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International Conference of Sustainable Earth Resources engineering 2020, SERIES 2020

作者： Susilo, Hendro Purwantoro, Dwi Rahadiansyah, Shakti Doctoral Program of Water Resources Engineering Brawijaya University Malang Indonesia BBWS Serayu Opak Ministry of PUPR Indonesia

Ground Water Irrigation Network (GWIN) make a major contribution to agricultural production in karst mountainous areas, especially in the dry season. A performance index model would be developed to measure the level of success in developing groundwater irrigation systems. Performance evaluation of groundwater irrigation systems was a value called the Groundwater Irrigation Performance Index. This study aims to obtain determinants of the parameters of the index performance of groundwater irrigation that based on technical and non-technical aspects by analyzing the variables that give effect to the both aspects. Technical variables were physical infrastructure, supporting facilities, geological conditions, vegetation conditions, water recharge area conditions, direction of spatial planning, documentation, and crop productivity. Non-technical aspects were personnel organizations, P3A (Association of Water User Farmers'), and economic-socio-cultural. The locus of research was in the Karst Mountains area of Gunung Kidul Regency, Yogyakarta. The method used in collecting data was direct measurement of site visit and secondary data from the field. The non-technical aspects of using questionnaire data as qualitative data were converted into quantitative data. Smart Partial Least Squares was used for filtering variables. GRG-Generalized Reduced Gradient method was used to solve the non-linear equation with the objective-objective assumption and constraints. The model calibration test was calibrated and simulated on the parameters used. The results were a performance index model to measure the level of success in developing groundwater irrigation systems. The performance index evaluation of the developed groundwater irrigation system was an accumulation of technical indexes and non-technical indices involving influential variables. © Published under licence by IOP Publishing Ltd.

关键词： Irrigation

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Privacy Preserving Electronic Health Record with Consortium Blockchain 6

Privacy Preserving Electronic Health Record with Consortium ...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Gautama, Adri Rochim, Adian Fatchur Bayuaji, Luhur Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

ISBN: (纸本)9798350399615

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, Science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance. © 2022 IEEE.

关键词： Consortium Blockchain e-health EHR Privacy

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Leveraging Heart Rate Variability and Machine Learning for Daily Activity Classification in Healthy Adult Males

Leveraging Heart Rate Variability and Machine Learning for D...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Vidya Ikawati Rahadian Yusuf Ary Setijadi Prihatmanto Tatang Sudrajat Didin Saepudin Welly Surjono Doctoral Program of School of Electrical and Information Engineering Institute of Technology Bandung Bandung Indonesia Departement of Electrical and Information Engineering Institute of Technology Bandung Bandung Indonesia Universitas Sangga Buana Bandung Indonesia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

Advancements in machine learning techniques for analyzing heart rate variability (HRV) show great potential in accurately measuring various activities in the field of health monitoring. This work examines the feasibility of using non-invasive HRV data gathered during everyday activities to train machine learning models for accurate activity classification. In the field of personalization, the study focuses on the difficulty of adapting to individual differences in baseline HRV. It also acknowledges that HRV is influenced by stress, physical activity, and external influences, and that it is a dynamic measure. The experimental methodology consists of a pilot study using a retrospective observational cohort approach. It utilizes a waistband device to continuously record heart rate variability (HRV) during different daily activities. The study utilizes a thorough methodology that includes statistical data description, correlation analysis, variance analysis, outlier detection, and identification of cycle periods using power spectral density. The performance of various machine learning models, such as Random Forest, Naïve Bayes, Decision Tree, and Logistic Regression, was evaluated for activity classification. The Decision Tree model exhibited superior performance. The work provides useful insights into the potential of machine learning for HRV-based activity monitoring, highlighting the importance of collecting reliable data and developing tailored models to improve accuracy and practicality.

关键词： Accuracy Databases Data models Telecommunications Decision trees Reliability Heart rate variability Monitoring Stress Stress measurement

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Avoiding Lookup Table in AES Algorithm 6

Avoiding Lookup Table in AES Algorithm

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6th International Conference on Informatics and Computing, ICIC 2021

作者： Prayitno, Ragiel Hadi Sudiro, Sunny Arief Madenda, Sarifuddin Gunadarma University Doctoral Program in Information Technology Jakarta Indonesia STMIK Jakarta STIK Electrical Engineering Jakarta Indonesia

ISBN: (纸本)9781665421553

This article describes the AES encryption and decryption process without using lookup tables in the MixColumns transformation. The encryption process consists of transforming subbytes, shiftrows, mixcolumns and addroundkey. The process was carried out for 10 rounds, but in round 10 the mixcolumns transformation was not carried out. The decryption process consists of inverse mixcolumns, inverse shiftrows, inverse subbytes and addroundkey. In this study, the AES encryption and decryption process was carried out using two methods, namely based on the lookup table and without using the lookup table on the MC/IMC transformation. The method in this article is applied to Matlab software. The experimental results show that the encryption and decryption process using a lookup table is slower than the method without a lookup table. The encryption process without a lookup table on the MC transformation takes 0.091 seconds while using a lookup table takes 0.399 seconds. The decryption process without a lookup table on the IMC transformation takes 0.149 seconds while using a lookup table takes 0.206 seconds. © 2021 IEEE.

