Clustering Tendency Research Articles

A novel data visualization method for the effective assessment of cluster tendency through the dark blocks image pattern analysis

Data clustering is the most promising unsupervised technique, and it can be used to partition the data objects into different clusters. The data objects are placed according to derived similarity features using distance metrics, such as Euclidean, cosine, etc. The top clustering algorithms include k-means, and hierarchical clustering algorithms are extensively used and succeeded in many real-life applications. These algorithms are suffered from cluster tendency problems. They detect the number of clusters before the unlabelled dataset is called cluster tendency. Various pre-cluster tendency methods are studied, and it found that visual assessment of cluster tendency (VAT) solves the issue of cluster tendency. VAT cannot effectively derive the number of clusters for compact-separated (CS) datasets and obtain fewer groups for non-compact separated (non-CS) datasets. The Spectral-based implementations are recommended for the best assessment of cluster tendency, especially for the type of non-CS datasets. With this motivation, current data visualization methods are enhanced with spectral modeling to overcome the problem of cluster tendency over the non-CS datasets, which recognizes the cluster patterns efficiently with the spectral features of non-CS datasets. Comparative demonstrations were presented for the experimental study of existing and proposed data visualization methods using various performance parameters.

Exploring clusters of rare events using unsupervised random forests

Given highly imbalanced data, most learning algorithms face the challenge of accurately predicting rare events, while such cases are the ones that carry importance and useful knowledge. In a binary class label dataset, the rare events are the ones in the minority class. This study used a stroke dataset with a binary class label and the class imbalance ratio was 54:1. In addition to that, the dataset contained missing values and mixed data types. To identify the intrinsic structures in the minority class (the stroke group), Random Forest Clustering was used to produce the proximity matrix and fed to Partition around Medoid (PAM) clustering method to identify the optimal number of clusters. The proximity plot seems to show there could be cluster tendency as Hopkins’s statistics test value was H = 0.8735 and k=2 was identified to be the optimal number of clusters. Based on the internal cluster validation, however, the silhouette coefficient width was small (0.1), indicating that many of the data objects were within the other boundary of the other class. We have suggested a further investigation plan in this paper for the next action.

Spatial distribution and determinant factors of unmet need for family planning among all reproductive-age women in Ethiopia: a multi-level logistic regression modelling approach

BackgroundUnmet need for family planning has been remaining high in developing countries than developed countries, notably in sub-Saharan Africa. Data on unmet needs can help countries set service priorities. This study aimed to explore the geographical disparities of unmet need among reproductive-age women in Ethiopia using a 2016 national population-based survey.MethodsThis study was based on the nationally representative 2016 Ethiopian Demographic and Health Survey data. We used a total weighted sample of 15,683 reproductive-aged women. A multi-level logistic regression analysis was used to account for the Demographic Health Survey data’s hierarchal nature. In the multivariable multi-level analysis, those variables with a p-value < 0.05 were significantly associated with unmet needs. Spatial autocorrelation techniques were used to explore the clustering tendencies of unmet needss using Getis-Ord Gi* statistics.ResultsOverall, 15.2% (95% Confidence Interval (CI): 14.63, 15.76) of women of the reproductive age group in Ethiopia had an unmet need for family planning in 2016. In multivariable multilevel logistic regression analysis; individual-level variables such as being married (Adjusted odds ratio (AOR) = 25.7,95% CI: 11.50,60.42), lowest wealth status (AOR = 1.43,95% CI:1.14,1.79), having five or more children (AOR = 1.98, 95% CI:1.62,2.41), being a follower of Muslim religion (AOR = 1.35,95% CI:1.03,1.76) and protestant religion (AOR = 0.73,95% CI: 0.53,0.99) than orthodox Christian followers were statistically associated factors with unmet need. Among community-level variables; being in rural residency (AOR = 1.37, 95% CI: 1.01, 1.93), belong to the Oromia region (AOR = 1.53, 95%CI: 1.10, 2.11) and Somali region (AOR = 0.37, 95% CI: 0.22, 0.61) were significantly associated unmet need. The spatial analysis of unmet need among all women revealed that Oromia, Southern Nations, and Nationality of People and Gambela regions had high hotspots than other parts of the country.ConclusionsIn this study, the prevalence of unmet needs was high. Significant regional unmet need variation was indicated among reproductive-age women in Ethiopia, specifically in western parts of the country. Wealth status, number of children, marital status, residence, and religion were the most important associated factors with unmet needs. Addressing unmet needs targeted rural residents with low socioeconomic status, and western regions should be given top priority.

