nils-herrmann/clean_scirepeval huggingface dataset:
Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure.

Atherosclerotic cardiovascular disease and malnutrition are widely recognized as leading causes of the increased morbidity and mortality observed in uremic patients. C-reactive protein (CRP), an acute-phase protein, is a predictor of cardiovascular mortality in nonrenal patient populations. In chronic renal failure (CRF), the prevalence of an acute-phase response has been associated with an increased mortality.



One hundred and nine predialysis patients (age 52 +/- 1 years) with terminal CRF (glomerular filtration rate 7 +/- 1 ml/min) were studied. By using noninvasive B-mode ultrasonography, the cross-sectional carotid intima-media area was calculated, and the presence or absence of carotid plaques was determined. Nutritional status was assessed by subjective global assessment (SGA), dual-energy x-ray absorptiometry (DXA), serum albumin, serum creatinine, serum urea, and 24-hour urine urea excretion. The presence of an inflammatory reaction was assessed by CRP, fibrinogen (N = 46), and tumor necrosis factor-alpha (TNF-alpha; N = 87). Lipid parameters, including Lp(a) and apo(a)-isoforms, as well as markers of oxidative stress (autoantibodies against oxidized low-density lipoprotein and vitamin E), were also determined.



Compared with healthy controls, CRF patients had an increased mean carotid intima-media area (18.3 +/- 0.6 vs. 13.2 +/- 0.7 mm2, P < 0.0001) and a higher prevalence of carotid plaques (72 vs. 32%, P = 0.001). The prevalence of malnutrition (SGA 2 to 4) was 44%, and 32% of all patients had an acute-phase response (CRP > or = 10 mg/liter). Malnourished patients had higher CRP levels (23 +/- 3 vs. 13 +/- 2 mg/liter, P < 0.01), elevated calculated intima-media area (20.2 +/- 0.8 vs. 16.9 +/- 0.7 mm2, P < 0.01) and a higher prevalence of carotid plaques (90 vs. 60%, P < 0.0001) compared with well-nourished patients. During stepwise multivariate analysis adjusting for age and gender, vitamin E (P < 0.05) and CRP (P < 0.05) remained associated with an increased intima-media area. The presence of carotid plaques was significantly associated with age (P < 0.001), log oxidized low-density lipoprotein (oxLDL; P < 0.01), and small apo(a) isoform size (P < 0.05) in a multivariate logistic regression model.



These results indicate that the rapidly developing atherosclerosis in advanced CRF appears to be caused by a synergism of different mechanisms, such as malnutrition, inflammation, oxidative stress, and genetic components. Apart from classic risk factors, low vitamin E levels and elevated CRP levels are associated with an increased intima-media area, whereas small molecular weight apo(a) isoforms and increased levels of oxLDL are associated with the presence of carotid plaques.
Enhancing Memory Retention by Increasing Alpha and Decreasing Beta Brainwaves using Music

Brain waves can aptly define the state of a person's mind. High activity and attention lead to dominant beta waves while relaxation and focus lead to dominant alpha waves in the brain. Alpha state of mind is ideal for learning and memory retention. In our experiment we aim to increase alpha waves and decrease beta waves in a person with the help of music to measure improvement in memory retention. Our hypothesis is that, when a person listens to music which causes relaxation, he is more likely to attain the alpha state of mind and enhance his memory retention ability. To verify this hypothesis, we conducted an experiment on 5 participants. The participants were asked to take a similar quiz twice, under different states of mind. During the experimentation process, the brain activity of the participants was recorded and analyzed using MUSE, an off-the-shelf device for brainwave capturing and analysis.
A monocycle pulse generator with variable capacitance diodes for radar target detecting application

The high amplitude pulses played a crucial role and is of paramount importance for obtaining a good radar range, especially when performing the task of penetrating thick and lossy building walls. The outputs of monocycle pulse generator affect the performance directly. Aimed at increasing the output power of the transmitter and improving the present pulse generator, in this paper, we present a novel monocycle pulse generator. This generator can operate well at the pulse repetition frequency (PRF) up to 5 MHz and generate a monocycle pulse with good symmetry and very low ringing. It combines wideband bipolar junction transistors (BJTs), linear power amplifier transistor, step recovery diode (SRD), variable capacitance diodes (VCDs) as a unique pulse compressor, LC high-pass filter as a differentiator and short-circuited microstrip line to generate the monocycle pulse directly. Measurement results show that the output peak-to-peak amplitude of the monocycle pulse is 25V and the duration is 450ps. These characters has significant potential in applications where long detection range and a high resolution are concerned, especially when penetrating thick and lossy medium for applications in through-wall and ground penetrating radar sensors.
Orbital Septorhaphy for the Correction of Baggy Upper and Lower Eyelids

