Shape Model Research Articles

Pneumatic separation and process simulation of waste printed circuit boards pyrolysis residue

In this study, the application of Z-shape pneumatic separator for separating metal and nonmetal particles in pyrolysis residue of waste printed circuit boards was investigated, and the flow field characteristics and particle motion behavior in the separator were revealed through flow field simulation. The pneumatic separation results indicated that pre-screening significantly enhanced the separation efficiency of metals and nonmetals in pyrolysis residue. The simulation results showed that the large gradient of airflow velocity distribution and the turbulent flow field within the Z-shape separation cavity are beneficial for dispersing particles and reducing the influence between particles of different properties, thus enhancing separation efficiency. Using Rocky DEM for modeling of different shapes and properties of particles, and then combined with ANSYS Fluent can better simulate the particle separation behavior in the pneumatic separator.

Changes in femoral anteversion after intramedullary nailing for pediatric femoral shaft fracture: a multicenter study

BackgroundThe rotational change after using a flexible intramedullary (IM) nail for femoral shaft fractures has been a concern for many surgeons. Recently, a statistical shape model (SSM) was developed for the three-dimensional reconstruction of the femur from two-dimensional plain radiographs. In this study, we measured postoperative femoral anteversion (FAV) in patients diagnosed with femoral shaft fractures who were treated with flexible IM nails and investigated age-related changes in FAV using the SSM.MethodsThis study used radiographic data collected from six regional tertiary centers specializing in pediatric trauma in South Korea. Patients diagnosed with femoral shaft fractures between September 2002 and June 2020 and patients aged < 18 years with at least two anteroposterior (AP) and lateral (LAT) femur plain radiographs obtained at least three months apart were included. A linear mixed model (LMM) was used for statistical analysis.ResultsOverall, 72 patients were included in the study. The average patient age was 7.6 years and the average follow-up duration was 6.8 years. The average FAV of immediate postoperative images was 27.5 ± 11.5°. Out of 72 patients, 52 patients (72.2%) showed immediate postoperative FAV greater than 20°. The average FAV in patients with initial FAV > 20° was 32.74°, and the LMM showed that FAV decreased by 2.5° (p = 0.0001) with each 1-year increase from the time of initial trauma.ConclusionsThis study explored changes in FAV after femoral shaft fracture using a newly developed technology that allows 3D reconstruction from uncalibrated 2D images. There was a pattern of change on the rotation of the femur after initial fixation, with a 2.5° decrease of FAV per year.

Non-stationary models for hydrological extremes in the mountain rivers of the Argentinean Central Andes

ABSTRACT The assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region.

Statistical shape modeling of the geometric morphology of the canine femur, tibia, and patella.

Bone morphometry varies among dogs of different sizes and breeds. Studying these differences may help understand the predisposition of certain breeds for specific orthopedic pathologies. This study aimed to develop a statistical shape model (SSM) of the femur, patella, and tibia of dogs without any clinical orthopeadic abnormalities to analyze and compare morphological variations based on body weight and breed. A total of 97 CT scans were collected from different facilities and divided based on breed and body weight. The 3D models of the bones were obtained and aligned to a coordinate system. The SSM was created using principal component analysis (PCA) to analyze shape variations. The study found that the first few modes of variation accounted for a significant percentage of the total variation, with size/scale being the most prominent factor. The results provide valuable insights into normal anatomical variations and can be used for future research in understanding pathological bone morphologies and developing 3D imaging algorithms in veterinary medicine.

Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms

Challenging infrarenal aortic neck characteristics have been associated with increased risk of a type Ia endoleak after endovascular aneurysm repair (EVAR). Short apposition (< 10 mm circumferential shortest apposition length [SAL]) on the first post-operative computerised tomography angiography (CTA) has been associated with type Ia endoleak. Therefore, this study aimed to develop a model to predict post-operative SAL in patients with an abdominal aortic aneurysm based on the pre-operative shape. A statistical shape model was developed to obtain principal component scores. The dataset comprised patients treated with standard EVAR without complications (n = 93) enriched with patients with a late type Ia endoleak (n = 54). The infrarenal SAL was obtained from the first post-operative CTA and subsequently binarised (< 10 mm and ≥ 10 mm). The principal component scores that were statistically different between the SAL groups were used as input for five classification models, and evaluated by means of leave one out cross validation. Area under the receiver operating characteristics curves (AUC), accuracy, sensitivity, and specificity were determined for each classification model. Of the 147 patients, 24 patients had an infrarenal SAL < 10 mm and 123 patients had a SAL ≥ 10 mm. The gradient boosting model resulted in the highest AUC of 0.77. Using this model, 114 (78.0%) patients were correctly classified; sensitivity (< 10 mm apposition was correctly predicted) and specificity (≥ 10 mm apposition was correctly predicted) were 0.70 and 0.79, and were based on a threshold of 0.21, respectively. A model was developed to predict which patients undergoing EVAR will achieve sufficient graft apposition (≥ 10 mm) in the infrarenal aortic neck based on a statistical shape model of pre-operative CTA data. This model can help vascular specialists during the planning phase to accurately identify patients who are unlikely to achieve sufficient apposition after standard EVAR.

Accuracy tradeoffs between individual bone and joint-level statistical shape models of knee morphology

Statistical shape models (SSMs) are useful tools in evaluating variation in bony anatomy to assess pathology, plan surgical interventions, and inform the design of orthopaedic implants and instrumentation. Recently, by considering multiple bones spanning a joint or the whole lower extremity, SSMs can support studies investigating articular conformity and joint mechanics. The objective of this study was to assess tradeoffs in accuracy between SSMs of the femur or tibia individually versus a combined joint-level model. Three statistical shape models were developed (femur-only, tibia-only, and joint-level) for a training set of 179 total knee arthroplasty (TKA) patients with osteoarthritis representing both genders and several ethnicities. Bone geometries were segmented from preoperative CT scans, meshed with triangular elements, and registered to a template for each SSM. Principal component analysis was performed to determine modes of variation. The statistical shape models were compared using measures of compactness, accuracy, generalization, and specificity. The generalization evaluation, assessing the ability to describe an unseen instance in a leave-one-out analysis, showed that errors were consistently smaller for the individual femur and tibia SSMs than for the joint-level model. However, when additional modes were included in the joint-level model, the errors were comparable to the individual bone results, with minimal additional computational expense. When developing more complex SSMs at the joint, lower limb, or whole-body level, the use of an error threshold to inform the number of included modes, instead of 95 % of the variation explained, can help to ensure accurate representations of anatomy.

OpenHands: An Open-Source Statistical Shape Model of the Finger Bones

This paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset of computed tomography (CT) scans of 10 right hands (5F:5M, 27–37 years, free from disease or injury) imaged at 0.3 mm resolution, segmented, meshed and aligned. Model generated included pose neutralisation to remove joint angle variation during imaging. Repositioning was successful; no joint flexion variation was observed in the resulting model. The first principal component (PC) of morphological variation represented phalanx size in all fingers. Subsequent PCs showed variation in position along the palmar-dorsal axis, and bone breadth: length ratio. Finally, the models were interrogated to provide gross measures of bone lengths and joint spaces. These models have been published for open use to support wider community efforts in hand biomechanical analysis, providing bony anatomy descriptions whilst preserving the security of the underlying imaging data and privacy of the participants. The model describes a small, homogeneous population, and assumptions cannot be made about how it represents individuals outside the training dataset. However, it supplements anthropometric datasets with additional shape information, and may be useful for investigating factors such as joint morphology and design of hand-interfacing devices and products. The model has been shared as an open-source repository (https://github.com/abel-research/OpenHands), and we encourage the community to use and contribute to it.

Probabilistic Finite Element Analysis of Human Rib Biomechanics: A Framework for Improved Generalizability.

