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Lumpy skin disease (LSD) is a serious, transboundary disease that affects cattle all over the world and results in considerable productivity losses. Although Türkiye’s first outbreak of LSD was reported in August 2013, there is very little information available about the outbreak’s spatiotemporal distribution or severity. GIS-based data analysis provides crucial tools for describing the spatial epidemiology of the disease by assessing the spatial distribution of LSD across time. This study used information on outbreaks reported to the the World Animal Health Organization (WOAH-OIE) between 2013 and 2021 to conduct a retrospective study on the epidemiology of LSD in Türkiye. Differences in the number of reported outbreaks and cases across different regions, provinces, months, and years were evaluated and descriptive statistics were calculated. In addition, spatial statistical tests (Local Moran’s I and Getis-Ord Gi*) and Geographical Information Systems (GIS) were used to assess LSD outbreaks that had taken place at the province level in Türkiye. Possible epidemiological clusters of LSD were identified. A total of 1787 outbreaks and 10109 cases of LSD were reported from 75 out of 81 provinces of Türkiye during the course of the nine-year period. Hotspots for the circulation of LSD were identified in the Aegean, Southeastern and Eastern regions using spatial cluster analyses and it was observed that the spatial autocorrelation of LSD cases is positive across the country. The findings from this study, it may help us comprehend the disease’s spatial character and offer authorities the beneficial information for surveillance efforts.

Abstract Ossicular fixation disturbs the mobility of the ossicular chain and causes conductive hearing loss. To diagnose the lesion area, otologists typically assess ossicular mobility through intraoperative palpation. Quantification of ossicular mobility and evidence-based diagnostic criteria are necessary for accurate assessment of each pathology, because diagnosis via palpation can rely on the surgeons’ experiences and skills. In this study, ossicular mobilities were simulated in 92 pathological cases of ossicular fixation as compliances using a finite-element (FE) model of the human middle ear. The validity of the ossicular mobilities obtained from the FE model was verified by comparison with measurements of ossicular mobilities in cadavers using our newly developed intraoperative ossicular mobility measurement system. The fixation-induced changes in hearing were validated by comparison with changes in the stapedial velocities obtained from the FE model with measurements reported in patients and in temporal bones. The 92 cases were classified into four groups by conducting a cluster analysis based on the simulated ossicular compliances. Most importantly, the cases of combined fixation of the malleus and/or the incus with otosclerosis were classified into two different surgical procedure groups by degree of fixation, i.e., malleo-stapedotomy and stapedotomy. These results suggest that pathological characteristics can be detected using quantitatively measured ossicular compliances followed by cluster analysis, and therefore, an effective diagnosis of ossicular fixation is achievable.

Abstract Background The tribe Ampelopsideae plants are important garden plants with both medicinal and ornamental values. The study of codon usage bias (CUB) facilitates a deeper comprehension of the molecular genetic evolution of species and their adaptive strategies. The joint analysis of CUB in chloroplast genomes (cpDNA) offers valuable insights for in-depth research on molecular genetic evolution, biological resource conservation, and elite breeding within this plant family. Results The base composition and codon usage preferences of the eighteen chloroplast genomes were highly similar, with the GC content of bases at all positions of their codons being less than 50%. This indicates that they preferred A/T bases. Their effective codon numbers were all in the range of 35–61, which indicates that the codon preferences of the chloroplast genomes of the 18 Ampelopsideae plants were relatively weak. A series of analyses indicated that the codon preference of the chloroplast genomes of the 18 Ampelopsideae plants was influenced by a combination of multiple factors, with natural selection being the primary influence. The clustering tree generated based on the relative usage of synonymous codons is consistent with some of the results obtained from the phylogenetic tree of chloroplast genomes, which indicates that the clustering tree based on the relative usage of synonymous codons can be an important supplement to the results of the sequence-based phylogenetic analysis. Eventually, 10 shared best codons were screened on the basis of the chloroplast genomes of 18 species. Conclusion The codon preferences of the chloroplast genome in Ampelopsideae plants are relatively weak and are primarily influenced by natural selection. The codon composition of the chloroplast genomes of the eighteen Ampelopsideae plants and their usage preferences were sufficiently similar to demonstrate that the chloroplast genomes of Ampelopsideae plants are highly conserved. This study provides a scientific basis for the genetic evolution of chloroplast genes in Ampelopsideae species and their suitable strategies.

The research subject is the application of geographic information systems (GIS) in assessing land suitability for agriculture in the Sana River Basin in Bosnia and Herzegovina. The aim of the research is to apply the analytic hierarchy process (AHP) in identifying suitable areas for agricultural production. Within the AHP framework, the following factors were considered: pedology, land use, elevation, slope, aridity index, and distance from rivers. The results of the suitability assessment underwent reclassification (RP) and cluster analysis processes (CAPs). It was found that very unsuitable land (1) covers an area of 0.15% (RP) or 5.83% (CAP), unsuitable land (2) covers 3.44% (RP) or 17.52% (CAP), conditionally suitable land (3) covers 32.11% or 28.47% (CAP), suitable land (4) covers 56.29% or 28.57% (CAP), and very suitable land (5) covers 7.98% (RP) or 19.59% (CAP). At the study area level, a supervised classification process was conducted to identify land use classes: meadows/pastures, water, forest, agricultural, and built-up areas. RP and CAP results were overlaid with supervised classification results to determine the amount of land used for agricultural purposes within each suitability class. It was determined that currently, for agricultural purposes, 0.04 km2 (RP) or 0.88 km2 (CAP) of very unsuitable land (1) is used, 0.41 km2 (RP) or 7.28 km2 (CAP) of unsuitable land (2), 15.75 km2 (RP) or 27.52 km2 (CAP) of conditionally suitable land (3), 185.15 km2 (RP) or 107.06 km2 (CAP) of suitable land (4), and 42.99 km2 (RP) or 101.65 km2 (CAP) of very suitable land (5). The research findings hold substantial importance in elucidating both the potential and constraints of land use practices as a vital natural resource within agriculture. They also have practical importance for relevant institutions in terms of agricultural sector development and making timely land use planning decisions for sustainable development.

Деформация литосферного слоя под действием сейсмических процессов изменяет характеристики геоакустической эмиссии. Изучение особенностей динамики геоакустической эмиссии направлено на поиск признаков предсейсмических событий. Существует проблема качественной обработки сигналов геоакустической эмиссии и классификации получаемых результатов. Исследование направлено на поиск лучшей комбинации средств предобработки и кластеризации импульсного потока геоакустической эмиссии для выделения особенностей изменчивости характеристик такого сигнала. Обрабатываемые сигналы получены в ходе многолетних измерений в поверхностных слоях литосферы сейсмоактивного района Камчатского полуострова. Для выявления особенностей изменчивости характеристик сигналов геоакустической эмиссии осуществляется их структурно-лингвистическое преобразование в трехмерные отображения. Отображения обрабатываются, сравниваются и кластеризуются с применением сверточных нейроных сетей различной архитектуры. Лучший результат оценивается по трем подобранным критериям качества. Разработана методика поиска лучшего результата предобработки и кластеризации отображений. Представлен анализ результатов экспериментов

Abstract Background Studies of time trends in Nordic adolescents' mental health have often relied on analyses of adolescents’ psychosomatic symptoms. In this study, we examine adolescents' self-reports on mental health in the context of the dual factor model, which encompasses both overt manifestations of mental health symptoms and subjective perception of one's health status. Method The objective of this study was to employ a person-oriented approach utilizing cluster analysis to discern time trends in mental health profiles of Nordic adolescents, using their psychosomatic complaints and their perception of their overall health as cluster variables. The resulting health profiles were then subjected to a comparative analysis with regard to different measures of psychological and social adjustment. The mental health profiles were based on data from the Health Behaviour in School-aged Children (HBSC) survey, which was conducted among almost 50000 15-year-olds in five Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) between 2002 and 2022. Results Mental health profiles exhibiting comparable content were observed in all Nordic countries, including profiles indicating adequate health, perceived good health, perceived poor health, high psychosomatic complaints, and dual health problems (defined as both high psychosomatic complaints and perceived poor health). These health profiles showed similar trends over time in the Nordic countries. Significant gender differences were observed. In 2002, adequate health was the dominant profile for both sexes. After 20 years, however, the high psychosomatic profile became the most common profile among girls. Among the three risk profiles, namely perceived poor health, high psychosomatic complaints and dual health problems, adolescents in the dual problems profile had the most psychological and social adjustment problems. Conclusions The comparatively lower incidence of adjustment problems among adolescents in the high psychosomatic profile relative to the dual mental health group challenges the prevailing view that there has been a sharp increase in mental health problems among Nordic adolescents. This view was largely based on the observed rise in psychosomatic symptoms. Indeed, there was a doubling in the proportion of adolescents in the high psychosomatic complaints profile between 2002 and 2022. This increase was considerably more pronounced than that observed for the dual health problems profile which exhibited most problems.

