Miracle-dz/UniFAR-FTUs huggingface dataset:
Tests and Analysis of a Small-Scale Hybrid-Type DC SFCL Prototype

In recent years, high-voltage dc power transmission is widely developed in the world. However, it is difficult for the existing dc breaker to cut off the fault transmission line with large short-circuit fault current. In a short-circuit fault of dc systems, short-circuit fault current is composed of initial transient impulse current and steady-state fault current. Therefore, a hybrid-type dc superconducting fault current limiter (SFCL) is proposed with two-stage current limiting capability. In the early stage of short-circuit fault, initial transient impulse current is limited by inductance and resistance. In the later stage of short-circuit fault, a sustained and steady current-limiting resistance is provided by the dc SFCL. A small-scale prototype of the dc SFCL is developed and the parameter design of the dc SFCL prototype is presented. According to the experimental tests and simulation analysis, the design scheme's effectiveness can be confirmed from high current-limiting ratio. Based on experimental tests and simulation analysis, the current-limiting characteristics and energy loss characteristics of the hybrid-type dc SFCL have been well verified.
Doping, Defects And Solar Cell Performance Of Cu-rich Grown CuInSe2

Cu-rich grown CuInSe2 thin-film solar cells can be as efficient as Cu-poor ones. However record lab cells and commercial modules are grown exclusively under Cu-poor conditions. While the Cu-rich material’s bulk properties show advantages, e.g. higher minority carrier mobilities and quasi-Fermi level splitting - both indicating a superior performance - it also features some inherent problems that led to its widespread dismissal for solar cell use. Two major challenges can be identified that negatively impact the Curich’s performance: a too high doping density and recombination close to the interface. In this work electrical characterisation techniques were employed to investigate the mechanisms that cause the low performance. Capacitance measurements are especially well suited to probe the electrically active defects within the space-charge region. Under a variation of applied DC bias they give insights into the shallow doping density, while frequency and temperature dependent measurements are powerful in revealing deep levels within the bandgap. CuInSe2 samples were produced via a thermal co-evaporation process and subsequently characterized utilizing the aforementioned techniques. The results have been grouped into two partial studies. First the influence of the Se overpressure during growth on the shallow doping and deep defects is investigated and how this impacts solar cell performance. The second study revolves around samples that feature a surface treatment to produce a bilayer structure - a Cu-rich bulk and a Cu-poor interface. It is shown that via a reduction of the Se flux during absorber preparation the doping density can be reduced and while this certainly benefits solar cell efficiency, a high deficit in open-circuit voltage still results in lower performance compared to the Cu-poor devices. Supplementary measurements trace this back to recombination close to the interface. Furthermore a defect signature is identified, that is not present in Cu-poor material. These two results are tied together via the investigation of the surface treated samples, which do not show interface recombination and reach the same high voltage as the Cu-poor samples. The defect signature, normally native to the Cu-rich material, however is not found in the surface treated samples. It is concluded that this deep trap acts as a recombination centre close to the interface. Shifting it towards the bulk via the treatment is then related to the observed increase in voltage. Within this thesis a conclusive picture is derived to unite all measurement results and show the mechanisms that work together and made it possible to produce a high efficient Cu-rich thin-film solar cell.
Randomized dimensionality reduction of deep network features for image object recognition

This study investigates data dimensionality reduction for image object recognition. The dimensionality reduction was applied to features extracted from an existing pre-trained Deep Neural Network (DNN) structure, the AlexNet. An analysis of the neurons in different layers of the AlexNet revealed an incremental increase in the pair-wise orthogonality between weight vectors of neurons in each layer, towards higher-level layers. This observation motivated the current study to evaluate the possibility of performing randomized dimensionality reduction by mimicking the observed orthogonality property of the high-level layers on activations of low-level layers of the AlexNet. Image object classification experiments have shown that the proposed random orthogonal projection method performed well in multiple tests, consistently outperforming the well-known statistics-based sparse random projection. Apart from being data independent, the proposed approach achieved performances comparable with the state-of-the-art techniques, but with lower computational requirements.
A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects

The state of energy (SOE) is a key indicator for the energy optimization and management of lithium-ion (Li-ion) battery-based energy storage systems in smart grid applications. To improve the SOE estimation accuracy, a Li-ion battery model is presented in this study against dynamic loads and battery ageing effects. First, an electrical battery model is combined with an analytical model in order to take advantages of both models for accurate prediction of battery terminal voltage characteristics, SOE, and remnant runtime. Second, a novel method to separate the fast and slow dynamics of the electrical battery model is developed, and its superior performance is presented. Third, the effects of the battery initial SOC, load current rate and direction, operating temperature, and ageing level are systematically scrutinized and involved in the proposed model for robust SOE and terminal voltage prediction. Commercial Li-ion batteries are then tested under dynamic loads and at an arbitrary battery ageing level to validate the effectiveness and robustness of the proposed model. The laboratory-scale experimental test results show superb accuracy and reliability of the proposed battery model for estimating battery SOE and terminal voltage under dynamic loads and battery ageing conditions.
Caregiver perceptions of the food marketing environment of African-American 3–11-year-olds: a qualitative study

