Miracle-dz/UniFAR-FTUs huggingface dataset:
Defining the must-run and must-take units in a deregulated market

Reliability must-run (RMR) units are generation facilities that are necessary during certain operating conditions in order to maintain the security of power systems in a competitive environment. Absence of RMR units may result in insecure operation of a system. From a financial point of view, RMR units may become a safety net for independent power producers (IPPs) who enjoy steady incomes or a place for those who prefer not to participate in the competitive market. This paper presents a practical method to determine RMR units and their must-run output in the Thailand power market, according to the proposed structure of the Thailand deregulated market to be implemented in 2003 (forecasted opening date of the operation based on information of August 1999). The study determines the amount of must-run output in RMR units by finding the necessary amount of generation to avoid the violation of voltage magnitude, thermal limit, and voltage stability limit. The proposed procedure to determine RMR units defines the amount of generation capacity to be assigned as RMR and allows must-run units to sell surplus generation capacity to the system.
Cardiorespiratory Responses to Underwater Treadmill Running Versus Land-Based Treadmill Running

Underwater (UW) running provides a low load-bearing form of supplementary training that can be used for recovery and rehabilitation while maintaining cardiovascular fitness. Whether it elicits a cardiorespiratory training stimulus comparable to that of land-based running is seemingly unclear. The purpose of this study was to compare cardiorespiratory responses between underwater treadmill running and land-based running. Fourteen male triathletes completed trials at maximal and submaximal workloads for each of three conditions: running on an underwater treadmill with AQx® water running shoes, running on an underwater treadmill barefoot, and running on a land-based treadmill. No differences between groups were found for measures of oxygen consumption (VO2), rating of perceived exertion (RPE), or respiratory exchange ratio (RER) across modalities for maximal trials; however, heart rate (HR) was greater during land-based running than underwater treadmill running. No group differences were found for HR, RPE, and RER across modalities during submaximal trials; however, VO2 was significantly greater during land-based running than underwater treadmill running. We concluded that the cardiorespiratory training stimulus during underwater treadmill running was comparable to that of land-based running at maximal exertion levels, with the exception of HR, and therefore could be an effective form of supplemental training during rehabilitation. At submaximal levels, underwater treadmill running elicited a less rigorous training stimulus than land-based running in terms of VO2 and therefore is a less effective form of supplemental training.
Stock Market Reactions during Different Phases of the COVID-19 Pandemic: Cases of Italy and Spain

The COVID-19 pandemic and pandemic-induced lockdowns and quarantine establishments have inevitably affected individuals, businesses, and governments. At the same time, the spread of the COVID-19 pandemic had a dramatic impact on financial markets all over the world and caused an increased level of uncertainty; the stock markets were no exception either. Most of the studies on the impact of the COVID-19 pandemic on stock markets are based either on the analysis of a relatively short period (the beginning of pandemic) or a longer period, which, in turn, is very heterogeneous in terms of both the information available on the COVID-19 virus and the measures taken to contain the virus and address the consequences of the pandemic. However, it is very important to assess the impact not only at the beginning of the pandemic but also in the subsequent periods and to compare the nature of this impact; the studies of this type are still fragmentary. Therefore, this research aims to investigate the impact of the COVID-19 pandemic on stock markets of two of the most severely affected European countries—Italy and Spain. To reach the aim of the research OLS regression models, heteroscedasticity-corrected models, GARCH (1,1) models, and VAR-based impulse response functions are employed. The results reveal that the stock market reaction to the spread of the COVID-19 pandemic differs depending on the country and period analyzed: OLS regression and heteroscedasticity-corrected models have not revealed the statistically significant impact of the spread of the COVID-19 pandemic, while impulse response functions demonstrated the non-zero primary response of analyzed markets to the COVID-19 shock, and GARCH models (in the case of Spain) confirmed that the COVID-19 pandemic increased the volatility of stock market return. This research contributes to the literature by providing a comprehensive impact assessment both during the whole pre-vaccination period of the pandemic and during different stages of this period.
The Investor's Guide to the Net: Making Money Online

