Statistical Clustering Research Articles

Post-drainage vegetation, microtopography and organic matter in Arctic drained lake basins

Wetlands in Arctic drained lake basins (DLBs) have a high potential for carbon storage in vegetation and peat as well as for elevated greenhouse gas emissions. However, the evolution of vegetation and organic matter is rarely studied in DLBs, making these abundant wetlands especially uncertain elements of the permafrost carbon budget. We surveyed multiple DLB generations in northern Alaska with the goal to assess vegetation, microtopography, and organic matter in surface sediment and pond water in DLBs and to provide the first high-resolution land cover classification for a DLB system focussing on moisture-related vegetation classes for the Teshekpuk Lake region. We associated sediment properties and methane concentrations along a post-drainage succession gradient with remote sensing-derived land cover classes. Our study distinguished five eco-hydrological classes using statistical clustering of vegetation data, which corresponded to the land cover classes. We identified surface wetness and time since drainage as predictors of vegetation composition. Microtopographic complexity increased after drainage. Organic carbon and nitrogen contents in sediment, and dissolved organic carbon (DOC) and dissolved nitrogen (DN) in ponds were high throughout, indicating high organic matter availability and decomposition. We confirmed wetness as a predictor of sediment methane concentrations. Our findings suggest moderate to high methane concentrations independent of drainage age, with particularly high concentrations beneath submerged patches (up to 200 μmol l−1) and in pond water (up to 22 μmol l−1). In our DLB system, wet and shallow submerged patches with high methane concentrations occupied 54% of the area, and ponds with high DOC, DN and methane occupied another 11%. In conclusion, we demonstrate that DLB wetlands are highly productive regarding organic matter decomposition and methane production. Machine learning-aided land cover classification using high-resolution multispectral satellite imagery provides a useful tool for future upscaling of sediment properties and methane emission potentials from Arctic DLBs.

Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis

Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.

PPS: Fair and efficient black-box scheduling for multi-tenant GPU clusters

Multi-tenant GPU clusters are common, where users purchase GPU quota to run their neural network training jobs. However, strict quota-based scheduling often leads to cluster under-utilization, while allowing quota groups to use excess GPUs improves utilization but results in fairness problems. We propose PPS, a probabilistic prediction based scheduler, which uses job history statistics to predict future cluster status for making good scheduling decisions. Different from existing schedulers that rely on deep learning frameworks to adjust bad scheduling decisions and/or require detailed job information, PPS treats jobs as black boxes in that PPS runs a job to completion without adjustment once scheduled and requires only aggregate job statistics. The black-box feature is favorable due to its good generality, compatibility and security, and made possible by the predictability of aggregate resource utilization statistics of large clusters. Extensive experiments show that PPS achieves high cluster utilization and good fairness simultaneously.

Perceptions from Member-Consumers of a University Community for Sustainable and Healthy Eating: Evidence from Greece

Healthy eating is a vital component of a sustainable daily life, especially after the COVID-19 pandemic. In this study, we investigated the perceptions and attitudes of the members of a Greek university community, especially the young members, on: (i) health itself, (ii) healthy eating, and (iii) healthy eating campaigns today. The research was conducted electronically in the last three months of 2023 via a questionnaire survey and 1046 member-consumers participated. Statistical analysis, including descriptive and cluster analysis to group respondents into homogenous segments, was performed by employing a one-way ANOVA. The highlights of the results indicate that consumers perceive physical and mental health and energy efficiency as health preconditions, while they perceive a balanced and sustainable diet as the main parameter of healthy eating. They are influenced mostly by doctors and health providers and use information mostly based on common sense and their upbringing for their healthy eating choices. The cluster analysis revealed two distinctive groups of consumers categorized by this study as the “approachables” (54.5%), affected by today’s sustainable knowledge and concepts on healthy eating, and the “conservatives” (45.5%), who are reluctant to accepted these as such.

