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Background: Refugees and asylum seekers (ASRs) are frequently exposed to loss in addition to a variety of other stressors and often display high levels of various psychological symptoms.Objective: The study aimed to primarily determine clusters of prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD), and depression symptoms in bereaved ASRs and secondly identify predictors of cluster membership. Sociodemographic  - and flight-related variables were investigated in exploratory analyses.Method: ASRs in Germany (N = 92) with interpersonal loss exposure, i.e. at least one missing or deceased relative or friend, were assessed with interview-based questionnaires for PGD, PTSD, and depressive symptoms. We used k-means cluster analysis to distinguish symptom profiles and logistic regression analyses to identify predictors of cluster membership.Results: We found a three-cluster-solution. The PGD-cluster (30%) was characterised predominantly by PGD symptoms, while the PGD/PTSD-cluster (32%) had high PGD and PTSD and moderate depressive symptoms. The resilient cluster (38%) showed low symptoms overall. insecure residence status predicted membership in the PGD and PGD/PTSD clusters relative to the resilient cluster, whilst higher attachment anxiety predicted membership in the PGD/PTSD cluster relative to the other clusters. Explorative analysis revealed duration of stay as a significant predictor.Conclusion: Findings can extend the current knowledge about different symptom profiles among bereaved ASRs in Europe. Insights to attachment  - and migration-related variables distinguishing between these profiles offer starting points for interventions.

Bereaved asylum seekers and refugees in Germany can be grouped into three symptom clusters: (1) predominantly prolonged grief, (2) high prolonged grief, high posttraumatic stress, and moderate depressive symptoms, and (3) low symptom load.Attachment  ­ and migration-related variables (i.e. residence status, duration of stay, and attachment anxiety) distinguish between cluster membership.Results highlight the importance of attending to profiles and not only single categories of symptoms and attachment features in bereaved asylum seekers and refugees.

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Treatment for diabetes includes anti-diabetic medication in addition to lifestyle improvements through diet and exercise. In Japan, protocol-based pharmacotherapy management allows drug treatment to be provided through cooperation between physicians and pharmacists, based on a protocol that is prepared and agreed upon in advance. However, there are no studies to clarify the relationship between patient characteristics and therapeutic effects after pharmacist intervention in protocol-based pharmacotherapy management for patients with diabetes. Therefore, this study aimed to use protocol-based reports from pharmacies to understand the status of outpatient diabetes medication compliance. We classified patients with diabetes on the basis of patient characteristics that can be collected in pharmacies and investigated the characteristics that impacted diabetes treatment. Patients were prescribed oral anti-diabetic drugs at outpatient clinics of Hitachinaka General Hospital, Hitachi, Ltd., from April 2016 to March 2021. Survey items included patient characteristics (sex, age, number of drugs used, observed number of years of anti-diabetic drug prescription, number of anti-diabetic drug prescription days, and presence or absence of leftover anti-diabetic drugs) and HbA1c levels. Graphical analyses indicated the relationship between each categorised patient characteristic using multiple correspondence analyses. Subsequently, the patients were clustered using K-means cluster analysis based on the coordinates obtained for each patient. Patient characteristics and HbA1c values were compared between the groups for each cluster. A total of 1,910 patients were included and classified into three clusters, with clusters 1, 2, and 3 containing 625, 703, and 582 patients, respectively. Patient characteristics strongly associated with Cluster 1 were ages between 65 and 74 years, use of three or more anti-diabetic drugs, use of 3 years or more of anti-diabetic drugs, and leftover anti-diabetic drugs. Furthermore, Cluster 1 had the highest number of patients with worsening HbA1c levels compared with other clusters. Using the leftover drug adjustment protocol, we clarified the patient characteristics that affected the treatment course. We anticipate that through targeted interventions in patients exhibiting these characteristics, we can identify those who are irresponsibly continuing with drug treatment, are not responding well to therapy, or both. This could substantially improve the efficacy of their anti-diabetic care.

