Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the parasitic resistances on the voltage gain in a dc-dc modular system

In many circumstances, ignoring the parasitic resistances of the inductors in DC-DC converter is recommended, which results in simplifying state equations and its following calculations. Nevertheless, when the duty cycle values are high, the converter may work in a region of its characteristic that is unstable, so the maximum attainable gain would be limited. In this paper, the voltage ratio of a modular DC-DC converter is analyzed. This studied converter is based on the three-level boost converter. This converter structure has already proposed for making dc voltage balance. The simulations are carried out in MATLAB and SIMULINK environment. The system simulation waveforms are compared with the waveforms obtained from the theoretical calculations. The comparison is made for different scenarios. Two different operating points are set. At the first operating point, the nominal load resistance is considered. At another operating point, half of the nominal value for the load resistance is fixed.
A novel localization methodology for partial discharge in power transformer considering internal structure

Ultrasonic method has been widely studied in the field of partial discharge location of power transformer. Previous studies mainly focused on solving the time difference of arrival (TDOA) equations by using different optimization algorithms. However, the complex structure in the transformer will have a great influence on the propagation path and arrival time of ultrasonic signal, and then affect the positioning accuracy. In this paper, the influence of the internal structure of transformer on the ultrasonic propagation paths is explained in detailed. Based on the theoretical analysis, a numerical node model of transformer is established. Based on the model, A* routing algorithm considering the influence of internal structure is proposed to calculate the fastest propagation path between nodes. In order to verify the accuracy of the positioning algorithm, the positioning experiment is carried out on a 35kV transformer.
Deep-Learning-Based Intelligent PotholeEye+ Detection Pavement Distress Detection System

Road maintenance is one of the most critical issues confronting agricultural based countries. Road foundations are vital to society since most traffic accidents are caused by poor road conditions such as potholes. Potholes are caused by poor quality and a failure to keep up with the roadways. Furthermore, the continuous rise of overweight vehicles, such as trucks, are responsible for the potholed roads. These poor quality roads will extremely damage the automobiles in terms of underinflated tires and accidents. Hence, a A suitable structure for monitoring road conditions and planning future works should be developed. Pits are generally formed by mature roadways and poor road support structures, and their number grows with time. Potholes compromise road safety and reduce transportation efficiency. This research study proposes a portable framework called PotholeEye + to consequently observe the outer layer of a road and progressively distinguish asphalt problems through video investigation. As you drive the road director, PotholeEye+ pre-processes the images, removes the inclusion and sort out the misery. This research work is testing PotholeEye+ on a real road constantly for a year by using original settings, a camera, a small-scale PC, a beneficiary GPS, etc. Subsequently, PotholeEye + has recognized asphalt problems with 92% typical accuracy, 87% accuracy and 74% review when driving at a typical speed of 110 km/h on an actual highway.
Willingness to share anonymised routinely collected clinical health data in Switzerland: a cross-sectional survey.

AIMS OF THE SURVEY Routinely collected health data (or real-world data) from hospitals is becoming increasingly important to advance medical progress. Anonymisation of these data facilitates data sharing processes. This allows stakeholders of the healthcare system to compliantly access this anonymised data to address epidemiological questions, advance precision medicine, support drug development or address other medical needs. As the willingness of the general Swiss population to share anonymised health data has been uncertain, a survey was conducted to better understand their perception of sharing such data for research purposes. The present survey focused on the re-use (secondary use) of hospital-derived health data in anonymised form. METHODS A cross-sectional survey was conducted in a representative random sample (n = 1006) of the general Swiss population. The general population was contacted by phone between 14 September and 3 October 2020. The survey was also conducted in an additional population with chronic disease (n = 225) via an online panel. An independent research organisation (gfs-zürich) was commissioned to conduct the survey. The survey participation was anonymous and voluntary. The demographic composition of the interviewed participants from the general population was specifically constructed to be representative of the 18+-year-old French- and German-speaking population of Switzerland, according to the quota features gender, age and language region. Representativeness of the chronic disease population is unclear. RESULTS 71% of the general population and 81% of the chronic disease group reported that they would share their anonymised health data for medical research. The drivers were mainly of an altruistic nature. Hurdles concern mainly data protection issues, potential misuse or disadvantages, e.g., by health insurers. About 56% of the general population would like to be better informed about the use of their personal health data, and 69% spontaneously reported health authorities as the stakeholder responsible for providing such information. CONCLUSIONS The survey showed that the Swiss population is willing to share anonymised health data given that some key concerns are addressed. Our findings underline that a better understanding of the standards and processes around health data privacy and transparent data usage is important to build trust in the public eye. An open dialogue is required to develop a common consent on data governance for Switzerland, which would allow health data sharing with third parties. This open dialogue should involve all stakeholders of the healthcare system, so as to strive towards both a more personalised and a more sustainable Swiss healthcare system.
Research and implementation of safety assessment software for chemical plant

