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Slow feature analysis (SFA) has shown great advantages in process monitoring. However, there is no limitation imposed on the serial correlation of feature except the first-order autocorrelation, so the residual feature may not exhibit the white noise-like behaviors and may include useful information. In this paper, according to the properties of white noise, we propose a serial correlation analysis (SCA) method to improve the performance of feature extraction, where the residual feature is guaranteed to conform to the characteristics of white noise as much as possible by minimizing the sum of the squares of the multi-order autocorrelation coefficients, and the principal features are those features with a large sum of the squares. In addition, the determination of parameters is discussed, and monitoring indices are constructed for the principal feature and the residual feature to evaluate the static and dynamic status of the process operation. Finally, the proposed SCA method is applied in two numerical cases and the Tennessee Eastman (TE) process, and the results verify the effectiveness of the proposed method. • The statistical properties of residual features extracted by SFA are analyzed. • A serial correlation analysis method is proposed to fully extract data information. • SCA is used to evaluate the process steady operation status and dynamics change. • The proposed method is applied in two numerical cases and the TE process. [ABSTRACT FROM AUTHOR]

To forecast extinction risks of natural populations under climate change and direct human impacts, an integrative understanding of both phenotypic plasticity and adaptive evolution is essential. To date, the evidence for whether, when, and how much plasticity facilitates adaptive responses in changing environments is contradictory. We argue that explicitly considering three key environmental change components – rate of change, variance, and temporal autocorrelation – affords a unifying framework of the impact of plasticity on adaptive evolution. These environmental components each distinctively effect evolutionary and ecological processes underpinning population viability. Using this framework, we develop expectations regarding the interplay between plasticity and adaptive evolution in natural populations. This framework has the potential to improve predictions of population viability in a changing world. Global biodiversity is jeopardised by unprecedented environmental change, the hallmark of the Anthropocene. To estimate the extinction risks of species, understanding how individuals and populations respond to changing environments is crucial. Adaptive evolution and phenotypic plasticity are two key mechanisms by which natural populations avoid extinction in the face of environmental change. However, the relative roles and interplay between the two are still unresolved. Whether plasticity hinders (H1) or facilitates (H2) adaptive evolution has been ardently researched, but without cross-study standardization of how changing environments impact whether (H1) or (H2) is more likely over time. We propose an integrative framework based on how key environmental components influence the 'building blocks' of ecoevolutionary responses to examine when plasticity aids or hinders adaptive evolution. We synthesise key microevolutionary and ecological processes regarding how natural populations respond to environmental change. Studies may benefit from this framework to deepen our understanding of how plasticity influences adaptive evolution by reframing H1 and H2 in the context of environmental change, and will thus increase our ability to forecast extinction risks in the Anthropocene. [ABSTRACT FROM AUTHOR]

This article studies the limit of the empirical distribution induced by a mutation-selection multi-allelic Moran model. Our results include a uniform in time bound for the propagation of chaos in L p of order N , and the proof of the asymptotic normality with zero mean and explicit variance, when the number of individuals tend towards infinity, for the approximation error between the empirical distribution and its limit. Additionally, we explore the interpretation of this Moran model as a particle process whose empirical probability measure approximates a quasi-stationary distribution, in the same spirit as the Fleming – Viot particle systems. [ABSTRACT FROM AUTHOR]

Hosting mega sports events is a key driver for sustainable development, particularly through fostering tourism marketing and planning. Although the 2022 FIFA World Cup has received considerable attention, as the first global event hosted in the Middle East and Arab world, studies that highlight and investigate the potential benefits of hosting such an event are rare. This study assessed the geographical accessibility to archaeological sites, monuments, and museums across Qatar, providing clear guidelines on how to represent the national cultural and historical heritage to global audiences during the 2022 mega event, to maximise socioeconomic revenue. Spatial data were assembled within GIS platforms to assess accessibility utilising geospatial techniques, such as the average nearest neighbour, near analysis, spatial autocorrelation (Moran 'I Index), and cost distance to estimate travel times. The analysis indicated that the archaeological sites are spatially clustered, predominantly concentrated in Doha and Al-Rayan municipalities, where historical and cultural landmarks are located. However, most castles, forts, and archaeological sites located outside the capital zone are less accessible, and at a long distance from hotels and residential areas. As the hosting stadiums are located along the northeast coast and within the most populous zone, the museums, towers, and cultural landscapes are easily accessible within a short travel time (less than 10 min). As a host community, planners and policymakers in Qatar may benefit from this research, as a spatial guideline to promote sustainable development of tourism, through facilitating accessibility to monuments and museums, as well as enriching the representation of the national tangible heritage to a global audience. • For host country, FIFA World Cup has crucial impacts on sustainable tourism. • Qatar World Cup 2022 offers a great opportunity to represent the national heritage. • Facilitating accessibility to monuments is a strategic pillar for the host country. • Geospatial techniques have been utilised to measure accessibility patterns. • Monuments within the capital zone are spatially clustered and easily accessed. [ABSTRACT FROM AUTHOR]

This study aimed to determine the spatiotemporal distribution and epidemiological characteristics of hospital admissions for carbon monoxide poisoning (COP) in Guangdong, China, from 2013 to 2020. Data on age- and sex- specific numbers of hospital admissions due to COP in Guangdong (2013–2020) were collected. Daily temperatures were downloaded through the China Meteorological Data Sharing Service System. We analyzed temporal trends through time series decomposition and used spatial autocorrelation analysis to detect spatial clustering. The distributed lag nonlinear model was used to quantify the effects of temperature. There were 48,854 COP admissions over the study period. The sex ratio (male to female) was 1:1.74. The concentration ratios (M) ranged from 0.73–0.82. The highest risk occurred in January (season index = 3.59). Most cases were concentrated in the northern mountainous areas of Guangdong with high-high clustering. COP in the study region showed significant spatial autocorrelation, and the global Moran's I value of average annual hospital admission rates for COP was 0.447 (P < 0.05). Low temperatures were associated with high hospital admission rates for COP, with a lag lasting 7 days. With a lag of 0 days, the effects of low temperatures [5th (12 °C)] on COP were 2.24–3.81, as compared with the reference temperature [median (24 °C)]. COP in Guangdong province showed significant temporal and spatial heterogeneity. Low temperature was associated with a high risk of COP, and the influence had a lag lasting 7 days. [ABSTRACT FROM AUTHOR]

Extracting weak impulses from vibration signals is a tricky technique in fault diagnosis and condition monitoring of the rotating machinery. The autocorrelation function is an effective method for enhancing repetitive signals, however, the effect deteriorates as the point number of autocorrelation calculation decreases. To resolve this problem, a novel algorithm entitled extended autocorrelation function is presented. Besides, an algorithm called local maximum instantaneous extraction transform is proposed to further improve the enhancement effect, combining short-time Fourier transform, gamma transform, and local maximum sampling. Therefore, the presented algorithm is called the local maximum instantaneous extraction transform based on the extended autocorrelation function (LMIET-EACF). Firstly, we discuss the mathematical statistics theory of the autocorrelation function for realizing signal enhancement. It is illustrated that, when the number of data points involved in the calculation decreases, the amplitude variances of the noise components will increase. This is the limitation of the autocorrelation function. Secondly, an extension operation is conducted in the extended autocorrelation function to make the variance constant by keeping the number of calculation points constant. This operation can improve the enhancement effect for the fault impulses. Furtherly, the short-time Fourier transform is employed to suppress the noise. The contrast of the time–frequency distribution is improved by the gamma transform. And the local maximum sampling is employed to extract the fault impulse peaks to improve the time–frequency concentration. These operations constitute the local maximum instantaneous extraction transform. The proposed method is validated by a series of simulated signals and two public experimental signals. And a comparison is done between the LMIET-EACF and other mainstream signal enhancement algorithms. The results indicate that the proposed method performs better than several common time–frequency distributions, by quantitatively evaluating with the Rényi entropy. In addition, it can catch the fault signal in a lower signal-to-noise ratio, compared with some novel post-processing methods. [ABSTRACT FROM AUTHOR]

