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Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions (MVC). However, understanding the spatial distribution of TBI risk and their correlation with area-based deprivation remains limited. This study addresses this gap by employing a Bayesian spatial modeling approach to analyze the spatial patterns of fall and MVC-related TBI and their associations with area-based deprivation. Data on TBI cases from 2003 to 2019 in the Halifax Regional Municipality were sourced from the Nova Scotia Trauma Registry. Patients' residential postal codes were geocoded to dissemination areas (DAs), and area-based deprivation status from the national census data was linked to DAs. Bayesian spatial models were utilized to analyze TBI incidence rates resulting from falls and MVCs across DAs. The analysis revealed a substantial spatial disparity in TBI risk, illustrated through maps showing both the relative risk and posterior exceedance probability. Furthermore, area-based deprivation emerged as a significant factor associated with TBI risk. Specifically, areas within the most economically deprived quintile increased the risk of fall related TBI nearly threefold (RR = 2.7, 95%CI 1.98–3.66) compared to those in the least deprived quintile. The most residentially unstable quintile had a 35% greater risk of fall-related TBI than the least deprived quintile (RR = 1.35, 95%CI = 1.01–1.80). The risk of MVC-related TBI increased by 71% among areas in the second most deprived quintile (RR = 1.71; 95% CI 1.18–2.49), highlighting situational vulnerability as a significant risk factor. Deprivation was mapped within a social determinants of health framework to understand the downstream effects of residential instability, economic dependency, and situational vulnerability. This study reveals the unique spatial risk patterns of TBI resulting from falls and MVCs and demonstrates the impact of areal-level deprivation on injury risk. By identifying high-risk areas and understanding the pathways through which deprivation acts on the physical environment and community behavior, public health interventions can be developed to mitigate injury risk. [ABSTRACT FROM AUTHOR]

The long-term monitoring of freshwater ecosystems has been globally recognized for its critical role in preserving natural biodiversity and human well-being over the past century, yet monitoring efforts remain heterogeneously distributed. The Anatolia region in Turkey is known to host three out of the world's 34 biodiversity hotspots, boasting high biodiversity, but is increasingly threatened by anthropogenic effects and invasive freshwater fish species. Although the introduction of non-native fish species and pollution has detrimental effects on different river catchments in this area, long-term biomonitoring studies in Anatolia remain scarce and inconsistent. Collecting information of the Gediz River basin, a major Anatolian river catchment as a model that has been significantly impacted by human activities, published between 1971 and 2023, a total of 21 fish species from 12 families were identified. The spatial and temporal occurrences of these species fluctuated significantly due to heterogeneous sampling efforts that did not follow a clear pattern, indicating a complex ecological and conservation scenario characterized by both spatial and temporal variability in species prevalence, endemism and abundance. The presented results emphasize the need for comprehensive long-term biodiversity monitoring for anthropogenically impacted catchments like the Gediz River basin. Only with a coherent basis of high-resolution monitoring data can the multiple stressors threatening Anatolia's freshwater biodiversity be disentangled and, subsequently, be mitigated. Using the Gediz River basin as an example, this study emphasizes the urgent need for comprehensive, long-term biodiversity monitoring in understudied regions worldwide to protect regional biodiversity and ensure ecosystem health. [ABSTRACT FROM AUTHOR]

Agricultural nutrients nitrogen and phosphorus can subsequently be transported to waterways and are often managed through the adoption of best management practices (BMPs). However, we have a poor understanding of how the use of BMPs varies spatially and how BMP adoption might be related to nutrient yields in surface waters. To address this, we performed a survey of agricultural landowners across New York State and compared this with estimates of annual incremental nitrogen and phosphorus yields of agricultural origin from the Spatially Referenced Regressions On Watershed attributes (SPARROW) model. Using these socio-behavioral data and SPARROW predictions, we perform colocation analysis to identify areas where watersheds with high nutrient yield from agriculture are collocated with non-use of agricultural BMPs. This colocation analysis offers a novel methodology for identifying areas where monitoring of waterways and promotion of best management practices could be targeted to achieve the greatest benefits. [ABSTRACT FROM AUTHOR]

Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People's Democratic Republic (Lao PDR). This study aimed to describe spatial–temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR. [ABSTRACT FROM AUTHOR]

In this study, we ran a meta-analysis of neuroimaging studies to pinpoint the neural regions that are commonly activated across space, time, and numerosity, and we tested the existence of gradient transitions among these magnitude representations in the brain. Following PRISMA guidelines, we included in the meta-analysis 112 experiments (for space domain), 114 experiments (time domain), and 115 experiments (numerosity domain), and we used the activation likelihood estimation method. We found a system of brain regions that was commonly recruited in all the three magnitudes, which included bilateral insula, the supplementary motor area (SMA), the right inferior frontal gyrus, and bilateral intraparietal sulci. Gradiental transitions between different magnitudes were found along all these regions but insulae, with space and numbers leading to gradients mainly over parietal regions (and SMA) whereas time and numbers mainly over frontal regions. These findings provide evidence for the GradiATOM theory (Gradient Theory of Magnitude), suggesting that spatial proximity given by overlapping activations and gradients is a key aspect for efficient interactions and integrations among magnitudes. [ABSTRACT FROM AUTHOR]

This study aims to analyze the determinants of processes of knowledge creation and knowledge spillovers that affect the volume of innovative goods and services. It investigates the role of spatial and non-spatial proximities in knowledge creation in Russian regions within a spatial econometric modeling framework. This study also estimates the effect of expenditure on technological innovations and human capital on the knowledge production function in the spatial autoregressive model, the spatial Durbin model, and the extended spatial error model. The results indicate that, whereas cognitive proximity enhanced knowledge spillovers in 2005 and had a positive impact on the volume of innovative goods and services, technological proximity had no effect on knowledge creation. Spatial proximity existed between Russian regions but was not based on either R&D or human capital. The results of the study can be used for developing policies oriented towards integration projects and joint regional programs of the Russian Federation. [ABSTRACT FROM AUTHOR]

Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction between detection/non-detection and presence/absence by treating true occupancy as a latent process. In this paper, we propose a multi-species, multi-season occupancy model to jointly model detection/non-detection data on multiple species. Existing literature has shown that models that account for various sources of dependence in the latent occupancy process improve estimation, especially in the single-survey setting. However, the detection process in the model has not received much attention, even though detectability of a species is expected to relate to the detectability of other species and to detectability in previous time periods. In this work, we propose a model to capture this phenomenon by incorporating a multivariate temporal random effect in the detection process. We perform a simulation study to show that the proposed model yields more accurate inference than models that only use covariates to quantify detection. We apply our model to detection/non-detection data on three species—Thomson's gazelle, zebra, and wildebeest—in Serengeti National Park of Tanzania, Africa. [ABSTRACT FROM AUTHOR]

High-quality geospatial data are required to examine how the places in which we reside, work and play determine health outcomes; however, seldom is the quality of nationwide geospatial data reported. We examined the quality of geospatial data of public drinking water distribution zones (WDZ) across all territorial authorities in Aotearoa New Zealand to investigate structural inequities in data quality. In our national dataset of WDZ, we identified several differences in the quality of geospatial information that are associated with the population, area-level deprivation, ethnicity and most of all urban/rural classification. Our research highlights structural data inequity, which may undermine efforts to reduce health inequity. [ABSTRACT FROM AUTHOR]

