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Motion sickness (MS) is a common physiological response that often occurs when individuals are exposed to environments with repeated acceleration stimuli. MS results from a mismatch between the vestibular system and visual and proprioceptive inputs. As a crucial organ for sensing acceleration stimuli, the vestibular system is closely related to the onset of MS. However, the complex pathogenesis of MS has led to its diagnosis primarily relying on subjective questionnaires, with a lack of objective indicators for evaluation. To identify objective indicators for evaluating MS, we conducted rotating chair stop experiments based on the principle of vestibulo-ocular reflex with 65 volunteers, obtaining their nystagmus slow-phase velocity and related time constants. Additionally, we conducted detailed MS questionnaires with these volunteers to assess their MS susceptibility. Through correlation analysis, we explored whether the nystagmus slow-phase velocity and related time constants significantly correlated with the MS questionnaire scores. The results showed significant positive correlations between the maximal nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and the nystagmus duration with the MS questionnaire scores in the 65 volunteers. These results indicated that these nystagmus parameters could serve as objective indicators for assessing MS susceptibility. Using K-Means clustering analysis, we classified MS susceptibility into categories I, II, III, and IV, and conducted K-Means clustering analysis on the corresponding nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and nystagmus duration. The magnitude and range of these indicators at different levels was quantified, offering objective and quantitative indicators for the clinical diagnosis of MS. [ABSTRACT FROM AUTHOR]

Abstract Motion sickness (MS) is a common physiological response that often occurs when individuals are exposed to environments with repeated acceleration stimuli. MS results from a mismatch between the vestibular system and visual and proprioceptive inputs. As a crucial organ for sensing acceleration stimuli, the vestibular system is closely related to the onset of MS. However, the complex pathogenesis of MS has led to its diagnosis primarily relying on subjective questionnaires, with a lack of objective indicators for evaluation. To identify objective indicators for evaluating MS, we conducted rotating chair stop experiments based on the principle of vestibulo-ocular reflex with 65 volunteers, obtaining their nystagmus slow-phase velocity and related time constants. Additionally, we conducted detailed MS questionnaires with these volunteers to assess their MS susceptibility. Through correlation analysis, we explored whether the nystagmus slow-phase velocity and related time constants significantly correlated with the MS questionnaire scores. The results showed significant positive correlations between the maximal nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and the nystagmus duration with the MS questionnaire scores in the 65 volunteers. These results indicated that these nystagmus parameters could serve as objective indicators for assessing MS susceptibility. Using K-Means clustering analysis, we classified MS susceptibility into categories I, II, III, and IV, and conducted K-Means clustering analysis on the corresponding nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and nystagmus duration. The magnitude and range of these indicators at different levels was quantified, offering objective and quantitative indicators for the clinical diagnosis of MS.

Taiwan's provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. Methods: This is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 h. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 h, on day 3, and on day 7 after the diagnosis of ARDS. Results: We collected 5054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P

• Chronic kidney disease (CKD) represents a significant global health burden. • This study emphasized the need for individualized management strategies beyond lipid profile management for CKD prevention. • It highlights the importance of measuring and managing inflammation in participants with dyslipidemia or insulin resistance • By elucidating the effects of dyslipidemia, inflammation, and insulin resistance, we can identify individuals at a high risk of CKD. There remains a limited comprehensive understanding of how dyslipidemia and chronic inflammation collectively contribute to the development of chronic kidney disease (CKD). We aimed to identify clusters of individuals with five variables, including lipid profiles and C-reactive protein (CRP) levels, and to assess whether the clusters were associated with incident CKD risk. We used the Korean Genome and Epidemiology Study-Ansan and Ansung data. K-means clustering analysis was performed to identify distinct clusters based on total cholesterol, triglyceride, non-high-density lipoprotein (HDL)-C, HDL-C, and CRP levels. Cox proportional hazards models were used to examine the association between incident CKD risk and the different clusters. During the mean 10-year follow-up period, CKD developed in 1,645 participants (690 men and 955 women) among a total of 8,053 participants with a mean age of 51.8 years. Four distinct clusters were identified: C1, low cholesterol group (LC); C2, high-density lipoprotein cholesterol group (HC); C3, insulin resistance and inflammation group (IIC); and C4, dyslipidemia and inflammation group (DIC). Cluster 4 had a significantly higher risk of incident CKD compared to clusters 2 (hazard ratio (HR) 1.455 [95% confidence interval (CI) 1.234-1.715]; p < 0.001) and cluster 1 (HR 1.264 [95% CI 1.067-1.498]; p = 0.007) after adjusting for confounders. Cluster 3 had a significantly higher risk of incident CKD compared to clusters 2 and 1. Clusters 4 and 3 had higher risk of incident CKD compared to clusters 2 and 1. The combination of dyslipidemia with inflammation or insulin resistance with inflammation appears to be pivotal in the development of incident CKD. [ABSTRACT FROM AUTHOR]