From the Publisher: The Internet is the undisputed information medium of today. Using an extremely nontechnical, applications oriented approach, this practical guide is a basic start-up kit for investing in Cyberspace. Coverage includes hardware/software requirements, what bulletin boards to scan, using the electronic business news services, tapping into individual and government databases, accessing online newsletters and much more. Contains numerous screen captures and accessible illustrations with an actual map for Internet investing.
Bridging the gap between academia and industry through user-centred training

Bridging the Gap is a project-based online track within TU-Towson University's undergraduate curriculum that will provide industry and theoretical background through a module- based e-learning environment. Bridging the Gap will develop and enhance TU's existing IT courses with the addition of self-paced e-learning modules and hands-on industry projects. The project will assist faculty members with developing and administering class assignments by utilizing modulated project repository and providing them with continuous real-time technology updates, required skill sets and knowledge of industry trends. The business community will also benefit from the User Centred Software Engineering and Web Technology Lab; the lab will serve as a test bed for industry applications. Active collaboration with industry will result in the development of new research initiatives for undergraduate and graduate students while, at the same time, helping them develop real-world skill sets to compliment what they will learn through self-paced e-learning modules. Bridging the Gap's approach will lead to the development of a project- based course curriculum, which will benefit the students, TU and other universities, and ultimately the IT industry.
Optimasi Training Neural Network Menggunakan Hybrid Adaptive Mutation PSO-BP

Optimization of training neural network using particle swarm optimization (PSO) and genetic algorithm (GA) is a solution backpropagation’s problem. PSO often trapped in premature convergent (convergent at local optimum) and GA takes a long time to achieve convergent and crossover makes worse the results. In this research adaptive mutation particle swarm optimization and backpropagation (AMPSO-BP) is used for training the neural network of the iris plant, breast cancer, wine, glass identification and pima indian diabetes. The addition of PSO with adaptive mutation to prevent premature convergent and BP to increase the efficiency of local searching. AMPSO-BP training results will be compared with the GA and BP. The test results showed AMPSO-BP is able to optimize the process of training the neural network. AMPSO-BP more rapidly achieve the minimum error (global minimum), fast convergent and have the ability memorization and generalization with more accurate results than the other methods. Index Terms—Adaptive Mutation, Backpropagation, Particle Swarm Optimization, Training Neural Network.
Enhancing the Team Experience in Service Learning Courses

Service learning is pervasive in higher education today, with 31 percent of students at Campus Compact member schools engaging in service activities (Campus Compact, 2009) and universities’ missions and strategic planning documents increasingly aimed at developing engaged citizens. Service learning has many potential benefits for college students; among those benefits is the opportunity to develop and practice teamwork skills. The present paper describes the strategies used in a team-based service learning course to support positive team experiences for students. Service Learning and Teamwork Service learning is a part of the broader experiential education movement that has its underpinnings in the work of the philosopher John Dewey (Katula & Threnhauser, 1999). It is defined as: a credit bearing educational experience in which students participate in an organized service activity that meets identified community needs and reflect on the service activity in such a way as to gain further understanding of the course content, a broader appreciation of the discipline, and an enhanced sense of service responsibility (Bringle & Hatcher, p. 222).
Diversification, Industrial Synergy and Firm Performance of State-Owned Enterprises - Taking Coal Business Groups as an Example

Diversified industrial combination of state-owned enterprise is often not optimal, it can also maintain in the industry boom, but once the main market atrophy, the entire firm’s development will be in trouble, such as coal business groups. To solve this problem, this paper use industrial synergy as an intermediary variable and develop the traditional dualism, which is the relationship between diversification and firm performance, to trialism, which are relationships among diversification, industrial synergy and firm performance and take coal enterprises listing Corporations as an example to analyze relationships of above three points. Results show vertical business synergy in related diversification and horizontal function synergy in unrelated diversification are all have a significant positive impact on firm performance. This conclusion is confirmed the important hypothesis in theory that industrial synergy will promote firm performance and has important practical significance to guide state-owned enterprises’ management practice.
Assessments of right ventricular strain using cardiac magnetic resonance imaging following kidney transplantation

