Miracle-dz/UniFAR-FTUs huggingface dataset:
The transmission of the ECB ’ s recent non-standard monetary policy measures

This article evaluates the transmission through bank intermediation, bank lending and money of the ECB’s non-standard measures announced since June 2014, namely the credit easing package, focusing on the targeted longer-term refinancing operations (TLTROs), and the expanded asset purchase programme (APP), focusing on the public sector purchase programme (PSPP). The results presented suggest that these measures have significantly lowered yields in a broad set of financial market segments, with the effects generally increasing with maturity and riskiness. Both programmes have contributed to a reduction in banks’ funding costs, which has incentivised them to pass on the cost relief to final borrowers by granting more credit at better conditions. Overall, the improved credit conditions in the euro area have helped push the monetary policy accommodation through the intermediation chain to reach households and firms.
Do Acquirers Overstate Earnings Prior to a Share for Share Bid ?

Earnings management by acquirers ahead of share for share bids may affect whether a bid succeeds, and hence which management team controls the target’s assets, as well as the distribution of gains between target and acquirer shareholders. This paper tests for such earnings management for the UK, the world’s second largest takeover market in the period 1997-2001, when M&A reached record levels and share for share deals came to account for the majority of expenditure. Using a range of approaches originating in Jones’ model, the paper finds evidence consistent with earnings management ahead of share-financed bids.
Dynamic modeling of flexure jointed hexapods for control purposes

A number of researchers have been investigating the use of Stewart platforms (or hexapods) for precision applications. This paper presents a new dynamic model suitable for control of flexure jointed hexapods. Novel contributions include: 1) base acceleration inputs are included; 2) the dynamic model is experimentally verified; 3) the model is developed so that it is suitable for control, and 4) a decoupled force control is derived.
A NUMERICAL STUDY USING MIXTURES OF WATER-ETHYLENE GLYCOL BASED NANOFLUIDS ON LAMINAR HEAT TRANSFER OF AN ANNULUS

In this study, developing laminar flow and heat transfer behaviour of ethylene glycol (EG) and water mixture based SiO2 nanofluids in an annulus have been numerically investigated. A constant heat flux was applied to the inner walls of the annulus with 100 W / m2. Water 100% EG 0%, water 50% EG 50% and water 0% EG 100% mixtures have been utilized as the base fluids. SiO2 nanoparticles have been used with d = 20 nm and volume fractions φ =0%-4%. The Reynolds number varies from 200 to 1000. The physical model of the test section mainly consists of two concentric horizontal cylinders that form an annular space ranging from two interconnected elliptical tubes with axis ratio (r1/r2=1/2) placed at the centre of a circular cylinder with major radius of 2r2 with the length of 1 m. Governing equations have been solved with Ansys Fluent programme. The velocity distribution, temperature contours, average Nusselt number and thermal-hydraulic performance have been analysed and presented. The effects of nanofluids have been examined on heat and flow fields and it has been observed that the heat transfer increases together with the nanoparticle volume concentration. When the nanofluid is used in a forced convection, the amount of heat transfer increases as the Reynolds number increases. The highest value of the average Nusselt number was obtained in the EG based nanofluid with φ=4% and Re=1000 as 29.14, and the lowest value was obtained in the water-based nanofluid with φ=4% and Re=200 as 5.61. Results show that the use of nanofluid in the annulus channel increases the thermal performance of systems.
Using Face Images to Investigate the Differences between LDA and SVM Separating Hyperplanes

