Hierarchical Partitioning Research Articles

How do urban green space attributes affect visitation and satisfaction? An empirical study based on multisource data

Urban green spaces (UGSs) are major settings of human–nature interaction and important to public well-being. Visitation and satisfaction are indicators that reflect the utilization degree of and feedback regarding UGSs. The focus of this study is to analyze the visitation and satisfaction characteristics of different types of UGSs, analyze the dominant factors affecting these indicators, and rank their importance to provide strategies for improving management. Here, 50 typical UGSs in Shanghai are selected. All-subset regression, hierarchical partitioning analysis and other methods are comprehensively used to explore the influencing factors. The results are as follows: 1) Comprehensive parks have the highest visitation rates, whereas community parks have the lowest visitation rates but the highest satisfaction rates. 2) UGS size has the greatest influence on visitation, followed by connectivity, building shape index, and edge density, with contributions of 69.35 %, 16.40 %, 8.50 %, and 5.75 %, respectively. 3) Transportation facility density and edge density have the greatest influences on satisfaction, with contributions of 51.49 % and 48.51 %, respectively. In this study, the applicability of using multisource data to analyze UGS attributes and their factors affecting visitation and satisfaction are demonstrated. Targeted strategies for constructing UGSs will help authorities plan and manage UGSs effectively.

Dynamic evolution trend and driving mechanisms of water conservation in the Yellow River Basin, China

Water conservation (WC) is a critical ecological service function in the Yellow River Basin (YRB). There is currently a lack of detailed exploration of WC development processes and the impact mechanisms of driving factors at spatiotemporal scales in the YRB. By collecting data on DEM, land use, soil, meteorology, reservoirs, and observed discharge, this study established a large-scale WC model using the soil and water assessment tool (SWAT). The abrupt change test, empirical orthogonal function (EOF), wavelet analysis, hierarchical partitioning analysis (HPA), geodetectors, and aridity index were employed to analyze the multi-spatiotemporal characteristics and driving forces of WC calculated using the water balance method. The results are as follows: (1) The average WC among the YRB was 9.11 mm (74.68 × 108 m3) from 1960 to 2020. Pasture and forests contributed to 48.65% and 22.05% of the average annual WC, respectively. (2) WC exhibited four forms: less/more in the YRB, more in the southeast (northwest), and less in the northwest (southeast). (3) Forests and pastures in land use had higher average WC capacity, while Gansu, Shaanxi, and Qinghai ranked in the top three for average WC among the nine provinces. (4) Precipitation was the major driving force affecting WC variations, with the interaction between precipitation and actual evapotranspiration being the most significant. (5) Drought was a significant cause of negative WC. Protecting and managing crucial WC areas was essential for improving the ecological environment. This research elucidates the driving forces of WC in the YRB, providing scientific support for improving regional WC and promoting sustainable development.

Adaptive Privacy-Preserving Coded Computing with Hierarchical Task Partitioning.

Coded computing is recognized as a promising solution to address the privacy leakage problem and the straggling effect in distributed computing. This technique leverages coding theory to recover computation tasks using results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, designed to be applicable to various types of computation tasks, including polynomial and non-polynomial functions, and to adaptively provide accurate or approximated results. We prove the optimality of APCC in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding, based on the optimal recovery threshold of Lagrange Coded Computing. We demonstrate that APCC guarantees information-theoretical data privacy preservation. Mitigation of the straggling effect in APCC is achieved through hierarchical task partitioning and task cancellation, which further reduces computation delays by enabling straggling workers to return partial results of assigned tasks, compared to conventional coded computing strategies. The hierarchical task partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. The simulation results show that APCC can reduce the task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks.

A hierarchical data partitioning strategy for irregular applications: a case study in digital pathology

A hierarchical data partitioning strategy for irregular applications: a case study in digital pathology

HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains.

Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuroimaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups.

