Spatial Similarity Research Articles

Mapping of Spatial Variance of Family Policy that Could Increase Fertility: Indices for 23 OECD Countries across 21 Year Period

In a period of falling fertility rates and increasing number of various family policies (FPs) that are designed to address this trend, our study explores spatial and temporal variance of such FP measures which could be associated with increased fertility. They have been implemented in a range of countries, but their variance has not yet been systematically mapped. We employ a novel approach, examining gender and economic dimensions of FP measures to develop FP indices scoring 23 European OECD countries throughout the 2000–2021 period and uncover spatial similarities and discrepancies therein. Our findings indicate that degenderizing FP measures are increasingly being adopted across countries, with both genderizing and generous financial measures also enduring. Interestingly, despite an overall decline in fertility rates, FP has become more robust across countries according to the indices. We agree with recent studies that degenderizing FPs are most common in Nordic countries and least so in Central and Eastern Europe. We provide evidence that Hungary and Estonia are exceptions, with different types and more extensive FP. We also found that countries categorised in different contextual clusters sometimes score similarly strongly on genderizing FP (Czechia, Luxembourg, Spain) or weakly on financial FP measures (Slovakia, the UK, Greece). In countries where index scores increased only recently (e.g., Switzerland, Spain), we expect potential increases in fertility rates in the future.

Personalized in silico model for radiation-induced pulmonary fibrosis.

Radiation-induced pulmonary fibrosis (RIPF) is a severe late-stage complication of radiotherapy (RT) to the chest area, typically used in lung cancer treatment. This condition is characterized by the gradual and irreversible replacement of healthy lung tissue with fibrous scar tissue, leading to decreased lung function, reduced oxygen exchange and critical respiratory deficiencies. Currently, predicting and managing lung fibrosis post-RT remains challenging, with limited preventive and treatment options. Accurate prediction of fibrosis onset and progression is therefore clinically crucial. We present a personalized in silico model for pulmonary fibrosis that encompasses tumour regression, fibrosis development and lung tissue remodelling post-radiation. Our continuum-based model was developed using data from 12 RT-treated lung cancer patients and integrates computed tomography (CT) and dosimetry data to simulate the spatio-temporal evolution of fibrosis. We demonstrate the ability of the in silico model to capture the extent of fibrosis in the entire cohort with a less than 1% deviation from clinical observations, in addition to providing quantitative metrics of spatial similarity. These findings underscore the potential of the model to improve treatment planning and risk assessment, paving the way for more personalized and effective management of RIPF.

MM-IRSTD: Conv Self-Attention-Based Multi-Modal Small and Dim Target Detection in Infrared Dual-Band Images

Infrared multi-band small and dim target detection is an important research direction in the fields of modern remote sensing and military surveillance. However, achieving high-precision detection remains challenging due to the small scale, low contrast of small and dim targets, and their susceptibility to complex background interference. This paper innovatively proposes a dual-band infrared small and dim target detection method (MM-IRSTD). In this framework, we integrate a convolutional self-attention mechanism module and a self-distillation mechanism to achieve end-to-end dual-band infrared small and dim target detection. The Conv-Based Self-Attention module consists of a convolutional self-attention mechanism and a multilayer perceptron, effectively extracting and integrating input features, thereby enhancing the performance and expressive capability of the model. Additionally, this module incorporates a dynamic weight mechanism to achieve adaptive feature fusion, significantly reducing computational complexity and enhancing the model’s global perception capability. During model training, we use a spatial and channel similarity self-distillation mechanism to drive model updates, addressing the similarity discrepancy between long-wave and mid-wave image features extracted through deep learning, thus improving the model’s performance and generalization capability. Furthermore, to better learn and detect edge features in images, this paper designs an edge extraction method based on Sobel. Finally, comparative experiments and ablation studies validate the advancement and effectiveness of our proposed method.