Statistical separating hyper-planes have played an important role of characterizing differences betwee n a reference group of patterns and the population under inv estigation. In this paper, we compare LDA-based an d SVM separating hyper-planes for extracting group-differ ences between images. Our experimental results on face images indicate that SVM is a more robust technique for classification than the LDA-based method. How ever, the hyper-planes found by both approaches are disti nct from the point of view of detecting discriminat ive information from images. The experiments of this study support the hypothesis that extracting and classify ing groupdifferences are two different problems. Variations within each group captured by the LDA-based method , although not important for classification, are very u seful for extracting discriminative information. I n contrast, the SVM discriminative direction that focuses on th e data at the boundary of the classes extracts grou pdifferences that are less perceivable on the origin al image space. This is an important distinction b etween the two approaches, particularly in extracting discrimi nat ve information from groups of patterns where gr ound truth might be unknown, such as in medical image an alysis of a particular brain disorder.
2019-0205 A Disintegrin and Metalloproteinase 9 Domain ( ADAM 9 ) Is a Major Susceptibility Factor in the Early Stages of Encephalomyocarditis Virus Infection

Encephalomyocarditis virus (EMCV) is a picornavirus that produces lytic infections in murine and human cells. Employing a genome-wide CRISPR-Cas9 knockout screen to find host factors required for EMCV infection, we identified a role for ADAM9 in EMCV infection. CRISPR-mediated deletion of ADAM9 in multiple human cell lines rendered the cells highly resistant to EMCV infection and cell death. Primary fibroblasts from ADAM9 KO mice were also strongly resistant to EMCV infection and cell death. In contrast, ADAM9 KO and WT cells were equally susceptible to infection with other viruses, including the picornavirus Coxsackie virus B. ADAM9 KO cells failed to produce viral progeny when incubated with EMCV. However, bypassing EMCV entry into cells through delivery of viral RNA directly to the cytosol yielded infectious EMCV virions from ADAM9 KO cells, suggesting that ADAM9 is not required for EMCV replication post-entry. These findings establish that ADAM9 is required for the early stage of EMCV infection, likely for virus entry or viral genome delivery to the cytosol. IMPORTANCE Viral myocarditis is a leading cause of death in the United States, contributing to numerous unexplained deaths in people 35 years old. Enteroviruses contribute to many cases of human myocarditis. Encephalomyocarditis virus (EMCV) infection causes viral myocarditis in rodent models, but its receptor requirements have not been fully identified. CRISPR-Cas9 screens can identify host dependency factors essential for EMCV infection and enhance our understanding of key events that follow viral infection, potentially leading to new strategies for preventing viral myocarditis. Using a CRISPR-Cas9 screen, we identified a disintegrin and metalloproteinase 9 domain (ADAM9) as a major factor required for the early stages of EMCV infection in both human and murine infection.
A Bayesian Approach to Finding Compact Representations for Reinforcement Learning

Feature-based function approximation methods have been applied to reinforcement learning to learn policies in a data-efficient way, even when the learner may not have visited all states during training. For these methods to work, it is important to identify the right set of features in order to reduce over-fitting, enable efficient learning, and provide insight into the structure of the problem. In this paper, we propose a Bayesian method for reinforcement learning that finds a policy by identifying a compact set of high-performing features. Empirical results in classic RL domains demonstrate that our algorithm learns concise representations which focus representational resources on regions of the state space that are necessary for good performance.
The Scientific Bulletin of VALAHIA University – MATERIALS and MECHANICS – Nr . 5 ( year 8 ) 2010 50 EN NORMS FOR FOUNDRY ALUMINIUM ALLOYS

Being light Al is a metal with an increasing usage in order to reduce energy consumption and limit the material thrown into landfills. In the metallurgy sector, aspects involving norms have greatly diversified the offer of foundry alloy and aluminium casting produced inside the European Community. EN norms for castings and EN 1676 for alloys are discussed in the paper for aluminium and its alloys together with technical tests that guarantee the correct performance of the castings.
Flood Inundation Mapping of Floodplain of the Jamuna River Using HEC-RAS and HEC-GeoRAS *