Classification of montane grasslands in central Argentina

AbstractQuestionWe address the following questions: (1) which are the main plant communities in montane grasslands in central Argentina; (2) do those communities differ in terms of floristic composition, endemisms, chorotypes and life forms; and (3) which are the major environmental variables (climatic, topographic and edaphic) associated with the heterogeneity of the vegetation?LocationPampean Mountains (Sierras Pampeanas), Córdoba Province, central Argentina, an area of ca. 37,000 km2.MethodsThe survey was based on a data set comprising 746 relevés and 822 vascular plant species, collected in accordance with the Braun‐Blanquet sampling method. The data were classified using the hierarchical ISOmetric feature mapping and Partitioning Around Medoids, and ordinated through isometric feature mapping (ISOPAM). The environmental variables were related to the ISOPAM ordination.ResultsThe ISOPAM classification identified two clusters, five vegetation types and 17 plant communities, which were distributed across different elevations. There were notable differences in the diagnostic species, average percentages of endemic species, chorotypes and life forms among the various vegetation types and plant communities. The primary gradient in grassland vegetation was found to be associated with elevation and related climatic variables.ConclusionsThe study provides valuable insights into the montane grasslands of the Sierras Pampeanas in central Argentina. It presents a comprehensive classification of the region's grasslands, including tall tussock grasslands, humid grasslands, stony grasslands, and grasslands with scattered shrubs and trees. Each recognized unit is distinguished by diagnostic species and a distinct floristic composition. Our findings emphasize a significant division between grasslands at higher elevation, abundant in Andean species, and Piedmont and shrubby grasslands at lower elevations, characterized by a higher prevalence of Chacoan and other Neotropical species. Grassland vegetation in other regions of the Sierras Pampeanas remains poorly studied, requiring additional research to determine the syntaxonomic position of this vegetation.

Accelerating large-scale multi-scalar multiplication in Zk-SNARK through exploiting its multilevel parallelism

In the context of zk-SNARK, MSM emerges as a major computational bottleneck, particularly due to its high computational and memory overhead. In this work, we exploit multiple levels of parallelism to effectively accelerate largescale MSM in zk-SNARKs. Firstly, a distributed parameter generation method is proposed in this paper to replace that of centralized method. Based on this methodology, the paper realizes a system with extraordinary scalability, capable of computing large-scale MSMs. Subsequently, the approach presented in this paper elevates the computation of Bellperson, the most prominent zero-knowledge proof system in real-world applications, from a single-node computation to a clustering mode, significantly enhancing its computational performance – a crucial advancement for practical applications. Finally, we implement a multi-level, fully parallelised MSM computing system by leveraging hierarchical sub-task partitioning and cross-node communication optimization, thereby thoroughly exploiting parallelism at diverse granularities. Experimental results show that in the cluster scenario, the proposed approach achieves acceleration ratios of approximately 3.60 and 6.50 times compared to the cutting-edge heterogeneous version Bellperson in dual-node and quad-node settings, respectively. On a single node, the proposed optimization approach achieves an acceleration ratio of 1.38 times compared to the current State-of-the-Art MSM calculation module of cuZK, outperforms the industry-popular Bellman by 186 times and the Bellperson by 1.96 times.

Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.

Fuzzy machine learning predictions of settling velocity based on fractal aggregate physical features in water treatment

The dynamics of gravitational sedimentation in water treatment are crucial for optimising particulate matter removal. This study addresses the effect of fractal aggregate features on settling velocity and explores fuzzy machine learning (ML) for predicting this phenomenon. Particle image velocimetry determined aggregate velocities within a sedimentation column, with features identified concurrently. Using a comprehensive methodological framework, significant predictors were selected through various statistical analyses. The fuzzy ML model, developed with the ‘FisPro’ package in R, incorporated a hierarchical partitioning scheme using Lukasiewicz and Sum operators for conjunction and disjunction processes, respectively. The Wang-Mendel method extracted fuzzy rules, with defuzzification achieved using the maximum crisp operator. Hyperparameter optimization was conducted through grid search techniques, and model performance was evaluated using 3-fold cross-validation. The findings reveal that Margination, Radius, and Clumpiness significantly impact settling velocity. The model demonstrates exceptional predictive accuracy (R2 = 0.923) across both training and validation datasets, highlighting its potential for forecasting terminal velocity in water treatment. This research suggests that precise predictions of sedimentation dynamics can improve particulate matter removal efficiency and encourages further investigations into diverse aggregate types and environmental scenarios, advocating for integrating physics-informed ML approaches to enhance the model.

