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Development of laboratory scale model of field automatic water control system with sheetpipe technology

IOP Conference Series: Earth and Environmental science 2021年第1期871卷

作者： S K Saptomo C Arif W B Suwarno H Agustina A G Putra Wiranto K Tamura H Matsuda B I Setiawan Department of Civil and Environmental Engineering IPB University Bogor Indonesia 16680 Department of Agronomy and Horticulture IPB University Bogor Indonesia 16680 Study Program of Agricultural Engineering Science IPB University Bogor Indonesia 16680 Meteo Nusantara Instrumen Bogor Indonesia 16620 Kyouwa Kensetsu Kogyo 2370 Oaza Tsubaki Hagi City Yamaguchi Prefecture Japan 758-0061 Yamaguchi University 16-1 Tokiwadai 2-chome Ube-shi Yamaguchi Japan 755-8611.

In this study, a 2mx2mx0.5m box is modeled, filled with soil having a thickness of 40 cm rice fields and sheet pipe subsurface drainage installed 30 cm below the surface. The sheet pipe is connected to the air duct and reservoir upstream and the drainage outlet pipe downstream. Automation of the water level control in the reservoir is done utilizing a digital controller which has been programmed and connected to a water level sensor and a solenoid valve. If the water level is detected falls below the minimum threshold, the controller will open the solenoid valve and flow water to the reservoir until the specified level. This system is connected to the server via wireless internet, data monitoring and control settings can be done by the user via a dashboard in an internet browser. The test was carried out for approximately 3 months with rice planting and un-inundated irrigation. The test results show that the system has been able to function in remote monitoring and regulation of the controller, automatic irrigation operations, and water supply in the land as needed in the rice planting experiment with the control error of 2.6 cm.

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Evaluation of Palm Sap (Neera) Quality (Arenga pinnata Merr) in Processing of House Hold Palm Sugar (Case study on Aren Farmers in Gunung Halu Village, Gunung Halu District, West Bandung Regency)

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IOP Conference Series: Earth and Environmental science 2020年第1期466卷

作者： W Gunawan R R Maulani E P Hati F Awaliyah A H Afif R G Albab Magister Program of Biomanajemen School of Science and Technology Institute Technology of Bandung. Jl. Ganeca 10 Bandung Indonesia 40132 Study Program of Post-Harvest Technology School of Science and Technology Institute Technology of Bandung. Jl. Ganeca 10 Bandung Indonesia 40132 Magister of Bio-Manajemen ITB-Bandung Bachelor of Agricultural Engineering ITB-Bandung

Palm sap (Neera) is a sweet liquid that is obtained from the cutting of flower bunches of sugar palm (Arenga pinnata Merr). Nira used as a raw material for making palm sugar. Palm sugar with good quality depends on the quality of the sap processed. The research was conducted to study the different quality of nira in household processing of palm sugar based on differences in storage containers (lodong), namely traditional containers using bamboo and modern containers using aluminium. Harvesting of the nira was conducted in the morning and afternoon time. Observations were carried on chemical quality (pH and total soluble solid), organoleptic perception (color, aroma, and taste), and the number of bacterial colonies (total plate count). The results showed that then treatments using aluminium container had better quality of nira with a mean value of pH 6.95, mean value of TSS 13.7 Brix, the organoleptic perceptions of color is more clear, taste is sweet, and fragrant smell; and the mean number of bacterial colonies is 12.5.

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Fungal diversity notes 1512–1610: taxonomic and phylogenetic contributions on genera and species of fungal taxa

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Fungal Diversity 2022年第6期117卷 1-272页