Bangladesh lies at the confluence of world’s three major rivers, namely the Ganges, the Jamuna and the Meghna. In Bangladesh, normally four types of flood, expressly river flood, flash flood, tidal flood and storm surge flood appear. The Jamuna River is the most vulnerable to river flood. As a low lying country, flood occurs repeatedly in Bangladesh and causes tremendous losses in terms of property and life. Therefore, a study is carried out to develop flood extend map and flood inundation depth map of the Jamuna River. One dimensional hydraulic model HEC-RAS with HEC-GeoRAS interface in coordination with ArcView-GIS is applied for the analysis. The Nash-Sutcliffe Coefficient (NSE) values have been found greater than 0.60 for both calibration and validation. The percentages of area inundated by 2, 5, 10, 25, 50 and 100-year return period’s floods are 26, 46, 51, 55, 57 and 59% respectively. The classification of flood depth area showed that most of the flooding area had water depth between 1.2 m to 3.6 m. Thus, finding of the study may help in planning and management of flood plain area of the Jamuna River to mitigate future probable disaster through technical approach. The automated floodplain mapping and analysis using these tools provide more efficient, effective and standardized results and saves time and resources.
Encoding of speaker identity in a Neural Network model of Visually Grounded Speech perception

This thesis presents research on how the unique characteristics of a voice are encoded in a Recurrent Neural Network (RNN) trained on Visually Grounded Speech signals. Multiple experiments were executed to determine to what extent speaker identity is encoded. These experiments were executed against raw Mel Frequency Cepstral Coefficient (MFCC) vectors, a convolutional layer and the recurrent layers of the RNN. This thesis also describes the process of annotating perceived gender of the speakers in two audio caption corpora. The outcomes of this procedure were used to examine how well the trained RNN could distinguish male and female speakers in each layer. The most important takeaway from these experiments is that in general gender and speaker encoding are most prevalent in the first few layers of the RNN. This finding aligns with the research that has been done on encoding of phonemes in this type of model: ‘form-related aspects’ are most prevalent in the first few layers while semantics are better encoded in the deeper layers. An experiment comparing the performance between male and female speakers revealed that differences depending on the dataset occur, which were most notable in the classification on the MFCC vectors and the convolutional layer. It is not entirely certain why these differences occur but an important reason could be that the amount of male and female speakers differ per dataset.
A comparative screen of the human HSPB family of molecular chaperones reveals HSPB7 as a superior suppressor of aggregation of polyglutamine containing proteins

A small number of heat shock proteins have previously been shown to act protectively on aggregation of several proteins containing an extended polyglutamine tract, which are linked to several neurodegenerative diseases. A specific subfamily of heat shock proteins is formed by the HSPB family of molecular chaperones, of which a few members are known to act as anti-aggregation proteins in vitro. Furthermore, mutations in a subset of HSPB members are linked to neuropathies, underlining the importance of this subfamily in protein folding, aggregation and neurological diseases. Using overexpression, we screened the HSPB family for novel inhibitors of polyglutamine aggregation and provide a comparative overview on the human HSPB family in view of preventing polyglutamine aggregation. This screen revealed several HSPB members as novel suppressors of polyglutamine aggregation, with HSPB7 as the most active one. Interestingly, this HSPB protein was found to function independently of the HSP70 machine and heat-shock respons. Furthermore, it did not target polyglutamine proteins for proteasomal degradation, but was found to depend in part on autophagy for its activity. Moreover, it prevented polyglutamine induced toxicity in cells and in an in vivo model for SCA3, showing the potential of HSPB7 as a potential therapeutic target.
Filtering Unwanted Message using Firewall Technique in Online Social Networks

Internet develops keen on more popular in the day to day activities of user’s. In current years online social networks (OSN) also increased rapidly. The users know how to communicate and share their views and content through online social networking services. The portion between the users is supposed to be several types of content like image, audio, video etc. The mainly significant draw-back of these Online Social Networking (OSN) armed forces is the lack of isolation for the user’s own classified space. The users can’t have the ability to direct control to prevent the undesired communication post on their individual private walls. Online Social Networks (OSN) becomes an inner part of many people life today. So Online Social Networks (OSN) should be extremely secured to prevent the individual’s privacy. Up to now the Online Social Network (OSN) provides the security events are incomplete. To filter the unwanted messages, in this paper we proposed an enhanced filtering system by using Firewall learning technique based on a content filtering.
Scalable RDF Stream Reasoning in the Cloud

