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A Modified Recommended Food Score Is Associated with a Lower Incidence of High Blood Pressure in Middle-Aged and Older Korean Men: The Korean Genome and Epidemiology Study

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Current Developments in Nutrition 2022年 6卷 908-908页

作者： Hwang, Jiyoung Kim, Jeongsu Kim, Hyesook Kwon, Oran Ewha Womans University Department of Nutritional Science and Food Management Graduate Program in System Health Science and Engineering Department of Nutritional Science and Food Management Graduate Program in System Health Science and Engineering Ewha Womans University

Objectives:This cohort study investigated the association between the modified RFS (Recommended Food Score) and incident high blood pressure in Korean adults from the Korean Genome and Epidemiology Study (KoGES). Methods:We prospectively analysed 2,854 (1,028 men and 1,556 women) participants aged 40–69 years without high blood pressure at baseline during a 16-year follow-up period. A dish-based, semi-quantitative, 103-item food frequency questionnaire was used to assess dietary intakes at baseline. The modified RFS (a total of 65 points) is based on the reported consumption of foods recommended in the Dietary Approaches to Stop Hypertension (DASH) diet modified for Korean foods. High blood pressure included hypertension and prehypertension, also known as stage 1 hypertension (a systolic blood pressure ≥120 mmHg, diastolic blood pressure ≥80 mmHg). Cox proportional hazards model was used to examine the association between modified RFS scores and the risk of incident high blood pressure. Results:A total of 1,663 participants (761 men and 902 women) developed high blood pressure during an average follow-up of 87 months (range 17–183 months). The modified RFS was positively correlated with the DASH diet score in men (r = 0.6492; p < 0.0001) and women (r = 0.7291; p < 0.0001), respectively. Men with the highest quintile of the modified RFS had a 24% lower incidence of high blood pressure than those with the lowest quintile (adjusted hazard ratio = 0.76, 95% confidence interval = 0.60–0.96, p-trend = 0.0218). However, there was no association between modified RFS and incident high blood pressure in women. Conclusions:A higher modified RFS was associated with a reduced risk of incident high blood pressure in middle-aged and older Korean men. Further prospective studies of larger scale or intervention studies are needed to confirm these findings. Funding Sources:This work was supported by the Bio & Medical Technology Development program of the National Research Foundation

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Promotion of the Glutathione Defense System From Aronia Berry Extract With Acute Aerobic Exercise: A Double-Blind Randomized Controlled Trial in Healthy Adults.

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Current Developments in Nutrition 2022年 6卷 1166-1166页

作者： Kim, JiEon Chung, Jae Won Nam, Yea-eun Jang, Jaehui Kwon, Oran Ewha Womans University Department of Nutritional Science and Food Management Graduate Program in System Health Science and Engineering Department of Nutritional Science and Food Management Graduate Program in System Health Science and Engineering Ewha Womans University

Objectives:This study aimed to examine the protective effects of Aronia melanocarna berry extract (ABE) supplementation against the acute aerobic exercise (AAE) – induced oxidative stress in healthy subjects. Methods:A 8-week, randomized, double-blind, parallel, placebo-controlled trial were conducted with 70 healthy middle-aged adults. We assessed a battery of AAE-induced oxidate stress biomarkers at pre-exercise, immediately post-exercise, and 30-min post-exercise. The AAE challenge model, as a treadmill exercise for 30 minutes at 60% VO2max, was applied to load oxidative stress at the end of the study. A linear mixed-effect model was used to test differences of safety and effectiveness between two groups and Pearson’s correlation coefficients assessed the statistical association. Results:The AAE-induced time course-dependent oxidative stress was well-observed in the placebo group regarding the glutathione peroxidase activity and glutathione availability for antioxidant defense; and erythrocyte malondialdehyde, interleukin-6, and lactate levels for oxidative damage. Meanwhile, the ABE supplementation improved the glutathione defense system by increasing glutathione availability and glutathione peroxidase activity during exercise and post-exercise recovery periods. In addition, the scatter plot and linear regression analysis revealed strong negative correlations of GSH availability with oxidized LDL and erythrocyte malonaldehyde levels. Conclusions:These finding suggest ABE supplementation might affect to improve antioxidant defense mechanism in healthy adults with low fruit and vegetable intake. Funding Sources:This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and the National Research Foundation of Korea (NRF), and the Bio & Medical Technology Development program of the National Research Foundation (NRF), funded by the Ministry of science & ICT (2012M3A9C4048761).