This paper studied the design and implementation of safety assessment software for chemical plant. The common safety assessment methods and accident consequence models are analysed, and the research idea and design scheme of safety assessment with computer technology are determined. Then a set of assessment software has been developed using Visual Basic 6.0 Database Access technology, including document management module, assessment method module, accident prediction module, occupational health module and system help module. Visual Chm software is used to prepare legal and regulatory documents. This file can be called at any time for reference of relevant standard and norms during assessment, which provides standard support for the preparation of safety checklist. Therefore, the assessment software developed in this paper can quantitatively assess risks of petrochemical production, storage and other plants, featuring engineering value.
Successive electromagnetic transient diagnosis using unsupervised circulation-learning strategy

In this paper, we present an unsupervised learning diagnosis method for successive EMT. This method can not only segment these EMT but also diagnose them separately. In the method, a feature extractor constructed by CNN is to extract principal features. And then, a point-wise confidence interval is used to segment successive EMT and a Rule is made to determine the digital-labels of the EMT segments based on the principal features. Eventually, a correspondence Table of digital-label and type of EMT is built to achieve successive EMT diagnosis. To train the feature extractor in the method, an unsupervised circulation-learning strategy is also proposed. The strategy can utilize unlabelled EMT to update the CNN parameters in the feature extractor. Further, the proposed method is tested under a measured EMT dataset, which contains successive EMT. The favourable segmentation and diagnosis results of the EMT are obtained after parameters in the method are determined. Results show that the proposed method is effective and accurate in diagnosing successive EMT.
Partial discharge analysis of power electronic transformer under high frequency pulse voltage

Power electronic transformers (PETs) are becoming popular in modern DC distribution grid due to their high efficiency and flexibility. To increase the power density of PETs, high switching frequency would be adopted for the power switches. However, this may eventually increase the risk of partial discharge (PD) as the internal insulation space is reduced. In order to investigate PD characteristics in PETs under high frequency pulse voltage, an experimental platform was set up in the laboratory, in which a twisted pair of wires model was used to test the insulation failure between windings in PETs. The PD behaviour were thoroughly investigated in the frequency range of 50 Hz to 20 kHz by time-resolved partial discharge (TRPD). The results reveal that at high frequency, the initial discharge voltage decreases and discharge repetition would increase, while the PD amplitude decreases. Moreover, the PD pulses mainly occur at the field strength inversion of the rising and falling edges, the discharge time width tends to increase at high frequency. Under high frequency voltage, the total amplitude of PD is much higher than 50 Hz, which may be the main reason for high-frequency insulation failure prematurely.
Microwave photonics‐based channelized receiving and reconstruction for broadband RF signal

