Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF THERMAL BEHAVIOR FOR A POLYESTER FABRIC WATERPROOF BREATHABLE LAMINATED , USING SKIN MODEL BASED ON SENSOR

The paper presents an analysis of the thermal behavior of a woven polyester structure, laminated with waterproof breathable polyurethane film, using skin model. The skin model depends on sweating guarded hotplate [1, 2] that describes the sensorial skin behavior [3]. For evaluating the thermal behavior for polyester fabric have been performed tests in controlled environment (air velocity, temperature, humidity) by using thermal hotplate for 1 hour. Thermal comfort is an important environmental factor that can indicate the parameters for quality of life and work. For the skin model were performed tests in controlled environment humidity 65% and by varying the thermal sensor values, in the climatic chamber, between 0 and 10 Celsius degree. The values received from sensor, such as REF (evaporative resistance for fabric), skin temperature, relative humidity (RH), air velocity and ambient temperature, have been analyzed using the program MATLAB.
Biofilm formation and multidrug resistance in nosocomial isolates of Acinetobacter

Purpose: To study the biofilm formation and to examine the correlation between antibiotic resistance and biofilm formation among the clinical isolates of Acinetobacter. Materials and Methods: A total of 43 isolates of Acinetobacter collected from samples like peripheral venous catheter tips, urine from Foley’s catheter, central venous catheter tips and endotracheal tube aspirates were preserved and processed. The tube method was performed to qualitatively detect Biofilm production. Antimicrobial susceptibility was done as per CLSI guidelines. Place of Study and Study Period: Yenepoya medical college, Mangaluru and June 2015 to December 2015. Study Design: Prospective Results: Out of 43 isolates of Acinetobacter, 26(60%) showed biofilm production, among which 19 isolates (73%) were Multidrug resistant (MDR) and only 7 isolates (27%) were Non-MDR. Highest sensitivity was seen to colistin (100%) followed by imipenem (73.7%) and least sensitivity to ampicillin (0%) followed by ciprofloxacin (3.8%). Conclusion: Antibiotic resistance was found to be significantly higher among biofilm producing Acinetobacter isolates than nonbiofilm isolates. Routine and advanced studies of the biofilm production will help in making better usage of the invasive devices with less critical complications. Keywords: Biofilms, Acinetobacter, Multidrug resistance.
Cost Inefficiency in the Pakistan Banking Sector 2002-2009

This paper uses the Simar-Wilson bootstrap technology to estimate cost inefficiency in the Pakistan banking sector for the period 2002-2009. Several models of outputs including bad output are considered alongside a common set of inputs. Cost inefficiency is decomposed into its Technical and Allocative inefficiency components. Panel regression methods are used to model the drivers of inefficiency. In general the findings suggest that inefficiency is declining over time and that there is strong conditional convergence to peer group clusters based on branch levels, ownership and specialism. It is found that in general banks with more branches have higher cost inefficiency, the one foreign bank operates on lower cost inefficiency and Islamic banks have higher allocative inefficiency which is offset by a lower technical inefficiency.
AUTOMATIC DETECTION AND RECOGNITION OF MAN-MADE OBJECTS IN HIGH RESOLUTION REMOTE SENSING IMAGES USING HIERARCHICAL SEMANTIC GRAPH MODEL

Abstract. In this paper, we propose a hierarchical semantic graph model to detect and recognize man-made objects in high resolution remote sensing images automatically. Following the idea of part-based methods, our model builds a hierarchical possibility framework to explore both the appearance information and semantic relationships between objects and background. This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. After training local classifiers to calculate parts properties, we use belief propagation to transmit messages quantitatively, which could enhance the utilization of spatial constrains existed in images. Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. The experimental results demonstrate that this method is able to detect manmade objects in complicated surroundings with satisfactory precision and robustness.
Inequality Aversion, Populism, and the Backlash Against Globalization

Motivated by the recent rise of populism in western democracies, we develop a tractable equilibrium model in which a populist backlash emerges endogenously in a strong economy. In the model, voters dislike inequality, especially the high consumption of "elites. " Economic growth exacerbates inequality due to heterogeneity in preferences, which generates heterogeneity in returns on capital. In response to rising inequality, rich-country voters optimally elect a populist promising to end globalization. Equality is a luxury good. Countries with more inequality, higher financial development, and trade deficits are more vulnerable to populism, both in the model and in the data.
Evaluation of the Three Gorges Dam project using multi-criteria analysis (MCA) based on a sustainable perspective

