Miracle-dz/UniFAR-FTUs huggingface dataset:
Ticagrelor does not affect left ventricular remodeling following acute myocardial ischemia

Despite ticagrelor’s proven superiority to clopidogrel in treating patients with acute coronary syndrome, it is unclear whether its actions are mediated by antiplatelet inhibition or by some other pleotropic effect. In this study, we used a porcine model of acute myocardial infarction (AMI) to evaluate the efficacy of ticagrelor administered at a similar level of platelet inhibition to that seen with clopidogrel. Twenty pigs were grouped according to the P2Y12-receptor inhibitors (ticagrelor or clopidogrel). Platelet inhibition was monitored by measuring adenosine diphosphate - induced platelet aggregation. A chronic ischemic heart model was artificially generated by embolization of the middle left anterior descending coronary artery. Animals received a maintenance dose of the antiplatelet agents. Two-dimensional echocardiography was performed on the surviving animals 2 weeks later. Both 180 mg ticagrelor and 600 mg clopidogrel exerted a significant and consis-tent antiplatelet effect showing platelet aggregation inhibition of 50.7±22.9% and 45.5±21.0%, respectively, p=0.614), which persisted for up to 2 weeks. However, no significant differences were observed in ventricular arrhythmia (40% vs. 50%, respectively; p=0.886). The ejection fraction measured 2 weeks after surgery was 44.6±7.4% in the ticagrelor group and 36.9±5.7% in the clopidogrel group (p=0.091). In a chronic ischemic heart failure model with a similar level of platelet inhibition, ticagrelor was no better than clopidogrel in reducing mortality and improving cardiac function.
Wastewater reuse in irrigation: short-term effect on soil carbon and nitrogen stocks in Brazilian semi-arid region

The main process that opposes the Greenhouse Effect is called “carbon sequestration”, a phenomenon that essentially seeks to establish a dynamic balance between greenhouse gas (GHG) emissions from the immobilization of its elements, especially C and N. This work evaluated the stocks of carbon and nitrogen in soils of the Brazilian semi-arid submitted to irrigation with treated wastewater from domestic sewage. For this purpose, the carbon and nitrogen stocks in the soil of the semi-arid region of Brazil were compared for four different uses: Open Native Caatinga (ONC); Treated Wastewater Effluent Irrigation (TSEI); Surface Water Irrigation (SWI); and Traditional Rainfed Agriculture (TRA). The hypothesis considered in this research was that the application of treated effluents in agriculture, besides being an alternative that can mitigate the problem of water scarcity of the semiarid region, is also capable of influencing the storage capacity of C and N in the soil, due to its organic load. The results indicate that among the different land uses evaluated, soil C and N stocks were highest in ONC, and decreased with the agricultural land use. The greatest accumulation of C and N in Caatinga is due to the presence of forest litter, and the influence of irrigation with treated sewage effluent was not detected in the present study. It can be concluded that the contribution of vegetation residues to the soil surface is the main factor contributing to C and N storage.
Preparation of NixTiy compound via Ni plating followed by thermal treatment of Ti powder

ABSTRACT In this research, NixTiy compound was prepared by thermal treatment of Ni-plated Ti powder. For this purpose, Ti powder was plated in an electroless Ni bath for various times (120, 225, 300, and 720 min). Hydrazine hydrate was used as a reductant for the deposition of pure Ni on the Ti particles. The plated powder (225 min) was heat treated under argon atmosphere to achieve NixTiy powder. Finally, the heated/plated powder was pressed by CIP followed by sintering at 980°C for prepare the NixTiy bulk sample. The plated powders as well as sintered one were characterized using scanning electron microscopy, energy dispersive spectrometer, X-ray fluorescence, X-ray diffraction and differential scanning calorimetric. The NiTi2, NiTi, and Ni3Ti phases were detected in the XRD patterns of heated/plated Ti powder. According to DSC data, the heated/plated Ti powder showed reversible martensitic transformation at temperature range of −38.0°C to +38.1°C, while sintered/heated/plated Ti powder displayed reversible transformation at temperature range of 16.0°C–15.4°C.
Improvement of radar detection capabilities for fluctuating targets using convolutional error control coding technique