Spatial variations of COVID-19 risk by age in Toronto, Canada.

The risk of coronavirus disease 2019 (COVID-19) may vary by age, biological, socioeconomic, behavioural and logistical reasons may be attributed to these variations. In Toronto, Canada, the aging population has been severely impacted, accounting for 92% of all COVID-19 deaths. Four age groups: 60-69 years, 70-79 years, 80-89 years and ≥90 years in Toronto neighbourhoods were investigated for clustering tendencies using space-time statistics. Cohen's Kappa coefficient was computed to assess variations in risk by neighbourhood between different age groups. The findings suggest that knowledge of health risks and health behaviour varied by age across neighbourhoods in Toronto. Therefore, understanding the socioecological context of the communities and targeting age-appropriate intervention strategies is important for planning an effective mechanism for controlling the disease.

Scalable Cluster Tendency Assessment for Streaming Activity Data using Recurring Shapelets.

Automatic interpretation of cluster structure in rapidly arriving data streams is essential for timely detection of interesting events. Human activities often contain bursts of repeating patterns. In this paper, we propose a new relative of the Visual Assessment of Cluster Tendency (VAT) model, to interpret cluster evolution in streaming activity data where shapes of recurring patterns are important. Existing VAT algorithms are either suitable only for small batch data and unscalable to rapidly evolving streams, or cannot capture shape patterns. Our proposed incremental algorithm processes streaming data in chunks and identifies repeating patterns or shapelets from each chunk, creating a Dictionary-of-Shapes (DoS) that is updated on the fly. Each chunk is transformed into a lower dimensional representation based on it's distance from the shapelets in the current DoS. Then a small set of transformed chunks are sampled using an intelligent Maximin Random Sampling (MMRS) scheme, to create a scalable VAT image that is incrementally updated as the data stream progresses. Experiments on two upper limb activity datasets demonstrate that the proposed method can successfully and efficiently visualize clusters in long streams of data and can also identify anomalous movements.

Principal component analysis in long term assessment of total viable plate count of municipal water distribution network system in healthcare facility

The microbial quality of water is a critical safety aspect that should not be overlooked, especially when considering human consumption in the facilities for healthcare and the treatment of health compromised populations. The present work aimed to study a municipal network for city water within a healthcare facility microbiologically. The implementation of multivariate analysis was conducted over collected data to decipher trends of the microbiological count of samples from different points-of-use. The statistical study involved an Eigenvalue determination, score graph, loading plot study and outlier diagram. Data showed clustering tendency with aberrant values could be observed. The first component appeared to be associated with bioburden density in the water. While the other component showed a link to the relative locations of the distribution points in the facility and the length of the water lines from the source feeding city water to a great extent. The two components explained more than 55% of the variations in data. A property that highlighted a strong agreement between the order of points-of-use in the loading plot with that of ratio factor which was obtained from the Total Viable Count over the hypothetical distance of sampling port from the reference feeding entrance. Thus, there was evidence of variation in the microbial quality affected by the biological stability of the water distribution system that needed attention and an improvement plan for better control of microbial content in city water to prevent possible excursions in the future. The study showed a valuable perspective approach for the quantitative examination of the microbiological stability of the municipal water distribution network system.

Microarchitectural Changes of Cardiovascular Calcification in Response to In Vivo Interventions Using Deep-Learning Segmentation and Computed Tomography Radiomics.