Conservation of forest resources is a prerequisite for sustainable development of human society, both in the context of preventing negative climate change and for economic growth. The study aims to establish or refute the co-dependence between the level of forest cover in European countries and the production of gross domestic product. The study object is the socio-economic systems of the national economies of European countries in relation to the totality of forest resources of the continent. Studying the dynamics of forest cover indicators (the share of forests in the total area of the country and forest area per capita), weighted within the internationally recognized regions of Europe, it is confirmed that the level of forest cover of European countries is gradually increasing. The analysis of forest fire area maps identifies three main groups by the level of vulnerability to forest fires: safe (Northern European countries), conditionally safe (Western European countries), and dangerous (Eastern and Southern European countries).Denmark, Finland, France, Norway, and Finland show a direct correlation between the level of forest cover of a country’s territory and gross domestic product. The results of cluster analyses based on the data from 2000, 2010, 2015, and 2020 confirm the existence of a stable cluster of European countries (34 countries) in which there is one type of relationship between the production of gross domestic product and the level of forest cover of the territory. AcknowledgmentThis study was funded by the grant support from the Philipp Schwarz Initiative of the Alexander von Humboldt Foundation.

Introduction: Environmental factors such as temperature, precipitation, and various other pollutants in the air like nitrogen dioxide (NO2), CO2, etc., have been studied for sustenance and transmission of Coronavirus disease-19 (COVID-19). Studies from India are lacking about the distribution of COVID-19 cases according to air pollutants and meteorological factors. Hence, the present study was conducted to study the relationship between COVID-19 and environmental factors with the objectives of Cluster mapping of the COVID-19 cases district-wise for the state of Maharashtra. Methodology: The air pollution variables NO2 column density (mol/m2), sulfur dioxide column density (mol/m2), Aerosol absorbing index (Ai), Carbon monoxide (CO) column density (mol/m2), and Ozone column density (mol/m2) were extracted from Sentinel-5P datasets available in Google Earth Engine (GEE) (https://code.earthengine.google.com/). The meteorological variables precipitation (m), temperature (t), and humidity (k) were extracted from the European Environmental Agency 5-Land reanalysis dataset in GEE, which provides data at a resolution of 1113.2 m. The preprocessing and retrieval of data were carried out in GEE. These values were correlated with daily cases, density, and other demographic factors. Results: The study identified a cluster of cases in few districts of Maharashtra. These districts were Pune, Ahmednagar, Mumbai, Nashik, and Nagpur. Population density and NO2 were important factors in most of the monthly data. Conclusion: The present study used data available on public platforms to assess the association between COVID-19 and environmental factors. The study found that various environmental factors such as NO2, CO, and Ai may be associated with increase cases of COVID-19 in the district. However, ecological bias may be kept in mind.

A comprehensive and intuitive evaluation system for the quality of crabapples (Malus spp.) was established. In this study, 14 varieties of crabapples were tested. The core quality indexes were selected through variability analysis, correlation analysis, and principal component analysis (PCA) to establish the comprehensive quality evaluation model. The reliability of the model was verified by sensory evaluation and cluster analysis. In addition, the characteristics and utilization values of these varieties were investigated by cluster analysis. The results showed that there were significant differences in 18 quality indexes among the varieties and significant correlations among these quality indexes. PCA showed that the cumulative contribution rate of the first 5 principal components was 89.253%. Combining this result with those of correlation analysis, we found that single fruit mass, solid/acid ratio, sugar/acid ratio, and total flavanol content were the major quality indexes of crabapples. According to the comprehensive evaluation model, ‘123 Xiaopingguo’, ‘Longfeng’, ‘Cuifeng’ and ‘Huangtaiping’ had better fruit quality than the other varieties. The antioxidant activity of crabapple polyphenols showed a positive linear correlation with the contents of polyphenols and flavonoids with correlation coefficients of R2 = 0.837 and 0.728 for 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) cation radical scavenging capacity, R2 = 0.829 and 0.768 for 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) radical scavenging capacity, and R2 = 0.575 and 0.969 for ferric ion reducing antioxidant power (FRAP), respectively. Likewise, the sensory evaluation scores were linearly correlated with the PCA scores (R2 = 0.862), indicating the reliability of the comprehensive evaluation model. The results of cluster analysis showed that the crabapple varieties could be divided into 4 categories. Category I included M. orbusta and ‘Haihong’, which could be processed into health products with high VC content. Category II included ‘Jixinguo’, ‘Longfeng’, ‘Cuifeng’ and ‘123 Xiaopingguo’, with good taste and being suitable for fresh consumption. Category III included ‘Adam’, ‘Profusion’, M. micromalus, ‘Brandywine’, and ‘Red Splender’, which are suitable for making polyphenols-rich health foods. Category IV included ‘K9 Qingshaguo’, ‘Kelsey’ and ‘Huangtaiping’, which can be processed into preserved fruit, juice, wine and other products. The results were consistent with those of PCA, corroborating the reliability of the model. The comprehensive quality evaluation system can provide a theoretical basis for the development and utilization of different varieties of crabapples and also provide a good scheme for the quality evaluation of crabapples.

Abstract Background Nurses are increasingly demanded to achieve gold-standards of care with fewer resources. Dealing effectively with stress experienced in their daily-work-life is thus crucial. This study is based on the Demands-Resources-and-Individual-Effects (DRIVE) Nurses Model and applied the person-centred approach with a twofold objective: 1. to identify patterns of coping strategies (Problem-Focused; Seek-Advice; Self-Blame; Wishful-Thinking; Escape/Avoidance) adopted by nurses to deal with perceived stress; 2. to explore potential differences in perceived Demands (Effort), Resources (Rewards, Job-Control, Social-Support), and Psychopathological Symptoms (Anxiety, Phobic-Anxiety, Obsessive–Compulsive, Somatization, Depression, Interpersonal-Sensitivity, Hostility, Psychoticism, Paranoid-Ideation) according to the emerged patterns. Method This cross-sectional study was reported by using the STROBE Checklist. Overall, 265 nursing professionals completed self-report measures. Non-hierarchical k-means-cluster-analysis was employed to derive patterns of coping. MANOVAs were used to test differences in Demands, Resources, and Psychopathological Symptoms according to the emerged patterns. Results Three stable and meaningful patterns of coping were identified and labelled as Active/Solution-Oriented, Dysregulated/Emotion-focused, and Passive/Disengaged. Nurses belonging to Dysregulated/Emotion-focused group emerged to be at higher risk (higher effort/psychopathological suffering; lower resources) – followed by Passive/Disengaged group – in comparison with nurses belonging to Active/Solution-Oriented group. Conclusion Fostering nurses’ awareness of their latent coping patterns and supporting active approaches/emotional regulation strategies for stress management should represent a key goal when defining interventions promoting nurses’ health within/beyond the healthcare settings.

Abstract The biopharmaceutical sector is of considerable interest during the COVID-19 pandemic. This study aims to investigate the biopharmaceutical sector using the Shenwan Industry Classification and provides insights into investment strategies. We combine factor and cluster analyses to reduce data dimensions and detect their latent similarities. Specifically, the biopharmaceutical sector is divided into six categories based on second-level industry classification. It is observed that medical devices, medical services, biological products, and chemical pharmaceuticals maintained their upward tendency, while Chinese medicine and pharmaceutical commerce declined slightly. We also develop optimal investment strategies using various metrics for different investor types.

This study evaluated the juice processing characteristics of seven typical blueberry fruit cultivars: Windsor, Susieblue, Lanmei 1, Sharpblue, Sweetheart, Titan, and Gardenblue from three major strains (Southern highbush, Northern highbush and rabbiteye). The juice yield, color, physicochemical properties, functional component contents and antioxidant capacities were analyzed. In addition, correlation, factor, and cluster analyses were used to evaluate comprehensively the suitability of each cultivar for juice processing. The results indicated significant differences in the juice processing characteristics of the blueberry cultivars (PTitan>Lanmei 1>Windsor>Sharpblue>Susieblue>Sweetheart. Among the seven blueberry cultivars, the most suitable for juice processing was Gardenblue, followed by Titan. This study provides a theoretical reference for selecting blueberry cultivars for juice processing.

Cermet coatings are one of the best surface protection of machine elements against wear. On the other hand, the most universal and economically justified method of applying such coatings is high velocity oxy-fuel (HVOF) spraying. This method makes it possible to produce coatings characterized by compact structure, low porosity and very good adhesion to the substrate. All these fundamental properties contribute to the high wear resistance of these coatings. However, carrying out full wear tests (e.g. ball-on-disc) is time-consuming, especially when it is necessary to select the proper feedstock material and carefully selected process parameters. The aim of the following researches was to statistically investigate the possibility of replacing long-term wear resistance tests with estimation of this performance on the basis of determining the fundamental mechanical properties of the coatings. Three types of coating materials were selected: WC-12Co, WC-10Co-4Cr and WC-20Cr3C2-7Ni, which were deposited on AZ31 magnesium alloy substrates from three different spray distances: 320, 360 and 400 mm. On the basis of the tests carried out and using cluster analysis techniques (the Ward and k-means methods), the relative similarity between the obtained coatings was determined. The applied methodology allowed to select from the analyzed cermet coatings such samples that were characterized by improved resistance to abrasive wear. The obtained results of the analyzes were also referred to the results of tests of resistance to abrasive wear.

In the context of retail distribution, this study employs recursive K-means clustering on retail sales data to optimize clusters of nearest-distance stores for salesperson route recommendations. This approach addresses the stochastic salesperson problem by generating effective routes, enhancing cost reduction, and improving service efficiency. The recursive K-means algorithm dynamically adjusts to continuous changes in store numbers, locations, and transaction data. Consequently, this research successfully developed a model that automatically re-clusters the data with each change, providing continuously updated and effective store recommendations.

