Definition Of Clusters Research Articles

Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 (“Westernized Millennial”, n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 (“Healthy”, n = 10616) consisted of healthy adults; Cluster 3 (“Mediterranean Young Adult”, n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 (“Pre-morbid”, n = 600) was characterized by healthy adults with declined mood; Cluster 5 (“Pro-morbid”, n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses (“lifemetabotypes”). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.

Communication Breakdown - Of Disease Clusters, a Trillium and One Health.

One health is based on an interdisciplinary collaboration across professions using a common language. Geographic epidemiology is the study of spatial patterns of population health in a study area. Such spatial patterns (trend, cluster and clustering) require clear definition to be meaningful in science communication. However, the term "disease cluster" has been defined in the literature in various and rather different ways. When geographic epidemiology is unable to make sense of its own concepts it is questionable how respective research results can benefit one health. The goal of this study was to clarify the disease cluster concept. Examples of disease cluster definitions from the literature were used for illustration. The epidemiological triangle of causation (agent, host and environment) was used to conceptualize geographic epidemiological data analysis. The term disease cluster was distinguished from related concepts (clustering, high-risk area, hot spot and outbreak) additionally the semantics and statistical meaning of expectation and prediction were reviewed to further identify the cluster concept as a statistical outlier. The new paradigm of the geographic epidemiological trillium is proposed here and embedded within the spatial generalized linear mixed model to clarify concepts of spatial patterns and guide epidemiological research and teaching.

Comparative Evaluation of K-Means, Hierarchical Clustering, and DBSCAN in Blood Donor Segmentation

Clustering techniques are pivotal in the fields of data analysis and pattern recognition, offering significant insights by grouping data points with similar characteristics. This study aims to perform a comprehensive comparison of three widely used clustering algorithms—K-Means, Hierarchical Clustering, and DBSCAN—on a dataset of blood donors. The objective is to determine which algorithm achieves the most precise and effective clustering of the data, taking into account factors such as donor location, blood type, and donation frequency. The study presents a novel approach by integrating a web-based platform that allows blood donors to register online. This platform not only facilitates the real-time updating of the dataset but also enhances the overall relevance and applicability of the clustering model by continuously incorporating new data entries. By leveraging such a dynamic dataset, the clustering algorithms can adapt to evolving patterns and trends, ensuring more accurate and meaningful insights over time.To rigorously evaluate the performance of each clustering method, several well-established metrics are employed, including the Silhouette Score, which assesses how similar each data point is to its own cluster compared to other clusters; the Davies-Bouldin Index, which evaluates the average similarity ratio of each cluster with its most similar cluster; and the Calinski-Harabasz Index, which measures the ratio of the sum of between-clusters dispersion and of within-cluster dispersion for all clusters. The results of this study indicate that the K-Means algorithm consistently outperforms both Hierarchical Clustering and DBSCAN in terms of accuracy and the clarity of cluster definitions. The findings underscore the robustness of K-Means for applications involving blood donor data, where capturing precise donor groupings can have substantial implications for healthcare logistics and resource allocation. These insights pave the way for further research into the optimization of clustering techniques in dynamic datasets and their practical applications in medical and other domains.

Риски в направлениях сетевого взаимодействия в области обеспечения региональной экономической безопасности

The article is a study of the issue of risks in the directions of network communication cross providing regional economic safety. A definition of risk and networking is presented. The connection between network communication and the cluster as a factor in sustainable and innovative economic development is shown. An overview of the definition of clusters is given. Regional management, as part of the public management, is aimed at overcoming crisis situations in various areas. The authors pay attention to the digital transformation of the economy and name individual risks that are conduits to the destabilization of regional economic security. The consolidated influence of the state and cluster process participants have a positive effect on the formation of conditions for economic safety. The article makes an attempt to identify the impulses of the impact of network communication for stable regional sustainability and economic safety.

