Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation into Effect of Butt Welding Parameters on Weldment Mechanical Properties

Butt Fusion is the most important welding technique used for joining materials of similar compositions and melting points, resulting in welded joints as strong as the materials itself. The principle of butt fusion is to heat the materials ends to a designated temperature under pressure for a specified amount of time, then the materials ends are fused together under pressure for a period of time. In the present work a set of experiments has been conducted on base material of steel 52-3N (DIN 17100) to study the effect of butt welding process variables on the temperature distributions, welding thermal cycles behavior, peak temperature and the cooling rate for the steel butt-welding joints in the multiple pass welding process by means of the practically measurements. Also the effect of heat input which presented by the welding process variables on the mechanical properties of the welded joints has been investigated to reach the optimum welding variables which give the optimum mechanical properties. It is observed through the experiments that the optimum mechanical properties can be achieved at interpass temperature of 200°, welding voltage of 35V and welding speed of 6 mm/sec.
Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks

The Multiple Sclerosis (MS) lesion segmentation is critical for the diagnosis, treatment and follow-up of the MS patients. Nowadays, the MS lesion segmentation in Magnetic Resonance Image (MRI) is a timeconsuming manual process carried out by medical experts, which is subject to intraand interexpert variability. Machine learning methods including Deep Learning has been applied to this problem, obtaining solutions that outperformed other conventional automatic methods. Deep Learning methods have especially turned out to be promising, attaining human expert performance levels. Our aim is to develop a fully automatic method that will help experts in their task and reduce the necessary time and effort in the process. In this paper, we propose a new approach based on Convolutional Neural Networks (CNN) to the MS lesion segmentation problem. We study different CNN approaches and compare their segmentation performance. We obtain an average dice score of 57.5% and a true positive rate of 59.7% for a real dataset of 59 patients with a specific CNN approach, outperforming the other CNN approaches and a commonly used automatic tool for MS lesion
Clustered deferred and forward shading

This paper presents and investigates Clustered Shading for deferred and forward rendering. In Clustered Shading, view samples with similar properties (e.g. 3D-position and/or normal) are grouped into clusters. This is comparable to tiled shading, where view samples are grouped into tiles based on 2D-position only. We show that Clustered Shading creates a better mapping of light sources to view samples than tiled shading, resulting in a significant reduction of lighting computations during shading. Additionally, Clustered Shading enables using normal information to perform per-cluster back-face culling of lights, again reducing the number of lighting computations. We also show that Clustered Shading not only outperforms tiled shading in many scenes, but also exhibits better worst case behaviour under tricky conditions (e.g. when looking at high-frequency geometry with large discontinuities in depth). Additionally, Clustered Shading enables real-time scenes with two to three orders of magnitudes more lights than previously feasible (up to around one million light sources).
A new method to characterize function of the Drosophila heart by means of optical flow

ABSTRACT The minuteness of Drosophila poses a challenge to quantify performance of its tubular heart and computer-aided analysis of its beating heart has evolved as a resilient compromise between instrumental costs and data robustness. Here, we introduce an optical flow algorithm (OFA) that continuously registers coherent movement within videos of the beating Drosophila heart and uses this information to subscribe the time course of observation with characteristic phases of cardiac contraction or relaxation. We report that the OFA combines high discriminatory power with robustness to characterize the performance of the Drosophila tubular heart using indicators from human cardiology. We provide proof of this concept using the test bed of established cardiac conditions that include the effects of ageing, knockdown of the slow repolarizing potassium channel subunit KCNQ and ras-mediated hypertrophy of the heart tube. Together, this establishes the analysis of coherent movement as a suitable indicator of qualitative changes of the heart's beating characteristics, which improves the usefulness of Drosophila as a model of cardiac diseases. Summary: The analysis of coherent movement in Drosophila is established as a suitable indicator of qualitative changes of the heart's beating characteristics, which improves the usefulness of Drosophila as model of cardiac diseases.
Anaesthesia for caesarean deliveries and maternal complications in a Nigerian teaching hospital.

