Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelo cognitivo-contextual del conflicto interparental y la adaptación de los hijos

Title: A cognitive-contextual framework of the marital conflict and child- ren's adjustment. Abstract: The aim of these research is to check cognitive-contextual model's validity in Spanish population as a theoretical framework which can explain the connection between marital conflict and childrens' adjust- ment. 3957 students were included in the sample (50.2% female, 49.8% male; Average age= 15.68 years old). The following variables were as- sessed through questionnaires: children perception of interparental con- flict (CPIC), children's psychological well-being (YSR), academic perform- ance (CDE) and family satisfaction (FS). Hierarchical regression models are able to explain the 22.6% of the variance on children's emotional unrest, and the 26% on academic performance. A covariance structure model (CFI, GFI y AGFI >.95) has confirmed a clear and direct effect of interparental conflict on children's affectivity, but no on academic per- formance, which is more directly affected by children's emotional unrest. In other words, the relation between interparental conflict and academic performance is not direct, it is mediated by the level of affectivity.
Making sense of sensitivity: extending omitted variable bias

We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, naturally handles multiple confounders, possibly acting non‐linearly, exploits expert knowledge to bound sensitivity parameters and can be easily computed by using only standard regression results. In particular, we introduce two novel sensitivity measures suited for routine reporting. The robustness value describes the minimum strength of association that unobserved confounding would need to have, both with the treatment and with the outcome, to change the research conclusions. The partial R2 of the treatment with the outcome shows how strongly confounders explaining all the residual outcome variation would have to be associated with the treatment to eliminate the estimated effect. Next, we offer graphical tools for elaborating on problematic confounders, examining the sensitivity of point estimates and t‐values, as well as ‘extreme scenarios’. Finally, we describe problems with a common ‘benchmarking’ practice and introduce a novel procedure to bound the strength of confounders formally on the basis of a comparison with observed covariates. We apply these methods to a running example that estimates the effect of exposure to violence on attitudes toward peace.
Incidence and risk assessment of venous thromboembolism in cancer patients admitted to intensive care unit for post-operative care

Correspondence to: Hong-Zhi Wang, MD. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Department of Critical Care Medicine, Peking University Cancer Hospital and Institute,Beijing, China, 52 Fucheng Street, Haidian District, 100142 Beijing, China. Tel: +86 013811587782, E-mail: doctorwhz@163.com Received: 01/09/2017; Accepted: 23/09/2017 Incidence and risk assessment of venous thromboembolism in cancer patients admitted to intensive care unit for postoperative care
TEMPORAL DYNAMICS AND RELATIONSHIP OF LAND USE LAND COVER AND LAND SURFACE TEMPERATURE IN DHAKA , BANGLADESH

Analysis of change in LULC and LST helps to understand the UHI effect, as rapid urbanization and industrialization often take place without any coherent planning and control policies in cities of developing countries. This study aims to detect the pattern of LULC change in Dhaka City, the capital of Bangladesh, from the Landsat images of the year 1988, 2002 and 2016, considering four major LULC types named Built Up Area, Bare Lands, Vegetation and Wetlands/Lowlands. Along with the LULC, LST of those years have also been computed from the thermal band of the satellite images using the calibration of spectral radiance and emissivity correction of remote sensing, to investigate the changing nature of the climate. The change detection study has revealed that Dhaka can be characterized by increased Built Up Area and decreased Vegetation and Wetlands within these 28 years. Due to excess population pressure and rapid urbanization, Built Up Area has increased and encroached the Bare Lands and the Wetlands/Lowlands. Regression analysis has been used to characterize the relationship between LULC types and LST, which has revealed a very strong positive correlation (r2=0.9281) between Built Up Area and LST and a very strong negative correlation (r2=0.9556) between Wetlands/Lowlands and LST. This study will facilitate the land use managers and policy makers to monitor how the land is used and understand the development and directions of various kinds of land use, in particular, urban land use in the past, present, and future and to promote substantial and sustainable development accordingly.
Novel Ultrasonic Instrumentation Developments for Real-time Monitoring of Binary Gas Mixtures and Flow : Description and Applications

