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The zero-coupon yield curve is a common input for most financial purposes. We consider three popular yield curve datasets and explore the extent to which the decision as to what dataset to use for a particular application may have an impact on the results. Many term structure papers evaluate alternative models for estimating zero coupon bonds based on their ability to replicate bond prices. However, in this paper we take a step forward by analyzing the consequences of using these alternative datasets in estimates of other moments and variables such as interest rate volatilities or the resulting forward rates and their correlations. After finding significant differences, we also explore the existence of volatility spillover effects among these three datasets. Finally, we illustrate the relevance of the choice of one particular dataset by examining the differences that may arise when testing the expectations hypothesis. In the conclusions, we provide guidance to end users in selecting a particular dataset. [ABSTRACT FROM AUTHOR]

With the increasing demands for data, the subscription scheme came into being in the face of pricing for an extensive and unfixed number of data items. However, in the existing subscription scheme, a diversity of customers in the real market may lead to the lack of stability, which means risking the failure of pricing. Additionally, the study involves arbitrage-free, an essential economics concept, which is not reasonable on data items. To address these problems, this paper provides insights for designing an improved subscription scheme that includes two components: the calculation and the specific validity. On the one hand, the calculation improves the existing scheme by building a new structure that combines different customers' behaviours instead of the separated calculation in the existing scheme, and can steadily set prices for subscriptions to maximise the sellers' profit even in a real market. On the other hand, the specific validity shows the improvement towards arbitrage-free by taking the characteristics of data subscriptions into account. In other words, the specific validity endows the scheme with more rationality. [ABSTRACT FROM AUTHOR]

Information is valuable if it derives from reliable data. However, measurements for data reliability have not been widely established in the area of information systems (IS). This paper attempts to draw some concepts of reliability from the field of quality control and to apply them to IS. The paper develops three measurements for data reliability: internal reliability--reflects the "commonly accepted" characteristics of various data items; relative reliability--indicates compliance of data to user requirements; and absolute reliability--determines the level of resemblance of data items to reality. The relationships between the three measurements are discussed, and the results of a field study are displayed and analyzed. The results provide some insightful information on the "shape" of the database that was inspected, as well as on the degree of rationality of some user requirements. General conclusions and avenues for future research are suggested. [ABSTRACT FROM AUTHOR]

The use of information technology during the COVID-19 pandemic raises significant questions around the protection of personal data in a disaster. This paper considers how the clear benefits of using and sharing such data in disaster scenarios can be achieved while recognising an individual's right to privacy through examining the experiences of Taiwan and New Zealand. These states represent two successful COVID-19 response strategies which utilised different approaches to the use of technology. In Taiwan, the response made significant use of personal data and information technology. New Zealand, by contrast, has relied upon stringent lockdowns and extreme limits upon personal freedoms. The paper considers the different approaches to data and privacy that underpinned these responses and considers whether New Zealand can learn from the Taiwanese experience in future disaster planning. In doing so, the paper concludes by examining the wider question of when and if the community's expectation of a safe environment should trump the rights of individuals to retain personal data both in the context of pandemics and in other emergency or disaster scenarios. It also raises deeper questions, exposed by the COVID-19 response, about whether our current approach to privacy is sustainable in the digital age. [ABSTRACT FROM AUTHOR]

The article focuses on modelling reference crop evapotranspiration using vector machines. It relates the evapotranspiration modelling work of O. Kisi and M. Cimen wherein support vector machines and artificial neural networks were applied to a non-complex hydrological modelling problem. It mentions that four meteorological input measurements were utilized in the modeling work including solar radiation (Rs), air temperature (T), and relative humidity (RH). It suggests that measurement units for all input and output variables must be provided in each scientific reporting of data in data-driven modelling paper. It adds that each paper must consist of a numerical description of every variable used in the model.

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions. [ABSTRACT FROM AUTHOR]

Accurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the least-squares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average. [ABSTRACT FROM AUTHOR]

