Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Linear Algebra and Applications, Second Edition

This second edition of the author s acclaimed textbook covers the major topics of computational linear algebra, including solution of a system of linear equations, least-squares solutions of linear systems, computation of eigenvalues, eigenvectors, and singular value problems. Important features of the original edition have been updated and improved. Drawing from numerous disciplines of science and engineering, the author covers a variety of motivating applications. When a physical problem is posed, the scientific and engineering significance of the solution is clearly stated. Each chapter contains a summary of the important concepts developed in that chapter, suggestions for further reading, and numerous exercises, both theoretical and MATLAB and MATCOM based. The author also provides a list of key words for quick reference. The MATLAB toolkit MATCOM contains implementations of the major algorithms associated with the book and enables students to study different algorithms for the same problem, comparing efficiency, stability, and accuracy. Additional online content includes appendices containing MATLAB codes and the MATCOM toolkit solutions to selected problems as well as an extra chapter on special topics. The topics of generalized and quadratic eigenvalue problems, which arise in practical engineering applications, are described in great detail. This feature, along with an important overview of Krylov subspace methods and an extensively updated bibliography, enhances the book s value as a reference for both engineers and students. Audience: This book is intended for undergraduate and graduate students in applied and computational mathematics, scientific computing, computer science, financial mathematics, actuarial sciences, and electrical and mechanical engineering. It will also appeal to researchers in mathematics, computer science, physics, chemistry, biology, economics, statistics, and aerospace, electrical, mechanical, and chemical engineering as well as practicing engineers and industrial mathematicians. Contents: Preface; Chapter 1: Linear Algebra Problems, Their Importance, and Computational Difficulties; Chapter 2: A Review of Some Required Concepts from Core Linear Algebra; Chapter 3: Floating Point Numbers and Errors in Computations; Chapter 4: Stability of Algorithms and Conditioning of Problems; Chapter 5: Gaussian Elimination and LU Factorization; Chapter 6: Numerical Solutions of Linear Systems; Chapter 7: QR Factorization, Singular Value Decomposition, and Projections; Chapter 8: Least-Squares Solutions to Linear Systems; Chapter 9: Numerical Matrix Eigenvalue Problems; Chapter 10: Numerical Symmetric Eigenvalue Problem and Singular Value Decomposition; Chapter 11: Generalized and Quadratic Eigenvalue Problems; Chapter 12: Iterative Methods for Large and Sparse Problems: An Overview; Chapter 13: Key Terms in Numerical Linear Algebra; Bibliography; Index
Avaliação de Potencial de Liquefação Cíclica por Vibrações de Obra de Descaracterização de Barragem de Rejeitos

: One of the concerns associated with the project to de-characterize a tailings dam, is that the vibrations caused by the equipment to be used in the work and by the detonations carried out in pits around it, induce the liquefaction of the tailings susceptible to this phenomenon. In a first step, a methodology based on the shear wave velocity parameter (Vs) was adopted, which can be obtained in the Seismic Cone Penetration Test (SCPT), to define a peak particle velocity (PPV) limit so that there is no generation of pore pressures in the tailings and a consequent increase in the risk of liquefaction. At the same time, field experiments were carried out in an area similar to the dam was implemented, to assess, through the use of geophones, the levels of vibration generated by the activities programmed for the work. From the PPV pre-determined limits, an instrumentation plan was developed and implemented for measurement and control of vibrations in the dam area, associated with the monitoring of pore pressures in the reservoir, allowing the de-characterization works to be done in a safer way.
Solar pv fed stand-alone excitation system of a synchronous machine for reactive power generation

This paper presents a model of a stand-alone solar energy conversion system based on synchronous machine working as a synchronous condenser in overexcited state. The proposed model consists of a Synchronous Condenser, a DC/DC boost converter whose output is fed to the field of the SC. The boost converter is supplied by the modelled solar panel and a day time variable irradiance is fed to the panel during the simulation time. The model also has one alternate source of rechargeable batteries for the time when irradiance falls below a threshold value. Also the excess power produced when there is ample irradiance is divided in two parts and one is fed to the boost converter while other is utilized to recharge the batteries. A simulation is done in MATLAB-SIMULINK and the obtained results show the utility of such modelling for supplying reactive power is feasible.
Climate Change, Aging, and Well-being: How Residential Setting Matters