Fluctuating targets are important phenomenons that affect detection probability. Several investigations about this topic have been carried on with good results to radar capabilities improvement. However, the increasing development of stealth technology has weakened traditional surveillance systems. This work has as main objective to improve radar detection of fluctuating targets using error control codes techniques. The purposed novel idea consists on a long pulse divided into a number of shorter sub-pulses, equally spaced in time duration. The binary phases of these sub-pulses are selected in accordance with Barker codes. Convolutional coding and Binary Phase Shift Keying(BPSK) modulation techniques are used. We determinate the probability of detection computing the bit error rate values. Matlab software is used to make several simulations that back up this theory. The results demonstrate that the probability of detection for Swerling I and Swerling III fluctuating target models is increased with the use of convolutional coding technique to detect and correct sub-pulse errors. This work offers a preliminary analysis of the detection improvement method with the use of error-control coding on radar pulsed with quite generalized assumptions thereby left a bigger space for future work with assumptions to fit practical implementation of the method.
Variations of Thermospheric Winds Observed by a Fabry–Perot Interferometer at Mohe, China

A Fabry–Perot interferometer (FPI) system was deployed to observe the thermospheric winds at Mohe (53.5°N, 122.3°E), the northernmost observatory of space environment in the mainland of China, in July 2019. Thermospheric winds variations revealed from the 1 year FPI observations are as follows: (1) For the diurnal variation, southward meridional winds prevail at night and peak after midnight with amplitudes from 70 to 110 m/s; meridional winds are northward (∼40 m/s) at dusk and dawn in winter. For the zonal winds, eastward winds prevail only at dusk in summer, while it can sustain after midnight in winter. The maximum amplitudes of eastward winds are 50–130 m/s. Westward winds are strongest at dawn, with amplitudes of 75–120 m/s. (2) For the seasonal variation, both the meridional and zonal winds are dominated by annual oscillations. The semi‐annual oscillations can be observed at midnight. (3) The eastward (southward) winds become strong (weak) at midnight in January and December. An empirical model of thermospheric winds was established based on Mohe FPI observations and compared with HWM14. The variation trends of thermosphere winds described by HWM14 is basically consistent with the observations at Mohe. Moreover, Mohe FPI observations were compared with those by Kelan (38.7°N, 111.6°E) FPI. Variations of thermospheric winds showed some discrepancies between these two locations. The southward winds were significantly stronger at Mohe than at Kelan after midnight; and the semi‐annual and tri‐annual oscillations in the midnight zonal winds are observed at Mohe but not significant at Kelan.
Cross-domain Knowledge Discovery based on Knowledge Graph and Patent Mining

This paper studies an approach on cross-domain knowledge discovery to assist the conceptual stage of the design process related to mechanical engineering. Variable methods and tools are proposed to obtain knowledge within a given domain until now. However, methods on cross-domain knowledge analysis is under-developed. In this paper, domain knowledge graph is built automatically by employing natural language process (NLP) and patent mining. They comprise patent documents obtaining and knowledge extraction. Then according to the international patent classification (IPC), the knowledge elements are divided to some different categories. The elements are stored in Databases and then the given domain knowledge graph is constructed after correlation analyses. The cross-domain knowledge surrounding the given domain knowledge is found by mining the correlation among cross-domain knowledge. The cross-domain knowledge can inspire designers about new design of a given domain. And the efficiency of knowledge reusing can be improved by domain knowledge graphs.
Image recognition based billing system for fruit shop using raspberry PI

This work describes a system that automatically does the billing of fruits in the retail market. Smart billing system is used for identifying and classifying the fruit types and to calculate the bill amount based on the type and quantity of fruits purchased by the consumer. Fruit identification is done using image processing technique and for weight measurement a load cell is used in the smart system. In this system an SVM classifier is used for fruit recognition. This smart billing system analyzes and classifies fruits successfully and bill calculation is performed accurately.
Hurdle Blockmodels for Sparse Network Modeling