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Chemical composition and oxidative potential of fine particulate matter (PM_2.5) in urban parks of Bangkok, Thailand: Implication for public health

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Environmental Challenges 2025年 20卷

作者： Thaveevong, Phailin Ahmad, Mushtaq Panyametheekul, Sirima Chetwittayachan, Tassanee Zhang, Yan Department of Environmental and Sustainable Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand Atmospheric Research Unit National Astronomical Research Institute of Thailand Chiang Mai 50180 Thailand Energy Research Institute Chulalongkorn University Bangkok 10330 Thailand Center of Excellence in Indoor Air Quality and Well-being Chulalongkorn University Bangkok 10330 Thailand International Program in Hazardous Substance and Environmental Management Chulalongkorn University Bangkok 10330 Thailand Department of Environmental Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

PM2.5 poses serious health risks, worsening respiratory and cardiovascular conditions and increasing chronic disease risk. Weather conditions and emission sources affect their composition. PM2.5 samples are collected ... 详细信息

PM_2.5 poses serious health risks, worsening respiratory and cardiovascular conditions and increasing chronic disease risk. Weather conditions and emission sources affect their composition. PM_2.5 samples are collected from Lumphini and Thonburirom Parks in Bangkok from January 30 to February 8, 2024, and analysed its chemical components and the oxidative potential (OP) using the dithiothreitol (DTT) assay. A correlation analysis was conducted to assess the contributions of PM_2.5 components to and its OP in both parks. The enrichment factor and principal component analysis (PCA) were used to identify potential sources, while multiple linear regression (MLR) analysis quantified their contributions. Additionally, the study investigates the cancer and non-cancer risks associated with heavy metals. Lumphini Park has a mean PM_2.5 concentration slightly higher than Thonburirom Park. Both parks show a higher concentration of organic carbon (OC) in their PM_2.5 levels. Additionally, Thonburirom Park exhibits a higher volume-normalized OP (DTT_v) of PM_2.5 than Lumphini Park, suggesting that inhaling PM_2.5 may induce redox activity and oxidative stress. In Lumphini Park, DTT_v strongly correlate with OC, elemental carbon (EC), and metals, while in Thonburirom Park, it correlates with metals and secondary organic carbon (SOC). PCA identifies primary sources of PM_2.5 as mixed combustion sources (including vehicle emissions and biomass burning), industrial emissions, secondary aerosol (SA) formation, and road dust resuspension. The MLR analysis indicates that mixed combustion sources dominate PM_2.5 concentration in Lumphini Park, while Thonburirom Park is primarily affected by industrial emissions and SA formation. Cr is notably enriched in both parks, indicating significant anthropogenic sources. Furthermore, both parks exceed the acceptable cancer risk threshold

关键词： DTT activity Enrichment factor Health risk Oxidative potential Principal component analysis Source apportionment

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Export- and import-based economic models for predicting global trade using deep learning

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Expert Systems with Applications 2023年第1期218卷

作者： Yang, Cheng-Hong Lee, Cheng-Feng Chang, Po-Yin Department of Information Management Tainan University of Technology Tainan City710302 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung80708 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung80708 Taiwan Department of Business Administration National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen County 892 Taiwan

Forecasting global foreign trade is essential for developing government trade policies and management strategies for multinational corporations. However, achieving an accurate trade forecast is challenging because of the complex structural relationships between exports, imports and other economic variables. Many traditional forecasting models, such as time series, econometric, and machine learning, provide less accurate forecasts for trade data. This paper proposes an ensemble learning approach to improve forecasting performance by hybridizing the structural relationships between trade and deep learning models to predict foreign trade for ten major countries. The proposed method first establishes a cointegration relationship between exports and imports and their structural variables. The cointegrated models are then used to predict the future of trade, which is used as a benchmark model for comparison. A hybrid deep learning algorithm uses the cointegrated variables as input variables to predict trade data, and then are compared with time-series forecasts and economic structural models. The experimental results reveal that the ensemble learning method can achieve excellent forecasting performance for the tested periods of trade data. In most cases, the root means square error and mean absolute percentage error values are smaller than the time series and economic structural models. © 2023 Elsevier Ltd

关键词： Forecasting

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Performance LoRa Technology for Autonomous Vehicles

Performance LoRa Technology for Autonomous Vehicles

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Puput Dani Prasetyo Adi Iwan Purnama Arfanda Anugrah Siregar Angga Putra Juledi Firman Edi Abdul Karim Yuyu Wahyu Fairuz Iqbal Maulana Suhardi Atmoko Budi Susilo Toni Ilham Maliki Harahap Lintang Patria National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Science and Technology Labuhanbatu University Labuhanbatu North Sumatra Indonesia Telecommunication Engineering Study Program Politeknik Negeri Medan Medan City North Sumatra Indonesia Department of Engineering Management Faculty of Industrial Technology Batam Institute of Technology Batam City Riau Islands Indonesia Department of Information Technology Budi Darma University Medan North Sumatra Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Teacher Training and Education Labuhanbatu University Labuhanbatu North Sumatra Indonesia Faculty of Engineering Universitas Terbuka Jakarta Indonesia

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss.