The growing demand for spatially detailed population products in various fields continues to rise, as users shift their focus from aggregated areal totals to high-resolution grid estimates. Aggregating demographic data to areas, such as census tracts or block groups, can mask localized heterogeneities within those areas. This paper presents a new pycnophylactic (density-preserving) geospatial model for disaggregating population to high-resolution grids. We describe a Bayesian Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM), which incorporates land cover covariates and two levels of spatial autocorrelation. We evaluated the model's predictive ability first with simulation studies, and then by disaggregating census population data for Davidson County, TN, from the census tract-level to a fine grid and comparing predicted to actual block-level population counts. The interpolated population map successfully identified spatial heterogeneities, such as hot- and cold-spots within census tracts. The HPDSRM model out-performed three other types of disaggregation modeling, which suggests the value of incorporating spatial autocorrelation. Based upon this study, HPSDRM has potential for disaggregating other demographic data, such as socioeconomic indicators. • We introduced a new pycnophylactic geospatial model, the Bayesian Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM). • The Bayesian Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM) incorporates hierarchical structures in the latent field to model the spatial autocorrelations. • The evaluation of the HPSDRM model's predictive ability using simulation studies and real-world census population data in the Davidson County, Nashville, demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

The spatial information has been widely utilized in Spectral Mixture Analysis (SMA) studies over the years. However, the spatial autocorrelation, as a prerequisite of these studies, was seldomly examined. In the complex urban systems, it remains largely unknown whether and how land cover spectra at different locations are spatially autocorrelated and to which extent spatial autocorrelation can explain the great spectral variability. This study proposed an SMA method with Spatial Endmember Modeling (SEMSMA) to improve the fraction estimation of major urban land cover types. SEMSMA used the Incremental Spatial Autocorrelation (ISA) analysis to quantify spatial autocorrelation patterns in broadband reflectance. With the patterns, SEMSMA predicted endmember signatures that incorporated both spatial (location-oriented) and non-spatial spectral variability via the Restricted Maximum Likelihood-Empirical Best Linear Unbiased Predictor (REML-EBLUP) and Monte Carlo sampling. The spectral variabilities were then addressed in pixel unmixing by an iterative mixture analysis method. Results of ISA demonstrated diverse spatial autocorrelation patterns in reflectance of urban land covers. These patterns can be related to the pathways that spatial processes acted on land cover properties. The predicted endmember signatures explained most of the spectral variability for all land cover types. Subpixel fraction estimates showed that SEMSMA outperformed multiple endmember SMA (MESMA) and spatially adaptive SMA (SASMA), based on the root mean square error (0.04–0.15), the mean absolute error (0.01–0.09), and the Systematic Error (SE) (−80.00–0.01). The low SE of SEMSMA revealed unbiased estimation of land cover fractions. Results of this study underscored the necessity of spatial autocorrelation examination and the advantage of rigorous spatial endmember modeling when incorporating spatial information into SMA. [ABSTRACT FROM AUTHOR]

This paper devotes to give a comprehensive review of Z -valued time series models, which allow negative autocorrelations besides positive autocorrelations. Z -valued versions of integer-valued autoregressive (INAR) models are mainly based on three different operators: signed thinning operators, difference operators and rounding operators, while Z -valued versions of integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models can be classified into four categories: some Z -valued discrete distributions, difference of two independent nonnegative INGARCH models, nonnegative INGARCH models multiplying by some special Z -valued discrete random variables, and random rounding operators. We review stochastic properties, briefly discuss the estimation methods of the Z -valued INAR-type and INGARCH-type models and use two real examples to show their applications. In addition, we also consider some other Z -valued models, including the Bernoulli difference model with taking values − 1 , 0 , 1 , and the discrete ARMA model. Finally, some possible extensions are discussed. [ABSTRACT FROM AUTHOR]

What are the patterns and predictors of corruption across space and time? Existing studies have approached this question either through descriptive statistics or spatiotemporal autocorrelation analysis. Moreover, they have primarily relied on ad hoc predictor sets. This paper, however, implements emerging hot spot analysis and geographically and temporally weighted regression (GTWR) to examine corruption heterogeneity across Vietnam. In so doing, it draws on ecological complexity theory and informal payments data over the 2006–2020 period. Subsequently, emerging hot spot analysis indicates corruption is declining in every Vietnamese region. At the same time, GTWR elaborates on corruption's spatiotemporal heterogeneity: (a) only two predictors (i.e., net migration and proactivity) have consistent positive or negative associations with corruption; and (b) the variance of most predictors' corruption impact is high. Furthermore, whereas labor has the strongest corruption impact in North Vietnam, provincial leaders' proactivity takes precedence in Central and South Vietnam. These findings, then, underscore the significance of incorporating ecological complexity theory into the study of corruption. [ABSTRACT FROM AUTHOR]

The uncertain mechanical parameters of clay layer under torrential rain are the key to the dynamic evolution process and stability assessment of landslide geological hazards. Due to the complex environment, engineering geology and physical chemistry process, the mechanical parameters of clay layer show significant spatial variability and correlation. In addition, due to technical and economic conditions constraints, the actual investigation and test data of soft cohesive soil are very limited, which seriously restricts the stability evaluation of clay slope and the prevention of instability disaster. To characterize anisotropic spatial variations of uncertain mechanical parameters for clay layer using incomplete probability data, the elastic modulus, Poisson ratio and shear strength under saturated conditions were measured, and statistical data and variation properties of uncertain mechanical parameters were analyzed. A modeling approach was proposed for characterizing incomplete probability data of clay layer. The accuracy of the proposed approach is verified by comparison of the statistical characteristic for measured data and simulated data. A novel linear fitting method was proposed for assessing scale of fluctuation and autocorrelation distances. The variability and correlation of uncertain mechanical properties for soft cohesive soil layer are discussed. The results show that the mechanical properties of the clay layer are uncertain in spatial position. Both the original observation data and the simulated data of three mechanical parameters have symmetrical correlation structure. The clay layer display the horizontal layered structure on the soil profile, and the vertical autocorrelation distances are shorter than the horizontal distances. This paper clearly illustrates the anisotropic spatial variations of uncertain mechanical parameters for clay layer using incomplete probability data and it can provide scientific data for the uncertainty analysis and risk assessment of clay slope under torrential rain conditions. • A modeling approach was proposed for characterizing incomplete probability data of clay layer. • Statistical data and variation properties of uncertain mechanical parameters were analyzed. • A novel linear fitting method was proposed for assessing scale of fluctuation of spatial variations. • Variability and correlation of uncertain mechanical properties for clay layer were discussed. [ABSTRACT FROM AUTHOR]

