nils-herrmann/clean_scirepeval huggingface dataset:
[Potentials of individual muscle fibers evoked by stimulation of prejunctional nerve endings in healthy subjects (preliminary observations)].

Electrical responses of the single muscle fibre (SFER) by stimulation of the motor terminal nerve-endings have been investigated in normal subjects at various ages in vivo. Shape, latency, rise-time and interspike distance seem to be SFER's most interesting parameters of the functional organisation of the motor subunits and their terminal fractions. "Time" parameters of SFER are in agreement with the anatomo-functional characteristics of the excited tissues during ageing.
Impact of certain plants and synthetic molluscicides on some fresh water snails and fish.

The LC50 (78, 85 ppm) and LC90 (88, 135 ppm) of Anagalis arvensis and Calendula micrantha respectively against Biomphalaria alexandrina were higher than those of the non-target snails, Physa acuta, Planorbis planorbis, Helisoma duryi and Melanoides tuberculata. In contrast, the LC50 of Niclosamide (0.11 ppm) and Copper sulphate (CuSO4) (0.42 ppm) against B. alexandrina were lower than those of the non-target snails. The mortalities percentage among non-target snails ranged between 0.0 & 20% when sublethal concentrations of CuSO4 against B. alexandrina mixed with those of C. micrantha and between 0.0 & 40% when mixed with A. arvensis. Mortalities ranged between 0.0 & 50% when Niclosamide was mixed with each of A. arvensis and C. micrantha. A. arvensis induced 100% mortality on Oreochromis niloticus after 48 hrs exposure and after 24 hrs for Gambusia affinis. C. micrantha was non-toxic to the fish. The survival rate of O. niloticus and G. affinis after 48 hrs exposure to 0.11 ppm of Niclosamide were 83.3% & 100% respectively. These rates were 91.7% & 93.3% respectively when each of the two fish species was exposed to 0.42 ppm of CuSO4. Mixture of sub-lethal concentrations of A. arvensis against B. alexandrina and those of Niclosamide or CuSO4 at ratios 10:40 & 25:25 induced 66.6% mortalities on O. niloticus and 83.3% at 40:10. These mixtures caused 100% mortalities on G. affinis at all ratios. A. arvensis CuSO4 mixtures at 10:40 induced 83.3% & 40% mortalities on O. niloticus and G. affinis respectively and 100% mortalities on both fish species at ratios 25:25 & 40:10. A mixture of sub-lethal concentrations of C. micrantha against B. alexandrina and of Niclosamide or CuSO4 caused mortalities of O. niloticus between 0.0 & 33.3% and between 5% & 35% of G. affinis. The residue of Cu in O. niloticus were 4.69, 19.06 & 25.37 mg/1kgm fish after 24, 48 & 72 hrs exposure to LC0 of CuSO4 against B. alexandrina respectively.
Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing

Demand response (DR) is very important in the future smart grid, aiming to encourage consumers to reduce their demand during peak load hours. However, if binary decision variables are needed to specify start-up time of a particular appliance, the resulting mixed integer combinatorial problem is in general difficult to solve. In this paper, we study a versatile convex programming (CP) DR optimization framework for the automatic load management of various household appliances in a smart home. In particular, an L1 regularization technique is proposed to deal with schedule-based appliances (SAs), for which their on/off statuses are governed by binary decision variables. By relaxing these variables from integer to continuous values, the problem is reformulated as a new CP problem with an additional L1 regularization term in the objective. This allows us to transform the original mixed integer problem into a standard CP problem. Its major advantage is that the overall DR optimization problem remains to be convex and therefore the solution can be found efficiently. Moreover, a wide variety of appliances with different characteristics can be flexibly incorporated. Simulation result shows that the energy scheduling of SAs and other appliances can be determined simultaneously using the proposed CP formulation.
The Kinematics Analysis and Simulation of Stacking Robot