In dynamic impact events, thoracic injuries often involve rib fractures, which are closely related to injury severity. Previous studies have investigated the behavior of isolated ribs under impact loading conditions, but often neglected the variability in anatomical shape and tissue material properties. In this study, we used probabilistic finite element analysis and statistical shape modeling to investigate the effect of population-wide variability in rib cortical bone tissue mechanical properties and rib shape on the biomechanical response of the rib to impact loading. Using the probabilistic finite element analysis results, a response surface model was generated to rapidly investigate the biomechanical response of an isolated rib under dynamic anterior-posterior load given the variability in rib morphometry and tissue material properties. The response surface was used to generate pre-fracture force-displacement computational corridors for the overall population and a population sub-group of older mid-sized males. When compared to the experimental data, the computational mean response had a RMSE of 4.28N (peak force 94N) and 6.11N (peak force 116N) for the overall population and sub-group respectively, whereas the normalized area metric when comparing the experimental and computational corridors ranged from 3.32% to 22.65% for the population and 10.90% to 32.81% for the sub-group. Furthermore, probabilistic sensitivities were computed in which the contribution of uncertainty and variability of the parameters of interest was quantified. The study found that rib cortical bone elastic modulus, rib morphometry and cortical thickness are the random variables that produce the largest variability in the predicted force-displacement response. The proposed framework offers a novel approach for accounting biological variability in a representative population and has the potential to improve the generalizability of findings in biomechanical studies.

Morphology of the scaphotrapeziotrapezoid joint: A multi-domain statistical shape modeling approach.

The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three-dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi-domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM-generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three-dimensional triangular surface meshes. A multi-domain, particle-based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface-normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities-a proxy for joint space-were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging-derived STT joint space.

Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction.

It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e', >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01-1.04]; P=0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00-1.04]; P=0.043). LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621.

Improving radiological quantification of levator hiatus features with measures informed by statistical shape modeling.

The measures that traditionally describe the levator hiatus (LH) are straightforward and reliable; however, they were not specifically designed to capture significant differences. Statistical shape modeling (SSM) was used to quantify LH shape variation across reproductive-age women and identify novel variables associated with LH size and shape. A retrospective study of pelvic MRIs from 19 nulliparous, 32 parous, and 12 pregnant women was performed. The LH was segmented in the plane of minimal LH dimensions. SSM was implemented. LH size was defined by the cross-sectional area, maximal transverse diameter, and anterior-posterior (A-P) diameter. Novel SSM-guided variables were defined by regions of greatest variation. Multivariate analysis of variance (MANOVA) evaluated group differences, and correlations determined relationships between size and shape variables. Overall shape ( ), SSM mode 2 (oval to -shape, ), mode 3 (rounder to broader anterior shape, ), and maximal transverse diameter ( ) significantly differed between groups. Novel anterior and posterior transverse diameters were identified at 14% and 79% of the A-P length. Anterior transverse diameter and maximal transverse diameter were strongly correlated ( , ), while posterior transverse diameter and maximal transverse diameter were weakly correlated ( , ). The traditional maximal transverse diameter generally corresponded with SSM findings but cannot describe anterior and posterior variation independently. The novel anterior and posterior transverse diameters represent both size and shape variation, can be easily calculated alongside traditional measures, and are more sensitive to subtle and local LH variation. Thus, they have a greater ability to serve as predictive and diagnostic parameters.

Poster 354: Tibiofemoral Bone Shape Features Are Associated with Re-Tear after Anterior Cruciate Ligament Reconstruction

