Selected Time Window Research Articles

Charge Transfer Kinetics in Halide Perovskites: On the Constraints of Time-Resolved Spectroscopy Measurements.

Understanding photophysical processes in lead halide perovskites is an important aspect of optimizing the performance of optoelectronic devices. The determination of exact charge carrier extraction rate constants remains elusive, as there is a large and persistent discrepancy in the reported absolute values. In this review, we concentrate on experimental procedures adopted in the literature to obtain kinetic estimates of charge transfer processes and limitations imposed by the spectroscopy technique employed. Time-resolved techniques (e.g., transient absorption-reflection and time-resolved photoluminescence spectroscopy) are commonly employed to probe charge transfer at perovskite/transport layer interfaces. The variation in sample preparation and measurement conditions can produce a wide dispersion of the measured kinetic parameters. The selected time window and the kinetic fitting model employed introduce additional uncertainty. We discuss here evaluation strategies that rely on multiexponential fitting protocols (regular or stretched) and show how the dispersion in the reported values for carrier transfer rate constants can be resolved.

Research on the minimum measurable mass of explosives hidden in concrete walls based on SVM and tagged neutron method

A method based on the combination of the tagged neutron method and the support vector machine (SVM) algorithm was proposed for the fast and effective identification of explosives behind concrete walls. Geant4 was used for modeling the experimental setup. The high degree of agreement in the experimental and simulated gamma spectra from selected time window validated the developed methodology. The difference between the experimental and simulated spectra could be inferred that the difference between the experimental and simulated spectra mainly comes from the difference in the composition of the cement. Geant4 was used for modeling the situation where explosives were hidden behind walls with thicknesses of 5 cm, 10 cm, and 15 cm, and the minimum detectable mass was calculated. The minimum detection limit (MDL) for C element in explosives hidden behind concrete walls is used to determine the minimum mass of explosives that can be detected and identified. The types of explosives hidden inside the wall were identified using SVM. The mass of explosives with 95% identification accuracy was considered to be the minimum measurable mass that can identify the type of explosives hidden within the wall. Cph/N and Cse/N were selected as input vectors to the SVM where Cph is the peak area of the inelastic scattering peak of element C, Cse is the peak area of the first escape peak of element C, and N is the peak area of the inelastic scattering peak of element N. Calculations of the minimum detectable mass of the type of explosive (TNT, AN, RDX) that could be effectively identified behind walls of 5, 10, and 15 cm resulted in 452 g, 539 g, and 893 g, respectively.

Performance index of a network of ground-based optical sensors for space objects observation and measurements

The total number of space objects in Earth orbits is estimated to be over 600 thousands over 1 cm, while the current number of those that are constantly tracked and cataloged is around 25 thousands. In our era, where the space traffic is increasing every year, and so the risk of possible collisions, there is a global need to take control of the near Earth space environment, in particular the low Earth orbit. This is a common problem for every country and it can be solved with a global collaboration between nations. In addition, the uncertainty associated with the measured position of orbiting objects is one of the main factors impacting performance, accuracy and timeliness. For this reason, aiming for the coordination of a multitude of sensors is one of the most important aspects targeted in the domain. This paper proposes an algorithm to estimate the performance of a globally-distributed network of optical assets, equipped with off-the-shelf components, deployed in multiple sites distributed across different locations. The quantitative performance measure is calculated as the portion of total cataloged debris that is visible by the network in a 24 h time window, considering space objects of size down to 3 cm. The proposed algorithm takes as input all objects of the NORAD catalog, the whole set of objects’ physical data provided by the DISCOS and SATCAT catalogs, and optical and atmospheric data. It then propagates the space object population to obtain their position in the selected time window, filters out all the objects that are not in the ground station network line-of-sight for a sufficient amount of time to guarantee a feasible orbit determination, and on the remaining ones it estimates the signal-to-noise Ratio achievable by the assets, leveraging an advanced algorithm that models their optical performance. These values are directly translated into a probability of detection, thus providing a performance index for the given ground sensor network configuration that can be used as an objective function to be optimized when evaluating different architectures.