High childhood disease prevalence and under-five mortality rates have been consistently reported in Nigeria. Vaccination is a cost-effective preventive strategy against childhood diseases. Therefore, this study aimed to identify the determinants of Incomplete Vaccination (IV) among children aged 12–23 months in Nigeria. This cross-sectional design study utilized the 2018 Nigeria Demographic and Health Survey (NDHS) dataset. A two-stage cluster sampling technique was used to select women of reproductive age who have children (n = 5475) aged 12–23 months. The outcome variable was IV of children against childhood diseases. Data were analyzed using Integrated Nested Laplace Approximation and Bayesian binary regression models (α0.05). Visualization of incomplete vaccination was produced using the ArcGIS software. Children’s mean age was 15.1 ± 3.2 months and the median number of vaccines received was four. Northern regions contributed largely to the IV. The likelihood of IV was lower among women aged 25–34 years (aOR = 0.67, 95% CI = 0.54–0.82, p < 0.05) and 35–49 years (aOR = 0.59, 95%CI = 0.46–0.77, p < 0.05) compared to younger women in the age group 15–24 years. An increasing level of education reduces the risk of odds of IV. Other predictors of IV were delivery at the health facility (aOR = 0.64, 95% CI = 053–0.76, p < 0.05), and media exposure (aOR = 0.63, 95%CI = 0.54–0.79, p < 0.05). Mothers’ characteristics explained most of the variability in the IV, relatively to smaller overall contributions from the community and state-level factors (p < 0.05). The level of IV against childhood diseases was high in Nigeria. However, disparities exist across the regions and other socioeconomic segments of the population. More efforts are required to improve vaccination sensitization programs and campaigns in Nigeria. [ABSTRACT FROM AUTHOR]

Everyone learns differently, but individual performance is often ignored in favour of a group-level analysis. Using data from four different experiments, we show that generalised linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns. Producing ellipsoids and cluster analyses based on predicted random effects, individual learning patterns can be identified, clustered and used for comparisons across various experimental conditions or groups. This analysis can handle a range of datasets including discrete, continuous, censored and non-censored, as well as different experimental conditions, sample sizes and trial numbers. Using this approach, we show that learning a face-named paired associative task produced individuals that can learn quickly, with the performance of some remaining high, but with a drop-off in others, whereas other individuals show poor performance throughout the learning period. We see this more clearly in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals who produced a rapid learning and another showing a slow and gradual learning pattern. Using data from another spatial learning task (Sea Hero Quest), we show that individuals' performance generally reflects their age category, but not always. Overall, using this analytical approach may help practitioners in education and medicine to identify those individuals who might need extra help and attention. In addition, identifying learning patterns may enable further investigation of the underlying neural, biological, environmental and other factors associated with these individuals. [ABSTRACT FROM AUTHOR]

Competition within and between species affects community structure and is well documented in raptors. We investigated the spatial distribution of two sympatric raptors in Cyprus, the Bonelli's Eagle (BE) and the Long-legged Buzzard (LLB). Both species are facultative tree/cliff nesters, with overlapping nesting and foraging areas. In 2021, the BE population consisted of 40 pairs with a density of 1,3 pairs/100 km2, and the LLB population of 113 pairs with 2,9 pairs/100 km2. About 80% of BE nesting sites and 43% of LLB nesting sites were situated within the Natura 2000 (N2K) protected areas network while 97% of BE nesting sites and 91% of LLB's were situated within 2.5 km from N2K areas. The spatial distribution was compared between and within species based on Nearest Neighbour Distance (NND) analysis. Average intraspecific NND for BE was 6.158 ± 2.207 m (SD), almost double compared to LLB which was 3.151 ± 1.730 m (SD). The distribution of BE nesting territories showed a regular spacing, indicating a stronger effect of intraspecific competitive interactions, whereas LLB nesting territories did not differ significantly from random. Average interspecific NND for BE was 5.339 ± 3.719 m (SD) and for LLB 5.673 ± 3.931 m (SD). Only the LLB had a regular pattern, suggesting that LLB pairs take into account neighbouring BE active territories when establishing their own, while the opposite is not the case. Therefore, the LLB, which is the smaller of the two raptors, avoids BE nesting sites by placing its nests further than to conspecifics. NNDs in 2021 were lower compared to previous studies in Cyprus, in 2005 for LLB and 2009 for BE, indicating an increase in both species' densities. [ABSTRACT FROM AUTHOR]

This study proposed a spatially explicit non-linear function for estimating the benefits following the release and establishment of the koinobiont endoparasitoid, Fopius arisanus at a landscape scale in Kenya to control the oriental fruit fly, Bactrocera dorsalis. The proposed model relates the bioecology of the parasitoid to its impacts on fruit growers' economic benefits per fruit farm. In addition, we used financial information attributed to the cost of importation, conducting associated research, and the release of F. arisanus to estimate the return on investment from the parasitoid's release in Kenya. We observed distinct variations in potential annual benefits across the diverse mango cultivation regions throughout Kenya. In Kenya, the potential benefits range from negligible benefit to high benefit per acre (US$201- US$255) annually when classical biological control is applied. Furthermore, the cost-benefit ratio analysis showed that in Kenya, for every US$1 invested, the return benefit was US$33.92. In addition in Kenya, the investment yielded a significant net present value of US$13.7 million over a 16-year period and an attractive internal rate of return of 73% compared to the reference discount rate of 12%. The study suggests the need to facilitate further release of the parasitoid (F. arisanus) across major fruit production regions of countries affected by the invasive fruit fly (B. dorsalis). Highlights: This study presents a spatially explicit non-linear function for estimating the economic benefits at scale following parasitoid release and establishment to control an invasive fruit fly. Costs incurred in the exploration, importation, lab research, and field release were employed to estimate the return on investment in the bio-control of fruit flies using F. arisanus as an agent in Kenya. The findings will stimulate government investment in mass rearing of the parasitoid for up-scaling sustainable biological control techniques of invasive agricultural pests. [ABSTRACT FROM AUTHOR]

Compositional Data Analysis (CoDa) has gained popularity in recent years. This type of data consists of values from disjoint categories that sum up to a constant. Both Dirichlet regression and logistic-normal regression have become popular as CoDa analysis methods. However, fitting this kind of multivariate models presents challenges, especially when structured random effects are included in the model, such as temporal or spatial effects. To overcome these challenges, we propose the logistic-normal Dirichlet Model (LNDM). We seamlessly incorporate this approach into the R-INLA package, facilitating model fitting and model prediction within the framework of Latent Gaussian Models. Moreover, we explore metrics like Deviance Information Criteria, Watanabe Akaike information criterion, and cross-validation measure conditional predictive ordinate for model selection in R-INLA for CoDa. Illustrating LNDM through two simulated examples and with an ecological case study on Arabidopsis thaliana in the Iberian Peninsula, we underscore its potential as an effective tool for managing CoDa and large CoDa databases. [ABSTRACT FROM AUTHOR]