Functional and phylogenetic structure of mammals along elevational gradients in the Central and East Himalayas

BackgroundMountain ecosystems are critical hotspots of global biodiversity, yet the dynamics of community assembly along their elevational gradients are not well understood. This gap is primarily due to the complexity of environmental and biotic interactions that influence species distribution and community structure. Although extensive research has been conducted on certain taxa, such as small mammals and bats, comprehensive studies encompassing entire mammal assemblages are lacking. Our research aims to bridge this gap by examining the taxonomic, phylogenetic, and functional diversity, as well as the assemblage structures of mammal communities across elevational gradients in the Central and East Himalayas, exploring how diverse ecological and evolutionary processes impact community assembly.MethodsWe analyzed mammal elevational diversity patterns using species richness, functional diversity (FD), and phylogenetic diversity (PD). We compared the observed values of community structure, such as mean pairwise phylogenetic distance (MPD) and mean pairwise functional distance (MFD), with null-model corrected effect sizes to identify patterns and processes of community assembly. Using structural equation modeling and hierarchical partitioning, we investigated the relationships between climate, productivity, and various facets of diversity, describing the organization of each component across different elevations.ResultsTaxonomic, functional, and phylogenetic diversity generally decreased with elevation but showed varied patterns such as mid-elevation peaks, low-elevation plateaus, or monotonic declines across different regions. Richness-controlled functional diversity increased towards mid-low elevations and decreased at higher elevations in both regions, whereas richness-controlled phylogenetic diversity lacked consistent patterns. Phylogenetic structures tended to cluster from mid to high elevations, indicating closer relationships than those observed in random communities, likely due to significant environmental turnover near tree lines. Functional structure showed greater clustering at high elevations and increased over-dispersion at lower elevations, suggesting that species are more functionally similar than expected at higher elevations and more diverse at lower elevations. Our results revealed that environmental factors, evolutionary histories, and trait-driven ecological processes collectively shape species richness along these gradients.ConclusionsOur results showed incongruent community structures across phylogenetic and functional diversity. Generally, functional traits are closely linked to environmental conditions, reducing the chance of observing traits that are misaligned with their surroundings. Species with similar ecological roles or distinct evolutionary lineages often show convergent adaptations to highland environments. Additionally, our findings emphasize that community assembly varies with the biogeography and diversification history of individual mountain ranges, complicating the development of a generalized theory. Using multiple measures is important for accurate community structure assessments and effective conservation planning, as variable elevational patterns exist across different diversity dimensions.

Soil fungi lead to stronger ‘diminishing returns’ in fine‐root length versus mass allometry towards earlier successional tropical forests

Abstract Decreasing returns in resource acquisition ability with increasing leaf mass investment is called ‘diminishing returns’, which provides important insights into plant economy. Yet, whether this is true for fine roots and how root resource acquisition strategies change with forest succession remain unclear. We investigated the scaling relationship between fine‐root length (L) and mass (M) for 215 topsoil cores from 24 plots across four successional stages in tropical forests of Xishuangbanna, southwestern China. We also assessed the relative effects of edaphic conditions, leaf functional traits, tree species diversity and soil fungal factors on L versus M scaling relationship using hierarchical variation partitioning. Our results revealed the existence of diminishing returns in root length (L vs. M scaling exponent <1), and that the exponent was higher in late‐ than early‐successional forests, corresponding to a strategy shifting from ‘do‐it‐yourself’ in the late‐successional stage to ‘outsourcing’ resource uptake by soil fungi in the early‐successional stage. Soil fungal abundance was the main driver of changes in the L versus M scaling exponent across plots (explained 58% of variances), with root endophytic fungi the strongest predictor (22.11%), followed by mycorrhizal fungi (10.41%), while other factors (leaf functional traits, edaphic nutrient conditions and tree species diversity) exerted weak effects. Our results suggest that root endophytic and mycorrhizal fungi act as key modulators of root economy changes during forest succession, but the former has received less attention previously. L versus M scaling exponent may be a better indicator for shifts in root resource acquisition strategy than the commonly used specific root length. Read the free Plain Language Summary for this article on the Journal blog.