Abstract . The Three Gorges dam of China is one of the largest and expensive hydropower projects of the world. The four main purposes of the project are flood control,energy production, improved navigation and fresh water supply. The dam project has been completed and running successfully with the potential benefits. However, this project is still a controversial issue among many environmentalists and socialists due to various impacts. This study focuses on the benefit and the impacts of the project, and also evaluates the performance of the project using multi-criteria analysis (MCA) approach from a sustainable perspective. Different sustainability criteria related with the dam project have been identified and used for the ranking and rating process. The final result of MCA comes with this scoring process and pairwise comparison, which evaluates the performance of the project considering different positive and negative aspects.
Culturally Embedded Organizational Learning for Global Responsibility

This article proposes a multilevel model of Global Responsibility as a culturally embedded organizational learning process. The model enables an analysis of the way culture influences how responsibilities are defined and distributed in a culture at a given point in time, and how organizations learn to address new responsibilities in new ways when the context changes. The model starts at the organizational level and zooms in on the individual level as well as outward to the local, national, and international levels. The case of a French multinational company subsidiary in Brazil illustrates how the model can be used to show the relative relevance of the different sources of cultural influences on key stages in organizational learning processes. The authors include the arts as an inherent dimension of culture that tends to be overlooked in the management literature, and the case illustrates how the arts can play a role in organizational learning for Global Responsibility.
Designing of Information Grounds in a College Town as a Medium for Facilitating Communication and Social Interaction

This paper is to propose the design of information grounds in terms of placerelated information grounds in college towns to enhance social interaction through communication. In this particular study, a college town is chosen because of the increasing number of information grounds in college towns and effectiveness of social interaction in education. Social interaction is the primary activity at information grounds; therefore, this paper is focused on creating place-related information grounds for enhancing communication and social interaction with interiors based on human emotions and behaviors toward physical settings. The theoretical framework is constructed based on four major areas (social factors of third place, place attachment, place-related information ground factors and M-R model). Specifically, social and physical factors for placerelated information grounds, which include non-purposive and simultaneous focal activity, conviviality, comfort levels, location and permanence, privacy and ambient noise, and relations between physical factors and M-R model, will be mainly analyzed and utilized for the framework. Through this study, the importance of place-related information grounds is presented. Also, it is beneficial to contribute to education by fulfilling the needs of information grounds in a college town. The theoretical framework may be adapted for designing of information grounds in a college town as well as other settings in the design field. Thus, the rationale of making an informational ground for communication and social interaction is a significant aspect of design and the influences in interior space are addressed. The framework may also be used to simulate new insight and investigate multidimensional research.
Establishing the Mechanism of Glutaredoxin

Glutaredoxin (Grx) is a redox enzyme and it belongs to thioredoxin-family with Cys-ProTyr-Cys active site motif. This enzyme catalyzes the reduction of glutathione disulfide (GSSG) to glutathione (GSH) through the dithiol mechanism that includes a Grx-S-S-G mixed disulfide intermediate between the enzyme (Grx) and the substrate (GSSG). Glutaredoxin functions as both electron donors and as regulators of cellular function in response to oxidative stress. Additionally, it functions in sulfur assimilation, dehydroascorbate reduction, and the regulation of cellular differentiation, transcription and apoptosis. Grx and other redox enzymes play a very crucial role in the protection of dopaminergic neurons. The loss of dopaminergic neurons results in Parkinson’s disease; hence, the study of this enzyme becomes important. The work presented in this thesis contains the outcomes of the studies of the catalytic mechanism of the reduction of glutathione disulfide (GSSG) by glutaredoxin (Grx) using the ONIOM extrapolative QM/MM scheme as implemented in Gaussian09 package. The whole thesis contains four chapters. Chapter one presents an introductory information about the enzyme, its characteristic reaction; thiol-disulfide exchange, and the catalytic mechanism of the enzyme. Chapter two dwells on discussing the computational methods as applied to biological systems. This chapter contains four sections: (i) Quantum Mechanics (QM) Methods, (ii) Molecular Mechanics (MM) Methods, (iii) Hybrid Methods with emphasis on QM/MM Methods, and (iv) Computational Enzymatic Catalysis. Chapter three describes the studies that were carried out with the results obtained for the two mechanistic steps of the reaction as well as the conclusions drawn from each step. Lastly, chapter four outlines the general conclusions from the whole work.
What Drives Business Cycle Fluctuations: Aggregate or Idiosyncratic Uncertainty Shocks?