• Analysis of fatal nighttime pedestrian collisions 2002–2017 using FARS data. • Post-2009 fatality increase most strongly correlated with infrastructure factors. • Unmarked non-intersections saw 80.8% of additional fatalities and arterials 81.1%. • 40–45 mph roads (54.6% of additional); five-lane roads (40.7%); urban (99.7%). • SUVs involved in 39.7% of additional fatalities, overrepresenting fleet share. • Age of pedestrians killed increased more (18.1%) than the national average (3.2%). Introduction: Pedestrian fatalities in the United States increased 45.5% between 2009 and 2017. More than 85% of those additional pedestrian fatalities occurred at night. Method: We examine Fatality Analysis Reporting System (FARS) data for fatal pedestrian crashes that occurred in the dark between 2002 and 2017. Within-variable and before/after examinations of crashes in terms of infrastructure, user, vehicle, and situational characteristics are performed with one-way analysis of variance (ANOVA) and two-sample t -tests. We model changes in crash characteristic proportions between 2002–2009 and 2010–2017 using linear regressions and test for autocorrelation with Breusch-Godfrey tests. Results: The increase in fatal nighttime pedestrian crashes is most strongly correlated with infrastructure factors: non-intersection unmarked locations (saw 80.8% of additional fatalities); 40–45 mph roads (54.6%); five-lane roads (40.7%); urban (99.7%); and arterials (81.1%). In addition, SUVs were involved in 39.7% of additional fatalities, overrepresenting their share of the fleet. Increased pedestrian alcohol and drug involvement warrant further investigation. The age of pedestrians killed increased more (18.1%) than the national average (3.2%). Conclusions: By identifying factors related to the increase in nighttime pedestrian fatalities, this work constitutes a vital first step in making our streets safer for pedestrians. Practical Applications: More research is needed to understand the efficacy of different solutions, but this paper provides guidance for such future research. Engineering solutions such as road diets or traffic calming may be used to improve identified infrastructure issues by reducing vehicle speeds and road widths. Rethinking vehicle design, especially high front profiles, may improve vehicle issues. However, the problems giving rise to these pedestrian fatalities are likely a result of not only engineering issues but also interrelated social and political factors. Solutions may be correspondingly comprehensive, employing non-linear, systems-based approaches such as Safe Systems. [ABSTRACT FROM AUTHOR]

• Sudden Oak Death showed strong spatial autocorrelation in Europe and some in the US. • Peaks of sudden oak death was mainly in May and June, and had obvious seasonality. • The spread was spatiotemporally clustered, in many areas with multiple outbreaks. • La niña had a 6-month lagged impact on the sudden oak death outbreak in the US. Sudden Oak Death (SOD) is a devastating forest disease caused by Phytophthora ramorum , leading to rapid branch and leaf wilting. This study analyzed SOD data from various sources to study the spatiotemporal distribution of this pathogen globally. The spatial autocorrelation analysis shows that seven counties in the US have positive spatial correlations with high clustering values, whereas, the other six counties have negative correlations with low clustering values. In Europe, the regions with positive spatial correlations with high clustering values are located in England, Scotland, southern Finland, northern Germany, western France, and northern Spain, and many areas show negative correlations with low clustering values. The results of time series analysis reveal a clear seasonal pattern of pathogen incidence in the western US, with the peak of pathogen occurrences mainly in May and June. Furthermore, our spatiotemporal permutation scanning approach detects six clusters in the US and four in Western Europe during 2005–2006, 2012–2013, and 2021, providing insight into temporal dynamics and geographical hotspots of SOD. Our study also explored the impact of El Niño-Southern Oscillation (ENSO) on SOD for the first time, revealing a lag correlation up to 6 months. By improving our understanding of SOD spatiotemporal patterns and dynamics, we can better predict future trends and mitigate its impact. We recommend strengthening priority protection of host plants in areas where SOD outbreaks were clustered during La Niña events, and containing outbreak risks through effective detection, early warning and isolation of susceptible plants. This study provides a basis for global forest protection and disease prevention efforts to save susceptible plants. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) can result in suboptimal monitoring performance due to nonlinearity, dynamics, and local characteristics in modern complex industrial processes. To surmount these limitations, this paper first proposes a novel data construction method to model the short-term autocorrelation and spatial correlations as a three-dimensional matrix and then reorder the elements of it to better encode the local and temporal structures. Subsequently, we design a new structure called Long-range Discriminative Attention (LDA) based on the self-attention mechanism to enlarge the receptive field of the original convolutional neural networks (CNNs) to extract global features. Finally, we propose a monitoring model named Long-range Discriminative Attention Autoencoder (LDCA) based on LDA to extract structural features between long-range and local variables from the constructed matrix. The effectiveness of the method in fault detection is verified by numerical examples and a three-phase flow process. • Model the spatiotemporal correlation into a three-dimensional tensor. • Reorder elements in data tensor to present the meaning of images. • Long-range discriminative attention mechanism for feature extraction. [ABSTRACT FROM AUTHOR]

Hourly precipitation forecasting is considered a spatiotemporal sequence forecasting problem that plays an increasingly important role in early warning of rainfall-induced floods and secondary disasters. Current popular precipitation forecasting methods usually ignore the spatial autocorrelation features, resulting in limited spatial information representations and extractions. In this work, a deformable convolution long short-term memory model considering spatial autocorrelation (DConvLSTM-SAC) is proposed for short-term precipitation forecasting. The proposed model extracts irregularly distributed precipitation information by deformable convolution with better generalization ability and calculates the spatial autocorrelation of inputs by calculating the local Moran index of precipitation images to fully utilize the spatial distribution information of features. In the experiment, the consecutive 3-h precipitation image sequences are used as the input data for the model to predict the precipitation images for the next 3 h. Four commonly used heuristic forecasting methods are adopted as baseline models, including decision tree regression (DTR), random forest regression (RF), recurrent neural network (RNN) and convolutional long short-term memory (ConvLSTM) methods. The proposed DConvLSTM-SAC model exhibits higher predictive accuracy than the four baseline models during the experiments and effectively improves the spatial patterns, with the most significant improvement in the first-hour lead time. The average R2 values for the first-hour prediction are 0.842, 0.838, 0.834, 0.850 and 0.892 for the DTR, RF, RNN, ConvLSTM and DConvLSTM-SAC models, respectively. As a result, the average R2 increased by 4.96%, and the average RMSE (MAE) decreased by 15.21% (9.01%) for the proposed DConvLSTM-SAC model relative to the ConvLSTM method during the first-hour prediction. • Deformable convolution is better suited to extract precipitation information. • Spatial autocorrelation fully utilizes the aggregation information of precipitation. • The DConvLSTM-SAC model outperforms DTR, RF, RNN and ConvLSTM predictions. [ABSTRACT FROM AUTHOR]

While research on new cities is emerging across African cities, focusing on housing, infrastructure, and service provision, little is known about walking accessibility for residents within and outside these new cities. Using the national capital of Ghana as a case study, the purpose of this paper is twofold: (a) it measures walking accessibility within and outside new cities in Accra; and (b) it compares walking accessibility to urban amenities between residents in and outside new cities. Publicly available data on the location and distribution of various urban amenities in Accra were obtained, and accumulated distance and spatial autocorrelation statistics were used. Our findings demonstrate that while at the metropolitan level, less than 50% of residents have walking accessibility to the nearest parks, schools, and new job opportunities within 15 min' walk, those in new cities were significantly disadvantaged in terms of walking accessibility to parks, job opportunities, schools, and public transport stops compared to residents outside. The findings show that the location of new cities does not consider walking accessibility in Accra. This finding underscores the uncoordinated and splintering phenomenon that epitomizes the spatial location of new cities and their contribution to unsustainable urban forms. Given that new cities are rapidly emerging in African cities, we recommend a more coordinated and balanced provision of urban amenities within walking distance in new cities and peripheral areas of African cities to generate efficient, livable, and sustainable urban forms. • This paper examines walking accessibility for residents within and outside new cities. • Walking accessibility to six work and non-work activities is computed. • Findings indicate new cities are disadvantaged in terms of walking accessibility to amenities. • It recommends a more coordinated and balanced provision of amenities within walking distance in new cities. [ABSTRACT FROM AUTHOR]

