Miracle-dz/UniFAR-FTUs huggingface dataset:
PARALLEL SOLUTION ON A PC CLUSTER AND BEHAVIOR ANALYSIS OF BI AND THREE-DIMENSIONAL STRUCTURAL PROBLEMS UTILIZING THE FINITE ELEMENT METHOD

Large and complex engineering problems often need too much computing time and storage to run on single processor computers. Even if they can be solved, more powerful computing capabilities are required to obtain more accurate and reliable results within reasonable time. Parallel processing, the method of having many small tasks to solve one large problem, successfully fulfill such requirements for high-performance computing. We evaluate and compare the performance of two communication libraries, mpich-1.2. 5 and pvm3.4. 4, on a Linux PCs cluster connected by a gigabit Ethernet network, solving two structural problems applied to linear elasticity, utilizing the Finite Element Method for modeling and the Conjugate Gradient Method for the solution of the system equations. This appears to be a viable low cost option, since we utilize already consolidated technologies, with open architecture and public domain softwares. In this work, the mesh generation was done using the program GiD as a preprocessor. For mesh partition, the software package METIS was employed, and the visualization of the partitions was aided by the PMVis program. This article summarizes some numerical simulation results, and the behavior analysis for the two codes developed changing both the mesh granularity and the number of processors.
Implementing Active Learning Approaches to Enhance Learning for Students with Diverse Backgrounds in a First Year Engineering Course

CONTEXT Introduction to Structures 1801ENG, is a first year engineering course which aims to introduce structural concepts to Architecture and Industrial Design students. The students are from different age groups, cultural and educational backgrounds and bring with them different needs and academic potential. Over the past two years, the teaching team has observed that the majority of the students seem to have adopted a surface learning approach, and thus did not retain the knowledge of the material taught over the course of the semester. Previous researchers have shown that approaches to learning are associated with the students’ perceptions of their learning environment Parpala, Lindblom-­‐Ylänne, Komulainen, Litmanen, and Hirsto (2010) as well as different approaches to teaching Trigwell, Prosser, and Waterhouse (1999). It is believed that the passive teaching scheme and the lack of consideration of students’ backgrounds in the design of the learning activities has had a negative effect on students’ engagement, and has encouraged them to adopt surface learning approaches towards the course material.
Performance and Egg Quality Characteristics of Isa White Strain of Layer Chicken Fed Different Energy Levels in a Semi-Arid Zone of Nigeria

An experiment was conducted to evaluate performance of Isa White strain of layer chicken fed diets of different energy levels. A total of one hundred and sixty (160) layer birds aged 42 weeks were used in the experiment. The birds were randomly allocated into four (4) treatment groups of four energy levels (2500, 2600, 2700 and 2800 Metabolizable Energy kcal/kg) replicated four times; each replicate containing 10 birds in a completely Randomized Design (CRD). Daily feed intake and egg production were recorded for eight (8) weeks at mean temperature of 35.5 0 C and relative humidity of 29%. Egg quality assessment was conducted at the end of the feeding trial. The results indicated that the daily feed intake was significantly higher foranimals fed diets containing lowest energy content (2500 Kcals/Kg). Results indicated that hen day egg production and egg shell thicknessdid not differ significantly (P>0.05) between the treatments.Feed Conversion Ratio (FCR)and egg Haugh unit was betteras the energy content of the diets increased from 2500 to 2800Kcal/Kg. However, weight of egg increased with increasing energy content. Cost of feed consumed was better for animals fed diet containing 2600 Kcal/Kg ME. It was concluded that 2600 Kcals/Kg diet resulted in optimum performance of the birds in terms of feed intake, egg production and cost of feed consumed.
EVALUATION ON EQUATION MODELS BASED ON NONNEGATIVE MATRIX FACTORIZATION FOR HYPERSPECTRAL IMAGE FUSION WITH A MULTISPECTRAL IMAGE

The precise spectral information and continuous spectrum of hyperspectral images allow to identify materials of the earth by their reflectance for detailed analysis of remote sensing data. Due to the physical limitation of sensor systems, hyperspectral images usually have lower spatial resolution than panchromatic and multispectral images. Therefore, image fusion techniques of hyperspectral images with additional images having higher spatial resolution are studied to enhance the performance of hyperspectral image analysis. A number of hyperspectral fusion algorithms adopted spectral unmixing technique, which estimates the materials, called endmembers, in a hyperspectral image and the fraction of each endmembers, within a pixel. Recently, nonnegative matrix factorization (NMF) has been studied in various fields, such as text mining and spectral unmixing techniques, for its low complexity and ability to easily include physical constraints based on the non-negativity property, and many studies introduced image fusion, classification, and target detection techniques using hyperspectral images based on NMF. In this study, we constructed and evaluated different combinations of equations for the constrained version of NMF to optimize the spectral characteristics of hyperspectral images and the spatial information of multispectral images in the fusion results. We rearranged equations in different ways of iteration process based on coupled NMF to set the CNMF equation models and applied them to hyperspectral and multispectral images datasets. The fusion results of the suggested equation model could represent the spectral characteristics of hyperspectral images on the fusion results although hyperspectral and multispectral images were collected from different sensors.
Good , Better , Best : The Social Context of Labour-market Success