Introduction: Knee bony morphology is linked to anterior cruciate ligament (ACL) injury, ACL graft failure, and osteoarthritis (OA) risk, and employing tools such as statistical shape modeling could identify individuals prone to these joint conditions. Re-tear after ACL reconstruction remains a common and devastating complication, and therefore, recognizing patients at high risk of graft failure before primary ACL reconstruction is crucial for potential intervention to reduce failure rates. Magnetic resonance imaging (MRI) is most commonly obtained in the setting of possible ACL injury and before ACL reconstruction surgery, yet currently available MRI-based bone shape assessment methods require high-resolution sequences or manual segmentation, limiting feasibility of large-scale analysis. The development of automated tools for bone shape extraction from clinical MRI could allow for three-dimensional statistical shape modeling across large datasets. We established an automated pipeline for bone segmentation from proton density (PD)-weighted MRI scans, aiming to extract knee bony morphology from preoperative clinical MRIs in patients with ACL injuries. We hypothesized that our segmentation pipeline could uncover specific tibiofemoral joint bone shape features associated with ACL re-tear after reconstruction. Objective: To identify bone shape features prior to initial primary ACL reconstruction in patients with eventual ACL graft re-tear compared to those with a known, intact ACL graft. Methods: Patients who retrospectively experienced a subsequent re-tear after reconstruction within three years of initial injury were compared to those without a re-tear within three years (20 without subsequent re-tear, 22 with subsequent re-tear for distal femur; 22 without subsequent re-tear, 19 with subsequent re-tear for proximal tibia). The control group had MRI scans at 3 years after reconstruction that confirmed an intact ACL graft. An automatic femur and tibia segmentation framework was developed by training two deep convolutional neural networks using clinical, two-dimensional PD-weighted knee MRI sequences (3.5 mm slice thickness) from patients with ACL tears. Using the segmentation pipeline, bone was automatically segmented from preoperative clinical PD-weighted knee MRI sequence (3 mm slice thickness), three-dimensional triangulated meshes were generated, scaled to a reference, and aligned and matched to an atlas using Iterative Closest Point algorithm. Modes of shape variance were extracted from principal component analysis (PCA). The effect of each mode is modeled by three-dimensional surfaces for each specific shape feature. Modes were considered statistically significant if the mean principal component score between groups was p&lt;0.05 using Students T-test. Results: Statistical shape modeling of the femoral condyles using 15 PCA modes reveals significant differences (p &lt; 0.05) in Modes 1 and 4 at the initial injury in patients who subsequently experienced ACL re-tear after reconstruction compared to those without re-tear. Femur Mode 1 is characterized by a narrower intercondylar notch width, a wider medial condylar width, and an increased ratio of femoral condylar offset in patients with ACL re-tear. Femur Mode 4 illustrates a more prominent trochlear sulcus and increased surface area along the lateral femoral condyle relative to the medial condyle in patients with ACL re-tear. The initial 15 PCA modes of the distal femur collectively account for 75.04% of the normalized accumulated total variance. Statistical shape modeling of the tibial plateau with 15 PCA modes reveals that Mode 7 is significantly different (p &lt; 0.05) at the initial injury in patients who experienced subsequent ACL re-tear after reconstruction compared to those without re-tear. Tibia Mode 7 indicates diminished ACL facet prominence, a squared lateral tibial plateau, and a broader, flatter tibial spine in patients with subsequent ACL re-tear. The first 15 PCA modes of the proximal tibia collectively explain 73.60% of the normalized accumulated total variance. Conclusions: Using an automatic bone segmentation pipeline on preoperative clinical MRIs, we identify specific shape features in the tibiofemoral joint associated with ACL re-tear after reconstruction. Patients with subsequent ACL re-tear exhibit a variety of distinct three-dimensional morphology differences, including reduced intercondylar notch width, wider medial condylar width, increased lateral tibial plateau convexity, and broader, lateralized tibial spine, uncovered using statistical shape modeling. Statistical shape modeling identifies three-dimensional morphology patterns, thus capturing differences otherwise unaccounted for by traditional two-dimensional measurements. This approach offers predictive potential for patients at risk of ACL re-tear, improving treatment decisions for those with high-risk morphologic features.

Facial Masculinization Surgery Using Polyetheretherketone Alloplasty: Statistical Shape Modeling-based Implant Designs.

The face is the initial feature used to judge gender in public spaces; it is also a source of significant gender dysphoria. Surgical techniques are available for non-cisgender male patients who desire a more masculine face by augmenting certain features to change the bony framework of the skull. Augmentation using virtually designed patient-specific polyetheretherketone implants has now become a more widely applied method in maxillofacial surgery. When designing implants for augmentation, a three-dimensional (3D) reference or template is very useful. Hence, a 3D statistical shape model was developed of a male skull shape from information from a population of 40 male patients containing the mean shape and principal components of shape variation. By overlaying the template and the patient's 3D skull model, this method identified the regions of gender dimorphism in this case to be the orbital ridge, zygomatic regions, and frontal bossing area. Based on the 3D template overlay, polyetheretherketone augmentation implants were virtually designed in close consultation with a patient to augment the aforementioned regions. The virtual statistical shape modeling template offered an objective reference, and the possibility to fully involve the patient in the treatment planning.

Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study.

The study aims to identify differences in tibiofemoral joint morphology between responders (R group, no pain) to arthroscopic partial medial meniscectomy (APMM) versus medial postmeniscectomy syndrome patients (MPMS group, recurrent pain at 2 years postmeniscectomy) in a clinically neutrally aligned patient population. The second aim was to build a morphology-based predictive algorithm for response to treatment (RTT) in APMM. Two patient groups were identified from a large multicentre database of meniscectomy patients at 2 years of follow-up: the R group included 120 patients with a KOOS pain score > 75, and the MPMS group included 120 patients with a KOOS pain score ≤ 75. Statistical shape models (SSMs) of distal femur, proximal tibia and tibiofemoral joint were used to compare knee morphology. Finally, a predictive model was developed to predict RTT, with the SSM-derived morphologic variables as predictors. No differences were found between the R and MPMS groups for patient age, sex, height, weight or cartilage status. Knees in the MPMS group were significantly smaller, had a wider femoral notch and a smaller medial femoral condyle. A morphology-based predictive model was able to predict MPMS at 2 years follow-up with a sensitivity of 74.9% (95% confidence interval [CI]: 74.4%-75.4%) and a specificity of 81.0% (95% CI: 80.6%-81.5%). A smaller tibiofemoral joint, a wider intercondylar notch and smaller medial femoral condyle were observed shape variations related to medial postmeniscectomy syndrome. These promising results are a first step towards a knee morphology-based clinical decision support tool for meniscus treatment. Case-control study. Level IIIb.

How Systems Thinking Provides the Foundation for A W‐Shaped Model of an Effective Technical Leader

AbstractTechnical Leadership is a relatively new subject that has not been adequately addressed in literature. Behaviors and skills of effective technical individuals and leaders are defined and cited often, including various shape models of individuals. However, a Systems Thinking approach for combining these two mindsets has not been documented. This paper's goal is to provide a background on effective technical and leadership behaviors and skills, relate them to the various shape‐based models of individuals, and ultimately present a novel W‐shaped model describing an effective Technical Leader whose foundation is a Systems Thinking mindset.

Poster 200: Statistical Shape Modeling of an Experimental Induced FAI Cam Deformity in a Rabbit Model: A Platform to Study Mechanism of Hip Disease

Poster 200: Statistical Shape Modeling of an Experimental Induced FAI Cam Deformity in a Rabbit Model: A Platform to Study Mechanism of Hip Disease

Predictions for Sparse Photometry of Jupiter-family Comet Nuclei in the LSST Era

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory will deliver high-quality, temporally sparse observations of millions of solar system objects on an unprecedented scale. Such data sets will likely enable the precise estimation of small-body properties on a population-wide basis. In this work, we consider the possible applications of photometric data points from LSST to the characterization of Jupiter-family comet (JFC) nuclei. We simulate sparse-in-time lightcurve points with an LSST-like cadence for the orbit of a JFC between 2024 and 2033. Convex lightcurve inversion is used to assess whether the simulation input parameters can be accurately reproduced for a sample of nucleus rotation periods, pole orientations, activity onsets, shapes, and sizes. We find that the rotation period and pole direction can be reliably constrained across all nucleus variants tested, and that the convex shape models, while limited in their ability to describe complex or bilobed nuclei, are effective for correcting sparse photometry for rotational modulation to improve estimates of nucleus phase functions. Based on this analysis, we anticipate that LSST photometry will significantly enhance our present understanding of the spin state and phase function distributions of JFC nuclei.