We study jointly the roles of aggregate and idiosyncratic uncertainty shocks in driving business cycle fluctuations. By decomposing total stock return volatility of over 20,000 publicly-listed US firms from 1962 to 2012, we construct separate indices for aggregate and idiosyncratic uncertainty, and run a horse race between them in an otherwise standard macroeconomic VAR. We find that idiosyncratic uncertainty shocks account for a large fraction of fluctuations in economic activity at business cycle frequencies, whereas the impacts of aggregate uncertainty are negligible. Idiosyncratic uncertainty, and not aggregate uncertainty, shocks produce the "sharp drop and rapid rebound" response in activity characterized in Bloom (2009). Idiosyncratic uncertainty shocks to large firms have more powerful macroeconomic impacts than small firms, suggesting "Granular" origins to the role of uncertainty in the macroeconomy. We also find evidence of an economy-wide "buffering effect", in which the effects of large and small firms’ shocks exhibit a negative covariance which dampens down the aggregate effects of idiosyncratic uncertainty shocks on economic fluctuations.
Scalable Bayesian Record Linkage

Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity without a unique identifier. Bayesian methods provide a powerful mechanism for characterizing uncertainty in links between records (via the posterior distribution). The computational complexity of existing Bayesian approaches to PRL severely limits the size of problems to which these techniques can be successfully applied. We propose a new computationally efficient and scalable approach, providing both a fast algorithm for generating a point estimate of the linkage structure that properly accounts for one-to-one matching and a restricted MCMC algorithm that samples from an approximate posterior distribution. These advances make it possible to perform Bayesian PRL for larger problems, and to perform sensitivity analysis with respect to different prior specifications. We demonstrate the methods on simulated and real data.
Variability of the Meridional Overturning in the North Atlantic from the 50-Year GECCO State Estimation

Abstract The German partner of the consortium for Estimating the Circulation and Climate of the Ocean (GECCO) provided a dynamically consistent estimate of the time-varying ocean circulation over the 50-yr period 1952–2001. The GECCO synthesis combines most of the data available during the entire estimation period with the ECCO–Massachusetts Institute of Technology (MIT) ocean circulation model using its adjoint. This GECCO estimate is analyzed here for the period 1962–2001 with respect to decadal and longer-term changes of the meridional overturning circulation (MOC) of the North Atlantic. A special focus is on the maximum MOC values at 25°N. Over this period, the dynamically self-consistent synthesis stays within the error bars of H. L. Bryden et al., but reveals a general increase of the MOC strength. The variability on decadal and longer time scales is decomposed into contributions from different processes. Changes in the model’s MOC strength are strongly influenced by the southward communication of d.
The Distribution of Lifetime Earnings Returns to College

I use Swedish registry data to estimate lifetime earnings returns to college and how they vary with observed and unobserved characteristics. The richness of the data also allows me to examine heterogeneity with respect to cognitive and noncognitive ability and parental earnings. Local instrumental variable analysis is used to recover marginal and average treatment effects under selection on gains. The findings support the notion of self-selection, but mainly on observed characteristics. Returns vary little with parental earnings but substantially with respect to both cognitive and noncognitive ability, thus suggesting important complementarities between formal schooling and informal skills.
Phylogenetic Analysis of Enterococcus, Lactobacillus and Streptococcus Strains on the Basis of abc (Atp Binding Protein) Gene Sequences

Phylogenetic analysis of about 14 strains of Enterococcus, Lactobacillus and Streptococcus was carried out using the nucleotide sequence of the gene for abc (ATP binding protein), The result establish a new phylogenetic tree for the classification of Enterococcus, Lactobacillus and Streptococcus. In comparison with 16s rRNA analysis, the abc sequence indicated a greater evolutionary divergence for the bacteria. Thus, in screening for the presence of bacteria, the abc gene might be a useful tool for differentiating between closely related species of bacteria such as Lactobacillus species, and Streptococcus. At present, 16s rRNA sequence analysis is an accurate and rapid method for identifyieng most unknown bacteria to the genus level because the highly conserved 16Ss rRNA region is easy to amplify; however, analysis of the more variable abc sequence region can identify unknown bacteria to the species level. In summary, we have shown that abc sequence analysis is a useful alternative to 16s rRNA analysis for constructing the phylogenetic relationships of bacteria, in particular for the classification of closely related bacterial species.
Three-Dimensional Seismic Response of Humboldt Bay Bridge-Foundation-Ground System

