检索结果-内蒙古大学图书馆

Modeling the volatility of gross domestic product based on the volatility of money supply, inflation, interest rate, and exchange rate using the fractional cointegration model

引用

Journal of Physics: Conference Series 2020年第1期1538卷

作者： A Abdullah H Kuswanto D D Prastyo Suhartono Department of Statistics Faculty of Mathematics Computing and Data Science- Institut Teknologi Sepuluh Nopember (ITS) Indonesia

Economic growth is a quantitative index to determine the level of economic progress that can be measured by gross domestic product (GDP). Various literatures aim to solve problems regarding patterns of economic growth as well as about the determinants wherein the analysis volatility models are used. Some indicators of economic growth are money supply, inflation, interest rates, and exchange rates. Economic variables are recorded as time series data with different periods, and hence temporal aggregation is required to analyze. Economic time series data showing the existence of long memory and structural breaks, which leads to the difficulty on explaining the data only with a general model. Therefore, both long memory models and structural break are proposed to be taken into account in the model. In this analysis, the residual from a linear combination of economic variables follows a long memory process indicating a cointegration relationship between these variables. This paper models GDP volatility based on money supply, inflation, interest rate and exchange rate volatility in Indonesia, Philippines, Malaysia, and Singapore using the Fractional Cointegration model with long memory and structural break. The analysis shows that structural break is only found in the volatility of GDP of Indonesia, Malaysia, and Singapore while the nature of long memory is found in almost all economic variables' volatility. Furthermore, we found the cointegration model, stationary and persistent noise in all countries.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spatial extreme modeling using student t copula approach in Ngawi Regency

引用

Journal of Physics: Conference Series 2020年第1期1538卷

作者： M Fauziyah Sutikno J D T Purnomo Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember (ITS) Indonesia

Extreme rainfall is an unpredictable phenomenon which causes suffering effect such as flooding. Located on the Equator area, Indonesia results in a high intensity of extreme rainfall. Initial information regarding the patterns, characteristics, and rainfall prediction is needed in order to minimize the negative effect of such phenomenon. A method that can be used to predict extreme rainfall is the spatial extreme value using the copula approach. The copula approach used in this study is student-t copula. The Generalized Extreme Value (GEV) distribution used for the student-t copula with parameter estimation is Pseudo-Maximum Likelihood Estimation (PMLE). The proposed method was applied to model the extreme rainfall atNgawi Regency. An extreme spatial dependency on location is shown by extreme coefficient graphic. The best model that is obtained is based on Akaike Information Criteriation's (AIC) lowest value. The best model then continues to be used to predict the rainfall intensity return level. The prediction result of the rainfall intensity return level value shows that the maximum value of rainfall intensity increases from year to year in each station.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bias correction and statistical downscaling of earth system models using quantile delta mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ)

引用

Journal of Physics: Conference Series 2020年第1期1538卷

作者： F Fauzi H Kuswanto R M Atok Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember (ITS) Indonesia

Earth System Models (ESM) is a model that can simulate, predict climate change that occurred in the past, present, and create climate change scenarios in the future. ESM output has not been able to represent local scale climate. Statistical Downscaling (SD) is a static downscaling process in which data on a large-scale grids in a certain period and time period is used as a basis for determining data on small-scale grids. SD results still have a sizeable bias, so we need a method that works to reduce the bias. The bias correction method used in this research is Quantile Delta Mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ). This study downscales the rainfall and maximum temperature data generated from Beijing Normal University Earth System Model (BNU-ESM) and ERA-Interim as the proxy of the observation. The skill is verified by means of Taylor Diagram showing the correlation value, the Root Mean Square Error (RMSE), and standard deviation. Based Taylor Diagram the QDM has better performance compared to the BCCAQ method. The performance of downscaling and bias correction during September-October-November (SON) is the best compared to other seasons.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Beware of "Explanations" of AI

arXiv

引用

arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

引用

Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

来源：评论

学校读者我要写书评

暂无评论

Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

引用

Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

来源：评论

学校读者我要写书评

暂无评论

Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

引用

Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

来源：评论

学校读者我要写书评

暂无评论

The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

引用

science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

来源：评论

学校读者我要写书评

暂无评论

Molecular dynamics simulation of thermal behavior of ammonia refrigerant in the presence of copper nanoparticles