PSO-based Quaternion Fourier Transform steganography: Enhancing imperceptibility and robustness through multi-dimensional frequency embedding

This paper presents a novel steganography technique using the Quaternion Fourier Transform (QFT) in the 4D frequency domain to enhance imperceptibility and robustness in digital image embedding. Steganography, the art of hiding information within media, faces challenges in balancing security, imperceptibility, and robustness. To address this, we leverage the multi-dimensional properties of quaternions, enabling the embedding of secret data in both grayscale and color images. For grayscale images, two quaternion dimensions are utilized for intensity and secret data, while for color images, all four dimensions are employed with one reserved for metadata. The research question centers on how to maximize spatial dispersion and color similarity while maintaining high imperceptibility and robustness against attacks. Experimental results show that the proposed method improves visual imperceptibility by more than 4 % and exhibits a 13 % increase in robustness against common steganalysis attacks compared to the best state-of-the-art existing technique. These advancements highlight the potential of this method for applications in secure communication, digital watermarking, and copyright protection. By combining the quaternion mathematical framework with a novel optimization strategy, this approach significantly improves upon traditional steganography methods.

Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time

Background: Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time. Methods: For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databases, we propose using restricted mean survival time or restricted mean time lost, which are applicable even in competing risk situations. We demonstrate their application by assessing the compliance of patients with acute myocardial infarction (AMI) to high-power statins over 12 months in Austria’s political districts, using pseudo-observations and employing causal inference methods to achieve regional comparability. Results: We analyzed the compliance of 31,678 AMI patients from Austria’s 116 political districts with index AMI between 2011 and 2015. The results revealed considerable compliance variations across districts but also plausible spatial similarities. Conclusions: Restricted mean survival time and restricted mean time lost provide interpretable estimates of patients’ expected time in compliance (lost), well-suited for risk-adjusted entity comparisons in the presence of (measurable) confounding, censoring, and competing risks.

Cortical Surface Area Profile Mediates Effects of Childhood Disadvantage on Later-Life General Cognitive Ability.

Childhood disadvantage is associated with lower general cognitive ability (GCA) and brain structural differences in midlife and older adulthood. However, the neuroanatomical mechanisms underlying childhood disadvantage effects on later-life GCA remain poorly understood. Although total surface area (SA) has been linked to lifespan GCA differences, total SA does not capture the nonuniform nature of childhood disadvantage effects on neuroanatomy, which varies across unimodal and transmodal cortices. Here, we examined whether cortical SA profile-the extent to which the spatial patterning of SA deviates from the normative unimodal-transmodal cortical organization-is a mediator of childhood disadvantage effects on later-life GCA. In 477 community-dwelling men aged 56-72 years old, childhood disadvantage index was derived from four indicators of disadvantages and GCA was assessed using a standardized test. Cortical SA was obtained from structural magnetic resonance imaging. For cortical SA profile, we calculated the spatial similarity between maps of individual cortical SA and MRI-derived principal gradient (i.e., unimodal-transmodal organization). Mediation analyses were conducted to examine the indirect effects of childhood disadvantage index through cortical SA profile on GCA. Around 1.31% of childhood disadvantage index effects on later-life GCA were mediated by cortical SA profile, whereas total SA did not. Higher childhood disadvantage index was associated with more deviation of the cortical SA spatial patterning from the principal gradient, which in turn related to lower later-life GCA. Childhood disadvantage may contribute to later-life GCA differences partly by influencing the spatial patterning of cortical SA in a way that deviates from the normative cortical organizational principle.

A precise and efficient K-means-ELM model to improve ultra-short-term solar irradiance forecasting

