explains the decline in the relative price of investment. Research intensity is higher in the investment sector if the innovation rate is higher in that sector. The introduction of information technology can increase the productivity of R&D in the investment sector, as the innovation of new investment goods is facilitated. A shift that increases the innovation rate in the investment sector will lead to a higher research intensity in the investment sector and a faster decline of the relative price of investment goods, as observed in the data. The implications for the consumption sector are ambiguous, but the new equilibrium could indeed explain the observed productivity slowdown in the consumption sector.
β-Lactamases of Kluyvera ascorbata, Probable Progenitors of Some Plasmid-Encoded CTX-M Types

ABSTRACT Kluyvera ascorbata produces a β-lactamase that results in an atypical susceptibility pattern, including low-level resistance to penicillins, cephalothin, and cefuroxime, but this resistance is reversed by clavulanate. Ten nucleotide sequences of the corresponding gene, blaKLUA, were obtained and were found to have minor variations (96 to 100%). Otherwise, blaKLUA was found to be similar (95 to 100%) to some plasmid-encoded CTX-M-type β-lactamases. Finally, mobilization of blaKLUA on a plasmid was found to be mediated probably by a genetic mobile element like ISEcp1.
Java-based approach for high level OpenMP loops synthesis

This paper presents an automatic VHDL code generation method based on the OpenMP parallel programming specification. In order to synthesize C code for loops into hardware, we applied the directives of OpenMP, which specifies portable implementations of shared memory parallel programs. The proposed design flow using this method is described and its implementation details are provided. Experimental results show that the generated vhdl code from OpenMP is competitive with optimized code.
Model and Simulation of Driving Decision-Making Behavior in Curve Movement on ACT-R Cognitive Framework

The method based on ACT-R cognitive framework involved in bend movement is presented in this paper, which defines perception-action modules, memory modules and core unit of decision-making cycle which includes visual and tactile perception of traffic conditions rules. The paper introduces nine components of state to describe bend movement. Bend movement is divided into normal rational driving behavior and non-rational driving behavior which can be affected by drivers' fatigue, characters, etc. This paper also analyzes the impact of actions and requirements of action generation. Utility function is used to evaluate drivers' actions on the basis of driver's perception expressed by the actions under the new state. The validity of these models is tested by a number of simulations, recommended parameter values are given in this paper to illustrate the effectiveness of decision-making behavior based on ACT-R model.
CRITERION (PREDICTIVE) VALIDATION STUDY OF RELIGIOUS SCHEME SCALE

The purpose of this study was to confirm the factors of Religious Scheme Scale (RSS) using Indonesian Christian student sample. Participants were 230 students (male n= 100, female = 130) with age ranged from 15 – 23 years old (M = 16.8, SD = 1.588). Factor Analysis using Confirmatory Factor Analysis (CFA) was conducted using AMOS 21 did not confirm the 15 – item 3 – factor as proposed by Streib et al. (2010) but 13 – item 3 factor model. The unsatisfactory 2 variables were found in xenosophia sub scale. This study was considered similar to Proios’ study (2015) in Greece which dropped 2 items from xenosophia sub scale. The predictive validity of RSS showed a good predictor of identity fusion. In conclusion, RSS is satisfactory to be implemented in Indonesian Christian student samples. Keyword : Religious Scheme Scale, Factor analysis, Indonesian Christian students sample, predictive validity, identity fusion
Visual Analytics on the Financial Market: Pixel-based Analysis and Comparison of Long-Term Investments

In this paper, we describe solutions how pixel-based visualization techniques can support the decision making process for investors on the financial market. We especially focus on explorative interactive techniques where analysts try to analyze large amounts of financial data for long-term investments, and show how visualization can effectively support an investor to gain insight into large amounts of financial time series data. After presenting methods for improving the traditional performance/risk computation in order to take user-specific regions of interest into account, we present a novel visualization approach that demonstrates how changes in these regions of interest affect the ranking of assets in a long-term investment strategy.
A Particle Swarm Optimization Algorithm for Batch Processing Workflow Scheduling

Aiming at shortcomings in existing scheduling methods for batch processing workflow, this paper attempt to investigate and solve the optimization problem for grouping and scheduling multiple activity instances in batch processing workflow. A multiple objective optimal model of problem with constraints is presented firstly. Then, a discrete particle swarm optimization algorithm is proposed to produce a set of optimal Pareto solutions by optimizing the two objective functions simultaneously. The result of simulation experiment shows the effectiveness of this algorithm.
Roles of PI3K/AKT/PTEN Pathway in the Pathogenesis of Parkinson’s Disease and the Neuropsychiatric Symptoms

Parkinson's disease is a neurodegenerative disorder associated with loss of dopaminergic neurons in substantianigra caused by severe neuro-degeneration, which is the second most common neurodegenerative disorder after Alzheimer’s disease.Parkinson's disease has a high prevalence of psychiatric comorbidity including depression. The neuropsychiatric symptoms are common in Parkinson's disease and may precede onset of motor symptoms. Increasing interest is often addressed to the selective targeting of some of metabotropic glutamate receptors that inhibit the transmitter release at synapses in the basal ganglia. The metabotropic glutamate receptors may be coupled to the phosphatidylinositol-3-kinase (PI3K), AKT, and PTEN pathways, whichplay a central role in cell survival. A better understanding of the molecular connections in the PI3K pathways could uncover new targets for drug development in Parkinson's disease.
The behaviour of the complete eigenstructure of a polynomial matrix under a generic rational transformation

Given a polynomial matrix P(x) of grade g and a rational function $x(y) = n(y)/d(y)$, where $n(y)$ and $d(y)$ are coprime nonzero scalar polynomials, the polynomial matrix $Q(y) :=[d(y)]^gP(x(y))$ is defined. The complete eigenstructures of $P(x)$ and $Q(y)$ are related, including characteristic values, elementary divisors and minimal indices. A Theorem on the matter, valid in the most general hypotheses, is stated and proved.
FINITE ELEMENT METHOD FOR CONSTRAINED OPTIMAL CONTROL PROBLEMS GOVERNED BY NONLINEAR ELLIPTIC PDES

In this paper, we study the finite element method for constrained optimal control problems governed by nonlinear elliptic PDEs. Instead of the standard error estimates under $L^2$- or $H^1$- norm, we apply the goal-oriented error estimates in order to avoid the difficulties which are generated by the nonsmoothness of the problem. We derive the a priori error estimates of the goal function, and the error bound is $O(h^2)$, which is the same as one for some well known quadratic optimal control problems governed by linear elliptic PDEs. Moreover, two kinds of practical algorithms are introduced to solve the underlying problem. Numerical experiments are provided to confirm our theoretical results.