CSF sTREM2 has opposite association with brain structural damage rate at early and late stage of Alzheimer’s disease

AbstractBackgroundThe role of neuroinflammation in the pathogenesis of Alzheimer’s disease (AD) is still controversial. Recent studies proposed that microglial response may have a stage‐dependent effect on disease progression. Considering the CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) has emerged as a promising marker of microglial activation, we aimed to further test this hypothesis by studying the association between baseline sTREM2 with dynamic brain structural changes in different stage of AD.Method22 amyloid‐positive (A+) and tau‐positive (T+) AD patients and 24 A+T+ MCI patients were identified from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The patients had baseline measurements of CSF A‐beta, P‐Tau, sTREM2, baseline and follow‐up T1‐weighted and diffusion tensor imaging (DTI) scans, and were followed up for at least one year. Voxel‐based morphometry (VBM) analysis was performed to assess grey matter volumetric change, while fractional anisotropy (FA) and mean diffusivity (MD) were derived from DTI scans to evaluate white matter microstructural integrity. Linear mixed effect models were then applied to model the longitudinal change of grey and white matter integrity, and to analyze the influence of baseline sTREM2 on the rate of brain structural changes.ResultIn A+T+ AD patients, baseline CSF sTREM2 was associated with faster FA decline and MD increase in association fibers including corpus callosum, inferior longitudinal fasciculus, cingulum, inferior fronto‐occipital fasciculus, and superior longitudinal fasciculus. In A+T+ MCI patients, baseline CSF sTREM2 was associated slower grey matter volumetric loss in regions including Alzheimer’s signature cortices such as angular gyrus and precuneus, while mixed findings were found regarding white matter integrity, with a protective effect with regard to MD but a potential negative effect against FA values in association fibers.ConclusionThe current study suggests that microglial activation at early AD stage might have a protective effect against neurodegeneration, while at late stage, it might facilitate neurodegenerative process. These observations support the hypotheses that microglial response is an active factor in the process of Alzheimer’s pathogenesis, and that it has stage‐dependent effects on disease progression. Future efforts on modulating microglial activation could be promising, given a carefully selected time window for intervention.

Cerebrospinal Fluid sTREM2 Has Paradoxical Association with Brain Structural Damage Rate in Early- and Late-Stage Alzheimer's Disease.

Recently it has been proposed that microglial response has a stage-dependent effect on the progression of Alzheimer's disease (AD). Cerebrospinal fluid (CSF) sTREM2 has emerged as a promising microglial activation marker. To test the stage-dependent role of microglia by studying the association between baseline sTREM2 and dynamic brain structural changes in AD and mild cognitive impairment (MCI) patients. 22 amyloid-β-positive (A+) and tau-positive (T+) AD and 24 A+T+MCI patients were identified from the Alzheimer's Disease Neuroimaging Initiative. The patients had baseline CSF amyloid-β, phosphorylated-tau, and sTREM2, and were followed up for at least one year by T1-weighted and diffusion tensor imaging scans. Gray matter volumes and white matter microstructural integrity were evaluated. Linear mixed models were applied to analyze how baseline sTREM2 may influence the rate of brain structural changes while adjusting for the effects of age, APOE4 status, and the CSF core markers. In A+T+AD patients, baseline CSF sTREM2 was associated with faster mean diffusivity increase in the bilateral posterior corona radiata and right superior longitudinal fasciculus. In A+T+MCI patients, baseline CSF sTREM2 was associated slower gray matter volumetric loss in parahippocampal gyrus, left fusiform cortex, left middle temporal gyrus, and left lateral occipital cortex. Baseline CSF sTREM2 also had a protective effect against mean diffusivity increase in right inferior fronto-occipital fasciculus, left superior longitudinal fasciculus, left forceps minor, and left uncinate fasciculus. Microglial activation at early stage might have a protective effect against neurodegeneration, while at late stage it might facilitate AD. Future efforts on modulating microglial activation could be promising, given a carefully selected time window for intervention.

Decomposition of the vehicle routing problem with time windows on the time dimension

The Vehicle Routing Problem with Time Windows (VRPTW) asks for the optimal set of routes to be performed by a fleet of vehicles to serve a set of customers within their assigned time windows. In this work, we propose a matheuristic for the VRPTW which utilizes the sub-problem constituted by optimizing only a selected time window of the VRPTW whereas the tours outside of this time window are regarded as fixed. We call this problem the Single Time Window Vehicle Routing Problem (STWVRP). For applying the STWVRP, we must assume that several customers are assigned to the same time window, i.e., the number of time windows is much smaller than the number of customers. An exact problem description of the STWVRP is given in the form of a Mixed-Integer Linear Programming formulation. We apply this exact formulation within a matheuristic for the VRPTW. The paper concludes with extensive computational experiments.

