Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasible Power-Flow Solution Analysis of DC Microgrids Under Droop Control

DC Microgrids have been widely used due to their high efficiency, high reliability and flexibility. A sine qua non condition for the correct operation of systems is the existence of a feasible power-flow solution. This paper analyzes the existence of the feasible power-flow solution of the DC microgrid under droop control. Firstly, the power-flow mathematical model of DC microgrid is established. Then, based on the nested interval theorem, we obtain the sufficient conditions of the existence of the feasible power-flow solution, and the uniqueness of the feasible power-flow solution is proved. Moreover, the iterative algorithm of the feasible power-flow solution is proposed, which is proved to be monotonically exponentially convergent. The proposed algorithm’s domain of attraction is derived, thus, the initial iterative value of which can easily be chosen to guarantee its convergence. Finally, case studies are given in this paper to verify the correctness and effectiveness of the proposed theorems.
New data-driven approaches to improve probabilistic model structure learning

To learn the network structures used in probabilistic models (e.g., Bayesian network), many researchers proposed structure learning algorithms to extract the network structure from data. However, structure learning is a challenging problem due to the extremely large number of possible structure candidates. One challenge relates to structure learning in Bayesian network is the conflicts among local structures obtained from the local structure learning algorithms. This is the so-called symmetry correction problem. Another challenge is the V-structure selection problem, which is related to the determination of edge orientation in Bayesian network. In this thesis, we investigate the above two challenges in structure learning and propose novel data-driven approaches to overcome these challenges when building a Bayesian network. First, two new data-driven symmetry correction methods are developed to learn an undirected graph of Bayesian network. The proposed methods outperform the existing heuristic rule. Second, a weighted maximum satisfiability (MAX-SAT) problem is formulated to solve the V-structures selection problem. The weights are learned from data to quantify the strength of the Vstructures. Our proposed solution outperforms existing methods. Besides, we investigate how transfer learning can be used for structure learning with limited training examples and a source structure. In particular, we propose a transfer learning approach to learn the structure of a Sum-Product Network (SPN) which can be converted to a Bayesian network under certain conditions. Our novel approach allows one to construct the target SPN with limited training examples, given an existing source SPN from a similar domain.
Random Walks: A Review of Algorithms and Applications

A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together.
Development and Effectiveness of Tailored Education and Counseling Program for Patients with Coronary Artery Disease undergoing Percutaneous Coronary Intervention

Purpose: This study was to develop the patient-tailored education and counseling program (PTECP) for patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI), and to identify the effects of PTECP on awareness of risk factors, knowledge, self-efficacy, self-care behavior, and blood cholesterol profile (BCP). Methods: A non-equivalent control group pre-post design was used. Sixty patients who were newly diagnosed with CAD and treated PCI were recruited and participated either in an intervention group or a control group, thirty each. The PTECP focused on managing their own risk factors consisted of two individual educations, two individual counselings, three telephone counselings, and two short message services for eight weeks. Data were collected two times at baseline and 8 weeks after beginning of the program. Results: After eight weeks, the patients’ awareness of risk factor (p<.001), knowledge (p=.015), self-efficacy (p<.001), and self-care behavior (p<.001) in the intervention group were significantly different from those of the control group. However, no difference in BCP was found between the groups. Conclusion: The PTECP was effective in improving the patients’ awareness of CAD risk factors, knowledge, self-efficacy, and self-care behavior. Successful PTECP might lead to better health outcome in patients with CAD through effective self-management.
Market-Based Private Equity Returns

Using the universe of Business Development Companies (BDCs), a unique publicly traded segment of U.S. Private Equity (PE), for the period 1998-2017 we provide the first in depth examination of their performance and risk adjusted characteristics. More importantly, we show that the readily available BDC market returns explain the cash flow based proxies of traditional PE fund returns over and above other traded factors. Our findings highlight the importance of BDCs in providing public access to the PE asset class and a readily available market-based benchmark for evaluating both traditional PE investments and transaction based proxies of PE market returns.
A Feedforward Noise Reduction Technique in Capacitive MEMS Accelerometer Analog Front-End for Ultra-Low-Power IoT Applications

