Miracle-dz/UniFAR-FTUs huggingface dataset:
Image quality modeling and characterization of Nyquist-sampled framing sensors with operational considerations for remote sensing

Abstract. The effect of increasing Q on image interpretability is explored, and the fidelity of general image quality equation (GIQE) predictions is assessed for Nyquist-sampled (Q=2) imagery at low signal-to-noise ratio. A digital image chain simulation is developed and validated against a laboratory test bed using objective and subjective assessments. Using the validated model, additional test cases are simulated to study the effects of increased detector sampling on image quality with operational considerations for space-based remote sensing. Variants of the GIQE are evaluated against subject-provided ratings, and modifications that increase prediction accuracy for Q=2 imagery are proposed. Finally, using the validated simulation and modified image quality equation, trades are conducted to ascertain the feasibility of implementing Q=2 designs in future electro-optical systems.
The Poverty and Income Inequality Impacts of South Asian Trade Liberalisation on the Sri Lankan Economy

The impacts of South Asian trade liberalisation on poverty and income inequality in the Sri Lankan economy are examined using a multi-country computable general equilibrium (CGE) model. A non-parametric extended representative household-agent approach is used to estimate the income inequality and poverty effects using micro household survey data. Two trade liberalisation policy simulations are investigated (i) the formation of the South Asian Free Trade Agreement (SAFTA) and (ii) unilateral trade liberalisation in South Asia. Poverty in Sri Lanka is predominantly rural and the findings suggest that poverty and income inequality is reduced in the urban, rural and estates sectors in Sri Lanka under both trade liberalisation policies.
Frequency and scale domain analysis of complex quadrature embolic Doppler ultrasound signals

Early and accurate detection of emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. The windowed Fourier transform has been widely used by commercial Doppler ultrasonic systems. An alternative approach is the wavelet transform. Here, we present comparison of time-frequency (based on complex fast Fourier transform) and time-scale (based on complex wavelet transform) analysis. The results reveal that the wavelet transform provides an optimized temporal resolution and time localization for analysis and detection of embolic signals.
Motion-compensated hybrid coding at 50 kb/s

A system for coding of NTSC color video signals is presented. It combines several bit rate reduction techniques: spatial and temporal subsampling, motion-compensated prediction, adaptive transform coding, and Huffman coding. The system gives good picture quality at 50 kb/s for scenes with moderate motion. The quality degrades gracefully for critical scenes.
Short Software Descriptions

This paper briefly presents the software for interactive decision support that was developed in 1990-1991 within the Contracted Study Agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions, namely: Institute of Automatic Control (Warsaw University of Technology); Institute of Computing Science (Technical University of Poznaii); Institute of Informatics (Warsaw University); and Systems Research Institute of the Polish Academy of Sciences. This Contracted Study Agreement has been a continuation of the same type of activity conducted since 1985. Therefore many of the software packages are actually improved versions of the programs developed in 1985-1989. The theoretical part of the results developed within this scientific activity is presented in the IIASA Collaborative Paper CP-90-008 by A. Ruszczynski, T. Rogowski and A.P. Wierzbicki entitled "Contributions to Methodology and Techniques of Decision Analysis (First Stage). " Detailed descriptions of the methodology and the user guide for each particular software package are published in separate Collaborative Papers. Each software package described here is available in executable form for non-profit educational and scientific purposes, however, any profit-oriented or commercial application requires a written agreement with IIASA. Inquires about the software should be directed to the System and Decision Sciences Program at IIASA, Methodology of Decisions Analysis Project.
Drivers of Advertising Spending : Evidence from Cross-Country Analysis

An important and long-standing question in international marketing concerns the factors associated with cross-country variation in the development of the advertising industry. We estimated time series cross section models for 64 economies and supplemented them with crosssectional OLS models. We found that the three predictor variables have significant effects on ad spending. Moreover, the income effect is more salient in more sophisticated markets than it is in less sophisticated markets. Evidence of foreign MNCs’ influence on ad spending, on the other hand, is more readily apparent in less sophisticated markets.
THE IMPACT OF LAND USE CHANGES OVER THE LOW LEVEL CIRCULATION RELATED TO THE NORTHWESTERN ARGENTINEAN LOW

The Northwestern Argentinean Low (NAL) is a low pressure system with its center located approximately at 66oW30oS, close to the eastern Andean slopes. Previous studies show that NAL life cycle and position is strongly conditioned by the heat budget at the surface (particularly during the warm season). This leads to the hypothesis that NAL could be very sensitive to changes of surface parameters which ultimately modulate the surface heat balance. In order to investigate land/ atmosphere interactions over the NAL and surrounding areas, several sensitivity studies employing different land use and soil moisture patterns are proposed. They have been done using WRF (Weather Research and Forecasting) model, for a particular NAL episode observed during SALLJEX (South America Low level jet experiment). Results reveals a strong sensitivity to an augmentation of soil dryness that causes a deepening of the NAL and an acceleration of the northerly flow to the east of the NAL. This kind of response also leads to a change in the position of the related precipitation area.
Trajectory Studies through Numerical Simulation of Ion Thruster Grid Region Plasma with PIC-DSMC Approach in 3 D

