Clustering Effect Research Articles

Media Use in Daily Routines and Young Children’s Social Skills

ABSTRACT Research Findings: The current study examined (1) whether children’s media use during afterschool or bedtime routines, beyond their total media use, contributes uniquely to their peer social skills, and (2) whether joint media engagement serves as a protective factor for any negative associations between media use in daily routines and peer social skills. The study followed 459 children in a Midwestern U.S. state from kindergarten to Grade 1. Teachers reported on children’s peer social skills at both time points. Parents reported frequency of media use in afterschool and bedtime routines, daily media use duration, and joint media engagement. Hierarchical linear modeling was utilized to account for class-level clustering effects. Frequency of media use in afterschool routines negatively predicted children’s peer social skills in first grade, controlling for total media use, peer social skills in kindergarten, and demographics. However, media use in bedtime routines did not significantly predict social skills. Neither of these associations was significantly moderated by parental joint media engagement. Practice or Policy: The current results call for future research focused on effective means to regulate children’s media use in their daily routines to foster positive social development.

Determinants of animal source food consumption among children aged 6–23 months in sub-Saharan Africa: multilevel mixed effect model

A diversified diet in early childhood that comprises plant and animal source foods contributes to better health outcomes throughout the life course. However, inadequate infant and young child-feeding practices are serious public health problems in Africa. Evidence on the magnitude and determinants of inadequate animal source food consumption (ASF) in sub-Saharan Africa (SSA) is not yet well established. Hence, this study aimed to estimate the pooled magnitude and determinants of ASF consumption in SSA. A total weighted sample of 92,309 children aged 6–23 months from the Demographic and Health Survey dataset of 35 SSA countries was used for this study. A multilevel mixed-effect model was fitted. The intra-class correlation coefficient (ICC) and likelihood ratio (LR) tests were used to assess the presence of the clustering effect. The model comparison was made using deviance information criteria (DIC). A p-value of p < 0.05 was used to declare statistical significance. The pooled magnitude of ASF consumption among children aged 6–23 months in SSA was 23.7% (95% CI: 23.4, 23.9). Across the SSA regions, the Southern Africa region had the highest consumption of ASF (51.35%), and the lowest was from the Eastern Africa region (21.41%). Younger children aged 6–9 months (AOR = 3.39; 95% CI: 3.14, 3.65), mothers who had no formal education (AOR = 1.46; 95% CI: 1.37, 1.56), children from mothers with no media exposure (AOR = 1.17; 95% CI: 1.11, 1.24), children from the poorest households (AOR = 2.59; 95% CI: 2.36, 2.84), rural residence (AOR = 1.20; 95% CI: 1.12, 1.28), living in low-income countries (AOR = 2.43; 95% CI: 2.03, 2.90), and children from the East Africa region (AOR = 1.24; 95% CI: 1.14, 1.35) were among the significant predictors for not consuming ASF. In this study, ASF consumption was markedly low. Almost three-fourths of children aged 6–23 months in SSA did not consume ASF in the previous day. Age of the child, ANC visits, maternal educational status, maternal marital status, media exposure, and household wealth index were significant individual-level predictors of ASF consumption, whereas the place of residence, country’s income level, and regions of SSA were found to be significant community-level predictors of ASF consumption. Thus, the study highlighted that policymakers, governments, and their partners should demonstrate rigorous efforts to improve dietary diversification with due consideration to the identified predictors of ASF.

Magnetocaloric effect in a high-spin ferromagnetic molecular cluster

Magnetocaloric effect in a high-spin ferromagnetic molecular cluster

Associations between family factors and physical activity clustering in preschool children: a cross-sectional study

