Segment Size Research Articles

Interventional Management of Portal Vein Stenosis after Liver Transplant: Single Center Experience.

Portal vein stenosis is a relatively rare complication after liver transplant but has severe consequences. We evaluated the efficacy and longterm results of the endovascular treatment methods for portal vein stenosis. From October 2011 to October 2022, we treated 22 patients (5 female, 17 male) with portal vein stenosis using endovascular methods. Doppler ultrasonography was used for initial diagnosis, with consideration of flow rate increase over stenosis, absence of flow, or reduced anastomotic segment size (>50%). Angiography served as the gold standard, with a pressure gradient above 5 mm Hg indicating the need for treatment. Technical success criteria were defined as <50% stenosis remaining and/or a pressure gradient <5mm Hg. The transhepatic approach was used for all patients. Balloon angioplasty was initially performed, and stents were reserved for patients in the early postoperative period or those unresponsive to balloon angioplasty. The technical success rate was 100%. Mean age was 27.1 years (SD 22.4; range, 4 months to 63 years). Mean time from transplant to intervention was 317 days (range, 0-3135 days). Angioplasty was successful for 7 patients (13.8%). Of 15 patients who underwent stent placement, 9 (40.9%) were in the early postoperative period; in the other 6 patients (27%), results of angioplasty were not satisfactory, and stents were placed. Within 3 months of transplant, 3 patients died because of other complications. Among patients with stents, 2 required reintervention, resulting in reestablishment of good portal venous flow. During the mean follow-up of 24 months (range, 15 days to 9 years), 19 patients (86%) had portal flows within reference limits. The endovascular approach is a safe and effective treatment option for management of portal vein stenosis in both adult and pediatric liver transplant recipients in the early or late period.

Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach

Abstract. Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images to derive acceptable FSDs at local and global scales.

Characterization of runs of Homozygosity revealed genomic inbreeding and patterns of selection in indigenous sahiwal cattle.

Runs of homozygosity (ROH) are contiguous genomic regions, homozygous across all sites which arise in an individual due to the parents transmitting identical haplotypes to their offspring. The genetic improvement program of Sahiwal cattle after decades of selection needs re-assessment of breeding strategy and population phenomena. Hence, the present study was carried out to optimize input parameters in PLINK for ROH estimates, to explore ROH islands and assessment of pedigree and genome-based inbreeding in Sahiwal cattle. The sliding window approach with parameters standardized to define ROH for the specific population under study was used for the identification of runs. The optimum maximum gap, density, window-snp and window-threshold were 250Kb, 120Kb/SNP, 10, 0.05 respectively and ROH patterns were also characterized. ROH islands were defined as the short homozygous genomic regions shared by a large proportion of individuals in a population, containing significantly higher occurrences of ROH than the population specific threshold level. These were identified using the -homozyg-group function of the PLINK v1.9 program. Our results indicated that the Islands of ROH harbor a few candidate genes, ACAD11, RFX4, BANP, UBA5 that are associated with major economic traits. The average FPED (Pedigree based inbreeding coefficient), FROH (Genomic inbreeding coefficient), FHOM (Inbreeding estimated as the ratio of observed and expected homozygous genotypes), FGRM (Inbreeding estimated on genomic relationship method) and FGRM0.5 (Inbreeding estimated from the diagonal of a GRM with allele frequencies near to 0.5) were 0.009, 0.091, 0.035, -0.104 and -0.009, respectively. Our study revealed the optimum parameter setting in PLINK viz. maximal gaps between two SNPs, minimal density of SNPs in a segment (in kb/SNP) and scanning window size to identify ROH segments, which will enable ROH estimation more efficient and comparable across various SNP genotyping-based studies. The result further emphasized the significant role of genomics in unraveling population diversity, selection signatures and inbreeding in the ongoing Sahiwal breed improvement programs.

New Method for Microstructure Segmentation and Automatic Grain Size Determination Using Computer Vision Technology during the Hot Deformation of an Al-Zn-Mg Powder Metallurgy Alloy

New Method for Microstructure Segmentation and Automatic Grain Size Determination Using Computer Vision Technology during the Hot Deformation of an Al-Zn-Mg Powder Metallurgy Alloy

