Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling enhanced impurity sputtering due to rf sheaths in front of ICRH actuators

One of the main concerns over using the Ion Cyclotron Resonance Heating (ICRH) is the enhanced impurity sputtering phenomenon due to the emergence of RF sheaths near the Faraday Screen of the ICRH antenna. Here we present a semi-analytical fluid plasma model able to capture the enhanced sputtering yield from the Faraday Screen and the PlasmaFacing Components of an Ion Cyclotron Resonance Heating antenna in a fusion machine. The model is a one-dimensional phase-resolved model of a rectified radio frequency sheath in a magnetic field at an angle with respect to the material surface, solving for the momentum transport of both the ions and the impurities. The sputtering model of the impurities coming off from the wall is based on lookup tables obtained from the plasma-material interaction code Fractal-Tridyn. This study analyzes a range of magnetic angles and wave frequencies to parametrically investigate their effect on the energy-angle distributions of the impacting ions and sputtered impurities. Finally, an estimate of the impurity fluxes and of the gross-erosion rate is provided for ITER-relevant conditions.
Influence of the Composition on the Exploitation Properties of Combined Medium Density Fibreboards Manufactured with Coniferous Wood Residues

One of the main disadvantages of medium density fibreboards (MDF) in comparison with particleboards is the higher price of the panels, due to the energy-intensive defibration process. Studies on the possibilities for replacing some part of the wood fibre mass in the composition of MDF with coniferous sawmill residues (shavings) obtained from bandsaw, are presented in this article . The experimental plan is designed using the McLean and Anderson method for studying the properties of multi component systems in the presence of constraints on the components . The content of coniferous wood shavings varies up to 40%. The panels are manufactured with a density of 720 kg . m -3 . The content of urea - formaldehyde resin varies from 8 to 14% in order to compensate the negative impact of the inclusion of coniferous wood shavings in the composition of the manufactured MDF panels . The main exploitation properties of the panels are determined . Experimental and statistical models on the influence of the studied factors are obtained by applying stepwise regression and optimization is performed to obtain the best exploitation properties of MDF panels. As a result of the study it was determined that in order to achieve the values of MDF properties, required by the respective standards, the maximum permissible content of coniferous wood shavings should be up to 10.6%, in which case the content of urea-formaldehyde resin should be above 10%. If the content of urea-formaldehyde resin is below 10%, the maximum permissible content of coniferous wood shavings should be up to 5%.
Increased eNO and pulmonary iNOS expression in eNOS null mice

Nitric oxide (NO) is a major regulatory molecule of the cardiovascular system; however, measurement of vascular NO synthesis in vivo represents a major challenge. NO stemming from the lower respiratory tract has been used as a marker of vascular endothelial function. Experimental evidence for this concept is lacking. Therefore, the aim of the present study was to investigate this relationship. Lower respiratory tract exhaled NO concentration, together with systemic and pulmonary artery pressure, was measured in endothelial nitric oxide synthase (NOS) (eNOS) null mice (eNOS-/-). Similar studies were performed in inducible NOS (iNOS) null mice (iNOS-/-). Defective endothelial NO synthesis in eNOS-/- mice (evidenced by systemic and pulmonary hypertension) was associated with augmented exhaled NO levels (12.5±1.9 versus 9.8±1.2 parts per billion (ppb), eNOS-/- versus wild type), whereas normal endothelial NO synthesis in iNOS-/- mice was associated with decreased exhaled NO levels (4.3±1.5 ppb). Augmented exhaled NO levels in eNOS-/- mice were associated with upregulation of iNOS expression in the lung. These results indicate that inducible nitric oxide synthase is a major determinant of gaseous nitric oxide production in the lung, and lower respiratory tract exhaled nitric oxide does not always represent a marker of vascular endothelial nitric oxide synthesis.
Avoidance and Inflexibility as a Common Clinical Pathway in Obesity and Smoking Treatment