Geospatial analysis of groundwater arsenic and fluoride in Quaternary aquifers of southern Ontario, Canada

The geological setting of an area plays a critical role in the transfer and ultimate distribution of hydrochemical constituents present in groundwater. In southern Ontario, Canada, the present physiography was significantly influenced by glacial processes during the Quaternary period. The heterogeneous nature and complex pattern of shallow subsurface glacial overburden sediments, likely affect the fate of different groundwater constituents. In this study, arsenic (As) and fluoride (F−) concentrations from 515 water wells, that are constructed within overburden sediment, were analyzed with the physiographic map of southern Ontario along with other related variables. Geospatial mapping and several spatial statistical analyses were performed to examine the possible geological influence on As and F− distribution and water-well susceptibility at a regional scale. Key findings suggest four physiographic settings were significant variables influencing the distribution of As and F− in differently constructed bored/dug and drilled wells. Bored/dug wells in Bevelled Till Plains and bored/dug wells in Undrumlinized and Bevelled Till Plains were found to be relatively susceptible to As and F− contamination respectively. In contrast, bored/dug and drilled wells in Drumlinized Till Plains and Drumlins and drilled wells in Sand Plains seemed to be relatively safe from F− and As respectively. The statistical regression analyses suggested that other variables, such as the application of phosphate fertilizer and the textures of till, influenced the spatial distribution of As and F− as well as which types of wells (bored/dug or drilled) were impacted. The geospatial mapping and statistical cluster analysis indicated that the possible sources of elevated As and F− in drilled wells are the clasts of underlying bedrock. The relationship between physiographic settings and impacted overburden wells in southern Ontario provides planners with an approach to water-well susceptibility assessments at the regional scale, which in turn can guide further local analysis for water resource management.

Mock observatory: Two thousand light-cone mock catalogues of luminous red galaxies from the Hyper Suprime-Cam Survey for the cosmological large-scale analysis

ABSTRACT Estimating a reliable covariance matrix for correlation functions of galaxies is a crucial task to obtain accurate cosmological constraints from galaxy surveys. We generate 2000 independent light-cone mock luminous red galaxy (LRGs) catalogues at 0.3 ≤ z ≤ 1.25, designed to cover CAMIRA LRGs observed by the Subaru Hyper Suprime-Cam Subaru Strategic Programme (HSC SSP). We first produce full-sky light-cone halo catalogues using a COmoving Lagrangian Acceleration (COLA) technique, and then trim them to match the footprints of the HSC SSP S20A Wide layers. The mock LRGs are subsequently populated on to the trimmed halo catalogues according to the halo occupation distribution model constrained by the observed CAMIRA LRGs. The stellar mass (M⋆) is assigned to each LRG by the subhalo abundance-matching technique using the observed stellar-mass functions of CAMIRA LRGs. We evaluate photometric redshifts (photo-z) of mock LRGs by incorporating the photo-z scatter, which is derived from the observed M⋆–photo-z-scatter relations of the CAMIRA LRGs. We validate the constructed full-sky halo and light-cone LRG mock catalogues by comparing their angular clustering statistics (i.e. power spectra and correlation functions) with those measured from the halo catalogues of full N-body simulations and the CAMIRA LRG catalogues from the HSC SSP, respectively. We detect clear signatures of baryon acoustic oscillations (BAOs) from our mock LRGs, whose angular scales are well consistent with theoretical predictions. These results demonstrate that our mock LRGs can be used to evaluate covariance matrices at large scales and provide predictions for the BAO detectability and cosmological constraints.

Экспресс-диагностика профессиональной психологической пригодности военнослужащих по призыву

Summary. The purpose of the study is to develop a model for express diagnostics of the professional psychological suitability of conscript military personnel. Materials and methods of research. The object of the study was randomly selected conscript military personnel (n=60), whose average age was (23.78±1.27) years. Criteria for inclusion in the study were conscription age and written consent of the subject to participate in the study; exclusion criteria from the study – lack of written consent to participate in the study, incorrect testing, severe illness – high fever, chills, etc. When processing the research results, descriptive statistics methods and cluster analysis using the K-means method were used. The significance of differences between groups was assessed using Student’s t-test; to develop decision rules, the multiple regression method and forward discriminant analysis stepwise with inclusion were used (F-enter = 2.0); (F-remove=1.9) and p<0.05. The critical value of the significance level was considered to be 0.05. Mathematical data processing was carried out using the STATISTICA v.10.0 software package. Research results and their analysis. Using the example of conscripted military personnel, the possibility of predicting professional psychological suitability using express testing based on vibraimage technology is shown. The results of testing aimed at assessing personality accentuations, abilities (types of multiple intelligences according to G. Gardner), as well as the moral qualities of military personnel were analyzed. Decisive rules have been constructed that make it possible to predict the professional psychological suitability of a serviceman with an accuracy of more than 91%.

