Miracle-dz/UniFAR-FTUs huggingface dataset:
Bubble detection in Bitcoin and Ethereum and its relationship with volatility regimes

PurposeThe aim of this paper is to test the existence of bubbles for the daily prices of cryptocurrencies Bitcoin and Ethereum and verify if there is a relationship between bubbles and volatility regimes.Design/methodology/approachThe authors test the presence of bubbles with the generalized supremum augmented Dickey–Fuller (GSADF) test using critical values simulated by the bootstrap procedures of Gutierrez (2011), Harvey et al. (2016) and Pedersen and Schütte (2020). Also, the authors estimate Markov regime switching generalized autoregressive conditional heteroskedasticity model for these cryptocurrencies.FindingsThe GSADF test result indicates the presence of bubbles for both cryptocurrencies. Simulating critical values by wild-bootstrap, which is robust to non-stationary volatility, leads to the highest number of bubbles in both cryptocurrencies. In addition, based on the estimates of conditional variance models with regime changes, the authors find that the bubbles identified are associated with a regime of low returns volatility, indicating a change in the trade-off between risk and return when the prices of cryptocurrencies differ from their fundamental values.Originality/valueTo the best of the authors knowledge, there are no studies that test the explosive behavior for cryptocurrencies by the GSADF test using the bootstrap method to simulate critical values from the procedures of Harvey et al. (2016) or Pedersen and Schütte (2020). These bootstrapping procedures are robust to heteroscedasticity and avoid the detection of false bubbles. Further, the advantage of Harvey et al. (2016) procedure is the robustness to non-stationary volatility.
Fluid flow and transport in defined biofilms: experiments and numerical simulations on a microscale

As in other fields of science and engineering, the need to understand and predict biofilm systems both on a physical and on a technical scale results in an increasing demand for reliable and efficient numerical simulations of the different processes that take place within biofilms. For that, the expertise of biofilm specialists on the one hand and modern computational techniques on the other hand have to be brought together. In this paper, we present results of a combined and interdisciplinary attempt to simulate fluid flow and transport in defined pure-culture biofilms at the level of continuum mechanics. In order to provide a realistic setup for the simulation and to offer possibilities for a validation of the obtained results, our approach is embedded into state-of-the-art confocal laser scanning microscopy including automated image acquisition and semi-automated image analysis within extended 3D-regions of biofilm structures, which allow both the generation of real-life starting geometries for simulation purposes and the direct comparison of computed and experimental data. An important issue of our approach is flexibility, and the used code NaSt++ offers extensions for taking into account biomass growth or EPS as well as for dealing with more than one substrate.
Online Detection and Location of Eccentricity Fault in PMSG With External Magnetic Sensing

The detection and location of eccentricity faults in a permanent magnet synchronous generator (PMSG) become increasingly essential, especially in safety-critical applications. However, the existing approaches for monitoring eccentricity subject to many limitations. In this article, a novel method for online detection and location of the eccentricity fault in a PMSG is proposed based on the external magnetic sensing. The noninvasiveness facilitates installation and maintenance work. In this article, the effect of different eccentricity faults on the external leakage flux density is analyzed theoretically with the magnetic equivalent circuit method first. Then, two indicators are proposed for detecting static eccentricity (SE) and dynamic eccentricity (DE), respectively. The amplitudes of the SE and DE indicators are proportional to the fault degree. The angle of the SE indicator proves to be the direction of SE. Meanwhile, two indicators show excellent robustness and are independent of loads and rotation speed. Moreover, these indicators can be applied in any surface-mounted PMSG with a three-multiple number of slots. Accordingly, an algorithm to diagnose the eccentricity fault is shown based on two indicators from three sensing points outside. The computation of the method is light, which leads to fast detection speed. Finally, the accurate results in multioperating conditions from simulations and experiments verify the feasibility of the proposed approach.
Effects of Dietary Ferulic Acid Supplementation on Hepatic Injuries in Tianfu Broilers Challenged with Lipopolysaccharide

