nils-herrmann/clean_scirepeval huggingface dataset:
Compact Double-Sided Decoupled Coils-Based WPT Systems for High-Power Applications: Analysis, Design, and Experimental Verification

Wireless power transfer (WPT) system is a practical and promising way for charging electric vehicles due to its security, convenience, and reliability. The requirement for high-power wireless charging is on the rise, but implementing such a WPT system has been a challenge because of the constraints of the power semiconductors and the installation space limitation at the bottom of the vehicle. In this paper, bipolar coils and unipolar coils are integrated into the transmitting side and the receiving side to make the magnetic coupler more compact while delivering high power. The same-side coils are naturally decoupled; therefore, there is no magnetic coupling between the same-side coils. The circuit model of the proposed WPT system using double-sided LCC compensations is presented. Finite-element analysis tool ANSYS MAXWELL is adopted to simulate and design the magnetic coupler. Finally, an experimental setup is constructed to evaluate the proposed WPT system. The proposed WPT system achieved the dc–dc efficiency at 94.07% while delivering 4.73 kW to the load with a vertical air gap of 150 mm.
Comparing Machine Learning Approaches for Table Recognition in Historical Register Books

We present in this paper experiments on Table Recognition in hand-written register books. We first explain how the problem of row and column detection is modelled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archives of the Diocese of Passau. With an F-1 score of 89, both methods provide a quality which allows for Information Extraction. Software and dataset are open source/data.
Effects of strength, explosive and plyometric training on energy cost of running in ultra-endurance athletes.

The aim of the present study was to evaluate the effects of a 12-week home-based strength, explosive and plyometric (SEP) training on the cost of running (Cr) in well-trained ultra-marathoners and to assess the main mechanical parameters affecting changes in Cr. Twenty-five male runners (38.2 ± 7.1 years; body mass index: 23.0 ± 1.1 kg·m-2; V˙O2max: 55.4 ± 4.0 mlO2·kg-1·min-1) were divided into an exercise (EG = 13) and control group (CG = 12). Before and after a 12-week SEP training, Cr, spring-mass model parameters at four speeds (8, 10, 12, 14 km·h-1) were calculated and maximal muscle power (MMP) of the lower limbs was measured. In EG, Cr decreased significantly (p < .05) at all tested running speeds (-6.4 ± 6.5% at 8 km·h-1; -3.5 ± 5.3% at 10 km·h-1; -4.0 ± 5.5% at 12 km·h-1; -3.2 ± 4.5% at 14 km·h-1), contact time (tc) increased at 8, 10 and 12 km·h-1 by mean +4.4 ± 0.1% and ta decreased by -25.6 ± 0.1% at 8 km·h-1 (p < .05). Further, inverse relationships between changes in Cr and MMP at 10 (p = .013; r = -0.67) and 12 km·h-1 (p < .001; r = -0.86) were shown. Conversely, no differences were detected in the CG in any of the studied parameters. Thus, 12-week SEP training programme lower the Cr in well-trained ultra-marathoners at submaximal speeds. Increased tc and an inverse relationship between changes in Cr and changes in MMP could be in part explain the decreased Cr. Thus, adding at least three sessions per week of SEP exercises in the normal endurance-training programme may decrease the Cr.
Development of a cloud-based automatic irrigation system: A case study on strawberry cultivation

High increment rate of human population brings about the necessity of efficient utilization of world resources. One way of achieving this is providing the plants with the optimum amount of water at the right time in agricultural applications. In this paper, a cloud-based drip irrigation system, which determines the amount of irrigation water and performs the irrigation process automatically, is presented. Basically, water level in a Class A pan is continuously measured via a water level sensor and duration of irrigation is calculated using total amount of level decrement in a given time interval. The irrigation process is initialized by powering solenoid valves through a microcontroller board. To measure the environmental quantities such as temperature, humidity and pressure, an extra sensor is included in the system. A GSM/GPRS module enables internet connection of the system and all the sensor data as well as system status data are recorded in a cloud server. Furthermore, an accompanying Android application is developed to monitor the instantaneous status of the system. The system is tested on a strawberry field in a greenhouse. Currently, it is active for about four months and first observations imply that the system is capable of successfully perform the irrigation task.
Optical and Electronic Packaging Processes for Silicon Photonic Systems