Abstract Background Recent studies have demonstrated that individuals hospitalized due to COVID-19 can be affected by “long-COVID” symptoms for as long as one year after discharge. Objectives Our study objective is to identify data-driven clusters of patients using a novel, unsupervised machine learning technique. Methods The study uses data from 437 patients hospitalized in New York City between March 3rd and May 15th of 2020. The data used was abstracted from medical records and collected from a follow-up survey for up to one-year post-hospitalization. Hospitalization data included demographics, comorbidities, and in-hospital complications. The survey collected long-COVID symptoms, and information on general health, social isolation, and loneliness. To perform the analysis, we created a graph by projecting the data onto eight principal components (PCs) and running the K-nearest neighbors algorithm. We then used Louvain’s algorithm to partition this graph into non-overlapping clusters. Results The cluster analysis produced four clusters with distinct health and social connectivity patterns. The first cluster (n = 141) consisted of patients with both long-COVID neurological symptoms (74%) and social isolation/loneliness. The second cluster (n = 137) consisted of healthy patients who were also more socially connected and not lonely. The third cluster (n = 96) contained patients with neurological symptoms who were socially connected but lonely, and the fourth cluster (n = 63) consisted entirely of patients who had traumatic COVID hospitalization, were intubated, suffered symptoms, but were socially connected and experienced recovery. Conclusion The cluster analysis identified social isolation and loneliness as important features associated with long-COVID symptoms and recovery after hospitalization. It also confirms that social isolation and loneliness, though connected, are not necessarily the same. Physicians need to be aware of how social characteristics relate to long-COVID and patient’s ability to cope with the resulting symptoms.

Objectives: From a cross-cultural perspective, aging well may encompass pertinent challenges in terms of adjustment, sexual well-being, and satisfaction with life in the late years. Considering the paucity of empirical data concerning cultural diversity of experiencing aging, this study aims to help fill this gap by assessing the specific patterns of sexual satisfaction, adjustment to aging (AtA), and life satisfaction with life (SwL) of older adults in Portugal and Spain. Methods: This cross-national study included 326 older adults, age 65 and older, from Portugal and Spain. Five instruments were applied: (a) Adjustment to Aging Scale (ATAS); (b) Satisfaction with Life Scale (SwLS); (c) New Sexual Satisfaction Scale-Short (NSSS-S); (d) Mini-Mental State Exam; and (e) Sociodemographic, Health and Lifestyle questionnaire. K-means cluster analysis was employed to identify and characterize the clusters considering adjustments to aging, sexual satisfaction, and life satisfaction. One-way ANOVAs were conducted to analyze differences in sexual well-being among clusters. Results: Findings indicated three clusters, which explained 77.7% (R-sq = 0.777) of the total variance: Cluster 1: “Most skilled” (n = 26, 8.0%), Cluster 2: “Least adjusted” (n = 115, 35.3%), and Cluster 3: “Aging strivers” (n = 185, 56.7%). Participants in Cluster 1 were mostly Portuguese, with high levels of AtA, sexual satisfaction, and SWL. Conversely, Cluster 2 included mostly Portuguese participants with moderate sexual satisfaction and lower levels of AtA and SwL. Participants from Cluster 3 were mostly Spanish, with moderate levels of AtA and reduced sexual satisfaction and SwL. Conclusions: This study innovates by exploring the elaborate interplay among sexual satisfaction, AtA, and SwL in a cross-cultural perspective, with implications for tailoring interventions, service planning, development, and evaluation of culturally diverse older populations.

Background. Special attention is paid in many regions to invasive plant species introduced into natural plant communities. Studying the degree of naturalization of Berberis aquifolium Pursh and Daphne laureola L. on the Crimean Peninsula is important because these species are currently observed in different types of forest vegetation, including Protected Areas. Materials and methods. Forest plant communities with B. aquifolium and D. laureola served as the research target. A geobotanical survey was conducted in 2019–2021 using the Braun-Blanquet approach. The JUICE platform and the PC-ORD 5.0 algorithm were employed to perform the cluster analysis, and the PAST 3.26 software for the ordination analysis. Twelve factors were considered: soil humidity (Hd), humidity fluctuation (fH), substrate acidity (Rc), anionic composition (Tr), carbonate content (Ca), nitrogen content (Nt), granulometric (mechanical) composition of the substrate (Ae), illuminance of the community (Lc), thermal regime (Tm), aridity/humidity (Om), cryoregime (Cr), and climate continentality (Kn). Phytoindication technique was used to determine the parameters of ecological niches. Results and conclusion. The communities with B. aquifolium and D. laureola on the Southern Coast of Crimea were found to belong to the Quercetea pubescentis and Erico-Pinetea classes of vegetation. The species appeared to be most widespread in the pine-oak-hornbeam and oak-hornbeam-dogwood forest communities where they occurred mainly along riverbeds, near springs, and along gully bottoms. According to a majority of edaphoclimatic factors, their habitat conditions corresponded to the parameters of the fundamental niches for the studied species, which explains their high degree of adaptation to the natural environments on the Southern Coast of Crimea.

Abstract Monthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability patterns and regimes. To accomplish this, the T-mode of principal component analysis was applied to the correlation matrix of the data. Based on the maximum loading values of the rotational principal component scores, the T-mode indicated three separate subregions with varying precipitation patterns over time. Four clusters with distinct rainfall patterns were identified when cluster analysis was performed on the principal component scores. An assessment of the homogeneity of the clusters was performed using L-moment’s heterogeneity measure (H). Statistical analysis was employed to model annual rainfall data using five commonly used rainfall analysis probability distribution functions: normal, lognormal, gamma, Weibull, and Gumbel. The probability distributions with the greatest fit were determined based on the maximum overall score, which was calculated by adding the individual point scores of three chosen goodness-of-fit tests. Each cluster exhibited distinct probability distribution functions, with the gamma, Gumbel, lognormal, and Weibull distributions providing the most accurate descriptions.

Abstract Background Multimorbidity (MM) is generally defined as the presence of 2 or more chronic diseases in the same patient and seems to be frequently associated with frailty and poor quality of life. However, the complex interplay between MM and functional status in hospitalized older patients has not been fully elucidated so far. Here, we implemented a 2-step approach, combining cluster analysis and association rule mining to explore how patterns of MM and disease associations change as a function of disability. Methods This retrospective cohort study included 3366 hospitalized older patients discharged from acute care units of Ancona and Cosenza sites of Italian National Institute on Aging (INRCA-IRCCS) between 2011 and 2017. Cluster analysis and association rule mining (ARM) were used to explore patterns of MM and disease associations in the whole population and after stratifying by dependency in activities of daily living (ADL) at discharge. Sensitivity analyses in men and women were conducted to test for robustness of study findings. Results Out of 3366 included patients, 78% were multimorbid. According to functional status, 22.2% of patients had no disability in ADL (functionally independent group), 22.7% had 1 ADL dependency (mildly dependent group), and 57.4% 2 or more ADL impaired (moderately-severely dependent group). Two main MM clusters were identified in the whole general population and in single ADL groups. ARM revealed interesting within-cluster disease associations, characterized by high lift and confidence. Specifically, in the functionally independent group, the most significant ones involved atrial fibrillation (AF)-anemia and chronic kidney disease (CKD) (lift = 2.32), followed by coronary artery disease (CAD)-AF and heart failure (HF) (lift = 2.29); in patients with moderate-severe ADL disability, the most significant ARM involved CAD-HF and AF (lift = 1.97), thyroid dysfunction and AF (lift = 1.75), cerebrovascular disease (CVD)-CAD and AF (lift = 1.55), and hypertension-anemia and CKD (lift = 1.43). Conclusions Hospitalized older patients have high rates of MM and functional impairment. Combining cluster analysis to ARM may assist physicians in discovering unexpected disease associations in patients with different ADL status. This could be relevant in the view of individuating personalized diagnostic and therapeutic approaches, according to the modern principles of precision medicine.

In the southeast coast of China, typhoon rainstorms resulted in lots of landslides. Investigating rainfall thresholds and patterns of typhoons is of great importance for the early warning of landslides. In this study, the landslide information obtained from multiple sources and typhoon rainstorm data were used to analyze landslide and rainfall characteristics in Taishun 2007 to 2022; the rainfall threshold of typhoon rainstorm-induce landslides was defined for Taishun area based on the rainfall intensity–duration (I-D) model. It finds that a large number of typhoon rainstorm landslides occur at the windward slope close to the landing direction of the typhoon, where the main vegetation types of the landslide are arbors and herbs with tall trunks and shallow roots. The duration of rainfall events inducing landslides is usually 2～3 days, with concentrated rainfall and total rainfall more than 200 mm. The threshold of typhoon rainstorm-induced landslides is significantly higher than that of rainfall-induced landslides in other regions. With the backward position of rainfall peak in rainfall process, the thresholds become lower and steeper, indicating that more rainfall is needed to trigger landslides when the rainfall peak appears earlier than when it is late. Typhoon rainstorm-induced landslides are more sensitive to the heavy rainfall event after a long rainfall. The threshold model established in this study is consistent with the observation, indicating that it is effective for landslide monitoring during the typhoon season.

