bluuebunny/crossref_metadata_2025_split huggingface dataset:
In vivo evaluation of vulvar lichen sclerosus with reflectance confocal microscopy and therapeutic monitoring in children
Authors: Chen Lixin , Wang Ying , Gao Xibo , Qin Bei , Ren Min , Zhang Wanxing , Wei Ran , Su Haihui , Li Qinfeng 
Published: November 2022

AbstractBackgroundVulvar lichen sclerosus (VLS) in girls presents with itching, dysuria, and constipation and may result in the loss of vulvar architecture. In patients with an ambiguous clinical presentation, reflectance confocal microscopy (RCM) could be a helpful noninvasive diagnostic tool. The aim of this study was to describe the RCM characteristics of VLS and explore the clinical application value of RCM in therapeutic monitoring.MethodsSixteen patients with VLS were included in the study. All patients were periodically evaluated clinically with RCM, and different treatment regimens were given based on the patient's clinical appearances and RCM features.ResultsSome major key diagnostic features of VLS can be observed by RCM, including round to oval cyst‐like structures with medium‐to‐low‐refractive keratinoid substances (75%), thinning of the epidermal thickness (100%), destruction of the ring‐like structures around dermal papillae (100%), disorderly distributed coarse medium‐refractive fibrous material (100%),polygonal, plump, high‐refractive cellular structures and linear low‐refractive canalicular structures (100%). All of these characteristics had a high correspondence with histopathological features. The clinical manifestations improved after individualized treatment regimens based on the clinical appearances and RCM features.ConclusionRCM allows the visualization of major key diagnostic features of VLS and represents a valid option for objective therapeutic monitoring.
Fungal infection and colonization in lung transplant recipients with chronic lung allograft dysfunction
Authors: Pennington Kelly M. , Aversa Meghan , Martinu Tereza , Johnson Bradley , Husain Shahid 
Published: November 2022

AbstractBackgroundThe incidence and impact of de novo fungal airway colonization and infection in lung transplant recipients (LTRs) with known chronic lung allograft dysfunction (CLAD) has not been established. We aimed to determine the 1‐year cumulative incidence and risk factors of de novo fungal colonization or infection in LTRs with CLAD and assess the impact of colonization or infection on post‐CLAD survival.MethodsProspectively collected Toronto Lung Transplant Program database and chart review were used for double‐LTRs who were diagnosed with CLAD from January 1, 2016 to January 1, 2020 and who were free of airway fungi within 1 year prior to CLAD onset. International Society for Heart and Lung Transplantation definitions were used to define clinical syndromes. Cox‐Proportional Hazards Models were used for risk‐factor analysis. Survival analysis could not be completed secondary to low number of fungal events; therefore, descriptive statistics were employed for survival outcomes.ResultsWe found 186 LTRs diagnosed with CLAD meeting our inclusion criteria. The 1‐year cumulative incidence for any fungal event was 11.8% (7.0% for infection and 4.8% for colonization). Aspergillus fumigatus was a causative pathogen in eight of 13 (61.5%) patients with infection and six of nine (66.7%) patients with colonization. No patients with fungal colonization post‐CLAD developed fungal infection. Peri‐CLAD diagnosis (3 months prior or 1 month after) methylprednisolone bolus (hazards ratio: 8.84, p = .001) increased the risk of fungal events. Most patients diagnosed with fungal infections (53.8%) died within 1‐year of CLAD onset.ConclusionDe novo IFIs and fungal colonization following CLAD onset were not common. Fungal colonization did not lead to fungal infection. Methylprednisolone bolus was a significant risk factors for post‐CLAD fungal events.
image
Dynamic and fluctuating generation of hydrogen peroxide via photorespiratory metabolic channeling in plants
Authors: Li Xiangyang , Liao Mengmeng , Huang Jiayu , Chen Linru , Huang Haiyin , Wu Kaixin , Pan Qing , Zhang Zhisheng , Peng Xinxiang 
Published: November 2022

SUMMARYThe homeostasis of hydrogen peroxide (H2O2), a key regulator of basic biological processes, is a result of the cooperation between its generation and scavenging. However, the mechanistic basis of this balance is not fully understood. We previously proposed that the interaction between glycolate oxidase (GLO) and catalase (CAT) may serve as a molecular switch that modulates H2O2 levels in plants. In this study, we demonstrate that the GLO–CAT complex in plants exists in different states, which are dynamically interchangeable in response to various stimuli, typically salicylic acid (SA), at the whole‐plant level. More crucially, changes in the state of the complex were found to be closely linked to peroxisomal H2O2 fluctuations, which were independent of the membrane‐associated NADPH oxidase. Furthermore, evidence suggested that H2O2 channeling occurred even in vitro when GLO and CAT worked cooperatively. These results demonstrate that dynamic changes in H2O2 levels are physically created via photorespiratory metabolic channeling in plants, and that such H2O2 fluctuations may serve as signals that are mechanistically involved in the known functions of photorespiratory H2O2. In addition, our study also revealed a new way for SA to communicate with H2O2 in plants.
The trade‐GDP ratio as a measure of openness
Authors: Bleaney Michael , Tian Mo 
Published: November 2022

