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River water pollution and the subsequent degradation of water quality for irrigation and drinking are reported worldwide, especially in tropical regions with excess population pressure. The present study intends to investigate irrigation and drinking water quality and assess their suitability in the subtropical Damodar River in India using hydrochemical indices during pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) periods. The water quality index (WQI) results reveal that the river’s water is unsuitable for drinking, as 68.92% (52.95% in PRM, 86.54% in MON, and 66.88% in POM) of samples are found to be unfit for consumption in the temporal dimension. However, in the spatial dimension, the percentage of unsuitable water samples is primarily high near the village of Mujher Mana station, with 97.20% of samples (97.87% in PRM, 97.91% in MON, and 95.83% in POM) deemed unfit for drinking. This suggests the Damodar River water in MON and near the village of Mujher Mana needs treatment before drinking. The study’s findings from the irrigation hazards indices and the local farmers’ feedback indicate that the river water is suitable for irrigation use. Moreover, SAR, %Na, KR, and PS are high at Mujher Mana village, RSC at Raniganj downstream (Ds), PI at Barakar, and MAR at Durgapur upstream (Us) in terms of spatial extent. The ANOVA test indicates a significant variation in river water quality across different spatio-temporal dimensions in the study area. Water pollution is mainly attributed to the discharge of untreated industrial and urban effluents directly into rivers, without undergoing water treatment. Therefore, it is imperative to address the issue promptly to reinstate the river water quality.

In the domain of medical science, emotion recognition based on electroencephalogram (EEG) has been widely used in emotion computing. Despite the prevalence of deep learning in EEG signals analysis, standard convolutional and recurrent neural networks fall short in effectively processing EEG data due to their inherent limitations in capturing global dependencies and addressing the non-linear and unstable characteristics of EEG signals. We propose a dual transfer learning method based on 3D Convolutional Neural Networks (3D-CNN) with a Vision Transformer (ViT) to enhance emotion recognition. This paper aims to utilize 3D-CNN effectively to capture the spatial characteristics of EEG signals and reduce data covariance, extracting shallow features. Additionally, ViT is incorporated to improve the model’s ability to capture long-range dependencies, facilitating deep feature extraction. The methodology involves a two-stage process: initially, the front end of a pre-trained 3D-CNN is employed as a shallow feature extractor to mitigate EEG data covariance and transformer biases, focusing on low-level feature detection. The subsequent stage utilizes ViT as a deep feature extractor, adept at modeling the global aspects of EEG signals and employing attention mechanisms for precise classification. We also present an innovative algorithm for data mapping in transfer learning, ensuring consistent feature representation across both spatio-temporal dimensions. This approach significantly improves global feature processing and long-range dependency detection, with the integration of color channels augmenting the model’s sensitivity to signal variations. In a 10-fold cross-validation experiment on the DEAP, experimental results demonstrate that the proposed method achieves classification accuracies of 92.44% and 92.85% for the valence and arousal dimensions, and the accuracies of four-class classification across valence and arousal are HVHA: 88.01%, HVLA: 88.27%, LVHA: 90.89%, LVLA: 78.84%. Similarly, it achieves an accuracy of 98.69% on the SEED. Overall, this methodology not only holds substantial potential in advancing emotion recognition tasks but also contributes to the broader field of affective computing.

Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they cannot preserve the multi-way signal characteristics as the dimension of arrays expands. In this paper, we propose a structural-missing tensor completion algorithm for robust DOA estimation with uniform rectangular array (URA), which exhibits a high robustness to non-ideal sensor failure conditions. Specifically, the signals received at the impaired URA are represented as a three-dimensional incomplete tensor, which contains whole fibers or slices of missing elements. Due to this structural-missing pattern, the conventional low-rank tensor completion becomes ineffective. To resolve this issue, a spatio-temporal dimension augmentation method is developed to transform the structural-missing tensor signal into a six-dimensional Hankel tensor with dispersed missing elements. The augmented Hankel tensor can then be completed with a low-rank regularization by solving a Hankel tensor nuclear norm minimization problem. As such, the inverse Hankelization on the completed Hankel tensor recovers the tensor signal of an unimpaired URA. Accordingly, a completed covariance tensor can be derived and decomposed for robust DOA estimation. Simulation results verify the effectiveness of the proposed algorithm.

Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time. Here, we introduce an innovative spatio‐temporal machine learning methodology that harnesses such crowd‐sourced data to quantify phenological dynamics across taxa, space and time. Our algorithm links individual phenological responses across thousands of species and geographical locations, using a similarity measure. The analysis draws on nearly ten million plant observations collected through the AI‐based plant identification app Flora Incognita in Germany from 2018 to 2021. Our method quantifies changes in synchronisation across the annual cycle. During the growing season, synchronised behaviour can be encoded by a few characteristic macrophenological patterns. Nonlinear spatio‐temporal changes of these patterns can be efficiently quantified using a data compressibility measure. Outside the growing season, the phenological synchronisation diminishes introducing noise into the patterns. Despite biases and uncertainties associated with crowd‐sourced data, for example due to human data collection behaviour, our study demonstrates the feasibility of deriving meaningful indicators for monitoring plant macrophenology from individual plant observations. As crowd‐sourced databases continue to expand, our approach holds promise to study climate‐induced phenological shifts and feedback loops.

