Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Green Manure on Soil Enzyme Activity

As a nitrogen-fixing and carbon-reducing crop,leguminous green manure(GM) played an important role in improving soil fertility and reducing nitrogenous fertilizer usage.By field experiment,soil enzyme activities was studied,which catalyze recycle of carbon,nitrogen,phosphorus,sulfur and other material in soil after the application of three GM(soybean,huai bean,green beans) and different usages of nitrogenous fertilizer.The results showed that GM had an important effect on soil enzyme activity.In most cases,huai bean and soybean could activated soil enzyme activity,while green bean could inhibited soil urease activity.Nitrogenous fertilizer application could inhibited soil invertase and urease activity,activated arylsulphatase and dehydrogenase activity,have less impact on phosphatase activity,GM,nitrogenous fertilizer and their interaction had significant or remarkably significant influence on soil enzyme activityis,their relationship reached significant or remarkably significant positive correlation between soil enzyme activity and soil organic matter;The order of total enzyme activity(TEI) was: Huai beansoybeangreen beanfallow,the correlation coefficient were higher than signal enzyme with soil properties.Revealed that TEI was an better index to monitor soil fertility levels,GM could increase the soil enzyme activity,improve soil fertility,was a better environment-friendly fertilization.In Weibei Plateau,huai beans was better green manure species.
On the dissolution state of cellulose in aqueous tetrabutylammonium hydroxide solutions

We have characterized the dissolution state of microcrystalline cellulose (MCC) in aqueous 40 wt% tetrabutylammonium hydroxide (TBAH) using a combination of light and small angle X-ray scattering, up to 0.1 g cm−3. In dilute solutions, cellulose–cellulose interactions are repulsive, as seen by a positive second virial coefficient. Above 0.04 g cm−3, interactions shift from repulsive to be effectively attractive and cellulose begin to form aggregates that can be modelled as mass fractal clusters. The small angle X-ray scattering data also indicate that cellulose is preferentially solvated by TBA+ ions. We propose that the apparent shift in effective interactions is due to a phase transition where less soluble cellulose II precipitates while cellulose I still dissolves.
Providing Electromagnetic Compatibility of High-Power Frequency Converters with Active Rectifiers at Internal Power Supply System of Cherepovets Steel Mill

This paper presents the method of ensuring electromagnetic compatibility of frequency converters with active rectifiers ACS6000 as a part of main electric drives of four-stand mill with 10 kV distribution network of in-plant power supply system at JSC “Severstal” Cherepovets Steel Mill. The essence of the used method was in application of improved algorithms of PWM for active rectifiers of frequency converters ACS6000 ensuring harmonic composition alteration of currents consumed by ARs with exclusion of harmonics falling into the zone of resonances in the frequency response of the network relative to sections of 10 kV GPP-2. Theoretical studies were conducted on a complex mathematical model of the main electric drives of the mill and the internal 10 kV distribution network implemented in the mathematical package Matlab-Simulink. The analysis of research results showed the effectiveness of the proposed method in terms of reducing the total harmonic distortion factor on the sections of the main step-down substation of the plant, which are intrafactory points of common connection.
Artificial Intelligence And The Multivariate Approach In Predictive Analysis Of The Small Cap Index Of The Brazilian Stock Exchange

Looking for greater returns, the Brazilian investors have increasingly resorted to the stock exchange. In this way, companies classified as Small Cap appear as a great opportunity for significative profitability given the higher volatility. In this scenario, studies aiming to explain the behavior of the stock market are becoming even more important. This article aims to develop prediction models for the Small Cap Index in order to assess whether the series under study can be explained by economic and financial variables. The monthly data used were collected from October 2005 to December 2019. The methods applied for the development of predictive models were multiple linear regression and the multilayer perceptron artificial neural network with backpropagation supervised learning algorithm. For the first forecasting method, the model presents, based on 10 predicting variables, approximately 91% of the explanatory capacity of the Small Cap Index variability, against approximately 98% of the second method, which is based on 16 variables for the forecast. Although the artificial neural network presents better prediction results, both models are satisfactory to explain the behavior of the index under study.
Comparative Study of Electrocardiogram Signals Acquired from Different Smart Textile Shirts