AbstractThe ratio of trade to GDP is often used as a summary measure of a country's openness to the rest of the world. It is well‐known that the trade–GDP ratio is affected by relatively time‐invariant factors, such as country size and remoteness from trading partners, that can largely be controlled for in cross‐country panels by using country fixed effects. It is shown here that there are also other important, time‐varying influences on the trade‐GDP ratio that have been little investigated, such as the prices of commodity exports and imports, the real effective exchange rate, and the ratio of investment to GDP. These factors are shown to be significant, and not only in the short run, and need to be taken into account in estimating the long‐run effects of transport costs or trade policy on the trade/GDP ratio.
CT features of malignant tubular genital tract tumors in seven goats
Authors: Collins‐Webb Alexandra G. , Löhr Christiane V. , Stieger‐Vanegas Susanne M. 
Published: November 2022

ABSTRACTNeoplasia of the tubular genital tract in goats, while rarely described, is most commonly reported as uterine adenocarcinoma, leiomyoma, or leiomyosarcoma. In this retrospective, single‐center, case series, medical records were searched for goats with a computed tomography (CT) diagnosis of tubular genital mass and a definitive histologic (surgical biopsy or necropsy) diagnosis of malignant neoplasia. Data recorded from CT images were presence of peritoneal/retroperitoneal fluid, urinary tract obstruction, abdominal lymphadenomegaly, additional abdominal nodules/masses, and pulmonary nodules. For masses, maximum cross‐sectional area, contrast enhancement, and uterine luminal fluid accumulation were also recorded. Seven goats met the inclusion criteria (leiomyosarcoma n = 5, adenocarcinoma n = 2). Both goats with adenocarcinoma had upper urinary tract obstruction, moderate to severe regional lymphadenopathy, peritoneal fluid, and peritoneal or hepatic nodules/masses; one goat with adenocarcinoma was discharged and subsequently euthanized, and the other had palliative mass debulking and was lost to follow up. Goats with leiomyosarcoma had infrequent, mild peritoneal fluid and mild sublumbar lymphadenopathy. Of the goats with leiomyosarcoma, two were euthanized at or near the time of CT imaging, two were euthanized at the time of surgery due to perceived mass non‐resectability, and one had mass regression approximately four months post ovariohysterectomy but was subsequently lost to follow up. Five goats had pulmonary nodules, three of which had pathologic confirmation (pulmonary metastasis in a single patient with adenocarcinoma, and lungworm granulomas in two goats with leiomyosarcoma). Severe sublumbar lymphadenopathy and obstructive uropathy were sequelae in the two caprine patients with genital adenocarcinoma, and in none with leiomyosarcoma.
A guide to using technological applications to facilitate systematic reviews
Authors: Gibbs Karen DiValerio , Loveless Jennifer , Crane Stacey 
Published: November 2022

AbstractBackgroundA systematic review (SR) synthesizes evidence in a reproducible way and informs evidence‐based decision‐making. SRs are time‐intensive, particularly with respect to staying organized, maintaining records, and managing different phases of the process. Although there are numerous methodological guides to lead researchers in the approach to SRs to minimize bias and enhance rigor, there is less focus on technological approaches that can make the SR process easier for researchers.AimTo guide researchers through the currently available technological applications that can assist with the SR process and synthesis of scientific literature.MethodologyKey ways that technological applications can facilitate the SR process are examined.ResultsSpecific applications are discussed and stratified by their support of one or multiple phases of the systematic review process. Key features, strengths, and limitations are provided for technological applications that support the SR process.Linking Evidence to ActionThis paper guides researchers in different ways technology can support SRs. Through use of these applications, the researcher can complete SRs in a timely manner and manage the process effectively.
Experimental Study of Effect of Injection Timing on Port Fuel Injection Gasoline, Port Fuel Injection Compressed Natural Gas, and Direct Injection Compressed Natural Gas Engine Performance, Combustion, and Emissions Characteristics
Authors: Sahoo Sridhar , Srivastava Dhananjay Kumar 
Published: November 2022