Robust K-means algorithm with weighted window for seismic facies analysis

Abstract Seismic facies analysis can generate a map to describe the spatial distribution characteristics of reservoirs, and therefore plays a critical role in seismic interpretation. To analyse the characteristics of the horizon of interest, it is usually necessary to extract seismic waveforms along the target horizon using a selected time window. The inaccuracy of horizon interpretation often produces some inconsistent phases and leads to inaccurate classification. Therefore, the developed adaptive phase K-means algorithm proposed a sliding time window to extract seismic waveforms. However, setting the maximum offset of the sliding window is difficult in a real data application. A value that is too large may cause the cross-layer problem, whereas a value that is too small reduces the flexibility of the algorithm. To address this disadvantage, this paper proposes a robust K-means (R-K-means) algorithm with a Gaussian-weighted sliding window for seismic waveform classification. The used weights punish those windows distant from the interpretation horizon in the objective function, consequently producing a smaller range of horizon adjustments even when using relatively large maximum offsets and benefitting the generation of stable and reliable seismic facies maps. The application of real seismic data from the F3 block proves the effectiveness of the proposed algorithm.

Comparison on PMT waveform reconstructions with JUNO prototype

JUNO is a 20 kton underground liquid scintillator detector aiming to determine the neutrino mass hierarchy and neutrino measurements. A prototype of JUNO is designed and set up for better understanding sub-systems of future detector. The preliminary results show that trigger rate is about 290 Hz at threshold 0.3 MeV (gamma) on the surface of earth, containing cosmic muon rate ∼35 Hz identified by detected energy higher than 20 MeV . For a better detector understanding with PMT signal, three waveform reconstruction algorithms are compared for PMTs with different overshoot ratios, including charge integration, waveform fitting, and deconvolution. It is concluded that the deconvolution algorithm is best to handle larger overshoot (∼10%) while a selected time window length would affect the reconstruction uncertainty. Further, the three reconstruction algorithms show similar performance on consistency and uncertainty with small overshoot (∼1%) waveforms and different amplitudes, which means that the simplest charge integral method is still working well for quick waveform reconstruction in this case.

IQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks.

Resource interferences caused by concurrent queries is one of the key reasons for unpredictable performance and missed workload SLAs in cluster computing systems. Analyzing these inter-query resource interactions is critical in order to answer time-sensitive questions like 'who is creating resource conflicts to my query'. More importantly, diagnosing whether the resource blocked times of a 'victim' query are caused by other queries or some other external factor can help the database administrator narrow down the many possibilities of query performance degradation. We introduce iQCAR, an inter-Query Contention Analyzer, that attributes blame for the slowdown of a query to concurrent queries. iQCAR models the resource conflicts using a multi-level directed acyclic graph that can help administrators compare impacts from concurrent queries, identify most contentious queries, resources and hosts in an online execution for a selected time window. Our experiments using TPCDS queries on Apache Spark show that our approach is substantially more accurate than other methods based on overlap time between concurrent queries.

Investigation of Spatio-temporal Satellite Thermal IR Anomalies Associated with the Awaran Earthquake (Sep 24, 2013; M 7.7), Pakistan

The reliable forecast of imminent future earthquakes is still questionable due to uncertainties regarding the complex phenomena of earthquake occurrence and the hierarchical nature of the lithosphere. However, numerous retrospective studies suggest the implications of earthquake-accompanying processes within the context of earthquake forecasting. In this connection, an attempt is made here to verify the enhancement in thermal IR emissions associated with the extraordinary major (M 7.7) and shallow (depth ≈ 15 km) Awaran earthquake (Sep 24, 2013). The satellite thermal imagery record is analyzed to identify the possible effect of pre- and post-seismic changes around the epicentral region (latitude 25–29°N; longitude 63–67°E) for a suitable selected time window (July 27, 2013 to Nov 16, 2013). The pre-earthquake satellite imagery records reveal a very clear and distinct thermal anomaly developed within the earthquake preparation zone almost 6–10 days earlier, while the analysis of post-earthquake imagery record shows a decreasing trend of thermal anomaly as a function of time. The daily land surface temperature (LST) shows an anomalous rise of 7–9 °C on Sep 18, 2013, almost 6 days prior to the event occurrence, which is further authenticated by the statistical criterion $$(\bar{x} \pm 2\sigma )$$ with a confidence interval of 95%. Furthermore, the comparative and percentile analysis of daily and 5-year-averaged LST also exhibits abnormal increase associated with this particular event. In addition to all above, the present study is also consistent with the earlier findings and suggests a multi-precursory strategy for earthquake forecasting research.