Abstract How do older people’s living environments influence their vulnerabilities to climate change? Much has been written about the physiological consequences of climate change for older individuals, particularly the dangers of increased incidence of severe heat. Less is known about how older people’s residential settings moderate their exposure to climate stressors, their particular sensitivities to the effects of climate change, or their capacities to respond to extreme events or adapt to long-term environmental changes. Drawing on literature in English, with a focus on work relevant to the United States, we examine how the housing, neighborhood, and urban or rural contexts in which older people live shape their experiences of climate change, moderating their exposure to risks related to climate change, sensitivity to those events and trends, and their capacities to adapt and recover. Older people face multiple life changes, making prioritizing climate readiness more challenging. They are also diverse, with different vulnerabilities and perceptions of risks and the ability to manage them. This paper lays out an agenda where additional research can inform policy and planning efforts aimed at reducing older individuals’ risk and building the capacity to adapt to climate change. The agenda includes understanding specific vulnerabilities and how older people and their housing providers are already responding.
Impact of evening carbohydrate intake on the resolution of persistent night sweats in patients with long COVID: a case series

Up to 25% of patients with long COVID experience persistent night sweats. However, in most cases they remain untreated until they disappear on their own. Since SARS-CoV-2 is known to disrupt glucose homeostasis, we hypothesized that impaired mitochondria would result in faster glycogen depletion at night due to reduced ATP production yield, inducing adrenaline production ultimately leading to the onset of persistent night sweats. To test our hypothesis we investigated whether incorporating carbohydrates into the diet of three non-diabetic patients with long COVID before bedtime would have any effect decreasing their night sweats. Remarkably, after one week with the dietary intervention, the patients reported that their night sweats had completely disappeared. Therefore, we propose carbohydrate supplementation as an affordable solution for night sweats in long COVID patients.
Arginine and Total Free Amino Acids As Indicators of the Nitrogen Status of Grapevines1

Growth and composition of ‘Thompson Seedless’ rootings grown in a perelite-vermiculite mixture (1:1 v/v), and receiving Hoagland nutrient solutions containing 0, ½, 1, 2, 4, or 8 mM NO3 were investigated over a 3-year period. Dry wt, no. of nodes, and total leaf area per vine, trunk circumference, and chlorophyll concn in leaves increased linearily with increasing concn of NO3 from 0 to 4 mM, and then remained relatively constant at 4 and 8 mM. Control vines (no NO3) had significantly less area per leaf blade and dry wt per unit leaf area than vines receiving ½ to 8 mM NO3. However, among vines irrigated with ½ to 8 mM NO3 differences in leaf area and leaf wt were not statistically significant. Highly significant linear (correlation coefficients 0.76 to 0.98) and 4th degree polynomial (correlation coefficients 0.92 and 0.99) regression equations were obtained between the levels of NO3 in the nutrient solution and the concn of arginine in canes, trunk, and roots at 3 different stages of vine development; i.e., mid-dormancy, budbreak, and bloom. Total free amino acids and NO3 in petioles and leaf blades were also positively correlated with the level of N in the Hoagland solutions, and with each other. The increases in concn of arginine and total free amino acids (relative to the control) in various parts of vines irrigated with different levels of NO3 were greatest at bloom, followed by budbreak and then dormancy, and were generally in the order of roots>trunk>canes>leaves. The relative increases of arginine reserves in vines following budbreak was first from canes, then from trunk, and lastly from roots.
A Model of Optimal Gas Supply to a Set of Distributed Consumers

