Two-step Clustering Algorithm Research Articles

Research on the difference of stroke characteristics and stroke effect between different stroke duration of table tennis players

This study aimed to explore the differences in joint kinematic characteristics and stroke effect between fast and slow strokes of table tennis players. Thirty-four table tennis players were randomly selected as participants for this experiment. A high-speed infrared motion capture system was used to collect kinematic data of the right-side upper and lower limb joints during strokes. Simultaneously, a high-speed camera was used to measure the stroke effect. According to the duration of a stroke, the stroke data were classified into two types: fast-stroke and slow-stroke using a two-step clustering algorithm. The duration of the three phases of fast-stroke was shorter, with faster ball speed and stronger spin. Compared with slow-stroke, fast-stroke exhibited shorter displacements in the three lower limb joints and faster linear velocities in the three upper limb joints. In addition, the motion angles of the knee and ankle joints were smaller, while the angles of the hip, shoulder, and elbow joints were larger, with higher angular velocities. Increasing the extension speed of the lower limbs, the flexion speed of the elbow joints, and the wrist movement speed can achieve fast strokes, enhancing ball speed and spin. Small and quick hip joint movement can effectively transfer energy to the upper limbs, increasing the movement speed of the elbow and wrist joints, thus improving the ball speed and spin of the stroke.

Applying a two-step cluster algorithm in traffic accident data analysis

Cluster analysis is often employed as the initial stage in organizing heterogeneous data into homogeneous groups. Choosing an effective clustering approach and an ideal number of clusters in a traffic accident dataset might be complex and challenging. This study aims to evaluate the effectiveness of k-means and two-step cluster methods. Subsequently, the two-step cluster method and GIS are applied to analyze the traffic accident datasets from 2015 to 2017 in Hanoi, Vietnam. First, according to the Silhouette score, the two-step cluster method achieved a higher score of 0.563, while the k-means method scored 0.341. A higher Silhouette score indicates more well-defined clusters. Second, the research suggests combining the two-step cluster method with GIS for analyzing traffic accident datasets. The outcome identifies five typical types of accidents in Hanoi. In addition, the locations of various accident types were visually illustrated on a map, enabling traffic officials to recommend precise and urgent countermeasures. Importantly, the clustering results reveal that the two-step cluster method exhibits a significantly higher rate of homogeneous data in the clusters compared to the k-means method. This study demonstrates that the two-step cluster method is not only more effective than the k-means method in terms of clustering ability but also in data pre-processing. The study's results enable authorities to gain a more detailed understanding of typical traffic accident patterns in Hanoi. Besides, the employed methods could potentially be applied to other regions, providing an additional avenue for analysis

Research on automotive scrap metal classification method using laser-induced breakdown spectroscopy and two-step clustering algorithm

In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing laser-induced breakdown spectroscopy (LIBS) and two-step clustering algorithm (K-means, hierarchical clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the multiplicative scatter correction to the spectral data for calibration, the initial clustering clusters were determined using the Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient. Then, the Kruskal–Wallis test was conducted on each cluster to check the significance. The clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: long-term deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslide-prone areas.

Mobile communication channel resource allocation technology in interference environment based on clustering algorithm

The rapid development of urbanization has led to the gradual increase of urban residential density. Relatively speaking, the spectrum resources are increasingly scarce, which leads to the increasingly serious interference between communities, and the system performance is also greatly limited. Therefore, in order to improve the efficiency of spectrum resources and solve the problem of user interference between cells, the experiment combines the advantages of clustering by fast search and find of Density Peaks Clustering (DPC), and proposes a two-step clustering algorithm. This method is proposed based on the core idea of DPC after in-depth study of the downlink multi-cell orthogonal frequency division multiplexing system architecture. The proposed model is compared with Matching Pursuit (MP) algorithm and Graph-based algorithm with the sum of clustering distance Rs, 1.5⁢Rs. The results show that the two-step clustering algorithm can significantly improve the spectrum efficiency and network capacity while ensuring good quality of service under the condition of channel tension or not. In addition, the minimum SINR value of the two-step clustering algorithm can reach 75 dB. Compared with the 220 dB of the Graph-based algorithm with the clustering distance Rs, it has extremely obvious advantages. Therefore, the two-step clustering algorithm constructed in this study can effectively reduce system interference, and has certain research and application value in solving the problem of mobile communication channel resource shortage.