Effects of Environmental Factors on the Diversity of Grasshopper Communities along Altitude Gradients in Xizang, China.

To determine the grasshopper species composition, altitudinal distribution patterns, and their main drivers, we conducted a study in Xizang using 33 sample plots ranging from 600 to 4100 m. Grasshoppers were collected from August to October during 2020-2022 using sweep nets. A total of 1159 grasshoppers from six families, 28 genera, and 44 species were identified, with Omocestus cuonaensis and Aserratus eminifrontus as the dominant species, comprising 30.03% and 10.26% of total grasshoppers, respectively. The results showed that species richness and the Margalef richness index of grasshopper communities decreased significantly (p < 0.05) with increasing altitude, peaking at 1100-1600 m and lowest values at 2600-3100 m. Similarly, the Shannon-Wiener index and Simpson dominance index also decreased significantly (p < 0.05) with an increase in altitude, showing the highest and lowest values at 600-1100 m and 3100-3600 m, respectively. The Jaccard similarity coefficients among grasshopper communities varied from 0 to 0.40 across altitudinal gradients, indicating different degrees of dissimilarity. The results of Pearson correlation analyses showed that the Shannon-Wiener index, species richness, Margalef richness index, and Simpson dominance index of grasshopper communities were significantly negatively correlated with the temperature factors and soil pH, but they were significantly positively correlated with the moisture factors. Hierarchical partitioning identified annual mean temperature-daily difference, precipitation in the coldest season, and driest month precipitation as the primary factors explaining variance in grasshopper community diversity in Xizang. These findings provided greater insights into the mechanisms underlying insect community structure, distribution patterns, and diversity in Xizang ecosystems, including implications for the effects of global warming on insect communities.

Trapezoidal neutrosophic teaching learning-based optimization in enhancing accuracy of diabetes prognosis

Diabetes is one of chronic diseases in which blood glucose (sugar) level soar up high where human body are incapable to absorb it properly. It is important to have an appropriate diagnosis for proper management and treatment. The aim of this manuscript is to provide a more accurate diabetes prediction model through the new adaptive Trapezoidal Neutrosophic Teaching Learning-Based Optimization (TLBO) method. In order to address the inherent uncertainties and imprecisions in medical data, the suggested model makes use of the resilience of Trapezoidal Neutrosophic sets. The Trapezoidal Neutrosophic set theory provides a suitable basis for developing rule/knowledge-based systems in the medical field. The present investigation makes use of the dataset acquired from the Pima Indians Diabetes Database (PIDD) website, which has an extensive global collection of diabetes datasets. The performance of our model is evaluated against several existing methodologies, including Intuitionistic Neuro-Fuzzy System (INFS) Structure, Fuzzy Logic based Diabetes Diagnosis System (FLDDS), Fuzzy Verdict Mechanism (FVM) for Diabetes Decision, (Fuzzy Expert System) FES, and Hierarchical Neuro-Fuzzy Binary space partitioning System (HNFB-1). Quantitative analysis validates that proposed methodology achieves an exceptional predictive accuracy of 99.89 %, which is substantially higher than the comparative methodologies, namely INFS Structure (88.76 %), FLDDS (87.2 %), FVM for Diabetes Decision (85.03 %), FES (81.7 %), and HNFB-1 (78.26 %). These enhancements demonstrate show how well the suggested model works to lower diagnostic errors and increase dependability.

A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate.

