Cluster Groups Research Articles

Sustainable transformation of energy sector: Cluster analysis for the sustainable development strategies of selected European countries

Implementing measures by the European countries within the framework of the ratification of the Paris Climate Agreement should ensure an enlarged share in energy production from alternative energy sources and the introduction of new alternative energy capacities, which would provide an annual decrease in carbon dioxide emissions growth rate, reduce anthropogenic pressure on the natural environment and the use of non-renewable natural resources while preserving biodiversity. This paper aims at forming a system of indicators of sustainable transformation of the energy sector and studying their influence on the policy of environmental-oriented development. The paper applies Ward's method for determining the optimal cluster number and discriminant analysis for checking which variables significantly influence the formation of countries' clusters. The six clusters of countries obtained describe the different features of decarbonisation and climate change mitigation policies. Using discriminant analysis helped to single out the most relevant indicators of the energy sector's transformation in the direction of its decarbonisation and climate change mitigation. The most significant indicators (energy efficiency, electricity production capacities for renewables and wastes, and share of energy from renewable sources) largely determine the pace and features of forming sustainable development strategies for the energy sector. By highlighting the relevant cluster groups in the study, their characteristic features, and ways of transformational processes in the energy sector were discovered. This enabled the authors to form specific directions for strengthening and activating the processes of decarbonisation of the energy sector and climate change mitigation. There outcomes can become the basis for further research and the formation of appropriate strategies for sustainable development and transformation of the energy sector. The results stemming from this study could help scholars and other affiliated persons determine future strategies for research for implementing Sustainable Development Goals 2030.

The Best Tourism Recommendation Intelligent System Model: Weighted Product and K-Means Methods

Magelang Regency, with a wealth of tourist destinations with a variety of things, such as Borobudur Temple and Nepal Van Java, has extraordinary tourism potential. With this diversity, it is a challenge for tourists to determine the best tourist objects that suit their preferences. This study aims to develop an intelligent system for recommending the selection of the best tourist attractions in Magelang Regency by integrating the Weighted Product and K-Means Clustering Methods. The system is designed to provide accurate recommendations based on tourist criteria such as location, facilities, tickets, and security, as well as group attractions based on their level of potential. The Weighted Product method is used to determine the best tourist attractions, while K-Means Clustering groups tourist destinations into high, medium, and low potential categories. In this study, several stages were carried out: literature study, data collection, system design, data analysis, implementation, and system testing to produce an effective and efficient recommendation system for tourists in Magelang Regency. The results of this research obtained the best tourism, namely at Borobudur Temple.

A Magnesium Organic Framework Fluorescent Sensor for Selective Detection of Nitrofuran Antibiotics and Inorganic Pollutants

ABSTRACTA novel 3D magnesium‐based metal organic framework (Mg‐MOF) has been solvothermally synthesized using 5‐(pyridin‐4‐yl)isophthalic acid. The ligand has demonstrated to preserve two types of coordination modes depending on the connection mode of the pyridine group. The uncoordinated pyridine groups provide Lewis base sites (LBSs), whereas the coordinated ones promote to form a rarely reported eight‐connected tetranuclear Mg clusters. Benefiting from the multinuclearity with high number of connections, the framework keeps good performance of high thermal stability until 500°C. Furthermore, the framework exhibits potential open metal sites (OMSs) benefiting from the terminal coordinated water groups of the metal clusters. Owing to the abundant active sites, the Mg‐MOF exhibits good sensitivity to nitrofuran antibiotics and Cr2O72−, with high KSV (at 104 M−1 level) and low limit of detection (LOD) values (∼10−6 M). Density functional theory (DFT) investigation reveals that the interchange of the HOMO–LUMO energy levels of the framework and antibiotics is responsible for the sensing activity. In addition, the Mg‐MOF is greatly recyclable in five cycles while maintaining the structural rigidity and sensing activity, making it a promising luminescence probe material.

