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High-temperature irradiation-induced amorphization in SiC is a crucial factor leading to swelling. This study employs atomistic simulations to clarify the effects of experimental irradiation damage (1.0 dpa) in polycrystalline 3C-SiC at 1000 K. The findings reveal amorphization and swelling thresholds at 0.45 dpa and 7.45 %, respectively. Interestingly, dose threshold for amorphization does not align with chemical bonding, volume swelling, or enthalpy thresholds. Instead, a competitive mechanism emerges between grain boundaries and the grain core in the evolution of chemical disorder and clusters. Moreover, as amorphization saturates and grain boundaries annihilate, the degradation of tensile strength and Young's modulus stabilizes at 21.1 GPa and 124.2 GPa, respectively. Notably, grain boundaries exert a significant influence on defect cluster formation during amorphous evolution and dominate the initial deterioration of mechanical properties at low damage doses. This study enhances our understanding of how grain boundaries and chemical disorder mechanisms impact amorphization SiC. [ABSTRACT FROM AUTHOR]

Two closely related yet distinctly different cationic clusters, [Dy52Ni44(HEIDA)36(OH)138(OAc)24(H2O)30]10+ (1) and [Dy112Ni76(HEIDA)44(EIDA)24(IDA)4(OH)268(OAc)48(H2O)44]4+ (2) (HEIDA=N‐(2‐hydroxyethyl)iminodiacetate), each featuring a multi‐shell core of Platonic and Archimedean polyhedra, were obtained. Depending on the specific conditions used for the co‐hydrolysis of Dy3+ and Ni2+, the product can be crystallized out as one particular type of cluster or as a mixture of 1 and 2. How the reaction process was affected by the amount of hydrolysis‐facilitating base and/or by the reaction temperature and duration was investigated. It has been found that a reaction at a high temperature and/or for an extended period favors the formation of the compact and thermodynamically more stable 1, while a brief reaction with a large amount of the base is good for the kinetic product 2. By tuning these intertwining conditions, the reaction can be regulated toward a particular product. [ABSTRACT FROM AUTHOR]

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value. [ABSTRACT FROM AUTHOR]

There are debates about cluster heterogeneity, network structures, and innovations. The difference in the degree to which firms joined in the internal and external networks of heterogeneous clusters can affect innovation performance. There is still a lack of empirical evidence on how networks or spatial factors can differentially affect innovation efficiency due to cluster heterogeneity. China has endogenous clusters formed by small and medium‐sized firms and numerous clusters mainly based on government planning instrument, such as industrial zones and high‐tech parks. There are controversies over these planned cluster, such as insufficient firm connections and weak innovation effectiveness.Cluster innovation is a complex socio‐economic process that combines endogenous context, exogenous factors and interacts with multi‐spatial relationships. This perspective may explain the differences in which heterogeneous clusters improve efficiency. This paper draws on first‐hand data obtained from 188 questionnaires. The Lingang Equipment Manufacturing Cluster in Shanghai and the Taizhou Machine Tool Manufacturing Cluster in Zhejiang serve as examples of heterogeneous clusters. We combine the cluster's endogenous and exogenous characteristics, network size and strength of network ties, and local and non‐local innovation spaces to discuss the impact on innovation efficiency. Expecting to provide a reference for improving the innovation efficiency of heterogeneous clusters in developing economies. The results suggest that regardless of local or non‐local scales, exogenous clusters have a more pronounced effect of local network size and non‐local tie strength on innovation performance based on demand for proximity to customers and suppliers. [ABSTRACT FROM AUTHOR]

How to redefine tourism development from a holistic regional perspective? On Costa Brava (Spain), known as a sun and beach destination, alternative strategies are being explored to strengthen territorial resilience through the promotion of existing products, such as music festivals. A mixed methods research based on the qualitative analysis of interviews with key informants and the quantitative analysis of a battery of indicators will provide the necessary information to analyse the potential of these festivals in a context where the dependence towards tourism and the nature of the tourism model itself are being contested. Lessons learnt from last decade might contribute to a more adequate tourism development in the future. [ABSTRACT FROM AUTHOR]

The Cluster mission will always be the first ever multi-spacecraft mission mapping the Earth magnetosphere in three dimensions. Launched in 2000 and originally planned to operate for two years, it has been orbiting Earth for more than two solar cycles. Over the course of its lifetime, its data have enabled the scientific community to conduct pioneer science. Recent scientific highlights will be presented first, followed by the latest scientific objectives that have guided the Cluster mission operations from 2021 until 2024. Early September 2024, one spacecraft of this veteran constellation will re-enter in a controlled manner the Earth's atmosphere, followed by its companions in 2025 and 2026. As we will see, this will be a unique opportunity to improve the ESA space debris re-entry models. Lastly, preparation of its legacy phase will be presented. [ABSTRACT FROM AUTHOR]

Tuning the interfacial structure of metal oxide substrates is an essential strategy to induce electronic structure reconstruction of supported catalysts, which is of great importance in optimizing their catalytic activities. Herein, vanadium oxides‐supported Ir catalysts (Ir‐V2O3, Ir‐VO2, and Ir‐V2O5) with different interfacial bonding environments (Ir‐V, Ir‐Obri, and Ir‐O, respectively) were investigated for hydrogen evolution reaction (HER). The regulating mechanism of the influence of different interfacial bonding environments on HER activity was investigated by both experimental results and computational evidence. Benefiting from the unique advantages of interfacial Ir‐V direct metal bonds in Ir‐V2O3, including enhanced electron transfer and electron donation ability, an optimized HER performance can be obtained with lowest overpotentials of 16 and 26 mV at 10 mA cm−2, high mass activities of 11.24 and 6.66 A mg−1, and turnover frequency values of 11.20 and 6.63 s−1, in acidic and alkaline conditions respectively. Furthermore, the assembled Ir‐V2O3||RuO2 anion exchange membrane (AEM) electrolyzer requires only 1.92 V to achieve a high current density of 500 mA cm−2 and realizes long‐term stability. This study provides essential insights into the regulating mechanism of interfacial chemical bonding in electrocatalysts and offers a new pathway to design noble metal catalysts for different applications. [ABSTRACT FROM AUTHOR]