Abstract
Compressed natural gas (CNG) has gained popularity due to its wide availability, higher efficiency, and lower emissions compared to gasoline. However, the lower flame speed characteristics of CNG with conventional port injection reduce the CNG engine volumetric efficiency and power output. CNG's lower gas jet momentum during a low load operation creates a non-uniform air-fuel mixture that affects ignition and combustion quality. Direct injection of CNG with optimum injection timing is expected to improve volumetric efficiency, ignition quality, and combustion process. In this study, a comparative study on the effect of end-of-injection (EOI) timing on volumetric efficiency, thermal efficiency, combustion duration, and emissions was carried out in a single-cylinder port fuel injection (PFI) spark-ignition engine using gasoline and CNG, and direct injection (DI) spark ignition engine using CNG. The experiments were performed at two-part load operations of 20% and 40% throttle at 900 and 1500 rpm. Experimental results indicate that the PFI CNG engine is more influential in EOI timing than gasoline engines. The performance of the PFI CNG engine is improved when the injection occurs during the intake valve open period compared to the closed valve period with higher thermal efficiency, volumetric efficiency, and indicated mean effective power (IMEP). A shorter flame development angle and combustion duration were observed when EOI timing was in the open intake valve condition. DI CNG improved volumetric efficiency at advanced EOI timing compared to the PFI CNG engine. However, the combustion process is critically dependent on injection timing and air-fuel mixing duration. A three-dimensional computational fluid dynamics simulation was conducted to evaluate the effect of advanced and retarded EOI timing on DI CNG engine's in-cylinder turbulent kinetic energy development and in-cylinder equivalence ratio near the ignition point. An excess advanced EOI timing resulted in stratified rich and retarded EOI timing results in loss of turbulence energy, leading to a slightly rich and lean mixture for advanced and retarded EOI timing, respectively. Hence, an optimum EOI timing provides a conducive environment to initiate the combustion and flame front propagation. Further, advanced EOI timing was required at higher throttle opening and engine speed. The emissions in DI CNG were also greatly affected by EOI timing.
Controlling Damage Evolution in Geometrically Identical Cold Forged Parts by Counterpressure
Authors: Gitschel Robin , Hering Oliver , Schulze André , Erman Tekkaya A. 
Published: November 2022

Abstract
It is investigated to what extent the evolution of ductile damage in cold forging can be controlled without changing the geometry of the produced part. Besides the effects of strain hardening and residual stresses, damage, which is the nucleation, growth and coalescence of voids on microscopic level, affects product properties of the manufactured components such as fatigue strength, impact strength, or elastic stiffness. Former investigations have shown that the load path-dependent damage evolution in forward rod extrusion, and thus, the performance of produced parts can be controlled by the process parameters extrusion strain and shoulder opening angle. As these parameters also affect the geometry of extruded parts, design requirements of components might be violated by varying these. Thus, counterpressure is used to superpose purely hydrostatic stresses to forward rod extrusion in order to decrease triaxiality in the forming zone without causing geometric variations in the produced parts. The counterpressure is either introduced by a counterpunch or by modified process routes. The achieved improvements in product performance are in agreement with results obtained by variation of extrusion strain and shoulder opening angle as described in the literature. In addition, it is observed in tensile tests that damage in cold extruded parts does not significantly affect flow stress. All advancements in product performance are realized without affecting the products’ geometries.
Large Eddy Simulation of Transitional Separated Flow Over a Low Reynolds Number Cambered Airfoil
Authors: Zilstra Alison , Johnson David A. 
Published: November 2022

Abstract
The accurate simulation of the aerodynamic behavior of low Reynolds number (Re) cambered airfoils requires the ability to capture the transitional separated boundary layer (BL) that occurs naturally on the surface of the airfoil. In this study, simulations are performed using a modern cambered airfoil designed for use in low Re applications, which are an advancement from previous studies using flat plate geometries or symmetric NACA airfoils. The cambered SD 7037 airfoil is simulated using wall-resolved large eddy simulation (LES) at a modest Re of 4.1×104 and at 1 deg, 5 deg, and 7 deg angles of attack (AOAs), with results validated against experimental data. Simulated predictions of pressure and skin friction coefficients clearly capture the correct location of the laminar separated bubble (LSB) which forms during the natural BL transition process. Sensitivity to elevated inflow turbulence is found to cause early BL reattachment at higher AOAs without impacting the location of BL separation. An integral BL analysis verifies the accuracy of the simulated velocity profiles against experimental values. The scale of horseshoe structures visualized in the transitional BL is larger in comparison to airfoil chord length than what is seen in previous simulations at Re of the order of 105, which highlights the importance of investigating cambered airfoils at a modest Re.
Synthetic Image Assisted Deep Learning Framework for Detecting Defects During Composite Sheet Layup
Authors: Manyar Omey M. , Cheng Junyan , Levine Reuben , Krishnan Vihan , Barbič Jernej , Gupta Satyandra K. 
Published: November 2022