Abstract Objective To assess caregivers’ perceptions of the extent to which the food marketing environment influences food consumption among African-American children (aged 3–11 years) in order to generate potential strategies to make the marketing environment more favourable to healthier eating. Design Individual semi-structured interviews with caregivers were conducted by trained community leaders to ascertain their awareness of and perceptions about food marketing environments contributing to African-American children's food consumption. Setting Six predominantly African-American communities in metro Birmingham, Alabama, USA with high proportions of school-age children and lower-income residents. Subjects Caregivers (n 25) were predominantly female (93 %) and either parents/guardians (64 %) or grandparents (28 %) of African-American children aged 3–11 years. Caregiver mean age was 43 years and 46 % had lived in their current residence for over 10 years. Results Caregivers reported all aspects of the food marketing matrix as supporting unhealthy eating among African-American youth. Child preference for foods higher in fat and sugar, lower pricing of less healthy foods, limited access to healthier food retailers and targeted advertisements were particularly influential on the food selection, acquisition and consumption of children. Company loyalty, corporate sponsorship of local events and conflicts over parental v. food company responsibility contributed to less consensus about the overall impact (positive or negative) of food companies in African-American communities. Conclusions While caregivers perceived aspects of their food marketing environments as primarily contributing to unhealthy eating among African-American children, framing the demand for changes in the food marketing environments of African-American youth may be particularly challenging.
Complex activity recognition system based on cascade classifiers and wearable device data

This paper proposes a system for recognizing human complex activities by using unobtrusive sensors such as smartphone, smartwatch and bluetooth beacons. The method encapsulates two classification stages. The former is composed of two parallel processes: the Main Activity Detection (MAD) and the Room Detection (RD). The latter implements the Complex Activity Detection (CAD) process by exploiting the outputs of the first stage and the accelerometer data of the smartwatch. The cascade classification approach that combines the room detection with the main/complex activities recognition task constitutes the novelty of the work. Preliminary results demonstrate the reliability of the system in terms of accuracy and macro-Fl score.
Leader-Following Consensus of Multi-Agent Systems With Switching Networks and Event-Triggered Control

This paper addresses the event-based leader-following consensus of a class of multi-agent systems with switching networks. Fully distributed event-triggered functions are proposed to select the sampling instants automatically. Detailed algorithms are given to design the feedback gains and coupling strength. The positive lower bound of the sampling intervals is captured to exclude Zeno behaviours. Finally, simulations are also carried out to verify the effectiveness of the event-triggered control scheme.
The Efficacy of Psychological Interventions for Managing Fatigue in People With Multiple Sclerosis: A Systematic Review and Meta-Analysis

Background Multiple sclerosis (MS) is a complex, demyelinating disease of the central nervous system. Fatigue is commonly reported by people with MS (PwMS). MS-related fatigue severely affects daily activities, employment, socioeconomic status, and quality of life. Objective We conducted this systematic review and meta-analysis to determine whether psychological interventions are effective in managing fatigue in PwMS. Data sources We performed systematic searches of Medline, EMBASE, PsycINFO, and CINAHL to identify relevant articles published from database inception to April 5, 2017. Reference lists from relevant reviews were also searched. Study selection and design Two independent reviewers screened the papers, extracted data, and appraised the included studies. A clinical psychologist verified whether interventions were psychological approaches. A narrative synthesis was conducted for all included studies. For relevant randomized controlled trials that reported sufficient information to determine standardized mean differences (SMDs) and 95% confidence intervals (CIs), meta-analyses were conducted using a random-effects model. Results Of the 353 identified articles, 20 studies with 1,249 PwMS were included in this systematic review. Narrative synthesis revealed that psychological interventions reduced fatigue in PwMS. Meta-analyses revealed that cognitive behavioral therapy decreased levels of fatigue compared with non-active controls (SMD = −0.32; 95% CI: −0.63 to −0.01) and compared with active controls (relaxation or psychotherapy) (SMD = −0.71; 95% CI: −1.05 to −0.37). Meta-analyses further showed that both relaxation (SMD = −0.90; 95% CI: −1.30 to −0.51), and mindfulness interventions (SMD = −0.62; 95% CI: −1.12 to −0.12), compared with non-active control, decreased fatigue levels. The estimates of heterogeneity for the four meta-analyses varied between none and moderate. Conclusion This study found that the use of psychological interventions for MS-related fatigue management reduced fatigue in PwMS. While psychological interventions are generally considered first-line therapy for MS-related fatigue, further studies are needed to explore the long-term effect of this therapy.
Semantic Illustration Retrieval for Very Large Data Set

