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Determination of Mung Bean Seed Viability Change in Vacuum Packaging during Storage in Different Temperatures

IOP Conference Series: Materials Science and engineering 2019年第1期557卷

作者： M. Faisal U. Ahmad D. Wulandani Postharvest Technology Master Course Program Graduate School Bogor Agricultural University Indonesia Dept. of Mechanical and Bio-system Engineering IPB Bogor Agricultural University Indonesia

Mung bean seeds decreased their viability very fast during storage, especially in tropical area without proper packaging. The purpose of this study is to observethe decrease in viability of mung bean seeds during storage and to build a mathematical model for shelf life prediction. Dried mung bean seeds were vacuum packed and stored in Accelerated Shelf-life Testing chamber at five different temperatures of 30° C, 35° C, 40° C, 45° C, 50° C with approximately 80% in relative humidity, to facilitate rapid deterioration with different rate of physical and chemical change in the seed. The results show that there was an increase in moisture content and the germination decreased in different rates. Changes in values for some parametersof the seeds affected the viabilty of the seeds during rapid deterioration. The value of constant (k)for viability decreasing was calculated and used to build the mathematical model for predicting the shelf life of mung bean seeds during storage at any temperature. Using the developed model, it was observed that the shelf life of the Sriti variety of mung bean seeds in vacuum packaging could maintain 90% of germination level for 421 days or 14.3 months if stored at 20 °C, and for 173.37 days or 5.78 months if stored at 25 °C.

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Performance evaluation of four-wheel driving wheel tractor with diesel engine using biodiesel fuel

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IOP Conference Series: Earth and Environmental Science 2022年第1期963卷

作者： D A Sasmito L T Mulyantara A Budiman Master Program in System Engineering Universitas Gadjah Mada 55281 Yogyakarta Indonesia Indonesian Centre for Agricultural Engineering Research and Development 15338 Tangerang Banten Indonesia Chemical Engineering Department Universitas Gadjah Mada 55281 Yogyakarta Indonesia

The Indonesian government is committed to increasing biodiesel usage as renewable energy by 23% of national energy consumption by 2025. Implementing the mandatory biodiesel program must be carried out sustainably to help reduce fossil energy consumption, reduce air pollution, and reduce the effect of greenhouse gases. The Ministry of Agriculture studied applying biodiesel from palm oil in agricultural machinery, especially four-wheeled tractors, to help implement agricultural mechanization in Indonesia. The research objective was to determine the effect of biodiesel on the performance of a four-wheeled tractor engine in several variations of the mixture to obtain the composition of biodiesel mixture that gave optimal engine performance. The experiment was conducted by testing the performance of a four-wheeled tractor engine on a laboratory scale referring to SNI 7146-2019, using biodiesel fuel with a mixture of B10 to B100 variations. The result showed that biodiesel affects the performance of a four-wheeled tractor engine. The efficiency of continuing engine power using biodiesel was the largest at B20 of 80.15% at the maximum governor and 80.97% at the standard PTO rotation of 540 rpm. The smallest power forwarding efficiency on the B100 was 76.12% at the maximum governor and 76.55% at 540 rpm PTO rotation. Biodiesel B20 provided the greatest maximum power of 23.55 kW, 44 kgf of torque, specific fuel consumption of 250.54 g/kW hours. At 540 rpm, PTO rotation produced a maximum power of 23.79 kW, 42 kgf of torque, the specific fuel consumption of 256.12 g/kW hours

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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Computer system, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network

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Journal of Neuro-Oncology 2024年第1期166卷 167-174页

作者： Chou, Chi-Jen Yang, Huai-Che Chang, Po-Yao Chen, Ching-Jen Wu, Hsiu-Mei Lin, Chun-Fu Lai, I-Chun Peng, Syu-Jyun Division of Neurosurgery Department of Surgery Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei Taiwan Department of Electrical Engineering National Central University Taoyuan Taiwan Department of Neurological Surgery University of Virginia Health System Charlottesville 22903 VA United States Department of Radiology Taipei Veterans General Hospital Taipei Taiwan Department of Heavy Particles & Radiation Oncology Taipei Veterans General Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Purpose: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. Methods: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. Results: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. Conclusion: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep learning neural network Identification Metastatic brain tumors Perilesional edema Quantification

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Design of customer knowledge management system for Aglaonema Nursery in South Tangerang, Indonesia

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IOP Conference Series: Materials Science and engineering 2017年第1期277卷