Although kidney transplantation (KT) has been shown to ameliorate adverse left ventricular (LV) remodelling associated with end stage kidney disease, its effects on the right ventricle have not been well studied. Recently, strain imaging has been shown to be a sensitive measure of early subclinical myocardial dysfunction. Using cardiac magnetic resonance imaging (MRI), we examined the effects of KT on right ventricular (RV) strain parameters. In a cohort of 81 patients (39 patients underwent KT and 42 patients remained on dialysis as control group), cardiac MRI studies were obtained at baseline and at 1 year follow‐up. There were no significant differences in RV strain values between the groups at baseline. After 1 year, RV strain values did not significantly change in patients who received KT, and changes in RV strain over 1 year were not significantly different between the KT and the dialysis groups. Given the previously demonstrated improvement in LV strain post‐KT, the current study suggests that RV and LV remodelling post‐KT may have different mechanisms. Further studies elucidating the effects of KT on RV remodelling are needed.
System Identification of Internet transmission rate control factors

: As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.
Big Data in Asian Central Banks

This paper reviews the use of big data in Asian central banks, leveraging on a survey conducted among the members of the Irving Fischer Committee. The analysis reveals four main insights. First, Asian central banks define big data in a more encompassing way. Second, interest in big data appears higher in Asia, including at the senior policy level. Third, Asian central banks report dealing with big data to support a wide range of tasks. Fourth, big data poses new challenges and increases the need for international policy cooperation, especially to make use of payments data and promote innovative technological solutions.
EVALUATIOOF WOUDHEALIG ACTIVITY OF LEAVES OF URTICA PARVIFLORA ROXB AD CALLICARPA ARBOREA ROXB IRATS

Summary Leaves of Urtica parviflora Roxb and Callicarpa arborea Roxb were chosen to investigate wound healing property on rats. Wound healing study was performed by excision, incision and dead space wound models by administering the methanolic extracts of the two plants at the dose of 300mgkg '1 day '1 p.o and topically applying alcoholic extracts (5% w/w) formulated as an ointment prepared by Indian Pharmacopoeia method. Healing was assessed by the rate of wound contraction, time until complete epithelialization, granulation tissue weight, breaking strength, estimation of hydroxyproline and histopathological parameters. Complete wound contraction was shown by both the plants in the study period. In excision, incision and dead space wound models, all the test drugs showed significant (P<0.0001) wound healing activities compared to the control. Moreover the ointment formulation of Callicarpa arborea Roxb had been observed to have equipotent wound healing activity as of the standard drug Framycetin. Histological examination of granulation tissue showed the enhanced wound healing property of the extracts by decrease number of macrophages and increased deposition of fibroblasts and collagen.
Object-oriented sequence-domain representation of transformers for network analysis

An object-oriented admittance matrix representation of transformers with any number of circuits is developed for direct use in modern network analysis programs. An analysis of manually adjusted and load-tap-changing taps shows that the effective impedance can vary by 2:1 over the full range of taps and should therefore be considered-for accurate network studies. The principal innovation lies in the preservation of the neutral nodes of Y-connected circuits by including their axes in the admittance matrix. This makes it possible to attach neutral impedances and connections to the neutral points, retaining their identities also instead of modifying the transformer zero-sequence parameters to account for them. Conversion of the transformer admittance matrix to engineering units makes it possible to use neutral impedances in ohms without conversion to any per-unit base.
A comparison study on the number of wavelength converters needed in synchronous and asynchronous all-optical switching architectures

The objective of this study is to investigate the performances of packet-switching architectures working in a synchronous and asynchronous way; in such architectures, the packet contention is resolved in the wavelength domain and the used wavelength converters are shared. We investigate on the saving of the number of converters that the sharing technique allows to obtain in the synchronous and asynchronous architectures and compare the obtained results. These ones show that when a packet loss probability is fixed, in the synchronous case a greater number of converters is saved. In some cases, the gain is 40% more than the asynchronous case. Furthermore, in the asynchronous case, a more expensive switching matrix is needed. The analysis is performed by introducing analytical and simulation models, and when both unicast and multicast traffic scenarios are considered.
E-Intelligence Form Design and Data Preprocessing in Health Care