Digital transformation with a lightweight on-premise PaaS

The rise of cloud computing has been enabled by advances in virtualization and containerization technology. Over the past decade, the use of cloud computing has grown rapidly and has had a significant impact on digital transformation with many enterprises migrating to public clouds. While convenient and cost efficient, such approaches are prone to certain data privacy, compliance and security risks. The ongoing democratization of cloud technologies represented by the increasing number of open source projects, has enabled certain enterprises to easily develop their on-premise cloud infrastructure. However, these open source projects are largely enterprise level and still too complex for small and micro enterprises and academic environments. To further decrease the on-premise infrastructure deployment and management barrier, we first provide an analysis of the existing on-premises PaaS workflows and solutions, along with the complexity of their deployment models, and identify the requirements for simple PaaS solutions for small environments with limited resources. We then introduce Kubitect as an enabler of on-premises PaaS democratization and expedite digital transformation. Kubitect is a lightweight single file declarative infrastructure configuration solution for on-premises cluster definition, instantiation and update. Our qualitative and quantitative evaluation shows the advantage of Kubitect for small environments where simplicity is more important than deploy time, assuming the latter is relatively comparable with alternative solutions.

Advancing Predictive Maintenance with PHM-ML Modeling: Optimal Covariate Weight Estimation and State Band Definition under Multi-Condition Scenarios

The proportional hazards model (PHM) is a vital statistical procedure for condition-based maintenance that integrates age and covariates monitoring to estimate asset health and predict failure risks. However, when dealing with multi-covariate scenarios, the PHM faces interpretability challenges when it lacks coherent criteria for defining each covariate’s influence degree on the hazard rate. Hence, we proposed a comprehensive machine learning (ML) formulation with Interior Point Optimizer and gradient boosting to maximize and converge the logarithmic likelihood for estimating covariate weights, and a K-means and Gaussian mixture model (GMM) for condition state bands. Using real industrial data, this paper evaluates both clustering techniques to determine their suitability regarding reliability, remaining useful life, and asset intervention decision rules. By developing models differing in the selected covariates, the results show that although K-means and GMM produce comparable policies, GMM stands out for its robustness in cluster definition and intuitive interpretation in generating the state bands. Ultimately, as the evaluated models suggest similar policies, the novel PHM-ML demonstrates the robustness of its covariate weight estimation process, thereby strengthening the guidance for predictive maintenance decisions.

Data-driven identification of earthquake clusters: Clusters before the 2010 El mayor-Cucapah earthquake MW 7.1, Baja California, Mexico

Seismic clusters in background seismicity have been associated with high stress levels and can be an important precursor to large earthquakes, but there is not a unanimous concept of cluster and most cluster identification methods are cumbersome and involve a priori assumptions. We propose a simple definition of seismic cluster and a straightforward method of identification involving a minimum of parameters that can be objectively determined in a data-driven way according to a principle of low random occurrence. As an illustration, definition and method were applied to the identification of cluster activity from October 1979 to March 2010 in northern Baja California, Mexico, between 118°W to 113°W and 30°N to 33°N, a tectonically complex seismic region with several fault systems. Twenty-one clusters were identified, of which 17 located around the places at the northeastern corner of the study area that would be ruptured on April 4, 2010 by the El Mayor-Cucapah Mw 7.1 earthquake, the largest recorded earthquake in Baja California, Mexico, and the four others occurred within 9 km from its epicenter. Clustering also became slightly more frequent as the time of the earthquake approached, so that if the clustering survey had been carried out before the whole northern Baja California area, the clustering might have identified the future epicentral region as a region of interest to be closely monitored (this earthquake featured foreshock activity starting some 15 days before the main event). Although the reliability of clusters as precursors to large earthquakes is still to be studied, it is certainly useful to have a reliable and simple method to identify and characterize them.

How Do Secondary-School Teachers Design STEM Teaching–Learning Sequences? A Mixed Methods Study for Identifying Design Profiles