To address the problems of intermittent and uncontrollable solar irradiance faced by grid-connected photovoltaic power plants, this paper proposes an ultra-short-term solar irradiance prediction model that combines K-means clustering with the Extreme Learning Machine (ELM). The K-means algorithm is first applied to cluster solar irradiance data from different seasons in Shanghai based on spatial similarity. Subsequently, the ELM algorithm is employed to train the model, significantly improving both prediction accuracy and training speed. Compared with the prediction accuracy of the model before clustering, the root-mean-square error (RMSE) of the clustered model is significantly reduced by 28.75 % in spring, and 0.30 %, 6.70 % and 5.92 % in summer, Autumn and winter, respectively. In addition, the model demonstrates stable predictive performance at different time resolutions (5, 10 and 15 minutes) with R2 values close to 1, confirming its accuracy and stability under small sample conditions. In terms of training speed, ELM’s training time is more than 100 times faster than Support Vector Regression (SVR) and significantly shorter than traditional models such as PSO-BP and Random Forest (RF), showing its great advantage in application scenarios that require fast response. Overall, the K-means-ELM model is able to accurately capture the overall trend and sudden changes in solar irradiance, which is of great application value for enhancing the efficiency and stability of solar power generation systems.

Machine learning based classification of excessive smartphone users via neuronal cue reactivity

Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. This study aims to address this gap by leveraging functional magnetic resonance imaging (fMRI) data and machine learning techniques to classify ESU and non-excessive smartphone users (n-ESU) based on their neural Cue-Reactivity (CR) signatures. By conducting a CR task and analyzing brain activation patterns, we identified spatial similarities between addictive smartphone use and established addictive disorders. Our approach involved employing Support Vector Machines (SVM) for classification, enhanced with feature selection methods such as Recursive Feature Elimination (RFE) and Model-based Selection and dimensionality reduction methods such as and Principal Component Analysis (PCA) to mitigate the challenges posed by limited dataset size and high dimensionality of fMRI data. The results demonstrate the effectiveness of our classification model, achieving accuracies of up to 79.9 %. Furthermore, we observed region-specific activations contributing significantly to classification accuracy, highlighting the potential biomarkers associated with ESU. External validation on longitudinal data revealed the necessity for larger training datasets to improve model generalizability. Additionally, feature selection techniques proved crucial for optimizing model performance, particularly in datasets with combined information from multiple sources. Our findings underscore the importance of incorporating more data to enhance model stability and generalizability, with implications for advancing the understanding and treatment of ESU and related disorders. Overall, our study demonstrates the promise of machine learning approaches in elucidating neural correlates of ESU and informing targeted interventions for affected individuals.

Local molecular and connectomic contributions of tau-related neurodegeneration.

Neurodegeneration in Alzheimer's disease (AD) is known to be mostly driven by tau neurofibrillary tangles. However, both tau and neurodegeneration exhibit variability in their distribution across the brain and among individuals, and the relationship between tau and neurodegeneration might be influenced by several factors. I aimed to map local molecular and connectivity characteristics that affect the association between tau pathology and neurodegeneration. The current study was conducted on the cross-sectional tau-PET and longitudinal T1-weighted MRI scan data of 186 participants from the ADNI dataset including 71 cognitively unimpaired (CU) and 115 mild cognitive impairment (MCI) individuals. Furthermore, the normative molecular profile of a region was defined using neurotransmitter receptor densities, gene expression, T1w/T2w ratio (myelination), FDG-PET (glycolytic index, glucose metabolism, and oxygen metabolism), and synaptic density. I found that the excitatory-inhibitory (E:I) ratio, myelination, synaptic density, glycolytic index, and functional connectivity are linked with deviation in the relationship between tau and neurodegeneration. Furthermore, there was spatial similarity between tau pathology and glycolytic index, synaptic density, and functional connectivity across brain regions. The current study demonstrates that the regional susceptibility to tau-related neurodegeneration is associated with specific molecular and connectomic characteristics of the affected neural systems. I found that the molecular and connectivity architecture of the human brain is linked to the different effects of tau pathology on downstream neurodegeneration.