©Padma Pedarla 2004 ii I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. iii Abstract Clinical data systems continue to grow as a result of the proliferation of features that are collected and stored. Demands for accurate and well-organized clinical data have intensified due to the increased focus on cost-effectiveness, and continuous quality improvement for better clinical diagnosis and prognosis. Clinical organizations have opportunities to use the information they collect and their oversight role to enhance health safety. Due to the continuous growth in the number of parameters that are accumulated in large databases, the capability of interactively mining patient clinical information is an increasingly urgent need to the clinical domain for providing accurate and efficient health care. Simple database queries fail to address this concern for several problems like the lack of the use of knowledge contained in these extremely complex databases. Data mining addresses this problem by analyzing the databases and making decisions based on the hidden patterns. The collection of data from multiple locations in clinical organizations leads to the loss of data in data warehouses. Data preprocessing is the part of knowledge discovery where the data is cleaned and transformed to perform accurate and efficient data mining results. Missing values in the databases result in the loss of useful data. Handling missing values and reducing noise in the data is necessary to acquire better quality mining results. This thesis explores the idea of either rejecting inappropriate values during the data entry level or suggesting various methods of handling missing values in the databases. E-Intelligence form is designed to perform the data preprocessing tasks at different levels of the knowledge discovery process. Here the minimum data set of mental health and the breast cancer data set are used as case studies. Once the missing values are handled, decision trees are used as the data mining tool to perform the classification of the diagnosis of the databases for analyzing the results. Due to the ever increasing mobile devices and internet in health iv care, the analysis here also addresses issues relevant hand-held computers and communicational devices or web based applications for quick and better access.
Parametric Effects on the Induced Voltages on Overhead Lines by Lightning Strokes to Nearby Ground

Outages of low-voltage overhead lines are caused primarily by lightning strokes to nearby ground. These lightning strokes are generally known as indirect lightning strokes. Because of the complex interaction among the various line and lightning parameters, predicting the worst possible severity of indirect lightning strokes on an overhead line is difficult. The paper discusses the effects of some of these parameters, based on the work previously presented. [1,2] The effects of the following parameters on the induced voltage are discussed: 1. Line parameters a. Height of the line above ground, and b. Distance along the line from the struck point. 2. Lightning-stroke parameters a. Return-stroke current, b. Return-stroke velocity, c. Perpendicular distance of lightning stroke from overhead line, and d. Height of cloud charge center. The magnitude of the induced voltage will be directly proportional to the height of the overhead line above ground. In general, the severity of an indirect lightning stroke is not a strong function of the height of the cloud charge center; however, the waveshape of the induced voltage changes considerably for low cloud heights. The analysis shows that the highest induced voltage on an overhead line by a nearby lightning strike to ground may not appear at the point directly opposite the stroke. This phenomenon has also been observed indirectly during field investigations where distribution transformers were damaged at points on the line away from the location of the lightning stroke to ground.
Nearest Neighbors Search Using Multi-GPU

Meshless methods are increasingly gaining space in the study of electromagnetic phenomena as an alternative to traditional mesh-based methods. One of their biggest advantages is the absence of a mesh to describe the simulation domain. Instead, the domain discretization is done by spreading nodes along the domain and its boundaries. Thus, meshless methods are based on the interactions of each node with all its neighbors, and determining the neighborhood of the nodes becomes a fundamental task. The k-nearest neighbors (kNN) is a well-known algorithm used for this purpose, but it becomes a bottleneck for these methods due to its high computational cost. One of the alternatives to reduce the kNN high computational cost is to use spatial partitioning data structures (e.g., planar grid) that allow pruning when performing the k-nearest neighbors search. Furthermore, many of these strategies employed for kNN search have been adapted for graphics processing units (GPUs) and can take advantage of its high potential for parallelism. Thus, this paper proposes a multi-GPU version of the grid method for solving the kNN problem. It was possible to achieve a speedup of up to 1.99x and up to 3.94x using two and four GPUs, respectively, when compared against the single-GPU version of the grid method.