AbstractDue to the increasing presence of the Science, Technology, Engineering, and Mathematics (STEM) education paradigm in Spain, many teachers have embarked on the design of specific Teaching–Learning Sequences (TLS) to be implemented in schools. Understanding the views and perceptions about STEM that take shape in specific teachers’ designs should enrich the way in which STEM education is designed based on a more focused approach. This study aims to characterise how secondary school teachers from Catalonia (Spain) design STEM TLS, to identify specific design profiles that can be related to different understandings of STEM education based on a mixed-method analytical approach. We collected 345 canvases from teachers participating in a national STEM education training programme, outlining STEM TLS. The canvases were analysed with an assessment rubric consisting of 8 instructional components (Interdisciplinarity, STEM practices, Information and Communications Technology tools, Formalisation, Openness, Alignment, Authenticity and Values). We identified patterns in teachers’ designs while implementing a hierarchical cluster analysis of the results, obtaining 6 different clusters of 39, 36, 66, 49, 90, and 65 TLS, respectively. The diverse components prioritised or balanced in each cluster suggest how STEM education can be conceived of differently by participating teachers through the lens of component analysis. While authenticity appears to be a major force in the clustering process, direct relationships between components can be found (i.e., between Formalisation and Alignment), as well as inverse relationships (i.e., between Openness and Practices). These findings provide important clues to understand STEM TLS design and recognise the rubric and the cluster definition as powerful tools for teacher training and evaluation in STEM education.

Biosignatures of defective sebaceous gland activity in sebum-rich and sebum-poor skin areas in adult atopic dermatitis.

Atopic dermatitis (AD) is a composite disease presenting disruption of the skin permeability barrier (SPB) in the stratum corneum (SC). Recent evidence supports derangement of the sebaceous gland (SG) activity in the AD pathomechanisms. The objective of this study was to delineate profiles of both sebaceous and epidermal lipids and of aminoacids from SG-rich (SGR) and SG-poor (SGP) areas in AD. Both sebum and SC were sampled from SGR areas, while SC was sampled also from SGP areas in 54 adult patients with AD, consisting of 34 and 20 subjects, respectively with and without clinical involvement of face, and in 44 age and sex-matched controls. Skin biophysics were assessed in all sampling sites. Disruption of the SBP was found to be associated with dysregulated lipidome. Abundance of sapienate and lignocerate, representing, respectively, sebum and the SC type lipids, were decreased in sebum and SC from both SGR and SGP areas. Analogously, squalene was significantly diminished in AD, regardless the site. Extent of lipid derangement in SGR areas was correlated with the AD severity. The abundance of aminoacids in the SC from SGR areas was altered more than that determined in SGP areas. Several gender-related differences were found in both controls and AD subgroups. In conclusion, the SG activity was differently compromised in adult females and males with AD, in both SGR and SGP areas. In AD, alterations in the aminoacidome profiles were apparent in the SGR areas. Lipid signatures in association with aminoacidome and skin physical properties may serve the definition of phenotype clusters that associate with AD severity and gender.

A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior.

To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

496 - Conjunctivitis does not increase with longer duration of lebrikizumab exposure in patients with moderate-to-severe atopic dermatitis

Abstract IntroductionIntegrated safety data for lebrikizumab (LEB) treatment in moderate-to-severe atopic dermatitis (AD) has been previously published. Conjunctivitis and keratitis were identified as adverse events (AEs) of special interest in the AD ­program. Objectives Further characterize patient-reported conjunctivitis and keratitis AEs in LEB clinical trials for AD. Methods Data from adult and adolescent patients were analyzed in 2 groups: a) LEB 250mg every 2 weeks (LEBQ2W, N=783) vs placebo (PBO, N=404), weeks 0-16 (PC 0-16wk) from 4 clinical trials (ADvocate1, ADvocate2, ADhere, Phase 2b study); and b) patients who received at least one dose of LEB (ALL-LEB, N=1720) from 8 clinical trials (ADvocate1, ADvocate2, ADhere, ADore, ADjoin (ongoing), ARBAN, TREBLE, Phase 2b study). Conjunctivitis and keratitis refer to cluster definitions defined by MedDRA preferred terms conjunctivitis, allergic conjunctivitis, bacterial conjunctivitis, viral conjunctivitis, giant papillary conjunctivitis; and keratitis, atopic keratoconjunctivitis, allergic keratitis, ulcerative keratitis, vernal keratoconjunctivitis, respectively. Exposure adjusted incidence rates (IR) are provided as per 100 patient-years. Results In PC 0-16wk, at baseline, similar proportions of LEB (21.5%) and PBO (19.3%) patients had medical history (MH) of conjunctivitis. Of ALL-LEB, 22.2% had MH of conjunctivitis; of those with conjunctivitis or keratitis AE, 40% had MH of conjunctivitis. In PC 0-16wk, conjunctivitis cluster was reported by 8.5% (IR:30.6) LEBQ2W vs 2.5% (IR:8.9) PBO; keratitis cluster was reported by 0.6% (IR:2.2) and 0.3% (IR:0.9) of patients, respectively. All conjunctivitis and keratitis events in PC 0-16wk were mild or moderate in severity; 5 events (3 conjunctivitis, 2 keratitis) led to treatment discontinuation (LEBQ2W) vs 1 conjunctivitis event (PBO). ALL-LEB treatment duration ranged from 1 dose to 100 weeks. In ALL-LEB, conjunctivitis cluster was reported by 10.6% (IR:12.2), with the majority being mild or moderate (10.3%) and 0.3% severe. Keratitis was reported by 0.5% (IR:0.6), with 1 severe event of vernal keratoconjunctivitis. Conjunctivitis and keratitis events resulted in 17 (1.0%) and 3 (0.2%) treatment discontinuations, respectively. Similar proportions of adult (11.3%; 0.6%) and adolescent (8.3%; 0.3%) patients reported conjunctivitis and keratitis events. The majority were reported within first 16 weeks of treatment. ConclusionsConjunctivitis is an AE reported by LEB-treated patients. Patients with MH of conjunctivitis may have higher risk of developing treatment-emergent conjunctivitis or keratitis. Most events were mild or moderate in severity, did not lead to treatment discontinuation, were reported within first 16 weeks of treatment, and IR did not increase with longer duration of exposure.

