nils-herrmann/clean_scirepeval huggingface dataset:
Overview of energy storage in renewable energy systems

In this paper, we present an overview of energy storage in renewable energy systems. In fact, energy storage is a dominant factor. It can reduce power fluctuations, enhances the system flexibility, and enables the storage and dispatching of the electricity generated by variable renewable energy sources such as wind and solar. Different storage technologies are used in electric power systems. They can be chemical, electrochemical, mechanical, electromagnetic or thermal. Energy storage facility is comprised of a storage medium, a power conversion system and a balance of plant. In this work, an application to photovoltaic and wind electric power systems is made. The results obtained under Matlab/Simulink are presented.
Caffeine and the central nervous system: mechanisms of action, biochemical, metabolic and psychostimulant effects

Caffeine is the most widely consumed central-nervous-system stimulant. Three main mechanisms of action of caffeine on the central nervous system have been described. Mobilization of intracellular calcium and inhibition of specific phosphodiesterases only occur at high non-physiological concentrations of caffeine. The only likely mechanism of action of the methylxanthine is the antagonism at the level of adenosine receptors. Caffeine increases energy metabolism throughout the brain but decreases at the same time cerebral blood flow, inducing a relative brain hypoperfusion. Caffeine activates noradrenaline neurons and seems to affect the local release of dopamine. Many of the alerting effects of caffeine may be related to the action of the methylxanthine on serotonin neurons. The methylxanthine induces dose-response increases in locomotor activity in animals. Its psychostimulant action on man is, however, often subtle and not very easy to detect. The effects of caffeine on learning, memory, performance and coordination are rather related to the methylxanthine action on arousal, vigilance and fatigue. Caffeine exerts obvious effects on anxiety and sleep which vary according to individual sensitivity to the methylxanthine. However, children in general do not appear more sensitive to methylxanthine effects than adults. The central nervous system does not seem to develop a great tolerance to the effects of caffeine although dependence and withdrawal symptoms are reported.
A 5b 800MS/s 2mW asynchronous binary-search ADC in 65nm CMOS

Digital wireless communication applications such as UWB and WPAN necessitate low-power high-speed ADCs to convert RF/IF signals into digital form for subsequent baseband processing. Considering latency and conversion speed, flash ADCs are often the most preferred option. Generally, flash ADCs suffer from high power consumption and large area overhead. On the contrary, SAR ADCs have low power dissipation and occupy a small area. However, a SAR ADC needs several comparison cycles to complete one conversion, which limits its conversion speed. The highest single-channel operation speed of previously reported SAR ADCs is 625MS/s . The ADC in  utilizes a 2b/step structure. For non-multi-bit/step SAR ADCs, the highest reported conversion rate is 300MS/s . The structure of a comparator-based binary-search ADC is between that of flash and SAR ADCs . Compared to a flash ADC (high speed, high power) and a SAR ADC (low speed, low power), a binary-search ADC achieves balance between operation speed and power consumption. This paper reports a 5b asynchronous binary-search ADC with reference-range prediction. The maximum conversion speed of this ADC is 800MS/s at a cost of 2mW power consumption.
Decomposition Rate and Pattern in Hanging Pigs.

Accurate prediction of the postmortem interval requires an understanding of the decomposition process and the factors acting upon it. A controlled experiment, over 60 days at an outdoor site in the northwest of England, used 20 freshly killed pigs (Sus scrofa) as human analogues to study decomposition rate and pattern. Ten pigs were hung off the ground and ten placed on the surface. Observed differences in the decomposition pattern required a new decomposition scoring scale to be produced for the hanging pigs to enable comparisons with the surface pigs. The difference in the rate of decomposition between hanging and surface pigs was statistically significant (p=0.001). Hanging pigs reached advanced decomposition stages sooner, but lagged behind during the early stages. This delay is believed to result from lower variety and quantity of insects, due to restricted beetle access to the aerial carcass, and/or writhing maggots falling from the carcass.
Analytic Method for the Computation of the Total Harmonic Distortion by the Cauchy Method of Residues

The total harmonic distortion (THD) is an important performance criterion for almost any communication device. In most cases, the THD of a periodic signal, which has been processed in some way, is either measured directly or roughly estimated numerically, while analytic methods are employed only in a limited number of simple cases. However, the knowledge of the theoretical THD may be quite important for the conception and design of the communication equipment (e.g. transmitters, power amplifiers). The aim of this paper is to present a general theoretic approach, which permits to obtain an analytic closed-form expression for the THD. It is also shown that in some cases, an approximate analytic method, having good precision and being less sophisticated, may be developed. Finally, the mathematical technique, on which the proposed method is based, is described in the appendix.
A Short Survey on Online and Offline Methods for Search Quality Evaluation

