Miracle-dz/UniFAR-FTUs huggingface dataset:
Capability analysis of a D-STATCOM integrated to a single-phase to three-phase converter for rural grids

This paper presents a capability study of a static converter system to provide balanced three-phase four-wire with grounded neutral from single-phase network. The system is connected in parallel with the single-phase grid, that matches one of the output three-phase terminal. The converter absorbs active power from single phase grid to produce two additional phases of the three-phase system. Furthermore, it allows the voltage compensation or power factor correction through reactive power injection. This system also allows the connection of alternative sources to DC bus.
Hand Jitter Reduction Algorithm Software Test Automation Using Robotic Arm

Hand jitter reduction (HJR) is an algorithm developed to offset the jitter effect caused by camera users. The hand jitter algorithm can be added and implemented in digital signal processing using hardware acceleration to execute these noise reduction algorithms. HJR involves the detection of noise in the camera images and the application of noise reduction algorithms to smoothen these noises. HJR tests are critical for validating the authenticity of the HJR algorithm. The challenge here is to be able to simulate similar motions to what a real-world user would experience while using the camera. We have inducted and programmed a robotic arm to simulate real-world user motions. We have developed software to simulate different motions, such as the generation of sine waves, and we also generate a randomized motion that is a combination of different motions, such as a sine wave, a cosine wave, and vertical, horizontal, and angular motions. In the robotic arm setup, the robot comprises six joints, and we can rotate the joints to generate a specific motion. We have developed an algorithm to find the locations of joints depending on the motion we need to simulate. For example, if we need a 30° rotation while the camera is at a specific location, we can calculate the joint value for specific joints in the robotic arm. The goal of HJR tests is also to categorize the results of the camera as having acceptable or non-acceptable results based on the induced motions. The test automation has immensely helped us to objectively benchmark the performance of the algorithm over several software builds. We have provided test results of computer vision use cases of camera panorama to show the effect of hand jitter on the quality of the software.
Nonparametric analysis of dependently interval‐censored failure time data

Failure time studies based on observational cohorts often have to deal with irregular intermittent observation of individuals, which produces interval‐censored failure times. When the observation times depend on factors related to a person's failure time, the failure times may be dependently interval censored. Inverse‐intensity‐of‐visit weighting methods have been developed for irregularly observed longitudinal or repeated measures data and recently extended to parametric failure time analysis. This article develops nonparametric estimation of failure time distributions using weighted generalized estimating equations and monotone smoothing techniques. Simulations are conducted for examination of the finite sample performance of proposed estimators. This research is motivated in part by the Toronto Psoriatic Arthritis Cohort Study, and the proposed methodology is applied to this study.
Clinical evaluation of a novel commercial multiplex‐based PCR diagnostic test for differential diagnosis of dermatomycoses

Dermatomycoses are very common worldwide with increasing prevalence. An accurate and rapid detection of fungi is most important for the choice of antimycotics and the success of treatment. The aim of this study was to evaluate a new commercial multiplex‐based PCR which allows the detection and differentiation of the most relevant human pathogen fungi causing dermatomycoses in Europe. The accuracy and reproducibility of this application were verified in a clinical performance assessment in comparison to direct microscopy and culture using DNA isolates from 253 clinical samples. Sensitivity, specificity, positive predictive value and negative predictive value of 87.3%, 94.3%, 87.3% and 94.3%, respectively, were calculated for dermatophytes when confirmed by direct microscopy, culture or both. The corresponding values for Candida spp. were 62.7%, 93.5%, 77.8%, and 87.4%, respectively. Furthermore, in comparison to culture, the multiplex PCR was able to detect additional 38 Trichophytum rubrum and 12 Trichophytum interdigitale infections. These results were confirmed by independent PCR analysis. From DNA isolation to diagnosis the multiparameter diagnostic kit gives rise to a 1‐day workflow, enables fast clarification of disease aetiology and, thus, contributes to specific therapy selection. The latter is particularly important in light of growing resistance to antimycotics.
If Docker is the Answer, What is the Question?

