Miracle-dz/UniFAR-FTUs huggingface dataset:
Image classification of cashmere and wool fiber based on LC-KSVD

The identification of cashmere and wool fibers has always been a key issue in the textile industry. The classification method of cashmere wool based on microscope image processing technology mainly studies fiber structure characteristics, and the steps are complicated. Affected by changes in breeding methods, breed improvements and climate change, the output of cashmere has decreased, and cashmere fibers have more and more variations, which makes the classification of cashmere wool more difficult. In order to accurately identify cashmere and wool fibers, this paper proposes a multi-feature fusion cashmere wool classification algorithm based on the LC-KSVD algorithm. According to the characteristics of the microscope fiber image, the key information and texture features of the fiber image are extracted, and then the multiple types of texture features are merged into Multi-dimensional feature matrix vector, and finally realize the classification of cashmere wool through the LC-KSVD algorithm. Experiments show that compared with other classification algorithms, the classification accuracy of this method can reach up to 90%, which can be used for subsequent cashmere wool fiber classification and identification.
Clinical Efficacy and Prognosis of the Large Dose Methylprednisolone Impulse Therapy in Combination with the Surgery in Treatment

147 *Address for correspondence E-mail: hen058@sina.com Sun et al.: Serum Clinical efficacy and prognosis of the large-dose methylprednisolone impulse therapy in combination with the surgery It is aimed to investigate the clinical efficacy and prognosis of large-dose methylprednisolone impulse therapy in combination with the surgery in treatment of the spinal damage complicated with the spinal cord injury. A retrospective analysis was conducted over the clinical data of 90 spine damage patients complicated with the spinal cord injury who underwent the surgical treatment in this hospital between October 2015 and September 2018. In the control group, patients only took the surgical treatment, while those in the observation group adopted the large-dose methylprednisolone impulse therapy. Following treatment, the improvement of vertebral body, Harris score, self-care ability of daily living and the overall effective rate of treatment were evaluated in two groups. In the observation group, the compression height of the anterior vertebral edge was (6.64±2.20) mm, the compression height of the posterior vertebral edge was (5.86±2.63) mm, and the Cobb’s angle was (8.62±2.51), which were significantly improved when compared with those in the control group, and the differences had statistical significance (t=21.75, 11.37 or 12.64; p<0.05). In the observation group, patients also had a higher Harris score (66.11±3.97) points than their counterparts in the control group (5.68±2.26) points (t=83.42; p< 0.05). Besides, the scores of the selfcare ability of daily life [Dressing: (8.91±2.33) points; diet: (8.63±2.47) points; walking: (14.33±2.96) points; moving ability of the bed or chair: (13.11±2.97) points; control of defecation: (15.34±4.70) points] in the observation group were all higher than the control group [Dressing: (3.54±2.60) points; diet: (3.16±2.11) points; walking: (1.62±3.11) points; moving ability of the bed or chair: (1.74±3.20) points; control of defecation (2.86±6.12) points] (t=9.69, 10.61, 18.67, 16.42 and 10.22; p< 0.05). In the observation group, the total effective rate of treatment was 95.56 %, also significantly higher than 80.00 % in the control group (ꭓ2 = 4.113, p<0.05). After the large-dose methylprednisolone impulse therapy in combination with the surgical treatment, spine-damaged patients with the spinal cord injury gain the promising improvement in the vertebra and prognosis, significantly enhancing the self-care ability of daily life, with recovery in the nerve function and muscle strength. Thus, this strategy is worthy of being promoted in clinical practice.
Quantized vs. Analog Channel Feedback for FDD Massive MIMO Systems with Multiple-Antenna Users

In this paper, we consider the problem of channel feedback in massive multiple-input-multiple-output (MIMO) systems. For the downlink scenario, we present a detailed comparison between the performance of the quantized and the analog channel feedback schemes for the case of having multiple antenna users. Both schemes’ performance is evaluated by deriving an upper bound on the rate gap between the rate of the system with perfect channel state information (CSI) and with imperfect CSI for both feedback schemes. We compare the two schemes, namely, quantized channel feedback and analog channel feedback, under the same resources allocated for channel feedback for a fair comparison. Moreover, we consider two different downlink transmission schemes; the first one does not consider power allocation across the streams and the second one does power allocation (water-filling) across the streams. Our results show that the analog feedback scheme performs better in the low signal to noise (SNR) region when performing power allocation across the multiple data streams. However, the quantized channel feedback scheme performs better at the high SNR region, where the quantized CSI can provide a better approximation of the actual CSI. Finally, simulation results are presented to verify our theoretical analysis and demonstrate our conclusions.
Response Generation based on the Big Five Personality Traits

