nils-herrmann/clean_scirepeval huggingface dataset:
A new ART-counterpropagation neural network for solving a forecasting problem

This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. q 2004
Managing flood disasters on the built environment in the rural communities of Zimbabwe: Lessons learnt

This article is about managing flood disasters affecting the built environment in the rural communities of Zimbabwe. Using Tsholotsho district in Matabeleland North province as a case study, the authors argue that flooding has adversely impacted the built environment through destroying infrastructure. The principal objectives of this study were to establish the impact of flood disasters on the built environment, to demarcate factors that perpetuate communities' vulnerabilities to flooding and to delineate challenges that negate the management of flood disasters in the built environment. This qualitative study was based on a purposive sample of 40 participants. Data were collected through semi-structured interviews and observation methods. The findings were that floods can damage human shelter, roads, bridges and dams. Locating homesteads near rivers and dams, using poor-quality construction materials, and lack of flood warning were found to perpetuate vulnerability to flooding. Poverty and costs of rebuilding infrastructure, lack of cooperation between the communities and duty-bearers, and failure to use indigenous knowledge were found to be impeding the management of flood disasters. The study concluded that flood disasters can wipe out community development gains accumulated over many years. Further, community vulnerability to flooding in the built environment is socially constructed. The study posits that addressing the root causes, reducing flood vulnerability and avoiding risk creation are viable options to development in the built environment. Lastly, reconstruction following flood disasters is arduous and gruelling, and not an easy exercise.
Energy-Efficient Hierarchical Routing for Wireless Sensor Networks: A Swarm Intelligence Approach

Energy efficient routing in wireless sensor networks (WSNs) require non-conventional paradigm for design and development of power aware protocols. Swarm intelligence (SI) based metaheuristic can be applied for optimal routing of data, in an energy constraint WSNs environment. In this paper, we present BeeSwarm, a SI based energyefficient hierarchical routing protocol for WSNs. Our protocol consists of three phases: (1) Set-up phase-BeeCluster, (2) Route discovery phase-BeeSearch and (3) Data transmission phase-BeeCarrier. Integration of three phases for clustering, data routing and transmission, is the key aspect of our proposed protocol, which ultimately contributes to its robustness. Evaluation of simulation results show that BeeSwarm perform better in terms of packet delivery, energy consumption and throughput with increased network life compared to other SI based hierarchical routing protocols.
Turning the Waiting Room into a Classroom: Weekly Classes Using a Vegan or a Portion-Controlled Eating Plan Improve Diabetes Control in a Randomized Translational Study.

In research settings, plant-based (vegan) eating plans improve diabetes management, typically reducing weight, glycemia, and low-density lipoprotein (LDL) cholesterol concentrations to a greater extent than has been shown with portion-controlled eating plans.



The study aimed to test whether similar benefits could be found using weekly nutrition classes in a typical endocrinology practice, hypothesizing that a vegan eating plan would improve glycemic control, weight, lipid concentrations, blood pressure, and renal function and would do so more effectively than a portion-controlled eating plan.



In a 20-week trial, participants were randomly assigned to a low-fat vegan or portion-controlled eating plan.



Individuals with type 2 diabetes treated in a single endocrinology practice in Washington, DC, participated (45 starters, 40 completers).



Participants attended weekly after-hours classes in the office waiting room. The vegan plan excluded animal products and added oils and favored low-glycemic index foods. The portion-controlled plan included energy intake limits for weight loss (typically a deficit of 500 calories/day) and provided guidance on portion sizes.



Body weight, hemoglobin A1c (HbA1c), plasma lipids, urinary albumin, and blood pressure were measured.


STATISTICAL ANALYSES PERFORMED
For normally distributed data, t tests were used; for skewed outcomes, rank-based approaches were implemented (Wilcoxon signed-rank test for within-group changes, Wilcoxon two-sample test for between-group comparisons, and exact Hodges-Lehmann estimation to estimate effect sizes).



Although participants were in generally good metabolic control at baseline, body weight, HbA1c, and LDL cholesterol improved significantly within each group, with no significant differences between the two eating plans (weight: -6.3 kg vegan, -4.4 kg portion-controlled, between-group P=0.10; HbA1c, -0.40 percentage point in both groups, P=0.68; LDL cholesterol -11.9 mg/dL vegan, -12.7 mg/dL portion-controlled, P=0.89). Mean urinary albumin was normal at baseline and did not meaningfully change. Blood pressure changes were not significant.



