nils-herrmann/clean_scirepeval huggingface dataset:
Constraint-induced movement therapy as a rehabilitation intervention for upper extremity in stroke patients: systematic review and meta-analysis.

Constraint-induced movement therapy (CIMT) is a neurorehabilitation technique designed to improve upper extremity motor functions after stroke. This review aimed to investigate evidence of the effect of CIMT on upper extremity in stroke patients and to identify optimal methods to apply CIMT. Four databases (MEDLINE, EMBASE, CINHAL, and PEDro) and reference lists of relevant articles and reviews were searched. Randomized clinical trials that studied the effect of CIMT on upper extremity outcomes in stroke patients compared with other rehabilitative techniques, usual care, or no intervention were included. Methodological quality was assessed using the PEDro score. The following data were extracted for each trial: patients' characteristics, sample size, eligibility criteria, protocols of CIMT and control groups, outcome measurements, and the PEDro score. A total of 38 trials were identified according to the inclusion criteria. The trials included were heterogeneous in CIMT protocols, time since stroke, and duration and frequency of treatment. The pooled meta-analysis of 36 trials found a heterogeneous significant effect of CIMT on upper extremity. There was no significant effect of CIMT at different durations of follow-up. The majority of included articles did not fulfill powered sample size and quality criteria. The effect of CIMT changed in terms of sample size and quality features of the articles included. These meta-analysis findings indicate that evidence for the superiority of CIMT in comparison with other rehabilitative interventions is weak. Information on the optimal dose of CIMT and optimal time to start CIMT is still limited.
"Ooh Aah... Just a Little Bit" : A Small Amount of Side Channel Can Go a Long Way

We apply the FLUSH+RELOAD side-channel attack based on cache hits/misses to extract a small amount of data from OpenSSL ECDSA signature requests. We then apply a “standard” lattice technique to extract the private key, but unlike previous attacks we are able to make use of the side-channel information from almost all of the observed executions. This means we obtain private key recovery by observing a relatively small number of executions, and by expending a relatively small amount of post-processing via lattice reduction. We demonstrate our analysis via experiments using the curve secp256k1 used in the Bitcoin protocol. In particular we show that with as little as 200 signatures we are able to achieve a reasonable level of success in recovering the secret key for a 256-bit curve. This is significantly better than prior methods of applying lattice reduction techniques to similar side channel information.
Impact of mindfulness on the neural responses to emotional pictures in experienced and beginner meditators

There is mounting evidence that mindfulness meditation is beneficial for the treatment of mood and anxiety disorders, yet little is known regarding the neural mechanisms through which mindfulness modulates emotional responses. Thus, a central objective of this functional magnetic resonance imaging study was to investigate the effects of mindfulness on the neural responses to emotionally laden stimuli. Another major goal of this study was to examine the impact of the extent of mindfulness training on the brain mechanisms supporting the processing of emotional stimuli. Twelve experienced (with over 1000 h of practice) and 10 beginner meditators were scanned as they viewed negative, positive, and neutral pictures in a mindful state and a non-mindful state of awareness. Results indicated that the Mindful condition attenuated emotional intensity perceived from pictures, while brain imaging data suggested that this effect was achieved through distinct neural mechanisms for each group of participants. For experienced meditators compared with beginners, mindfulness induced a deactivation of default mode network areas (medial prefrontal and posterior cingulate cortices) across all valence categories and did not influence responses in brain regions involved in emotional reactivity during emotional processing. On the other hand, for beginners relative to experienced meditators, mindfulness induced a down-regulation of the left amygdala during emotional processing. These findings suggest that the long-term practice of mindfulness leads to emotional stability by promoting acceptance of emotional states and enhanced present-moment awareness, rather than by eliciting control over low-level affective cerebral systems from higher-order cortical brain regions. These results have implications for affect-related psychological disorders.
Coal Exploration Based on a Multilayer Extreme Learning Machine and Satellite Images

The demand for coal has been on the rise in modern society. With the number of opencast coal mines decreasing, it has become increasingly difficult to find coal. Low efficiencies and high casualty rates have always been problems in the process of coal exploration due to complicated geological structures in coal mining areas. Therefore, we propose a new exploration technology for coal that uses satellite images to explore and monitor opencast coal mining areas. First, we collected bituminous coal and lignite from the Shenhua opencast coal mine in China in addition to non-coal objects, including sandstones, soils, shales, marls, vegetation, coal gangues, water, and buildings. Second, we measured the spectral data of these objects through a spectrometer. Third, we proposed a multilayer extreme learning machine algorithm and constructed a coal classification model based on that algorithm and the spectral data. The model can assist in the classification of bituminous coal, lignite, and non-coal objects. Fourth, we collected Landsat 8 satellite images for the coal mining areas. We divided the image of the coal mine using the constructed model and correctly described the distributions of bituminous coal and lignite. Compared with the traditional coal exploration method, our method manifested an unparalleled advantage and application value in terms of its economy, speed, and accuracy.
Better management of blood supply-chain with GIS-based analytics