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Particulate matter with a diameter ≤ 2.5 µm (PM2.5) is a complex mixture of particles with a variety of compositions potentially including sulfate ions (SO42-), nitrate ions (NO3-), ammonium ions (NH4+), organic and inorganic elemental carbon, and metals. Here, the temporal composition evolution of PM2.5 was analyzed to characterize its emission source, origin, and external influences. The concentrations of wintertime PM2.5 chemical compositions in Seoul, Korea during the period of 2012-2021 were classified into four representative clusters using a K-means cluster analysis method. Cluster 1 exhibited high concentrations of NO3- and NH4+ ions mainly due to the prevalence of emissions from domestic manure and fertilizer sources in the northeast. High concentrations of these two ions are conducive to generation of ammonium nitrate (NH4NO3) through atmospheric chemical reactions, resulting in relatively long-lasting high PM2.5 concentrations in Seoul. In cluster 2, high concentrations of SO42-, vanadium, and nickel were observed in frequent south-westerly winds, indicating the domestic influence of industrial facilities. Cluster 3 showed high concentrations of potassium ions and organic carbon, highlighting a pronounced external influence transported from China via prevailing westerly winds. Cluster 4 showed low PM2.5 concentrations accompanied by strong winds in warm environments, which are uncommon in winter. This study revealed that the air quality in Seoul, which was influenced by many factors, could be classified into four representative patterns. Our results provide insights into the emission sources, major influences, and responsible mechanisms of high PM2.5 concentrations in Seoul, which can help with air quality policies.

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The provenance study of archaeological materials is an important step in understanding the cultural and economic life of ancient human communities. One of the most popular approaches in provenance studies is to obtain the chemical composition of material and process it with chemometric methods. In this paper, we describe a combination of the total-reflection X-ray fluorescence (TXRF) method and chemometric techniques (PCA, k-means cluster analysis, and SVM) to study Neolithic ceramic samples from eastern Siberia (Baikal region). A database of ceramic samples was created and included 10 elements/indicators for classification by geographical origin and ornamentation type. This study shows that PCA cannot be used as the primary method for provenance purposes, but can show some patterns in the data. SVM and k-means cluster analysis classified most of the ceramic samples by archaeological site and type with high accuracy. The application of chemometric techniques also showed the similarity of some samples found at sites located close to each other. A database created and processed by SVM or k-means cluster analysis methods can be supplemented with new samples and automatically classified.

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The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, ß-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

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This study has several purposes. First, identify indicators contributing to the performance of small and medium-sized enterprises (SMEs) that could be affected by the COVID-19. Second, formulate the framework to measure the level of vulnerability of SMEs. Third, assign the SMEs into several clusters. Data used in this research were collected through web-based closed questionnaires and short telephone interviews. This study used Content Validity Analysis, Analytical Hierarchy Process, Multi-Attribute Value Theory approach, K-means Clustering Analysis, and Discriminant Analysis for data processing. The data processing results indicated that the 44 valid indicators belonging to ten dimensions could be used to measure the level of vulnerability of SMEs whose performance was affected by the COVID-19 pandemic. The surveyed SMEs can be segmented into four clusters, namely resilient cluster, low vulnerability cluster, moderate vulnerability cluster, and high vulnerability cluster. Most of the surveyed SMEs belong to the moderate and high vulnerability clusters. The differences between the clusters were based on 16 indicators. These indicators include levels of supplier disruption and the SMEs' market in which the SMEs operate or expect to operate. The results of this study help quantify how the pandemic could generate different levels of impact on each indicator that could depend on the business and what policymakers should consider as they contemplate the scale of the required intervention. Overall, this study contributes to the literature on the effects of the pandemic on SMEs by synthesizing the findings of studies on the impact of COVID-19 on SMEs. The study also determined the framework and the equation for measuring the level of SME vulnerability caused by the pandemic.