作者： Ruvishika S.Jayawardena Kevin D.Hyde Song Wang Ya‑Ru Sun Nakarin Suwannarach Phongeun Sysouphanthong Mohamed A.Abdel‑Wahab Faten A.Abdel‑Aziz Pranami D.Abeywickrama Vanessa P.Abreu Alireza Armand AndréAptroot Dan‑Feng Bao Dominik Begerow Jean‑Michel Bellanger Jadson D.P.Bezerra Digvijayini Bundhun Mark S.Calabon Ting Cao Taimy Cantillo João LVRCarvalho Napalai Chaiwan Che‑Chih Chen Régis Courtecuisse Bao‑Kai Cui Ulrike Damm Cvetomir M.Denchev Teodor T.Denchev Chun Y.Deng Bandarupalli Devadatha Nimali Ide Silva Lidiane Ados Santos Nawal K.Dubey Sylvain Dumez Himashi SFerdinandez André L.Firmino Yusufon Gaforov Achala J.Gajanayake Deecksha Gomdola Sugantha Gunaseelan Shucheng‑He Zin H.Htet Malarvizhi Kaliyaperumal Martin Kemler Kezhocuyi Kezo Nuwan DKularathnage Marco Leonardi Ji‑Peng Li Chunfang Liao Shun Liu Michael Loizides Thatsanee Luangharn Jian Ma Hugo Madrid S.Mahadevakumar Sajeewa S.N.Maharachchikumbura Dimuthu S.Manamgoda María P.Martín Niranjan Mekala Pierre‑Arthur Moreau Yan‑Hong Mu Pasouvang Pahoua Dhandevi Pem Olinto L.Pereira Wiphawanee Phonrob Chayanard Phukhamsakda Mubashar Raza Guang‑Cong Ren Andrea C.Rinaldi Walter Rossi Binu C.Samarakoon Milan CSamarakoon Vemuri V.Sarma Indunil C.Senanayake Archana Singh Maria F.Souza Cristina M.Souza‑Motta Adriano A.Spielmann Wenxin Su Xia Tang XingGuo Tian Kasun M.Thambugala Naritsada Thongklang Danushka S.Tennakoon Nopparat Wannathes DingPeng Wei Stéphane Welti Subodini N.Wijesinghe Hongde Yang Yunhui Yang Hai‑Sheng Yuan Huang Zhang Jingyi Zhang Abhaya Balasuriya Chitrabhanu SBhunjun Timur S.Bulgakov Lei Cai Erio Camporesi Putarak Chomnunti Y.S.Deepika Mingkwan Doilom Wei‑Jun Duan Shi‑Ling Han Naruemon Huanraluek EBGareth Jones NLakshmidevi Yu Li Saisamorn Lumyong Zong‑Long Luo Surapong Khuna Jaturong Kumla Ishara S.Manawasinghe Ausana Mapook Wilawan Punyaboon Saowaluck Tibpromma Yong‑Zhong Lu JiYe Yan Yong Wang Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Innovative Institute for Plant Health Zhongkai University of Agriculture and EngineeringGuangzhou 510225P.R.China Department of Plant Pathology College of AgricultureGuizhou UniversityGuiyang 550025GuizhouChina Research Center of Microbial Diversity and Sustainable Utilization Chiang Mai UniversityChiang Mai 50200Thailand Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Biotechnology and Ecology Institute Ministry of Agriculture and ForestryP.O.Box:811Vientiane CapitalLao PDR Department of Botany and Microbiology Faculty of ScienceSohag UniversitySohag 82524Egypt Beijing Key Laboratory of Environment-Friendly Management on Fruit Diseases and Pests in North China Institute of Plant ProtectionBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Departamento de Fitopatologia Universidade Federal de ViçosaViçosaMinas GeraisBrazil Laboratório de Botânica/Liquenologia Instituto de BiociênciasUniversidade Federal de Mato Grosso do SulAvenida Costa e Silva S/NBairro UniversitárioCampo GrandeMato Grosso do Sul CEP 79070-900Brazil College of Agriculture and Biological Sciences Dali UniversityDali 671003YunnanChina Department of Entomology and Plant Pathology Faculty of AgricultureChiang Mai UniversityChiang Mai 50200Thailand Institute of Plant Science and Microbiology Universität HamburgOrganismic Botany and MycologyOhnhorststr.1822609 HamburgGermany CEFE CNRSUniv.MontpellierEPHEIRDINSERM1919Route de Mende34293 Montpellier Cedex 5France Setor de Micologia Departamento de Biociências e TecnologiaInstituto de Patologia Tropical e Saúde PúblicaUniversidade Federal de GoiásRua 235S/NSetor UniversitárioGoiâniaGO CEP:74605-050Brazil Division of Biological Sciences College of Arts and SciencesUniversity of the Philippines Visayas5023 MiagaoIloiloPhilippines CA