In the pursuit of sustainable urban development, it is imperative to address strategies for optimizing and harmonizing the configuration of urban spatial layouts, especially in response to rapid urbanization. Urban road networks play a pivotal role in shaping intricate urban spatial patterns as they intricately weave throughout the cityscape, significantly influencing land utilization, spatial arrangements, and the mobility of residents. The urban environment is a complex system teeming with multifaceted human activities. With the continuous evolution of advanced data and computational technologies, it is now feasible to construct intricate models for an in-depth exploration of urban spatial structures. In this study, we constructed an analytical framework that leveraged road network image recognition, multi-scale influence factors analysis, and predictions using codeless visual programming. Within this framework, deep learning and unsupervised clustering are applied to complete the hierarchical clustering of road networks and exploit the intrinsic association between cities. Meanwhile, hierarchical clustering and natural and humanistic attributes of cities are connected to investigate the strength of different attributes on specific clusters. The research has the following results: (1) In addition to the alignment of urban road network clustering outcomes, quantitative unevenness and geographical heterogeneity are also observed. (2) Multiple attributes have a complicated impact on urban space, including city size, and socioeconomic and geographic characteristics. Among these factors, socio-economic variables emerge as the principal drivers influencing urban spatial structure. (3) The research enhances the model’s generalization capability and predictive accuracy. As attributes increase and model learning improves, both the Area Under Curve (AUC) and F-1 scores exhibit continuous enhancements, ultimately achieving error-free predictions. The study’s findings offer valuable insights into urban spatial structures and systems, supporting urban decision-makers in addressing challenges associated with urban growth.

To explore the quality evaluation indicators of walnut and pecan varieties cultured in Yunnan, the contents of 12 nutritional indicators which included in mineral elements, protein, crude fat, and vitamin C of 27 walnut varieties were determined. 12 indicators were comprehensively explored by correlation analysis, principal component analysis and cluster analysis to reveal variation and comprehensive quality of walnut resources in Yunnan Province. Results showed that the variation of the content of 12 indicators was relatively rich, the variation coefficient ranged from 5% to 83%, among which the variation coefficient of vitamin C was the largest and the variation coefficient of crude fat was the smallest. The order of the average contents of 9 mineral elements was K>P>Mg>Ca>Mn>Na>Fe>Zn>Cu. The average contents of protein, fat and vitamin C were 15.62%, 68.63% and 4.66 mg/100 g, respectively. As demonstrated by the results of correlation analysis on 12 indicators, there was a significant positive correlation between protein and P, Fe, Mg, Ca, and Cu. The principal component analysis was used to comprehensively evaluate, showed that the comprehensive quality of walnut varieties 'Xiangcha' 'Yongping' 'Wanshu' and 'Xiaoyuanguo' were the best and the pecan variety 'Kaduo' was the better. The clustering analysis results showed that it can be divided into two major categories, namely the group rich in mineral elements and the group with high fat content. Subsequently, by combining with cluster analysis, K, Fe, Mg, Ca, Cu, protein and fat were determined as the critical indicators to evaluate the quality of walnut and pecan varieties cultured in Yunnan. Pecan varieties were suitable for extracting oil, and 22 main walnut varieties were suitable for direct consumption or the development of functional beverages rich in mineral elements and protein. The study clarified the critical indicators for evaluating the quality of Yunnan walnut and explored its quality characteristics, providing a scientific classification method, and laying a theoretical basis for the construction of the Yunnan walnut quality evaluation system.

In order to study the differences in the comprehensive quality and processing characteristics of different varieties of wampee (Clausena lansium (Lour.) Skeels), seven kinds of wampee ('Bingtanghuangpi' 'Jixinhuangpi' 'Jinhuangpi' 'Guifeisanhao' 'Baimihuangpi' 'Huananyihao' 'Wuhehuangpi') were taken as the research objects, the physical property, chemical composition and antioxidant capacity of the fruits were analyzed, and the processing characteristics of the seven kinds of wampee were evaluated and categorized using principal component analysis and cluster analysis. The results showed that the pulping rate, soluble sugar and organic acid content of seven kinds of wampee ranged from 50.51% to 60.71%, 18.93 to 48.91 mg/g and 8.13 to 14.00 mg/g. Respectively, the 'Bingtanghuangpi' had the highest pulping rate, the 'Baimihuangpi' had the highest soluble sugar content, the 'Jixinhuangpi' had the highest organic acid content. The total phenol content of pericarp, total phenol content of fruit pulp, total flavonoid content of pericarp and total flavonoid content of fruit pulp ranged from 2446.4 to 4045.5 mg/kg DW, 763.7 to 118.2 mg/kg, 234.1 to 678.7 mg/kg DW, and 96.8 to 143.4 mg/kg. Respectively, the 'Guifeisanhao' had the highest total phenolic and total flavonoid content, and the strongest antioxidant capacity (ferric reducing antioxidant power (FRAP), DPPH radical scavenging capacity). Nine phenolic compounds in wampee were identified by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and rutin was the main phenolic compound. The results of principal component analysis showed that 25 indicators reflecting the quality of different varieties of wampee, that can be expressed by four principal components (the cumulative contribution rate reached 87.51%), of which 'Guifeisanhao' scored the highest in the comprehensive evaluation of principal component analysis, and 'Bingtanghuangpi' scored the lowest. The results of cluster analysis were basically consistent with the results of principal component analysis. Overall, 'Guifeisanhao' and 'Wuhehuangpi' had the highest content of total phenolics, total flavonoids and the strongest antioxidant ability, but the soluble sugar content was relatively low, which was more suitable for the extraction of active substances. 'Bingtanghuangpi' and 'Baimihuangpi' had high soluble sugar content and low organic acid content, with moderate sweet and sour taste, which was suitable for fresh food, and the high organic acid content of 'Jinhuangpi' 'Huananyihao' and 'Jixinhuangpi' was more suitable for processing into food ingredient.

Abstract This study examines the impact of fire incidents on wildlife and habitats in the western oak forests of Iran (Zagros region). These forests are globally recognized for their exceptional biodiversity but are frequently threatened by wildfires. To achieve this, the study uses the space–time scan statistics permutation (STSSP) model to identify areas with a higher frequency of fires. The study also analyzes the effects of fires on the Zagros forests from 2000 to 2021 using remote-sensing MODIS data. Also, to understand the elements at risk of fire, burned areas were assessed based on the richness of vertebrate species, determined by the distribution of 88 vertebrate species. The results show that the annual fire rate in the Zagros forests is 76.2 (fire occurrences per year), calculated using the Poisson distribution. Findings show the highest fire rates are found in the northwest and a part of the south of the Zagros. The northwest of the Zagros also has the largest number of single fires and clusters, indicating a wide spatial distribution of fire in these regions. On the other side, it was unexpectedly found that these regions have the richest number of species and higher habitat value. The results demonstrate a significant correlation between the value of the habitat and the extent of burned areas (p

Decision-making based on complex human-machine algorithms can lead to discrimination of citizens based on gender, race and other grounds. However, in world science there is no idea of algorithmically conditioned discrimination of citizens by their place of residence. This also applies to the adoption of algorithmic decisions on the socio-economic development of regions. Therefore, the purpose of our study was to detect algorithmic bias in the results of socio-economic clustering of Russian regions. To achieve this goal, it was necessary to identify sensitive operations in cluster analysis that could lead to spatial injustice, form an array of articles on socio-economic clustering of subjects (regions) of the Russian Federation, analyze all articles for the possibility of algorithmic bias and identify Russian regions with potentially biased attitudes towards them as a result of clustering. The term ‘spatial algorithmic bias’ is proposed. Using the author’s semantic search algorithm in bibliographic databases, six hundred articles with empirical results of cluster analysis of Russian regions by socio-economic indicators were identified. The characteristics of the identified articles are given. The analysis of all the articles showed that algorithmic bias is most evident in the four operations of the clustering algorithm – deploying a conceptual model into an optimal set of indicators, selecting regions, choosing a way to combine regions into clusters and determining the number of clusters. Examples of discriminated Russian regions are presented for each operation. Three directions of further research are indicated. Practical significance may be associated with the adoption of unbiased decisions on regional socio-economic development based on fair clustering of the Russian Federation’s subjects

The tectonic zoning of the African-Antarctic sector of the Southern Ocean was carried out on the basis of cluster analysis of a large volume of heterogeneous geophysical and geological data. It is known that the degree of geological and geophysical exploration of this part of the ocean by marine surveys and drilling remains extremely low. The results of such an analysis make it possible in areas with weak geophysical knowledge to quickly draw up tectonic zoning schemes based on regional geophysical data that are freely available throughout the entire territory of the world ocean and use them as a basis for further interpretation. The following materials were used as input data or classification features: ocean floor topography, gravity anomalies in free air and in the Bouger reduction, ∆Ta anomalous magnetic field model, ocean floor age, geoid height data, and the SL2013sv seismotomography model. «K-means» algorithms and the «Gaussian mixtures» method used for clustering. Based on the results of the application of these algorithms, cluster models of the study region were obtained, in which the selected classes fully reflect the main tectonic elements.

