Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and thermodynamics of liquid transition metals: integral-equation study of Fe, Co and Ni

The structure and some thermodynamic properties of iron, cobalt and nickel are investigated in the framework of integral-equation theory with the hybridized mean spherical approximation. A local form factor, which combines the empty-core model and s-d mixing through an inverse scattering approach, is used to deduce effective-pair potentials suitable for transition metals via the second-order pseudopotential perturbation method. The resulting internal energy and thermodynamic quantities, necessary to achieve thermodynamic self-consistency, contain in addition a d-band contribution expressed by the Friedel rectangular model for the density of d states. The calculated structure factors S(q) of Fe, Co and Ni near the melting point are found to be in good agreement with experiment and the energies, obtained self-consistently, coincide reasonably well with other published theoretical data.
Some Aspects of the Modelling of Thin-Layer Drying of Sawdust

Drying of spruce, beech, willow, and alder sawdust was examined in a laboratory type dryer. The effect of drying air temperature T (25, 60, and 80 °C) and airflow velocity v (0.01, 0.15, and 1.5 m/s) was investigated. The obtained results demonstrated that drying air temperature and airflow velocity have impacts on the drying of sawdust. The experimental dehydration data of sawdust obtained were fitted to theoretical, semi-theoretical, and empirical thin-layer models. The accuracies of the models were estimated using the correlation coefficient (R), root mean square error (RMSE), and reduced chi-square (χ2). All models except the theoretical model of a sphere described the drying characteristics of sawdust satisfactorily. The effect of T and v on the parameters (constants and coefficients) of the drying models were determined. The effect, by the proposed equations, was also described. This work combines aspects of mechanical engineering and modelling of the drying process.
Image of Transition: Using Embodied Pedagogy to Facilitate Difficult Topics and Build Empathy

In this paper, we share the process of introducing embodied learning techniques to a kinesiology undergraduate class that critically examines power relations and (in)active bodies through a social justice perspective. Drawing on insights from the sociology of the body and applied theatre pedagogy, we sought to use bodily intelligence to interrupt the typical focus on linguistic engagement in the classroom. More specifically, we implemented and assessed an embodied learning activity called “image of transition” which is a branch of Boal’s system called “theatre of oppressed” (1979). “Image of transition” seeks to put the challenges of people’s social lives at the heart of the theatrical process, and to empower audience-participants to create, rehearse, perform, and analyze aesthetic enactments of social problems and potential interventions. In this paper, we reflect on relevant aspects of the facilitation process (what worked well, what did not, and changes made), and also assess the efficacy of our intervention, with a specific focus on whether or not our intervention facilitated the examination of socially-charged topics and if/how it engendered empathy, and the intellectual and affective impact of this perspective taking on students. By way of conclusion, we identify three insights for future research and practice using embodied pedagogy in critical health education contexts.
Supplier Qualification Model (SQM): A Quantitative Model for Supplier Agreements Evaluation

Recently software outsourcing has increasingly widespread due to the valuable economical and technical benefits it introduced to the software development industry. Where the software development organizations adopt a third party to acquire a software project component (product, service). In the acquisition, process companies rely on the CMMI supplier agreement management (SAM) process area to select the potential supplier. Potential suppliers (vendors) are carefully selected through a dedicated process to ensure the delivery of high-quality and reliable services. Most of the published work in the context of how to evaluate and select the right supplier is based on a normal process with plain steps, nevertheless, no literature was reported to evaluate suppliers in a measurable way and select the potentials depending on a quantitative model. The purpose of this paper is to propose a practical quantitative model called the Supplier Qualification Model that enables the organizations to easily evaluate and select the potential suppliers through a measurable approach depends on monitoring and executing the SLAs of the SAM. The proposed model has been verified by implementing it through building an extension for one of the worldwide leading Agile management platforms according to Gartner (Microsoft Team Foundation Server). Multiple versions of the extension were implemented to target the major versions of Microsoft Team Foundation Server and validated by using them in 426 worldwide companies. This proves the suitability of the model to be used. Keywords—Agile practices; vendor selection; CMMI; outsourcing; software acquisition; supplier agreement management; supplier selection; supplier evaluation
An Overview of Sentiment Analysis

: Companies selling products online often ask their customers to review the purchased products and the associated services. As online shopping is becoming more and more popular, the number of reviews received by product from customer is growing rapidly. For a trending product, the number of reviews can be in hundreds or even thousands which makes it difficult for a potential customer to read them and make the correct decision on whether to purchase the product or not. It also makes it difficult for the manufacturing company of the product to keep track of customer opinions and to maintain brand value of the product. For the manufacturer, there are different challenges because many e-commerce sites may sell the same product and the manufacturer normally produces various kinds of products. Sentiment analysis is the technique to understand the sentiment behind given piece of text. Sentiment analysis can be implemented using following approaches: Machine Learning and Lexicon based approach. In this paper, the main focus is on the frequently used techniques for implementing sentiment analysis of the online product reviews.
LEADERSHIP COMPETENCIES FOR POST-SECONDARY ENGINEERING EXPERIENCES : A Delphi Study of Industry Perspectives