This article is the 14th in the Fungal Diversity Notes series,wherein we report 98 taxa distributed in two phyla,seven classes,26 orders and 50 families which are described and *** in this study were collected from Australia,Brazil,Burkina Faso,Chile,China,Cyprus,Egypt,France,French Guiana,India,Indonesia,Italy,Laos,Mexico,Russia,Sri Lanka,Thailand,and *** are 59 new taxa,39 new hosts and new geographical distributions with one new *** 59 new species comprise Angustimassarina kunmingense,Asterina lopi,Asterina brigadeirensis,Bartalinia bidenticola,Bartalinia caryotae,Buellia pruinocalcarea,Coltricia insularis,Colletotrichum fexuosum,Colletotrichum thasutense,Coniochaeta caraganae,Coniothyrium yuccicola,Dematipyriforma aquatic,Dematipyriforma globispora,Dematipyriforma nilotica,Distoseptispora bambusicola,Fulvifomes jawadhuvensis,Fulvifomes malaiyanurensis,Fulvifomes thiruvannamalaiensis,Fusarium purpurea,Gerronema atrovirens,Gerronema favum,Gerronema keralense,Gerronema kuruvense,Grammothele taiwanensis,Hongkongmyces changchunensis,Hypoxylon inaequale,Kirschsteiniothelia acutisporum,Kirschsteiniothelia crustaceum,Kirschsteiniothelia extensum,Kirschsteiniothelia septemseptatum,Kirschsteiniothelia spatiosum,Lecanora immersocalcarea,Lepiota subthailandica,Lindgomyces guizhouensis,Marthe asmius pallidoaurantiacus,Marasmius tangerinus,Neovaginatispora mangiferae,Pararamichloridium aquisubtropicum,Pestalotiopsis piraubensis,Phacidium chinaum,Phaeoisaria goiasensis,Phaeoseptum thailandicum,Pleurothecium aquisubtropicum,Pseudocercospora vernoniae,Pyrenophora verruculosa,Rhachomyces cruralis,Rhachomyces hyperommae,Rhachomyces magrinii,Rhachomyces platyprosophi,Rhizomarasmius cunninghamietorum,Skeletocutis cangshanensis,Skeletocutis subchrysella,Sporisorium anadelphiae-leptocomae,Tetraploa dashaoensis,Tomentella exiguelata,Tomentella fuscoaraneosa,Tricholomopsis lechatii,Vaginatispora favispora and Wetmoreana *** new combin

关键词： 59 New taxa 39 New records Ascomycota Basidiomycota Dothideomycetes Lecanoromycetes Leotiomycetes Sordariomycetes Ustilaginomycotina

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Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Natalina Br Sitepu Sawaluddin M Zarlis Syahril Efendi Hanna Willa Dhany Informatic Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatra Utara Medan - Indonesia

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy.

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Weed Management in Dryland Paddy Farming for Environmental Conservation

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IOP Conference Series: Earth and Environmental science 2019年第1期239卷

作者： Priyaji Agung Pambudi Suyud Warno Utomo Tarsoen Waryono Djoko M. Hartono School of Environmental Science University of Indonesia Department of Geography Faculty of Mathematics and Nature Science University of Indonesia Environmental Engineering Study Program Faculty of Engineering University of Indonesia

Weeds become the major thread in the agriculture system especially for rice crops. Individual density per 0,1 m could decrease 57% of dryland rice production. We aimed to determine the strategy for manage the rice weeds in eco-friendly dryland as the effort to conserve the biodiversity. The methodology used is mixed method with observation and in-depth interview. The research was conducted in July – Oktober 2018, Sudimoro district, Pacitan Regency, East Java. The weeds that dominantly by important value index is Alternanthera sessilis (L.) ***. Ex DC (4,41) and Synedrella nodiflora (L.) J. Gaertner (2,84). The farmer tends to manage the weed chemically; even 100% respondent admitted the use of 42.86% chemical and mechanical to manage the weeds, meanwhile the rest 57.14% used only chemical (herbicide). To the farmers, the use of herbicide is believed as the most effective solution, it takes only few times, less energy, and more affordable than mechanic control. However, 42.86% of respondents indicated that the weed turned more resistant because even it was sprayed by herbicide, the weeds kept growing up. In the other side, the existence of pollinator insect and odonata were no longer as much as in the past time, moreover, somehow, odonata was totally disappeared. This reflection shows that the quality of ecosystem was disturbed by the use of herbicide. The management using the prior ecological principles is the best solution to conserve the ecosystem. This one can be gained through combining mechanical and biologycal management.