Personality is one’s intrinsic property that is hard to change and closely tied with one’s natural language expression, so it is a potential key to make a dialogue system consistent. While there exist many studies on generating responses conditioned on certain pro(cid:12)les, little work has been done on incorporating real personalities in dialogue systems. In this study, we (cid:12)rstly constructed a personality identi(cid:12)er by using the myPersonality dataset for detecting trait values from utterances. We then used the constructed identi(cid:12)er to label all the conversations in the Cornell Movie-dialogs Corpus. Finally, we used a GRU-based seq2seq model with attention mechanism to generate responses conditioned on a certain personality by including personality information in the decoder part. The personality model used in our experiment is called the Big Five, which contains (cid:12)ve personality dimensions named neuroticism, openness, extraversion, agreeableness and conscientiousness. Our experiment shows that when adjusting the trait values along extraversion and neuroticism dimensions, the generated responses can re(cid:13)ect the prescribed personality slightly while for the other three dimensions there is no improvement observed.
Prediction of Relative Risk “SUSPECT” and “CONFIRM” COVID- 19 Using Meta-Analysis Approach

Background: Coronavirus Disease 2019 (COVID-19) is a disease caused by a new type of virus called Severa Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2). The number of COVID-19 cases in Indonesia continues to increase every day, followed by an increasing number of deaths. During a pandemic like this, it is important for a country to control the rate of increase in Covid-19 patients and the rate of patient mortality from Covid-19 disease. As a real first step in determining who is affected by Covid-19 and not, this is what is important, and then there will be appropriate handling of each of the positive and negative ones. If the treatment is right, then the handling will be good and have an impact on a lower risk of death. Materials and Methods: This study uses secondary data per district of Sidoarjo Regency, which is obtained from https://covid19.sidoarjokab. go.id/#angka as of 13/12/2020 at 17.00 WIB. The data obtained are data on the number of “Suspect” and “Confirm” COVID-19, for each district in Sidoarjo Regency. The method of analysis in this research is meta-analysis for binary data (“Suspect” or “Confirm”) which will calculate the effect size in the form of a risk ratio, risk difference, and odds ratio. Furthermore, to determine the prediction of pessimistic, actual and optimistic risks for the "Suspect" and "Confirm" groups who experienced the event (died). Results: Relative risk groups can be grouped into 3 parts. Group 1 consists of a relative risk of less than 0.225, namely Sidoarjo district (0.051), Jabon district (0.036), Krembung district (0.069), Tulangan district (0.123), and Krian district (0.028). Group 2 consists of relative risks from 0.225 to 0.567, namely Buduran district (0.258), Candi district (0.414), Porong district (0.470), Taman district (0.317), Sedati district (0.274), Waru district (0.252), and Tarik district (0.301). Group 3 consists of risks above 0.567, namely Gedangan district (0.645), Tanggulangin district (1.353), Wonoayu district (0.748), Prambon district (0.790), Sukodono district (1.070), and Balongbendo district (0.728). The results of the forest plot in Relative Risk (log scale) Group 1, hereinafter referred to as the green zone, are in Sidoarjo district , Buduran district , Jabon district , Taman district , Krembung district , Tulangan district , Krian district , Sedati district , Waru district , and Tarik district . Group 2, hereinafter referred to as the Yellow Zone, is in the Candi subdistrict, the Gedangan subdistrict, the Porong subdistrict, the Wonoayu district, the Prambon subdistrict, and the Balongbendo district. Group 3, hereinafter referred to as the Orange Zone, is in the Tanggulangin district and Sukodono district . Conclusion: The "Confirm" group had a greater risk of dying than the "Suspect" group. The districts in Sidoarjo Regency with the highest risk of dying in confirmed cases are Tanggulangin district, Wonoayu district, Prambon district, Sukodono district. While the lowest relative risk of dying was in Krian district, Jabon district, Krembung district and Sidoarjo district.
The impact of the freeze–melt cycle of land-fast ice on the distribution of dissolved organic matter in the Laptev and East Siberian seas (Siberian Arctic)