Loss experiences in old age and their impact on the social network and depression– results of the Leipzig Longitudinal Study of the Aged (LEILA 75+)

BackgroundLoss experiences and bereavement are common among the oldest old. This study aims to investigate the effects of loss experiences and the social network type on depression in old age. MethodsAs part of the Leipzig Longitudinal Study of the Aged (LEILA 75+), a representative population-based cohort study, 783 persons aged 75+ years were assessed via standardized interviews including the Practitioner Assessment of Network Type Instrument (PANT) and the Center for Epidemiologic Studies-Depression Scale (CES-D). Effects of loss experiences and network type on depression were analysed cross-sectionally (baseline survey) using logistic regressions. Effects over time were analysed longitudinally (follow-up1 and follow-up2) using hybrid techniques. ResultsMore than half of the elderly (57%) continuously lived in a restricted network. Only 12.1% lived in an integrated network. Although 30.9% had a change in their network, no significant association with loss experiences was found. Nevertheless, loss experiences (OR 7.56 (1.60–35.72)) and a restricted social network (OR 4.08 (1.52–10.95)) appeared to be the significant predictors of depression. LimitationsOur study captures only a selected time window of the individual life and loss experience was only assessed at the time of the baseline survey. ConclusionOur findings revealed that elderly individuals, who experienced social loss or lived in restricted social networks, were more likely to develop depression compared to individuals who lived in integrated social networks or without loss experiences. The social integration of elderly individuals is an urgent issue that should be addressed in order to reduce depression in old age.

Thermal IR satellite data application for earthquake research in Pakistan

The scientific progress in space research indicates earthquake-related processes of surface temperature growth, gas/aerosol exhalation and electromagnetic disturbances in the ionosphere prior to seismic activity. Among them surface temperature growth calculated using the satellite thermal infrared images carries valuable earthquake precursory information for near/distant earthquakes. Previous studies have concluded that such information can appear few days before the occurrence of an earthquake. The objective of this study is to use MODIS thermal imagery data for precursory analysis of Kashmir (Oct 8, 2005; Mw 7.6; 26 km), Ziarat (Oct 28, 2008; Mw 6.4; 13 km) and Dalbandin (Jan 18, 2011; Mw 7.2; 69 km) earthquakes. Our results suggest that there exists an evident correlation of Land Surface Temperature (thermal; LST) anomalies with seismic activity. In particular, a rise of 3–10 °C in LST is observed 6, 4 and 14 days prior to Kashmir, Ziarat and Dalbandin earthquakes. In order to further elaborate our findings, we have presented a comparative and percentile analysis of daily and five years averaged LST for a selected time window with respect to the month of earthquake occurrence. Our comparative analyses of daily and five years averaged LST show a significant change of 6.5–7.9 °C for Kashmir, 8.0–8.1 °C for Ziarat and 2.7–5.4 °C for Dalbandin earthquakes. This significant change has high percentile values for the selected events i.e. 70–100% for Kashmir, 87–100% for Ziarat and 84–100% for Dalbandin earthquakes. We expect that such consistent results may help in devising an optimal earthquake forecasting strategy and to mitigate the effect of associated seismic hazards.