Freshwater lakes play a vital role in global hydrological and biogeochemical cycles, serving various functions and maintaining ecological balance. However, freshwater resources are more vulnerable to deterioration due to multiple stressors. Gaoyou Lake is one of the impounded lakes of the Eastern route of South-to-North Water Diversion Project in China, and as an important source of drinking water, the lake has been routinely monitored. Long-term monitoring of water quality in Gaoyou Lake showed that concentrations of nutrients and chlorophyll a as well as trophic state in the water column increased while water transparency decreased, indicating that the water quality has declined during the last 12 years. Specifically, there was a notable and statistically significant increase in chlorophyll a concentrations, averaging an annual rate of 9.9%. Despite a slight decline in trophic level index until 2014, subsequent years saw an upward trend, ranging from 50.7 to 56.4 and indicating a light eutrophic state. Spatially, the western area displayed higher nutrient and chlorophyll a concentrations. Changes in hydro-meteorological variables and nutrients from inflowing rivers were the main factors correlated with water quality in Gaoyou Lake. Thus, pollution source apportionment and management within Huaihe River basin should be considered to reduce the external loadings of nutrients in order to improve and sustain long-term water quality. [ABSTRACT FROM AUTHOR]

In developing countries like Iran, where information is scarce, understanding the spatial variability of soil available phosphorous (SAP), one of the three major nutrients, is crucial for effective agricultural ecosystem management. This study aimed to predict and digitally map the spatial distribution and related uncertainty of SAP while also assessing the impact of environmental factors on SAP variability in the topsoils. A study area from northern Khuzestan province, Iran was selected as case study area. Three machine learning (ML) models, namely, Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were used to develop predictive relationship between surface soil (0–10 cm) SAP content and environmental covariates derived from a digital elevation model and Landsat 8 images. A total of 250 topsoil samples were collected following the conditioned Latin Hypercube Sampling (cLHS) approach and several soil properties were measured in the laboratory. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Lin's Concordance Correlation Coefficient (LCCC) were used to determine the accuracy of models. The findings indicated that the RF algorithm demonstrated the most favorable performance, with a mean absolute error (MAE) of 0.85 mg SAP kg−1, the lowest root mean square error (RMSE) of 0.99 mg SAP kg−1, and the highest linear correlation coefficient (LCCC) values of 0.96. This suggests that the RF algorithm had the least tendency to overestimate or underestimate SAP contents compared to other methods. Consequently, the RF algorithm was selected as the optimal choice. Predictive ML models were employed to digitally map SAP contents within the region. Spatial patterns of SAP contents showed an increasing gradient from west to east. The spatial variability information provides a basis for developing sustainable production system in the area. [ABSTRACT FROM AUTHOR]

With the increasing popularity of digital data in the healthcare domain, data hiding has become a hot research topic for covert communication and privacy protection. Existing data-hiding methods often tend to results in increased imperceptibility and robustness, which are also needed to simultaneously improve the system's security and embedding capacity. To solve this problem, a robust and high-capacity data hiding technique, called HIDEmarks, our study proposed a combination of integer wavelet transform (IWT) and least significant bit (LSB) for healthcare. Specifically, the IWT-LSB was used to embed multiple marks into the medical colour image. The technique first transformed cover images into three channels, and then each channel was transformed using IWT. After this, multiple marks were concealed into the cover media with the help of the LSB scheme. Meanwhile, a lossless soft method was adopted to compress the image mark prior to embedding, thereby reducing storage and transmission overhead and improving the embedding capacity of the marked colour image. Experimental results show that the proposed HIDEmarks achieved superior perceptual quality, robustness and capacity compared with the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

This paper examines the spatial, environmental, and temporal patterns of 10,488 stranger rapes committed over a 15-year period in Greater London, UK. We distinguished between two types of stranger rapes according to perpetrator method of approach, i.e. absent/fleeting interaction with victim on approach (S1) or extended interaction with victim on approach (S2). There were a range of locational settings in which perpetrators both encountered their victims and where the offence took place, and these differed by method of approach. The highest number of S1 offences occurred outdoors, with 74% of approaches and 55% of offences located recorded as outside. For S2 rapes, there was more variety in approach locations with only 32% outside. The level of locational correspondence between approach and offence location was 71% for S1 rapes and 28% for S2 rapes. A series of negative binomial regression models identified variables predictive of stranger rape offence location. There were significant associations with transport connections and the night-time economy for both S1 and S2 rapes. Other significant predictors were deprivation score, the percentage of one person properties, and the percentage of private rented properties in a location. The percentage of green space was a significant predictor for S1 rapes only. The current findings challenge the popular narrative that stranger rape occurs in a specific setting (i.e. outside in a secluded location at night) and have implications for place-based crime prevention policy. [ABSTRACT FROM AUTHOR]

Digital technologies have changed almost every aspect of our lives, including the way we access heritage. Following the pandemic caused by COVID-19 and the technological evolution of recent years, museums and institutions, among others, have changed the way they display their collections, taking a greater interest in new technologies, platforms and digital software. This technological boom finds its greatest transformation with the implementation of Virtual Reality (VR) and Metaverse in the museum sector. This article shows the concrete influence of VR/Metaverse in a museum room previously digitised through different techniques. Subsequently, the impact over user experience in the VR scenario versus on-site visit has been measured. In parallel, to measure the enzyme alpha-amylase in saliva, a cognitive test and usability test (SUS) were carried out to determine the learning capacity and degree of satisfaction obtained with experience alongside the room of the Museo de la Sangre in Murcia (Spain). [ABSTRACT FROM AUTHOR]

Purpose: This research aims to critically assess the suitability of current ISO life cycle assessment standards and practices for the challenges of decarbonisation through the use of carbon capture and the circular economy. Currently, the handling of wastes, including carbon dioxide, in life cycle assessment varies from sector to sector. We propose several methodological innovations to improve transparency and comparability of life cycle assessments to aid in the decarbonisation transition. Methods: Three approaches have been used to analyse the shortcomings of life cycle assessment for carbon capture and circular practices: Recent standards and practices have been assessed to determine their suitability for decarbonisation; Life cycle assessment and industry experts were consulted through a workshop held at the University of Bath in September of 2022; Case studies of industrial decarbonisation projects funded by IDRIC have been conducted to apply solutions to the shortcomings identified through the former methods. The issues identified have been categorised into three key areas: (i) guidelines and standards; (i) temporal and regional issues and (iii) data quality analysis. Results and discussion: The methods of handling carbon capture, waste valorisation and circular practices were found to vary significantly from sector to sector. Temporal aspects are frequently aggregated in a specific point of time, leading to low resolution by neglecting emissions over the duration of the process that creates them. Regionalisation was found to be hampered by regional characterisation factors being representative of larger regions but unsuitable at county or state scale. Uncertainty and sensitivity analyses, which are key to assessing the validity of the impacts of new and emerging technologies, were found to be neglected or only partially conducted. Conclusions and recommendations: The ISO life cycle assessment standards require updating to provide consistency in methodologies to make them suitable for use with carbon capture and circular systems and to avoid ambiguity. We recommend that the life cycle assessment community focuses on developing more consistent standards and practices between sectors to address carbon capture and circularity; improving the implementation of temporal aspects of impacts; increasing the number of studies including uncertainty and sensitivity analyses and moving towards global uncertainty in favour of local sensitivity. [ABSTRACT FROM AUTHOR]

This preregistered scoping review provides an account of studies which have examined the spatial patterning of emergency reactive police demand (ERPD) as measured by calls for service data. To date, the field has generated a wealth of information about the geographic concentration of calls for service, but the information remains unsynthesised and inaccessible to researchers and practitioners. We code our literature sample (N = 79) according to the types of demand studied, the spatial scales used, the theories adopted, the methods deployed and the findings reported. We find that most studies focus on crime-related call types using meso-level (e.g., neighborhood) spatial scales. Descriptive methods demonstrate the non-random distribution of calls, irrespective of their type, while correlational findings are mixed, providing minimal support for theories such as social disorganization theory. We conclude with suggestions for future research, focusing on how the field can better exploit open data sources to 'scale-up' analyses. [ABSTRACT FROM AUTHOR]