Evaluation has always been the cornerstone of scientific development. Scientists come up with hypotheses (models) to explain physical phenomena, and validate these models by comparing their output to observations in nature. A scientific field consists then merely by a collection of hypotheses that could not been disproved (yet) when compared to nature. Evaluation plays the exact key role in the field of information retrieval. Researchers and practitioners develop models to explain the relation between an information need expressed by a person and information contained in available resources, and test these models by comparing their outcomes to collections of observations. This article is a short survey on methods, measures, and designs used in the field of Information Retrieval to evaluate the quality of search algorithms (aka the implementation of a model) against collections of observations. The phrase “search quality” has more than one interpretations, however here I will only discuss one of these interpretations, the effectiveness of a search algorithm to find the information requested by a user. There are two types of collections of observations used for the purpose of evaluation: (a) relevance annotations, and (b) observable user behaviour. I will call the evaluation framework based on the former a collection-based evaluation, while the one based on the latter an in-situ evaluation. This survey is far from complete; it only presents my personal viewpoint on the recent developments in the field.
A General Approach to Terrain Relative Navigation for Planetary Landing

We describe an algorithm for navigation state estimation during planetary descent to enable precision landing. The algorithm automatically produces 2D-to-3D correspondences between descent images and a surface map and 2D-to-2D correspondences through a sequence of descent images. These correspondences are combined with inertial measurements in an extended Kalman filter that estimates lander position, velocity and attitude as well as the time varying biases of the inertial measurements. The filter tightly couples inertial and camera measurements in a resource-adaptive and hence real-time capable fashion. Results from a sounding rocket test, covering the dynamic profile of typical planetary landing scenarios, show estimation errors of magnitude 0.16 m/s in velocity and 6.4 m in position at touchdown. These results vastly improve current state of the art and meet the requirements of future planetary exploration missions.
Tendon disorders of the hand.

After reading this article, the participant should be able to: 1. Make decisions on flexor tendon repair based on current evidence. 2. Perform some important tendon transfers after viewing Dr. Kozin's videos. 3. Inject local anesthesia for wide-awake flexor tendon repair after viewing the appropriate videos in the article. 4. Use relative motion extension splints for the postoperative management of extensor tendon injuries.



This article provides a practical, clinically useful overview of some of the current best techniques and evidence available to the plastic surgeon in the treatment of flexor and extensor tendon injuries, tendon transfers, trigger fingers, mallet fingers, boutonniere deformities, and De Quervain tenosynovitis. Twelve short movies and drawings emphasize important points of diagnosis and treatment of tendon disorders.
Polycystic Ovary Syndrome: Insights into the Therapeutic Approach with Inositols

Polycystic ovary syndrome (PCOS) is characterized by hormonal abnormalities that cause menstrual irregularity and reduce ovulation rate and fertility, associated to insulin resistance. Myo-inositol (cis-1,2,3,5-trans-4,6-cyclohexanehexol, MI) and D-chiro-inositol (cis-1,2,4-trans-3,5,6-cyclohexanehexol, DCI) represent promising treatments for PCOS, having shown some therapeutic benefits without substantial side effects. Because the use of inositols for treating PCOS is widespread, a deep understanding of this treatment option is needed, both in terms of potential mechanisms and efficacy. This review summarizes the current knowledge on the biological effects of MI and DCI and the results obtained from relevant intervention studies with inositols in PCOS. Based on the published results, both MI and DCI represent potential valid therapeutic approaches for the treatment of insulin resistance and its associated metabolic and reproductive disorders, such as those occurring in women affected by PCOS. Furthermore, the combination MI/DCI seems also effective and might be even superior to either inositol species alone. However, based on available data, a particular MI:DCI ratio to be administered to PCOS patients cannot be established. Further studies are then necessary to understand the real contents of MI or DCI uptaken by the ovary following oral administration in order to identify optimal doses and/or combination ratios.
Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review.

