Combination Of Cluster Analysis Research Articles

Effects of water and fertilizer coupling on the physiological characteristics and growth of rabbiteye blueberry.

Understanding the impact of irrigation and fertilizer on rabbiteye blueberry (Vaccinium virgatum) physiology is necessary for its precision planting. Here, we applied varied irrigation and fertilizer under completely randomized experimental design to see its impact on the physiological characteristics and bush growth of rabbiteye blueberries. A comprehensive evaluation of the membership function was used to establish the best water-fertilizer coupling regimes. Rabbiteye blueberry enhanced the net photosynthetic rate, stomatal conductance and transpiration rate of leaf and improved its photosynthetic capacity at maximum level of irrigation water and fertilizer application (F3W4). The high fertilizer-medium water treatment (F3W3) increased leaf-soluble protein contents. The medium fertilizer-medium water treatment (F2W3, F2W2) increased leaf- soluble sugar, superoxide dismutase, and chlorophyll contents; decreased the malondialdehyde content; and enhanced leaf resistance and metabolism. It also promoted the growth of flower buds and new shoots. Combined membership function and cluster analyses revealed that the optimal water and fertilizer conditions for promoting rabbiteye blueberry plant growth were the medium fertilizer-medium water [(NH4)2SO4:Ca(H2PO4)2:K2SO4 at 59:10:20 g plant-1; 2.5 L water plant-1], medium fertilizer-medium-high water [(NH4)2SO4:Ca(H2PO4)2:K2SO4 at 59:10:20 g plant-1; 3.75 L water plant-1], and high fertilizer-medium-high water [(NH4)2SO4:Ca(H2PO4)2:K2SO4 at 118:20:40 g plant-1; 3.75 L water plant-1] treatments. The findings of this study could be used in improving the precision and efficacy of rabbiteye blueberry planting in Guizhou, China. Such an approach can increase the productivity and profitability for local fruit farmers.

Development of revised diagnostic criteria for Fuchs’ uveitis syndrome in a Chinese population

Background/aimsFuchs’ uveitis syndrome (FUS) is one of the frequently misdiagnosed uveitis entities, which is partly due to the absence of internationally recognised diagnostic criteria. This study was performed to develop...

Spider web biomonitoring: A cost-effective source apportionment approach for urban particulate matter

Elevated levels of particulate matter (PM) in urban atmospheres are one of the major environmental challenges of the Anthropocene. To effectively lower those levels, identification and quantification of sources of PM is required. Biomonitoring methods are helpful tools to tackle this problem but have not been fully established yet. An example is the sampling and subsequent analysis of spider webs to whose adhesive surface dust particles can attach. For a methodical inspection, webs of orb-weaving spiders were sampled repeatedly from 2016 to 2018 at 22 locations in the city of Jena, Germany. Contents of Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, La, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sb, Si, Sn, Sr, Th, Ti, V, Y, Zn and Zr were determined in the samples using inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-optical emission spectroscopy (ICP-OES) after aqua regia digestion.Multivariate statistical methods were applied for a detailed evaluation. A combination of cluster analysis and principal component analysis allows for the clear identification of three main sources in the study area: brake wear from car traffic, abrasion of tram/train tracks and particles of geogenic origin. Quantitative source contributions reveal that high amounts of most of the metals are derived from a combination of brake wear and geogenic particles, the latter of which are likely resuspended by moving vehicles. This emphasizes the importance of non-exhaust particles connected to road traffic.Once a source identification has been performed for an area of interest, classification models can be applied to assess air quality for further samples from within the whole study area, offering a tool for air quality assessment. The general validity of this approach is demonstrated using samples from other locations.

Development of healthcare use across contemporary retirement pathways: results from a register based cohort study.

Aim:We aimed to understand the interplay between retirement pathways and healthcare use in the postponed and structurally changing context of retirement.Methods:Based on Swedish register data on income and healthcare use, we applied combined sequence and cluster analysis to identify typical pathways into retirement and analysed their relation to healthcare use developments.Results:We detected five distinct pathways into retirement. Level of healthcare use was significantly higher for the pathway via disability pensions. We saw an overall increase in healthcare use across the retirement process that was related to age rather than to the different pathways.Conclusions:Level of healthcare use at the beginning of the retirement process may be related to selection into different pathways of retirement. We did not find clear evidence across several healthcare measures that different pathways lead to different developments in healthcare use.