The national society of professional engineers has said, “engineering has a direct and vital impact on the quality of life for all people. ” The demand for career-ready engineering professionals has been a consistent, high priority area for the U.S. workforce for the last several decades. Specifically, this call expresses a desire for engineers that are deeply prepared in their technical areas, but also broadly capable as participatory leaders and team members. Thus, leadership-coupled professional competencies that enhance teamwork and problem solving are in high demand from the engineering industry. However, contemporary research suggests that postsecondary engineering programs do not adequately prepare graduates in these areas. This Delphi study identified the consensus perspective of an industry panel regarding the most valuable competencies within the organizational culture of engineering firms. After three iterative rounds, 14 leadership-coupled competencies were identified. Introduction/Literature Review The U.S. Bureau of Labor Statistics recognizes that Engineering is making steady growth across its disciplines and accelerated growth in areas such as civil engineering (Bureau of Labor Statistics, 2019). It is reasonable to expect this trend to continue, given the growing global demand to meet infrastructural, energy-based, and technological challenges. To this end, the United States has identified Science, Technology, Engineering, and Mathematics (STEM) development as a priority in American education (Committee on STEM Education & National Science and Technology Council, 2013). The challenges of today and tomorrow require increasingly cooperative, complex, and creative solutions driven by innovative teams of professionals. Thus, the call to bolster the U.S. workforce pathways in STEM, in part, requires more collaborative and professionally skilled engineers. Professional organizations within the engineering field have expressed a need for a workforce equipped with professional skillsets that match technical competencies (American Society for Engineering Education, 2015; National Academy of Engineering, 2004; Williams & Emerson, 2019). Many of the competencies articulated as professional skills within the engineering industry, such as communication skills, problem-solving skills, etc., are thought of as components of leadership among leadership scholars (Mumford, Zaccaro, Connelly, & Marks, 2000; Seemiller, 2016). In an effort to avoid semantic confusion, such competencies will hereafter be referred to as leadership-coupled competencies. The ABET Student outcomes for accreditation of engineering programs (see table 1) serve as the best representation of how leadership-coupled competencies are intended to be integrated into engineering curricula.
Wearable Banking: A Study of Adoption Factors in Islamic Banking Institutions

—Wearable banking has been coined by banking institutions in providing the latest technology to their customers. This study investigates the determinants which attract the Islamic banks’ customers to adopt wearable banking in Malaysia. The sample size of 350 respondents has been selected from ten Islamic banks in Malaysia using the survey research instrument questionnaire. Data has been analyzed using software SPSS 15.0. The confirmatory factor analysis with multiple regressions technique has been applied. The result of regression analysis shows that Perceived Usefulness, Information of Wearable Banking, Perceived Risk, Security and Privacy shows more influence in increasing the intention of customers to adopt wearable banking services. Meanwhile, Government Support provides more influence for the bankers in adoption of wearable banking services. This study proves that bankers should emphasize more on wearable banking, since bank customers believe it is a convenient method when adopting the banks services.
Postfunctionalization of Azido or Alkyne Poly(3,4-ethylenedioxythiophene) Surfaces: Superhydrophobic and Parahydrophobic Surfaces

Postfunctionalization is a key tool for the elaboration of hydrophobic surfaces. For that purpose, the elaboration of surfaces suitable for the Huisgen reaction allows for functionalization with various hydrophobic side chains. Here the synthesis of azido or alkyne monomers is reported to prepare platform surfaces suitable for click chemistry. The surface hydrophobicity and morphology are investigated. Superhydrophobic and parahydrophobic properties are obtained depending on the starting monomer and the molecule used for the postfunctionalization.
Small-Angle Neutron Scattering from Diblock Copolymer Poly(styrene-d8)-b-poly(γ-benzyl l-glutamate) Solutions: Rod−Coil to Coil−Coil Transition

The elastic properties of rod-coil diblock copolymer poly(styrene-d8)-b-poly(γ-benzyl L-glutamate) (PSd-b-PBLG) rod-coil diblock copolymer solutions were investigated using small-angle neutron scattering (SANS). PBLG is known to undergo a transition from a rodlike α-helical secondary structure to a random coil conformation under specific solvent conditions: the PBLG is (i) rodlike in dioxane and (ii) random coil in dioxane/trifluoroacetic acid (80/20). For the PSd-b-PBLG diblock copolymer, this conformational transition results in a topological change from a rod-coil to a coil-coil type structure. SANS results indeed show the presence of the expected scattering peaks-copolymer signature-for both conformational topologies. The position and the height of these scattering peaks are found in good agreement with a recent theoretical model that describes the conformational change in rod-coil diblock copolymer solutions. Circular dichroism experiments confirmed the change in secondary structure in the peptide block that accompanies the topological transition observed by SANS.
Changes in Health Behaviors, Mental and Physical Health among Older Adults under Severe Lockdown Restrictions during the COVID-19 Pandemic in Spain