An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.

Кластеры как драйверы регионального экономического развития: практика США

Clusters are becoming one of the drivers of regional and national economic growth. Accumulating around themselves a number of advantages of territorial concentration of economic activity, clusters contribute to innovative development, improving the quality of labor resources, promoting companies to world markets, increasing their competitiveness. The purpose of the article is to define the cluster as a driver of regional economic development based on the analysis of US practice. To do this, the definition of clusters is given and their types are highlighted, the key subjects and the benefits created are identified, the direction of influence on the economy of the regions is formulated, the analysis of US clusters is carried out and modern development trends are identified. It is certain that the cluster in the region has an impact on economic development, forming the sectoral structure of the economy, creating political incentives, increasing competitiveness and productivity, attracting new companies and in other ways. It is shown that clusters can be divided into two groups: oriented to the external market (traded) and internal (local). This classification is important because clusters of the two groups manifest themselves differently in the regions. “External” clusters are interconnected by common technologies and highly skilled workers, they are concentrated in several regions, form the “core” of the regional economy and create a block of service industries. The development of this cluster group is well explained by the theories of exporting regions. The analysis of economic clusters according to the Institute of Strategy and Competitiveness of Harvard Business School showed that the average number of industries in clusters of this group is higher, there is close connectivity between clusters, there is growth in innovative, creative, educational and environmental clusters. The clusters of the group are oriented to the external market, and therefore they are distinguished by a high number of patents received. The construction of the model showed that the growth of the cluster depends on the initial number of employees, the level of wages and the number of patents received. Clusters focused on the domestic market are larger in the number of industries, differ in low wages and require little patenting. The analysis of the distribution of clusters by the number of employees according to Zipf’s law (the “rank-size” rule) showed the validity of such a division of clusters into groups. In conclusion, some current trends in the development of clusters were formulated.

Convergence Trends in Euro Economies: Financial Crisis Recovery and the COVID-19 Pandemic

The configurative comparative method, Dynamic Pattern Synthesis (DPS) is used to replicate previous research into the impact of the euro on economic convergence. The DPS method ensures a forensic examination of the diverse variable patterns that influence cluster memberships. As with previous research conclusions, there are multiple patterns of convergence and divergence. Consistent clusters across the time periods compared are Germany, the Netherlands, Luxembourg, and Ireland; Slovakia and Estonia; Italy, Spain, and Slovenia; and Portugal and Greece. The variable patterns most likely to influence cluster definitions are differences in GDP per capita, productivity, and investment, although there are other differing variable patterns that influence specific smaller cluster memberships and the consistency of memberships over time. Externalities undermine nominal convergence. An example is the divergence of the experience of consumer inflation between 2016 and 2022. Nevertheless, some convergence in long-term interest rates is achieved. There is also divergence in the real convergence target of GDP per capita. As regards structural changes, productivity differences widen, and investment as a percentage of GDP converges during COVID-19. The theoretical implications are that the complex dynamics between collaboration, competitive markets, and global instabilities makes convergence unlikely. Real convergence, such as reducing the distribution differences of GDP per capita, is only likely to be possible over many decades, and needs considerable government interventions. Complex systems theory informs us that limits to convergence are inevitable in dynamic systems where events bring unplanned divergences.