Abstract
Automation of high-performance manufacturing processes such as prepreg composite layup has been gaining a lot of interest lately. Reliable and accurate defect detection methods play a crucial role in the automation of such processes to maintain the desired quality. The composite prepreg layup process involves manipulation of sheet-like material. Traditional machine vision-based defect detection techniques are inept in detecting defects for such complex processes due to the nature of the defects. Advanced defect detection techniques enabled by deep learning are the key for such applications. However, Deep learning usually requires an enormous amount of physical images of the process which is infeasible in high-mix manufacturing applications. In this paper, we resolve the data generation problem for deep learning by presenting an approach where with a combination of finite element-based simulation and advanced graphics techniques we generate a dataset of photorealistic images of the defects. Approximately, 10000 synthetic images are generated and combined with around 1000 images of real sheets to train a ResNeSt-based deep learning model. We have also devised an efficient 2-stage methodology for training the deep learning network to detect wrinkle-like defects. With the trained model and data augmentation techniques, our method can achieve a mean Average Precision (mAP) of 0.98 on actual production data for detecting defects. The code and the entire dataset are available at: https://github.com/RROS-Lab/DeepSynthDefectDetector.
A Scalable Graph Learning Approach to Capacitated Vehicle Routing Problem Using Capsule Networks and Attention Mechanism
Authors: Paul Steve , Chowdhury Souma 
Published: November 2022

Abstract
This paper introduces a new graph neural network architecture for learning solutions of Capacitated Vehicle Routing Problems (CVRP) as policies over graphs. CVRP serves as an important benchmark for a wide range of combinatorial planning problems, which can be adapted to manufacturing, robotics and fleet planning applications. Here, the specific aim is to demonstrate the significant real-time executability and (beyond training) scalability advantages of the new graph learning approach over existing solution methods. While partly drawing motivation from recent graph learning methods that learn to solve CO problems such as multi-Traveling Salesman Problem (mTSP) and VRP, the proposed neural architecture presents a novel encoder-decoder architecture. Here the encoder is based on Capsule networks, which enables better representation of local and global information with permutation invariant node embeddings; and the decoder is based on the Multi-head attention (MHA) mechanism allowing sequential decisions. This architecture is trained using a policy gradient Reinforcement Learning process. The performance of our approach is favorably compared with state-of-the-art learning and non-learning methods for a benchmark suite of Capacitated-VRP (CVRP) problems. A further study on the CVRP with demand uncertainties is conducted to explore how this Capsule-Attention Mechanism architecture can be extended to handle real-world uncertainties by embedding them through the encoder.
Hall Effect Sensor Design Optimization With Multi-Physics Informed Gaussian Process Modeling
Authors: Xu Yanwen , Zheng Zhuoyuan , Arora Kanika , Senesky Debbie G. , Wang Pingfeng 
Published: November 2022

Abstract
Magnetic field sensor devices have been widely used to track changes in magnetic flux concentration, and the Hall sensors are promising in many engineering applications. Design optimization of the Hall effect sensor is required to ensure the quality and capability of the device when in service. Even though there has been empirical models established from experiments to guide the design of the Hall effect sensor, the underlying relationship in Hall effect sensor design parameters and corresponding performances has not been looked into thoroughly. This paper presents a physics-informed machine learning technique to optimize the geometry design of Hall magnetic sensors for a low offset and high sensitivity characteristic. Multi-physics based finite element models were first developed to simulate and predict the Hall voltage, offset voltage and sensor sensitivity of different Hall effect sensors with various geometries. In addition, to improve the design efficiency, Gaussian Process (GP) based surrogate models were constructed from multiphysics-based simulation results to effectively investigate the Hall sensor performances with an adaptive sampling strategy. Three types of geometries of Hall sensor were studied and optimized with the proposed physics-informed GP model, the obtained results were consistent with the empirical experimental result.
Exploring the Perceived Complexity of 3d Shapes: Towards a Spatial Visualization VR Application
Authors: Busheska Angela , Lopez Christian 
Published: November 2022

Abstract
The objective of this work is to explore the perceived complexity of 3D shapes used in spatial visualization tasks and leverage Machine Learning to create a model that can predictthis perceived complexity using the visual features of the shapes. This could help automate the process of generating 3D shapes for a Virtual Reality (VR) application designed to help develop spatial visualization skills. Spatial visualization skills are important skills needed in the STEM fields. While VR has been used to help develop these skills, most of the existing applications do not necessarily tailor their content to the skills level of individuals. Automatically generating shapes can help VR applications tailor spatial visualization tasks to the skills level of users. However, in order to do this, it is important to first understand how humans perceive the complexity of 3D shapes, and how this relates to their performance in spatial visualization tasks. The results of this work indicate that while participants perceived complexity of 3D shapes is correlated to their performance in spatial visualization tasks that use the same 3D shapes, this perceived complexity by itself is not enough to predict their performance in such tasks. Moreover, the results indicate that certain visual features of 3D shapes can help explain the perceived complexity of the shape as well as the performance of individuals in spatial visualization tasks that implement those 3D shapes.