关键词： Cryptography

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Fucoxanthin induces human melanoma cytotoxicity by thwarting the JAK2/STAT3/BCL-xL signaling axis

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Environmental Toxicology 2024年第6期39卷 3356-3366页

作者： Kuo, Min-Yung Dai, Wen-Chyi Chang, Jie-Li Chang, Jo-Shu Lee, Tse-Min Chang, Chia-Che Pediatric Surgery Division Department of Surgery Tungs' Taichung MetroHarbor Hospital Taichung Taiwan Doctoral Program in Biotechnology Industrial Innovation and Management National Chung Hsing University Taichung Taiwan Taichung Municipal Taichung First Senior High School Taichung Taiwan Department of Chemical and Materials Engineering Research Center for Smart Sustainable Circular Economy Tunghai University Taichung Taiwan Department of Life Sciences National Chung Hsing University Taichung Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung Taiwan Graduate Institute of Biomedical Sciences Master Program in Precision Health Doctoral Program in Translational Medicine Rong Hsing Research Center for Translational Medicine The iEGG and Animal Biotechnology Research Center National Chung Hsing University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Traditional Herbal Medicine Research Center Taipei Medical University Hospital Taipei Taiwan

Melanoma is the most lethal skin malignancy. Fucoxanthin is a marine carotenoid with significant anticancer activities. Intriguingly, Fucoxanthin's impact on human melanoma remains elusive. Signal Transducer and Activator of Transcription 3 (STAT3) represents a promising target in cancer therapy due to its persistent activation in various cancers, including melanoma. Herein, we revealed that Fucoxanthin is cytotoxic to human melanoma cell lines A2758 and A375 while showing limited cytotoxicity to normal human melanocytes. Apoptosis is a primary reason for Fucoxanthin's melanoma cytotoxicity, as the pan-caspase inhibitor z-VAD-fmk drastically abrogated Fucoxanthin-elicited clonogenicity blockage. Besides, Fucoxanthin downregulated tyrosine 705-phosphorylated STAT3 (p-STAT3 (Y705)), either inherently present in melanoma cells or inducible by interleukin 6 (IL-6) stimulation. Notably, ectopic expression of STAT3-C, a dominant-active STAT3 mutant, abolished Fucoxanthin-elicited melanoma cell apoptosis and clonogenicity inhibition, supporting the pivotal role of STAT3 blockage in Fucoxanthin's melanoma cytotoxicity. Moreover, Fucoxanthin lowered BCL-xL levels by blocking STAT3 activation, while ectopic BCL-xL expression rescued melanoma cells from Fucoxanthin-induced killing. Lastly, Fucoxanthin was found to diminish the levels of JAK2 with dual phosphorylation at tyrosine residues 1007 and 1008 in melanoma cells, suggesting that Fucoxanthin impairs STAT3 signaling by blocking JAK2 activation. Collectively, we present the first evidence that Fucoxanthin is cytotoxic selectively against human melanoma cells while sparing normal melanocytes. Mechanistically, Fucoxanthin targets the JAK2/STAT3/BCL-xL antiapoptotic axis to provoke melanoma cell death. This discovery implicates the potential application of Fucoxanthin as a chemopreventive or therapeutic strategy for melanoma management. © 2024 Wiley Periodicals LLC.

关键词： Carotenoids

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Optimizing Student Performance with GridSearchCV Using Random Forest Classifier Method to Enhance Learning Outcome Prediction

Optimizing Student Performance with GridSearchCV Using Rando...

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International Conference on Informatics and Computing (ICIC)

作者： Riza Arifudin Rizal Isnanto Budi Warsito Doctoral Program of Information System Diponegoro University Semarang Indonesia Departementof Computer Engineering Diponegoro University Semarang Indonesia Department of Statistics Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Predicting student performance is becoming increasingly important because it plays a major role in the sustainable development of countries, which depend on effective education systems to produce a generation of leaders in various aspects of life, including science, economy, social, and military. Advances in educational technology have changed academic performance analysis, allowing for more accurate and databased predictions of student performance This research aims to optimize the performance of student learning outcome prediction by using the Random Forest Classifier method which is improved through the GridSearchCV technique. This research focuses on improving accuracy and efficiency in predicting student learning outcomes based on historical data and variables used. The data used in this study were collected from various educational sources that included student academic achievement records, demographic data, and other related variables. The Random Forest Classifier method is applied as the main prediction model. The GridSearchCV technique is used to find optimal combinations of parameters that improve the model’s performance. Model evaluation is carried out using accuracy, precision, recall, and F1-score metrics to ensure the reliability of predictions. The results show that the use of GridSearchCV significantly improves the performance of the Random Forest Classifier model. The optimized model is able to achieve a higher prediction accuracy of 0.89. This improvement was also seen in other evaluation metrics, which showed an overall improvement in the model’s predictive ability to student learning outcomes.