Analysis with principal points is a useful statistical tool for summarizing large data. In this paper, we propose a subgradient-based algorithm to calculate a set of principal points for multivariate binary data by the formulating it as a p-median problem. This enables us to find a globally optimal set of principal points or an ε-optimal solution in the middle of the calculation by combining an upper bound found using the greedy method. This algorithm is an iterative procedure where each iteration can be calculated in an efficient manner. We investigate the applicability of the proposed framework with questionnaire data and arXiv co-authors data. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture (InSAR) has been widely applied to natural disaster monitoring. However, it has limitations due to the influence of noise sources such as atmospheric and topographic artefacts, data processing errors, etc. In particular, atmospheric effect is one of the most prominent noise sources in InSAR for the monitoring of small magnitude deformations. In this paper, we proposed an efficient multi-temporal InSAR (MTInSAR) approach to measure small co-seismic deformations by minimizing atmospheric anomalies. This approach was applied to investigate the 18 September 2004 earthquake over Huntoon Valley, California, using 13 ascending and 22 descending ENVISAT synthetic aperture radar (SAR) images. The results showed that the co-seismic deformation was ±1.5 and ±1.0 cm in the horizontal and vertical directions, respectively. The earthquake source parameters were estimated using an elastic dislocation source from the ascending and descending acquisitions. The root mean square errors between the observed and modelled deformations were improved by the proposed MTInSAR approach to about 3.8 and 1.8 mm from about 4.0 and 5.2 mm in the ascending and descending orbits, respectively. It means that the MTInSAR approach presented herein remarkably improved the measurement performance of a small co-seismic deformation. [ABSTRACT FROM AUTHOR]

In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas. [ABSTRACT FROM AUTHOR]

Landslides are a pervasive natural disaster, resulting in severe social, environmental and economic impacts worldwide. The tropical, mountainous landscape in South-West Mexico is predisposed to landslides because of frequent hurricanes and earthquakes. The main goal of this study is to compare landslide susceptibility maps in Guerrero derived using high-resolution LIDAR (light detection and ranging) data from both a manual landslide event inventory and an automated landslide inventorying algorithm. The paper also highlights the importance of applying LIDAR data in landslide inventorying and susceptibility mapping. We mapped landslides based on two approaches: (1) manual mapping using satellite images and (2) automatic identification of landslide morphology employing the Contour Connection Method (CCM). We produced a landslide susceptibility map by computing the probability of landslide occurrence from statistical relationships of inventoried landslides detected with LIDAR digital terrain models (DTMs) and derived landslide-causing factors using the logistic regression method. Our results suggest that the automated inventory derived through the CCM algorithm with LIDAR DTMs effectively minimizes the time-consuming and subjective manual inventorying process. The high overall prediction accuracy (up to 0.83) from logistic regression demonstrates the validity and applicability deriving reliable landslide susceptibility maps from an automated inventory; however, LIDAR data are required. [ABSTRACT FROM AUTHOR]

In this paper, parameters of north Tabriz fault are studied using 3D displacement field and artificial neural networks (ANNs). We provide the 3D surface displacement along the north Tabriz fault using an integration of tropospherically corrected InSAR, GPS and precise levelling data. To perform the InSAR analysis, we use the 17 ENVISAT radar acquisitions. The line of sight (LOS) displacement field was corrected using the ERA-Interim global meteorological reanalysis models. In order to calculate the 3D displacement, we use a Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements approach. ANNs used to estimate the parameters. 3D displacement field and ANN algorithm yields an average slip rate of 6.1 ± 0.01 mm/year with a locking depth of 13.4 ± 0.01 km. This rate is consistent with previous geodetic estimates based on recent global positioning system measurements and InSAR analysis. In addition, the length, width and dip angle of fault are about 101.2 ± 0.01 km, 25.06 ± 0.03 km and 63 ± 0.05 deg. This study demonstrates that interseismic displacement with a sub-centimetre rate can be successfully detected by integration of multi temporal InSAR techniques, GPS and precise leveling data and shows that ANN algorithm is suitable for estimating the parameters of north Tabriz fault. [ABSTRACT FROM AUTHOR]

Aim: The ICPerMed, international initiative promoting personalized medicine, has realized a survey among a group of experts, to define a common vision for the deployment of personalized medicine across healthcare systems until 2030. Materials & methods: ICPerMed defined five perspectives (p.4) and addressed an online questionnaire to 97 international experts to collect their views. Results: Seventy (72%) of the 97 experts effectively answered the survey from which 69 answers were exploitable. Respondents from a variety of international profiles approved the five proposed perspectives and reported required actions and best practices. Conclusion: There is a large consensus among experts directly involved in shaping international strategies and policies, calling for voluntarist public policies, new IT platforms enabling data-driven approaches, large-scale educational programs and new financing models. [ABSTRACT FROM AUTHOR]

In many clinical disciplines, especially in subjective sensitivity research, data are collected on the ratio scale. This is also true for vibration perception thresholds (VPTs). Hence, such data may be of heteroscedastic nature, where the amount of measurement error increases as the values increase. Heteroscedastic data is a great scientific concern, since many standard inferential statistical tests assume homoscedasticity and may, therefore, lead to incorrect interpretations. Unfortunately, only few studies even address heteroscedasticity, and even if they do, in many cases this is achieved inappropriately. Therefore, in this paper, we first show a simple approach for how to detect heteroscedasticity in VPTs measured at the plantar aspect of the hallux. Second, we show how to correct for heteroscedasticity by means of logarithmizing the raw data. Finally, we also demonstrate the importance of how data benefits from this transformation. Hence, if data takes benefit from the transformation, we recommend using logarithmized data to conduct further statistical approaches. We also interpret the results presented here. Furthermore, we briefly mention alternatives in case the transformation is not successful in eliminating heteroscedasticity. [ABSTRACT FROM AUTHOR]