This paper presents a novel application of operations research, data mining and geographic information-systems-based analytics to support decision making in blood supply chain management. This, blood reserve availability assessment, tracking, and management system (BRAMS), research project has been funded by the Office of the Secretary of Defense.  The rapidly increasing demand, criticality of the product, strict storage and handling requirements, and the vastness of the theater of operations, make blood supply-chain management a complex, yet vital problem for the department of defense. In order to address this problem a variety of contemporary analytic techniques are used to analyze inventory and consumption patterns, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The current implementation of the system is being actively used by 130 mangers at different levels in the supply chain including facilities at Osan Air Force Base in South Korea and Incirlik Air Force Base in Turkey.
Power control in AC autonomous and isolated microgrids with renewable energy sources and energy storage systems

This paper presents a new strategy to control the generated power that comes from the energy sources existing in autonomous and isolated Microgrids. In this particular study, the power system consists of a power electronic converter supplied by a battery bank, which is used to form the AC grid (grid former converter), an energy source based on a wind turbine with its respective power electronic converter (grid supplier converter), and the power consumers (loads). The main objective of this proposed strategy is to control the state of charge of the battery bank limiting the voltage on its terminals by controlling the power generated by the energy sources. This is done without using dump loads or any physical communication among the power electronic converters or the individual energy source controllers. The electrical frequency of the microgrid is used to inform to the power sources and their respective converters the amount of power they need to generate in order to maintain the battery-bank state of charge below or equal its maximum allowable limit. It is proposed a modified droop control to implement this task.
A highly integrated RF frontend module including Doherty PA, LNA and switch for high SHF wide-band massive MIMO in 5G

A highly integrated RF frontend module including a three-stage power amplifier (PA), a two-stage low noise amplifier (LNA) and a switch (SW) is presented for high SHF wide-band massive MIMO in 5G. In order to achieve efficient operation over wide-band frequency, Doherty PA configuration using a parasitic output capacitance neutralization technique is proposed for final stage PA. To improve LNA's gain flatness over wide frequency band, two R-LC stabilization circuits with different resonance frequencies are proposed. They are fabricated with 0.15-μm GaAs process, and integrated into a 5 × 5 mm2 QFN package. The state-of-the-art measured results show that final stage PA and three-stage PA achieve a drain efficiency (DE) at 8dB back-off of more than 22 % and 12 %, respectively, and a two-stage LNA achieves a noise figure (NF) of less than 1.4dB over 14.5–15.0 GHz.
Bifurcations of Recurrent Neural Networks in Gradient Descent Learning

Asymptotic behavior of a recurrent neural network changes qualitatively at certain points in the parameter space, which are known as \bifurcation points". At bifurcation points, the output of a network can change discontinuously with the change of parameters and therefore convergence of gradient descent algorithms is not guaranteed. Furthermore, learning equations used for error gradient estimation can be unstable. However, some kinds of bifurcations are inevitable in training a recurrent network as an automaton or an oscillator. Some of the factors underlying successful training of recurrent networks are investigated, such as choice of initial connections, choice of input patterns, teacher forcing, and truncated learning equations.
SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks

Community detection is an important task for mining the structure and function of complex networks. Generally, there are several different kinds of nodes in a network which are cluster nodes densely connected within communities, as well as some special nodes like hubs bridging multiple communities and outliers marginally connected with a community. In addition, it has been shown that there is a hierarchical structure in complex networks with communities embedded within other communities. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm SHRINK by combining the advantages of density-based clustering and modularity optimization methods. Based on the structural connectivity information, the proposed algorithm can effectively reveal the embedded hierarchical community structure with multiresolution in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the sensitive threshold problem of density-based clustering algorithms and the resolution limit possessed by other modularity-based methods. To illustrate our methodology, we conduct experiments with both real-world and synthetic datasets for community detection, and compare with many other baseline methods. Experimental results demonstrate that SHRINK achieves the best performance with consistent improvements.
Relationship between Resilience and Self-regulation: A Study of Spanish Youth at Risk of Social Exclusion