BACKGROUND The aim of this audit was to evaluate the frequency of caesarean delivery, anaesthetic techniques employed, investigate potential trends and the rate of maternal complications associated with general or regional anaesthesia in our institution. METHODS We reviewed data collected on all deliveries from patients' medical records, anaesthetic charts and relevant surgical notes from 1 January 2008 to 31 December 2010. RESULTS A total of 10,911 deliveries were conducted during the study period and there were 3389 caesarean sections, giving a rate of 31.1%; which showed an upward trend from 27.8% in the first year to 34% in the third year. Our data showed a predominant use of regional anaesthesia for caesarean section generally (86.2%) and 83.8% for emergency caesarean deliveries in line with global trends. The overall complication rate was 10.5%. However, 34.5% of parturients who had general anaesthesia in contrast with 6.7% who had regional techniques had anaesthesia-related complications, postoperative intensive care unit admission rather than recovery room care, intra-operative cardiac arrest and haemorrhage exceeding 1200 ml (p = 0.001). Haemodynamic fluctuations were the most common anaesthesia-related complication. Our data revealed that general anaesthesia was a significant risk factor for maternal complications. CONCLUSION Obstetric general anaesthesia is low in our hospital. Our result showed that general anaesthesia was a significant risk factor for maternal complications during caesarean section.
Potential of plant polyphenols to combat oxidative stress and inflammatory processes in farm animals

Polyphenols are secondary plant metabolites which have been shown to exert antioxidative and antiinflamma tory effects in cell culture, rodent and human studies. Based on the fact that conditions of oxidative stress and inflammation are highly relevant in farm animals, polyphenols are considered as promising feed additives in the nutrition of farm animals. However, in contrast to many studies existing with model animals and humans, potential antioxidative and antiinflammatory effects of polyphenols have been less investigated in farm animals so far. This review aims to give an overview about potential antioxidative and antiinflammatory effects in farm animals. The first part of the review highlights the occurrence and the consequences of oxidative stress and inflammation on animal health and performance. The second part of the review deals with bioavailability and metabolism of polyphenols in farm animals. The third and main part of the review presents an overview of the findings from studies which investigated the effects of polyphenols of various plant sources in pigs, poultry and cattle, with particular consideration of effects on the antioxidant system and inflammation.
A Quinoline Compound Inhibits the Replication of Dengue virus Serotypes 1–4 in Vero Cells

Background The global occurrence of dengue, a mosquito-borne viral disease caused by four distinct dengue viruses (DENV-1, -2, -3 and -4), is reported to have increased approximately 30-fold in the last 50 years, causing approximately 400 million infections a year. A limited use, sub-optimal live attenuated dengue vaccine has become available recently. It is becoming apparent that antibodies to DENVs can promote infection by Zika virus (ZIKV), a related mosquito-borne flavivirus. A drug to treat these flaviviral infections continues to be an unmet public health need. Methods We screened an ‘in-house’ library of approximately 2,000 small molecules for inhibitors of cloned DENV-2 protease. Putative inhibitor binding to DENV-2 protease was analysed by in silico docking. Anti-DENV activity was analysed by monitoring viral antigen synthesis by ELISA, viral RNA synthesis by reverse-transcription coupled to real-time polymerase chain reaction and infectious virus production by plaque assay, in DENV-infected Vero cells. Results A quinoline derivative, BT24, was identified for the first time as a potent inhibitor of the cloned DENV-2 protease (half maximal inhibitory concentration [IC50]=0.5 μM). In silico analysis revealed that BT24 binds to an allosteric site in the vicinity of the active site of DENV-2 protease. Cell-based assays demonstrated that BT24 can inhibit all four DENVs in infected Vero cells. Conclusions BT24 is a DENV-2 protease inhibitor which manifests the capacity to inhibit the replication of all four DENVs in cultured cells. It may provide a lead for a pan-DENV inhibitory drug.
Salient object detection of social images based on semantic tag context

