Miracle-dz/UniFAR-FTUs huggingface dataset:
Degradation Assessment of Oil-Impregnated Paper Insulation for Converter Transformer Based on FDS using Digital Image Processing

- The Oil-Impregnated Paper (OIP) insulation is commonly utilized as the foremost insulation type in the case of a converter transformer, which is constantly and unavoidably affected by elevated temperature and different stresses arising during the mode of operation. It causes a safety risk to the insulation system of the converter transformer. Because of this, the present study examines the effect on OIP insulation using the FDS technique as a function of frequency and elevated temperature. The Frequency Domain Spectroscopy (FDS) and Atomic Force Microscopy (AFM) techniques were carried out for condition evaluation and surface morphological changes of OIP insulation. The experimental results show that elevated temperature viz 30°C, 50°C, 70°C, 90°C, 110°C and 130°C produces irreversible damage to the surface of OIP, which can lead to morphological changes. The frequency-dependent permittivity studies also confirm the deterioration of OIP insulation as permittivity decreases with increased frequency. However, the synergistic effect generated on the OIP insulation can also be analyzed by image processing-based evaluation methods dependent on the average of four local areas of AFM images. One disk of a valve side star winding single phase converter transformer is developed in MATLAB Simulink. An impulse of 20kV, 1.2/50µsec is applied to study the correlation among insulation degradation across the turn by considering elevated temperature and frequency dependence of OIP insulating material by wavelet transform. The energy of the wavelet coefficient is utilized to analyze the insulation degradation of insulation of the converter transformer. Thus the effectiveness of the FDS study revealed the condition monitoring of converter transformer insulation, and the presented results agree with the published work.
Who can help me? Understanding the antecedent and consequence of medical information seeking behavior in the era of bigdata

Introduction The advent of bigdata era fundamentally transformed the nature of medical information seeking and the traditional binary medical relationship. Weaving stress coping theory and information processing theory, we developed an integrative perspective on information seeking behavior and explored the antecedent and consequence of such behavior. Methods Data were collected from 573 women suffering from infertility who was seeking assisted reproductive technology treatment in China. We used AMOS 22.0 and the PROCESS macro in SPSS 25.0 software to test our model. Results Our findings demonstrated that patients’ satisfaction with information received from the physicians negatively predicted their behavior involvement in information seeking, such behavior positively related to their perceived information overload, and the latter negatively related to patient-physician relationship quality. Further findings showed that medical information seeking behavior and perceived information overload would serially mediate the impacts of satisfaction with information received from physicians on patient-physician relationship quality. Discussion This study extends knowledge of information seeking behavior by proposing an integrative model and expands the application of stress coping theory and information processing theory. Additionally, it provides valuable implications for patients, physicians and public health information service providers.
Residential location, high capacity transportation infrastructure and their influences on emissions and travel dispersal

Recently, there has been some literature to look at the impact of the urban built environment on transport-related CO2. It has tended to use relatively coarse calculations for transport-related CO2 and the built environment. This paper uses an approach developed by Beckman, Golob and Zahavi (1983) to analyze the effect of proximity to high capacity transport infrastructure on activity spaces and extends it to include more types of infrastructure, as well as to analyze the effect of infrastructure access on transport-related CO2. Data from the region of Montreal is used to generate activity spaces and transport-related CO2 emissions for groups of households at different distances from the CBD close to different types of transport infrastructure. Results indicate that both activity spaces and CO2 are related to transport infrastructure in predictable ways: emissions are on average higher for households living close to expressways, and activity spaces and emissions are similar for those close to metros and commuter rail. We also find that emissions (and more weakly activity spaces) exhibit an inverted U-shape (for households near all infrastructure types apart from expressways) - at first increasing with distance from the CBD and then decreasing. We argue that this is related to households "falling out of the orbit" of the city.
Application to the Whittier Narrows California aftershock sequence