Permafrost degradation in the catchment of major Siberian rivers, combined with higher precipitation in a warming climate, could increase the flux of terrestrially derived dissolved organic matter (tDOM) into the Arctic Ocean (AO). Each year,∼ 7.9 Tg of dissolved organic carbon (DOC) is discharged into the AO via the three largest rivers that flow into the Laptev Sea (LS) and East Siberian Sea (ESS). A significant proportion of this tDOM-rich river water undergoes at least one freeze–melt cycle in the land-fast ice that forms along the coast of the Laptev and East Siberian seas in winter. To better understand how growth and melting of land-fast ice affect dissolved organic matter (DOM) dynamics in the LS and ESS, we determined DOC concentrations and the optical properties of coloured dissolved organic matter (CDOM) in sea ice, river water and seawater. The data set, covering different seasons over a 9-year period (2010– 2019), was complemented by oceanographic measurements (T , S) and determination of the oxygen isotope composition of the seawater. Although removal of tDOM cannot be ruled out, our study suggests that conservative mixing of high-tDOM river water and sea-ice meltwater with low-tDOM seawater is the major factor controlling the surface distribution of tDOM in the LS and ESS. A case study based on data from winter 2012 and spring 2014 reveals that the mixing of about 273 km3 of lowtDOM land-fast-ice meltwater (containing ∼ 0.3 Tg DOC) with more than 200 km3 of high-tDOM Lena River water discharged during the spring freshet (∼ 2.8 Tg DOC yr−1) plays a dominant role in this respect. The mixing of the two low-salinity surface water masses is possible because the meltwater and the river water of the spring freshet flow into the southeastern LS at the same time every year (May– July). In addition, budget calculations indicate that in the course of the growth of land-fast ice in the southeastern LS,∼ 1.2 Tg DOC yr−1 (± 0.54 Tg) can be expelled from the growing ice in winter, together with brines. These DOC-rich brines can then be transported across the shelves into the Arctic halocline and the Transpolar Drift Current flowing from the Siberian Shelf towards Greenland. The study of dissolved organic matter dynamics in the AO is important not only to decipher the Arctic carbon cycle but also because CDOM regulates physical processes such as radiative forcing in the upper ocean, which has important effects on sea surface temperature, water column stratification, biological productivity and UV penetration.
Dialogue Quality Distribution Prediction based on a Loss that Compares Adjacent Probability Bins

The NTCIR-14 Short Text Conversation Dialogue Quality subtask requires participating systems to predict a distribution of dialogue quality scores for each given customer-helpdesk dialogue. The gold distribution represents the views of multiple annotators, and the systems are evaluated by comparing the two distributions over ordinal bins of scores. In this study, we propose a loss function that is based on comparing the probabilities in adjacent bins and demonstrate its eﬀectiveness for the task. Our proposed model outperformed the baseline model in terms of every measure for Chinese dataset
Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies

Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
Depth distribution of mineralizable nitrogen pools in contrasting soils in a semi-arid climate

Abstract: A better understanding of the depth distribution of soil mineralizable nitrogen (N) pools is important to improve prediction of net soil N mineralization. However, our understanding of the depth distribution of these N pools under the semi-arid conditions of western Canada is limited. This study examined the depth distribution of soil mineralizable N pools (k S, the rate constant of a nondepleting zero-order stable N pool, and N L, the size of a depleting first-order labile N pool) of six sites in western Canada chosen to vary with respect to soil zone, soil texture, and cropping system. The depth distribution of mineralizable N pools varied substantially among sites, indicating that this distribution needs to be considered in making predictions of net soil N mineralization. A single regression equation including soil total nitrogen (STN), Pool I (a labile mineralizable N pool determined through a 14-day aerobic incubation), and soil pH explained 67% of the variation in k S across sites and soil depths. In addition, 95% of the variation in N L was explained by a regression model with Pool I. Thus, although the depth distribution of soil mineralizable N pools can vary substantially among sites, the mineralizable N parameters can be adequately predicted across sites and soil depths from simple soil properties. Comparison with a study using surface soils under humid conditions in New Brunswick suggests that the relationship between N L and Pool I is applicable across a wide range of soils, climatic zones, and cropping systems, whereas the regression model to predict k S varied with climatic zone, perhaps reflecting different pedogenic processes stabilizing the organic matter in these climatic zones.
Action Research on Impact of Project-based Learning on Intrinsic Motivation of Private College Students Learning English in China

The study investigates the effect of project-based learning on the intrinsic motivation of private college students learning English in China. Motivation is instrumental to students’ learning process, private college students in China are less academically motivated than their counterparts in public institutions. Project-based learning is an effective instructional technique that empowers students to be the center of learning. In this action research, 21 students learning English in a private Chinese college were administered with the Intrinsic Motivation Inventory to examine the difference of their intrinsic motivation level before and after a project. The result of the study shows significant difference existing in interest, autonomy, competence and pressure students perceived before and after the project, which supports project-based learning has a positive impact on students’ intrinsic motivation for learning. Based on this result, it is highly suggested that more considerations should be given to incorporate PBL in course design to enhance intrinsic motivation of private college students in China.
Screening of 200 Core SNPs and the Construction of a Systematic SNP-DNA Standard Fingerprint Database with More Than 20,000 Maize Varieties