Salient object detection is an important process for machines to understand visual contents as humans. Typically, most previous studies on salient object detection infer salient map by only using the visual features. In this paper, we propose a new paradigm on salient object detection, which aims at producing more reliable results by mining the context information from user's annotated tags. To address this problem, we firstly construct a large scale salient object dataset, which includes 5429 images from the NUSWIDE dataset (a real world web image database from National University of Singapore) with tag information and accurate human-labeled masks. Moreover, a specialised conditional random field (CRF) model is also proposed which takes account of both tag contexts and appearance cues. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects.
Occupation and Risk of Bladder Cancer in Nordic Countries

Objective: The purpose of the study was to describe the variation of bladder cancer incidence according to occupational categories in the Nordic countries. Methods: The study cohort comprised 15 million individuals older than 30 years who participated in one or more population censuses in 1960, 1970, 1980/1981, and/or 1990. Standardized incidence ratios (SIRs) were estimated for 53 occupational categories. Results: Significantly increased SIRs were observed among tobacco workers (1.57; 95% confidence interval [CI] 1.24 to 1.96), chimney sweeps (1.48; 95% CI 1.21 to 1.80), waiters (1.43; 95% CI 1.33 to 1.53), hairdressers (1.28; 95% CI 1.18 to 1.40), seamen (1.22; 95% CI 1.16 to 1.30), printers (1.21; 95% CI 1.14 to 1.30), and plumbers (1.20; 95% CI 1.13 to 1.30). A significantly decreased risk of bladder cancer was observed among gardeners (0.78, 0.75 to 0.80), forestry workers (0.74; 95% CI 0.70 to 0.78), and farmers (0.70; 95% CI 0.68 to 0.71). Conclusions: The SIR of bladder cancer was overall similar across the Nordic countries. The study suggests that occupation is evidently associated with bladder cancer risk.
Complex-Valued Restricted Boltzmann Machine for Direct Speech Parameterization from Complex Spectra

This paper describes a novel energy-based probabilistic distribution that represents complex-valued data and explains how to apply it to direct feature extraction from complex-valued spectra. The proposed model, the complex-valued restricted Boltzmann machine (CRBM), is designed to deal with complex-valued visible units as an extension of the well-known restricted Boltzmann machine (RBM). Like the RBM, the CRBM learns the relationships between visible and hidden units without having connections between units in the same layer, which dramatically improves training efficiency by using Gibbs sampling or contrastive divergence (CD). Another important characteristic is that the CRBM also has connections between real and imaginary parts of each of the complex-valued visible units that help represent the data distribution in the complex domain. In speech signal processing, classification and generation features are often based on amplitude spectra (e.g., MFCC, cepstra, and mel-cepstra) even if they are calculated from complex spectra, and they ignore phase information. In contrast, the proposed feature extractor using the CRBM directly encodes the complex spectra (or another complex-valued representation of the complex spectra) into binary-valued latent features (hidden units). Since the visible-hidden connections are undirected, we can also recover (decode) the complex spectra from the latent features directly. Our speech coding experiments demonstrated that the CRBM outperformed other speech coding methods, such as methods using the conventional RBM, the mel-log spectrum approximate (MLSA) decoder, etc.
Antagonistic Coevolution over Productivity Gradients

