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2021 ◽  
Vol 7 (2) ◽  
pp. 393
Author(s):  
Linda Khajornkhae ◽  
Prasart Nuangchalerm

Socioscientific-issues based instruction can promote science to students as a tool for necessary learning in the disruptive world. This instruction helps students critique and response as its nature of science, gaining higher-ordered thinking, and discussing with scientific reasoning. The objectives of this study were to compare learning achievement and scientific reasoning of grade 10 students. The topic “DNA technology” was employed with 90 grade 10 students from 2 classrooms. The quasi-experimental research was designed by comparing learning achievement and scientific reasoning between 2 learning organizations. The research tools were socioscientific-issues based and inquiry-based lesson plans, the achievement test consist of 30 items of 4 choices multiple test and scientific reasoning test. The statistic used to test the hypothesis was independent t-test. The results indicated that students had no difference score of learning achievement between learning organizations. While socioscientific-issues based learning had score of scientific reasoning higher than inquiry-based learning at the .05 level of statistically significance. The study can summarize that socioscientific-issues based learning can promote scientific reasoning to science classroom.


2021 ◽  
Vol 9 ◽  
Author(s):  
Jielin Lu ◽  
Xuehai Fu ◽  
Junqiang Kang ◽  
Ming Cheng ◽  
Zhenzhi Wang

The accurate characterization of coal pore structure is significant for coalbed methane (CBM) development. The splicing of practical pore ranges of multiple test methods can reflect pore structure characteristics. The pore\fracture compressibility is the main parameter affecting the porosity and permeability of coal reservoirs. The difference in compressibility of different coal rank reservoirs and pore\fracture structures with changing stress have not been systematically found. The pore structure characteristics of different rank coal samples were characterized using the optimal pore ranges of high-pressure mercury intrusion (HPMI), low-temperature liquid nitrogen adsorption (LT-N2A), low-pressure carbon dioxide adsorption (LP-CDA), and nuclear magnetic resonance (NMR) based on six groups of different rank coal samples. The compressibility of coal matrix and pore\fracture were studied using HPMI data and NMR T2 spectrum under effective stress. The results show that the more accurate full pore characterization results can be obtained by selecting the optimal pore range measured by HPMI, LT-N2A, and LP-CDA and comparing it with the NMR pore results. The matrix compressibility of different rank coal samples shows that low-rank coal > high-rank coal > medium-rank coal. When the effective stress is less than 6 MPa, the microfractures are compressed rapidly, and the compressibility decreases slowly when the effective stress is more than 6 MPa. Thus, the compressibility of the adsorption pore is weak. Nevertheless, the adsorption pore has the most significant compression space because of the largest proportion in different pore structures. The variation trend of matrix compressibility and pore\fracture compressibility is consistent with the increase of coal rank. The compressibility decreases with the rise of reservoir heterogeneity and mechanical strength. The development of pore volume promotes compressibility. The research results have guiding significance for the exploration and development of CBM in different coal rank reservoirs.


Author(s):  
Lucas Böttcher ◽  
Maria R. D'Orsogna ◽  
Tom Chou

We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios. This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.


2021 ◽  
pp. 35-55
Author(s):  
Thorsten Dickhaus ◽  
André Neumann ◽  
Taras Bodnar

2021 ◽  
pp. 11-34
Author(s):  
Ajit C. Tamhane ◽  
Jiangtao Gou

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 295-296
Author(s):  
Aiden E Juge ◽  
Courtney L Daigle