Coronary calcification associates closely with cardiovascular risk, but its progress is accelerated in response to some interventions widely used to reduce risk. This paradox suggests that qualitative, not just quantitative, changes in calcification may affect plaque stability. To determine if the microarchitecture of calcification varies with aging, Western diet, statin therapy, and high intensity, progressive exercise, we assessed changes in a priori selected computed tomography radiomic features (intensity, size, shape, and texture). Longitudinal computed tomography scans of mice (Apoe-/-) exposed to each of these conditions were autosegmented by deep learning segmentation, and radiomic features of the largest deposits were analyzed. Over 20 weeks of aging, intensity and most size parameters increased, but surface-area-to-volume ratio (a measure of porosity) decreased, suggesting stabilization. However, texture features (coarseness, cluster tendency, and nonuniformity) increased, suggesting heterogeneity and likely destabilization. Shape parameters showed no significant changes, except sphericity, which showed a decrease. The Western diet had significant effects on radiomic features related to size and texture, but not intensity or shape. In mice undergoing either pravastatin treatment or exercise, the selected radiomic features of their computed tomography scans were not significantly different from those of their respective controls. Interestingly, the total number of calcific deposits increased significantly less in the 2 intervention groups compared with the respective controls, suggesting more coalescence and/or fewer de novo deposits. Thus, aging and standard interventions alter the microarchitectural features of vascular calcium deposits in ways that may alter plaque biomechanical stability.

Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology.

Recent multi‐omics analyses paved the way for a comprehensive understanding of pathological processes. However, only few studies have explored Alzheimer’s disease (AD) despite the possibility of biological subtypes within these patients. For this study, unsupervised classification of four datasets (genetics, miRNA transcriptomics, proteomics, and blood‐based biomarkers) using Multi‐Omics Factor Analysis+ (MOFA+), along with systems‐biological approaches following various downstream analyses are performed. New subgroups within 170 patients with cerebral amyloid pathology (Aβ+) are revealed and the features of them are identified based on the top‐rated targets constructing multi‐omics factors of both whole (M‐TPAD) and immune‐focused models (M‐IPAD). The authors explored the characteristics of subtypes and possible key‐drivers for AD pathogenesis. Further in‐depth studies showed that these subtypes are associated with longitudinal brain changes and autophagy pathways are main contributors. The significance of autophagy or clustering tendency is validated in peripheral blood mononuclear cells (PBMCs; n = 120 including 30 Aβ‐ and 90 Aβ+), induced pluripotent stem cell‐derived human brain organoids/microglia (n = 12 including 5 Aβ‐, 5 Aβ+, and CRISPR‐Cas9 apolipoprotein isogenic lines), and human brain transcriptome (n = 78). Collectively, this study provides a strategy for precision medicine therapy and drug development for AD using integrative multi‐omics analysis and network modelling.

Phylogenetic and protein prediction analysis reveals the taxonomically diverse distribution of virulence factors in Bacillus cereus strains.

Bacillus cereus is a food contaminant with widely varying enterotoxic potential due to its virulence proteins. In this article, phylogenetic analysis of the amino acid sequences from the whole-genomes of 41 strains, evolutionary distance calculation of the amino acid sequences of the virulence genes, and functional and structural predictions of the virulence proteins were performed to reveal the taxonomically diverse distribution of virulence factors. The genome evolution of the strains showed a clustering trend based on the protein-coding virulence genes. The strains of B. cereus have evolved into non-toxic risk and toxic risk clusters with medium-high- and medium-low-risk subclusters. The evolutionary transfer distances of incomplete virulence genes relative to housekeeping genes were greater than those of complete virulence genes, and the distance values of HblACD were higher than those of nheABC and CytK among the complete virulence genes. Cytoplasmic localization was impossible for all the virulence proteins, and NheB, NheC, Hbl-B, and Hbl-L1 were predicted to be extracellular. Nhe and Hbl proteins except CytK had similar spatial structures. The predicted structures of Nhe and Hbl mainly showed ‘head’ and ‘tail’ domains. The ‘head’ of NheA and Hbl-B, including two α-helices separated by β-tongue strands, might play a special role in the formation of Nhe trimers and Hbl trimers, respectively. The ‘cap’ of CytK, which includes two ‘latches’ with many β-sheets, formed a β-barrel structure with pores, and a ‘rim’ balanced the structure. The evolution of B. cereus strains showed a clustering tendency based on the protein-coding virulence genes, and the complete virulence-gene operon combination had higher relative genetic stability. The beta-tongue or latch associated with β-sheet folding might play an important role in the binding of virulence structures and pore-forming toxins in B. cereus.