This study addresses the question of how spatial heterogeneity in prey productivity and migration act to determine geographic patterns in antagonistic coevolution with a predator. We develop and analyze a quantitative coevolutionary model for a predator‐prey interaction. If the model is modified appropriately, the results could broadly apply to multispecies communities and to herbivore‐plant, parasite‐host, and parasitoid‐host associations. Model populations are distributed over a gradient in prey birth rate (as a measure of productivity). Each population, in each patch, is made up of a suite of strains. Each strain of the predator has a certain ability to successfully attack each strain of the prey. We consider scenarios of isolated patches, global migration, and stepping‐stone (i.e., local) migration over a linear string of patches. The most pervasive patterns are the following: investments in predator offense and prey defense are both maximal in the patches of highest prey productivity; when there are no constraints on maximal investment, mean predation evolves to highest levels in the most productive patches; similarly, the predator has a greater impact (measured as the percentage reduction in prey density) on the prey population in high productivity patches as compared with low productivity ones—in spite (even after evolution) of prey abundance being highest in the most productive patches; and migration has the net effect of shunting relatively offensive and defensive strains from productive patches to nonproductive ones, potentially resulting in the elimination of otherwise rare, low‐investment clones. A modification of the model to gene‐for‐gene type interactions predicts that generalist strains (in terms of the range of strains the predator can exploit or the prey can fend off) dominate in productive areas of the prey, whereas specialists prevail in marginal habitats. Assuming a wide range of productivities over the prey's geographical distribution, the greatest strain diversity should be found in habitats of intermediate productivity. We discuss the implications of our study for adaptation and conservation. Empirical studies are in broad accord with our findings.
PLA/PLGA nanoparticles for delivery of drugs across the blood-brain barrier

Abstract The blood-brain barrier (BBB), which protects the central nervous system (CNS) from unnecessary substances, is a challenging obstacle in the treatment of CNS disease. Many therapeutic agents such as hydrophilic and macromolecular drugs cannot overcome the BBB. One promising solution is the employment of polymeric nanoparticles (NPs) such as poly (lactic-co-glycolic acid) (PLGA) NPs as drug carrier. Over the past few years, significant breakthroughs have been made in developing suitable PLGA and poly (lactic acid) (PLA) NPs for drug delivery across the BBB. Recent advances on PLGA/PLA NPs enhanced neural delivery of drugs are reviewed in this paper. Both in vitro and in vivo studies are included. In these papers, enhanced cellular uptake and therapeutic efficacy of drugs delivered with modified PLGA/PLA NPs compared with free drugs or drugs delivered by unmodified PLGA/PLA NPs were shown; no significant in vitro cytotoxicity was observed for PLGA/PLA NPs. Surface modification of PLGA/PLA NPs by coating with surfactants/polymers or covalently conjugating the NPs with targeting ligands has been confirmed to enhance drug delivery across the BBB. Most unmodified PLGA NPs showed low brain uptake (<1%), which indirectly confirms the safety of PLGA/PLA NPs used for other purposes than treating CNS diseases.
Variational Bayesian Matrix Factorization for Bounded Support Data

A novel Bayesian matrix factorization method for bounded support data is presented. Each entry in the observation matrix is assumed to be beta distributed. As the beta distribution has two parameters, two parameter matrices can be obtained, which matrices contain only nonnegative values. In order to provide low-rank matrix factorization, the nonnegative matrix factorization (NMF) technique is applied. Furthermore, each entry in the factorized matrices, i.e., the basis and excitation matrices, is assigned with gamma prior. Therefore, we name this method as beta-gamma NMF (BG-NMF). Due to the integral expression of the gamma function, estimation of the posterior distribution in the BG-NMF model can not be presented by an analytically tractable solution. With the variational inference framework and the relative convexity property of the log-inverse-beta function, we propose a new lower-bound to approximate the objective function. With this new lower-bound, we derive an analytically tractable solution to approximately calculate the posterior distributions. Each of the approximated posterior distributions is also gamma distributed, which retains the conjugacy of the Bayesian estimation. In addition, a sparse BG-NMF can be obtained by including a sparseness constraint to the gamma prior. Evaluations with synthetic data and real life data demonstrate the good performance of the proposed method.
A viscoelastic–viscoplastic constitutive model considering damage evolution for time dependent materials: Application to asphalt mixes

