Agglomerative Hierarchical Clustering Research Articles

Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals

We propose the HOSA algorithm to pick P-wave arrival times on seismic arrays. HOSA comprises two stages: a single-trace stage (STS) and a multi-channel stage (MCS). STS seeks deviations in higher-order statistics from background noise to identify sets of potential onsets on each trace. STS employs various thresholds and identifies an onset only for solutions that are gently variable with the threshold. Uncertainty is assigned to onsets based on their variation with the threshold. MCS verifies that detected onsets are consistent with the array geometry. It groups onsets within an array by hierarchical agglomerative clustering and selects only groups whose maximum differential times are consistent with the P-wave travel time across the array. HOSA needs a set of P-onsets to be calibrated. These sets may be already available (e.g., preliminary catalogs) or retrieved from picking (manually/automatically) a subset of traces in the target area. We tested HOSA on 226 microearthquakes recorded by 20 temporary arrays of 10 stations each, deployed in the Irpinia region (Southern Italy), which, in 1980, experienced a devastating 6.9 Ms earthquake. HOSA parameters were calibrated using a preliminary catalog of onsets obtained using an automatic template-matching approach. HOSA solutions are more reliable, less prone to false detection, and show higher inter-array consistency than template-matching solutions.

Insights into the contribution of multiple factors on Ixodes ricinus abundance across Europe spanning 20 years using different machine learning algorithms.

The interplay of biotic and abiotic factors driving Ixodes ricinus abundance trends are not fully understood. Machine learning (ML) approaches are being increasingly used to explore this and predict future abundance patterns of this species, however, the studies focusing on this to date have had limitations (including short study duration, limited sample size, narrow geographical range and use of a single ML model). This study was undertaken to address these limitations by applying 11 predictive ML models (across three data clustering techniques) to a large I. ricinus occurrence dataset (27,150 records) containing geographical and temporal data from a 20-year period across 30 European countries, coupled with data covering a range of climatic and habitat features (temperature, rainfall, Normalised Difference Vegetation Index (NDVI), percentage of discontinuous urban fabric and land use category). To assess which ML model was most suited to prediction of I. ricinus abundance, four performance metric values were calculated per model: Normalised Root Mean Square Error (NRMSE), Scatter Index (SI), Mean Absolute Percentage Error (MAPE) and R2, all of which describe the statistical relationship between predicted and actual I. ricinus abundance values. Furthermore, using a Random Forest (RF) model across three clustering methods, we determined which features most significantly impacted upon I. ricinus abundance. The study demonstrated that Agglomerative Hierarchical Clustering (AC) methods and Linear Regression (LR) modelling performed best with this dataset. Our findings revealed that land use and rainfall were the primary contributors to I. ricinus abundance, with temperature playing a lesser role. This was measured according to the extent of prediction error increase following exclusion of that factor from the analysis. We provide a summary of the factors most strongly linked to I. ricinus abundance, which can be used to guide interventions to aid the control of ticks and tick-borne disease across Europe.

Categorization of Beef Longissimus Lumborum and Gluteus Medius Muscles Based on Metabolic Attributes Is More Informative Than Muscle pH