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Monitoring System for Elderly Health Care Using Smart Band, Raspberry Pi, and Node-Red

Monitoring System for Elderly Health Care Using Smart Band, ...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Yudhi Ardiyanto Kusworo Adi Kurnianingsih Karisma Trinanda Putra Pradityo Utomo Doctoral Program of Information System School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Physics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Politeknik Negeri Semarang Semarang Indonesia Department of Informatics Management Faculty of Engineering Universitas Merdeka Madiun Madiun Indonesia

ISBN: (纸本)9781665465427

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity.

关键词： Heart rate Electrical engineering Cloud computing Databases Medical services Receivers Older adults

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Prediction of Forest Fire Using Machine Learning Algorithms: The Search for the Better Algorithm

Prediction of Forest Fire Using Machine Learning Algorithms:...

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IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)

作者： Pranati Rakshit Srestha Sarkar Sambit Khan Pritam Saha Sonali Bhattacharyya Nilarpan Dey Sardar M.N. Islam Souvik Pal Computer Science of Engineerng JIS College of Engineering Kalyani India ISILC and Decision Sciences and Modeling Program Victoria University Australia Computer Science of Engineerng Global Institute of Management and Technology Krishnanagar India

ISBN: (纸本)9781665417853

Forest fire has several devastating effects on the natural vegetation and the forest lives. The forest fire plays an important role in everyone’s lives and also in our environment. Forest fire is an integral part of many ecosystems such as grassland, temperate forest etc. The ability to predict the area where the forest fire may occur will help in optimizing the situation. The paper presented the prediction of forest fire risk with the help of a machine learning algorithm by using meteorological data. From the existing literature and Limitations, we can show that Different studies have shown the amount of burnt area due to the forest fire, and many have proposed different models to predict forest fire. But there is no such literature which predicts the depth of risk for this forest fire specifically. For that reason, the objective of this work is to predict the risk of forest fire by identifying the particular area as highly prone, moderately prone, low prone and no fire prone area. As a Present Research, in this paper we have worked with different classification models to check which models work best to predict forest fire with greater accuracy. The results we have obtained with the help of various classifiers in machine learning are much better and reliable than the results obtained by traditional computing methods. Thus, this paper indicates a deeper investigation in the field of predicting forest fire risk through machine learning. As a contribution, in this paper we have used SVM, KNN, Decision Tree, Naive Bayes classifier for prediction purposes. The main objective of this paper is to predict the possibility of forest fire with its intensity in specific atmospheric conditions in a given location. We have made comparison of the performance analysis of the different machine learning classifiers. At the end of the abstract, we got the highest AUC value of 0.99 and classification accuracy of 0.98 using Decision Tree to predict the same.

关键词： Dimensionality reduction Machine learning algorithms Biological system modeling Computational modeling Atmospheric modeling Forestry Vegetation

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Cascaded Organic Rankine Cycles (ORCs) for Simultaneous Utilization of Liquified Natural Gas (LNG) Cold Energy and Low-Temperature Waste Heat 9th

Cascaded Organic Rankine Cycles (ORCs) for Simultaneous Util...

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9th International Workshop of Advanced Manufacturing and Automation, IWAMA 2019

作者： Liu, Fuyu Hu, Xiangping Yu, Haoshui Zhang, Baosheng School of Economics and Management China University of Petroleum Beijing102249 China Industrial Ecology Program Department of Energy and Process Engineering Norwegian University of Science and Technology Trondheim7491 Norway Department of Energy and Process Engineering Norwegian University of Science and Technology Trondheim7491 Norway

ISBN: (纸本)9789811523403

Liquified Natural Gas (LNG) is a good way to transport natural gas from suppliers to end consumers. LNG contains a huge amount of cold energy due to the energy consumed in the liquefaction process. Generally, the LNG cold energy is lost during the regasification process at the receiving terminal. Power generation with LNG as the heat sink is an energy-efficient and environment-friendly way to regasify LNG. Among different kinds of power generation technologies, Organic Rankine Cycle (ORC) is the most promising power cycle to recover LNG cold energy. ORC has been widely used to convert low-temperature heat into electricity. If low-temperature waste heat and LNG cold energy utilization are utilized simultaneously, the efficiency of the whole system can be improved significantly. However, due to the large temperature difference between the low-temperature waste heat source and LNG, one stage ORC cannot exploit the waste heat and LNG cold energy efficiently. Therefore, a cascaded ORC system is proposed in this study. The optimization of the integrated system is challenging due to the non-convexity and non-linearity of flowsheet and the thermodynamic properties of the working fluids. A simulation-based optimization framework with Particle Swarm Optimization algorithm is adopted to determine the optimal operating conditions of the integrated system. The maximum unit net power output of the integrated system can reach 0.096 kWh per kilogram LNG based on the optimal results. © Springer Nature Singapore Pte Ltd 2020.