This brief study presents reductions in avoidance and inflexibility as a potential common clinical pathway to intervene on for both smoking cessation and weight control. A mediation analysis—using the Avoidance and Inflexibility Scale (AIS)—was conducted on a group of participants (N = 84) who were shown to improve weight control outcomes after receiving an Acceptance and Commitment Therapy (ACT) intervention. Results were then compared to a previous study using the same process measure (AIS) for smoking cessation. Results suggest a common clinical pathway.
AUTONOMIC NERVOUS SYSTEM IN CHILDREN WITH AUTISM SPECTRUM DISORDER

Background. Autism spectrum disorder (ASD) is a neurodevelopmental disorder of unknown etiology, defined by impaired social communication and interaction, and by the presence of restrictive and repetitive behaviour. The autonomic imbalance, in which one branch of the autonomic nervous system (ANS) dominates over the other, is associated with a lack of dynamic flexibility and adaptability. The aim of this article was to summarize the current state of knowledge regarding ANS activity in children with ASD. Methods and Results. We have collected the latest information on autism and ANS regulation from available medical resources (PubMed). We focused on two main physiological parameters such as heart rate variability (HRV) and electrodermal activity (EDA) as the noninvasive indices of vagal and sympathetic activity, respectively. The studies showed atypical autonomic activity at rest as well as in response to stress. Conclusion. ASD symptoms are associated with pervasive abnormalities in the central nervous system including structures and networks involved in the complex ANS regulation. Importantly, the autonomic imbalance in a manner of higher sympathetic activity associated with lower vagal activity is associated with increased risk of cardiovascular, and other adverse outcomes in ASD. Therefore, we suggest that detailed complex analysis of physiological parameters may illuminate the pathway linking ASD and autonomic nervous system activity.
Linear-After-The-Exponential (LATE)–PCR: An advanced method of asymmetric PCR and its uses in quantitative real-time analysis

Conventional asymmetric PCR is inefficient and difficult to optimize because limiting the concentration of one primer lowers its melting temperature below the reaction annealing temperature. Linear-After-The-Exponential (LATE)–PCR describes a new paradigm for primer design that renders assays as efficient as symmetric PCR assays, regardless of primer ratio. LATE-PCR generates single-stranded products with predictable kinetics for many cycles beyond the exponential phase. LATE-PCR also introduces new probe design criteria that uncouple hybridization probe detection from primer annealing and extension, increase probe reliability, improve allele discrimination, and increase signal strength by 80–250% relative to symmetric PCR. These improvements in PCR are particularly useful for real-time quantitative analysis of target numbers in small samples. LATE-PCR is adaptable to high throughput applications in fields such as clinical diagnostics, biodefense, forensics, and DNA sequencing. We showcase LATE-PCR via amplification of the cystic fibrosis CFΔ508 allele and the Tay-Sachs disease TSD 1278 allele from single heterozygous cells.
Integrating analysis of customers' processes into roadmapping: The value-creation perspective

Roadmapping has been recognized as a critical activity for combining a business strategy with release planning. Furthermore, the marketing literature has placed increasing emphasis on the importance of understanding customers' processes and customer value. This paper presents an action research study on the roadmapping problems of two software product companies. The key findings were that customer knowledge was fragmented across different functions of the companies and that roadmapping neglected service development. To solve these problems, analysis of customer value and customers' processes was integrated into roadmapping. Facilitated workshops with cross-functional teams were used to analyze the customers' processes in their entirety and to create a holistic view to roadmapping. The lessons learned offer practical means with which software companies can shift their focus from the prioritization of software features to the analysis of customers' processes and the prioritization of customers' activities. The shift promotes value creation from the customers' perspective and the discovery of new service business opportunities.
Development and validation of a multiplex add-on assay for sepsis biomarkers using xMAP technology.