Muscle metabolism is generally monitored using muscle pH. However, pH does not account for all metabolic effects on meat quality. We evaluated the effectiveness of agglomerative hierarchical clustering in creating clusters of beef longissimus and gluteus medius muscles based on metabolic traits. Beef carcasses (n = 100) were selected at grading based on longissimus thoracis pH (< 5.6, 5.60 to 5.74, 5.75 to 5.9, and > 5.9). Metabolic traits characterizing oxidative and glycolytic metabolism were measured on each muscle. A subset of longissimus lumborum muscles were placed in an in vitro glycolytic system with 2 temperature decline rates to evaluate glycolytic efficiency. Gluteus medius muscles exhibited more oxidative metabolism than longissimus lumborum muscles. Metabolic traits measured in one muscle were generally positively correlated to the same trait measured in the other muscle. Clustering of metabolic traits within each muscle produced similar dendrograms. Clustering of longissimus lumborum muscles based on metabolic traits produced 4 distinct clusters (High pH, Glycolytic, Chaperone, and Soluble). Clustering of the high pH was generally, but not totally, in agreement with classifications based on pH. The remaining longissimus lumborum clusters did not differ in pH. Similar to the longissimus lumborum clusters, the gluteus medius clusters included High pH and Glycolytic clusters and a cluster with low values for protein solubility and peroxiredoxin 2 abundance. In the in vitro system, pH decline was affected by a cluster × temperature decline rate interaction (P < 0.05). The soluble cluster had the least extensive pH decline under the fast temperature decline but had the most rapid pH decline at the slower pH decline. These results indicate that clustering muscles based on several metabolic factors was more effective than categorizing muscles based on muscle pH. Metabolic variation identified by clustering was related to differences in the glycolytic machinery that can be differentially impacted by chilling rate.

Persistently different patterns of patient’s global assessment of health in rheumatoid arthritis are associated with pain and impaired function more than with inflammation: an inception cohort study over 15 years

ObjectiveTo assess patient’s self-reported global assessment of health (PGA) in relation to inflammation, pain and disability in patients with rheumatoid arthritis (RA).MethodsData were obtained from the Better Anti-Rheumatic PharmacOTherapy early...

Chilean Mediterranean forest on the verge of collapse? Evidence from a comprehensive risk analysis.

World forests are experiencing significant modification due to the confluence of climate change and deforestation, with Mediterranean forests facing particularly acute threats. The Chilean Sclerophyllous Mediterranean Forest is considered a world biodiversity hotspot, a restricted ecosystem type that is highly affected by global change drivers. Despite the high ecological and environmental importance of this ecosystem, an integrated assessment of its risk derived from climate and land-use change is lacking. This study estimates the level of risk of all sclerophyll forest stands in Chile, by assessing their exposure to several factors linked with climate change, land cover change, wildfires, change in the vegetation functional properties, and habitat fragmentation. We also generated a spatially explicit estimate of the main factors underlying the risk of each sclerophyllous forest stand. We constructed a multifactorial risk index based on the different analyzed variables, exploring the main drivers associated with the risk of each forest stand using principal components analysis with agglomerative hierarchical clustering. We found that 39.8% of sclerophyll forest stands reached a high or very high-risk index, with the highest levels concentrated between 32°S and 34°S. The vigor and net primary production of sclerophyll forest stands decreased in 90.9% and 86.6%, respectively. We identified three groups of forest stands: A) northern group (29.9°S to 33.5°S), which has experienced high decrease of net primary production and vegetation vigor; B) central group (32°S to 35°S), also affected by degradation and replacement by croplands; and C) southern group (34°S to 39°S), affected by drought and exotic forest plantations. The results of this study provided clarity on the current state of the sclerophyll forest, allowing for the identification of spatial differences in the risk and their underlying factors. Our results and data can be useful for informing policy and supporting sustainable management of government agencies and practitioners.

Volcanic aerosols captured by plants: A study of nanoparticles and their chemical composition.