Abstract Capitalizing on canine olfactory capacity is a promising strategy for detecting and diagnosing human, animal, and plant diseases. The purpose of this review was to assess the extent of current research in canine disease detection. In this systematic review, multiple databases were searched for studies in which dogs were trained to detect diseases or health conditions. Following PRISMA guidelines, 1689 studies were screened and 50 relevant studies identified. The majority of studies (n = 31, 66%) took place in Europe. Lung cancer (n = 11, 22%), prostate cancer (n = 8, 16%), and breast cancer (n = 7, 14%) were the most frequently-studied conditions. Urine (n = 17, 34%) and breath (n = 9, 18%) were the most common sample types. Across all studies, 166 unique detection dogs were tested. The most numerous breed was Labrador Retrievers (n = 24, 14.46%). The median number of dogs per study was 2 (range: 1–20). To analyze experimental design and results, studies including multiple test paradigms were divided into sub-studies (n = 90). In 84.4% of sub-studies (n = 76), dogs were presented with sets of samples and 74.4% (n = 67) reported a constant number of samples per trial. The median number of samples per trial was 7 (range: 2–100). Of the sub-studies reporting a fixed number of positive samples (range: 1–10; n = 55), 87% (n = 48) presented one positive sample per trial. A plurality of sub-studies (n = 44, 49%) presented samples in a lineup. Sensitivity (median: 0.90; range: 0.13 to 1.0; n = 77) and specificity (median: 0.96; range: 0.08 to 1.0; n = 69) were the predominant measures of detection success, although reporting strategies were inconsistent. Dogs appear to have the capacity to detect disease via olfaction; yet the nascent nature of this discipline yields little consistency across studies.


2021 ◽  
Vol 156 (Supplement_1) ◽  
pp. S101-S102
Author(s):  
R Haider ◽  
T S Shamsi ◽  
N A Khan

Abstract Introduction/Objective Key challenges against early diagnosis of COVID-19 are its symptoms sharing nature and prolong SARS-CoV-2 PCR turnaround time. Hither machine learning (ML) tools experienced by routinely generated clinical data; potentially grant early prediction. Methods/Case Report Routine and earlier diagnostic data along demographic information were extracted for total of 21,672 subsequent presentations. Along conventional statistics, multilayer perceptron (MLP) and radial basis function (RBF) were applied to predict COVID-19 from pre-pandemic control. Three feature sets were prepared, and performance evaluated through stratified 10-fold cross validation. With differing predominance of COVID-19, multiple test sets were created and predictive efficiency was evaluated to simulate real-fashion performance against fluctuating course of pandemic. Models validation was also inducted in prospective manner on independent dataset, equating framework forecasting to conclusions from PCR. Results (if a Case Study enter NA) RBF model attained superior cross entropy error 20.761(7.883) and 20.782(3.991) for Q-Flags and Routine Items respectively while MLP outperformed for cell population data (CPD) parameters with value of 6.968(1.259) for ‘training(testing)’. Our CPD driven MLP framework in challenge of lower (<5%) COVID-19 predominance affords greater negative predictive values (NPV >99%). Higher accuracy (%correct 92.5) was offered during prospective validation using independent dataset. Sensitivity analysis advances illusive accuracy (%correct 94.1) and NPV (96.9%). LY-WZ, Blasts/Abn Lympho?, ‘HGB Interf?’, and ‘RBC Agglutination?’ are among novel enlightening study attributes. Conclusion CPD driven ML tools offer efficient screening of COVID-19 patients at presentation to hospital to backing early expulsion and directing patients’ flow-from amid the initial presentation to hospital.


2021 ◽  
Vol 9 (2) ◽  
pp. 202
Author(s):  
Agnesia Arista Wijaya AK ◽  
Ni Luh Yulianti ◽  
Ida Bagus Putu Gunadnya

