Indigenous utilization of termite mounds and their sustainability in a rice growing village of the central plain of Laos

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to investigate the indigenous utilization of termite mounds and termites in a rain-fed rice growing village in the central plain of Laos, where rice production is low and varies year-to-year, and to assess the possibility of sustainable termite mound utilization in the future. This research was carried out from 2007 to 2009.</p> <p>Methods</p> <p>The termites were collected from their mounds and surrounding areas and identified. Twenty villagers were interviewed on their use of termites and their mounds in the village. Sixty-three mounds were measured to determine their dimensions in early March, early July and middle to late November, 2009.</p> <p>Results</p> <p>Eleven species of Termitidae were recorded during the survey period. It was found that the villagers use termite mounds as fertilizer for growing rice, vegetable beds and charcoal kilns. The villagers collected termites for food and as feed for breeding fish. Over the survey period, 81% of the mounds surveyed increased in volume; however, the volume was estimated to decrease by 0.114 m³mound^-1year^-1on average due to several mounds being completely cut out.</p> <p>Conclusion</p> <p>It was concluded that current mound utilization by villagers is not sustainable. To ensure sustainable termite utilization in the future, studies should be conducted to enhance factors that promote mound restoration by termites. Furthermore, it will be necessary to improve mound conservation methods used by the villagers after changes in the soil mass of mounds in paddy fields and forests has been measured accurately. The socio-economic factors that affect mound utilization should also be studied.</p

近代デザイン全般の中でのエディトリアルデザインの成立に関する研究/杉浦康平デザイン研究の継承と展開

本研究は平成24 年度共同研究からの継続として位置づけられる。杉浦康平名誉教授の1950 年代からはじまるデザイン活動の包括的な研究を目指すものである。1970 年代～ 80 年代の杉浦名誉教授の活動を中心に成立したと思われる「エディトリアルデザイン」概念の成立過程の検証も目的とする。また、本研究のひとつの中核をなすものとして杉浦名誉教授デザインによるポスター作品の収集・整理・分析の活動である「ポスターアーカイブ・プロジェクト」がある。26 年度共同研究の過程で浮かびあがってきたのが「インフォグラフィックス・デザイン（情報デザイン）」の概念である。ビジュアルデザイン学科棟資料室に蔵された多量のポスターのリスト内容の更新の過程で、分析の軸をなす概念としてこの新しい視座を得た。杉浦デザインの多様性に対して、ひとつの強力な分析の切り口を得たものと考える。70 年代に多くの成果を生んだダイアグラム・デザインとの関連も重要であり、集大成的な出版によって、さらに多くのことが明示化されてきた。また、本共同研究では例年同様、いくつかの関連企画を開催した。10 月のレクチャー・研究会、11 月のインフォグラフィックスの歴史を通観する「系統樹の森展」、3 月の卒展選抜展としての「PLATEAU展」などである。特に2 つの展示企画については新設された梅田サテライトの活用としても意味を持つものである。The objective of this course, originated from the 2012 joint research program, is a comprehensive study of Professor Emeritus Kohei Sugiura\u27s design activity which started in the fifties. The course also aims at examining how the concept of "editorial design" was developed around the activities of Professor Emeritus Sugiura in the seventies and eighties. One of the key activities of the course is the Poster Archive Project which entails the collection, classification and analysis of posters designed by Sugiura. During the 2014 joint research program, we came up to the concept of "infographic design" in the process of updating the list of the posters archived in Department of Visual Design. The concept offers a new, insightfulperspective to analyze the diversity of Sugiura design. The course also focuses on diagram design, one of the key achievement of Sugiura which flourished in the seventies, and clarifies many important ideas. In this program, as in the past programs, we hold several events including a lecture/workshop in October, Phylogentic Forests in November (an exhibition focusing on the history of infographics), and 2015 PLATEAU OSAKA in March (an exhibition of selected works of faculty members of the department.) The latter two attract attention also as meaningful ways to use newly-built CURIO-CITY in Umeda, Osaka

Predicting sensory evaluation of spinach freshness using machine learning model and digital images.

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels

Effect of moderation on rubric criteria for inter-rater reliability in an objective structured clinical examination with real patients

Objectives: Objective structured clinical examinations (OSCEs) are used to assess clinical competence in medical education. Evaluations using video-recorded OSCEs are effective in reducing costs in terms of time and human resources. To improve inter-rater reliability, these evaluations undergo moderation in the form of a discussion between the raters to obtain consistency in grading according to the rubric criteria. We examined the effect of moderation related to the rubric criteria on the inter-rater reliability of a video-recorded OSCE with real patients. METHODS: Forty OSCE videos in which students performed range-of-motion tests at shoulder abduction on real patients were assessed by two raters. The two raters scored videos 1 to 10 without moderation and videos 11 to 40 with moderation each time. The inter-rater reliability of the OSCE was calculated using the weighted kappa coefficient. RESULTS: The mean scores of the weighted kappa coefficients were 0.49 for videos 1 to 10, 0.57 for videos 11 to 20, 0.66 for videos 21 to 30, and 0.82 for videos 31 to 40. CONCLUSIONS: An assessment of video-recorded OSCEs was conducted with real patients in a real clinical setting. Repeated moderation improved the inter-rater reliability. This study suggests the effectiveness of moderation in OSCEs with real patients

Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall

The presence of plaques in the coronary arteries is a major risk to the patients' life. In particular, non-calcified plaques pose a great challenge, as they are harder to detect and more likely to rupture than calcified plaques. While current deep learning techniques allow precise segmentation of real-life images, the performance in medical images is still low. This is caused mostly by blurriness and ambiguous voxel intensities of unrelated parts that fall on the same value range. In this paper, we propose a novel methodology for segmenting calcified and non-calcified plaques in CCTA-CPR scans of coronary arteries. The input slices are masked so only the voxels within the wall vessel are considered for segmentation, thus, reducing ambiguity. This mask can be automatically generated via a deep learning-based vessel detector, that provides not only the contour of the outer artery wall, but also the inner contour. For evaluation, we utilized a dataset in which each voxel is carefully annotated as one of five classes: background, lumen, artery wall, calcified plaque, or non-calcified plaque. We also provide an exhaustive evaluation by applying different types of masks, in order to validate the potential of vessel masking for plaque segmentation. Our methodology results in a prominent boost in segmentation performance, in both quantitative and qualitative evaluation, achieving accurate plaque shapes even for the challenging non-calcified plaques. Furthermore, when using highly accurate masks, difficult cases such as stenosis become segmentable. We believe our findings can lead the future research for high-performance plaque segmentation.Comment: Extended abstract (see SPIE for final published version