Nanoparticles (NPs) exhibit high reactivity and mobility in the environment, and a significant capacity to penetrate living organisms, potentially leading to harmful effects. Volcanoes are the second major source of natural NPs emitted into the atmosphere, with an estimated flux of 342 Tg/year. Few studies have focused on their fate. Thanks to technological advances in single-particle inductively coupled plasma mass spectrometry (spICP-MS), this trend is starting to reverse. La Soufrière volcano in Guadeloupe, chosen as a case study, exhibits increasing hydrothermal activity since its last eruption in 1530. This study aims to characterize NPs produced during volcanic activity by analysing ancient ash deposits, as well as those formed during periods of volcanic inactivity by examining condensates near fumaroles, as ultrafine particles are primarily generated through gas condensation. In this study, plants are utilized as samplers for NPs produced by fumarole activity. The use of a ICP-MS time-of-flight in single particle mode (spICP-ToF-MS), combined with data processing techniques such as hierarchical agglomerative clustering, enables the detailed characterization of NPs by determining their multi-element composition, concentration, and mass distribution. The results demonstrate that plants can effectively serve as samplers, even under the extreme environmental conditions present at the volcano's summit. However, differences in their efficiency at trapping particles on leaf surfaces can be attributed to varying physical characteristics of the plants. The spICP-ToF-MS analysis identified three types of multi-elemental NPs (NP-Al+Si, NP-Al+Fe, NP-Ti+Al) and three mono-elemental NPs (NP-Al, NP-Si, NP-Fe). Additionally, NPs containing trace elements were detected exclusively in undiluted Sphagnum pore water, where one tri-elemental NP (Sr-Ce-La), one bi-elemental NP (CeLa), and nine mono-elemental NP families (Cr, Cu, Zn, Sr, Y, Zr, Ba, La, Ce) were identified. Elements with potential negative effects on biota such as Cu, Zn, and Cr were also highlighted. Furthermore, the composition of the total of NPs (excluding families) is compared with elemental ratios from materials of different origins (volcanic, detrital, atmospheric) to validate their volcanic source.

Sepsis subtypes and differential treatment response to vitamin C: biological sub-study of the LOVIT trial.

We hypothesised that the biological heterogeneity of sepsis may highlight sepsis subtypes with differences in response to intravenous vitamin C treatment in the Lessening Organ Dysfunction with VITamin C (LOVIT) trial. Our aims were to identify sepsis subtypes and to test whether sepsis subtypes have differences in treatment effect to vitamin C and describe putative biological effects of vitamin C treatment. We measured biomarkers of inflammation, at baseline and at 7days post-randomisation, in 457/863 (53.0%) of participants with plasma samples in the LOVIT trial. We used agglomerative hierarchical clustering on log10-transformed baseline data of 26 biomarkers to identify sepsis subtypes. We analysed differences in vitamin C treatment effect with regression models incorporating robust standard errors to report odds ratio and 95% confidence intervals (OR(95% CI)). All analyses were completed blinded to treatment allocation. Our cohort included 233/429 (54.3%) allocated to vitamin C and 224/434 (51.6%) allocated to placebo. A three-subtype model best explained the variance in our data. Subtype-2 had the highest, and subtype-3 had the lowest levels of inflammatory response. In paired longitudinal samples, vitamin C did not have discernible anti-inflammatory effects, with anti-inflammatory effects related to time since randomisation and concomitant hydrocortisone treatment. The treatment effect estimates (OR (95% CI)) for subtype-1, subtype-2 and subtype-3 were 1.04 (0.63-1.73), 1.33 (0.53-3.36) and 1.95 (0.85-4.49), respectively (test of heterogeneity p = 0.002). We report three sepsis subtypes based on inflammatory response profile. No subtype benefitted from vitamin C treatment in the LOVIT trial, with heterogeneity of treatment effect in the magnitude of harm. Funded by the Lotte and John Hecht Memorial Foundation; LOVIT ClinicalTrials.gov number,NCT03680274.

Resting-state microstate dynamics abnormalities in children with ADHD and co-occurring sleep problems.

