nils-herrmann/clean_scirepeval huggingface dataset:
A biomechanical investigation of ankle injury under excessive external foot rotation in the human cadaver.

Numerous studies on the mechanisms of ankle injury deal with injuries to the syndesmosis and anterior ligamentous structures but a previous sectioning study also describes the important role of the posterior talofibular ligament (PTaFL) in the ankle's resistance to external rotation of the foot. It was hypothesized that failure level external rotation of the foot would lead to injury of the PTaFL. Ten ankles were tested by externally rotating the foot until gross injury. Two different frequencies of rotation were used in this study, 0.5 Hz and 2 Hz. The mean failure torque of the ankles was 69.5+/-11.7 Nm with a mean failure angle of 40.7+/-7.3 degrees . No effects of rotation frequency or flexion angle were noted. The most commonly injured structure was the PTaFL. Visible damage to the syndesmosis only occurred in combination with fibular fracture in these experiments. The constraint of the subtalar joint in the current study may have affected the mechanics of the foot and led to the resultant strain in the PTaFL. In the real world, talus rotations may be affected by athletic footwear that may influence the location and potential for an ankle injury under external rotation of the foot.
Optimal Surface Smoothing as Filter Design

For a number of computational purposes, including visualization, smooth surfaces are approximated by polyhedral surfaces. An inherent problem of these approximation algorithms is that the resulting polyhedral surfaces appear faceted. A signal processing approach to smoothing polyhedral surfaces was recently introduced [10, 11]. Within this framework surface smoothing corresponds to low-pass filtering. In this paper we look at the filter design problem in more detail. We analyze the stability properties of the low-pass filter described in [10, 11], and show how to minimize its running time. Then we show that most classical techniques used to design finite impulse response (FIR) digital filters can also be used to design significantly faster smoothing filters. Finally, we describe an algorithm to estimate the power spectrum of a signal, and use it to evaluate the performance of the different filter design techniques described in the paper.
Measurement Method and Empirical Research on the Sustainable Development Capability of a Regional Industrial System Based on Ecological Niche Theory in China

From the analytical view of a recycling economy, the regional system achieves the goal of sustainable development through improving resource utilization efficiency, reducing energy consumption and ameliorating the quality of water and air. The regional economic system’s potential for sustainable development is significantly influenced by regional industrial operational efficiency, which measures the cost of ecology, environment, energy and resources accompanying the economic growth. It is vital for national and regional governments to accelerate harmonious development between products of industrial department, consumption of energy and pollutants discharged. Under the guidance of ecological niche theory and recycling economy theory, the theoretical analysis on efficient relations between regional industrial growth, energy consumption, resources utilization and environmental carrying capacity has been carried out from horizontal and vertical respects. Industrial operational efficiency and the sensitivity coefficient in response to the change of every input and output index can be calculated and critical factors, which restrict sustainable development capability, can be found out so that quantitative references could be provided for administrative decisions. As for the measurement method, a super efficiency mixed data envelopment OPEN ACCESS Sustainability 2014, 6 8486 analysis model, which wipes off self-limited condition and either contains both meeting cone characteristic indexes or not, has been established and applied.  On the basis of further analysis, an adjustment strategy can be constituted to improve the capability for sustainable development.
Hyponatremia and hypernatremia in the newborn: in medio stat virtus.

Hyponatremia and hypernatremia are complex clinical problems that occur frequently in full term newborns and in preterm infants admitted to the Neonatal Intensive Care Unit (NICU) although their real frequency and etiology are incompletely known. Pathogenetic mechanisms and clinical timing of hypo-hypernatremia are well known in adult people whereas in the newborn is less clear how and when hypo-hypernatremia could alter cerebral osmotic equilibrium and after how long time brain cells adapt themselves to the new hypo-hypertonic environment. Aim of this review is to present a practical approach and management of hypo-hypernatremia in newborns, especially in preterms.
Offline EEG-based driver drowsiness estimation using enhanced batch-mode active learning (EBMAL) for regression

There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.
A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.
Three-Dimensional Bipedal Walking Control Based on Divergent Component of Motion

In this paper, the concept of divergent component of motion (DCM, also called “Capture Point”) is extended to 3-D. We introduce the “Enhanced Centroidal Moment Pivot point” (eCMP) and the “Virtual Repellent Point” (VRP), which allow for the encoding of both direction and magnitude of the external forces and the total force (i.e., external plus gravitational forces) acting on the robot. Based on eCMP, VRP, and DCM, we present methods for real-time planning and tracking control of DCM trajectories in 3-D. The basic DCM trajectory generator is extended to produce continuous leg force profiles and to facilitate the use of toe-off motion during double support. The robustness of the proposed control framework is thoroughly examined, and its capabilities are verified both in simulations and experiments.
Natively Unfolded Nucleoporins Gate Protein Diffusion across the Nuclear Pore Complex

Nuclear pore complexes (NPCs) form aqueous conduits in the nuclear envelope and gate the diffusion of large proteins between the cytoplasm and nucleoplasm. NPC proteins (nucleoporins) that contain phenylalanine-glycine motifs in filamentous, natively unfolded domains (FG domains) line the diffusion conduit of the NPC, but their role in the size-selective barrier is unclear. We show that deletion of individual FG domains in yeast relaxes the NPC permeability barrier. At the molecular level, the FG domains of five nucleoporins anchored at the NPC center form a cohesive meshwork of filaments through hydrophobic interactions, which involve phenylalanines in FG motifs and are dispersed by aliphatic alcohols. In contrast, the FG domains of four peripherally anchored nucleoporins are generally noncohesive. The results support a two-gate model of NPC architecture featuring a central diffusion gate formed by a meshwork of cohesive FG nucleoporin filaments and a peripheral gate formed by repulsive FG nucleoporin filaments.
TLRs, future potential therapeutic targets for RA.