High-speed and heavy-load stacking robot technology is a common key technique in nonferrous metallurgy areas. Specific layer stacking robot of aluminum ingot continuous casting production line, which has four-DOF, is designed in this paper. The kinematics model is built and studied in detail by D-H method. The transformation matrix method is utilized to solve the kinematics equation of robot. Mutual motion relations between each joint variables and the executive device of robot is got. The kinematics simulation of the robot is carried out via the ADAMS-software. The results of simulation verify the theoretical analysis and lay the foundation for following static and dynamic characteristics analysis of the robot.
A Miniaturized Wideband Three-Section Branch-Line Hybrid With Harmonic Suppression Using Coupled Line and Open-Ended Stubs

In this letter, a wideband three-section branch-line hybrid with harmonic suppression is designed using a novel transmission line model. The proposed topology is constructed using a coupled line, two series transmission lines, and open-ended stubs. The required design equations are obtained by applying even- and odd-mode analysis. To support these equations, a three-section branch-line hybrid working at 0.9 GHz is fabricated and tested. The physical area of the prototype is reduced by 87.7% of the conventional hybrid and the fractional bandwidth is greater than 52%. In addition, the proposed technique can eliminate second harmonic by a level better than 15 dB.
Rigid versus flexible dentine-like endodontic posts--clinical testing of a biomechanical concept: seven-year results of a randomized controlled clinical pilot trial on endodontically treated abutment teeth with severe hard tissue loss.

This is the first clinical long-term pilot study that tested the biomimetic concept of using more flexible, dentine-like (low Young modulus) glass fiber-reinforced epoxy resin posts (GFREPs) compared with rather rigid, stiff (higher Young modulus) titanium posts (TPs) in order to improve the survival rate of severely damaged endodontically treated teeth.



Ninety-one subjects in need of postendodontic restorations in teeth with 2 or less remaining cavity walls were randomly assigned to receive either a tapered TP (n = 46) or a tapered GFREP (n = 45). The posts were adhesively luted using self-adhesive resin cement. The composite core build-ups were prepared ensuring a circumferential 2-mm ferrule. The primary endpoint was a loss of restoration for any reason. To study group differences, the log-rank test was calculated (P < .05). Hazard plots were constructed.



After 84 months of observation (mean = 71.2 months), 7 restorations failed (ie, 4 GFREPs and 3 TPs). The failure modes were as follows: GFREP:root fracture (n = 3), core fracture (n = 1) and TP:endodontic failure (n = 3). No statistical difference was found between the survival rates (GFREPs = 90.2%, TPs = 93.5%, P = .642). The probability of no failure was comparable for both post materials (risk ratio; 95% confidence interval, 0.965-0.851/1.095).



When using self-adhesive luted prefabricated posts in severely destroyed abutment teeth with 2 or less cavity walls and a 2-mm ferrule, postendodontic restorations achieved high long-term survival rates irrespective of the post material used (ie, glass fiber vs titanium).
A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets

We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging from social networks, to power grids, to the influence of jazz musicians, and is also incorporated into the DARPA HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph analytics. We design an optimized implementation of betweenness centrality for the massively multithreaded Cray XMT system with the Thread-storm processor. For a small-world network of 268 million vertices and 2.147 billion edges, the 16-processor XMT system achieves a TEPS rate (an algorithmic performance count for the number of edges traversed per second) of 160 million per second, which corresponds to more than a 2× performance improvement over the previous parallel implementation. We demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for the large IMDb movie-actor network.
Attentive Systems: A Survey

Visual saliency analysis detects salient regions/objects that attract human attention in natural scenes. It has attracted intensive research in different fields such as computer vision, computer graphics, and multimedia. While many such computational models exist, the focused study of what and how applications can be beneficial is still lacking. In this article, our ultimate goal is thus to provide a comprehensive review of the applications using saliency cues, the so-called attentive systems. We would like to provide a broad vision about saliency applications and what visual saliency can do. We categorize the vast amount of applications into different areas such as computer vision, computer graphics, and multimedia. Intensively covering 200+ publications we survey (1) key application trends, (2) the role of visual saliency, and (3) the usability of saliency into different tasks.
Oscillations in Notch Signaling Regulate Maintenance of Neural Progenitors