Weekly classes, integrated into a clinical practice and using either a low-fat vegan or portion-controlled eating plan, led to clinical improvements in individuals with type 2 diabetes.
CDM ESD protection in CMOS integrated circuits

The impacts of charged-device-model (CDM) electrostatic discharge (ESD) events on integrated circuit (IC) products are presented in this paper. The mechanism of chip-level CDM ESD event is introduced with some case studies on CDM ESD damages. Besides the chip-level CDM ESD event, the board-level CDM ESD event, which had been reported to cause damages in many customer-returned ICs, is also investigated in this work. The chip-level and board-level CDM ESD levels of several test devices and test circuits fabricated in CMOS processes are characterized and compared. The experimental results have shown that the board-level CDM ESD level of the test circuit is much lower than the chip-level CDM ESD level, which indicates that the board-level CDM ESD test is more critical than the chip-level CDM ESD test in the field applications. In addition, failure analysis reveals that the failure on the test circuit under board-level CDM ESD test is much severer than that under chip-level CDM ESD test.
GPS spoofer countermeasure effectiveness based on signal strength, noise power, and C/N0 measurements

Spoofing sources can effectively disrupt a GPS receiver during the acquisition phase by generating multiple false correlation peaks and increasing the noise floor. Such deceptive correlation peaks can mislead the GPS receiver into acquiring the spoofer generated signals rather than the authentic signals. Also, the spoofer can increase the receiver noise floor to bury the authentic signals in the noise and at the same time generate correlation peaks with amplitudes commensurate with reasonable C/N0 expectations. The main focus of this paper is on assessment of the reduced effectiveness of the GPS spoofer countermeasure during acquisition where the GPS receiver utilizes C/N0 discrimination. As shown, whereas the C/N0 discrimination is of limited effectiveness, with a modest circuit modification, the receiver can measure the absolute power of the correlation peaks, which is an effective means of detecting and discriminating spoofer sources. It will be shown that employing absolute power monitoring technique considerably reduces the vulnerability region of the receiver compared with the C/N0 monitoring techniques.
FaCT++ Description Logic Reasoner: System Description

This is a system description of the Description Logic reasoner FaCT++. The reasoner implements a tableaux decision procedure for the well known SHOIQ description logic, with additional support for datatypes, including strings and integers. The system employs a wide range of performance enhancing optimisations, including both standard techniques (such as absorption and model merging) and newly developed ones (such as ordering heuristics and taxonomic classification). FaCT++ can, via the standard DIG interface, be used to provide reasoning services for ontology engineering tools supporting the OWL DL ontology language.
Degraded dot matrix character recognition using CSM-based feature extraction

This paper presents an OCR method for degraded character recognition applied to a reference number (RN) of 15 printed characters of an invoice document produced by dot-matrix printer. First, the paper deals with the problem of the reference number localization and extraction, in which the characters tops or bottoms are or not touched with a printed reference line of the electrical bill. In case of touched RN, the extracted characters are severely degraded leading to missing parts in the characters tops or bottoms. Secondly, a combined recognition method based on the complementary similarity measure (CSM) method and MLP-based classifier is used. The CSM is used to accept or reject an incoming character. In case of acceptation, the CSM acts as a feature extractor and produces a feature vector of ten component features. The MLP is then trained using these feature vectors. The use of the CSM as a feature extractor tends to make the MLP very powerful and very well suited for rejection. Experimental results on electrical bills show the ability of the model to yield relevant and robust recognition on severely degraded printed characters.
Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and e®ectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classi ̄cation and lower dimension features can be got by applying weighted PCA algorithm. Finally, the di®erent classi ̄ers are used to select the optimal classi ̄cation categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12 10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.
Performance of a high frequency quasi-resonant inverter with variable-frequency output for induction heating