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

The rapid development of information technology and mobile Internet technology has brought about innovations in the field of education. The application of financial statement automation analysis methods in the teaching of accounting majors in higher vocational colleges and universities is conducive to improving the quality of the training of accounting talents in higher vocational colleges and universities so as to accelerate the construction of the modern accounting industry college. This study proposes the application of relevant deep learning techniques in the process of automated analysis of financial statements in terms of recognition and analysis, such as text detection based on CTPN and text recognition technology based on CRNN-Attention, which makes the processing of financial statements more automated and intelligent. It is also proposed to utilize the K-means algorithm to perform cluster analysis on the model output to extract the financial status of the company. To evaluate the model’s performance, several financial statement images of 12 companies are sampled for example analysis. The experimental data shows that the model has a high percentage of image skew correction, e.g., the ratio of image skew correction for the income statement and cash flow statement is 100%. The recognition speed of the model is also faster, with the consumption time of the profit and loss statement recognition task being around 0.3 seconds in 20 tests. In the financial statement analysis session, each of the three clusters of companies under the K-means algorithm clustering has its characteristics, with the first cluster of companies having a stronger quality of earnings, the third cluster of companies having outstanding profitability, and the second cluster of companies having average indicator data.

This study utilized a mixed model approach, incorporating k-means clustering analysis for data examination, discriminant analysis for classification, and multilayer perceptron neural network analysis for prediction. After removing inadequate samples and outliers, the total number of observations was 19,692 for this study, which was collected through an interactive online personality test (i.e., Big Five Personality Traits) in 2012. The empirical results based on the k-means clustering analysis identified four different personality clusters using the total score of Big Five Personality Traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience). The empirical results obtained from the k -means clustering analysis revealed the presence of four distinct personal clusters, determined by the total scores of the Big Five Personality Traits. The accuracy of the clustering analysis was further tested using discriminant analysis, which indicated significant difference among the cluster means and correctly classified 95.5% of the original grouped cases. For predictive modeling, a multilayer perceptron neural network framework was used. The network had a 5-6-4 structure and was employed to determine the personality classification of participants. Notably, the model achieved 99.4% accuracy in correctly classifying the training grouped cases and 99.2% accuracy for the testing grouped cases. The results of this study offer valuable insights into understanding the personalities of participants, with implications for various domains such as psychology, social sciences, cultural studies, and economics. [ABSTRACT FROM AUTHOR]

In order to solve the problem that a single V-I track feature can not effectively identify similar track features and the extracted features are prone to redundacy or even noise features in non-invasive load identification,a hierarchical non-invasive load identification method based on K-means clustering and PSO feature optimization was proposed.Firstly,K-means algorithm was used to initially classify the HOG features of load V-I trajectories,and the appliances with similar trajectories were classified into one category.Then,multi-dimensional features are extracted from electrical current data in each category and the optimal feature subset is selected by PSO algorithm.Finally,KNN model was used for secondary load identification.The experimental results show that this method effectively improves the accuracy of load identification.Extracting the HOG feature of V-I trajectory solves the problem of fluctuation of the same electrical appliance.PSO feature optimized KNN secondary classification is carried out for each category after the first level classification,which solves the problem of poor adaptability of some electrical appliances to feature subset.The proposed method solves the influence of redundant features and even noise features on the identification accuracy to a certain extent,and provides a new idea for the selection of load features,which has important reference significance for the practical application of load identification.