ObjectiveThis study aimed to examine the association between family factors and physical activity (PA) clustering in preschool children.MethodsPreschoolers’ PA and sedentary behavior (SB) were assessed consecutively for 7 days using ActiGraph accelerometers based on the cutoff counts developed by Pate et al. Information about children, their parents, and their families was collected using questionnaires. We developed a two-step approach to clustering PA both inside and outside of kindergarten. The Euclidean distance metric was utilized to distinguish between groups, while the Schwarz–Bayesian criterion was applied to identify the most optimal sub-group model. A one-way ANOVA was used to assess the clustering effect, and logistic regression was used to analyze the influencing factors of different clustering.ResultsWe collected data from 291 preschool children aged 3 to 6 years and divided them into three clusters—Inactive (50.2%), Active in kindergarten (26.8%), and Active outside kindergarten (23.0%)—with significant differences in PA and SB, revealing distinct temporal and spatial clustering patterns (silhouette coefficient = 0.3, p &amp;lt; 0.05). Furthermore, preschooler activity levels correlated significantly with factors including gender (OR = 0.35, 95% CI: 0.19–0.66), age (OR = 1.05, 95% CI: 1.00–1.10), birth weight (OR = 1.79, 95% CI: 1.16–2.76), paternal age (OR = 1.01, 95% CI: 1.00–1.02), and maternal income (OR = 0.68, 95% CI: 0.48–0.96).ConclusionThis study shows that the family environment or parents significantly influence the PA of preschool children. Older fathers may promote preschool children’s PA through greater educational focus and financial stability, while higher maternal income can provide more opportunities and resources for preschool children to engage in active lifestyles. Thus, it is suggested that families providing more attention and exercise opportunities for preschool children’s education can help improve their PA levels in the future.

The Effect of Heart Failure Symptom Clusters on Quality of Life: The Moderating Effect of Self-Care Behaviours.

Although heart failure (HF) symptoms affect patients' quality of life (QoL), improving patients' QoL requires certain self-care behaviours. However, the specific role of self-care behaviours in the relationship between HF symptoms and QoL has not been clarified. To evaluate the status of symptom clusters, self-care behaviours and QoL in HF patients, and to analyse and test the moderating effect of self-care behaviours between symptom clusters and QoL. This study is a cross-sectional study. A total of 320 HF patients who treated in thethree hospitals in Chengdu, China, from December 2022 to July 2023 were selected as the research subjects. The patients were evaluated using The General Information Questionnaire, Memorial Symptom Assessment Scale Heart Failure, Self-Care of Heart Failure Index and Minnesota Living with Heart Failure Questionnaire. The statistical analysis methods were exploratory factor analysis, Pearson correlation analysis, hierarchical regression analysis and simple slope analysis. There were five symptom clusters in HF patients: emotional symptom cluster (sadness, anxiety, irritability, feeling nervous), digestive symptom cluster (lack of appetite, dry mouth, weight loss, nausea, abdominal distension), ischemic symptom cluster (dizziness, chest pain, palpitations, fatigue), dyspnoea symptom cluster (difficulty breathing when lying flat, waking up breathless at night, sleep difficulty) and congestion symptom cluster (cough, shortness of breath, oedema). There was a significant correlation between HF symptom group, self-care behaviours and QoL (p < 0.05). Both self-care maintenance (β = -0.262, p < 0.001) and self-care management (β = -0.258, p < 0.001) had a moderating effect between symptom clusters and QoL. There are a variety of symptom clusters in HF patients. Improving the self-care behaviours ability of HF patients is conducive to reducing the impact of HF symptom clusters on QoL. The study used the STROBE checklist for reporting. Medical staff should focus on the impact of HF symptom clusters and self-care behaviours on QoL, and formulate corresponding interventions for HF symptom clusters and self-care behaviours to improve the QoL of patients. The head nurse of the cardiovascular department actively assisted us in collecting questionnaires from HF patients, and all HF patients surveyed participated in this study seriously.

Clinical study on the effects of cluster needling at scalp points with needle retaining combined with the training of the upper-limb intelligent rehabilitation robot on shoulder function in stroke patients during convalescence

