Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of methods applied for wind power
generation forecasting

A bstrAct : The dynamic development of wind power in recent years has generated the demand for production forecasting tools in wind farms. The data obtained from mathematical models is useful both for wind farm owners and distribution and transmission system operators. The predictions of production allow the wind farm operator to control the operation of the turbine in real time or plan future repairs and maintenance work in the long run. In turn, the results of the forecasting model allow the transmission system operator to plan the operation of the power system and to decide whether to reduce the load of conventional power plants or to start the reserve units. The presented article is a review of the currently applied methods of wind power generation forecasting. Due to the nature of the input data, physical and statistical methods are distinguished. The physical approach is based on the use of data related to atmospheric conditions, terrain, and wind farm characteristics. It is usually based on numerical weather prediction models (NWP). In turn, the statistical approach uses historical data sets to determine the dependence of output variables on input parameters. However, the most favorable, from the point of view of the quality of the results, are models that use hybrid approaches. Determining the best model turns out to be a complicated task, because its usefulness depends on many factors. The applied model may be highly accurate under given conditions, but it may be completely unsuitable for another wind farm. K
Extreme Environment Integrated Circuits Based on Enhancement Mode SiC JFETs

This paper presents results on developing high temperature capable SiC JFET based IC technology that can operate at temperatures up to 500 °C. All JFET devices are fully planar, formed by ion implantation, and the device design allows the use of semi-insulating or conductive SiC substrates. Basic analog and logic ICs were built in order to demonstrate the technology high temperature capability. All circuits used enhancement mode n-channel JFETs as active transistors, and depletion mode transistors as active loads. The logic circuits built included NOT, NAND, and NOR gates. The analog circuits built included a simple one-stage operational amplifier. JFETs and ICs were packaged in ceramic packages and tested at temperatures up to 500 °C.
Emergence of Vancomycin Resistant and Methcillin Resistant Staphylococus aureus in Patients with Different Clinical Manifestations in Khartoum State, Sudan

Staphylococcus aureus (Staph. aureus), a major cause of potentially life-threatening infections acquired in healthcare and community settings, has developed resistance to most classes of antimicrobial agents as determined by the dramatic increase The present study aimed to determine the prevalence of MRSA, and VRSA in patients with different clinical manifestations in Khartoum state. The study population (n, 426) were males and females with different age categories, suffering either from wound infections (105), ear infections (121), or UTI (101), in addition to nasal carriers of medical staff (100). Cultures, Gram staining and other biochemical tests were performed for conventional identification. Modified Kirbry-Bauer disk diffusion method was applied and DNA was extracted from MRSA and VRSA isolates and PCR was then performed for amplification of arc, mecA, VanA and VanB genes. The results confirmed the existence of Staph. aureus in 49/426 (11.5%) cases among which MRSA were isolated from 34/49 (69.4%) when modified Kirbry-Bauer disk diffusion method was applied. Ten out of these 34 MRSA were confirmed as VRSA by cultures on BHI agar containing 6μg/ml vancomycin according to NCCLS criteria. PCR revealed that out of the 34 MRSA isolates, 26 were mecA positive (76.5%) while 8 (23.5%) were arcC positive. No vanA or VanB genes were detected. Molecular method confirmed the results for MRSA through the presence of either arcC or mecA genes while it failed to approve the occurrence of VRSA since neither VanA or VanB genes were detected. Thus, VRSA may be attributed to other factors. [Maimona A. E. Elimam, Suhair Rehan, Miskelyemen A Elmekki and Mogahid M Elhassan. Emergence of Vancomycin Resistant and Methcillin Resistant Staphylococus aureus in Patients with Different Clinical Manifestations in Khartoum State, Sudan. J Am Sci 2014;10(6):106-110]. (ISSN: 1545-1003). http://www.jofamericanscience. org. 13
Knowledge of nutrition facts on food labels and their impact on food choices on consumers in Koforidua, Ghana: a case study