We extend recent work on relative utility functions to the context of promotions, and labour-market success in general, based on innovative work by Runciman. Receiving a promotion or a pay rise is, as might be expected, associated with greater levels of job satisfaction. However, doing better on the labour market is not the only thing that matters. The best of all is receiving a pay rise that is higher than that received by others. The two sets of ‘relevant others’we consider here are the individual’s partner, and other workers in the same occupational group by region and by year. We also uncover some evidence that the well-being return of having experienced a promotion at work is relative. Journal of Economic Literature Classification No.: D63.
Understanding the risk preferences of the poor

We investigate the extent to which economic experiments and choice models can characterize individual preferences in the presence of decision error and/or confusion. To do this, we use standard experimental methods to collect choices over risky outcomes in a representative sample of over 9,600 poor rural Peruvians, including couples and single individuals. Standard methods are challenging to implement in these populations because of low literacy and numeracy, but choice-based modelling can help purge these biases. Estimates of preferences that account for these biases are significantly correlated with field behavior across several domains (e.g. participating in formal or informal credit markets, agricultural production decisions, timing of first pregnancy) and personal characteristics while naive measures that ignore measurement error are not. We also find that a participant’s propensity to choose at random predicts field behavior and is less frequent among those experiencing recent, bad shocks. The risk preferences of husbands and wives are positively and significantly correlated and both are correlated with the field behavior of the household. Our study highlights the importance of repeated individual measures of preferences and accounting for decision error. JEL codes: XX, YY, C91
Eﬀects of Unsupervised Participation over the Internet on a Usability Study about Map Animation

The Internet has become a major platform for experimental research for several kinds of scientiﬁc and commercial questionnaires, polls, and surveys, including geovisualization and cartographic topics. At the same time, the volume of Internet use by an individual person has increased heavily, and maintaining focus on experimental tasks performed on the Internet can be questioned. Previous comparisons between supervised laboratory and unsupervised Internet conditions have prompted experimental studies over the Internet. This extended abstract compares supervised laboratory conditions with unsupervised conditions over the Internet in a geovisual-ization usability experiment on map animation. The same experimental procedure was run for experimental sessions in both conditions. The results show that reaction times are signifantly aﬀected by the experimental environment, as participants reacted remarkably faster to all tasks over the Internet than in the laboratory. However, quality of answers was aﬀected only in two of ten cases. The results of the reaction times may be explained by a more relaxed and focused environment causing less self-control over the Internet. The results suggest that running experiments over the Internet is appropriate for human experimental research on geovisual tasks. We call for further studies on evaluating the Internet as an experimental research platform. 1998 ACM
Effectiveness of eHealth Interventions in Improving Treatment Adherence for Adults With Obstructive Sleep Apnea: Meta-Analytic Review

Background Poor adherence to continuous positive airway pressure (CPAP) treatment by adults with obstructive sleep apnea (OSA) is a common issue. Strategies delivered by means of information and communication technologies (ie, eHealth) can address treatment adherence through patient education, real-time monitoring of apnea symptoms and CPAP adherence in daily life, self-management, and early identification and subsequent intervention when device or treatment problems arise. However, the effectiveness of available eHealth technologies in improving CPAP adherence has not yet been systematically studied. Objective This meta-analytic review was designed to investigate the effectiveness of a broad range of eHealth interventions in improving CPAP treatment adherence. Methods We conducted a systematic literature search of the databases of Cochrane Library, PsycINFO, PubMed, and Embase to identify relevant randomized controlled trials in adult OSA populations. The risk of bias in included studies was examined using seven items of the Cochrane Collaboration risk-of-bias tool. The meta-analysis was conducted with comprehensive meta-analysis software that computed differences in mean postintervention adherence (MD), which was defined as the average number of nightly hours of CPAP use. Results The meta-analysis ultimately included 18 studies (N=5429 adults with OSA) comprising 22 comparisons between experimental and control conditions. Postintervention data were assessed at 1 to 6 months after baseline, depending on the length of the experimental intervention. eHealth interventions increased the average nightly use of CPAP in hours as compared with care as usual (MD=0.54, 95% CI 0.29-0.79). Subgroup analyses did not reveal significant differences in effects between studies that used eHealth as an add-on or as a replacement to care as usual (P=.95), between studies that assessed stand-alone eHealth and blended strategies combining eHealth with face-to-face care (P=.23), or between studies of fully automated interventions and guided eHealth interventions (P=.83). Evidence for the long-term follow-up effectiveness of eHealth adherence interventions remains undecided owing to a scarcity of available studies and their mixed results. Conclusions eHealth interventions for adults with OSA can improve adherence to CPAP in the initial months after the start of treatment, increasing the mean nightly duration of use by about half an hour. Uncertainty still exists regarding the timing, duration, intensity, and specific types of eHealth interventions that could be most effectively implemented by health care providers.
Automated Identification of Libraries from Vulnerability Data