Children with attention-deficit/hyperactivity disorder often experience sleep problems, exacerbating symptoms, and cognitive deficits. However, the neurophysiological mechanisms underlying such deficits remained unclear. This study aims to use resting-state microstate analysis to investigate the neurophysiological characteristics in children with ADHD and sleep problems and explore whether neurophysiological abnormalities are associated with sleep problems. Five-minute eyes-closed resting-state EEG data were collected in 34 children with both ADHD and sleep problems, 32 children with ADHD without sleep problems, and 22 healthy controls aged 6-12 years. Participants' parents completed the ADHD Rating Scale, Behavioral Rating Inventory of Executive Function, and Children's Sleep Habits Questionnaire. Five minutes of eyes-closed resting-state EEG data were collected, and agglomerative hierarchical clustering was used for microstate analysis. ANCOVAs, adjusted for sex and IQ, were used to compare ADHD symptoms, executive function, and microstate parameters across groups. Pearson partial correlations, controlling for sex and IQ, examined the association between microstate parameters and sleep problems. Children with both ADHD and sleep problems exhibited more severe inattentive (20.4±3.1 vs 18.0±3.4, p<0.05) and total symptoms (14.3±6.0 vs 13.0±5.3, p<0.05), along with greater deficits in emotional regulation (1.88±0.58 vs 1.60±0.42, p<0.05, organizational of materials 2.56±0.41 vs 2.20±0.55, p<0.05, behavioral regulation (1.88±0.40 vs 1.70±0.36, p<0.05), and global executive function (2.14±0.30 vs 1.95±0.30, p<0.05), compared to children with ADHD without sleep problems. Moreover, Both ADHD groups exhibited significantly reduced microstate D occurrence and lower transition probability from microstate C to D compared to healthy controls (all p<0.05). Additionally, no significant correlation was found between sleep problems and microstate parameters in all three groups after adjustment (all p>0.05). Children with both ADHD and sleep problems showed greater symptom severity and more pronounced executive function deficits compared to children with ADHD without sleep problems and healthy controls. Additionally, both ADHD groups showed overlapping atypical microstate parameters, suggesting children with co-occurring ADHD and sleep problems may share similar aberrant neurophysiological characteristics with children with ADHD alone. Resting-state EEG microstate parameters may serve as a sensitive tool for assessing sleep problems in children with ADHD, distinguishing them from typically developing children.

Woody species diversity, structure and community distribution along environmental gradients of Seqela Dry Afromontane forest in Northwestern Ethiopia.

Dry evergreen Afromontane forests are severely threatened due to the expansion of agriculture and overgrazing by livestock. The objective of this study was to investigate the composition of woody species, structure, regeneration status and plant communities in Seqela forest, as well as the relationship between plant community types and environmental variables. Systematic sampling was used to collect vegetation and environmental data from 52 (20 m x 20 m) (400 m2) plots. Density, Diameter at Breast Height (DBH), basal area, frequency, and importance value index (IVI) of woody species were computed to characterize the vegetation structure of the forest. Agglomerative hierarchical cluster analysis and Canonical Correspondence Analysis (CCA) with R software were used to identify plant communities and analyse the relationship between plant community types and environmental variables, respectively. A total of 68 woody plant species belonging to 63 genera and 44 families were identified. The Shannon diversity index and evenness values of the study area were 2.12 and 0.92, respectively. The total basal area and density of woody species were 27.4 m2 ha-1 and 1079.3 individual ha-1, respectively. The most frequent woody species in the Seqela forest included Albizia gummifera (51.92%), Croton macrostachyus (44.23%), Olinia rochetiana and Teclea nobilis (36.54%). Additionally, the most dominant species, as indicated by their importance value index (IVI), were Erythrina brucei (IVI = 11.24), Prunus africana (IVI=8.68), and Croton macrostachyus (IVI=7.38). Four plant community types were identified: Albizia gummifera - Ekebergia capensis, Prunus africana - Croton macrostachyus, Vachellia abyssinica - Dombeya torrida and Schefflera abyssinica - Teclea nobilis. The CCA results showed that the variation of species distribution and plant community formation were significantly (P < 0.05) related to altitude, organic matter, aspect, slope and soil available phosphorus. The regeneration status assessment of the forest revealed a good regeneration status, which was linked to diverse and abundant seed bank in the soil can ensure a continuous supply of seeds for regeneration; therefore, it is recommended to implement periodic soil seed bank assessments to monitor seed diversity and abundance and inform targeted conservation actions.