Expression of the Notch effector gene Hes1 is required for maintenance of neural progenitors in the embryonic brain, but persistent and high levels of Hes1 expression inhibit proliferation and differentiation of these cells. Here, by using a real-time imaging method, we found that Hes1 expression dynamically oscillates in neural progenitors. Furthermore, sustained overexpression of Hes1 downregulates expression of proneural genes, Notch ligands, and cell cycle regulators, suggesting that their proper expression depends on Hes1 oscillation. Surprisingly, the proneural gene Neurogenin2 (Ngn2) and the Notch ligand Delta-like1 (Dll1) are also expressed in an oscillatory manner by neural progenitors, and inhibition of Notch signaling, a condition known to induce neuronal differentiation, leads to downregulation of Hes1 and sustained upregulation of Ngn2 and Dll1. These results suggest that Hes1 oscillation regulates Ngn2 and Dll1 oscillations, which in turn lead to maintenance of neural progenitors by mutual activation of Notch signaling.
Dynamic modeling of a VSC-HVDC converter

The aim of this paper is to present a nonlinear dynamic model for Voltage Source Converter-based HVDC (VSC-HVDC) links that can be used for dynamic studies. It includes the main physical elements and is controlled by PI controllers with antiwindup. A linear control model is derived for efficient tuning of the controllers of the nonlinear dynamic model. The nonlinear dynamic model is then tuned according to the performance of an ABB HVDC Light model.
Hepatoprotective and antidiabetic effects of apple cider vinegar (A Prophetic Medicine Remedy) on the liver of male rats

Diabetes mellitus is associated with biochemical and pathological alterations in the liver. The aim of this study was to investigate the effects of apple cider vinegar (ACV) on serum biochemical markers and histopathological changes in the liver of diabetic rats for 30 days. Effects were evaluated using streptozotocin (STZ)-induced diabetic rats as an experimental model.  Diabetes mellitus was induced by a single dose of STZ (65 mg/kg) given intraperitoneally. Thirty wistar rats were divided into three groups: control group, STZ-treated group and STZ plus ACV treated group (2 ml/kg BW). Animals were sacrificed 30 days post treatment.  Biochemical results indicated that, ACV caused a significant decrease in glucose, TC, LDL-c and a significant increase in HDL-c. Histopathological examination of the liver sections of diabetic rats showed fatty changes in the cytoplasm of the hepatocytes in the form of accumulation of lipid droplets, lymphocytic infiltration. Electron microscopic studies revealed aggregations of polymorphic mitochondria with apparent loss of their cristae and condensed matrices. Besides, the rough endoplasmic reticulum was proliferating and fragmented into smaller stacks. The cytoplasm of the hepatocytes exhibited vacuolations and displayed a large number of lipid droplets of different sizes. On the other hand, the liver sections of diabetic rats treated with ACV showed minimal toxic effects due to streptozotocin. These ultrastructural results revealed that treatment of diabetic rats with ACV led to apparent recovery of the injured hepatocytes. In prophetic medicine, Prophet Muhammad peace is upon him strongly recommended eating vinegar in the Prophetic Hadeeth: "vinegar is the best edible".  This study showed that ACV, in early stages of diabetes inductioncan decrease the destructive progress of diabetes and cause hepatoprotection against the metabolic damages resulting from streptozotocininduced diabetes mellitus.
Multi-antenna Wireless Energy Transfer for Backscatter Communication Systems