Context-Aware DGCN-Based Ship Formation Recognition in Remote Sensing Images

Ship detection and formation recognition in remote sensing have increasingly garnered attention. However, research remains challenging due to arbitrary orientation, dense arrangement, and the complex background of ships. To enhance the analysis of ship situations in channels, we model the ships as the key points and propose a context-aware DGCN-based ship formation recognition method. First, we develop a center point-based ship detection subnetwork, which employs depth-separable convolution to reduce parameter redundancy and combines coordinate attention with an oriented response network to generate direction-invariant feature maps. The center point of each ship is predicted by regression of the offset, target scale, and angle to realize the ship detection. Then, we adopt the spatial similarity of the ship center points to cluster the ship group, utilizing the Delaunay triangulation method to establish the topological graph structure of the ship group. Finally, we design a context-aware Dense Graph Convolutional Network (DGCN) with graph structure to achieve formation recognition. Experimental results on HRSD2016 and SGF datasets demonstrate that the proposed method can detect arbitrarily oriented ships and identify formations, attaining state-of-the-art performance.

3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China

Mineralization distribution is commonly heterogeneous in space due to the various geology and structure conditions or fluid flow and evoluation, causing the possible difference in mineralization distribution regularity between shallow and deep at a deposit. This presents important challenges for three-dimensional mineral prospectivity modeling (3D MPM) for deep zones with the scarcity data conditions. Transfer learning has shown promising generalization performance in tasks involving shifts in data distribution, reducing reliance on labeled samples and enhancing learning capability with limited data. In this study, we propose an approach of 3D MPM, namely DAN-CBAM, based on the deep adaptation network (DAN) augmented with the convolutional block attention module (CBAM). It theoretically can harmonize the distribution of ore-controlling features between shallow and deep zones of deposits while effectively extract critical high-dimensional features and spatial patterns. The Xiadian orogenic gold deposit was selected as a case study to validate the approach. The DAN integrates multi-layer, multi-kernel adaptation at the top layer of the CNN, resulting in improved alignment of marginal distributions across domains. Metrics for distribution similarity such as Wasserstein distance, were reduced by 0.250 and KL divergence decreased by 0.032. Additionally, the inclusion of the CBAM module led to a lower MK-MMD loss and a faster convergence rate. And the DAN-CBAM model achieves superior prediction accuracy (0.85) compared to traditional deep neural network (DNN) models (0.81). These highlight CBAM's effectiveness in enhancing the model's ability to capture spatial similarities in mineralization. Furthermore, the area under the curve (AUC) value of the DAN-CBAM model (0.869) significantly outperforms traditional machine learning methods, including CNN (0.786) and Random Forest (0.703) models, underscoring its superior predictive efficiency in 3D MPM for deep mineralization. Therefore, the proposed DAN-CBAM model is promising to be applied in the 3D MPM, in particular for the deposits with different mineralization distribution regularities in space.

Similar patterns of brain activity for holistic representation of working memory: a study in children and adults.

Working memory is the fundamental function of the various cognitive processes and abilities in the overall trajectory of development. Significant advances in multivariate analysis of human functional magnetic resonance imaging data have converged functional segregation models toward integrated representation-based models. However, due to the inherent limitations of the multi-voxel pattern analysis method, we are unable to determine whether the underlying neural representations are spatially similar in the brain. Our study attempts to answer this question by examining the spatial similarity of brain activity during the working memory task in children and adults. Our results reveal similar patterns of activity between the regions involved in working memory. This functional network of similar spatial patterns was observed in both normally developing children and adults. However, the between-region similarity was more pronounced in adults than in children and associated with better performance. We propose an exchange of similar information flows through the brain at an integrated level of working memory processes, underpinning the holistic nature of working memory representation.

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

Improving the performance of evidential belief function model using five machine learning models in earth fissure susceptibility mapping