To strengthen the management of maize varieties and the protection of intellectual property rights to new varieties, we constructed a comprehensive single nucleotide polymorphism (SNP)-DNA standard fingerprint database of 20,075 materials covering nationally and provincially approved maize hybrid lines, hybridized combinations, and inbred lines. The database was based on 200 core SNPs selected from 60 K SNPs distributed in intragenic regions, including 106 (53.0%) located in exons. Average minor allele frequencies (MAF) of the 200 SNPs in 6755 maize hybrids, 7837 hybridized combinations, and 3478 inbred lines were 0.385, 0.350, and 0.378, respectively, with corresponding average polymorphism information content (PIC) values of 0.354, 0.335, and 0.351. Heterozygous genotype frequencies of maize hybrids, hybridized combinations, and inbred lines averaged 0.48, 0.47, and 0.012, respectively. The number of different loci in the three different maize groups ranged from one up to 164, 160, and 140, respectively. The percentage of different SNPs within 5% (the number of difference SNPs is less than 10) accounted for 0.013%, 0.011%, and 0.030% among pairwise comparisons of samples within hybrid lines, hybridized combinations and inbred lines, respectively. Genetic distances between varieties based on the 200 core SNPs were highly correlated with those obtained using 60 K SNPs, with a correlation coefficient of 0.82 and 0.87 in in inbred and hybrid lines, respectively. The maize SNP-DNA fingerprint database established in this study can play an important role in variety authentication, purity determination and the protection of variety rights, thereby providing reliable, comprehensive data support for use in the seed industry.
Adaptive Virtual Environments using Machine Learning and Artificial Intelligence

Advances in off-the-shelf sensor technology have aided the collection of psychophysiological data in real-time. Utilizing these low-cost sensors, researchers can monitor a person’s cognitive, behavioral, and affective states (in real-time) as they interact within virtual environments. Moreover, this psychophysiological data can be used to develop adaptive virtual environments. In this paper, we explore electroencephalography-based algorithms to optimize flow models. These algorithms use various combinations of brain wave channels to develop indices of task engagement (Beta / (Alpha + Theta)), arousal (BetaF3 + BetaF4) / (AlphaF3 + AlphaF4), and valence (AlphaF4 / BetaF4) - (AlphaF3 / BetaF3). Results support accurate determination of when a person has left a state of flow. Moreover, the reported results can be further modeled using machine learning (e.g., Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor) to develop training classifiers used in our adaptive virtual environments. We purpose a set of rules for the development of an adaptive virtual environment that can adjust environmental stimuli to keep the user in a state of flow.
Experimental validation of simulation models for the emitter coil of an WPT system

This article investigates the magnetic field distribution generated by the emitter coil of a Wireless Power Transfer (WPT) system, in two ways, through simulation and experimental measurement. More exactly, a comparative analysis is made in several relevant points from the selected 2D plans through the center of the coil. Data captured from perpendicular vertical XZ plane and YZ respectively, were used as reference values. For the investigations, a commercial double planar coil was considered as device under test (DUT). In both investigations, simulated and measured, the same initial conditions were considered, for the current density of the coil materials and for its geometric dimensions. Experimental values of magnetic field distribution were captured with a high-resolution measurement system developed in the laboratory. Finally, the simulated result of magnetic field distribution for considered emitter coil were compared with measurements of the real product. The profile of the simulated field in the selected plans overlaps the profile of the field distribution from measurements. Also, the standard deviation errors are presented in the results section. General conclusions were formulated based on the comparison results.
An Efficient Short-Term Electric Power Load Forecasting Using Hybrid Techniques

The power system plays an essential part in the transmission of electricity. Generation, transmission and distribution are the main parts of electricity consumption, and day to day, the demand for electricity increases. So, the prediction of price and load is significant in the power system. The short term load forecasting (STLF) predicts the load 24 hours ahead or a week ahead. This paper investigates the effect of price and load on short term load forecasting with two hybrid techniques (HT). The first hybrid technique consists of wavelet as well as Generalized Autoregressive Conditionally Heteroscedastic (GARCH) analysis methods and the second hybrid technique consists of GARCH, EGARCH, GJR models and Particle swarm optimization (PSO). The first hybrid technique (FHT) is analyzed by taking the Price from the Ontario grid and the second hybrid technique (SHT) is diagnosed by taking the load from the Xintai power plant. The prediction of Price and load depends on input data. The results are significant and its accuracies are effective. The analysis of two hybrid techniques clearly defines its work and the ability to produce the result. In the first HT, the wavelet decomposes the data and the GARCH models analyze the decomposition data. In the second HT, the GARCH, EGARCH, GJR models are executed the input data and PSO brings the smoothness of the result. The forecasting price results and load calculation indicates less error and it is essential for short term load forecasting.