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In the context of urban stock renewal, the spatial arrangement of public cultural facilities (PCFs) should follow the principles of equity and efficiency to ensure that residents have equitable access to and quality of public cultural services. The aim of this article is to study the spatial distribution of PCFs and the coupling of supply and demand of cultural resources in Tianjin’s central area. By building a supply-demand coupling coordination model and other methods, the equalization of the spatial distribution of PCFs is measured from various perspectives, and the results suggest that more than half of the sub-districts are in a situation of supply and demand imbalance. To fulfill the purpose of meeting residents’ actual needs, balancing supply and demand for cultural resources, and coordinating the increase in stock, these sub-districts’ facilities enter the step of optimization. Depending on the circumstances, the quality and scale of these facilities are optimized, or new facility points are added based on the maximized coverage model. The optimization is shown to be beneficial in terms of updating design and coverage quantity using two real-world cases. Finally, the coverage of facilities in the study area is maximized, facility utilization is made more efficient, and residents’ needs for public cultural services are satisfied.

This paper was a part of studies conducted within an island population of the ragwort Senecio umbrosus (White Mt, southeastern Poland), a vulnerable element of xerothermic grasslands. Special attention was paid to the effects of expansive grass encroachment vs. grassland burning episodes on spatiotemporal patterns and life-stage structure of individuals in the population. The population traits were investigated nine times from 1990 to 2010, within three permanent patches differing in soil properties, initial floristic composition, grassland cover (particularly the cover of Brachypodium pinnatum), ragwort cover and density, shrub/tree cover influencing light intensity (full light-shadow), and grassland burning (zero-six episodes). There was a drastic decline in ragwort abundance within all the study patches accompanied by a decrease in the population clustering coefficient and a gradual equalization of the spatial distribution of ramets. The abundance was negatively correlated (PCA analysis) with an increase in B. pinnatum cover and positively correlated with the number of burning episodes, which temporarily delimited persistent litter cover and facilitated recruitment of new individuals. The decrease in ramet abundance ranged from 3.8 times (medium-high, moderately shadowed grassland; six cases of burning) to 8.3 times (high, dense, and shadowed grassland; four cases of burning). The patch of low, loose, sunlit, and never-burned grassland with the greatest initial density of ragwort (a 6.8-fold decrease in abundance) has evolved with time into a high and dense grassland with a greater coverage of B. pinnatum and Calamagrostis epigejos, additionally shaded by shrubs and young trees. Keywords Brachypodium pinnatum; grassland burning; grassland overgrowing; plant succession; spatial pattern equalization; vulnerable species; xerothermic grasslands

This paper proposes a novel method of image enhancement via energy curve equalization (ECE). Histogram equalization (HE) appears to be the most straightforward method for image contrast enhancement. Many modifications are already recommended to overcome the confines of HE. The computation of the histogram does not consider the spatial correlations among the surrounding pixels. The current method utilizes the energy curve as a substitute for the histogram of an image. In contrast to the histogram, this approach covers the spatial context information of neighboring pixels. The modified Hopfield neural network (HNN) architecture is employed to compute an image's energy curve. It has peaks and valleys and appears smoother than the image histogram. The average value of the mean and median of the energy curve is used as the plateau limit to control the enhancement rate. The clipped energy curve is partitioned into three individual regions based on the standard deviation value. The final transformation function is utilized to remap the pixel intensity values and is formulated by integrating the individual portions of equalized energy curves. The simulation results confirm the projected technique's effectiveness compared to conventional HE-based methods with and without plateau limit.

Wei, C. and Theuerkauf, S.J., 2021. A novel multitemporal approach for satellite-derived bathymetry for coastal waters of Palau. Journal of Coastal Research, 37(2), 336–348. Coconut Creek (Florida), ISSN 0749-0208. Shallow water bathymetry is important for understanding biogeophysical and socioeconomic processes in coastal areas. In recent years, satellite-derived bathymetry (SDB) methods have been increasingly used to provide high-resolution bathymetry estimation in different regions using single remote sensing images. To tackle the common issues of single-image SDB, such as data gaps due to cloud coverage and false bathymetry due to water turbidity, this study applied a novel multitemporal workflow for SDB estimation in Palau, a Pacific Island nation. This workflow implements the typical empirical SDB steps to calculate relative water depth (i.e. log ratio of the blue and green band) of 20 Landsat 8 images that were composited and subsequently related to in situ depth measurements to estimate true depth. Before composition, a histogram equalization approach was employed to normalize the images and identify clear water areas of each image pair by applying a 1% difference threshold. To achieve better performance, different methodological options at three key steps were evaluated, including temporal composition (mean vs. median), point data extraction (direct vs. bilinear interpolation), and regression (linear vs. piecewise vs. polynomial). Among 12 models, the polynomial model built upon bilinearly interpolated mean composition data performed the best, accurately estimating water depth up to the extinction depth of 13.7 m (45 ft), with a root mean square error of 1.76 m (5.77 ft). This multitemporal approach, with proper methodological choices according to local circumstances, could be applied to other regions to derive gap-free and accurate bathymetry estimations.