The recent rise of cloud computing poses serious challenges for software engineering because it adds complexity not only to the platform and infrastructure, but to the software too. The demands on system scalability, performance and reliability are ever increasing. Industry solutions with widespread adoption include the microservices architecture, the container technology and the DevOps methodology. These approaches have changed software engineering practice in such a profound way that we argue that it is becoming a paradigm shift. In this paper, we examine the current support of programming languages for the key concepts behind the change in software engineering practice and argue that a novel programming language is required to support the new paradigm. We report a new programming language CAOPLE and its associated Integrated DevOps Environment CIDE and demonstrate the utility of both.
A cure‐rate model for Q‐learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients

Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft‐versus‐host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two stages of treatment may lead to a cure has not yet been considered. Using a sample of 9563 patients treated for blood and bone cancers by allogeneic hematopoietic cell transplantation drawn from the Center for Blood and Marrow Transplant Research database, we provide a case study of a novel approach to Q‐learning for survival data in the presence of a potentially curative treatment, and demonstrate the results differ substantially from an implementation of Q‐learning that fails to account for the cure‐rate.
Meta-analysis of the association between rumination and reduced autobiographical memory specificity

ABSTRACT The CaRFAX model, proposed by Williams J. M. G. (2006. Capture and rumination, functional avoidance, and executive control (CaRFAX): Three processes that underlie overgeneral memory. Cognition and Emotion, 20, 548–568. doi:10.1080/02699930500450465; Williams, J. M. G. , Barnhofer, T. , Crane, C. , Herman, D. , Raes, F. , Watkins, E. , & Dalgleish, T. (2007). Autobiographical memory specificity and emotional disorder. Psychological Bulletin, 133(1), 122–148. doi:10.1037/0033-2909.133. 1.122) posits that reduced autobiographical memory specificity, a key factor associated with the emergence and maintenance of emotional disorders, may result from heightened rumination. We provide the first meta-analysis of the relation between autobiographical memory specificity and trait rumination. PsycINFO, PsycARTICLES and MEDLINE databases were searched and the following were extracted: the correlation between the number of specific memories recalled in the Autobiographical Memory Test and self-reported trait rumination scores, and its sub-factors – brooding and reflection. The pooled effect size for the correlation between memory specificity and trait rumination was small (d = −.05) and did not differ significantly from zero (p = .09). The effect sizes for the correlation with brooding and reflection were not significantly different from zero. There is limited support for the association between trait rumination and memory specificity suggested in CaRFAX.
Microfluidic Biosensor Based on Microwave Substrate-Integrated Waveguide Cavity Resonator

A microfluidic biosensor is proposed using a microwave substrate-integrated waveguide (SIW) cavity resonator. The main objectives of this noninvasive biosensor are to detect and analyze biomaterial using tiny liquid volumes (3 μL). The sensing mechanism of our proposed biosensor relies on the dielectric perturbation phenomenon of biomaterial under test, which causes a change in resonance frequency and return loss (amplitude). First, an SIW cavity is realized on a Rogers RT/Duroid 5870 substrate. Then, a microwell made from polydimethylsiloxane (PDMS) material is loaded on the SIW cavity to observe the perturbation phenomenon. The microwell is filled with phosphate-buffered saline (PBS) solution (reference biological medium). To demonstrate the sensing behavior, the fibroblast (FB) cells from the lungs of a human male subject are analyzed and one-port S-parameters are measured. The resonance frequency of the structure with FB cells is observed to be 13.48 GHz. The reproducibility and repeatability of our proposed biosensor are successfully demonstrated through full-wave simulations and measurements. The resonance frequency of the FB-loaded microwell showed a shift of 170 MHz and 20 MHz, when compared to those of empty and PBS-loaded microwells. Its analytical limit of detection is 213 cells/μL. Our proposed biosensor is noncontact and reliable. Furthermore, it is miniaturized, inexpensive, and fabricated using simple- and easy-design processes.
Influenza D Virus Infection in Feral Swine Populations, United States

Influenza D virus (IDV) has been identified in domestic cattle, swine, camelid, and small ruminant populations across North America, Europe, Asia, South America, and Africa. Our study investigated seroprevalence and transmissibility of IDV in feral swine. During 2012–2013, we evaluated feral swine populations in 4 US states; of 256 swine tested, 57 (19.1%) were IDV seropositive. Among 96 archived influenza A virus–seropositive feral swine samples collected from 16 US states during 2010–2013, 41 (42.7%) were IDV seropositive. Infection studies demonstrated that IDV-inoculated feral swine shed virus 3–5 days postinoculation and seroconverted at 21 days postinoculation; 50% of in-contact naive feral swine shed virus, seroconverted, or both. Immunohistochemical staining showed viral antigen within epithelial cells of the respiratory tract, including trachea, soft palate, and lungs. Our findings suggest that feral swine might serve an important role in the ecology of IDV.
Application of GA with SVM for Stock Price Prediction in Financial Market