Background: Metabolic clusters can stratify subgroups of individuals at risk for type 2 diabetes mellitus and related complications. Since obesity and insulin resistance are closely linked to alterations in hemostasis, we investigated the association between plasmatic coagulation and metabolic clusters including the impact on survival. Methods: Utilizing data from the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, we assigned 917 participants without diabetes to prediabetes clusters, using oGTT-derived glucose and insulin, high-density lipoprotein cholesterol, triglycerides, and anthropometric data. We performed a comprehensive analysis of plasmatic coagulation parameters and analyzed their associations with mortality using proportional hazards models. Mediation analysis was performed to assess the effect of coagulation factors on all-cause mortality in prediabetes clusters. Results: Prediabetes clusters were assigned using published tools, and grouped into low-risk (clusters 1,2,4; n = 643) and high-risk (clusters 3,5,6; n = 274) clusters. Individuals in the high-risk clusters had a significantly increased risk of death (HR = 1.30; CI: 1.01 to 1.67) and showed significantly elevated levels of procoagulant factors (fibrinogen, FVII/VIII/IX), D-dimers, von-Willebrand factor, and PAI-1, compared to individuals in the low-risk clusters. In proportional hazards models adjusted for relevant confounders, elevated levels of fibrinogen, D-dimers, FVIII, and vWF were found to be associated with an increased risk of death. Multiple mediation analysis indicated that vWF significantly mediates the cluster-specific risk of death. Conclusions: High-risk prediabetes clusters are associated with prothrombotic changes in the coagulation system that likely contribute to the increased mortality in those individuals at cardiometabolic risk. The hypercoagulable state observed in the high-risk clusters indicates an increased risk for cardiovascular and thrombotic diseases that should be considered in future risk stratification and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Lung cancer is currently the leading cause of cancer-related death. To lower the mortality from lung cancer, early detection is crucial. A precise and effective method of diagnosis by medical professionals is necessary for early detection of lung-related cancers in order to maximize the success rate of treatment. It is particularly difficult to catch it in the early stages of dissemination because there are no symptoms in the early stages. Using specific image processing ideas on computed tomography (CT), we can identify the tumor state in the early phases of spread and diagnose this at an early level. The early detection is crucial for curing and restraining the spread of uncontrolled malignant cells. The best outcomes for accurately identifying malignant tumors in their early stages come from combining the effects of morphological filtering and a contour-based picture segmentation technique. The segmented lung CT scan pictures that were malignant were separated from the rest of the testing dataset by a support vector machine (SVM) classifier in the subsequent analysis phase. Various image quality and performance metrics, such as accuracy, sensitivity, precision and specificity are used to validate the proposed technique. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy. [ABSTRACT FROM AUTHOR]

Objectives: Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods. Methods: We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence. Results: While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters. Conclusions: We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence. [ABSTRACT FROM AUTHOR]

The equilibrium geometry, tensors of diamagnetic, paramagnetic and magnetic susceptibility of (TiO2)n clusters (n = 1–4, 10–16) and supramolecular complexes [(TiO2)10(H2O)m] (m = 1–12) are calculated using the density functional theory in the M06/6-31G(d,p) approximation. A conclusion is reached on the predominance of the paramagnetic contribution and the strong effect the size and hydration of clusters have on the isotropic magnetic susceptibility. Correlations are established and regression equations are proposed for the isotropic magnetic susceptibility and the numbers of electrons in clusters (TiO2)n and water molecules in complexes (TiO2)10(H2O)m. [ABSTRACT FROM AUTHOR]

Summary: Los Alamos National Laboratory has deployed (over the last year and a half) a pair of Cray Shasta machines—a development testbed named Guaje and and production machine named Chicoma, which will soon comprise the bulk of LANL's open science research computing portfolio. In the process, we have encountered a number of problems and challenges in several realms—authentication and authorization, cluster health management, image management, and configuration management. Both independently and in collaboration with Cray/HPE, we have found solutions and brought the system into stable production. The presentation will discuss the solutions and how they came about, and issues we are working to resolve in the near future. [ABSTRACT FROM AUTHOR]