A microwave photonics‐based channelized receiver for broadband vector signals is proposed and experimentally demonstrated. A 4‐line optical frequency comb (OFC) generated by a dual‐parallel Mach‐Zehnder modulator (DPMZM) is used as local oscillator (LO) signal in the in‐phase/quadrature (I/Q) receiver for coherently down‐converting the broadband signal into different channelized bands. By properly setting the frequency difference between the OFC comb line and the broadband signal, either homodyne or heterodyne detection of I/Q receiver is realized. To compensate the amplitude difference and phase difference of adjacent channel signals, an overlap ratio of adjacent slices is introduced for channel equalization and signal reconstruction. The simulated result shows the error vector magnitude of 4‐channel channelization for a reconstructed 8 GBaud quadrature phase shift keying (QPSK) signal is very close to the original one. In the experiment, 2‐channel channelization for a 2 GBaud QPSK is realized and the whole signal is reconstructed successfully. The experimental result also proves the quality improvement of reconstructed signal with an increase of channel overlap ratio.
Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
Expression, Purification, and Characterisation of South African Cassava Mosaic Virus Cell-to-Cell Movement Protein

South African cassava mosaic virus (SACMV) is a circular ssDNA bipartite begomovirus, whose genome comprises DNA-A (encodes six genes) and DNA-B (encodes BC1 cell-to-cell movement and BV1 nuclear shuttle proteins) components. A few secondary and tertiary structural and physicochemical characteristics of partial but not full-length begomovirus proteins have been elucidated to date. The full-length codon-optimised SACMV BC1 gene was cloned into a pET-28a (+) expression vector and transformed into expression host cells E. coli BL21 (DE3). The optimal expression of the full-length BC1-encoded movement protein (MP) was obtained via induction with 0.25 mM IPTG at an OD600 of ~0.45 at 37 °C for four hours. Denatured protein fractions (dialysed in 4 M urea), passed through an IMAC column, successfully bound to the nickel resin, and eluted using 250 mM imidazole. The protein was refolded using stepwise dialysis. The molecular weight of MP was confirmed to be 35 kDa using SDS–PAGE. The secondary structure of SACMV MP presented as predominantly β-strands. An ANS (1-anilino-8-naphthalene sulphonate)-binding assay confirmed that MP possesses hydrophobic pockets with the ability to bind ligands such as ANS (8-anilino-1-naphthalenesulphonic acid). A 2′ (3′)-N-methylanthraniloyl-ATP (mant-ATP) assay showed binding of mant-ATP to MP and indicated that, while hydrophobic pockets are present, MP also exhibits hydrophilic regions. Intrinsic tryptophan fluorescence indicated a significant conformational change in the denatured form of BC1 in the presence of ATP. In addition, a phosphatase assay showed that MP possessed ATPase activity.
An evaluation of geo-located Twitter data for measuring human migration

Abstract This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.
Nonlinear Adaptive Backstepping Controller Design for Three-Phase Grid-Connected Solar Photovoltaic Systems

Abstract A cascaded control structure is proposed in this paper for injecting active and reactive power in a three-phase grid-connected solar photovoltaic (PV) system by considering external disturbances. In the proposed cascaded control structure, there are two control loops—the outer loop voltage controller is used to ensure a continuous balance in power flow between the PV arrays and electrical power grid whereas the inner loop current controller controls the output current of the inverter. Moreover, the DC-DC boost converter is controlled to achieve a constant voltage at the input of the inverter. In order to obtain the power balance and extract maximum power, an incremental conductance (IC) based maximum power point tracking (MPPT) method is used in this paper. The current controller is designed using a nonlinear adaptive backstepping technique to regulate the active and reactive components of the grid current. The regulation of these currents towards desired values which in turn control the active and reactive power delivered into the grid. The overall stability analysis of the system is performed based on the formulation of control Lyapunov functions (CLFs). Finally, the performance of the designed controller is tested on three-phase grid-connected PV systems with single as well as multiple PV units under different environmental conditions and compared with an existing sliding mode controller. Simulation results confirm the effectiveness of the proposed adaptive backstepping control scheme and demonstrate the superior performance over the sliding mode controller.
An Empirical Investigation of the Sponsor's Role in Project Initiation

