bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design Rules and In-Situ Quality Monitoring of Thin-Wall Features Made Using Laser Powder Bed Fusion
Authors: Gaikwad Aniruddha , Imani Farhad , Rao Prahalad , Yang Hui , Reutzel Edward 
Published: November 2019

Abstract
The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold:
1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l) to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine.
2) Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network.
To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.
A New Micromechanical Model to Study Transformation Plasticity in High-Carbon Steels
Authors: Voothaluru Rohit , Bedekar Vikram , Pauskar Praveen 
Published: November 2019

Abstract
Hardened steels in engineering applications tend to have gradient microstructures with varying amounts of retained austenite alongside harder phases such as martensite or bainite. However, the metastable austenite can transform into martensite under mechanical loads, resulting in an inelastic strain within the material from the volumetric mismatch between FCC austenite and BCT martensite. In this work, a new constitutive formulation based upon the critical driving force for austenite transformation is presented. The model was implemented into a crystal plasticity formulation, and empirical data from in-situ neutron diffraction was used to determine the local micro-plasticity and transformation plasticity parameters. The results from finite element modeling also show that using a homogenized finite element approach could help to establish a material model that can capture the transformation plasticity within these materials with good accuracy.
Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data
Authors: Decker Nathan , Huang Qiang 
Published: November 2019

Abstract
While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.
Simulation of Cutting Edge Wear Model Based on Chip Production Rate in Micro-Endmilling
Authors: Lee Jue-Hyun , Sodemann Angela A. 
Published: November 2019

Abstract
In this paper, simulation of cutting edge wear rate model based on the chip production rate in micro-endmilling is conducted in order to understand the state of the interaction between the tool and the workpiece. In micro-endmilling, the chip production rate changes due to the cutting edge wear and it can be explained by the minimum chip thickness effect. If the cutting edge radius increases due to the tool wear until the minimum chip thickness becomes larger than the uncut chip thickness, the chips will not be generated with the cutting tooth sliding on the workpiece. If the new tool with the sharp cutting edge is used, the chips will be generated without the cutting tooth sliding on the workpiece. From this point of view, the cutting edge wear could be observed by measuring the chip production rate in micro-endmilling. Therefore, the cutting edge wear rate model is proposed and the simulation of the cutting edge wear rate estimation is conducted. Our proposed cutting edge wear rate model could be used in improving the tool life and the surface quality by estimating the cutting edge wear rate.
Severe illness getting noticed sooner – SIGNS-for-Kids: developing an illness recognition tool to connect home and hospital
Authors: Gilleland Jonathan , Bayfield David , Bayliss Ann , Dryden-Palmer Karen , Fawcett-Arsenault Joelle , Gordon Michelle , Hartfield Dawn , Iacolucci Anthony , Jones Melissa , Ladouceur Lisa , McNamara Martin , Middaugh Kristen , Moore Gregory , Murray Sean , Noble Joanna , Singh Simran , Stuart-Minaret Jane , Williams Carla , Parshuram Christopher S 
Published: November 2019

BackgroundDelays to definitive treatment for time-sensitive acute paediatric illnesses continue to be a cause of death and disability in the Canadian healthcare system. Our aim was to develop the SIGNS-for-Kids illness recognition tool to empower parents and other community caregivers to recognise the signs and symptoms of severe illness in infants and children. The goal of the tool is improved detection and reduced time to treatment of acute conditions that require emergent medical attention.MethodsA single-day consensus workshop consisting of a 17-member panel of parents and multidisciplinary healthcare experts with content expertise and/or experience managing children with severe acute illnesses was held. An a priori agreement of ≥85% was planned for the final iteration SIGNS-for-Kids tool elements by the end of the workshop.ResultsOne hundred percent consensus was achieved on a five-item tool distilled from 20 initial items at the beginning of the consensus workshop. The final items included four child-based items consisting of: (1) behaviour, (2) breathing, (3) skin, and (4) fluids, and one context-based item and (5) response to rescue treatments.ConclusionsSpecific cues of urgent child illness were identified as part of this initial development phase. These cues were integrated into a comprehensive tool designed for parents and other lay caregivers to recognise the signs of serious acute illness and initiate medical attention in an undifferentiated population of infants and children. Future validation and optimisation of the tool are planned.
A mixed methods study examining teamwork shared mental models of interprofessional teams during hospital discharge
Authors: Manges Kirstin , Groves Patricia S , Farag Amany , Peterson Ryan , Harton Joanna , Greysen S Ryan 
Published: November 2019

