A Study on Reinforcement of Self Directed Learning using Controlling Face Emotion

This paper deals with emotion-based self-directed teaching and learning in online education. Teachers and learners cannot understand how much their communication exchanges well with each other. So, their teaching and learning efficiency decreases than their expectation. To increase teaching and learning efficiency, this paper analyzes face emotional patterns to figure out which emotion segments have dominant facts in teaching and learning through Korean women’s face data. These dominant factors are sent to control for improving self-directed learning. In the control system, deep learning compares face data with reference data and finally decides the control signal to improve self-directed learning. Keywords: Face Emotion, Online Education, Self-Directed Teaching and Learning, Emotion Reinforcement.

Challenges to Implementing Environmental-DNA Monitoring in Namibia

By identifying fragments of DNA in the environment, eDNA approaches present a promising tool for monitoring biodiversity in a cost-effective way. This is particularly pertinent for countries where traditional morphological monitoring has been sparse. The first step to realising the potential of eDNA is to develop methodologies that are adapted to local conditions. Here, we test field and laboratory eDNA protocols (aqueous and sediment samples) in a range of semi-arid ecosystems in Namibia. We successfully gathered eDNA data on a broad suite of organisms at multiple trophic levels (including algae, invertebrates and bacteria) but identified two key challenges to the implementation of eDNA methods in the region: 1) high turbidity requires a tailored sampling technique and 2) identification of taxa by eDNA methods is currently constrained by a lack of reference data. We hope this work will guide the deployment of eDNA biomonitoring in the arid ecosystems of Namibia and neighbouring countries.

Designing a Biogas Plant for an Educational and Scientific Livestock Complex

To design biogas plants, it is necessary to have accurate data about the properties and biogas productivity of the available substrates. Reference data should not be used because the performance of the same substrate can vary significantly. In this research,chicken, horse, sheep and rabbit manure from one of the farms inthe Belgorod region of Russia were analyzed, and the parameters of a biogas station for the processing of this raw material were calculated.The biogas yield of the substrates was determined using the Hohenheim Biogas Yield Test. It was found that the specific biogas yield from the droppings of broilers, laying hens, rabbits, sheep, and horses, and from corn silage were, respectively, 456, 363, 390, 189, 116 and 618 ml/g оDM. The methane content in the biogas was 58.00, 58.50, 57.00, 62.00, 65.00 and 53.60%, respectively. In most cases, the obtained results differed significantly from the data presented in publications of other researchers and reference books.The biogas plant parameter calculations were made according to generally accepted equations, taking into account the characteristics of the studied substrates. Based on the results, it can be concluded that to dispose of the animal excrement of this farm, it is necessary to build a biogas plant with a bioreactor of volume 102.2 m3 and an engine with a power of 12 to 31 kW. The planned output of electric and thermal energy would be 246.19 and 410.27 kWh/day, respectively. Keywords: Hohenheim Biogas Yield Test, rabbit manure, horse dung, sheep manure, chicken droppings, biogas yield of substrates

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Background Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. Results In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. Conclusions The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions

The Subadult Virtual Anthropology Database (SVAD): An Accessible Repository of Contemporary Subadult Reference Data

The Subadult Virtual Anthropology Database (SVAD) is the largest available repository of contemporary (2010–2019) subadult reference data from around the world. It is composed of data collected from individuals aged between birth and 22 years. Data were collected from skeletal remains (n = 43, Colombia) and medical images (n = 4848) generated at medical examiner’s offices in the United States (full-body Computed Tomography (CT) scans), hospitals in France, The Netherlands, Taiwan (region-specific CT scans), and South Africa (full-body Lodox Statscans), a private clinic in Angola (region-specific conventional radiographs), and a dental practice in Brazil (panoramic radiographs). Available derivatives include individual demographics (age, sex) with standardized skeletal and/or dental growth and development indicators for all individuals from all samples, and segmented long bone and innominate surfaces from the CT scan samples. Standardized protocols for data collection are provided for download and derivatives are freely accessible for researchers and students.

Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction

The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2.

Effects of Different Irradiation Treatments on Total Saponins Content of Sapindus mukorossi

Sapindus mukorossi Gaertn is also known as Mu Huanzi, You Huanzi, soap tree, etc. The pericarp of Sapindus mukorossi contains many saponins, which is a type of natural non-ionic surfactant. Its extract has vigorous surface activity and biological activities such as bacteriostasis, oxidation resistance, and free radical scavenging. The Sapindus mukorossi extract is an environmentally friendly washing product that microorganisms can be rapidly decompose in nature without any environmental pollution.This study aims to investigate the effects of E-beam and Co60-γ irradiation on the total saponins content in the crude extract of the S mukorossi. The S mukorossi powder is irradiated with E-beam and Co60-γ ray at doses of 0, 4, 6, 8, 10, and 12 kGy for E-beam and 0, 50, 100, 150, and 200 Gy, respectively, for Co60-γ ray. The changes in the content of total saponins in the crude extract, total detergency, and the bacteriostatic abilities before and after the irradiation were analyzed. The results showed that the content of total saponins in samples irradiated by E-beam was significantly higher than that in non-irradiated samples. The saponins yield was the highest at a radiation dose of 6 kGy, and the detergency and bacteriostatic ability were also the strongest. After low-dose Co6-γ irradiation, the total saponins in the S mukorossi crude extract, and detergency and bacteriostatic ability had no apparent change. Conclusion: E-beam irradiation at a dose of 6 kGy can effectively improve the content of total saponins in the crude extract of S mukorossi powder. In addition, its effects on detergency and bacteriostatic abilities are relatively significant. The findings provide sufficient reference data for the further development of S mukorossi commodities.

Research on Automatic Profile Generation Based on 3D Model

In this paper, the technology of profile generation based on 3D model is studied. The main steps are as follows: (1) the location where the profile needs to be generated in 3D model design; (2) Using 3D data cutting technology to realize the generation of geological lines in profile; (3) Read the basic exploration data related to profile position in the database; (4) According to the data generated in the first three steps, the cross-section is automatically drawn after data coordinate transformation. The above method can quickly generate the geological profile of any location according to the 3D geological model, which is helpful for geological analysis and provides reference data for engineering design.