Miracle-dz/UniFAR-FTUs huggingface dataset:
Embrittlement in Weld Strain- Affected Zone in Carbon Steel

Based on the work of an earlier report (Ref. 1), the authors have tried to find the cause of embrittlement in the strain-affected zone of carbon steel weldments using postheating conditions as a parameter. First, various properties at the tip of the center notch of welded wideplate tensile specimens (Ref. 1) were investigated to find effective tests for revealing the material -differences between as-welded and postheated specimens. Experiment showed that the following tests are effective: (1) ha rdness , (2) f r ac tu re t rans i t i on temperature by Charpy impact test, (3) absorbed energy by modif ied halfsize sharp notch Charpy impact test, and (4) amount of plastic strain by the x-ray diffraction method. Next, to reproduce the embritt lement in the weld strain-affected zone by thermal straining, the specimens were heated to the peak temperature, 450 ± 20 C (842 ± 36 F), under uniaxia l , b iaxial , and tr iaxial restraint, and also heated without restraint. These specimens were then postheated under various heating conditions, and their properties were investigated using the abovementioned tests. This data was compared with that at the notch tip and with the T. TOYOOKA is Consulting Engineer, Welding Research Department, Technical Research Institute, Kawasaki Heavy Industries, Ltd., Kobe, Japan. M. WATANABE is Professor and Director General, Welding Research Institute, Osaka University, Osaka, Japan. Paper was presented at the 56th A WS Annual meeting held in Cleveland, Ohio, April 21-25, 1975. fracture stress data of the previous report using statistical methods.
Performance Analysis of LDPC Coded OFDM System

The standard of 802.11n adopts Low Density Parity Check Codes (LDPCC) as an optional coding scheme. This coding offers high reliability, which is important factor for modern systems. To illustrate the performance of LDPC coded OFDM system over AWGN or Channel with Rayleigh fading, a simulation model was implemented. This paper presents impact of modulation and coding schemes on the LDPC codes efficiency. Also average number of needed iterations to successfully decode LDPC codeword was measured.
Peptide-Carbon Quantum Dots conjugate, Derived from Human Retinoic Acid Receptor Responder Protein 2, against Antibiotic-Resistant Gram Positive and Gram Negative Pathogenic Bacteria

Antibiotic-resistant bacterial infections have become global issues for public health, which increases the utter need to develop alternatives to antibiotics. Here, the HSER (Homo sapiens retinoic acid receptor) peptide was designed from retinoic acid receptor responder protein 2 of Homo sapiens, and was conjugated with synthesized CQDs (carbon quantum dots) for enhanced antibacterial activity in combination, as individually they are not highly effective. The HSER–CQDs were characterized using spectrophotometer, HPLC coupled with electrospray-ionization quadrupole time-of-flight mass spectrometer (ESI–qTOF) mass spectrometer, zeta potential, zeta size, and FTIR. Thereafter, the antibacterial activity against Vancomycin-Resistant Staphylococcus aureus (VRSA) and Escherichia coli (carbapenem resistant) was studied using growth curve analysis, further supported by microscopic images showing the presence of cell debris and dead bacterial cells. The antibacterial mechanism of HSER–CQDs was observed to be via cell wall disruption and also interaction with gDNA (genomic DNA). Finally, toxicity test against normal human epithelial cells showed no toxicity, confirmed by microscopic analysis. Thus, the HSER–CQDs conjugate, having high stability and low toxicity with prominent antibacterial activity, can be used as a potential antibacterial agent.
CryptoMiniSat 2.5.1

Binary xor clauses are handled specially. Firstly, they are regularly searched for using a special heuristic. Secondly, a forest structure is built from them, indicating which variable is equior antivalent with which variable. The top of the trees are regularly replaced with those lower in the tree, reducing the number of clauses and variables in the problem, and usually leading to variable assignments (and possibly even more binary xor clauses).
MicroRNA Prediction Using Small RNA-Sequencing, Advanc- es and Challenges