Electrocardiogram (ECG) wearable smart textile has received a lot of attention due to its high flexibility, reusability, comfort, and ability to be used for home-based, real-life activities and real-time data acquisition. ECG wearable smart textile is a shirt with an embedded textile sensor that can capture ECG data in more convenient and user-friendly ways, particularly for continuous and long-term ECG data acquisition outside of the laboratory and clinical environment. However, the current challenge with wearable ECG smart textile is wearable smart textile data quality and reliability. Eight healthy volunteers participated in this study, whereas four wore the Hexoskin smart textile and four wear the HeartIn smart textile for the heart rate and ECG data acquisition. The study concludes that both wearable smart textiles, even though in different materials and different types of textile ECG electrodes, are suitable for ECG biometrics on-the-go. Although, both wearable smart textile data acquisition is affected by its wearer’s motion artefacts, concerning the shifting of body postures and the wearer body’s physical states. However, the heart rate and ECG morphology have yielded a promising results in real life and as on-the-go ECG biometric readiness.
Optimizing Heater Electrode and Temperature Distributions on Large Scale Sheet Metal Heaters

We present a method to design specific heater electrode distributions on substrates with high thermal conductivity such as sheet metal. The substrate is covered with a thin polymer insulation layer on both sides, which are, in turn, carrying screen printed, electrically conductive heater electrodes and another protective polymer layer. The temperature inside a predetermined optimization area is demanded to be as uniform as possible, which is required for various high-precision heating applications. A uniform temperature distribution is required when realizing a newly proposed "lab on metal" approach. For this purpose, an optimization algorithm yields the required distances between the electrodes. The resulting electrode distribution was numerically obtained for certain optimization areas. We show the electrode positions relative to the distance from the centermost electrode and the optimization error for each distribution. Finally we describe the fabrication of a test device and show infrared measurements of the FEM modeled uniform temperature distribution on the experimental realized device.
Modeling and Analysis of Current Mode and V2 Controls with Adaptive Voltage Positioning (AVP) Design

Adaptive voltage positioning (AVP) is used in CPU application to reduce voltage spike during load transient. A lot of different methods have been proposed to achieve AVP function. In this paper, the active droop control are discussed and analyzed based on the advanced modeling techniques. With the accurate small signal model, the design guidelines are simplified for active droop control. Then the AVP design with constant on-time V2 control is given. The inherent DC offset of V2 control is analyzed under VR13 spec. A universal structure, which is modified from average current mode control, is given to solve the inherent DC offset of current mode and V2 controls. Simulation and experimental results show the good transient response, accurate load line and zero DC offset of the structure.
High-temperature Schottky diode characteristics of bulk ZnO

Current–voltage (I–V) measurements of Ag/n-ZnO have been carried out at temperatures of 200–500 K in order to understand the temperature dependence of the diode characteristics. Forward-bias I–V analysis results in a Schottky barrier height of 0.82 eV and an ideality factor of 1.55 at room temperature. The barrier height of 0.74 eV and Richardson constant of 0.248 A K−2 cm−2 were also calculated from the Richardson plot, which shows nearly linear characteristics in the temperature range 240–440 K. From the nkbT/q versus kbT/q graph, where n is ideality factor, kb the Boltzmann constant, T the temperature and q the electronic charge we deduce that thermionic field emission (TFE) is dominant in the charge transport mechanism. At higher sample temperatures (>440 K), a trap-assisted tunnelling mechanism is proposed due to the existence of a deep donor situated at Ec—0.62 eV with 3.3 × 10−15 cm2 capture cross section observed by both deep-level transient spectroscopy (DLTS) and lnI0 versus 1/kbT plots. The ideality factor almost remains constant in the temperature range 240–400 K, which shows the stability of the Schottky contact in this temperature range.
Solution composition and particle size effects on the dissolution and solubility of a ThO2 microstructural analogue for UO2 matrix of nuclear fuel

