Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Formula for the Magnetic-to-Electric Field Coupling of Magnetization in the Partial Element Equivalent Circuit Method

In this paper, the analytical formula for the magnetic-to-electric field coupling of time-varying magnetization is presented in the framework of the partial element equivalent circuit (PEEC) method with a Manhattan-type tessellation. In the virtue of the results presented in a previous work, the proposed formula allows obtaining a quasi-magnetostatic PEEC solver in which all the matrix entries can be computed analytically. This ensures a significant speedup of the resulting PEEC solver if compared to the one where the evaluation of the integral was performed numerically. The numerical results confirm the accuracy and robustness of the proposed formula and the expected significant speedups. Detailed pseudocodes are provided to make the results easily reproducible.
Modeling, Tuning, and Performance Comparison of Second-Order-Generalized-Integrator-Based FLLs

The second-order-generalized-integrator-based freq-uency-locked loop (SOGI-FLL) is a well-known tool for filtering and synchronization purposes in the power and energy applications. The SOGI-FLL, nevertheless, has a limited ability in rejecting the grid voltage disturbances. To deal with this problem, two advanced SOGI-FLLs have been proposed recently. In the first one, a SOGI-based filter is included inside the standard SOGI-FLL control loop, and in the second one, a SOGI-based filter is employed as the prefiltering stage of the SOGI-FLL. The small-signal modeling, tuning procedure, and detailed performance comparison of these advanced SOGI-FLLs have not been carried out yet. The aim of this paper is to cover these issues.
Using Big Data Technology to Analyze the Fluctuation Trends in Stock Price – As a Practice of Taiwan Financial Stock Market

With the development of cloud computing and ICT at present, financial technology has undergone major changes in recent years. The big data technology has been used to combine internal and external information, called weighted forecasting. The fundamentals of internal information include company financial statements, after-hours transaction information and other information; the technical facet analysis aspects include moving average and a variety of indicators. Also, external information includes Taiwan stock market related articles called chip facet analysis.In this paper, three facets were systematically analyzed to find the weights and calculate the fluctuation trend of individual stock, thereby more accurately predicting fluctuations in the following month. Also, this paper took Taiwan’s financial stock as an example. The simulation-based period was from January to September 2017. The external information sources include: CNYES.com. and MoneyDJ. First, the initial weighted ratio of 8:2 for the internal and external information was found, and rolling learning was done to make adjustments and predict the monthly fluctuation from October to December 2017. Empirical results show that the accuracy of the traditional forecasting in predicting actual fluctuation trend is 62.25%. The internal information is the data reference of the fundamental and technical facets analysis. After coupled with the chip facet analysis was added and the three facets analyzed, the accuracy of the actual individual stock fluctuations obtained using weighted forecasting is 73.55%. Overall, the weighted forecasting can be used to calculate fluctuations of individual stock, also enhancing the accuracy of predicting stock fluctuations the following month, also serve as a reference for experts and investors.
Analysis of effective capacity over Beaulieu-Xie fading model

This paper presents an exact analysis of effective capacity over the Beaulieu-Xie fading by introducing the concept of delay constraints. This fading model is composed of multiple no. of line-of-sight (LOS) components with some diffuse power. To analyze the effective capacity of this fading channel, the closed-form expressions are derived. Also, the asymptotically analysis by considering the cases of high and low signal-to-noise ratio (SNR) is also investigated to explore the effects of fading parameters on the effective capacity of this wireless fading channel. The obtained analytical results are validated by reducing the expression as a special case of existing fading channels. The numerical results are presented to show the effect of several parameters on the effective capacity.
Re-Watching Lectures as a Study Strategy and Its Effect on Mind Wandering

Material re-exposure (e.g., re-reading) is a popular mnemonic strategy, however, its utility has been questioned. We extend research on re-reading to re-watching – an emerging mnemonic technique given the increased use of recorded lectures today (e.g., in online courses). Consistent with findings from recent investigations of re-reading, there were no benefits of massed re-watching on memory for lecture material and re-watching increased rates of mind wandering. We discuss implications for understanding the cognitive consequences of re-exposure-based mnemonics.
A PV-Inspired Low-Common-Mode Dual-Active-Bridge Converter for Aerospace Applications

In the framework of the more electric aircraft, the use of isolated dc–dc power conversion for the electrical power distribution system is one of the most investigated solutions. If the dc–dc converter produces a variable common-mode voltage, the leakage current can flow in the interwinding parasitic capacitance of the high-frequency transformer, leading to insulation deterioration and early failure. This paper proposes to use modified H-bridge structures, already employed in the photovoltaic system for dc–ac power converters, to enable a constant common-mode voltage for isolated dc–dc converters. The analysis shows that this solution can achieve the same efficiency as the conventional one, while simulations and experiments show a strong reduction of the common-mode current flowing through the transformer. A reliability analysis showed that the lifetime of the high-frequency transformer can be extended with the proposed solution.
Cn-MAKG: China Meteorology and Agriculture Knowledge Graph Construction Based on Semi-structured Data