New Evidence on a Distinction between Aβ40 and Aβ42 Amyloids: Thioflavin T Binding Modes, Clustering Tendency, Degradation Resistance, and Cross-Seeding.

The relative abundance of two main Abeta-peptide types with different lengths, Aβ40 and Aβ42, determines the severity of the Alzheimer’s disease progression. However, the factors responsible for different behavior patterns of these peptides in the amyloidogenesis process remain unknown. In this comprehensive study, new evidence on Aβ40 and Aβ42 amyloid polymorphism was obtained using a wide range of experimental approaches, including custom-designed approaches. We have for the first time determined the number of modes of thioflavin T (ThT) binding to Aβ40 and Aβ42 fibrils and their binding parameters using a specially developed approach based on the use of equilibrium microdialysis, which makes it possible to distinguish between the concentration of the injected dye and the concentration of dye bound to fibrils. The binding sites of one of these modes located at the junction of adjacent fibrillar filaments were predicted by molecular modeling techniques. We assumed that the sites of the additional mode of ThT-Aβ42 amyloid binding observed experimentally (which are not found in the case of Aβ40 fibrils) are localized in amyloid clots, and the number of these sites could be used for estimation of the level of fiber clustering. We have shown the high tendency of Aβ42 fibers to form large clots compared to Aβ40 fibrils. It is probable that this largely determines the high resistance of Aβ42 amyloids to destabilizing effects (denaturants, ionic detergents, ultrasonication) and their explicit cytotoxic effect, which we have shown. Remarkably, cross-seeding of Aβ40 fibrillogenesis using the preformed Aβ42 fibrils changes the morphology and increases the stability and cytotoxicity of Aβ40 fibrils. The differences in the tendency to cluster and resistance to external factors of Aβ40 and Aβ42 fibrils revealed here may be related to the distinct role they play in the deposition of amyloids and, therefore, differences in pathogenicity in Alzheimer’s disease.

Non-Communicable Disease Risk Factors Among Caregivers of Patients Attending a Tertiary Cardiovascular Hospital in Tanzania.

BackgroundNotwithstanding the ever-present burden of infectious diseases, the sub-Saharan Africa (SSA) region has experienced a 67% rise in the non-communicable disease (NCD) burden in less than three decades. Furthermore, regardless of the increased recognition of NCDs threat in the region, reliable local estimates and associated drivers are generally lacking. We therefore conducted this cross-sectional study to establish the pattern and correlates of the modifiable NCD risk factors among caregivers of patients attending a tertiary cardiovascular centre in Tanzania.MethodsA cross-sectional survey was conducted at Jakaya Kikwete Cardiac Institute, Dar es Salaam, Tanzania. We used a structured questionnaire bearing a modified WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) tool to explore the modifiable behavioral and modifiable biological NCD risk factors.ResultsA total of 1063 caregivers were enrolled in this study. The mean age was 40.5 years, and 55.7% were female. Nearly 80% of participants had a good knowledge regarding NCDs and 85.4% had a positive family history of NCDs. Overall, 1027 (96.6%) participants had at least one modifiable NCD risk factor while 510 (48.0%) had three or more (i.e., clustering). With respect to modifiable behavioral NCD risk factors, 34 (3.2%) were tobacco users, 56 (5.3%) had harmful alcohol consumption, 691 (65%) had unhealthy eating behavior, and 820 (77.1%) were physically inactive. Pertaining to modifiable biological NCD risk factors, 710 (66.8%) had excess body weight, 420 (39.5%) had hypertension and 62 (5.8%) were diabetic.ConclusionA vast majority of caregivers of NCD patients in this tertiary setting were found to have modifiable NCD risk factors with a strong tendency of clustering. These findings call for intensification of both population strategies and targeted group interventions for better control of the NCD threat and its correlates.