Toll like receptors (TLR)s have a central role in regulating innate immunity and in the last decade studies have begun to reveal their significance in potentiating autoimmune diseases such as rheumatoid arthritis (RA). Earlier investigations have highlighted the importance of TLR2 and TLR4 function in RA pathogenesis. In this review, we discuss the newer data that indicate roles for TLR5 and TLR7 in RA and its preclinical models. We evaluate the pathogenicity of TLRs in RA myeloid cells, synovial tissue fibroblasts, T cells, osteoclast progenitor cells and endothelial cells. These observations establish that ligation of TLRs can transform RA myeloid cells into M1 macrophages and that the inflammatory factors secreted from M1 and RA synovial tissue fibroblasts participate in TH-17 cell development. From the investigations conducted in RA preclinical models, we conclude that TLR-mediated inflammation can result in osteoclastic bone erosion by interconnecting the myeloid and TH-17 cell response to joint vascularization. In light of emerging unique aspects of TLR function, we summarize the novel approaches that are being tested to impair TLR activation in RA patients.
InAlGaN/GaN-HEMT device technologies for W-band high-power amplifier

We demonstrated an excellent output power (Pout) density performance using a novel InAlGaN/GaN-HEMT with an 80-nm gate for a W-band amplifier. To eliminate current collapse, a unique double-layer silicon nitride (SiN) passivation film with oxidation resistance was adopted. The developed discrete GaN-HEMT achieved a Pout density of 3.0 W/mm at 96 GHz, and we fabricated W-band amplifier MMIC using the air-bridge wiring technology. The Pout density of the MMIC reached 3.6 W/mm at 86 GHz. We proved the potential of the developed InAlGaN/GaN-HEMT experimentally using our unique device technology. With the aim of future applications, we developed a novel wiring-inter-layer technology. It consists of a cavity structure and a moisture-resistant dielectric film technology. We demonstrated excellent high-frequency performances and low current collapse originating in humidity-degradation using AlGaN/GaN-HEMT. This is also a valuable technology for InAlGaN/GaN-HEMT.
Approximation algorithms for budgeted learning problems

We present the first approximation algorithms for a large class of budgeted learning problems. One classicexample of the above is the budgeted multi-armed bandit problem. In this problem each arm of the bandithas an unknown reward distribution on which a prior isspecified as input. The knowledge about the underlying distribution can be refined in the exploration phase by playing the arm and observing the rewards. However, there is a budget on the total number of plays allowed during exploration. After this exploration phase,the arm with the highest (posterior) expected reward is hosen for exploitation. The goal is to design the adaptive exploration phase subject to a budget constraint on the number of plays, in order to maximize the expected reward of the arm chosen for exploitation. While this problem is reasonably well understood in the infinite horizon discounted reward setting, the budgeted version of the problem is NP-Hard. For this problem and several generalizations, we provide approximate policies that achieve a reward within constant factor of the reward optimal policy. Our algorithms use a novel linear program rounding technique based on stochastic packing.
Mild Fe-deficiency improves biomass production and quality of hydroponic-cultivated spinach plants (Spinacia oleracea L.).

It is of great practical importance to improve yield and quality of vegetables in soilless cultures. This study investigated the effects of iron-nutrition management on yield and quality of hydroponic-cultivated spinach (Spinacia oleracea L.). The results showed that mild Fe-deficient treatment (1 μM FeEDTA) yielded a greater biomass of edible parts than Fe-omitted treatment (0 μM FeEDTA) or Fe-sufficient treatments (10 and 50 μM FeEDTA). Conversely, mild Fe-deficient treatment had the lowest nitrate concentration in the edible parts out of all the Fe treatments. Interestingly, all the concentrations of soluble sugar, soluble protein and ascorbate in mild Fe-deficient treatments were higher than Fe-sufficient treatments. In addition, both phenolic concentration and DPPH scavenging activity in mild Fe-deficient treatments were comparable with those in Fe-sufficient treatments, but were higher than those in Fe-omitted treatments. Therefore, we concluded that using a mild Fe-deficient nutrition solution to cultivate spinach not only would increase yield, but also would improve quality.
Stability analysis of cascaded converters for bidirectional power flow applications

