Monitoring House Vacancy Dynamics in The Pearl River Delta Region: A Method Based on NPP-VIIRS Night-Time Light Remote Sensing Images

Liu, Xuan; Li, Zehao; Fu, Xinyi; Yin, Zhengtong; Liu, Mingzhe; Yin, Lirong; Zheng, Wenfeng

doi:10.3390/land12040831

Cited by 73 publications

(17 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EEJ variations at different longitudes are due to the underlining effects of local winds, which are responsible for EEJ driving [65][66][67][68][69]. In addition, longitudinal differences in EEJ are caused by the different nature of the propagating diurnal tides, the meridional winds, and the dynamics of the migratory tides [70][71][72][73][74]. All the satellite and ground data showed significant storm-time variations in different phases [75][76][77][78][79].…”

Section: Discussionmentioning

confidence: 98%

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

et al. 2023

View full text Add to dashboard Cite

We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator.

show abstract

Section: Discussionmentioning

confidence: 98%

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

et al. 2023

View full text Add to dashboard Cite

show abstract

“…A smart grid has some fundamental characteristics [88]. It can accommodate growing customer demand without needing more infrastructure to be developed.…”

Section: Contemporary Infrastructure For Utility Gridsmentioning

confidence: 99%

“…Conversely, district energy networks primarily deal with load management and energy efficiency enhancement. Demand response, the construction of energy-efficient buildings and districts, and communication difficulties are all addressed by a number of European infrastructure and facilities programs [75][76][77][78][79][80][81][82][83][84][85][86][87][88].…”

Section: Advanced Approaches In Smart City Energy Modeling and The In...mentioning

confidence: 99%

A Review of Smart Energy Management in Residential Buildings for Smart Cities

Qayyum,

Jamil,

Ali

2023

Energies

View full text Add to dashboard Cite

This survey critically examines the integration of energy management systems within smart residential buildings, serving as key nodes in the smart city network. It systematically maps out the intricate relationships between smart grid technologies, energy storage capabilities, infrastructure development, and their confluence in residential settings. From the evolution of power generation methods, incorporating both traditional and renewable sources, to the cutting-edge progress in energy-efficient transport systems, we assess their cumulative impact on the smart urban environment. While our approach is rooted in theoretical exploration rather than mathematical modeling, we provide a comprehensive review of the prevailing frameworks and methodologies that drive energy management in smart urban ecosystems. We also discuss the implications of these systems on urban sustainability and the critical importance of integrating various energy domains to facilitate effective energy governance. By bringing together a diverse array of scholarly insights, our paper aspires to enhance the understanding of energy interdependencies in smart cities and to catalyze the development of innovative, sustainable policies and practices that will define the future of urban energy management. Through this expanded perspective, we underscore the necessity of cross-disciplinary research and the adoption of holistic strategies to optimize energy usage, reduce carbon footprints, and promote resilient urban living in the era of smart cities.

show abstract

“…Finally, software evolution and maintenance involve changing the program to suit changing consumer and market desires [5]. They include sub-events, such as requirement authentication, architectural strategy, testing, and supportive events, such as configuration and modification administration, quality guarantee, and project administration [6][7][8]. Other actions involve providing new technological tools, following best practices, and standardizing processes.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Babatunde,

Ogundokun,

Adeoye

et al. 2023

Mathematics

View full text Add to dashboard Cite

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

show abstract

Monitoring House Vacancy Dynamics in The Pearl River Delta Region: A Method Based on NPP-VIIRS Night-Time Light Remote Sensing Images

Cited by 73 publications

References 30 publications

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

A Review of Smart Energy Management in Residential Buildings for Smart Cities

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Contact Info

Product

Resources

About