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Understanding the groundwater hydrogeochemical processes and aquifer hydraulic connections are essential for effective prevention of water inrush in concealed coal mines. In this study, 40 groundwater samples were collected from the loose layer aquifer (LA), coal measure aquifer (CA), and limestone aquifer (LA) in the Pansan coal mine, Huanan coalfield, China, and the major ion concentrations were analyzed by bivariate diagrams (Na+ + K+ - Cl- versus Ca2+ + Mg2+ - SO4 2- - HCO3 - and CAI-I versus CAI-II), multivariate statistical methods, and receptor model in order to identify the water-rock interactions and aquifer hydraulic connections. Piper diagram showed that groundwater in LA and TA was dominated by the Na-Cl type, while groundwater in CA was mainly of the Na-HCO3 type. Based on the results of bivariate diagrams and PCA/FA, weathering of silicate minerals and cation exchange (source 1), sulfate dissolution (source 2) and chloride dissolution (source 3) were the main processes controlling the groundwater chemistry. Unmix model revealed that the mean contribution of source 1 to CA samples was 74%, while LA and TA samples have higher contributions from evaporite dissolution (source 2 and source 3) relative to CA samples. Moreover, both clustering analysis methods (Q-type hierarchical and K-means cluster) confirmed the existence of a hydraulic connection between LA and TA in the northeastern part of the study area. It is concluded that the application of multivariate statistical analysis to interpret groundwater chemistry can provide useful guidance to prevent water inrush in coal mines.

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RATIONALE AND OBJECTIVES: Drug-seeking behavior occurs more readily in some individuals than others. This phenomenon is considered in studies of drug self-administration in which high drug-seeking/taking individuals can be identified. In contrast, studies of conditioned place preference (CPP) often involve a random sample of drug-naïve rodents that includes phenotypes not considered relevant to addiction. The main objective of the current studies was to determine if a priori identification of different conditioning phenotypes could improve the validity and sensitivity of CPP expression as a preclinical test for vulnerability to addiction. METHODS AND RESULTS: Analysis of cocaine place conditioning data from 443 Swiss-Webster mice revealed a trimodal distribution with peaks corresponding to means of k = 3 clusters. The cluster means occurred at high, low, or negative preference scores, the latter suggesting a phenotype acquiring conditioned place aversion (CPA). The same clusters were identified in mice conditioned with methamphetamine, MDPV, or amphetamine, and these clusters remained stable and reliable during three additional expression tests spaced at 24 h. A meta-analysis of effect sizes obtained from CPP literature revealed a positively skewed distribution affected by sample size, consistent with the existence of a CPA phenotype within the populations tested. A dopamine receptor antagonist, flupentixol, blocked cocaine CPP expression in a group containing all phenotypes, but sensitivity improved markedly when CPA phenotypes were excluded from the dataset. CONCLUSIONS: These studies suggest that taking phenotype into consideration when designing place conditioning studies will improve their application as a preclinical tool in addiction biology and drug discovery.

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It is known that complex growth environments often induce inhomogeneity in two-dimensional (2D) materials and significantly restrict their applications. In this paper, we proposed an efficient method to analyze the inhomogeneity of 2D materials by combination of Raman spectroscopy and unsupervised k-means clustering analysis. Taking advantage of k-means analysis, it can provide not only the characteristic Raman spectrum for each cluster but also the cluster spatial maps. It has been demonstrated that inhomogeneities and their spatial distributions are simultaneously revealed in all CVD-grown MoS2, WS2 and WSe2 samples. Uniform p-type doping and varied tensile strain were found in polycrystalline monolayer MoS2 from the grain boundary and edges to the grain center (single crystal). The bilayer MoS2 with AA and AB stacking are shown to have relatively uniform p-doping but a gradual increase of compressive strain from center to the periphery. Irregular distribution of 2LA(M)/E2g1 mode in WS2 and E2g1 mode in WSe2 is revealed due to defect and strain, respectively. All the inhomogeneity could be directly characterized in color-coded Raman imaging with correlated characteristic spectra. Moreover, the influence of strain and doping in the MoS2 can be well decoupled and be spatially verified by correlating with the clustered maps. Our k-means clustering Raman analysis can dramatically simplify the inhomogeneity analysis for large Raman data in 2D materials, paving the way towards direct evaluation for high quality 2D materials.