Software Composition Analysis (SCA) has gained traction in recent years with a number of commercial offerings from various companies. SCA involves vulnerability curation process where a group of security researchers, using various data sources, populate a database of open-source library vulnerabilities, which is used by a scanner to inform the end users of vulnerable libraries used by their applications. One of the data sources used is the National Vulnerability Database (NVD). The key challenge faced by the security researchers here is in figuring out which libraries are related to each of the reported vulnerability in NVD. In this article, we report our design and implementation of a machine learning system to help identify the libraries related to each vulnerability in NVD. The problem is that of extreme multi-label learning (XML), and we developed our system using the state-of-the-art FastXML algorithm. Our system is iteratively executed, improving the performance of the model over time. At the time of writing, it achieves F1 @1 score of 0.53 with average F1 @k score for $k=1,2,3$ of 0.51 $(F_{1}$ @k is the harmonic mean of precision@k and recall@k). It has been deployed in Veracode as part of a machine learning system that helps the security researchers identify the likelihood of web data items to be vulnerability-related. In addition, we present evaluation results of our feature engineering and the FastXML tree number used. Our work formulates and solves for the first time library name identification from NVD data as XML, and deploys the solution in a complete production system.CCS CONCEPTS• Security and privacy → Software security engineering; • Software and its engineering → Software maintenance tools.
OUTPUT-ONLY MODAL ANALYSIS USING FREQUENCY DOMAIN DECOMPOSITION

The advantage of this method is that no artificial excitation needs to be applied to the structure and if artificial loading is required, the force does not need to be measured. Then the excitation can be the own operation condition of the structure. All parameter estimation is based on the response signals only. The modal analysis using output-only focus this problem. In this case, the deterministic knowledge of the input it is replaced by the assumption that the input is a realization of a white noise process and the structural modal parameters of the model is evaluated such as a stochastic identification problem. The present paper discusses the use of frequency domain techniques for the modal identification of output-only mechanical structures. The study described in this paper presents the main results of the Experimental Modal Analysis using output-only. The problem is formulated from the output power spectrum density matrix, which is the only available measured data. The matrix is decomposed in a set of spectral density functions by Singular Value Decomposition (SVD) and the corresponding functions are treated as single degree of freedom system, in order to estimate the modal parameters of the model the implement approach is evaluated with experimental data.
Effect of Health Education on Self Care among known Diabetic Patients

Diabetes is a chronic illness that re-quires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. The prevalence of diabetes mellitus (DM) has increased enormously as there were an estimated 30 million cases in 1985 versus 177 million in 2000 1 . Based on these current trends, >360 million individuals will have diabetes by the year 2030 and the increased prevalence of type 2 DM is likely due to increased obesity and reduced activity levels.We conducted a study to find out the knowledge and practice related to self care among diabetic patients and to assess the improvement in knowledge and practice after health education.It was an Interventional ( experimental) study by taking a pre-test, giving health education and later a post-test conducted in HUDCO colony, Peelamedu involving known Type 2 Diabetic patients. On anlaysis we concluded that theknowledge and practice related to self care in diabetes among known Type 2 Diabetic patients was low and inadequate. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 23-07-2018 Date of acceptance: 07-08-2018 -------------------------------------------------------------------------------------------------------------------------------------
Car cinogenesis in Mouse Stomach by Simultaneous Activation of the Wnt Signaling and Prostaglandin $E_2$ Pathway.