Proper design of the accelerator grids plays a crucial role in the performance of an ion thruster. A PIC-DSMC code is developed to simulate the accelerator grid region plasma on a three-dimensional domain in cylindrical coordinates to assess the performance of ion thruster grids. The solution domain is a 30 slice of a 2 mm radius cylindrical domain with 5 mm axial length. This shape provides the smallest representative domain for the assumed ion thruster accelerator grid configuration. Two species, ions and neutrals, are simulated with macro particles whereas electrons are handled with an analytical model. Collisions between the heavy species are handled using DSMC approach. Electric potential is evaluated by solving the Gauss’ Law, which takes the form of the Poisson’s equation. The code is implemented using the C++ programming language with multi-core parallelization. Preliminary results for various particle trajectories are presented.
Crawl-O-Matic-O-Matic Automated Optimization of Expert Systems With Machine Learning

This project involves utilizing machine learning techniques to attempt to optimize an already existing artificial intelligence (AI) agent that plays the roguelike game Dungeon Crawl Stone Soup (DCSS). An expert system based AI agent has been developed that is capable of winning a game of DCSS without human assistance with a low frequency of success. This project aims to utilize machine learning to automatically tune the agent to improve its performance by optimizing values stored in its knowledge base concerning monster threat levels.
Contribution of New Information Technology to the Growth of the Japanese Economy for 1974-85 Am Application of Input-Output Model and Productivity Analysis 1 1 By

The development of new information technology for production and other activities (i.e., informatization) has been a major source of the growth of the Japanese economy since 1970s. The present work is an attempt to measure the contribution of informatization. We develop an inputoutput method to decompose the value of produced goods and the capital stock into two components: the part attributable to the development of new information technology and the remaining part. We apply this method to time series of the Japanese economy for the period 1974-85. By using the factor-productivity analysis to the decomposed time series, we find that ten to twenty percent of the annual growth of the Japanese economy during this period was explained by the development of new information technology and that, without such technology, per capita national income of Japan in 1985 would have been lower than its actual level by approximately twelve percent. The paper is the first part of the work, which is composed of two parts.
Neural networks for financial forecasting

From the Publisher: When applied to the world of finance, neural networks are automated trading systems, based on mapping inputs and outputs for forecasting probable future values. In Neural Networks for Financial Forecasting - the first book to focus on the role of neural networks specifically in price forecasting - traders are provided with a solid foundation that explains how neural nets work, what they can accomplish, and how to construct, use, and apply them for maximum profit. It is written by an acknowledged authority who is, himself, the developer of several successful networks. Neural Networks for Financial Forecasting enables you to develop a usable, state-of-the-art network from scratch all the way through completion of training. There are spreadsheets and graphs throughout to illustrate key points, and an appendix of valuable information, including neural network software suppliers and related publications.
A Fuzzy Logic Controlled Solar Power Generation with Integrated Maximum Power Point Tracking using Multi-level inverter

This paper presents a grid-connected photovoltaic (PV) power conversion system based on a new single-phase multilevel inverter and DC/DC converter. First, configuration and structural parts of the PV assisted inverter system are introduced in detail. This new seven level inverter comprises of capacitor selection circuit which is connected in cascade with full bridge power converter circuit. There are two output voltages in the capacitor selection circuit. Two output voltage sources of dc–dc power converter converts into a three-level dc voltage using capacitor selection circuit. The full-bridge power converter further converts this three-level dc voltage into a seven-level ac voltage. There are six power electronic switches in seven level inverter which simplifies the circuit configuration and losses. In this way, the proposed gridconnected PV power conversion system generates seven-level output voltage and a sinusoidal output current which is inphase with the utility voltage and is fed into the grid. A digital PI current control algorithm is used to remain the current injected into the grid sinusoidal and to achieve high dynamic performance with low total harmonic distortion (THD). The simulations
An Investigation of Antioxidant Enzymes Activities in Liver of Cyprinus carpio Taken from Different Stations in the Karakaya Dam Lake

In this study, we examined the activity of the antioxidant enzymes such as Catalase (CAT), Superoxide dismutase (SOD) and Glutathione peroxidase (GSH-Px) in liver of Cyprinus carpio that has economical importance in the Karakaya Dam Lake. The fish samples were obtained from stations (Hasırcılar, Boran, and İmikuşağı) at Karakaya Dam Lake between March and July 1999. Spectrofotometric method was used to determine the activities of enzymes. We found higher SOD activity in Cyprinus carpio livers taken from Hasırcılar than in livers of Cyprinus carpio taken from Boran and İmikuşağı (p>0.05). CAT and GSH-Px activities were found to be lower in Cyprinus carpio in relatively uncontaminated Imikusagi than in Cyprinus carpio in Boran and Hasırcılar (p>0.05).
Investor relations and information asymmetry * , * *