In this paper, a method of constructing China meteorology and agriculture knowledge graph based on semi-structured data is proposed. Firstly, demand analysis, determine the boundary of knowledge, according to which design the schema of Cn-MAKG. Then the semi-structured knowledge data is preprocessed in accordance with the standard indexes in the domain of meteorology and agriculture. Finally, the graph database Neo4j is used to store the knowledge graph, so as to realize the construction of Cn-MAKG. At present, the knowledge graph has been successfully applied to the automatic generation of crop meteorological reports.
An innovative panel to assess endothelial integrity of pedicled and skeletonized internal thoracic artery used as aortocoronary bypass graft: a randomized comparative histologic and immunohistochemical study.

Background Optimal preservation of endothelial integrity of the vessels used as aortocoronary grafts is a crucial determinant of long-term clinical success of coronary artery bypass grafting (CABG). The purpose of this study was to evaluate an impact of two common techniques to harvest left internal thoracic artery (LITA) on endothelial integrity. Methods One hundred twenty consecutive patients (84 males and 36 females) with a mean age of 64.9±8.8 years undergoing CABG were randomized to receive pedicled (group P; n=60) or skeletonized (group S; n=60) LITA grafts. During surgery LITA was harvested by the same experienced cardiac surgeon. The most peripheral surplus segments of LITA were obtained and then analysed histologically under light microscope. Additionally, endothelial expression of CD31, CD34, CD133 and nitric oxide synthase (eNOS) were evaluated by means of immunohistochemistry. Results In both groups, no cases of major arterial wall damage such as disruption, dissection, thrombosis or subadventitial hematoma were noted on LITA cross sections. Immunohistochemical assessment of protein expression revealed no differences in endothelial expression of CD133, CD34 antigens (markers of regeneration potential) and eNOS (indicating preserved functional integrity) between studied groups. Contrary to them, endothelial immunoreactivity of CD31, a marker of the morphological integrity of the endothelium, was revealed to be stronger in group P. Conclusions The skeletonized method of LITA harvesting may be associated with worse preservation of morphological integrity of endothelium but without compromising functional integrity and potential for tissue regeneration.
Coherence based Dual Microphone Source Separation in Low SNR Noisy Environments

A coherence function based dual-microphone source separation method in the adverse noisy environments is presented in this paper. The method is based on the coherence between the clean speech and noise signal for the noise reduction. The technique is applied to closely spaced microphones array and assumed that noise signals are captured by microphones are highly correlated. The presented technique is straightforward to realize and does not need any noise statistics estimation. The technique is evaluated in terms of the speech intelligibility, perceptual quality, segmental SNR (SNRSeg), speech distortion and residual noise objective measures. The experimental results indicate improved gains in perceptual quality, and speech intelligibility comparative to the noisy speech. Moreover, the presented technique yielded substantial improvements in terms of the SNRSeg, speech distortion and residual noise, respectively in low SNR (-5 and -10 dB) noisy environments than noisy speech. The time-varying spectral and time-domain waveform analysis indicated a low residual noise and less speech distortion in the spectrums of speech processed by the coherence based source separation technique.
Nurse Prescribing From the Global Vantage Point: The Intersection Between Role and Policy

Nurses around the world are increasingly prescribing and managing pharmaceutical agents. Prescribing by nurses is currently based on varying nursing roles, depending on national and regional norms and practices. Prescribing occurs within the advance practice, advanced level, and task-sharing roles, depending on the country. It is evolving both within and outside of traditional regulatory frameworks. Therefore, the purpose of this article is to describe the nurse prescribing globally among various nursing roles that support and facilitate the practice. We gathered practice, statutory, and regulatory information from gray and peer-reviewed literature, Google search and Google scholar, government websites, PubMed, and CINAHL electronic databases. In contrast to previous global policy reviews that focus primarily on high-income nations, our findings suggest that nurse prescribing occurs extensively in all six continents. Nurse prescribing within the context of advanced practice nursing occurs mostly in high-income countries. However, the predominant model of nurse prescribing from the global context occurs within the advanced level role by postbasic or postprofessional nurses. Additional nurse prescribing occurs through less formal task-sharing arrangements, primarily in low- to middle-income countries. In general, nurse prescribing is evolving rapidly around the world but within highly variable roles and regulatory frameworks. Codifying these roles by strengthening of educational and regulatory standards may serve to enhance the health system capacity, especially in low- to middle-income countries.
Improving On-Road Vehicle Detection Performance by Combining Detection and Tracking Techniques