Eyelid bags are the result of relaxation of lid structures like the skin, the orbicularis muscle, and mainly the septum, with subsequent protrusion or pseudo herniation of intraorbital fat contents. The logical treatment of baggy upper and lower eyelids should therefore include repositioning the herniated fat into the orbit and strengthening the attenuated septum in the form of a septorhaphy as a hernia repair. The preservation of orbital fat results in a more youthful appearance. The operative technique of the orbital septorhaphy is demonstrated for the upper and lower eyelid. A prospective series of 60 patients (50 upper and 90 lower blepharoplasties) with a maximum follow-up of 17 months were analyzed. Pleasing results were achieved in 56 patients. A partial recurrence was noted in 3 patients and widening of the palpebral fissure in 1 patient. Orbital septorhaphy for baggy eyelids is a rational, reliable procedure to correct the herniation of orbital fat in the upper and lower eyelids. Tightening of the orbicularis muscle and skin may be added as usual. The procedure is technically simple and without trauma to the orbital contents. The morbidity is minimal, the rate of complications is low, and the results are pleasing and reliable.
Interactive Image Segmentation with Latent Diversity

Interactive image segmentation is characterized by multimodality. When the user clicks on a door, do they intend to select the door or the whole house? We present an end-to-end learning approach to interactive image segmentation that tackles this ambiguity. Our architecture couples two convolutional networks. The first is trained to synthesize a diverse set of plausible segmentations that conform to the user's input. The second is trained to select among these. By selecting a single solution, our approach retains compatibility with existing interactive segmentation interfaces. By synthesizing multiple diverse solutions before selecting one, the architecture is given the representational power to explore the multimodal solution space. We show that the proposed approach outperforms existing methods for interactive image segmentation, including prior work that applied convolutional networks to this problem, while being much faster.
A 5 GHz Digital Fractional-N PLL Using a 1-bit Delta – Sigma Frequency-to-Digital Converter in 65 nm CMOS

A highly digital two-stage fractional-N phaselocked loop (PLL) architecture utilizing a first-order 1-bit frequency-to-digital converter (FDC) is proposed and implemented in a 65 nm CMOS process. Performance of the first-order 1-bit FDC is improved by using a phase interpolatorbased fractional divider that reduces phase quantizer input span and by using a multiplying delay-locked loop that increases its oversampling ratio. We also describe an analogy between a time-to-digital converter (TDC) and a FDC followed by an accumulator that allows us to leverage the TDC-based PLL analysis techniques to study the impact of FDC characteristics on FDC-based fractional-N PLL (FDCPLL) performance. Utilizing proposed techniques, a prototype PLL achieves 1 MHz bandwidth, −101.6 dBc/Hz in-band phase noise, and 1.22 psrms (1 kHz–40 MHz) jitter while generating 5.031 GHz output from 31.25 MHz reference clock input. For the same output frequency, the stand-alone second-stage fractional-N FDCPLL achieves 1 MHz bandwidth, −106.1 dBc/Hz in-band phase noise, and 403 fsrms jitter with a 500 MHz reference clock input. The two-stage PLL consumes 10.1 mW power from a 1 V supply, out of which 7.1 mW is consumed by the second-stage FDCPLL.
Comments-oriented document summarization: understanding documents with readers' feedback

Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multi-relation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.
Categorization Using Improved k-Nearest neighbour Algorithm

The quantity of text information published in Arabic language on the net requires the implementation of effective techniques for the extraction and classifying of relevant information contained in large corpus of texts. In this paper we presented an implementation of an enhanced k-NN Arabic text classifier. We apply the traditional k-NN and Naive Bayes from Weka Toolkit for comparison purpose. Our proposed modified k-NN algorithm features an improved decision rule to skip the classes that are less similar and identify the right class from k nearest neighbours which increases the accuracy. The study evaluates the improved decision rule technique using the standard of recall, precision and f-measure as the basis of comparison. We concluded that the effectiveness of the proposed classifier is promising and outperforms the classical k-NN classifier.
Suicide Note Classification Using Natural Language Processing: A Content Analysis.

Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.
Alcohol's role in gastrointestinal tract disorders.

When alcohol is consumed, the alcoholic beverages first pass through the various segments of the gastrointestinal (GI) tract. Accordingly, alcohol may interfere with the structure as well as the function of GI-tract segments. For example, alcohol can impair the function of the muscles separating the esophagus from the stomach, thereby favoring the occurrence of heartburn. Alcohol-induced damage to the mucosal lining of the esophagus also increases the risk of esophageal cancer. In the stomach, alcohol interferes with gastric acid secretion and with the activity of the muscles surrounding the stomach. Similarly, alcohol may impair the muscle movement in the small and large intestines, contributing to the diarrhea frequently observed in alcoholics. Moreover, alcohol inhibits the absorption of nutrients in the small intestine and increases the transport of toxins across the intestinal walls, effects that may contribute to the development of alcohol-related damage to the liver and other organs.
Phase-sensitive observation of a spin-orbital Mott state in Sr2IrO4.

Measurement of the quantum-mechanical phase in quantum matter provides the most direct manifestation of the underlying abstract physics. We used resonant x-ray scattering to probe the relative phases of constituent atomic orbitals in an electronic wave function, which uncovers the unconventional Mott insulating state induced by relativistic spin-orbit coupling in the layered 5d transition metal oxide Sr2IrO4. A selection rule based on intra-atomic interference effects establishes a complex spin-orbital state represented by an effective total angular momentum = 1/2 quantum number, the phase of which can lead to a quantum topological state of matter.
Diagnosis and treatment of acute uncomplicated cystitis.

Urinary tract infections are the most common bacterial infections in women. Most urinary tract infections are acute uncomplicated cystitis. Identifiers of acute uncomplicated cystitis are frequency and dysuria in an immunocompetent woman of childbearing age who has no comorbidities or urologic abnormalities. Physical examination is typically normal or positive for suprapubic tenderness. A urinalysis, but not urine culture, is recommended in making the diagnosis. Guidelines recommend three options for first-line treatment of acute uncomplicated cystitis: fosfomycin, nitrofurantoin, and trimethoprim/sulfamethoxazole (in regions where the prevalence of Escherichia coli resistance does not exceed 20 percent). Beta-lactam antibiotics, amoxicillin/clavulanate, cefaclor, cefdinir, and cefpodoxime are not recommended for initial treatment because of concerns about resistance. Urine cultures are recommended in women with suspected pyelonephritis, women with symptoms that do not resolve or that recur within two to four weeks after completing treatment, and women who present with atypical symptoms.
A CMOS analog front-end IC for portable EEG/ECG monitoring applications

A new digital programmable CMOS analog front-end (AFE) IC for measuring electroencephalograph or electrocardiogram signals in a portable instrumentation design approach is presented. This includes a new high-performance rail-to-rail instrumentation amplifier (IA) dedicated to the low-power AFE IC. The measurement results have shown that the proposed biomedical AFE IC, with a die size of 4.81 mm/sup 2/, achieves a maximum stable ac gain of 10 000 V/V, input-referred noise of 0.86 /spl mu/ V/sub rms/ (0.3 Hz-150 Hz), common-mode rejection ratio of at least 115 dB (0-1 kHz), input-referred dc offset of less than 60 /spl mu/V, input common mode range from -1.5 V to 1.3 V, and current drain of 485 /spl mu/A (excluding the power dissipation of external clock oscillator) at a /spl plusmn/1.5-V supply using a standard 0.5-/spl mu/m CMOS process technology.
Chinese-English mixed text normalization