With the recent availability of high-frequency financial data the long-range dependence of volatility regained researchers' interest and has led to the consideration of long-memory models for volatility. The long-range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short-memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern. In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. A simulation study shows that long memory processes as well as short memory processes with structural breaks can be well approximated by this local approach. Furthermore, using S&P500 data we find that our local modeling approach outperforms long-memory type models and models with structural breaks in terms of predictability. [ABSTRACT FROM AUTHOR]

An important way for the agrifood industry to continue to feed a growing world population as well as reduce its environmental impact is to extensively adopt Information Technologies that will "datafy" the industry. While the promises of datafication are high, the realization is lagging behind. We identify and discuss from an institutional economics point of view three related issues that help explain needs run behind realization: supply of data is separated from use of data; data quality, interoperability and standardization; and data completeness. [ABSTRACT FROM AUTHOR]

This paper provides a discussion of a recently introduced final-year econometrics module designed to capture methodological debates, advances in technology and increased data availability via a structure emphasising the practical nature of econometrics. The justification for the provision of such a module is presented clearly, with further support for its proposal from the broader educational literature provided also. Evaluation of the module shows its success in terms of student satisfaction, student learning and progress and staff satisfaction. Consequently, it is suggested that colleagues should be encouraged to develop similar modules emphasising the relevance of the material they cover in a topical manner exploiting all available technological resources as appropriate. [ABSTRACT FROM AUTHOR]

Universal health coverage (UHC) aims to ensure people have access to the health services they need. Sixteen tracer indicators were developed for implementation by countries to measure UHC in the health system. South Africa uses 15 of the proposed 16 indicators. Operational managers in the public health care sector collect data and report on these indicators at a primary health clinic level. This qualitative study explored the knowledge and attitudes of managers toward data and UHC service indicators in a sub-district in Ugu, KwaZulu-Natal, South Africa. Operational managers saw data collection as information gathering, measuring performance and driving action. They understood UHC indicators as 'health for all' linking them to National Department of Health Strategic plans and saw the value of indicators for health promotion. They found the lack of training, inadequate numeracy skills, requests for data from multiple spheres of government and the indicator targets that they had to reach as challenging and untenable. While operational managers made the link between data, measuring performance and action, the limited training, skills gaps and pressures from higher levels of government may impede their ability to use data for local level planning and decision making. [ABSTRACT FROM AUTHOR]

Research over the years has delved into the role of culture in advertising from various perspectives using both cross-country comparisons and within-country investigations. An assessment of recent research offers observations for scholars to consider for future work in this area: continue to expand theories, sample outside of the usual cultures, assess comparability, identify obstacles to publishing, consider changes in data collection and related issues, and connect the dots by providing periodic state-of-the-knowledge reviews and analyses. At the same time, it would be worthwhile for future work to respond to changes in the environment and new communication needs by studying consumer ethnocentrism during a period of heightened trade tensions and a slowdown in globalization, and by looking into philanthropy and corporate social responsibility communication for initiatives to help solve local, regional, and global problems. [ABSTRACT FROM AUTHOR]

We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail. [ABSTRACT FROM AUTHOR]

A mixed data frame (MDF) is a table collecting categorical, numerical, and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF exhibits simultaneously main effects, such as row, column, or group effects and interactions, for which a low-rank model has often been suggested. Although the literature on low-rank approximations is very substantial, with few exceptions, existing methods do not allow to incorporate main effects and interactions while providing statistical guarantees. The present work fills this gap. We propose an estimation method which allows to recover simultaneously the main effects and the interactions. We show that our method is near optimal under conditions which are met in our targeted applications. We also propose an optimization algorithm which provably converges to an optimal solution. Numerical experiments reveal that our method, mimi, performs well when the main effects are sparse and the interaction matrix has low-rank. We also show that mimi compares favorably to existing methods, in particular when the main effects are significantly large compared to the interactions, and when the proportion of missing entries is large. The method is available as an R package on the Comprehensive R Archive Network. for this article are available online. [ABSTRACT FROM AUTHOR]

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators. for this article are available online. [ABSTRACT FROM AUTHOR]