Accurate multi-energy load forecasting is prerequisite for achieving balance between supply and demand in building energy system. The continuous development of building flexibility control techniques has led to increased complexity and variability in characteristics of multi-energy flexibility loads under application of various building flexibility control strategies. Existing load forecasting methods lack the ability to recognize the flexibility features and face challenges in considering complex coupling relationships and variable characteristics of multi-energy flexibility loads. To address this gap, we propose a multi-energy flexibility load forecasting method, denoted as (BFFR-MTL-LSTM), which incorporates building flexibility feature recognition (BFFR) and multi-task learning (MTL) model utilizing Long Short-Term Memory (LSTM) neural networks as the shared layer. A novel ToU-K-means method combining ToU with K-means algorithm is proposed firstly as BFFR technique to accurately recognize the flexibility features under various energy consumption patterns. The forecasting accuracy of proposed method is validated using actual operational data under various energy consumption patterns with average R2 of 0.973. Additionally, through comparisons with numerous existing models, the proposed method demonstrates a forecasting accuracy improvement ranging from 33 % to 47 %. This validation supports that the proposed method adeptly recognizes energy consumption patterns, resulting in more accurate forecasts of multi-energy flexibility loads. • A BFFR-MTL-LSTM method was proposed for multi-energy flexibility load forecasting. • A ToU-K-means method was proposed as BFFR to recognize the flexibility features. • The application value of proposed method was validated through comparison analysis. • The proposed method demonstrates a maximum forecasting accuracy improvement of 47 %. [ABSTRACT FROM AUTHOR]

Multi-functional rainwater storage space is a practical solution to flood and runoff pollution in high-density built-up areas. This study presents a location decision method to improve the control effect of total runoff volume, runoff peak flow, and runoff pollution. The results show that this method can accurately and quickly screen available locations for multi-functional rainwater storage space and prioritize them as optimum, suitable, less suitable, unsuitable, and not available for construction. In the case of this study, greening the road can achieve a better effect on runoff control. [ABSTRACT FROM AUTHOR]

Copyright of Revista Caatinga is the property of Revista Caatinga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: To explore the characteristics of the clinical phenotype of ARDS based on Machine Learning., Methods: This is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 h. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 h, on day 3, and on day 7 after the diagnosis of ARDS., Results: We collected 5054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P < 0.05), with phenotype I having the lowest in-hospital mortality (10 %) and phenotype II having the highest (31.8 %). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P < 0.05) under phenotypic classification., Conclusions: The clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis ., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In order to reduce the safety accidents caused by the unsafe behaviors in the managed operation of coal mines, we conducted a scientific classification study on the unsafe behaviors of personnel under such operation mode. The field investigation on the “three violations” behaviors of 1 996 coal miners in six coal mines in Inner Mongolia, Ningxia and Xinjiang from 2017 to 2018 was conducted. All the three violations were statistically classified from three aspects of time, job type and characteristics of SCM behavior generation. A data set of 8 indicators and 4 subcategories was established based on K-means clustering algorithm, and a visual clustering scatter diagram was drawn through PCA dimension reduction. The analysis shows that: using SCM and K-means clustering to calculate the proportion of four kinds of unsafe action classification, the same conclusion was obtained as manual analysis, in all unsafe action, lawless accounted for the biggest, errors accounted for the smallest proportion. The research results have certain guiding significance for reducing unsafe behaviors of coal mine employees and preventing safety accidents by classification.

In order to make the sponge city construction model more consistent with the inherent characteristics of the city, this paper constructs classification of the sponge city construction index system. We carry out multivariate statistical and cluster analysis based on geographic information system (GIS) and classify the different regions of mainland China, putting forward key points of construction for different types of regions. The results show that climatic factor, hydrological factor and soil factor are the main factors affecting sponge city construction, followed by city scale and level of urban economic development; the third is underlying surface type and geomorphological type. The cities are classified into ten clusters, and they present a continuously zonal or flaky distribution from northeast to southwest on the whole; more than 80% of cities obviously present a continuously zonal or flaky distribution. Each of the nine clusters has at least one pilot city, which can be used as a reference for construction. HIGHLIGHTS This paper puts forward the method of cluster analysis to classify Chinese cities.; By using principal component analysis, it is found that the climatic, soil and hydrological factors are the most related to the results of city classification.; Based on the construction experience of existing pilot cities, the construction emphasis of sponge cities in various types of cities is proposed.;