Evaluating and Modelling Soil Detachment Capacity in Rills After Treatment With Biochar From Solid Waste of Olive Oil Mills

ABSTRACTAlthough being a viable substrate to reduce soil erosion, the effects of biochar from solid waste of olive oil mills (OMSW biochar) on soil detachment capacity in rills (Dc) has never been explored. Furthermore, no equations have been proposed to predict important parameters (soil erodibility factor, Kr, shear stress, τ, and its critical value, τc, for rills) of this hydrological process in treated soils. This study was conducted in agro‐forest sites of Northern Iran to evaluate Dc and key properties of soils–organic carbon (OC), aggregate stability (MWD), bulk density (BD) and carbon exchange capacity (CEC)—treated with OMSW biochar in comparison to untreated sites through flume experiments. Moreover, regression models were developed to predict Dc, Kr and τc for both treated and untreated soils. Compared with the untreated soil, the application of this biochar noticeably increased OC (+85%), MWD (+51%) and CEC (+101%), and reduced BD (−11%) and Dc (−31%). The correlation analysis revealed significant but not high associations between physical properties on one the hand, and soil detachment capacity in rills, on the other hand. Overall, the soil treatment with OMSW biochar impacted agro‐forest soils to a severe extent. Treated soils were discriminated from untreated sites into two distinct groups by the principal component analysis and agglomerative hierarchical cluster analysis. The linear equations interpolating Dc and τ estimated Kr, and τc with accuracy in treated and untreated soils (r2 &gt; 0.74, p &lt; 0.05). The best prediction capacity of Dc was given by power equations applied to the stream power (r2 &gt; 0.78, p &lt; 0.05). The multiple regression equation developed to estimate Dc from the water flow rate and soil slope was also very accurate (r2 &gt; 0.95 and NSE, coefficient of Nash and Sutcliffe, &gt; 0.89). These results help land managers and hydrologists to control and predict rill detachment in long and steep hillslopes against the risk of soil erosion.

Relationship between deadwood structural diversity and carbon stock along environmental and disturbance gradients in Tropical dry forests

Abstract Deadwood is a substantial component of forest ecosystems playing a vital role in maintaining ecosystem health and functioning. However, there is limited information on deadwood stand structure which encompasses attributes such as type, quantities and distribution of deadwood pieces and how it is related to its biomass. This study examined the relationship between deadwood species structural diversity and carbon stock along different environmental and disturbance factors in forest and woodland ecosystems. An agglomerative hierarchical clustering analysis was used to identify species communities, followed by indicator species analysis which was done to determine the species significantly associated with each community. Species richness, evenness and Shannon-Wiener diversity index were calculated to determine deadwood species diversity in both ecosystems. Multimodel inference approach was used to analyse the relationship between deadwood carbon stock and diversity indices, soil properties, climate and proximity to roads and settlements. Three communities were identified from forest ecosystems while four communities were from woodland. Multimodel analysis found a positive significant relationship between deadwood carbon stock and species abundance, Shannon-Wiener diversity, soil moisture and proximity to roads in both ecosystems. These findings provide insights into conservation strategies that prioritize protection and restoration of ecosystems as carbon reservois.

Knowledge Graphs to Accumulate and Convey Knowledge from Past Experiences in Search and Rescue Planning and Resource Allocation

ABSTRACT Planning and allocation of resources in search and rescue operations is critical and complex. Those tasks require consideration of several factors such as the nature of the emergency, location, people involved, and weather conditions. In this paper, we investigate the specific requirements for effective decision-making and planning in search and rescue operations in Norway. These requirements were determined using data gathered through expert interviews and analysis of mission reports. We propose a framework designed for search and rescue decision support, including its architecture and components. We also discuss the implementation of the service layer. We used information retrieval methods, i.e. BM25 and TF-IDF, for distance computation and compared their performance using k-means and hierarchical agglomerative clustering methods. The evaluation through silhouette score and Davies-Bouldin score shows that the hierarchical agglomerative clustering using BM25 performs well for the given dataset. The search and rescue mission reports published by the Norwegian Safety Authority were used as a dataset. In addition, the overall framework is evaluated by the domain experts using qualitative analysis. Finally, we discuss potential framework implementation challenges and provide suggestions for future research directions, in addition to presenting insights from the framework design process.

