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Mechanical Properties of Hydraulic Concretes with Partial Replacement of Portland Cement by Pozzolans Obtained from Agro-Industrial Residues: A Review

SSRN

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SSRN 2024年

作者： De Jesus Torres-Ortega, Ramon Torres-Sanchez, Diego Luis Lopez-Lara, Teresa Civil Engineering Program University of Cartagena Cartagena de Indias130015 Colombia Department of Civil and Engineering Environmental Engineering Florida International University United States Faculty of Engineering Autonomous University of Queretaro Qro Santiago de Queretaro76010 Mexico

The following article presents an overview of the research conducted on the use of pozzolans obtained from agro-industrial by-products such as rice husk, palm oil husk, and sugarcane bagasse. The improvements these pozzolans provide in the mechanical properties of compressive, flexural, and tensile strength in concretes with percentages of substitution of conventional Portland cement by these materials have been investigated. This situation is mainly due to their high content of amorphous silica that reacts with calcium hydroxide, when the hydration of Portland cement begins, giving rise to the production of calcium silicate hydrate (CSH) gel, with cementitious properties. In this way, an analysis of the chemical properties of these pozzolans is presented based on their basic oxide content, in particular, the amorphous silica they contain and their reactive activity. Thus, the influence of the temperature and grinding processes used to obtain it will be considered, finding that the optimal temperatures for obtaining amorphous silica range between 600°C and 700 °C. On the other hand, the optimal replacement doses to improve the mechanical properties of concretes are between 10% and 20% replacement. © 2024, The Authors. All rights reserved.

关键词： Tensile strength

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Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array.

arXiv

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arXiv 2024年

作者： Ginio, Noam Lindenbaum, Michael Fishbain, Barak Liberzon, Dan Faculty of Civil and Environmental Engineering Technion Haifa320003 Israel Faculty of Computer Science Technion Haifa320003 Israel Interdisciplinary Program for Marine Engineering Technion Haifa320003 Israel

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction. © 2024, CC BY-NC-ND.

关键词： Water waves

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Ensemble Deep Learning NARX for Estimating Time Series of Earthquake Occurrence

Ensemble Deep Learning NARX for Estimating Time Series of Ea...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Hapsoro Agung Nugroho Eka Yuni Astuty Aries Subiantoro Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Predicting earthquake occurrences in time series data remains a challenging task in seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have recently shown promise for accurate predictions. While previous research has demonstrated NARX's effectiveness in estimating earthquake frequencies, the architecture still relies on several pre-determined parameters. These parameters include the number of hidden layers, number of neurons, and delay time. This study aims to investigate the effectiveness of Ensemble Deep Learning NARX neural networks in predicting time series of earthquakes. The proposed ensemble model integrates multiple NARX neural networks, each trained on a distinct subset of earthquake data. This approach aims to enhance prediction accuracy and bolster model robustness. The dataset consists of time series records detailing earthquake occurrence frequencies and magnitudes. The results show performance evaluation metrics in terms of Mean Square Error (MSE) values. For training frequency data, the MSE is 4.10e-26, and for testing, it is 6.05e-22. Regarding training magnitude data, the MSE is 2.86e-21 for training and 3.17e-19 for testing. This study makes a valuable contribution to the advancement of earthquake prediction techniques, underscoring the potential of Ensemble Deep Learning NARX neural networks for precise time series estimation in seismology.

关键词： Deep learning Training Time series analysis Earthquakes Seismology Estimation Predictive models

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Performance Analysis of YOLO-DeepSORT on Thermal Video-Based Online Multi-Object Tracking

Performance Analysis of YOLO-DeepSORT on Thermal Video-Based...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Nur Ibrahim Arsyad Ramadhan Darlis Herianto Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Recently, thermal cameras have been used in various fields, including surveillance systems and advanced driver assistance systems (ADAS), as they perform better in low light than visible-light cameras. Some challenges in the surveillance system or ADAS field related to thermal cameras are occlusion and thermal crossover between objects with similar appearances during object detection or object tracking tasks, which can lead to misdetection, false positives, and lost tracking. In this paper, performance analysis of you-only-look-once (YOLO) combined with deep online real-time tracking (DeepSORT) on thermal video-based online multi-object tracking (MOT) in occlusion and thermal crossover scene is presented. YOLO, as one of state-of-the-art method for detection task, is used for detection system. Then, the detected object from YOLO is tracked using DeepSORT. The results demonstrate that the online MOT of sequential thermal images using YOLO-DeepSORT achieved a MOTA score of 44.2% and IDF1 of 45.3%. Thus, negative example was added in YOLO training process to reduce false detection, and it gives improvement with MOTA score of 63.8% and IDF1 score of 54.6%.

关键词： YOLO Training Surveillance Cameras Real-time systems Thermal analysis Performance analysis

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LoRa Network Design for Monitoring Landslides on 150 KV Delivery Tower

LoRa Network Design for Monitoring Landslides on 150 KV Deli...