• Developed a time series model for monthly MSW generation. • Used Non-linear autoregressive neural models to forecast monthly MSW generation. • Constructed self-lagged NAR model for waste prediction along with model comparison. • Used Partial-autocorrelation in NAR model to forecast with seasonal variations. Municipal solid waste (MSW) generation is a multi-variable dependent process and hence its quantification is relatively not easy. The estimations for monthly MSW generation are required to provide theoretical guidelines for understanding and designing the disposal system. These estimations help in budgetary planning for the handling of future waste with optimized waste management system. This study forecasts the monthly MSW generation in Nagpur (India) for the year 2023 using non-linear autoregressive (NAR) models. The classical multiplicative decomposition model with simple linear regression in time series was constructed with maximum absolute error of 6.34% to overcome the problem of data availability. It was observed that NAR neural models were able to predict short-term monthly MSW generation with absolute maximum error of 6.45% (Model A) and 3.05% (Model B) for the observation period. It was also concluded that the variation in MSW generation was best captured when yearly lagged values were used for the construction of NAR model and coefficient of efficiency (E) was 0.99 and 0.97 during testing and validation, respectively. It was found that in the year 2023, the city will record minimum waste generation in the month of February and maximum in the month of December. For the year 2023, it had been estimated that the maximum 48504 ± 1569 tons of waste in December and minimum 39682 ± 471 tons in February will be generated. It had also been estimated that the minimum waste generation from the year 2017 to 2023 will increase by approximately 5345 tons. [ABSTRACT FROM AUTHOR]

Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally highest, and a change in the scale could cause an abrupt change in these measures. Unsupervised parameter optimization methods typically use global measures of spatial and spectral properties calculated from all image objects in all bands as the target criteria to determine the optimal segmentation scale. However, no studies consider the effect of noise in image spectral bands on the segmentation assessment and scale selection. Furthermore, these global measures could be affected by outliers or extreme values from a small number of objects. These issues may lead to incorrect assessment and selection of optimal scales and cause the uncertainties in subsequent segmentation and classification results. These issues become more pronounced when segmenting hyperspectral data with large spectral variability across the spectrum. In this study, we propose an enhanced method that 1) incorporates the band's inverse noise weighting in the segmentation and 2) detects and removes outliers before determining segmentation scale parameters. The proposed method is evaluated on three well-established segmentation approaches – k-means, mean-shift, and watershed. The generated segments are validated by comparing them with reference polygons using normalized over-segmentation (OS), under-segmentation (US), and the Euclidean Distance (ED) indices. The results demonstrate that this proposed scale selection method produces more accurate and reliable segmentation results. The approach can be applied to other segmentation selection criteria and are useful for automatic multi-parameter tuning and optimal scale parameter selections in OBIA methods in remote sensing. [ABSTRACT FROM AUTHOR]

• Bike-sharing networks have a small-world property. • The connectivity of bike stations is highly related to their accessibility, while not to their intermediateness. • The spatial distributions of connectivity and accessibility of bike stations have a strong spatial autocorrelation. • Bike-sharing networks have a polycentric structure. To solve the last-mile problem and promote low carbon transportation, hundreds of cities around the world have launched bike-sharing services. A growing number of studies have been mining bike-sharing data from different cities to understand bike-sharing systems. However, there is still a lack of comprehensive understanding of a bike-sharing network structure. Thus, this paper attempts to use complex network theory and spatial autocorrelation analysis approaches to examine structural properties of bike-sharing systems, quantify the importance of bike stations in the network, and evaluate their spatial clustering patterns. This research analyzes bike-sharing trip data from five different sized bike-sharing systems in the United States, Canada, and China. The results of network analysis reveal that the bike-sharing networks have a small-world property with a small average path length and a high clustering coefficient. Compared with medium-scale networks, the connectivity and accessibility of bike stations are relatively low in the large-scale bike-sharing network. Also, the connectivity of bike stations is positively related to their accessibility while they are not highly correlated with their intermediateness. The spatial clustering analysis results indicate that the spatial distributions of connectivity and accessibility of bike stations have a strong global spatial autocorrelation. Also, the stations with high connectivity and accessibility are concentrated in the urban centers, and the stations with low connectivity and accessibility are clustered in the periphery of the urban areas. However, their intermediateness does not show a strong global spatial clustering pattern, which implies that bike-sharing networks consist of multiple sub-groups. The findings provide new insights for transport planners and managers to understand bike-sharing systems and to improve their service quality. [ABSTRACT FROM AUTHOR]

This study tests whether the built environment influences total distance traveled by auto for non-work trips on a weekday. Using cross-sectional data from Halifax, Canada, we identify a strong causal relation between the built environment and auto distance traveled by worker and non-worker for a selection of non-work travel. We apply linear regression and the spatial lag model to control for spatial autocorrelation and find that the presence of built environment variables in linear regression handles the autocorrelation problem. We use attitude variables to control for residential self-selection. The importance of measuring the built environment near home and workplace is demonstrated. Also, we find that an empirically derived geographical scale of measuring the built environment outperforms the commonly used quarter-mile scale. The study demonstrates the importance of selecting a suitable set of trips for travel behavior-built environment analysis and suggests that future studies should classify travel based on trippurpose and examine what types of trips are influenced by the built environment. • Impact of the built environment on total distance traveled by auto for non-work trips on a weekday. • Data are from the Halifax Space-Time Activity Research (STAR) Project. • Built environment characteristics are measured for empirically-derived geographical scales. • Spatial lag models are estimated for workers and non-workers to account for spatial autocorrelation. • Built environment variables improve the goodness-of-fit of the models. [ABSTRACT FROM AUTHOR]

We consider a Lévy driven continuous time moving average process X sampled at random times which follow a renewal structure independent of X. Asymptotic normality of the sample mean, the sample autocovariance, and the sample autocorrelation is established under certain conditions on the kernel and the random times. We compare our results to a classical non-random equidistant sampling method and give an application to parameter estimation of the Lévy driven Ornstein–Uhlenbeck process. • Analysis of Lévy moving average processes sampled at a renewal sequence. • Stationarity of moving averages when randomly sampled is shown to be preserved. • Asymptotic normality of the sample mean and the autocorrelation is established. • Parameter estimation of an Ornstein–Uhlenbeck process sampled at Poisson times. [ABSTRACT FROM AUTHOR]

The ionosphere in low-latitude regions has intense dynamics with great variability – not only spatially but also temporally. The most critical effect found in the low-latitude region is the scintillation in phase and amplitude due to plasma bubble occurrence in the ionospheric layer. To augmentation systems like GBAS or SBAS, designed to provide Category-I (CAT I) precision approach service in low-visibility conditions, plasma bubbles can compromise the performance requirements for air navigation precision approach in a way that the service provided by the system is considered unviable. In practical terms, this is the main reason why the Brazilian Department of Airspace Control (DECEA) still has not implemented any augmentation system in Brazil. Scintillation may lead to loss of signal lock of the affected satellite. In this context, a better understanding of the scintillation pattern and its statistical properties gain particular relevance, once this is the most concerning issue for the performance of any GNSS receiver in low-latitude regions especially in the context of augmentation systems for air navigation. Previous works validated the use of the α-μ distribution to characterize amplitude scintillation. The present work is concerned with proposing a new method for the estimation of the α-μ coefficients based on the α-μ autocorrelation function. The method is validated using field data and comparing the results from empirical autocorrelation function with the results obtained from the moment-based estimator. Additionally, the efficiency of the method is proven by analyzing measurements of Level-Crossing Rate (LCR) and Average Fading Duration (AFD) with their respective theoretical formulation. [ABSTRACT FROM AUTHOR]

In order to represent the spatial variability of layered rock mass mechanical parameters in Finite Difference Method (FDM) grids, an approach based on the numerical simulation of cross-correlated non-Gaussian random fields was proposed in this paper. First, the discretization of a Gaussian random field was performed using the Karhunen-Loève (K-L) expansion. Then, the Gaussian random field for layered rock mass parameter was built by geostatistics and the random field size calculation. Next, the cross-correlated non-Gaussian random fields were simulated using Nataf transformation and isoprobabilistic transformation methods. Furthermore, a grid model coupled to the random fields was finally built using regroupment and element traversal methods. The engineering application showed that the spatial variability, autocorrelation, cross-correlation and non-Gaussianity described by the random fields agreed closely with the statistical characteristics. The correspondence between the random fields and grid model verified that the random parameters in the grid model can represent the anisotropy of layered rock mass parameters. This study provides a computational basis for quantitative risk assessment during tunnel excavation in layered rock mass. [ABSTRACT FROM AUTHOR]