Along with the expansion of globalization, multilingualism has become a popular social phenomenon. More than one language may occur in the context of a single conversation. This phenomenon is also prevalent in China. A huge variety of informal Chinese texts contain English words, especially in emails, social media, and other user generated informal contents. Since most of the existing natural language processing algorithms were designed for processing monolingual information, mixed multilingual texts cannot be well analyzed by them. Hence, it is of critical importance to preprocess the mixed texts before applying other tasks. In this paper, we firstly analyze the phenomena of mixed usage of Chinese and English in Chinese microblogs. Then, we detail the proposed two-stage method for normalizing mixed texts. We propose to use a noisy channel approach to translate in-vocabulary words into Chinese. For better incorporating the historical information of users, we introduce a novel user aware neural network language model. For the out-of-vocabulary words (such as pronunciations, informal expressions and et al.), we propose to use a graph-based unsupervised method to categorize them. Experimental results on a manually annotated microblog dataset demonstrate the effectiveness of the proposed method. We also evaluate three natural language parsers with and without using the proposed method as the preprocessing step. From the results, we can see that the proposed method can significantly benefit other NLP tasks in processing mixed text.
Data set for fall events and daily activities from inertial sensors

Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data sets.
 This paper presents a description of the data set and the experimental protocols designed by the authors for the simulation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection algorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%.
An integrator circuit using differential difference current conveyor transconductance amplifier

An integrator circuit is presented in this paper that has used Differntial Difference Current Conveyor Transconductance Amplifier (DDCCTA). It has one DDCCTA and one passive component. It has been realized only with first order low pass response. The operation of the circuit has been observed and enforced at a supply voltage of ± 1.8V (bias current 50pA) using cadence and the model parameters of gpdk 180nm CMOS technology. The worthy of the proposed circuit has been test checked using DDCCTA and further tested for its efficiency on a laboratory breadboard. In this commercially available AD844AN and LM13600 ICs are used. Further, the circuit presented in this paper is impermeable to noise, possessing low voltage and insensitive to temperature.
Scalable systems for early fault detection in wind turbines : A data driven approach

The world has changed dramatically for wind farm operators and service providers in the last decade. Organizations whose turbine portfolios was counted in 10-100s ten years ago are now managing large scale operation and service programs for fleet sizes well above one thousand turbines. A big challenge such organizations now face is the question of how the massive amount of operational data that are generated by large fleets are effectively managed and how value is gained from the data. A particular hard challenge is the handling of data streams collected from advanced condition monitoring systems. These data are highly complex and typically require expert knowledge to interpret correctly resulting in poor scalability when moving to large Operation and Maintenance (O&M) platforms.
A Passive Filter Building Block for Input or Output Current Ripple Cancellation in a Power Converter

This paper analyzes a coupled inductor based LL-LC network as a building block to achieve input or output current ripple cancellation in power converters. The proposed current ripple cancellation technique works on the principle of inductance matching. It also provides reduced current stress on the switching devices of the converter compared with the other current ripple cancellation networks discussed in the literature. The current ripple cancellation technique is verified in achieving input and output current ripple minimization in different converters. The effect of inductance mismatch, inductor and capacitor tolerances, and circuit parasitic is also studied. Some of the published research works are demonstrated to be a special case of the proposed idea. The effectiveness of the proposed method is validated by demonstrating the zero-ripple input current using a 31 V dc to 60 V dc, 240 W boost converter prototype and a 31 V dc to 115 V (rms) ac, 320 W current-fed-switched inverter prototype, and the zero-ripple output current using a 62 V to 42 V dc, 450 W buck converter prototype.
1 IMPLEMENTING TOTAL PRODUCTIVE MAINTENANCE IN NIGERIAN

Remarkable improvements have occurred recently in the maintenance management of physical assets and productive systems, so that less wastages of energy and resources occur. The requirement for optimal preventive maintenance using, for instance, justin-time (JIT) and total quality-management (TQM) techniques has given rise to what has been called the total productive-maintenance (TPM) approach. This study explores the ways in which Nigerian manufacturing industries can implement TPM as a strategy and culture for improving its performance and suggests self-auditing and bench-marking as desirable prerequisites before TPM implementation. ABBREVIATIONS (commonly employed and used in this text) BSC Balanced score-card CBM Condition-based maintenance FMEA Failure modes and effects analysis JIT Just-in-time LCC Life-cycle cost MLDT Mean logistic down-time Mt Maintainability MTBF Mean time between failures (=1/λ) MTTR Mean time-to-repair (=τ=1/μ) MWT Mean waiting-time OEE Overall equipment’s effectiveness PLC Programmable logic-controller PM Preventive maintenance RCM Reliability-centred maintenance RTF Run-to-failure SMED Single minute exchange of die SPC Statistical process-control TPM Total productive-maintenance TPS Team problem-solving TQM Total quality-management ZQC Zero quality-control