BACKGROUND Sepsis is a common and often fatal disease. Because sepsis can be caused by many different organisms, biomarkers that can aid in diagnosing sepsis and monitoring treatment efficacy are highly warranted. New sepsis markers may provide additional information to complement the currently used markers. METHODS We used a combination of in-house and commercially available multiplex immunoassays based on Luminex xMAP technology to assay biomarkers of potential interest in EDTA-plasma samples. RESULTS A 3-plex assay for soluble urokinase plasminogen activator receptor (suPAR), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and macrophage migration inhibiting factor (MIF) was developed and validated in-house. This 3-plex assay was added to a commercially available interleukin-1beta (IL-1beta), IL-6, IL-8, granulocyte/macrophage colony-stimulating factor, and tumor necrosis factor-alpha human cytokine panel. No cross-reactivity was observed when the assays were combined. Correlation between values obtained with the 8-plex, the 5-cytokine panel, the 3 in-house 1-plex assays, and a suPAR ELISA ranged from 0.86 to 0.99. Mean within- and between-run CVs were 8.0% and 11%, respectively. Recoveries of suPAR, sTREM-1, and MIF calibrators were 108%, 88%, and 51%, respectively. In plasma collected from 10 patients with bacterial sepsis confirmed by blood culture, the assay detected significantly increased concentrations of all 8 analytes compared with healthy controls. CONCLUSIONS A commercially available xMAP panel can be expanded with markers of interest. The combined multiplex assay can measure the 8 analytes with high reproducibility. The xMAP technology is an appealing tool for assaying conventional cytokines in combination with new markers.
ATG18 and FAB1 Are Involved in Dehydration Stress Tolerance in Saccharomyces cerevisiae

Recently, different dehydration-based technologies have been evaluated for the purpose of cell and tissue preservation. Although some early results have been promising, they have not satisfied the requirements for large-scale applications. The long experience of using quantitative trait loci (QTLs) with the yeast Saccharomyces cerevisiae has proven to be a good model organism for studying the link between complex phenotypes and DNA variations. Here, we use QTL analysis as a tool for identifying the specific yeast traits involved in dehydration stress tolerance. Three hybrids obtained from stable haploids and sequenced in the Saccharomyces Genome Resequencing Project showed intermediate dehydration tolerance in most cases. The dehydration resistance trait of 96 segregants from each hybrid was quantified. A smooth, continuous distribution of the anhydrobiosis tolerance trait was found, suggesting that this trait is determined by multiple QTLs. Therefore, we carried out a QTL analysis to identify the determinants of this dehydration tolerance trait at the genomic level. Among the genes identified after reciprocal hemizygosity assays, RSM22, ATG18 and DBR1 had not been referenced in previous studies. We report new phenotypes for these genes using a previously validated test. Finally, our data illustrates the power of this approach in the investigation of the complex cell dehydration phenotype.
Trends and challenges in dynamic optimization problems

This paper presents the challenges and problems faced in dynamic environment optimization problems. Real world problems are mostly dynamic thus such algorithms are required which can track the moving optima. Classical Optimization algorithm searches sequentially for the solution and are based on differential equations. In order to track the moving optima algorithm will have to be restarted and there is no guarantee that algorithm will find the optimum solution. On the other, Evolutionary algorithms are more suitable for such kind of problems as they can find the multiple optima in parallel. Evolutionary algorithms can track the optima and then repeatedly balance the need to exploit known optima with the need to search for new optima. A number of techniques have been proposed to deal with the dynamic environment problems. Different challenges and problems faced in dynamic environment whether it is for a multimodal functions or multi-objective have been discussed here.
Scale-invariant filtering design and analysis for edge detection