To explore the effects of cluster needling at scalp points with needle retaining combined with the training of the upper-limb intelligent rehabilitation robot on shoulder function in stroke patients during convalescence. Ninety stroke patients during convalescence were collected and randomized into scalp point group, robot training group and combined intervention group, with 30 cases each. In the scalp point group, the cluster needling was delivered at scalp points with the needles retained. In the robot training group, the patients were trained with the upper-limb intelligent rehabilitation robot. In the combined intervention group, the patients received both the cluster needling and the training of the upper-limb intelligent rehabilitation robot. In each group, the treatment was given once daily, 5 treatments a week with 2 days rest, for consecutive 4 weeks. Separately, before and after treatment, the score of Fugl-Meyer assessment upper extremity scale (FMA-UE) and the score of activity of daily living (ADL) were evaluated. Using the in-built assessment system of the upper-limb intelligent rehabilitation robot, the range of motion (ROM) of forward flexion, horizontal abduction and horizontal adduction of the affected shoulder joint were evaluated. With surface electromyogram (sEMG), the sEMG value of the deltoid muscle and the pectoralis major on the affected side were detected. Compared with the values before treatment, ADL score and FMA-UA score increased in patients of the three groups (P<0.05), and ROM of forward flexion, horizontal abduction and horizontal adduction of the affected shoulder joint was larger (P<0.05), the sEMG value of the fasciculi pectoralis major anterior of patients in the combined intervention group was reduced (P<0.05), and the sEMG of anterior deltoid tract was elevated in the three groups (P<0.05). When compared with the scalp point group and the robot group, in the combined intervention group, after treatment, ADL score and FMA-UA score were higher (P<0.05), ROM of forward flexion, horizontal abduction and horizontal adduction of the affected shoulder joint elevated (P<0.05) and sEMG value of the fasciculi pectoralis major anterior reduced (P<0.05), while which of the anterior deltoid tract was elevated (P<0.05). The cluster needling at scalp points with needle retaining, combined with the training of the upper-limb intelligent rehabilitation robot, improves the upper limb motor function and the range of motion of the shoulder joint in stroke patients during convalescence.

The carbon emission reduction benefits of the transformation of the intensive use of cultivated land in China

The transformation of the intensive use of cultivated land has an important impact on agricultural carbon emissions (ACE). However, the existing research on the impact mechanisms of the transformation of intensive use of cultivated land on carbon emissions is not clear. In view of this, this study constructs the impact mechanism of the transformation of intensive use of cultivated land on carbon emissions from the perspective of production factors substitution based on the theory of induced technological change and the theory of marginal returns. Using the Cobb-Douglas production function, this study identifies the transformation patterns of intensive use of cultivated land in China from 1980 to 2021. Furthermore, this study simulates the carbon emission reduction benefits brought about by the transformation of the intensive use of cultivated land by considering the cost of agricultural production. The results show that, first, from 1980 to 2021, the price of agricultural labor increased by 224.10%, while the price of other agricultural production factors significantly decreased. China’s intensive use of cultivated land methods has undergone four stages: agricultural labor–intensive (ALI), yield–enhancing factor–intensive (YEFI), labor–saving factor–intensive (LSFI), and innovation factor–intensive (IFI). In 2021, most of China’s provinces were in the YEFI or ALI stages. Second, between 1993 and 2021, the transformation of China’s intensive use of cultivated land brought about an 87.48% increase in carbon emissions, while the spatial clustering effect of carbon emissions weakened. Furthermore, the results show that, the transition of the use of cultivated land from ALI to YEFI and YEFI to LSFI will reduce carbon emissions by 7.89%, and input factors costs will increase by 23.85%. If all regions, except for those in the IFI stage, transition to the LSFI model, it will result in a 17.56% reduction in carbon emissions, with a corresponding increase of 33.40% of the input factors input costs. This research enriches the theoretical foundation of literature about the transformation of the intensive use of cultivated land. It also expands the research perspective on agricultural carbon emissions. This research also helps provide references for the direction of the transformation of the intensive use of cultivated land in China and the achievement of the dual carbon goals.

Application of Hard and Soft Acid-base Theory to Construct Heterometallic Materials with Metal-oxo Clusters.