Introduction: Claw lesions significantly contribute to lameness, greatly affecting sow welfare. This study investigated different factors that would impact the severity of claw lesions in the sows of Brazilian commercial herds. Methods: A total of 129 herds (n = 12,364 sows) were included in the study. Herds were in the Midwest, Southeast, or South regions of Brazil. Inventory sizes were stratified into 250-810 sows, 811-1,300 sows, 1,301-3,000 sows, and 3,001-10,000 sows. Herds belonged to Cooperative (Coop), Integrator, or Independent structures. The herd management was conducted either maintaining breeds from stock on-site (internal), or through purchase of commercially available genetics (external). Herds adopted either individual crates or group housing during gestation. Within each farm, one randomly selected group of sows was scored by the same evaluator (two independent experts evaluated a total of 129 herds) from 0 (none) to 3 (severe) for heel overgrowth and erosion (HOE), heel-sole crack (HSC), separation along the white line (WL), horizontal (CHW) and vertical (CVW) wall cracks, and overgrown toes (T), or dewclaws (DC) in the hind legs after parturition. The study assessed differences and similarities between herds using Principal Component Analysis (PCA) and Hierarchical Agglomerative Clustering (HAC) analysis. The effects of factors (i.e., production structure, management, housing during gestation, and region) were assessed using the partial least squares method (PLS). Results and discussion: Heel overgrowth and erosion had the highest prevalence, followed by WL and CHW, while the lowest scores were observed for T DC, and CVW. Herds were grouped in three clusters (i.e., Cl, C2, and C3). Heel overgrowth and erosion, HSC, WL, CHW, CVW, and T were decreased by 17, 25, 11, 25, 21, and 17%, respectively, in C3 compared to CI and 2 combined. Independent structure increased the L-lndex in all three clusters. Furthermore, individual housing increased the L-lndex regardless of the cluster. The results suggest that shifting toward larger, more technologically advanced herds could potentially benefit claw health. Additionally, adopting group gestation housing appears to mitigate the adverse effects on claw health, although further validation is necessary, as Brazil has only recently transitioned from individual housing practices. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium hirsutum L.) is a vital cash crop in Pakistan, but climate change scenarios threatened its production by biotic and abiotic stress, especially drought. Nutrient management, specifically potassium (K) fertilization, typically alleviates the effects of drought. To this end, a greenhouse experiment evaluated the genotypes for drought stress tolerance and its management by K fertilization. The experiment consisted of 70 cotton genotypes factorially combined with two water levels (standard irrigation and drought stress) and two potassium levels (control and 102 mg/kg of potassium). Data collection occurred for shoot and root lengths, fresh shoot and root weights, dry shoot and root weights, root shoot ratio, total dry matter production, and K uptake after 45 days of germination. Results depicted that mean squares for genotypes, drought, potassium, and their interaction were significant for shoot and root lengths, fresh shoot and root weights, dry shoot and root weights, total dry weight, and potassium uptake, while some traits showed nonsignificant differences. Based on the principal component analysis, membership function value, and genotypic diversity, five genotypes emerged as tolerant: CIM-496, IR-3701, Cp-15/2, FH-113, and CIM-1100, and three, i.e., 4-F, MNH-129, and FH-1000, as susceptible. Tolerant and susceptible genotypes can further benefit breeding programs to develop cotton genotypes adaptable to drought stress and with better K uptake. [ABSTRACT FROM AUTHOR]

The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms and severity score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals with long COVID. A 3-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against hierarchical clustering and the Gaussian Mixture Model. The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (r s = −0.55), EuroQol visual analog scale (r s = −0.368), Patient Health Questionnaire-9 (r s = 0.538), Beck Anxiety Inventory (r s = 0.689), and Insomnia Severity Index (r s = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies. The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for patients with long COVID. • This study introduces the long COVID symptoms and severity score (LC-SSS), a novel, validated tool incorporating unsupervised machine learning to categorize symptom severity. This addresses a critical gap in standardized and quantifiable monitoring of long COVID, going beyond the limitations of traditional patient-reported outcome measures and offering a refined, personalized assessment framework. • The research demonstrates that the LC-SSS is strongly correlated with existing health quality measures, showing high construct and structural validity, excellent internal consistency, and satisfactory test-retest reliability. K-means clustering further identified 3 distinct severity categories among patients with long COVID, which can guide personalized treatment strategies, highlighting the tool's efficacy in capturing the complexity of long COVID symptoms. • By enabling precise classification of symptom severity, the LC-SSS facilitates tailored treatment plans and informs healthcare resource allocation and policy formulation for long COVID management. Its integration into clinical practice promises to improve diagnostic accuracy, enhance care quality, and potentially uplift patient outcomes in the context of health economics and outcomes research, making a significant contribution to the field. [ABSTRACT FROM AUTHOR]

As a Pt-group element, Pd has been regarded as one of the alternatives to Pt-based catalysts for the hydrogen evolution reaction (HER). Herein, we performed density functional theory (DFT) computations to explore the most stable structures of PdxBy (x = 6, 19, 44), revealed the in situ structural reconstruction of these clusters under acidic conditions, and evaluated their HER activity. We found that the presence of B can prevent underpotential hydrogen adsorption and activate the H atoms on the cluster surface for the HER. The theoretical calculations show that the reaction barrier for the HER on ~1 nm sized Pd44B4 can be as low as 0.36 eV, which is even lower than for the same-sized Pt and Pd2B nanoparticles. The ultra-high HER activity of sub-nanosized PdxBy clusters makes them a potential new and efficient HER electro-catalyst. This study provides new ideas for evaluating and designing novel nanocatalysts based on the structural reconstruction of small-sized nanoparticles in the future. [ABSTRACT FROM AUTHOR]

In a two-year field study across 58 isolated wetlands in Texas (USA), we examined whether odonate (Insecta: Odonata) assemblages were structured by local environmental filters or instead simply reflected the use of any available water in this semi-arid region. Cluster analysis resolved three wetland groupings based on environmental characteristics (hydroperiod, water chemistry, vegetation); 37 odonate species were detected at these wetlands. The most speciose assemblages occurred at wetlands with longer hydroperiods; these sites also had the most species found at no other wetland type. Ordination plots indicated some filtering with respect to the hydroperiod, but there was only mixed or weak support with respect to other local factors. Because water persistence was the strongest driver maintaining odonate diversity in this region, regardless of water quality or vegetation, beggars cannot be choosers in this system and conservation efforts can focus on water maintenance or supplementation. [ABSTRACT FROM AUTHOR]

