1,721,001 research outputs found
On the stochastic significance of peaks in the least-squares wavelet spectrogram and an application in GNSS time series analysis
Full text linkIn this paper, the mathematical derivation of the underlying probability distribution function for the normalized least-squares wavelet spectrogram is presented. The impact of empirical and statistical weights on the estimation of the spectral peaks and their significance are demonstrated from the statistical point of view both theoretically and practically. The simulation results show an improvement of approximately 0.02mm (RMSE) for annual signal estimation when statistical weights are considered in the least-squares wavelet analysis (LSWA). The weighted LSWA estimates the signals more accurately than the ordinary LSWA for different percentage amount of missing data. As a real-world application, Global Navigation Satellite Systems (GNSS) time series for a station in Rome, Italy are analyzed. The analyses of the GNSS time series provided by different agencies for the same station reveal statistically significant annual peaks, more significant in 2010 but less significant between 2018 and 2020, while the higher frequency components show different spectral patterns over time. A declining trend of approximately -0.42 mm/year since 2004 is estimated for the GNSS height time series, likely due to gradual land subsidence. The results not only highlight the advantages of LSWA but can also help to better understand the uncertainties involved in signal estimation
Ground deformation monitoring via PS-InSAR time series. An industrial zone in Sacco River Valley, central Italy
No full textPersistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment
Cayley graphs of order 30p are Hamiltonian
Full text linkAbstractSuppose G is a finite group, such that |G|=30p, where p is prime. We show that if S is any generating set of G, then there is a Hamiltonian cycle in the corresponding Cayley graph Cay(G;S)
Coherency and phase delay analyses between land cover and climate across Italy via the least-squares wavelet software
Full text linkLand cover and climate monitoring is a crucial task in agriculture, forestry, hazard management, and ecosystems assessment. In this paper, normalized difference vegetation index (NDVI), land surface temperature (LST), and land cover products by the moderate resolution imaging spectroradiometer (MODIS) as well as precipitation were utilized to monitor the spatiotemporal dynamics of vegetation and climate along with their correlation and coherency across Italy during 2000–2021. The analyses were performed on both pixel and ecoregion levels via the least-squares wavelet software (LSWAVE). It was found that relatively more areas in all ecoregions had positive NDVI gradients than negative for each month since 2000. It was estimated that the average NDVI has increased by 0.07 since 2000 for all ecoregions. Except the southern ecoregion which showed an insignificant daytime cooling, other ecoregions have been warming by less than 0.05 °C/year since 2000. Furthermore, precipitation had an insignificant decreasing trend for almost all ecoregions over the past two decades. The annual coherency between NDVI and LST was found much stronger than the annual coherency between NDVI and precipitation. The annual cycles of NDVI and LST were out-of-phase for the southern ecoregion while the annual cycle of precipitation led the one in NDVI by about one month for this ecoregion, the only ecoregion showing the highest Pearson correlation (53%) and annual coherency (39%) between NDVI and precipitation. For other ecoregions, the annual cycles of NDVI and LST were approximately in-phase, i.e., less than a month phase delay
A comparative study of estimating hourly images of MODIS land surface temperature using diurnal temperature cycle models in arid regions
Full text linkThermal monitoring of different regions is usually limited to meteorological data in ground stations. Meteorological networks are limited in arid and semi-arid areas, where monitoring climatic conditions is not possible. The aim of this study is to estimate the land surface temperature (LST) hourly for Yazd-Ardakan plain by modeling the diurnal temperature cycle (DTC) using LST imagery of moderate resolution imaging spectroradiometer (MODIS). First, MODIS imagery are reconstructed using the multi-channel singular spectrum analysis, and the complete time series without missing values are created. Then, six DTC models are compared. The accuracy of DTC models is examined by ground LST measurements, air temperature, humidity, and wind speed. In addition, the results of examining the root mean square error (RMSE) images obtained from cross-validation based on MODIS LST imagery show that DTC2 has the highest error, where 73% of the area has RMSE greater than 3degree celsius . In DTC1 and DTC2, 64% and 5.8% of the study region has RMSE less than 2degree celsius. In general, DTC1, DTC6, DTC5, DTC4, DTC3, and DTC2 models have shown the highest to lowest accuracy in modeling the LST diurnal cycle. In addition, the difference between LST in mountain and plain lands is greater at the time of maximum temperature than at other hours of the day and night. The findings of this research are crucial in studies concerning climate change and land environmental monitoring in arid/semi-arid regions
Trend analysis of MODIS land surface temperature and land cover in Central Italy
Full text linkLand Surface Temperature (LST) is an important climate factor for understanding the relationship between the land surface and atmosphere. Furthermore, LST is linked to soil moisture and evapotranspiration, which can potentially alter the severity and regime of wildfires, landslide-triggering precipitation thresholds, and others. In this paper, the monthly daytime and nighttime LST products of Moderate Resolution Imaging Spectroradiometer (MODIS) are employed for the period 2000–2023 in order to find areas that have been cooling or warming in a region of great interest in Central Italy, due to its complex geological and geomorphological settings and its recent seismic sequences and landslide events. The annual MODIS land cover images for 2001–2022 are also utilized to investigate the interconnection between LST and land cover change. The results of the non-parametric Mann–Kendall trend test and its associated Sen’s slope reveal a significant nighttime warming trend in the region, particularly in July, linked to forest and woodland expansion. Grasslands toward the coastline with low elevation (less than 500 m a.s.l.) have experienced significant heat waves during the summer, with an LST of more than 35 °C. A significant negative correlation between the elevation and LST is observed for each calendar month. In particular, the daytime and nighttime LST have more than 80% correlation with elevation during winter and summer, respectively. In addition, nighttime warming and gradual drainage are noticed in Lake Campotosto. The results of this study could be useful for wildfire and landslide susceptibility analyses and hazard management