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Confocal Raman Microspectroscopy (CRM) was employed to clarify the cellular response of cisplatin in osteosarcoma (OS) cells with different dosages and incubation times. The K7M2 mouse osteosarcoma cells were treated by cisplatin in 0 µM (UT group), 20 µM (20 T group), and 40 µM (40 T group) doses for 24-h (24H group) and 48-h (48H group), respectively. Raman spectroscopy was utilized to analyze the drug induced variations of intracellular biochemical components in osteosarcoma cells. The spectral results shows that the main changes in its biochemical composition come from nucleic acids. By adopting three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)), principal component analysis combined with support vector machine models (PCA-SVM) was built to address the spectral variations among all investigated groups. Meanwhile, multivariate curve resolution alternating least squares (MCR-ALS) was further utilized to discuss on the chemical interpretation on the acquired spectral results. Moreover, Raman spectral images, which is reconstructed by K-means cluster analysis (KCA) with point-scanned hyperspectral dataset, was applied to illustrate the drug induced compositional and morphological variations in each subcellular region. The achieved results not only prove the application potential of Raman based analytical technique in non-labeled intracellular studies, but also illustrate the detailed compositional and structural information of cisplatin induced OS cell responses from the perspective of multivariate analysis and imaging of Raman spectroscopy.

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BACKGROUND: Education on internet games for parents and internet game literacy are needed to prevent problematic internet game playing in Korea. We created an 18-item Internet Game Literacy Scale (IGLS). It is a valuable tool for assessing the positive and negative aspects of internet game play. We aimed to determine the validity of the IGLS and the cut-off for the tendency for internet gameplay. METHODS: An online research company gathered data from 300 participants. Factor analysis, including Cronbach's α and consistency coefficient, exploratory factor analysis, and confirmatory factor analysis were conducted to verify the 18 items of the IGLS. Additionally, a K-means cluster analysis was performed to determine the cut-off values for positive and negative IGLS scores. RESULTS: The 18 items of the IGLS were proven to be reliable, as evidenced by a high Cronbach's alpha (α = 0.892). The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.903, and Bartlett's test of sphericity was good (χ² = 1,623.314, P < 0.001). All 18 items were segregated into two factors, with nine items each. The eigenvalue of all 18 items was significant at > 0.4. In the analysis of the validity of the 18-item IGLS with confirmatory factor analysis (CFA) (maximum likelihood estimation, with an oblique method), the fit indices of the standard three-factor model reached acceptable standards. The cut-off point of the total score between the low positive and average positive groups was 23, and the cut-off point of the total score between the average positive and high positive groups was 30. The cut-off point of the total score between the low negative and the average negative groups was 24. The cut-off point of the total score between the average negative group and the high negative group was 32. CONCLUSION: The study assessed the reliability and validity of the IGLS and suggested a cut-off for low, average, and high Internet game literacy degree with 300 Korean adults aged 21-49 years. The current results suggest that the IGLS has good internal consistency and a proper cut-off for positive and negative internet game literacy degrees.

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The plant health is governed by many factors: soil playing a central role which exhibits huge variability in its characteristics. Micronutrients even though needed in small quantities by plants play an indispensable role in affecting the crop growth and development. The assessment of spatial variability of different soil parameters is incumbent for tackling the loss of crop productivity on account of non-receipt of desirable inputs. The present investigation centered on the spatial distribution of soil parameters and micronutrients was conducted to delineate management zones (MZs) in cold arid region (Kargil) of India for proficient micronutrient management. Overall 454 georeferenced representative soil samples at the depths of 0-15 cm were garnered. The soil samples were processed and analyzed for different soil parameters encompassing pH, EC (electrical conductivity), SOC (soil organic carbon), and available micro-nutrient (iron, manganese, zinc, copper and nickel) concentrations. The distinct variation in the soil properties including micronutrients was identified with coefficient of variation ranging as low as 5.62% to moderate (21.16 to 42.49%) and as high as 159.63%. Semivariogram analysis and ordinary kriging of soil variables under study revealed diverse spatial distribution exhibiting medium to high spatial dependence in the region. PCA (principal component analysis) and K-means clustering were expended for the delineation of MZs. Four principal components (PCs) having eigen values > 1 and accounting for 70% of the total variation were subjected to further analysis. The five potential MZs were demarcated on the basis of K-means cluster performance index, and heterogeneity in parameters was discerned. The results of study corroborate that the spatial variability analysis of different soil parameters for delineation/identification of MZs might be effectually employed for site-specific micronutrient management.