This paper presents the performance of a high frequency quasi-resonant inverter using power Metal Oxide Semiconductor Field Effect Transistor (MOSFETs) as switching devices is described. This inverter circuit can output the variable frequency from 160kHz to 400kHz by power MOSFET for induction heating. The induction heating is often used for the heat-treatment of a metal work-piece. In hardening of a gear, since the bottom or tips are heated at lower frequency fL or higher frequency fH respectively, the heating at two different frequencies for uniform heat-treatment is required. Therefore, two inverters are necessary to output different resonant frequency each other. To solve this problem a single adjustable frequency quasi-resonant inverter circuits. This inverter circuit includes the first resonant capacitor and that of the second with a one-way short-circuit switch. Synthetic series capacitance is varied by manipulating the switch and then the resonant frequency can be adjusted. The power MOSFET has the disadvantages of withstand voltage and current. To solve this problem by the methods of connecting switching devices to series . Voltage across the series connected power MOSFETs are balanced by magnetically coupling all gate lines using a simple two-winding transformer.
Islanding detection of active distribution system with parallel inverters

Average absolute frequency deviation value (AFDVavg) based active islanding detection technique is recently introduced for islanding detection of active distribution system with a single active DG. This paper implements, evaluates and analyze the performance of AFDVavg technique for active distribution system with parallel inverter. The main focus is on islanding detection of an ADN with parallel inverters including the worst case i.e. zero power imbalance condition. The case study is done on an ADN which is energized by two parallel inverter based DGs. Worst case of islanding has been simulated successfully by adjusting the equivalent load of ADN to match the total generation. The resultant quality factor of the equivalent load is kept 1 for testing islanding. Computer simulations are done in MATLAB.
Neural influences on sprint running: training adaptations and acute responses.

Performance in sprint exercise is determined by the ability to accelerate, the magnitude of maximal velocity and the ability to maintain velocity against the onset of fatigue. These factors are strongly influenced by metabolic and anthropometric components. Improved temporal sequencing of muscle activation and/or improved fast twitch fibre recruitment may contribute to superior sprint performance. Speed of impulse transmission along the motor axon may also have implications on sprint performance. Nerve conduction velocity (NCV) has been shown to increase in response to a period of sprint training. However, it is difficult to determine if increased NCV is likely to contribute to improved sprint performance. An increase in motoneuron excitability, as measured by the Hoffman reflex (H-reflex), has been reported to produce a more powerful muscular contraction, hence maximising motoneuron excitability would be expected to benefit sprint performance. Motoneuron excitability can be raised acutely by an appropriate stimulus with obvious implications for sprint performance. However, at rest H-reflex has been reported to be lower in athletes trained for explosive events compared with endurance-trained athletes. This may be caused by the relatively high, fast twitch fibre percentage and the consequent high activation thresholds of such motor units in power-trained populations. In contrast, stretch reflexes appear to be enhanced in sprint athletes possibly because of increased muscle spindle sensitivity as a result of sprint training. With muscle in a contracted state, however, there is evidence to suggest greater reflex potentiation among both sprint and resistance-trained populations compared with controls. Again this may be indicative of the predominant types of motor units in these populations, but may also mean an enhanced reflex contribution to force production during running in sprint-trained athletes. Fatigue of neural origin both during and following sprint exercise has implications with respect to optimising training frequency and volume. Research suggests athletes are unable to maintain maximal firing frequencies for the full duration of, for example, a 100m sprint. Fatigue after a single training session may also have a neural manifestation with some athletes unable to voluntarily fully activate muscle or experiencing stretch reflex inhibition after heavy training. This may occur in conjunction with muscle damage. Research investigating the neural influences on sprint performance is limited. Further longitudinal research is necessary to improve our understanding of neural factors that contribute to training-induced improvements in sprint performance.
WHY PEOPLE STAY : USING JOB EMBEDDEDNESS TO PREDICT VOLUNTARY TURNOVER

A new construct, entitled job embeddedness, is introduced. Assessing factors from on and off the job, it includes an individual’s (a) links to other people, teams and groups, (b) perception of their fit with their job, organization and community and (c) what they say they would have to sacrifice if they left their job. A measure of job embeddedness is developed with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and voluntary turnover, and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives and job search. Implications for theory and practice are discussed.
Learning How to Combine Internal and External Denoising Methods