Experiments with different earthquake location methods applied to aftershocks of the October 1, 1987, Whittier Narrows earthquake in California (ML=5.9) suggest that local event locations can be greatly improved through the use of the L1 norm, station corrections and waveform cross correlation. The Whittier Narrows sequence is a compact cluster of over 500 events at 12 to 18 km depth located within the dense station coverage of the Southern California Seismic Network (SCSN), a telemetered network of several hundred short-period seismographs. SCSN travel time picks and waveforms obtained through the Southern California Earthquake Center are examined for 589 earthquakes between 1981 and 1994 in the vicinity of the mainshock. Using a smoothed version of the standard southern California velocity model and the existing travel time picks, improved location accuracy is obtained through use of the L1 norm rather than the conventional least squares (L2 norm) approach, presumably due to the more robust response of the former to outliers in the data. A large additional improvement results from the use of station terms to account for three-dimensional velocity structure outside of the event cluster. To achieve greater location accuracy, waveforms for these events are resampled and low-pass filtered, and the P and S wave cross-correlation functions are computed at each station for every event pair. For those events with similar waveforms, differential times may be obtained from the cross-correlation functions. These times are then combined with the travel time picks to invert for an adjusted set of picks that are more consistent than the original picks and include seismograms that were originally unpicked. Locations obtained from the adjusted picks show a further improvement in accuracy. Location uncertainties are estimated using a bootstrap technique in which events are relocated many times for sets of picks in which the travel time residuals at the best fitting location are used to randomly perturb each pick. Improvements in location accuracy are indicated by the reduced scatter in the residuals, smaller estimated location errors, and the increased tendency of the locations to cluster along well-defined fault planes. Median standard errors for the final inversion are 150 m in horizontal location and 230 m in vertical location, although the relative locations within localized clusters of similar events are better constrained. Seismicity cross sections resolve the shallow dipping fault plane associated with the mainshock and a steeply dipping fault plane associated with a ML=5.3 aftershock. These fault planes appear to cross, and activity began on the secondary fault plane prior to the large aftershock.
Overlooked Long‐Term Atmospheric Chemical Feedbacks Alter the Impact of Solar Geoengineering: Implications for Tropospheric Oxidative Capacity

Studies of the impacts of solar geoengineering have mostly ignored tropospheric chemistry. By decreasing the sunlight reaching Earth's surface, geoengineering may help mitigate anthropogenic climate change, but changing sunlight also alters the rates of chemical reactions throughout the troposphere. Using the GEOS‐Chem atmospheric chemistry model, we show that stratospheric aerosol injection (SAI) with sulfate, a frequently studied solar geoengineering method, can perturb tropospheric composition over a span of 10 years, increasing tropospheric oxidative capacity by 9% and reducing methane lifetime. SAI decreases the overall flux of shortwave radiation into the troposphere, but increases flux at certain UV wavelengths due to stratospheric ozone depletion. These radiative changes, in turn, perturb tropospheric photochemistry, driving chemical feedbacks that can substantially influence the seasonal and spatial patterns of radiative forcing beyond what is caused by enhanced stratospheric aerosol concentrations alone. For example, chemical feedbacks decrease the radiative effectiveness of geoengineering in northern high latitude summer by 20%. Atmospheric chemical feedbacks also imply the potential for net global public health benefits associated with stratospheric ozone depletion, as the decreases in mortality resulting from SAI‐induced improvements in air quality outweigh the increases in mortality due to increased UV radiation exposure. Such chemical feedbacks also lead to improved plant growth. Our results show the importance of including fuller representations of atmospheric chemistry in studies of solar geoengineering and underscore the risk of surprises from this technology that could carry unexpected consequences for Earth's climate, the biosphere, and human health.
Noncontact Cardiac Parameters Estimation Using Radar Acoustics for Healthcare IoT

Novel use of radar acoustics for noncontact cardiac parameter estimation is presented as a potentially invaluable addition to wireless sensor networks in Healthcare IoT. Here, radar acoustics refers to the radar captured high-frequency mechanical motion, beyond 20 Hz, on the skin surface induced by heart sound (HS) acoustic waves. Conventional radar-based heart rate (HR) detection methods rely on detecting the heartbeat motion, around 1 Hz, while the unaddressed respiration motion coupling issue prevents consistent heartbeat estimation accuracy in the presence of stronger respiration interference, below 1 Hz. Another issue common to prior methods is the nonadaptive spectral filter design for separating heartbeat signal. As a consequence, these methods cannot be generalized to measurement spaces with high heartbeat variability as demonstrated in this study. To effectively address these limitations, a new HR detection method using radar is proposed to derive a high-fidelity pulse template from the smoothed envelogram of HS measurements. What is more, the feasibility study of radar HS-based HR variability analysis is performed, in which inter-beat-internal is derived from the first and second HS signatures. To that end, automatic identification of HS signatures and signal denoising techniques are developed accordingly. Multiple validation experiments are conducted on human subjects. Our results imply that the new methodology effectively improves remote cardiac sensing performance using the radar technology and presents an exciting opportunity to expand Healthcare IoT, improving patient experiences and outcomes.
Boosting Stock Price Prediction with Anticipated Macro Policy Changes