The frequency and occurrence of earth fissures threaten human settlements, infrastructure, and agricultural lands in many parts of the globe. This natural hazard has particularly posed great challenge in the North, Central, and Eastern parts of the Najaf Abad Basin of Iran. This paper presents a comparative analysis of machine learning algorithms for earth fissure susceptibility assessment, and involves the creation of an earth fissure database by selecting and validating 200 fissure locations. To improve the accuracy of the fissure locations, various geographic data sources, such as Google Earth images, field surveys, and GPS measurements, were employed. The locations were divided into training and validation sets based on sixteen conditioning factors for finding the earth fissure susceptible zones using EBF (Evidential belief function), EBF – Multilayer Perceptron (MLP), EBF – Function Tree (FT), EBF – Alternating decision tree (ADtree), EBF – Adaptive Boosting (AdaBoost), EBF – Random SubSpace (RSS), EBF – Naive Bayes Tree (NBTree) models. The results show that earth fissures occur in greater concentrations where groundwater levels are lowest, and their prevalence is closely associated to both agricultural activities and the presence of clay, clayey sand, and silty clay soils. Susceptibility analysis results revealed that 15–21 % agricultural dependent population falls under the very high susceptibility risk zone in and around the major cities of the Najaf Abad region. The receiver operating characteristic (ROC) curve analysis showed that the EBF-RSS algorithm has higher accuracy (>99 %) compared to other algorithms (>85 %) in training and validation phases for earth fissure susceptibility prediction. Our findings emphasise the need to prioritise these susceptible areas for sustainable land and water management strategies and implement effective measures to mitigate the impact of earth fissures. Our study also advances our knowledge on geospatial analysis and earth fissures-related hazard assessment by applying five machine learning algorithms that shows spatial similarity in susceptibility categories with splendid accuracy performance.

The Ecological Risks in Arid Zones from a Production–Living–Ecological Space Perspective: A Case Study of the Tuha Region in Xinjiang, China

The ecological and environmental problems of arid zones have become an urgent global concern. Current research on ecological risk is based mainly on the dominant functions of land use, with a primary focus on land use landscape projections and less consideration of potential risks to ecosystems, system resilience and interactions between nature and future sustainable development. In this study, a potential–connectivity–resilience ecological risk assessment model based on the SDGs was constructed using multisource data to spatially quantify indicators at the grid scale in the Turpan and Hami regions of Xinjiang, China. This model was used as a basis for studying ecological risk in arid zones from a production–living–ecological space (PLES) perspective. The results revealed that, during the period 2000–2020, PLESs in the Turpan and Hami regions presented significant spatial similarity, with an increasing trend in overall risk. The production space in the Turpan and Hami regions showed a parabolic trend of increasing and then decreasing, whereas the living space and ecological space in the Hami region showed continuous linear upward trends. The state of ecological security in the Turpan and Hami regions is gradually deteriorating, and comprehensive ecological protection and restoration measures are urgently needed to rationally allocate the structure and layout of the production-–living-–ecological space. The study of ecological risk from a PLES perspective not only helps in fully understanding the development trend of the arid zone; it also provides new ideas and methods for evaluating regional ecological environmental safety and scientific references for formulating regional sustainable development, ecological risk prevention and control and the rational allocation of resources.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

Monitoring the Soil Copper of Urban Land with Visible and Near-Infrared Spectroscopy: Comparing Spectral, Compositional, and Spatial Similarities

Heavy metal contamination in urban land has become a serious environmental problem in large cities. Visible and near-infrared spectroscopy (vis-NIR) has emerged as a promising method for monitoring copper (Cu), which is one of the heavy metals. When using vis-NIR spectroscopy, it is crucial to consider sample similarity. However, there is limited research on studying sample similarities and determining their relative importance. In this study, we compared three types of similarities: spectral, compositional, and spatial similarities. We collected 250 topsoil samples (0–20 cm) from Shenzhen City in southwest China and analyzed their vis-NIR spectroscopy data (350–2500 nm). For each type of similarity, we divided the samples into five groups and constructed Cu measurement models. The results showed that compositional similarity exhibited the best performance (Rp2 = 0.92, RPD = 3.57) and significantly outperformed the other two types of similarity. Spatial similarity (Rp2 = 0.73, RPD = 1.88) performed slightly better than spectral similarity (Rp2 = 0.71, RPD = 1.85). Therefore, we concluded that the ranking of the Cu measurement model’s performance was as follows: compositional similarity > spatial similarity > spectral similarity. Furthermore, it is challenging to maintain high levels of similarity across all three aspects simultaneously.