Rapid detection of SARS-CoV-2 infection by multicapillary column coupled ion mobility spectrometry (MCC-IMS) of breath. A proof of concept study

There is an urgent need for screening of patients with a communicable viral disease to cut infection chains. Recently, we demonstrated that ion mobility spectrometry coupled with a multicapillary column (MCC-IMS) is able to identify influenza-A infections in patients’ breath. With a decreasing influenza epidemic and upcoming SARS-CoV-2 infections we proceeded further and analyzed patients with suspected SARS-CoV-2 infections. In this study, the nasal breath of 75 patients (34 male, 41 female, aged 64.4 ± 15.4 years) was investigated by MCC-IMS for viral infections. Fourteen were positively diagnosed with influenza-A infection and sixteen with SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) of nasopharyngeal swabs. In one patient RT-PCR was highly suspicious of SARS-CoV-2 but initially inconclusive. The remaining 44 patients served as controls. Breath fingerprints for specific infections were assessed by a combination of cluster analysis and multivariate statistics. There were no significant differences in gender or age according to the groups. In the cross validation of the discriminant analysis 72 of the 74 clearly defined patients could be correctly classified to the respective group. Even the inconclusive patient could be mapped to the SARS-CoV-2 group by applying the discrimination functions. Conclusion: SARS-CoV-2 infection and influenza-A infection can be detected with the help of MCC-IMS in breath in this pilot study. As this method provides a fast non-invasive diagnosis it should be further developed in a larger cohort for screening of communicable viral diseases. A validation study is ongoing during the second wave of COVID-19.Trial registration: ClinicalTrial.gov, NCT04282135 Registered 20 February 2020—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04282135?term=IMS&draw=2&rank=1

Dark Energy Survey Year 3 results: Optimizing the lens sample in a combined galaxy clustering and galaxy-galaxy lensing analysis

We investigate potential gains in cosmological constraints from the combination of galaxy clustering and galaxy-galaxy lensing by optimizing the lens galaxy sample selection using information from Dark Energy Survey (DES) Year 3 data and assuming the DES Year 1 Metacalibration sample for the sources. We explore easily reproducible selections based on magnitude cuts in $i$-band as a function of (photometric) redshift, $z_{\rm phot}$, and benchmark the potential gains against those using the well established redMaGiC sample. We focus on the balance between density and photometric redshift accuracy, while marginalizing over a realistic set of cosmological and systematic parameters. Our optimal selection, the MagLim sample, satisfies $i < 4 \, z_{\rm phot} + 18$ and has $\sim 30\%$ wider redshift distributions but $\sim 3.5$ times more galaxies than redMaGiC. Assuming a wCDM model and equivalent scale cuts to mitigate nonlinear effects, this leads to $40\%$ increase in the figure of merit for the pair combinations of $\Omega_m$, $w$, and $\sigma_8$, and gains of $16\%$ in $\sigma_8$, $10\%$ in $\Omega_m$, and $12\%$ in $w$. Similarly, in LCDM we find an improvement of $19\%$ and $27\%$ on $\sigma_8$ and $\Omega_m$, respectively. We also explore flux-limited samples with a flat magnitude cut finding that the optimal selection, $i < 22.2$, has $\sim 7$ times more galaxies and $\sim 20\%$ wider redshift distributions compared to MagLim, but slightly worse constraints. We show that our results are robust with respect to the assumed galaxy bias and photometric redshift uncertainties with only moderate further gains from increased number of tomographic bins or the inclusion of bin cross-correlations, except in the case of the flux-limited sample, for which these gains are more significant.

Combining fuzzy analytic hierarchy process with concentration–area fractal for mineral prospectivity mapping: A case study involving Qinling orogenic belt in central China