Soil-structure interaction may play a major role in the seismic response of a bridge structure. Specifically, soil layers of low stiffness and strength may result in permanent displacement of the abutments and foundations, thus imposing important kinematic conditions to the bridge structure. A study to illustrate such phenomena is undertaken based on three-dimensional nonlinear dynamic finite-element (FE) modeling and analysis (for a specific bridge configuration under a given seismic excitation). A bridge-foundation-ground model is developed based on the structural configuration and local soil conditions of the Humboldt Bay Middle Channel Bridge. The FE model and nonlinear solution strategy are built in the open-source software platform OpenSees of the Pacific Earthquake Engineering Research Center. Based on the simulation results, the overall system seismic response behavior is examined, as well as local deformations/stresses at selected critical locations. It is shown that permanent ground deformation may induce settlement and longitudinal/transversal displacements of the abutments and deep foundations. The relatively massive approach ramps may also contribute to this simulated damage condition, which imposes large stresses on the bridge foundations, supporting piers, and superstructure.
Hospital readmissions in frail older people

The majority of hospital in-patients are older people, and many of these are at increased risk of readmission, which can be an adverse outcome for the patient. Currently there is poor understanding as to how best to reduce the risk of readmission. We searched MEDLINE, EMBASE and the Cochrane library for high quality review articles about readmissions. Each review was quality assessed by two reviewers. Grouped data and evidence from original papers is cited with 95% confidence intervals when possible. Nine review studies of sufficient quality were included. Two addressed risk factors for readmission, which included: age, poor functional status prior to admission, length of stay during the index admission, depression, cognitive impairment, malnutrition, social support and social networks/support. The seven other reviews addressed interventions to reduce readmission, which included: discharge planning, post-discharge support, post-discharge case management, and nutritional supplementation. It is possible to identify older people at risk of readmission using well-established risk factors; discharge planning, post-discharge support and nutritional interventions appear to be effective in reducing readmission. Combined interventions appear to be more effective than isolated interventions.
Seasonal Prediction of Tropical Cyclone Frequency over the East China Sea through a Bayesian Poisson-Regression Method

In this study, a Bayesian method has been used to predict the seasonal number of tropical cyclones (TCs) over the East China Sea (25°N-35°N, 120°E-130°E). The method considers two periods each year and provides several different predictions of the number of TCs that will enter the target area during a typhoon season, assigning a probability value to indicate the likelihood of each prediction. The method was used to forecast the number of TCs that would occur over the extended season (June-September) issued by June 1 and over the peak season (July-September) issued by July 1. The three parameters of sea surface temperature (SST), outgoing longwave radiation (OLR), and 850-hPa vorticity (VOR850) were used as predictors, based on lag correlation coefficients with the TCs over the target region for 1979-2003. For the extended season prediction, the SST for February-April, the VOR850 for February 16-May 15, and the OLR for May 1-May 15 were chosen as predictors in the TC forecast system. The three predictors for the peak season prediction are delayed one month relative to those for the extended season prediction. The observed TCs over the target region mainly fall into the prediction range within 25% probability from the median during the period from 1979 to 2007. The method predicts the mean TC frequency with a high level of accuracy, yielding a correlation coefficient of 0.69 and 0.71 and a root mean square error of 1.48 and 1.20 between the predicted and observed TCs for the extended and peak season forecasts, respectively. This TC prediction method may soon be used for operational purposes.
Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.
Is There Still a Case for Merchant Interconnectors? Insights from an Analysis of Welfare and Distributional Aspects of Options for Network Expansion in the Baltic Sea Region

Despite the ongoing appetite of financial investors for merchant investments into the European electricity network, the EC is reluctant to approve such undertakings, thus implicitly favoring regulated investments. Based on a two-level model, we analyze the impact of profit-maximizing merchant transmission investment as compared to welfare-maximizing regulated transmission investment. We apply the model to the Baltic Sea region, which has in the past been subject to rapid interconnector development and still would benefit from increased interconnection. We obtain stable results indicating that merchant investment may well contribute to overall welfare, but at the same time, “the merchant takes it all”, i.e. in many cases merchant profits are close to the overall efficiency gain, and sometimes even higher. These results underline that that distributional aspects, besides mere welfare arguments should be taken into account when analyzing the impact of merchant transmission investment.