Determination of clay minerals in Brunei's outcrops and spatial variability of the radioisotopes

Shale with the presence of clay minerals is one of the problematic formations to be encountered in the oil and gas industry. Drilling into these formations will be a challenge since shale sloughs and clay swells. Clay minerals impact hydrocarbon production and drilling operations due to clay hydration. Thus, the main objective of this paper is to identify the clay minerals from Brunei Darussalam outcrops based on the cross plot of thorium and potassium from the components of the radioisotopes of spectral gamma ray logging (SGRL). The majority of the outcrops in Brunei Darussalam contains montmorillonite which swell in contact with water. Geostatistical methods are implemented to gather spatial data over an area of interest. Variogram therefore is used to describe and characterize the variability of the radioisotopes by fitting a model to the experimental semi-variogram. The estimation of the measured values at any unknown location are then calculated by the kriging process. Semi variogram and corresponding kriging maps for thorium, uranium, and potassium components were prepared. The result indicated a good spatial similarity and areas with the highest number of the radioisotopes of spectral gamma ray logging presented the relationship of the radioisotopes in the surrounding area.

Hydrochemical Evolution Process and Mechanism of Groundwater in the Hutuo River Alluvial Fan, North China

Due to extensive groundwater exploitation, a groundwater funnel has persisted in the Hutuo River alluvial fan in Shijiazhuang since the 1980s, lasting nearly 40 years and significantly impacting the groundwater chemical characteristics. In this study, based on the groundwater level and chemistry data, the hydrochemical evolution processes and mechanisms of the groundwater during the 1980 groundwater funnel period and the post-2015 artificial governance period were investigated using traditional hydrogeochemical methods and inverse hydrogeochemical simulations. The results show the following: (1) The ion concentrations gradually increased along the groundwater flow path, where they displayed a pattern of lower levels in the northwest and higher levels in the southeast. From 1980 to 2021, the concentrations of major ions were increased. (2) In 1980s, the groundwater hydrochemical type predominantly exhibited HCO3—Ca. From 1980 to 2015, the hydrochemical types diversified into HCO3·Cl—Ca, HCO3—Ca·Mg, and HCO3·SO4—Ca types. Following the artificial governance, the groundwater level rise led to an increase in the concentrations of SO42− and Mg2+. Post-2015, the prevailing hydrochemical type changed to HCO3·SO4—Ca·Mg. (3) The changes in the groundwater level and ion concentrations were quantitatively strongly correlated and exhibited spatial similarity. (4) In the 1980s, the groundwater hydrochemical composition was primarily controlled by the dissolution of albite, dolomite, halite, and quartz; reverse cation exchange; and groundwater exploitation. Since 2015, the hydrochemical composition has mainly been influenced by the dissolution of albite, calcite, and quartz; positive cation exchange; river–groundwater mixing; and industrial activities, with increasing intensities of both water–rock interactions and human activities.

Generalisation to novel exemplars of learned shape categories based on visual and auditory spatial cues does not benefit from multisensory information.

Although the integration of information across multiple senses can enhance object representations in memory, how multisensory information affects the formation of categories is uncertain. In particular, it is unclear to what extent categories formed from multisensory information benefit object recognition over unisensory inputs. Two experiments investigated the categorisation of novel auditory and visual objects, with categories defined by spatial similarity, and tested generalisation to novel exemplars. Participants learned to categorise exemplars based on visual-only (geometric shape), auditory-only (spatially defined soundscape) or audio-visual spatial cues. Categorisation to learned as well as novel exemplars was then tested under the same sensory learning conditions. For all learning modalities, categorisation generalised to novel exemplars. However, there was no evidence of enhanced categorisation performance for learned multisensory exemplars. At best, bimodal performance approximated that of the most accurate unimodal condition, although this was observed only for a subset of exemplars within a category. These findings provide insight into the perceptual processes involved in the formation of categories and have relevance for understanding the sensory nature of object representations underpinning these categories.