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach.
Latent Stochastic Differential Equations for Change Point Detection

Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.
Yarn tension pattern retrieval system based on Gaussian maximum likelihood

The unusual yarn tension inspection plays an important role in yarn quality measurement. The patterns of unusual tension, which are captured and recorded by an on-line yarn tension monitor system, can ensure precise recognition of the unusual type of tension in order to solve the problem immediately. However, it is not easy for operators of twister machines to recognize the patterns of unusual tension without related training. The traditional on-line yarn tension monitor systems only detect unusual variation in tension, but cannot identify the patterns of unusual tension for operators, especially in the improved quality yarns. To assist the operators in the pattern recognition of unusual tension, in this paper, we propose an unusual yarn tension retrieval system based on Gaussian maximum likelihood classification algorithm. The proposed system uses four features to describe the tension patterns, and includes the following processes: pattern generation, feature calculation, similarity degree measurement, new class detection and pattern retrieval. Experimental results show that the proposed system can serve as an efficient and fast tool to identify unusual tension patterns.
Identification and Genome Characterization of a Dahlia Common Mosaic Virus Isolate from China

Dahlia (Dahlia variabilis) is a widely cultivated ornamental and medicinal plant in China. Recently, dahlia plants with symptoms of leaf mottling and distortion were collected in Hohhot, Inner Mongolia, China. The presence of dahlia common mosaic virus (DCMV), an unassigned species in the genus Caulimovirus, was confirmed by high-throughput sequencing. Three fragments of DCMV Inner Mongolia isolate (DCMV-IN) were PCR-amplified with specific primers, sequenced and assembled into the complete genome sequence with a GenBank accession number of OR494328. The double-stranded circular DNA genome of DCMV-IN consists of 7949 bp and contains six open reading frames (ORFs). Sequence analysis showed that DCMV-IN shared high sequence identities with other DCMV isolates available in the GenBank database. Phylogenetic analysis of DCMV isolates and other representative caulimoviruses based on genome sequence clustered four DCMV isolates to a single branch which was closest to dahlia mosaic virus (DMV). No recombination event was detected among the four DCMV isolates.
An Attitude toward the Collaborative Information Behavior: A Systematic Review

Abstract Objective Collaborative information behavior researches explain different types of functions and activities that people utilize in teamwork information-oriented environments by interacting with each other. The aim of this paper is to analyze the methodological structure and identify the main variables in existing CIB, CIS, and CIR studies. Methodology All papers extracted from international databases including Scopus and Web of Science from 1997 to 2019 were reviewed in terms of quality and appropriateness. Then the full text of 91 selected articles was assessed. Based on a prepared checklist, information was extracted in a variety of fields such as first author, year, main subject, type of study, analysis tools, variables studied, and number and type of participants. Finding The evaluation of the articles’ methodology showed that the most methodological structure was qualitative in 39 papers. Also, an investigation of the measurement tools showed that data were collected in 28 articles by questionnaire, 13 articles by interview, and questionnaire. Seventy-four percent of the selected studies have been done by mixed methods in the last five years. Conclusion This systematic review investigated the published papers in the field of CIB, CIS & CIR to evaluating of the research content structures and components. Team awareness, interaction, information sharing, collaboration, division of labor, communication, problem-solving, and cognitive dimension have been identified as the main elements of CIB.
Characterization of Distinct Chondrogenic Cell Populations of Patients Suffering from Microtia Using Single-Cell Micro-Raman Spectroscopy