Implementasi Algoritma K-Means Guna Pengelompokkan Data Penjualan Berdasarkan Pembelian

Information technology can help solve problems faced by humans by facilitating performance. Information technology and information systems are difficult to separate in the business world. Data mining is the core of the KDD process, which involves inferring algorithms that explore data, developing models and finding previously unknown patterns. KDD is often referred to as knowledge discovery in databases. The KDD process generally consists of 5 stages, namely data selection, pre-processing/cleaning, transformation, data mining and interpretation/evaluation. K-Means is a clustering algorithm in data mining to be able to produce groups of large amounts of data with a point-based partition method with fast and efficient computing time. Clustering is the process of dividing objects from a data set into several homogeneous clusters. The main purpose of the cluster method is to group a number of data/objects into clusters (groups) so that each cluster will contain data that is as similar as possible. This study aims to provide real solutions to UD. Martua in order to know which items are selling well and which items are not selling well so that the object can know which items need to be added to the stock and which items need to be reduced. The method used in this study is the K-Means method with stages, namely data selection, pre-processing, data transformation, information extraction and evaluation of results. The data consists of 30 item data, there are 8 as members of C1 and are best-selling items and 22 as members of C2 and are not selling items. The conclusion that can be obtained from this study is that the K-Means method can group items at UD. Martua. This study shows that the implementation of the K-Means method with the support of the RapidMiner application is effective in grouping item data at UD. Martua.

Improving embedding-based link prediction performance using clustering

Incomplete knowledge graphs are common problem that can impair task accuracy. As knowledge graphs grow extensively, the probability of incompleteness increases. Link prediction addresses this problem, but accurate and efficient link prediction methods are needed to handle incomplete and extensive knowledge graphs. This study proposed modifications to the embedding-based link prediction using clustering to improve performance. The proposed method involves four main processes: embedding, clustering, determining clusters, and scoring. Embedding converts entities and relations into vectors while clustering groups these vectors. Selected clusters are determined based on the shortest distance between the centroid and the incomplete knowledge graph. Scoring measures relation rankings, and link prediction result is selected based on highest scores. The link prediction performance is evaluated using Hits@1, Mean Rank, Mean Reciprocal Rank and prediction time on three knowledge graph datasets: WN11, WN18RR, and FB13. The link prediction methods used are TransE and ComplEx, with BIRCH as the clustering technique and Mahalanobis for short-distance measurement. The proposed method significantly improves link prediction performance, achieving accuracy up to 98% and reducing prediction time by 99%. This study provides effective and efficient solution for improving link prediction, demonstrating high accuracy and efficiency in handling incomplete and extensive knowledge graphs.

Distribution characteristics of microplastics in the surface mixed layer of the western Indian Ocean

The abundance, characteristics, and distribution of microplastics in almost all aquatic environments have been well documented. However, the distribution of microplastics in open oceans, especially in the western Indian Ocean, is rarely studied. Hence, for the first time, we determined the meridional distribution characteristics of microplastics in the surface mixed layer of the western Indian Ocean during the boreal summer monsoon in 2017. We used the multiple opening and closing nets and environmental sampling system to collect microplastics from 11 stations between 5°N and 16°S along 67°E in the western Indian Ocean; the surveyed stations with similar temperature and salinity profiles were clustered into four groups. Microplastics were detected at all sampling stations. The mean abundance of microplastics in the study area was 0.23 ± 0.04 particles/m3, and black-colored polyester fibers 1.0–5.0 mm in size were the most abundant type of microplastics among the samples collected. However, the mean abundance of microplastics differed among the clustered groups due to the influence of different ocean currents associated with each group, indicating meridional heterogeneity in the distribution of microplastics in the surface mixed layer of the western Indian Ocean. These findings provide reliable information for a better understanding of the characteristics of microplastics in the surface waters of the western Indian Ocean.

