Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China.

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of "overall dispersion, partial concentration", with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

Ecological setting of phlebotomine sand flies in the Republic of Kosovo.

Sand flies (Diptera, Psychodidae) are the principal vectors of Leishmania spp., the causative agents of leishmaniasis, as well as phleboviruses. In the Balkans, the endemicity and spreading of sand fly-borne diseases are evident, particularly in the Republic of Kosovo, a country with a predominantly humid continental climate. To date, understanding the drivers behind the spatial structure and diversity patterns of sand fly communities in humid continental regions remains limited. Therefore, elucidating the geographical and ecological factors contributing to the presence of potential vector species in the country is crucial. We aimed to enhance our understanding of factors influencing sand fly occurrence in cool and wet wintering humid continental areas, which could serve as a model for other countries with similar climatic conditions. Therefore, we assessed the currently known sand fly fauna through detailed environmental analyses, including Voronoi tessellation patterns, entropy calculations, Principal Coordinate and Component Analyses, Hierarchical Clustering, Random Trees, and climatic suitability patterns. Notable differences in the ecological tolerance of the species were detected, and the most important climatic features limiting sand fly presence were wind speed and temperature seasonality. Sand flies were observed to prefer topographical environments with little roughness, and the modelled climatic suitability values indicated that, dominantly, the western plain regions of Kosovo harbour the most diverse sand fly fauna; and are the most threatened by sand fly-borne diseases. Phlebotomus neglectus and P. perfiliewi, both confirmed vectors for L. infantum and phleboviruses, were identified as two main species with vast distribution in Kosovo. Contrary to this, most other present species are relatively sparse and restricted to temperate rather than humid continental regions. Our findings reveal a diverse potential sand fly fauna in Kosovo, indicating the need for tailored strategies to address varying risks across the country's western and eastern regions in relation to leishmaniasis control amidst changing environmental conditions.

Spatial patterns of Hyalomma marginatum-borne pathogens in the Occitanie region (France), a focus on the intriguing dynamics of Rickettsia aeschlimannii.

Hyalomma marginatum is an invasive tick species recently established in mainland southern France. This tick is known to host a diverse range of human and animal pathogens. While information about the dynamics of these pathogens is crucial to assess disease risk and develop effective monitoring strategies, few data on the spatial dynamics of these pathogens are currently available. We collected ticks in 27 sites in the Occitanie region to characterize spatial patterns of H. marginatum-borne pathogens. Several pathogens have been detected: Theileria equi (9.2%), Theileria orientalis (0.2%), Anaplasma phagocytophilum (1.6%), Anaplasma marginale (0.8%), and Rickettsia aeschlimannii (87.3%). Interestingly, we found a spatial clustered distribution for the pathogen R. aeschlimannii between two geographically isolated areas with infection rates and bacterial loads significantly lower in Hérault/Gard departments (infection rate 78.6% in average) compared to Aude/Pyrénées-Orientales departments (infection rate 92.3% in average). At a smaller scale, R. aeschlimannii infection rates varied from one site to another, ranging from 29% to 100%. Overall, such high infection rates (87.3% on average) and the effective maternal transmission of R. aeschlimannii might suggest a role as a tick symbiont in H. marginatum. Further studies are thus needed to understand both the status and the role of R. aeschlimannii in H. marginatum ticks.IMPORTANCETicks are obligatory hematophagous arthropods that transmit pathogens of medical and veterinary importance. Pathogen infections cause serious health issues in humans and considerable economic loss in domestic animals. Information about the presence of pathogens in ticks and their dynamics is crucial to assess disease risk for public and animal health. Analyzing tick-borne pathogens in ticks collected in 27 sites in the Occitanie region, our results highlight clear spatial patterns in the Hyalomma marginatum-borne pathogen distribution and strengthen the postulate that it is essential to develop effective monitoring strategies and consider the spatial scale to better characterize the circulation of tick-borne pathogens.

Assessing desertification vulnerability and mitigation strategies in northern Nigeria: A comprehensive approach.