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International Conference on Smart Computing & Communications (ICSCC)

作者： I Wayan Dikse Pancane I Putu Gelgel Astika Putra I Wayan Gede Suarjana Agus Supranartha Electrical Engineering Study Program Universitas Pendidikan Nasional Denpasar Indonesia Department of Public Health Faculty of Sport Science and Public Health Universitas Negeri Manado Sulawesi Indonesia Electro Enginering Elektrical Engineering Program Study Politechnic Negeri Bali Badung Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The objective of this research is to design and develop an integrated landslide early detection system at Tower Number Eight, 150 kV Gianyar. The research is expected to result in the construction of a communication system based on LoRa technology that will serve to reduce the potential damage from landslides. In order to achieve this, several parameters must be considered as benchmarks. These include RSSI, SNR, BER and PDR. The device operates by monitoring changes in position, soil moisture and rainfall, and then transmitting the data via LoRa technology. At the sink, the data is recorded and monitored for RSSI, SNR, PDR and BER values. In the test, the source 1 node, situated 300 metres away, exhibited an RSSI value of −100 to −109 dBm and an SNR value of 6.75 to −6.7 in both sunny and rainy weather, with a PDR value of 100%. The source 2 node, situated 600 metres away, exhibited an RSSI value of −97 to −109 dBm and an SNR value of 8.5 to −11.75 dB, with a PDR value of 70 to 100%. It should be noted that the number of bits in the sensor data sent is always complete. This is evidenced by the bit error ratio (BER) value, which is always 0 on both source nodes.

关键词： Rain Electricity Poles and towers Soil moisture LoRa Terrain factors Reliability Monitoring Signal to noise ratio Meteorology

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Evaluation Electrical System of Mini Plant Waste Energy on Institut Teknologi PLN

Evaluation Electrical System of Mini Plant Waste Energy on I...

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Converging Technology in Electrical and Information engineering (ICCTEIE), International Conference on

作者： Pawenary Dwi Listiawati Hendri Electrical Engineering Study Program Faculty of Electricity and Renewable Energy Insitut Teknologi PLN Jakarta Indonesia

Waste is a problem in almost every region, especially related to how the processing of waste can be converted into other forms as a form of energy that can be utilized, one of which is waste processing by pyrolysis is one of the technologies to convert waste into useful energy or fuel. At this time waste processing requires waste processing machines ranging from organic and un-organic waste shredding machines, briquette processing machines, pellets, using star-delta 3-phase motors, generally still conventional/manually. Current research, the author began to develop a waste and flue gas particle handling machine, a non-incinerator pyrolysis machine with the wet method, a high efficiency cyclone system which originally used a machine manually/ conventionally, replaced with a Delta series Star - Delta series with interlock and interface) automatically. Garbage processing The machine in addition to aiming to reduce waste waste, the machine must be made automatically and friendly to the operator who runs it.

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Integrating clustering algorithms and machine learning to optimize regional snapshot municipal solid waste management for achieving sustainable development goals

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Environmental Advances 2025年 19卷

作者： Taweesan, Achara Kanabkaew, Thongchai Surinkul, Nawatch Polprasert, Chongrak Department of Environmental Science Faculty of Science Ramkhamhaeng University Bangkok Thailand Occupational and Environmental Health Program Faculty of Public Health Thammasat University Pathumthani Thailand Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Thailand Department of Civil Engineering Faculty of Engineering Thammasat University Pathumthani Thailand

Effective municipal solid waste management (MSWM) is essential for sustainable urban development, significantly impacting environmental health, economic efficiency, and social well-being. It aligns with Sustainable Development Goal 11 (SDG11), which aims to make cities inclusive, safe, resilient, and sustainable by ensuring waste is safely managed from collection to disposal. This study evaluates the performance of MSWM practices across various cities in different regions, updated as of March 2023, to assess the achievement of SDG11. By reviewing published literature and conducting questionnaire surveys and key informant interviews with government authorities and service providers, data on access to managed MSW facilities were collected from 470 cities across various income-level regions to monitor MSWM progress toward SDG11 targets. Clustering algorithms and machine learning were employed to identify patterns and enhance MSWM practices across these regions, focusing on achieving SDG11. Hierarchical cluster analysis identified four clusters based on MSWM performance: high-income, upper-middle-income, lower-middle-income, and low-income regions. The developed decision-tree models provided a comprehensive analysis of effective MSW collection and disposal, achieving accuracies of approximately 75 % and 73 %, respectively. The models highlighted the importance of high collection coverage, proper disposal facilities, and robust institutional frameworks in achieving effective MSWM. The findings emphasize the need for targeted interventions and advanced analytical methods to enhance regional MSWM strategies, supporting the attainment of SDG11. © 2024 The Author(s)

关键词： Clustering algorithms Collection efficiency Machine learning Municipal solid waste management Sustainable development goal Treatment efficiency Waste management practices

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Unravelling biosynthesis and biodegradation potentials of microbial dark matters in hypersaline lakes

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Environmental Science and Ecotechnology 2024年第4期20卷 177-188页