Microtia is a congenital condition of abnormal development of the outer ear. Tissue engineering of the ear is an alternative treatment option for microtia patients. However, for this approach, the identification of high regenerative cartilage progenitor cells is of vital importance. Raman analysis provides a novel, non-invasive, label-free diagnostic tool to detect distinctive biochemical features of single cells or tissues. Using micro-Raman spectroscopy, we were able to distinguish and characterize the particular molecular fingerprints of differentiated chondrocytes and perichondrocytes and their respective progenitors isolated from healthy individuals and microtia patients. We found that microtia chondrocytes exhibited lower lipid concentrations in comparison to healthy cells, thus indicating the importance of fat storage. Moreover, we suggest that collagen is a useful biomarker for distinguishing between populations obtained from the cartilage and perichondrium because of the higher spectral contributions of collagen in the chondrocytes compared to perichondrocytes from healthy individuals and microtia patients. Our results represent a contribution to the identification of cell markers that may allow the selection of specific cell populations for cartilage tissue engineering. Moreover, the observed differences between microtia and healthy cells are essential for gaining better knowledge of the cause of microtia. It can be useful for designing novel treatment options based on further investigations of the discovered biochemical substrate alterations.
Error Performance of NOMA System With Outdated, Imperfect CSI, and RHI Over $\alpha-\mu$ Fading Channels

This article investigates the joint impact of residual hardware impairment (RHI) and outdated and imperfect channel state information (CSI) on the performance of NOMA-based vehicular networks over <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> fading channels, while taking into account the effect of imperfect successive interference cancellation (ipSIC). Particularly, we present a novel pairwise error probability (PEP) expression with arbitrary <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> users, which applies to most channels (e.g., Rayleigh, Rice, Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula>, and Weibull distributions). Closed-form and asymptotic PEP expressions are derived to characterize each user's performance in different scenarios. Following that, the PEP is used to evaluate the union bound on the bit error rate of NOMA users. Furthermore, the asymptotic PEP expression is employed to quantify the diversity order of each user. The analysis is supported by numerical and Monte Carlo simulation results. We show that each NOMA user's effective diversity order equals its order precisely. We further show that ipCSI has a significant influence on PEP performance in the presence of RHI, particularly for weak users, but has almost no effect in an ideal RF environment with the vehicles sitting motionless. In a mobile environment, however, the impact of ipCSI on PEP performance for all users is almost ignored no matter whether RHI exists or not. Additionally, regardless of whether ipCSI or pCSI is used, RHI has a clear influence on PEP performance.
Sensing-Aided Hybrid Precoding for Efficient Terahertz Wideband Communications in Multiuser High-Data-Rate IoT

Terahertz (THz) massive MIMO with wideband hybrid precoding has been considered one of the crucial techniques to compensate for the high-path loss in 6G high-data-rate Internet of Things (IoT). However, the beam split in wideband hybrid precoding makes the beam of different subcarriers aiming at different directions, which results in only partial channel state information (CSI) from the users to the BS. The efficiency of the CSI-based terahertz (THz) wideband beamforming scheme which is more efficient than the hardware-based scheme in narrow-band would degrade severely. To address the degradation, in this article, we first propose a sensing-aided THz wideband hybrid precoding which restores the full CSI. Through sensing and deducing the angle-frequency information, we construct a channel-selecting matrix and inverse the full CSI from our complete channel dictionary. Moreover, in order to satisfy the multiuser access requirements in IoT, we also propose dynamic radio frequency (RF) chains and dynamic power allocation schemes to further enhance the performance in multiuser scenarios based on a new precoding perspective in which each RF chain serves only one user. This benefits from the highly sparse THz channel characteristic. The spectral efficiency and energy efficiency are employed to validate that the proposed is efficient. The numerical results demonstrate that our proposed sensing-aided wideband hybrid precoding scheme achieves similar performance to the optimal precoding and much better performance to the true time delay scheme and the full CSI-based scheme.
Building Blocks for a Model for using EDI and ERP to Improve Supply Chain Performance in Ugandan PDEs

The study's overarching goal is to provide a theoretical foundation for the integration of EDI and ERP systems into Ugandan procurement and disposal entities' supply chains to optimize their performance. A design science approach was used as a research strategy and for data collection; a cross-sectional survey using qualitative techniques, including in-depth interviews was employed. From the findings, a model was developed for data visualization, report production, analytics, and transformation. This study therefore provides an in-depth evaluation of the effectiveness of procurement and disposal organizations in Uganda with the help of EDI and ERP technologies to enhance supply chain management.