Fiber optic interconnection processes and hybrid integration of electronic devices for high speed Si photonic systems are presented. Thermal effects arising from these hybrid integration processes are also investigated. An overview of ePIXfab which offers affordable access to an advanced Si photonic foundry service is also presented. This includes the presentation of fundamental photonic packaging design rules which can greatly reduce the time and cost associated with the development of complex Si photonic devices.
Implications of Placebo and Nocebo Effects for Clinical Practice: Expert Consensus.

Placebo and nocebo effects occur in clinical or laboratory medical contexts after administration of an inert treatment or as part of active treatments and are due to psychobiological mechanisms such as expectancies of the patient. Placebo and nocebo studies have evolved from predominantly methodological research into a far-reaching interdisciplinary field that is unravelling the neurobiological, behavioural and clinical underpinnings of these phenomena in a broad variety of medical conditions. As a consequence, there is an increasing demand from health professionals to develop expert recommendations about evidence-based and ethical use of placebo and nocebo effects for clinical practice.



A survey and interdisciplinary expert meeting by invitation was organized as part of the 1st Society for Interdisciplinary Placebo Studies (SIPS) conference in 2017. Twenty-nine internationally recognized placebo researchers participated.



There was consensus that maximizing placebo effects and minimizing nocebo effects should lead to better treatment outcomes with fewer side effects. Experts particularly agreed on the importance of informing patients about placebo and nocebo effects and training health professionals in patient-clinician communication to maximize placebo and minimize nocebo effects.



The current paper forms a first step towards developing evidence-based and ethical recommendations about the implications of placebo and nocebo research for medical practice, based on the current state of evidence and the consensus of experts. Future research might focus on how to implement these recommendations, including how to optimize conditions for educating patients about placebo and nocebo effects and providing training for the implementation in clinical practice.
A virtual coach for upper-extremity myoelectric prosthetic rehabilitation

Successful prosthesis use is largely dependent on providing patients with high-quality, individualized pre-prosthetic training, ideally completed under the supervision of a trained therapist. Computer-based training systems, or ‘virtual coaches,’ designed to augment rehabilitation training protocols are an emerging area of research and could be a convenient and low-cost alternative to supplement the therapy received by the patient. In this contribution we completed an iterative needs focus group to determine important design elements required for an effective virtual coach software package.
Personalized Context-Aware Point of Interest Recommendation

Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). In this article, we propose a probabilistic model to find the mapping between user-annotated tags and locations’ taste keywords. Furthermore, we introduce a dataset on locations’ contextual appropriateness and demonstrate its usefulness in predicting the contextual relevance of locations. We investigate four approaches to use our proposed mapping for addressing the data sparsity problem: one model to reduce the dimensionality of location taste keywords and three models to predict user tags for a new location. Moreover, we present different scores calculated from multiple LBSNs and show how we incorporate new information from the mapping into a POI recommendation approach. Then, the computed scores are integrated using learning to rank techniques. The experiments on two TREC datasets show the effectiveness of our approach, beating state-of-the-art methods.
RGD liposome-protamine-siRNA (LPR) nanoparticles targeting PAX3-FOXO1 for alveolar rhabdomyosarcoma therapy.