To explore the differences in the composition, content and comprehensive quality of free amino acids (FAA) in millet from Longzhong area in Gansu Province，12 different cultivars of millet were used as research objects, the types and contents of FAA were determined by liquid chromatography-tandem mass spectrometry. Comprehensive evaluation of FAA quality was performed by principal component analysis (PCA) and hierarchical cluster analysis (HCA). The results showed that 19~20 kinds of FAA were detected in all kinds of millet, and the total content of FAA ranged from 1046.80~1773.85 mg/kg, asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu) and glycine (Gly) were the four main FAA in millet. There were significant differences in the contents of FAA (Pbitter amino acids>sweet amino acids>aromatic amino acids. 9 Medicinal amino acids and 3 branched chain amino acids were found in millet, the average content of medicinal amino acids was 706.34 mg/kg, the percentages of medicinal amino acids to total FAA was 53.63%, so millet had many potential medicinal values. There was a significant relation with FAA in different cultivars of millet, five principal components were extracted after PCA, and the cumulative contribution ratio of the five components was 86.20%, the FAA comprehensive scores of LG-23、LG-11 and YG-18 were ranked as the top three. The 12 different cultivars millet were divided into four groups by HCA, FAA comprehensive quality performances of groups Ⅰ (LG-23) and Ⅲ (LG-11 and YG-18) were optimal, the content of total FAA was significantly higher than the other two groups (P

The research investigated changes in surface water quality (SWQ) in the canals and rivers of Can Tho province in the Mekong Delta in southern Vietnam. Surface water was sampled at 34 points through months of the end of the rainy and flood season (October 2023), the beginning of the dried season (December 2023), and the middle of the dried season (February 2024). The SWQ parameters were compared with limited values of the national technical standard on SWQ and the Vietnamese water quality index. We used principal component analysis (PCA) to find sources causing surface water pollution and cluster analysis (CA) to identify the similarities in SWQ parameters among sampling points. The research showed that Can Tho's average SWQ value ranged at a good level. The SWQ in the dry season is worse than in the rainy season. Pollution from suspended solids, organic matter, nutrients, and coliform was found in Can Tho's surface water. The PCA resulted in the three key sources of PCs 1, 2, and 3 being elucidated by 65.3% of SWQ changes. Twelve current monitoring parameters, including temperature, pH, dissolved oxygen, total suspended solids, total organic carbon, biological oxygen demand, chemical oxygen demand, ammonium, nitrite, nitrate, phosphate, and coliform, are influencing SWQ. It is suggested from the CA results that the 34 monitoring points could decrease to 30 points in the case of financial issues. The results are beneficial in reviewing the sampling points and monitoring parameters for SWQ in Can Tho.

Background Traditional Chinese Medicine (TCM) has a systematic understanding of the occurrence, development, diagnosis and treatment of Hashimoto's thyroiditis complicated with thyrotoxicosis. However, at present, there are few studies on the clinical symptoms and syndrome distribution of TCM in this disease population, and there is also a lack of description of the syndrome distribution and syndrome diagnostic criteria of this disease in the national industry standards. Objective Using factor analysis combine with cluster analysis to explore the distribution of clinical symptoms and syndromes of Hashimoto's thyroiditis complicated with thyrotoxicosis, provide a basis for clinical syndrome differentiation and promote the standardization of Hashimoto's thyroiditis complicated with thyrotoxicosis syndrome. Methods From December 2020 to December 2021, 171 patients with Hashimoto's thyroiditis complicated with thyrotoxicosis who met the diagnostic criteria in the outpatient department of thyroid disease of Dongcheng District, Tongzhou District of Dongzhimen Hospital, Beijing University of Chinese Medicine and Sunsimiao Hospital was collected. The symptoms, signs, tongue, pulse and other four diagnostic information were collected by using the four diagnostic information collection table of Hashimoto's thyroiditis complicated with thyrotoxicosis'. Based on factor analysis and cluster analysis, the symptoms and syndrome distribution of Hashimoto's thyroiditis complicated with thyrotoxicosis were studied. Results Among the 171 patients with Hashimoto's thyroiditis complicated with thyrotoxicosis, there were 17 males and 154 females, with an average age of (39.98±13.30) years. Patients aged 20-60 years accounted for 87.72%. The symptoms with high frequency in the distribution of symptoms were fatigue, palpitation, irritability or impatience. Signs were swelling of the neck, finger tremor. The tongue image with higher frequency had red, thin tongue and tooth marks on the tongue. The moss were white and thin. The pulse condition were pulse string and pulse number. Factor analysis of 82 four diagnostic items of the questionnaire was collected, and 25 common factors were extracted. The cumulative variance contribution rate was 70.562%, and 62 meaningful symptoms were screened out. The 25 common factor results obtained by factor analysis were used as variables to perform R-type system cluster analysis, and a total of 5 types of syndrome types were obtained, namely: pattern of phlegm congealing due to liver depression, pattern of yin deficiency with effulgent fire, pattern of yang deficiency in spleen and kidney, pattern of qi stagnation due to liver depression, pattern of dual deficiency of qi and nutrient phases. Conclusion The basic TCM syndromes of Hashimoto's thyroiditis complicated with thyrotoxicosis can be divided into pattern of phlegm congealing due to liver depression, pattern of yin deficiency with effulgent fire, pattern of yang deficiency in spleen and kidney, pattern of qi stagnation due to liver depression, pattern of dual deficiency of qi and nutrient phases.

Benchmarking has been deemed useful for the study of regional competitiveness and innovation and is quickly expanding. The aim of this work is the formulation and validation of a method to select the right regions to compare. The proposed method is of general application and can therefore be used by any region across the world wishing to benchmark against others in the field of competitiveness and innovation. This method is carried out in two successive stages. In the first stage, regions with higher innovation and competitiveness than the region under study are identified. In the second, a subset of regions with a resource base similar to the one under study is selected, which makes them suitable for the comparison. The method is validated by applying it to a European region. The results show the validity and robustness of the proposed method.

The Italian Alpine region has a long-standing connection between the binary system of the tourism industry and silvopastoral sectors that offer different economic, ecological, and cultural benefits. This research investigates the perception of ecosystem services (ES) provided by mountain animal production among tourists in a specific mountain area (Upper Ellero Valley, North-West Italy). A total of 216 visitors were surveyed online between June and October 2022. The questionnaire was designed to explore the following aspects: (1) interviewees’ socio-demographic characteristics; (2) the perceived impacts of alpine livestock systems on ecosystem services, including also the animal welfare variable; (3) the heterogeneity of hikers in response to their perception of ES and (4) the assessment of the individuals’ opinion towards selected valorisation strategies of the herd-grazing production system. The responses about the ES perception were analysed using the Principal Component Analysis. The new principal components were employed to cluster the sample in the function of individuals’ perceptions of ecosystem services. Finally, the Correspondence Analysis was adopted to analyse the association between the three hikers’ groups and the proposed strategies for mountain area valorisation. This research revealed a positive perception of visitors towards the impact of herds on the ES. In addition, different opinions emerged among clusters related to the valorisation strategies adoptable for mountain area development exploiting the positive connection between animal farming and the environment. These findings could have concrete implications on the definition of social and economic development strategies for the alpine mountain valleys, representing an important source of production for national mountain pasture livestock farming.

Anomaly separation based on stream sediment data is an important step for mineral exploration. In this article, three methods of cluster analysis, factor analysis and fractal geometry have been used to separate the anomalous and suspected mineralization areas from the background areas. By combining these three methods, a possible mineralization was found in the Maleksiahkuh area. In addition, the relationship between the anomalies and the anomaly's host rocks was discussed. Maleksiahkuh is located 35 kilometers north of Zahedan and in the eastern part of the Flysch zone, Iran. Multivariate statistical analysis was performed. The results show a positive correlation between copper and molybdenum. The amount of chromium from the field is relatively high. Chromium is rich in the host mafic rocks. The presence of large concentrations of chromium in the region can be attributed to the presence of mafic rocks. The highest positive correlation was observed between manganese and cobalt, which is about 0.997. In addition, iron with titanium has a correlation of 0.984. Cobalt with iron has a correlation of 0.975. The cluster analysis for the region confirmed the existence of three clusters. The third cluster containing elements As, Sr, Sn, Sb, Pb, Cu, and Ag is probably related to the base-metal mineralization. Factor analysis was performed on the elemental concentrations. The sixth factor, which Cu and Ag elements have the highest weightage, was considered as another mineralization factor. The location of the most concentrated copper in the map derived from the Number-size (N-S) fractal method corresponds to the highest score in the factor rating map. There is a good match between copper anomalies and mafic rocks. Green crests have always been associated with mineralization, and studies show that there is a good relationship between mineralization and these rocks.

An intelligent tool wear prediction model has been proposed for the micro-grinding tool, optimized using a genetic algorithm (GA) based BP neural network. The GA-BP prediction model is applied with in-situ tool wear detection to obtain training set data and combines cluster analysis to divide the tool wear stages. To represent the uncertainty in wear characteristics, the loss of cross-sectional area of the micro-grinding tool has been selected as an index to evaluate tool wear loss. The K-means clustering algorithm is used to cluster and analyze the tool wear stages under different process parameters. The GA-BP neural network includes five neurons in the input layer: rotating speed, feed rate, cutting depth, grinding length, and the initial cross-sectional area of the tool. The output layer neuron predicts the loss of the tool's cross-sectional area. To validate the method, a series of micro-grinding experiments were performed under different parameters for the micro-groove array of monocrystalline silicon. The loss of the tool's cross-sectional area was measured by a self-made visual inspection system, providing learning samples for the prediction model. The predicted results of the GA-BP neural network model were compared with the traditional Gaussian process regression method. The results show that the GA-BP neural network model can correctly predict tool wear loss and identify wear stages under different process parameters and grinding lengths. It has higher prediction accuracy during the self-learning process, with an average error of 5% .