We study RF-enabled wireless energy transfer (WET) via energy beamforming, from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs) in a backscatter communication system such as RFID. The acquisition of the forward-channel (i.e., ET-to-ER) state information (F-CSI) at the ET (or RFID reader) is challenging, since the ERs (or RFID tags) are typically too energy-and-hardware-constrained to estimate or feedback the F-CSI. The ET leverages its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze the harvested energy obtained using the estimated BS-CSI. Furthermore, we optimize the resource allocation to maximize the total utility of harvested energy. For WET to single ER, we obtain the optimal channel-training energy in a semiclosed form. For WET to multiple ERs, we optimize the channel-training energy and the energy allocation weights for different energy beams. For the straightforward weighted-sum-energy (WSE) maximization, the optimal WET scheme is shown to use only one energy beam, which leads to unfairness among ERs and motivates us to consider the complicated proportional-fair-energy (PFE) maximization. For PFE maximization, we show that it is a biconvex problem, and propose a block-coordinate-descent-based algorithm to find the close-to-optimal solution. Numerical results show that with the optimized solutions, the harvested energy suffers slight reduction of less than 10%, compared to that obtained using the perfect F-CSI.
Optimal control of three-phase PWM inverter for UPS systems

This work presents a new digital control strategy of a three-phase PWM inverter for uninterruptible power supplies (UPS) systems. To achieve a fast transient response, a good voltage regulation, nearly zero steady state inverter output voltage error, and low total harmonic distortion (THD), the proposed control method consists of two discrete-time feedback controllers: a discrete-time optimal + sliding-mode voltage controller in outer loop and a discrete-time optimal current controller in inner loop. To prove the effectiveness of the proposed technique, various simulation results using Matlab/Simulink are shown under both linear and nonlinear loads.
A Model and Decision Procedure for Data Storage in Cloud Computing

Cloud computing offers many possibilities for prospective users, there are however many different storage and compute services to choose from between all the cloud providers and their multiple datacenters. In this paper we focus on the problem of selecting the best storage services according to the application's requirements and the user's priorities. In previous work we described a capability based matching process that filters out any service that does not meet the requirements specified by the user. In this paper we introduce a mathematical model that takes this output lists of compatible storage services and constructs an integer linear programming problem. This ILP problem takes into account storage and compute cost as well as performance characteristics like latency, bandwidth, and job turnaround time, a solution to the problem yields an optimal assignment of datasets to storage services and of application runs to compute services. We show that with modern ILP solvers a reasonably sized problem can be solved in one second, even with an order of magnitude increase in cloud providers, number of datacenters, or storage services the problem instances can be solved under a minute. We finish our paper with two use cases, BLAST and MODIS. For MODIS our recommended data allocation leverages both cloud and local resources, it incurs in half the cost of a pure cloud solution and the job turnaround time is 52% faster compared to a pure local solution.
Implementable Smart Contract using 3-SmartContract Model View project

An emergent use of the blockchain technology is to enable the transfer of digital assets between two parties. An extension to this is the Smart Property in which physical assets could be transferred too. Another extension is the exchange of services of all kinds in form of digitally executed contracts. In this paper, the problems with existing attempts to implement an all-inclusive smart contract platform were identified and a new framework proposed. In this framework, the technical and legal terms of any contract could be executed digitally if prepared with appropriate legal prose and required parameters for each of the terms of the contract. The cores of the framework are the technical, business and legal models, which are connected to each other. The technical model adapts block chain technology while ensuring granularity in implementing the terms of the contract as presented by the legal model using legal prose and necessary parameters. Using the proposed framework, some questions that have persisted with current implementation of Smart contracts that involves the blockchain were answered. The framework improves the efficiency and practicability of using smart contract for physical assets and non-financial services with emphasis. The contribution is mainly on ensuring an adoptable and practicable smart contract platform.
A neutral templating route to mesoporous molecular sieves.