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Literature suggests that music performance anxiety (MPA) is prevalent in adolescence, a developmental period with increased likelihood of experiencing anxiety under evaluative conditions. Evidence also indicate that individuals may respond to evaluative situations in distinct ways. Factors contributing to the individuality of responses in evaluative situations (such as test taking and musical performance) are not yet fully understood. This study investigated student typologies in adolescent instrumental learners. Participants included 410 learners who completed the Young Musicians' Performance Questionnaire. K-Means cluster analysis revealed three typologies: Cluster 1 - moderately anxious students evidencing lower levels of motivation and feeling ineffective but guarding their self-esteem; Cluster 2 - highly anxious students evidencing negative self-perceptions and being susceptible to experiencing maladaptive MPA; Cluster 3 - low anxious students evidencing high levels of motivation and confidence and inclined toward experiencing adaptive MPA. The 3-cluster solution effectiveness was validated with discriminant analysis. Significant associations between examination achievement and cluster membership revealed variations between clusters. Thematic analysis of qualitative data facilitated further understanding of their characteristics. This study adds to the body of MPA literature by exploring the different ways with which adolescent musicians interpret and respond to anxiety inducing situations. Findings have implications for clinical and educational practice.

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The aim of this study was to identify serum ferritin (SF) cut-off points (COPs) in a cohort of healthy full-term normal birth weight infants who had repeated measurements of SF and haemoglobin every 3 months during the first year of life. The study included 746 full-term infants with birth weight ≥2,500 g, having uncomplicated gestations and births. Participants received prophylactic iron supplementation (1 mg/day of iron element) from the first to the 12th month of life and did not develop anaemia during the first year of life. Two statistical methods were considered to identify COPs for low iron stores at 3, 6, 9 and 12 months of age: deviation from mean and cluster analysis. According to the K-means cluster analysis results by age and sex, COPs at 3 and 6 months for girls were 39 and 21 µg/L and for boys 23 and 11 µg/L, respectively. A single COP of 10 µg/L was identified, for girls and boys, at both 9 and 12 months. Given the physiological changes in SF concentration during the first year of life, our study identified dynamic COPs, which differed by sex in the first semester. Adequate SF COPs are necessary to identify low iron stores at an early stage of iron deficiency, which represents one of the most widespread public health problems around the world, particularly in low- and middle-income countries.

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Burial is applied to dispose of livestock carcasses due to its convenience and cost efficiency despite concerns about groundwater contamination by leachate from burial pits. In particular, the burial method has caused debates about groundwater contamination sources around on-farm livestock burial sites because of pre- and coexisting contamination from livestock production and agriculture. To assess the causes of groundwater contamination around poultry burial pits that were constructed after an outbreak of avian influenza in 2010-11 in Korea, hydrochemical data of groundwater samples from monitoring wells (MWs, n = 14) and household wells (HWs, n = 30) were monitored to differentiate contamination sources. Hydrochemical data indicated that groundwater from MWs is characterized by higher enrichments of inorganic constituents including electrical conductivity (EC), NH4, Ca, Mg, K, SO4, HCO3, Fe(Total), and Mn(Total), but lower concentrations of DO than groundwater from HWs. The combined use of the principal component analysis (PCA) and K-means cluster analysis (KCA) indicated that groundwater in seven MWs was affected by leachate. The parameters such as NH4, Ca, Mg, K, SO4, HCO3, Fe(Total), and Mn(Total) are expected to be useful to identify the impact of leachate on groundwater in agricultural areas. This study suggests that (1) regional hydrochemical characteristics should be assessed to distinguish the effect of livestock burial leachate from other contamination sources and (2) the combined use of PCA and KCA is effective to identify the weakened impact of leachate leakage among overlapping multiple sources and processes of groundwater contamination.

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Multiple goals balancing is an important but not yet fully validated dimension of complex problem solving (CPS). The present study used process data to explore how solvers clarify goals, set priorities, and balance conflicting goals. We extracted behavioral indicators of goal pursuit from the log data of 3,201 students on the third subtask of the "Ticket" task in the PISA 2012 CPS test. Cluster analysis was used to identify 10 groups that varied in goal pursuit behavior. Logistics and least-squares regression analysis were used to explore how goal pursuit affected task scores and CPS proficiency. The results showed that competent solvers clarified goals and weighed priorities more effectively. They also made trade-offs between conflicting goals. The importance of theoretically-driven log data analysis and coping strategies in the face of multiple goals conflict scenarios was discussed.