The culmination of conventional yield improving parameters has widened the margin between food demand and crop yield, leaving the potential yield productivity to be bridged by the manipulation of photosynthetic processes in plants. Efficient strategies to assess photosynthetic capacity in crops need to be developed to identify suitable targets that have the potential to improve photosynthetic efficiencies. Here, we assessed the photosynthetic capacity of the Japanese soybean mini core collection (GmJMC) using a newly developed high-throughput photosynthesis measurement system "MIC-100" to analyze physiological mechanisms and genetic architecture underpinning photosynthesis. K-means clustering of light-saturated photosynthesis (Asat) classified GmJMC accessions into four distinct clusters with Cluster2 comprised of highly photosynthesizing accessions. Genome-wide association analysis based on the variation of Asat revealed a significant association with a single nucleotide polymorphism (SNP) on chromosome 17. Among the candidate genes related to photosynthesis in the genomic region, variation in expression of a gene encoding G protein alpha subunit 1 (GPA1) showed a strong correlation (r = 0.72, p < 0.01) with that of Asat. Among GmJMC accessions, GmJMC47 was characterized by the highest Asat , stomatal conductance (gs), stomatal densit y (SDensity), electron transfer rate (ETR), and light use efficiency of photosystem II (Fv'/Fm′) and the lowest non-photochemical quenching [ NPQ (t)], indicating that GmJMC47 has greater CO2 supply and efficient light-harvesting systems. These results provide strong evidence that exploration of plant germplasm is a useful strategy to unlock the potential of resource use efficiencies for photosynthesis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Collecting and mining customer consumption data is crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved random forest model, by adding a new indicator joining the RFMS-based method to a K-means algorithm with the entropy weight method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies. [ABSTRACT FROM AUTHOR]

The culmination of conventional yield improving parameters has widened the margin between food demand and crop yield, leaving the potential yield productivity to be bridged by the manipulation of photosynthetic processes in plants. Efficient strategies to assess photosynthetic capacity in crops need to be developed to identify suitable targets that have the potential to improve photosynthetic efficiencies. Here, we assessed the photosynthetic capacity of the Japanese soybean mini core collection (GmJMC) using a newly developed high-throughput photosynthesis measurement system “MIC-100” to analyze physiological mechanisms and genetic architecture underpinning photosynthesis. K-means clustering of light-saturated photosynthesis (Asat) classified GmJMC accessions into four distinct clusters with Cluster2 comprised of highly photosynthesizing accessions. Genome-wide association analysis based on the variation of Asat revealed a significant association with a single nucleotide polymorphism (SNP) on chromosome 17. Among the candidate genes related to photosynthesis in the genomic region, variation in expression of a gene encoding G protein alpha subunit 1 (GPA1) showed a strong correlation (r = 0.72, p

• This paper systematically creates a typology of Chinese secondary cities based on their HQD performance; • TOPSIS evaluation and K-means clustering are the main methods employed; • We identify five distinctive secondary city types in Chinese mega-regions facing different challenges; • The typology is a valuable foundation for target policymaking and implementing HQD in secondary cities. The governmental initiative of high-quality development (HQD) marks a shift in the Chinese development paradigm from prioritizing speed to prioritizing quality towards comprehensive goals of economic growth, social vitality, innovation capacity, industrial upgrading, regional cooperation, and green transformation. This initiative is increasingly discussed within the framework of mega-regions, with prior studies demonstrating that they are critical arenas for promoting HQD visions. However, unevenness within mega-regions has become an important limitation to this vision. Namely, significant disparities exist between mega-regional core cities and the smaller neighboring cities in most HQD indicators. This paper conceptualizes these smaller players as secondary cities. Based on this, this paper aims to understand and differentiate the specific challenges of secondary cities facing intra-regional unevenness in the context of HQD. We build an evaluation framework and employ the TOPSIS method to evaluate 34 core cities and 180 secondary cities. Then, we introduce typological thinking to develop a meaningful classification of secondary cities based on the results of these evaluations. K-means clustering analysis identifies five secondary city types with similar profiles. The analysis supports the discussion of the characteristics and challenges of each type and may contribute to policy recommendations for a balanced HQD in mega-regional secondary cities. [ABSTRACT FROM AUTHOR]

The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord Gi* statistic and K-means clustering analysis were performed on 15 PTEs in 6,862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6,862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies.