Relationship between body sway and body build in healthy adult men and women

Studies investigating the relationship between balance ability and body size, build and proportions tend to concentrate on body mass and height rather than breadth parameters or size of individual body segments. The purpose of this study was to determine a relationship between the ability to keep balance and the size, build and proportions, based on breadth and length dimensions of the body in healthy adult men and women during a position of free standing. This study also aimed to investigate how the lack of visual control affects the analyzed relationship. The study group consisted of 102 adults of both sexes. The investigations encompassed anthropometric measurements of the body and the ability to keep balance. The analysis covered a of series anthropometric parameters, 9 indices of body proportions, mean velocity of the COP movement (MV) and ellipse area (EA). A statistical analysis of the results was carried out taking into consideration the division into groups due to sexes. The results of the Pearson correlation have revealed that there is a statistically significant correlation (weak or moderate degree) between anthropometric parameters of the body and stabilographic values. Results differ between sexes and depend on whether Romberg’s test was performed with open or closed eyes. The obtained results showed that the surface area of ellipse significantly depends on the dimensions of these body elements which relate to the position of the centre of mass. The obtained results, which differ depending on sex, show that the values of the body sways in a position of free standing depend on breadth and length dimensions of the body, visual control and the analyzed parameter of balance.

Optimal decisions for hybrid manufacturing and remanufacturing with trade-in program and carbon tax

With the practical implementation of carbon taxes and the adoption of trade-in programs, the decision environment for manufacturing and remanufacturing has become increasingly complicated. Taking the carbon tax and the trade-in program into consideration, this study develops manufacturing-remanufacturing models for two market scenarios (i.e., emerging market and mature market) when cores for remanufacturing are limited by used product collection, and finds that the determining factors of optimal decisions are different across the two market scenarios. In addition to the carbon tax and carbon emissions of remanufactured product, the optimal decisions are also affected by the segment size of the replacement consumer in the mature market. Furthermore, we find that carbon tax policy does not always encourage the manufacturer to work on remanufacturing, and it may prevent remanufacturing under certain circumstances. Moreover, the impacts of supply chain parameters on optimal decisions, total carbon emissions, and profits are explored. The carbon tax has similar impacts across the two market scenarios; however, the carbon emissions of remanufactured product and the remaining value of the used product show different impacts. From the social welfare perspective, the optimal carbon tax depends on the regulator’s environmental concern.

Standardized 2D atrial mapping and its clinical applications

The visualization and comparison of electrophysiological information in the atrium among different patients could be facilitated by a standardized 2D atrial mapping. However, due to the complexity of the atrial anatomy, unfolding the 3D geometry into a 2D atrial mapping is challenging. In this study, we aim to develop a standardized approach to achieve a 2D atrial mapping that connects the left and right atria, while maintaining fixed positions and sizes of atrial segments across individuals. Atrial segmentation is a prerequisite for the process. Segmentation includes 19 different segments with 12 segments from the left atrium, 5 segments from the right atrium, and two segments for the atrial septum. To ensure consistent and physiologically meaningful segment connections, an automated procedure is applied to open up the atrial surfaces and project the 3D information into 2D. The corresponding 2D atrial mapping can then be utilized to visualize different electrophysiological information of a patient, such as activation time patterns or phase maps. This can in turn provide useful information for guiding catheter ablation. The proposed standardized 2D maps can also be used to compare more easily structural information like fibrosis distribution with rotor presence and location. We show several examples of visualization of different electrophysiological properties for both healthy subjects and patients affected by atrial fibrillation. These examples show that the proposed maps provide an easy way to visualize and interpret intra-subject information and perform inter-subject comparison, which may provide a reference framework for the analysis of the atrial fibrillation substrate before treatment, and during a catheter ablation procedure.

Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning

ABSTRACT Natural sounds are easily perceived and identified by humans and animals. Despite this, the neural transformations that enable sound perception remain largely unknown. It is thought that the temporal characteristics of sounds may be reflected in auditory assembly responses at the inferior colliculus (IC) and which may play an important role in identification of natural sounds. In our study, natural sounds will be predicted from multi-unit activity (MUA) signals collected in the IC. Data is obtained from an international platform publicly accessible. The temporal correlation values of the MUA signals are converted into images. We used two different segment sizes and with a denoising method, we generated four subsets for the classification. Using pre-trained convolutional neural networks (CNNs), features of the images were extracted and the type of heard sound was classified. For this, we applied transfer learning from Alexnet, Googlenet and Squeezenet CNNs. The classifiers support vector machines (SVM), k-nearest neighbour (KNN), Naive Bayes and Ensemble were used. The accuracy, sensitivity, specificity, precision and F1 score were measured as evaluation parameters. By using all the tests and removing the noise, the accuracy improved significantly. These results will allow neuroscientists to make interesting conclusions.