A better design of gas supply chains may lead to a more efficient use of locally available resources, cost savings, higher energy efficiency and lower impact on the environment. In optimizing the supply chain of liquefied natural gas (LNG), compressed natural gas (CNG) or biogas for smaller regions, the task is to find the best supplier and the most efficient way to transport the gas to the customers to cover their demands, including the design of pipeline networks, truck transportation and storage systems. The analysis also has to consider supporting facilities, such as gasification units, truck loading lines and CNG tanking and filling stations. In this work a mathematical model of a gas supply chain is developed, where gas may be supplied by pipeline, as compressed gas in containers or as LNG by tank trucks, with the goal to find the solution that corresponds to lowest overall costs. In order to efficiently solve the combinatorial optimization problem, it is linearized and tacked by mixed integer linear programming. The resulting model is flexible and can easily be adapted to tackle local supply chain problems with multiple gas sources and distributed consumers of very different energy demands. The model is illustrated by applying it on a local gas distribution problem in western Finland. The dependence of the optimal supply chain on the conditions is demonstrated by a sensitivity analysis, which reveals how the model can be used to evaluate different aspects of the resulting supply chains.
Epidemiology and Survival of Kaposi’s Sarcoma by Race in the United States: A Surveillance, Epidemiology, and End Results Database Analysis

Abstract The introduction of highly active antiretroviral treatment (HAART) for acquired immunodeficiency syndrome (AIDS) has led to a significant decrease in the incidence of Kaposi’s sarcoma (KS) in recent years. However, a concomitant increase in racial disparities in KS has arisen. Here, we analyze data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program for epidemiology and survival of Kaposi’s sarcoma by race. The highest incidence was observed among Black patients (incidence rate (IR), 1.189; 95% confidence interval (CI), 1.092–1.189), followed by White patients (IR, 0.486; 95% CI, 0.473–0.498). Relative survival at 1, 3, and 5 years differed significantly by race, with White patients having the highest survival rates (84.26%, 77.79%, and 74.79% at 1, 3, and 5 years respectively) and Black patients demonstrating the lowest (71.50%, 61.37%, and 57.38%), with intermediate survival rates in Asians/Pacific Islanders (AAPI) (80.62%, 71.19%, and 67.28%) and American Indians/Alaska Natives (AIAN) (80.56%, 61.45%, and 61.45%). We conclude that there are significant racial disparities in the incidence, epidemiology, and survival of KS.
Raising Environmental and Social Sustainability through Green Human Resources Practices in Egyptian Tourism & Hospitality Organisations; the Mediating Role of Pro-Environmental Behaviors

Governments, organisations and consumers are facing growing green challenges and opportunities. Particularly, tourism organisations are confronting numerous social and environmental issues; human resource management (HRM) is one of the important business functions that can address and respond to such issues by implementing sustainable strategies such as green human resource management (GHRM) practices. Thus, there is a need to examine how green HRM can raise the Social and Environmental Sustainability of tourism and hospitality organizations, especially in response to Egypt's vision 2030 of sustainability. Accordingly, this study investigates the relation between GHRM practices and the organizations‟ Social and environmental responsibility through developing Pro-Environmental Behaviors among their employees. A model was proposed for the analysis of this relationship within Egyptian Tourism& Hospitality (T&H) organisations. Seeking to the quantitative research method, a survey research tool was also employed for testing the model. Using the partial least squares structural equation modeling (PLS-SEM) results show the different consequences and effects of the study variables. The study provides theoretical contributions to HRM literature; Specific implications for all stakeholders within the industry will arise considering the results.
Estimation des données occultées : modèles statistiques à noyau

In this paper, we deal with the pattern recognition problem using non-linear statistical models based on Kernel Principal Component Analysis. Objects that we try to recognize are defined by ordered sets of points. We present here two types of models: the first one uses an explicit projection function, the second one uses the Kernel trick. The present work attempts to estimate the localization of partially visible objects. Both are applied to the cephalometric problem with good results.
Improving Translation Quality By Using Ensemble Approach

Machine translation (MT) has been a topic of great research during the last sixty years, but, improving its quality is still considered an open problem. In the current paper, we will discuss improvements in MT quality by the use of the ensemble approach. We performed MT from English to Hindi using 6 MT different engines described in this paper. We found that the quality of MT is improved by using a combination of various approaches as compared to the simple baseline approach for performing MT from source to target text.
Interaction of Nitrogenous Fertilizers and Steam on Soil Chemicals and Carnation Growth1

