Miracle-dz/UniFAR-FTUs huggingface dataset:
Universal Testing Machine Motion Control System

Universal testing machine is a kind of basic experimental equipment which can test many mechanical properties of materials such as its stress and strain it is named after the fact that it can perform many standard tensile and compression test on materials, components and structures[1]. All the traditional machine use hydraulic systems which do not have real time control over the loading process for example if the required load to be applied on the specimen is consider 100kg ,then the user only should stop the machine manually and there is no guaranty that the machine stops at 100kg so if the applied load exceeds maximum rated capacity of specimen then the specimen will be damaged. In order to over come this disadvantage of the traditional systems this project aims to achieve real time control over loading process and monitoring the applied load using LabVIEW so that the applied load on the specimen does not exceed the maximum rated capacity. Hence there is a need to improve the control of motion of universal testing machine.
Sentiment Analysis and Challenges Involved : A Survey

Web is a global fence with enormous opportunities for people to share their thoughts and to know their opinions about almost anything in the world. Influence of what other people think has always been a prime part of ones life from generations for a better decision making. The abundant amount of opinions or views expressed by internet users today not only consists of a wealth of intelligent information but also gains monetary benefits, and the urge to be benefited from this knowledge source has led to emergence of a field known as Sentiment Analysis. Opinion Mining is a computational study of opinions which applies Natural Language Processing techniques and computational linguistics which extracts the view of users from various sources on web and analyzes them to identify their polarity. The study investigated briefs about the framework of a Sentiment Analysis system used in general, the various supervised and lexicon based approaches used for the classification, the challenges that are confronted in building an efficient system and lastly, it covers the various applications of Sentiment Analysis.
ACCOUNTING FOR SPATIAL AUTOCORRELATION IN MODELING THE DISTRIBUTION OF WATER QUALITY VARIABLES

OF THESIS ACCOUNTING FOR SPATIAL AUTOCORRELATION IN MODELING THE DISTRIBUTION OF WATER QUALITY VARIABLES Several studies in hydrology have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this thesis, I hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. I selected ten watersheds in the USA and analyzed them to determine whether water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R2) and greater decreases in residual SAC (rSAC) after spatial modeling. I compared non-spatial ordinary least squares to two spatial regression approaches, namely, spatial lag and error models. The predictors were the principal components of topographic, land cover, and soil group variables. The results revealed that water quality variables with higher inherent SAC showed more substantial increases in R2 and decreases in rSAC after performing spatial regressions. In this study, I found a generally linear relationship between the spatial model outcomes (R2 and rSAC) and the degree of SAC in each water quality variable. I suggest that the inherent level of SAC in response variables can predict improvements in models before spatial regression is performed. The benefits of this study go beyond modeling selection and performance, it has the potential to uncover hydrologic connectivity patterns that can serve as insights to water quality managers and policy makers.
Performance Improvement in Adaptive Routing Strategy in Mobile Adhoc Network

: It has been a big challenge to develop a routing protocol that can meet different application needs and optimize routing paths according to the topology change in mobile adhoc networks. Based on their forwarding decisions only on the local topology, geographic routing protocols have drawn a lot of attention in recent years. In routing, nodes need to maintain up-to-date status of their immediate neighbors for making effective forwarding decisions. The periodic broadcasting of beacon packets that contain the geographical location coordinates of the nodes is a popular method used by most routing protocols to maintain neighbor status. Each node can determine and adjust the protocol parameter values independently according to different network environments, data traffic conditions and node’s own requirements. The project offers the Adaptive Status Update strategy for routing, which dynamically adjusts the frequency of status updates based on the mobility dynamics of the guests and the forwarding patterns in the network based on nodes whose movements are harder to predict update their status more frequently, and nodes closer to forwarding paths update their Status more frequently.
Promotion Signals, Experience, and Education

