Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud computing models

Information Technology has always been considered a major pain point of enterprise organizations, from the perspectives of both cost and management. However, the information technology industry has experienced a dramatic shift in the past decade – factors such as hardware commoditization, open-source software, virtualization, workforce globalization, and agile IT processes have supported the development of new technology and business models. Cloud computing now offers organizations more choices regarding how to run infrastructures, save costs, and delegate liabilities to third-party providers. It has become an integral part of technology and business models, and has forced businesses to adapt to new technology strategies. Accordingly, the demand for cloud computing has forced the development of new market offerings, representing various cloud service and delivery models. These models significantly expand the range of available options, and task organizations with dilemmas over which cloud computing model to employ. This thesis poses analysis of available cloud computing models and potential future cloud computing trends. Comparative analysis includes cloud services delivery (SaaS, PaaS, IaaS) and deployment models (private, public, and hybrid). Cloud computing paradigms are discussed in the context of
Securing Networked Microgrids Operation through DNP3 Protocol Implementation

The paper introduces the microgrid and its components that are networked within a local area. This is to satisfy the supply demand requirements within the microgrid with the hosting grid at the point of common coupling (PCC). A complete control model is presented for an inverter based microgrid. The results show that the distributed control can achieve the set frequency and voltage points within the whole microgrid. The paper further introduces a secured networked distributed microgrid by utilizing the DNP3 protocol as a case study for the distributed control system. The DNP3 implementation as middleware communication protocol is detailed here. The test system utilizes an embedded computer as the distributed control layer for the grid and a Python-based dynamical model is developed to emulate the actual power system dynamics.
The Perspective of Administrators of Intellectual Disability Organizations on the COVID-19 Pandemic

Objective: To learn about the challenges, policies, and needed resources to serve people with intellectual disability and protect staff during the COVID-19 pandemic. From the perspective of intellectual disability service providers. Methods: We conducted in-depth qualitative interviews with 16 intellectual disability organization administrators throughout Illinois, USA from November 2020 through February 2021. We coded and analyzed the data using thematic analysis. Results: Three major themes emerged: (1) COVID-19 caused considerable challenges to people with intellectual disability and staff and service providers, (2) intellectual disability organizations reinvented service provisions in response to COVID-19 challenges, and (3) the interrelatedness of intellectual disability organizations, public policies, and community entities became evident. Conclusions: Exhibiting responsiveness to needs and developing innovative solutions were strategies championed by intellectual disability organizations during the pandemic. Fostering collaboration with community entities may assist these organizations in navigating pandemic challenges and developing resilient infrastructure for future environmental threats.
Nanotechnology-based approaches for regenerative medicine and biosensing

OF THE DISSERTATION Nanotechnology-based Approaches for Regenerative Medicine and Biosensing by Aniruddh P. Solanki Dissertation Director: Professor Ki-Bum Lee The recent emergence of nanotechnology has set high expectations in many fields of science, especially in biology and medicine. Nanotechnology-based approaches are expected to solve key questions in the emerging field of regenerative medicine. Regenerative medicine essentially deals with regeneration of cells, ultimately leading to the formation of tissues and organs. For this purpose, stem cells (embryonic stem cells or adult stem cells) are thought to be ideal resources. However, controlling the differentiation of stem cells into cells of a specific lineage is extremely vital and challenging. Addressing this challenge, in this work, novel nanotechnology-based approaches for controlling the differentiation of neural stem cells (NSCs) into neurons has been presented. Regeneration of damaged neurons, due to traumatic injuries or degenerative diseases, is extremely challenging. For this purpose, NSCs can be used as resources that can differentiate into neurons, thus having great potential in solving needs of many patients suffering from such conditions. The initial part of this work presents the use of nanomaterials for efficiently delivering soluble cues such as small molecules and small interfering RNA (siRNA) into NSCs for controlling their differentiation into neurons. However, for regenerative purposes, it is preferred that least amounts of the delivery vehicle be used. Thus, the following
Vision-Based UAV Distribution Line Inspection Using Deep Learning

Regular inspection of distribution line is an important link to maintain the normal operation of distribution network. Using unmanned aerial vehicle (UAV) instead of manpower can save the cost of inspection. With the universal application of vision sensor in UAV and the rapid development of deep learning, Convolutional Neural Networks (CNN) is applied to the detection of power line in UAV visible images. In view of the lack of application environment inspection methods for distribution line, a vision-based UAV distribution line inspection method using deep learning and a dataset for the deep learning method of distribution line inspection task are proposed in this paper. The method proposed predicts distribution line area through the encoder-decoder structure network firstly. Image processing operation and sampling clustering are used to remove the interference. Finally, the UAV tracking direction of distribution power line is calculated according to the detected distribution line. The method can reach an inspection speed of nearly 77ms per frame, the range of heading deviation error can reach (-1.52°, 1.36°), the tracking rate nearly 100%. Through the test of network and inspection method on dataset, the results show that the method proposed in this paper can be effectively, quickly and accurately applied to UAV distribution line inspection.
A Review on Manufacturing and Post-Processing Technology of Vascular Stents