A neutral templating route for preparing mesoporous molecular sieves is demonstrated based on hydrogen-bonding interactions and self-assembly between neutral primary amine micelles (S degrees ) and neutral inorganic precursors (l degrees ). The S degrees l degrees templating pathway produces ordered mesoporous materials with thicker framework walls, smaller x-ray scattering domain sizes, and substantially improved textural mesoporosities in comparison with M41S materials templated by quaternary ammonium cations of equivalent chain length. This synthetic strategy also allows for the facile, environmentally benign recovery of the cost-intensive template by simple solvent extraction methods. The S degrees 1 degrees templating route provides for the synthesis of other oxide mesostructures (such as aluminas) that may be less readily accessible by electrostatic templating pathways.
Intrusion Detection System using Fuzzy Genetic Approach

Network security is of primary concerned now days for large organizations. The intrusion detection systems (IDS) are becoming indispensable for effective protection against attacks that are constantly changing in magnitude and complexity. With data integrity, confidentiality and availability, they must be reliable, easy to manage and with low maintenance cost. Various modifications are being applied to IDS regularly to detect new attacks and handle them. This paper proposes a fuzzy genetic algorithm (FGA) for intrusion detection. The FGA system is a fuzzy classifier, whose knowledge base is modeled as a fuzzy rule such as "if-then" and improved by a genetic algorithm. The method is tested on the benchmark KDD'99 intrusion dataset and compared with other existing techniques available in the literature. The results are encouraging and demonstrate the benefits of the proposed approach. Keywordsgenetic algorithm, fuzzy logic, classification, intrusion detection, DARPA data set
Employing a fully convolutional neural network for road marking detection

Since road markings are one of the main landmarks used for traffic guidance, perceiving them may be a crucial task for autonomous vehicles. In visual approaches, road marking detection consists in detecting pixels of an image that corresponds to a road marking. Recently, most approaches have aimed on detecting lane markings only, and few of them proposed methods to detect other types of road markings. Moreover, most of those approaches are based on local gradient, which provides noisy detections caused by cluttered images. In this paper, we propose an alternative approach based on a deep Fully Convolutional Neural Network (FCNN) with an encoder-decoder architecture for road marking detection and segmentation. The experimental results reveal that the proposed approach can detect any road marking type in a high level of accuracy, resulting in a smooth segmentation.
Recognition of Handwritten Roman Script Using Tesseract Open source OCR Engine

In the present work, we have used Tesseract 2.01 open source Optical Character Recognition (OCR) Engine under Apache License 2.0 for recognition of handwriting samples of lower case Roman script. Handwritten isolated and free-flow text samples were collected from multiple users. Tesseract is trained to recognize user-specific handwriting samples of both the categories of document pages. On a single user model, the system is trained with 1844 isolated handwritten characters and the performance is tested on 1133 characters, taken form the test set. The overall character-level accuracy of the system is observed as 83.5%. The system fails to segment 5.56% characters and erroneously classifies 10.94% characters.
Active and passive touch differentially activate somatosensory cortex in texture perception.

The neural mechanisms behind active and passive touch are not yet fully understood. Using fMRI we investigated the brain correlates of these exploratory procedures using a roughness categorization task. Participants either actively explored a surface (active touch) or the surface was moved under the participant's stationary finger (passive touch). The stimuli consisted of three different grades of sandpaper which participants were required to categorize as either coarse, medium, or fine. Exploratory procedure did not affect performance although the coarse and fine surfaces were more easily categorized than the medium surface. An initial whole brain analysis revealed activation of sensory and cognitive areas, including post-central gyrus and prefrontal cortical areas, in line with areas reported in previous studies. Our main analysis revealed greater activation during active than passive touch in the contralateral primary somatosensory region but no effect of stimulus roughness. In contrast, activation in the parietal operculum (OP) was significantly affected by stimulus roughness but not by exploration procedure. Active touch also elicited greater and more distributed brain activity compared with passive touch in areas outside the somatosensory region, possibly due to the motor component of the task. Our results reveal that different cortical areas may be involved in the processing of surface exploration and surface texture, with exploration procedures affecting activations in the primary somatosensory cortex and stimulus properties affecting relatively higher cortical areas within the somatosensory system.