Existing edge detection filters work well on straight edges but make significant errors near sharp corners by producing rounded corners. This is due to the fact that the edge maps produced by these filters are scale variant. We enhance Canny’s optimality criteria to incorporate detection performance near corners as an explicit design objective. The resulting optimal filter, termed ‘Bessel integral filter’, can be derived analytically and exhibits superior performance over recent alternatives, both in terms of numerical accuracy and experimental fidelity. A noise-free localization index is also derived here to account for the detection accuracy of discontinuities forming sharp corners in the absence of noise. We prove here that edges detected by the filters that are not optimal with respect to this noise-free localization index are scale variant. However, the Bessel integral filter proposed here is optimal with respect to the noise-free localization index and therefore it is a scale-invariant filter.
Touching Floating Objects in Projection-based Virtual Reality Environments

Touch-sensitive screens enable natural interaction without any instrumentation and support tangible feedback on the touch surface. In particular multi-touch interaction has proven its usability for 2D tasks, but the challenges to exploit these technologies in virtual reality (VR) setups have rarely been studied. In this paper we address the challenge to allow users to interact with stereoscopically displayed virtual environments when the input is constrained to a 2D touch surface. During interaction with a large-scale touch display a user changes between three different states: (1) beyond the arm-reach distance from the surface, (2) at arm-reach distance and (3) interaction. We have analyzed the user's ability to discriminate stereoscopic display parallaxes while she moves through these states, i. e. , if objects can be imperceptibly shifted onto the interactive surface and become accessible for natural touch interaction. Our results show that the detection thresholds for such manipulations are related to both user motion and stereoscopic parallax, and that users have problems to discriminate whether they touched an object or not, when tangible feedback is expected.
Approximate Stream Analytics in Apache Flink and Apache Spark Streaming

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing - based on the chosen sample size - can make a systematic trade-off between the output accuracy and computation efficiency. Unfortunately, the state-of-the-art systems for approximate computing primarily target batch analytics, where the input data remains unchanged during the course of sampling. Thus, they are not well-suited for stream analytics. This motivated the design of StreamApprox - a stream analytics system for approximate computing. To realize this idea, we designed an online stratified reservoir sampling algorithm to produce approximate output with rigorous error bounds. Importantly, our proposed algorithm is generic and can be applied to two prominent types of stream processing systems: (1) batched stream processing such as Apache Spark Streaming, and (2) pipelined stream processing such as Apache Flink. We evaluated StreamApprox using a set of microbenchmarks and real-world case studies. Our results show that Spark- and Flink-based StreamApprox systems achieve a speedup of $1.15\times$-$3\times$ compared to the respective native Spark Streaming and Flink executions, with varying sampling fraction of $80\%$ to $10\%$. Furthermore, we have also implemented an improved baseline in addition to the native execution baseline - a Spark-based approximate computing system leveraging the existing sampling modules in Apache Spark. Compared to the improved baseline, our results show that StreamApprox achieves a speedup $1.1\times$-$2.4\times$ while maintaining the same accuracy level.
Interference Mitigation by Statistical Interference Modeling in an Impulse Radio UWB Receiver

Some impulse radio UWB (IR-UWB) networks may allow concurrent transmissions without power control (for example MAC protocols that do not use power control, or co-existing, non-coordinated piconets). In such cases, it has been proposed to mitigate multi-user interference (MUI) at the physical layer, but existing proposals for interference mitigation do not account for the multipath nature of UWB channels. We address this problem and propose a receiver that employs a combination of statistical interference modeling and thresholding to mitigate MUI. We find that in a multipath environment the proposed receiver significantly outperforms existing receiver designs that either completely neglect the effect of MUI or only use a simple threshold to reject samples from interfering users. Further, in contrast to successive interference cancellation schemes, our receiver does not require active decoding of each interferer. Thus there is no need to synchronize the receiver with all the interfering users, which would be impractical in an IR-UWB system that is likely to be run in ad hoc mode. To model MUI we consider a hidden Markov model (HMM) and a Gaussian mixture model (GMM). We find that the HMM models interference better than the GMM. However, the resulting performance difference is not huge and comes at the cost of increased receiver complexity
A fast color feature for real-time image retrieval