The study analyzed the individual performance of partition cluster algorithms and selected Kmeans, Kmeans9+, Kmedoid, and Fuzzy Cmeans algorithms as base algorithms for the ensemble. The cluster performance is assessed using UCI data sets as well as other common public data sets. The quality of cluster results depends on the base cluster algorithm used. The efficiency of base algorithms is added based on the ensemble models. We developed two ensemble models: a simple hard voting ensemble and a soft boosting ensemble based on the bagging and boosting ensemble technique. Ensemble of different cluster algorithms can generate the most accurate clusters. Both models show better cluster results than their base cluster algorithms for the small and big data sets. When using most data sets, the Soft Boosting Ensemble model achieves 100% cluster accuracy. The cluster evaluating functions are the benchmark for assessing the quality of the cluster. All the cluster evaluating indices show better performance for developed ensemble models. Internal cluster-evaluating indices as well as external cluster-evaluating indices are used to compare the cluster quality of the individual cluster algorithm and generated ensemble cluster models. The work establishes that the developed ensemble methods improved the quality of the generated clusters. [ABSTRACT FROM AUTHOR]

This article analyzes the current situation and existing problems of the construction and development of Guangxi's mango industry cluster, and proposes countermeasures and suggestions such as strengthening the construction of high standard orchards, extending and strengthening the chain, cultivating leading enterprises, and strengthening brand building. This provides reference for promoting the upgrading of the mango industry form from "small mangoes" to advantageous and characteristic "big industries". [ABSTRACT FROM AUTHOR]

Although a memristor model, subjected to electrochemical metallization mechanism, has been proposed based on the spontaneous decay of clusters in the previous work, it does not agree with the human forgetting accurately. Therefore, an improved model is meaningfully presented for the memristor with the cluster spontaneous decay by adding the residual effect. The former is due to the inward contraction of atoms driven by surface energy, while the latter is because of the balance of attractive and repulsive forces between atoms. The model fits well with the actual device. The forgetting is caused by the spontaneous decay. Memory retention is generated due to the added effect, which is also the internal cause of good agreement with the actual forgetting. Additionally, short‐term plasticity is converted to long‐term plasticity through the repeated learning. The efficiency of experiential learning using this model is much higher than that using the previous. It is shown that the physical mechanism of spontaneous decay in the cluster‐based channel is different from that in vacancy‐based or atom‐based channel. The model working under a non‐ideal condition with the temperature influence is discussed. Potential applications based on the model are stated. [ABSTRACT FROM AUTHOR]

A series of RE (Sm, Nd, Pr and Ho)-doped Rh@NSPC (NSPC stands for N, S co-doped porous carbon nanosheets) are prepared by a simple joule-heat pyrolysis method for the first time. The optimized Sm-Rh@NSPC achieves highly efficient alkaline hydrogen evolution with superior electrocatalytic property. [Display omitted] • RE (Sm, Nd, Pr and Ho)-doped Rh@NSPC are prepared by joule-heat pyrolysis method. • Collaborative electronic interactions between Sm and Rh clusters. • The overpotential of Sm-Rh@NSPC is merely 18.1 mV at current density of 10 mAcm−2. The unique electronic and crystal structures of rare earth metals (RE) offer promising opportunities for enhancing the hydrogen evolution reaction (HER) properties of materials. In this work, a series of RE (Sm, Nd, Pr and Ho)-doped Rh@NSPC (NSPC stands for N, S co-doped porous carbon nanosheets) with sizes less than 2 nm are prepared, utilizing a simple, rapid and solvent-free joule-heat pyrolysis method for the first time. The optimized Sm-Rh@NSPC achieves HER performance. The high-catalytic performance and stability of Sm-Rh@NSPC are attributed to the synergistic electronic interactions between Sm and Rh clusters, leading to an increase in the electron cloud density of Rh, which promotes the adsorption of H+, the dissociation of Rh-H bonds and the release of H 2. Notably, the overpotential of the Sm-Rh@NSPC catalyst is a mere 18.1 mV at current density of 10 mAcm−2, with a Tafel slope of only 15.2 mV dec-1. Furthermore, it exhibits stable operation in a 1.0 M KOH electrolyte at 10 mA cm−2 for more than 100 h. This study provides new insights into the synthesis of composite RE hybrid cluster nanocatalysts and their RE-enhanced electrocatalytic performance. It also introduces fresh perspectives for the development of efficient electrocatalysts. [ABSTRACT FROM AUTHOR]

Solar‐driven CO2 reduction and water oxidation to liquid fuels represents a promising solution to alleviate energy crisis and climate issue, but it remains a great challenge for generating CH3OH and CH3CH2OH dominated by multi‐electron transfer. Single‐cluster catalysts with super electron acceptance, accurate molecular structure, customizable electronic structure and multiple adsorption sites, have led to greater potential in catalyzing various challenging reactions. However, accurately controlling the number and arrangement of clusters on functional supports still faces great challenge. Herein, we develop a facile electrosynthesis method to uniformly disperse Wells‐Dawson‐ and Keggin‐type polyoxometalates on TiO2 nanotube arrays, resulting in a series of single‐cluster functionalized catalysts P2M18O62@TiO2 and PM12O40@TiO2 (M=Mo or W). The single polyoxometalate cluster can be distinctly identified and serves as electronic sponge to accept electrons from excited TiO2 for enhancing surface‐hole concentration and promote water oxidation. Among these samples, P2Mo18O62@TiO2‐1 exhibits the highest electron consumption rate of 1260 μmol g−1 for CO2‐to‐CH3OH conversion with H2O as the electron source, which is 11 times higher than that of isolated TiO2 nanotube arrays. This work supplied a simple synthesis method to realize the single‐dispersion of molecular cluster to enrich surface‐reaching holes on TiO2, thereby facilitating water oxidation and CO2 reduction. [ABSTRACT FROM AUTHOR]