We combined cluster analysis, the fuzzy analytic hierarchy process (fuzzy AHP), and the concentration–area (C-A) fractal for mineral prospectivity mapping. First, cluster analysis was used to determine the indicator elements for orogenic Au deposits (omitting redundant geochemical elements). Subsequently, according to pairwise comparisons of mineralization alternatives performed by three exploration experts, a series of fuzzy evaluation matrices were constructed, and the corresponding normalized weights were calculated. Furthermore, on the basis of the multifractal theory, two thresholds for each alternative were obtained (each alternative was divided into a background region, a general anomaly region, and a high anomaly region), and the fuzzy membership function was used to obtain the normalized score of each alternative. Finally, the weight of each alternative was multiplied by the normalized score, and fuzzy superposition was employed to generate a mineralization prediction map, which consisted of high, medium, low, and weak metallogenic potential areas, accounting for 7.05%, 19.67%, 40.99%, and 32.29% of the study area, which contained 4, 1, 1, and 0 of the six known mine occurrences, respectively. The prediction area (P-A) plot indicated that the result predicted 83.3% of Au deposits with an area of 16.7%, indicating that the combination of cluster analysis, the fuzzy AHP, and the concentration–area method is an efficient and economical forecasting method that has guiding significance for mineral prospectivity mapping (MPM).

Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

Shared DNA-based copy number with divided methylation changes differentiate and clinically associate early stage pancreatic cancer tumors.

434 Background: There is no known molecular taxonomy of pancreatic cancer that can guide therapeutic strategies. Understanding the fundamental molecular mechanism underlying pancreatic cancer biology remains an unmet need. We explore the extent to which combinations of DNA-based molecular changes in copy number (CN) and methylation separate early stage PAAD tumors and associated with survival outcomes. Methods: We performed genome-wide combined cluster analyses on DNA-based CN and methylation changes using TCGA data. We examined cluster associations with clinical outcomes by comparing in months (mos), Kaplan--Meier estimated overall survival (OS) and disease-free interval (DFI) using a log-rank test. We performed additional comparisons among CN-Methylation derived clusters with respect to PAAD phenotypes. Results: Using 78 early stage pancreatic cancer tumors from TCGA with CN, methylation and clinical outcomes data, we identified two patient clusters with distinct gene copy number signatures that when combined with three methylation signatures, resulted in three additional clusters. Thus, the same gene CN signature, when combined with different methylation signatures, further differentiated tumors into sub-clusters with varying levels of associations with clinical outcome. Among them, analogous to current gene-expression based subtypes, we also identified an immune-rich subtype that was associated with improved overall survival (n=21, median OS=16mos; DFI=16mos), and an extracellular matrix (ECM)-rich with worse survival (n=19, median OS=12mos; DFI=8mos). Unlike previous expression subtypes, we identified another metabolic-enriched subtype with the same worse median OS and DFI, differentiated by methylation with the ECM-rich subtype. The improved OS cluster had a signature of CN neutral and increased methylation, while the poor cluster had a signature of CN gains and increased methylation among a set of genes distinct from the improved cluster. No significant differences in age, site, microsatellite instability and KRAS status among clusters were noted. Notably, in a multivariable model that included gene expression-based subtypes, only our DNA-level subtypes remained significantly associated with overall survival. Conclusions: While RNA-level changes often display large variations, DNA-level changes are more robust. Copy number changes appear to separate tumors into poor and improved prognosis clusters, while methylation appears to inform on the further separation of poor prognosis into various levels. A DNA-based molecular taxonomy for early stage pancreatic cancer could prove invaluable when used in combination with methylation-based circulating tumor DNA assays for clinical trial monitoring of tumor responses.

Optimal energy management strategy for plug-in hybrid electric vehicles based on a combined clustering analysis

Based on the optimization results of dynamic programming, this paper proposes an optimal rule design method for a series-parallel plug-in hybrid electric vehicle mode division strategy combining K-means clustering and improved artificial bee colony. The improved artificial bee colony algorithm uses the average of the sum of the squares of the distances method to initialize the colony to overcome the randomness of initialization and best utilize the useful information, constructs a fitness function adapted to the clustering analysis, and introduces an acceleration factor into the search equation to dynamically adjusted search scope. Combining the improved artificial bee colony algorithm with the K-means clustering algorithm, the clustering performance is improved and the optimal rule-based mode division strategy is obtained. The simulation results show that the new strategy reduces fuel consumption by 4.78% and electricity consumption by 1.81% comparing with the original strategy.