The ability to self-regulate behavior is one of the most important protective factors in relation with resilience and should be fostered especially in at-risk youth. Previous research has characterized these students as having behaviors indicating lack of foresight. The aim of the present study was to test the hypothetical relationship between these personal variables. It was hypothesized that self-regulation would be associated with and would be a good predictor of resilience, and that low-medium-high levels of self-regulation would lead to similar levels of resilience. The participants were 365 students -aged 15 and 21- from Navarre (Spain) who were enrolled in Initial Vocational Qualification Programs (IVQP). For the assessment, the Connor Davidson Resilience Scale (CD-RISC) and the Short Self-Regulation Questionnaire (SSRQ) were applied. We carried out linear association analyses (correlational and structural) and non-linear interdependence analyses (MANOVA) between the two constructs. Relationships between them were significant and positive. Learning from mistakes (self-regulation) was a significant predictor of coping and confidence, tenacity and adaptation, and tolerance to negative situations (resilience). Likewise, low-medium-high levels of self-regulation correlated with scores on resilience factors. Implications of these results for educational practice and for future research are discussed.
Spatio-temporal clustering methods classification

Nowadays, a vast amount of spatio-temporal data ar e being generated by devices like cell phones, GPS and remote sensing devices and therefore discovering interesting patterns in such data becam n interesting topics for researchers. One of these topics has been spatio-te mporal clustering which is a novel sub field of data mining and Recent researches in this area has focused on new methods and ways which are adapting previous me thods and solutions to the new problem. In this paper we first define what t e spatio-temporal data is and what different it has with other types of data. Then try to classify the clustering methods and done works in this area base d on the proposed solutions. classification has been made based on this fact tha t how these works import and adapt temporal concept in their solutions.
Microbiological Analysis of Street Vended Fruit Juices from Mumbai City , India

In developing countries, fruit and vegetable juices sold by street vendors are widely consumed by millions of people. These juices provide a source of readily available and affordable source of nutrients to many sectors of the population, including the urban poor. Unpasteurized juices are preferred by the consumers because of the “fresh flavor” attributes and hence, in recent times, their demand has increased. They are simply prepared by extracting, usually by mechanical means, the liquid and pulp of mature fruit and vegetables. The final product is an unfermented, clouded, untreated juice, ready for consumption. Pathogenic organisms can enter fruits and vegetables through damaged surfaces, such as punctures, wounds, cuts and splits that occur during growing or harvesting. Contamination from raw materials and equipments, additional processing conditions, improper handling, prevalence of unhygienic conditions contribute substantially to the entry of bacterial pathogens in juices prepared from these fruits or vegetables (Victorian Government Department of Human Services 2005; Oliveira et al., 2006; Nicolas et al., 2007). In countries, where street food vending is prevalent, there is commonly a lack of information on the incidence of food borne diseases related to the street vended foods. However, microbial studies on such foods in American, Asian and African countries have revealed increased bacterial pathogens in the food. There have been documented outbreaks of illnesses in humans associated with the consumption of unpasteurized fruit and vegetable juices and fresh produce. A report published by Victorian Government Department of Abstract: Fresh squeezed juices of sugarcane, lime and carrot sold by street vendors in Mumbai city were analyzed for their microbial contents during the months of June 2007 to September 2007. The total viable counts of all 30 samples were approximately log 6.5 cfu/100ml with significant load of coliforms, faecal coliforms, Vibrio and Staphylococcal counts. Qualitative counts showed the presence of coagulase positive S.aureus in 5 samples of sugarcane and 2 samples of carrot juice. Almost 70% of the ice samples collected from street vendors showed high microbial load ranging from log 58.5. Our results demonstrate the non hygienic quality of three most popular types of street vended fruit juices and ice used for cooling of juices suggesting the urgent need for government participation in developing suitable intervention measures to improve microbial quality of juices.
DNA sequencing with chain-terminating inhibitors.

A new method for determining nucleotide sequences in DNA is described. It is similar to the "plus and minus" method [Sanger, F. & Coulson, A. R. (1975)  but makes use of the 2',3'-dideoxy and arabinonucleoside analogues of the normal deoxynucleoside triphosphates, which act as specific chain-terminating inhibitors of DNA polymerase. The technique has been applied to the DNA of bacteriophage varphiX174 and is more rapid and more accurate than either the plus or the minus method.
The modified lateral brow lift.