Euclid preparation

Aims. We validate a semi-analytical model for the covariance of the real-space two-point correlation function of galaxy clusters. Methods. Using 1000 PINOCCHIO light cones mimicking the expected Euclid sample of galaxy clusters, we calibrated a simple model to accurately describe the clustering covariance. Then, we used this model to quantify the likelihood-analysis response to variations in the covariance, and we investigated the impact of a cosmology-dependent matrix at the level of statistics expected for the Euclid survey of galaxy clusters. Results. We find that a Gaussian model with Poissonian shot-noise does not correctly predict the covariance of the two-point correlation function of galaxy clusters. By introducing a few additional parameters fitted from simulations, the proposed model reproduces the numerical covariance with an accuracy of 10%, with differences of about 5% on the figure of merit of the cosmological parameters Ωm and σ8. We also find that the covariance contains additional valuable information that is not present in the mean value, and the constraining power of cluster clustering can improve significantly when its cosmology dependence is accounted for. Finally, we find that the cosmological figure of merit can be further improved when mass binning is taken into account. Our results have significant implications for the derivation of cosmological constraints from the two-point clustering statistics of the Euclid survey of galaxy clusters.

Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran

The crucial parameters influencing drilling operations, reservoir production behavior, and well completion are lithology and reservoir rock. This study identified optimal reservoir rocks and facies in 280 core samples from a drilled well in the Asmari reservoir of the Mansouri field in SW Iran to determine the number of hydraulic flow units. Reservoir samples were prepared, and their porosity and permeability were determined by measuring devices. The flow zone index (FZI) was calculated for each sample using MATLAB software; then, a histogram analysis was performed on the logarithmic data of the FZI, and the number of hydraulic flow units was determined based on the obtained normal distributions. Electrical facies were determined based on artificial neural network (ANN) and multi-resolution graph-based clustering (MRGC) approaches. Five electrical facies with dissimilar reservoir conditions and lithological compositions were ultimately specified. Based on described lithofacies, shale and sandstone in zones three and five demonstrated elevated reservoir quality. This study aimed to determine the Asmari reservoir’s porous medium’s flowing fluid according to the C-mean fuzzy logic method. Furthermore, the third and fourth flow units in the Asmari Formation have the best flow units with high reservoir quality and permeability due to determining the siliceous–clastic facies of the rock units and log data. Outcomes could be corresponded to the flow unit determination in further nearby wellbores without cores.

An Overview of Bioinformatics and its Application: A Ray of Hope towards Microbial Biotechnology

A new era in the analysis of biological data has been sparked by the revolutionary development in processing speed and memory capacity. With the sequencing of hundreds of microbial and numerous eukaryotic genomes—including a clearer draft of the human genome—better control over microbes is anticipated. The objectives are very high and include the creation of sensible medications and antimicrobial agents, the creation of novel, improved bacterial strains for bioremediation and pollution control, the creation of more effective vaccines that are simple to administer, the creation of protein biomarkers for a variety of bacterial diseases, and a deeper comprehension of the interactions between hosts and bacteria to prevent bacterial infections. Bioinformatics research currently being conducted on the basis of Genomics, Proteomics, cell visualization and application to the development of drugs and antimicrobial agents. The main contributions of bioinformatics research include automated genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to determine the function of the genome, automated metabolic pathway derivation, automated gene expression analysis to determine regulatory pathways, automated statistical technique development, data mining and clustering techniques to determine protein-protein and protein-DNA interactions, modeling of 3D protein structure and 3D docking between proteins and biochemicals for rational drug design, and analysis of differences between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents.

Regression Test Suite Study Using Classic Statistical Methods and Machine Learning

This work is interdisciplinary in nature. This work tries to apply latest discoveries in Artificial Intel-ligence to classic testing methodologies. Machine Learning which is the field of Artificial Intelligence is explored in this work. The work demonstrates that provided the test team maintains the required data, Machine Learning Algorithms can aid in deciphering patterns from the test data. Patterns of interest are the relation between testers experience in the project and bugs uncovered, relations between the testers experience and the efficiency of test case with respect to code coverage and test execution time. Relation between testers experience and efficiency of test case with respect to code coverage and execution time, relation between testers experience and bugs uncovered are explored using classic statistical techniques and clustering Machine Learning Algorithms. This clustering can be of immense help in test selection, prioritization, pruning and Regression test execution time reduction.