引用

International Journal of Thermofluids 2025年 28卷

作者： Hawzhen Fateh M. Ameen Ali B.M. Ali Ahmed Shawqi Sadeq Narinderjit Singh Sawaran Singh Soheil Salahshour Sh. Baghaei Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Erbil Kurdistan Region Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Chemical Engineering Department University of Basrah Basrah Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Nanofluids are mixtures of a base fluid and nanoparticles (also known as nano-scaled particles), and they were used within advanced heat transfer applications with known aggregation issues as well as unreliability in performance. Molecular dynamics simulations can effectively look at nanofluid behavior with no disruptions, especially when considering the complications and limitations involved with performing experiments at the nano-scale. We conducted molecular dynamics simulations that investigate the thermal and atomic behaviors of a nanofluid, which involved ammonia nanofluids with copper nanoparticles in aluminum nanochannels. Our results focused on evaluating the outflow of the nanofluid and on determining the primary factors including maximum velocity, temperature heat flux and nanoparticle aggregation time while modifying the initial conditions of temperature (300-350 K), and pressure (1-5 bar). Furthermore, we found the thermophysical properties of the nanofluids were heavily dependent on the initial temperature and pressure. By improving the initial temperature and pressure, thermal systems can support the promotion of efficiency and sustainability. We also measured the kinetic and potential energies, with the potential energies measuring -8399.15 eV and 80.69 eV after 5 ns with no indications of structural instabilities. The results indicated that as the initial temperature was increased, maximum velocity increased from 0.00086 to 0.00099 Å/ps and maximum temperature increased from 240 to 258 K. Furthermore, heat flux decreased from 1411 to 1397 W/m² and aggregation time decreased from 3.96 to 3.93 ns. On the other hand, maximum velocity decreased to 0.00078 Å/ps and maximum temperature decreased to 234 K, as well as heat flux increased to 1436 W/m² and aggregation time increasing time was increased to 4.07 ns, with the increasing initial pressure. These results provided some insight into the optimization of nanofluids for energy conserving thermal control

关键词： Initial temperature Initial pressure Heat flux Nanofluids Thermal properties Aggregation time Maximum velocity

来源：评论

学校读者我要写书评

暂无评论

The comparison of exponentially weighted moving variance and double moving average-S control charts based on neoteric ranked set sampling

引用

Journal of Physics: Conference Series 2020年第1期1538卷

作者： R J M Putri M Mashuri Irhamah Department of Statistics Faculty of Mathematic Computing and Data Science Institut Teknologi Sepuluh Nopember (ITS) Surabaya Indonesia

In manufacturing industry, quality is very important, because it can determine customers' satisfaction and distinguish the product from others. The effort that can be made by companies to maintain the products quality is by monitoring and controlling them. One of the statistical methods that can be used for monitoring and controlling quality is control charts. Generally, there are two types of control charts, control chart for mean and control chart for variability. Three models of control charts, recently, have been developed, such as Shewhart, Cumulative Sum (CUSUM), and Exponentially Weighted Moving Average (EWMA). This research will be stated Exponentially Weighted Moving Variance (EWMV) and Double Moving Average-S (DMA-S) for monitoring variability based on Neoteric Ranked Set Sampling (NRSS). EWMV and DMA-S control charts can detect small shifts, and NRSS has better performance than Simple Random Sampling (SRS) and Ranked Set Sampling (RSS). Furthermore, the performance of EWMV based on NRSS and DMA-S basd on NRSS control charts will be compared and evaluated by using Average Run Length (ARL) value with Monte Carlo simulation approach to detect any particular shifts. Both of the control chart models will be applied in Combined Cycle Power Plant (CCPP) case. By this evaluation, the result shows that the DMA-S control chart based on NRSS performs better than the EWMV control chart based on NRSS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：