关键词： Rankine cycle

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Work capacity of compost applicator on paddy field

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AIP Conference Proceedings 2023年第1期2596卷

作者： Iqbal Muhammad Reza Fikri Azhar Mahmud Achmad Muhammad Tahir Sapsal Andi Azizah Agricultural Engineering Study Program Hasanuddin University Makassar Indonesia Environmental Quality Management Study Program Wrocław University of Science and Technology Wrocław Poland

The addition of compost (organic fertilizer) is essential to maintain agricultural land productivity. Nutrients have a role in plant growth and development, especially in paddy plants. The application of distributing compost (organic fertilizer) on agricultural land requires considerable energy and costs, especially if the land is large. In order to overcome this, an applicator is used to reduce costs and energy and streamline the fertilization process time. This applicator is specially designed to spread compost (organic fertilizer) to agricultural land with a certain dose. This study aimed to calculate the working capacity and efficiency of the compost applicator in paddy fields. The study was conducted by direct testing in the fields with three different speed treatments, namely at 2.71 km/hour, 3.38 km/hour, and 4.62 km/hour. The calculation results show that the optimum speed of the applicator in paddy fields is 2.71 km/hour. The working capacity obtained is 0.35 ha/hour for theoretical field capacity and 0.26 ha/hour for effective field capacity with a field efficiency of 74.74%.

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First-Attempt Application of Sponge Membrane Bioreactor Treating 3,4-Dichlorophenol Contaminated Wastewater at Different Hydraulic Retention Times

SSRN

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SSRN 2024年

作者： Nhu Nguyet, Phan Nguyen, Thi-Kim-Ngan Thuan, Ngo Thi Yen, Duong Hoang Vo, Thi-Kim-Quyen Nguyen, Thi-Yen-Phuong Bui, Xuan-Thanh Watari, Takahiro Yamaguchi, Takashi Hatamoto, Masashi Faculty of Environment University of Science Ho Chi Minh City Viet Nam Ho Chi Minh City Viet Nam Ho Chi Minh City Viet Nam School of Chemical and Environmental Engineering International University Ho Chi Minh City Viet Nam Master Program in Water Technology Reuse and Management Vietnamese German University Binh Duong Province Viet Nam Ho Chi Minh City Viet Nam Department of Civil and Environmental Engineering Nagaoka University of Technology Niigata Japan Department of Science of Technology Innovation Nagaoka University of Technology Niigata Japan

Chlorophenols (CPs) are priority pollutants which persist in the environment for up to 28 years. The release of Chlorophenol derivatives into aquatic environments poses serious risks to living beings because of their mutagenic, carcinogenic, genotoxic, and teratogenic effects. This study highlights the first application of a Sponge Membrane Bioreactor (Sponge-MBR) for treating 3,4-Dichlorophenol (3,4-DCP)-contaminated wastewater. Herein, a lab-scale Sponge-MBR was conducted to treat 3,4-DCP-contaminated wastewater at various hydraulic retention times (HRTs) ranging from 5 to 15 h in terms of organic matter and 3,4-DCP removal, and microbial community shifts. The sludge characteristics were also performed. Results showed that a rise in HRT at 5, 10 and 15 h increased the removal efficiencies of COD (84 – 94%) and 3,4-DCP (90 – 96%). Under 30-day operation period at optimal HRT (15 h), corresponding to OLR of 2.84 ± 0.18 kg COD·m-3·day-1, the removal efficiencies of both 3,4-DCP and COD were 96% with the average removal rate of 356 ± 83 mg COD·g-SS-1·day-1 and 1.62 ± 0.38 mg 3,4-DCP·g-SS-1·day -1. The attached biomass in sponge media was recorded higher than suspended biomass and contributed 49 – 66% of the total biomass, indicating that the microbial community shifts could degrade organic matter and remove 3,4-DCP. Proteobacteria dominated the microbial community because of their adaptability to environmental changes. The findings derived from this study provide useful perspectives on the application of Sponge-MBR treating 3,4-DCP-contaminated wastewater and offer Sponge-MBR as a potential alternative for treating wastewater contaminated with toxic chemicals. © 2024, The Authors. All rights reserved.

关键词： Biomass

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