Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic characterization of porcine sapoviruses isolated from pigs during a diarrhea outbreak in Iowa, 2019.

Porcine Sapovirus (SaV) was first identified by electron microscopy in the United States in 1980 and has since been reported from both asymptomatic and diarrheic pigs usually in mixed infection with other enteric pathogens. SaV as the sole etiological agent of diarrhea in naturally infected pigs has not previously been reported in the United States. Here, we used four independent lines of evidence including metagenomics analysis, real-time RT-PCR (rRT-PCR), histopathology, and in situ hybridization to confirm porcine SaV genogroup III (GIII) as the sole cause of enteritis and diarrhea in pigs. A highly sensitive and specific rRT-PCR was established to detect porcine SaV GIII. Examination of 184 fecal samples from an outbreak of diarrhea on a pig farm showed that pigs with clinical diarrhea had significantly lower Ct values (15.9 ± 0.59) compared to clinically unaffected pigs (35.8 ± 0.71). Further survey of 336 fecal samples from different states in the United States demonstrated that samples from pigs with clinical diarrhea had a comparable positive rate (45.3%) with those from asymptomatic pigs (43.1%). However, the SaV-positive pigs with clinical diarrhea had significantly higher viral loads (Ct = 26.0 ± 0.5) than the SAV-positive but clinically healthy pigs (Ct = 33.2 ± 0.9). Phylogenetic analysis of 20 field SaVs revealed that all belonged to SaV GIII and recombination analysis indicated that intra-genogroup recombination had occurred within the field isolates of SaV GIII. These results suggest that porcine SaV GIII plays an important etiologic role in swine enteritis and diarrhea and rRT-PCR is a reliable method to detect porcine SaV. Our findings provide significant insights to better understand the epidemiology and pathogenicity of porcine SaV infection.
Human-in-the-Loop Adaptation for Interactive Musical Beat Tracking

In music information retrieval (MIR), beat-tracking is one of the most fundamental and important task. However, a perfect algorithm is difﬁcult to achieve. In addition, there could be a no unique correct answer because what one interprets as a beat differs for each individual. To address this, we propose a novel human-in-the-loop user interface that allows the system to interactively adapt to a speciﬁc user and target music. In our system, the user does not need to correct all errors manually, but rather only a small portion of the errors. The system then adapts the internal neural network model to the target, and automatically corrects remaining errors. This is achieved by a novel adaptive runtime self-attention in which the adaptable parameters are intimately integrated as a part of the user interface. It enables both low-cost training using only a local context of the music piece, and by contrast, highly effective runtime adaptation using the global context. We show our framework dramatically reduces the user’s effort of correcting beat tracking errors in our experiments.
Call for Papers Corporate Governance: An International Review Special Issue on “Meta-analyses on Contemporary Corporate Governance” Due Date to Submit Proposals: May 1, 2022 Guest Editors

Meta-analyses that advance theory via the synthesis and integration of previously discordant findings have the potential to be highly influential. For example, Post and Byron (2015) advanced our understanding of the effects of board gender diversity on organizational performance via meta-analysis. Similarly, a recent meta-analysis challenged assumptions about the effects of ownership type on the frequency of tunnelling activity and advanced our understanding of the role of ownership in firms (Solarino & Boyd, 2020). Meta-analytic tools also permit the development of more nuanced and novel hypotheses, including both moderation (e.g., Gonzalez-Mulé & Aguinis, 2018) and mediation effects (e.g., Bergh et al., 2016). Consequently, metaanalyses do far more than summarize work to date: they extend and advance theory (Post, Sarala, Gatrell, & Prescott, 2020). Furthermore, meta-analyses are beneficial for both emerging and mature bodies of work, making this methodology relevant to a wide range of corporate governance topics.
Pulse clarity metrics developed from a deep learning beat tracking model

In this paper we present novel pulse clarity metrics based on different sections of a state-of-the-art beat tracking model. Said model consists of two sections: a recurrent neural network that estimates beat probabilities for audio and a dynamic Bayesian network (DBN) that determines beat moments from the neural network’s output. We obtained pulse clarity metrics by analyzing periodical behavior from neuron activation values and we interpreted the probability distribution computed by the DBN as the model’s certainty. To analyze whether the inner workings of the model provide new insight into pulse clarity, we also proposed reference metrics using the output of both networks. We evaluated the pulse clarity metrics over a wide range of stimulus types such as songs and mono-tonal rhythms, obtaining comparable results to previous models. These results suggest that adapting a model from a related task is feasible for the pulse clarity problem. Additionally, results of the evaluation of pulse clarity models on multiple datasets showed that, with some variability, both ours and previous work generalized well beyond their original training datasets.
Influence of the parameters of the extrusion process on the properties of PLA composites with the addition of graphite