Heterometallic cluster-based materials offer the potential to incorporate multiple functionalities, leveraging the aggregation effects of clusters and translating this heterogeneity and complexity into unexpected properties that are more than just the sum of their components. However, the rational construction of heterometallic cluster-based materials remains challenging due to the complexity of metal cation coordination and structural unpredictability. This minireview provides insights into a general synthetic strategy based on Hard and Soft Acids and Bases (HSAB) theory, summarizing its advantages in the designed synthesis of discrete heterometallic clusters (intracluster assembly) and infinite heterometallic cluster-based materials (intercluster assembly). Furthermore, it emphasizes the potential to exploit the intrinsic properties of mixed components to achieve breakthroughs across a broad range of applications. The insights from this review are expected to drive the progress of heterometallic cluster-based materials in a controllable and predictable manner.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Scenario-Based Analysis of Electrification Effects on Value Creation and Employment Structures for the Automotive Industry in the Federal State of Baden-Wuerttemberg, Germany

The transformation path to electric mobility will have fundamental impacts on the existing value chain and employment structures in the automotive industry. The purpose of this paper is to derive and examine these effects based on two different electric mobility market scenarios for the European (EU27) passenger car as well as truck market 2040 with special focus on the highly export-oriented industrial automotive cluster in Baden-Wuerttemberg. To achieve this, both a moderate and a progressive market scenario were simulated using the scientifically validated DLR VECTOR21 vehicle technology scenario model, based on two different parameter sets derived from actual and possible framework conditions on the European car and truck markets. Based on a detailed analysis of the industrial branch, value creation, and employee structure in Baden-Wuerttemberg and its automotive cluster, the effects resulting from the transformation to electric mobility will be displayed. With detailed Fade-In and Fade-Out analysis, the shifts from internal combustion engine components to electrified components will be derived and illustrated for each employee segment in the automotive cluster in Baden-Wuerttemberg, which leads to completely new and original results at this level of detail. Furthermore, the detailed display of the automotive cluster in this study allows for regionalized statements on employment effects, considering, for the first time, not only the car but also the truck segment. The analysis shows that battery electric vehicles will achieve a share of 34% or 57% for new registrations on the German car market in 2030, depending on the scenario framework conditions. The resulting employment effects for the entire automotive cluster in Baden-Wuerttemberg reach −37,000 (−8%) or −66,000 (−14%) by 2030 with further negative development until 2040 (−155,000, −30%) for the respective scenarios. Employment segments, in particular powertrain-dependent production employees, are at risk, with a potential decline of up to −60%. R&amp;D employees could be also be significantly, affected with a reduction in workforce of about −50%.

Urban Heat Island Differentiation and Influencing Factors: A Local Climate Zone Perspective

With the acceleration of urbanization, the urban heat island (UHI) effect has become a major environmental challenge, severely affecting the quality of life of residents and the ecological environment. Quantitative analysis of the factors influencing urban heat island intensity (UHII) is crucial for precise urban planning. Although extensive research has investigated the causes of UHI effects and their spatial variability, most studies focus on macro-scale analyses, overlooking the spatial heterogeneity of thermal characteristics within local climate zones (LCZs) under rapid urbanization. To address this gap, this study took the central urban area of Chengdu, constructing a LCZ map using multisource remote sensing data. Moran’s Index was employed to analyze the spatial clustering effects of UHI across different LCZs. By constructing Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models, the study further explored the influencing factors within these climate zones. The results showed that: (1) Chengdu’s built and natural environments had comparable proportions, with the scattered building zone comprising the highest proportion at 22.12% in the built environment, and the low vegetation zone accounting for 21.8% in the natural environment. The UHII values in this study ranged from 10.2 °C to −1.58 °C, based on specific measurement conditions. Since UHII varied with meteorological conditions, time, seasons, and the selection of rural reference points, these values represented dynamic results during the study period and were not constant. (2) Chengdu’s urban spatial morphology and UHII exhibited significant spatial heterogeneity, with a global Moran’s I index of 0.734, indicating a high degree of spatial correlation. The highest local Moran’s I value was found in the proportion of impervious surfaces (0.776), while the lowest is in the floor area ratio (0.176). (3) The GWR model demonstrated greater explanatory power compared to the OLS model, with a fit of 0.827. The impact of spatial morphological factors on UHII varied significantly across different environments, with the most substantial difference observed in the sky view factor, which has a standard deviation of 13.639. The findings provide precise recommendations for ecological spatial planning, aiming to mitigate the UHI effect and enhance the quality of life for urban residents.