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Testing Real-Time Applications on Windows 10 IOT Using the Nyquist Theory

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Zulfahmi Syahputa M. Zarlis Beny B. Nasution Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan-Indonesia

Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT.

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Application of artificial neural network for type 2 diabetes mellitus detection using buccal cell images

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Journal of International Dental and Medical Research 2017年第2期10卷 253-259页

作者： Wardhani, Priyanka Kusuma Widiyanti, Prihartini Arisgraha, Franky Chandra Satria Biomedical Engineering Study Program Faculty of Science and Technology Universitas Airlangga Indonesia Institute of Tropical Disease Universitas Airlangga Indonesia

Diabetes mellitus (DM) is metabolic disease causing hyperglicemia due to insulin action anomali. DM can cause cellular changes, including buccal cell. Blood tests are used to diagnosis diabetes, so non-invasive test is required for diagnosis of diabetes. Accordingly, this research aims to design non-invasive system based on artificial neural network for type 2 DM detection using buccal cell images. Buccal cells smears were obtained from 30 subjects suffering from type 2 DM and 30 normal subjects. The smears were stained by using Papanicolaou method. Each slide were observed under digital microscope and were evaluated. The system was designed by using MATLAB with image processing and Probabilistic Neural Network (PNN) algorithm to classify features. Buccal cell images were segmented to get features. The features used in this study were nucleus area, nucleus perimeter and nucleus circularity. Nucleus areas and perimeters in type 2 DM group were higher than those in control group with similar nucleus roundness in both groups. Forty nucleus feature datasets were used for training process, while 20 nucleus feature datasets were used for testing process. The optimal PNN value was 0.4 g constant. The optimal accuracy of training was 92.5%, while the optimal accuracy of testing was 90%.

关键词： Artificial neural network Buccal cell images Type 2 diabetes mellitus

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The Application of Fuzzy Logic Method in the Debtors Eligibility Assessment System of Microfinance Institution

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IOP Conference Series: Materials science and engineering 2020年第1期879卷

作者： S L B Ginting M R M Rizky Y R Ginting Sutono Computer System Study Program Faculty of Engineering and Computer Science Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung 40132 Indonesia. Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 125 Simpang Baru Pekanbaru 28293 Indonesia.

Decision-making processes in determining loan eligibility are often subjective which leads to imprecise credit predictions. Due to inaccurate inquiry on prospective customers done by field survey officers of Bank Perkreditan Rakyat (BPR) Bandung City, it has experienced credit complications such as bad credits. Therefore, this study aims to help decision-makers in determining creditworthiness and preventing bad credits from occurring. To realize this solution, the study uses the Fuzzy Logic method to calculate the creditworthiness of each prospective loaner based on the inquiries done in the field survey. Fuzzy Logic is known to be a 'counting' methodology with varying words. In addition, it can implement human expertise into machine language with ease and adequately. Based on numerous testing performed, the results demonstrate a level of 90% in accuracy when inputting within the valid ranges of each fuzzy set and membership function. However, the level of accuracy is only based on the clarification result which is determined by a researcher and BPR director, not a general level of accuracy for other microfinance institutions. Nevertheless, the findings of this study prove the method has a high enough accuracy to support decision-makers in determining the loan eligibility of prospective loaners and through this application in the surveying process, survey workers can work more efficiently. Hence, in future has a higher chance of predicting bad credits from potential loaners.

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Waste management for achieving sustainable management of water and sanitation in Universitas Sebelas Maret Indonesia 4

Waste management for achieving sustainable management of wat...

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4th International Workshop on UI GreenMetric World University Rankings, IWGM 2018

作者： Karsidi, Ravik Parama Astirin, Okid Astuti, Winny Rector Universitas Sebelas Maret Campus UNS Kentingan Surakarta Indonesia Department of Biology Faculty of Mathematics and Natural Science UNS Green Campus Team Universitas Sebelas Maret Indonesia Study Program of Urban and Regional Planning Faculty of Engineering UNS Green Campus Team Universitas Sebelas Maret Indonesia