Specific responses in soil metabolite alteration and fungal community decline to the long-term monocropping of lisianthus

Continuous cropping obstacle (CCO) is becoming a serious threat to the quality production of lisianthus (Eustoma grandiflorum). However, investigations of the causal mechanism of CCO in lisianthus are still lacking. To this end, soil biotic dynamics and metabolites alterations were illuminated using metagenomic sequencing and metabolomic profiling along a chronosequence that has cultivated lisianthus from 2 weeks to 6 years. Soil EC and nutrient contents increased significantly as the cultivation duration prolonged. Fungal community has undergone significant decline in their diversity and biomass. Nevertheless, no apparent accumulation of pathogenicity-related microbes was observed along the investigated chronosequence. The alterations of soil metabolites along the chronosequence reflect the decline of microbial activity, the accumulation of phytotoxic compounds and the loss of metabolite involved in beneficial plant-microbe interactions. As a result, lisianthus plants in the longer continuous cropping soil experienced stressful condition as evidenced by the increasing trend of stress responsive metabolites and the overall soil function deterioration as indicated by soil enzymes. Hierarchical partitioning analysis showed that the investigated factors can explain a total of 92.25 % of the variations in the soil function degradation. Of which, soil metabolite alteration was the most important factor in explaining the CCO development, covering 24.55 % of the explainable variations. Spearman's correlation results further implied that soil metabolite alteration is the main driver in the development of CCO in lisianthus, especially by mediating the plant-microbes interactions. Our findings revealed a general pattern of soil biotic and abiotic response to the continuous monocropping of lisianthus and suggest that the overcome of the CCO in lisianthus requires further studies focusing on the crucial metabolites and their interactions with microbes.

Tree richness increased biomass carbon sequestration and ecosystem stability of temperate forests in China: Interacted factors and implications

Biodiversity loss and forest degradation have received increasing attention worldwide, and their effects on forest biomass carbon storage and stability have not yet been well defined. This study examined 1275 tree plots using the field survey method to quantify the effects of tree diversity, tree sizes, and mycorrhizal symbiont abundance on biomass carbon storages (Cs) and NDVI (Normalized Difference Vegetation Index)-based ecosystem stability (standard deviation/mean NDVI = NDVI_S) during the field survey period from 2008 to 2018. Our data showed Cs and NDVI_S averaged at 31–108 t ha−1 and 32.04–49.28, respectively, and positive relations between Cs and NDVI_S were observed (p < 0.05). Large forest-type and regional variations were found in these two parameters. Broadleaf forests had 74% of Cs (p < 0.05) of the conifer forests, but no differences were in NDVI_S. Cold regions at high latitudes had 71% of NDVI_S in the warm regions at low latitudes, while no differences were in Cs. Moist regions at high longitudes had 2.04 and 1.28-fold higher Cs and NDVI_S (p < 0.05). The >700 m a.s.l. regions had 1.24-fold higher Cs (p < 0.01) than the <700 m a.s.l. regions, but similar NDVI_S (p > 0.05). Nature Reserves had 1.94-fold higher Cs but 30% lower NDVI_S than outside Reserves (p < 0.001). > 40-year-old forests had 1.3- and 2-fold higher Cs and NDVI_S than the young forests. Structural equation modeling and hierarchical partitioning revealed the driving paths responsible for these variations. Tree richness was positively associated with Cs and ecosystem stability, contributing 21.6%–30.6% to the total effects on them; tree sizes significantly promoted the Cs, but had negligible impacts on NDVI_S. MAT's total effects on NDVI_S of conifer forests were 40% higher than that of broadleaf forests, MAP's total effects on Cs varied with forest types; arbuscular mycorrhizal tree dominance exhibited a smaller positive impact on Cs and ecosystem stability in comparison to other factors. Our findings underscore that the significance of climatic-adapted forest management, diversity conservation, and big-sized tree protections can support the achievement of carbon neutrality in China from biomass carbon sequestration and ecosystem stability.