Financial resilience, growth and risk sharing in the EU

We build a financial resilience index capturing the structure and stability features of financial systems and benchmark EU financial systems against their ability to enhance stable growth and international risk sharing. The index comprises financial openness, market orientation, equity deepening, maturity structure, and institutional soundness. Our results show that: (i) EU financial systems are highly heterogeneous and converge to a clustered pattern; (ii) the index is highly significant in growth regressions, suggesting that financial structure and institutional soundness are key to enduring growth; (iii) the heterogeneity of EU financial systems has implications for the vulnerability to domestic output shocks: the risk-sharing mechanism is more effective in the market-based and institutionally sound economies that group in the top financial clusters, whereas unsmoothed consumption is higher in economies belonging to the low-resilience clusters, especially in the aftermath of the global financial crisis, when the credit channel is significantly downsized.

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.

Sex-specific dietary patterns and their association with metabolic syndrome: Insights from a cross-sectional analysis

AimsThis study aims to identify a posteriori dietary patterns with a sex approach and to evaluate their association with metabolic syndrome criteria. MethodsCross-sectional study conducted in 6821 men and women between 55 and 75 years of age. Forty-two food groups were analyzed from dietary information collected with food frequency questionnaires, using principal component analysis and cluster analysis and then information from both statistical methods was compared. Prevalences were calculated foreach cluster group, based on the number and types of metabolic syndrome criteria they met. ResultsFollowing principal component analysis, two dietary patterns labeled “healthy” and “unhealthy” were identified in both men and women, due to the presence of foods that are considered more or less healthy. These same dietary patterns were found in cluster analysis plus an “intermediate” cluster consisting of both healthy and unhealthy foods. The presence of metabolic syndrome is related to the “healthy” dietary pattern in women and to the “unhealthy” dietary pattern in men. Comparison of the two statistical approaches showed a high level of correlation between them (weighted Kappa = 0.703 in women and weighted Kappa = 0.691 in men). ConclusionsAdherence to both healthy and unhealthy dietary pattern appears to be related to the development of MS. The differences found by sex make it necessary to develop interventions with a sex-specific approach.

Metacognitive Abilities and Socio-Psychological Adaptation of People of Mature Age: Features of Relationships

The analysis is aimed at determining characteristics of relationships between metacognitive abilities and socio-psychological adaptation in homogeneous cluster groups represented by people of mature age. The main research methods were: cluster analysis, which allows us to identify relatively homogeneous groups; “bootstrap” method to check the normality of the distribution. The study also used factor analysis to determine the relationship between the characteristics of metacognitive abilities and socio-psychological adaptation. The total following variance in the factor models (86.24%, 99.77%, and 100%) as well as the levels of predictive consistency (p≤0.01 to p≤0.05) reflect the reliability and significance of the results obtained. Data processing and interpretation were carried out using qualitative and quantitative statistical methods: Fisher’s angular transformation criterion (φ*) for comparing two samples according to the occurrence frequency of the effect which is interesting to the researcher; Kruskal-Wallis rank sum test (IBM SPSS Statistics).The results of the study allow us to talk about ambivalence in the manifestation of metacognitive abilities formation in people of mature age: on the one hand, a general tendency towards a decrease in the development of metacognitive abilities was discovered, on the other hand, an increase in the degree of structural organization of the entire metacognitive system was revealed in the process of growing up, which generally compensates for the natural decline of potential resource capabilities of adults. This ambiguity in the manifestation of metacognitive abilities is reflected in their relationship with indicators of socio-psychological adaptation of adults. The identified features of the relationship in terms of harmony/inconsistency, integrity, and the content of crystallizing factors generally indicate a multidimensional structure of the relationship between metacognitive abilities and socio-psychological adaptation, when only in some cases can we talk about a direct correlation of these variables: a high level of development of metacognitive abilities determines high social-psychological adaptation.

Perceptions Matter! Active Physical Recreation Participation of Children with High and Low Actual and Perceived Physical Competence.