It is imperative to foster data acumen in our university student population in order to respond to an increased attention to statistics in society and in the workforce, as well as to contribute to improved career preparation for students. This article discusses 13 learning outcomes that represent achievement of undergraduate data acumen for university level students across different disciplines. [ABSTRACT FROM AUTHOR]

We present single imputation method for missing values which borrows the idea of data depth—a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly defined statistical depth function. For each single iteration, imputation reverts to optimization of quadratic, linear, or quasiconcave functions that are solved analytically by linear programming or the Nelder–Mead method. As it accounts for the underlying data topology, the procedure is distribution free, allows imputation close to the data geometry, can make prediction in situations where local imputation (k-nearest neighbors, random forest) cannot, and has attractive robustness and asymptotic properties under elliptical symmetry. It is shown that a special case—when using the Mahalanobis depth—has direct connection to well-known methods for the multivariate normal model, such as iterated regression and regularized PCA. The methodology is extended to multiple imputation for data stemming from an elliptically symmetric distribution. Simulation and real data studies show good results compared with existing popular alternatives. The method has been implemented as an R-package. for the article are available online. [ABSTRACT FROM AUTHOR]

Massive spatio-temporal big data about human mobility have become increasingly available. Revealing underlying dynamic patterns from these data is essential for understanding people's behavior and urban deployment. Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time. The most widely used spatial autocorrelation measurements, such as Moran's I and local indicators of spatial association (LISA), only apply to static data, so are powerless to spatio-temporal big data about human mobility. Thus, we proposed a new method by extending Moran's I to measure the spatial autocorrelation of time series data. Then the method was applied to taxi ride data in Beijing, China to reveal the spatial pattern of collective human mobility. The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road, weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road, and almost no spatio-temporal autocorrelation between the roads. Local spatial patterns of taxi travel were also recognized. This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility. [ABSTRACT FROM AUTHOR]

A simple sorting approach for inducing any desired Pearson or Spearman correlation to independent bivariate data, whose marginals can be of any distributional type and nature is described and illustrated through examples that span a broad range of situations. The proposed method has substantial potential in simulated settings that involve random number generation. [ABSTRACT FROM AUTHOR]

The boxplot is an effective data-visualization tool useful in diverse applications and disciplines. Although more sophisticated graphical methods exist, the boxplot remains relevant due to its simplicity, interpretability, and usefulness, even in the age of big data. This article highlights the origins and developments of the boxplot that is now widely viewed as an industry standard as well as its inherent limitations when dealing with data from skewed distributions, particularly when detecting outliers. The proposed Ratio-Skewed boxplot is shown to be practical and suitable for outlier labeling across several parametric distributions. [ABSTRACT FROM AUTHOR]

The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods. [ABSTRACT FROM AUTHOR]

The focus of the present study is to explain the stair-step brittle deformation evolutionary pattern of the landslide and reveal the corresponding internal physical mechanism. Based on the concept of high strength geological body (HSGB), similar to rock bridges in the intermittence jointed rock masses, a conceptual geological model and the corresponding mechanical model are successively outlined. Through reanalysing typical stair-step displacement records of a wedge rockslide, it was found that the HSGB model can reasonably explain the stair-step deformation feature and reveal its internal essence. Finally, a displacement criterion is derived through combining the two-dimensional renormalization group model with the shear creep constitutive model. By means of this displacement criterion, one can quantitatively describe the displacement ratio between the onset of the accelerating creep and brittle rupture of HSGB. In order to make the reader understand this displacement criterion better, a detailed application process is demonstrated through a case study. To some extent, the present study has shed light on the failure prediction of the HSGB-type landslide, whose stability is mainly controlled by HSGB. [ABSTRACT FROM AUTHOR]

Population density is one of the key parameters for assessing the magnitude of population exposed to risk, and the better quality data we have, the better the assessment of risk. The aim of this study is to elaborate a high-resolution spatially distributed population density grid, which estimates population at the commune scale with a reliability of over 90%. The novelty of the approach is population density estimation in a regular European grid, based on buildings vector data collected in the national topographic database. Using abductive reasoning in combination with statistics and spatial analysis, the authors extract approximate information about a population from the large-scale topographic data. Moreover, linking the obtained population data with the cadastral data – by unique building identifier – allows for regular, quick and census survey-independent updates of the population surface. A shortcoming of the approach is the issue of the possible existence of two houses per family, which leads to an overestimation of population. However, in the study area it affected only two of the total 14 communes by 7%–9%. [ABSTRACT FROM AUTHOR]