Environmental risks in swine biogas slurry-irrigated soils: A comprehensive analysis of antibiotic residues, resistome, and bacterial pathogens

Simple anaerobic digestion is insufficient to completely remove residual parent antibiotics and antibiotic resistance genes (ARGs) from animal manure. ARG prevalence in swine biogas slurry-irrigated soils threatens human health. However, comprehensive analysis of antibiotic residues, high-resolution resistance gene profiles, and pathogenic microbiomes in biogas slurry-irrigated soils is very limited. Here, we comprehensively determined the antibiotics, resistome, and potential pathogens distribution in these soils, using high-performance liquid chromatography-tandem mass spectrometry, high-throughput quantitative PCR, and 16S rRNA gene sequencing. The results revealed a significant enrichment of tetracyclines and fluoroquinolones antibiotics and ARGs in soils with prolonged biogas slurry irrigation, with a total of 12 antibiotics, 175 unique ARGs, and 9 mobile genetic elements (MGEs) detected. Quantification of veterinary antibiotic residues (especially chlortetracycline) showed significant correlations with multiple ARGs. The abundance of ARGs and MGEs was highest in the biogas slurry-irrigated soils, denoting a tight link between the application of biogas slurry and the spread of antibiotic resistance. The presence of 50 potential pathogenic bacterial genera, including 13 with multidrug resistance, was identified. Variation partitioning, combined with hierarchical partitioning analysis, indicated that Firmicutes, MGEs, and tetracyclines were the key drivers shaping the ARG profiles in biogas slurry-irrigated soils. The findings offer insights into the mechanisms of antibiotic residue and ARGs spread from the agricultural practice of biogas slurry irrigation, underscoring the necessity of sustainable soil management to mitigate the spread of antibiotic resistance.

Effects of Community Assets on Major Health Conditions in England: A Data Analytic Approach.

The broader determinants of health including a wide range of community assets are extremely important in relation to public health outcomes. Multiple health conditions, multimorbidity, is a growing problem in many populations worldwide. This paper quantified the effect of community assets on major health conditions for the population of England over six years, at a fine spatial scale using a data analytic approach. Community assets, which included indices of the health system, green space, pollution, poverty, urban environment, safety, and sport and leisure facilities, were quantified in relation to major health conditions. The health conditions examined included high blood pressure, obesity, dementia, diabetes, mental health, cardiovascular conditions, musculoskeletal conditions, respiratory conditions, kidney and liver disease, and cancer. Cluster analysis and dendrograms were calculated for the community assets and major health conditions. For each health condition, a statistical model with all community assets was fitted, and model selection was performed. The number of significant community assets for each health condition was recorded. The unique variance, explained by each significant community asset per health condition, was quantified using hierarchical variance partitioning within an analysis of variance model. The resulting data indicate major health conditions are often clustered, as are community assets. The results suggest that diversity and richness of community assets are key to major health condition outcomes. Primary care service waiting times and distance to public parks were significant predictors of all health conditions examined. Primary care waiting times explained the vast majority of the variances across health conditions, with the exception of obesity, which was better explained by absolute poverty. The implications of the combined findings of the health condition clusters and explanatory power of community assets are discussed. The vast majority of determinants of health could be accounted for by healthcare system performance and distance to public green space, with important covariate socioeconomic factors. Emphases on community approaches, significant relationships, and asset strengths and deficits are needed alongside targeted interventions. Whilst the performance of the public health system remains of key importance, community assets and local infrastructure remain paramount to the broader determinants of health.

SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration

Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates. At the same time, a tension has arisen between explicit scene representations amenable to rasterization and neural fields built on ray marching, with state-of-the-art instances of the latter surpassing the former in quality while being prohibitively expensive for real-time applications. We introduce SMERF, a view synthesis approach that achieves state-of-the-art accuracy among real-time methods on large scenes with footprints up to 300 m 2 at a volumetric resolution of 3.5 mm 3 . Our method is built upon two primary contributions: a hierarchical model partitioning scheme, which increases model capacity while constraining compute and memory consumption, and a distillation training strategy that simultaneously yields high fidelity and internal consistency. Our method enables full six degrees of freedom navigation in a web browser and renders in real-time on commodity smartphones and laptops. Extensive experiments show that our method exceeds the state-of-the-art in real-time novel view synthesis by 0.78 dB on standard benchmarks and 1.78 dB on large scenes, renders frames three orders of magnitude faster than state-of-the-art radiance field models, and achieves real-time performance across a wide variety of commodity devices, including smartphones. We encourage readers to explore these models interactively at our project website: https://smerf-3d.github.io.