Character association and genetic diversity analyses were performed in the present study for the morphological and yield traits of Nerium oleander L. accessions to find out the major contributing traits towards flower yield. A panel of 20 diverse nerium accessions were evaluated at the Floriculture Research Station, Thovalai, Kanyakumari, Tamil Nadu during 2018 to 2022. Correlation analysis revealed that number of flowers per spikes had highly significant and positive correlation with flower yield. From the path analysis, the number of flowers per spike, days for bud initiation, flower weight, plant height and number of shoots/plants were identified to have high positive direct effect on flower yield. Clustering of nerium germplasm, revealed two distinct groups. Cluster I, comprising NI-1 to NI-19, suggested shared genetic traits among them, while Cluster II, represented by NI-20, showcases unique attributes. High inter-cluster distance between Cluster I and Cluster II suggested that the genotypes in these clusters had a significant degree of genetic diversity between them and that crossings between the genotypes in the cluster I with NI-20 would produce desirable transgressive segregants.

Extended Abstract Background: Salinity is one of the major abiotic stresses and the most limiting factor in agricultural production worldwide, affecting the growth, development, and final yields of crops. Rapeseed is one of the most important sources of oilseeds in the world, and its seeds contain more than 40% of oil. Moreover, the meal obtained from oil extraction has more than 35% protein, hence it currently ranks third among oil crops in the world after soybean and oil palm, making it necessary to identify the genotypes that tolerate salinity stress. The development and improvement of rapeseed cultivars with salinity tolerance and acclimation offer promising prospects for improving sustainable production in this area. Therefore, the current study aimed to investigate the responses of rapeseed genotypes to salinity stress through analyses of agronomic and biochemical traits. Methods: The genetic diversity between rapeseed lines in terms of agronomic, morphological, and physiological traits in saline soils was investigated in an experiment based on a randomized complete block design with 17 autumn rapeseed genotypes with three replicates in the research farm of East-Azarbaijan Agricultural and Natural Resources Research and Education Center. The measured traits were plant height, the number of fertile pods, number of seeds per pod, pod length, pod area, plant growth rate, 1000-seed weight (TSW), seed yield, oil content, and oil yield. The relationships between yield, yield components, and morphological traits were explored using the analysis of variance (ANOVA), comparison of averages, correlation analysis, cluster analysis, and biplot to understand the relative importance of traits affecting the yield of the studied genotypes. Results: The studied genotypes were significantly different from each other in pod length, pod area, number of fertile pods, number of seeds per pod, plant growth rate, seed oil percentage, plant height, TSW, grain yield, and oil yield. However, there were no significant differences between the studied genotypes in terms of harvest index and number of actual pods to potential pods. According to the mean comparisons, genotypes 5, 11, and 15 can be introduced as salinity-tolerant lines, and genotypes 2, 4, 6, 9, and 12 can be considered salinity-sensitive lines. According to the other traits, genotype 11 produced a high pod length, number of fertile pods, oil percentage, and oil yield, genotype 5 had a high growth rate and oil percentage, and genotype 15 presented a high height and number of fertile pods. According to the cluster analysis, the second and third groups contained tolerant and susceptible genotypes, respectively. The genotypes in the second group had the highest percentage of positive deviation from the overall mean for grain yield, plant height, harvest index, seed oil percentage, pod length, pod area, and number of fertile pods. Based on the biplot analysis, Karaj 8 and 14 genotypes had a strong relationship with the number of fertile pods, number of seeds per pod, pod length, pod area, and plant growth rate. Based on the obtained results, the plant height, TSW, seed yield, and oil content traits were closely correlated with Karaj 5, 7, 11, 10, and 15 genotypes. Based on the results of correlation analysis, the correlation coefficient of seed yield was positive and significant for three traits, i.e., plant height, oil percentage, and number of fertile pods, and the highest correlation coefficient (r = 0.879) was obtained for seed yield with seed oil percentage. Positive and significant correlations were measured for the number of seeds in pods with pod length (r = 0.699), pod area (r = 0.555), number of fertile pods (r = 0.678), and number of actual pods. Therefore, genotypes characterized by longer and more abundant pods play a crucial role in improving seed quantity, a key component of grain yield in saline environments. Consequently, the size and number of pods per plant serve as indicators of high-yield potential under such conditions. Based on the results of the principal component analysis (PCA), the first and second components had the highest relative variances, accounting for 44.66% and 31.22% of the total variance, respectively. Together, these two components accounted for 75.88% of the total variance. Factor loadings showed that traits such as number of fertile pods, seed yield, oil yield, and seed oil content had the highest factor loadings in the first component. Similarly, the plant growth rate had the highest factor load in the second component among all the studied traits. Cluster analysis divided the genotypes into four groups, and its dendrogram showed that all the studied genotypes were divided into four separate groups based on all the measured traits. The first group comprised three Karaj 1, 13, and 16 genotypes, the fourth group (like the first group) contained three Karaj 5, 11, and 15 genotypes, and the third group had five genotypes, namely Karaj 3, 7, 8, 10, and 14. The remaining genotypes were assigned to the second group. Conclusion: The results of the present study demonstrate acceptable genetic diversity among rapeseed genotypes in terms of the evaluated traits in saline lands. This shows the importance and the possibility of using these genetic resources to achieve promising and superior genotypes in breeding programs.

Extended Abstract Background: Wheat is a plant that is cultivated in different environments around the world and provides approximately 20% of the energy and 25% of the protein needs of the world population. Durum wheat (Triticum turgidum L. var durum) is an industrial product that is mainly used in the pasta production industry. In addition, the difference in the price of durum wheat in the world markets compared to bread wheat has prompted some countries to increase the area under cultivation and production of this crop and start its export to supply their bread wheat needs through imports, thereby saving large sums of foreign currency. Although it is often believed that durum wheat produces less yield than bread wheat, the results of national uniformity tests conducted in recent years have shown that not only the yield of durum wheat is not lower than that of bread wheat but also the promising lines of durum wheat has an increase in yield in the conditions of the tests. If we consider a 20% increase in the price of its grain, the development and promotion of its cultivation in tense areas can provide more economic benefits for the producers and the country. The production of crop cultivars with appropriate characteristics, high yields, and stable production is one of the main goals of wheat breeding programs. Therefore, the identification of suitable lines and cultivars for cultivation in each geographical region is of special importance. It is possible to achieve such a goal by evaluating the set of new lines of each plant in each region. According to the special program of the Seed and Plant Improvement Institute (SPII), which plans to increase the self-reliance coefficient of wheat for the introduction of new varieties, this study also aims to obtain more productive and promising durum wheat genotypes in the northern region of Khuzestan province. Methods: To identify the best durum wheat lines in terms of yield and other agricultural traits, an experiment was carried out during two 2020-2021 and 2021-2022 crop years in the research farm of Safiabad Agricultural and Natural Resources Research Center, Dezful. New durum wheat lines (n = 76) were evaluated observationally along with three control varieties Aran, Hana, and Mehregan in the first year and 37 best lines of the first year were evaluated in the form of an alpha lattice design, along with three control varieties, in the second year. In addition to seed yield, other important agricultural traits, including heading date, physiological maturity date, length of seed filling period, lodging rate, plant height, and 1000 kernel weight, were calculated and considered in the final selection. For the results of each year, statistical parameters (indices of central tendency) were measured using Excel software. The correlation between traits was measured using SPSS software. In addition, MetaR software was used to analyze the variance of the Alphalatis design. To group the tested lines, a dendrogram was drawn using the Ward method and SPSS software. Results: The results of the trait analysis in the first year showed that the tested lines had a very high variety so that their yield ranged from 5883 kg/ha for the weakest line to 8350 kg/ha for the best line, and the average yield of all tested lines was 7231 kg/ha. In addition, the average yield of 37 selected lines in the second year was about 6423 kg/ha, which was about one ton less than the yield of the same lines in the first year (7397 kg/ha). One of the main reasons for this decrease in yield can be attributed to the increase in the temperature of March in the second year (20 °C) compared to the first year (16 °C), which caused the average grain filling period from 49.3 days in the first year to reach 45.1 days in the second year. Finally, according to the total measured traits of each line during two crop years and cluster analysis, lines 6, 38, 57, 58, 59, and 73 were selected as the best lines. The yields of these lines were 8350, 8303, 7917, 7537, 8000, and 8130 kg/ha in the first year, and 6933, 6917, 6775, 7045, 7178, and 6815 kg/ha in the second year, respectively. The average yields of Aran, Hana, and Mehregan controls were 7207, 7148, and 7109 kg/ha in the first year, and 5698, 6208, and 5927 kg/ha in the second year, respectively. The dendrogram drawn using the total of the measured traits placed the 40 studied lines in three different groups, each of which had two subgroups. An important point about the cluster analysis is that the selected top lines (6, 38, 57, 58, 59, and 73) are placed in one group, and only line 61 is added to them. Conclusion: In the pedigree study of the top lines, a common parent named PLATA was found in the pedigree of the four lines, which probably made these lines superior to the other tested lines. According to these results, it is possible to propose and implement the use of this line to improve the wheat breeding program in the hot and dry areas of the south.