Nonlinear optical (NLO) crystals that can extend the frequency range of coherent light have significant applications in laser technology, semiconductor manufacturing, and lithography. Herein, two interesting NLO crystals, A2Pb3[H2N(CH2COO)2]3Cl5 (A = K, Rb), are rationally obtained. Their porous structures are constructed on [Pb3Cl5O6] clusters that possess large hyperpolarizability and polarizability anisotropy, and are arranged in a parallel manner. Remarkably, A2Pb3[H2N(CH2COO)2]3Cl5 (A = K, Rb) exhibits comprehensive NLO performance, including strong phase‐matching second‐harmonic generation (SHG) response, the widest bandgap among lead oxychloride NLO crystals, larger laser damage threshold than 127 MW cm−2, suitable birefringence of 0.11@1064 nm, high thermal stability, non‐deliquescence, as well as facile crystal growth. Theoretical calculations and structural analysis demonstrate that their strong SHG response is predominantly originated from the [Pb3Cl5O6] cluster. This study will provide a unique method for the discovery of new interesting NLO crystals in future. [ABSTRACT FROM AUTHOR]

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning's advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) are suspected reservoirs of Legionella pneumophila (Lp). The required aeration and mixing steps lead to the emission and dispersion of bioaerosols potentially harboring Lp. The aim of the project is to evaluate municipal WWTPs as a possible source of legionellosis through the statistical analysis of case clusters. A space–time scanning statistical method was implemented in SaTScan software to identify and analyze WWTPs located within and close to spatiotemporal clusters of legionellosis detected in Quebec between 2016 and 2020. In parallel, WWTPs were ranked according to their pollutant load, flow rate and treatment type. These parameters were used to evaluate the WWTP susceptibility to generate and disperse bioaerosols. Results show that 37 of the 874 WWTPs are located inside a legionellosis cluster study zone, including six of the 40 WWTPs ranked most susceptible. In addition, two susceptible WWTPs located within an extended area of 2.5 km from the study zone (2.5-km buffer) were included, for a total of 39 WWTPs. The selected 39 WWTPs were further studied to document proximity of population, dominant wind direction, and surrounding water quality. Samples collected from the influent and the effluent of six selected WWTPs revealed the presence of Legionella spp. in 92.3% of the samples. Lp and Lp serogroupg 1 (Lp sg1) were detected below the limit of quantification in 69% and 46% of the samples, respectively. The presence of Legionella in wastewater and the novel statistical approach presented here provides information to the public health authorities regarding the investigation of WWTPs as a possible source of Legionella exposure, sporadic cases, and clusters of legionellosis. [ABSTRACT FROM AUTHOR]

Atmospheric new particle formation (NPF), which exerts comprehensive implications for climate, air quality and human health, has received extensive attention. From molecule to cluster is the initial and most important stage of the nucleation process of atmospheric new particles. However, due to the complexity of the nucleation process and limitations of experimental characterization techniques, there is still a great uncertainty in understanding the nucleation mechanism at the molecular level. Laboratory‐based molecular beam methods can experimentally implement the generation and growth of typical atmospheric gas‐phase nucleation precursors to nanoscale clusters, characterize the key physical and chemical properties of clusters such as structure and composition, and obtain a series of their physicochemical parameters, including association rate coefficients, electron binding energy, pickup cross section and pickup probability and so on. These parameters can quantitatively illustrate the physicochemical properties of the cluster, and evaluate the effect of different gas phase nucleation precursors on the formation and growth of atmospheric new particles. We review the present literatures on atmospheric cluster formation and reaction employing the experimental method of laboratory molecular beam. The experimental apparatuses were classified and summarized from three aspects of cluster generation, growth and detection processes. Focus of this review is on the properties of nucleation clusters involving different precursor molecules of water, sulfuric acid, nitric acid and NxOy, respectively. We hope this review will provide a deep insight for effects of cluster physicochemical properties on nucleation, and reveal the formation and growth mechanism of atmospheric new particle at the molecular level. [ABSTRACT FROM AUTHOR]

The HIV-1 pandemic, spanning four decades, presents a significant challenge to global public health. This study aimed to understand the molecular transmission characteristics of newly reported HIV infections in Taiyuan, Shanxi Province, China, to analyze the characteristics of subtypes and the risk factors of the transmission network, providing a scientific basis for precise prevention and intervention measures. A total of 720 samples were collected from newly diagnosed HIV-1 patients residing in Taiyuan between 2021 and 2023. Sequencing of partial genes of the HIV-1 pol gene resulted in multiple sequence acquisitions and was conducted to analyze their subtypes and molecular transmission networks. Out of the samples, 584 pol sequences were obtained, revealing 17 HIV-1 subtypes, with CRF07_BC (48.29%), CRF01_AE (31.34%), and CRF79_0107 (7.19%) being the dominant subtypes. Using a genetic distance threshold of 1.5%, 49 molecular transmission clusters were generated from the 313 pol gene sequences. Univariate analysis showed significant differences in the HIV transmission molecular network in terms of HIV subtype and household registration (p < 0.05). Multivariate logistic regression analysis showed that CRF79_0107 subtype and its migrants were associated with higher proportions of sequences in the HIV transmission network. These findings provide a scientific foundation for the development of localized HIV-specific intervention strategies. [ABSTRACT FROM AUTHOR]