Non-destructive method for evaluating local integrity of model piles using electromagnetic waves

The objective of this study is to suggest a more accurate integrity estimation method in drilled shafts using electromagnetic waves propagating through transmission lines. Model piles, whose dimensions were 3.0 m in length, 0.2 m in height, and 0.2 m in width, were prepared. In the center of model piles along the length, the rebar and transmission line with 0, 1, 2, or 3 connectors were installed. In the middle of model piles, defect segments with length of 1.0 m were prepared The defect segments were filled with air, sands with different water contents, and water. The experimental results show that the velocity of the electromagnetic waves are dependent on the properties of the medium surrounding the transmission line. Although defects were detected in the transmission line without the connector, the location and size of defect were not clearly identified. However, for the transmission line with connectors, the location and size of sound and defect segments were more clearly identified using the velocity of electromagnetic waves determined between the connectors. This study suggests that transmission line with connectors may be effectively used for a more accurate evaluation of the integrity of drilled shafts.

The specially designed nudging tableware promotes healthy food choices: Evidence from a randomized crossover trial in normal-weight young adults

PurposeTo evaluate the effects of the specially designed nudging tableware, including a plate and bowl, on individual food choices in normal-weight young adults and preliminarily explore its mechanisms. We hypothesized that the toolset could increase the choice of vegetables and decrease that of rice. MethodsA randomized, single-blind, two-period crossover trial was carried out among 40 normal-weight university students in China. All subjects completed two buffets separated by an interval of one week, wearing the eye tracker. Vegetable choice, evaluated through the proportion of vegetables, was the primary outcome, and the weight of vegetables and rice were the secondary outcomes. The mechanisms of the decision-making process were preliminarily explored through eye tracking. ResultsThe usage of the nudging tableware significantly increased the proportion of vegetables and decreased the amount of rice taken (P<0.05), while insignificantly increased the weight of vegetables (P = 0.079). Eye tracking shows that the nudging plate significantly prolonged the food-choosing process and fixation duration on vegetables (P<0.05), and the latter was positively correlated to the increased quantity of vegetables while using the nudging plate (r = 0.493, P<0.01). ConclusionThe specially designed nudging tableware might be an effective and practical tool to promote the choice of less rice and more vegetables. Mechanisms behind this change might include automatic and unconscious processes with the inconspicuously smaller capacity of the bowl and larger portion size of the vegetable segment, and increased attention triggered by the vegetable patterns and larger green underpainting.

CROSS SECTIONAL RETROSPECTIVE ANALYTICAL STUDY OF MORPHOMETRY OF INTERNAL CAROTID ARTERY

Background: The internal carotid artery (ICA) is essential for diagnosing and treating cerebrovascular diseases and plays a significant role in the human head and neck circulatory system. Traditional techniques of ICA research have their limitations, but more recent developments in medical imaging—particularly retrospective angiography—offer more thorough evaluation options. Objective: This study aimed to investigate variations and morphometry of the ICA using 2D digital subtraction angiography (DSA) images, with a focus on age and gender-related differences. Methods: The 149 individuals with subarachnoid hemorrhage who had cerebral angiography were included in the research. Analysis was done on 2D ICA pictures without any obvious pathology. PACS imaging techniques were used to measure morphometric characteristics such as the length, angle, and diameter of the ICA segments. Results: According to the study, ICA length and direction were not substantially affected by age, but segment diameters were highly associated with age, indicating that segment diameters decreased with age. However, gender indicated statistically significant variations in several segment sizes, with males typically having greater diameters than females. Gender had little effect on length and orientation. Conclusion: This study sheds light on ICA changes and morphometry, emphasizing age-related diameter decreases and gender-related variations in segment diameters. These findings highlight the need to take age and gender into account when conducting clinical evaluations and cerebrovascular disease therapies.

Analysis of Pneumonia Area Development in Covid-19 Patients based on Sequential X-Ray Images Using the Seq_UB Architecture