This paper deals with landscape to examine the ways people associate meanings to their environment through the cultural perceptions of space and time. It aims to explain the transformation from natural to cultural landscape in the cultural context of socio-spatial and socio-temporal organization in rural Pakistan. It is argued here that spatial and temporal references are pivotal in constructing, perceiving, and memorizing cultural landscape. The paper is based on ethnography of a Pakistani village, using participant observation in addition to interviews from thirty-two participants. These interviews included narratives, photo-elicitation, and memory imagework to analyze the spatial and temporal aspects of landscape from socio-cultural perspectives in rural Pakistan. The study shows that the interrelationship of space and time in rural social organization is evident in the social construction of landscape. [ABSTRACT FROM AUTHOR]

This study examined the long-term (1980–2019) spatio-temporal trends, variability and teleconnections of Indian summer monsoon rainfall (ISMR) of all districts of Haryana, India and their impact on agricultural productivity. The gridded datasets of India Meteorological Department (IMD) were used to statistically analyse the rainfall distribution, trend, coefficient of variation and intensity of rainfall. The gridded datasets of European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were examined for lower and upper tropospheric wind circulation (850 hPa and 200hpa), vertically integrated moisture transport (VIMT) and surface moisture flux (SMF). The datasets of National Oceanic and Atmospheric Administration (NOAA) were correlated with ISMR and composite deviation of rainfall and rainfall intensity during El Niño and La Niña from neutral years was examined at district level. Our analysis revealed that districts lying in eastern agroclimatic zone (EAZ) of Haryana received more ISMR during each month of monsoon season as compared to the ones situated in western agroclimatic zone (WAZ). Trend-free pre-whitening Mann–Kendall (TFPW-MK) test revealed that Kurukshetra, Panipat, Ambala, Rohtak, Faridabad, Jhajjar, Sonipat, Fatehabad and Palwal have shown a decreasing trend while Mahendragarh and Panchkula have shown an increasing trend of rainfall. During the El Niño years, most of the locations in the state received deficient to large deficient category, whereas during the La Niña episodes, most of the locations received excess to large excess category of ISMR, which is indicative of the influence of El Niño–Southern Oscillation (ENSO) on the regional scale. The influence of ISMR on bajra productivity for the districts lying in WAZ and rice productivity for the districts lying in EAZ was undertaken. This study is beneficial for understanding the impacts of climate change and climate variability on ISMR dynamics in Haryana which may further guide the policy-makers and beneficiaries for optimising the use of hydrological resources. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrialization, atmospheric pollution and health challenges induced by the escalation of aerosol optical depth (AOD) become severe. Therefore, health risk due to AOD has become a hot research topic these days. In this study, we use the AOD dataset of the MCD19A2 product of MODIS C6 and PM2.5 from 2000 to 2022. This study uses the MAIAC AOD of 13 selected cities to evaluate the spatial and temporal variation of AOD and PM2.5. AOD in Pakistan continuously increased from 2000 to 2022. The average value of AOD is 3.67. The average value of AOD is high in Bahawalpur (0.73). We observe significant differences in the seasonal variation of AOD across Pakistan. Therefore, the highest value of AOD occurred in summer. AOD had the highest value (0.76) in July. The dry areas of Pakistan have a high value of AOD. Dust and anthropogenic activities are the major sources of high AOD in Pakistan. Generally, the AOD is increasing from 2000 to 2022. The population exposure risk is increasing continuously. The regions that are at high risk are (>9) in Punjab (Lahore, Gujranwala, Faisalabad, Bahawalpur, Sialkot, Sahiwal, and Rawalpindi) and Sindh (Karachi, Hyderabad). We also evaluate the slope of the population exposure risk. The slope of the population exposure risk keeps increasing from 2000 to 2022. [ABSTRACT FROM AUTHOR]

The spatial and spatiotemporal autoregressive conditional heteroscedasticity (STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive (STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data. [ABSTRACT FROM AUTHOR]

Occupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities—including probabilities conditional to the other species' presence. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. We further evaluate the model's ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. Most parameters are estimated with low uncertainty (i.e. narrow posterior distributions). More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

South Asia has a remarkable diversity of squirrels and they play an important role in the ecosystem functioning of this region. An evaluation on a global scale showed that the tropics of South and Southeast Asia, whilst being the centre of high diversity are areas that have given rise to very less scientific knowledge. Thus, this study aims to understand the temporal and spatial variation in squirrel research in South Asia and to map the thematic research areas on squirrel research and understand, how much funding has the squirrel research attracted in the past few decades. Keeping the research goals in mind, a systematized search for papers following the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses" (PRISMA) framework was followed. The articles were searched in Scopus. It was found that the greatest number of studies were published between 2011–2020 showing a positive trend on a temporal scale. A total of 43 studies have been done which comprises to 76 percent of the total studies conducted are in India and India shares a huge percentage of major Sciuridae species research in South Asia. Also, majority of the research is conducted on three species of giant squirrels in South Asia. [ABSTRACT FROM AUTHOR]

This study offers a literature review and bibliometric analysis aiming to enhance our understanding of the actual contribution of resilience approaches to spatial and territorial development and planning studies. Using citation link-based clustering and statistical text-mining techniques (in terms of prevalence of topics, over time, extraction of relevant terms, keywords frequencies), our study maps scientific domains that include the spatial dimension of resilience thinking. It offers a systematic assessment of modern approaches by connecting profoundly theoretical views to more instrumental and policy-oriented approaches. Firstly, the theoretical background of spatial resilience used in numerous studies in various fields is analysed from the viewpoint of the type of embedded resilience (engineering, ecological, social-ecological, economic, social etc.). Secondly, we review and discuss the significance of three main and consistent research directions in terms of different scales and political/institutional contexts that matter from the viewpoint of spatial and territorial planning. Our findings show that spatial resilience debates are far from being settled, as according to many scientists, resilience measurements are often based on technical-reductionist frameworks that cannot comprehensively reflect the complex systems and issues they address. Our conclusions highlight the necessity of a harmonized framework and integrated perspective on resilience in sustainable territorial planning and development, in both theoretical and empirical contexts. [ABSTRACT FROM AUTHOR]

Spatial and temporal information are two major feature dimensions of human movements. How these two types of information are represented in working memory—whether as integrated units or as individual features—influences how much information might be retained and how the retained information might be manipulated. In this study, we investigated how spatial (path/trajectory) and temporal (speed/rhythm) information of complex whole-body movements are represented in working memory under a more ecologically valid condition wherein the spatiotemporal continuity of movement sequences was considered. We found that the spatial and temporal information are not automatically integrated but share the storage capacity and compete for a common pool of cognitive resources. The finding rejects the strong form of object-based representation and supports the partial independence of spatial and temporal processing. Nevertheless, we also found that contextual factors, such as the way movements are organized and displayed, can further modulate the level of object-based representation and spatiotemporal integration. [ABSTRACT FROM AUTHOR]