作者： D Sugiarto I Mardianto TS Dewayana M Khadafi Faculty of Industrial Technology Information System Program Trisakti University Jakarta 11440 Indonesia Faculty of Industrial Technology Informatics Program Trisakti University Jakarta 11440 Indonesia Faculty of Industrial Technology Master of Industrial Engineering Program Trisakti University Jakarta 11440 Indonesia

The purpose of this paper is to describe the design of customer knowledge management system to support customer relationship management activities for an aglaonema nursery in South Tangerang, Indonesia. system. The steps were knowledge identification (knowledge about customer, knowledge from customer, knowledge for customer), knowledge capture, codification, analysis of system requirement and create use case and activity diagram. The result showed that some key knowledge were about supporting customer in plant care (know how) and types of aglaonema including with the prices (know what). That knowledge for customer then codified and shared in knowledge portal website integrated with social media. Knowledge about customer were about customers and their behaviour in purchasing aglaonema. Knowledge from customer were about feedback, favorite and customer experience. Codified knowledge were placed and shared using content management system based on wordpress.

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Product Distribution and Characteristic from Pyrolysis of Indonesia Palm Oil Residues

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IOP Conference Series: Materials Science and engineering 2020年第2期736卷

作者： R. B. Cahyono G. I. Adhityatama G.B. Persada A. Prasetya T. Ariyanto Waste Refinery Center (WRC Faculty of Engineering Universitas Gadjah Mada Department of Chemical Engineering Faculty of Engineering Universitas Gadjah Mada Master Program on System Engineering Faculty of Engineering Universitas Gadjah Mada JI. Grafika 2 Bulaksumur Yogyakarta 55281 Indonesia

During the production, palm oil industry generated around 4 ton of dry biomass waste such empty fruit bunch (EFB), palm kernel shell (PKS), and palm oil fiber (PF) for each 1 ton of palm oil extracted. As main industry in Indonesia, palm oil industry offered not only the oil product but also biomass waste which was potential for clean energy sources. The utilization of those biomass residue is still limited due to lack of information related to conversion method as well as characteristic of various product. This research focuses on the product distribution and characterization of biomass pyrolysis especially PKS and PF. Pyrolysis experiments were conducted using both thermogravity (TG) method and stainless steel reactor at temperature range of 450–550°C. Based on the TG method that biomass was heated up until 1000°C in nitrogen atmosphere, PKS exhibited slower decomposition process rather than PF because of high value of hardness and fixed carbon. Two strong peaks were seen clearly which the first peak correspond to hemicellulose and lignin decomposition that proceed in the temperature range of 200 – 300°C. While, the second peak represented to cellulose and lignin decomposition that occurred in the temperature range of 300 – 500°C. Pyrolysis at higher temperature initiated smaller amount of solid product due to volatilization and higher ash content. The ash composed of several chemical such as Si, Al, Ca, Mn etc which would still remain in the solid product when temperature was increased. However, the fixed carbon and heating value enhanced significantly as increasing temperature. Clearly, based on the SEM images, the biochar hold better pore structure compared to raw material. In the pyrolysis process, the volatile matter moved to gas phases and create abundant vacant as pore structure in the solid product. These results offers opportunities of better utilization of palm oil residues not only energy conversion but also other area such as adsorbent, bio-fertilizer/

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Hydro-char production from press-mud wastes of the sugarcane industry by hydrothermal treatment with natural zeolite addition

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AIP Conference Proceedings 2018年第1期2026卷

作者： Asroful Abidin Chandra Wahyu Purnomo Rochim Bakti Cahyono 1Master Program of System Engineering Universitas Gadjah Mada Yogyakarta Indonesia 2Chemical Engineering Department Universitas Gadjah Mada Yogyakarta Indonesia 3Agrotechnology Innovation Center PIAT-UGM Sleman Yogyakarta Indonesia

Hydrothermal treatment (HT) is a biomass conversion by thermal process that using water as solvent and reactant. This technology is superior in treating biomass with a high water content into char and liquid product. This research aims to observe the zeolite incorporation in HT as an additive material to increase the acidity for hydrolysis enhancement. The effects of temperature and zeolite addition on biochar of press-mud biomass characteristic has been observed. The temperature variation were 180, 200, and 220°C with a water-biomass ratio of 1:5 and residence time of sixty minutes. Zeolite was added as much as 20% of the sample weight. The HT char was characterized using proximate analysis, heating value, and FTIR. The solid product had a heating value from 1886.419 to 2047.559 cal/g. It is observed that temperature variation affect the caloric value, ash content, fix carbon, and energy densification, while zeolite addition increased solid yield, energy yield, and liquid product acidity.