Background: This study aimed to analyze trends in oral health research among people with disabilities. Methods: Data were obtained from 70 oral studies on individuals with disabilities from 2000 to 2024. Keywords were analyzed. Frequency, betweenness centrality, and cluster analyses were performed using NetMiner. Results: The main keywords for oral health research on disabled people were oral health, dental caries, DMFT (decayedmissing- filled-teeth), dental treatment, oral health centers, and disabled children. As a result of the centrality analysis, DMFT had the highest connectivity, followed by disabled children, special care dentistry, oral health behavior, periodontitis, and health insurance. Cluster analysis results of research on disabled people: Group 1, oral diseases and functions of disabled people; Group 2, oral care for disabled children; Group 3, dental treatment for disabled people; Group 4, oral health policy; Group 5, oral care by dental hygienists; and Group 6, conservative dentistry. Conclusion: Considering the increase in the number of disabled people in a super-aging society, research on ways to promote oral health for disabled people, oral health policies, and training of oral health experts for disabled people is required.

Abstract Modern teaching has made significant progress, with many advanced equipment and technologies being introduced into the teaching process. Experimental teaching of engineering design courses is important. Due to limited teaching resources, engineering students need effective guidance during limited laboratory time. We will introduce artificial intelligence solutions to engineering education. We will use artificial intelligence technology for classroom behavior analysis to improve engineering design practice courses' teaching effectiveness. In an instructional milieu, image acquisition tools such as cameras are capable of real-time data capture, facilitating the identification and enumeration of students' emotional states. Concurrently, analytical software gauges the students' interaction patterns and performs comprehensive cluster analysis. Such multifaceted information provides valuable insights into the students' educational engagement, allowing educators to tailor their approach, thereby fostering enhanced pedagogical outcomes. The emotion recognition model we have developed, namely ERAM, demonstrates a rapid response rate coupled with dependable accuracy, making it a robust tool for classroom implementation. In contrast to the conventional post-lesson evaluations, our proposed technique furnishes immediate feedback throughout the instructional process. This real-time approach heralds a significant shift in instructional methodology, promoting timely intervention and adaptive teaching strategies. The control group experiment showed that intelligent systems improved teaching effectiveness by 8.44%. Intelligent systems can help teachers understand students' learning status and improve laboratory teaching quality in engineering design courses.

Abstract Background Arteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is increasingly used for limb salvage in ASO patients, but the outcomes vary. The development of prediction models using unsupervised machine learning may lead to the identification of novel subtypes to guide patient prognosis and treatment. Methods This retrospective study analyzed clinical data from 448 patients with ASOs who underwent endovascular therapy. Unsupervised machine learning algorithms were employed to identify subgroups. To validate the precision of the clustering outcomes, an analysis of the postoperative results of the clusters was conducted. A prediction model was constructed using binary logistic regression. Results Two distinct subgroups were identified by unsupervised machine learning and characterized by differing patterns of clinical features. Patients in Cluster 2 had significantly worse conditions and prognoses than those in Cluster 1. For the novel ASO subtypes, a nomogram was developed using six predictive factors, namely, platelet count, ankle brachial index, Rutherford category, operation method, hypertension, and diabetes status. The nomogram achieved excellent discrimination for predicting membership in the two identified clusters, with an area under the curve of 0.96 and 0.95 in training cohort and internal test cohort. Conclusion This study demonstrated that unsupervised machine learning can reveal novel phenotypic subgroups of patients with varying prognostic risk who underwent endovascular therapy. The prediction model developed could support clinical decision-making and risk counseling for this complex patient population. Further external validation is warranted to assess the generalizability of the findings.

The purpose of this paper is to identify the peculiarities of banks’ business models and assess their risks, which is especially relevant in the context of the war in Ukraine since 2014. The information base is the published statements for each month of 63 Ukrainian banks for the period from 1 January 2018 to 1 January 2024. The number of indicators is chosen in an empirical manner. Business models are investigated using the method of structural-functional groups of banks, which allows estimating large arrays of financial indicators, grouping banks with similar characteristics and drawing conclusions about the main risks. It is convenient to use neural networks, namely Kohonen’s self-organizing maps, to estimate large data sets. The largest group of banks places a significant part of assets in government securities and has an unstable resource base. The share of these banks in the system as of January 1, 2024 is 38% and total assets are 10%. The second group by number of banks is focused on corporate lending with a high share of current resources in liabilities, and includes 21% of banks, whose assets account for 31% of total assets. State-owned banks, PrivatBank and OschadBank, account for 35% of total assets. The business models of these banks are characterized by dependence on retail funds, a high share of investment operations, and high credit and currency risks. Ukraine’s banking system has significantly developed a risk-oriented approach to management, which allowed it to maintain stability in the face of a full-scale war.

In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.

Abstract Background In recent years data-driven population segmentation using cluster analyses of mainly health care utilisation data has been used as a proxy of future health care need. Chronic conditions patterns tended to be examined after segmentation but may be useful as a segmentation variable which, in combination with utilisation could indicate severity. These could further be of practical use to target specific clinical groups including for prevention. This study aimed to assess the ability of data-driven segmentation based on health care utilisation and comorbidities to predict future outcomes: Emergency admission, A&E attendance, GP practice contacts, and mortality. Methods We analysed record-linked data for 412,997 patients registered with GP practices in 2018-19 in Cwm Taf Morgannwg University Health Board (CTM UHB) area within the Secure Anonymised Information Linkage (SAIL) Databank. We created 10 segments using k-means clustering based on utilisation (GP practice contacts, prescriptions, emergency and elective admissions, A&E and outpatients) and chronic condition counts for 2018 using different variable compositions to denote need. We assessed the characteristics of the segments. We employed a train/test scheme (80% training set) to compare logistic regression model predictions with observed outcomes on follow-up in 2019. We assessed the area under the ROC curve (AUC) for models with demographic variables, with and without the segments, as well as between segmentation implementations (with/without comorbidity and primary care data). Results Adding the segments to the model with demographic covariates improved the prediction for all outcomes. For emergency admissions this increased discrimination from AUC 0.65 (CI 0.64–0.65) to 0.73 (CI 0.73–0.74). Models with the segments only performed nearly as well as the full models. Excluding comorbidity showed reduced predictive ability for mortality (similar otherwise) but most pronounced reduction when excluding all primary care variables. Conclusions This shows that the segments have satisfactory predictive ability, even for varied outcomes and a broad range of events and conditions used in the segmentation. It suggests that the segments can be a useful tool in helping to identify specific groups of need to target with anticipatory care. Identification may be refined with selected diagnoses or more specialised tools such as risk stratification.

According to V.A. Dragavtsev's concept, the reaction of the annual ring to the provocative environmental background of different years creates a “portrait” of adaptive polygenic genotype systems. By comparing the reactions of the main forest-forming species from an ecologically homogeneous habitat, it is possible to assess the level of similarity of their ecological properties. Cluster analysis of tree-ring chronologies of aspen and species growing together with it, correlation analysis of chronologies and indexed chronologies, as well as time series of meteorological factors and Fourier spectral analysis have been used for a dendroecological research into aspen growth features in the conditions of the Molokchinskiy Botanical and Entomological Reserve of the Sergiev Posad District of the Moscow Region. To compare with the chronologies of aspen from two stands, the chronologies of European white birch, English oak, grey alder and Norway spruce have been used. It has been established that the cluster analysis of indexed chronologies based on calculating the squared Euclidean distance and combining the chronologies into groups according to the rule of complete linkage distinguishes aspen chronologies into a separate cluster, very distant from the other species under consideration. According to the correlation analysis of indexed chronologies, the dynamics of radial increment of aspen is most closely related to the dynamics of radial increment of European white birch and gray alder. Moreover, the maximum correlation coefficient (0.71) is observed for the chronologies of aspen from two different stands. Among the climatic factors, the main limiting influence on aspen increment is exerted by July precipitation in the year of the annual ring formation and August precipitation in the year preceding the year of the annual ring formation. The response to precipitation in August last year is specific to aspen stands and has several options for ecophysiological interpretation. According to Fourier spectral analysis, aspen chronologies have the most pronounced cyclic component with a period of 10.4 years, which completely coincides with the cyclic component of the time series of Wolf numbers in the time interval under consideration. This distinguishes aspen from the rest of the studied forest-forming species. A conclusion has been made about the specificity of the dynamics of the radial growth of aspen in comparison with other forest-forming species under study.