Long COVID presents with diverse symptoms after COVID-19. Different clusters of symptoms have been reported; however, their persistence beyond 2 years after COVID-19 remains unclear. In this cohort study, we prospectively evaluated individuals with previous severe COVID-19 presenting with long COVID at a two-year follow-up. We characterized the included patients and performed a cluster analysis of symptoms through multiple correspondence analysis and hierarchical clustering. A total of 199 patients with long COVID were included. The median age was 58 years (48–69), 56% were male, and the median follow-up time since the COVID-19 diagnosis was 26 months (IQR: 25, 27). Three symptom clusters were identified: Cluster 1 is characterized by fatigue, myalgia/arthralgia, a low prevalence of symptoms, and a lack of specific symptoms; Cluster 2 is defined by a high prevalence of fatigue, myalgia/arthralgia, and cardiorespiratory symptoms, including palpitations, shortness of breath, cough, and chest pain; and Cluster 3 is demonstrated a high prevalence of ageusia, anosmia, fatigue, and cardiorespiratory symptoms. Our study reinforces the concept of symptom clustering in long COVID, providing evidence that these clusters may persist beyond two years after a COVID-19 diagnosis. This highlights the chronic and debilitating nature of long COVID and the importance of developing strategies to mitigate symptoms in these patients. [ABSTRACT FROM AUTHOR]

Ensuring the quality and safety of meat from slaughter animals is a matter of global concern. Among the factors that must be taken care of are the activities that generate stress to the animal during antemortem handling (transport, rest, and stunning), postmortem carcass handling (aging and storage), and hygiene practices in facilities and staff. This work aimed to characterize sheep slaughter units within the municipality of Capulhuac de Mirafuentes, State of Mexico, based on current Mexican regulations. For this, a principal component (PC) analysis was carried out, highlighting that those that represented the highest variability in the slaughter centers were the price of the carcasses and their products, place of marketing, slaughter volume, sex of the animal, and safety of the carcasses, which represented 50.4 % of the explained variance. A cluster analysis was also carried out, which represented the integration of four groups of slaughter descriptors (P<0.05). As a result, it was found that 65 % of animals are slaughtered in commercial premises and houses that do not comply with the technification described in the regulations; they also present deficient antemortem and postmortem handling of animals; it was also observed that 98.3 % of the establishments use a slaughter method called "descabellado" (pithing), not reported in NOM-033-SAG/ZOO/2014, coupled with the lack of knowledge of the staff on animal welfare issues. This affects the quality and safety of meat and puts consumers' health at risk. [ABSTRACT FROM AUTHOR]

A large-scale street lights send data to the cloud platform, causing load imbalance among nodes of the server in high concurrency, resulting in communication failure. In view of this problem, based on microservices, this study proposes a dynamic weight load balancing algorithm based on the smart street lamp cloud platform. The algorithm calculates the respective weight coefficients and the initial weights of each node according to the hardware performance of each server during initialization, and dynamically adjusts the server weights according to the idle rate of CPU(Central Processing Unit) and network bandwidth during the request process to realize load optimization. By setting the minimum threshold and comparing it with the calculated remaining load rate, the weight of the server that reaches the upper limit of the load is set to 0 to prevent the server from being overloaded. The test results show that the proposed algorithm has a better load balancing effect than the minimum number of connections algorithm and the smooth weighted round robin algorithm in the experimental environment. Compared with the dynamic weight algorithm, the average response time and the actual number of concurrent connections of the proposed method are also improved. [ABSTRACT FROM AUTHOR]

Traditional performance analysis methods for high performance applications have shortcomings such as additional overhead during the analysis process and inaccurate analysis results, resulting in users spending more time and domain knowledge. To address these issues, this paper transforms the problem of program performance analysis into a multi-classification problem of unbalanced small sample datasets under high-dimensional features. By collecting 500 pieces of performance data that include seven types of metrics such as the number of process switches, memory utilization, and disk I/O load during program runtime, after data preprocessing such as PCA dimensionality reduction, a program performance problem analysis model is trained using a random forest classifier. Experimental validation shows that the model can identify five types of performance issues, including excessive memory utilization and heavy disk I/O load. To evaluate the effectiveness of the model's guidance, this paper collects performance data generated by the HotSpot3D program and the LU-Decomposition program during runtime. Based on the model's output guidance, the two validation programs are optimized at the runtime level and the compilation level. Experimental results indicate that the proposed method can effectively guide the optimization of program performance, with speedup ratios of 1.056 and 5.657 for the two programs, respectively. [ABSTRACT FROM AUTHOR]