Background: We aimed to examine main changes in health behaviors, mental and physical health among older adults under severe lockdown restrictions during the COVID-19. Methods: We used prospective data from 3041 participants in four cohorts of community-dwelling individuals aged [≥]65 years in Spain. Data were obtained using validated questionnaires through a pre-pandemic face-to-face interview and a telephone interview conducted between weeks 7 to 15 after the beginning the COVID-19 lockdown. Lineal or multinomial, as appropriate, regression models with adjustment for the main confounders were used to assess changes in the outcome variables from the pre-pandemic to the confinement period, and to identify their associated factors. Results: On average, the confinement was not associated with a deterioration in lifestyle risk factors (smoking, alcohol intake, diet or weight), except for a decreased physical activity and increased sedentary time, which reversed with the end of confinement. However, chronic pain worsened, and moderate declines in mental health, that did not seem to reverse after restrictions were lifted, were observed. Several subgroups of individuals were at increased risk of developing unhealthier lifestyles or mental health decline with confinement: (i)-males (for physical activity and sedentariness), (ii)-those with greater social isolation (for diet, physical activity, mental health), (iii)-feelings of loneliness (for diet, sleep quality, mental health), (iv)-poor housing conditions (for diet, physical activity, TV viewing time), (v)-unhealthy sleep duration (for physical activity and sedentariness), and (vi-worse overall health or chronic morbidities (for physical activity, screen time, mental health). On the other hand, previously having a greater adherence to the Mediterranean diet and doing more physical activity protected older adults from developing unhealthier lifestyles with confinement. Conclusions: The lockdown during the first wave of the COVID-19 in Spain, which was one of the most restrictive in Europe, only led to minor average changes in health behaviors among older adults. However, mental health was moderately affected. If another lockdown were imposed on this or future pandemics, public health programs should specially address the needs of older individuals with male sex, greater social isolation, poor housing conditions and chronic morbidities, because of their greater vulnerability to the enacted movement restrictions
Self-Supervised Features Improve Open-World Learning

This is a position paper that addresses the problem of Open-World learning while proposing for the underlying feature representation to be learnt using self-supervision. We also present an unifying open-world framework combining three individual research dimensions which have been explored independently \ie Incremental Learning, Out-of-Distribution detection and Open-World learning. We observe that the supervised feature representations are limited and degenerate for the Open-World setting and unsupervised feature representation is native to each of these three problem domains. Under an unsupervised feature representation, we categorize the problem of detecting unknowns as either Out-of-Label-space or Out-of-Distribution detection, depending on the data used during system training versus system testing. The incremental learning component of our pipeline is a zero-exemplar online model which performs comparatively against state-of-the-art on ImageNet-100 protocol and does not require any back-propagation or retraining of the underlying deep-network. It further outperforms the current state-of-the-art by simply using the same number of exemplars as its counterparts. To evaluate our approach for Open-World learning, we propose a new comprehensive protocol and evaluate its performance in both Out-of-Label and Out-of-Distribution settings for each incremental stage. We also demonstrate the adaptability of our approach by showing how it can work as a plug-in with any of the recently proposed self-supervised feature representation methods.
CVD of Nanosized ZnS and CdS Thin Films from Single-Source Precursors

iPrXan)2 @M 5 Zn,Cd; Oi PrXan 5 S2COCH(CH3) 2] as single-source precursors. Thermal decomposition and fragmentation of M(Oi PrXan)2 compounds were investigated by thermal analyses and mass spectrometry. The sulfide films were thoroughly characterized in their composition, nanostructure, and morphology by means of several analytical techniques. Surface and in-depth chemical composition was studied by X-ray photoelectron spectroscopy, X-ray excited Auger electron spectroscopy, and secondary ion mass spectrometry. Film nanostructure and surface topography were investigated as a function of the synthesis conditions by glancing incidence X-ray diffraction and atomic force microscopy, respectively. Optical absorption properties were also studied. Nanophasic and contaminant-free ZnS and CdS thin films with an average crystallite size lower than 25 nm were obtained. The layers mainly contained the hexagonal ~a! sulfide phase and displayed a smooth and regular surface morphology. In the present work, the influence of synthesis conditions on film characteristics is analyzed and discussed.
Controlling False Discovery Rates Using Null Bootstrapping