Clustering-based Recommendations for Enhancing Students’ Academic Performance by Recognizing Prevalent Assessment Method using Exploratory Data Analysis

Objectives: To analyse students’ academic performance based on assessment methods and determine the most prevalent one through which students can be categorised for recommending optimal student-centered pedagogies that enhance students’ performance. Methods: Exploratory Data Analysis identifies the implications of the assessment methods based on the marks obtained by students in Continuous Assessments (CA) and the Cumulative Test (CT). Continuous Assessment (CA) and Cumulative Test (CT) marks of three subjects that come under foundation science, elective, and skill-based course of 100 undergraduate students are collected from a reputed Arts and Science Institution using stratified sampling technique, analyzed, and the recommendations are made based on the statistical observations and cluster analysis. Clustering recognises learning patterns of the students’ on the learners’ data. The Elbow method determines the number of clusters where the Silhouette score identifies the best suitable clustering technique for the dataset. K-Means Clustering categorises students based on their performance, that helps to give recommendations to improve. Findings: Based on Univariate and Bivariate analysis on the dataset, this work identifies Continuous Assessment (CA) as a prevalent evaluation strategy that motivates students to get engaged throughout rather than just before the exam. Based on the Silhouette Score (above .5), K-Means clustering is chosen to discover hidden patterns in the assessment marks depending on the three clusters determined by the Elbow method. It helps to identify the underperformers (46%) and suggest personalised recommendations for improving student’s academic performance as per clusters. Novelty: This work integrates Statistical Analysis and Clustering Analysis as per the optimal clusters determined by the Elbow method for identifying patterns hidden in assessment marks based on the prevalent assessment types. As a result, it enables more personalised recommendations for recognising the predominant assessment method and boosting academic achievement. Keywords: Continuous Assessment, Cumulative Test, Statistical Analysis, Exploratory Data Analysis, Univariate, Bivariate, Cluster Analysis, Elbow Method, K­Means Clustering

AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from the angular power spectrum and correlation function

We study the tomographic clustering properties of the photometric cluster catalogue derived from the third data release of the Kilo Degree Survey (KiDS), focusing on the angular correlation function and its spherical harmonic counterpart: the angular power spectrum. We measured the angular correlation function and power spectrum from a sample of 5162 clusters, with an intrinsic richness of λ* ≥ 15, in the photometric redshift range of z ∈ [0.1, 0.6]. We compared our measurements with theoretical models, within the framework of the Λ cold dark matter cosmology. We performed a Markov chain Monte Carlo (MCMC) analysis to constrain the cosmological parameters, Ωm and σ8, as well as the structure growth parameter, S8 ≡ σ8√Ωm/0.3. We adopted Gaussian priors on the parameters of the mass-richness relation, based on the posterior distributions derived from a previous joint analysis of cluster counts and weak-lensing mass measurements carried out on the basis of the same catalogue. From the angular correlation function, we obtained Ωm = 0.32−0.04+0.05, σ8 = 0.77−0.09+0.13, and S8 = 0.80−0.06+0.08, which are in agreement, within 1σ, with the 3D clustering result based on the same cluster sample and with existing complementary studies on other data sets. For the angular power spectrum, we checked the validity of the Poissonian shot noise approximation, also considering the mode-mode coupling induced by the mask. We derived statistically consistent results, in particular, Ωm = 0.24−0.04+0.05 and S8 = 0.93−0.12+0.11; while the constraint on σ8 alone is weaker with respect to the one provided by the angular correlation function, σ8 = 1.01−0.17+0.25. Our results show that the 2D clustering from photometric cluster surveys can provide competitive cosmological constraints with respect to the full 3D clustering statistics. We also demonstrate that they can be successfully applied to ongoing and forthcoming spectrometric and photometric surveys.