Automated calibration of consensus weighted distance-based clustering approaches using sharp.

In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms. We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximizing the sharp score compared to existing calibration scores and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes. The R package sharp (version ≥1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.

(Re)defining Esotericism

Abstract This article moves past discussing definitions of esotericism to clarifying the nature of definition itself. After reviewing different approaches to definition, we argue that rigid definitions (which propose necessary and sufficient conditions) are problematic, and that fluid definitions (which seek to clarify, explain, teach etc.) are a better alternative. We then discuss homeostatic property cluster definitions, which are like family-resemblance definitions, but with explicit justification for the particular set of defining criteria. We propose a related but more flexible stable property cluster definition of esotericism. It is intended as a useful focus for ongoing discussion, not as a candidate for the single correct definition. Throughout the article, we contrast meaning realism and meaning antirealism (not ontological realism/antirealism), making a case that the latter is more useful in this context. We end by suggesting that our definitional approach has value for exploring both the global spread of western esotericisms and, more importantly, the extent to which esotericism intersects with the semantic fields of comparable categories in other languages and cultures.

Insights into early evolutionary adaptations of the Akkermansia genus to the vertebrate gut.

Akkermansia, a relevant mucin degrader from the vertebrate gut microbiota, is a member of the deeply branched Verrucomicrobiota, as well as the only known member of this phylum to be described as inhabitants of the gut. Only a few Akkermansia species have been officially described so far, although there is genomic evidence addressing the existence of more species-level variants for this genus. This niche specialization makes Akkermansia an interesting model for studying the evolution of microorganisms to their adaptation to the gastrointestinal tract environment, including which kind of functions were gained when the Akkermansia genus originated or how the evolutionary pressure functions over those genes. In order to gain more insight into Akkermansia adaptations to the gastrointestinal tract niche, we performed a phylogenomic analysis of 367 high-quality Akkermansia isolates and metagenome-assembled genomes, in addition to other members of Verrucomicrobiota. This work was focused on three aspects: the definition of Akkermansia genomic species clusters and the calculation and functional characterization of the pangenome for the most represented species; the evolutionary relationship between Akkermansia and their closest relatives from Verrucomicrobiota, defining the gene families which were gained or lost during the emergence of the last Akkermansia common ancestor (LAkkCA) and; the evaluation of the evolutionary pressure metrics for each relevant gene family of main Akkermansia species. This analysis found 25 Akkermansia genomic species clusters distributed in two main clades, divergent from their non-Akkermansia relatives. Pangenome analyses suggest that Akkermansia species have open pangenomes, and the gene gain/loss model indicates that genes associated with mucin degradation (both glycoside hydrolases and peptidases), (micro)aerobic metabolism, surface interaction, and adhesion were part of LAkkCA. Specifically, mucin degradation is a very ancestral innovation involved in the origin of Akkermansia. Horizontal gene transfer detection suggests that Akkermansia could receive genes mostly from unknown sources or from other Gram-negative gut bacteria. Evolutionary metrics suggest that Akkemansia species evolved differently, and even some conserved genes suffered different evolutionary pressures among clades. These results suggest a complex evolutionary landscape of the genus and indicate that mucin degradation could be an essential feature in Akkermansia evolution as a symbiotic species.