Introduction: People with multiple sclerosis (PwMS) exhibit a spectrum of needs that extend beyond solely disease-related determinants. Investigating unmet needs from the patient perspective may address daily difficulties and optimize care. Our aim was to identify patterns of unmet needs among PwMS and their determinants. Methods: We conducted a cross-sectional multicentre study. Data were collected through an anonymous, self-administered online form. To cluster PwMS according to their main unmet needs, we performed agglomerative hierarchical clustering algorithm. Principal component analysis (PCA) was applied to visualize cluster distribution. Pairwise comparisons were used to evaluate demographics and clinical distribution among clusters. Results: Out of 1764 mailed questionnaires, we received 690 responses. Access to primary care was the main contributor to the overall unmet need burden. Four patterns were identified: cluster C1, 'information-seekers with few unmet needs'; cluster C2, 'high unmet needs'; cluster C3, 'socially and assistance-dependent'; cluster C4, 'self-sufficient with few unmet needs'. PCA identified two main components in determining the patterns: the 'public sphere' (access to information and care) and the 'private sphere' (need for assistance and social life). Older age, lower education, longer disease duration and higher disability characterized clusters with more unmet needs in the private sphere. However, demographic and clinical factors failed in explaining the four identified patterns. Conclusion: Our study identified four unmet need patterns among PwMS, emphasizing the importance of personalized care. While clinical and demographic factors provide some insight, additional variables warrant further investigation to fully understand unmet needs in PwMS. [ABSTRACT FROM AUTHOR]

Edge cloud technologies working in conjunction with AI-powered solutions can help surmount the challenges associated with the distributed execution of immersive services and contribute to delivering a positive end-user experience. Intelligent resource management, orchestration, and predictive systems can enhance service deployment, adapt to changing demands, and ensure seamless service operation. This paper introduces an innovative architectural paradigm that enables multi-domain edge orchestration for highly distributed immersive services by leveraging various AI solutions and technological tools to support multi-domain edge deployments. The proposed architecture is designed to function based on multi-level specification blueprints, separating high-level user-intent infrastructure definition from AI-driven orchestration and the final execution plan. This architectural design enables the incorporation of AI solutions to be conducted in a modular manner. Furthermore, the Application Management Framework provides a visual language and tool as an alternative to formal methods for creating intent blueprints. The proposed architecture is evaluated within the frame of an immersive virtual touring use-case scenario. [ABSTRACT FROM AUTHOR]

Atomically precise nanoclusters hold promise for supramolecular assembly and (opto)electronic‐ as well as magnetic materials. Herein, this work reports that treating palladium(0) precursors with a triphosphirane affords strongly colored Pd8(PDip)6 that is fully characterized by mass spectrometry, heteronuclear and Cross‐Polarization Magic‐Angle Spinning (CP‐MAS) NMR‐, infrared (IR), UV–vis, and X‐ray photoelectron (XP) spectroscopies, single‐crystal X‐Ray diffraction (sc‐XRD), mass spectrometry, and cyclovoltammetry (CV). This coordinatively unsaturated 104‐electron Pd(0) cluster features a cubic Pd8‐core, µ4‐capping phosphinidene ligands, and is air‐stable. Quantum chemical calculations provide insight to the cluster's electronic structure and suggest 5s/4d orbital mixing as well as minor Pd─P covalency. Trapping experiments reveal that cluster growth proceeds via insertion of Pd(0) into the triphosphirane. The unsaturated cluster senses ethylene and binds isocyanides, which triggers the rearrangement to a tetrahedral structure with a reduced frontier orbital energy gap. These experiments demonstrate facile cluster manipulation and highlight non‐destructive cluster rearrangement as is required for supramolecular assembly. [ABSTRACT FROM AUTHOR]

In order to define the concept of nearness between L -fuzzy rough sets, we introduce the concept of Čech L -fuzzy rough proximity spaces. On this new nearness structure, we prove some basic topological results. To support the proposed approach, examples are given. Further, we introduce L -fuzzy rough grills, L -fuzzy rough filters and L -fuzzy rough clusters and on Čech L -fuzzy rough proximity spaces, and find the relationship between them. [ABSTRACT FROM AUTHOR]

The tick Amblyomma lepidum is an ectoparasite of veterinary importance due to its role in transmitting livestock diseases in Africa, including heartwater. This study was conducted in 2023 to monitor Amblyomma spp. infestation in dromedary camels imported from Somalia, Ethiopia, and Sudan to Egypt. This study inspected 200 camels at the Giza governorate’s camel market that had been imported from Somalia, 200 from Ethiopia, and 200 from Sudan for tick infestation. Specimens were identified using morphological characteristics and phylogenetic analyses of the 12S and 16S rRNA genes. Clusters were calculated using an unweighted pair-group method with arithmetic averages (UPGMA) dendrogram to group the specimens according to their morphometric characteristics. The morphometric analysis compared the body shape of ticks collected from different countries by analyzing dorsal features. Principal component analysis (PCA) and canonical variate analysis (CVA) were performed to obtain body shape variation among specimens from different countries. Results indicated that camels were infested by 57 males Amblyomma lepidum, and no female specimens were observed; among these specimens, one may have a morphological abnormality. The results suggest that A. lepidum specimens collected from camels imported to Egypt from African countries exhibit locally adapted morphology with variations among specimens, particularly variations in body size. This adaptation suggests minimal potential for genetic divergence. Ecological niche modeling was used to predict the areas in Africa with suitable climates for A. lepidum. The study confirmed that East African countries might have the most favorable climatic conditions for A. lepidum to thrive. Interestingly, the amount of rain during the wettest quarter (Bio16) had the strongest influence on the tick’s potential distribution, with suitability decreasing sharply as rainfall increased. Future predictions indicate that the climatic habitat suitability for A. lepidum will decrease under changing climate conditions. However, historical, current, and future predictions indicate no suitable climatic habitats for A. lepidum in Egypt. These findings demand continuous surveillance of A. lepidum in camel populations and the development of targeted strategies to manage tick infestations and prevent the spread of heartwater disease. [ABSTRACT FROM AUTHOR]