Cosmological constraints from the abundance, weak lensing, and clustering of galaxy clusters: Application to the SDSS

Aims. The clustering of galaxy clusters is a powerful cosmological tool. When it is combined with other cosmological observables, it can help to resolve parameter degeneracies and improve constraints, especially on Ωm and σ8. We aim to demonstrate its potential in constraining cosmological parameters and scaling relations when combined with cluster counts and weak-lensing mass information. As a case study, we use the redMaPPer cluster catalog derived from the Sloan Digital Sky Survey (SDSS). Methods. We extended a previous analysis of the number counts and weak-lensing signal by the two-point correlation function. We derived cosmological and scaling relation posteriors for all possible combinations of the three observables to assess their constraining power, parameter degeneracies, and possible internal tensions. Results. We find no evidence for tensions between the three data sets we analyzed. We demonstrate that the constraining power of the sample can be greatly improved by including the clustering statistics because this can break the Ωm − σ8 degeneracy that is characteristic of cluster abundance studies. In particular, for a flat ΛCDM model with massive neutrinos, we obtain Ωm = 0.28 ± 0.03 and σ8 = 0.82 ± 0.05, which is an improvement of 33% and 50% compared to the posteriors derived by combining cluster abundance and weak-lensing analyses. Our results are consistent with cosmological posteriors from other cluster surveys, and also with Planck results for the cosmic microwave background (CMB) and DES-Y3 galaxy clustering and weak-lensing analysis.

Accurate Fourier-space statistics for line intensity mapping: Cartesian grid sampling without aliased power

ABSTRACT Estimators for n-point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this transformation in the context of pixelized line intensity maps (LIM), highlighting potential biasing effects on power-spectrum measurements. Current analyses often avoid a complete resampling of the data by approximating survey geometry as rectangular in Cartesian space, an increasingly inaccurate assumption for modern wide-sky surveys. Our simulations of a $20\, {\times }\, 20\, \text{deg}^2$ 21 cm LIM survey at $0.34\, {\lt }\, z\, {\lt }\, 0.54$ show this assumption biases power-spectrum measurements by ${\gt }\, 20~{{\ \rm per\ cent}}$ across all scales. We therefore present a more robust framework for regridding the voxel intensities on to a 3D FFT field by coordinate transforming large numbers of Monte-Carlo sampling particles. Whilst this unbiases power-spectrum measurements on large scales, smaller scale discrepancies remain, caused by structure smoothing and aliasing from separations unresolved by the grid. To correct these effects, we introduce modelling techniques, higher order particle assignments, and interlaced FFT grids to suppress the aliased power. Using a piecewise cubic spline (PCS) particle assignment and an interlaced FFT field, we achieve sub-per cent accuracy up to 80 per cent of the Nyquist frequency for our 21 cm LIM simulations. We find a more subtle hierarchical improvement in results for higher order assignment schemes, relative to the gains made for galaxy surveys, which we attribute to the extra complexity in LIM from additional discretizing steps. python code accompanying this paper is available at github.com/stevecunnington/gridimp.

Statistical groupings of mental and social health measurements correlate with musculoskeletal capability – A cross sectional study

ObjectiveA better understanding of the degree to which social health factors contribute uniquely to statistical clusters associated with variation in levels of capability might inform targeted whole person care strategies for more comprehensive management of musculoskeletal health. Therefore, we asked: (1) What are the statistical groupings of social and mental health measurements in patients seeking specialty care for musculoskeletal conditions? (2) Do identified psychosocial groupings correspond with different mean magnitudes of incapability accounting for demographic and clinical factors? MethodsWe included 158 patients seeking musculoskeletal specialty care and collected measures of magnitude of incapability, unhelpful thoughts and distress regarding symptoms, symptoms of depression, symptoms of anxiety, and social health. A k-means clustering algorithm was fit to the data and a linear regression model compared mean PROMIS-PF CAT scores for grouping. ResultsA quantitative social health measure contributed to 4 statistical clusters as follows: 1) relatively low levels of all mental health measures and high social health; 2) greater unhelpful thoughts and distress regarding symptoms, average symptoms of general anxiety and depression, and average social health; 3) higher levels of all mental health measures and severely compromised social health; and 4) severely compromised mental health and lower social health. Magnitude of incapability was significantly greater for groups with worse mental and social health. ConclusionThe finding of a relatively independent association of social and mental health factors with greater incapability supports the importance of introducing comprehensive health strategies in musculoskeletal specialty care. Strategies may include mindset training and case management of social unmet needs. Level of evidenceLevel III; Cross-sectional study.

Lip color diversity: An intricate study.