Himalayan glaciers are showing reliable evidences of climate change. Glacier status in Bhilangna basin of Garhwal Himalaya were mapped from Corona and Cartosat satellite images acquired in 1965 and 2014. The main Khatling trunk glacier that receded 4340 m resulted in fragmentation of the compound basins trunk glacier into multiple valley glaciers, which increased the number of glaciers in the basin from 20 to 33 and loss of 10% glacier area during 1965 to 2014. The Glacial erosional and depositional landforms reveal evidences of two stages of glaciation in the basin. The Khatling glacier paleoextent was reconstructed to 8.4 Km from the present snout to downstream. The satellite image interpretation and field evidences show that morphology and dynamics of the glacier has a strong influence on the faster recession of Khatling glacier. Systematic observation of glaciers from oldest Corona and recent Cartosat satellite images provide reliable information on glacier dynamics in the inaccessible terrain of the Himalaya. [ABSTRACT FROM AUTHOR]

MODIS-NDVI data from 2002 to 2014 were analysed to evaluate the effect of fire on vegetation in a test site located in Daxing'anling region (Inner Mongolia and Heilongjiang Province). Fire-affected and fire-unaffected areas were processed using two statistical approaches: detrended fluctuation analysis (DFA) and Fisher–Shannon (FS) method. The DFA allows the detection of scaling behaviour in nonstationary signals, whereas the FS method permits to identify the organization/order structure in complex signals. Our findings show that the results obtained by jointly using the two methods are consistent, enabling the characterization and discrimination between the fire-affected and fire-unaffected areas. In particular, among the investigated indices, the mean value of Fisher information measure (FIM) represents the most significant in discriminating between burned and unburned sites; its mean value for burned sites is about 2.5 that is significantly larger than that obtained for unburned sites (∼1.3). FIM is also characterized by the larger effectiveness in discriminating the two classes of sites on the base of its receiver operating characteristic based performance. The scaling exponents estimated by means of the DFA of the fire-affected pixels are averagely higher than those of the fire-unaffected ones, which, furthermore, are characterized by lower organization and higher disorder degree. Both of the two methods would contribute to identify the impact of fires on vegetation. [ABSTRACT FROM AUTHOR]

China is one of the most earthquake-prone countries in the world. The highest-priority mission after an earthquake is to rapidly save lives, and to minimize the loss of life. Rapid judgment of the trapped personnel location is the important basis to identify the emergency supply demands and carry out the search and rescue work after the earthquake. Through analyzing the main influencing factors, we constructed an assessment model of people trapped in collapsed buildings caused by the earthquakes. The accuracy of the estimation results from the model was then tested against the actual investigation data in 2014 Ludian earthquake-hit area. Results showed that, the trapped personnel distribution assessed by this model is generally concordant with that obtained by the actual survey in Ludian earthquake. The grid-based assessment of people trapped in earthquakes can meet the requirements of key search and rescue zone identification and rescue forces allocation in the early stage of earthquake emergency. Although there were some limitations in the study, it offers a simple and rapid approach for assessing the trapped people losses based on basic empirical data. The approach can be further improved to provide more information and suggestions for earthquake emergency search and rescue. [ABSTRACT FROM AUTHOR]

This study used multi-stage remote sensing data in the analysis of the evolution of deep-seated landslides at Putanpunas stream, Taiwan. Terrain analysis, landslide interpretation, and the assessment of underlying mechanisms were included. Our results show that the evolution of deep-seated landslides can be divided into four stages: (1) erosion and decompression of the gully, (2) deformation of the rock slope, (3) the development of sliding surfaces, and (4) movement of the sliding mass. A large quantity of colluvium has accumulated on the slopes and in upstream and midstream gullies. In the future, we predict that the erosion of this colluvium will greatly influence the direction of flow and terrain in the alluvial fan. This activity is also expected to threaten the safety of inhabitants and property in the area intersecting Laonong stream. [ABSTRACT FROM AUTHOR]

Fault displacements are being measured by geological observations using the method of detecting and evaluating marker rocks. Thus, the length of total displacement in a fault zone relates to position detection of marker rocks. Due to limits of human eye, we used remote sensing data and terrestrial spectral measurements at 229 locations for measuring the total offset along the Kelkit Valley segment of the North Anatolian Fault Zone (NAFZ). We examined the lithology, especially ophiolites that are older than the fault zone and can be a good marker for detecting the total offset in the region. The Advanced Spaceborne Thermal Emission and Reflection Radiometer images are subjected to Spectral Angle Mapper (SAM) method. Principal component analysis, decorrelation stretching and geological map were used to compare the SAM results. Ophiolites on either side of the fault zone were clearly classified and identified with the SAM analysis. As a result of comparison of SAM with image enhancement methods and the geological map, we measured the total fault displacement on the NAFZ in the part of the Kelkit Valley. Along the fault zone to the north and south of the ophiolites providing a right lateral offset was measured as 90 ± 5 km. [ABSTRACT FROM AUTHOR]