Desertification constitutes a grave threat to the environmental and socio-economic stability of desertification frontline states in Northern Nigeria. From 2003 to 2020, this research comprehensively analyzes desertification vulnerability, integrating parameters such as NDVI, LST, TVDI, MSAVI, and Albedo. Key factors contributing to land degradation are identified, along with the spatial patterns and trends of desertification over the two-decade period. The consequences are profound, with Northern Nigeria's ecosystem experiencing a steady decline in vegetation cover. Agriculture, vital to the region's economy, faces increased aridity and reduced arable land, jeopardizing food security. Diminishing water resources exacerbates scarcity issues, placing additional strain on communities. These environmental changes lead to severe socio-economic implications, including displacement, loss of livelihoods, and heightened vulnerability to climate-related risks. Urgent, comprehensive, and strategic interventions are imperative. Policy recommendations underscore revising and enforcing land use regulations, promoting sustainable agricultural practices, and establishing monitoring systems to guide decision-making. This research contributes practical strategies to enhance the resilience of desertification frontline states, safeguard livelihoods, and align with Nigeria's sustainable development objectives. Findings from the study indicate that only a tiny percentage (6.7 %) of the study area remains unaffected by desertification. Moreover, 13.3 % exhibit light vulnerability, 20 % demonstrate moderate exposure, and 60 % fall into the severe (26.7 %) and compelling (33.3 %) vulnerability categories. These statistics underscore the gravity of desertification in the study area, emphasizing the urgent need for effective mitigation measures to address its impact comprehensively.

Exploring the Spatial and Temporal Patterns of Children and Adolescents with COVID-19 Infections in Slovakia during March 2020 to July 2022.

Background and Objectives: The COVID-19 pandemic has had a significant global impact, necessitating a comprehensive understanding of its spatiotemporal patterns. The objective of this study is to explore the spatial and temporal patterns of COVID-19 infections among five age groups (<1, 1-4, 5-9, 10-14, and 15-19 years) in 72 districts of Slovakia on a quarterly basis from March 2020 to July 2022. Material and Methods: During the study period, a total of 393,429 confirmed PCR cases of COVID-19 or positive antigen tests were recorded across all studied age groups. The analysis examined the spatiotemporal spread of COVID infections per quarter, from September 2021 to May 2022. Additionally, data on hospitalizations, intensive care unit (ICU) admissions, pulmonary ventilation (PV), and death cases were analyzed. Results: The highest number of COVID-19 infections occurred between September 2021 and May 2022, particularly in the 10-14-year-old group (68,695 cases), followed by the 15-19-year-old group (62,232 cases), while the lowest incidence was observed in the <1-year-old group (1235 cases). Out of the total confirmed PCR cases, 18,886 individuals required hospitalization, 456 needed ICU admission, 402 received pulmonary ventilation, and only 16 died. The analysis of total daily confirmed PCR cases for all regions showed two major peaks on 12 December 2021 (6114 cases) and 1 February 2022 (3889 cases). Spatial mapping revealed that during December 2021 to February 2022, the highest number of infections in all age groups were concentrated mainly in Bratislava. Moreover, temporal trends of infections within each age group, considering monthly and yearly variations, exhibited distinct spatial patterns, indicating localized outbreaks in specific regions. Conclusions: The spatial and temporal patterns of COVID-19 infections among different age groups in Slovakia showed a higher number of infections in the 10-14-year-old age group, mainly occurring in urban districts. The temporal pattern of the spread of the virus to neighboring urban and rural districts reflected the movement of infected individuals. Hospitalizations, ICU and PV admissions, and deaths were relatively low. The study highlights the need for more proactive measures to contain outbreaks promptly and ensure the resilience of healthcare systems against future pandemics.

Challenges in moving towards fog's contribution to spatial patterns of atmospheric deposition fluxes on a national scale.

Fog is an important environmental phenomenon affecting, among other things, geochemical cycles via atmospheric deposition pathways. It is generally accepted that fog contributes substantially to atmospheric deposition fluxes especially in mountain forests. Nevertheless, due to intrinsic constraints, fog pathway has thus far been neglected in the quantification of atmospheric deposition and fog pathway has not been accounted for in nation-wide spatial patterns of atmospheric deposition of air pollutants. In this review we explore the causes as to why it is so complex to create a spatial pattern of fog contribution to atmospheric ion deposition fluxes on a national scale. Physical and chemical principles of fog formation are presented and factors influencing the abrupt temporal and spatial changes in both fog occurrence and fog chemistry are elucidated. The focus is on both constituents essential for fog deposition flux quantification, i.e. (i) hydrological input on fog water and (ii) chemistry of fog water.

Aerosol in global oceanic regions: Four-decade trends, spatial patterns, and policy implications.