With the proliferation of location-based social media, it is of great importance to provide web users and mobile users with timely and high-quality information. In this light, we study the problem of continuous spatial keyword search over a stream of spatio-temporal messages by taking spatial relevance, textual relevance, and result diversification into consideration. We define a novel continuous query named Diversified Continuous Spatial Keyword (DCSK) query. A DCSK query consists of a spatial region, a set of query keywords, a similarity threshold 휃, and the number of query results k. Given a stream of spatio-temporal messages, the DCSK query continuously receive spatio-temporal messages such that: (1) they are located inside the query region and contain at least one query keyword, and (2) the similarities between each message and its previous k messages are all mess than 휃. Compared to traditional continuous spatial keyword query, the DCSK query can provide subscribers with spatio-temporal messages of higher quality because that the DCSK query takes both spatio-temporal relevance and query result diversification into consideration. We develop a Spatio-temporal Diversified Publish/Subscribe (STD-PS) framework to process a large number of DCSK queries efficiently. We conduct extensive experiments with real-world datasets. Our experimental results confirm the capability of our proposal in terms of result diversity, efficiency, and salability. [ABSTRACT FROM AUTHOR]

The assessment of genetic diversity and karyotypes can help to identify new species and taxonomic units and contribute to species conservation. Ecological niche modeling can be used to predict species distribution and to estimate the impact of climate changes on its survival. The Alouatta guariba group is one of the most threatened primates in the Americas, with two endemic Atlantic Forest subspecies: A. g. guariba and A. g. clamitans, referred to as Northern and Southern populations, respectively. Although extensively studied, the distribution and the genetic diversity of these subspecies remains unclear. We assessed species relationships and genetic diversity in the A. guariba group and evaluated the potential future distribution of populations. We sequenced the mitochondrial cytochrome b gene of 14 captive and 108 free-living individuals to generate a maximum likelihood phylogenetic tree and median-joining haplotype network and to estimate the nucleotide diversity between the taxa. We conducted neutrality tests to test for population expansion. We also linked ten new karyotypes, obtained from cell cultures from blood samples of captive individuals, with the positions of the clades in the gene tree. Finally, we used ecological niche modeling to predict the potential distribution of the Northern and Southern populations. We found three lineages within the A. guariba group: North A and North B associated with A. g. guariba, and South C associated with A. g. clamitans. The results showed that A. guariba group is a species complex, with reduced haplotype and nucleotide diversity within each lineage, compared with other Alouatta species. The neutrality tests and median-joining analysis suggest a recent diversification and expansion of each lineage, and the geographical data, based on the localities of the individuals that we analyzed genetically, increase the geographic distribution of A. g. guariba, expanding the distribution farther south in southeastern Brazil than previously described. Our findings show the distribution of the A. guariba group and genetic diversity of recently expanding populations, contributing to the assessment of this Critically Endangered species. We also suggest that environmental losses and climate change are major factors in the drastic population decline of the A. guariba group. [ABSTRACT FROM AUTHOR]

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity—an intrinsic characteristic embedded in spatial data—poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. Moreover, we propose a spatial moderator to generalize learned space partitionings to new test regions. Finally, we extend the framework by integrating meta-learning-based training strategies into both spatial transformation and moderation to enhance knowledge sharing and adaptation among different processes. Experiment results on real-world datasets show that the framework can effectively capture flexibly shaped heterogeneous footprints and substantially improve prediction performances. [ABSTRACT FROM AUTHOR]

Exposure to pesticides may cause adaptation not only in agricultural pests and pathogens, but also in non-target organisms. Previous studies mainly searched for adaptations in non-target organisms in pesticide-polluted sites. However, organisms may propagate heritable pesticide effects, such as increased tolerance, to non-exposed populations through gene flow. We examined the pesticide tolerance—as one of the pre-assumptions of local adaptation—of the freshwater crustacean Gammarus spp. (at genus level reflecting the gammarid community). The pesticide tolerance was quantified in acute toxicity tests using the insecticide imidacloprid. Gammarids were sampled at pesticide-polluted agricultural sites (termed agriculture), least impacted upstream sites (termed refuge) and transitional sites (termed edge) in six small streams of south-west Germany. Furthermore, we examined the population genetic structure of Gammarus fossarum and the energy reserves (here lipid content) of G. fossarum as well as of Gammarus spp. at the three site types (i.e. agriculture, edge and refuge). We found significantly lower imidacloprid tolerance of Gammarus spp. from agricultural sites compared to edge and refuge sites, potentially due to higher environmental stress at agricultural sites, as indicated by a slightly lower lipid content per mg gammarid tissue. We found no differences in pesticide tolerance between edge and refuge populations, indicating no propagation of pesticide effects to edges. The genetic structure among G. fossarum populations showed significant differentiation between streams, but not within a stream across the site types. We suggest that high gene flow within each stream hindered population differentiation and resulted in similar (pre)adaptations to local stress levels between site types, although they exhibited different pesticide pollution. Further studies on target genes (e.g., conferring pesticide tolerance), population structure and fitness of different phenotypes in particular among non-target organisms are required for adjacent pristine ecosystems to detect potential propagations of pesticide effects. [ABSTRACT FROM AUTHOR]

In the contemporary robotizing knowledge economy, robots take increasing responsibility for accomplishing knowledge-related tasks that so far have been in the human domain. This profoundly changes the knowledge-creation processes that are at the core of the knowledge economy. Knowledge creation is an interactive spatial process through which ideas are transformed into new and justified outcomes, such as novel knowledge and innovations. However, knowledge-creation processes have rarely been studied in the context of human–robot co-creation. In this article, we take the perspective of key actors who create the future of robotics, namely, robotics-related students and researchers. Their thoughts and actions construct the knowledge co-creation processes that emerge between humans and robots. We ask whether robots can have and create knowledge, what kind of knowledge, and what kind of spatialities connect to interactive human–robot knowledge-creation processes. The article's empirical material consists of interviews with 34 robotics-related researchers and students at universities in Finland and Singapore as well as observations of human–robot interactions there. Robots and humans form top-down systems, interactive syntheses, and integrated symbioses in spatial knowledge co-creation processes. Most interviewees considered that robots can have knowledge. Some perceived robots as machines and passive agents with rational knowledge created in hierarchical systems. Others saw robots as active actors and learning co-workers having constructionist knowledge created in syntheses. Symbioses integrated humans and robots and allowed robots and human–robot cyborgs access to embodied knowledge. [ABSTRACT FROM AUTHOR]

Estuaries across the globe have been subject to extensive abiotic and biotic changes and are often monitored to track trends in species abundance. The San Francisco Estuary has been deeply altered by anthropogenic factors, which is reflected in substantial declines in some native and introduced fishes. To track trends in fish abundance, a multitude of monitoring programs have conducted regular fish surveys, some dating back to the late 1950s. While these surveys are all designed to track population-scale changes in fish abundance, they are methodologically distinct, with different target species, varying spatial coverage and sampling frequency, and different gear types. To compensate for individual survey limitations, we modeled pelagic fish distributions with integrated data from many sampling programs. We fit binomial generalized linear mixed models with spatial and spatiotemporal random effects to map annual trends in the spatially explicit detection probabilities of striped bass, Delta smelt, longfin smelt, threadfin shad, and American shad for the years 1980 to 2017. Overall, detection probability has declined by approximately 50% for striped bass and is now near zero for the two smelt species, while threadfin shad and American shad have both experienced fluctuations with only slightly reduced detection probabilities by 2017. Detection probabilities decreased dramatically for these fishes in the Central and South Delta, especially after the year 2000. In contrast, Suisun Marsh and the North Delta acted as refuge habitats with reduced levels of decline or even increased detection probabilities for some species. Our modeling approach, using disparate datasets, demonstrates the simultaneous spatially driven decline of pelagic fish species in a highly altered estuary. [ABSTRACT FROM AUTHOR]