Alveolar rhabdomyosarcoma (ARMS) are aggressive soft tissue tumors harboring specific fusion transcripts, notably PAX3-FOXO1 (P3F). Current therapy concepts result in unsatisfactory survival rates making the search for innovative approaches necessary: targeting PAX3-FOXO1 could be a promising strategy. In this study, we developed integrin receptor-targeted Lipid-Protamine-siRNA (LPR) nanoparticles using the RGD peptide and validated target specificity as well as their post-silencing effects. We demonstrate that RGD-LPRs are specific to ARMS in vitro and in vivo. Loaded with siRNA directed against the breakpoint of P3F, these particles efficiently down regulated the fusion transcript and inhibited cell proliferation, but did not induce substantial apoptosis. In a xenograft ARMS model, LPR nanoparticles targeting P3F showed statistically significant tumor growth delay as well as inhibition of tumor initiation when injected in parallel with the tumor cells. These findings suggest that RGD-LPR targeting P3F are promising to be highly effective in the setting of minimal residual disease for ARMS.
Torque Ripple Minimization Using Online Estimation of the Stator Resistances With Consideration of Magnetic Saturation

The tolerances and limitations in design and manufacturing of both electrical motors and inverters lead to parasitic resistances in the phases. Therefore, the phase resistances are asymmetric, which causes an asymmetry among the phase currents and a resulting increase in torque ripple. This paper proposes a novel scheme for torque ripple minimization using online estimation of unbalanced stator resistances of a permanent-magnet synchronous motor. The identifiability of the stator resistances in a nonlinear model is investigated by employing a sensitivity analysis. The phase resistances are estimated by recursive least squares in the rotating reference frame in real time. Using the estimated resistances, torque ripple reduction is achieved by a compensation scheme in the stationary reference frame. The effectiveness of the proposed scheme is verified in both simulation and test-bench experiments. Results with and without consideration of magnetic saturation are compared.
Analysis of transmission lines matching using quarter-wave transformer

Due to conformal capability, research on transmission lines has received much attention lately. Many studies have been reported in the last decade in which transmission lines have been analyzed extensively using various techniques. It is well known that transmission lines are used for transmission of information, but in this case the main aim is to deliver information from generator to receiver with low attenuation. To achieve this, the load should be matched to the characteristic impedance of the line, meaning that the wave coefficient should be near 1 (one). One of the most important methods for line matching is through quarter-wavelength line (quarter-wave transformer). Analysis of transmission lines using numerical methods is difficult because of any possible error that can occur. Therefore, the best solution in this case would be the use of any software package which is designed for analysis of transmission lines. In this paper we will use Sonet software which is generally used for the analysis of planar lines.
AOT-controlled dual-mode AVP buck regulator with AEAF mechanism

A novel adaptive voltage positioning (AVP) buck regulator using adaptive on-time (AOT) control targeted for applications with low-ESR output capacitors is proposed. In this work, AOT control is adapted to keep the system's switching frequency quasi-fixed or independent of the input supply voltage and the AVP mechanism is realized without the need to use conventional error amplifier compensator or extra current-sensing circuit. For ensuring the system's switching frequency not entering the range of acoustic frequency at light load, an AEAF (avoid entering acoustic frequency) circuit is also proposed. For comparison purpose, the implemented buck regulator can be set to operate under AVP or non-AVP mode. This work has been fabricated and verified with a standard 0.18μm CMOS technology. Experimental results show excellent transient recovery time of 4μs (under AVP mode), ±0.11% switching frequency variation (for the specified input voltage range), and 91% peak conversion efficiency.
New rate-2 STBC design for 2 TX with reduced-complexity maximum likelihood decoding

We propose a new full-rate space-time block code (STBC) for two transmit antennas which can be designed to achieve maximum diversity or maximum capacity while enjoying optimized coding gain and reduced-complexity maximum-likelihood (ML) decoding. The maximum transmit diversity (MTD) construction provides a diversity order of 2Nr for any number of receive antennas Nr at the cost of channel capacity loss. The maximum channel capacity (MCC) construction preserves the mutual information between the transmit and the received vectors while sacrificing diversity. The system designer can switch between the two constructions through a simple parameter change based on the operating signal-to-noise ratio (SNR), signal constellation size and number of receive antennas. Thanks to their special algebraic structure, both constructions enjoy low-complexity ML decoding proportional to the square of the signal constellation size making them attractive alternatives to existing full-diversity full-rate STBCs in ,  which have high ML decoding complexity proportional to the fourth order of the signal constellation size. Furthermore, we design a differential transmission scheme for our proposed STBC, derive the exact ML differential decoding rule, and compare its performance with competitive schemes. Finally, we investigate transceiver design and performance of our proposed STBC in spatial multiple-access scenarios and over frequency-selective channels.
An Entropy-Based Subspace Clustering Algorithm for Categorical Data