• Capability of maximum likelihood estimator for autocorrelation distances is assessed. • Effect of autocorrelation structure on autocorrelation distance estimation is studied. • A clustered borehole layout scheme is used to estimate autocorrelation distances. • A resampling method is proposed to estimate autocorrelation distances. • The proposed methods are illustrated using some real SPT and CPT data. A simultaneous estimation of the vertical and horizontal autocorrelation distances (ACDs) for geotechnical properties under the condition of limited data (fewer than one hundred data points) is not well studied. This paper evaluates the performance of the maximum likelihood estimator (MLE) in estimating autocorrelation distances using some simulated data. The effect of the autocorrelation structure on the accuracy of ACD estimation is also investigated. It is found that MLE may produce highly biased estimations if limited data are available. Hence, a clustered borehole layout scheme and a resampling method are proposed to improve the estimation accuracy. The methods are illustrated using some simulated data, standard penetration test (SPT) data and cone penetration test (CPT) data. It is found that the clustered borehole layout scheme and resampling method yield more accurate estimations of ACDs in analyses using both the simulated data and real data. [ABSTRACT FROM AUTHOR]

Highlights • We study the stochastic behavior of the LMS algorithm when the input signal is a cyclostationary colored Gaussian process. • The analysis is performed for cyclostationary input signals modeled as colored Gaussian random processes with periodically time-varying power. • A new model is proposed for the autocorrelation matrix of an input vector of samples of a second order wide sense cyclostationary signal. • The proposed model is a good approximation of the autocorrelation matrix of an autoregressive signal with periodically varying power. • Simulation results show excellent agreement with the theoretically predicted behavior. Abstract This paper studies the stochastic behavior of the LMS algorithm for a system identification framework when the input signal is a cyclostationary colored Gaussian process. The unknown system is modeled by the standard random walk model. Well-known results for the LMS algorithm are extended to the cyclostationary case and used for predicting the mean-square weight deviation (MSD) and excess mean-square error (EMSE) behavior of the algorithm. Monte Carlo simulations provide strong support for the theory. [ABSTRACT FROM AUTHOR]

Highlights • We analyze changes in fine-scale diversity and spatial structure of tree assemblages. • Fifty stands dominated by pine and/or eucalypt were selected in Northwestern Iberia. • Pines show higher tree diversity, more heterogeneous structure and complex patterns. • Dominant tree species and human management reflect differently the forest types. • Conservation and production aims can benefit from co-existent pine-eucalypt forests. Abstract The conversion of natural and semi-natural forests into plantations is an increasing trend across European landscapes under abandonment. In Northwest Iberian Peninsula, current land use changes, namely neglected management (or even abandonment) of maritime pine (Pinus pinaster) and blue gum eucalypt (Eucalyptus globulus) stands by individual landowners, represents a major challenge for landscape planning and management. The consequences of these changes for within-stand tree diversity and structure are still under-investigated. Here, we evaluate how fine-scale diversity and spatial structure of tree assemblages in the core area of stands change with land conversion. Fifty stands were selected in a farming landscape of Northwest Portugal. At the center of each stand, a 100 m2 plot was surveyed for tree species richness and structural diversity, diameter at breast height (DBH) and spatial location of individual trees. Spatial distribution patterns of trees were evaluated using autocorrelation tests and point pattern analyses. Overall, the interior of pine stands showed higher tree diversity, with more heterogeneous DBH structure and complex patterns of tree distribution, compared to eucalypt and even abandoned mixed stands. Such differences seem to result both from the inherent characteristics of the dominant tree species, and from the differential management applied within each type of forest mosaics. Therefore, active landscape management in the study area should be promoted, particularly focusing on pine co-existence with eucalypt stands in the same landscape as a sustainable strategy to achieve both conservation and production goals. [ABSTRACT FROM AUTHOR]

Abstract Data-intelligent algorithms designed for forecasting significant height of coastal waves over the relatively short time period in coastal zones can generate crucial information about enhancing the renewable energy production. In this study, a machine learning model is designed and evaluated for forecasting significant wave height (Hs) within the eastern coastal zones of Australia. The extreme learning machine (ELM) model is coupled with an improved complete ensemble empirical mode decomposition method with adaptive noise (ICEEMDAN) to design the proposed ICEEMDAN-ELM model. This model incorporates the historical lagged series of Hs as the model's predictor to forecast future Hs. The ICEEMDAN algorithm demarcates the original Hs data from January-2000 to March-2018, recorded at 30-min intervals, into decomposed signals i.e. , intrinsic mode functions (IMFs) and a residual component. After decomposition, the partial autocorrelation function is determined for each IMF and the residual sub-series to determine the statistically significant lagged input dataset. The ELM model is applied for forecasting of each IMF by incorporating the significant antecedent Hs sub-series as inputs. Finally, all the forecasted IMFs are summed up to obtain the final forecasted Hs. The results are benchmarked with those from an online sequential extreme learning machine (OSELM) and random forest (RF) integrated with ICEEMDAN, i.e. , the ICEEMDAN-OSELM and ICEEMDAN-RF models. The proposed ICEEMDAN-ELM model is tested geographically at two coastal sites of the Queensland state, Australia. The testing performance of all the standalone (ELM, OSELM, RF) and integrated models (ICEEMDAN-ELM, ICEEMDAN-OSELM, ICEEMDAN-RF), according to robust statistical error metrics, is satisfactory; however, the hybrid ICEEMDAN-ELM model is found to be a beneficial Hs forecasting tool in accordance to high performance accuracy. The proposed ICEEMDAN-ELM model can be considered as a pertinent decision-support framework and is vital for designing of reliable ocean energy converters. Highlights • ICEEMDAN hybridized ELM model is designed to forecast half-hourly wave height. • ICEEMDAN-ELM is benchmarked against ICEEMDAN-OSELM and ICEEMDAN-RF. • Evaluation of ICEEMDAN-ELM is performed at two wave energy potential sites. • Hybrid ICEEMDAN-ELM outperforms comparative models in Australia's energy sites. [ABSTRACT FROM AUTHOR]

Abstract This paper is devoted to a method for the forecasting of seasonal time series. The core of our approach is based on the fuzzy transform and fuzzy natural logic (FNL) techniques. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and an irregular fluctuation, the forecasting is a combination of individual forecasting of each of these constituents. More precisely, the trend-cycle and the seasonal component are predicted with the help of fuzzy transform, pattern recognition and fuzzy natural logic techniques. To model the irregular component, we apply the Box–Jenkins approach. In the paper, we compare the suggested method with two other well-known methods, namely STL (see [14]) and ARIMA ones. [ABSTRACT FROM AUTHOR]

Abstract A theoretical model for interferometric autocorrelation with long nonlinear crystal (input depleted) has been developed and applied to the measurement of the duration of ultrashort pulses. The phase-matching condition is assumed throughout pulse spectrum. The interferometric autocorrelation trace of a mode-locked fibre laser (20 nJ energy, 100 kW peak power, centred at 1595 nm) has been measured by employing a fibre interferometer to avoid misalignment effects and a BBO nonlinear crystal as long as 2 mm, in order to generate higher second-harmonic power. BBO crystal was used because it can keep the phase-matching condition throughout a wide spectrum around 1600 nm. By fitting the experimental measurements and computing according to the theoretical model exposed, it has been demonstrated that the autocorrelator sensitivity is clearly enhanced by increasing the nonlinear crystal length. A temporal duration of 0.18 ps has been obtained by fitting theoretical and experimental values. [ABSTRACT FROM AUTHOR]