Abstract Objective: The aim of this study was to investigate consumers’ knowledge of food labels and how this knowledge guides their decisions when making purchasing choices with regard to food. Design: This was a descriptive research design study. Setting: The setting was the suburb of Koforidua Municipality in the Eastern region of Ghana. Subjects: One hundred and forty-three customers were observed in store from four randomly sampled supermarkets. One hundred of these customers completed a self-administered questionnaire. Outcome measures: Measurements included observation and a self-administered questionnaire that elicited information on label-reading habits, attitudes towards health awareness and the influence of food labels on food choices. Data were analysed using descriptive statistics. Results: Sixty-five of the 100 consumers (54 females and 46 males) who consented to respond to the questionnaire looked at or read food labels before selecting a food to purchase. Seventy-five per cent reported reading the food labels prior to selecting food. This study found that nutrition knowledge had a low to average impact on consumers’ food choices. Half of the consumers who reported reading the food labels did not do so regularly. This could have implications on how often such information is used when purchasing food. Also, only 22% of the study respondents answered correctly when asked to explain “26% RDA (recommended dietary allowance) vitamin A per serving” on a food label, even though 45% of the respondents had a tertiary education. Conclusion: These findings indicate awareness and knowledge of food labelling which may not always adequately impact on food choices, even though study respondents indicated high awareness and low to average reading of labels prior to purchasing food.
Kick-Start for Connectivity - How to Implement Digital Platforms Successfully in Industry 4.0

Lassen, 2017), which can significantly profit from their great adaptability across industry sectors and value chain stages. Therefore, not only the choice of a product or a technology is crucial, but also the choice of the best platform strategy in combination with the best ecosystem, in order to gain a competitive advantage (Cusumano, 2010). However, little is known
Consistency of the Spectral Seriation Algorithm

Given n arbitrary objects x1, x2, . , xn and a similarity matrix P = (pi,j)1≤i,j≤n, where pi,j measures the similarity between xi and xj. If the objects can be ordered along a linear chain so that the similarity decreases as the distance increase within this chain, then the goal of the seriation problem is to recover this ordering π given only the similarity matrix. When the data matrix P is completely accurate, the true relative order can be recovered from the spectral seriation algorithm [1]. In most applications, the matrix P is noisy, but the basic spectral seriation algorithm is still very popular. In this thesis, we study the consistency of this algorithm for a wide variety of statistical models, showing both consistency and bounds on the convergence rates. More specifically, we consider a model matrix P satisfying certain assumptions, and construct a noisy matrix P̂ where the input (i, j) is a coin flip with probability pi,j. We show that the output π̂ of the spectral seriation algorithm for the random matrix is very close to the true ordering π.
Link-state QoS routing protocol under various mobility models

Mobile Ad Hoc Network (MANET) consists of a group of mobile or wireless nodes that are placed randomly and dynamically that causes the continual change between nodes. A mobility model attempts to mimic the movement of real mobile nodes that change the speed and direction with time. The mobility model that accurately represents the characteristics of the mobile nodes in an ad hoc network is the key to examine whether a given protocol. The aim of this paper is to compare the performance of four different mobility models (i.e. Random Waypoint, Random Direction, Random walk, and Steady-State Random Waypoint) in MANET. These models were configured with Optimized Link State Routing (OLSR) protocol under three QoS (Quality of Service) metrics such as the Packet Delivery Ratio (PDR), Throughput, End-to-End delay. The simulation results show the effectiveness of Steady-State Random Waypoint Mobility Models and encourage further investigations to extend it in order to guarantee other QoS requirements.
Differential Police Treatment of Male-on-Female Spousal Violence

The assumption that police are relatively lenient with men who assault their spouses has led several U.S. political jurisdictions to enact mandatory arrest-for-wife-assault legislation. Although the leniency thesis underlying this legislation is widely believed, it is directly addressed by only three studies of varying rigor and generalizability. With new data, the present research uses multivariate techniques to reexamine the hypothesis that police are less aggressive in arresting wife assaulters, with results that support the leniency thesis. In addition, results suggest that police are more likely to arrest if the victim of spousal violence is a woman who is affluent, White, older, or suburban. The implications of these findings for mandatory arrest laws, crime control, and the debate on race and crime are discussed.
Predictors of Employment Outcomes for Vocational Rehabilitation Consumers With HIV/AIDS: 2002–2007

This study examined the predictability of two employment outcomes—employment status and weekly earnings at closure— from consumer demographic, medical, and service variables for multiple groups of vocational rehabilitation (VR) consumers with HIV/AIDS retrieved from the RSA-911 data for fiscal years 2002 through 2007. A logistic regression analysis was used to determine predictors of employment status at closure across years, and a multiple regression analysis was conducted to identify predictors of weekly wage at closure. Consistently significant predictors of employment status at closure across the 6 years of data included job placement assistance, service expenditures, and length of time in rehabilitation. Consumer demographic variables, including receipt of Supplemental Security Income/Social Security Disability Income at referral, Medicare/Medicaid at referral, and level of education along with receipt of training services (i.e., college, vocational, or on-the-job training), were found to be the most reliable, significant predictors of weekly earnings at closure across the 6 years. Implications of the findings for practice and research are provided.
Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness

The star‐rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the “true quality” of products and services is challenging. The mean‐rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the “helpfulness” social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on “helpfulness” achieve better performance than the statistical and heuristic baseline approaches.
Optimizing Parallel I/O Accesses through Pattern-Directed and Layout-Aware Replication

As the performance gap between processors and storage devices keeps increasing, I/O performance becomes a critical bottleneck of modern high-performance computing systems. In this paper, we propose a pattern-directed and layout-aware data replication design, named PDLA, to improve the performance of parallel I/O systems. PDLA includes an HDD-based scheme H-PDLA and an SSD-based scheme S-PDLA. For applications with relatively low I/O concurrency, H-PDLA identifies access patterns of applications and makes a reorganized data replica for each access pattern on HDD-based servers with an optimized data layout. Moreover, to accommodate applications with high I/O concurrency, S-PDLA replicates critical access patterns that can bring performance benefits on SSD-based servers or on HDD-based and SSD-based servers. We have implemented the proposed replication scheme under MPICH2 library on top of OrangeFS file system. Experimental results show that H-PDLA can significantly improve the original parallel I/O system performance and demonstrate the advantages of S-PDLA over H-PDLA.
Lyapunov approach to higher-order sliding mode design

Analysis and design of the well-known higher-order sliding mode (HOSM) controllers are usually made by means of homogeneity and contraction properties. Up to now, HOSM algorithms have not been properly addressed within a Lyapunov framework, and there is no Lyapunov-based analysis and design, despite the fact that Lyapunov's methods are one of the most important analysis and design tools in modern control theory of nonlinear systems. In this chapter, we design new families of homogeneous HOSM controllers for a class of single-input-single-output uncertain systems. In contrast to the well-known quasi-continuous and nested HOSM control (HOSMC) families, the proposed families of HOSM controllers are obtained by using the concept of control Lyapunov functions (CLFs). The CLFs are constructed explicitly by a modification of the wellknown Backstepping technique and applying properties of homogeneous systems. These ingredients allow us to construct continuously differentiable CLFs recursively, avoiding the problem of using complex mathematical tools for non-continuously differentiable Lyapunov functions. A Lyapunov framework for the HOSM design leads to synthesize different families of homogeneous HOSM controllers and it will allow us to investigate important nonlinear properties of these algorithms which cannot be easily studied with the known techniques hitherto established in HOSM theory.
What Makes Cryptocurrencies Special? Investor Sentiment and Return Predictability

The 2017 bubble on the cryptocurrency market recalls our memory in the dot-com bubble, during which hard-to-measure fundamentals and investors' illusion for brand new technologies led to overvalued prices. Benefiting from the massive increase in the volume of messages published on social media and message boards, we examine the impact of investor sentiment, conditional on bubble regimes, on cryptocurrencies aggregate return prediction. Constructing a crypto-specific lexicon and using a local-momentum autoregression model, we find that the sentiment effect is prolonged and sustained during the bubble while it turns out a reversal effect once the bubble collapsed. The out-of-sample analysis along with portfolio analysis is conducted in this study. When measuring investor sentiment for a new type of asset such as cryptocurrencies, we highlight that the impact of investor sentiment on cryptocurrency returns is conditional on bubble regimes.
Verification of Floating Offshore Wind Linearization Functionality in OpenFAST

The wind engineering community relies on multiphysics engineering software to run nonlinear time-domain simulations, e.g., for design standards-based loads analysis. Although most physics involved in wind energy are nonlinear, linearization of the underlying nonlinear system equations is often advantageous to understand the system properties and exploit well-established methods and tools for analyzing linear systems. Previous work in this area has focused on the development of the new linearization functionality of the open-source engineering tool OpenFAST for floating offshore wind turbines, as well as the concepts and mathematical background needed to understand and apply it correctly. This paper focuses on the verification of this new linearization functionality, which is carried out by comparing results to previous stable versions of FAST. A nonlinear time-domain simulation for a floating offshore platform is also compared to the time-domain response of the linearized state-space model. The linearized results show good alignment between OpenFAST and previous versions of FAST, as well as with the time-domain simulations, thereby showing the accuracy of the new features in OpenFAST.
Primary hyperaldosteronism in cats: expanding the diagnostic net