Establishment of discrete reference interval and next-generation reference interval for copper and zinc during pregnancy using real-world data

BackgroundCopper and zinc are two trace elements that are essential to maintain normal pregnancy and fetal development. But only few research established specific reference intervals (RIs) for pregnant women. In this study, we aim to establish discrete RIs and next-generation RIs for copper and zinc during pregnancy by real-world data. MethodWe retrospectively collected 710 healthy pregnant women and 300 age-matched non-pregnant women attending the hospital and compared copper and zinc levels among them. Further, we analyzed multiple factors (gestational age, maternal age, and parity) that may affect copper and zinc during pregnancy by multivariate regression. Two types of reference intervals (RIs) of copper and zinc for pregnant women were established: discrete reference intervals (RIs) by non-parametric method and next-generation RIs by Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model. ResultCopper levels were higher (median: 1st trimester: 1203.00 μg/L, 2nd trimester: 1818.00 μg/L, 3rd trimester: 1795.00 μg/L) than in non-pregnant women (median: 900.00 μg/L, p＜0.001), whereas zinc levels were lower in pregnant women (median: 1st trimester: 836.00 μg/L, 2nd trimester: 639.00 μg/L, 3rd trimester: 618.00 μg/L) than in non-pregnant women (median: 767.00 μg/L, p＜0.001). Additionally, copper and zinc levels varied among trimesters. Moreover, copper and zinc were affected significantly by gestational age but maternal age only had a weak effect on them. Based on the effect of gestational age, we established discrete RIs partitioned by trimesters and next-generation RIs for copper and zinc respectively during pregnancy. ConclusionTaken together, this research elucidated the remarkable effect of gestational age on copper and zinc during pregnancy. The next-generation RIs visualized trends and subtle changes in copper and zinc levels during pregnancy. The discrepancy between discrete RIs and next-generation RIs suggested a more detailed continuous RIs could be considered for pregnant women.

Non-stationary low flow frequency analysis under climate change

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.

YADE - An extensible framework for the interactive simulation of multiscale, multiphase, and multiphysics particulate systems

This contribution presents the key elements of YADE, an extensible open-source framework for dynamic simulations. During the past 19 years, YADE has evolved from “Yet Another Dynamic Engine” to a versatile multiscale and multiphysics solver, counting a large, active, and growing community of users and developers. The computationally intense parts of the source code are written in C++, using flexible object models that allow for easy implementation of new features. The source code is wrapped in Python, equipping the software with an interactive kernel used for rapid and concise scene construction, simulation control, post-processing, and debugging. The project, including documentation and examples, is hosted on https://yade-dem.org, while the source code is freely available on GitLab. Over the last decade, YADE has expanded in terms of capabilities thanks to the contribution of many developers from different fields of expertise, including soil and rock mechanics, chemical engineering, physics, bulk material handling, and mineral processing. The rapid growth of YADE can be attributed to (1) the careful and robust design of the framework core, (2) a continuous integration pipeline with fully embedded thorough tests which are executed upon each merge request, ensuring stable compilation for various operating systems, and (3) user-friendliness, facilitated by the Python interface, detailed documentation, and rigorous user support. In this paper, we review the main features of YADE, highlighting its versatility in terms of applications, its flexibility in terms of code development, as well as recent improvements in terms of computational efficiency. Program summaryProgram Title: YADE - Yet Another Dynamic EngineCPC Library link to program files:https://doi.org/10.17632/n4f5fw97rd.1Developer's repository link:https://gitlab.com/yade-dev/trunkLicensing provisions: GNU General Public License 2Programming language: C++, PythonNature of problem: Numerical simulation of many-particle systems requires accurate models for particle-to-particle interactions, efficient contact detection between objects of various shapes, and robust time integration. In addition, the flow of fluids, thermal effects, as well as other coupled problems in the presence of particles are found in many fundamental and practical applications and they need dedicated computational tools. YADE provides a computational framework to perform such simulations using the discrete element method and multiple extensions of it.Solution method:YADE simulates particulate systems using the Discrete Element Method (DEM) in a flexible platform combining C++ and Python. It provides a large variety of shape and interaction models, an explicit time-integration scheme, and many post-processing tools. YADE features dedicated solvers for both fluid and heat fluxes at the pore scale, and it supports couplings with third-party software such as Escript (finite element method) and OpenFOAM (computational fluid dynamics).