This paper uses a nation-wide representative survey of employees to examine whether more informative job promotions carry larger wage increases. In job assignment models with asymmetric information, unexpected promotions send a signal to the external labor market to revise upward their assessment of a worker's ability. The employing firm must then increase wages to prevent the worker from being bid away. Less educated workers are assumed to come from a group with lower average ability. Their promotion is hypothesized to induce a larger positive update of the assessment of their ability than the promotion of more educated workers. Promotions of less experienced workers, with less known about their abilities, should also result in strong signaling effects. We obtain regression results consistent with our hypotheses, although the size and significance of the estimates hinge on the promotion definition. Inexperienced workers gain more from promotions that entail new managerial responsibilities, whereas less educated workers gain more from nonmanagerial promotions. This sensitivity to the definition of promotion suggests that promotions reveal information on different dimensions of ability for different types of workers.
1 Examining the genetic background of porcine muscle 2 growth and development based on transcriptome and 3 miRNAome data 4

Recently, selection in pigs has been focused on improving the lean meat content in 16 carcasses; this focus has been most evident in breeds constituting a paternal component in breeding. 17 Such sire-breeds are used to improve the meat quantity of cross-breed pig lines. However, even in 18 one breed, a significant variation in the meatiness level can be observed. In the present study, the 19 comprehensive analysis of genes and microRNA expression profiles in porcine muscle tissue was 20 applied to identify the genetic background of meat content. The comparison was performed 21 between whole gene expression and miRNA profiles of muscle tissue collected from two sire-line 22 pig breeds (Piertain, Hampshire). The RNA-seq approach allowed the identification of 627 and 416 23 differentially expressed genes (DEGs) between pig groups differing in terms of loin weight between 24 Pietrain and Hampshire breeds, respectively. The comparison of miRNA profiles showed 25 differential expression of 57 microRNAs for Hampshire and 34 miRNAs for Pietrain pigs. Next, 43 26 genes and 18 miRNAs were selected as differentially expressed in both breeds and potentially 27 related to muscle development. According to Gene Ontology analysis, identified DEGs and 28 microRNAs were involved in the regulation of the cell cycle, fatty acid biosynthesis and regulation 29 of the actin cytoskeleton. The most deregulated pathways dependent on muscle mass were the 30 Hippo signalling pathway connected with the TGF-beta signalling pathway and controlling organ 31 size via the regulation of ubiquitin-mediated proteolysis, cell proliferation and apoptosis. The 32 identified target genes were also involved in pathways such as the FoxO signalling pathway, 33 signalling pathways regulating pluripotency of stem cells and the PI3K-Akt signalling pathway. The 34 obtained results indicate molecular mechanisms controlling porcine muscle growth and 35 development. Identified genes (SOX2, SIRT1, KLF4, PAX6 and genes belonging to the transforming 36 growth factor beta superfamily) could be considered candidate genes for determining muscle mass 37 in pigs. 38
Measurements of DNA Twist Using the Freely-orbiting Magnetic Tweezers

Single-molecule techniques make it possible to investigate the behavior of individual biological molecules in solution in real time. These techniques include so-called force spectroscopy approaches such as atomic force microscopy, optical tweezers, flow stretching, and magnetic tweezers. Amongst these approaches, magnetic tweezers have distinguished themselves by their ability to apply torque while maintaining a constant stretching force. Here, it is illustrated how such a “conventional” magnetic tweezers experimental configuration can, through a straightforward modification of its field configuration to minimize the magnitude of the transverse field, be adapted to measure the degree of twist in a biological molecule. The resulting configuration is termed the freely-orbiting magnetic tweezers. Additionally, it is shown how further modification of the field configuration can yield a transverse field with a magnitude intermediate between that of the “conventional” magnetic tweezers and the freely-orbiting magnetic tweezers, which makes it possible to directly measure the torque stored in a biological molecule. This configuration is termed the magnetic torque tweezers. The accompanying video explains in detail how the conversion of conventional magnetic tweezers into freely-orbiting magnetic tweezers and magnetic torque tweezers can be accomplished, and demonstrates the use of these techniques. These adaptations maintain all the strengths of conventional magnetic tweezers while greatly expanding the versatility of this powerful instrument.
Reliable e-Assessment with GIT - Practical Considerations and Implementation

The introduction of e-Assessment is hindered by reservations, despite e-Assessment being a valuable tool for teaching and examining in general. These reservations concern mainly the fairness and reliability of e-Assessment as well as financial questions. The latter can be solved in practice by utilizing BYOD, since most of today’s students already possess devices that are capable of being used for e-Assessment. This poses, however, additional challenges to the fairness and reliability of e-Assessment. This paper presents an approach that utilizes cryptographic methods, like public key cryptography and digital signatures, and the version control system GIT in order to overcome this challenges. Based on these techniques a framework for e-Assessment was implemented as client-server architecture.
Matching Bundles of Axons Using Feature Graphs