GPR Clutter Amplitude Processing to Detect Shallow Geological Targets

The analysis of clutter in A-scans produced by energy randomly scattered in some specific geological structures, provides information about changes in the shallow sedimentary geology. The A-scans are composed by the coherent energy received from reflections on electromagnetic discontinuities and the incoherent waves from the scattering in small heterogeneities. The reflected waves are attenuated as consequence of absorption, geometrical spreading and losses due to reflections and scattering. Therefore, the amplitude of those waves diminishes and at certain two-way travel times becomes on the same magnitude as the background noise in the radargram, mainly produced by the scattering. The amplitude of the mean background noise is higher when the dispersion of the energy increases. Then, the mean amplitude measured in a properly selected time window is a measurement of the amount of the scattered energy and, therefore, a measurement of the increase of scatterers in the ground. This paper presents a simple processing that allows determining the Mean Amplitude of Incoherent Energy (MAEI) for each A-scan, which is represented in front of the position of the trace. This procedure is tested in a field study, in a city built on a sedimentary basin. The basin is crossed by a large number of hidden subterranean streams and paleochannels. The sedimentary structures due to alluvial deposits produce an amount of the random backscattering of the energy that is measured in a time window. The results are compared along the entire radar line, allowing the location of streams and paleochannels. Numerical models were also used in order to compare the synthetic traces with the field radargrams and to test the proposed processing methodology. The results underscore the amount of the MAEI over the streams and also the existence of a surrounding zone where the amplitude is increasing from the average value to the maximum obtained over the structure. Simulations show that this zone does not correspond to any particular geological change but is consequence of the path of the antenna that receives the scattered energy before arriving to the alluvial deposits.

Study on the “Warming Hole” in America Based on CMIP5 Models

Studying the “warming hole” in the eastern United States can help us understand the warming’ mechanisms and its relationship with nature climate variability. Here we explored the relationships between the calculation of trends and the selected time window and between the “warming hole” and AMO, PDO and IPO, along with the temporal variability for 30 years running temperature trends in America and the North Atlantic. We also evaluated the ability of 21 CMIP5 climate models to simulate the “warming hole” and AMO in the twentieth century and conducted the twenty-first-century “warming hole” and AMO projections for two future emission scenarios based on the known uncertainty in CMIP5 twentieth-century simulations. The results indicate that the selected time window has a great impact on the calculation of trends, the observed United States warming hole is closely associated with AMO, PDO and IPO, the temporal variability for 30 years running temperature trends in America and the North Atlantic is sinusoidal and has a 67-yr cycle and that most CMIP5 models simulate significantly lower relative power in AMO than observed. The results also show that GFDL-ESM2G, MIROC-ESM and CanESM2 from 19 CMIP5 models have the best performances in capturing features related to the twentieth-century “warming hole” and that there is about 10% chance in a PCR4.5 scenario and no chance in a PCR8.5 scenario for the warming hole’s reappearance in the twenty-first century.

Water Ingress Detection in Low-Pressure Gas Pipelines Using Distributed Temperature Sensing System

In a distribution network of low-pressure gas pipelines, the situation of gas leak can be further aggravated when groundwater enters the pipeline through leaks and eventually blocks the gas flow. This will have critical implications on the gas supply to the customers. This is termed as “water ingress,” which typically happens only in low-pressure distribution networks, and not in high-pressure transmission networks. In order to find the location of water ingress, distributed temperature sensing (DTS) system has been used experimentally. The results show significant temperature change immediately after the onset of water ingress, and with data post-processing based on temporal difference, location information of the leak can be obtained. With a selected time window of interest, the inclination of gas pipeline is also indicated by the differenced temperature profiles. The DTS system is still capable of identifying the position, even if the location of water ingress is changed.

Model of selecting prediction window in ramps forecasting

Prediction of wind power ramp events is important to the stability operation of power system, it is realized by combining wind power prediction of several continuous units for long-term power prediction, then using detecting algorithms to extract ramps. A prediction unit is a prediction time window. Its size impacts the accuracy of predicting ramps, and an optimization model is proposed to select the suitable window size. First, a swinging door algorithm is applied to extract ramp events from historical data. A model for optimizing the time window size is established based on the minimum non-ramp data in a ramp window. The solution of the proposed model is discussed, including the selection of variables, constraints and algorithm. The model presented in this paper is tested, and performance of selected time window is discussed. Computational analysis demonstrates the validity of the model.

Optimal Acquisition Time Window and Simplified Quantification of Dopamine Transporter Availability Using 18F-FE-PE2I in Healthy Controls and Parkinson Disease Patients.