Root loss, interveinal chlorosis, wilting, and reduced growth were typical symptoms of carnations grown in a soil mixture steamed at 100°C and to which a high ratio of NH4+ to NO3--N in the fertilizer was applied. These symptoms were less apparent and growth was greater in 100°C-treated soil with high NO3-N and in 71°- or 60°C-treated soil with high NH4+-N. No symptoms were apparent on plants grown in soil steamed at 71° and 60°C and treated with high NO3- fertilizer. Decreases in growth and visual symptoms were associated with high soil concn of soluble and exchangeable Mn or with high concn of NH4+ and NO2-- N. The primary cause of post steaming injury is believed to be due to excesses of NH4+ and NO2- rather than Mn. However, Mn appeared to be involved directly in the accumulation of NH4+ and NO2- in the soil.
Multi-Omics Approaches to Improve Clubroot Resistance in Brassica with a Special Focus on Brassica oleracea L.

Brassica oleracea is an agronomically important species of the Brassicaceae family, including several nutrient-rich vegetables grown and consumed across the continents. But its sustainability is heavily constrained by a range of destructive pathogens, among which, clubroot disease, caused by a biotrophic protist Plasmodiophora brassicae, has caused significant yield and economic losses worldwide, thereby threatening global food security. To counter the pathogen attack, it demands a better understanding of the complex phenomenon of Brassica-P. brassicae pathosystem at the physiological, biochemical, molecular, and cellular levels. In recent years, multiple omics technologies with high-throughput techniques have emerged as successful in elucidating the responses to biotic and abiotic stresses. In Brassica spp. , omics technologies such as genomics, transcriptomics, ncRNAomics, proteomics, and metabolomics are well documented, allowing us to gain insights into the dynamic changes that transpired during host-pathogen interactions at a deeper level. So, it is critical that we must review the recent advances in omics approaches and discuss how the current knowledge in multi-omics technologies has been able to breed high-quality clubroot-resistant B. oleracea. This review highlights the recent advances made in utilizing various omics approaches to understand the host resistance mechanisms adopted by Brassica crops in response to the P. brassicae attack. Finally, we have discussed the bottlenecks and the way forward to overcome the persisting knowledge gaps in delivering solutions to breed clubroot-resistant Brassica crops in a holistic, targeted, and precise way.
Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching

As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition accuracy and recognition efficiency. There are few research studies on music recognition based on deep neural networks. This paper expounds on the basic principles of deep learning and the basic structure and training methods of neural networks. For two kinds of commonly used deep networks, convolutional neural network and recurrent neural network, their typical structures, training methods, advantages, and disadvantages are analyzed. At the same time, a variety of platforms and tools for training deep neural networks are introduced, and their advantages and disadvantages are compared. TensorFlow and Keras frameworks are selected from them, and the practice related to neural network research is carried out. Training lays the foundation. Results show that through the development and experimental demonstration of the prototype system, as well as the comparison with other researchers in the field of humming recognition, it is proved that the deep-learning method can be applied to the humming recognition problem, which can effectively improve the accuracy of humming recognition and improve the recognition time. A convolutional recurrent neural network is designed and implemented, combining the local feature extraction of the convolutional layer and the ability of the recurrent layer to summarize the sequence features, to learn the features of the humming signal, so as to obtain audio features with a higher degree of abstraction and complexity and improve the performance of the humming signal. The ability of neural networks to learn the features of audio signals lays the foundation for an efficient and accurate humming recognition process.
Prevalence and Factors Associated with Metabolic Syndrome in Patients at a Psychosocial Care Center: A Cross-Sectional Study