Universitas Sebelas Maret (UNS) is the biggest university in Surakarta Central Java Indonesia, which was established on March 11th, 1976 through Presidential Decree No. 10 of 1976. Commitmen of UNS toward Green Campus began in 2012 by Rector Decree 7nd of August 2012. Land size of UNS, whis is only 60 ha becomes constraints in development of Green Campus Initiatives. In 2030 agenda of Sustainable Development Goals gives attention to huge increased of urbanization thus cities and human settlements face the problems of high density, economies of agglomeration link economy, energy, environment, science, technology and social and economic output [2]. The Problem of Water and Sanitation becomes big issues of Sustainable Development Goals (SDGs), which is stated in the Goal 6 out of 17 Goals. Goal 6 not only addresses Clean water sanitation and health but also quality and sustainability of water resources all over the world. Universitas Sebelas Maret is one of Campus in Indonesia, which has awareness toward water and sanitation, especially related to the problem of waste. Liquid waste has become problems for a long time in UNS, when domestic liquid waste has come from residential areas surrounding campus passed through the lake in campus before finally flowing to Bengawan Solo River. This causes euthropication and sedimentation of the lake of UNS, which had impact on extremelly decreased the function of the pond estetically and functionally. By partnership with the Ministry of Public Works anad Spatial Planning in 2017, UNS has built Integrated Waste Water Treatment, which treats domestic liquid waste from residential area surrounding campus and internal campus becomes recycled clean water. In spite of that, UNS also treats organic waste of fall leafs through composting units and manages chemical hazardous waste according to the Indonesian regulation issued by Ministry of Environment. Several efforts of UNS related to Waste Management have contribution to achieving SDGs e

关键词： Lakes

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Entanglement properties of disordered quantum spin chains with long-range antiferromagnetic interactions

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Physical Review B 2020年第21期102卷 214201-214201页

作者： Y. Mohdeb J. Vahedi N. Moure A. Roshani Hyun-Yong Lee R. N. Bhatt Stefan Kettemann Stephan Haas Department of Physics and Earth Sciences Jacobs University Bremen Bremen 28759 Germany Technische Universität Braunschweig Institut für Mathematische Physik Mendelssohnstr. 3 38106 Braunschweig Germany Department of Physics Sari Branch Islamic Azad University Sari 48164-194 Iran Department of Physics and Astronomy University of Southern California Los Angeles California 90089-0484 USA Department of Applied Physics Graduate School Korea University Sejong 30019 Korea Division of Display and Semiconductor Physics Korea University Sejong 30019 Korea Interdisciplinary Program in EċICT-Culture-Sports Convergence Korea University Sejong 30019 Korea Department of Electrical Engineering Princeton University Princeton New Jersey 08544 USA School of Natural Sciences Institute for Advanced Study Princeton New Jersey 08540 USA Division of Advanced Materials Science Pohang University of Science and Technology (POSTECH) Pohang 790-784 Korea

Entanglement measures are useful tools in characterizing otherwise unknown quantum phases and indicating transitions between them. Here we examine the concurrence and entanglement entropy in quantum spin chains with random long-range couplings, spatially decaying with a power-law exponent α. Using the strong disorder renormalization group (SDRG) technique, we find by analytical solution of the master equation a strong disorder fixed point, characterized by a fixed point distribution of the couplings with a finite dynamical exponent, which describes the system consistently in the regime α>12. A numerical implementation of the SDRG method yields a power-law spatial decay of the average concurrence, which is also confirmed by exact numerical diagonalization. However, we find that the lowest-order SDRG approach is not sufficient to obtain the typical value of the concurrence. We therefore implement a correction scheme which allows us to obtain the leading-order corrections to the random singlet state. This approach yields a power-law spatial decay of the typical value of the concurrence, which we derive both by a numerical implementation of the corrections and by analytics. Next, using numerical SDRG, the entanglement entropy (EE) is found to be logarithmically enhanced for all α, corresponding to a critical behavior with an effective central charge c=ln(2), independent of α. This is confirmed by an analytical derivation. Using numerical exact diagonalization (ED), we confirm the logarithmic enhancement of the EE and a weak dependence on α. For a wide range of partition size l, the EE fits a critical behavior with a central charge close to c=1, which is the same as for the clean Haldane-Shastry model with a power-law-decaying interaction with α=2. Only for small l≪L, in a range which increases with the number of spins N, we find deviations which are rather consistent with the strong disorder fixed point central charge c=ln(2). Furthermore, we find using ED that the conc

关键词： Magnetism Quantum entanglement Disordered systems Doped semiconductors Density matrix methods Exact diagonalization Renormalization group Tensor network methods

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