IMI2: A fuzzy clustering validity index for multiple imbalanced clusters

The fuzzy c-means (FCM) clustering algorithm requires the pre-definition of the number of clusters. When used for imbalanced datasets, FCM tends to equalize the sizes of clusters and thus produces bad clustering results. Although clustering validity index (CVI) is commonly employed to determine the number of clusters, it is often ineffective for bad clustering results of FCM. To address this issue, we have previously proposed a fuzzy CVI, namely IMI, for imbalanced datasets. However, IMI encounters issues when the number of clusters exceeds three, as it tends to divide the majority clusters into multiple subclusters. In this paper, a novel CVI, IMI2, is proposed to enhance the performance of IMI. IMI2 adopts a two-step clustering algorithm that merges some clusters from the IMI result based on their separation values. This enhancement overcomes the limitations of IMI, rendering IMI2 an excellent CVI capable of accurately determining the number of clusters for datasets with multiple imbalanced clusters. The performance of IMI2 is evaluated using several synthetic and UCI datasets. The results demonstrate that IMI2 outperforms other methods in terms of its superior performance for multiple imbalanced clusters. IMI2 retains the good performance of IMI for datasets with two or three clusters, while significantly improving it for multiple imbalanced clusters.

Interactions between Metabolic Syndrome, MASLD, and Arterial Stiffening: A Single-Center Cross-Sectional Study.

Metabolic-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), has emerged as a prominent global cause of chronic liver disease and is increasingly recognized as associated with atherosclerotic vascular illness, consolidating its position along traditional cardiovascular risk factors. Individuals with MASLD exhibit a combination of metabolic syndrome risk factors, carotid atherosclerosis, and increased arterial stiffness, hinting at shared pathogenesis. In this study, we aim to explore liver involvement and arterial stiffness within metabolic syndrome. We enrolled 75 patients (30 male and 45 female) with either liver steatosis on conventional ultrasound, altered liver function tests, or the presence of cardiometabolic risk factors after excluding liver pathology other than MASLD. Clinical evaluation, laboratory measurements, abdominal and carotid ultrasounds, vibration-controlled transient elastography (VCTE, Fibroscan), and assessment with the Arteriograph (Tensiomed) were performed. The 26 patients diagnosed with MetS had significantly higher liver involvement as quantified via the hepatic steatosis index (HSI), Fibrosis-4 (FIB4), aspartate aminotransferase to platelet ratio index (APRI) category, and VCTE measurements, as well as Agile 3+ and Agile 4 scores which use a combination of clinical and laboratory parameters together with results obtained from VCTE to reflect the probability of advanced liver fibrosis or cirrhosis. Patients with MetS also exhibited more pronounced vascular involvement as quantified via arterial stiffness measurements and CIMT (carotid intima-media thickness). We applied a two-step clustering algorithm to enhance our analysis, which gave us pertinent insight into the interplay between metabolic syndrome elements and typologies of hepatic steatosis and arterial stiffness degrees. Notably, of the three obtained clusters, the cluster showing increased levels of hepatic steatosis and arterial stiffness also exhibited the highest prevalence of metabolic syndrome and its constituting components. The results have significant clinical implications, advocating for a comprehensive diagnostic approach when MetS or MASLD is suspected.

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5 µm3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250 µm3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. This work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

Hepatic Involvement across the Metabolic Syndrome Spectrum: Non-Invasive Assessment and Risk Prediction Using Machine Learning.

Metabolic-dysfunction-associated steatotic liver disease (MASLD) and metabolic syndrome (MetS) are inextricably linked conditions, both of which are experiencing an upward trend in prevalence, thereby exerting a substantial clinical and economic burden. The presence of MetS should prompt the search for metabolic-associated liver disease. Liver fibrosis is the main predictor of liver-related morbidity and mortality. Non-invasive tests (NIT) such as the Fibrosis-4 index (FIB4), aspartate aminotransferase-to-platelet ratio index (APRI), aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), hepatic steatosis index (HIS), transient elastography (TE), and combined scores (AGILE3+, AGILE4) facilitate the detection of liver fibrosis or steatosis. Our study enrolled 217 patients with suspected MASLD, 109 of whom were diagnosed with MetS. We implemented clinical and biological evaluations complemented by transient elastography (TE) to discern the most robust predictors for liver disease manifestation patterns. Patients with MetS had significantly higher values of FIB4, APRI, HSI, liver stiffness, and steatosis parameters measured by TE, as well as AGILE3+ and AGILE4 scores. Machine-learning algorithms enhanced our evaluation. A two-step cluster algorithm yielded three clusters with reliable model quality. Cluster 1 contained patients without significant fibrosis or steatosis, while clusters 2 and 3 showed a higher prevalence of significant liver fibrosis or at least moderate steatosis as measured by TE. A decision tree algorithm identified age, BMI, liver enzyme levels, and metabolic syndrome characteristics as significant factors in predicting cluster membership with an overall accuracy of 89.4%. Combining NITs improves the accuracy of detecting patterns of liver involvement in patients with suspected MASLD.