The interest in attribute weighting for soft subspace clustering have been increasing in the last years. However, most of the proposed approaches are designed for dealing only with numeric data. In this paper, our focus is on soft subspace clustering for categorical data. In soft subspace clustering, the attribute weighting approach plays a crucial role. Due to this, we propose an entropy-based approach for measuring the relevance of each categorical attribute in each cluster. Besides that, we propose the EBK-modes (entropy-based k-modes), an extension of the basic k-modes that uses our approach for attribute weighting. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four state-of-the-art algorithms, using three well-known evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the EBK-modes outperforms the algorithms that were considered in the evaluation, regarding the considered metrics.
Occipito-atlanto-axial Hypermobility : Clinical Features and Dynamic Analysis of Cranial Settling and Posterior Gliding of Occipital Condyle : Part 1 : Findings in Patients with Hereditary Disorders of Connective Tissue and Ehlers-Danlos Syndrome

To investigate hereditary disorders of connective tissue(HDCT)and Ehlers-Danlos syndromes(EDS)that can present with lower brain stem symptoms attributable to occipito-atlanto-axial hypermobility and cranial settling, and relationship to Chiari malformation typeI(CMI). The diagnostic criteria for EDS and related HDCT were prospectively met by 155 patients. Osseous structures comprising the craniocervical junction were investigated morphometrically using reconstructed 2D-CT and plain x-ray images in 135 patients with HDCT・EDS and the results were compared to those in patients with normal controls(n=55). There were 124 cases(80%) in HDCT・EDS of CMI. The diagnostic features of HDCT・EDS with CMI had a greater incidence of lower brain stem symptoms and signs. The measured distances of the basion-dens interval(BDI), basion-atlas interval(BAI), atlas-dens interval (ADI), dens-atlas interval(DAI), clivus-atlas angle(CAA), clivus-axis angle(CXA), and atlas-axis angle(AXA)were the same in the supine and upright positions in normal controls. There was reduction of the BDI(3.3 mm), enlargement of the BAI(2.8 mm), and reduction of the CXA(10.8°), CAA (5.8°, p<0.001), and AXA(12.3°)upon assumption of the upright position. These changes were reducible by cervical traction. Morphometric evidence of cranial settling, posterior gliding of the occipital condyles in the HDCT・EDS cohort suggests hypermobility of the atlanto-occipital and atlanto-axial joints. This hypermobility induces more prominent brain stem symptoms in patients associated with CMI. The patients with CMI have greater hypermobility of occipito-atlanto-axial joints.
Chapter 2 Accelerating Lloyd ’ s Algorithm for k-Means Clustering

The k-means clustering algorithm, a staple of data mining and unsupervised learning, is popular because it is simple to implement, fast, easily parallelized, and offers intuitive results. Lloyd’s algorithm is the standard batch, hill-climbing approach for minimizing the k-means optimization criterion. It spends a vast majority of its time computing distances between each of the k cluster centers and the n data points. It turns out that much of this work is unnecessary, because points usually stay in the same clusters after the first few iterations. In the last decade researchers have developed a number of optimizations to speed up Lloyd’s algorithm for both lowand high-dimensional data. In this chapter we survey some of these optimizations and present new ones. In particular we focus on those which avoid distance calculations by the triangle inequality. By caching known distances and updating them efficiently with the triangle inequality, these algorithms can provably avoid many unnecessary distance calculations. All the optimizations examined produce the same results as Lloyd’s algorithm given the same input and initialization, so are suitable as drop-in replacements. These new algorithms can run many times faster and compute far fewer distances than the standard unoptimized implementation. In our experiments, it is common to see speedups of over 30–50x compared to Lloyd’s algorithm. We examine the trade-offs for using these methods with respect to the number of examples n, dimensions d , clusters k, and structure of the data.
Is there Really a Need for Using NLP to Elicit Requirements? A Benchmarking Study to Assess Scalability of Manual Analysis