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New microscope for measuring the distribution of nonlinear dielectric properties

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART II-ELECTRONICS 1996年第6期79卷 68-75页

作者： Cho, Y Kirihara, A Saeki, T Department of Electrical and Electronic Engineering Faculty of Engineering Yamaguchi University Ube Japan 755 Received his B.E. M.S. and Dr. of Eng. degrees from Tohoku University in 1980 1982 and 1986 respectively. He joined the University as a Research Associate in the Research Institute of Electrical Communication in 1985. He joined Yamaguchi University in 1990 as an Associate Professor in the Electrical and Electronic Engineering Department. He has been involved in research on nonlinear phenomenon of piezoelectric materials and their application. Received his B.E. degree in Electrical and Electronic Engineering from Yamaguchi University in 1995. He is working at present for the University as a technical official. He has been involved in research on the telecommunication electronic circuits and the application of the piezoelectric materials. Received his B.E. degree in Electrical and Electronic Engineering from Yamaguchi University in 1995 and has been involved in research and development of the scanning nonlinear dielectric microscope system. He is at present in the Master's program in the Electrical Engineering Department of Kyushu University.

This paper describes a new technique for imaging the distribution of nonlinear dielectric constants of ferroelectric materials. This technique is based on the principle that the nonlinear (third-order) dielectric constants (third-rank tensor) are very sensitive to the state of the spontaneous polarization and the quality of the crystallization of materials, while the linear (second-order) dielectric constants (second-rank tensor) are insensitive to them. Applying this principle, a new microscope for measuring the distribution of nonlinear dielectric properties is developed. This new microscope is used to observe the distribution of the polarization of ferroelectric materials such as lead zirconate titanate (PZT) ceramic and piezoelectric polymer.

关键词： ferroelectric materials nonlinear dielectric constant microscope piezoelectric materials

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QUALITY MANAGEMENT AND master ORDNANCE REPAIR

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NAVAL ENGINEERS JOURNAL 1993年第5期105卷 68-76页

作者： STIMSON, WA MARTINEZ, E William A. Stimson:received his B.S. degree in mathematics from the University of Texas at El Paso in 1964 and his M.S. degree in engineering from the University of Santa Clara in 1971. Mr. Stimson was employed in the aerospace industry for ten years then joined the Naval Ship Weapon Systems Engineering Station in Port Hueneme Calif in 1973. At NSWSES he served ten years as a ship qualification trials project supervisor and seven years as program manager of the Master Ordnance Repair Program. Today Mr. Stimson lives in Charlottesville Va. and is a Ph.D candidate in systems engineering at the University of Virginia majoring in quality management and statistical quality control. Ernest Martinez:received his B.S. degree in computer sciences from National University in 1985. Mr. Martinez was head of combat systems at the Naval Ship Repair Facility Yokosuka Japan for five years returning in 1983 to the Naval Sea Support Center Pacific San Diego Calif. During the next five years he managed the Fleet Modernization Program and was coordinator of the Combat System Readiness Test Program. Today Mr. Martinez lives in San Diego and is employed by the Naval Surface Warfare Center Port Hueneme Calif. as MOR program manager.

Because of the redefinition of national defense needs and the reduction of defense budgets, the management of complex combatant repair is more imperative than ever. To this end, the U.S. Navy is calling for Total Quality Leadership (TQL) strategies from its agencies and contractors. These strategies are derived from well known initiatives of quality management, such as those of Deming. They define the basis of any quality program. Quality methodologies have proven highly effective in the manufacturing sector, but many remain unproven or tentative in service and repair industries. The authors believe that TQL methods can be effective in the repair industry also, but must be developed or adapted to suit the unique characteristics of that industry: high skill, non-rote crafts, with continual customer interface at all levels of operation. In establishing a direction for TQL development, the authors have chosen two elements. The first is the Malcolm Baldrige Quality Improvement program, which provides guidelines for direction. The second is the master Ordnance Repair (MOR) program, which provides the management vehicle. From its beginning in 1983, MOR has been a program of management. The task is explicit in its charter, NAVSEAINST 8000.2, which requires that a MOR contractor manage combat system work and testing. Quality management methods are applied to the MOR program to achieve MOR objectives using Baldrige measurements. Methods are presented which deploy the quality function across the organization. Techniques are provided to improve the company's ability to control its processes and the robustness of its product. These include methods of process analysis that determine added value, ownership, and internal customer. Stress is placed on in-process verification rather than on end-of-process inspection. The recommendations of this paper require no extraordinary skills or costs, but do require a formal and systematic approach to quality issues. The authors believe that

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