Extreme weather is a global challenge due to its severe impact on environment and society. Existing studies focus on specific extreme events or long‐term characteristics of individual indices. Event‐based concept was proposed to more accurately understand features of extreme events and achieve results closer to realistic extreme processes. In this study, the event‐based concept was extended to spatial dimension to simultaneously investigate multiple features in space and time. After homogeneous regions were identified, extreme events in each region were clustered into different types and then subsequent statistical analyses were conducted. The event‐based concept provides further insight into potential driving mechanisms by synoptic features analysis. To evaluate our framework, heatwave in China was chosen as a case study due to its severe impact on socioeconomy and China's complex topography and climate. The results revealed details about the regional features in northwest China and vast spatial extent along the middle and lower Yangtze River. Different heatwave types showed different characteristics in different regions. Severer heatwaves generally had larger magnitude in various features. Synoptic feature analysis revealed that heatwave events in northwest China were influenced by the Iranian Subtropical High and surface temperature advection from the Qinghai‐Tibet Plateau. The western Pacific subtropical high impacts the heatwave events in eastern China. The western spread of the subtropical high and cold waves from north spawn and finish heatwaves in Sichuan Basin. The correlation analysis between circulation anomalies at the start of heatwaves and the overall features of events was concluded in the most heat‐impacted region. Key Points: Event‐based heatwave feature analysis was achieved with regionalization and clustering methodFeatures and frequency of different types of heatwave events in eight regions of China were revealedThe evolution of synoptic feature associated with different types of heatwave events were revealed [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the accuracy and efficiency of reliability assessment for complex structures with small failure probability and time-consuming model， a structural reliability analytical method based on PC-Kriging (polynomial-chaos-based Kriging) model and adaptive k-means clustering analysis was proposed. PC-Kriging’s regression basis function approximated the global behavior of the numerical model by using the sparse polynomial optimal truncation set， and Kriging was used to deal with the local variation of the output of the model. PC-Kriging used least angle regression (LAR) to calculate the number of possible polynomial basis function sets of performance function， and adopted Akaike information criterion (AIC) to determine the optimal polynomial form. The adaptive k-means clustering analysis ensured that some of the significant contribution sample points toward the failure probability can be added as the new training samples in each iteration. The results of two numerical examples indicated that the proposed method can not only guarantee the validity and accuracy of the estimation of failure probability but also reduce structural performance function evaluation times. [ABSTRACT FROM AUTHOR]

It is difficult to detect end pose of auto electrocoating conveying parallel robot based on binocular vision because of less features, noise and blurred edges in image. So a pose detection method is presented. Firstly, according to distance and similarity between image points, the bilateral filtering is adopted to reduce noise. Secondly, for extracting straight lines on blurred edges and feature points in lines accurately, a Hough-K-means algorithm using K-means clustering analysis in parameter space after Hough transform is proposed. Thirdly, the discrete Gaussian–Hermite moment is used as feature descriptor to solve the low accuracy problem of principal directions of feature points described by speeded-up robust features (SURF) descriptor. The extracted feature points are selected according to the similarity metric between constructed feature vectors. Finally, the vision model is developed to calculate the three-dimensional pose parameters of parallel robot based on obtained point pairs. The experiment results show that, compared with the SURF algorithm, the average matching time decreases by 21.58%, the average deviations of detected poses in x, z and β decrease by 1.149 mm, 0.646 mm and 1.164° respectively by using the proposed method. The speed and accuracy of pose detection of parallel robot can be improved.

Copyright of Journal of Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article seeks to evaluate color quality of blue sapphire and also address environmental impacts related to precious stones. Mining for gold, silver, diamonds, sapphires, gemstones, and other metals can result in water pollution, greenhouse gas emission, and soil erosion. Through the measurement, screening and analysis of color parameters of Gem Dialogue TM Color Comparison Charts, the feasibility of its application to the color evaluation of blue sapphire was explored in terms of ecological evaluation. Charts with strong blue tone in GemdialoguetTM Color Comparison Charts--charts G2B, CYAN, B, P2B and B2P--were measured under illuminants A, D65 and CWF, by X-rite SP60 Portable spectrophotometer. The law of the charts color variation, environmental issues and the influence of illuminant variation on color were explored. And D65 is recommended to be used as the color comparison and ecological evaluation illuminant of blue sapphire. Screen the measured data in reference to the specified color range of Chinese National Standard "GB/T 32862-2016 Sapphire Grading". The valid color data was classified into five categories through KMeans clustering analysis of SPSS21.0 and taxonomic ecology, and the taxonomy accuracy rate reached 98.3% by the verifying of Fisher analysis. Combined with the environmental effect of color simulation, the result of classification was ecologically accurate and ideal. Applying the GemdialogueTM Color Comparison Charts to the color quality evaluation of blue sapphire is practicable. By screening the data according to the environmental characteristics of different gems, this method can be extended to help establish the color evaluation system of other blue color gems. It is also suggested to support organizations working to improve the global mining industry. Ethical Metal Smiths and Fair Jewelry Action are two such organizations. [ABSTRACT FROM AUTHOR]