Emerging evidence suggests that the accuracy of a child's perceived physical competence (PPC) impacts participation in physical activity. We examined differences in active physical recreation (APR) participation based on clusters of high and low motor competence and perceptions from grades 3-5. Participants were a longitudinal sample (n = 155; 85 girls) of grade three, four, and five children. Fundamental motor skills (FMSs) were assessed using the TGMD-2, perceptions were assessed using the Self-Perception Profile for Children, and APR was measured using the Children's Assessment of Participation and Enjoyment. K-means cluster analysis was used to create four clusters per grade based on participants' FMSs and PPC z-scores. Differences in FMSs, PPC, and APR with cluster group as a factor were examined using a series of factorial ANOVAs. In each grade, participants in the high-high cluster participated in significantly more APR than those in the low-low cluster (p = 0.002 in grades three and four; p < 0.001 in grade five). Among the less accurate clusters (e.g., low FMSs with high PPC) was a trend toward positive FMSs growth among the children with higher perceptions. Results show that combinations of FMSs and PPC influence patterns of engagement or disengagement in active physical recreation persistently across middle childhood.

Naturalistic driving analysis of situational, behavioral, and psychosocial determinants of speeding

The present analysis used full-trip naturalistic driving data along with driver behavioral and psychosocial surveys to understand the individual and contextual predictors of speeding. The data were collected over a three-week period from 44 drivers and contain 3,798 full trips, with drivers speeding 7.8 % of the time. Speeding events were identified as periods when participants traveled at a velocity greater than five mph over the speed limit for at least five seconds. Data were analyzed using the Comprehensive Driver Profile (CDP) framework which uses principal component analysis (dimensionality reduction), random forest (predictive modeling), k-means clustering (grouping and profiling), and bootstrapping (profile stability) to decompose the predictive variables and driver characteristics. The final dataset included 188 candidate independent variables from the CDP framework and one dependent variable (speeding). Nine variables emerged as significant predictors of speeding onset with an AUC of 0.88, including the percent of trip time spent idling and speeding, highway driving in low traffic conditions, and positive attitudes toward phone use. Percent of trip speeding was associated with a higher likelihood of speeding by up to 42 percent, and percent trip idling was associated with it by up to 30 percent. Driver profile clusters revealed four types: Traffic & Idling Speeders, Infrequent Speeders, Frequent Speeders, and Situational Speeders. The present analysis demonstrates the importance of situational factors and individual differences in motivating speeding behavior. Countermeasures targeting speeding may be more effective if they address the root causes of the behavior in addition to the behavior itself.

Are They Prepared? Comparing Intern Milestone Performance of Accelerated 3-Year and 4-Year Medical Graduates.

Accelerated 3-year programs (A3YPs) at medical schools were developed to address student debt and mitigate workforce shortage issues. This study investigated whether medical school length (3 vs 4 years) was associated with early residency performance. The primary research question was as follows: Are the Accreditation Council for Graduate Medical Education Milestones (MS) attained by A3YP graduates comparable to graduates of traditional 4-year programs (T4YPs) at 6 and 12 months into internship? The MS data from students entering U.S. medical schools in 2021 and 2022 from the 6 largest specialties were used: emergency medicine, family medicine, internal medicine, general surgery, psychiatry, and pediatrics. Three-year and 4-year graduates were matched for analysis (2,899 matched learners: 182 in A3YPs and 2,717 in T4YPs). The study used a noninferiority study design to examine data trends between the study cohort (A3YP) and control cohort (T4YP). To account for medical school and residency program effects, the authors used cross-classified random-effects regression to account for clustering and estimate group differences. The mean Harmonized MS ratings for the midyear and end-year reporting periods showed no significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = 0.01 [0.02], P = .77). Mean MS ratings across internal medicine MS for the midyear and end-year reporting periods showed no significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = -0.03 [0.03], P = .31). Similarly, for family medicine, there were no statistically significant differences between the A3YP and T4YP groups (mean [SE] cross-classified coefficient = 0.01 [0.02], P = .96). For the specialties studied, there were no significant differences in MS performance between 3-year and 4-year graduates at 6 and 12 months into internship. These results support comparable efficacy of A3YPs in preparing medical students for residency.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization. This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score. Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

The Hybridization of Polymers with Metal Oxide Clusters for the Design of Non-Fluorinated Proton Exchange Membranes.