Lipopolysaccharide (LPS) is an endotoxin that can cause an imbalance between the oxidation and antioxidant defense systems and then induces hepatic damages. Ferulic acid (FA) has multiple biological functions including antibacterial and antioxidant activities; however, the effect of FA on lipopolysaccharide-induced hepatic injury remains unknown. The purpose of this study was to investigate the mechanism of action of dietary Ferulic acid against Lipopolysaccharide-induced hepatic injuries in Tianfu broiler chickens. The results showed that supplementation of FA in daily feed increased body weight (BW) and decreased the feed conversion ratio (FCR) in LPS treatment broilers significantly (p < 0.05). Additionally, supplement of FA alleviated histological changes and apoptosis of hepatocytes in LPS treatment broilers. Supplement of FA significantly decreases the activities of ROS. Interestingly, the levels of antioxidant parameters including total superoxide dismutase (T-SOD), total antioxidant capacity (T-AOC), and glutathione (GSH) in LPS group were significantly increased by the FA supplementation (p < 0.05). Nevertheless, administration of LPS to broilers decreased the expressions of Nrf2, NQO1, SOD, GSH-Px, CAT and Bcl-2, whereas it increased the expressions of Bax and Caspase-3 (p < 0.05). Moreover, the expressions of Nrf2, NQO1, SOD, CAT, Bcl-2 were significantly upregulated and Caspase-3 were significantly downregulated in the FL group when compared to LPS group (p < 0.05). In conclusion, supplementation of FA in daily feed improves growth performance and alleviates LPS-induced oxidative stress, histopathologic changes, and apoptosis of hepatocytes in Tianfu broilers.
Phosphorylation but Not Oligomerization Drives the Accumulation of Tau with Nucleoporin Nup98

Tau is a neuronal protein that stabilizes axonal microtubules (MTs) in the central nervous system. In Alzheimer’s disease (AD) and other tauopathies, phosphorylated Tau accumulates in intracellular aggregates, a pathological hallmark of these diseases. However, the chronological order of pathological changes in Tau prior to its cytosolic aggregation remains unresolved. These include its phosphorylation and detachment from MTs, mislocalization into the somatodendritic compartment, and oligomerization in the cytosol. Recently, we showed that Tau can interact with phenylalanine-glycine (FG)-rich nucleoporins (Nups), including Nup98, that form a diffusion barrier inside nuclear pore complexes (NPCs), leading to defects in nucleocytoplasmic transport. Here, we used surface plasmon resonance (SPR) and bio-layer interferometry (BLI) to investigate the molecular details of Tau:Nup98 interactions and determined how Tau phosphorylation and oligomerization impact the interactions. Importantly, phosphorylation, but not acetylation, strongly facilitates the accumulation of Tau with Nup98. Oligomerization, however, seems to inhibit Tau:Nup98 interactions, suggesting that Tau-FG Nup interactions occur prior to oligomerization. Overall, these results provide fundamental insights into the molecular mechanisms of Tau-FG Nup interactions within NPCs, which might explain how stress-and disease-associated posttranslational modifications (PTMs) may lead to Tau-induced nucleocytoplasmic transport (NCT) failure. Intervention strategies that could rescue Tau-induced NCT failure in AD and tauopathies will be further discussed.
Driver Distraction Detection Using Bidirectional Long Short-Term Network Based on Multiscale Entropy of EEG

Driver distraction diverting drivers’ attention to unrelated tasks and decreasing the ability to control vehicles, has aroused widespread concern about driving safety. Previous studies have found that driving performance decreases after distraction and have used vehicle behavioral features to detect distraction. But how brain activity changes while distraction remains unknown. Electroencephalography (EEG), a reliable indicator of brain activities has been widely employed in many fields. However, challenges still exist in mining the distraction information of EEG in realistic driving scenarios with uncertain information. In this paper, we propose a novel framework based on Multi-scale entropy (MSE) in a sliding window and Bidirectional Long Short-term Memory Network (BiLSTM) to explore the distraction information of EEG to detect driver distraction based on multi-modality signals in real traffic. Firstly, MSE with sliding window is implemented to extract the EEG features to determine the distraction position. Statistical analysis of vehicle behavioral data is then performed to validate driving performance indeed changes around distraction position. Finally, we use BiLSTM to detect driver distraction with MSE and other traditional features. Our results show that MSE notably decreases after distraction. Consistent with the result of MSE, driving performance significantly deviates from the normal state after distraction. Besides, BiLSTM performance of MSE outperforms other entropy-based methods and is better than behavioral features. Additionally, the accuracy is improved again after adding MSE feature to behavioral features with a 3% increasement. The proposed framework is useful for mining brain activity information and driver distraction detection applications in realistic driving scenarios.
Overeducation and R&D: Theoretical Aspects and Empirical Evidence