The main purpose of the work is to determine the influence of the screw diameter on the parameters of the single screw extrusion process, such as power consumption, torque, throughput, the actual temperature of the melt on the extruder die, as well as phase s with the use of a laboratory single-screw extruder. The research was carried out with the use of various plasticizing systems. Two types of graphite: pre-expanded and normal grade with different grain sizes were used in the research. Flammability tests of the obtained composites were carried out using cone calorimeter.
Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors

This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.
The Concept of Implementation of Multifrequency RFID System Industrial Involvement in Laboratory Conditions

People have been dealing with the correct identification of objects for a long time. In industry, we cannot avoid this area, whether it is to identify people, semi-finished products or final products. Therefore, this article deals with the design of a multifrequency RFID system for industry 4.0. The idea of the article is to implement one type of identification technology for tracking objects using the radio frequency spectrum at different wavelengths. We have based our design on the built industrial-assembly line in the SmartTechLab laboratory, where we have implemented LF, HF and UHF systems connected by an industrial PLC into a complex system. In this article, we gradually focus on the selection of RFID systems, their cooperation and the design of connection to one portable box. Using an RFID box, we can monitor different types of objects and verify RFID reading using a single reading device or by creating portal RFID gateways. The implemented system consists of four middleware and four independent antennas that can cooperate. For proper operation, there is necessary implement not only hardware but also necessary software. The system can identify RFID tags in the range of 1 cm to several meters. Also, the advantage of the design is that it identifies all types of tags (industry, label, ceramic, laundry, paper). One of the main benefits of the design is modularity, mobility and the creation of a robust design that can be used for measurements in companies and also for educating students in laboratory conditions. The whole system is designed to meet the requirements of Industry 4.0 and improve the competitiveness of businesses.
A Depth Map Generation Method for Path Planning of Mobile Robot

Recently, the unmanned mobile robots have received broad applications, such as industrial and security inspection, disinfection and epidemic prevention, warehousing logistics, agricultural picking, etc. In order to drive autonomously from departure to destination, an unmanned mobile robot mounts different sensors to collect information around it and further understand its surrounding environment based on the perceptions. Here we proposed a method to generate high-resolution depth map for given sparse LiDAR point cloud. Our method fits the point cloud into a 3D curve and projects LiDAR data onto the curve surface, and then we make appropriate interpolations of the curve and finally implement the Delaunay triangulation algorithm to all the data points on the 3D curve. The experimental results show that our approach can effectively improve the resolution of depth maps from sparse LiDAR measurements.
Key Techniques of SINS/DVL Integrated Navigation System

Doppler Velocity Log (DVL) aided Strapdown Inertial Navigation System (SINS) is commonly used for the applications of Autonomous Underwater Vehicles (AUVs). In lack of other aiding sensors, how to maintain precise integrated navigation is still a challenge issue. In this paper, the structure and basic principles of the SINS is presented. Alignment calibration of SINS and DVL, fast in-motion alignment and data fusion are key factors which has great influence on the navigation accuracy. Research efforts taken in these fields in recent years are surveyed. This paper may provide a firm foundation for the researchers in related areas.
Research on 3D Reconstruction Technology of Power Line Based on Image Semantic Segmentation

Because it was difficult to distinguish the characteristics of the power lines by the traditional methods of extracting the power lines, which led to the current situation of incomplete reconstruction and a large number of noise in the process of rebuilding the power lines only by the inclined photographing. In this paper, the power line information in the image is segmented pixel by pixel by introducing in-depth learning semantics segmenting neural network. The three-dimensional coordinates of the power line are calculated by the principle of multi-view three-dimensional reconstruction. Finally, the power line is fitted by the catenary equation to complete the incomplete power line reconstruction. The results show that the fitted power line model has high accuracy and meets the requirements of power related applications. Based on the traditional three-dimensional reconstruction, a new idea for power line reconstruction is proposed.
Research on Deterministic Measurement Matrix of Power Line Carrier Compressed Sensing