This paper presents the combination method of detection and tracking to improve the recall of on-road vehicle detection. The vehicle detection method is a kind of the object detection where the vehicle in front of a car is detected using a camera installed on the car. In the proposed method, the Viola Jones detection and the support vector machine technique are combined complementary. Further the Lucas Kanade tracking technique is introduced to gain the true positive detection when both detection techniques fail to detect the vehicle. The experimental results show that the proposed method achieves the recall of 0.97, the precision of 0.96 and the frame rate of 25.54 fps (frames per second).
The goal bias revisited: A collostructional approach

Abstract There is a goal bias in the description of motion events: adverbials specifying goals are preferred over adverbials specifying source. Two broad explanations have been suggested to account for this: first, a general cognitive bias towards the aims of human actions, and second, the higher information value of goal adverbials in conceptualizing a motion event in its entirety. The second explanation predicts that the goal bias should be verb-specific. In particular, verbs whose lexical semantics focus on trajectories or sources of motion events (such as stroll and escape respectively), should not display a goal bias but instead prefer adverbials corresponding to this focus. Stefanowitsch and Rohde (2004) present case studies of ten English motion verbs that confirm this prediction. The current study takes up this research and complements it with a collostructional analysis over a large sample of 248 English motion verbs. The study shows, first, that goal adverbials dominate among strongly-associated pairs of motion verbs and prepositions in the English Intransitive Motion Construction, confirming a general goal bias for this construction; and second, that while goal adverbials are significantly associated with generic motion verbs as well as motion verbs specifying trajectories, trajectory adverbials and goal adverbials are significantly associated with trajectory- and goal-oriented verbs respectively, adding largescale quantitative confirmation to the previous study.
BC-MED: Plataforma de Registros Médicos Electrónicos Sobre Tecnología Blockchain

Contar con una historia clínica completa de un paciente, en cualquier momento, constituye una herramienta valiosa que permite a los actores del sistema de salud, tales como médicos, enfermeras, farmaceutas, laboratoristas e incluso al mismo paciente, tomar mejores decisiones sobre los tratamientos médicos a seguir al contar con todos los antecedentes clínicos bien documentados. Sin embargo, en la actualidad solo se cuenta con un sistema fragmentado, no estandarizado, no interconectado, que Iimita el acceso a esta información de manera integral. En los últimos tiempos se ha venido trabajando mecanismos para Iograr que esta centralización sea una realidad. Un ejemplo de ello es la Blockchain, que surge una tecnología que permitiría la construcción de un sistema con información centralizada, pero no dependiente de una única instancia, por su carácter participativo. Esta característica serviría para implementar sistemas EMR (Electronic Medical Records) flexible, robusto y seguro para almacenar la información médica de los pacientes. Este trabajo presenta el diseño de la plataforma BD-MED para el uso de redes blockchain para el acceso, registro y almacenamiento del historial clínico de los pacientes, incluso en escenarios con fallas de conectividad.
Hybrid Classification Model for Twitter Data - A Recursive Preprocessing Approach

Tremendous advancements in communication systems and computational power have ushered rapid unremitting increase in the size of data. Tweets, blogs, product and service reviews are a few among main sources of user generated text data. These sources depict public reaction and response about an entity. Sentiment analysis, a major application of Text Analytics, aims to know about collective sentiment of people about the entity under discussion. This work encompass a novel hybrid method involving recursive data munging module with machine learning techniques to glean classification of closed domain twitter dataset on the issue of global warming and climate changes. Experimental work proved that this hybrid model gave better results and achieved up to 86.18% accuracy for the given twitter dataset in comparison to base-line classification model.
Micro-Synchrophasors for Event Detection in an Electric Power System Simulator

The complexity of the electric power system and its short-term variabilities prompt the need for high precision and high resolution monitoring of network states. Modern development of synchrophasor measurement devices have provided new perspectives for observing operation behavior and transient phenomena in distribution networks. This study uses microsynchrophasor measurement devices (uPMU) in a hardwarebased electric power system simulator to simulate substation operation and power system events. We monitor PV current, perform fault studies and compare observability with a SCADA monitoring system. We find there is much potential for distribution network event detection and state estimation through synchronized measurement and high resolution monitoring.
Squash: Approximate Square-Accumulate With Self-Healing