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Confocal Raman microspectral imaging (CRMI) is an advanced cell-imaging method that maps endogenous molecular compositions with their unique spectral fingerprint indicators. The aim of this work was to provide a visualized understanding of subcellular features of live osteosarcoma cells using a 532-nm laser excitation without the use of dyes or molecular probes. Both malignant osteoblast and spindle osteosarcoma cells derived from the BALB/c mouse osteosarcoma cell line K7M2 were investigated in this work. After preprocessing the obtained spectral dataset, K-means cluster analysis (KCA) is employed to reconstruct Raman spectroscopic maps of single biological cells by identifying regions of the cellular membrane, cytoplasm, organelles, and nucleus with their corresponding mean spectra. Principal component analysis (PCA) was further employed to indicate variables of significant influence on the separation of the spectra of each cellular component. The biochemical components of the two cell types were then extracted by showing the spectral and distribution features attributed to proteins, lipids, and DNA. Using this standardized CRMI technique and multivariate analysis approaches, the results obtained could be a sound foundation for a typical Raman imaging protocol of live cellular biomedical analysis.

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Vegetation restoration often has a significant effect on the supply of an ecosystem service (ES). Assessment of this effect is crucial for informed decision-making in sustainable ecosystem management. In this respect, this study analyses three regulating, two provisioning, and a single cultural ES over a 30-year period (1985 to 2015, with 15 years pre-restoration and 15 years post-restoration) in the Loess Plateau, China, using data from a combination of modelling and statistical yearbooks. On applying a suite of standard statistical tools, results indicate: (1) regional scale restoration promotes the increase of vegetation cover as the coverage increased faster between 2000 and 2015 than between 1985 and 2000; (2) vegetation restoration changes the temporal trend of regulating ESs, and enhances the supply of provisioning and cultural ESs; (3) the 40 municipalities of the Loess Plateau can be divided into four ES categories where areas with poor ES delivery account for about 30% of the Loess Plateau; (4) vegetation restoration changes the interaction among ESs, resulting in synergistic relationships between provisioning and regulating ESs; (5) precipitation has a significant impact on regulating ESs, while population density is critical for provisioning and cultural ESs. This study demonstrates that ESs, their interactions and their groupings can change across both time and space following the implementation of a vegetation restoration programme, which makes understanding ES dynamics complicated. Recommendations are provided for improved and coherent ecosystem management.

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Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers' risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention.

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The coasts of the West Antarctic Peninsula are strongly influenced by glacier meltwater discharge. The spatial structure and biogeochemical composition of inshore habitats are shaped by large quantities of terrigenous particulate material deposited in the vicinity of the coast, which impacts the pelagic and benthic ecosystems. We used a multitude of geochemical and environmental variables to identify the radius extension of the meltwater impact from the Fourcade Glacier into the fjord system of Potter Cove, King George Island. The k-means cluster algorithm, canonical correspondence analysis, variance analysis and Tukey's post hoc multiple comparison tests were applied to define and cluster coastal meltwater habitats. A minimum of 10 clusters were needed to classify the 8 km2 study area into meltwater fjord habitats (MFHs), fjord habitats and marine habitats. Strontium content in surface sediments is the main geochemical indicator for lithogenic creek discharge in Potter Cove. Furthermore, bathymetry, glacier distance and geomorphic positioning are the essential habitats explaining variables. The mean and maximum MFH extent amounted to 1 km and 2 km, respectively. Extrapolation of the identified meltwater impact ranges to King George Island coastlines, which are presently ice-covered bays and fjord areas, indicated an overall coverage of 200-400 km2 MFH, underpinning the importance of better understanding the biology and biogeochemistry in terrestrial marine transition zones.This article is part of the theme issue 'The marine system of the West Antarctic Peninsula: status and strategy for progress in a region of rapid change'.