Geographical origin identification and chemical markers screening of Chinese green tea using two-dimensional fingerprints technique coupled with multivariate chemometric methods

Identifying the geographical origins of green teas produced in specific regions is of significance since the geographical origin of tea influences its quality and price greatly. In this work, a novel two-dimensional (2D) fingerprints acquired by high-performance liquid chromatography-diode array detector (HPLC-DAD) was firstly proposed to identify geographical origins of Chinese green teas. A total number of 62 chemical components were extracted from 2D HPLC-DAD fingerprints of 78 tea samples by multivariate curve resolution-alternating least squares (MCR-ALS) algorithm. Afterward, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to classify tea samples based on the extracted components. Inspection of PCA score plots of two scaling types (UV and Par) showed that tea samples from two different geographical origins have an obvious clustering tendency. As for the OPLS-DA analysis, the Q 2 cum of two types-scaling OPLS-DA models are greater than 0.75, and the total recognition rates for test set are 92.86%. What's more, according to p value of t -test, VIP values, V-plot and S-plot, 17 characteristic components were screened and identified as chemical markers to distinguish between Zhejiang teas and Shandong teas. This work indicated that the proposed strategy is suitable for identifying geographical origins of Chinese green teas. • 2D HPLC-DAD fingerprints was proposed to identify geographical origins of tea. • 62 chemical components were extracted from 2D HPLC-DAD fingerprints by MCR-ALS. • PCA and OPLS-DA were used to classify tea samples using the extracted components. • 17 characteristic components were screened and identified as chemical markers. • There are correlations between chemical components and geographical origins of tea.

Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer

BackgroundTo report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum.MethodsConsecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak’s classification.ResultsTwo-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63).ConclusionsRadiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Spatial distribution characteristics of discontinuous hillslope gullies on the Loess Plateau of China: A special focus on spoon gullies

Gullies characterized by frequent material exchanges and dramatic morphological changes sculpt fragmented surface landscapes and generate severe soil erosion, yet research on discontinuous gullies is currently lacking on the Loess Plateau of China. A special kind of discontinuous gully isolated on a hillslope, termed a spoon gully, was observed during a field investigation on the Loess Plateau. Spoon gullies are permanent gullies that are basically spoon-shaped with fat heads and thin tails. The confluences between spoon gullies and other gullies mainly occur in the subsurface. According to image interpretations and field verifications, 4077 spoon gullies were recognized on the Loess Plateau. These gullies were mainly distributed in the western part and northern fringes of the Loess Plateau with an obvious spatial clustering tendency, forming four concentrated areas with high gully densities. The topography distinctly affects the spatial distribution of spoon gullies. A total of 81.92% of the spoon gullies were located on slopes of 10–30°, and 73.29% of these gullies were located on semi-sunny or semi-shady slopes. Furthermore, the spatial distribution of spoon gullies was associated with loess landforms and soil erosion intensities. A total of 66.37% of the spoon gullies occurred in loess ridge, loess tableland, or loess hill regions, and more than 50% of them arose in areas of strong or moderate water erosion. Overall, spoon gullies were mainly distributed in areas where continuous gullies developed insufficiently, and this finding can be regarded as the integrated result of precipitation, loess thickness and texture, vegetation coverage, and agricultural activities, suggesting that spoon gullies may represent an early stage in the development of continuous loess gully systems. These results not only provide new insights for understanding the formation and evolution of gully landforms but also have significant implications for soil erosion management on the Loess Plateau.

Spatio-Temporal Disparity and Driving Forces of the Supply Level of Healthcare Service in the Yangtze River Delta.