Abstract A variety of random graph models have been proposed in the literature to model the associations within an interconnected system and to realistically account for various structures and attributes of such systems. In particular, much research has been devoted to modeling the interaction of humans within social networks. However, such networks in real-life tend to be extremely sparse and existing methods do not adequately address this issue. In this article, we propose an extension to ordinary and degree corrected stochastic blockmodels that accounts for a high degree of sparsity. Specifically, we propose hurdle versions of these blockmodels to account for community structure and degree heterogeneity in sparse networks. We use simulation to ensure parameter estimation is consistent and precise, and we propose the use of likelihood ratio-type tests for model selection. We illustrate the necessity for hurdle blockmodels with a small research collaboration network as well as the infamous Enron E-mail exchange network. Methods for determining goodness of fit and performing model selection are also proposed. Supplementary materials for this article are available online.
Intelligent Expeditious Accident Detection and Prevention System

Nowadays, many of the vehicle accident occur due to over speed, drowsy driving and drunken drivers. According to statistics, for every four minutes, a person dies because of road accidents and also thousands of accidents are recorded in 2017 for which people are died in India, which is more in number compared to all other accidents. But their lives could have been saved in the presence of emergency indicators. So we are going to implement our ideas, regarding the life saving measures and precautionary measure to prevent the accident. In this proposal, by using GPS, the exact accident location is identified automatically and GSM can send the data to nearby ambulance vehicle, the police station and family members of that person. The occurrence of accident can be detected using accelerometer sensor and severity of the accident is detected using tilt sensor. It helps to monitor the tilt angle of the chassis. The drowsiness of the driver is detected using drowsiness detector. By measuring the percentage of eyelid closure over the time, the driver’s drowsy state is detected and warning is given to the driver through the alarm. By this method we can measure the speed of a vehicle which gives reason for the accident and vehicle GPS location can be view through mobile application for theft detection. All the data can be processed in microcontroller with help of sensor value and send location to cloud. The major advantages of this system are cost effective, assured safety, victim life can be save quickly, low power consumption, better accuracy, efficient time consumption, reduce the chance of human error.
The Electrochemical Behavior and Heat Removal Study during the Solidification Process of an Al-base Alloy

We study the effect that physicochemical phenomena attack the electrochemical degradation of ternary aluminum alloy. The chemical reaction on corrosion behavior has been correlated with extraction of heat during the solidification process. The scope of the present work is the analysis of the evolution of chemical degradation into aluminum alloys by the influence of the imposing high cooling rate during liquid to solid formation. Within the framework of a thermodynamic phenomenon, the cooling rate has been estimated as the single most important influence on the microstructural behavior. The first approach involves the effects of dendrite arm size and the solute distribution for the ternary aluminum alloy. In fact, the extreme rates of heat removal have been correlated with a good corrosion resistance for the aluminum alloy, which has been linked with the appropriate composition and refined microstructural features. Moreover, the evolution of the growth rate of the solid, which occurs during the casting, might lead both to their mechanical properties, and chemical degradation. Several metallic systems have been considered for use as approach to explain of such chemical mechanism to corrosion degradation. The effect of electrochemical parameters such as Ecorr and Icorr values has been deduced, and will also be discussed the initial corrosion kinetics and the corrosion resistance behavior.
A Crash Recovery Scheme for a Hybrid Mapping FTL in NAND Flash Storage Devices