High aerosol loadings are observed not only in megacities on continents but also in oceanic regions like the Bohai Sea. This work provides a comprehensive analysis of the spatial and temporal variations in Aerosol Optical Depth (AOD) across different ocean regions worldwide over the past four decades, using remote sensing reanalysis data. The mean AOD value across all oceanic grids is approximately 0.112, with higher levels recorded in the Central Atlantic (~0.206), followed by the North Indian Ocean (~0.201), and the Western North Pacific (~0.197). A latitudinal analysis reveals that high AOD values are predominantly found in the Northern Hemisphere's oceanic regions, especially between latitudes 0° and 70° N. Except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Among which, the Bohai Sea stands out as the most polluted oceanic region with AOD value of 0.35. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5 % of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S-30° N). Investigation into inland seas shows that nearly two-thirds have experienced a declining AOD trend, while sharply upward trends in AOD are primarily found in Asia. The Bohai Sea shows the largest increase in AOD, with an annual growth rate of 1.4 %. The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. To improve air quality in inland seas like the Bohai Sea, adjusting industrial layouts, such as relocating heavy industries from the surrounding coastal cities' proximities to areas near open seas, could significantly benefit public health.

Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia.

Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.

Demographic effects of aggregation in the presence of a component Allee effect.

The component Allee effect (AE) is the positive correlation between an organism's fitness component and population density. Depending on the population spatial structure, which determines the interactions between organisms, a component AE might lead to positive density dependence in the population per-capita growth rate and establish a demographic AE. However, existing spatial models impose a fixed population spatial structure, which limits the understanding of how a component AE and spatial dynamics jointly determine the existence of demographic AEs. We introduce a spatially explicit theoretical framework where spatial structure and population dynamics are emergent properties of the individual-level demographic and movement rates. This framework predicts various spatial patterns depending on its specific parametrization, including evenly spaced aggregates of organisms, which determine the demographic-level by-products of the component AE. We find that aggregation increases population abundance and allows population survival in harsher environments and at lower global population densities when compared with uniformly distributed organisms. Moreover, aggregation can prevent the component AE from manifesting at the population level or restrict it to the level of each independent aggregate. These results provide a mechanistic understanding of how component AEs might operate for different spatial structures and manifest at larger scales.

Near-natural streams: Spatial factors are key in shaping multiple facets of zooplankton α and ß diversity.

In near-natural basins, zooplankton are key hubs for maintaining aquatic food webs and organic matter cycles. However, the spatial patterns and drivers of zooplankton in streams are poorly understood. This study registered 165 species of zooplankton from 147 sampling sites (Protozoa, Rotifers, Cladocera and Copepods), integrating multiple dimensions (i.e., taxonomic, functional, and phylogenetic) and components (i.e., total, turnover, and nestedness) of α and ß diversity. This study aims to reveal spatial patterns, mechanisms, correlations, and relative contribution of abiotic factors (i.e., local environment, geo-climatic, land use, and spatial factors) through spatial interpolation (ordinary kriging), mantel test, and variance partitioning analysis (VPA). The study found that α diversity is concentrated in the north, while ß diversity is more in the west, which may be affected by typical habitat, hydrological dynamics and underlying mechanisms. Taxonomic and phylogenetic ß diversity is dominated by turnover, and metacommunity heterogeneity is the result of substitution of species and phylogeny along environmental spatial gradients. Taxonomic and phylogenetic ß diversity were strongly correlated (r from 0.91 to 0.95), mainly explained by historical/spatial isolation processes, community composition, generation time, and reproductive characteristics, and this correlation provides surrogate information for freshwater conservation priorities. In addition, spatial factors affect functional and phylogenetic α diversity (26%, 28%), and environmental filtering and spatial processes combine to drive taxonomic α diversity (10%) and phylogenetic ß diversity (11%). Studies suggest that spatial factors are key to controlling the community structure of zooplankton assemblages in near-natural streams, and that the relative role of local environments may depend on the dispersal capacity of species. In terms of diversity conservation, sites with high variation in uniqueness should be protected (i) with a focus on the western part of the thousand islands lake catchment and (ii) increasing effective dispersal between communities to facilitate genetic and food chain transmission.

The role of memory-based movements in the formation of animal home ranges.

Most animals live in spatially-constrained home ranges. The prevalence of this space-use pattern in nature suggests that general biological mechanisms are likely to be responsible for their occurrence. Individual-based models of animal movement in both theoretical and empirical settings have demonstrated that the revisitation of familiar areas through memory can lead to the formation of stable home ranges. Here, we formulate a deterministic, mechanistic home range model that includes the interplay between a bi-component memory and resource preference, and evaluate resulting patterns of space-use. We show that a bi-component memory process can lead to the formation of stable home ranges and control its size, with greater spatial memory capabilities being associated with larger home range size. The interplay between memory and resource preferences gives rise to a continuum of space-use patterns-from spatially-restricted movements into a home range that is influenced by local resource heterogeneity, to diffusive-like movements dependent on larger-scale resource distributions, such as in nomadism. Future work could take advantage of this model formulation to evaluate the role of memory in shaping individual performance in response to varying spatio-temporal resource patterns.

Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet.

Introduction: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery. Methods: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction. Results and discussion: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

Cancer incidence trends in New York State and associations with common population-level exposures 2010-2018: an ecological study.

The impact of common environmental exposures in combinations with socioeconomic and lifestyle factors on cancer development, particularly for young adults, remains understudied. Here, we leveraged environmental and cancer incidence data collected in New York State at the county level to examine the association between 31 exposures and 10 common cancers (i.e., lung and bronchus, thyroid, colorectal, kidney and renal pelvis, melanoma, non-Hodgkin lymphoma, and leukemia for both sexes; corpus uteri and female breast cancer; prostate cancer), for three age groups (25-49, 50-69, and 70-84 year-olds). For each cancer, we stratified by age group and sex, and applied regression models to examine the associations with multiple exposures simultaneously. The models included 642,013 incident cancer cases during 2010-2018 and found risk factors consistent with previous reports (e.g., smoking and physical inactivity). Models also found positive associations between ambient air pollutants (ozone and PM2.5) and prostate cancer, female breast cancer, and melanoma of the skin across multiple population strata. Additionally, the models were able to better explain the variation in cancer incidence data among 25-49 year-olds than the two older age groups. These findings support the impact of common environmental exposures on cancer development, particularly for younger age groups.

Tracing spatial patterns of lacustrine groundwater discharge in a closed inland lake using stable isotopes.

Tracing lacustrine groundwater discharge (LGD) is essential for understanding the hydrological cycle and water chemistry behaviour of lakes. LGD usually exhibits large spatial variability, but there are few reports on quantitatively revealing the spatial patterns of LGD at the whole lake scale. This study investigated the spatial patterns of LGD in Daihai Lake, a typical closed inland lake in northern China, based on the stable isotopes (δ2H and δ18O) of groundwater, surface water, and sediment pore water (SPW). The results showed that there were significant differences between the δ2H and δ18O values of different water bodies in the Daihai Lake Basin: groundwater < SPW < lake water. The LGD through SPW was found to be an important recharge pathway for the lake. Accordingly, stable isotopes of SPW showed that LGD in the northeastern and northwestern of Daihai Lake was significantly greater both horizontally and vertically than that in the other regions, and the proportions of groundwater in SPW in these two regions were 55.53% and 29.84%, respectively. Additionally, the proportion of groundwater in SPW showed a significant increase with profile depth, and the proportion reached 100% at 50 cm below the sediment surface in the northeastern of the lake where the LGD intensity was strongest. The total LGD to Daihai Lake was 1.47 × 107 m3/a, while the LGD in the northeastern and northwestern of the lake exceeded 1.9 × 106 m3/a. This study provides new insights into assessing the spatial patterns of LGD and water resource management in lakes.

Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.

The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, making a BCI is difficult in part because of the enormous dimensionality of the data. Three stages make up the suggested methodology: pre-processing, extraction of features, selection, and categorization. To remove unwanted artifacts, the EEG signals are filtered by a fifth-order Butterworth multichannel band-pass filter. This decreases execution time and memory use, both of which improve system performance. Then a novel multichannel optimized CSP-ICA feature extraction technique is used to separate and eliminate non-discriminative information from discriminative information in the EEG channels. Furthermore, CSP uses the concept of an Artificial Bee Colony (ABC) algorithm to automatically identify the simultaneous global ideal frequency band and time interval combination for the extraction and classification of common spatial pattern characteristics. Finally, a Tunable optimized feed-forward neural network (FFNN) classifier is utilized to extract and categorize the temporal and frequency domain features, which employs an FFNN classifier with Tunable-Q wavelet transform. The proposed framework, therefore optimizes signal processing, enabling enhanced EEG signal classification for BCI applications. The result shows that the models that use Tunable optimized FFNN produce higher classification accuracy of more than 20% when compared to the existing models.

Cluster pattern analysis of environmental stressors and quantifying their impact on all-cause mortality in Belgium.