Copyright of Studies & Work of the Collegium of Management & Finance / Studia i Prace Kolegium Zarzadzania i Finansów is the property of SGH Warsaw School of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article discusses the issues of cluster policy formation in the Republic of Kazakhstan on the basis of studying the experience of leading countries. The research aim is to find new effective tools and institutions for the development of the cluster structuring of the agro-industrial complex economy of Kazakhstan. Cluster policy in the field of supporting regional clusters starts with the identification of already existing clusters in the region, because by viewing the regional economy through the prism of various local industries and innovative enterprises, regional authorities can identify measures of effective impact and support for their clusters. This research examines the possibilities of using clusters and cluster policy as one of the most important components of the policy for the development and support of small and medium-sized enterprises in Kazakhstan's agro-industrial complex. The research methodology includes qualitative and quantitative data analysis methods, comparative analysis, and mathematical processing (the Pearson correlation coefficient), as well as the modeling of possible development scenarios. The obtained results show that there are significant opportunities for a wider involvement of small and medium-sized enterprises in the formation of cluster structures of the agro-industrial sector through joint efforts by the government and regional centers in the conditions of innovative development of the country's economy. [ABSTRACT FROM AUTHOR]

Given a strongly connected mixed graph G = (V , E , A) , where V represents the vertex set, E is the undirected edge set, and A is the directed arc set, R ⊆ E is a subset of required edges and is divided into p clusters R 1 , R 2 , ⋯ , R p , and A is a set of required arcs and is partitioned into q clusters A 1 , A 2 , … , A q . Each edge in E and each arc in A are associated with a nonnegative weight and the weight function satisfies the triangle inequality. In this paper we consider two clustered arc routing problems. The first is the Clustered Rural Postman Problem, in which A is empty and the objective is to find a minimum-weight closed walk such that all the edges in R are serviced and the edges in R i ( 1 ≤ i ≤ p ) are serviced consecutively. The other is the Clustered Stacker Crane Problem, in which R is empty and the goal is to find a minimum-weight closed walk that traverses all the arcs in A and services the arcs in A j ( 1 ≤ j ≤ q ) consecutively. For both problems, we propose constant-factor approximation algorithms with ratios 13/6 and 19/6, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Between 2019–2021, facing public concern, a scientific expert committee (SEC) reanalysed suspected clusters of transverse upper limb reduction defects (TULRD) in three administrative areas in France, where initial investigations had not identified any risk exposure. We share here the national approach we developed for managing suspicious clusters of the same group of congenital anomalies occurring in several areas. Methods: The SEC analysed the medical records of TURLD suspected cases and performed spatiotemporal analyses on confirmed cases. If the cluster was statistically significant and included at least three cases, the SEC reviewed exposures obtained from questionnaires, environmental databases, and a survey among farmers living near to cases' homes concerning their plant product use. Results: After case re-ascertainment, no statistically significant cluster was observed in the first administrative areas. In the second area, a cluster of four children born in two nearby towns over two years was confirmed, but as with the initial investigations, no exposure to a known risk factor explaining the number of cases in excess was identified. In the third area, a cluster including just two cases born the same year in the same town was confirmed. Discussion: Our experience highlights that in the event of suspicious clusters occurring in different areas of a country, a coordinated and standardised approach should be preferred. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally, it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops. These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios. Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes. The evaluation spans varying sensor densities, wireless channel conditions, and packet transmission rates, showcasing the protocol's superiority in ensuring reliable and efficient data transfer. Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs. By prioritizing fairness, reliability, and efficiency, the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services, thereby offering a valuable contribution to the field of medical event-drivenWSNs. [ABSTRACT FROM AUTHOR]

Abstract Background Metabolic clusters can stratify subgroups of individuals at risk for type 2 diabetes mellitus and related complications. Since obesity and insulin resistance are closely linked to alterations in hemostasis, we investigated the association between plasmatic coagulation and metabolic clusters including the impact on survival. Methods Utilizing data from the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, we assigned 917 participants without diabetes to prediabetes clusters, using oGTT-derived glucose and insulin, high-density lipoprotein cholesterol, triglycerides, and anthropometric data. We performed a comprehensive analysis of plasmatic coagulation parameters and analyzed their associations with mortality using proportional hazards models. Mediation analysis was performed to assess the effect of coagulation factors on all-cause mortality in prediabetes clusters. Results Prediabetes clusters were assigned using published tools, and grouped into low-risk (clusters 1,2,4; n = 643) and high-risk (clusters 3,5,6; n = 274) clusters. Individuals in the high-risk clusters had a significantly increased risk of death (HR = 1.30; CI: 1.01 to 1.67) and showed significantly elevated levels of procoagulant factors (fibrinogen, FVII/VIII/IX), D-dimers, von-Willebrand factor, and PAI-1, compared to individuals in the low-risk clusters. In proportional hazards models adjusted for relevant confounders, elevated levels of fibrinogen, D-dimers, FVIII, and vWF were found to be associated with an increased risk of death. Multiple mediation analysis indicated that vWF significantly mediates the cluster-specific risk of death. Conclusions High-risk prediabetes clusters are associated with prothrombotic changes in the coagulation system that likely contribute to the increased mortality in those individuals at cardiometabolic risk. The hypercoagulable state observed in the high-risk clusters indicates an increased risk for cardiovascular and thrombotic diseases that should be considered in future risk stratification and therapeutic strategies. Graphical Abstract

Abstract Objectives Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods. Methods We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence. Results While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters. Conclusions We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence.

Abstract Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy.