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The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan

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REVIEW OF REGIONAL RESEARCH-JAHRBUCH FUR REGIONALWISSENSCHAFT 2021年第1期41卷 1-32页

作者： Peng, Ti-Ching Natl Taipei Univ Dept Real Estate & Built Environm 151Univ Rd New Taipei 23741 Taiwan

The occurrence of 6.7-magnitude Hokkaido Eastern Iburi Earthquake on 6th September 2018 made local residents in Japan realise the underneath hazardous threat to their own safety and residential dwellings. Japan has faced various natural disasters for a long time, and Government chose to unveil these hazard data in details to the public to raise every one's alertness. Despite their long-term awareness of hazards, this short-term unexpected event should have impact on local real estate markets. Given the transaction data of properties and lands, this study used machine-learning algorithms to examine whether the unexpected hazard shock (i.e. 2018 Hokkaido Earthquake) or the observed local hazard information in geographic areas (i.e. long-term evaluation of each real estate) would be capitalised into the prices of residential properties and lands in the case of Hokkaido, Japan. It is assumed that the difference in the characteristics of disaster notification would alter individuals' risk perception and thus the evaluation of properties;compared to uncertain, infrequent occurrence of earthquakes in the unknown future (i.e. beyond the scope of objective estimation), the release of long-term hazardous information (tied to each property or land), which is more perceivable in near future, is more likely to be capitalised into real estate. It is found that housing/land attributes are still the key features to real estate values in Hokkaido. In comparison with short-term unexpected hazard shock (i.e. 2018 Earthquake), the long-term hazard threats are more influential to prices of properties and lands. This is possibly due to people's awareness of the hazard conditions of local communities while deciding where to reside.

关键词： Natural hazard Japan Property prices Land prices machine-learning algorithms

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Using machine-learning-driven approaches to boost hot-spot's knowledge

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2022年第5期12卷 e1602-e1602页

作者： Rosario-Ferreira, Nicia Bonvin, Alexandre M. J. J. Moreira, Irina S. Univ Coimbra Fac Sci & Technol CQC Coimbra Chem Ctr Chem Dept Coimbra Portugal Univ Coimbra CIBB Ctr Innovat Biomed & Biotechnol Coimbra Portugal Univ Utrecht Sci Fac Chem Bijvoet Ctr Biomol Res Utrecht Netherlands Univ Coimbra Dept Life Sci Calcada Martim de Freitas P-3000456 Coimbra Portugal Univ Coimbra CNC Ctr Neurosci & Cell Biol CIBB Coimbra Portugal

Understanding protein-protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein-protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

关键词： binding hot-spots computational alanine scanning mutagenesis interaction energetics machine-learning algorithms protein-protein interactions

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Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System

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ENERGIES 2022年第19期15卷 7070页

作者： Sun, Yongchao Sun, Pengyuan Zhang, Zhixiang Zhang, Shuchao Zhao, Jian Mei, Ning Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Xiamen Univ Coll Energy Xiamen 361005 Peoples R China Dezhou State Owned Sports Ind Dev Ltd Dezhou 253300 Peoples R China Qingdao City Univ Coll Mech & Elect Engn Qingdao 266106 Peoples R China

The output of the absorption refrigeration system driven by exhaust gas is unstable and the efficiency is low. Therefore, it is necessary to keep the performance of absorption refrigeration systems in a stable state. This will help predict the dynamic parameters of the system and thus control the output of the system. This paper presents a machine-learning algorithm for predicting the key parameters of an ammonia-water absorption refrigeration system. Three new machine-learning algorithms, Elman, BP neural network (BPNN), and extreme learning machine (ELM), are tested to predict the system parameters. The key control parameters of the system are predicted according to the exhaust gas parameters, and the cooling system is adjusted according to the predicted values to achieve the goal of stable cooling output. After comparison, the ELM algorithm has a fast learning speed, good generalization performance, and small test set error sum, so it is selected as the final optimal prediction algorithm.

关键词： exhaust gas heat recovery ammonia-water-based absorption refrigeration quantitative control of refrigeration output machine-learning algorithms prediction

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Analyzing the impact of temperature on PV module surface during electricity generation using machine learning models

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Cleaner Energy Systems 2024年 9卷

作者： Rezaul Karim, S.M. Sarker, Debasish Monirul Kabir, Md. Department of Electrical and Electronic Engineering International University of Business Agriculture and Technology Department of Mechanical Engineering International University of Business Agriculture and Technology Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology

Use of fossil fuel in industries causes Carbon emission, which is mostly responsible for global warming. Another aspect is that environment friendly energy production and sustainable development goal is highly dependent on the production of clean energy. According to the IEA solar energy has a huge potential and will contribute up to 16 % of the global electricity by 2050. Hence, prediction of solar energy production has a great deal of demand in renewable energy sector. This paper compares machine-learning algorithms to evaluate the impact of PV module back surface temperature (degC) on the generated power. Support Vector machine for Regression (SMOreg), Multilayer Perceptron (ANN), Linear Regression, M5 Rules, k-Nearest-Neighbor (Ibk) and Random Forest methods are employed to test their performance in different ratio of training and testing data. The dataset comprises five independent parameters such as PV module back surface temperature (degC), Dry bulb temperature (degC), Relative humidity (%RH), Atmospheric pressure (mb), and Precipitation (mm). The dependent parameter is Maximum power of PV module (W). The correlation coefficient was determined by varying the percentage of training data from 60 % to 85 %. The numerical tests were done for two data sets, one dataset includes all the independent variables and another one excluded the PV module back surface temperature. Except for M5 Rules, other models exhibit consistent correlation coefficients with several of training data. All models demonstrate a dependency on the PV module back surface temperature, with Random Forest surpassing others in overall performance with a correlation coefficient of 0.9713 at 75 % of training set. © 2024 The Authors

关键词： Correlation coefficient machine-learning algorithms PV module back surface temperature PV module output

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Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines

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IFAC-PapersOnLine 2022年第12期55卷 450-455页

作者： Abdeltif Boujamza Saad Lissane Elhaq Team of production system optimization and energy Laboratory of Engineering research Hassan II University National Higher School of Electricity and Mechanics Casablanca Morocco

In a critical business sector such as the aviation industry, remaining useful life (RUL) prediction helps engineers schedule maintenance to avoid the risk of catastrophic failure in both the manufacturing and the servicing sectors. This paper attempts to review and evaluate various RUL predictive models for aircraft engines and compare their performance with a proposed Long-Short Term Memory (LSTM) method based on a data-driven machine learning approach. This study uses the C-MAPSS datasets in order to evaluate the performance and the results of each approach. The obtained outcomes show that the modified LSTM method with Attention mechanism improves the RUL prediction for aircraft engines and provides better performance.

关键词： Remaining useful life prognostic health management predictive maintenance aircraft Engines neural network machine-learning algorithms

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The Game of Drones: rapid agent-based machine-learning models for multi-UAV path planning

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COMPUTATIONAL MECHANICS 2020年第1期65卷 217-228页

作者： Zohdi, T. I. Univ Calif Berkeley Dept Mech Engn 6195 Etcheverry Hall Berkeley CA 94720 USA

The goal of this article is to provide basic modeling and simulation techniques for systems of multiple interacting Unmanned Aerial Vehicles, so called "swarms", for applications in mapping. Also, the paper illustrates the application of basic machine-learning algorithms to optimize their information gathering. Numerical examples are provided to illustrate the concepts.

关键词： Drones UAVs Simulation machine-learning algorithms

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Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

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Geoscience Frontiers 2020年第4期11卷 1203-1217页

作者： Hamid Reza Pourghasemi Amiya Gayen Mohsen Edalat Mehrdad Zarafshar John P.Tiefenbacher Department of Natural Resources and Environmental Engineering College of AgricultureShiraz University71441-65186ShirazIran Department of Geography Ballygunge Science CollegeUniversity of CalcuttaKolkataWest Bengal700019India Crop Production and Plant Breeding Department School of AgricultureShiraz University71441-65186ShirazIran Natural Resources Department Fars Agricultural and Natural Resources Research and Education CenterAREEOShirazFarsIran Department of Geography Texas State UniversitySan MarcosTX78666USA

Natural hazards are often studied in ***,there is a great need to examine hazards holistically to better manage the complex of threats found in any *** regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is *** parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and *** effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and *** study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern *** do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these *** floods,landslides,and forest fires were surveyed and *** locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data *** conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and *** outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each *** areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,***,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to

关键词： Multi-hazard risk mapping Considering flood Landside and forest fire jointly machine-learning algorithms

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A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020年 363卷 112907-000页

作者： Zohdi, T. I. Univ Calif Berkeley Dept Mech Engn 6195 Etcheverry Hall Berkeley CA 94720 USA