Introduction During the covid pandemic, radiologists diagnosed covid patients by analyzing chest X-ray images. The existence of a quantitative system can help visual observation of lung conditions by radiologists. Purpose of research This research aims to develop an image segmentation algorithm, Seq_UB, which can monitor the development of the lung condition of Covid-19 patients periodically. The Seq_UB algorithm was developed by modifying the UNet system using different CNN layers according to the UBNet v1 algorithm. Materials and methods The dataset for training uses the Montgomery USA Dataset, which contains 138 CXRs with a resolution of 4,020×4,892, of which there are 80 normal CXR images and 58 CXR images of patients identified with tuberculosis; the Shenzhen Hospital Dataset consists of 662 CXR images, of which there are 336 abnormal CXR images. Result and discussion The results show that reducing the input image size does not significantly affect the accuracy of the segmentation results. However, reducing the input image size will affect the resolution of the segmentation results. Where the smaller the input image size, the lower the resolution obtained. This will have an impact on the final interpretation of the segmentation results. Research shows that an input image size of 512 is the best because the resolution of the segmentation results is still very accurate. This study shows that the Seq_UB architecture can perform X-ray image segmentation with relatively stable accuracy and lower computational burden. An interesting pattern was found, where Covid-19 patients quantitatively experienced fluctuations in image segmentation size. Conclusions The Seq_UB system can perform well with a segmentation accuracy of 96 %, and the processing speed takes 0.91 s. A desktop GUI was designed to segment X-ray images more effectively. HIGHLIGHTS Development of the Seq_UB algorithm: This research presents the development of an X-ray image segmentation algorithm with a computational workload that is faster, more accurate, and lighter Quantitative estimation of the lung area percentage of Covid-19 patients based on segmentation with the Seg_UB model Monitoring the development of lung conditions of Covid-19 patients quantitatively by comparing it with normal CXR using the Seg_UB model GRAPHICAL ABSTRACT

Effect of transported segment size on the new bone formation of the rabbit femur in the Ilizarov bone transport method.

This study aimed to compare histological and radiological union in the bone transport of 3 segments of different sizes to reconstruct the rabbit femur's bone defects. Thirty rabbits were divided into 3 groups; a 1-cm defect was created in the femur in all rabbits. The length of the segment to be transferred was 10% of the femur length in group 1, 15% in group 2, and 20% in group 3. All defects were reconstructed by applying bone transport. At the end of the consolidation period, the distraction zone was compared radiologically and histologically. While there was no radiological difference between the groups, the highest histological scores were obtained from group 3. Osteocalcin staining revealed similar involvement in groups 2 and 3, butless involvement in group 1. Evidence from this study has shown that as the size of the segment used for bone transport increases, more stable fixation and better histological union tissue can be obtained in the rabbit femoral defect model.

Variations of science-related populism in comparative perspective: A multilevel segmentation analysis of supporters and opponents of populist demands toward science

Many countries worldwide have seen populist resentment against scientists, which can manifest as “science-related populist attitudes” among the population. These attitudes can be assumed to divide populations into multiple segments—each endorsing or rejecting different facets of science-related populism, with segment sizes and characteristics varying between countries and cultural contexts. This study tests this with a secondary analysis of four public opinion surveys from Austria, Germany, Switzerland, and Taiwan (total N = 4598), combining a Most Similar Systems Design (MSSD) and a Most Different Systems Design (MDSD). It uses fixed-effects latent class analysis to demonstrate that Austrian, German, Swiss, and Taiwanese publics can be grouped into three segments: Full-Fledged Populists, People-Centric Non-Populists, and Deferent Anti-Populists. A large majority in all countries can be classified as Non-Populist or Anti-Populists, whereas Populists, who support the entire spectrum of science-related populism, make up the smallest segment. Bayesian regression shows that Populists are older and more likely to support right-leaning political views. Cross-country and cross-cultural comparisons reveal differences in segment sizes and characteristics: For example, Populists are more prevalent in Austria, while Germany has a large proportion of Anti-Populists. These are less widespread in Taiwan, where Non-Populists form a particularly big segment. The findings can be explained with national political, cultural, and historical contexts to some degree. Eventually, they are discussed against the backdrop implications for science communication and future scholarship on public science skepticism.

Towards Environment-Aware Fall Risk Assessment: Classifying Walking Surface Conditions Using IMU-Based Gait Data and Deep Learning.

Fall risk assessment (FRA) helps clinicians make decisions about the best preventative measures to lower the risk of falls by identifying the different risks that are specific to an individual. With the development of wearable technologies such as inertial measurement units (IMUs), several free-living FRA methods based on fall predictors derived from IMU-based data have been introduced. The performance of such methods could be improved by increasing awareness of the individuals' walking environment. This study aims to introduce and analyze a 25-layer convolutional neural network model for classifying nine walking surface conditions using IMU-based gait data, providing a basis for environment-aware FRAs. A database containing data collected from thirty participants who wore six IMU sensors while walking on nine surface conditions was employed. A systematic analysis was conducted to determine the effects of gait signals (acceleration, magnetic field, and rate of turn), sensor placement, and signal segment size on the method's performance. Accuracies of 0.935 and 0.969 were achieved using a single and dual sensor, respectively, reaching an accuracy of 0.971 in the best-case scenario with optimal settings. The findings and analysis can help to develop more reliable and interpretable fall predictors, eventually leading to environment-aware FRA methods.