An FTL (flash translation layer), which most flash storage devices are equipped with, needs to guarantee the consistency of modified metadata from a sudden power failure. This crash recovery scheme significantly affects the writing performance of a flash storage device during its normal operation, as well as its reliability and recovery performance; therefore, it is desirable to make the crash recovery scheme efficient. Despite the practical importance of a crash recovery scheme in an FTL, few works exist that deal with the crash recovery issue in FTL in a comprehensive manner. This study proposed a novel crash recovery scheme called FastCheck for a hybrid mapping FTL called Fully Associative Sector Translation (FAST). FastCheck can efficiently secure the newly generated address-mapping information using periodic checkpoints, and at the same time, leverages the characteristics of an FAST FTL, where the log blocks in a log area are used in a round-robin way. Thus, it provides two major advantages over the existing FTL recovery schemes: one is having a low logging overhead during normal operations in the FTL and the other to have a fast recovery time in an environment where the log provisioning rate is relatively high, e.g., over 20%, and the flash memory capacity is very large, e.g., 32 GB or 64 GB.
Behavioural aspects of China's P2P lending

In this paper, we argue that China's P2P lending is influenced by the behavioural factors of P2P platforms. This is because severe information asymmetry results in high uncertainty surrounding China's P2P lending industry. Specifically, we examine three behavioural aspects of P2P lending: lending sentiments, herding, and speculation. Using a sample of 918 P2P platforms from October 2015-September 2019, we document that positive P2P news release in the media (sentiments) encourages P2P lending; P2P platforms herd on their peers in making lending decisions; and P2P lending contains speculative elements and is driven by real estate bubbles. Moreover, we find that these behavioural effects are less profound if P2P platforms adopt a fund custody mechanism in which commercial banks provide custodian services for investor funds used for P2P lending. This result suggests that behavioural factors are more important in explaining P2P lending when information asymmetry is more severe. We obtain these results by controlling for other usual factors that can explain P2P lending, including characteristics of P2P platforms, macroeconomic variables, and other variables reflecting features of P2P operating environment. Our results suggest that regulators should monitor risk management of P2P platforms and reduce asymmetric information faced by participants in China's P2P lending market.
On the receptivity of employees to just-in-time self-tracking and eCoaching for stress management: a mixed-methods approach

ABSTRACT Smartphones are powerful tools for reaching the user when it is most needed, i.e. Just-In-Time (JIT). In the context of stress management in professionals, self-tracking can create awareness about stress and eCoaching can provide personalised JIT coping suggestions. Employees should also be receptive to take in or act upon the JIT-messages. Therefore, this study aims to explore what factors (emotional state, events or conditions, and content of the message) affect the employees’ receptivity to JIT-messages. 17 participants were invited to use a prototype of the Resilience Navigator app for two weeks. The mixed-methods approach consisted of mixed effects models analysis on data collected via the app (receptivity and the factors of interest) and qualitative analysis on semi-structured interview data collected after the study period. The overall finding was that the participants’ receptivity in the context of stress management often mismatches with the most relevant moments for JIT-messages. For example, emotions with a negative valence seemed to influence the receptivity towards JIT-messages negatively, although the perceived relevance was high. As technology can pinpoint the most receptive and relevant moment for sending JIT-messages, we advocate to further study this topic with more robust quantitative data.
Raman imaging processed cheese and its components

Controlling the quality of cheese involves monitoring its taste, texture, stability and appearance. One method of doing so involves the analysis of cheese microstructure. This research investigated the use of confocal Raman microscopy for observing the microstructure of processed cheese including the distribution of additives therein. Reference Raman spectra were obtained for several additives commonly present in processed cheese including: trisodium citrate (TSC), sorbic acid, paprika and corn starch. Low and high spatial resolution Raman images were obtained from commercial and custom formulated processed cheeses. These were created using band integrals and principal component analysis scores values. Results of this investigation show that fat, protein, water, TSC, paprika and starch distributions can be imaged effectively using the many Raman imaging methods, including high spatial resolution Raman microscopy. Copyright © 2016 John Wiley & Sons, Ltd.
A Train Positioning Method Based-On Vision and Millimeter-Wave Radar Data Fusion