At the beginning of 2008, disastrous weather with snowstorms truck South China. Analysis of satellite and in situ data revealed obvious changes in ecological environments in the northern South China Sea after snowstorms. The sea surface temperature (SST) data from NOAA and Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, and the chlorophyll-a (Chla) concentration from MODIS are analysed. In the study area, the multiple-day-averaged SST dropped by 20.01% and Chla concentration increased by 22.42% during and after snowstorm. SST decreased more significantly and Chla increased in the coastal waters, while suspended sediment concentration (SSC) increased more remarkably offshore. The biggest drop of SST reached 6°C and striking elevation in Chla with 52.55% in the Taiwan Strait, and SSC changed significantly near to the Taiwan bank after the snowstorm. Further analysis indicated that the remarkable reductions in SST were caused by large cold water input from Min-Zhe coastal water and the Pearl River Estuary, as a result of strong sea-land-air interaction in the coastal waters during the snowstorm. The increase in Chla may be related to the abundant nutrients from the plenty of cold water, a strong front and vertical mixing in the coastal waters. [ABSTRACT FROM AUTHOR]

A novel approach is presented to assess the agriculture performance in the states of Telangana and Andhra Pradesh using long-term fortnightly satellite, meteorological and irrigation data-sets. National Oceanic and Atmospheric Administration (NOAA), Global Inventory Modeling and Mapping Studies (GIMMS) and Normalized Difference Vegetation Index (NDVI) data for the period 1982–2000 were used to study the pattern of anomalies in the NDVI anomalies and Standardized Precipitation Index (SPI) in the agriculture areas to capture the drought events. After hierarchical image classification and field observations, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product was used to generate the cropped area. The deviation of the NDVI (NDVIDev) was used to understand the agricultural growth/stress, variations in the cropped area and per cent fluctuation in space and time. The SPI, derived from the rainfall data pertaining to 2000–2015, was used to determine the distribution of precipitation. The crop area (%) and growth conditions during the different cropping seasons agree well with the prevailing drought conditions. A significant (p< 0.05) relation was found between the NDVI and precipitation in the summer monsoon each year except during excellent summer monsoon year. The seasonal precipitation, residual soil moisture and source of irrigation were also found to have significant (p< 0.05) impacts on the winter and summer crops. [ABSTRACT FROM AUTHOR]

Kopili River Basin is one of the chronic flood affected basins of Brahmaputra River, lies in north-eastern part of India. This study attempts to utilize the historical spatial data on flood inundation layers derived from multi-temporal remote sensing images for identifying villages falling in various flood hazard severity zones. A total of 183 flood events were mapped in the basin in the last two decades. About 3.89 lakh hectares which is 29% of Kopili River Basin area is affected by floods during 1977, 1988 and 1998–2015. The flood hazard zonation frequency is determined treating each village as minimum unit of entity and based on the number of times affected by flood events in a given year. About 742 villages are categorized as very low to low and 396 villages fall in moderate flood hazard zone and more than 150 villages are categorized between high to very high flood hazard zones. [ABSTRACT FROM AUTHOR]

An accurate gridded climatological temperature data-set can be a reliable basis for studying the issues that concern climate change, natural disasters, among others. In this study, the climate standard value data and annual observed data collected by 104 meteorological sites in Northeast China from 1971 to 2000 are used to interpolate the annual mean air temperature of the region with different spatial interpolation techniques. Efforts have also been made to verify the accuracy of the results generated by each interpolation method with error indicator, temperature characteristic value and temperature variation curves. The results show at the spatial scale, the partial thin-plate smoothing spline (PTPSS) scheme generates the most desirable temperature interpolations with a root-mean-square error at 0.34 °C and an average standard error at 0.52 °C. At the temporal scale, the PTPSS-based 1971–2000 temperature curves agree the best with the gridded temperature curves, with the correlation coefficients of means, minimums and maximums between the two being larger than 0.9. Meanwhile, the PTPSS produce the smallest error approaching zero among all the interpolation schemes tested in the study. In this context, the PTPSS scheme stands out as the most desirable interpolation method for the annual mean temperature in Northeast China. [ABSTRACT FROM AUTHOR]