In this paper, employing the definition of an effective configuration in the continuum damage mechanics, mechanical response of a compressible viscoelastic–viscoplastic material is investigated. We present a phenomenological viscoelastic–viscoplastic–viscodamage constitutive model, developed within the framework of irreversible thermodynamics. The formulation is based on the additive decomposition of the total strain into elastic, viscoelastic, and viscoplastic parts in the effective configuration. Thus, the Helmholtz energy is partitioned into elastic, elastic–viscoelastic, and viscoplastic parts. Then, each one is divided into deviatoric and volumetric energies. Considering the Clausius–Duhem inequality and Drucker–Prager potential function, the effective stress and effective viscoelastic and viscoplastic strain evolution equations are achieved. A damage evolution equation is proposed in the form of a prescribed function. Then, utilizing the semi-implicit discretization method, all the internal variables are found in each increment. The material parameters are calibrated using the creep and creep-recovery tests in the literature. Finally, capability of the proposed model is demonstrated using the model prediction of creep-recovery and repeated creep-recovery tests compared with the experimental data available in the literature for asphalt mixes, where a good agreement is revealed.
Phase structure and mechanical properties of PP/EPR/CaCO3 nanocomposites: Effect of particle's size and treatment

Calcium carbonate (CaCO3) reinforced polypropylene/ethylene propylene rubber (PP/EPR) copolymer composites for automotive use were developed by means of extrusion and injection molding process. Three kinds of CaCO3 (stearic acid treated and untreated) nanoparticles and microparticles were used as fillers. The influence of stearic acid, particle size, and filler content on the state distribution and morphology were investigated by SEM and rheological measurements. Two different morphologies were observed: EPR and CaCO3 dispersed in the PP matrix and a core shell structure, depending on the interactions between EPR and CaCO3. Toughening mechanisms and mechanical properties of the different systems were investigated. Significant improvement in tensile modulus is observed in all composites, depending on filler content. Elongation and notched impact strength were drastically decreased, especially for composites with nano CaCO3. Better impact properties were obtained with low content of treated particles, showing the importance of filler treatment. POLYM. ENG. SCI., 2015. © 2015 Society of Plastics Engineers
Investigation on Mining Subsidence Based on Multi-Temporal InSAR and Time-Series Analysis of the Small Baseline Subset - Case Study of Working Faces 22201-1/2 in Bu'ertai Mine, Shendong Coalfield, China

High-intensity coal mining (large mining height, shallow mining depth, and rapid advancing) frequently causes large-scale ground damage within a short period of time. Understanding mining subsidence under high-intensity mining can provide a basis for mining-induced damage assessment, land remediation in a subsidence area, and ecological reconstruction in vulnerable ecological regions in Western China. In this study, the mining subsidence status of Shendong Coalfield was investigated and analyzed using two-pass differential interferometric synthetic aperture radar (DInSAR) technology based on high-resolution synthetic aperture radar data (RADARSAT-2 precise orbit, multilook fine, 5 m) collected from 20 January 2012 to June 2013. Surface damages in Shendong Coalfield over a period of 504 days under open-pit mining and underground mining were observed. Ground deformation of the high-intensity mining working faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield was monitored using small baseline subset (SBAS) InSAR technology. (1) DInSAR detected and located 85 ground deformation areas (including ground deformations associated with past-mining activity). The extent of subsidence in Shendong Coalfield presented a progressive increase at an average monthly rate of 13.09 km2 from the initial 54.98 km2 to 225.20 km2, approximately, which accounted for 7% of the total area of Shendong Coalfield; (2) SBAS-InSAR reported that the maximum cumulative subsidence area reached 5.58 km2 above the working faces 22201-1/2. The advance speed of ground destruction (7.9 m/day) was nearly equal to that of underground mining (8.1 m/day).
Interface Engineering of Inorganic Thin‐Film Solar Cells – Materials‐Science Challenges for Advanced Physical Concepts

The challenges and research needs for the interface engineering of thin‐film solar cells using inorganic‐compound semiconductors are discussed from a materials‐science point of view. It is, in principle, easily possible to define optimized device structures from physical considerations. However, to realize these structures, many materials' limitations must be overcome by complex processing strategies. In this paper, interface properties and growth morphology are discussed using CdTe solar cells as an example. The need for a better fundamental understanding of cause–effect relationships for improving thin‐film solar cells is emphasized.