Comparative Study of Cox Proportional Hazard and Accelerated Failure Time Models on Survival of Diabetes Mellitus and Hypertension Patients

Comparative analysis of survival models for hypertension and diabetics carried out in this study. The study compared the performance of Cox Proportional Hazard (CPH) and Accelerated Failure Time (AFT) models with and without random effects in analyzing hypertension and diabetes survival data obtained from General Hospital Keffi and General Hospital Nasarawa for the period of five years (2018-2022). Models evaluated include CPH, AFT (Exponential, Weibull, Log-Logistic) and their corresponding random effect counterparts. The Weibull AFT random effect model outperformed all other models in terms of goodness-of-fit, predictive accuracy and interpretability. This model accounted for individual heterogeneity and clustering effects, providing a more nuanced understanding of risk factors’ impact on survival rates. The Weibull AFT random effects model is the most suitable choice for analyzing hypertension and diabetes survival data, offering improved predictive power and insight into the complex relationships between risk factors and time-to-event outcomes. These findings have significant implications for healthcare professionals, policymakers, and researchers seeking to optimize survival analysis methodologies.

Experimental investigation of nickel-based nanofluid on the performance of evacuated tube solar collector

This paper conducts an experimental investigation of an evacuated tube solar collector type solar collector employing a unique Ni/water nanofluid to assess its exergetic and energy performance. The investigation sheds light on the intricate behavior of nanofluid and their consequential impact on the efficiency, thermal properties, and entropy generation within the framework of evacuated tube solar collector (ETSC). The study reveals that an escalation of nickel nanoparticles volumetric concentration results in a reduction of specific heat capacity, attributed in part to the clustering effect of nanoparticles mixed with working fluid. Nevertheless, higher temperatures counterintuitively lead to an augmentation in specific heat capacity. The study presents the comparative behavior of nanofluid and the parent base fluid. The study further elucidates that augmenting the volumetric concentration of nanoparticles causes a reduction in the temperature difference between the collector's both side inlet and outlet, affecting thermal efficiency positively. Moreover, with mass flow rate contribute and increased volumetric concentration to a diminished outlet temperature, thereby enhancing the efficiency of ETSC. The study also analyzed the thermophysical properties such as thermal conductivity, viscosity, and specific heat of the Ni/water nanofluid across different volumetric concentrations and temperature ranges from 25 °C to 50 °C. Furthermore, the thermal performance of the collector was evaluated at various mass flow rates ranging from 0.0085 kg/s to 0.051 kg/s. The findings revealed a maximum energy efficiency of 73.14% at a volumetric concentration of 1% and a mass flow rate of 0.041 kg/s.

A Method for Predicting the Impact of Labor Education at Institutions of Higher Education Based on a Two-Stage Clustering Algorithm

In order to develop more accurate teaching plans and methods based on students’ characteristics and needs, and improve the effectiveness of labor education, a two-stage clustering algorithm based method for predicting the effectiveness of labor education in universities is studied. A two-stage clustering algorithm-based method for predicting the effectiveness of labor education in universities has been proposed. Through in-depth analysis of labor education data in universities, principal component analysis (PCA) was used to select 16 most representative variables from the original features. Subsequently, self-organizing map (SOM) neural network was used for preliminary clustering, and the optimal number of clusters was determined to be 8. Furthermore, the K-means clustering algorithm performs fine clustering based on the initial centers provided by SOM, significantly improving the stability and accuracy of clustering. At the same time, processing the outliers in the clustering results has reduced their impact on the clustering effect. Finally, the processed data was input into an improved XGBoost prediction model, which achieved a true positive rate (TPR) of over 85% on the ROC curve when predicting the effectiveness of labor education in universities. While maintaining a low false positive rate (FPR), the model outperformed other comparison methods significantly. This achievement not only validates the effectiveness of the proposed method in feature selection, clustering optimization, and prediction model construction, but also confirms its ability to accurately predict the labor education effectiveness of students in different grades, providing strong support for higher education institutions to optimize teaching plans and improve educational outcomes.