The aim of this study is to build a flood simulation model for the city of Hafr Al-Batin catchment area. The model consists of a hydrologic model, a hydraulic model and pre- and post-processing tools. A geographic information system-based modelling interface was used to pre-process the terrain and rainfall data and generate input files for hydrologic and hydraulic models. Soil map data, land cover map, land use map and digital elevation model was used to delineate the physical watershed's characteristics. The runoff estimation was based on the widely known Soil Conservation Service curve number approach. The hydrologic/hydraulic model simulations simulated the runoff hydrograph corresponding to different design storms and helped to delineate the resulting flood inundation maps. The results indicate good agreement between the delineated inundation map and the hazard map developed by the municipality. They also show that the location of the city complicates the runoff response for small storms creating two distinct peaks. The results of this study can be utilized for planning purposes and in the design of flood control structures as the study has estimated the runoff corresponding to different design storms and used hydraulics and geospatial data in delineating the flood zones. [ABSTRACT FROM AUTHOR]

Although leaf area index (LAI) is one of the essential parameters employed to monitor global vegetation, no global LAI products which use a fine spatial resolution yet exist. To remedy this, we herein outline an adapted LAI retrieval algorithm which employs HuanJing-1 charge-coupled device (HJ-1 CCD) data, which was originally determined using Landsat thematic mapper (TM) data. Validation of this adapted LAI retrieval algorithm via field measurements demonstrates that errors for ∼72% of the sample sites were within a range of 0.2 for the Bashang Grassland in Hebei Province, and within 1.0 for the Taihe region in Jiangxi Province. In addition, the correlation coefficients (R) of HJ-1 LAI and ground-measured LAI data were similar to those of MODIS LAI and ground-measured LAI datasets for both regions. These results demonstrate the potential for transposing a mature Landsat LAI-retrieval algorithm to HJ-1 data. Furthermore, we generated 30 m HJ-1 LAI datasets for China for the summer of 2012. An analytical comparison between retrieved HJ-1 LAI and MODIS LAI data showed the coherence of the LAI distribution. Finally, a specific post-forest fire area was mapped using HJ-1 LAI datasets. [ABSTRACT FROM AUTHOR]

The aim of the present study is motivated to build an inundation library for a range of gauge heights, which can be used by decision-makers to anticipate the likely extent of inundation and provide quick response towards warning the habitation at threat. In the present study, two approaches for developing a series of static flood-inundation extent libraries for a range of potential flood levels using historical satellite images, gauge heights and digital elevation model (DEM) are demonstrated. First method is based on the geotagging of gauge height data with corresponding satellite observed inundation extent and the other method supplements the first method in the absence of adequate satellite data-sets by simulating inundation using gauge data and DEM for a range of gauge heights. Simulated inundation extents are validated with the satellite-derived reference inundation extents using spatial statistics, which measure the correspondence between the estimated and observed occurrence of events like probability of detection (POD), false-alarm ratio, and critical success index (CSI). A good correlation between the simulated inundation and satellite-derived inundation extents, with POD varying between 87% and 94%, CSI between 75% and 80% is observed. [ABSTRACT FROM AUTHOR]

On 8 May 1939, an earthquake (Ms7.1) occurred near the Azores archipelago, with an epicentre located close to the western end of the Gloria fault. Previous studies present different epicentre locations spreading over a large area, and two different types of focal mechanisms. Given these uncertainties, the interpretation of the seismological information in a complex tectonic environment between the Gloria Fault and the Terceira Ridge is a matter of debate. The event caused a small tsunami recorded in the Azores Islands. In this study, we use the tsunami observations and tsunami numerical modelling to select the earthquake fault rupture that best fits the tsunami observations. We consider the different focal mechanism solutions, perform tsunami numerical modelling, and compute synthetic tsunami waveforms at the tide gauge locations. We find that an earthquake caused by a low-angle dipping fault with dominant strike–slip movement generates a tsunami that reproduces well the record at Ponta Delgada tide gauge. Finally, in areas where earthquakes are rare, the study of ancient earthquakes must use all information available, namely tsunami observations and mareograph data. [ABSTRACT FROM AUTHOR]

Landslide detection and mapping are essential issues for reducing impact of such natural disasters, and for improving the future built-up expansion and planning strategies, especially in developing countries where a reasonable land-use design is an important concern for sustainable growth and environmental management. Armenia is a landlocked country and its urban development is strongly tied to the improvement of infrastructures, which must takes into account the environmental setting and the slope instability of the area, in order to identify risks and possible damages to settlements and economic activities. The use of satellite-based Earth Observation data has advanced significantly in the last decade and has turned out to be very useful for measuring and monitoring slow-moving surface deformation phenomena with millimetric precision. In this framework, this study aims at providing a remote sensing-based Landslide Inventory Map (LIM) and a Landslide Susceptibility Map (LSM) over Dilijan (Armenia) area, performed within the Secondary Cities Urban Development in Armenia project. In particular, LIM and LSM in the study area were produced by using ground deformation measurements derived from satellite Synthetic Aperture Radar (SAR) data, acquired by ALOS and ENVISAT sensors from 2003 up to 2010, and integrated with photo-interpretation of recent optical images and morphological analysis of Digital Elevation Model (DEM). Given the extensive presence of vegetation in the area of interest, satellite SAR images were processed to produce both SqueeSAR™ and Temporary Coherent Scatterers data, which are PSI (Persistent Scatterer Interferometry) data conceived as evolution of PSInSAR™ approach and particularly suited for non-urban and rural areas characterized by low density of coherent terrain benchmarks over time. Landslide mapping produced through this work identifies the most hazardous landslide-affected and landslide-prone areas around Dilijan city, and can be used for further estimating environmental risks for urban infrastructure development in the area. [ABSTRACT FROM AUTHOR]