Suboptimal nutrition is a leading cause of poor health. Nutrition and policy science have advanced rapidly, creating confusion yet also providing powerful opportunities to reduce the adverse health and economic impacts of poor diets. This review considers the history, new evidence, controversies, and corresponding lessons for modern dietary and policy priorities for cardiovascular diseases, obesity, and diabetes mellitus. Major identified themes include the importance of evaluating the full diversity of diet-related risk pathways, not only blood lipids or obesity; focusing on foods and overall diet patterns, rather than single isolated nutrients; recognizing the complex influences of different foods on long-term weight regulation, rather than simply counting calories; and characterizing and implementing evidence-based strategies, including policy approaches, for lifestyle change. Evidence-informed dietary priorities include increased fruits, nonstarchy vegetables, nuts, legumes, fish, vegetable oils, yogurt, and minimally processed whole grains; and fewer red meats, processed (eg, sodium-preserved) meats, and foods rich in refined grains, starch, added sugars, salt, and trans fat. More investigation is needed on the cardiometabolic effects of phenolics, dairy fat, probiotics, fermentation, coffee, tea, cocoa, eggs, specific vegetable and tropical oils, vitamin D, individual fatty acids, and diet-microbiome interactions. Little evidence to date supports the cardiometabolic relevance of other popular priorities: eg, local, organic, grass-fed, farmed/wild, or non-genetically modified. Evidence-based personalized nutrition appears to depend more on nongenetic characteristics (eg, physical activity, abdominal adiposity, gender, socioeconomic status, culture) than genetic factors. Food choices must be strongly supported by clinical behavior change efforts, health systems reforms, novel technologies, and robust policy strategies targeting economic incentives, schools and workplaces, neighborhood environments, and the food system. Scientific advances provide crucial new insights on optimal targets and best practices to reduce the burdens of diet-related cardiometabolic diseases.
Patient self-management of chronic disease in primary care.

Patients with chronic conditions make day-to-day decisions about--self-manage--their illnesses. This reality introduces a new chronic disease paradigm: the patient-professional partnership, involving collaborative care and self-management education. Self-management education complements traditional patient education in supporting patients to live the best possible quality of life with their chronic condition. Whereas traditional patient education offers information and technical skills, self-management education teaches problem-solving skills. A central concept in self-management is self-efficacy--confidence to carry out a behavior necessary to reach a desired goal. Self-efficacy is enhanced when patients succeed in solving patient-identified problems. Evidence from controlled clinical trials suggests that (1) programs teaching self-management skills are more effective than information-only patient education in improving clinical outcomes; (2) in some circumstances, self-management education improves outcomes and can reduce costs for arthritis and probably for adult asthma patients; and (3) in initial studies, a self-management education program bringing together patients with a variety of chronic conditions may improve outcomes and reduce costs. Self-management education for chronic illness may soon become an integral part of high-quality primary care.
Design of a New Hysteretic PWM Controller for All Types of DC-to-DC Converters

A new control method using a hysteretic PWM controller for all types of converters and its proper design method are presented. The triangular voltage obtained from a simple RC network connected between comparator output and converter output is superimposed to the output voltage and as a feedback signal to a hysteretic comparator. Since the hysteretic PWM controller essentially has derivative characteristics and has no error amplifier, the presented method provides no steady-state error voltage on the output and excellent dynamic performances for the load current transient by choosing proper values of time constants in the RC network. Performances of the proposed controller are experimentally verified for the buck, buck-boost and boost converters.
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT

Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from overand under-translation issues [Tu et al., 2016], of which studies have become research hotspots in NMT. At present, these studies mainly apply the dominant automatic evaluation metrics, such as BLEU, to evaluate the overall translation quality with respect to both adequacy and fluency. However, they are unable to accurately measure the ability of NMT systems in dealing with the above-mentioned issues. In this paper, we propose two quantitative metrics, the Otem and Utem, to automatically evaluate the system performance in terms of overand under-translation respectively. Both metrics are based on the proportion of mismatched n-grams between gold reference and system translation. We evaluate both metrics by comparing their scores with human evaluations, where the values of Pearson Correlation Coefficient reveal their strong correlation. Moreover, in-depth analyses on various translation systems indicate some inconsistency between BLEU and our proposed metrics, highlighting the necessity and significance of our metrics.  Evaluation Metric Neural Machine Translation Overtransaltion Under-transaltion.
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