MicroRNAs (miRNAs) are basically a type of short (~22 nt) single strand RNA molecules predominantly shown in eukaryotes [1]. Most identified miRNAs showed evolutionary conservation throughout different species, some miRNAs even span both plants and animals and showed important and similar gene expression regulation pattern in different model species [2]. Another non-coding small RNA products called small interfering RNA (siRNA) showed similar to miRNA in biogenesis and downstream regulation. The processing of both miRNA and siRNAs are almost same mediated by Dicer and RISC, and the length are 21 to 23 nucleotides for siRNA and 18-25 nt for miRNA, generally. Unlike siRNA, which in general have perfectly complementarity to shut down gene expression. MiRNA, somehow, relies no so much perfect match to target its transcripts which makes it able to target a number of different transcripts. Another basic difference is siRNAs in general shut down gene expression at a post-transcriptional level through mRNA degradation, while miRNAs silence their target genes mainly through translational repression.
Culture in management from the perspective of cultural discourse in the social sciences

The development of the cultural concept of organization and management is a part of a broader thought concerning the study of culture in the social sciences and humanities. There are numerous complex relations between management and other sciences such as sociology, social psychology etc. The subject of the article is the historical analysis of a cultural trend in management in relation to changes in the theory of culture.
What Triggers Stock Market Jumps?

We examine next-day newspaper accounts of large daily jumps in 16 national stock markets to assess their proximate cause, clarity as to cause, and the geographic source of the market-moving news. Our sample of 6,200 market jumps yields several findings. First, policy news – mainly associated with monetary policy and government spending – triggers a greater share of upward than downward jumps in all countries. Second, the policy share of upward jumps is inversely related to stock market performance in the preceding three months. This pattern strengthens in the postwar period. Third, market volatility is much lower after jumps triggered by monetary policy news than after other jumps, unconditionally and conditional on past volatility and other controls. Fourth, greater clarity as to jump reason also foreshadows lower volatility. Clarity in this sense has trended upwards over the past century. Finally, and excluding U.S. jumps, leading newspapers attribute one-third of jumps in their own national stock markets to developments that originate in or relate to the United States. The U.S. role in this regard dwarfs that of Europe and China. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber. org.
D1.4.1b Kernel Canonical Correlation Analysis

Kernel Canonical Correlation Analysis (KCCA) is a technique for finding common semantic features between different views of data. It can be used for finding semantics from different languages that share the same meaning. This information can then be used for mining databases with multilingual text documents. In this report we present theory behind KCCA and application of it on cross-language information retrieval and classification Keyword list: Kernel Canonical Correlation Analysis, semantic space, cross-language text mining, language indepandent representation of text, cross -language information retrieval, cross-language classification WP1 Prototype/Report PU Contractual date of delivery: 31.12. 2004 Actual date of delivery: 29.12. 2004 D1.4. z / Kernel Canonical Correlation Analysis
Emmanuel Flachaire Measuring mobility Article (Published version)

Our new approach to mobility measurement involves separating out the valuation of positions in terms of individual status (using income, social rank, or other crite-ria) from the issue of movement between positions. The quantiﬁcation of movement is addressed using a general concept of distance between positions and a parsimonious set of axioms that characterize the distance concept and yield a class of aggregative indices. This class of indices induces a superclass of mobility measures over the different status concepts consistent with the same underlying data. We investigate the statistical inference of mobility indices using two well-known status concepts, related to income mobility and rank mobility. We also show how our superclass provides a more consistent and intuitive approach to mobility, in contrast to other measures in the literature, and illustrate its performance using recent data from China. This paper focuses on the second interpretation of mobility measurement—mobility measures in the abstract. It develops an approach that is sufﬁciently ﬂexible to cover income or wealth mobility on the one hand, and on the other, various types of “rank” mobility including cases where the underlying data are categorical. There are two reasons why such an approach is needed.
Ocean Remote Sensing in Support of Sea Level Anomalies and Mean Sea Surface Modelling

