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In this treatise, we evaluate the curriculum for STEP, created in Junior High School Attached to Faculty of Education, MieUniversity in 2019, and analyses the achievements and challenges. The evaluation and the analysis is based on students’questionnaire survey and our original competency for climate change education. We also report the guideline and prospectof 2021 STEP Curriculum.In each learning process of STEP , we Þnd out what students have learned and what problems there have been from studentsÕquestionnaire survey, it is the students’ feedback. Due to that, we can correct the course of the curriculum of next STEP andit helps students learn deeper and grow their inner skills better. In addition, by reßecting the studentsÕ feedback on thelearning activities, 2021STEP must be deeper and more experiential learning stage., 本稿では，平成31年度から三重大学教育学部附属中学校で創設した「STEP」を中心としたカリキュラムを質問紙調査と本校独自の気候変動教育コンピテンシーを基に評価し，成果と課題を分析するとともに，2021年度以降からの第２期STEPにおけるカリキュラムの指針と展望を報告する。STEPの各学習過程において，生徒がどのような学びを得たのか，また，それぞれの学習活動の中にどのような課題が見られたのかを学習者の生徒自身の声である「質問紙調査」を中心に据えて分析することで，第２期STEPで修正，改善を加え，生徒がより深い学びを得られ，彼らの資質・能力をより伸長させることになる。また，生徒の声を学習活動に反映させることにより，第２期STEPはより生徒の主体的な学習活動の場となる。

In this treatise, we clarify the issues of the previous practical research of Education for Sustainable Development (ESD) and report these two things: the contents of the curriculum for Save The Earth Projects (STEP) , created in Junior High School Attached to Faculty of Education, Mie University in 2019, and the status of progress until November, 2019.Due to the revision of the education guidelines, whole teachers need to make the research followed the curriculum management more concrete, not only working on “in-depth study by thinking for themselves through discussion” in each subject.We propose a certain direction of the curriculum planning of the SDGs’ contents, which focus on “Think locally, act globally” and the importance that students and teachers learn together., 本稿では，ESDに関係する先行実践の課題を明らかにするとともに，平成31年度から三重大学教育学部附属中学校で創設した「STEP」を中心としたカリキュラムの内容と，2019年11月までの実践に関わる進捗状況を報告する。学習指導要領の改訂によって，各教科で主体的・対話的で深い学びを実現するための取組を行うだけでなく，学校全体でカリキュラム・マネジメントに則した研究のあり方を具体化する必要性が出てきた。生徒と教師が共に学び合い，特に“Think locally, act globally”を意識した学習活動となるようなSDGsの内容に特化したカリキュラム開発の方向性を提示する。

Aims: Fusarium wilt of melon (Cucumis melo L.), which is caused by Fusarium oxysporum f. sp. melonis is regarded as a severe disease worldwide. Among the races (0, 1, 2 and 1,2), no gene conferringhigh resistance to race 1,2 has been identified in melon Wild Cucumis species have been reported to possess resistance to some melon diseases, and some of the methods of overcome to the reproductive barriers in genus Cucumis interspecific crosses were reported. We evaluated C. ficifolius for novel resistance to F. oxysporum f. sp. melonis and searched for novel sources of resistance. Study Design: A total of 10–20 plants were evaluated for each accession. Results were expressed as mean values and standard errors. Disease severity was log-transformed and data were analyzed using least-squares analysis of variance (ANOVA). A post hoc Tukey- HSD test was performed to compare disease severity among the accessions. Place and Duration of Study: Tokyo University of Agriculture and Technology, and Plant Biotechnology Institute, Ibaraki Agricultural Center between October 2008 and September 2011. Methodology: Inoculation was conducted using a root dip method. The resistance to each race was evaluated 21 days after inoculation according to a 0–3 disease severity scale (0=no symptoms, 1=small lesions on leaves, 2=leaves strongly affected, 3=plant death). Results:All inoculated accessions showed susceptibility both in race 0 and 1. In race 2 and 1,2, only the accession PI 273648 showed resistance to both. However, the other accessions showed susceptibility to race 2 and 1,2 and the symptoms were almost severe. Conclusion: These results suggest that the accession PI 273648 would be a suitable genetic resource to breed the melon cultivar, which has resistance to race 2 and 1,2.

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Feature selection from sparse and high dimension features using conventional greedy based boosting gives classifiers of poor generalization. We propose a novel “shrink boost” method to address this problem. It solves a sparse regularization problem with two iterative steps. First, a “boosting” step uses weighted training samples to learn a full high dimensional classifier on all features. This avoids over fitting to few features and improves generalization. Next, a “shrinkage” step shrinks least discriminative classifier dimension to zero to remove the redundant features. In our object detection system, we use “shrink boost” to select sparse features from histograms of local binary pattern (LBP) of multiple quantization and image channels to learn classifier of additive lookup tables (LUT). Our evaluation shows that our classifier has much better generalization than those from greedy based boosting and those from SVM methods, even under limited number of train samples. On public dataset of human detection and pedestrian detection, we achieve better performance than state of the arts. On our more challenging dataset of bird detection, we show promising results.