We consider controlling the false discovery rate for many tests with unknown correlation structure. Given a large number of hypotheses, false and missing discoveries can plague an analysis. While many procedures have been proposed to control false discovery, they either assume independent hypotheses or lack statistical power. We propose a novel method for false discovery control using null bootstrapping. By bootstrapping from the correlated null, we achieve superior statistical power to existing methods and prove that the false discovery rate is controlled. Simulated examples illustrate the efficacy of our method over existing methods. We apply our proposed methodology to financial asset pricing, where the goal is to determine which “factors” lead to excess returns out of a large number of potential factors.
Boosting Deep Transfer Learning For Covid-19 Classification

COVID-19 classification using chest Computed Tomography (CT) has been found pragmatically useful by several studies. Due to the lack of annotated samples, these studies recommend transfer learning and explore the choices of pre-trained models and data augmentation. However, it is still unknown if there are better strategies than vanilla transfer learning for more accurate COVID-19 classification with limited CT data. This paper provides an affirmative answer, devising a novel ‘model’ augmentation technique that allows a considerable performance boost to transfer learning for the task. Our method systematically reduces the distributional shift between the source and target domains and considers augmenting deep learning with complementary representation learning techniques. We establish the efficacy of our method with publicly available datasets and models, along with identifying contrasting observations in the previous studies.
Scaling Up Exact Neural Network Compression by ReLU Stability

We can compress a rectifier network while exactly preserving its underlying functionality with respect to a given input domain if some of its neurons are stable. However, current approaches to determine the stability of neurons with Rectified Linear Unit (ReLU) activations require solving or finding a good approximation to multiple discrete optimization problems. In this work, we introduce an algorithm based on solving a single optimization problem to identify all stable neurons. Our approach is on median 183 times faster than the state-of-art method on CIFAR-10, which allows us to explore exact compression on deeper (5 x 100) and wider (2 x 800) networks within minutes. For classifiers trained under an amount of L1 regularization that does not worsen accuracy, we can remove up to 56% of the connections on the CIFAR-10 dataset. The code is available at the following link, https://github.com/yuxwind/ExactCompression.
BrowseLite: A Private Data Saving Solution for the Web

The median webpage has increased in size by more than 80% in the last 4 years. This extra complexity allows for a rich browsing experience, but it hurts the majority of mobile users which still pay for their traffic. This has motivated several data-saving solutions, which aim at reducing the complexity of webpages by transforming their content. Despite each method being unique, they either reduce user privacy by further centralizing web traffic through data-saving middleboxes or introduce web compatibility (Web-compat) issues by removing content that breaks pages in unpredictable ways. In this paper, we argue that data-saving is still possible without impacting either users privacy or Web-compat. Our main observation is that Web images make up a large portion of Web traffic and have negligible impact on Web-compat. To this end we make two main contributions. First, we quantify the potential savings that image manipulation, such as dimension resizing, quality compression, and transcoding, enables at large scale: 300 landing and 880 internal pages. Next, we design and build BrowseLite, an entirely client-side tool that achieves such data savings through opportunistically instrumenting existing server-side tooling to perform image compression, while simultaneously reducing the total amount of image data fetched. The effect of BrowseLite on the user experience is quantified using standard page load metrics and a real user study of over 200 users across 50 optimized web pages. BrowseLite allows for similar savings to middlebox approaches, while offering additional security, privacy, and Web-compat guarantees.
Meta Back-translation

Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data. However, several recent works have found that better translation quality of the pseudo-parallel data does not necessarily lead to better final translation models, while lower-quality but more diverse data often yields stronger results. In this paper, we propose a novel method to generate pseudo-parallel data from a pre-trained back-translation model. Our method is a meta-learning algorithm which adapts a pre-trained back-translation model so that the pseudo-parallel data it generates would train a forward-translation model to do well on a validation set. In our evaluations in both the standard datasets WMT En-De'14 and WMT En-Fr'14, as well as a multilingual translation setting, our method leads to significant improvements over strong baselines. Our code will be made available.
Optimal Power Flow Considering Time of Use and Real-Time Pricing Demand Response Programs

In recent years, the implementation of the demand response (DR) programs in the power system's scheduling and operation is increased. DR is used to improve the consumers' and power providers' economic condition. That said, optimal power flow is a fundamental concept in the power system operation and control. The impact of exploiting DR programs in the power management of the systems is of significant importance. In this paper, the effect of time-based DR programs on the cost of 24-hour operation of a power system is presented. The effect of the time of use and real-time pricing programs with different participation factors are investigated. In addition, the system's operation cost is studied to analyze the DR programs' role in the current power grids. For this aim, the 14-bus IEEE test system is used to properly implement and simulate the proposed approach.