Percutaneous coronary intervention (PCI) with stent implantation is one of the most effective treatments for cardiovascular diseases (CVDs). However, there are still many complications after stent implantation. As a medical device with a complex structure and small size, the manufacture and post-processing technology greatly impact the mechanical and medical performances of stents. In this paper, the development history, material, manufacturing method, and post-processing technology of vascular stents are introduced. In particular, this paper focuses on the existing manufacturing technology and post-processing technology of vascular stents and the impact of these technologies on stent performance is described and discussed. Moreover, the future development of vascular stent manufacturing technology will be prospected and proposed.
Identification and description of patients with multisystem inflammatory syndrome in adults associated with SARS-CoV-2 infection using the Premier Healthcare Database

Abstract Multisystem inflammatory syndrome in adults (MIS-A) is a hyperinflammatory illness related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The characteristics of patients with this syndrome and the frequency with which it occurs among patients hospitalised after SARS-CoV-2 infection are unclear. Using the Centers for Disease Control and Prevention case definition for MIS-A, we created ICD-10-CM code and laboratory criteria to identify potential MIS-A patients in the Premier Healthcare Database Special COVID-19 Release, a database containing patient-level information on hospital discharges across the United States. Modified MIS-A criteria were applied to hospitalisations with discharge from March to December 2020. The proportion of hospitalisations meeting electronic health record criteria for MIS-A and descriptive statistics for patients in the potential MIS-A cohort were calculated. Of 34 515 SARS-CoV-2-related hospitalisations with complete clinical and laboratory data, 53 met modified criteria for MIS-A (0.15%). The median age was 62 years (IQR 52–74). Most patients met the severe cardiac illness criterion through either myocarditis (66.0%) or new-onset heart failure (35.8%). A total of 79.2% of patients required ICU admission, while 43.4% of patients in the cohort died. MIS-A appears to be a rare but severe outcome of SARS-CoV-2 infection. Additional studies are needed to investigate how this syndrome differs from severe coronavirus disease 2019 (COVID-19) in adults.
Imaging Cerebral Arteries Tortuosity and Velocities by Transcranial Doppler Ultrasound Is a Reliable Assessment of Brain Aneurysm in Mouse Models

Background and Purpose Intracranial aneurysms (IAs) are common vascular abnormalities of the brain with a prevalence of 3.2% in the general population. In the past few decades, several pathophysiological processes leading to IA rupture were identified, including irregular IA shape, an altered hemodynamic stress within the IA and vessel wall inflammation. The use of preclinical models of IA and imaging tools are paramount to better understand the underlying disease mechanisms. Therefore, there is a need for imaging methods to monitor intracranial aneurysm formation. Methods We used two established mouse models of IA and we analyzed the progression of the IA by magnetic resonance imaging (MRI), transcranial Doppler (TCD), and histological studies. Results In both models of IA, we observed by TCD a significant decrease of the blood velocities and wall shear stress of the internal carotid arteries (ICA). We also observed the formation of tortuous arteries in both models which were correlated with the presence of an aneurysm as confirmed by MRI and histological analysis. A high grade of tortuosity has been associated with a significant decrease of the mean blood flow velocities and a greater artery dilation. Conclusions TCD is robust and easy imaging method to evaluate the progression of IA. The decrease of the blood flow velocities and the tortuosity can be used as reliable readout for IA detection.
High‐power gamma‐related delta phase alteration in schizophrenia patients at rest

Information processing is supported by the cortico‐cortical transmission of neural oscillations across brain regions. Recent studies have demonstrated that the rhythmic firing of neural populations is not random but is governed by interactions with other frequency bands. Specifically, the amplitude of gamma‐band oscillations is associated with the phase of lower frequency oscillations in support of short and long‐range communications among networks. This cross‐frequency relation is thought to reflect the temporal coordination of neural communication. While schizophrenia patients show abnormal oscillatory responses across multiple frequencies at rest, it is unclear whether the functional relationships among frequency bands are intact. This study aimed to characterize the lower frequency (delta/theta, 1–8 Hz) phase and the amplitude of gamma oscillations in healthy subjects and schizophrenia patients at rest.
Sensitivity Analysis of Offshore Wind Cost of Energy (Poster)