BackgroundLittle is known about how team processes impact providers’ abilities to prepare patients for a safe hospital discharge. Teamwork Shared Mental Models (teamwork-SMMs) are the teams’ organised understanding of individual member’s roles, interactions and behaviours needed to perform a task like hospital discharge. Teamwork-SMMs are linked to team effectiveness in other fields, but have not been readily investigated in healthcare. This study examines teamwork-SMMs to understand how interprofessional teams coordinate care when discharging patients.MethodsThis mixed methods study examined teamwork-SMMs of inpatient interprofessional discharge teams at a single hospital. For each discharge event, we collected data from the patient and their discharge team (nurse, physician and coordinator) using interviews and questionnaires. We quantitatively determined the discharge teams’ teamwork-SMM components of quality and convergence using the Shared Mental Model Scale, and then explored their relationships to patient-reported preparation for posthospital care. We used qualitative thematic analysis of narrative cases to examine the contextual differences of discharge teams with higher versus lower teamwork-SMMs.ResultsThe sample included a total of 106 structured patient interviews, 192 provider day-of-discharge questionnaires and 430 observation hours to examine 64 discharge events. We found that inpatient teams with better teamwork-SMMs (ie, higher perceptions of teamwork quality or greater convergence) were more effective at preparing patients for post-hospital care. Additionally, teams with high and low teamwork-SMMs had different experiences with team cohesion, communication openness and alignment on the patient situation.ConclusionsExamining the quality and agreement of teamwork-SMMs among teams provides a better understanding of how teams coordinate care and may facilitate the development of specific team-based interventions to improve patient care at hospital discharge.
Genetic variants in incident SUDEP cases from a community-based prospective cohort with epilepsy
Authors: Ge Yan , Ding Ding , Zhu Guoxing , Kwan Patrick , Wang Wenzhi , Hong Zhen , Sander Josemir W 
Published: November 2019

ObjectiveSudden unexpected death in epilepsy (SUDEP) is a leading cause of epilepsy-related mortality in young adults. It has been suggested that SUDEP may kill over 20 000 people with epilepsy in China yearly. The aetiology of SUDEP is unclear. Little is known about candidate genes for SUDEP in people of Chinese origin as most studies have ascertained this in Caucasians. No candidate genes for SUDEP in Chinese people have been identified.MethodsWe performed whole exome sequencing (WES) in DNA samples collected from five incident cases of SUDEP identified in a large epilepsy cohort in rural China. We filtered rare variants identified from these cases as well as screened for SUDEP, epilepsy, heart disease or respiratory disease-related genes from previous published reports and compared them with publicly available data, living epilepsy controls and ethnicity-match non-epilepsy controls, to identify potential candidate genes for SUDEP.ResultsAfter the filtering process, the five cases carried 168 qualified mutations in 167 genes. Among these genetic anomalies, we identified rare variants inSCN5A(1/5:20% in our cases),KIF6(1/5:20% in our cases) andTBX18(1/5:20% in our cases) which were absent in 330 living epilepsy control alleles from the same original cohort and 320 ethnicity-match non-epilepsy control alleles.ConclusionsThese three genes were previously related to heart disease, providing support to the hypothesis that underlying heart disorder may be a driver of SUDEP risk.
Control-flow analysis of procedural skills competencies in medical training through process mining
Authors: de la Fuente Rene , Fuentes Ricardo , Munoz-Gama Jorge , Riquelme Arnoldo , Altermatt Fernando R , Pedemonte Juan , Corvetto Marcia , Sepúlveda Marcos 
Published: November 2019