As the key component of various energy storage and conversion devices, proton exchange membranes (PEMs) have been attracting significant interest. However, their further development is limited by the high cost of perfluorosulfonic acid polymers and the poor stability of acid-dopped non-fluorinated polymers. Recently, a new group of PEMs has been developed by hybridizing polyoxometalates (POMs), a group of super acidic sub-nanoscale metal oxide clusters, with polymers. POMs can serve simultaneously as both proton sponges and stabilizing agents, and their complexation with polymers can further improve polymers' mechanical performance and processability. Enormous efforts have been focused on studying supramolecular complexation or covalent grafting of POMs with various polymers to optimize PEMs in terms of cost, mechanical properties and stabilities. This concept summarizes recent advances in this emerging field and outlines the design strategies and application perspectives employed for using POM-polymer hybrid materials as PEMs.

Cognitive subtypes in youth at clinical high risk for psychosis.

Schizophrenia is a mental health condition that severely impacts well-being. Cognitive impairment is among its core features, often presenting well before the onset of overt psychosis, underscoring a critical need to study it in the psychosis proneness (clinical high risk; CHR) stage, to maximize the benefits of interventions and to improve clinical outcomes. However, given the heterogeneity of cognitive impairment in this population, a one-size-fits-all approach to therapeutic interventions would likely be insufficient. Thus, identifying cognitive subtypes in this population is crucial for tailored and successful therapeutic interventions. Here we identify, validate, and characterize cognitive subtypes in large CHR samples and delineate their baseline and longitudinal cognitive and functional trajectories. Using machine learning, we performed cluster analysis on cognitive measures in a large sample of CHR youth (n = 764), and demographically comparable controls (HC; n = 280) from the North American Prodrome Longitudinal Study (NAPLS) 2, and independently validated our findings with an equally large sample (NAPLS 3; n = 628 CHR, 84 HC). By utilizing several statistical approaches, we compared the clusters on cognition and functioning at baseline, and over 24 months of followup. We further delineate the conversion status within those clusters. Two main cognitive clusters were identified, "impaired" and "intact" across all cognitive domains in CHR compared to HC. Baseline differences between the cognitively intact cluster and HC were found in the verbal abilities and attention and working memory domains. Longitudinally, those in the cognitively impaired cluster group demonstrated an overall floor effect and did not deteriorate further over time. However, a "catch up" trajectory was observed in the attention and working memory domain. This group had higher instances of conversion overall, with these converters having significantly more non-affective psychotic disorder diagnosis versus bipolar disorder, than those with intact cognition. In the cognitively intact group, we observed differences in trajectory based on conversion status, where those who start with intact cognition and later convert demonstrate a sharp decline in attention and functioning. Functioning was significantly better in the cognitively intact than in the impaired group at baseline. Most of the cognitive trajectories demonstrate a positive relationship with functional ones. Our findings provide evidence for intact and impaired cognitive subtypes in youth at CHR, independent of conversion status. They further indicate that attention and working memory are important to distinguish between the CHR with intact cognition and controls. The cognitively intact CHR group becomes less attentive after conversion, while the cognitively impaired one demonstrates a catch up trajectory on both attention and working memory. Overall, early evaluation, covering several cognitive domains, is crucial for identifying trajectories of improvement and deterioration for the purpose of tailoring intervention for improving outcomes in individuals at CHR for psychosis.