Measuring dense false positive regions from segmentation result for whole slide tissue histology image

Evaluating a segmentation model is critical for constructing computer-aided diagnosis (CAD) systems, in which the segmented regions will be used for downstream analysis. However, existing segmentation evaluation metrics may not reflect every aspect of a trained segmentation model performance in the context of the whole slide tissue histology images (WSIs). Specifically, existing segmentation metrics generally ignore the impact of densely packed false positive pixels (DFP) in the WSI segmentation result. This study presents a new and efficient metric, named MAFaR, accounting for DFP regions in digital WSI segmentation. The proposed metric consists of two modules: 1) Estimation of DFP regions; 2) Calculation of MAFaR score. In module 1, a Gaussian Kernel Density Estimation method was used to estimate the density of the false positive (FP) regions in segmentation result (SR). Then a two-step Mean-shift clustering algorithm was applied to the high-density FP regions to estimate the DFP regions. In module 2, the ratio of DFP regions area to the positive regions area were used for MAFaR calculation. We conducted two experiments to evaluate the effectiveness of the MAFaR score. In the first experiment, we compared MAFaR with existing metrics related to FP regions, the proposed MAFaR score can reflect the impact of DFP regions. In the second experiment, MAFaR scores were compared with the manual evaluation scores given by three experienced engineers and found high correlation (Spearman’s rank correlation coefficients greater than 0.7) and high agreement (Kendall coefficient = 0.839). Therefore, the MAFaR can be used with other segmentation metrics for evaluating WSI segmentation.

Individualized Recommendation Method of Multimedia Network Teaching Resources Based on Classification Algorithm in a Smart University

Abstract Current recommendation methods of multimedia network teaching resources cannot classify the resources with high precision, which leads to large deviation of recommendation results and low recall. Therefore, a personalized recommendation method of multimedia network teaching resources based on classification algorithm is proposed. Adaptive sliding window mutual information method is used to process historical and incremental data of teaching resources and extract the characteristics of multimedia network teaching resources. Two-step clustering algorithm combined with business identifier code criteria are used to complete resource classification. Based on structured query language (SQL) multimedia network teaching resources data recommendation database, document query and processing, combined with SQL structure, a personalized recommendation of multimedia network teaching resources is completed. The experimental results show that the recall of the classification results is higher than 99.58 %, and the highest F1 value is 97.28 %. The precision of personalized recommendation of multimedia network teaching resources is always higher than 90 %, and the recall rate is also higher.

Identification of Stopping Points in GPS Trajectories by Two-Step Clustering Based on DPCC with Temporal and Entropy Constraints

The widespread adoption of intelligent devices has led to the generation of vast amounts of Global Positioning System (GPS) trajectory data. One of the significant challenges in this domain is to accurately identify stopping points from GPS trajectory data. Traditional clustering methods have proven ineffective in accurately identifying non-stopping points caused by trailing or round trips. To address this issue, this paper proposes a novel density peak clustering algorithm based on coherence distance, incorporating temporal and entropy constraints, referred to as the two-step DPCC-TE. The proposed algorithm introduces a coherence index to integrate spatial and temporal features, and imposes temporal and entropy constraints on the clusters to mitigate local density increase caused by slow-moving points and back-and-forth movements. Moreover, to address the issue of interactions between subclusters after one-step clustering, a two-step clustering algorithm is proposed based on the DPCC-TE algorithm. Experimental results demonstrate that the proposed two-step clustering algorithm outperforms the DBSCAN-TE and one-step DPCC-TE methods, and achieves an accuracy of 95.49% in identifying stopping points.