This paper establishes the criteria to ensure stable operation of two-stage, bidirectional, isolated AC-DC converters. The bi-directional converter is analyzed in the context of a building block module (BBM) that enables a fully modular architecture for universal power flow conversion applications (AC-DC, DC-AC and DC-DC). The BBM consists of independently controlled AC-DC and isolated DC-DC converters that are cascaded for bidirectional power flow applications. The cascaded converters have different control objectives in different directions of power flow. This paper discusses methods to obtain the appropriate input and output impedances that determine stability in the context of bi-directional AC-DC power conversion. Design procedures to ensure stable operation with minimal interaction between the cascaded stages are presented. The analysis and design methods are validated through extensive simulation and hardware results.
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion

A monocular 3D object tracking system generally has only up-to-scale pose estimation results without any prior knowledge of the tracked object. In this paper, we propose a novel idea to recover the metric scale of an arbitrary dynamic object by optimizing the trajectory of the objects in the world frame, without motion assumptions. By introducing an additional constraint in the time domain, our monocular visual-inertial tracking system can obtain continuous six degree of freedom (6-DoF) pose estimation without scale ambiguity. Our method requires neither fixed multi-camera nor depth sensor settings for scale observability, instead, the IMU inside the monocular sensing suite provides scale information for both camera itself and the tracked object. We build the proposed system on top of our monocular visual-inertial system (VINS) to obtain accurate state estimation of the monocular camera in the world frame. The whole system consists of a 2D object tracker, an object region-based visual bundle adjustment (BA), VINS and a correlation analysis-based metric scale estimator. Experimental comparisons with ground truth demonstrate the tracking accuracy of our 3D tracking performance while a mobile augmented reality (AR) demo shows the feasibility of potential applications.
Substrate integrated waveguide with corrugated wall

In this work we propose a new SIW structure, called the corrugated SIW (CSIW), which does not require conducting vias to achieve TE10 type boundary conditions at the side walls. Instead, the vias are replaced by quarter wavelength microstrip stubs arranged in a corrugated pattern on the edges of the waveguide. This, along with series interdigitated capacitors, results in a waveguide section comprising two separate conductors, which facilitates shunt connection of active components such as Gunn diodes.
Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals' privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches.
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data are stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65-nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep convolutional neural network (CNN) consuming 3.16pJ per equivalent reduced instruction set computer operation, local CNN-based face detection with secured remote recognition in 5.74pJ/op, and seizure detection with encrypted data collection from electroencephalogram within 12.7pJ/op.
Anti-neutrophil cytoplasmic (ANCA) and anti-glomerular basement membrane (GBM) autoantibodies in necrotizing and crescentic glomerulonephritis

Anti-neutrophil cytoplasmic autoantibodies (ANCA) and anti-glomerular basement membrane (GBM) necrotizing and crescentic glomerulonephritis are aggressive and destructive glomerular diseases that are associated with and probably caused by circulating ANCA and anti-GBM antibodies. These necrotizing lesions are manifested by acute nephritis and deteriorating kidney function often accompanied by distinctive clinical features of systemic disease. Prompt diagnosis requires clinical acumen that allows for the prompt institution of therapy aimed at removing circulating autoantibodies and quelling the inflammatory process. Continuing exploration of the etiology and pathogenesis of these aggressive inflammatory diseases have gradually uncovered new paradigms for the cause of and more specific therapy for these particular glomerular disorders and for autoimmune glomerular diseases in general.
Sacrococcygeal teratoma with anorectal malformation.

A 7-month-old child presented with imperforate anus, penoscrotal hypospadias and transposition, and a midline mucosa-lined perineal mass. At surgery the mass was found to be supplied by the median sacral artery. It was excised and the anorectal malformation was repaired by posterior sagittal anorectoplasty. Histologically the mass revealed well-differentiated colonic tissue. The final diagnosis was well-differentiated sacrococcygeal teratoma in association with anorectal malformation.
Velocity and shape from tightly-coupled LiDAR and camera

In this paper, we propose a multi-object tracking and reconstruction approach through measurement-level fusion of LiDAR and camera. The proposed method, regardless of object class, estimates 3D motion and structure for all rigid obstacles. Using an intermediate surface representation, measurements from both sensors are processed within a joint framework. We combine optical flow, surface reconstruction, and point-to-surface terms in a tightly-coupled non-linear energy function, which is minimized using Iterative Reweighted Least Squares (IRLS). We demonstrate the performance of our model on different datasets (KITTI with Velodyne HDL-64E and our collected data with 4-layer ScaLa Ibeo), and show an improvement in velocity error and crispness over state-of-the-art trackers.
What Causes Specific Language Impairment in Children?

Specific language impairment (SLI) is diagnosed when a child's language development is deficient for no obvious reason. For many years, there was a tendency to assume that SLI was caused by factors such as poor parenting, subtle brain damage around the time of birth, or transient hearing loss. Subsequently it became clear that these factors were far less important than genes in determining risk for SLI. A quest to find "the gene for SLI" was undertaken, but it soon became apparent that no single cause could account for all cases. Furthermore, although fascinating cases of SLI caused by a single mutation have been discovered, in most children the disorder has a more complex basis, with several genetic and environmental risk factors interacting. The clearest evidence for genetic effects has come from studies that diagnosed SLI using theoretically motivated measures of underlying cognitive deficits rather than conventional clinical criteria.