We study discrete panel data methods where unobserved heterogeneity is revealed in a first step, in environments where population heterogeneity is not discrete. We focus on two-step grouped fixed-effects (GFE) estimators, where individuals are first classified into groups using kmeans clustering, and the model is then estimated allowing for group-specific heterogeneity. Our framework relies on two key properties: heterogeneity is a function—possibly nonlinear and time-varying—of a low-dimensional continuous latent type, and informative moments are available for classification. We illustrate the method in a model of wages and labor market participation, and in a probit model with time-varying heterogeneity. We derive asymptotic expansions of two-step GFE estimators as the number of groups grows with the two dimensions of the panel. We propose a data-driven rule for the number of groups, and discuss bias reduction and inference.

Urban morphology exhibits fractal characteristics, which can be described by multifractal scaling. Multifractal parameters under positive moment orders primarily capture information about central areas characterized by relatively stable growth, while those under negative moment orders mainly reflect information about marginal areas that experience more active growth. However, effectively utilizing multifractal spectra to uncover the spatio-temporal variations of urban growth remains a challenge. To addresses this issue, this paper proposes a multifractal measurement by combining theoretical principles and empirical analysis. To capture the difference between growth stability in central areas and growth activity in marginal areas, an index based on generalized correlation dimension Dq is defined. This index takes the growth rate of Dq at extreme negative moment order as the numerator and that at extreme positive moment order as the denominator. During the stable stage of urban growth, the index demonstrates a consistent pattern over time, while during the active stage, the index may exhibit abnormal fluctuations or even jumps. This indicates that the index can reveal spatio-temporal information about urban evolution that cannot be directly observed through multifractal spectra alone. By integrating this index with multifractal spectra, we can more comprehensively characterize the evolutionary characteristics of urban spatial structure.

Accurate precipitation prediction is very important for social life and economical activity. Prediction of the quantitative precipitation in semi-arid areas is difficult because of rain scarcity and volatility. In this study, the 3-h precipitation situation in the semi-arid region of Lanzhou is predicted, that is, the precipitation status after 3 h is forecasted on 5 levels: ‘no rain’, ‘light rain’, ‘moderate rain’, ‘heavy rain’ and ‘torrential rain’. We selected the meteorological data from 25 stations in and nearby Lanzhou, and processed the data with lag, difference and multiplication. Due to the large number of features, we use Mutual Information (MI) feature extraction method to reduce feature dimension, extract the features that are highly correlated with the target variable, and introduce spatio-temporal information in this way. Precipitation in semi-arid areas also has the problem of sample imbalance. We oversampled the data using Adaptive Synthetic (ADASYN) sampling approach and generated some minority class samples. Based on the MI feature extraction method and the ADASYN oversampling method, we constructed an Adaptive Synthesis and Mutual Information extraction Matrix (ASMI-M), which is the feature matrix used for model training. Then we proposed a Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) model based on deep learning to predict the 3-h precipitation in Lanzhou City, which has achieved better prediction performance than traditional machine learning methods.

In this study, we present the fractal characteristics of the spatio-temporal sequence for seismic activity in the circum-Pacific seismic belt and vicinity regions, which is one of the most active seismic zones worldwide. We select the seismic dataset with magnitude M ≥ 4.4 in the circum-Pacific seismic belt region and its vicinity from 1900–2015 as the objects. Based on the methods of capacity dimension and information dimension, using ln ( 1 / δ ) – ln N ( δ ) of the relationship to evaluate and explain, the results show that (1) in the circum-Pacific seismic belt and the surrounding areas, for the seismic activity with magnitude M ≥ 4.4 , the time series dimension is 0.63, the spatial distribution dimension is 0.52 and they have fractal structure. (2) For the earthquakes with M ≥ 7.0 , the time series dimension increases greatly, which indicates that the cluster characteristics in time is greatly reduced. And the earthquakes with magnitude 7.0 ≥ M ≥ 4.4 have significant impact on the characterized by clustering in time in the study region. (3) There is significant fractal structure at spatio-temporal distribution of earthquakes in the circum-Pacific seismic belt. It reveals the tectonic movements keep continuous, obvious anisotropism characteristic of geological structure and the distribution of surface stress field is spatio-temporal heterogeneity in the study area.

Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.

Knowing the GIS expression of geological phenomena is an important basis for the combination of geology and GIS. Regional geological structures include folds, faults, strata, rocks, and other typical geological phenomena and are the focus of geological GIS research. However, existing research on the GIS expression of regional geological structure focuses on the expression of the spatial and attribute characteristics of geological structures, and our knowledge of the expression of the semantic, relationship, and evolution processes of geological structures is not comprehensive. In this paper, a regional geological structure scene expression model with the semantic terms positional accuracy, geometric shape, relationship type, attribute type, and time-type attributes and operations is proposed. A regional geological structure scenario markup language (RGSSML) and a method for mapping it with graphics are designed to store and graphically express regional geological structure information. According to the geological time scale, a temporal reference coordinate system is defined to dynamically express the evolution of regional geological structures. Based on the dynamic division of the time dimension of regional geological structures, the expression method of “time dimension + space structure” for the regional geological structure evolution process is designed based on the temporal model. Finally, the feasibility and effectiveness of the regional geological structure scene expression method proposed in this paper is verified using the Ningzhen Mountain (Nanjing section) as an example. The research results show that the regional geological structure scene expression method designed in this paper has the following characteristics: (1) It can comprehensively express the spatial characteristics, attribute characteristics, semantics, relationships, and evolution processes of regional geological structures; (2) it can be used to realize formalized expression and unified storage of regional geological information; and (3) it can be used to realize dynamic expression of the regional geological structure evolution process. Moreover, it has significant advantages for the expression of regional geological structure semantics, relationships, and evolution processes. This study improves our knowledge of the GIS expression of regional geological structures and is expected to further promote the combination and development of geology and GIS.