The exploitation of altimetric data sets from past and current satellite missions is fundamental to both oceanographic and geodetic applications. For oceanographic studies it allows the determination of sea level anomalies, as deviations from a static mean sea level, while it is also fundamental for marine geoid and gravity determination. With the advent of the recent gravity-field dedicated missions of GRACE and GOCE, an abundance of gravity data for the oceans became available as well. A crucial point to studying the variations of the sea surface is proper combination of heterogeneous data, with a statistically rigorous method. The optimal operator for such purposes, used in physical geodesy, is least squares collocation, which needs the input data and error variance-covariance matrices to be known. In this work, altimetric data sets from the satellite remote sensing missions of Jason-1 and ENVISAT have been used to study variation in the sea level and determine of Mean Sea Surface (MSS) models in the Mediterranean Sea. The raw data used are Sea Level Anomaly (SLA) values and their total inverse barometer corrections from the respective altimetric missions acquired by the on-board altimeters. Along-track records of the SLA have been used both to derive linear trends of the SLA variation in the area under study and come to some conclusions on the Mediterranean variability at scales as short as ten days. Empirical covariance functions and the statistical analysis of the SLA along-track repeated satellite records are presented and used to study the variations of the sea level between 2005 and 2010. Moreover, the estimated SLA variations are correlated with climatic indexes like SIO, NAO and MOI in order to conclude on the contribution of atmospheric forcing to the steric part of sea level fluctuations. Finally, the available SLAs are used along with the estimated covariance function to determine single and multi-satellite models of the mean sea surface through least squares collocation, which are then validated against the DTU2010 MSS model.
The Neotropical tree Ilex paraguariensis A. St. Hil. (Aquifoliaceae): pollen and seed dispersal in a fragmented landscape

Ilex paraguariensis is a dioecious, Neotropical tree endemic to South America with wide cultural, economic and ecological signi cance. However, due to extensive fragmentation and deforestation throughout its natural area of occurrence, studies on gene ow and genetic diversity are needed to drive genetic conservation and improvement strategies. Based on the sampling of all adult and juvenile trees in two I. paraguariensis populations, we investigate the reali ed pollen and seed ow and dispersal distance, spatial genetic structure (SGS) and genetic diversity using microsatellite markers. We found limited genetic di erentiation between populations (G’ st = 0.033), but signi cant di erences in terms of genetic diversity (R: 4.7 vs 3.9, H 0 : 0.495 vs 0.371, H e : 0.445 vs 0.375), pollen (10 vs. 23.3%) and seed immigration (0 vs. 3.3%), mating among relatives (t r : 16 vs 30%) and biparental inbreeding (F r : 0.253 vs. 0.345). Within populations, the genetic diversity was similar between adults and juveniles, but adults presented signi cantly lower xation index than juveniles, suggesting selection against inbred individuals between juvenile and adult life stages. We also found similar mean pollen (255 vs. 293 m) and seed (385 vs. 323 m) dispersal distances within populations. Our results show that the studied populations are not genetically isolated and some mating occurs among related trees due to SGS; however, the frequency of biparental inbred individuals decrease over the life course due to inbreeding depression. These results contribute directly to species management and seed collection and inform in situ and ex situ conservation programs.
WNT5A Signaling Contributes to A beta-Induced Neuroinflammation and Neurotoxicity