Context matters: The importance of investigating random effects in hierarchical models for early childhood education researchers

This paper highlights the importance of examining individual, classroom, and school-level variables simultaneously in early childhood education research. While it is well known that Hierarchical Linear Modeling (HLM) in school-based studies can be used to account for the clustering of students within classrooms or schools, less known is that HLM can use random effects to investigate how higher-level factors (e.g., effects that vary by school) moderate associations between lower-level factors. This possible moderation can be detected even if higher-level data are not collected. Despite this important use of HLM, a clear resource explaining how to test this type of effect is not available for early childhood researchers. This paper demonstrates this use of HLM by presenting three analytic examples using empirical early childhood education data. First, we review school-level effects literature and HLM concepts to provide the rationale for testing cross-level moderation effects in education research; next we do a short review of literature on the variables that will be used in our three examples (viz., teacher beliefs and student socioemotional behavior); next we describe the dataset that will be analyzed; and finally we guide the reader step-by-step through analyses that show the presence and absence of fixed effects of teacher beliefs on student social outcomes and the erroneous conclusions that can occur if school-level moderation (i.e., random effects) tests are excluded from analyses. This paper provides evidence for the importance of testing for how teachers and students impact each other as a function of school differences, shows how this can be accomplished, and highlights the need to examine random effects of clustering in educational models to ensure the full context is accounted for when predicting student outcomes.

Prediction of site-specific pharmacologic therapy among newborns with neonatal opioid withdrawal syndrome.

To predict pharmacotherapy for NOWS based on factors available shortly after birth. A multi-center, retrospective study of 1377 opioid exposed newborns between 2016 and 2017 dichotomized based on pharmacologic treatment (N = 665 treated, N = 712 not treated) was conducted. A multilevel mixed-effect logistic regression model that considered cluster effect from sites determined significant maternal and newborn factors associated with pharmacotherapy, which were combined in a nomogram to predict probability of treatment for infants at each participating site. Factors predictive of treatment were: no breastmilk (1.76, 1.30-2.39; p < 0.001), male sex (1.39, 1.05-1.83; p = 0.021), polysubstance exposure (1.53, 1.15-2.06; p = 0.004), inadequate prenatal care (1.51, 1.07-2.13; p < 0.018) and Methadone (6.08, 4.03-9.27; p < 0.001) or Buprenorphine (1.86, 1.29-2.69; p < 0.001). A site-specific nomogram provided the probability of treatment for individual newborns. Six factors available shortly after birth can be combined to predict site-specific use of medication for NOWS. If confirmed, the nomogram may identify at-risk newborns.

Kinetics of EBV antibody-based NPC risk scores in Taiwan NPC multiplex families.

Nasopharyngeal carcinoma (NPC) risk prediction models based on Epstein-Barr virus (EBV)-antibody testing have shown potential for screening of NPC; however, the long-term stability is unclear. Here, we investigated the kinetics of two EBV-antibody NPC risk scores within the Taiwan NPC Multiplex Family Study. Among 545 participants with multiple blood samples, we evaluated the stability of a 2-marker enzyme-linked immunosorbent assay score and 13-marker multiplex serology score using the intra-class correlation coefficient (ICC) by fitting a linear mixed model that accounted for the clustering effect of multiple measurements per subject and age. We also estimated the clustering of positive tests using Fleiss's kappa statistic. Over an average 20-year follow-up, the 2-marker score showed high stability over time, whereas the 13-marker score was more variable (p < .05). Case-control status is associated with the kinetics of the antibody response, with higher ICCs among cases. Positive tests were more likely to cluster within the same individual for the 2-marker score than the 13-marker score (p < .05). The 2-marker score had an increase in specificity from ~90% for single measurement to ~96% with repeat testing. The 13-marker score had a specificity of ~73% for a single measurement that increased to ~92% with repeat testing. Among individuals who developed NPC, none experienced score reversion. Our findings suggest that repeated testing could improve the specificity of NPC screening in high-risk NPC multiplex families. Further studies are required to determine the impact on sensitivity, establish optimal screening intervals, and generalize these findings to general population settings in high-risk regions.