Companies invest significant volumes of resources in investor relations (IR) departments. The professionals working in the IR department are responsible for communication between the company and the market, so that the information generated is widely disseminated and understood by investors. In this context, this research aims to investigate whether there is evidence that the IR activity decreases information asymmetry between the company and the market. Specifically, we evaluate the hypothesis that Brazilian companies with IR websites classified as more informative have a reduced bid-ask spread (proxy for asymmetry). Therefore, this paper classifies the informative content from IR websites of Brazilian companies for the years 2013 and 2014 and relates the outcomes obtained with information asymmetry metrics. Initially, the estimation considers the pooled ordinary least squares (POLS) model and, at a second moment, in order to mitigate potential endogeneity problems, the pooled two-stage least squares (2SLS) model is used. The results indicate that more informative IR websites are able to decrease the bid-ask spread of Brazilian listed companies. This finding strongly encourages companies to provide information to stakeholders on well-structured IR websites of their own.
Voltage Source Converter modeled in RTDS – experiences and comparison with field results

For the first time it has been possible to model a three-level Voltage Source Converter (VSC) and test the physical firing pulse controls using a Real Time Digital Simulator (RTDS). The new RTDS model is more flexible and comprehensive than both earlier digital versions and analogue TNA models. This allows more tests to be conducted in a shorter time and provides a more detailed power system representation for the tests. Using the new system to test the controls also simplifies the recording of results since all signals can now be gathered by the RTDS Simulator. For the case of Electric Arc Furnace (EAF) compensation, the new real time digital simulation has made it convenient and easy to demonstrate the flicker improvement factor provided by the actual VSC controls under realistic system conditions. The paper compares results from off-line and real time RTDS simulations, as well as from field measurements taken at a full scale ABB SVC Light installation. The good correlation between simulation results and field measurements give confidence to the digital modeling.
POLKADOT: VISION FOR A HETEROGENEOUS MULTI-CHAIN FRAMEWORK

Present-day blockchain architectures all suﬀer from a number of issues not least practical means of extensibility and scalability. We believe this stems from tying two very important parts of the consensus architecture, namely canonicality and validity , too closely together. This paper introduces an architecture, the heterogeneous multi-chain , which fundamentally sets the two apart. In compartmentalising these two parts, and by keeping the overall functionality provided to an absolute minimum of security and transport , we introduce practical means of core extensibility in situ. Scalability is addressed through a divide-and-conquer approach to these two functions, scaling out of its bonded core through the incentivisation of untrusted public nodes. The heterogeneous nature of this architecture enables many highly divergent types of consensus systems interop-erating in a trustless, fully decentralised “federation”, allowing open and closed networks to have trust-free access to each other.Weput forward a means of providing backwards compatibility with one or more pre-existing networks such as Ethereum [6,22]. We believe that such a system provides a useful base-level component in the overall search for a practically implementable system capable of achieving global-commerce levels of scalability and privacy.
Leadership and Management Commitment towards Logistics Performance Improvement in Malaysia with the Adoption of Green Logistics as the Moderating Variable Mohamad Fared

This study is to look into the leadership and management commitment of logistics service firms in Malaysia towards improving their logistics service performance by adopting the green logistics practices. As concern for many environmental issues that arises nowadays, logistics service firms must look more seriously of the logistics associated external costs mainly with the climate change, air pollution, noise pollution, land use, vibration, accidents and wastes. Apart from reducing the logistics costs as their focus, the world is concern about the issue of climate change and the increasing of carbon dioxide (CO2) emission from the operations of various transportation modes, especially on the road freight. Several researches conducted over a decade found out that human activities are releasing huge amounts of greenhouse gases into the atmosphere which had increase the greenhouse effect and warming the climate. In facing global warming and limited amount of energy resources, initiatives on green saving and low carbon emission of road transport service are among the major policy objectives that need to be implemented in Malaysia. Considering these externalities not only help the logistics service firms to reduce their operational costs but also helps to keep the globe green. The logistics services provided must be able to satisfy customer’s demand and most importantly, to come out with better solutions and new ideas to cope with the pace evolution in the manufacturing industry. This study adds to the existing literature by explaining conceptually the linkage between leadership and management commitment towards logistics performance improvement by adopting the green logistics practices.
OF PUBLIC KEY CRYPTOGRAPHY ON Wimax USING Rsa ALGORITHM