Reasoning over semantically annotated data is an emerging trend in stream processing aiming to produce sound and complete answers to a set of continuous queries. It usually comes at the cost of finding a trade-off between data throughput, latency and the cost of expressive inferences. Strider proposes such a trade-off and combines a scalable RDF stream processing engine with an efficient reasoning system. The main reasoning services are based on a query rewriting approach for SPARQL that benefits from an intelligent encoding of an extension of the RDFS (i.e., RDFS with owl:sameAs) ontology elements. Strider runs in production at a major international water management company to detect anomalies from sensor streams. The system is evaluated along different dimensions and over multiple datasets to emphasize its performance.
Impacts of agricultural research-led productivity on land-use change

This document reports the results of using the Global Trade Analysis Project (GTAP) Model to analyze the global economic effects of agricultural productivity through three different simulations that are of interest to the CGIAR-SPIA Secretariat. The first simulation is a re-analysis of the counterfactuals specified in Evenson and Rosegrant (2003), which in turn are based on Evenson (2003)’s estimates of the contributions of crop genetic improvement (CGI) in the growth in total factor productivity (TFP) of a broad group of staples in the developing world. Following Evenson and Rosegrant (2003), this simulation seeks to answer how agricultural prices, production, consumption, and trade would have been different in the year 2004 if the developing world had not experienced the agricultural productivity growth attributable to the establishment of National Agricultural Research Systems (NARS) and International Agricultural Research Centers (IARCs). The other two simulations focus on oilseeds in Brazil, Indonesia, and Malaysia. In the case of Brazil, soybean productivity has grown significantly since 1990. Thus, the second simulation investigates how different prices, production, and land use would have been in the year 2004 if productivity in Brazilian soybeans had stagnated at 1990 levels. Contrary to the two previous counterfactuals, productivity growth in oil palm in Indonesia and Malaysia has been quite limited. Because of this, the third simulation asks what would be the productive effects of an increase in the ∗Report prepared for the CGIAR Independent Science & Partnership Council’s Standing Panel on Impact Assessment (SPIA). †Nelson Villoria is a Research Assistant Professor with the GTAP Center, Department of Agricultural Economics, Purdue University. Postal address: 403 W. State Street, Room 570, West Lafayette, IN, , USA, 47906. Phone: +1 (765) 494-4303. email: nvillori@purdue.edu
Harnessing Technology Schools Survey 2009: data report – part 1, descriptive analysis

This document, the data report, is a reference document which presents the data in tabular form for anyone who wants to examine the findings of the Harnessing Technology Schools Survey (HTSS) in depth, for example in relation to specific areas of technology or policy, or by school sector. The findings for each question are also set out by school sector by primary, secondary and special school sub-samples.
Somatic Mosaicism in Epilepsy with Focal Cortical Dysplasia

Brain somatic variants are increasingly recognized as important causes of neurodevelopmental diseases, particularly in the pathogenesis of malformations of cortical development (MCDs), such as focal cortical dysplasia (FCD) and hemimegalencephaly. Therefore, the capability to detect such variants is critical to make genetic diagnosis in MCDs cases. Thanks to recent genomic technical improvements, multiple studies have implicated mTOR pathway brain somatic variants in various MCDs. The present review will show the main current approaches adopted to detect brain somatic variants, the role of mTOR signaling cascade in the pathogenesis of epilepsy with FCD and the therapeutic choices available at this time.
Brain Music System : Brain Music Therapy Based on Real-Time Sonified Brain Signals