Remote sensing-derived land surface temperature trends over South Asia
Full text linkSpatiotemporal changes in land surface temperature (LST) over South Asia were estimated using MODIS (moderate resolution imaging spectroradiometer) data from 2000 to 2021. We calculated the monthly and annual LST trends and magnitudes by applying the Mann–Kendall test and Sen's slope estimator at both ecoregion and pixel level. More ecoregions experienced daytime cooling than warming. Central and west South Asia showed the highest daytime cooling in December compared to the nighttime warming in the central and northwest in July and September. Nineteen ecoregions demonstrated monthly daytime cooling trends at the 99% confidence level (CL), with the highest record observed in ecoregion ‘Indus Valley desert’ in March with the magnitudes of −0.26 °C/yr. While the monthly and annual nighttime warming magnitude was the maximum in ‘Gissaro-Alai open woodlands’ in December (0.19 °C/yr at 95% CL), and ‘Indus River Delta-Arabian Sea mangroves’ at annual scale (0.06 °C/yr at 99% CL). To understand the influence of large-scale atmospheric oscillations on the trends, we also correlated the estimated LST trends with the selected oscillation indices. Sea surface temperature (SST) Niño 3.4 showed the most significant influence on the trends, where it was positively correlated with 38 ecoregions during nighttime over the year. A better understanding of temperature trends and impacts on South Asia would guide sustainable development and ensures the excessive demands on food, water, and energy supplies coping with the growing population
Personalized blood pressure control by machine learning for remote patient monitoring
Full text linkIn the midst of a global health crisis, it is of utmost importance for healthcare technologies to possess the capability to regulate and monitor the physiological variables of patients remotely and automatically. The effective control of mean arterial pressure (MAP) in a closed-loop manner is particularly critical for individuals who are critically ill or in the process of recovering from surgical procedures. Within the framework of the present research, an adaptive closed-loop structure has been formulated with the objective of controlling a patient's MAP through governed administration of the medication sodium nitroprusside (SNP), to attain the desired MAP levels under varying conditions. The proposed closed-loop technique incorporates an intelligent controller known as the active disturbance rejection control (ADRC) with the intention of tracking the desired MAP value, alongside the utilization of continuous action policy gradient (CAPG) for the optimization of the controller's coefficients. Under the DRL strategy, an actor is responsible for generating policy requests, while a critic assesses the efficacy of the actor's policy directives. This approach uses gradient descent to train the weight values of both actor and critic networks, and it is dependent on the reward return linked to the MAP fault. Upon comparing the outcomes of the recommended structure with conventional models, numerical simulation results demonstrate the superiority of the proposed system in coping with varying working conditions, key-value fluctuations, and uncertainties, while effectively maintaining the desired mean arterial pressure and drug administration rate
Ground deformation monitoring using InSAR and meteorological time series and least-squares wavelet software. A case study in Catania, Italy
Full text linkPersistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced satellite remote sensing technique which allows an effective monitoring of ground movement. In this work, PS-InSAR time series as well as precipitation and temperature time series in a region in Catania, Italy are utilized during 2018–2022, and their possible interconnections with land subsidence/uplift due to groundwater level change are investigated. First, the potential jumps in the displacement time series are removed, and then the Sequential Turning Point Detection (STPD) is applied to estimate the times when the velocity of the displacement time series changes. The results show a significant correlation between the frequency of turning points in displacement time series and precipitation trend change, particularly during the winter season. Furthermore, the Least-Squares Cross Wavelet Analysis (LSCWA) is applied to estimate the coherency and phase delay between the displacement and weather cycles in the time-frequency domain. The annual cycles of displacement and temperature show more coherency than the ones of displacement and precipitation across the study region. The results presented herein are important for infrastructure and water management planning
A New Task Scheduling Approach for Energy Conservation in Internet of Things
Full text linkInternet of Things (IoT) and mobile edge computing (MEC) architectures are common in real-time application scenarios for improving the reliability of service responses. Energy conservation (EC) and energy harvesting (EH) are significant concerns in such architectures due to the self-sustainable devices and resource-constraint edge nodes. The density of the users and service requirements are further reasons for energy conservation and the need for energy harvesting in these scenarios. This article proposes decisive task scheduling for energy conservation (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling. By classifying the energy requirements and the states of the mobile edge nodes, the allocation and queuing of data are determined, preventing overloaded nodes and dissemination. This process is recurrent for varying time slots, edge nodes, and tasks. The proposed method is found to achieve a high data dissemination rate (8.16%), less energy utilization (10.65%), and reduced latency (11.44%) at different time slots
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