Abstract The objective of this study was to investigate the dissolution rate of ThO2 which was synthesised to approximate, as closely as possible, the microstructure of UO2 in a nuclear fuel matrix. The optimal sintering temperature for ThO2 pellets was found to be 1750 ℃, which produced pellets with a microstructure similar to UO2 nuclear fuel pellets, with randomly oriented grains ranging in size from 10 to 30 μm. Dissolution was conducted using ThO2 particles of different size fractions (80 to 160 μm and 2 to 4 mm) in the presence and absence of carbonate, in solutions with pH from 2 to 8 and at 80 ℃. Dissolution rates were calculated from Th released from the solid phase to solution. Particles of ThO2 were also leached with 1 M HNO3 at 80 ℃ in order to investigate the morphological changes at the particle surfaces. The concentration of Th was found to be ≥ 10–9 mol/L at pH ≤ 4, lower than the theoretical solubility of crystalline ThO2. At higher pH values, from 4 to 8, the measured concentrations (10−10 to 10–12 mol/L) were between the theoretical solubility of ThO2 and Th(OH)4. Grain boundaries were shown to exert an influence on the dissolution of ThO2 particles. Using high resolution aqueous solution analysis, these data presented here extend the current understanding of Th solubility in solution.
Rise – E2e Simulator for the Generation of User Defined Spectral Imaging Data Sets

Current end-to-end (E2E) simulators support the development of satellite missions in various ways including the evaluation of technical requirements and the assessment of preliminary instrument performance. However, most E2E simulators are designed to be instrument-specific and are incapable of depicting the entire processing chain. This paper introduces a generic simulator, the Remote Sensing Image Simulation Environment (RISE), capable of covering a large spectrum of remote sensing applications. The generic design of RISE offers the user full control of every processing step including the simulation of real ground-truth to ground-truth data and instrument behaviour. Consequently, users can generate training data, necessary for deep learning (DL) algorithms in remote sensing applications, with substantially improved quality. RISE comprises a geometry module (GM), scene generation module (SGM), sensor module (SM), as well as Level 1 (L1) and Level 2 (L2) processing modules, which are currently under development.
Soft Clipping Mish - A Novel Activation Function for Deep Learning

This study aims to introduce a novel activation function, called Soft Clipping Mish. In other words, it brings improvements in order to increase the performance within the architecture. Its capability was tested on different scenarios using different datasets and using LeNet-5 architecture. So, these different testing conditions strengthen our proposal, the fact also emphasized in the experimental phase. We used such as datasets: MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100 for a classification task, and Beijing PM2.5 dataset for a prediction task to determine the rank of air pollution. We introduced two variants of this function, the first variant being Soft Clipping Mish with a predefined parameter and the second variant being Soft Clipping Mish learnable, the learnable parameter giving us more flexibility into weights updates. This learnable parameter was initialized during the training phase with a value equal to 0.25. Our proposal was inspired by a recent activation function called Mish.
Islanding Detection With Positive Feedback of Selected Frequency for DC Microgrid Systems

The oscillation frequency of the point of common coupling (PCC) voltage is proposed as a new detecting freedom for dc-based islanding events in this article. In the presented method, the voltage positive feedback with a frequency selection is applied to a distributed generator to make the PCC voltage oscillate at the selected frequency when islanding events occur. With the frequency information, islanding events can be identified by the small-amplitude oscillation superimposed on the normal dc voltage without the necessity of voltage amplitude to be shifted out of the normal range. Thus, the islanding events can be effectively distinguished from other abnormal conditions that lead to large voltage fluctuations, and so, the accuracy of islanding detection is improved. Also, the power quality at islanding events is enhanced, which provides important benefits for seamless transfer from the unintentional islanding conditions to the controlled islanding conditions. Moreover, the frequency selection is designed to only transfer the voltage signal at the sensitive frequency band of the islanding condition. As a result, the deterioration of stability caused by the voltage positive feedback can be relieved under the grid-connected condition compared to the existing methods. Finally, the effectiveness of the proposed method has been verified by both time-domain nonlinear simulation and experimental results.
Improvement of hydrogen gas sensing property of the sol-gel derived Pt/WO3 thin film by Ti-doping