Operational strategies to deal with the COVID-19 emergency: recommendations from the Italian national society SIAGASCOT following the introduction of vaccines against the SARS-CoV-2 infection

BackgroundThis article aims to present the operational recommendations adopted by the Italian national society for orthopaedic surgery, arthroscopy, and sports medicine (SIAGASCOT) in managing patients eligible to undergo elective orthopaedic surgery during the COVID-19 pandemic after the beginning of a national vaccination campaign.Materials and methodsAn extensive literature search, analysing medical databases and scientific societies protocols, was performed to support this document. A four-step approach was used: 1—definition of priorities; 2—definition of significant clusters of interventions; 3—extraction of recommendations from international literature; and 4—adaptation of the recommendations to the specific features of the Italian healthcare system.ResultsThree operational priorities were defined (“continuity of care and containment of the virus spread”, “examination of waiting lists”, and “definition of the role of vaccines”), six significant clusters of intervention were identified, and recommendations regarding the risk management for healthcare staff and hospital facility as well as the preoperative, in-hospital, and postoperative management were produced. Patient selection, preoperative screening, and pre-hospitalization procedures, which are regarded as pivotal roles in the safe management of patients eligible to undergo elective orthopaedic surgery, were analysed extensively.ConclusionsThis document presents national-wide recommendations for managing patients eligible to undergo elective orthopaedic surgery with the beginning of the vaccination campaign. This paper could be the basis for similar documents adapted to the local healthcare systems in other countries.Level of evidenceLevel IV.

Cardiovascular disease behavioural risk factors in rural interventions: cross-sectional study

This study aims to (1) assess the distribution of variables within the population and the prevalence of cardiovascular disease (CVD) behavioural risk factors in patients, (2) identify target risk factor(s) for behaviour modification intervention, and (3) develop an analytical model to define cluster(s) of risk factors which could help make any generic intervention more targeted to the local patient population. Study patients with at least one CVD behavioural risk factor living in a rural region of the Scottish Highlands. The study used the STROBE methodology for cross-sectional studies. Demographic and clinical data of patients (n = 2025) in NHS Highlands hospital were collected at the point of admission for PCI between 04.01.2016 and 31.12.2019. Collected data distributions were analysed by CVD behavioural risk factors for prevalence, associations, and direction of associations. Cluster definition was measured by assignment of a unit score each for the overall level of prevalence and significance of associations, and general logistics modelling for direction and significance of the risk. The mean (SD) age was 69.47(± 10.93) years [95% CI (68.99–69.94)]. The key risk factors were hyperlipidaemia, hypertension, and elevated body mass index (BMI). Approximately 40% of the population have multiple risk factor counts of two. Analytical measures revealed a population risk factor cluster with elevated BMI [77.5% (1570/2025)] that is mostly either hyperlipidaemic [9.43%, co-eff. (17), P = 0.007] or hypertensive [22.72%, co-eff. (17), P = 0.99] as key risk factor clusters. Carefully modelled analyses revealed clustered risk associated with elevated BMI. This information would support a strategy for targeting risk factor clusters in novel interventions to improve implementation efficiency. Exposure to and outcome of an elevated BMI is linked more to the population’s socio-economic outcomes rather than to regional rurality or urbanity.

Financial Implications of the COVID-19 Pandemic on the Container Ship Time Charter Business

This study examines the financial implications of the COVID-19 pandemic on the container ship time charter business. In this context, the container charter transactions were derived from the Clarksons Research Database, which included the ship types, daily charter fees, ship ages, and total charter days. The empirical analysis employed the K-Means Algorithm to cluster the observations in which the elbow curves revealed three cluster centers in the pre-COVID period and four in the post-COVID era, respectively. Based on the industry-wide used threshold definitions, the clusters were then named according to the mean value of given features. In addition, the relative weight of each cluster was disclosed based on the number of transactions falling into the respective cluster. Accordingly, the pre-COVID period clusters were described as intermediate-rated middle-termed young-aged intermediate-TEU container ships; low-rated middle-termed middle-aged feeders; and intermediate-rated long-termed middle-aged upper intermediate-TEU container ships. As for the post-COVID era, the cluster definitions were determined as intermediate-rated middle-termed young-aged feeders; intermediate-rated middle-termed old-aged feeders; high-rated long-termed middle-aged intermediate-TEU container ships; and high-rated short-termed middle-aged intermediate-TEU container ships. The findings suggested that the pandemic boosted the demand for relatively lower TEU container ships such as the feeders in which the criterium of ship age lost its importance due to availability reasons in the market. In addition, the pandemic led to higher charter rates which was a prioritized factor over the charter period.