In this paper, a fast color feature is presented for real-time image retrieval. The feature is based on Dense SIFT (DSIFT) in the multi-scale RGB space. A new sum function is proposed to accelerate feature extraction instead of Gaussian weighting function. In addition, a novel randomized segment-based sampling algorithm is introduced to filter out superfluous features. In the image retrieval stage, a similarity metric is provided to measure the match between the query and reference images. After the experiments are conducted, RGB-DSIFT is more resistant to common image deformations than the original DSIFT, and more efficient than SIFT, CSIFT, GLOH feature in the processing time.
Deep Machine Learning and Neural Networks: An Overview

Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.
DS14: Standard Assessment of Negative Affectivity, Social Inhibition, and Type D Personality

Objective: Type D personality—a joint tendency toward negative affectivity (NA) and social inhibition (SI)—is related to poor cardiac prognosis, but there is no standard for assessing Type D. This study reports on the Type D Scale-14 (DS14) as a standard measure of NA, SI, and Type D. Methods: The study included 3813 participants (2508 from the general population, 573 cardiac patients, 732 hypertension patients). They all filled out the DS14, containing 7-item NA and SI subscales; 275 subjects also completed the NEO-FFI, and 121 patients filled out the DS14 twice. Results: Factor analysis of the DS14 yielded 2 dominant traits; all of the NA and SI items loaded between 0.62 to 0.82 on their corresponding factor (N = 3678). The NA scale covered dysphoria, worry, and irritability; the SI scale covered discomfort in social interactions, reticence, and lack of social poise. The NA and SI scales were internally consistent (&agr; = 0.88/0.86; N = 3678), stable over a 3-month period (test–retest r = 0.72/0.82) and not dependent on mood and health status (N = 121). NA correlated positively with neuroticism (r = 0.68); SI correlated negatively with extraversion (r = −0.59/−0.65). Scale-level factor analysis confirmed the construct validity of the DS14 against the NEO-FFI. Using a cutoff of 10 (NA ≥10 and SI ≥10), 1027 subjects (28%) were classified as Type D, 21% in the general population versus 28% in coronary heart disease and 53% in hypertension (p ≤ .001). Age, sex, and Type D (odds ratio, 3.98; 95% confidence interval, 3.2–4.6; p <.0001) were independently associated with cardiovascular morbidity. Conclusion: The DS14 is a brief, psychometrically sound measure of negative affectivity and social inhibition that could readily be incorporated in epidemiologic and clinical research. CHD = coronary heart diseaseDS14 = Type D Scale-14GMS = Global Mood ScaleHCS = Health Complaints ScaleLVEF = left ventricular ejection fractionNA = negative affectivityNEO-FFI = NEO Five Factor InventorySI = social inhibition.
Using Energy-Efficient Wireless Sensor Network for Cold Chain Monitoring

Temperature control is a major concern in cold chain monitoring and traceability services for the largest cause of food poisoning. Considerable work has examined the use of wireless solutions in warehouses. However, the maintaining of the chain during the transportation phase remains very crucial. In this paper, we present new wireless sensor network (WSN) solution for cold chain monitoring. This solution is based on self-organizing protocols and aims at saving energy in order to increase the sensor lifetime and as a result the global network longevity within the given context. Furthermore, this solution does not need any base station or central equipment. To save energy, periodical active/sleep schedule is adopted in the design of our protocols which were dimensioned and validated by an analytical model described in this paper.
Stability in Graphs and Games

We study graphs and two-player games in which rewards are assigned to states, and the goal of the players is to satisfy or dissatisfy certain property of the generated outcome, given as a mean payoff property. Since the notion of mean-payoff does not reflect possible fluctuations from the mean-payoff along a run, we propose definitions and algorithms for capturing the stability of the system, and give algorithms for deciding if a given mean payoff and stability objective can be ensured in the system.