This work introduces R&D firms to a matching model to investigate their effect on overeducation. Moreover, we have enriched the empirical literature on the relationship between overeducation and R&D by evaluating whether the introduction of a tax credit to the R&D expenditures of Italian firms affected the PhDs’ probability of being overeducated. Our results first highlight that overeducation may be consistent with a share of graduate individuals who may be either lower (tightness dominance scenario) or higher (composition dominance scenario) than that optimally required by a social planner. Second, our model shows that the R&D incentives may decrease the likelihood of mismatches.
The Potential of Polyelectrolyte Multilayer Films as Drug Delivery Materials

Polyelectrolyte multilayers (PEMs) represent a group of polyelectrolyte complex (PEC)–based materials widely investigated in the biomedical and pharmaceutical sciences. Despite the unflagging popularity of the aforementioned systems in tissue engineering, only a few updated scientific reports concerning PEM potential in drug administration can be found. In fact, PEM coatings are currently recognized as important tools for functionalizing implantable scaffolds; however, only a small amount of attention has been given to PEMs as drug delivery materials. Scientific reports on PEMs reveal two dominant reasons for the limited usability of multilayers in pharmaceutical technology: complex and expensive preparation techniques as well as high sensitivity of interacting polyelectrolytes to the varieties of internal and external factors. The aim of this work was to analyze the latest approaches, concerning the potential of PEMs in pharmacy, chemical technology, and (primarily) tissue engineering, with special attention given to possible polymer combinations, technological parameters, and physicochemical characteristics, such as hydrophilicity, adhesive and swelling properties, and internal/external structures of the systems formed. Careful recognition of the above factors is crucial in the development of PEM-based drug delivery materials.
Adaptive nonlinear control of a permanent magnet synchronous motor

In this work an adaptive nonlinear control method is applied to a permanent magnet synchronous motor. Quadratic and direct currents decoupling and control are achieved through feedback linearization. Direct current is controlled to zero and torque becomes only proportional to the quadratic current. Also a near unity power factor is achieved. Parameters variation is compensated by using an adaptive scheme and no a priori knowledge of real parameters is required. The performance of the proposed adaptive nonlinear control scheme is demonstrated by simulation results. These results show that the proposed method achieves the same high dynamic performance as vector control. <<ETX>>
Rough-Fuzzy Support Vector Clustering with OWA Operators

Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm, which has been used already in many real-world applications. RFSVC’s strengths are its ability to handle arbitrary cluster shapes, identify the number of clusters, and e? ectively detect outliers by the means of membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers’ membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing ? nal membership degrees and at the same time allows a better interpretation of the cluster structures found. Particularly, we propose the induced OWA using weights determined by the employed kernel function. The computational experiments show that our approach outperforms the current version of RFSVC as well as alternative techniques ? xing the weights of the OWA operator while maintaining the level of interpretability of membership degrees for detecting outliers.
Empowering Data Driven Journalism in Macedonia

The need for independent media and powerful media is a must for any country aspiring to build upon values of a democratic society. Unfortunately, even though Macedonia has often been shaken by huge corruption scandals the general public is often ill informed or misguided by the media. This paper presents our efforts to create tools that will aid investigative journalists to better collect, analyze and present data to their audience. For this purpose a system that collects and organizes public procurement data in Macedonia has been generated. Furthermore, the developed system applies data mining algorithms against the datasets with the purpose of discovering trends, hidden information, relations between entities and anomalies.
Space-Based Sub-THz ISAR for Space Situational Awareness - Laboratory Validation

The advantages of sub-terahertz technology (sub-THz, 200–700 GHz) have already been advanced for imaging and recognition of a space object's state from a space-based sensor using inverse synthetic aperture radar (ISAR). The technology benefits from wide absolute signal bandwidths, giving high range resolution and from enhanced sensitivity to surface texture. An experimental validation of such a system in controlled laboratory conditions is presented. Possible image formation methods are proposed and evaluated experimentally. Images of real parts of satellites have been produced at sub-THz frequencies and compared to lower frequency images. Other results include sub-THz bistatic ISAR, and sub-THz cross-polarized images which highlight the scattering from rough surfaces while attenuating the very bright scatterers seen from conventional monostatic images.
YOLOv3-SORT: detection and tracking player/ball in soccer sport