Cluster analysis of social determinants of health and HIV/AIDS knowledge among Peruvian youths using Kohonen’s self-organized maps: a data-exploration study based on a Demographic and health survey

ABSTRACT Background Human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) have evolved into a global development burden, with nearly 40 million infections and 25 million deaths. Compared to other age groups, youth have increased risks of contracting the disease due to social and health structural factors; thus, additional efforts are needed to effectively tackle the challenges associated with this age group. Epidemiological studies employing unsupervised learning techniques are essential for shaping public health policies. Objective This study aimed to describe the Peruvian youth population based on their sociodemographic, health, and economic characteristics using an unsupervised learning approach through the development of a neural network model based on Kohonen’s self-organizing maps (SOMs), allowing the identification of social profiles in the study population. Methods This quantitative study used data from the 2019 Peruvian Demographic and Family Health Survey. An SOM network model for clustering individuals with similar attributes and clustering prototype vectors based on the agglomerative hierarchical clustering (AHC) method and their visualization on an SOM was applied to the study sample. Results Clustering of prototype vectors yielded four clusters, each of which represented a profile of Peruvian youths based on their knowledge of HIV/AIDS and structural health determinants. Conclusions Kohonen’s neural networks allowed the identification of patterns and behaviors among youths in Peru, quantifying and characterizing the four social clusters regarding HIV/AIDS and their social determinants. Kohonen’s maps may benefit healthcare professionals and policymakers by offering a useful method for tailoring interventions and policies based on the detected profiles, thereby enhancing the visibility of these focal points at the national level.

TAXONOMICAL IMPLICATIONS OF NATURAL COMPONENTS IN DELIMITATION OF SOME SPECIES OF ASTERACEAE FAMILY IN BAGHDAD

This study aimed to investigate the taxonomical relationship among eight species belonging to Asteraceae based on natural products as fresh leaves of eight species (Carthamus tinctorius L., Centaurea iberica Trevir. ex Spreng., Conyza bonariensis (L.) Cronquist, Conyza canadensis (L.) Cronquist, Launaea mucronata (Forssk.) Muschl., Senecio glaucus L., Sonchus asper (L.) Hill, and Sonchus tenerrimus L.) have been collected from different regions of Baghdad. The phytochemical screening of hexane extracts from eight plants showed different secondary metabolites in the studied species. Gas chromatography-mass spectrometry (GC-MS) analysis of eight hexane extracts showed the existence of eighty-one chemical compounds of eight taxa of Asteraceae, including hydrocarbons, alcohols, organic acids, esters, amines, and heterocyclic compounds. The agglomerative hierarchical clustering analysis dendrogram was carried out depending on 81 chemical compounds of the eight taxa of Asteraceae, gained by Euclidean distance using Ward's method. The HCA dendrogram showed the closeness of Sonchus asper and Sonchus tenerrimus and a divergence between Conyza canadensis and Conyza bonariensis and their affinity to Launaea mucronata while Carthamus tinctorius is the most divergent species from the rest of the species.

Risk factors for metabolic syndrome in the premetabolic state assessed using hierarchical clustering study in a health screening group

Early detection of a premetabolic status that is at risk for metabolic syndrome (MetS) but not meeting the criteria is crucial. This study examined 27,623 participants aged 20–50 (mean: 40.7) years who underwent initial health screening at Kangbuk Samsung Hospital (2011–2019), focusing on individuals with one or two MetS components. Hierarchical agglomerative clustering was used to form MetS risk clusters based on initial and follow-up data, including age, resting heart rate (rHR), serum uric acid (UA), C-reactive protein (CRP), gamma-glutamyl transpeptidase, and ferritin levels, and nonalcoholic fatty liver disease (NAFLD), periodontal disease, and Helicobacter pylori infection duration. Kaplan–Meier and generalized additive models were used to present the restricted mean survival time (RMST) and identify onset contributors. Clusters with NAFLD and elevated UA levels had the highest MetS risk, whereas those with uniformly low biomarker levels revealed the lowest risk. During follow-up, a cluster initially comprising 60.2% moderate-risk patients exhibited high biomarker levels and had the worst MetS prognosis (RMST: 211 days). UA, CRP levels, and rHR contributed to the incidence of MetS in the fitted model. Machine learning can predict the premetabolic state at MetS risk in a population-based cohort.