作者： Zhiguang Qiu Yuanyuan Zhu Qing Zhang Xuejiao Qiao Rong Mu Zheng Xu Yan Yan Fan Wang Tong Zhang Wei-Qin Zhuang Ke Yu School of Environment and Energy Peking University Shenzhen Graduate SchoolShenzhen518055China AI for Science(AI4S)-Preferred Program Peking UniversityShenzhen518055China Southern University of Sciences and Technology Yantian Hospital Shenzhen518081China Institute of Biomedicine and Biotechnology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China State Key Laboratory of Isotope Geochemistry CAS Center for Excellence in Deep Earth ScienceGuangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhou510640China School of Atmospheric Sciences Sun Yat-sen UniversitySouthern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai519082China Department of Civil Engineering University of Hong Kong999077Hong KongChina Department of Civil and Environmental Engineering Faculty of EngineeringUniversity of AucklandNew Zealand

Biosynthesis and biodegradation of microorganisms critically underpin the development of biotechnology,new drugs and therapies,and environmental ***,most uncultured microbial species along with their metabolic capacities in extreme environments,remain *** we unravel the metabolic potential of microbial dark matters(MDMs)in four deep-inland hypersaline lakes in Xinjiang,*** metagenomic binning,we uncovered a rich diversity of 3030 metagenomeassembled genomes(MAGs)across 82 phyla,revealing a substantial portion,2363 MAGs,as previously unclassified at the genus *** unknown MAGs displayed unique distribution patterns across different lakes,indicating a strong correlation with varied physicochemical *** analysis revealed an extensive array of 9635 biosynthesis gene clusters(BGCs),with a remarkable 9403 being novel,suggesting untapped biotechnological ***,some MAGs from potentially new phyla exhibited a high density of these *** biosynthesis,our study also identified novel biodegradation pathways,including dehalogenation,anaerobic ammonium oxidation(Anammox),and degradation of polycyclic aromatic hydrocarbons(PAHs)and plastics,in previously unknown microbial *** findings significantly enrich our understanding of biosynthesis and biodegradation processes and open new avenues for biotechnological innovation,emphasizing the untapped potential of microbial diversity in hypersaline environments.

关键词： Biosynthesis Biodegradation Hypersaline lakes Metagenome-assembled genomes Microbial dark matters

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Applying the TOD Standard for Potential Location Selection for Sustainable Transit-Oriented Development in Chiang Mai Province, Thailand

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Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences 2022年第9期49卷 67-73页

作者： Maneewan, Warakun Kronprasert, Nopadon Rinchumphu, Damrongsak Sangsrichan, Chaiwat Graduate Program in Construction Engineering and Management Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Civil Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Philosophy Program in Civil Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

Transit-oriented development (TOD) was organized as policies to promote the development of public transportation infrastructure in many countries, including the Thai government. Presently, Thailand is aiming for the growth of the city spreads in each region along with economic growth by stimulating sustainable development, especially in Chiang Mai Province. The purpose of this study was to implement the application of the TOD standard as an analytical tool in the context of Thailand. We adapted TOD standard as an instrument and indicator for physical data collection on areas that have the potential to be developed into TOD stations in Chiang Mai. The TOD standard as a selection tool will be used to score the selected three stations located on the Chiang Mai Red Line Mass Transit System Project that has the potential to develop into TOD. Data collection covers a radius from the stations of 500 meters and 500 – 1000 meters for commercial and residential land use, respectively. The scoring shows that the Airport station is the most potential area, followed by the Chiang Mai Rajabhat University and Nakornping hospital station. The results show that TOD standards had the potential to be used in the context of Thailand to enhance assessment, help in planning and drawing TOD policy for the upcoming station project, and identify specific TOD characteristics that need improvement. © 2022 Authors. All rights reserved.

关键词： Sustainable development

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Analysis of Lpg Conversion to Induction Stove from the Financial and Consumer Behavior Perspectives in Households Grade R1

SSRN

引用

SSRN 2023年

作者： Hidayat, Muhammad Fauzi S.T. Firmansyah Hakam, Dzikri S.T. Pawenary, M.T. ElectricalEngineering Masters Study Program Faculty ofEngineering PLNInstitute of Technology Jakarta Indonesia Main Supervisor Master’s PrograminElectricalEngineering Faculty of Engineering Institute of PLN Technology Electrical Engineering Masters Study Program PLN Institute of Technology Jakarta Jakarta Indonesia

The Indonesian government is currently implementing an induction stove program to replace the use of LPG stoves. The 2021 report from the Central Statistics Agency (BPS) shows that LPG is used by 83.36% of the Indonesian population as a cooking fuel. Expenditure on LPG subsidies is influenced by international crude oil prices set through the Contract Price Aramco (CPA). Analysis shows that the gas prices set by the CPA have significantly increased in the past five years, becoming one of the factors contributing to the subsidy burden on the state budget (APBN). Therefore, it is necessary to find breakthrough solutions to minimize future APBN expenditures. The LPG to induction stove conversion program is expected to reduce subsidy burdens, improve energy efficiency, and influence consumer behavior in choosing more efficient cooking appliances. © 2023, The Authors. All rights reserved.

关键词： Costs

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