Disentangling natural and anthropogenic effects on ecosystem condition can uncover bright spots in urban landscapes that are performing above expectations and so are potential sites for conservation or benchmarking, as well as sites performing below expectations that should be the focus of management. In this study, we tested for correlations between metrics indexing ecological condition (focusing on habitat-forming species) and a suite of spatial and environmental variables at 373 sites across four ecosystems (mangroves, seagrass, saltmarsh and rocky outcrops) and 13 estuaries in southeast Queensland, Australia. Ten condition metrics across the four ecosystems correlated with variables indexing the seascape context of sites, with condition metrics typically higher at sites more connected to natural features including the estuary mouth and mangroves. Urbanisation affected only two metrics, with rocky outcrop oyster cover being 75% lower at sites near extensive urbanisation and algae cover being highest at sites with intermediate urbanisation. We identified patterns in at least two variables from each ecosystem, meaning that decisions need to be made regarding optimal ecosystem states. Overall, management sites were more common than bright spots, with 50% more management sites in mangroves, 42.8% more in seagrasses, 38.5% more in saltmarshes and no bright spots in rocky outcrops (however, 10.3% of rocky outcrop sites were bright spots under an alternate ecosystem state model). We found that patterns in habitat condition across coastal seascapes can be predicted using spatial modelling approaches, and that these models can be readily used to prioritise management actions across the entire regions. [ABSTRACT FROM AUTHOR]

River water was sampled at 105 locations in the Ottawa River watershed and analysed for microplastics. Sampling techniques were standardised and replicated at each sample location to give an indication of the spatial extent of microplastics at the watershed scale. Microplastic concentrations remained largely uniform, with no clear accumulation of microplastics towards the lower reaches of the watershed. An ANCOVA analysis determined that the only significant relationships to microplastic concentration were distance downstream on the main channel and tributaries and an increase of microplastic concentrations at boat launch locations. However, these relationships were not strong (R2 value of 0.15) and suggest a more complex interaction of microplastics in large watersheds. It is recommended that further research on microplastic pollution in rivers needs to also focus on temporal factors in addition to considering sinks as an important element in the distribution of microplastics at the watershed scale. [ABSTRACT FROM AUTHOR]

Real-time processing of spatial keyword queries has been playing an indispensable role in location-based services. In this light, we propose and study a novel problem of processing continuous spatial keyword queries over geo-textual data streams. We define a new location-based continuously query that enable users to define personalized spatial requirement and textual requirement. Each query continuously feeds users with geo-textual objects that satisfy both spatial and textual requirements set by the query. To process massive-scale continuous spatial keyword queries efficiently, we develop a Continuous Spatial Keyword Query Matching (CSKQM) framework that takes a stream of queries as input and applies hierarchical dynamic grid cells to index each batch of queries. We also propose effective index update algorithm and efficient geo-textual object matching algorithm to process massive-scale continuous spatial keyword queries simultaneously over a stream of geo-textual objects. We conduct comprehensive experimental study on two real datasets to verify the performance of the CSKQM framework. [ABSTRACT FROM AUTHOR]

Geo-textual similarity join is a fundamental operation in spatial databases. With the continued proliferation of location-based social media, geo-textual data is becoming increasingly available over the past decades. Mobile users and location-based service providers may want to receive up-to-date similarity join results of massive-scale geo-textual objects over data streams. In this light, we propose and study a novel problem of Continuous Geo-Textual Similarity Join (CGTS-Join). Specifically, given a collection of geo-textual objects Q and a dynamic set of geo-textual objects P over geo-textual data streams, the problem CGTS-Join is to continuously maintain an up-to-date join result set containing object pairs such that the objects of each pair are similar to each other. For the purpose, we define an effective similarity metric that measures the similarity between two geo-textual objects by taking spatial, textual, and temporal aspects into consideration. Based on the similarity metric, we develop a Hybrid Grid Indexing Structure (HGI) and a tri-filtering framework that is capable of answering the CGTS-Join problem efficiently. We conduct extensive experiments on two real-world datasets to confirm the performance superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Focus on potentially increased frequency of extreme hydro-climatological events under climate change is intensified by Ghana's reliance on rainfed agriculture, and the question whether precipitation climatology has returned to conditions prevailing before the regional drought of the 1970s and 80s. This study examines long-term (44–51 years), complete, daily rainfall records from nineteen stations nationally to determine magnitude and frequency of totals in excess of the historical 90th percentile at each station. Results are divided into three sub-populations corresponding to pre-drought, drought, and post-drought periods. Four research questions are addressed; (1) Is there evidence of significant interdecadal scale variability in these variables, (2) are there significant differences in characteristics between periods, (3) are there any distinctive regional patterns in changes, and (4) can findings be extended to various definitions of what constitutes extreme rainfalls? The Mann–Whitney U test determines significant breaks that define the overarching temporal framework of the research. The hypergeometric distribution determines the statistical significance of changes in magnitude and frequency. Exponential, generalized Pareto and Poisson distributions are fit to the magnitude and frequency of post-drought data. Recovery is broadly defined as no difference between pre-drought and post-drought data. Some locations in mid-Ghana have recovered, while coastal areas have not recovered in frequency or magnitude. Outliers correspond to Axim in the southwest and Navrongo in the north. The anomalies at Kete Krachi, located on Lake Volta, may result from its local climatological setting. Contributions of the research include the first comprehensive, national review of the characteristics of magnitude and frequency of large rainfall totals. The outstanding question of whether "climate" has returned to pre-1970 levels has been explicitly addressed in the context of the important facet of precipitation climatology. Results provide some insight into one of the frequently expressed thoughts that climate change/variability may be most sensitively expressed through changes in the frequency of processes/extreme events. Finally, it suggests a potential tool that may be used to estimate risks of extremely rare events, based on the most recent (Period 3), which is a reasonably stationary dataset. [ABSTRACT FROM AUTHOR]

The spatial analysis of primary schools is needed to ensure that they meet standards set by the Government of India. Therefore, mapping of Prayagraj district primary schools is carried out in this research work. The spatial analysis of schools is performed using various parameters that include: literacy rate, student gender ratio, teacher taught ratio, and students' toilets ratio. Several problems were revealed in the GIS based analysis of schools by highlighting their locations on the map. The results identify the location of all those schools where there is a need to take corrective measures. Further, the route analysis between home and school is handled in this research work. Sometimes, commutation is the sole criteria in the selection of a school. Therefore, optimal path analysis has been performed based on three parameters, i.e. population density, traveling distance and traveling time. A combined weight function is also given to find the optimal path. This work will help people to select the best possible path based upon their requirements. There is often a requirement to reach all the schools to distribute the goods or other physical monitoring activities. This work gave the optimal route to visit all the schools by using the Travelling Salesman Problem (TSP). School accessibility results obtained in this research through TSP will assist in choosing the best possible route. This will help the government identify locations where new roads need to be constructed to have road connectivity. [ABSTRACT FROM AUTHOR]