ABSTRAK Penelitian ini bertujuan untuk menganalisa karakteristik  dan pengaruh jenis bahan baku dan persentase perekat yang berbeda terhadap mutu briket biomassa yang dihasilkan dan menentukan perlakuan manakah yang memberikan hasil terbaik terhadap karakteristik briket yang dihasilkan. Penelitian ini menggunakan Rancangan Acak Kelompok (RAK) Faktorial  dengan menggunakan 2 faktor dan 3 kali ulangan. Faktor pertama (A) adalah jenis bahan baku yang terdiri dari 3 taraf yaitu bambu tabah  ( A1), sekam padi (A2) dan campuran bambu tabah dan sekam padi (A3). Faktor kedua (B)  adalah persentase  perekat yang terdiri dari 3 taraf yatu  konsentrasi 10%, ( B1) 15% (B2) dan  20%(B3) . Parameter penelitian yang diamati adalah kadar air, kadar abu, kadar zat menguap dan laju pembakaran. Seluruh perlakuan diulang sebanyak 3 kali ulangan sehingga didapatkan 27 unit percobaan. Data yang diperoleh dianalisis dengan sidik ragam dan apabila terdapat pengaruh perlakuan yang signifikan, maka dilanjutkan dengan uji Duncan Multiple Test (DMRT). Berdasarakan hasil penelitian diketahui bahwa, interaksi  perlakuan memberikan pengaruh yang signifikan terhadap parameter kadar air, kadar abu, kadar zat menguap dan laju pembakaran. Selanjutnya  Kadar air yang didapat berkisar antar  2,30% bb - 4,78%,bb  kadar abu 5,88% - 34,85%, kadar zat menguap 31,30% - 51,59% dan laju pembakaran 73,20 gr/menit – 106,00 gr/menit. Kualitas briket yang paling baik  diperoleh pada perlakuan A2B3 (sekam 80 gram perekat 20 gram) dimana kadar air yang dihasilkan sebesar 2,30%bb, kadar abu 32,29%, kadar zat menguap 32,01% dan laju pembakarannya selama 92,60 gr/menit. ABSTRACT This study aims to analyze the characteristics and effects of different types of raw materials and adhesive percentages on the quality of the briquettes of the biomass produced and to determine which treatment gives the best results for the characteristics of the resulting briquettes. This study used a factorial randomized block design (RBD) using 2 factors and 3 replications. The first factor (A) is the type of raw material which consists of 3 levels, namely tabah bamboo (A1), rice husk (A2) and a mixture of tabah bamboo and rice husk (A3). The second factor (B) is the adhesive percentage consisting of 3 levels, namely 10% concentration, (B1) 15% (B2) and 20% (B3). The research parameters observed were moisture content, ash content, volatile substance content and combustion rate. All treatments were repeated 3 times in order to obtain 27 experimental units. The data obtained were analyzed using variance and if there was a significant treatment effect, it was followed by the Duncan Multiple Test (DMRT). Based on the results of the study, it is known that the treatment interaction has a significant effect on the parameters of moisture content, ash content, volatile substance content and combustion rate, then the moisture content obtained ranges from 2.30% bb - 4.78%, bb ash content 5, 88% - 34.85%, the volatile substance content was 31.30% - 51.59% and the combustion rate was 73.20 grams/minutes - 106.00 grams/minutes. The best quality of briquettes was obtained in A2B3 treatment (80 grams of rice husk 20 grams of adhesive) where the water content produced was 2.30%, the ash content was 32.29%, the vaporizing substance content was 32.01% and the burning rate was 92.60 grams. /minute.


2021 ◽  
Vol 33 (4) ◽  
pp. 756-767
Author(s):  
Momonosuke Shintani ◽  
Yuta Fukui ◽  
Kosuke Morioka ◽  
Kenji Ishihata ◽  
Satoshi Iwaki ◽  
...  

We propose a system in which users can intuitively instruct the robot gripper’s positions and attitudes simply by tracing the object’s grasp part surface with one stroke (one drag) of the laser beam. The proposed system makes use of the “real world clicker (RWC)” we have developed earlier, a system capable of obtaining with high accuracy the three-dimensional coordinate values of laser spots on a real object by mouse-operating the time-of-flight (TOF) laser sensor installed on the pan-tilt actuator. The grasping point is specified as the centroid of the grasp part’s plane region by the laser drag trajectory. The gripper attitude is specified by selecting the left and right drag modes that correspond to the PC mouse’s left and right click buttons. By doing so, we realize a grasping instruction interface where users can take into account various physical conditions for the objects, environments, and grippers. We experimentally evaluated the proposed system by measuring the grasping instruction time of multiple test subjects for various daily use items.


2021 ◽  
Author(s):  
Lucas Etourneau ◽  
Nelle Varoquaux ◽  
Thomas Burger

In proteomic differential analysis, FDR control is often performed through a multiple test correction (i.e., the adjustment of the original p-values). In this protocol, we apply a recent and alternative method, based on so-called knockoff filters. It shares interesting conceptual similarities with the target-decoy competition procedure, classically used in proteomics for FDR control at peptide identification. To provide practitioners with a unified understanding of FDR control in proteomics, we apply the knockoff procedure on real and simulated quantitative datasets. Leveraging these comparisons, we propose to adapt the knockoff procedure to better fit the specificities of quantitive proteomic data (mainly very few samples). Performances of knockoff procedure are compared with those of the classical Benjamini-Hochberg procedure, hereby shedding a new light on the strengths and weaknesses of target-decoy competition.


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