Time series forecasting is receiving remarkable attention from the research community in using data mining techniques to analyze the extensive historical datasets for solving prediction problems. For such type of forecasting the indicators are required to be derived from relevant time series. In stock price forecasting in the financial sector more than 100 indicators have been developed to understand stock market behavior and thus the identification of the right indicators is a challenging problem. In such a case, optimized computer algorithms need to be investigated and applied for identifying really necessary indicators. From the various machine learning techniques available one of the technique recently investigated for time series forecasting is the Support Vector Regression (SVR) or Support Vector Machine (SVM)(7). This study applies GA-SVM to predict the stock price index. In addition, the study also examines the feasibility of applying GA-SVM in financial forecasting by comparing it with Support Vector Machine (SVM) and case-based reasoning. The experimental results show that SVM with GA provides a more optimized and promising alternative to stock market prediction.
PNGV Equivalent Circuit Model and SOC Estimation Algorithm for Lithium Battery Pack Adopted in AGV Vehicle

Lithium battery is promising to power AGV vehicle. Given the inaccuracy of the state-of-charge (SOC) estimation, a partnership for a new generation of vehicle (PNGV) battery model is established, and an open-circuit voltage method is proposed in this paper. The model’s parameters in different SOC state are calculated through the lithium battery’s charge and discharge process; the internal resistance correction method and Kalman filter method are proposed separately on the basis of PNGV model. On that basis, the two methods are simulated and experimented to attain the open-circuit voltage and battery SOC. As indicated from the experimental results, the PNGV model is precise and able to describe the characterizations of battery in the discharge process. The accumulated error and inaccurate estimation of the initial value by the ampere-hour (or current integration) approach can be avoided through adopting the SOC estimations effectively. Additionally, the estimated SOC can be maintained highly accurate.
Application and Evaluation of the Growth Simulator SILVA 2 . 2 for Forest Stands , Forest Estates and Large Regions

The focus of this paper is on the application and validation of the forest growth simulator SILVA 2.2 at the level of forest stands, forestry enterprises and large regions. The Design concept and structure of this distance-dependent and site-sensitive individual tree simulator have been presented in PRETZSCH (1992), KAHN (1994), KAHN and PRETZSCH (1997), PRETZSCH (2001). The simulator owes its flexibility at stand level to the spatially explicit model approach and the parameter estimation, which is based on long-term experimental plots in pure and mixed stands, Some of the included time series date back to the year 1870.The simulator can be applied for various purposes and at different scales, and the results of the scenario calculations include estimates of timber yields as well as ecological and socio-economic indicators. In this paper, the development of a pure and mixed stand are compared by simulation at stand level. Thereafter, different silvicultural policies are investigated at enterprise level. The long-term consequences of forest management decisions at stand level become transparent once the growth projections are carried out at enterprise level. Thus, individual treatment regimes can be modified in order to achieve desired developments at enterprise level. Finally, the site-sensitive approach of SILVA 2.2, which is described in this paper, allows the analysis of the consequences of climate change. This is demonstrated in scenario calculations, which investigate the consequences of climate change on the growth and wood supply of spruce in Bavaria. The model validation was based on long-term experimental plots and showed a bias of -1.9 % to 4.8%, an accuracy of 19.2% to 38.6% and aprecision of 18.5% to 38.6% for the estimate of current stand volume increment. Similar results were obtained for mean diameter increment and; furthermore, for individual tree level. In case of short increment periods and extraordinary climatic conditions the values of bias and accuracy may prove less favourable. In a qualitative comparison with inventory data the prediction behaviour of the site-growth model was proved to be fairly robust. Currendy, however, the modellieg of regional and temporal peculiarities such as nitrogen immissions or snow-break events has to be improved.
The Association of Dietary Fiber Intake with Cardiometabolic Risk in Four Countries across the Epidemiologic Transition