To verify the advantages of deterministic matrix applied to power line carrier communication (PLCC) based on compressed sensing (CS). This article analyzed the research status of commonly used deterministic measurement matrices, and made simulation comparison. It is found that different types of deterministic measurement matrices generated based on chaotic mapping had higher reconstruction accuracy and higher reconstruction efficiency than Gaussian random matrix. Then, according to simulation results and the characteristics of PLCC signal, the Chebyshev sparse circulant (CSC) measurement matrix was designed by combining eighth-order Chebyshev chaotic and the idea of sparse and circulant. Actual circuit measurement shows that when compression rate was 40% and 60%, the reconstruction loss of CSC is 0.72dB and 0.49dB higher than that of Chebyshev chaotic measurement matrix and Chebyshev circulate measurement matrix, respectively. Obviously, the CSC measurement matrix designed in this paper can effectively improve the reconstruction accuracy.
NONLOCAL MEANS SCHEME FOR IMAGE NOISE REDUCTION

The nonlocal means (NLM) filtering scheme has gained an increasingly interest in the last decade for its great performance in image restoration. This scheme yields attractive results in removing Gaussian noise from an image by replacing the intensity value of each pixel by weighted average of the pixel intensities in a search neighborhood in the image. It is primarily based on repeated patterns that often exit in images. The selection of the kernel functions in such nonlocal means image restoration scheme is a major concern of researchers in order to improve the restored image quality. The Gaussian function is a standard kernel function commonly applied in the NLM filtering. In this paper, two functions for a nonlocal means image filtering scheme are proposed through a specific NLM method using an adaptive window size that varies according to the characteristics of the search regions in the image. Zero-mean Gaussian noise with different values of standard deviation corrupting various images of different characteristics has been used in the computer simulations. Mean squared error (MSE) and mean absolute error (MAE) have been used as measuring indices for the quality of the output restored image. Results show that these two functions work well and yield better performance mainly for images with a lot of details and edges than the conventional NLM scheme that uses the Gaussian kernel function and a fixed window size.
Universal Neural Network Acceleration via Real-Time Loop Blocking

There is a recent trend that the DNN workloads and accelerators are increasingly heterogeneous and dynamic. Existing DNN acceleration solutions fail to address these challenges because they either rely on complicated ad hoc mapping or clumpy exhaustive search. To this end, this paper first proposes a formalization model that can comprehensively describe the accelerator design space. Instead of enforcing certain customized dataflows, the proposed model explicitly captures the intrinsic hardware functions of a given accelerator. We connect these functions with the data reuse opportunities of the DNN computation and build a correspondence between DNN loop blocking and accelerator constraints. Based on this, we implement an algorithm that efficiently and effectively performs universal loop blocking for various DNNs and accelerators without manual specifications. The evaluation shows that our results manifest 2.1x and 1.5x speedup and energy efficiency over dataflow-defined algorithm as well as significant improvement in blocking latency compared with search-based methods.
New Simple Torque‐Sensorless Torque Control for Quasi‐perfect Compensation of Sixth‐Harmonic Torque Ripple Due to Nonsinusoidal Distribution of Back EMF of PMSM

This paper proposes a new torque‐sensorless torque control method for permanent‐magnet synchronous motors (PMSMs). The proposed method can almost perfectly compensate the sixth harmonic torque ripple that is caused by nonsinusoidal distributions of the back electromagnetic force and the rotor magnetic flux of PMSMs. The torque control system is, in principle, constructed on the basis of vector control, but has two new, dedicated speed‐varying devices: a harmonic torque observer and a current controller. The speed‐varying harmonic torque observer can estimate the harmonic component over a wide range of speeds, even when the generated torque is constant, and produce a suitable compensation signal. The speed‐varying current controller shows stable control performance over a wide range of speeds; it can fully track the compensated current command containing the DC and sixth harmonic components. The effectiveness of the proposed method is examined and verified through extensive numerical experiments.
S1473550420000361jra 1..8

Approximately 22% of sun-like stars have Earth-like exoplanets. Advanced civilizations may exist on these, and significant effort has been expended on the theoretical analysis of planetary systems, and accompanying practical detection instruments. The longevity of technological civilizations is unknown, as is the probability of less advanced societies becoming technological. Accordingly, searching for pre-industrial extraterrestrial societies may be more productive. Using the earth as a model, a consideration of possible detectible proxies suggests that observation of seasonal agriculture may be possible in the near future – particularly in ideal circumstances, for which quantitative analysis is provided. More speculatively, other detectible processes may include: species introduction; climate change; large urban fires and land-use or aquatic changes. Primitive societies may be both aware that their activities may be observed from other planets, and may deliberately adjust these activities to aid or conceal detection.
Evaluation Of Advantages Of Entrepreneurship Case Study In Czchia And Slovakia