The paper discusses a standardized therapeutic treatment using the Brain Music System, a system that uses Sonified Neurofeedback accurately and cost effectively to convert brainwaves into musical sound using Digital Signal Processing algorithms. A standard course of sonified neurofeedback therapy (for example 15 sessions), tailored specifically to individual patients suffering from a number of neurological conditions such as Autism Spectrum Disorder is a realistic possibility due to the inexpensive and portable nature of the system, and could be used both inside or even outside of a traditional clinical setting for subjects suffering from a wide array of mental and neurological conditions including Autism Spectrum Disorder. In a pilot study to test the algorithms and output of the Brain Music System, the distribution of the Alpha, Beta and Theta waves in normal subjects corresponds closely to that in published studies using standard high-end equipment (confined to expensive clinical setups). These results allows the Brain Music System to align its protocol to practice standards, and to better associate standard algorithmic tasks to each of the three mentioned brainwave types.
Active Load-Pull System in Doherty Power Amplifier Application Note # 63

02 AN63 Active Load Pull www.focus-microwaves.com In general terms, power amplifiers used in communications systems must be large enough to amplify envelope peaks. This leads to amplifiers that are designed to offer the best efficiency at peak power, while actually spending most of the time operating inefficiently, amplifying smaller signals. With the advancement in next generation communication systems, which employ signals with ever increasing peak to average ratio, the pressures on the PA design continue to grow. Techniques such as Doherty amplifier structures [1] are therefore used to extend high efficiency device operation to lower power levels, allowing for significantly improved average power amplifier (PA) efficiency. This white paper explains how the Mesuro Active Load-Pull measurement system can be used to aid the design and optimization of Doherty power amplifiers, and shows how the Measurement system can offer new, useful information that can improve Doherty designs.
MATLAB/Simulink Model Of Wind Farm To Weak Grid Connection

Wind Farms (WF) employing squirrel cage induction generator (SCIG) directly connected to the grid; represent a large percentage of the wind energy conversion systems around the world. In facilities with moderated power generation, the WF is connected through medium voltage (MV) distribution headlines. In this scheme, the power generated is comparable to the transport capacity of the grid. This case is known as Wind Farm to Weak Grid Connection, and its main problem is the poor voltage regulation at the point of common coupling (PCC). Thus, the combination of weak grids, wind power fluctuation and system load changes produce disturbances in the PCC voltage, worsening the Power Quality and WF stability. This situation can be improved using control methods at generator level, or compensation techniques at PCC. In case of wind farms based on SCIG directly connected to the grid, is necessary to employ the last alternative. Custom power devices technology (CUPS) results are very useful for this kind of application. In this paper is proposed a compensation strategy based on a particular CUPS device, the Unified Power Quality Compensator (UPQC). A customized internal control scheme of the UPQC device was developed to regulate the voltage in the WF terminals, and to mitigate voltage fluctuations at grid side. The internal control strategy is based on the management of active and reactive power in the series and shunt converters of the UPQC, and the exchange of power between converters through UPQC DC– Link. This approach increases the compensation capability of the UPQC with respect to other custom strategies that use reactive power only. MATLAB/Simulink ® Simulations results show the effectiveness of the proposed compensation strategy for the enhancement of Power Quality and Wind Farm stability.
A Comparison Analysis Study of Tools Support to Analysis Security Requirements for Internet of Things ( IoT ) Application

The growth of IoT has resulted in the need for analysing security requirements to ensure safe and secure Internet of Things (IoT) application experience to the users. However, poor elicited security requirements can lead to a failure in IoT development. Thus, this study evaluates various security requirement and analyses the existing gaps in security requirement tools for IoT application that exist in the market for commercial or research purposes. We report our findings from review and analysis of different studies on security requirement engineering for IoT application. The strengths and weaknesses of the features and utility are also presented to provide further understanding of the gaps and weaknesses of each tool. We conclude that these tools are still immature and need further improvements
Analysis of Tweets using Deep Neural Architectures

The task of sentiment classification of tweets is notoriously difficult due to both the brevity of the form and the common use of nonstandard spellings or slang terms. While many studies have described sentiment classification systems with incredibly high levels of accuracy, most have not been tested on Twitter data. This project describes my experiments with very-deep convolutional neural networks (VDCNNs) and Google’s BERT architecture for classifying tweets in the Sentiment140 data set as positive or negative, which ultimately led to the construction of a model that achieved an F1 score of 0.853 on the included test set.