An ultra-low-power (ULP) capacitive micro-electromechanical systems (MEMS) accelerometer analog front-end (AFE) chip with 290-<inline-formula> <tex-math notation="LaTeX">$\mu \text{g}/\surd $ </tex-math></inline-formula>Hz noise floor and 252-nW power consumption is presented. To achieve such low-noise characteristics under ULP operation, the proposed AFE utilizes a novel circuitry scheme, namely, feedforward noise reduction technique (FNRT), which cancels some of the correlated noise. The FNRT is a simple modification of the conventional self-balancing bridge (SBB) architecture without using any extra power and it can relax the noise requirement on amplifiers in AFE, thereby reducing its power consumption. The designed system uses an SAR ADC employing a simple signal-to-noise ratio (SNR) enhancement technique and includes a finite state machine (FSM) to enable dynamic configuration and duty-cycling operation. The AFE chip is implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> complementary metal–oxide–semiconductor (CMOS) process and achieves a dynamic range of 65 dB and the bias instability of 62.8 <inline-formula> <tex-math notation="LaTeX">$\mu \text{g}$ </tex-math></inline-formula>.
Kesiapan Technological, Pedagogical And Content Knowledge (Tpack) Calon Guru Bidang Teknik di Universitas Negeri Malang

Tujuan pendidikan nasional Republik Indonesia adalah mencerdaskan kehidupan bangsa. Tujuan tersebut merupakan suatu penghargaan agar bangsa Indonesia bisa bersaing dengan bangsa lain di kancah internaisional khususnya di era Revolusi Industri Keempat (R.I 4.0) dan era Masyarakat ekonomi Asean (MEA) saat ini. Sudah saatnya pendidikan di Indonesia berbenah agar bangsa bisa bersaing dengan bangsa lain. Pembenahan yang mendasar dalam pendidikan adalah terkait kualitas dan kompetensi guru maupun calon guru Indonesia. Calon guru harus mencermati bagaimana kualitasnya untuk menjadi guru, bagaimana menyadari posisi mereka dalam era revolusi industri saat ini. Hadirnya perkembangan ilmu pengetahuan dan teknologi (IPTEK) bisa dijadikan salah satu cara untuk bisa mewujudkan hal tersebut, seorang guru harus memiliki dan paham betul tentang kemampuan Technological Pedagogical Content Knowledge (TPACK) yang mengidentifikasi bahwa hal tersebut merupakan pengetahuan penting untuk pengembangan keterampilan professional guru dan calon guru.
Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.
Resilience Assessment of Interdependent Energy Systems Under Hurricanes

Energy systems, e.g., power systems and natural gas systems, are increasingly interdependent, which brings benefits under normal conditions and introduces risks under extreme weather events, e.g., hurricanes. A comprehensive method is proposed to reflect the spatial-temporal hurricane impacts on interdependent power and natural gas systems. Mechanism analysis based and data based component failure models are used to map hurricane effects on exposed component failure probabilities based on which system states are generated using Monte Carlo simulation. For system states with failed components, the integrated energy flow is developed to determine minimum joint load shedding considering inherent properties of each system and characteristics of interdependence coordinately. Operational metrics and infrastructure metrics are employed to quantify the service adequacy and network resistance of the interdependent energy systems, respectively. The modified IEEE 24-reliability test system and the 12-node natural gas system under a simulated hurricane are used to validate the effectiveness of the proposed method. Simulation results show the method can identify weak parts and quantify the performance of interdependent power and natural gas systems under hurricanes to provide information for effective and coordinative preparedness.
5G-Enabled Fault Detection and Diagnostics: How Do We Achieve Efficiency?

The fifth-generation (5G) wireless network technologies and mobile-edge computing (MEC) provide great promises of enabling new capabilities for the industrial Internet of Things (IoT). However, the solutions enabled by the 5G ultrareliable low-latency communication (URLLC) paradigm come with challenges, where URLLC alone does not necessarily guarantee the efficient execution of time-critical fault detection and diagnostics (FDD) applications. Based on the Tennessee Eastman (TE) process model, we propose the concept of the communication-edge-computing (CEC) loop and a system model for evaluating the efficiency of FDD applications. We then formulate an optimization problem for achieving the defined CEC efficiency and discuss some typical solutions to the generic CEC-based FDD services (FDDS) and propose a new uplink (UL)-based communication protocol called “ReFlexUp. ” From the performance analysis and numerical results, the proposed ReFlexUp protocol shows its effectiveness compared to the typical protocols, such as Selective Repeat automatic repeat request (ARQ), hybrid ARQ (HARQ), and “Occupy CoW” in terms of the key metrics, such as latency, reliability, and efficiency. These results are further convinced from the mmWave-based simulations in a typical 5G MEC-based implementation.
Online Product Reviews: Effects of Star Ratings and Valence on Review Perception among Those High and Low in Need for Cognition

