Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Dynamic cerebral autoregulation during cognitive task:Effect of hypoxia

Changes in cerebral blood flow (CBF) subsequent to alterations in the partial pressures of oxygen and carbon dioxide can modify dynamic cerebral autoregulation (CA). While cognitive activity increases CBF, the extent to which it impacts CA remains to be established. In the present study we determined whether dynamic CA would decrease during a cognitive task and whether hypoxia would further compound impairment. Fourteen young healthy subjects performed a simple Go/No-go task during normoxia and hypoxia (inspired O2 fraction = 12%), and the corresponding relationship between mean arterial pressure (MAP) and mean middle cerebral artery blood velocity (MCA Vmean) was examined. Dynamic CA and steady-state changes in MCA V in relation to changes in arterial pressure were evaluated with transfer function analysis. While MCA Vmean increased during the cognitive activity ( P &lt; 0.001), hypoxia did not cause any additional changes ( P = 0.804 vs. normoxia). Cognitive performance was also unaffected by hypoxia (reaction time, P = 0.712; error, P = 0.653). A decrease in the very low- and low-frequency phase shift (VLF and LF; P = 0.021 and P = 0.01) and an increase in LF gain were observed ( P = 0.037) during cognitive activity, implying impaired dynamic CA. While hypoxia also increased VLF gain ( P &lt; 0.001), it failed to cause any additional modifications in dynamic CA. Collectively, our findings suggest that dynamic CA is impaired during cognitive activity independent of altered systemic O2 availability, although we acknowledge the interpretive complications associated with additional competing, albeit undefined, inputs that could potentially distort the MAP-MCA Vmean relationship. NEW &amp; NOTEWORTHY During normoxia, cognitive activity while increasing cerebral perfusion was shown to attenuate dynamic cerebral autoregulation (CA) yet failed to alter reaction time, thereby questioning its functional significance. No further changes were observed during hypoxia, suggesting that impaired dynamic CA occurs independently of altered systemic O2 availability. However, impaired dynamic CA may reflect a technical artifact, given the confounding influence of additional inputs that could potentially distort the mean arterial pressure-mean middle cerebral artery blood velocity relationship. </jats:p

Automated classification of heat sources detected using SWIR remote sensing

Abstract The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale

The Influence of Case-Based Learning on Clinical Reasoning of New Graduate Occupational Therapists

This study aimed to explore the influence of case-based learning (CBL) on the clinical reasoning of new graduate occupational therapists. A quasi-experimental single-arm study with a convergent mixed methods approach was conducted. The intervention was the 10-week CBL program, which included (1) guidance and mentorship in clinical practice and (2) case reports and presentations. Quantitative data collection consisted of the self-assessment of clinical reasoning in occupational therapy (SA-CROT) and the professional identity scale (PI scale); paired t-tests were conducted (p \u3c.05). The qualitative data collection was through a questionnaire with one open-ended question and reflexive thematic analysis was performed. The quantitative analysis results indicated that the CBL program improved the total score of the SA-CROT (p = .001, effect size r = .65), and all four of the SA-CROT\u27s subfactors indicated improvement with moderate to large changes. In addition, the PI scale\u27s two subfactors improved. Qualitative analysis revealed that the CBL program was an experience of learning multidimensional thought processes and learning skills to improve clinical reasoning themselves for participants. This study\u27s results provide information on the positive influence of CBL on the clinical reasoning of new graduate occupational therapists and highlight the integration of the CBL program into continuing education, the importance of supervisors\u27 guidance and mentorship, and learners\u27 reflection and verbalization of clinical practice

Investigation of organic matter in the Allende meteorite using scanning transmission X-ray microscope at photon factory

第6回極域科学シンポジウム[OA] 南極隕石11月16日（月）　国立極地研究所１階交流アトリウ