Application of Strategic Management Through Military Leadership

The Indonesian National Armed Forces (TNI) unit has specific duties as mandated by Law No. 34 of 2004. Soldiers are expected to adhere to values derived from the nation's military doctrine, encapsulated in the Sapta Marga, the Soldier's Oath, and the 8 Obligations of the Indonesian National Armed Forces. Essential qualities for soldiers include loyalty to superiors, decisiveness, discipline, and responsibility towards their duties. The Indonesian National Armed Forces organizational structure is permanent and non-profit, which limits social interactions among soldiers, particularly those from different units or ranks. There are no specific guidelines for leadership styles at different rank levels, leading leaders to rely on the organizational culture established during military training. According to Wawan (2020), leadership in the military, underpinned by organizational culture values, motivates soldiers to improve their performance. Leaders play a critical role in applying organizational culture by making decisions based on Indonesian National Armed Forces doctrines. The responsibilities of officers within the Indonesian Army (TNI AD) evolve with their rank. Career development is determined by rank following the completion of general development education (Dikbangum), which involves rigorous selection processes evaluating various aspects supporting the required skills and competencies for higher ranks. Proportional stratified random sampling was used among officer students in Dikbangum for Secapa, Selapa, Sesko Indonesian National Armed Forces, and Seskoad. The study included 640 respondents: 111 from Secapa, 278 from Selapa, 190 from Seskoad, and 61 from Sesko Indonesian National Armed Forces. Research findings indicate that leadership positively affects soldier performance through organizational culture and work motivation within the Indonesian National Army. However, data from the overall sample revealed a negative impact of work motivation on soldier performance, both directly and through leadership roles incorporating work motivation elements. This negative effect is attributed to age, rank, job responsibility, and unit type differences among the research subjects in each cluster group. The study on officer students undergoing general development education in 2023 within the Indonesian National Army shows that leadership directly influences soldier performance, positively affects work motivation, and impacts organizational culture. Leadership also affects performance through work motivation and organizational culture. Leadership approaches at different rank levels have positively influenced soldiers' responses, enabling them to perform their duties effectively in accordance with directives from higher units, such as MABES Indonesian National Armed Forces.

An extended belief rule-based system with hybrid sampling strategy for imbalanced rule base

Imbalanced dataset is an important focus for classification. As the mainstream of addressing imbalanced dataset, data-level methods are trapped in facing difficultly-determined subjective parameters, and the inconsistency between new minority samples and original minority samples simultaneously. To address it, this paper develops an extended belief-rule-based (EBRB) system with hybrid sampling strategy, which is a white-box classifier. The hybrid sampling strategy is composed of an under-sampling process and an oversampling process, in which subjective parameters are not involved. The under-sampling is to identify and remove overlapping majority rules by iteratively determining an appropriate objective threshold for calculating the inconsistency degree of rule base, and to determine and remove redundant non-overlapping majority rules by using the density of non-overlapping rules in clustered groups. The oversampling is to design a differential evolution based iterative process to generate new minority rules in groups by minimizing the inconsistency of rule base. The distribution of original dataset is maintained extremely by balancing rules in clustered majority and minority groups, respectively. This EBRB system is used for the auxiliary diagnosis of thyroid nodules, and its superior performance is highlighted by the comparison with existing EBRB systems, representative data-level methods, and algorithm-level methods.