E synthase-1; MVD, microvessel density; PGE 2 , prostaglandin E 2 ; PPAR δ , Peroxisome proliferator-activated receptor δ ; RT-PCR, reverse-transcribed polymerase chain reaction; SPEM, spasmolytic polypeptide (TFF2)-expressing metaplasia; TFF2, trefoil factor 2; vWF, von Willebrand Factor. Abstract Background & Aims : Accumulating evidence indicate that prostaglandin E 2 (PGE 2 ), a downstream product of cyclooxygenase 2 (COX-2), plays a key role in gastric tumorigenesis. The Wnt pathway is also suggested to play a causal role in gastric carcinogenesis. However, the molecular mechanism remains poorly understood how the Wnt and PGE 2 pathways contribute to gastric tumorigenesis. To investigate the role of Wnt and PGE 2 in gastric cancer, we have generated transgenic mice that activate both pathways, and examined their phenotypes. Methods : We constructed K19-Wnt1 transgenic mice expressing Wnt1 in the gastric mucosa using the keratin 19 promoter. We then crossed K19-Wnt1 mice with another transgenic line K19-C2mE to obtain K19-Wnt1/C2mE compound transgenic mice. The K19-C2mE mice express COX-2 and microsomal prostaglandin E synthase-1 (mPGES-1) in the stomach, showing an increased gastric PGE 2 level. We examined the gastric phenotypes of both K19-Wnt1 and K19-Wnt1/C2mE mice. Results : K19-Wnt1 mice had a significant suppression of epithelial differentiation, and developed small preneoplastic lesions consisting of undifferentiated epithelial cells with macrophage accumulation. Importantly, additional expression of COX-2 and mPGES-1 converted the preneoplastic lesions in the K19-Wnt1 mice into dysplastic gastric tumors by 20 weeks of age. Notably, we found mucous cell metaplasia in the glandular stomach of the K19-Wnt1/C2mE mice as early as 5 weeks of age, before
Age-Related Glucose Metabolism in FDG-PET Brain Studies

Craniosynostosis syndromes are rare and prognosis would benefit from early diagnosis and treatment. We tried to establish a fast molecular diagnosis method to detect craniosynostosis syndromes.One patient aged 6 months was observed and diagnosed at first, and then another 10 patients were included in this study. All of them suffered from congenital hydrocephalus with normal head circumference. Karyotype analysis, chromosome genome microarray analysis and whole exome sequencing were used.The first patient was diagnosed as Apert Syndrome based on his clinical features and whole exome sequencing. The other 10 patients were also detected by the same protocol and one of them was found to be with Crouzon Syndrome. Both of the patients with craniosynostosis syndrome were treated in time and benefited from this new diagnosis protocol.Craniosynostosis syndromes cause hearing loss and deformity. It is worthy to detect mutations on FGFR1, FGFR2 and FGFR3 for children who suffer from congenital hydrocephalus with normal head circumference.
Intelligent crack extraction based on terrestrial laser scanning measurement

The hazards of cracks, which could badly decrease reliability and safety of structures, are receiving increasing attention with the popularity of tunnel constructions. Traditional crack inspection relies on visual examination, which is time-, cost- and labor-intensive. Therefore, how to identify and measure cracks intelligently is significantly essential. The paper focuses on the Canny method to extract cracks of tunnel structures by the intensity value of reflectivity. We propose and investigate a novel method which combines dilation and the Canny algorithm to identify and extract the cracks automatically and intelligently based on the point cloud data of terrestrial laser scanning measurement. In order for measurement of cracks, the projection of summed edge pixels is adopted, where a synthesis is carried out on the detection results with all sampling parameters. Based on the synthesized image, vertical crack presents two sharp peaks where the space of the peaks indicates the average width of the crack, as well as its position. The advantage of the method is that it does not require determination of Canny detector parameters. The deviation between manual measurement and Canny detection is 2.92%.
Deep Classiﬁcation of Time-Series Data with Learned Prototype Explanations

The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be conﬁdent applying them to high-risk decisions. In this paper, we leverage data from the latent space induced by deep learning models to learn stereotypical representations or ”prototypes” during training to elucidate the algorithmic decision-making process. We study how leveraging prototypes effect classiﬁcation decisions of two dimensional time-series data in two settings: (1) electrocardiogram (ECG) waveforms to detect clinical bradycardia, a slowing of heart rate, in preterm infants, and (2) audio waveforms to classify spoken digits 1 . We improve upon existing models by optimizing for increased prototype diversity and robustness, visualize how these pro-totypes in the latent space are used by the model to distinguish classes, and show that prototypes are capable of learning features on two dimensional time-series data to produce explainable insights during classiﬁcation tasks.
Developing a Customer-Driven Credit Card: Testing Consumer-Issuer Collaboration in the Barclays Ring Community

The Consumer Finance Institute hosted a workshop in November 2018, featuring Paul Wilmore, then chief marketing officer of Barclays US, to discuss the Barclays Ring credit card program. Through the Ring online community, Barclays engages cardholders directly in the program management, sharing detailed profit and loss data, seeking input on program features, and soliciting ideas on how to better create a relationship between the issuer and the customer. Wilmore reviewed the history of the Ring program and provided an overview of the key successes and challenges Barclays has encountered. This paper summarizes Wilmore? s presentation and provides additional research on online communities.