Integrating cluster analysis with MCDM methods for the evaluation of local agricultural production

This study aims to cluster Turkish cities based on their local agricultural production and rank them in terms of performance by combining cluster analysis and multi-criteria decision-making (MCDM) methods. In this context, a three-phase methodology is developed. In the first phase, Ward's method is utilized to cluster cities according to agricultural production characteristics. In the second phase, the objective criteria weights are determined using the Criteria Importance Through Intercriteria Correlation technique (CRITIC). In the third phase, to rank the clusters in terms of performance, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied. Due to the results, the 81 cities are divided into six clusters in terms of agricultural production features. The cluster with the highest performance is Cluster 6, in which Konya is alone. Cluster 4, which includes Antalya and Mersin, follows this cluster. Cluster 1 with 25 cities and Cluster 2 with 19 cities are the clusters with the lowest results. The results show that only a few cities such as Konya, Antalya, and Mersin are generating more than tens of them in combination. These findings reveal that local governments should reconsider their agricultural programs and develop new strategies under the direction of the central government.

Combining Cluster Analysis of Air Pollution and Meteorological Data with Receptor Model Results for Ambient PM2.5 and PM10.

Air pollution regulation requires knowing major sources on any given zone, setting specific controls, and assessing how health risks evolve in response to those controls. Receptor models (RM) can identify major sources: transport, industry, residential, etc. However, RM results are typically available for short term periods, and there is a paucity of RM results for developing countries. We propose to combine a cluster analysis (CA) of air pollution and meteorological measurements with a short-term RM analysis to estimate a long-term, hourly source apportionment of ambient PM2.5 and PM10. We have developed a proof of the concept for this proposed methodology in three case studies: a large metropolitan zone, a city with dominant residential wood burning (RWB) emissions, and a city in the middle of a desert region. We have found it feasible to identify the major sources in the CA results and obtain hourly time series of their contributions, effectively extending short-term RM results to the whole ambient monitoring period. This methodology adds value to existing ambient data. The hourly time series results would allow researchers to apportion health benefits associated with specific air pollution regulations, estimate source-specific trends, improve emission inventories, and conduct environmental justice studies, among several potential applications.

Distributed real-time SlowDoS attacks detection over encrypted traffic using Artificial Intelligence

SlowDoS attacks exploit slow transmissions on application-level protocols like HTTP to carry out denial of service against web-servers. These attacks are difficult to be detected with traditional signature-based intrusion detection approaches, even more when the HTTP traffic is encrypted. To cope with this challenge, this paper describes and AI-based anomaly detection system for real-time detection of SlowDoS attacks over application-level encrypted traffic. Our system monitors in real-time the network traffic, analyzing, processing and aggregating packets into conversation flows, getting valuable features and statistics that are dynamically analyzed in streaming for AI-based anomaly detection. The distributed AI model running in Apache Spark-streaming, combines clustering analysis for anomaly detection, along with deep learning techniques to increase detection accuracy in those cases where clustering obtains ambiguous probabilities. The proposal has been implemented and validated in a real testbed, showing its feasibility, performance and accuracy for detecting in real-time different kinds of SlowDoS attacks over encrypted traffic. The achieved results are close to the optimal precision value with a success rate 98%, while the false negative rate takes a value below 0.5%.

Explicit the urban waterlogging spatial variation and its driving factors: The stepwise cluster analysis model and hierarchical partitioning analysis approach

Urban waterlogging is a hydrological cycle problem that seriously affects people's life and property. Characterizing waterlogging variation and explicit its driving factors are conducive to prevent the damage of such disasters. Conventional methods, because of the high spatial heterogeneity and the non-stationary complex mechanism of urban waterlogging, are not able to fully capture the urban waterlogging spatial variation and identify the waterlogging susceptibility areas. A more robust method is recommended to quantify the variation trend of urban waterlogging. Previous studies have simulated the waterlogging variation in relatively small areas. However, the relationship between variables is often ignored, which cannot comprehensively reveal the dominant drivers affecting urban waterlogging. Therefore, a novel approach is proposed that combined stepwise cluster analysis model (SCAM) and hierarchical partitioning analysis (HPA) within a general framework and verifies the applicability through logistic regression, artificial neural network, and support vector machine. According to the dominant driving factors, different simulation scenarios are established to analyze waterlogging density variation. Results found that the SCAM provides accurate and detailed simulated results both in urban centers where waterlogging frequently occurs and urban fringe with few waterlogging events, which shows an excellent performance with a high classification accuracy and generalization capability. HPA detected that the impervious surface abundance (28.07%), vegetation abundance (20.80%), and cumulate precipitation (16.25%) are the dominant drivers of waterlogging. This result suggests that priority should be given to controlling these three factors to mitigate the risk of waterlogging. It is interesting to note that under different urbanization and rainfall scenarios, the urban waterlogging susceptibility has a considerable variation. The watershed spatial location and watershed characteristics are relevant aspects to be considered in identifying and assessing waterlogging susceptibility, which provides original insights that urban waterlogging mitigation strategies should be developed according to different local conditions and future scenarios.

Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning

In recommendation systems, the grey-sheep problem refers to users with unique preferences and tastes that make it difficult to develop accurate profiles. That is, the similarity search approach typically followed during the recommendation process fails to yield good results. Most research does not focus on such users and thus fails to cater to more exotic tastes and emerging trends, leading to a subsequent loss in revenue and marketing opportunities. One suggested solution is to use one-class classification to generate a prediction list for these users, where decision boundaries are learned that distinguish between normal and grey-sheep users. In this paper, we present the grey-sheep one-class recommendation (GSOR) framework designed to create accurate prediction models while taking both regular and grey-sheep users into account. In addition, we introduce a novel grey-sheep movie recommendation benchmark to be used by current and future researchers. When evaluating our GSOR framework against this benchmark, our results indicate the value of combining cluster analysis, outlier detection, and one-class learning to generate relevant and timely recommendation lists from data sets that contain grey-sheep users. Specifically, by employing one-class decision tree algorithms, our GSOR framework was able to outperform traditional collaborative filtering-based recommendation systems in both accuracy and model construction time. Furthermore, we report that having grey-sheep users in the system often had a positive impact on the learning and recommendation processes.

Indo-Aryan – a house divided? Evidence for the east–west Indo-Aryan divide and its significance for the study of northern South Asia

AbstractIn this study, we investigate the possible presence of an east–west divide in Indo-Aryan languages suggested in previous literature (Peterson, John. 2017a. Fitting the pieces together – towards a linguistic prehistory of eastern-central South Asia (and beyond). Journal of South Asian Languages and Linguistics 4(2). 211–257.), with the further hypothesis that this divide may be linked to the influence of the Munda languages, spoken in the eastern part of the subcontinent. Working with 217 fine-grained variables on a sample of 27 Indo-Aryan and Munda languages, we test the presence of a geographical divide within Indo-Aryan using computational methods such as cluster analysis in combination with visual statistical inference. Our results confirm the presence of a geographical divide for the whole dataset and most of the individual features. We then proceed to compute the degree of similarity between the Indo-Aryan languages and Munda, using a Bayesian alternative to a t-test. The results for most features support the claim that the languages identified in the eastern clusters are indeed more similar to Munda, thereby opening up further research scenarios for the history of this region.

Application of Cluster Analysis with Fuzzy Logic Elements for Ground Environment Assessment of Robotic Group

Emergency situations, that cause risks for human life and health, dictate elevated requirements to completeness and accuracy of the presentation of information about current ground environment. Modern robotic systems include sensors, that operate on different physical principles. This causes incrementation of information entering control system. Computing resources and technical capabilities of robotic systems are limited in range and detection probabilities of appearing objects. In case of insufficient performance of the on-board computer system and high uncertainties of ground environment, robotic systems are not able to perform without combining information from robotic group and producing a single view of ground environment. Complex information from a group of robotic systems occurs in real time and a non-deterministic environment.&#x0D; To solve the problem of identifying attribute vectors related to a single object, as well as to evaluate the effectiveness of obtained solutions, is possible using known formulas of the theory of statistical hypothesis testing and probability theory only under the normal distribution law with the known mathematical expectation of an attribute vector and a correlation matrix. However, these conditions are usually not met in practice. Problems also arise when methods of nonparametric statistics are used with an unknown law of probability distribution.&#x0D; The new method of identifying attribute vectors is proposed, that does not rely on a statistical approach and, therefore, does not require knowledge of the type of distribution law and the values of its parameters. Proposed method is based on the idea of combining cluster analysis and fuzzy logic, and is relatively simple to the basic methods of multidimensional nonparametric statistics.&#x0D; The results of modeling information processes are presented. The advantages of proposed method are shown. The comparative values for the number of false recognitions are given. The recommendations are given for constructing fuzzy inference rules when creating an expert system knowledge base.

Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project

BackgroundTuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study.MethodsAs part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix. Hierarchical cluster and factor analysis methods were used for data reduction and identification of natural TAND clusters.ResultsA total of 85 individuals with TSC (female:male, 40:45) from 7 countries were enrolled. Cluster analysis grouped the TAND variables into 6 clusters: a scholastic cluster (reading, writing, spelling, mathematics, visuo-spatial difficulties, disorientation), a hyperactive/impulsive cluster (hyperactivity, impulsivity, self-injurious behavior), a mood/anxiety cluster (anxiety, depressed mood, sleep difficulties, shyness), a neuropsychological cluster (attention/concentration difficulties, memory, attention, dual/multi-tasking, executive skills deficits), a dysregulated behavior cluster (mood swings, aggressive outbursts, temper tantrums), and an autism spectrum disorder (ASD)-like cluster (delayed language, poor eye contact, repetitive behaviors, unusual use of language, inflexibility, difficulties associated with eating). The natural clusters mapped reasonably well onto the six-factor solution generated. Comparison between cluster and factor solutions from this study and the earlier feasibility study showed significant similarity, particularly in cluster solutions.ConclusionsResults from this TOSCA research project in an independent international data set showed that the combination of cluster analysis and factor analysis may be able to identify clinically meaningful natural TAND clusters. Findings were remarkably similar to those identified in the earlier feasibility study, supporting the potential robustness of these natural TAND clusters. Further steps should include examination of larger samples, investigation of internal consistency, and evaluation of the robustness of the proposed natural clusters.

Social Cognition in Children With Non-specific Intellectual Disabilities: An Exploratory Study.

Social cognitive abilities – notably, Theory of Mind (ToM) and social information processing (SIP) – are key skills for the development of social competence and adjustment. By understanding affective and cognitive mental states and processing social information correctly, children will be able to enact prosocial behaviors, to interact with peers and adults adaptively, and to be socially included. As social adjustment and inclusion are major issues for children with intellectual disabilities (IDs), the present study aimed to explore their social cognitive profile by combining cluster analysis of both ToM and SIP competence, and to investigate the structure of relations between these skills in children with IDs. Seventy-eight elementary school children with non-specific IDs were recruited. They had a chronological age ranging from 4 years and 8 months to 12 years and 6 months and presented a preschool developmental age. Performance-based measures were administered to assess ToM and SIP abilities. Questionnaires were completed by the children’s parents to evaluate the children’s social competence and adjustment and their risk of developing externalizing or internalizing behaviors. Exploratory analysis highlighted strengths and weaknesses in the social cognitive profiles of these children with IDs. It also emphasized that the understanding of affective and cognitive mental states was used differently when facing appropriate vs. inappropriate social behaviors. The present study leads to a better understanding of the socio-emotional profile of children with IDs and offers some suggestions on how to implement effective interventions.

Variability of southwest Pacific tropical cyclone track geometry over the last 70 years

AbstractVariability in tropical cyclone (TC) track morphology, as it evolves post genesis, presents continued challenges in accurately forecasting TC movement. Therefore, an improved understanding of TC track climatology is essential, given that TCs are one of the most critical natural hazards in the southwest Pacific (SWP) region. We examine the historical variability of TC tracks within the SWP over the last 70 years (1948–2017) using 6‐hourly track data obtained from the South Pacific Enhanced Archive of Tropical Cyclones (SPEArTC) database. A probabilistic clustering technique is applied to separate TC tracks into distinct groups in order to assess the primary cyclone trajectories for the region and its relationship with the El Niño–Southern Oscillation (ENSO). TC tracks are also classified into four sinuosity categories: straight, recurving, sinuous and highly sinuous; and their spatial and temporal characteristics subsequently analysed. The results of the cluster analysis identified five optimal groups of TC tracks, four of which exhibited southeast propagation, except for the southwest moving tracks in Cluster 5. Temporally, significant trends were observed over the last seven decades, with Clusters 1, 3 and 4 becoming less frequent with a substantial increase in the occurrence of Cluster 2 tracks (representing TCs east of dateline), a geometry favoured by El Niño conditions. Further, the sinuosity analysis revealed continued dominance of straight TCs within the eastern SWP with a tendency of encountering TCs of other morphology types. Conversely, the western SWP region is typically exposed to highly sinuous tracks. We also observed a significant decrease (increase) in TCs with straight and quasi‐straight (highly sinuous) tracks, particularly during the last decade. These findings suggest that combined cluster analysis and TC track sinuosity analysis is an important tool in generalising the TC track regimes, refining predicted trajectories and understanding impacts on SWP island nations.