Revealing flat bands and hybridization gaps in a twisted bilayer graphene device with microARPES

Controlling the electronic structure of two-dimensional materials using the combination of twist angle and electrostatic doping is an effective means to induce emergent phenomena. In bilayer graphene with an interlayer twist angle near the magic angle, the electronic dispersion is strongly modified by a manifold of hybridizing moiré Dirac cones leading to flat band segments with strong electronic correlations. Numerous technical challenges arising from spatial inhomogeneity of interlayer interactions, twist angle and device functionality have so far limited momentum-resolved electronic structure measurements of these systems to static conditions. Here, we present a detailed characterization of the electronic structure exhibiting miniband dispersions for twisted bilayer graphene, near the magic angle, integrated in a functional device architecture using micro-focused angle-resolved photoemission spectroscopy. The optimum conditions for visualizing the miniband dispersion are determined by exploiting the spatial resolution and photon energy tunability of the light source and applied to extract a hybridization gap size of eV and flat band segments extending across a moiré mini Brillouin zone. In situ electrostatic gating of the sample enables significant electron-doping, causing the conduction band states to shift below the Fermi energy. Our work emphasizes key challenges in probing the electronic structure of magic angle bilayer graphene devices and outlines conditions for exploring the doping-dependent evolution of the dispersion that underpins the ability to control many-body interactions in the material.

Park Tag

A major challenge for modern cities is how to maximize the productivity and reliability of urban infrastructure, such as minimizing road congestion by making better use of the limited car parking facilities that are available. We approach a special system for smart parking reservation in a commercial car parking area in an urban environment. This system is mainly designed to avoid unnecessary time conception to find an empty slot in a car parking area. By the same case we can also save more than 80 percentage of fuel wastage in a car parking area to finds the empty parking slot. Car parking problem is a major contributor and has been, still a major problem with increasing vehicle size in the luxurious segment and confined parking spaces in urban cities.

A Novel Way of Optimizing Headlight Distributions Based on Real-Life Traffic and Eye-Tracking Data Part 2: Analysis of Real-World Traffic Environments Data in Germany

In order to find optimized headlight distributions based on real traffic data, a three-step approach has been chosen. Since the complete investigations are too extensive to fit into one single publication, this paper is the second of three papers. Over the course of these papers, a novel way to optimize automotive light distributions based on real-life traffic and eye-tracking data is presented. Over all three papers, 119 test subjects participated in the studies, with over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances and other objects in traffic. In the first paper, an ideal headlight distribution for straight roads with no other road users was identified. The second paper aims to collect the data required to modify this idealized headlight distribution for use on real roads. The first step is to find the extent to which real roads differ from an ideal, straight road. To do this, the German traffic space was analyzed. A new test vehicle recorded video and GPS data over a selected route. The video data were then evaluated by a machine learning algorithm. Object recognition software was used to find different traffic participants and road signs. Camera calibrations were used to find the exact angles of these objects. Using publicly available road data combined with the recorded GPS data, the video data were split into different road categories, and traffic object distributions were calculated for urban roads, country roads and motorways. The resulting analyses provided representative distributions of vehicles and highway signs along different types of roadways and roadway geometries. The GPS data were also used to find the curvature distributions along the selected route. These data were then used to optimize segment sizes for an adaptive driving beam. Overall, increasing the number of segments above 100 did not have appreciable benefits. These data will also be used in the third paper, where along the same route, the gaze distribution of drivers was recorded and analyzed.

Enhanced U-Net model for rock pile segmentation and particle size analysis

Segmenting blasted stockpile particles in open-pit mines is essential for improving mining operations. However, complex rock textures often challenge traditional segmentation models. This paper presents an enhanced U-Net model that leverages depth-separable convolution and feature depth concatenation to enhance segmentation performance while reducing model complexity and training time. We evaluate our model on a homemade open pit burst pile ore segmentation dataset and report an average accuracy improvement of 1.53 % over the U-Net model. Our work contributes to the field of mining engineering and shows the potential of deep learning methods to improve mining operations.

Deep U_ClusterNet: automatic deep clustering based segmentation and robust cell size determination in white blood cell

Deep U_ClusterNet: automatic deep clustering based segmentation and robust cell size determination in white blood cell