Abstract The Ljung–Box test is one of the most important tests for time series diagnostics and model selection. The Hassani's − 1 2 Theorem, however, indicates that the sum of sample autocorrelation function is always − 1 2 for any stationary time series with arbitrary length. In this paper, Hassani's − 1 2 Theorem is used to assess the sensitivity of the Ljung–Box test results to the number of lags. Bitcoin price return data is also used to examine the applicability of the theoretical results obtained here. The results confirm sensitivity of the Ljung–Box test to the number of lags involved in the test and therefore it should be used with extra caution. Highlights • An asymptotic is provided bound for Ljung–Box test statistics, using the Hassani's-1/2 Theorem. • The theoretical results confirm the sensitivity of the Ljung–Box test to the number of lags involved in the test. • The practical evidence from simulation study and Bitcoin Price time series data supports the theoretical results. [ABSTRACT FROM AUTHOR]

Highlights • A HMM with auto-correlated observations is developed. • A novel algorithm is presented to estimate the unknown parameters. • We discuss how to account for missing data and noise that accumulate over time. • Two RUL prediction methods based on the HMM-AO model are proposed. • An improved maintenance policy is developed based on the results of RUL prediction. Abstract In this paper, a hidden Markov model with auto-correlated observations (HMM-AO) is developed to handle the degradation modeling of manufacturing systems. Unlike the standard hidden Markov models (HMMs), the current observation in the HMM-AO model not only depends on the corresponding hidden system state, but also on the previous observations. A novel algorithm using the expectation maximum is presented to estimate the unknown parameters. Furthermore, missing data and noise that accumulate over time are also considered by modifying the proposed model. Then two remaining useful life prediction methods based on the HMM-AO model are developed. Predictive values of more accuracy can be obtained, since the autocorrelation of observations has been considered and the temporal evolution of degradation processes has been described properly. A case study is illustrated to highlight the advantages of HMM-AO and demonstrate the accuracy and efficiency of the prediction methods. Furthermore, an improved maintenance policy is developed based on the results of remaining useful life prediction. Finally, a comparison with a conventional condition-based maintenance policy is provided to prove the performance of this proposed policy. [ABSTRACT FROM AUTHOR]

Abstract Bio-inspired composites with hexagonal platelet and cylindrical inclusions were studied. A novel algorithm termed staggered hardcore algorithm (SHCA) was used to rapidly generate 3D periodic representative volume elements (RVE) for bio-inspired composites with staggered non-uniformly dispersed inclusions. The spatial dispersions of inclusions in these generated RVEs were assessed using autocorrelation analysis, demonstrating the effectiveness of the SHCA algorithm. Orthotropic elastic properties of two different bio-inspired composites were computed and compared with analytical models, namely modified shear-lag, Mori-Tanaka and Halpin-Tsai, as well as available experimental data from the literature. For lower inclusion volume fractions, the computed results correlated well with experimental data and the analytical results. However, for higher inclusion volume fractions and aspect ratios the analytical results diverged, particularly Mori-Tanaka and modified shear-lag models which was similarly reported in previous studies. The capabilities of the computational model were further demonstrated through a comparative study of orthotropic elastic constants for the cylindrical and hexagonal inclusion composites. The study revealed the necessity to use 3D micromechanical models with realistic inclusion dispersions for accurately assessing the response of high inclusion volume fraction bio-inspired composites. [ABSTRACT FROM AUTHOR]

Highlights • A method for simulation of spatial variability of gravity dams was established. • Mean value of the tensile strength has a great influence on the plastic damage. • Research results can be used as a reference for gravity dams with similar profiles. Abstract A method to simulate the random field of the strength parameters of gravity dams considering their spatial variability based on the theory of random fields and the autocorrelation function is proposed. Taking a gravity dam project as an example, the elastic modulus and tensile strength are selected as random variables and the corresponding random fields are generated. Based on the concrete damage plasticity (CDP) model, the effects of the spatial variability of parameters on the dynamic response characteristics of gravity dams are expressed from the standpoints of damage development, residual displacement, and energy dissipation. The impacts of the mean value, coefficients of variation, autocorrelation distance, realizations of the tensile strength random field, and seismic inputs on the structural damage are also investigated. It is found that the spatial variability of parameters, especially the tensile strength, has a great influence on the dynamic damage of gravity dams, and that increasing the mean value of the tensile strength can improve their seismic performance. [ABSTRACT FROM AUTHOR]

Abstract Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September 2015 to March 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with socio-demographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I = 0.10) and purchase restriction (Moran's I = 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Self-diffusion (D w) and D OH coefficients comparable up to 470 K. • Above 470 K D OH by 30% lower due to enhanced complexation of OH with H 2 O. • Continuous lifetime ∼10−14 s calculated for both H-donor and H-acceptor bond. • Intermittent lifetime 4 ps (373 K) drops to 1 ps (673 K) exceeding translational time. • Conclusions on OH aq translational dynamics depend on model potential used. Abstract MD simulations using flexible models were performed to show how velocity autocorrelation functions, diffusion (D OH) and self-diffusion (D w) coefficients, continuous and intermittent lifetimes of H-donor and H-acceptor bonds depend on: i) transformations in the hydrogen-bond network (HBN) of water below the critical point, and ii) decreasing density at the supercritical isotherm (∼670 K). Contrary to simulations neglecting short-range hydrogen-oxygen interactions, we show that complexation of OH aq with H 2 O is enhanced by breakage of the continuous HBN, resulting in the increase of OH aq hydrodynamic radius and the D OH values smaller than D w by 30%. Difference between D OH and D w increases to 65% at 670 K below the critical density. It is explained by enhanced aggregation of water molecules around OH aq , earlier recognized as the self-trapping mechanism. Except near-ambient conditions, the intermittent lifetime of H-bonds is longer than the translational time, indicating that OH aq is complexed with water and moves as a molecular aggregate. [ABSTRACT FROM AUTHOR]

Highlights • A SARIMAX model (to fit the error term) coupled with a Budyko approach is proposed. • SARIMAX / Budyko model outperforms benchmark models on 6 rivers on the monthly and annual scales. • The proposed model also outperforms a SARIMAX model with no Budyko input. • Sensitivity analysis performed on parameters to investigate water balance characteristics. Abstract The Budyko framework, used for water balance studies, is an effective tool that estimates the actual evapotranspiration, balancing between energy and water availabilities. In this study, we investigate eight Budyko framework equations as well as the GR1A and GR2M models on the Nile tributary Blue-Nile catchment. On the annual time scale, all investigated models and equations show poor results. Moreover, the resulting errors on the monthly scale show a clear pattern of seasonality and autocorrelation. To overcome such deficiencies, we propose a time series SARIMA model, to fit the error term of the best performing Budyko equation. Building on the time series capabilities, we extend the Budyko framework to allow the incorporation of values of precipitation and potential evapotranspiration from previous months. The selected performance criteria are the Nash-Sutcliffe and Kling-Gupta efficiencies, as well as the coefficient of determination and bias. The novel hybrid SARIMAX-Budyko model exhibits high performance not only on the Blue Nile but also on other five major rivers worldwide. The proposed model outperforms the previously published Budyko equations, as well as the GR1M and GR2A as benchmark models, in almost all performance criteria and especially on the annual time scale. [ABSTRACT FROM AUTHOR]