Custom ultrasonic instruments have been developed for continuous monitoring and real-time measurement of composition and flow in binary gas mixtures. These characteristics are derived from measurements of sound transit time along two opposite directions parallel or tilted to the gas flow direction. The flow rate is then calculated from the transit time difference while the average is used to compute sound velocity by comparison with a sound velocity/composition database. Five devices are integrated in the Detector Control System of the ATLAS experiment at the CERN Large Hadron Collider. Three instruments monitor C3F8 and CO2 coolant leaks into the nitrogen-purged envelopes of the inner silicon tracker; respectively with precisions better than ±2 × 10-5 and ±10-4. Two further instruments are used to monitor the new thermosiphon C3F8 evaporative coolant recirculator. One of these measures C3F8 vapour return flow to the condenser while the other tracks air ingress into the condenser with a precision better than 10-3. The precision of these instruments highlights their potential in other applications requiring continuous binary gas composition monitoring.
Physical Activity Levels at Work and Leisure Among Kindergarten Workers

Background: The low percentage of adults fulfilling the health recommendations standard of physical activity in Norway and in countries all over the world, is a major concern. The study examine how physical activity during work hours contributes to physical activity among kindergarten workers, and how these workers estimate their physical activity level. Methods: Accelerometers were used for seven days to derive percentages of time spent in moderate-to-vigorous physical activity (MVPA) among forty-six randomly selected kindergarten workers in Norway. A questionnaire was used to measure the subjects’ estimation of their own activity. Results: The study found that kindergarten workers achieved 57% of their MVPA during work on work days and 44% of their weekly MVPA through their work. Furthermore, the subjects underestimated their own MVPA during work, but not during leisure. Conclusion and recommendations: The results suggest that physically active work may be a major arena for daily physical activity, and that workers in physically active professions may be unaware of the positive outcomes regarding the contribution of work time to the daily MVPA level. The results point to the importance of making people aware of the positive outcome of physically demanding professions in relaton to fulfill the physical activity reccomendation.
Organizational and Environmental Determinants of Business Intelligence Systems Adoption in Croatian SME : A Case Study of Medium-Sized Enterprise

In the last few years, examples from scientific literature and business practices show that the adoption of technological innovations increases enterprises' performance. Recently, when it comes to the field of information technology innovation, business intelligence systems (BISs) have drawn a significant amount of attention of the scientific circles. BISs can be understood as a form of technological innovation which can bring certain benefits to the organizations that are adopting it. Therefore, the aim of this paper is twofold: (1) to define determinants of successful BISs adoption in small and medium enterprises and thus contribute to this neglected research area and (2) to present the current state of BISs adoption in small and medium-sized companies. In order to do so, determinants are defined and classified into three dimensions, according to the Technology – Organization – Environment (TOE) theoretical framework that describes the impact of each dimension on technological innovations adoption. Moreover, paper brings a case study presenting the adoption of BISs in practice within an organization from tertiary (service) industry sector. Based on the results of the study, guidelines for more efficient, faster and easier BISs adoption are presented. Keywords—Adoption, business intelligence, business intelligence systems, case study, TOE framework.
The Field of ‘ Between ’ – A New Principle for Interdisciplinary Epistemology

Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Global Journal of HUMAN-SOCIAL SCIENCE: H Interdisciplinary Volume 15 Issue 1 Version 1.0 Year 2015 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-460x & Print ISSN: 0975-587X
HOT-MELT EXTRUSION AND INJECTION MOULDING AS MANUFACTURING TOOLS FOR CONTROLLED RELEASE FORMULATIONS ➤

Hot-melt extrusion (HME) is a technique used in the pharmaceutical industry to process formulations containing release-controlling polymers into sustained release matrices. This paper presents two cases studies using HME as process technology to manufacture a multiparticulate dosage form, using ethylcellulose (EC) as matrix former, xanthan gum as hydrophilic additive and ibuprofen or metoprolol tartrate (MPT) as drug. These studies evaluated the influence of a number of formulation and process variables on the physicochemical properties and drug release of the mini-matrices (diameter 3 mm, length: 2 mm): drug type, xanthan gum concentration and grade, plasticizer type, process temperature, production rate, design of extrusion screw. The addition of xanthan gum as hydrophilic polymer was required as the release from drug/EC matrices was too slow. The release from EC/XG matrices depended on the XG concentration and was correlated with the swelling properties of the matrices due to the disruption of the matrix structure. Whereas EC/XG matrices containing ibuprofen did not require a plasticizer due to the plasticizing effect of ibuprofen on EC, the addition of a plasticizer was required to allow HME of MPT-containing formulations. The design of the extrusion screw and the production rate had a limited effect on the macroscopic properties of the mini-matrices, although at least one mixing zone was required to ensure a homogeneous drug distribution. Based on the plasma concentrations of ibuprofen and metoprolol tartrate obtained after oral administration of the matrices to dogs, the in-vivo sustained release properties of EC/XG mini-matrices were confirmed. HME was also combined with injection molding to prepare tablet-shaped sustained-release matrices formulated with ethylcellulose, hydroxypropylmethyl cellulose and metoprolol tartrate. Using dibutyl sebacate as plasticizer processing of these mixtures into high-quality tablets was possibly in a temperature interval between 110 and 140°C. Drug release from the matrices depended on the process temperature as well as on the HPMC concentration and grade.
NO . 13 Capital Flight and War