The study aimed to utilize Structural Equation Modeling (SEM) and K-means clustering for predicting real-time crash risks on freeways. The SEM was used to transform a number of correlated traffic variables into four independent latent traffic factors, and to establish the interrelationships among the traffic variables and crash risks. The segmentation analysis based on K-means clustering was then conducted to investigate the main traffic factors affecting crash risks in various traffic regimes. It was found that: (a) The measurement equations in SEM can effectively account for the correlations among traffic variables by transforming numerous correlated traffic variables into several latent traffic variables; (b) The SEM can both capture the direct and indirect effects of traffic flow variables on crash risks. This promotes a better understanding how traffic conditions affect crash risks; (c) The SEM produces more accurate estimates of crash risks than existing modeling technique. It can increase the crash prediction accuracy by an average of 7.6% compared with the commonly used logistic regression; and (d) Segmentation analysis results suggested that the traffic factors contributing to crash risks are various across different traffic regimes. The proactive crash prevention strategies for different traffic regimes were discussed based on the findings in the segmentation analysis. [ABSTRACT FROM AUTHOR]

Topically applied active cosmetic ingredients (ACI) or active pharmaceutical ingredients (API) efficacy is directly related to their efficiency of penetration in the skin. In vitro reconstructed human epidermis surrogate models offer in vivo like skin samples for transdermal studies. Using Delipidol®, an ACI currently used in the cosmetics industry, the capabilities to deliver accurate distribution maps and penetration profiles of this molecule by means of confocal Raman spectroscopic imaging have been demonstrated. Using a non‐negative constrained least squares (NCLS) approach, contribution of specific molecules can be estimated at each point of spectral maps in order to deliver semi‐quantitative heat maps representing the ACI levels in the different skin layers. The concentration profiles obtained are approximately single exponential for all 3 time points evaluated, with a consistent decay constant, which is independent of the sublayer structure. Notably, however, there is no significant penetration into the lower basal layers until a critical concentration is built up, after 3 hours. Combination of Raman confocal imaging with spectral unmixing methods such as NCLS is demonstrated to be a relevant approach for in vitro biological evaluation of cosmetic and pharmaceutical active ingredients and could easily be implemented as a screening tool for industrial use. [ABSTRACT FROM AUTHOR]

Following up on earlier investigations, the present paper analyzes construct validity of the impostor phenomenon. It examines the question whether the impostor phenomenon is a homogeneous construct or whether different types of persons with impostor self-concept can be distinguished on the basis of related characteristics. The study was conducted with professionals in leadership positions exhibiting a pronounced impostor self-concept (n = 183). Cluster-analytic procedures indicated the existence of two different types: one group which, in line with the literature (e.g., Clance, 1985), possessed traits classified as fairly unfavorable (“true impostors”) and another group which can be described as largely unencumbered (“strategic impostors”). The present study suggests two types of impostorism: “True” impostors characterized by the negative self-views associated with the construct definition, and more “strategic” impostors who seem to be less encumbered by self-doubt. It is assumed that “strategic impostors” are characterized by a form of deliberate self-presentation. Therefore, the impostor self-concept cannot principally be viewed as a dysfunctional personality style. This distinction should be more carefully considered in further research and in therapeutic interventions.