Highlights • Differences between EEG signals of visual artists and non-artists were investigated using a wide range of EEG features. • A similarity in the studied features was observed between the visual perception and the mental imagery tasks of each group. • The effective distinctive EEG features were selected using genetic algorithm. • Neurofeedback training protocols were proposed for performance improvement of novice visual artists. Abstract Differences in brain signals between skilled and non-skilled individuals led to the idea of using neurofeedback (NFB) in order to imitate brain activity patterns in skilled individuals, reinforce performance of novice people, and ultimately help them in terms of achieving skills in a faster manner. This study shed light on visual skills and aimed at designing a NFB training protocol specifically enhancing the performance of novice visual artists. For this purpose, a set of features (energy, mean and standard deviation of wavelet and cepstral coefficients, wavelength, the first zero crossing of autocorrelation function, correlation and fractal dimensions, approximate and sample entropy and scaling exponent) of EEG signals recorded from two groups of visual artists and non-artists were obtained during observation and mental imagery of several images. Then, the most effective distinctive features of the two groups and the associated channel were established by means of genetic algorithm. Fitness function in genetic algorithm was also defined using Mann-Whitney U test and Davies-Bouldin index (DB). Finally, the increase in the first zero crossing of the autocorrelation function in C4 channel was proposed as the protocol enhancing visual perception performance. Moreover, increased approximate entropy (ApEn) in Fp1 channel was suggested as a protocol for enhancing mental imagery among novice visual artists. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this study is to explore the impact of socioeconomic factors on PM 2.5 concentrations and to provide insights into air quality improvement. We firstly studied the spatial autocorrelations of PM 2.5 concentrations using global Moran's I and Local Indicators of Spatial Association, then explained the spatial heterogeneity of regional PM 2.5 concentrations and identify the driving factors on PM 2.5 by geographical detector technique, using data extracted from satellite observations over the years from 2000 to 2015. The results showed that, the annual average PM 2.5 concentration in China ranged from 11.5 μg/m³ to 18.7 μg/m³ with an upward trend in general, while PM 2.5 pollution were relatively serious in Beijing-Tianjin-Hebei region and Yangtze River Delta. Regional PM 2.5 concentrations showed significant global and local spatial autocorrelation. Regions of high PM 2.5 concentrations tend to cluster with regions of similar PM 2.5 concentrations. From a long-term perspective, population density has the greatest power of determinant on PM 2.5 , followed by electricity consumption, industry structure, coal consumption, number of vehicles per capita and GDP per capita. Over the study period, the impact of population density revealed a trend to first rise and then fall, and the impacts of GDP per capita showed a slightly upward trend. The impact trends of number of vehicle per capita and industry structure presented to be fluctuated. The determinant power of coal consumption and electricity consumption had significant downward trends. Highlights • Global and local spatial autocorrelations of PM 2.5 pollution exist in China. • Population density has the highest power of determinant on PM 2.5 concentration. • Coal consumption and electricity consumption have downward impact on PM 2.5 pollution. [ABSTRACT FROM AUTHOR]

Highlights • We investigated if there is a spatial structure of pine forest decline in Portugal. • Pine forest decline was spatially aggregated, presenting spatial autocorrelation. • Forest decline patterns could not be dissociated from climate effects. • Landscape diversity and forest density also seem to have an impact on pine decline. Abstract Context The pinewood nematode, Bursaphelenchus xylophilus , has been identified as being responsible for the decline of pine forests in Portugal, since its detection in the territory about 20 years ago. Forest decline due to disease and/or pests is a complex phenomenon, and a knowledge of the spatial patterns of tree mortality is important for providing an understanding of forest susceptibility and pest dynamics. Objectives The aim of the present study was to investigate the relationship between the spatial structure of pine mortality and that of different climatic, edaphic and landscape variables in an area of Portugal where B. xylophilus is known to have spread during the last decade. Methods Pine mortality was assessed in the field, in a large, complex area of Central-North Portugal. An ArcGis database with a range of environmental variables was created for the study area. Spatial autocorrelation of the different variables was investigated by means of Moran's I and Mantel r analysis. The relationship between pine decline and the different variables was analyzed using bivariate Moran's I. Results Environmental descriptors exhibit a clear spatial pattern, influenced by an undulating landscape. Pine mortality is spatially aggregated; warm, dry locations with higher evapotranspiration present high values, and in areas of extensive pine occupancy in a diverse landscape mortality decreases. Conclusions Forest mortality attributed to infestation by a non-native forest pathogen cannot be dissociated from the effects of climate and landscape diversity associated with pine forests. The analysis of landscape patterns appears to be crucial for gaining an understanding of forest pathology and decline. [ABSTRACT FROM AUTHOR]

Highlights • A computationally efficient coherent detection and parameter estimation algorithm via SAF-SFT is proposed. • The proposed algorithm could remove the range cell migration and Doppler spread effects without parameter-searching process. • SAF-SFT method could strike a good balance between computational cost and detection probability as well as the estimation performance. Abstract In this paper, a computationally efficient coherent detection and parameter estimation algorithm via symmetric autocorrelation function (SAF) and scaled Fourier transform (i.e., SAF-SFT) is proposed, involving range cell migration (RCM) and Doppler spread (DS) within the coherent integration (CI) time. In particular, the first SAF and SFT operations are applied to achieve the range and velocity estimations after the generalized keystone transform. With the estimations, the remaining RCM induced by target's velocity could be removed and the target signal could be extracted along the range cell. Then the second SAF and SFT operations are performed on the extracted signal, where the target energy could be coherent integrated and the acceleration estimation can be obtained. Cross term of SAF-SFT is also analyzed and its characteristic indicates the applicability in the scenario of multi-targets. Detailed comparisons of SAF-SFT with several typical algorithms with respect to computational cost, detection probability and parameter estimation ability show that the SAF-SFT could strike a balance between computational cost and detection probability as well as the estimation performance. Simulation results and real test experiment are given to verify the SAF-SFT based approach. [ABSTRACT FROM AUTHOR]

Abstract Noise distribution plays an essential role in state estimation using Kalman filter. However, statistical characteristics of the noise are often unknown in most practical applications. A second order mutual difference (SOMD) algorithm has been proposed to generate an estimation of the measurement noise covariance matrix R by calculating the autocorrelation of SOMD of redundant measurements, and thus it can avoid coupling with the state estimation error; however, the algorithm cannot be applied directly for a majority of practical systems due to the requirement of redundant measurements. In this paper, the SOMD algorithm is expanded to the system with single measurement by constructing a pseudo measurement. A non-zero estimation bias detection algorithm is presented to address the inconsistency between the mathematical model and the real. A modified robust adaptive Kalman filter (RAKF) is also developed to tackle this inconsistency and improve filtering accuracy by activating adaptive operation properly. The efficacy of the approach is demonstrated via a target tracking problem. Simulation results indicate that the proposed algorithm can reflect the noise properties accurately and outperform several reference algorithms in precision and robustness. [ABSTRACT FROM AUTHOR]