In this work we study the use of 3D hand poses to recognize first-person dynamic hand actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations. To obtain hand pose annotations, we used our own mo-cap system that automatically infers the 3D location of each of the 21 joints of a hand model via 6 magnetic sensors and inverse kinematics. Additionally, we recorded the 6D object poses and provide 3D object models for a subset of hand-object interaction sequences. To the best of our knowledge, this is the first benchmark that enables the study of first-person hand actions with the use of 3D hand poses. We present an extensive experimental evaluation of RGB-D and pose-based action recognition by 18 baselines/state-of-the-art approaches. The impact of using appearance features, poses, and their combinations are measured, and the different training/testing protocols are evaluated. Finally, we assess how ready the 3D hand pose estimation field is when hands are severely occluded by objects in egocentric views and its influence on action recognition. From the results, we see clear benefits of using hand pose as a cue for action recognition compared to other data modalities. Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks

Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies. We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.
Equal Partition Based Clustering Approach for Event Summarization in Videos

The rapid growth of video data demands both effective and efficient video summarization methods so that users are allowed to speedily browse and comprehend a large amount of video content. Hence, it is very challenging to store and access such audiovisual information in real time where an immense amount of recorded video content is rising within one second. In this paper we proposed an equal partition based clustering technique for summarizing the events in videos which can work better for real time applications (for e.g., surveillance video in various security systems). In clustering, the difficulty is to obtain the optimal set of clusters, which is gained by implementing Davies-Bouldin Index, a cluster validation technique which permits the users with free parameter based video summarization method for selecting the numbers of key–frames without incurring additional computational cost. The qualitative as well as quantitative evaluation is done in order to compare the performances of our proposed model and state-of-theart models. Experimental results on two benchmark datasets with various types of videos expose that the proposed method outperforms the state-of-the-art models with the best Precision and F–measure.
Recognizing the intended message of line graphs: methodology and applications

Information graphics (line graphs, bar charts, etc.) are common in popular media and periodicals. They are usually included in such documents to convey a message. This dissertation discusses the processing of one kind of information graphic, namely a line graph. It presents a learned model for segmenting a line graph into visually distinguishable trends and a Bayesian network inference model that hypothesizes the intended message of the graph based on communicative signals in the graphic. Besides recognizing the intended message of line graphs, this dissertation also presents a method for identifying the paragraph in the document that is most relevant to its information graphic. The research results provided by this dissertation can be used for several purposes: to give blind individuals access to information graphics in an article, to provide the basis for a longer summary of the graphic, to build a summary that captures both the article and its containing information graphics, and to indicate a graphic’s content when indexing it for retrieval in a digital library.
Bridgeless SEPIC PFC Rectifier With Reduced Components and Conduction Losses

In this paper, a new bridgeless single-ended primary inductance converter power-factor-correction rectifier is introduced. The proposed circuit provides lower conduction losses with reduced components simultaneously. In conventional PFC converters (continuous-conduction-mode boost converter), a voltage loop and a current loop are required for PFC. In the proposed converter, the control circuit is simplified, and no current loop is required while the converter operates in discontinuous conduction mode. Theoretical analysis and simulation results are provided to explain circuit operation. A prototype of the proposed converter is realized, and the results are presented. The measured efficiency shows 1% improvement in comparison to conventional SEPIC rectifier.
Digitised historical text: Does it have to be mediOCRe?

This paper reports on experiments to improve the Optical Character Recognition (ocr) quality of historical text as a preliminary step in text mining. We analyse the quality of ocred text compared to a gold standard and show how it can be improved by performing two automatic correction steps. We also demonstrate the impact this can have on named entity recognition in a preliminary extrinsic evaluation. This work was performed as part of the Trading Consequences project which is focussed on text mining of historical documents for the study of nineteenth century trade in the British Empire.
High efficiency bidirectional LLC resonant converter for 380V DC power distribution system using digital control scheme

A bidirectional full-bridge LLC resonant converter with a new symmetric LLC-type resonant network using a digital control scheme is proposed for a 380V dc power distribution system. This converter can operate under high power conversion efficiency since the symmetric LLC resonant network has zero voltage switching capability for primary power switches and soft commutation capability for output rectifiers. In addition, the proposed topology does not require any clamp circuits to reduce the voltage stress of the switches because the switch voltage of the primary inverting stage is confined by the input voltage, and that of the secondary rectifying stage is limited by the output voltage. Therefore, the power conversion efficiency of any directions is exactly the same as each other. In addition, intelligent digital control schemes such as dead-band control and switch transition control are proposed to regulate output voltage for any power flow directions. A prototype converter designed for a high-frequency galvanic isolation of 380V dc buses was developed with a rated power rating of 5kW using a digital signal processor to verify the performance of the proposed topology and algorithms. The maximum power conversion efficiency was 97.8% during bidirectional operations.