This article presents a user-oriented protocol that can be used to characterize chromatic attributes of an urban area. A need for both affordable and reliable assessment method of color has been observed in the field of urban design. The challenge is to provide statistical answers to these two questions: how is color organized, how does it develop its own structure in the city? The main purpose of the research is hence to investigate mean color types within the city via the implementation of a characterization tool; an urban area of the four main classes (historic center, periurban housing districts, working-class neighborhoods, and commercial zones) being characterized by a specific color palette, or a layout of specific color types. The developed instrumentation allows the assessment of the homogeneous and consistent features of an urban district. Their characterization protocol was tested through an application to several streets of the city of Liège (Belgium). © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 131-142, 2017 [ABSTRACT FROM AUTHOR]

Although diversity is a defining characteristic of much urban life, globalization and economic restructuring have led to an increase in socio-spatial stratification within modern cities. Socioeconomic differentiation is arguably the most important dichotomy within large metropolitan areas. However, demographic factors such as age, fertility, family and household structure, education level, and migration patterns of urban society also play key roles in the social life of urban areas. Thus, there are clear patterns to the geo-demographics of cities – in large part due to the tendency for certain social groups of individuals to occupy particular niches within the urban fabric. Using neighbourhood-level census and housing data from the year 2000, this paper focuses on the pattern of socio-spatial differentiation and segmentation in Ankara, Turkey’s capital city. The study applies k-means Clustering Analysis to identify the underlying dimensions of the formation of socio-spatial patterns in the city. The analysis supports the concept of emerging structural differentiations in a “sexpartite city”

To enhance the hydraulic energy utilization and torque output stability, a novel mechatronics-electro-hydraulic power coupling electric vehicle (MEH-PCEV) is proposed, integrating a hydraulic pump/motor and a motor into a single device for mutual energy conversion. For MEH-PCEVs equipped with multiple energy sources, a cluster analysis method is used to classify the actual road test dataset and provide guiding ideas for designing rule-based energy management strategies (RB-EMS). Simultaneously, for the output torque anomaly phenomenon in RB-EMS, an inverse thinking fuzzy logic optimization energy management strategy (FLO-EMS) conside ring multi-parameter objectives as input is used to adjust the electromagnetic torque in real-time and reasonably allocate the energy flow. The simulation results demonstrate that the electric and total torque output are more stable. The electric peak torque is relieved, with a corresponding increase in the percentage of electrical energy recovery. With the equal power demand, the overall efficiency of the motor working point is substantially improved, and the energy consumption rate is decreased by 24.42%. Under the active regulation of FLO-EMS, hydraulic energy is more reasonably utilized to meet the vehicle demand power while avoiding energy dissipation and waste. Moreover, this work is expected to reference the development and engineering applications of electro-hydraulic coupling systems. [Display omitted] • A novel mechatronics-electro-hydraulic power coupling system is proposed. • Actual test data is classified and guides the design of rule strategies. • Active energy regulation is achieved through a multi-parameter inverse control method. • A fuzzy logic optimization method with real-time adjustment of torque coefficients is proposed. • Actual results of driving conditions are analyzed utilizing data acquisition and simulation. [ABSTRACT FROM AUTHOR]

Bacteroides fragilis, member from commensal gut microbiota, is an important pathogen associated to endogenous infections and metronidazole remains a valuable antibiotic for the treatment of these infections, although bacterial resistance is widely reported. Considering the need of a better understanding on the global mechanisms by which B. fragilis survive upon metronidazole exposure, we performed a RNA-seq transcriptomic approach with validation of gene expression results by qPCR. Bacteria strains were selected after in vitro subcultures with subinhibitory concentration (SIC) of the drug. From a wild type B. fragilis ATCC 43859 four derivative strains were selected: first and fourth subcultures under metronidazole exposure and first and fourth subcultures after drug removal. According to global gene expression analysis, 2,146 protein coding genes were identified, of which a total of 1,618 (77%) were assigned to a Gene Ontology term (GO), indicating that most known cellular functions were taken. Among these 2,146 protein coding genes, 377 were shared among all strains, suggesting that they are critical for B. fragilis survival. In order to identify distinct expression patterns, we also performed a K-means clustering analysis set to 15 groups. This analysis allowed us to detect the major activated or repressed genes encoding for enzymes which act in several metabolic pathways involved in metronidazole response such as drug activation, defense mechanisms against superoxide ions, high expression level of multidrug efflux pumps, and DNA repair. The strains collected after metronidazole removal were functionally more similar to those cultured under drug pressure, reinforcing that drug-exposure lead to drastic persistent changes in the B. fragilis gene expression patterns. These results may help to elucidate B. fragilis response during metronidazole exposure, mainly at SIC, contributing with information about bacterial survival strategies under stress conditions in their environment. [ABSTRACT FROM AUTHOR]