AbstractBackgroundProcedural skills are key to good clinical results, and training in them involves a significant amount of resources. Control-flow analysis (ie, the order in which a process is performed) can provide new information for those who train and plan procedural training. This study outlines the steps required for control-flow analysis using process mining techniques in training in an ultrasound-guided internal jugular central venous catheter placement using a simulation.MethodsA reference process model was defined through a Delphi study, and execution data (event logs) were collected from video recordings from pretraining (PRE), post-training (POST) and expert (EXP) procedure executions. The analysis was performed to outline differences between the model and executions. We analysed rework (activity repetition), alignment-based fitness (conformance with the ideal model) and trace alignment analysis (visual ordering pattern similarities).ResultsExpert executions do not present repetition of activities (rework). The POST rework is lower than the PRE, concentrated in the steps of the venous puncture and guidewire placement. The adjustment to the ideal model measure as alignment-based fitness, expressed as a median (25th–75th percentile) of PRE 0.74 (0.68–0.78) is less than POST 0.82 (0.76–0.86) and EXP 0.87 (0.82–0.87). There are no significant differences between POST and EXP. The graphic analysis of alignment and executions shows a progressive increase in order from PRE to EXP executions.ConclusionProcess mining analysis is able to pinpoint more difficult steps, assess the concordance between reference mode and executions, and identify control-flow patterns in procedural training courses.
Delayed recovery from general anaesthesia associated with pre‐existing neurological disease in a horse
Authors: Lord Samuel , Hahn Caroline , Pollock Patrick J , Duncan Juliet Clare 
Published: November 2019

A seven‐year‐old gelding presented for surgical management of right‐sided olecranon bursitis. Preanaesthetic examination revealed a right‐sided head tilt which had been present since birth. General anaesthesia and surgery went smoothly without incident. Recovery was prolonged and the horse made no attempt to stand despite regaining motor function consistent with recovery from general anaesthesia. Increasingly loud auditory stimuli and mild physical stimuli yielded no attempt to move into sternal recumbency or stand. After over two hours in the recovery box an equine neurologist suggested a diagnosis of vestibular disease and proposed that without a horizon the horse would not be able to orientate itself, therefore advising that the horse be shown the horizon. The outside recovery box door was opened allowing the horse to see the horizon, on which the horse made an immediate coordinated attempt to stand, standing successfully on its first attempt.
Characterization of 30 $$^{76}$$Ge enriched Broad Energy Ge detectors for GERDA Phase II
Authors:   GERDA Collaboration, Agostini M. , Bakalyarov A. M. , Andreotti E. , Balata M. , Barabanov I. , Baudis L. , Barros N. , Bauer C. , Bellotti E. , Belogurov S. , Benato G. , Bettini A. , Bezrukov L. , Bode T. , Borowicz D. , Brudanin V. , Brugnera R. , Budjáš D. , Caldwell A. , Cattadori C. , Chernogorov A. , D’Andrea V. , Demidova E. V. , Di Marco N. , Domula A. , Doroshkevich E. , Egorov V. , Falkenstein R. , Freund K. , Gangapshev A. , Garfagnini A. , Gooch C. , Grabmayr P. , Gurentsov V. , Gusev K. , Hakenmüller J. , Hegai A. , Heisel M. , Hemmer S. , Hiller R. , Hofmann W. , Hult M. , Inzhechik L. V. , Csáthy J. Janicskó , Jochum J. , Junker M. , Kazalov V. , Kermaïdic Y. , Kihm T. , Kirpichnikov I. V. , Kirsch A. , Kish A. , Klimenko A. , Kneißl R. , Knöpfle K. T. , Kochetov O. , Kornoukhov V. N. , Kuzminov V. V. , Laubenstein M. , Lazzaro A. , Lehnert B. , Liao Y. , Lindner M. , Lippi I. , Lubashevskiy A. , Lubsandorzhiev B. , Lutter G. , Macolino C. , Majorovits B. , Maneschg W. , Marissens G. , Miloradovic M. , Mingazheva R. , Misiaszek M. , Moseev P. , Nemchenok I. , Panas K. , Pandola L. , Pelczar K. , Pullia A. , Ransom C. , Riboldi S. , Rumyantseva N. , Sada C. , Salamida F. , Salathe M. , Schmitt C. , Schneider B. , Schönert S. , Schütz A.-K. , Schulz O. , Schwingenheuer B. , Selivanenko O. , Shevchik E. , Shirchenko M. , Simgen H. , Smolnikov A. , Stanco L. , Vanhoefer L. , Vasenko A. A. , Veresnikova A. , von Sturm K. , Wagner V. , Wegmann A. , Wester T. , Wiesinger C. , Wojcik M. , Yanovich E. , Zhitnikov I. , Zhukov S. V. , Zinatulina D. , Zsigmond A. J. , Zuber K. , Zuzel G. 
Published: November 2019