Matching thin neuronal processes on nanometer scale between neighboring image volumes which are affected by distortions and noise is hard, especially when the processes appear in bundles. Classical image alignment methods mostly use feature points to minimize pixel-to-pixel dissimilarities. This, however, renders knowledge about the data like physical and biological properties useless by not involving it into the matching, which can lead to erroneous results in problematic matching regions. In this work, a pipeline approach to semantically match precarious bundles of axons using feature neighborhood graphs (FNG), in addition to a rather classical alignment method based on correspondences, is presented. The FNG approach provides a framework for data preprocessing, feature extraction, matching and evaluation applicable to the assignment of fiber bundles, considerable as collections of similar components, across overlapping datasets. Theoretical analysis on the efficiency of the developed methods is provided, showing that, though simple, FNG matching performs reasonably well on drastically distorted data and is highly effective when utilizing learned predictions from a trained model to assess match compatibility.
Towards Confidence Interval Estimation in Truth Discovery

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.
Kinematic control of male Allen's hummingbird wing trill over a range of flight speeds

ABSTRACT Wing trills are pulsed sounds produced by modified wing feathers at one or more specific points in time during a wingbeat. Male Allen's hummingbirds (Selasphorus sasin) produce a sexually dimorphic 9 kHz wing trill in flight. Here, we investigated the kinematic basis for trill production. The wingtip velocity hypothesis posits that trill production is modulated by the airspeed of the wingtip at some point during the wingbeat, whereas the wing rotation hypothesis posits that trill production is instead modulated by wing rotation kinematics. To test these hypotheses, we flew six male Allen's hummingbirds in an open-jet wind tunnel at flight speeds of 0, 3, 6, 9, 12 and 14 m s−1, and recorded their flight with two ‘acoustic cameras’ placed below and behind, or below and lateral to the flying bird. The acoustic cameras are phased arrays of 40 microphones that used beamforming to spatially locate sound sources within a camera image. Trill sound pressure level (SPL) exhibited a U-shaped relationship with flight speed in all three camera positions. SPL was greatest perpendicular to the stroke plane. Acoustic camera videos suggest that the trill is produced during supination. The trill was up to 20 dB louder during maneuvers than it was during steady-state flight in the wind tunnel, across all airspeeds tested. These data provide partial support for the wing rotation hypothesis. Altered wing rotation kinematics could allow male Allen's hummingbirds to modulate trill production in social contexts such as courtship displays. Summary: The sexually selected 9 kHz wing trill of male Allen's hummingbirds is substantially louder in maneuvers than in rectilinear flight, and appears to be produced in supination.
Exploiting Multilingual Corpora Simply and Efficiently in Neural Machine Translation

In this paper, we explore a simple approach for “Multi-Source Neural Machine Translation” (MSNMT) which only relies on preprocessing a N-way multilingual corpus without modifying the Neural Machine Translation (NMT) architecture or training procedure. We simply concatenate the source sentences to form a single, long multisource input sentence while keeping the target side sentence as it is and train an NMT system using this preprocessed corpus. We evaluate our method in resource poor as well as resource rich settings and show its effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using 5 source languages) and compare them against existing approaches. We also provide some insights on how the NMT system leverages multilingual information in such a scenario by visualizing attention. We then show that this multi-source approach can be used for transfer learning to improve the translation quality for single-source systems without using any additional corpora thereby highlighting the importance of multilingual-multiway corpora in low resource scenarios. We also extract and evaluate a multilingual dictionary by a method that utilizes the multi-source attention and show that it works fairly well despite its simplicity.
Peroxidasin contributes to lung host defense by direct binding and killing of gram-negative bacteria