The objective of this work is to illustrate how to algorithmically integrate machine-learning algorithms (MLA's) with multistage/multicomponent fire spread models. In order to tangibly illustrate this process, this work develops a framework for a specific model problem combining: (I) a meshless discrete element "submodel" that tracks the trajectory of airborne hot particles/embers, subject to prevailing wind velocities and updrafts, (II) a topographical "submodel" of the ambient combustible material whereby airborne embers that make contact are allowed to start secondary fires (if conditions are appropriate), combined with ground-based surface spread and burn rates for generating new embers, new updrafts (due to hot air), etc., and (III) a machine-learning Algorithm to rapidly ascertain the multi-submodel system parameters that force the overall model to match observations. The submodels compute both ground and airborne hot-ember driven fire propagation, as well as subsequent distribution of debris/soot, which is important for air-quality assessment. The overall framework is designed for use in digital twin technology, which refers to an adaptive digital replica of a physical system, whereby model updates are continuously in near real-time. This necessitates a rapid simulation paradigm that can easily interface with telecommunications, cameras and sensors. The presented framework is designed to run quickly on laptops and hand held devices, with the guiding principle being to make it potentially useful for first-responders in real-time. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Fire-propagation Ember flow Digital-twin machine-learning algorithms

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Investigations on the Use of Ensemble Methods for Specification-Oriented Indirect Test of RF Circuits

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2020年第2期36卷 189-203页

作者： El Badawi, H. Azais, Florence Bernard, S. Comte, M. Kerzerho, V Lefevre, F. Univ Montpellier CNRS LIRMM 161 Rue Ada F-34095 Montpellier France NXP Semicond 2 Esplanade Anton Phillips F-14000 Caen France

In order to reduce the costs of industrial testing of analog and Radio Frequency (RF) integrated circuits, a widely studied solution is indirect testing. Indeed, indirect testing is based on learning-machine algorithms to train a regression model that links the space of low-cost indirect measurements to the space of performance parameters guaranteed by datasheets, thus relaxing the constraints on expensive test equipment. This article explores the potential benefit of using ensemble learning in this context. Unlike traditional learning models that use a single model to estimate targeted parameters, ensemble-learning models involve training several individual regression models and combining their outputs to improve the predictive power of the ensemble model. Different ensemble methods based on bagging, boosting or stacking are investigated and compared to classical individual models. Experiments are performed on three RF performances of a LNA for which we have production test data and model quality is discussed in terms of goodness-of-fit, accuracy and reliability. The influence of the training set size is also explored. Finally, the efficiency of classical and ensemble models is compared in the context of a two-tier test flow that permits to tradeoff test cost and test quality.

关键词： Indirect testing RF integrated circuits machine-learning algorithms Ensemble methods Test efficiency

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Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: A case of KwaZulu-Natal, South Africa

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CLIMATE SERVICES 2022年 28卷

作者： Mugiyo, H. Chimonyo, V. G. P. Kunz, R. Sibanda, M. Nhamo, L. Masemola, C. Ramakgahlele Modi, A. T. Mabhaudhi, T. Univ KwaZulu Natal Ctr Transformat Agr & Food Syst Sch Agr Earth & Environm Sci P Bag X01 ZA-3209 Pietermaritzburg South Africa Int Maize & Wheat Improvement Ctr CIMMYT Zimbabwe POB MP 163 Harare Zimbabwe Univ KwaZulu Natal Ctr Water Resources Res Sch Agr Earth & Environm Sci P Bag X01 Scottsville South Africa Univ Western Cape Fac Arts Dept Geog Environm Studies & Tourism Bellville South Africa Water Res Commiss South Africa 4 Daventry St ZA-0081 Pretoria South Africa CSIR Precis Agr Grp Pretoria South Africa Int Water Management Inst IWMI GH West Africa Reg Off Accra Ghana

Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namely, sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth (Amaranthus) and taro (Colocasia esculenta) in the KwaZulu-Natal (KZN) province, South Africa. The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. The study showed an increase of 0.1-11.8% under highly suitable (S1), moderately suitable (S2), and marginally suitable (S3) for sorghum, cowpea, and amaranth growing areas from 2030 to 2070 across all RCPs. In contrast, the total highly suitable area for taro production is projected to decrease by 0.3-9.78% across all RCPs. The jack-knife tests of the MaxEnt model performed efficiently, with areas under the curve being more significant than 0.8. The study identified annual precipitation, length of the growing period, and minimum and maximum temperature as variables contributing significantly to model predictions. The developed maps indicate possible changes in the future suitability of NUS within the KZN province. Understanding the future distribution of NUS is useful for developing transformative climate change adaptation strategies that consider future crop distribution. It is recommended to develop regionally differen-tiated climate-smart agriculture production guidelines matched to spatial and temporal variability in crop suitability.

关键词： Food and nutrition security Land suitability analysis machine-learning algorithms Neglected and underutilised crop species

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