Approximate computing strives to achieve the highest performance-, area-, and power-efficiency for a given quality constraint and vice versa. Conventional approximate design methodology restricts the introduction of errors to avoid a high loss in quality. However, this limits the computing efficiency and the number of pareto-optimal design alternatives for a quality-efficiency tradeoff. This paper presents a novel self-healing (SH) methodology for an approximate square-accumulate (SAC) architecture. SAC refers to a hardware architecture that computes the inner product of a vector with itself. SH exploits the algorithmic error resilience of the SAC structure to ensure an effective quality-efficiency tradeoff, wherein the squarer is regarded as an approximation stage, and the accumulator as a healing stage. We propose to deploy an approximate squarer mirror pair, such that the error introduced by one approximate squarer mirrors the error introduced by the other, i.e., the errors generated by the approximate squarers are approximately the additive inverse of each other. This helps the healing stage (accumulator) to automatically average out the error originated in the approximation stage, and thereby to minimize the quality loss. For random input vectors, SH demonstrates up to 25% and 18.6% better area and power efficiency, respectively, with a better quality output than the conventional approximate computing methodology. As a case study, SH is applied to one of the computationally expensive components (SAC) of the radio astronomy calibration application, where it shows up to 46.7% better quality for equivalent computing efficiency as that of conventional methodology.
Low Complexity Event Detection Algorithm for Non- Intrusive Load Monitoring Systems

In order to tackle today’s energy and sustainability issues, there are two paths to be followed by the world community. Either they have to establish new generation plants with an expense of millions of dollars or to look deeper into the existing system to design and deploy techniques that can lead to a significant amount of energy saving. Today, researchers are extensively working towards energy efficiency and conservation via developing different techniques. In the said domain, energy monitoring is one of the key techniques which is an attractive and popular research topic in the field of sustainable energy. It is single-point sensing or commonly known as Non-Intrusive Load Monitoring (NILM) that is used to extract the appliance level energy consumption. For a NILM system, event detection plays a key role and is a pre-requisite for the later stages of the system. In this paper a simple and low complexity event detection algorithm is proposed. Digital simulation and sensitivity studies have been carried out using real world data to check the performance and sensitivity of the proposed algorithm. Furthermore, the results of the proposed algorithm are compared with the existing event detection algorithm for evaluation purposes.
ICL Multispectral Light Stage: Building a Versatile LED Sphere with Off-the-shelf Components

We describe the design and implementation of a versatile multispectral light stage (LED sphere) consisting of 168 RGB and color temperature controllable white (W+) lamps, respectively. The light stage is powered with two sets of off-the-shelf programmable MR16 LED lamps producing RGB and color temperature controllable white (2700K – 5700K) illumination. The design is heavily inspired by various USC-ICT light stages, particularly Light Stages 3, 5 and X. However, unlike a typical geodesic (subdivided icosahedron) dome structure, the structure of the LED sphere has been fabricated along spherical co-ordinates with latitude-longitude proﬁles for a simpliﬁed wiring and control layout of the LED lamps, and for simplifying polarization of incident illumination. These design decisions facilitate construction while providing a versatile solution for a variety of applications including reﬂectance capture, image-based lighting reproduction, and multiview facial geometry and appearance acquisition.
A Smart and Active Way for Protection of Local Cultural Heritages

The emergence of mobile Internet technology improved people's life environments, and brought opportunities never seen before for local culture dissemination and protection. Nowadays, many researchers engaged in looking for effective and efficient ways to broadcast and popularize local excellent culture and cultural heritages tangible and intangible supported by new internet technologies. In this research, we analyzed the thinking of how to protect local culture heritages tangible and intangible with new information technology, and then addressed a conception framework for local culture protection. According to this framework, we proposed a museum APP associated with iBeacon technology as a case to illustrate applying the new IT in local culture protection. The main functions of this APP are collection message pushing, relative keyword knowledge, local humanityies, indoor navigation and information center etc. We tested this prototype APP among university students, the questionnaire results indicated that this iBeacon based APP is popular, it can evoke users' interests to local culture knowledge due to the new IT's application.
Research on Fintech Industry Development and Talent Training Status

With the deep application and development of information technology in the financial field, Fintech (Financial Technology) has become a hot topic in the financial industry and the scientific and technological community. The Fintech industry's development model is continuously developing and innovating, and it constantly impacts and transforms the traditional financial industry. Fintech has become the country's policy direction and economic development trend, leading to an increase in Fintech's talent demand, and the development of Fintech's talent has become critical. This paper describes the situation of Fintech industry from three aspects: the development of Fintech industry at home and abroad, the demand of Fintech talents and the training status of Fintech talents. This paper also suggests that colleges and universities should seize the opportunity to explore new teaching modes such as school-enterprise cooperation for Fintech and providing good talents protection for the development of Fintech.