The equalization of healthcare supply is not only related to the people's need for a better life, but can also provide a strong guarantee for the high-quality and sustainable development of the Yangtze River Delta integration. By using exploratory spatial analysis techniques, this study analyzed the spatio-temporal evolution characteristics and heterogeneous influence effects of the supply level of healthcare service in the Yangtze River Delta from 2007 to 2019. It was found that the supply level of healthcare service in the Yangtze River Delta had improved significantly. The differences in the supply level of healthcare service between cities had tended to narrow without polarization, and the supply level of healthcare service generally showed a high spatial pattern in the south delta and low spatial pattern in the north delta. The higher the supply level of healthcare service was, the weaker the interannual variability was. The supply level of healthcare service in the Yangtze River Delta region presented obvious spatial association and differentiated tendency of local high and low spatial clusters. The relative length and curvature of the supply level of healthcare service in the Yangtze River Delta generally presented a spatial pattern with low values in the northeast and high values in the southwest. Population density and urban-rural income gap generally exhibited negative spatio-temporal impact on the supply level of healthcare service across most cities. On the other hand, urbanization level and per capita disposable income generally had positive spatio-temporal impact on the supply level of healthcare service across most cities. Per capita gross domestic product (GDP) showed an increasingly positive spatio-temporal impact on the supply level of healthcare service across most cities. While per capita fiscal expenditure exhibited significantly negative impact on the supply level of healthcare service across most cities in space.

Allele-specific aberration of imprinted domain chromosome architecture associates with large offspring syndrome

SummaryLarge offspring syndrome (LOS) and Beckwith-Wiedemann syndrome are similar epigenetic congenital overgrowth conditions in ruminants and humans, respectively. We have reported global loss-of-imprinting, methylome epimutations, and gene misregulation in LOS. However, less than 4% of gene misregulation can be explained with short range (<20kb) alterations in DNA methylation. Therefore, we hypothesized that methylome epimutations in LOS affect chromosome architecture which results in misregulation of genes located at distances >20kb in cis and in trans (other chromosomes). Our analyses focused on two imprinted domains that frequently reveal misregulation in these syndromes, namely KvDMR1 and IGF2R. Using bovine fetal fibroblasts, we identified CTCF binding at IGF2R imprinting control region but not KvDMR1, and allele-specific chromosome architecture of these domains in controls. In LOS, analyses identified erroneous long-range contacts and clustering tendency in the direction of expression of misregulated genes. In conclusion, altered chromosome architecture is associated with LOS.

Examining the Potential Scaling Law in Urban PM2.5 Pollution Risks along with the Nationwide Air Environmental Effort in China.

Urban scaling law provides a quantitative understanding of the fundamental nonlinear properties of how cities work. Addressing this, this study intended to examine the potential scaling law that may lie in urban air pollution. With ground-monitored PM2.5 data and statistical socioeconomic factors in 265 Chinese cities (2015–2019), a targeted analysis, based on the scaling power-law model and scale-adjusted metropolitan indicator (SAMI) was conducted. The main findings of this study were summarized as follows: (1) A significant sublinear scaling relationship between PM2.5 and urban population size indicated that air quality degradation significantly lagged behind urban growth, affirming the remarkable effectiveness of national efforts on atmospheric environment improvement. (2) SAMI analysis expressed the relative conflict risk between PM2.5 pollution and urbanization and showed significant spatial cluster characteristics. Cities in central China showed higher potential risk than other regions, and there was a clear southward tendency for the city clusters with increasing SAMIs during the study period. (3) During the study period, urbanization was not the reason affecting the human-land conflict in terms of air pollution. This study is significant in that it marked the first innovative incorporation of the scaling law model into an urban environmental risk study. It also offered a new perspective from which to reframe the urban PM2.5 pollution risk, along with the nationwide air environmental effort in China, which will benefit future research on multi-types of urban environmental issues.