Environmental stress represents an important burden on health and leads to a considerable number of diseases, hospitalisations, and excess mortality. Our study encompasses a representative sample size drawn from the Belgian population in 2016 (n = 11.26 million, with a focus on n = 11.15 million individuals). The analysis is conducted at the geographical level of statistical sectors, comprising a total of n = 19,794 sectors, with a subset of n = 18,681 sectors considered in the investigation. We integrated multiple parameters at the finest spatial level and constructed three categories of environmental stress through clustering: air pollution, noise stress and stress related to specific land-use types. We observed identifiable patterns in the spatial distribution of stressors within each cluster category. We assessed the relationship between age-standardized all-cause mortality rates (ASMR) and environmental stressors. Our research found that especially very high air pollution values in areas where traffic is the dominant local component of air pollution (ASMR + 14,8%, 95% CI: 10,4 - 19,4%) and presence of industrial land (ASMR + 14,7%, 95% CI: 9,4 - 20,2%) in the neighbourhood are associated with an increased ASMR. Cumulative exposure to multiple sources of unfavourable environmental stress (simultaneously high air pollution, high noise, presence of industrial land or proximity of primary/secondary roads and lack of green space) is associated with an increase in ASMR (ASMR + 26,9%, 95% CI: 17,1 - 36,5%).

Self-organization as a mechanism of resilience in dryland ecosystems.

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

Geographical patterns and determinants of insect biodiversity in China.

Insects play important roles in the maintenance of ecosystem functioning and the provision of livelihoods for millions of people. However, compared with terrestrial vertebrates and angiosperms, such as the giant panda, crested ibis, and the metasequoia, insect conservation has not attracted enough attention, and a basic understanding of the geographical biodiversity patterns for major components of insects in China is lacking. Herein, we investigated the geographical distribution of insect biodiversity across multiple dimensions (taxonomic, genetic, and phylogenetic diversity) based on the spatial distribution and molecular DNA sequencing data of insects. Our analysis included 18 orders, 360 families, 5,275 genera, and 14,115 species of insects. The results revealed that Southwestern and Southeastern China harbored higher insect biodiversity and numerous older lineages, representing a museum, whereas regions located in Northwestern China harbored lower insect biodiversity and younger lineages, serving as an evolutionary cradle. We also observed that mean annual temperature and precipitation had significantly positive effects, whereas altitude had significantly negative effects on insect biodiversity in most cases. Moreover, cultivated vegetation harbored the highest insect taxonomic and phylogenetic diversity, and needleleaf and broadleaf mixed forests harbored the highest insect genetic diversity. These results indicated that human activities may positively contribute to insect spatial diversity on a regional scale. Our study fills a knowledge gap in insect spatial diversity in China. These findings could help guide national-level conservation plans and the post-2020 biodiversity conservation framework.

Projecting global fertilizer consumption under shared socioeconomic pathway (SSP) scenarios using an approach of ensemble machine learning.

Comprehensively projecting global fertilizer consumption is essential for providing critical datasets in related fields such as earth system simulation, the fertilizer industry, and agricultural sciences. However, since previous studies have not fully considered the socioeconomic factors affecting fertilizer consumption, huge uncertainties may remain in fertilizer consumption projections. Here, an approach ensembled six machine learning algorithms was proposed in this study to predict global fertilizer consumption from 2020 to 2100 by considering the impact of socioeconomic factors under shared socioeconomic pathway (SSP) scenarios. It indicates that the proposed approach provides a rational and reliable framework for fertilizer consumption prediction that stably outperforms the single algorithms with relatively high accuracy (Nash-Sutcliffe efficiency of 0.93, Kling-Gupta efficiency of 0.89, and mean absolute percentage error of 10.97 %). We found that global N and P fertilizer consumption may decrease from 2020 to 2100, while K fertilizer may buck the trend. N fertilizer consumption showed a declining trend of -1 %, -17.13 %, and -3.43 % under the SSP1, SSP2, and SSP3 scenarios in 2100, respectively. For P fertilizer, those were -0.68 %, -9.68 %, and -2.03 %. In contrast, global K fertilizer consumption may increase by 18.03 %, 9.18 %, and 6.74 %, respectively. On average, N, P, and K fertilizer consumption is highest in China, and the lowest is in Kazakhstan. However, the hotspots of N fertilizer consumption may shift from China to Latin America and the Caribbean. This study highlighted the ensemble machine learning approach could potentially be a robust method for predicting future fertilizer consumption. Our prediction product will not only contribute to a better understanding of global fertilizer consumption trends and dynamics but also provide flexible and accurate key data/parameters for related research. The Projected Global Fertilizers Consumption Datasets are available at doi:https://doi.org/10.5281/zenodo.8195593 (Gao et al., 2023).