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X-ray diffraction and energy dispersion X-ray fluorescence data for cathode materials of Li-ion batteries. Samples named as Blend, Cath1, Cath2, Cath3.
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Determining how emotional experience influences attention is a long standing goal of cognitive psychologists. Emotion is often broken down into two main dimensions, arousal and valence. While many theories focus more on the influence of one dimension than the other, the systematic investigation of the independent influences of the two dimensions of emotion on attention has been slow in the coming. In order to examine the relevance of both aspects of emotion and their interplay on attention simultaneously, in the current experiment we induced low (satisfaction) and high (happiness) arousal positive emotions and low (sadness) and high (anger) arousal negative emotions in subjects before having them complete an inattentional blindness (IB) test. In line with theories that focus on the role of valence, we found that negative emotions led to more IB than did positive emotions, and that arousal did not influence attention. Data were from an extended version of the EMO 16 akt (Schmidt-Atzert & Hüppe, 1996), the Affect Grid (Russell, Weiss, & Mendelsohn, 1989), as well as an Inattentional Blindness task. The study comprised a one factorial design including the between-subjects factor “induced emotion” with the five levels happiness (positive valence, high arousal), satisfaction (positive valence, low arousal), neutral emotion (neutral valence, low arousal), sadness (negative valence, low arousal), and anger (negative valence, high arousal). The design can also be described as a 2 (valence: positive vs. negative) x 2 (arousal: high vs. low) design with an additional control group (neutral valence with low arousal). The dependent variables were detection rates of the critical stimuli in trial 3 (i.e., the critical trial) and trial 5 (divided attention trial). The extended EMO 16 akt as well as the Affect Grid were collected before emotion induction (_1), after emotion induction (_2), and after the inattentional blindness task (_3). The inattentional blindness task comprised six trials. The 3rd (critical trial, inattentional trial), 5th (divided attention trial), and 6th (full attention trial) included an unexpected stimulus.
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Raw data for FTIR, Hemocompatibility, Histomorphometry, Kimetics, Micro-CT and SEM-EDX
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We addressed the accuracy of Lung Ultrasound (LUS) to detect pneumothorax in children with acute chest pain evaluated in the pediatric Emergency Department (pED). Methods We prospectively analyzed patients from 5 to 17 years of age with acute chest pain and clinical suspicion of pneumothorax (PNX) evaluated at a tertiary level pediatric hospital. After clinical examination and before Chest X-Ray (CXR), children underwent LUS to evaluate the presence of PNX. Results We enrolled 77 children, 44 (57,1 %) male, with median age of 10 years and 3 months (IQR 6 years and 9 months - 14 years and 2 months). Thirty (39%) children had interstitial lung disease; 20/77 (26%) had pneumonia with or without pleural effusions; 7/77 (9,1%) had thoracic trauma; 7/77 (9,1%) had a final diagnosis of myo/pericarditis and 13 (16,9%) received a final diagnosis of PNX. In all 13 patients LUS showed the “bar-code sign” while in 12 (92,3%) there was the lung point, giving a diagnosis of PNX. All cases were confirmed by CXR. The lung point had a sensitivity of 92,3% and a specificity of 100%, a positive predictive value of 100% and a negative predictive value of 98,4 % for the detection of PNX. The “bar-code sign” had a sensitivity of 100% and a specificity of 100%, a positive predictive value of 100% and a negative predictive value of 100% for the detection of PNX. Conclusions LUS is highly accurate in detecting or excluding pneumothorax in children with acute chest pain evaluated in the pediatric emergency department. Importantly, both lung-point and M-mode need to be performed when PNX is suspected.
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In view of the irregular trace distribution of rock discontinuities, rock mass appears as both a statistical distribution and a texture distribution in the spatial image. This paper proposes a new method on statistical texture analysis for automated demarcating the homogeneous domains of trace distribution within a rock mass. Grey-Level Co-occurrence Matrix (GLCM) is used to quantify the statistical texture features of trace distribution. Relativity, Inverse Difference Moment and Entropy are screened from ten texture parameters of GLCM using robustness analysis and using principal components analysis. The reliability of three screened texture parameters is verified by comparing the Chebyshev polynomials fitting of three screened texture parameters with Normal distribution, Fisher distribution, and Exponent distribution using χ2 testing. Automated demarcation of the homogeneous domains is implemented by means of classifying three texture parameters of Relativity, Inverse Difference Moment and Entropy in a moving window using the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA). The screening process of texture parameters and a case study indicates that texture parameters and automated demarcation method is so robust, reliable, and efficient that it could replace the traditional representation of the probability statistics in trace distribution and greatly save a lot of manual labor in a large-scale domain.
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We collected cross-sectional data in Hubei province, China in June 2019, by collaborating with a professional Chinese online survey platform. Specifically, structured questionnaires were sent out with a simple random sampling through emails, QQ and WeChat, and each interviewee who provides a valid sample was compensated with 5 CNY. Interviewees were first asked to recall and report their consumption experience(s) of agritourism, and those who had no such experience would be signed as invalid samples. It is therefore guaranteed that all valid samples come from customers who have actually visited agritourism farms.
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Data are for a manuscript examining growth rates in eastern bluebird and black-capped chickadee nestlings in nests supplemented with 10 g mealworms per day vs. control nests.
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Brazilian dataset. Brazilian-Portuguese-TEIQue-SF
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The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script. The test data, or similar data, has been used for some publications, including: C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks,” in Proc WCX SAE World Congress Experience, Detroit, MI, Apr 2020 C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.
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The dataset contains survey data that examined personality and Islamic religiosity using a sample population of 277 Bruneian Malay Muslim university students. This includes demographics, and measures on personality and Islamic religiosity, as well as measures concerning psychological well-being, unethical behavior, and dark triad traits. All items had been properly labeled with values assigned, including ratings.
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