Drought severity and frequency are increasing in tropical regions and its occurrence is not uniform in space and time. The study assessed the spatio-temporal dynamics of agrometeorological drought in Mberengwa and Zvishavane districts between 1990 and 2020. An empirical research design was adopted in this study. GIS and remote sensing techniques were used to determine and analyze agricultural drought conditions and the standardized precipitation generator was used to compute SPI for meteorological drought monitoring and analysis. Microsoft Excel 2011 was adopted for the analysis of drought coverage and performing the Mann–Kendall trend test for precipitation trend analysis. Results indicated that the area covered by drought declined between the 1990 and 2000 and 2001–2010 decades before increasing during the 2011–2020 decade. Results indicated significant spatial dynamics of drought severity and frequency in Mberengwa and Zvishavane districts and the majority of wards experienced increased frequency of severe droughts during the 2011–2020 decade. It was highlighted that meteorological drought was not evenly distributed across all decades. Dry conditions in the first months (October and November) of the rainy season have been indicated during the 1990–2000 decade, followed by wetter conditions during the 2001–2010 decade and comparatively drier conditions in the same months during the 2011–2020 decade. This indicated the late onset of the rainfall season during the 1990–2000 and 2011–2020 decades compared to the 2001–2010 decade. It was also shown that rainfall cessation was earlier during the 2001–2010 and 2011–2020 decades and late during the 1990–2000 decade. The study concludes that drought has increased both in severity and frequency in Mberengwa and Zvishavane districts; hence, there is a need for more support to drought resilience-building initiatives in these areas. The government of Zimbabwe is advised to set up climate research centers in all provinces of the country to improve the availability of climate change–related data which is useful when addressing the impacts of climate change. [ABSTRACT FROM AUTHOR]

Despite popular culture’s promotion of the elephant’s ability to “never forget,” there is remarkably limited empirical research on the memory capacities of any living elephant species (Asian, Elephas maximus; African savanna, Loxodonta africana; African forest, Loxodonta cyclotis). A growing body of literature on elephant cognition and behavioral ecology has provided insight into the elephant’s ability to behave flexibly in changing physical and social environments, but little direct evidence of how memory might relate to this flexibility exists. In this paper, we review and discuss the potential relationships between what we know about elephant cognition and behavior and the elephants’ memory for the world around them as they navigate their physical, social, and spatial environments. We also discuss future directions for investigating elephant memory and implications for such research on elephant conservation and human–elephant conflict mitigation. [ABSTRACT FROM AUTHOR]

Target detection is the field of hyperspectral imaging where the materials or objects of interest are detected from images captured by hyperspectral sensors. This methodology has gained much significance in the area of military and defense, exploration of minerals, monitoring the food quality, and medical science. Various challenges such as errors occurring in the data at the time of image acquisition, data redundancy, and separation of background from desired targets however still exist. So in this study, we have given a brief introduction to hyperspectral imaging and have reviewed various atmospheric corrections, dimensionality reduction, and target detection techniques that help in overcoming these challenges related to hyperspectral data and target detection. Many of the researchers have worked significantly in this area and have acquired desired results by overcoming these challenges. Our review analysis has been approached from the perspectives of the synthetic dataset, public repositories, and developments made in pre-defined algorithms. Further, to percept the scope of the designed algorithms, the achieved results are also presented and compared for further improvements. The review study is presented in Filtering based and Optimization-based detection algorithms. The comparative analysis of various papers states that focus on optimization-based detection algorithms needs to be explored as these have gained much-desired results. [ABSTRACT FROM AUTHOR]

The use of geostatistical approaches in the structural dependency analysis and interpretation of magnetic response in conjunction with bedrock composition cannot be de-emphasised. This study utilised the two geostatistical tools namely; variogram and kriging in investigating the structural and spatial dependence index of the magnetic variation in different lithological bedrock constituents. Aeromagnetic data of Ilorin sheet 223 obtained from Nigeria Geological Survey Agency was analysed using a geostatistical approach. Nine lithological units (L1 – L9) were assessed such that their magnetic structural dependency was delineated by fitting the variogram models while the spatial magnetic variation was estimated using ordinary kriging method. Three variogram models; Spherical, Exponential and Gaussian models were adopted. The Nugget Sill Ratio (NSR) and Coefficient of Variability (CV) were as well deduced. Variogram cloud together with box and whisker plot were used to delineate the anomalous magnetic responses across the units. From the fitted models, the results accounted for NSR in the range of 4.1 – 46.2%, 4.0 – 42.4% and 4.1 – 43.7% for Spherical, Exponential and Gaussian models, respectively. Two structural dependencies levels; strong and moderate, were estimated across the nine lithological units. Six units revealed strong autocorrelation levels while three units give rise to a moderate level. Based on kriging estimation, three distinct levels; low, moderate and high magnetic response were identified across the nine lithological units. The least (- 482.6 nT) and highest (401.5 nT) magnetic responses were found associated with L6 lithological unit. Low variability (homogeneity) was found associated with four units (CV < 60%), while five units accounted for heterogeneous response (CV > 60%) in the source of magnetic intensity. In this study, geostatistical methods have proven to be good in estimating the magnetic structural and spatial dependency associated with Ilorin lithological bedrock units. Highlights: • Airborne magnetic data sets of Ilorin, North central Nigeria were analysed using Variogram and Kriging. • Three variogram models; Spherical, Gaussian and Exponential were adopted for estimating the degree of structural spatial dependency. • Strong and moderate spatial dependence were obtained across the nine (9) classified lithological units. • Based on kriging, the undifferentiated, granitic and granodiorite rock units have the highest magnetic responses. • The cross validation accounted for strong relationship between the observed and the predicted values thereby making kriging a good predictor of geophysical data set. [ABSTRACT FROM AUTHOR]