Background: Metabolic syndrome (MS) is associated with greater risk of morbimortality and it has high prevalence in people with mental illness. Objective: Estimate the prevalence of Metabolic Syndrome (MS) and its associated factors in the patients of a Psychosocial Care Center (CAPS in Brazilian Portuguese) in the city of Salvador, state of Bahia, Brazil. Method: Cross-sectional study set at CAPS in the city of Salvador-Bahia between August 2019 and February 2020. MS was evaluated according to the National Cholesterol Education Program’s Adult Treatment Panel III. In addition to descriptive statistics, gross and adjusted prevalence ratios were described. Results: MS was found in 100 (35.2%) individuals, 116 (40.9%) were obese and 165 (58.1%) had increased waist circumference. Polypharmacy was identified in 63 (22.3%) patients and 243 (85.9%) used antipsychotics. Under gross evaluation, women (PR = 1.88; 95%CI: 1.35–2.63) and those who used antidepressants (PR = 1.41; 95%CI: 1.05–1.88) showed an association with MS. After logistic regression, depression (PR = 1.86; 95%CI: 1.38–2.51), acanthosis (PR = 1.50; 95%CI: 1.18–1.90), use of antipsychotics (PR = 1.88; 95%CI: 1.13–2.75), and hypertriglyceridemic waist (PR = 3.33; 95%CI: 2.48–4.46) were associated with MS. Conclusion: The prevalence of MS signals multimorbidity among individuals with mental disorders and suggests a need for clinical screening.
Properties of triangular matrix and Gorenstein differential graded algebras

The main goal of this thesis is to investigate properties of two types of Differential Graded Algebras (or DGAs), namely upper triangular matrix DGAs and Gorenstein DGAs. In doing so we extend a number of corresponding ring theory results to the more general setting of DGAs and DG modules. Chapters 2 and 3 contain background material. In chapter 2 we give a brief summary of some important aspects of homological algebra. Starting with the definition of an abelian category we give the construction of the derived category and the definition of derived functors. In chapter 3 we present the basics about Differential Graded Algebras and Differential Graded Modules in particular extending the definitions of the derived category and derived functors to the Differential Graded case before providing some results on Recollement of DGAs, Dualising DG-modules and Gorenstein DGAs. Chapters 4 and 5 contain the bulk of the work for the Thesis. In chapter 4 we look at upper triangular matrix DGAs and in particular we generalise a result for upper triangular matrix rings to the situation of upper triangular matrix differential graded algebras. An upper triangular matrix DGA has the form [ R M 0 S ] where R and S are DGAs and M is a DG R-Sop-bimodule. We show that under certain conditions on the DG-module M , and given the existence of a DG R-module X from which we can build the derived category D(R), that there exists a derived equivalence between the upper triangular matrix DGAs [ R M 0 S ] and [ S M ′ 0 R′ ] , where the DG-bimodule M ′ is obtained from M and X, and R′ is the endomorphism differential graded algebra of a K-projective resolution of X. In chapter 5 we turn our attention to Gorenstein DGAs and generalise some results from Gorenstein rings to Gorenstein DGAs. We present a number of Gorenstein Theorems which state, for certain types of DGAs, that being Gorenstein is equivalent to the bounded and finite versions of the Auslander and Bass classes being maximal. We also provide a new definition of a Gorenstein morphism for DGAs by considering a DG bimodule as a generalised morphism of DGAs. We then show that some existing results for Gorenstein morphism extend to these “Generalised Gorenstein Morphisms”. We finally conclude with some examples of generalised Gorenstein morphisms for some well known DGAs.
A high linearity low-noise amplifier in 0.25 μm BiCMOS Qubic4x for GSM application

ABSTRACT In this paper, a low-noise amplifier (LNA) in 0.25 μm BiCMOS Qubic4x technology for Global System for Mobile communications (GSM) application is presented. The LNA uses inductive source degeneration to achieve noise and power gain matching simultaneously and parallel RC feedback to improve linearity. The LNA provides a forward gain (S21) of 10.3 dB with noise figure (NF) of 1.5 dB while drawing 50.6 mW power from a 1.6 V voltage supply. The simulated 1-dB compression point is 0 dBm at 942 MHz. The simulations are done in cadence virtuoso Spectre RF using 0.25 μm BiCMOS Qubic4x technology.