Soft Sensing of NOx Emissions From Thermal Power Units Based on Adaptive GMM Two-Step Clustering Algorithm and Ensemble Learning

The accurate measurement of NOx concentration is the basis of accurate ammonia injection in selective catalytic reduction (SCR) system of thermal power unit. Excessive or too little ammonia injection will cause ammonia escape or exceeding the standard of NOx emission, and cause environmental pollution. There exist a large measurement lag and errors during the purging process in the measurement of NOx concentration in the SCR system of thermal power units. Therefore, to enable the intelligent control of the SCR system, it is necessary to carry out soft sensing of NOx emissions. This paper proposes a soft sensing algorithm for NOx emissions of thermal power units based on an adaptive Gaussian Mixture Model (GMM) two-step clustering algorithm and ensemble learning. Adaptive GMM two-step clustering (AGTSC) algorithm is used to softly divide the historical data into working conditions. Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM) is also adopted to construct individual learners for each working condition. Here we use the parameter regression algorithm to train the combiner based on the output of the individual learner and the membership signal of the working condition as the input of the combiner. The clustering algorithm is tested with three datasets. The results show that this algorithm addresses the shortcomings of GMM including not being applicable to the clustering of non-convex datasets and manual selection of the number of clusters. The soft sensing algorithm is verified by using the historical data of the SCR system of 1000 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MW</i> thermal power units. The results show that the proposed method has higher accuracy and stronger generalization ability than the conventional method, hence providing an effective method for soft sensing of NOx emissions of thermal power units.

Выявление уникальных озер с использованием геоинформационных систем на примере Ненецкого автономного округа

The possibilities of using geographic information systems (GIS) and cluster methods for identifying anomalies to identify unique lakes in the Nenets Autonomous Okrug (NAO) are demonstrated. The following tasks have been solved: 1) determination of the morphometric characteristics of lakes using methods of remote sensing of the Earth; 2) identification of anomalous morphometric characteristics by mathematical methods; 3) expert evaluation of the lakes resulting from the analysis to confirm their unique characteristics for the purpose of subsequent research and assigning them a special status. The relevance of the work is caused by the vastness and inaccessibility of the northern regions, which leads to the need for preliminary identification of objects that are most interesting for expeditionary research. In protected areas, objects that differ in their anomalous characteristics may be of particular interest. The test region of the study was limited by the boundaries of specially protected natural areas of the NAO. The deciphering of the lakes was carried out using the Global Forest Change data set. Raster processing and extraction of areal characteristics of water bodies were carried out in the QGIS software environment. The entire data set was divided into several groups according to the genetic category of the surface, which were also analyzed when identifying anomalies. This approach makes it possible to identify anomalous objects within a particular landscape. For data processing, the IBM SPSS Modeler software application was used, where the anomaly search is based on a two-stage clustering model. The search for anomalies by cluster methods is based on the fact that if the values of an instance are removed from the center of the cluster by more than a certain amount, then the object is considered an anomaly. As a result of applying the TwoStep Cluster algorithm to the sample of morphometric parameters of lakes, 42 anomalous objects were identified. The expert assessment confirmed that the identified lakes are of interest for further research. The final set included such well-known lakes as Golodnaya Guba, Peschanka-To, Kuznetskoe-To, as well as a number of small water bodies that stand out sharply for their peculiar characteristics in comparison with most of the lakes in the study region. For sparsely populated and logistically complex northern territories, the use of such an approach is an important element of field work planning.

Two-step scalable spectral clustering algorithm using landmarks and probability density estimation

Spectral clustering is one of the most important clustering approaches, often yielding performance superior to other clustering approaches. However, it is not scalable to large data sets in its original form due to the computational burden of the required large-matrix eigen-decomposition. In this paper, a two-step spectral clustering algorithm is introduced by extending recent advances of scalable spectral clustering based on low-rank affinity matrix using landmarks. In the first step, a scalable spectral clustering algorithm using raw landmark-based affinity matrix is adopted. In the second step, a novel low-rank affinity matrix is learnt via the probability density estimators, constructed from the estimated clusters as derived from the first step. Since the prior information on cluster labels can be utilised in the second step, this learnt affinity matrix reflects intrinsic pairwise data relationships much better. While the proposed more complicated algorithm results in a higher computational cost than the previous landmark-based spectral research, it can be shown that the associated computational cost still scales well with data size. It is demonstrated that the proposed algorithm is capable of achieving far superior performance than other state-of-the-art algorithms for several benchmark multi-class image data sets.