This study seeks to understand online product review perception based on ratings, valence, and need for cognition. Review perception, review recollection, and intent to purchase after reading the reviews were being measured. Results showed that need for cognition had an effect on accuracy of review recognition and perceived review valence. Need for cognition and congruency (between rating of the review and valence of the review), as well as actual valence had an effect on perceived valence. Need for cognition, actual review valence, and review congruency all had an effect on purchase intention.
Autoimmune hemolytic anemia - progress in emerging treatment options

ABSTRACT Introduction: AIHA is a complex and heterogeneous disease involving antigen-autoantibody reaction, T-cell co-stimulation, complement activation, phagocytosis and bone marrow compensatory response. Several drugs targeting these mechanisms are under development in addition to standard therapies: steroids, immunesuppressors and splenectomy. Areas covered: Rituximab is the first biologic therapy used in AIHA, and its association with bendamustine and fludarabine has been shown more effective in relapse/refractory cold agglutinin disease. In these cases bortezomib was also beneficial with an overall response in about 30% of cases, and several complement inhibitors (eculizumab, BIVV009, and APL-2) are currently under investigation. B-cell receptor inhibitors (ibrutinib, acalabrutinib, and idelalisib) are promising therapeutic options for lymphoproliferative associated secondary forms. Finally, targeting IgG driven extravascular hemolysis (SYNT001 and fostamatinib) is an exciting new treatment approach. Expert opinion: AIHAs have been historically considered benign and easy to treat, however relapsing/refractory cases represent a clinical challenge. In these cases a target therapy would be ideal as traditional treatments are often ineffective/unfeasible. Moreover, the several mechanisms involved may be variably acting in the single patient and unpredictably changing overtime. Since several exciting target-therapies are emerging, only prospective studies would clarify the best choice, association, and sequence of these new drugs.
Analytical and numerical solutions of mathematical biology models: The Newell‐Whitehead‐Segel and Allen‐Cahn equations

In this paper, we combine the unified and the explicit exponential finite difference methods to obtain both analytical and numerical solutions for the Newell‐Whitehead‐Segel–type equations which are very important in mathematical biology. The unified method is utilized to obtain various solitary wave solutions for these equations. Numerical solutions of the specific case studies are investigated by using the explicit exponential finite difference method ensures the accuracy and reliability of the proposed scheme. After obtaining the approximate solutions, convergence analysis and error estimation (the error norms and absolute errors) are presented by comparing these results with the analytical obtained solutions and other methods in the literature through tables and graphs. The obtained analytical and numerical results are in good agreement.
Assessments of Functioning in Patients With Axial Spondyloarthritis

A standardized assessment of functioning in patients with axial spondyloarthritis (axSpA) is necessary in order to assess clinical manifestations, disease activity, and physical and overall functioning as objectively as possible. The standardized assessment is based on domains such as disease activity, quality of life, functioning and employment but also on individual aspects such as pain, arthritis and enthesitis. Domains and individual aspects are mainly collected and better known as patient reported outcomes. (J Rheum Dis 2020;27:22-29)
Removal of methylene blue dye by solvothermally reduced graphene oxide: a metal-free adsorption and photodegradation method

In this work, reduced graphene oxide (rGO) was fabricated at different reduction temperatures via an environmentally friendly solvothermal approach. The rGO formed at 160 °C clearly showed the partial restoration of the sp2 hybridization brought about by the elimination of oxygenated functionalities from the surface. Owing to the augmented surface area and the band gap reduction, rGO-160 exhibited the best adsorption (29.26%) and photocatalytic activity (32.68%) towards the removal of MB dye. The effects of catalyst loading, initial concentration of dye, light intensity, and initial pH of solution were evaluated. It was demonstrated that rGO-160 could achieve a higher adsorptive removal (87.39%) and photocatalytic degradation (98.57%) of MB dye when 60 mg of catalyst, 50 ppm of dye at pH 11, and 60 W m−2 of UV-C light source were used. The MB photodegradation activity of rGO-160 displayed no obvious decrease after five successive cycles. This study provides a potential metal-free adsorbent-cum-photocatalyst for the decontamination of dyes from wastewater.
Automatic Brazilian Mercosur license plate recognition: an approach with synthetic images