Different methods for image denoising have complementary strengths and can be combined to improve image denoising performance, as has been noted by several authors [11, 7]. Mosseri et al.  distinguish between internal and external methods depending whether they exploit internal or external statistics [13]. They also propose a rule-based scheme (PatchSNR) to combine these two classes of algorithms. In this paper, we test the underlying assumptions and show that many images might not be easily split into regions where internal methods or external methods are preferable. Instead we propose a learning based approach using a neural network, that automatically combines denoising results from an internal and from an external method. This approach outperforms both other combination methods and state-of-the-art stand-alone image denoising methods, hereby further closing the gap to the theoretically achievable performance limits of denoising [9]. Our denoising results can be replicated with a publicly available toolbox.
Frontal terminations for the inferior fronto-occipital fascicle: anatomical dissection, DTI study and functional considerations on a multi-component bundle

The anatomy and functional role of the inferior fronto-occipital fascicle (IFOF) remain poorly known. We accurately analyze its course and the anatomical distribution of its frontal terminations. We propose a classification of the IFOF in different subcomponents. Ten hemispheres (5 left, 5 right) were dissected with Klingler’s technique. In addition to the IFOF dissection, we performed a 4-T diffusion tensor imaging study on a single healthy subject. We identified two layers of IFOF. The first one is superficial and antero-superiorly directed, terminating in the inferior frontal gyrus. The second is deeper and consists of three portions: posterior, middle and anterior. The posterior component terminates in the middle frontal gyrus (MFG) and dorso-lateral prefrontal cortex. The middle component terminates in the MFG and lateral orbito-frontal cortex. The anterior one is directed to the orbito-frontal cortex and frontal pole. In vivo tractography study confirmed these anatomical findings. We suggest that the distribution of IFOF fibers within the frontal lobe corresponds to a fine functional segmentation. IFOF can be considered as a “multi-function” bundle, with each anatomical subcomponent subserving different brain processing. The superficial layer and the posterior component of the deep layer, which connects the occipital extrastriate, temporo-basal and inferior frontal cortices, might subserve semantic processing. The middle component of the deep layer could play a role in a multimodal sensory–motor integration. Finally, the anterior component of the deep layer might be involved in emotional and behavioral aspects.
Development and Applications of CRISPR-Cas9 for Genome Engineering

Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9 are enabling the systematic interrogation of mammalian genome function. Analogous to the search function in modern word processors, Cas9 can be guided to specific locations within complex genomes by a short RNA search string. Using this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice. Cas9-mediated genetic perturbation is simple and scalable, empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review, we describe the development and applications of Cas9 for a variety of research or translational applications while highlighting challenges as well as future directions. Derived from a remarkable microbial defense system, Cas9 is driving innovative applications from basic biology to biotechnology and medicine.
Stability of thin reinforced concrete walls under cyclic loads : state-ofthe-art and new experimental findings

Damage to structural walls in the recent earthquakes in Chile (2010) and New Zealand (2011) demonstrated that modern reinforced concrete (RC) walls may not achieve the expected ductile response but could possibly be triggered by out-of-plane displacements of the wall. Following a review of the mechanisms that cause global out-of-plane buckling of RC walls, relevant international code requirements, and past experimental tests, this paper describes the findings from quasi-static cyclic tests of two thin RC walls with single layers of vertical and horizontal reinforcement. The two walls were subjected to uni-directional (in-plane) and bi-directional (in-plane and out-of-plane) loading respectively. Both walls experienced significant out-of-plane displacements and damage caused by out-of-plane deformations ultimately triggered the wall in-plane failure. The data obtained with extensive instrumentation of the test units, which included optical measurements of the 3D displacement field, yield new insights into the development of out-of-plane displacements, in particular with regard to: evolution of out-of-plane displacements with imposed in-plane displacements, portion of height and length of the wall that are involved in the out-of-plane instability, influence of both local and global tensile strains on the buckling behaviour and role of bi-directional loading on out-of-plane instability. The tests showed that very significant out-of-plane displacements—larger than half of the wall thickness—can take place without causing out-of-plane wall failure. The damage caused by these large out-of-plane displacements, however, can lead to a premature in-plane failure of the wall. & Katrin Beyer katrin.beyer@epfl.ch Angelica Rosso angelica.rosso@epfl.ch João P. Almeida joao.almeida@epfl.ch 1 Earthquake Engineering and Structural Dynamics Laboratory (EESD), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), EPFL ENAC IIC EESD, GC B2 495, Station 18, 1015 Lausanne, Switzerland 123 Bull Earthquake Eng DOI 10.1007/s10518-015-9827-x