IntroductionBarley is the fourth most common cereal crop in the world after wheat, maize, and rice, which is used for animal and human feed and malt production. This plant stands out among crops due to its unexpectedly wide range of adaptation and distribution compared to other cereals, earning it recognition as a model species. Determining the variation in the morphological and physiological traits of roots and shoots in the collection of barley genotypes can provide the basis for breeding new cultivars with suitable traits for better adaptation to specific environments. So in this study, some morphophysiological characteristics of roots and shoots of commercial barley cultivars were studied to identify cultivars with a higher root-to-shoot ratio as donor parents for breeding projects.Materials and MethodsIn this study, 21 barley cultivars were grown in a randomized complete block design with six replications in greenhouse conditions. Morphological and physiological traits related to roots and shoots were measured during the tillering and heading stages. Also, some traits affecting photosynthesis and yield during reproductive growth and harvesting stages were measured. Analysis of variance, mean comparison by LSD test at 5% probability level, and multivariate statistical analyses including principal components analysis, cluster analysis by Ward method, correlation analysis, and path analysis were performed by JMP and R statistical software.Results and DiscussionSignificant genetic diversity was observed for most of the traits among cultivars (P < 0.05). At the tillering stage, Behrokh and Mehtab cultivars and in the heading stage, Jolgeh cultivar showed the highest dry weight of root to shoot ratio. Reyhan variety showed the lowest dry weight of root-to-shoot ratio in both stages. The carbohydrate content of the root in the tillering stage and leaf proline, root volume, and the ratio of chlorophyll a to carotenoid in the heading stage had the highest significant correlation coefficients with dry weight of root to shoot ratio (P < 0.05). Path analysis revealed that the root carbohydrate content in the tillering stage and the ratio of chlorophyll to carotenoids and proline in the heading stage are the most effective traits of the dry weight of root-to-shoot ratio. Principal components analysis showed that the first and second principal components explain 33.63% and the first seven principal components explain 72.66% of the total changes. Also, the first principal component showed a positive and significant correlation with root to root-to-shoot ratio in the heading stage, and the second principal component showed a negative and significant correlation with this ratio (P < 0.05). The studied cultivars were classified into four groups based on cluster analysis so that the fourth and second clusters in the tillering stage and the third and second clusters in the heading stage had the highest and the first cluster in both stages had the lowest dry weight of root to shoot ratio.ConclusionBased on the results, Jolgeh and Reyhan cultivars can be used in breeding programs to improve and produce cultivars with higher root-to-shoot ratio, optimal yield, and more adapted to specific environments. These cultivars had the highest and the lowest dry weight of root-to-shoot ratio in both stages, respectively. Also, they were placed in different dimensions of the biplot drawn based on the first two principal components. In addition, these cultivars were observed in different classes based on the cluster analysis.AcknowledgmentThis project related to the Ph.D. thesis was financially supported by the Vice President for Research of the Faculty of Agriculture, Ferdowsi University of Mashhad, and the seeds of the cultivars used in this study were provided by the Karaj Seed and Plant Improvement Institute, the authors are thankful for this cooperation.

Abstract Agricultural ecosystems managed by farmers provide and receive multiple ecosystem services that are essential to sustain human well‐being. Understanding how agricultural production systems generate agroecosystem services is as important as gaining deeper insights into how farmers perceive and value them to ensure the adoption and implementation of sustainable agricultural practices. We conducted a comprehensive review and in‐depth analysis of existing valuation studies on agroecosystem services to identify potential future research directions. We found two broad research strands of articles: one focusing on ‘General assessment of agroecosystem services’ and the other on ‘Implications for policy making’. The first strand focuses more on individual preferences and monetary values, which might limit a comprehensive assessment. The second strand emphasizes participatory and deliberative techniques to reflect the plurality of values and to provide empirical evidence for policymaking. We recommend to move beyond monetary valuation techniques, and to develop methods that bridge the two strands as a promising avenue for future inter‐ and transdisciplinary research. Read the free Plain Language Summary for this article on the Journal blog.

Abstract There is a need to synthesize the vast amount of empirical case study research on social‐ecological systems (SES) to advance theory. Innovative methods are needed to identify patterns of system interactions and outcomes at different levels of abstraction. Many identifiable patterns may only be relevant to small sets of cases, a sector or regional context, and some more broadly. Theory needs to match these levels while still retaining enough details to inform context‐specific governance. Archetype analysis offers concepts and methods for synthesizing and explaining patterns of interactions across cases. At the most basic level, there is a need to identify two and three independent variable groupings (i.e. dyads and triads) as a starting point for archetype identification (i.e. as theoretical building blocks). The causal explanations of dyads and triads are easier to understand than larger models, and once identified, can be used as building blocks to construct or explain larger theoretical models. We analyse the recurrence of independent variable interactions across 71 quantitative SES models generated from qualitative case study research applying Ostrom's SES framework and examine their relationships to specific outcomes (positive or negative, social or ecological). We use hierarchical clustering, principal component analysis and network analysis tools to identify the frequency and recurrence of dyads and triads across models of different sizes and outcome groups. We also measure the novelty of model composition as models get larger. We support our quantitative model findings with illustrative visual and narrative examples in four case study boxes covering deforestation in Indonesia, pollution in the Rhine River, fisheries management in Chile and renewable wind energy management in Belgium. Findings indicate which pairs of two (dyads) and three (triads) variables are most frequently linked to either positive or negative, social or ecological outcomes. We show which pairs account for most of the variation of interactions across all the models (i.e. the optimal suite). Both the most frequent and optimal suite sets are good starting points for assessing how dyads and triads can fulfil the role of explanatory archetype candidates. We further discuss challenges and opportunities for future SES modelling and synthesis research using archetype analysis. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Background Cancer has become a major health concern due to the increasing morbidity and mortality rates, and its negative social, economic consequences and the heavy financial burden incurred by cancer patients. About 40% of cancers are preventable. The aim of this study was to assess the knowledge, attitudes, and practices regarding cancer prevention, and associated characteristics to inform the development of targeted cancer prevention campaigns and policies. Methods We conducted a cross-sectional survey of adult patients at Mohamed Sekkat and Sidi Othmane Hospitals in Casablanca, Morocco. Data collection was conducted by two trained interviewers who administered the questionnaire in-person in the local language. An unsupervised clustering approach included 17 candidate variables for the cluster analysis. The variables covered a wide range of characteristics, including demographics, health perceptions and attitudes. Survey answers were calculated to compose qualitative ordinal categories, including a cancer attitude score and knowledge score. Results The cluster-based analysis showed that participants in cluster 1 had the highest mean attitude score (13.9 ± 2.15) and percentage of individuals with a high level of knowledge (50.8%) whereas the lowest mean attitude score (9.48 ± 2.02) and knowledge level (7.5%.) were found in cluster 3. The participants with the lowest cancer attitude scores and knowledge levels were aged 34 to 47 years old (middle age group), predominantly females, living in rural settings, and were least likely to report health professionals as a source of health information. Conclusions The findings showed that female individuals living in rural settings, belonging to an older age group, who were least likely to use health professionals as an information source had the lowest levels of knowledge and attitudes. These groups are amenable to targeted and tailored interventions aiming to modify their understanding of cancer in order to enhance the outcomes of Morocco’s on-going efforts in cancer prevention and control strategies.

Purpose: Grape (Vitis vinifera L.) is one of the most important horticultural products that are grown in different parts of Iran and has high nutritional values. In this study, the genetic diversity of cultivars and genotypes of some vineyards of Markazi province were investigated for the preliminary selection of superior cultivars and genotypes in terms of morphological and fruit characteristics for use in grape breeding programs. Research method: For this purpose, grouping and comparing 84 grape cultivars and genotypes were carried out using 70 traits including phenological and vegetative traits, trichome and stomata, bunch and berry traits. Findings: Based on the results, the “Sahebi Hazaveh” cultivar with 1000.17 g had highest an average bunch weight to compare other cultivars and genotypes. Results showed that, some traits such as bunch weight, bunch shoulders, fresh weight, rachis weight, the ratio of bunch weight to peduncle weight, the ratio of rachis weight to bunch weight, dry weight of bunch shoulders, length of the tail of bunch, berry weight, pedicel weight, seed weight and length of seed had a high coefficient of variation. Factor analysis reduced the evaluated traits to 10 main factors showed that they justified 78.38% of the total variance. Cluster analysis divided cultivars and genotypes into 4 main groups at five Euclidean distances. Limitations: No limitations were encountered. Originality/Value: This study indicated that grapes germplasm resources in zone are of noticeable diversities and can be promising for the utilization in the breeding programs. Based on the results, cultivars and genotypes of “Khalili Khondab” region, “Yaghouti”, “Sahebi”, “Fakhri”, “Kharvand” and “Kondori” Hazaveh region and “Sahebi” Aghbolagh region in leafing time, late flowering, sugar percentage, bunch and berry characteristics, stomatal density, standing and lying trichome density in leaves were superior to other cultivars and genotypes.

The aim of the article is to review the current literature in the field of agritourism. The data for the analysis was collected from the Web of Science database from 1990–2022: a total of 1,834 bibliometric publication records. The article presents both quantitative and qualitative analysis. The results indicated five main research areas: (a) the impact of the attitudes of the community on the perception and management of ecotourism and agritourism; (b) the impact of models of the perception of the quality of services in agritourism on customer satisfaction; (c) the quality of services and tourist culinary experiences that motivate the choice of agritourism destinations; (d) the typology of agritourism farms and their differentiation in the development of rural tourism; (e) the impact of state policy on the management of rural tourism and agritourism. The limitations of the conducted research, which mainly concern the methodological part, should also be borne in mind: including the selected publication database, and search criteria such as publication year or language.

In Austria, it is possible to enroll in an unlimited number of programs simultaneously and consecutively. Study duration and student performance are important performance indicators of the university system. The disciplines of psychology and sociology share similarities in their curriculum structures but are different in selectiveness due to their admission rules. They show differences in their motivations to enroll and their trajectories through higher education, leading to different constellations of “dropout” and “graduation.” The aim was to identify and compare groups of students’ outcomes in bachelor’s programs of psychology and sociology along the dimensions of workload and duration, incorporating the possibility of enrolling in multiple programs. The sample consisted of 6498 students between the academic years of 2010/11 and 2022/23 who either graduated or dropped out of one or more programs. Cluster analyses using four algorithms per individual outcome constellation, categorization, and aggregation of the results suggest a longer overall study duration for students with more than one program. In graduation–graduation and graduation–dropout clusters, groups with less overall workload could be identified. The transferability of passed exams may be an important component for students in multiple programs. Implications for policy and practice are discussed.