No matter the source, offshore wind energy plant cost estimates are significantly higher than for land-based projects. For instance, a National Renewable Energy Laboratory (NREL) review on the 2010 cost of wind energy found baseline cost estimates for onshore wind energy systems to be 71 dollars per megawatt-hour ($/MWh), versus 225 $/MWh for offshore systems. There are many ways that innovation can be used to reduce the high costs of offshore wind energy. However, the use of such innovation impacts the cost of energy because of the highly coupled nature of the system. For example, the deployment of multimegawatt turbines can reduce the number of turbines, thereby reducing the operation and maintenance (O&M) costs associated with vessel acquisition and use. On the other hand, larger turbines may require more specialized vessels and infrastructure to perform the same operations, which could result in higher costs. To better understand the full impact of a design decision on offshore wind energy system performance and cost, a system analysis approach is needed. In 2011-2012, NREL began development of a wind energy systems engineering software tool to support offshore wind energy system analysis. The tool combines engineering and cost models to represent an entire offshoremore » wind energy plant and to perform system cost sensitivity analysis and optimization. Initial results were collected by applying the tool to conduct a sensitivity analysis on a baseline offshore wind energy system using 5-MW and 6-MW NREL reference turbines. Results included information on rotor diameter, hub height, power rating, and maximum allowable tip speeds. « less
Getting the Human Factor into Traffic Flow Models: New Open-Source Design to Simulate Next Generation of Traffic Operations

Automated driving may lead to much higher road capacities, combined with increased road safety, increased driver comfort, and lower costs. Although this vision may hold ground in the long run, first a transitional period will take place in which increasing percentages of vehicles with many levels of automation will drive on the world’s road networks. This transition poses a fundamental scientific challenge. The models used today to simulate and predict vehicular traffic are not valid to predict emergent properties of traffic flows under increasing amounts of vehicle automation. For example, there is no idea of how drivers of nonautomated vehicles will respond to other drivers reading their morning papers behind the steering wheel or the consequences of such interactions on traffic safety and capacity. In this paper, the authors do not propose a new behavioral theory with which the effects of increasing vehicle automation can be predicted. What the authors propose is an advanced open-source simulation framework, OpenTrafficSim, which makes it possible to extend microscopic models incrementally with explanatory mental models, such that new behavioral theories can be tested and shared within the community. Given the societal importance of predicting the effects on safety and efficiency of vehicle automation, the authors sincerely hope this paper will fuel the discussion on how both open-source and closed-source simulation software can be adapted and made ready for the next generation of traffic simulation models that are needed in the coming decades.
STUDY ON TRANSMISSION ENERGY LOSSES AND FINDING THE HAZARDS USING WHAT IF ANALYSIS

Reducing electricity consumption in an educational institution is a very typical. This paper presents a detailed review of energy consumption in an educational institution. To determine the electrical cable loss is identified strategies to save electrical energy. This electrical energy audit presents the result of a preliminary audit carried out on 3 large building blocks of a famous educational institute in Salem district. Growing populations and educational institutions in our country create huge needs for electrical energy. Transmitting electrical energy through an underground cable leads to energy loss. An electrical consumption is a fundamental first step toward reducing utility bills in a preparing check list and what if analysis. By analyzing a college blocks major components including the building envelope, Class rooms, Computers, Electrical and Electronic laboratories and library, as well as of these components.
Sociodemographic and Clinical Characteristics Associated with Improvements in Quality of Life for Participants with Opioid Use Disorder

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant’s reported quality of life (QoL) at the beginning of the program and at successive intervals. Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL. Results: 71% of the participants (n = 494) experienced an increase in their QoL scores, with an average improvement of 15.8 ± 29 points out of a hundred. We identified 10 factors associated with a significant change in QoL. Participants who relapsed during treatment experienced minor increases in QoL, and participants who attended professional counseling experienced the largest increases in QoL compared with those who did not. Conclusions: Insight into significant factors associated with increases in QoL may inform programs on areas of focus. The inclusion of counseling and other services that address factors such as psychological distress were found to increase participants’ QoL and success in recovery.
Comparing Traditional Machine Learning Methods for COVID-19 Fake News

This article describes some supervised classification techniques for COVID-19 fake news detection in English, where the sources of data are annotated posts from various social media platforms such as Twitter, Facebook, or Instagram. The main objective is to examine the performance of traditional machine learning techniques of COVID-19 fake news detection. In this Situation, models trained with Support Vector Machine and Naïve Bayes algorithms outperformed all other strategies.
Novel Molecular Targets of Antidepressants

Antidepressants target a variety of proteins in the central nervous system (CNS), the most important belonging to the family of G-protein coupled receptors and the family of neurotransmitter transporters. The increasing number of crystallographic structures of these proteins have significantly contributed to the knowledge of their mechanism of action, as well as to the design of new drugs. Several computational approaches such as molecular docking, molecular dynamics, and virtual screening are useful for elucidating the mechanism of drug action and are important for drug design. This review is a survey of molecular targets for antidepressants in the CNS and computer based strategies to discover novel compounds with antidepressant activity.
Differences in cerebral blood flow measurement using arterial spin labeling MRI between patients with moyamoya disease and patients with arteriosclerotic cerebrovascular disease