Primary hyperaldosteronism or low-renin hyperaldosteronism in cats is characterized by inappropriately high aldosterone secretion from one or both adrenal glands, with systemic arterial hypertension and hypokalemia as leading clinical manifestations. In this thesis, non-tumorous primary hyperaldosteronism is documented, the relationship between primary hyperaldosteronism and chronic kidney disease is investigated, and a new diagnostic test for primary hyperaldosteronism is described. In Chapter 3 eleven cats with primary hyperaldosteronism are reported. Bilateral nodular hyperplasia of the adrenal zona glomerulosa was confirmed histologically in three cats and suspected in the other eight. One cat developed chronic kidney disease and in several others there was progression of chronic kidney disease. Histological examination of the kidneys of two of these cats revealed severe chronic inflammatory changes in the glomeruli, interstitium and arteries. These results suggest an association between low-renin hyperaldosteronism and progressive kidney disease in cats. These findings prompted exploratory investigation of the prevalence of primary hyperaldosteronism in cats with chronic kidney disease, as described in Chapter 4. Seven (14%) of 51 cats with chronic kidney disease had an elevated plasma aldosterone-to-renin ratio (ARR), pointing to inappropriately high aldosterone secretion. This warrants the investigation for primary hyperaldosteronism in cats with chronic kidney disease. The investigation of primary hyperaldosteronism can be rather complicated. The ARR has been widely accepted as a screening test, but is associated with some practical limitations. To circumvent these limitations, measuring aldosterone in urine was explored, as described in Chapter 5. The basal urinary aldosterone-to-creatinine ratio (UACR) was determined in 42 healthy cats and one cat with a confirmed aldosterone-producing adrenocortical carcinoma. The basal UACR in the cat with primary hyperaldosteronism was within the reference range of 46.5x10-9 points to primary hyperaldosteronism; and (3) in cats with a basal UACR between 7.5x10-9 and 46.5x10-9,
Studies in man with 18F.

1. The distribution of intravenously administered 18 F was investigated in thirty-one normal human volunteers, seven patients with acute or chronic renal failure and two patients with malignant disease. The object was to define some features of the bodily distribution and handling of 18 F in the hope that this will shed some light on fluoride physiology in health and disease. 2. Plasma radioactivity at any given time depends on the degree of diffusion of 18 F through the extracellular fluid as well as on the competing effects of renal excretion and tissue uptake. The factors governing these last two functions are discussed. 3. At 1 h after injection 40% of the administered dose of 18 F is contained in the extracellular fluid, 20% has been excreted and 40% taken up by the tissues, including 2·5% into erythrocytes.
DeepChannel: Wireless Channel Quality Prediction Using Deep Learning

Accurately modeling and predicting wireless channelquality variations is essential for a number of networking applications such as scheduling and improved video streaming over 4G LTE networks and bit rate adaptation for improved performance in WiFi networks. In this paper, we design DeepChannel, an encoder-decoder based sequence-to-sequence deep learning model that is capable of predicting future wireless signal strength variations based on past signal strength data. We consider two different versions of DeepChannel; the first and second versions use LSTM and GRU as their basic cell structure, respectively. In contrast to prior work that is primarily focused on designing models for particular network settings, DeepChannel is highly adaptable and can predict future channel conditions for different networks, sampling rates, mobility patterns, and communication standards. We compare the performance (i.e., the root mean squared error, mean absolute error and relative error of future predictions) of DeepChannel with respect to two baselines—i) linear regression, and ii) ARIMA for multiple networks and communication standards. In particular, we consider 4G LTE, WiFi, WiMAX, an industrial network operating in the 5.8 GHz range, and Zigbee networks operating under varying levels of user mobility and observe that DeepChannel provides significantly superior performance. Finally, we provide a detailed discussion of the key design decisions including insights into hyper-parameter tuning and the applicability of our model in other networking scenarios.
Virtual Brand Community Experience, Identification, and Electronic Word-of-mouth

ABSTRACT Despite recognizing the importance of virtual brand community (VBC) and the existence of different types of electronic word-of-mouth (eWOM) behaviors (e.g., opinion giving, opinion passing and opinion seeking), the existing literature remains inadequate regarding the influences of VBC experiences and identification on eWOM. This study, incorporating stimulus–organism–response theory and related research, examines the influences of various VBC experiences (e.g., intellectual, sensory, entertainment, interactive, affective, and relational experiences) on the different types of eWOM behaviors, mediated via two different types of identification (i.e., brand identification and community identification). Data are collected using an online survey and analyzed using structural equation modeling. Results show that VBC experiences (with the exception of interactive experience) have a significant impact on brand identification and community identification. Moreover, community identification and brand identification distinctively affect the different types of eWOM behaviors. Theoretical and practical implications, limitations, and future research directions are discussed.