*Department of Pure and Applied Chemistry, Faculty of Science and Technology, Tokyo University of Science, Noda, Chiba 278–8510, Japan **Department of Materials Science and Technology, Faculty of Industrial Science and Technology, Tokyo University of Science, Katsushika, Tokyo 125–8585, Japan ***Research Institute for Science and Technology, Tokyo University of Science, Noda, Chiba 278–8510, Japan
Prediction method of running fatigue based on depth image

The number of people who participate in running every year is huge, and in the United States alone, at least 60 million people participate in running every year. At the same time, the problem of stress fractures caused by running is also very common. The biggest cause of this injury is over fatigue exercise. To prevent over-fatigue exercises that may occur during running and accurately capture every small posture change of runners, this paper proposes a running fatigue prediction method based on the depth image. The method uses the Borg scale (RPE) as the label for measuring fatigue and the spatial information of joint points as the data describing the running posture to make a prediction of the current fatigue state of the tester, which truly achieves the three practical requirements of contact-free, non-interference and timely processing feedback for running fatigue prediction.
EVALUATING THE EXPRESSION OF OCT-4, NANOG, SOX2 AND NUCLEOSTEMIN IN COLON CANCER CELL LINES (CACO-2 AND HT-29)

Objective: Evaluating the expression of Oct-4, NANOG, Sox2 and Nucleostemin in colon cancer cell lines (Caco-2 and HT-29). Materials and Methods: Caco-2 and HT-29 human colon cancer cell lines were cultured in Dulbecco’s modified eagles medium (DMEM) and Roswell Park Memorial Institute medium (RPMI) respectively, containing 10% fetal bovin serum (FBS) with 1% peniciline and streptomycinen in 37°, 5% CO2 incubator. Total RNA was isolated using the ISOGEN method. RNA integrity was checked with the use of agarose gel electrophoresis and spectrophotometry. Reverse transcriptase polymerase chain reaction (RT-PCR) was used to examin the samples. The expression of Oct-4 and Nucleostemin at the protein level was further determined using immunocytochemistry. Results: RT-PCR analysis of Caco2 and HT-29 colon cancer cell lines showed expression of Oct-4, NANOG, Sox2 and Nucleostemin genes . Also immunocytochemical analysis confirmed the cytoplasmic and nuclear expression of the Oct-4 protein and Nucleostemin proteins. Conclusion: Collectively, our data confirmed the expression of Oct-4, NANOG, Sox2 and Nucleostemin in colon cancer cells and suggested that their expression can be used as potential tumor markers in diagnosis and /or prognosis of colon tumors. These results confirm the potential value of the cancer stem-cell theory in cancer therapy.
Test of a Reactor-Based Digestion Optimization Model for Nectar-Eating Rainbow Lorikeets

We tested predictions of a chemical reactor model of digestion of glucose solutions by rainbow lorikeets (Trichoglossus haematodus, 125 g). The optimization criterion was the maximization of net energy gain. The predictions were that residence time in the small intestine would be inversely related to sugar concentration, and extraction efficiency would be inversely related to sugar concentration and generally below 90%. Residence time in the small intestine was indexed by the transit time (TT) of the nonabsorbed marker polyethylene glycol (molecular weight = 4, 000), and extraction efficiency was measured by the inert indicator ratio technique. Birds were tested when ingesting 0.4 or 1.2 M D-glucose. All predictions were rejected: TT was independent of concentration (average = 37 ± 4 min, n = 7 birds), and extraction efficiency was uniformly very high (98.0% ± 0.4%) and also not influenced by glucose concentration. At low and high sugar concentration the rates of glucose absorption were similar (average = 4 mmol/ h). Extraction efficiency of sucrose solution at one concentration, 1.2 M, was 90.5% ± 1.0% (n = 6). Stomach-emptying rate appeared slower at high glucose concentration, perhaps owing to negative feedback arising from intestinal receptors. Total (mouth-to-anus) mean retention time was independent of glucose concentration and averaged 88 ± 12 min. Because the birds were not growing, storing fat, or reproducing, perhaps the assumption about energy maximization was inappropriate. The birds' responses were consistent with the goal of minimizing feeding time.
Explainable Deep Neural Models for COVID-19 Prediction from Chest X-Rays with Region of Interest Visualization