Background It is unclear whether the accuracy of arterial spin labeling (ASL) magnetic resonance imaging (MRI) is the same between moyamoya disease (MMD), which is known to have markedly elevated cerebral blood volume (CBV), and atherosclerotic intracranial arterial stenosis (AS), which has relatively less elevated CBV. Purpose To investigate how the differences in hemodynamics affect measurement of ASL-cerebral blood flow (CBF) using ASL for patients with MMD and AS. Material and Methods Fourteen MMD and ten AS patients were evaluated with ASL-MRI, magnetic resonance angiography (MRA), and 15O-gas positron emission computed tomography (PET). The regional CBF values of ASL using two post-labeling delays (PLDs; 1525 ms and 2525 ms) were compared with the PET-derived CBF, CBV, and mean transit time (MTT). Corresponding anterior circulation results were evaluated by flow territory map-based analysis. Results The correlation between the ASL-CBF values (2525 ms) and PET-CBF declined in the MMD group (r = 0.28; P < 0.01), while the AS group showed good correlation (r = 0.77; P < 0.01). In the MMD group, the ASL-CBF values (2525 ms) overestimated the PET-CBF values as the regional CBV values increased (r = 0.35; P < 0.01). When the regions of interest were divided into two subgroups according to the degree of arterial stenosis by MRA, the correlation coefficient between the ASL-CBF (2525 ms) and PET-CBF values improved (mild stenosis: r = 0.36; P = 0.06; severe stenosis: r = 0.51; P < 0.01). Conclusion The accuracy of CBF measurements using ASL-MRI differed between patients with MMD and AS. The prominent increase of CBV and the degree of arterial stenosis may have affected the accuracy of ASL-CBF in patients with MMD.
Precise orbit determination for LEO satellites: single-receiver ambiguity resolution using GREAT products

ABSTRACT In recent years, the large Low Earth Orbit (LEO) constellations have become a hot topic due to their great potential to improve the Global Navigation Satellite Systems (GNSS) positioning performance. One of the important focus is how to obtain the accurate and reliable orbits for these constellations with dozens of LEO satellites. The GNSS-based Precise Orbit Determination (POD) will be exclusively performed to achieve this goal, where the Integer Ambiguity Resolution (IAR) plays a key role in acquiring high-quality orbits. In this study, we present a comprehensive analysis of the benefit of the single-receiver IAR in LEO POD and discuss its implication for the future LEO constellations. We perform ambiguity-fixed LEO POD for four typical missions, including Gravity Recovery and Climate Experiment (GRACE) Follow-On (GRACE-FO), Swarm, Jason-3 and Sentinel-3, using the Uncalibrated Phase Delay (UPD) products generated by our GREAT (GNSS+ REsearch, Application and Teaching) software. The results show that the ambiguity fixing processing can significantly improve the accuracy of LEO orbits. There are negligible differences between our UPD-based ambiguity-fixed orbits and those based on the Observable Signal Bias (OSB) and Integer Recovery Clock (IRC) products, indicating the good-quality of UPD products we generated. Compared to the float solution, the fixed solution presents a better consistency with the external precise science orbits and the largest accuracy improvement of 5 mm is achieved for GRACE-FO satellites. Meanwhile, the benefit can be observed in laser ranging residuals as well, with a Standard Deviation (STD) reduction of 3–4 mm on average for the fixed solutions. Apart from the absolute orbits, the relative accuracy of the space baseline is also improved by 20–30% in the fixed solutions. The result demonstrates the superior performance of the ambiguity-fixed LEO POD, which appears as a particularly promising technique for POD of future LEO constellations.
Continuous Calibration D/A Conversion

In this chapter, the demands on the linearity of high-resolution D/A and A/D converters for measurement and digital audio equipment are currently so high that the achievable accuracy based on the matching of components in a standard process is not sufficient. Therefore, additional calibration techniques are used to achieve high resolution. A disadvantage of many calibration techniques is the need for a special calibration period. During this period, the converter cannot be used for conversion, which particularly limits the application range. Furthermore, a relatively large amount of chip area is needed to store the error signals. Other calibration techniques, such as laser trimming and external adjustment, take precious time and facilities and are sensitive to ageing and temperature, whilst dynamic element matching needs external components. In this chapter, the switched-current technique is used as a self calibration technique. Its use enables the design of D/A converters that need no calibration period, additional trimming or external components, and are insensitive to process variations.