Uncertain data density peak clustering algorithm based on JS divergence

Aiming at the defects of traditional density-based uncertainty clustering algorithms, such as parameter sensitivity and poor clustering results for complex manifold uncertain data sets, a new uncertainty data density peak clustering algorithm based on JS divergence (UDPC-JS) is proposed. The algorithm first removes noise points by using the uncertain natural neighbor density factor defined by the uncertain natural neighbor. Secondly, the local density of uncertain data objects is calculated by combining the uncertain natural neighbor and JS divergence. The initial clustering center of the uncertain data set is found by combining the idea of representative points, and the distance between the initial clustering centers is defined based on JS divergence and graph. Then, the decision graph is constructed on the initial clustering center using the local density calculated based on the uncertain natural neighbor and JS divergence and the newly defined distance between the initial clustering centers based on JS divergence and graph, and the final clustering center is selected according to the decision graph. Finally, the unassigned uncertain data objects are assigned to the cluster where their initial clustering center is located. Experimental results show that the algorithm has better clustering effect and accuracy than the comparison algorithm, and has a greater advantage in processing complex manifold uncertain data sets.

Identifying risk factors for recurrent multidrug resistant tuberculosis based on patient’s record data from 2016 to 2021: retrospective study

Globally, the prevalence of multidrug-resistant tuberculosis (MDR-TB) has been increasing recently. This is a major public health concern, as MDR-TB is more difficult to treat and has poorer outcomes compared to drug-sensitive tuberculosis. The main objective of the study was to identify risk factors for recurrent multidrug-resistant tuberculosis, at Alert Specialized Hospital, Addis Ababa, by using different parametric shared frailty models. From January 2016 to December 2021, a retrospective study was conducted on MDR-TB patients at Alert Specialized Hospital in Addis Ababa. The data for the study were collected from the medical records of MDR-TB patients at the hospital during this time period. Gamma and inverse-Gaussian shared frailty models were used to analyze the dataset, with the exponential, Weibull, and lognormal distributions included as baseline hazard functions. The data were analyzed using R statistical software. The median recurrence time of the patients was 12 months, and 149 (34.3%) had recurrences. The clustering effect was statistically significant for multiple drug-resistant tuberculosis patients’ recurrence. According to the Weibull-Inverse-Gaussian model, factors that reduced time to MDR-TB recurrence included lower weight (ɸ = 0.944), smoking (ɸ = 0.045), alcohol use (ɸ = 0.631), hemoptysis (ɸ = 0.041), pneumonia (ɸ = 0.564), previous anti-TB treatment (ɸ = 0.106), rural residence (ɸ = 0.163), and chronic diseases like diabetes (ɸ = 0.442) were associated with faster recurrence. While, higher education (ɸ = 3.525) and age (ɸ = 1.021) extended time to recurrence. For weight increment, smokers and alcohol users, clinical complications of hemoptysis and pneumonia, patients with pulmonary disease who had a history of previous anti-TB treatment, and being rural residents are prognostic factors. There was a significant clustering effect at the Alert Specialized Hospital in Addis Ababa, Ethiopia. The Weibull-Inverse Gaussian Shared Frailty Model was chosen as the best model for predicting the time to recurrence of MDR-TB.

Electronic and photocatalytic properties of Cu4 cluster-modified Ag/g-C3N4 single-atom catalysts: A hybrid DFT study

Increasing the active sites on the catalyst surface has been a key goal to increase the catalyst efficiency. This paper presents an efficient conversion of solar energy into chemical energy, realized by a Cu4 cluster-assisted single-atom catalyst (Ag-doped g-C3N4). The catalyst enhances charge transfer and light absorption, and the unique advantages of this approach in the field of photocatalysis are substantiated by density functional theory. Under the synergistic effect of Cu4 clusters, the electronic state density distribution of the Ag single-atom catalyst substrate is drastically altered, which realizes the increase of substrate active sites and rapid charge separation and transfer. Compared with the pristine substrate g-C3N4, the new electronic state distribution and the generation of impurity energy levels lead to the narrowing of the band gap from 2.77 to 1.83 eV, accelerating the transfer and separation of electrons and holes, and enlarging the visible light absorption range from 410 to 760 nm, thus improving the solar energy utilization. Despite the reduced band gap, the conduction and valance band edges of (Ag, Cu4) codoped g-C3N4 still have sufficient potential to split water. Therefore, to improve the photocatalytic performance under visible light, simultaneous codoping of the Ag single atom and Cu4 cluster on g-C3N4 is an effective strategy. The study provides theoretical support for further optimization of single-atom catalysts.