In this paper, we present a retrieval system that performs the illustration retrieval on very large data set. The traditional text-based retrieval systems often perform poorly on the illustration retrieval, because some illustrations are uncaptioned. Even worse, textual information are often mixed with noisy information and therefore fail to represent the illustrations accurately. To overcome the problem, we propose a semantic model for illustration retrieval. In this model, we first extract the shape information whose similarities are then used to construct two link graphs. Based on the graphs, we execute the auto-captioning procedure on the uncaptioned illustrations. In addition, cross-modal analysis is applied to get rid of the noisy information and reduce the dimensionality of the feature vectors. Finally, we introduce a re-rank scheme that returns as many subtopics related to the query as possible along with the improvement in relevance. Experiments on approximately 500,000 illustrations showed that our system performs efficiently in retrieving the illustrations with high relevance and diversity. Index Terms — illustration retrieval, feature extraction, cross-modal analysis, ranking
Impedance Control of Robots: An Overview

Impedance control has received significant efforts in recent decades in robotics as impedance control aims to achieve the desired mechanical interaction with uncertain environment. This paper is dedicated to the overview of basic concepts and principles, implementation strategies, challenging research problems and interests, etc. The objective is to help readers quickly get into problems of their interests related to impedance control and provide guidance and insights in finding appropriate strategies and solutions.
Error-bounded reduction of triangle meshes with multivariate data

Interactive visualization is complicated by the complexity of the objects being visualized. Sampled or computed scientific data is often dense, in order to capture high frequency components in measured data or to accurately model a physical process. Common visualization techniques such as isosurfacing on such large meshes generate more geometric primitives than can be rendered in an interactive environment. Geometric mesh reduction techniques have been developed in order to reduce the size of a mesh with little compromise in image quality. Similar techniques have been used for functional surfaces (terrain maps) which take advantage of the planar projection. We extend these methods to arbitrary surfaces in 3D and to any number of variables defined over the mesh by developing a algorithm for mapping from a surface mesh to a reduced representation and measuring the introduced error in both the geometry and the multivariate data. Furthermore, through error propagation, our algorithm ensures that the errors in both the geometric representation and multivariate data do not exceed a user-specified upper bound.
Current differential amplifiers: new circuits and applications

New CMOS current differential amplifiers are proposed suitable for analogue signal processing at high frequencies. They consist of simple current mirrors, which are easy to design and to implement in IC form. Low‐voltage low‐power design is feasible. Relying on these devices a number of applications are obtained, including lossy and lossless integrators, simulated inductors, active filters, and harmonic oscillators. Theoretical expressions are given for all of the proposed new circuits. The verification of the circuits is also achieved by simulation. Copyright 2001 © John Wiley & Sons, Ltd.
Effects of walnut consumption on blood lipids and other cardiovascular risk factors: a meta-analysis and systematic review.

BACKGROUND Consumption of nuts has been associated with a decreased risk of cardiovascular disease events and death. Walnuts in particular have a unique profile: they are rich in polyunsaturated fatty acids, which may improve blood lipids and other cardiovascular disease risk factors. OBJECTIVES We aimed to conduct a literature review and a meta-analysis to combine the results from several trials and to estimate the effect of walnuts on blood lipids. DESIGN Literature databases were searched for published trials that compared a specifically walnut-enhanced diet with a control diet. We conducted a random-effects meta-analysis of weighted mean differences (WMDs) of lipid outcomes. RESULTS Thirteen studies representing 365 participants were included in the analysis. Diets lasted 4-24 wk with walnuts providing 10-24% of total calories. When compared with control diets, diets supplemented with walnuts resulted in a significantly greater decrease in total cholesterol and in LDL-cholesterol concentrations (total cholesterol: WMD = -10.3 mg/dL, P < 0.001; LDL cholesterol: WMD = -9.2 mg/dL, P < 0.001). HDL cholesterol and triglycerides were not significantly affected by walnut diets more than with control diets (HDL cholesterol: WMD = -0.2, P = 0.8; triglycerides: WMD = -3.9, P = 0.3). Other results reported in the trials indicated that walnuts provided significant benefits for certain antioxidant capacity and inflammatory markers and had no adverse effects on body weight [body mass index (kg/m(2)): WMD = -0.4, P = 0.5; weight (kg): WMD = -0.05, P = 0.97]. CONCLUSIONS Overall, high-walnut-enriched diets significantly decreased total and LDL cholesterol for the duration of the short-term trials. Larger and longer-term trials are needed to address the effects of walnut consumption on cardiovascular risk and body weight.
Assembling behavioural characteristics of malicious software

In this paper we present our approach to assembling characteristics of malware, which may be useful for another researchers, in case they face limitations of hardware and software resources during their work. Our research aims at exploring malware characteristics on high level of abstraction and finding how much abstract can the characteristics be to bring results. To pursue our goals, we needed to analyse great amount of malware samples. Unfortunately, we faced obstacles which did not allow us to effectively perform malware analysis for obtaining data we required. As a result, we came up with different approach for assembling data form malware analysis by employing online resources. The paper describes realization of our approach, which allowed us to assemble large amount of malware behavioural characteristics and utilise them in our research.
Separate effects of reduced carbohydrate intake and weight loss on atherogenic dyslipidemia.

BACKGROUND Low-carbohydrate diets have been used to manage obesity and its metabolic consequences. OBJECTIVE The objective was to study the effects of moderate carbohydrate restriction on atherogenic dyslipidemia before and after weight loss and in conjunction with a low or high dietary saturated fat intake. DESIGN After 1 wk of consuming a basal diet, 178 men with a mean body mass index (in kg/m(2)) of 29.2 +/- 2.0 were randomly assigned to consume diets with carbohydrate contents of 54% (basal diet), 39%, or 26% of energy and with a low saturated fat content (7-9% of energy); a fourth group consumed a diet with 26% of energy as carbohydrate and 15% as saturated fat. After 3 wk, the mean weight loss (5.12 +/- 1.83 kg) was induced in all diet groups by a reduction of approximately 1000 kcal/d for 5 wk followed by 4 wk of weight stabilization. RESULTS The 26%-carbohydrate, low-saturated-fat diet reduced triacylglycerol, apolipoprotein B, small LDL mass, and total:HDL cholesterol and increased LDL peak diameter. These changes were significantly different from those with the 54%-carbohydrate diet. After subsequent weight loss, the changes in all these variables were significantly greater and the reduction in LDL cholesterol was significantly greater with the 54%-carbohydrate diet than with the 26%-carbohydrate diet. With the 26%-carbohydrate diet, lipoprotein changes with the higher saturated fat intakes were not significantly different from those with the lower saturated fat intakes, except for LDL cholesterol, which decreased less with the higher saturated fat intake because of an increase in mass of large LDL. CONCLUSIONS Moderate carbohydrate restriction and weight loss provide equivalent but nonadditive approaches to improving atherogenic dyslipidemia. Moreover, beneficial lipid changes resulting from a reduced carbohydrate intake were not significant after weight loss.
Incidence of bacterial enteropathogens in foods from Mexico

We examined food consumption patterns of U.S. students temporarily living in Guadalajara, Mexico. Consumption of foods prepared in Mexican homes was associated with an increased risk of acquisition of diarrhea. Foods from commercial sources and private Mexican homes in Guadalajara were subsequently examined for contamination with coliforms, fecal coliforms, and bacterial enteropathogens. For comparison, selected restaurant foods were obtained in Houston, Tex. Food obtained from Mexican homes showed generally higher counts of coliforms and fecal coliforms than those obtained from commercial sources in Mexico and Houston. The foods in Mexico, both from homes and commercial sources, commonly contained Escherichia coli and occasionally enterotoxigenic E. coli. Foods in Houston were not contaminated with E. coli or enterotoxigenic E. coli. Salmonella (17 isolates), Shigella (4 isolates), and Aeromonas hydrophila (1 isolate) were found only in the foods obtained from Mexican homes. Enterotoxigenic non-E. coli Enterobacteriaceae was recovered with approximately equal frequency from all food sources.
Combating Fake News with Stance Detection using Recurrent Neural Networks

Comparing the attitude that different news organizations have towards a certain claim or topic is an important part of the procedure used by human fact-checkers today for assessing the veracity of a news story reporting about the issue. In this thesis we focus on automating the challenging task of stance detection in the news domain, specifically determining the relative stance of an article towards a claim stated in an associated headline, making use of the labelled dataset delivered for supervision in the first stage of the Fake News Challenge. While the most successful approaches in this domain have used complex ensemble classifiers employing large sets of hand-engineered features, their performance is just marginally better than a simple bag-of-words model deriving only lexical similarity. Our approach makes use of recurrent neural networks that read headlines and articles word-by-word. The models we implement are comparable to the state-of-the-art systems, however, observing that severe overfitting occurs.
Light field compression by superpixel based filtering and pseudo-temporal reordering

In this paper we have addressed the topic of an evolutionary integration of light fields into standard image/video processing chains by pre-processing light fields with superpixel-based and structurally adaptive Gaussian pre-filters and circular pseudo-temporal sequencing to feed them into an HEVC-codec with low-delay predictive coding configuration. We could show significant bit rate reductions of up to 27% compared to pseudo-temporal sequencing without pre-processing. The paper includes experimental results showing that not only the perceived visual quality, but also the cornucopia of post-processing options is preserved.