The aim of paper was to find out how the students of university evaluate advantages of entrepreneurship in the Czech Republic and in the Slovak Republic. Then we made comparison of both countries based on established attitudes of the students of university. In connection with the stated aim of the research, we conducted questionnaire survey between the students of university in the Czech Republic and in the Slovak Republic. In this research, we contacted 409 students in the Czech Republic and 568 in Slovakia. We used statistical method of Zscores on to verification our scientific hypotheses. Results of our research offer many interesting findings. The results confirmed that the perception of the mentioned advantages that affect the decisions of pre-productive population in the countries are different. Keywords—college student; comparison; advantages of entrepreneurship
Artificial Intelligence Methods for Services and Product Sustaining Phase

—The main problem the project addresses is reducing time and effort spent by a vendor company on product sustaining phase. This is done by development of the approach for assisting with customer request resolution based on intellectual methods and machine learning algorithms. Solution collects resolved issues from bug tracking systems, local documentation and confluence pages, creates their respective vector models and teaches the algorithm on the data. For each retrieved unresolved issue, related resolved ones and documentation pages are found as a result of using trained algorithm. System also collects user-written rules, that are based on information from the issue, and checks them on each unresolved issue to give recommendations regarding its status change or additional needed information. Based on received information, the final report is constructed to show related issues, documentation and recommendations for each unresolved issue in a user-friendly manner. System was tested on Apache Kafka project issues and compared to manual approach performed on same data. The average time to analyze unresolved issues using the automated approach was 12.2 minutes, and the average time spent with the manual approach was 18.4 minutes, which means that our solution decreases complexity of issue analysis by ~33%.
Gold Creek dam and its unusual waste waterway (1890-1997): design, operation, and maintenance

Completed in 1885, the Gold Creek dam is an earthen dam located on the outskirts of Brisbane, Australia. The spillway system was refurbished three times, each time to increase the maximum overflow capacity. In 1890, a concrete stepped waterway was built to replace the damaged unlined-rock channel. This staircase chute is an unique structure: it is the first large man-made waste waterway built in Queensland, it is the only stepped weir built in Queensland before 1900, and it is, so far as the writers are aware, the first concrete-stepped spillway built for a large dam. The characteristics of the dam and its unusual stepped spillway are reviewed in a historical context. The design is compared with contemporary structures and present knowledge in stepped spillway design. The authors believe that the Queensland engineers gained expertise from overseas and within Australia for the stepped spillway design. However, the selection of concrete for the step construction was made by the local engineers and the reaso.
Maximum Power Point Tracking Techniques for Photovoltaic Systems-A Review

1PG Scholar, Dept. of Electrical Engineering, Ballarpur Institute of Technology Balharshah, Maharashtra, India. 2Assistant Professor, Dept. of Electrical Engineering, Ballarpur Institute of Technology Balharshah, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Photovoltaic energy has become one of the popular area in the field of electrical power system, due to rapid developments in the field of power electronics. The solar energy is clean, easily available, pollution free and unlimited. But there are two main drawbacks with PV plants, the high cost of PV cells and their conversion efficiency. In the I-V characteristics of PV module which is non-linear but has a unique maximum power point. To increase or to maximize the output power of photo-voltaic system Maximum power point tracking (MPPT) techniques are used. These techniques give maximum output power, irrespective of the irradiation condition, temperature and load electrical characteristics. For the purpose of tracking the maximum power the MPPT techniques use some electronic converters. In this paper, various algorithms used in MPPT for Photovoltaic (PV) systems has been studied and compared.
Structural, Optical and Magnetic Studies of Cr2O3 Nanoparticles Prepared by Microwave-Assisted

In this paper, quasi-rectangular Cr2O3 nanoparticles were prepared by microwave-assisted solution using Cr(NO3)2.6H2O and salicylic acid at the presence of NaOH in optimum conditions for the power of 360 W, the temperature of 50 oC and the time duration of 30 min. The as-prepared chromium precursor was annealed in an air atmosphere at two different temperatures, 500 oC and 600 oC, for 3 h. Quasi-rectangular Cr2O3 nanoparticles were prepared and characterized by Fourier transform infrared (FT-IR), and ultraviolet–visible (UV-Vis) spectroscopy, X-ray diffraction analysis (XRD), Transmission electron microscopy (TEM) and Vibrating sample magnetometer (VSM). The sharp peaks in FT-IR spectra and high intensity peaks in XRD patterns confirm that the single and pure phase existence of the Cr2O3 nanoparticles with an average nanoparticle size of 25 nm. The optical band gap (Eg) values of the as-prepared Cr2O3 nanoparticles calculated were in the range of 1.6 and 1.8 eV, respectively. The TEM images revealed a highly homogeneous quasi-rectangular morphology without any agglomeration. The magnetic properties of the Cr2O3 nanoparticles show weak ferromagnetic behavior.