Highlights • A spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. An exploratory spatial analysis and hotspot clustering method based on check-in data is proposed to detect urban hotspot and extract commercial area by geographic distribution metric with urban commercial hotspots. • An error ellipse is adopted to calculate the rough boundary of the commercial area, then a planar Delaunay iterative triangulation algorithm is proposed to determine the exact boundary of the commercial. Satellite images are used to adjust the fine boundary of the urban commercial area. • A time sequence extraction method of the commercial area is presented to analyze the trend of evolution in the city business space. Abstract Urban hot spot detection and commercial area analysis for city economic planning and dynamic urban planning is of vital importance. However, it is difficult to obtain a more accurate commercial area boundary in the past. Check-in data obtained by a social networking service (SNS) and/or a location-based service (LBS) is a type of crowd-sourced geographic data that can reveal mass daily life activities, which provides a new big data source for urban hot spot detection and commercial area analysis. In this paper, a dynamic urban commercial area extraction and monitoring approach is proposed using SINA Weibo (a social network) check-in data. First, a check-in data pre-process model is proposed to simplify the amount of check-in data and improve the efficiency of cluster analysis. The spatial autocorrelation validation is implemented to validate the significant patterns of the spatial clustering of check-in data. Then, an exploratory spatial analysis and hot spot clustering method based on check-in data is proposed to detect urban hot spots and extract commercial areas using a geographic distribution metric with urban commercial hot spots. Second, the hot spot cluster analysis results are taken to determine the center of the commercial area and calculate the distribution of an ellipse, which is adopted to obtain the rough boundary of the commercial area. A planar Delaunay iterative triangulation algorithm is presented to determine the exact boundary of the commercial area. Then, the time sequence extraction result of the commercial area is presented to analyze the evolutionary trend in the city business space. Finally, the Weibo check-in data from 2012 to 2014 of Wuhan city are taken as an example dataset for the commercial area extraction and detection with the proposed approach. The results show that the method can accurately determine the boundary of and changes within the commercial area in Wuhan city. This study provides a new method for the monitoring of hot spots and the geographical situations of city commercial areas. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Spatiotemporal characteristics of PM 2.5 are analyzed and its relationship between PM 2.5 and SO 2 , CO, NO 2 , and O 3 are explored. • Monthly average PM 2.5 concentrations were low in spring and summer but high in winter and autumn. • PM 2.5 concentrations decreased from north to south in Anhui Province, but the most cities didn't meet the national standards. • There was significant positive correlation between PM 2.5 and SO 2 , CO, and NO 2 , but negative with O 3. Abstract Air pollution is one consequence of rapid, massive, and disorganized urbanization. In China, pollution with PM 2.5 has caused widespread government and public concern. Many studies report that the PM 2.5 increase is associated with the increase of other molecules. Using statistics and spatial autocorrelation analysis methods, this study analyzes the temporal-spatial characteristics and agglomeration distribution of PM 2.5 concentrations in 21 cities of Anhui Province, China (one of the most polluted provinces in the Yangtze River Delta) over 2015 and 2016. In Hefei, the capital city of Anhui Province, a Pearson correlation coefficient was used to identify the relationship between PM 2.5 and SO 2 , CO, NO 2 , and O 3. The concentration levels show that (1) daily average PM 2.5 concentrations fluctuated wildly in different seasons of both years, (2) monthly average PM 2.5 concentrations were low in the spring and summer but high in the winter and autumn, and (3) PM 2.5 concentrations in 21 cities exceeded the annual PM 2.5 standards (≤ 35 μg/m³) and daily limit (≤ 75 μg/m³) with the second year showing lower levels. The annual average PM 2.5 had significant spatial autocorrelation and clustering characteristics with high concentrations of PM 2.5 expanding in northern Anhui, whereas lower concentrations occurred in the south. Additionally, SO 2 , CO, and NO 2 had significant correlations with PM 2.5 , but O 3 had no significant correlation with PM 2.5. This study provides a scientific basis for understanding the impact mechanisms of atmospheric pollution. It also outlines an understanding of pollution characteristics, which enables government to make efficient policies that jointly prevent and control pollution. [ABSTRACT FROM AUTHOR]

Abstract Home range estimation is an important analytical method in applied spatial ecology, yet best practices for addressing the effects of spatial variation in detection probability on home range estimates remain elusive. We introduce the R package "DiagnoseHR," simulation tools for assessing how variation in detection probability arising from landscape, animal behavior, and methodological processes affects home range inference. We demonstrate the utility of simulation methods for home range analysis planning by comparing bias arising from three home range estimation methods under multiple detection scenarios. We simulated correlated random walks in three landscapes that varied in detection probability and constructed home ranges from locations filtered through a range of sampling protocols. Home range estimates were less biased by reduced detection probability when sampling effort was increased, but the effects of sampling day distribution were minimal. Like others, we found that kernel density estimates were the least affected by variation in detection probability, while minimum convex polygons were most affected. Our results illustrate the value of quantifying uncertainty in home range estimates and suggest that field biologists working in environments with low detection may wish to weight sample-size greater than concerns about temporal autocorrelation when designing sampling protocols. [ABSTRACT FROM AUTHOR]

Abstract We investigate the structure of the crust beneath the main island of Tierra del Fuego through a teleseismic receiver function analysis and ambient seismic noise autocorrelation using data of four available broad-band stations. The area is situated in a remote region of southern Argentina where the interaction between the South American and Scotia plates define the Magallanes-Fagnano Fault System. Crustal thickness, Vp/Vs ratios and S-wave velocity models were estimated using the H-k stacking method and the inversion of receiver functions with the Neighbourhood Algorithm. Furthermore, we applied an ambient seismic noise autocorrelation procedure to identify PmP Moho reflections at all stations. The three methods provide consistent estimates for the crustal thickness which ranges from about 24.5 km in the northern part of the Island to about 39.2 km in the south. The lowest and highest values of S-wave velocity found, in the most superficial layers of the crust, correlate with the presence of Tertiary and upper Cretacic sediments in the north and with older geological units composed by more consolidated lithologies in the south, respectively. Despite the diverse geological and tectonic settings present, our results do not change significantly for different parameters or subsidiary data sets, i.e., showing the robustness of our models which likely represent closely the local structure beneath the Island. This area is considered a very seismically active one and its crustal structure has been little explored demonstrating the importance of having new models and the necessity to increase the station coverage to allow a more detailed study of the region. Furthermore, the models presented here provide better constraints on the crustal structure, and will facilitate studies of the regional seismicity that can improve our understanding of the seismic hazard posed by earthquakes such as the magnitude 7.5 and 7.8 events that occurred in 1949. Highlights • First receiver function and ambient seismic noise autocorrelation study for the Argentinian part of Tierra del Fuego • Velocity profiles, crustal thickness and Vp/Vs ratios are presented • The two-way traveltime of a PmP Moho reflection is observed at all the autocorrelgram sections • Results are robust with respect to different parameters or subsidiary data set [ABSTRACT FROM AUTHOR]

Abstract Two approaches of building up predictive models for acetylcholinesterase inhibitory activity of a broad dataset of 403 compounds, 2D Autocorrelation descriptors and 2D Monte Carlo descriptors, were compared. In a first approach, the molecular information has been encoded in 2D autocorrelation descriptors, obtained from different weighting schemes. Quantitative structure-activity relationships were built by multiple linear regression combined with evolutionary search. All 2D autocorrelation models were predictive according to internal and external validation experiments, including more than 30 descriptors. The 2D Autocorrelation space brings a major influence of Sanderson electronegativity weighted terms for three different splits. The Monte Carlo descriptors were calculated with the CORAL software (http://www.insilico.eu/coral). Advantages and disadvantages of the above approaches are discussed. Highlights • A broad dataset of 403 AChE inhibitors is compiled. • QSAR model considering 2D autocorrelation descriptors was developed. • QSAR model considering 2D Monte Carlo descriptors was performed. • Models were validated according to classical QSAR tests. [ABSTRACT FROM AUTHOR]

Highlights • In this article, we present a statistical method to describe the work mechanism of OPLL. • Use the cross-correlation function of phase-locked lasers to represent the effect of OPLL. • In this article, we analyze the phase-locked performance of OPLL with a non-ideal reference laser. • We prefer the ways about how to confirm the main parameters to make the phase-lock effect of OPLL meet the expected demand. Abstract In order to achieve the coherent combination of independent lasers by using optical phase-locked loop (OPLL), the working mechanism of OPLL is analyzed. By calculating the autocorrelation function and the cross-correlation function of lasers, the performance of phase-locking system has been analyzed while a non-ideal laser is employed as the reference laser. The results prove that the coherence of an ordinary laser can be improved obviously by using an OPLL. Furthermore, some main parameters of the phase-locking system have been discussed in detail to provide a theoretical basis for the design of the OPLL system. The results show that if the linewidth of an ordinary laser is 2 MHz, to meet the requirement of efficient coherent beam combining, the time constant of OPLL should be less than 10 ns and the linewidth of reference laser ought to be less than 20 kHz. [ABSTRACT FROM AUTHOR]