关键词： Measurement Accuracy Educational technology Predictive models Performance analysis Reliability Sustainable development Informatics Random forests Faces

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Estimation of Wrist Chattering Angle Based on Disturbance Observer 16

Estimation of Wrist Chattering Angle Based on Disturbance Ob...

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Zhang, Xinyu Sun, Zhenyu Shu, Bairui Yokoi, Hiroshi Yong, Xu Li, Guanglin Jing, Xiaobei The Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China The University of Electro-Communications Joint Doctoral Program for Sustainability Research Tokyo182-8585 Japan The University of Electro-Communications Department of Mechanical Engineering and Intelligent Systems Tokyo182-8585 Japan

ISBN: (纸本)9798350330755

Due to the physiological constraints and daily habits of hand movements, the wrist chattering effect (WCE) will inadvertently cause disturbance to the accuracy of fingers' pattern recognition, when healthy subjects or amputees undergoing mirror training control their fingers according to instructions. This paper proposes a method for estimating WCE angles using a disturbance observer. Based on the Denavit-Hartenberg (D-H) convention and inverse kinematics, the intervals of the WCE during finger flexion and extension are determined. The research objective is to eliminate disturbance terms containing redundant information referring to sliding mode anti-jittering methods, making subsequent machine learning models less susceptible to disturbances. © 2023 IEEE.

关键词： Inverse kinematics

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing Control of Nutrient Levels, Climate Conditions, and Plant Health

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Albert Good Fried Panggabean Rahmat Romadhon Ramot Lubis Endra Joelianto Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Indonesia Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health.

关键词： Smart agriculture Precision agriculture Reinforcement learning Humidity Aerospace electronics Nutrients Temperature control Informatics Monitoring Farming

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Preparing future entrepreneurs: reflections about the COVID-19 impacts on the entrepreneurial potential of Brazilian students

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Journal of Work-Applied Management 2021年第2期13卷 277-283页

作者： Cazeri, Gustavo T. Anholon, Rosley Rampasso, Izabela Simon Quelhas, Osvaldo L.G. Leal Filho, Walter School of Mechanical Engineering State University of Campinas Campinas Brazil PNPD/CAPES Program Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Master Program in Management Systems Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Faculty of Life Sciences Hamburg University of Applied Sciences Hamburg Germany

Purpose: This viewpoint aims to present reflections on the coronavirus disease 2019 (COVID-19) pandemic scenario and the entrepreneurial potential of Brazilian students. Guidelines are proposed to support debates in the academic environment and better prepare future entrepreneurs to the labour market. Design/methodology/approach: Since this is a viewpoint article, some statements presented here are characterised by the authors' opinions. However, for a better foundation, bibliographic research related to the theme was carried out. Findings: In the authors' point of view, the COVID-19 pandemic scenario may negatively influence the decisions of Brazilian students that are characterised as potential future entrepreneurs. Educators can use the guidelines presented here to conduct debates with students in order to clarify aspects related to profile, behaviour and other issues. Originality/value: By understanding better entrepreneurial aspects in the COVID-19 pandemic scenario, Brazilian students can make better decisions. This initiative will contribute to future success in the Brazilian entrepreneurial activity and national economy. © 2021, Gustavo T. Cazeri, Rosley Anholon, Izabela Simon Rampasso, Osvaldo L.G. Quelhas and Walter Leal Filho.

关键词： Brazilian higher education institutions COVID-19 pandemic Entrepreneurial activity Labour market

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Parallel Computing Approach for Preprocessing AIS Data in Transshipment Feature Extraction

Parallel Computing Approach for Preprocessing AIS Data in Tr...

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International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)

作者： Widyadi Setiawan Linawati Linawati I Made Oka Widyantara Dewa Made Wiharta Putu Arya Mertasana Sri Andriati Asri Doctoral Program of Engineering Science Udayana University Denpasar Indonesia Electrical Engineering Departement Udayana University Denpasar Indonesia Electrical Engineering Departement Politeknik Negeri Bali Denpasar Indonesia

AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent data processing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes data processing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality.

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