This paper provides empirical evidence on the effects of inflation on post-war capital flight flows. I test the hypothesis that inflation has a positive additional impact on capital flight flows after war. I use a new panel dataset of 77 developing countries, of which 35 experienced at least one episode of war between 1971 and 2000. I use a range of estimation methods and four capital flight measures – Cline, World Bank Residual, Morgan Guarantee and Dooley. The results consistently support the research hypothesis: Post-war inflation increases annual capital flight flows by about 0.005 to 0.01 percentage points of GDP. This effect is substantial in total at high inflation rates. The implication is that low inflation helps to curb capital flight in post-conflict
on Automation and Robotics in Construction ( ISARC 2018 ) Classification of Images from Construction Sites Using a Deep-Learning Algorithm

Field engineers take and collect several pictures from construction sites every day, and these pictures serve as records of a project. However, many of these images are loaded to and remain on computers in an unorganized manner because tagging, renaming, and organizing them is a time-consuming process. This paper proposes a method for automatically classifying construction photographs by job-type using a deep-learning algorithm. The first goal of this study is to classify construction images according to 27 job-types based on OmniClass Level 2. Google Inception v3—a deep learning algorithm used in this study as an image classifier—was trained using 1,208 construction pictures labeled by job-type. To improve the performance of the classifier, the optimized number of trainings was determined by examining the changes of accuracy and cross-entropy during trainings. The first result shows the incidence of several trainings over 50,000 was not meaningful. The retrained Google Inception as a construction image classifier was validated using a total of 235 images. The validation result shows that the classifier demonstrates an accuracy of 92.6% in classifying inputs properly and an average precision of 58.2% in correct classification. This means that retrained classifier can classify approximately nine out of every ten images correctly and that the deep-learning algorithm has high potential for use in the automatic classification of images from construction sites.
Effective Query Processing on Streamed XML Fragments

Query processing on streamed XML fragments is one of key issues in XML databases. In this paper, XFSeed (XML Fragment Processor with Seed label) is proposed to provide effective query processing by removing many redundant path evaluations and minimizing the number of fragments processed. The conducted experimental results reveal that the proposed scheme efficiently handles query processing and reduces memory usage.
ÏÐÀÊÒÈ×ÅÑÊÈÉ ÀÍÀËÈÇ Efficiency of the Banking Sector in the Russian Federation with International Comparison 1 )

Banks, in most countries, play a crucial role in providing financial intermediation between savers and those who demand credit. Providing low cost funds is essential for investment and growth while providing savers with a good return on their investments. In order for bank intermediation to be effective in economic growth, such intermediation should be done in an efficient manner. So, an efficient functioning banking system is essential for sustained growth. The efficiency of bank intermediation in the Russian Federation has not been addressed before. In this paper, we assess the level of efficiency of the Russian banking industry using bank specific data. We estimate the level of bank efficiency using a standard stochastic frontier model. We find the efficiency level of the Russian banks low compared to international evidence on other countries. Then, we estimate the contributions of different factors that influence the level of efficiency in the Russian banking system. The factors included in the estimations are bank specific factors summarized as CAMEL variables and macroeconomic risk factors such as interest rate and exchange rate risk. We find that equity to asset ratio, ratio of non-performing loans to total loans, interest rate volatility, inflation rate volatility and real effective exchange rate volatility significantly affect intermediation efficiency of banks in the Russian Federation.
SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation

An autonomous system consists of one or more physical or virtual agents that can perform tasks without continuous human guidance. In order to realise their promise, techniques for making such autonomous systems scrutable and transparent are therefore required. To address this issue the Scrutable Autonomous Systems (SAsSy) demo shows how argumentation and natural language can be combined to generate a human understandable dialog explaining the operation of an autonomous system. On the one hand argumentation theory is used to simulate human understandable reasoning mechanisms. On the other, natural language generation tools are used to translate logical statements into simple plain English. The idea is to generate a dialog that enables the user to understand and question the reasoning present in autonomous systems.
Geroch Group Description of Black Holes Bidisha Chakrabarty and Amitabh Virmani

On one hand the Geroch group allows one to associate spacetime independent matrices with gravitational configurations that effectively only depend on two coordinates. This class includes stationary axisymmetric fourand five-dimensional black holes. On the other hand, a recently developed inverse scattering method allows one to factorize these matrices to explicitly construct the corresponding spacetime configurations. In this work we demonstrate the construction as well as the factorization of Geroch group matrices for a wide class of black hole examples. In particular, we obtain the Geroch group SL(3,R) matrices for the five-dimensional Myers-Perry and Kaluza-Klein black holes and the Geroch group SU(2, 1) matrix for the fourdimensional Kerr-Newman black hole. We also present certain non-trivial relations between the Geroch group matrices and charge matrices for these black holes.
An efficient deterministic perturbation theory for selected configuration interaction methods

The interplay between advances in stochastic and deterministic algorithms has recently led to development of interesting new selected configuration interaction (SCI) methods for solving the many body Schr\"{o}dinger equation. The performance of these SCI methods can be greatly improved with a second order perturbation theory (PT2) correction, which is often evaluated in a stochastic or hybrid-stochastic manner. In this work, we present a highly efficient, fully deterministic PT2 algorithm for SCI methods and demonstrate that our approach is orders of magnitude faster than recent proposals for stochastic SCI+PT2. We also show that it is important to have a compact reference SCI wave function, in order to obtain optimal SCI+PT2 energies. This indicates that it advantageous to use accurate search algorithms such as 'ASCI search' rather than more approximate approaches. Our deterministic PT2 algorithm is based on sorting techniques that have been developed for modern computing architectures and is inherently straightforward to use on parallel computing architectures. Related architectures such as GPU implementations can be also used to further increase the efficiency. Overall, we demonstrate that the algorithms presented in this work allow for efficient evaluation of trillions of PT2 contributions with modest computing resources.
DEPTH FACE RECOGNITION THROUGH DEEP LEARNING NETWORKS FINE-TUNING Conference

Face recognition still has many challenges, and with the appearance of the Microsoft Kinect device, new potentials of research were discovered, trying to use the Kinect depth maps as a data source to recognize human faces was considered an interesting research area, mainly because the Kinect could provide the required data in an affordable and accurate way, but till this day no research managed to utilize the new and popular deep learning techniques to achieve higher accuracy and better results on face recognition using any deep learning technique and Kinect 2 depth map images. In this paper, with the goal of enhancing face recognition through the use of depth map images and the lack of a depth map datasets that can be used to train a deep learning network, we introduce a new practice for network fine-tuning, as the methodologies were applied with face depth maps to achieve high face recognition accuracy. The process of building a convolutional neural network and loading weights of a similar Image-net trained network was introduced, where the network was fine-tuned and trained to work with depth map images of faces. The results of the new training procedure presented superior performance compared to any previous methods where depth maps were the source of data.
Using Cognitive Load Theory to Improve Text Comprehension for Students with Dyslexia

Cognitive load theory (CLT) is a theory of instructional design built on our knowledge of human cognitive architecture. Based on the cognitive demands imposed by learning materials and instruction, the theory provides guidelines to manage learning tasks in order to increase knowledge. Whereas most students after grade 3 have automatized word recognition (with variations depending on several factors, such as the language domain), students with dyslexia do not fully automatize word recognition. Therefore, when they read texts, word recognition is so demanding that they have fewer cognitive resources available for comprehension. CLT can be applied to increase text comprehension for students with dyslexia, but, as far as we know, has not been used for this purpose. The chapter discusses how diverse, and sometimes contradictory, literature on increasing text comprehension for students with dyslexia can be interpreted and harmonized through CLT. Implications for practitioners are provided that summarize efficient ways to facilitate text comprehension for students with dyslexia — such as by changing text presentations, applying general CLT principles for students with limited cognitive resources, and using explicit instruction. We conclude that CLT has potential for facilitating learning to read by students with dyslexia.