COVID-19 has been designated as a once-in-a-century pandemic, and its impact is still being felt severely in many countries, due to the extensive human and green casualties. While several vaccines are under various stage of development, effective screening procedures that help detect the disease at early stages in a non-invasive and resource-optimized manner are the need of the hour. X-ray imaging is fairly accessible in most healthcare institutions and can prove useful in diagnosing this respiratory disease. Although a chest X-ray scan is a viable method to detect the presence of this disease, the scans must be analyzed by trained experts accurately and quickly if large numbers of tests are to be processed. In this paper, a benchmarking study of different preprocessing techniques and state-of-the-art deep learning models is presented to provide comprehensive insights into both the objective and subjective evaluation of their performance. To analyze and prevent possible sources of bias, we preprocessed the dataset in two ways - first, we segmented the lungs alone, and secondly, we formed a bounding box around the lung and used only this area to train. Among the models chosen to benchmark, which were DenseNet201, EfficientNetB7, and VGG-16, DenseNet201 performed better for all three datasets.
Temperature Rise Test on Medium Voltage Switchgear Assembly Based on IEC Standard

IEC 62271-1 clause 6.5. 2 states the measured temperature difference between the main circuit and the temporary test connections at a distance of 1 m should not exceed 5 K. In order to verify this technical aspect, a temperature rise test is conducted. Several arrangements are made with respect to the standard before the test is executed. Medium voltage (MV) vacuum insulated switchgear is selected for the test and the thermocouple method is implemented. The incomer busbar contacts, vacuum circuit breaker finger contacts, busbar compartment as well as the circuit breaker compartment are the thermocouple points. When the test is executed, 5 A of current from the load bank is fed onto the incomer. The current then flowed through the conductors and contacts of the switchgear which caused heat to be generated. This in turn caused the switching device to experience a temperature rise, especially on the thermocouple points. To measure the temperature, digital thermometers are used and the duration of this test is seven hours. The results are analysed after the test and the implication of the results are made. In the conclusion section, a set of recommendation is provided for the improvement of this project.
Changes in the soil C and N contents, C decomposition and N mineralization potentials in a rice paddy after long-term application of inorganic fertilizers and organic matter

ABSTRACT A long-term experiment on combined inorganic fertilizers and organic matter in paddy rice (Oryza sativa L.) cultivation began in May 1982 in Yamagata, northeastern Japan. In 2012, after the 31st harvest, soil samples were collected from five fertilizer treatments [(1) PK, (2) NPK, (3) NPK + 6 Mg ha−1 rice straw (RS), (4) NPK + 10 Mg ha−1 rice straw compost (CM1), and (5) NPK + 30 Mg ha−1 rice straw compost (CM3)], at five soil depths (0–5, 5–10, 10–15, 15–20 and 20–25 cm), to assess the changes in soil organic carbon (SOC) content and carbon (C) decomposition potential, total nitrogen (TN) content and nitrogen (N) mineralization potential resulting from long-term organic matter addition. The C decomposition potential was assessed based on the methane (CH4) and carbon dioxide (CO2) produced, while the N mineralization potential was determined from the potassium chloride (KCl)-extractable ammonium-nitrogen (NH4+-N), after 2, 4, 6 and 8 weeks of anaerobic incubation at 30°C in the laboratory. Compared to NPK treatment, SOC in the total 0–25 cm layer increased by 67.3, 21.0 and10.8%, and TN increased by 64.2, 19.7 and 10.6%, in CM3, RS and CM1, respectively, and SOC and TN showed a slight reduction in the PK treatment by 5.2 and 5.7%, respectively. Applying rice straw compost (10 Mg ha−1) instead of rice straw (6 Mg ha−1) to rice paddies reduced methane production by about 19% after the soils were measured under 8 weeks of anaerobic incubation at 30°C. Soil carbon decomposition potential (Co) and nitrogen mineralization potential (No) were highly correlated with the SOC and TN contents. The mean ratio of Co/No was 4.49, lower than the mean ratio of SOC/TN (13.49) for all treatments, which indicated that the easily decomposed organic matter was from soil microbial biomass and soil proteins.