Effective User Clustering and Power Control for Multiantenna Uplink NOMA Transmission

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

Novel peripheral blood immune cell populations correlate with disease progression and reveal systemic interactions in severe aortic valve stenosis patients

Abstract Background Osteoblastic differentiation is the key driver phenomenon underlying degenerative aortic valve stenosis (AS). It is characterized by a complex cellular interplay and infiltration by circulating immune cells. Moreover, AS demonstrates a diverse natural course, which is understudied. Purpose We sought to investigate the presence of AS clinical progression subphenotypes in the context of “AthenaValve” trial (NCT04312139). Methods Consecutive patients with severe AS undergoing TAVI or SAVR with complete clinical data, echocardiographic history (≥3 past years, corresponding from moderate to severe AS), along with matched healthy volunteers, were included. The median transaortic Vmax increase/month (Vi/m) was calculated and cluster analysis was used to reveal AS progression subgroups. Whole blood peripheral mononuclear cells (PBMCs) were isolated with density gradient centrifugation and cryopreserved into FBS-10% DMSO. PBMC populations were investigated with mass cytometry (CyTOF, Helios, Fluidigm) using a 30-marker antibody panel (Direct Immune Profiling Assay, Fluidigm) and analyzed with Flowjo and Cytobank software. Results A total of 67 patients fulfilling criteria were included (mean age 77.2±10 years, 61.2% males). Two-step cluster algorithm identified 2 clusters of AS progression: C1: 80%, mean Vi/m = 0.03±0.01 vs C2: 20%, Vi/m = 0.07±0.1 m/s/month, average silhouette = 0.7. Mean time to progression to severe AS differed significantly: C1: 59.5 (48.9,70.1) vs C2: 40.2 (29.2, 51.2) months, log rank p=0.05. Among those, 20 patients (C1: 10, C2: 10) and 10 healthy volunteers were included in CyTOF. Demographics, atherosclerotic clinical factors and renal function did not differ among patients-volunteers and C1-C2 groups. B cells, B-memory, B-naïve and pDendritic cells discriminated patients from healthy volunteers: ROC curve (AUC: 0.885, p=0.001), (0.913, p&amp;lt;0.001), (0.770, p=0.018), (0.282, p=0.056), profile (%): (5.3±3 vs 10.2±4), (0.86±0.4 vs 2.6±2), (4.4±3 vs 7.6±4), and (0.48±0.2 vs 0.3±0.1), respectively (mean live cells analyzed: 284028.8±52213.8). pDendritic cells and CD4-Th17-like cells discriminated C1 from C2 patients: ROC curve AUC: (0.804, p=0.005) and (0.268, p=0.031), profile (%): (0.6±0.2 vs 0.3±0.2) and (0.9±0.5 vs 2.6±0.9), respectively). Conclusions Aortic valve stenosis patients demonstrate two distinct phenotypes of disease progression from moderate to severe stage. Patients demonstrate reduced circulating B-cells and increased plasmatocytoid dendritic cells. The rapid progression subgroup of patients demonstrates reduced pDendritic and increased CD4-Th17-like cells. Further works are needed to expand and validate these results. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Co-Funded by Hellenic Ministry of Development and European Regional Development Fund in the context of an open call of “EATRIS-GR project: Infrastructure for preclinical and early-phase clinical development of drugs, therapeutics, and biomedical devices”

Rhythm of Transit Stations - Uncovering the Activity-Travel Dynamics of Transit-Oriented Development in the U.S.

Existing transit-oriented-development (TOD) classification studies primarily focus on the static characteristics around transit stations to measure the built environment’s density, diversity, and design. As a community development model, time-variant variables, dynamic human activities throughout different times of the day and week matter in further unpacking the characteristics of TODs. Given that this aspect has been under-discussed in most previous TOD literature, this research provides an activity-based framework to classify commuter transit station areas by considering the degree of local vibrancy - the temporal visiting pattern of all points of interest (POIs) that fall within the station areas. We apply a two-step semi-unsupervised clustering algorithm to classify 4,290 station areas from 54 metropolitan areas across the U.S. This method produces 13 distinct station area types. Next, we further examine the connection between station area types and neighborhood travel behavior. A cross-sectional comparison reveals that stations with consistent active morning activities are associated with a higher ratio of commuting by walking and biking and lower automobile usage measured in vehicle miles traveled (VMT). Using stations opened after 2009, we show that active weekend activity patterns are associated with a more significant increase in commuting by public transit.