A utilizacao de imagens para obter informacoes de placas veiculares nao e uma exclusividade dos dias atuais. Em 1987, Mizumachi ja apresentava um sistema que realizava a identificacao de numeros em placas veiculares. Sistemas como esses sao apresentados ate hoje, principalmente pela viabilizacao de aplicacoes que podem ser realizadas com essas extracoes, como: identificar veiculos roubados ou irregulares, monitorar vias para multar precisamente motoristas infratores e controlar o acesso a estacionamentos. Os metodos para solucionar esse problema, geralmente referenciados como de Reconhecimento Automatico de Placas (RAP) ou, em Ingles, Automatic License Plate Recognition (ALPR), consistem em, dada uma imagem, executar tres etapas: localizacao da placa na cena, segmentacao dos caracteres e reconhecimento optico dos caracteres segmentados. Apesar de existir inumeras solucoes na literatura, o fato de nao existir uma padronizacao mundial contribui para inumeras propostas. Devido a essa falta de padronizacao, em 2014, a Resolucao brasileira No 510 estabeleceu o sistema de placas de identificacao de veiculos no padrao Mercosul (CONSELHO NACIONAL DE TRÂNSITO (CONTRAN), 2014). Com esse novo padrao, as solucoes existentes para o padrao antigo nao se comportam como esperado, principalmente pelas mudancas de cores, formatos dos caracteres e ordem dos digitos. Entao, neste projeto, propoe-se um novo metodo para o reconhecimento automatico de placas veiculares do padrao Mercosul e uma metodologia de geracao artificial de placas veiculares do padrao Mercosul para acoplamento em imagens.
Direct 48-/1-V GaN-Based DC–DC Power Converter With Double Step-Down Architecture and Master–Slave AO2T Control

In modern power delivery systems, there has been an up-rising trend of migrating conventional two-stage dc–dc power conversion architecture to a single-stage one, in order to accomplish better efficiency and power density. In response to such a trend, this article presents a gallium nitride (GaN)-based power converter design that achieves direct 48-/1-V dc–dc power conversion with a single-stage double step-down (DSD) architecture. Operating at 2 MHz, it pushes the equivalent minimum duty ratio to a record low level of 2.1%. In order to improve closed-loop regulation, an adaptive ON- and OFF-time (AO2T) control with elastic ON-time modulation is proposed for both steady-state and transient response enhancement. For reinforced reliability in the dual-phase operation, a master-phase mirroring technique enables adaptive master–slave phase operation, accomplishing automatic phase current balancing. An IC prototype is implemented on a 180-nm high voltage (HV) bipolar, CMOS DMOS (BCD) process, with an active die area of 1.46 mm2. It achieves peak efficiencies of 85.4%, 79%, and 56.8% at 100 kHz, 250 kHz, and 2 MHz, respectively.
Indoor Reconstruction from Floorplan Images with a Deep Learning Approach

Although interest in indoor space modeling is increasing, the quantity of indoor spatial data available is currently very scarce compared to its demand. Many studies have been carried out to acquire indoor spatial information from floorplan images because they are relatively cheap and easy to access. However, existing studies do not take international standards and usability into consideration, they consider only 2D geometry. This study aims to generate basic data that can be converted to indoor spatial information using IndoorGML (Indoor Geography Markup Language) thick wall model or the CityGML (City Geography Markup Language) level of detail 2 by creating vector-formed data while preserving wall thickness. To achieve this, recent Convolutional Neural Networks are used on floorplan images to detect wall and door pixels. Additionally, centerline and corner detection algorithms were applied to convert wall and door images into vector data. In this manner, we obtained high-quality raster segmentation results and reliable vector data with node-edge structure and thickness attributes that enabled the structures of vertical and horizontal wall segments and diagonal walls to be determined with precision. Some of the vector results were converted into CityGML and IndoorGML form and visualized, demonstrating the validity of our work.
All-Metal 3-D Frequency-Selective Surface With Versatile Dual-Band Polarization Conversion

A dual-band periodic structure is here proposed, which allows for versatile frequency selectivity and polarization conversion functionality. The structure is a monolithic full-metal piece, and it is built from the arrangement of unit cells consisting of waveguide sections at cutoff loaded with compound perforations. The topology of such unit cells enables the efficient and independent interaction with vertical and horizontal electric field components illuminating the periodic structure. A circuit model is proposed to characterize the cells and understand their behavior. Three different examples of cells are designed to illustrate the structure versatile functionality. One of these examples is manufactured, consisting of a dual-band structure that converts linear polarization into circular with orthogonal senses of rotation at each band. The experimental results are in good agreement with the theoretical predictions, thus validating the underlying operation mechanism and its practical feasibility.