Accurate train positioning is crucial for train safety. In this paper, we propose a train positioning method which fuses vison and millimeter-wave radar data. The proposed method contains two parts: loop closure detection (LCD) and radar-based odometry. The loop closure detection part fuses the convolutional neural network (CNN) features and the line features to achieve accurate key location detection. The radar-based odometry part proposes a train speed measurement algorithm using millimeter-wave radar, and combines the results of loop closure detection to further realize train positioning. Experiments conducted on the Hong Kong metro Tsuen Wan line show that our proposed loop closure detection can achieve an efficient key location detection with 98.57% precision and 99.37% recall; the speed detection method fulfills the ETCS requirements; and the relative error of the proposed train positioning method is 0.45%. Besides, the proposed method has been applied on the Hong Kong Metro TSUEN WAN line.
Synthesis of Thermochromic Mo-Doped VO2 Particles

Mo-doped vanadium dioxide particles were produced by simple solution-based, rapid microwave assisted, and calcination in an inert gas method respectively. The precursors and the heat-treated final powders were characterized by X-ray diffractometer (XRD), differential scanning calorimeter (DSC), scanning electron microscope (SEM), and transmission electron microscope (TEM). The Mo dopant influenced the crystal structure and phase transition of VO2 (M) particles. The XRD pattern indicated that all Mo-doped VO2 particles were monoclinic crystal. The DSC results showed that phase transformation temperature of VO2 from monoclinic to tetragonal or rutile, which normally occurred at 67-68 oC was reduced to 43.18 oC, whereas over doping of Mo led to the increased transition temperature. The results suggested a significant effect of Mo doping content on the transition temperature of VO2. Doping with an optimum amount of Mo into VO2 structure can achieve the lowest phase transformation temperature of VO2 powders.
Environment, Biodiversity and Soil Security: A New Dimension in the Era of COVID-19

Undoubtedly, COVID-19 pandemic is one of the most devastating pandemics in the recent era and major problem for sustainability of life. This virus has severely impacted both environmental and human health. Moreover, it has become the top priority among other challenging global issues including pollution, climate change, urbanization, and unsustainable consumption, which have led to major environmental disturbances and biodiversity loss. The COVID-19 may have long-lasting impacts on the environment health, biodiversity, and soil security, consequently, will raising several scientific questions to be investigated in near future including the expected environmental impacts of COVID-19 on soil, water and air, connecting the missing links between environmental pollutions and COVID-19. Most importantly, unraveling the role of soil in spreading or reducing transmission of the COVID-19 pandemic, and soil xenobiotics status under the COVID-19 outbreak. Understanding the projected management scenario of soil and freshwater pollution in the post-COVID-19 era and the potential impact of COVID-19 on food and soil security would be of immense aid in the preparation of future pandemics. This opinion article aims to analyze and foresee some of the major issues for meeting the United Nation’s Sustainable Development Goals.
Hyperparameter Determines the Best Learning Curve on Single, Multi-Layer and Deep Neural Network of Student Grade Prediction of Pokhara University Nepal

This primary study tries to explore the best machine learning model of hyperparameter tuning using single, multiple, and deep neural networks for predicting engineering student grade of Pokhara University Nepal in the year 2019. Generally, hyperparameter algorithms are adjusted from the data sets automatically when the model was designed. This is not a good idea for all sort of algorithm, with less flexibility of user choices. Therefore this article ties to meet the research gap between automatic calculation vs external model hyperparameter calculation on the same data sets applying comparison. The single neuron predicts 92 percent accuracy of the student’s A, B, C, D grade of university examination with correlation with their internal marks respectively. The five-neuron multilayer neural network produces 72 percent accuracy and deep neural network with drop-out layer (32, 64) examines, the most suitable hyperparameter determines to produce an accurate result of 50 percent accurate when 32 hidden levels were used over 64 hyperparameters produce quite less accuracy (44 percent) of their final score in constituent campus Pokhara. The main objective of this study is to examine the comparison between various neural networks for educational settings for optimization of model deployment accurately when categorical large data prediction of student grade. Therefore, this study compares three comparisons of the neural network model with the same data sets for best hypermeter prediction.