Although most organizational projects have many interested parties or stakeholders, the executive with the fiscal authority, political clout, and personal commitment to see a project through is the project sponsor. Certainly, there are project sponsor tasks associated with the successful completion of a project. Yet, very little research exists that attempts to identify and validate a set of executive sponsor behaviors necessary for successful project implementation. Using a previously established database of project risk avoidance and mitigation strategies that was supplemented by an updated literature search, we examine and classify behaviors associated with the role of a project sponsor. The scope of this exploratory research includes: (1) identifying project sponsor-related behaviors; (2) validating and prioritizing the sponsor behaviors utilizing an established procedure; (3) empirically validating the behaviors; and (4) empirically testing the association of the project sponsor behaviors with various dimensions of project success (project outcomes).
First Demonstration of a Bidirectional Tandem-Pumped High-Brightness 8 kW Level Confined-Doped Fiber Amplifier

In this paper, we demonstrated a high-brightness bidirectional tandem-pumped confined-doped fiber amplifier with nearly 8 kW output power. The technical difficulties for realizing the bidirectional tandem-pumping scheme are comprehensively analyzed, according to which, the homemade high signal-isolation side-coupled pump and signal combiner as well as stable 1018 nm pump sources were developed and applied in the experimental study, successfully enabling the first demonstration of the bidirectional tandem pumping scheme. Moreover, benefiting from the confined-doped fiber design and the bidirectional tandem pumping technique, the transverse mode instability was successfully mitigated and the beam quality was well preserved during the power scaling process. Eventually, an output power of 7.88 kW was obtained with the beam quality factor of ∼1.97 and the optical-to-optical efficiency of ∼84.76%. This work provides a good reference for realizing large-mode-area fiber lasers with high output power and good beam quality by utilizing the confined-doped fiber design and bidirectional tandem-pumping technique.
A REVIEW OF PUBLIC-PRIVATE PARTNERSHIPS FOR RENEWABLE ENERGY PROJECTS IN DEVELOPING COUNTRIES

The increasing need for energy worldwide to gain energy security along with the negative externalities of fossil fuel have motivated some countries to seek new sources of energy known as renewable energy. The main benefit of these renewable energy sources is their ability to provide clean, reliable, affordable, and sustainable energy to millions of people. On the other hand, renewable energy technologies require high up-front investment, which is problematic especially for developing countries. Therefore, public-private partnerships (PPPs) have become an attractive delivery method in order to take advantage of the technology, innovation, and capital of the private sector to support the public sector in providing their services. However, there is a dearth of information on the current pattern, obstacles, as well as the success factors of PPPs in renewable energy in developing countries. Thus, this study reviews previous renewable energy public-private partnership projects with the objective of developing a detailed understanding of renewable energy projects delivered through these PPPs in developing countries. Additionally, this study identifies the characteristics of the PPP projects and the lessons learned obtained from these PPP projects. This will aid in identifying the critical success factors needed for the successful implementation of renewable energy PPP projects in developing countries.
Ruptured Hepatocellular Carcinoma: What Do Interventional Radiologists Need to Know?

Rupture of HCC (rHCC) is a life-threatening complication of hepatocellular carcinoma (HCC), and rHCC may lead to a high rate of peritoneal dissemination and affect survival negatively. Treatment for rHCC mainly includes emergency surgery, interventional therapies, and palliative treatment. However, the management of rHCC should be carefully evaluated. For patients with severe bleeding, who are not tolerant to open surgery, quick hemostatic methods such as rupture tissue ablation and TAE/TACE can be performed. We described clinical presentation, prognosis, complication, interventional management, and current evidence of rHCC from the perspective of interventional radiologists. Overall, our review summarized that interventional therapies are necessary for most patients with rHCC to achieve hemostasis, even in some patients with Child–Pugh C. Moreover, TAE/TACE followed by staged hepatectomy is a beneficial treatment for rHCC according to current clinical evidence. TAE/TACE is the first choice for most patients with rHCC, and appropriate interventional treatment may provide staged surgery opportunities for those who are not tolerant to emergency surgery to reach an ideal prognosis.