Innate immune recognition is classically mediated by the interaction of host pattern-recognition receptors and pathogen-associated molecular patterns; this triggers a series of downstream signaling events that facilitate killing and elimination of invading pathogens. In this report, we provide the first evidence that peroxidasin (PXDN; also known as vascular peroxidase-1) directly binds to gram-negative bacteria and mediates bactericidal activity, thus, contributing to lung host defense. PXDN contains five leucine-rich repeats and four immunoglobulin domains, which allows for its interaction with lipopolysaccharide, a membrane component of gram-negative bacteria. Bactericidal activity of PXDN is mediated via its capacity to generate hypohalous acids. Deficiency of PXDN results in a failure to eradicate Pseudomonas aeruginosa and increased mortality in a murine model of Pseudomonas lung infection. These observations indicate that PXDN mediates previously unrecognized host defense functions against gram-negative bacterial pathogens.
Algebraicity of the metric tangent cones and equivariant K-stability

We prove two new results on the K K -polystability of Q \mathbb {Q} -Fano varieties based on purely algebro-geometric arguments. The first one says that any K K -semistable log Fano cone has a special degeneration to a uniquely determined K K -polystable log Fano cone. As a corollary, we combine it with the differential-geometric results to complete the proof of Donaldson-Sun’s conjecture which says that the metric tangent cone of any point appearing on a Gromov-Hausdorff limit of Kähler-Einstein Fano manifolds depends only on the algebraic structure of the singularity. The second result says that for any log Fano variety with the torus action, K K -polystability is equivalent to equivariant K K -polystability, that is, to check K K -polystability, it is sufficient to check special test configurations which are equivariant under the torus action.
Survey on Recognize Malignant Facebook Application

With daily installs and use of third party apps are important reasons for the popularity and addictiveness of facebook. Hackers realized the potential of using apps for spreading spam and malware. Here the problem is already find out so it gives 13% of apps are malicious. So researchers are focused on detecting malicious posts and campaigns. Here question may arise that given a Facebook application, can we determine if it is malicious? So key is to developing REAppEFacebook’s Rigorous Application Evaluator is the first tool focused on detecting malicious apps on Facebook. In this paper we discussed the survey on different techniques used for malicious apps protection for facebook.
Mapping topsoil field-saturated hydraulic conductivity from point measurements using different methods

Abstract Topsoil field-saturated hydraulic conductivity, Kfs, is a parameter that controls the partition of rainfall between infiltration and runoff and is a key parameter in most distributed hydrological models. There is a mismatch between the scale of local in situ Kfs measurements and the scale at which the parameter is required in models for regional mapping. Therefore methods for extrapolating local Kfs values to larger mapping units are required. The paper explores the feasibility of mapping Kfs in the Cévennes-Vivarais region, in south-east France, using more easily available GIS data concerning geology and land cover. Our analysis makes uses of a data set from infiltration measurements performed in the area and its vicinity for more than ten years. The data set is composed of Kfs derived from infiltration measurements performed using various methods: Guelph permeameters, double ring and single ring infiltrotrometers and tension infiltrometers. The different methods resulted in a large variation in Kfs up to several orders of magnitude. A method is proposed to pool the data from the different infiltration methods to create an equivalent set of Kfs. Statistical tests showed significant differences in Kfs distributions in function of different geological formations and land cover. Thus the mapping of Kfs at regional scale was based on geological formations and land cover. This map was compared to a map based on the Rawls and Brakensiek (RB) pedotransfer function (mainly based on texture) and the two maps showed very different patterns. The RB values did not fit observed equivalent Kfs at the local scale, highlighting that soil texture alone is not a good predictor of Kfs.
Seasonality in Indian Stock Market: A re-examination of January Effect

A blend of evidences is on hand regarding the existence of January/November/December Effects on Indian stock market. The arguments in favor of January Effect claim highest average returns in the month of January in comparison with the expected returns in rest of the months of the year, individually. More than a few elucidations are provided by researchers for the existence of January Effect on various stock exchanges. The present study has spotlighted the existence of January/December/November/April effect anomalies in Indian stock market. It is based upon the empirical findings of the two major benchmarks of Bombay Stock Exchange for a period of more than ten years. The empirical results obtained in the study have discarded the possibility of January Effect on Indian Capital Market. But, in an attempt to examine the January Effect a few captivating information is discerned through which the investors may be able to strategize their trading activities in a way that may result in to abnormal returns to them. The overall findings stated that November and December months can be important for the investors to attain abnormal returns, instead of January and April. Further research can be made on the seasonality of the financial and commodity derivative market for these months to identify the more factual position of the seasonality over the Indian stock market.