The greatest burden of cardiovascular disease is now carried by developing countries with cardiometabolic conditions such as metabolic syndrome, obesity and inflammation believed to be the driving force behind this epidemic. Dietary fiber is known to have protective effects against obesity, type 2 diabetes, cardiovascular disease and the metabolic syndrome. Considering the emerging prevalence of these cardiometabolic disease states across the epidemiologic transition, the objective of this study is to explore these associations of dietary fiber with cardiometabolic risk factors in four countries across the epidemiologic transition. We examined population-based samples of men and women, aged 25–45 of African origin from Ghana, Jamaica, the Seychelles and the USA. Ghanaians had the lowest prevalence of obesity (10%), while Jamaicans had the lowest prevalence of metabolic syndrome (5%) across all the sites. Participants from the US presented with the highest prevalence of obesity (52%), and metabolic syndrome (22%). Overall, the Ghanaians consumed the highest dietary fiber (24.9 ± 9.7 g), followed by Jamaica (16.0 ± 8.3 g), the Seychelles (13.6 ± 7.2 g) and the lowest in the USA (14.2 ± 7.1 g). Consequently, 43% of Ghanaians met the fiber dietary guidelines (14 g/1000 kcal/day), 9% of Jamaicans, 6% of Seychellois, and only 3% of US adults. Across all sites, cardiometabolic risk (metabolic syndrome, inflammation and obesity) was inversely associated with dietary fiber intake, such that the prevalence of metabolic syndrome was 13% for those in the lowest quartile of fiber intake, compared to 9% those in the highest quartile of fiber intake. Notably, twice as many of participants (38%) in the lowest quartile were obese compared to those in the highest quartile of fiber intake (18%). These findings further support the need to incorporate strategies and policies to promote increased dietary fiber intake as one component for the prevention of cardiometabolic risk in all countries spanning the epidemiologic transition.
Gender differences in the effect of social resources and social status on the retirement satisfaction and health of retirees

ABSTRACT This study explores the effect of positive and negative social support, social reciprocity, and subjective social status on the retirement satisfaction and health of retirees and gender differences therein. Using cross-sectional data from the 2010 and 2012 waves of the Health and Retirement Study (HRS), we found that social support seems to matter more for the retirement satisfaction and health of women, while social reciprocity matters more for the health of men and subjective social status for the retirement satisfaction of men. Implications for the development of social programs and policies over the life course are discussed.
Isolation and Total Structure Determination of an All-Alkynyl-Protected Gold Nanocluster Au144.

Total structure determination of a ligand-protected gold nanocluster, Au144 , has been successfully carried out. The composition of title nanocluster is Au144 (C≡CAr)60 (1; Ar=2-FC6 H4 -). The cluster 1 exhibits a quasi-spherical Russian doll-like architecture, comprising a Au54 two-shelled Mackay icosahedron (Au12 @Au42 ), which is further enclosed by a Au60 anti-Mackay icosahedral shell. The Au114 kernel is enwrapped by thirty linear ArC≡C-Au-C≡CAr staple motifs. The absorption spectrum of 1 shows two bands at 560 and 620 nm. This spectrum is distinctly different from that of thiolated Au144 , which was predicted to have an almost identical metal kernel and very similar ligands arrangement in 1. These facts indicate the molecule-like behavior of 1 and significant involvement of ligands in the electronic structure of 1. The cluster 1 is hitherto the largest coinage metal nanocluster with atomically precise molecular structure in the alkynyl family. The work not only addresses the concern of structural information of Au144 , which had been long-pursued, but also provides an interesting example showing ligand effects on the optical properties of ligand protected metal nanoclusters.
An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.
Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

This paper examines the forecasting ability of the nonlinear specifications of the market model. We propose a conditional two-moment market model with a time-varying systematic covariance (beta) risk in the form of a mean reverting process of the state-space model via the Kalman filter algorithm. In addition, we account for the systematic component of co-skewness and co-kurtosis by considering higher moments. The analysis is implemented using data from the stock indices of several developed and emerging stock markets. The empirical findings favour the time-varying market model approaches, which outperform linear model specifications both in terms of model fit and predictability. Precisely, higher moments are necessary for datasets that involve structural changes and/or market inefficiencies which are common in most of the emerging stock markets. Copyright © 2016 John Wiley & Sons, Ltd.