Predictive value of neck circumference for cardiometabolic risk in children

Objective: To investigate the predictive value of neck circumference on cardiometabolic risk in children. Methods: This was a cross-sectional study of natural sources. As the prediction cohort, clinical data were collected from 3 443 children aged 5-14 years who underwent physical examination in the Department of Child Healthcare, Children's Hospital of Nanjing Medical University from July 2021 to September 2022. As the validation cohort for external validation, clinical data were collected from 604 children aged 5-14 years who underwent physical examination in the Department of Child Healthcare, Children's Hospital of Nanjing Medical University from October 2022 to March 2023. Height, weight, neck circumference, waist circumference and body composition were measured in both groups, and body mass index, neck circumference to height ratio (NHtR), waist circumference to height ratio, body fat percentage and skeletal muscle percentage were calculated. Systolic blood pressure, diastolic blood pressure, fasting blood glucose, blood lipid and uric acid and other cardiovascular and metabolic risk indicators were collected in both groups. The prediction cohort was further stratified into clustered and non-clustered groups based on the clustering of cardiometabolic risk factors (CCRF). Various variables between these 2 groups were compared using the Mann-Whitney U test. Pearson correlation and binary Logistic regression were conducted to investigate the correlations between neck circumference and cardiovascular metabolic risk factors. The accuracy of NHtR in predicting the CCRF was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC). The cutoff value was determined using the Youden index. The validation cohort was then divided into groups above and below the cutoff value, and the detection rate of CCRF between the 2 groups was compared using the χ2 test for validation. Results: In the prediction cohort of 3 443 children (2 316 boys and 1 127 girls), 1 395 (40.5%) children were overweight or obese, and 1 157 (33.6%) children had CCRF. Pearson correlation analysis revealed all significant positive correlations (all P<0.01) between neck circumference and systolic blood pressure (r=0.47, 0.39), diastolic blood pressure (r=0.27, 0.21), uric acid (r=0.36, 0.30), and triglycerides (r=0.20, 0.20) after adjusting for age in both males and females. Among both males and females, neck circumference both showed significant negative correlation (both P<0.01) with high-density lipoprotein cholesterol (r=-0.27, -0.28), and no correlation with fasting glucose levels (r=0.03, -0.03, both P>0.05). After adjusting for gender, age, and body fat percentage, increased body mass index, neck circumference, or waist circumference increased the risks of hypertension (OR=1.23, 1.39, 1.07, all P<0.001), hyperuricemia (OR=1.16, 1.23, 1.05, all P<0.001), hypertriglyceridemia (OR=1.08, 1.16, 1.02, all P<0.01), low high-density lipoprotein cholesterol (OR=1.10, 1.27, 1.03, all P<0.01), and the CCRF (OR=1.51, 1.73, 1.15, all P<0.01). The areas under the ROC curves of NHtR in predicting CCRF was 0.73, with sensitivity and specificity at 0.66 and 0.71, respectively. The corresponding optimal cut-off value was 0.21. Validation with 604 children confirmed that the detection of CCRF in the NHtR≥0.21 group was 3.29 times (60.5% (112/185) vs. 18.7% (79/422),χ2=107.82, P<0.01) higher compared to the NHtR <0.21 group. Conclusions: Neck circumference is associated with cardiovascular metabolic risks such as hypertension, hyperlipidemia, hyperglycemia, and hyperuricemia in children. When the NHtR is ≥0.21, there is an increased likelihood of CCRF.

Effects of Cluster Nursing Strategy on Neurological and Motor Functions in Patients with Moderate to Severe Traumatic Brain Injury.

This study aimed to explore the effects of the cluster nursing strategy applied to traumatic brain injury (TBI) patients. Ninety-eight TBI patients admitted to the hospital were selected as the study subjects. They were randomized into two groups, the control group and the cluster group, with 49 cases in each group. The control group received routine nursing methods, while the cluster group received cluster nursing strategy. The intervention effects were compared between the two groups. After 3 months, the total occurrence of complications in the cluster group was significantly lower than that in the control group. Postintervention, the cluster group had a significantly lower National Institutes of Health Stroke Scale score and significantly higher Fugl-Meyer score and Loewenstein Occupational Therapy Cognitive Assessment score compared with the control group. The serum level of glial fibrillary acidic protein in the control group was significantly higher than that in the cluster group, while the serum level of brain-derived neurotrophic factor was significantly lower. The application of the cluster nursing strategy in the care of patients with TBI could effectively reduce the risk of complications and improve neurological, motor, and cognitive functions.