Ten Parkinson disease (PD) patients and 10 controls were included. Ninety-three-min dynamic PET measurements with 18F-FE-PE2I were conducted using the high-resolution research tomograph (HRRT). The dynamic measurement was also smoothed to the resolution of a clinical PET system (HR). Regions of interest for the caudate, putamen, ventral striatum, substantia nigra (SN), and cerebellum were manually drawn on coregistered MR images. The outcome measure was the SBR, and the gold standard was the binding potential obtained with wavelet-aided parametric imaging (WAPI BPND). The cerebellum was used as a reference region. In a preliminary analysis, SBR was computed for 8 time windows (SBRdyn). Linear regression analysis and Bland-Altman plots were used to select the optimal acquisition time window. An average image from the selected time window was created, from which new SBR values (SBR calculated on the average image on the HRRT and SBR calculated on the average image on the simulated HR images) were calculated and compared with WAPI BPND The effect size was calculated. SBRdyn values for the time window between 16.5 and 42 min correlated best with WAPI BPND (r2 = 0.98, P < 0.001). Significant correlations (P < 0.001) were observed between SBRHR and WAPI-BPND (r2 = 0.95 in controls and 0.97 in PD patients). In the striatum, SBRHR values were 37% lower than BPND in controls, 29% in PD patients, whereas in the SN the underestimation was 22% in controls and 15% in PD patients. Similar effect sizes for BPND and SBRHR were found in the caudate (0.6), putamen (1.7 and 1.4), ventral striatum (0.7), and SN (0.5 and 0.4). A single 18F-FE-PE2I acquisition between 16.5 and 42 min provides the best outcome measure for simplified DAT quantification. Despite underestimation of the BPND, the SBR can be used in a clinical setting as a valid quantification method for DAT using 18F-FE-PE2I, because it provides differentiation similar to BPND between controls and PD patients.

Investigating the recurrence of meteorological hazards

The external hazards constitute a significant source of challenges for the safe operation of a NPP. An overview of the available operating experience is presented in this paper, in order to provide a better picture about the recurrence of meteorological hazards and their impact on the safety of nuclear installations. The IAEA International Reporting System was used as a reference database in the analysis.The meteorological related events identified in the selected time window were analyzed in detail, and the contributions of each external hazard that have induced the meteorological related events, together with the lessons learnt are presented and discussed. The obtained results and the conclusions regarding the occurrence of extreme meteorological hazards, together with the distribution of recurrent events for EU and non-EU areas are highlighted.

Effect of measurement noise and excitation on Generalized Response Surface Model Updating

This paper investigates the robustness of a parameter estimation procedure for nonlinear Finite Element (FE) model updating. Through this procedure, polynomial Response Surface (RS) models are constructed to approximate the response of a nonlinear FE model at every time step of the analysis. Subsequently, the optimization problem of model updating is solved iteratively in time which results in histograms of the updating parameters. With the assumption of White Gaussian measurement noise, it is shown that this parameter estimation technique has low sensitivity to the standard deviation of the measurement noise. In order to validate this, a parametric sensitivity study is performed through numerical simulations of nonlinear systems with single and multiple degrees of freedom. The results show the least sensitivity to measurement noise level, selected time window for model updating, and location of the true model parameters in RS regression domain, when vibration frequency of the system is outside the frequency bandwidth of the load. Further application of this method is also presented through a case study of a steel frame with bilinear material model under seismic loading. The results indicate the robustness of this parameter estimation technique for different cases of input excitation, measurement noise level, and true model parameters.

SPI Modes of Drought Spatial and Temporal Variability in Portugal: Comparing Observations, PT02 and GPCC Gridded Datasets

Regional drought modes in Portugal are identified applying the Principal Component Analysis (PCA) and Varimax rotation to the Standardized Precipitation Index (SPI) computed on various time scales using the three precipitation datasets covering the period 1950–2003: (i) The observation dataset composed of 193 rain-gauges distributed almost uniformly over the country, (ii) the PT02 high-resolution gridded dataset provided by the Portuguese Meteorological Institute, and (iii) the GPCC dataset with 0.5° spatial resolution. Results suggest that the three datasets well agree in identifying the principal drought modes, i.e. two sub-regions in northern and southern Portugal with independent climate variability. The two sub-regions appear stable when the SPI time scale is varied from 3- to 24-month, and the associated rotated principal component scores (RPCs) do not show any statistically significant linear trend. The degree of similarity between the rotated loadings or REOFs of different SPI time scales for the three used datasets was examined through the congruence coefficients, whose results show a good agreement between the three datasets in capturing the main Portuguese sub-regions. A third spatial mode in central-eastern Portugal was identified for SPI-24 in PT02, with the associated RPC characterized by a statistically significant downward trend. The stability of the identified sub-regions as a function of studied time period was also evaluated applying the same methodologies to a set of three different time windows and it was found that the southern sub-region is very stable but the northern and central-eastern sub-regions are very sensitive to the selected time window.