The growing interest of the requirements engineering (RE) community to elicit user requirements from large amounts of available online user feedback about software-intensive products resulted in identification of such data as a sensible source of user requirements. Some researchers proposed automation approaches for extracting the requirements from user reviews. Although there is a common assumption that manually analyzing large amounts of user reviews is challenging, no benchmarking has yet been performed that compares the manual and the automated approaches conderning their efficiency. We performed an expert-based manual analysis of 4,006 sentences from typical user feedback contents and formats and measured the amount of time required for each step. Then, we conducted an automated analysis of the same dataset to identify the degree to which automation makes the analysis more scalable. We found that a manual analysis indeed does not scale well and that an automated analysis is many times faster, and scales well to increasing numbers of user reviews.
A MEMS Dielectric Affinity Glucose Biosensor

Continuous glucose monitoring (CGM) sensors based on affinity detection are desirable for long-term and stable glucose management. However, most affinity sensors contain mechanical moving structures and complex design in sensor actuation and signal readout, limiting their reliability in subcutaneously implantable glucose detection. We have previously demonstrated a proof-of-concept dielectric glucose sensor that measured pre-mixed glucose-sensitive polymer solutions at various glucose concentrations. This sensor features simplicity in sensor design, and possesses high specificity and accuracy in glucose detection. However, lack of glucose diffusion passage, this device is unable to fulfill real-time in-vivo monitoring. As a major improvement to this device, we present in this paper a fully implantable MEMS dielectric affinity glucose biosensor that contains a perforated electrode embedded in a suspended diaphragm. This capacitive-based sensor contains no moving parts, and enables glucose diffusion and real-time monitoring. The experimental results indicate that this sensor can detect glucose solutions at physiological concentrations and possesses good reversibility and reliability. This sensor has a time constant to glucose concentration change at approximately 3 min, which is comparable to commercial systems. The sensor has potential applications in fully implantable CGM that require excellent long-term stability and reliability.
Improvement of wheat seed vitality by dielectric barrier discharge plasma treatment.

Influences of discharge voltage on wheat seed vitality were investigated in a dielectric barrier discharge (DBD) plasma system at atmospheric pressure and temperature. Six different treatments were designed, and their discharge voltages were 0.0, 9.0, 11.0, 13.0, 15.0, and 17.0 kV, respectively. Fifty seeds were exposed to the DBD plasma atmosphere with an air flow rate of 1.5 L min-1 for 4 min in each treatment, and then the DBD plasma-treated seeds were prepared for germination in several Petri dishes. Each treatment was repeated three times. Germination indexes, growth indexes, surface topography, water uptake, permeability, and α-amylase activity were measured. DBD plasma treatment at appropriate energy levels had positive effects on wheat seed germination and seedling growth. The germination potential, germination index, and vigor index significantly increased by 31.4%, 13.9%, and 54.6% after DBD treatment at 11.0 kV, respectively, in comparison to the control. Shoot length, root length, dry weight, and fresh weight also significantly increased after the DBD plasma treatment. The seed coat was softened and cracks were observed, systematization of the protein was strengthened, and amount of free starch grain increased after the DBD plasma treatment. Water uptake, relative electroconductivity, soluble protein, and α-amylase activity of the wheat seed were also significantly improved after the DBD plasma treatment. Roles of active species and ultraviolet radiation generated in the DBD plasma process in wheat seed germination and seedling growth are proposed. Bioelectromagnetics. 39:120-131, 2018.