Clinical phenotypes for risk stratification in small‐for‐gestational‐age fetuses

To evaluate whether clinical phenotypes of small-for-gestational-age (SGA) fetuses can be identified and used for adverse perinatal outcome risk stratification to facilitate clinical decision-making. This was a multicenter observational cohort study conducted in two tertiary care university hospitals. SGA fetuses were classified according to maternal, fetal and placental conditions using a two-step cluster algorithm, in which fetuses with more than one condition were assigned to the cluster associated with the highest mortality risk. Delivery and perinatal outcomes were compared using chi-square test among SGA clusters, and the associations between outcomes and each cluster were evaluated by calculating odds ratios (OR), adjusted for gestational age. The study included 17 631 consecutive singleton pregnancies, of which 1274 (7.2%) were defined as SGA at birth according to INTERGROWTH-21st standards. Nine SGA clinical phenotypes were identified using a predefined conceptual framework. All delivery and perinatal outcomes analyzed were significantly different among the nine phenotypes. The whole SGA cohort had a three-times higher risk of perinatal mortality compared with non-SGA fetuses (1.4% vs 0.4%; P < 0.001). SGA clinical phenotypes exhibited three patterns of perinatal mortality risk: the highest risk was associated with congenital anomaly (8.3%; OR, 17.17 (95% CI, 2.17-136.12)) and second- or third-trimester hemorrhage (8.3%; OR, 9.94 (95% CI, 1.23-80.02)) clusters; medium risk was associated with gestational diabetes (3.8%; OR, 9.59 (95% CI, 1.27-72.57)), preterm birth (3.2%; OR, 4.65 (95% CI, 0.62-35.01)) and intrauterine growth restriction (3.1%; OR, 5.93 (95% CI, 3.21-10.95)) clusters; and the lowest risk was associated with the remaining clusters. Perinatal mortality rate did not differ between SGA fetuses without other clinical conditions (54.1% of SGA fetuses) and appropriate-for-gestational-age fetuses (0.1% vs 0.4%; OR, 0.41 (95% CI, 0.06-2.94); P = 0.27). SGA combined with other obstetric pathologies increased significantly the risk of perinatal mortality, as demonstrated by the increased odds of perinatal death in SGA cases with gestational diabetes compared to non-SGA cases with the same condition (OR, 24.40 (95% CI, 1.31-453.91)). We identified nine SGA clinical phenotypes associated with different patterns of risk for adverse perinatal outcome. Our findings suggest that considering clinical characteristics in addition to ultrasound findings could improve risk stratification and decision-making for management of SGA fetuses. Future clinical trials investigating management of fetuses with SGA should take into account clinical information in addition to Doppler parameters and estimated fetal weight. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Influence factors on injury severity of bicycle-motor vehicle crashes: A two-stage comparative analysis of urban and suburban areas in Beijing

Objective More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the factors influencing the injury severity of bicycle-motor vehicle (BMV) crashes in Beijing (China) and discusses the commonalities and differences between the urban and suburban areas. Methods Information regarding 1,136 crashes between bicycles and motor vehicles were collected using police reported data from 2014 to 2015. A two-stage approach integrating random parameters logit (RP-logit) model and two-step clustering (TSC) algorithm was proposed to investigate the significant influence factors and their combination characteristics. Specifically, the RP-logit model was first used to identify the significant influence factors of urban and suburban areas, and then the TSC algorithm was applied to reveal the combination characteristics of significant influence factors for the fatal crashes. Results Five factors were found to be statistically significant and had random effects on the injury severity in urban areas, i.e., type of motor vehicle, motor vehicle license ownership, type of bicycle, signal control mode and lighting condition; and seven factors were found to be statistically significant on the injury severity in suburban areas, i.e., type of motor vehicle, motor vehicle license ownership, physical isolation facility, signal control mode, weather, visibility and lighting condition. Based on TSC, the combination of significant factors showed different characteristics for fatal crashes in urban and suburban areas, in which two types of the scene including five factors should be concerned in urban areas while one type of scene containing four factors in suburban areas. Conclusions The results suggest that different influence factors and individual heterogeneity exist in the RP-logit model for injury severity analysis of BMV crashes in urban and suburban areas. It shows that in urban areas, heavy truck, light truck and bus significantly increase the likelihood of fatal injury than that of suburban areas. These findings can provide valuable reference information for BMV crashes response, such as heavy truck restriction, to facilitate regional safety measures for urban and suburban areas.