Neurodegenration is a pathological hallmark of Alzheimer’s disease (AD), but the underlying molecular mechanism remains elusive. Here, we present evidence that reveals a crucial role of Wnt5a signaling in this process. We showed that Wnt5a and its receptor Frizzled-5 (Fz5) were up-regulated in the AD mouse brain, and that beta-amyloid peptide (Ab), a major constituent of amyloid plaques, stimulated Wnt5a and Fz5 expression in primary cortical cultures; these observations indicate that Wnt5a signaling could be aberrantly activated during AD pathogenesis. In support of such a possibility, we observed that inhibition of Wnt5a signaling attenuated while activation of Wnt5a signaling enhanced Ab-evoked neurotoxicity, suggesting a role of Wnt5a signaling in AD-related neurodegeneration. Furthermore, we also demonstrated that Ab-induced neurotoxicity depends on inflammatory processes, and that activation of Wnt5a signaling elicited the expression of proinflammatory cytokines IL-1b and TNF-a whereas inhibition of Wnt5a signaling attenuated the Ab-induced expression of the cytokines in cortical cultures. Our findings collectively suggest that aberrantly up-regulated Wnt5a signaling is a crucial pathological step that contributes to AD-related neurodegeneration by regulating neuroinflammation. Citation: Li B, Zhong L, Yang X, Andersson T, Huang M, et al. (2011) WNT5A Signaling Contributes to Ab-Induced Neuroinflammation and Neurotoxicity. PLoS ONE 6(8): e22920. doi:10.1371/journal.pone. 0022920 Editor: Colin Combs, University of North Dakota, United States of America Received February 8, 2011; Accepted July 7, 2011; Published August 17, 2011 Copyright: 2011 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the start-up funds from the University of Texas Medical Branch to S-JT and by grant No. 30873457 from the National Natural Science Foundation of China and No. 2008A060202010 and 2010B050700019 from the Scientific Technology Project of Guangdong Province of China to LZ. BL is a Ph.D. student fellow supported by China Scholarship Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: shtang@utmb.edu . These authors contributed equally to this work.
A Knowledge Graph for Ecotoxicological Risk Assessment and Effect Prediction

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this PhD research we aim at exploring the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph is being constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. We use ontology alignment techniques to integrate the effect data into the knowledge graph. Our preliminary experimental results show that the knowledge graph based approach improves the selected baselines.
Overview of the CanMoon Lunar Sample Return Analogue Mission

R. Osinski1, M. Bourassa1, C. M. Caudill1, E. A. Cloutis2, P. Christofferson1, P. J. A. Hill 1,3, Z. R. Morse1, J. D. Newman1, E. A. Pilles1, S. L. Simpson1, L. L. Tornabene1, T. Xie1 and the 2019 CanMoon Team. 1Department of Earth Sciences / Institute for Earth and Space Exploration, University of Western Ontario, London, ON, Canada. 2Department of Geography, University of Winnipeg, Winnipeg, MB, Canada. 3Department of Earth and Atmosphere, University of Alberta, Edmonton, AB, Canada (cmarion3@uwo.ca, gosinski@uwo.ca)
AN RFID AND AGENT TECHNOLOGIES BASED SYSTEM FOR THE IDENTIFICATION AND MONITORING OF PATIENTS

The paper summarizes our preliminary findings regarding the development and implementation of SIMPAC – a newly proposed RFID (Radio Frequency IDentification) and agent technologies-based system for the identification and monitoring of patients. The brief evaluation of its relevance for the Romanian health care system and services is accompanied by a short review of the technologies involved in the development of the prototype. The paper focuses upon three main aspects: the architecture of the system, its benefits and its potential users.
Associations between probable anxiety and mood disorder and measures of alcohol and cannabis use in young, middle-aged and older adults.