In the aging face, the lateral third of the brow ages first and ages most. Aesthetically, eyebrow shape is more significant than height and eyebrow shape is highly dependent on the level of the lateral brow complex. Surgical attempts to elevate the brow complex are usually successful medially, but often fail laterally. The "modified lateral brow lift" is a hybrid technique, incorporating features of an endoscopic brow lift (small hidden incisions, deep tissue fixation) and features of an open coronal brow lift (full thickness scalp excision). Sensory innervation of the scalp is preserved and secure fixation of the elevated lateral brow is achieved. Side effects and complications are minimal.
Unit sizing of stand-alone hybrid wind/PV/fuel cell power generation systems

An economic evaluation of a hybrid wind/photovoltaic/fuel cell generation system for a typical home in the Pacific Northwest is performed. In this configuration the combination of a fuel cell stack, an electrolyzer, and a hydrogen storage tank is used for the energy storage system. This system is compared to a traditional hybrid energy system with battery storage. A computer program has been developed to size system components in order to match the load of the site in the most cost effective way. A cost of electricity and an overall system cost are also calculated for each configuration. The study was performed using a graphical user interface programmed in MATLAB.
An affirmative intervention for families with gender variant children: parental ratings of child mental health and gender.

This is a report on parents who have children who exhibit gender variant behaviors and who contacted an affirmative program in the United States for assistance. All parents completed the Child Behavior Checklist, the Gender Identity Questionnaire, and the Genderism and Transphobia Scale, as well as telephone interviews. The parents reported comparatively low levels of genderism and transphobia. When compared to children at other gender identity clinics in Canada and The Netherlands, parents rated their children's gender variance as no less extreme, but their children were overall less pathological. Indeed, none of the measures in this study could predict parents' ratings of their child's pathology. These findings support the contention that this affirmative program served children who were no less gender variant than in other programs, but they were overall less distressed.
Multichannel bipotentiostat system for cellular monitoring platforms

A versatile 4-channel bipotentiostat system for biochemical sensing is presented. A 1pA current resolution and 8kHz bandwidth are suited for amperometric detection of neurotransmitters released by cells, monitored in a smart microfluidic culture chamber. Multiple electrochemical measurements can be carried out on arrays of microelectrodes. Key design issues are here discussed along with the results of extensive electrochemical experiments (cyclic voltammetry, chronoamperometry, redox recycling and potentiometry).
Error Detection and Correction in Indic OCRs

Indian languages have a rich literature that is not available in digitized form. Attempts have been made to preserve this repository of art and information by maintaining a digital library of scanned books. However, this does not fulfill the purpose as indexing and searching the documents is difficult in images. An OCR system can be used to convert the scanned documents to editable form. However, the OCR systems are error prone. These errors are largely unavoidable and occur due to issues like poorquality images, complex font, unknown glyphs etc. A post-processing system can help in improving the accuracy by using the information about the patterns and constraints in the word and sentence formation to identify the errors and correct them. OCR is considered to be a problem attempted with marked success in Latin scripts, especially English. This is not the case with Indic scripts as the error rates of various OCR systems available are comparatively high. The OCR pipeline includes three main stages, namely segmentation, text recognition and post-processing. We observe that Indic scripts have complex scripts and glyph segmentation itself is a challenge. The existence of visually similar glyphs also makes the recognition process difficult. The challenges faced in the post-processing stage are largely due to the properties of Indian languages. The inflectional properties of some languages like Telugu and Malayalam and agglutination of words creates issues due to the enormous and growing vocabulary in these languages. Unlike alphabet system in English, Indic scripts follow alphasyllabary writing system. Hence the choice of unicodes as the basic unit of a word is questionable. Aksharas which are a more meaningful unit is considered as a better alternative to unicodes. In this thesis, we analyze the challenges in building an efficient post-processor for Indic language OCRs and propose two novel error detection techniques. The post-processing module deals with the detection of errors in the recognized text and correction of those detected errors. To understand the issues in post-processing in Indian languages, we first perform a statistical analysis of the textual data. The unavailability of huge corpus prompted us to crawl various newspaper sites and Wikipedia dump to obtain the required text data. We compare the unique word distribution and word cover of popular Indian languages with English. We observe that languages like Telugu, Tamil, Kannada and Malayalam tend to have huge number of unique words compared to English. We also observe how many words get converted to other valid words in the language, using the Hamming distance between the words as a measure. We empirically analyze the effectiveness of statistical language models for error detection and correction. First we use an ngram model for detection of errors in the OCR output. We use akshara split