Abnormalities of resting-state EEG microstates in older adults with cognitive frailty.

This study aims to analyze the characteristics of EEG microstates across different cognitive frailty (CF) subtypes, providing insights for the prevention and early diagnosis of CF. This study included 60 eligible older adults. Their resting-state EEG microstates were analyzed using agglomerative adaptive hierarchical clustering. Microstate temporal parameters were extracted through global field power peak-based backfitting. Spearman's partial correlation analysis and linear mixed-effects models were employed to investigate the relationship between microstate temporal parameters and CF. Statistical differences were observed in transition probabilities (TPs) from microstate B to A between healthy controls (HCs) and reversible cognitive frailty (RCF) group (t = -2.076, P = 0.042). Potentially reversible cognitive frailty (PRCF) and RCF group also exhibited statistical differences in the TPs from microstate B to A (t = 3.122, P = 0.003). In the RCF group, the occurrence of microstates A and B differed significantly from microstate C (tAC = 3.455, PAC = 0.002; tBC = 3.108, PBC = 0.004). In the PRCF group, the occurrence of microstates A, B, and C differed significantly from microstate D (tAD = -3.688, PAD = 0.001; tBD = -3.334, PBD = 0.002; tCD = -4.188, PCD < 0.001). The neural networks and processing modes engaged by microstate D during executive memory tasks differ between RCF and PRCF. A decreased occurrence of microstate C and higher TPs of microstates A and B may serve as early warning signals for RCF. Conversely, an increased occurrence of microstate D and decreased TPs of microstates C and D indicate the onset of PRCF.

Vertiport Infrastructure Location Optimization for Equitable Access to Urban Air Mobility

Urban air mobility (UAM) has recently emerged as a promising new transportation mode, with various potential use cases. Facility location problems are well studied and of significant importance for various transportation modes. This work introduces a vertiport location identification framework, focusing on demand coverage and infrastructure accessibility. An Agglomerative Hierarchical Clustering (AHC) model was utilized for the identification of candidate vertiport locations, along with a k-means algorithm, for comparison and validation purposes, based on an estimated UAM demand pattern. A genetic algorithm (GA) was then formulated, for the solution of the proposed Uncapacitated and Capacitated Vertiport Location Problems (UVLP and CVLP, respectively), variations of the Uncapacitated and Capacitated Facility Location Problems. To evaluate and compare the introduced methodology, different existing facility location problems (FLPs) were considered and solved exactly using integer and linear programming. These are the Location Set Covering Problem (LSCP), the Maximal Coverage Location Problem (MCLP), and the p-median problem. The p-center problem was also considered and solved via a heuristic approach. The proposed framework is illustrated through applications in the Chicago Metropolitan Area, with the demand estimated on the basis of existing taxi and Transportation Network Company (TNC) data.

Agrupamiento de una muestra de las principales economías y mercados emergentes con respecto de su incertidumbre de la política económica

Objective: this study carries out pattern identification in a sample of 16 major and emerging economies in function of their economic policy uncertainty. Methodology: this paper applies for the groping procedure K-Means, Agglomerative Hierarchical Clustering (AHC), and Clustering and Density-Based Spatial Clustering with Noise (DBSCAN). Data: This research uses the Economic Policy Uncertainty (EPU) Index calculated monthly by the EPU Agency for several countries. In particular, it examines EPU indexes for a sample 16 countries in five crisis periods between 2008 and 2024; the sample was chosen based on data availability. Results: global crises have created distinct country clusters transcending traditional economic groupings based on development status or geographical location. Notably, in the COVID-19 pandemic it generated an unprecedented global EPU homogeneity among countries. High-uncertainty clusters consistently emerge, often comprising large economies directly affected by crises. Limitations: there are possible biases in news-based component of EPU indices. Originality: to the best of the authors’ knowledge, multiple clustering techniques for various crisis periods have not been implemented before. Conclusion: global crises can equalize policy uncertainty, challenging conventional notions of economic resilience. The empirical findings emphasize the importance of considering EPU in a global context for those responsible for improving the design of economic policy.