AbstractThe GERmanium Detector Array (Gerda) is a low background experiment located at the Laboratori Nazionali del Gran Sasso in Italy, which searches for neutrinoless double-beta decay of$$^{76}$$76Ge into$$^{76}$$76Se+2e$$^-$$-.Gerdahas been conceived in two phases. Phase II, which started in December 2015, features several novelties including 30 new76Ge enriched detectors. These were manufactured according to the Broad Energy Germanium (BEGe) detector design that has a better background discrimination capability and energy resolution compared to formerly widely-used types. Prior to their installation, the new BEGe detectors were mounted in vacuum cryostats and characterized in detail in theHadesunderground laboratory in Belgium. This paper describes the properties and the overall performance of these detectors during operation in vacuum. The characterization campaign provided not only direct input forGerdaPhase II data collection and analyses, but also allowed to study detector phenomena, detector correlations as well as to test the accuracy of pulse shape simulation codes.
Solve for Good
Authors: Ghani Rayid , Green Lisa , Bengoa Alberto , Shah Mohak 
Published: November 2019

Solve for Good is a platform for social good organizations to pose their problems that need data intensive help, and for volunteers to help solve those problems. Once the projects are submitted by the organization, they go through a scoping process (done by scoping volunteers and guided by our Data Science Scoping Process). Once a project scope is finalized, it becomes available for data science volunteers to start working on. The finished work is reviewed by a QA team consisting of volunteers and staff of the organization that submitted the project.
Solve for Good comes out of our experience working with government agencies, non-profits, universities, volunteers, professionals, students, and the private sector over the last several years. We repeatedly get contacted by governments, non-profits, and other social good organizations asking for help with data projects. We also have smart, passionate individuals who contact us offering their help, often in a volunteer capacity, on weekends, evenings, or for a few days or weeks. Solve for Good is our attempt at linking these two. We are just starting out, and looking for help in doing this better, and getting feedback from you. Join us at http://www.solveforgood.org as a volunteer to help solve problems, as an organization to submit problems, as partners to help us expand the platform and provide resources to run it, and as corporations or foundations to loan volunteers and donate resources used in solving these problems.
MCP-1 mediates ischemia-reperfusion-induced cardiomyocyte apoptosis via MCPIP1 and CaSR
Authors: Zhang Wei , Zhu Tiebing , Chen Lulu , Luo Wei , Chao Jie 
Published: November 2019

Monocyte chemotactic protein-1 (MCP-1) plays a crucial role in ischemia-reperfusion (I/R) injury; however, the detailed mechanism of MCP-1 in I/R injury-induced cardiomyocyte apoptosis remains unclear. In this study, we explored the cascade downstream of I/R-induced MCP-1 that modulates cell apoptosis and determined whether Ca2+-sensing receptors (CaSRs) are involved in the process. Protein levels were detected in a cardiac muscle cell line (HL-1) and primary cultured neonatal mouse ventricular cardiomyocytes using Western blotting and immunocytochemistry. Released MCP-1 was detected using ELISA. Both Hoechst staining and flow cytometry methods were used to measure cell apoptosis. Specific pharmacological inhibitors of CC chemokine receptor 2 (RS-102895) and CaSR (NPS-2143) as well as a CaSR activator (evocalcet) were applied to confirm the roles of these factors in I/R-induced cell apoptosis. I/R inhibited cell viability and upregulated cell apoptosis. Moreover, I/R induced the release of MCP-1 from both HL-1 cells and primary cardiomyocytes. Further research confirmed that CaSR acted as an upstream effector of monocyte chemotactic protein-1-induced protein-1 (MCPIP1) and coordinately regulated cell apoptosis, which was verified by addition of an inhibitor or activator of CaSR. Moreover, MCPIP1 induced cell apoptosis through endoplasmic reticulum (ER) stress but not autophagy induced by I/R. Based on these findings, I/R-induced MCP-1 release regulates cardiomyocyte apoptosis via the MCPIP1 and CaSR pathways, suggesting a new therapeutic strategy for I/R injury.NEW & NOTEWORTHY Ischemia-reperfusion (I/R)-induced monocyte chemotactic protein-1 release regulates cardiomyocyte apoptosis via the monocyte chemotactic protein-1-induced protein-1 (MCPIP1) and Ca2+-sensing receptor pathway. The functional changes mediated by MCPIP1 involve the activation of endoplasmic reticulum stress, but not the autophagy pathway, after I/R injury.