New technologies are evolving each day to facilitate human beings. From analog communication to 2G, 2G TO 3G and now we are heading towards Wimax (Worldwide Interoperability for Microwave Access), a telecommunications protocol that provides fixed and fully mobile internet access. The name "Wimax" was created by the Wimax Forum, which was formed in June 2001 to promote conformity and interoperability of the standard. Due to increased number of eavesdroppers in Wimax technology, the concern is now focused towards Wimax security. Though incorporating some security methods, IEEE 802.16e is still vulnerable to malicious attacks. In this paper, we point out several potential security threats and vulnerabilities. We propose some possible security improvements and solutions to eliminate the vulnerabilities using public key cryptography [1] in RSA.
Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 1: Labile Amino‐Nitrogen Concentrations

Current turfgrass fertilizer recommendations do not account for potential mineralizable N in the soil. The Solvita Soil Labile Amino-Nitrogen (SLAN) test measures a labile fraction of soil N. This study was conducted across 9 yr (2008– 2016) in Connecticut to determine if responses from predominately Kentucky bluegrass (Poa pratensis L.) and tall fescue [Schedonorus arundinaceus (Schreb.) Dumort. ] lawns are correlated to SLAN–N concentrations, and to determine the probability of turfgrass responses equaling or exceeding the response from benchmark urea rates in relation to SLAN–N concentrations. Randomized complete block design field experiments were set out with 23 rates of an organic fertilizer (0–2000 kg N ha−1 yr−1) and four different rates of urea (50, 100, 150, and 200 kg N ha−1 yr−1). Yearly spring soil samples were analyzed for SLAN–N concentrations, and turfgrass growth and quality responses were collected during the growing seasons. Turfgrass responded positively and linearly (P < 0.001) to SLAN–N concentrations, but correlations were relatively weak to moderate. When spring soil SLAN–N concentrations were 3158, 165, 198, and 217 mg kg−1, there was a 390% probability that overall combined responses across species and measured variables would be equal to or greater than responses obtained from 50, 100, 150, and 200 kg urea N ha−1 yr−1, respectively. The SLAN test has promise as an objective soil test to categorize the N fertilization response potential of turfgrass soils, and this would be helpful in guiding N fertilization. D.B. Moore, K. Guillard, and T.F. Morris, Dep. of Plant Science and Landscape Architecture, Unit 4067, Univ. of Connecticut, 1376 Storrs Road, Storrs, CT 06269; X. Geng. SINO-AGRI Group, 11th Floor, Global Finance and News Center Building C, Jia No.1, Xuanwumenwai Street, Xicheng District, Beijing 100052, China; W.F. Brinton, Woods End Laboratories, 290 Belgrade Rd., PO Box 297, Mt. Vernon, ME 04352. Received 25 Nov. 2018. Accepted 27 Mar. 2019. *Corresponding author (karl.guillard@uconn.edu). Assigned to Associate Editor Emily Merewitz. Abbreviations: ISNT, Illinois soil nitrogen test; NDVI, normalized difference vegetative index; SLAN, soil labile amino-nitrogen; SOM, soil organic matter. Published in Crop Sci. 59:1779–1788 (2019). doi: 10.2135/cropsci2018.11. 0706 © 2019 The Author(s). This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Published June 4, 2019
Big Data Discovery and Management By Sakhr

In this research, we developed a technique and manager for collecting metadata of existing Big Data in an enterprise or organization. All collected metadata are stored in metadata storage. Also, in this research, we developed a technique for discovering simple knowledge from existing Big Data (Twitter & websites) and extracted the correlation between different Big Data. Since Big Data are distributed across a large number of remote machines, we used mobile agents technology to build our manager. Using mobile agents and the metadata of Big Data will solve the Big Data transportation challenge in addition to the management challenge.
Data Grid Models for Preparation and Modeling in Supervised Learning Data Grid Models for Preparation and Modeling in Supervised Learning

This paper introduces a new method to automatically, rapidly and reliably evaluate the class conditional probability of any subset of variables in supervised learning. It is based on a partitioning of each input variable into intervals in the numerical case and into groups of values in the categorical case. The cross-product of the univariate partitions forms a multivariate partition of the input representation space into a set of cells. This multivariate partition, called data grid, is a piecewise constant nonparametric estimator of the class conditional probability. The best data grid is searched using a Bayesian model selection approach and an efficient combinatorial algorithm. We also extend data grids to joint density estimation in unsupervised learning and apply this extension to the problem of coclustering the instances and variables of a sparse binary dataset. We finally present three classification techniques, exploiting the maximum a posteriori data grid, an ensemble of data grids, or a coclustering data grid, and report results in the Agnostic Learning vs. Prior Knowledge Challenge, where our method achieved the best performance on two of the datasets. These experiments demonstrate the value of using data grid models in machine learning tasks, for conditional density estimation, data preparation, supervised classification, clustering and rule based explanation.