Dendrogram for Anthropometric and Biomechanical Variables Causing Foot Deformities by Using Hierarchical Cluster Analysis: A Cross-Sectional Study

ObjectiveThe purpose of this study was to determine the factors associated with pathomechanical conditions and to determine the clustering tendency of the variables by dendrogram. MethodsThe cross-sectional study was carried out with 108 physiotherapy students aged 21.36 ± 1.35 years. The variables included in the study were quadriceps (Q), subtalar, and valgus angles and medial longitudinal arch (MLA) and hallux valgus angles. The MLA and metatarsal width were measured and navicular drop tests performed under weighted and nonweighted conditions. Hierarchical cluster analysis was used to determine the clustering tendency of the variables and see how these clusters might converge. ResultsTwo main clusters of variables were obtained using hierarchical cluster analysis. In the first main cluster, the weighted and nonweighted metatarsal widths of the left and right sides, age, hallux valgus angle, and subtalar pronation angles of the left and right sides were found to be significantly related to each other. In the second main cluster, the weighted and nonweighted MLA, knee valgus and Q angles, weight, body mass index, weighted and nonweighted navicular drop values, gender, and height were found to be significantly related to each other. ConclusionThe relationships between variables such as MLA, hallux valgus angles, knee valgus and Q angles, weight, body mass index, weighted and nonweighted navicular drop values, gender, and height seem to be associated with pathomechanical conditions.

Hydrogen localization and cluster formation in α-Zr from first-principles investigations

Alpha-zirconium is a hydrogen host material used in nuclear energy and semiconducting applications due to Zr's excellent properties such as corrosion resistance, low thermal neutrons cross-section, and thermodynamically favorable interstitial sequestering of H atoms. Herein we use density functional theory simulations to systematically analyze the initial stages of H insertion in the Zr matrix, and determine optimal hydrogen localization, distribution, and clustering tendencies from a thermodynamics and kinetics perspective. Our results suggest that H occupation of tetrahedral sites is strongly favored, consistent with previous experimental and theoretical studies. Further, a cooperative effect in H localization is shown where H atoms favor clustering. The H pair is found to be the most stable by occupying tetrahedral sites with pairs at second nearest-neighbor positions (TTn2). Among the four H atom clusters, the most favorable configuration has two TTn2 pairs forming “box” or “zig-zag” shaped-cluster with the pairs separated by 3.03 or 3.25 Å, respectively. Nudged elastic band simulations suggest that the formation of TTn2 is facile and is limited by the small diffusion barrier of the monomer of less than 0.1 eV. Further, the formation routes for the 4H atom clusters are also presented. The current study can serve as a foundation to systematically model H atom clustering in metals and alloys.

Hydrogen storage in scandium decorated triazine based g-C3N4: Insights from DFT simulations

Hydrogen is being considered a ‘fuel of the future,’ a viable alternative to fossil fuels in fuel cell vehicles. Using Density Functional Theory simulations, reversible, onboard hydrogen storage in Sc-decorated triazine-based graphitic carbon nitride (g-C3N4) has been explored. Sc atom binds strongly on the g-C3N4 structure with a binding energy of −7.13 eV. Each Sc atom can reversibly bind 7 molecules of hydrogen, giving a net gravimetric storage capacity of 8.55 wt%, an average binding energy of −0.394 eV per H2, and a corresponding desorption temperature of 458.28 K, fulfilling the criteria prescribed by the US Department of Energy. The issue of transition metal clustering has been investigated by computing the diffusion energy barrier (2.79 eV), which may be large enough to hinder the clustering tendencies. The structural integrity of Sc-g-C3N4 has been verified through ab-initio Molecular Dynamics simulations. The interaction mechanism of Sc over g-C3N4 and H2 over Sc-g-C3N4 has been explored using density of states and charge transfer analysis. A flow of charge from valence 3d orbitals of Sc towards vacant orbitals of g-C3N4 during the binding of Sc over g-C3N4 is observed. The binding of H2 on Sc-g-C3N4 may be via Kubas type of interactions which is stronger than physisorption due to net charge gain by H 1s orbital from Sc 3d orbital. Our systematic investigations indicate that Sc-decorated g-C3N4 may be a high-performance material for reversible hydrogen storage applications.