Abstract. Soccer player and ball detection and tracking have emerged as an area of intense interest among many analysts and researchers. This is because it aids coaches in team performance evaluation and decision-making to achieve optimal results. However, existing methodologies have failed to effectively detect and track the ball when it moves at high velocity and also to track players under occlusion conditions. You only look once (YOLOv3) and simple online real-time (SORT)-based soccer ball and player tracking approach is proposed, for accurately classifying the detected objects in soccer video and track them in various challenging situations. The proposed methodology consists of two parts: (i) YOLOv3 can detect and classify the objects (i.e., player, soccer ball, and background) and eliminate the detected objects outside the playfield as background; (ii) tracking is achieved using SORT algorithm which employs a Kalman filtering and bounding box overlap. The proposed model achieves tracking accuracy of 93.7% on multiple object tracking accuracy metrics with a detection speed of 23.7 frames per second (FPS) and a tracking speed of 11.3 FPS.
Comprehensive Evolution Analysis of Public Perceptions Related to Pediatric Care: A Sina Weibo Case Study (2013–2020)

The frequent occurrence of doctor-patient disputes in pediatrics departments in China has led to more doctor-patient online public discussions. The effects of comprehensive evolution have become the focus of public opinion governance. This paper obtains social media data regarding pediatric care from Sina Weibo, and analyzes changes in emotional tendencies and the evolutionary characteristics of public emotional events. Simulation experiments are conducted by constructing a comprehensive evolution model of public emotion to reveal the derivation rules of the information community. The results of the study show that: (1) the Chinese public’s attention in the field of pediatrics in recent years is focused on children medicine; (2) emotional tendencies show a small fluctuation, and the positive emotions are mainly stable; (3) when the public opinion evolves, the evolution effect of the community has a great effect on the topic escalating and social emotions strengthening; (4) other relevant events could promote the exacerbation of current events, making the expansion of the topic larger and longer. Research on pediatric doctor-patient online public emotional events is beneficial to the public’s key needs for pediatric medical care, and provides effective information for stakeholders to effectively monitor and guide public emotional events.
Design of a millimeter‐wave wideband bandpass filter with novel‐slotted substrate integrated waveguide

A millimeter‐wave (mmW) wideband band‐pass filter (WBF) is proposed and designed by using novel‐slotted unites etched on the metal planes of SIW cavities in this paper. The novel‐slotted SIW cavity consists of two pairs of same size dumbbell slots etched on both the top and bottom metal planes of the SIW, which acts as a shunt resonator. And there is 90‐degree angle rotated between the novel‐slotted unites on top and bottom planes. The novel‐slotted SIW unit can be used to control the transmission zero and the cutoff frequency at upside. Finally, a five‐order millimeter‐wave wideband band‐pass filter is proposed and fabricated. The measured results show that the filter achieves insertion loss of −1.54 dB at 32.5 GHz, return loss of better than 12.5dB and 3dB fractional bandwidth (FBW) of 34.6%. A good agreement between simulated and measured results verifies the validity of this design methodology. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2406–2410, 2016
Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China

Due to the large amount of waste generated by urban construction, the transportation of construction waste has a significant impact on urban traffic. Understanding the transportation trajectory of garbage trucks can improve the management of transportation routes and reduce traffic accidents. This study analyzes electric waybill and state data of garbage trucks to identify hot nodes of construction waste transportation, where the volume of garbage trucks is relatively high. Management should strengthen the hot nodes to reduce traffic accidents. First, several machine learning methods are used to improve the prediction accuracy of electric waybill generation, where the garbage truck recorded on the electric waybill is regarded as a working truck. Second, the transportation trajectory of working trucks is extracted, and its spatiotemporal characteristics are further analyzed. Hot nodes are found based on density clustering. Finally, a case study is conducted based on the Shenzhen construction waste transportation system. The results show that the XGBoost model can improve the accuracy of the generation of waybill to 90.5% compared with the decision tree model, random forest, and GBDT. Moreover, the density clustering model can discover the hot nodes of construction waste transportation. Considering the minimum number of samples and the neighborhood radius, the clustering number is determined as 100. The ratio of noise points is determined as 0.79. The results can provide decision support for the management of electronic waybill and garbage truck transportation.
Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

Background A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.