In varietate concordia - cluster analysis of EQ-5D-5L value sets in European Union countries.

The transferability of health policies in the European Union (EU) faces challenges due to economic, political, and healthcare system factors,including cross-country differences in health preferences. In order to aid policymaking,previous research has grouped EU countries based on geographical proximity or literature-based criteria, but not on health preference data. The EQ-5D-5L instrument, which measures health-related quality of life and reflects unique national health preferences shaped by cultural and social factors, is used to evaluate policies, technologies and interventions, but has not been used to group countries. Thus, this study aims to identify clusters of EU-27 countries with similar preference patterns using published EQ-5D-5L value sets. Hierarchical agglomerative clustering was employed on 13 EU-27 countries' EQ-5D-5L value sets, first analyzing regression coefficients for each dimension-level and then creating a simulated coefficient distribution considering uncertainty. Five clusters of EU-27 countries emerged: (1) Belgium, Netherlands, Sweden, Germany); (2) Hungary, Italy, Portugal; (3) Poland, Romania; (4) France, Spain; (5) Denmark, Ireland. All clusters except cluster5 gave higher importance to "Pain and Discomfort"; all clusters except cluster1 gave less importance to "Usual Activities". "Anxiety and Depression" and "Pain and Discomfort" had the largest heterogeneity in valuation across severity level. Identified clusters of countries with distinct health preferences provide insights for health policy transferability in the EU. Recognizing countries with similar health preferences can aid EU policymaking and transferability efforts, particularly in early-stage policymaking, while also fostering closer collaboration on health policy decisions and best practice sharing. Further development of national value sets within the EU is necessary for a more accurate representation.

A Cheminformatics Workflow to Select Representative TSCA Chemicals for New Approach Methodology (NAM) Screening.

The Toxic Substances Control Act (TSCA) requires the US EPA to evaluate the hazard and exposure of new and existing chemicals. New chemical notifications are typically data-poor and EPA has historically relied upon approaches including chemical categories to fill data gaps. As part of a multi-year Research Program, opportunities are being explored to leverage New Approach Methods (NAMs) in hazard and exposure assessments. Data from a battery of in vitro NAMs will be generated to form a case study for an adaptable approach to inform new chemical assessments. Herein, a cheminformatics workflow was developed to identify a set of ∼300 representative candidate chemicals for in vitro screening from the TSCA non-confidential active inventory. The freely available web application ClassyFire was used to categorize all discrete organic structures from the TSCA inventory into one of 68 primary structural categories. Large primary categories were subcategorized into smaller categories using hierarchical agglomerative clustering, ultimately yielding 180 structural terminal categories. The inventory was filtered to substances that lacked previous ToxCast bioactivity screening, were associated with physicochemical property predictions indicating non-volatile solids or liquids, and had a higher chance of procurement. Amenability predictions for liquid chromatography-mass spectrometry were also generated to provide an indication of which chemicals lent themselves to aqueous-based screening and analytical verification in solvated samples. Structures associated with transformation in solvent, potentially explosive or highly reactive, were excluded. Potential candidate substances were selected on the basis of being structurally representative of the terminal category and meeting other screenability conditions. A final set of 318 candidate chemicals were proposed to undergo analytical quality control and screening in a range of broad and targeted biological technologies for human health-relevant end points. Finally, in silico tools were applied to explore predicted hazard profiles of these candidate substances relative to the full inventory.