Globally, hydroclimatic variables have been changed due to human activities and have an impact on agricultural production and water resource management. This study investigated the spatio-temporal variability of hydroclimatic variables in Zarima subbasin using rainfall and temperature data from 1984 to 2018 and stream flow data from 1990 to 2014. The data were analyzed using the coefficient of variation, standardized anomaly index, Mann–Kendall trend and Sen's slope estimator test. The results showed that rainfall, maximum and minimum temperatures of the Zarima subbasin varied spatially and temporally. The annual and Kiremit season rainfall and stream flow showed low to moderate variability (CV < 30%) whereas Bega and Belg season rain fall and stream flow showed high degree of variability (CV < 40%). The annual and Kiremit season rainfall showed relatively higher variability along the northern border of the subbasin. However, maximum temperature showed less degree of variability (CV < 10%) over the subbasin, whereas the annual and seasonal minimum temperatures showed less to moderate variability (CV = 2–28%). The minimum temperature showed moderate variability around the south east lower border of the subbasin on the annual time scale and the north east part of the subbasin during the Kiremit season. The Bega and Belg seasons mean minimum temperatures showed less to high degree of variability (CV = 0–40%). The south east lower part of the subbasin showed moderate to high variability during the Bega and Belg seasons. Results of a standardized anomaly index of the rainfall, temperature and stream flow confirmed that the sub basin experienced fluctuations between dry, wet, cool and warm years. The spatial representation of annual rainfall showed a statically significant increasing trend in some parts of the subbasin and an insignificant increasing trend in the majority of the subbasin up to 300 mm per decade, while the Bega and Belg seasons showed statistically insignificant increasing and decreasing trends. Kiremit season rainfall showed heterogenous results in both statistics and trend direction. The mean annual maximum showed an increasing trend from 0.35 to 0.9 °C. The Bega, Belg and Kiremit seasons' maximum temperatures increased by a range of 0.35 to 0.95 °C; 0.4 from 1.2 °C and 0.1 to 0.55 °C pre decade respectively. While the annual minimum temperature increased by 0.05 to 0.5 °C and decreasing trend by 0.1 to 0.3 °C around the south eastern part of the subbasin. The Bega and Belg season minimum temperatures increased up to 0.5 °C and 1 °C and temperatures decreased in the range of 0.1 to 0.5 oC per decade. The Kiremit season minimum temperature increased by a range of 0.05 to 0.5 °C. The annual, Bega, Belg and Kiremit season stream flow increased insignificantly by 0.32, 0.31, 0.02 and 0.68 mm3/s respectively. In general, the subbasin experienced hydroclimatic variability which affects the life of the people, agricultural production and the economy of the area. This needs further investigation for planning and management of water resource management for the success of future water resources projects. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether sociodemographic characteristics of US Census tracts and counties and state-level infertility insurance policy are associated with the presence of assisted reproductive technology (ART) clinics. Methods: Multilevel logistic regression analyses using publicly available reports of ART clinic locations (2014–2018) matched with sociodemographic data from the US Census Bureau and state infertility insurance policy information. Results: At the tract-level, multivariate multilevel logistic regression found significant associations with the likelihood of an ART clinic in a tract and the size of the tract population (adjusted odds ratio (aOR): 1.063, SE = 0.018, p <.001), the tract median household income (aOR = 0.990, SE = 0.002, p < 0.001), and the percentages of the population who were Hispanic (aOR = 0.975, SE = 0.007, p < 0.001), women over 25 with a bachelor's degree or higher (aOR: 1.052, SE = 0.004, p < 0.001), and foreign-born (aOR: 1.037, SE = 0.009, p <.001). At the county-level, significant associations were found with the county median household income (aOR: 1.016, SE = 0.006, p <.01) and the percentage of the population that identified as Black (aOR = 1.013, SE = 0.006, p <.05) and Hispanic (aOR = 1.028, SE = 0.009, p <.05). Multivariate models showed no associations between tract clinic counts and state infertility policy. Conclusion: There is mixed evidence that clinic concentration is associated with expected sociodemographics. In particular, physical proximity may not principally drive racial disparities in ART access. Furthermore, insurance mandates are not associated with the presence of an ART clinics in a tract, suggesting alternative policy levers may be needed to address differential access and utilization of ART services. [ABSTRACT FROM AUTHOR]

Microplastics are a widespread environmental contaminant that raises serious concern for aquatic organisms. Hence, the present study was conducted to investigate the spatial and seasonal variation of microplastics, their characteristics, polymer types and the risk assessment caused by the microplastics in six sampling sites along the Thoothukudi region. The average microplastic abundance ranged from 32 ± 26 to 232 ± 229 items/kg and 54 ± 41 to 619 ± 377 items/l in sediment and surface water, respectively, and they exhibited a significant spatial difference among the sampling sites. The microplastic abundance also showed a significant difference among the seasons with the monsoon significantly recording the highest mean microplastic abundance in sediment (160 ± 130 items/kg) and surface water (454 ± 374 items/l). In sediment and surface water, fragment (sediment: 52.72%, surface water: 40.89%), 0.5–1 mm (sediment: 43.96%, surface water: 31.11%) and blue-coloured (sediment: 52.33%, surface water: 41.85%) microplastics were dominant with no significant difference both spatially and seasonally. Polyethylene, the dominant polymer, was observed in both the sediment and surface water, accounting for about 47.58% and 49.83%, respectively, and it showed no significant difference among the selected sites. This signifies that they are homogenously distributed along the coast and further suggests that these particles persisted in the sediment and surface water for a longer period of time. The results of the polymer hazard index show that the sediment (PHI = 1181.63) and surface water (PHI = 1018.66) were severely contaminated (hazard level V) with microplastic polymers such as PE, PP, PS, PET and PA. It was also found that the degree of the microplastic contamination in sediment (PLI = 3.57) and surface water (PLI = 3.84) was lower (hazard level I). The overall risk index (RI) for sediment (253.48) and surface water (444.74) falls under the higher risk category. From the correlation analysis, a significantly positive relationship was observed between microplastics in sediment and surface water based on each classification (abundance, shape, size, colour and polymer). This suggests that microplastics rejoin the water column from the sediment through resuspension, which occurs due to the circulation, tides and sedimentation rate. This might be the reason for the higher microplastic abundance in the surface water than in the sediment. As a result, proper management measures to reduce plastic waste disposal in the marine environment should be implemented to lessen the effects of microplastics on marine biota and on public health. [ABSTRACT FROM AUTHOR]

The present study identifies the prevalence, pattern & geographical distribution of pregnancy loss and its various determinants. The choropleth equal interval maps and local indicators of spatial association maps were used to investigate the locations requiring attention. Spatial regression techniques were used to investigate the factors associated with pregnancy losses across India using data derived from the fourth round of National Family Health Survey. A decision tree was constructed to investigate the interaction effect of the significant factors.The percentage of pregnancy loss in India is noted to be 14.3% (95% BCa confidence interval: 13.47, 14.79). The spatial autocorrelation coefficient ( ρ = 0.61 , p < 0.001 ) indicated the spatial dependence in the data. Hotspots of response variable were observed in districts of Uttar Pradesh, Manipur, and Odisha. Locations, with high percentage of women who have attained education upto high school and above, women who did not want the pregnancy, and women whose family did not want more child, were likely to have increased percentage of pregnancy loss. Struggles in a womens life, family's pressure and own desire to have child coupled with their education status are related to the pregnancy loss. Preventing the unplanned pregnancies will play a pivotal role in decreasing the pregnancy loss which further prevents maternal mortality and morbidity. Timely and adequate supplies of contraception in public health institutions particulary sub-centre, primary health centre should be ensured. [ABSTRACT FROM AUTHOR]

Youth with disabilities face dual disadvantages as they are more likely to live in poverty. Children with disabilities have a lower probability of entering and advancing in school. The present article examines the prevalence of disability among Indian youth and its association with being socioeconomically disadvantaged. A secondary analysis was performed on the data obtained from the 76th round of NSS. This study considered disability among youth aged between 15 and 24 years. Descriptive statistics, bivariate estimates, geospatial methods like Moran's I, univariate LISA, bivariate LISA, and ordinary least square, spatial error model, and spatial lag model were used. Among the surveyed 103,801 youth, 12,951 (12.48%) had reported having a disability in any form. Locomotor disability is the most prevalent type of disability (45.9%) among all disabled youth. Near about 32% of the youth with disabilities were illiterate. Age, gender, education, wealth quintile, and place of residence were significantly associated with disabilities in India. A significant spatial dependence was observed. Regions with the highest disability requiring attention were from Haryana, Uttar Pradesh, Bihar, Sikkim, West Bengal, Jharkhand, Odisha, Chhattisgarh, Maharashtra, Andhra Pradesh, Tamil Nadu, and Kerala. Policymakers must work on developing customized interventions involving the modifiable factors in the locations requiring attention. Long term effects of the developed intervention should be ensured. [ABSTRACT FROM AUTHOR]

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