We recommend an approach to estimate a process performance measure (or parameter) at the present time from a stream of data where the performance may drift slowly over time. It is common practice to estimate current process performance using either present-time data only or including all historical data. When sample sizes by time period are small, an estimate based only on present-time data is imprecise. When the performance changes over time, including historical data in estimation trades more bias for less variability. We propose to regulate the bias/variance trade-off using estimating equations that down-weight past data. We derive approximations for the variance of the estimator and the distribution of a test statistic involving the estimator. The work is motivated by estimation of a customer loyalty measure where realistic data demonstrates the proposed approach. [ABSTRACT FROM AUTHOR]

The sizes of defect clusters, produced in materials by energetic ion or neutron impacts, are critically important input for models describing microstructural evolution of irradiated materials. We propose a model for the distribution of sizes of vacancy and self-interstitial defect clusters formed by high-energy impacts in tungsten, and provide new data fromin situion irradiation experiments to validate the model. The model predicts the statistics of sub-cascade splitting and the resulting distribution of primary defects extending over the entire range of cluster sizes, and is able to provide initial conditions for quantitative multi-scale simulations of microstructural evolution. IMPACT STATEMENT We present a model, parameterized for tungsten, for the distribution of defect sizes in primary radiation damage, as an essential step in multi-scale modelling of microstructural evolution in irradiated materials. [ABSTRACT FROM PUBLISHER]

The article discusses the relevance of high-volume and complex data on research and development citing a study by the non-profit Industrial Research Institute. Topics covered include the attributes of big data such as velocity, variability and veracity, the definition of data scientist and their expertise in chemistry, biology or business administration, and the benefits of innovation in predictive analytics, application of machine-generated hypothesis, and disruption in the business sector.

Many of the demands to perform next generation sequencing (NGS) in the clinical laboratory can be resolved using the principles of telepathology. Molecular telepathology can allow facilities to outsource all or a portion of their NGS operation such as cloud computing, bioinformatics pipelines, variant data management, and knowledge curation. Clinical pathology laboratories can electronically share diverse types of molecular data with reference laboratories, technology service providers, and/or regulatory agencies. Exchange of electronic molecular data allows laboratories to perform validation of rare diseases using foreign data, check the accuracy of their test results against benchmarks, and leverage in silico proficiency testing. This review covers the emerging subject of molecular telepathology, describes clinical use cases for the appropriate exchange of molecular data, and highlights key issues such as data integrity, interoperable formats for massive genomic datasets, security, malpractice and emerging regulations involved with this novel practice. [ABSTRACT FROM PUBLISHER]

The article discusess pros and cons associated with artificial intelligence (AI.) It informs that drastic increases in computing power, advances in machine learning algorithms, and increases in data volumes has accelerated AI's application across industries. It also mentions that AI helped in improving part of an ongoing operation and customer service, performing a diagnostic task, and optimizing inventory management.

We introduce Bayesian spatial change of support (COS) methodology for count-valued survey data with known survey variances. Our proposed methodology is motivated by the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that provides timely information on several key demographic variables. Specifically, the ACS produces 1-year, 3-year, and 5-year “period-estimates,” and corresponding margins of errors, for published demographic and socio-economic variables recorded over predefined geographies within the United States. Despite the availability of these predefined geographies, it is often of interest to data-users to specify customized user-defined spatial supports. In particular, it is useful to estimate demographic variables defined on “new” spatial supports in “real-time.” This problem is known as spatial COS, which is typically performed under the assumption that the data follow a Gaussian distribution. However, count-valued survey data is naturally non-Gaussian and, hence, we consider modeling these data using a Poisson distribution. Additionally, survey-data are often accompanied by estimates of error, which we incorporate into our analysis. We interpret Poisson count-valued data in small areas as an aggregation of events from a spatial point process. This approach provides us with the flexibility necessary to allow ACS users to consider a variety of spatial supports in “real-time.” We show the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data. [ABSTRACT FROM PUBLISHER]