This study examined the associations of cannabis use, alcohol use and alcohol problems with probable anxiety and mood disorders (AMD) in young, middle-aged and older adults. Method: Data are based on the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults aged 18 years and older. For the purposes of the current study, a merged dataset from the years 2001 through 2009 inclusive was separated into three individual datasets: 18-34 year olds (n=4,211), 35-54 year olds (n=7,874), and 55 years of age and older (n=6,778). The survey included the 12-item version of the General Health Questionnaire, which provides a measure of probable AMD for the general population. Logistic regression analyses examined the odds of probable AMD in three age groups associated with alcohol measures (number of drinks per day and alcohol problems (AUDIT 8+)) and cannabis use, while controlling for self-reported physical health, religious service attendance, and demographic factors. Due to listwise deletion, the logistic regression models were based on reduced samples. Results: Lifetime cannabis use and past year cannabis use predicted probable AMD in young and middle-aged adults, but only lifetime cannabis use predicted probable AMD among older adults. Alcohol problems predicted probable AMD among middle aged and older adults, but not among younger adults. No consistent link between recent alcohol consumption and probable AMD was observed. Conclusion: These analyses suggest that the impact of alcohol and cannabis use and problems on probable AMD may differ across age groups.
1 A Systematic Review of Fault Prediction Approaches used in Software Engineering

BACKGROUND – The accurate prediction of where faults are likely to occur in code is important because it can help direct test effort, reduce costs and improve the quality of software. OBJECTIVE – To summarise and analyse the published fault prediction studies in order to identify approaches used to build, measure and validate the performance of fault prediction models. METHOD – A systematic literature review of fault prediction in 148 studies published from January 2000 to December 2009. Studies are classified in terms of their context, the variables and methods used to build models as well as how the performance of a model is measured and validated. RESULTS – An increasing number of studies use machine learning approaches to predict where faults are likely to occur in code. These use a wide variety of methods to build models. Fault prediction models are based mainly on static code metrics, change data and previous fault data. The performance of models is measured in a range of ways that makes cross comparison very complex. The external validation of models is rarely demonstrated. Models reporting very high performance measures (eg. over 90%) need to be treated with particular caution. CONCLUSION – The literature in this area is just beginning to mature with a small but growing number of studies appearing that transparently report models built using rigorous techniques; the performance of these models have been measured in credible ways. However, many studies do not present their methods clearly enough to enable the performance of their models to be convincingly demonstrated. A more standardised way of building and reporting on such performance is needed before potential model users can confidently evaluate these studies.
Can Central Banks Lead Market Interest Rates? Evidence from Turkey

This study examines the validity of pass-through effect of interest rate and efficiency of monetary policy in Turkey by using monthly data belonging 01:2002 08:2015 period. The study benefits from the recent advance in the time and frequency domain causality tests and employs both conventional and bootstrap process based Toda Yamamoto, frequency domain and rolling windows causality methods. The econometric investigation provides strong evidence of long-run interest rate pass-through in Turkey.
Using Deconvolution and Machine Learning to Improve MSL Curiosity Images: Application to Chemcam/RMI And MAHLI

We investigate the performance of the recently released image processing software suite “Topaz Gigapixel” on martian images from the Mars Science Laboratory “Curiosity” rover. The objective is to evaluate the possibility to visually enhance the apparent resolution of acquired images, using cutting-edge image processing algorithms, in order to improve the mapping and interpretation of small scale features such as layers, laminations, cross-bedding, grains, veins, nodules.
State Machines All The Way Down

A useful pattern in dependently typed programming is to de ne a state transition system, for example the states and operations in a network protocol, as an indexed monad. We index each operation by its input and output states, thus guaranteeing that operations satisfy preand post-conditions, by typechecking. However, what if we want to write a program using several systems at once? What if we want to de ne a high level state transition system, such as a network application protocol, in terms of lower level states, such as network sockets and mutable variables? In this paper, we present an architecture for dependently typed applications based on a hierarchy of state transition systems, implemented in a generic data type ST. This is based on a monad indexed by contexts of resources, allowing us to reason about multiple state transition systems in the type of a function. Using ST, we show: how to implement a state transition system as a dependent type, with type level guarantees on its operations; how to account for operations which could fail; how to combine state transition systems into a larger system; and, how to implement larger systems as a hierarchy of state transition systems. We illustrate the system by implementing a number of examples, including a graphics API, POSIX network sockets, asynchronous programming with threads, and a high level network application protocol.