Lip investigations and characterizations in the literature are less prevalent than for skin, particularly on the topic of color diversity. However, as the consumer demand increases for a nude lip makeup result, that is, shades close to the bare lip color, the identification and modification of lip color is essential for the cosmetic industry. The objective was to highlight lip color diversity among three ethnicities (Caucasian, African and Hispanic), through the use of a spectral color measurement device especially adapted to the lip area, and to consider lip color ethnic specificities and overlaps. The inferior natural lip color was measured with a full-face hyperspectral imaging system, SpectraFace (Newtone Technologies, Lyon, France), on 410 healthy women aged 19 to 68 (Caucasian French, Caucasian American, African American, and Hispanic American women). A hierarchical ascending classification, was deployed to determine clusters based on the lip colorimetric parameters along two strategies to identify the best statistical analysis to preserve the lip color diversity. Lip color is a continuous color space, with great intra-ethnic and inter-ethnic diversity, especially for African American women in terms of chroma and lightness. Among the two strategies of data analysis, our two-step statistical clustering analysis yielded 11 groups (i.e., 11 lip tones), revealing an accurate representation of the scope of diversity, but also of the overlaps. The 11 lip tones/colors could potentially serve as target shades for the development of a more diverse and inclusive range of lip cosmetics, such as nude lipsticks.

The Aemulus Project. VI. Emulation of Beyond-standard Galaxy Clustering Statistics to Improve Cosmological Constraints

There is untapped cosmological information in galaxy redshift surveys in the nonlinear regime. In this work, we use the Aemulus suite of cosmological N-body simulations to construct Gaussian process emulators of galaxy clustering statistics at small scales (0.1–50 h −1 Mpc) in order to constrain cosmological and galaxy bias parameters. In addition to standard statistics—the projected correlation function w p(r p), the redshift-space monopole of the correlation function ξ 0(s), and the quadrupole ξ 2(s)—we emulate statistics that include information about the local environment, namely the underdensity probability function P U(s) and the density-marked correlation function M(s). This extends the model of Aemulus III for redshift-space distortions by including new statistics sensitive to galaxy assembly bias. In recovery tests, we find that the beyond-standard statistics significantly increase the constraining power on cosmological parameters of interest: including P U(s) and M(s) improves the precision of our constraints on Ωm by 27%, σ 8 by 19%, and the growth of structure parameter, f σ 8, by 12% compared to standard statistics. We additionally find that scales below ∼6 h −1 Mpc contain as much information as larger scales. The density-sensitive statistics also contribute to constraining halo occupation distribution parameters and a flexible environment-dependent assembly bias model, which is important for extracting the small-scale cosmological information as well as understanding the galaxy–halo connection. This analysis demonstrates the potential of emulating beyond-standard clustering statistics at small scales to constrain the growth of structure as a test of cosmic acceleration.

The dynamics of energy transition in European countries in years 2004-2021

This study delves into the critical contemporary issue of global energy transition, focusing on European countries from 2004 to 2021. Its objective is to identify countries with similar rates of energy transition, utilising statistical measures and clustering techniques on Eurostat data. The paper explores energy efficiency, renewable sources, and energy import reliance. Findings show reduced primary energy consumption in EU-27, varied energy efficiency trends, and notable differences in renewables and import dependence. Clustering countries unveil diverse transition speeds. Policymakers can tailor effective strategies based on regional challenges and successes, fostering a nuanced understanding of economic, social, and environmental dynamics in the energy transition. This analysis offers valuable insights for informed policy-making in the complex landscape of global energy transformation.

Severe weather events and cryptosporidiosis in Aotearoa New Zealand: A case series of space-time clusters.

Occurrence of cryptosporidiosis has been associated with weather conditions in many settings internationally. We explored statistical clusters of human cryptosporidiosis and their relationship with severe weather events in New Zealand (NZ). Notified cases of cryptosporidiosis from 1997 to 2015 were obtained from the national surveillance system. Retrospective space-time permutation was used to identify statistical clusters. Cluster data were compared to severe weather events in a national database. SaTScan analysis detected 38 statistically significant cryptosporidiosis clusters. Around a third (34.2%, 13/38) of these clusters showed temporal and spatial alignment with severe weather events. Of these, nearly half (46.2%, 6/13) occurred in the spring. Only five (38%, 5/13) of these clusters corresponded to a previously reported cryptosporidiosis outbreak. This study provides additional evidence that severe weather events may contribute to the development of some cryptosporidiosis clusters. Further research on this association is needed as rainfall intensity is projected to rise in NZ due to climate change. The findings also provide further arguments for upgrading the quality of drinking water sources to minimize contamination with pathogens from runoff from livestock agriculture.