We delve into whether the equalization of basic public services can mitigate regional disparities in China’s economic resilience. Our analysis reveals that COVID-19 has diminished economic resilience and exacerbated regional differences. Notably, these regional disparities constitute the primary cause of spatial variations in economic resilience. Despite the initially low level of basic public services in Chinese cities, there is a discernible upward trend, indicating a gradual narrowing of regional disparities. Furthermore, we uncover a substantial positive correlation between the equalization of public services and variations in regional economic resilience, thereby offering fresh empirical evidence that the equalization of public services can help bridge the gap in regional economic resilience.

Public services equalization is closely related to local economic and social development. Hence, it is crucial to explore the changing dynamics of public services equalization and its correlation with regional economic disparities. We first examine the changing spatiotemporal patterns of public services provision and local economic performance at the provincial level across China from 2003 to 2017, using a set of indicators and the Mann–Kendall test. It is found that different types of public services are divergent in both temporal trend and geographical locations. However, both income and expenditure have been significantly increased for all provinces during the study period. Second, we unravel the heterogeneous relationship between public services provision and local economy across time and space using the geographically and temporally weighted regression. Variance decomposition is further employed to quantify the relative contribution of public services provision to local economy. Results show that the impact of different types of public services on local economic system is divergent, which jointly affects local economy system together with political and other economic factors. Thirdly, we use the Theil index and traditional least square regression to further examine the relationships between public services equalization and regional economic disparities. We find that public services equalization is correlated with regional economic disparities at the national level, yet their interrelation varies significantly in different regions. Taken together, through revisiting the role of public services equalization in regional economic disparities and unpacking its geographical and temporal heterogeneity, this study fills salient research gaps and informs policymaking towards a long-term goal of social equalization.

Many species rely on olfaction to navigate towards food sources or mates. Olfactory navigation is a challenging task since odor environments are typically turbulent. While time-averaged odor concentration varies smoothly with the distance to the source, instaneous concentrations are intermittent and obtaining stable averages takes longer than the typical intervals between animals' navigation decisions. How to effectively sample from the odor distribution to determine sampling location is the focus in this article. To investigate which sampling strategies are most informative about the location of an odor source, we recorded three naturalistic stimuli with planar lased-induced fluorescence and used an information-theoretic approach to quantify the information that different sampling strategies provide about sampling location. Specifically, we compared multiple sampling strategies based on a fixed number of coding bits for encoding the olfactory stimulus. When the coding bits were all allocated to representing odor concentration at a single sensor, information rapidly saturated. Using the same number of coding bits in two sensors provides more information, as does coding multiple samples at different times. When accumulating multiple samples at a fixed location, the temporal sequence does not yield a large amount of information and can be averaged with minimal loss. Furthermore, we show that histogram-equalization is not the most efficient way to use coding bits when using the olfactory sample to determine location.

The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial–spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial–spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information’s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial–spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data.

Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios is proposed, where frequency-domain pilots of different transmit antennas occupy completely the same subcarriers in the frequency domain. Meanwhile, spatial–temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realised by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.

For the past few decades, brain tumors have had a substantial influence on human life, and pose severe health risks if not treated and diagnosed in the early stages. Brain tumor problems are highly diverse and vary extensively in terms of size, type, and location. This brain tumor diversity makes it challenging to progress an accurate and reliable diagnostic tool. In order to effectively segment and classify the tumor region, still several developments are required to make an accurate diagnosis. Thus, the purpose of this research is to accurately segment and classify brain tumor Magnetic Resonance Images (MRI) to enhance diagnosis. Primarily, the images are collected from BraTS 2019, 2020, and 2021 datasets, which are pre-processed using min–max normalization to eliminate noise. Then, the pre-processed images are given into the segmentation stage, where a Variational Spatial Attention with Graph Convolutional Neural Network (VSA-GCNN) is applied to handle the variations in tumor shape, size, and location. Then, the segmented outputs are processed into feature extraction, where an AlexNet model is used to reduce the dimensionality. Finally, in the classification stage, a Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to classify the brain tumor regions as gliomas and meningiomas. From the results, it is evident that the proposed VSA-GCNN-BiGRU shows superior results on the BraTS 2019 dataset in terms of accuracy (99.98%), sensitivity (99.92%), and specificity (99.91%) when compared with existing models. By considering the BraTS 2020 dataset, the proposed VSA-GCNN-BiGRU shows superior results in terms of Dice similarity coefficient (0.4), sensitivity (97.7%), accuracy (98.2%), and specificity (97.4%). While evaluating with the BraTS 2021 dataset, the proposed VSA-GCNN-BiGRU achieved specificity of 97.6%, Dice similarity of 98.6%, sensitivity of 99.4%, and accuracy of 99.8%. From the overall observation, the proposed VSA-GCNN-BiGRU supports accurate brain tumor segmentation and classification, which provides clinical significance in MRI when compared to existing models.