bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social determinants of health and preventable emergency department patient encounters among adults with hypertension
Authors: Ware Katilya S. , Chidume Tiffani , Chou Chiahung 
Published: November 2022

AbstractLimited access to resources promoting optimal health outcomes can lead to increased emergency department utilization for conditions often manageable by primary care resulting in rising healthcare costs, overcrowding in emergency departments, and poor patient outcomes. Social determinants of health often affect an individual's ability to achieve optimal health. To examine the association between social determinants of health and hypertension‐related patient encounters among adults, we analyzed data from a 340‐bed community hospital in the southeast region of the United States. Gender, race, and geographic location were strongly associated with hypertension‐related encounters. Hypertension‐related encounters in the emergency department were highest in women, African Americans, and young adults. Insurance status and geographic location were also strongly associated. Public health nursing is positioned to prevent disease and increase access to care, considering hypertension‐related encounters in the emergency department account for a significant number of overall visits.
Cardiorespiratory fitness, regular physical activity, and autonomic nervous system reactivity to laboratory and daily life stress
Authors: van der Mee Denise Johanna , Gevonden Martin J. , Westerink Joyce H. D. M. , de Geus Eco J. C. 
Published: November 2022

AbstractThe cross‐stressor adaptation hypothesis—which posits that adjustment to physical stress as a result of regular physical activity and its effects on fitness crosses over to psychological stress reactivity—has been around for over four decades. However, the literature has been plagued by heterogeneities preventing definitive conclusions. We address these heterogeneity issues in a combined laboratory and daily life study of 116 young adults (M = 22.48 SD = 3.56, 57.76% female). The exposure, i.e., the potential driver of adaptation, was defined in three ways. First, a submaximal test was performed to obtain aerobic fitness measured as the VO2max (kg/ml/min). Second, leisure time exercise behavior, and third, overall moderate‐to‐vigorous physical activity (MVPA), were obtained from a structured interview. Outcomes were autonomic nervous system (ANS) reactivity and affective responsiveness to stressors. ANS activity was measured continuously and expressed as inter‐beat‐interval (IBI), pre‐ejection‐period (PEP), respiratory sinus arrythmia (RSA), and non‐specific Skin Conductance Responses (ns.SCR). Negative and positive affect were recorded after each experimental condition in the laboratory and hourly in daily life with a nine‐item digital questionnaire. Linear regressions were performed between the three exposure measures as predictors and the various laboratory and daily life stress measurements as outcomes. Our results support the resting heart rate reducing effect of aerobic fitness and total MVPA in both the laboratory and daily life. We did not find evidence for the cross‐stressor adaptation hypothesis, irrespective of ANS or affective outcome measure or whether the exposure was defined as exercise/MVPA or aerobic fitness.
Severe acute haemolytic transfusion reaction secondary to a plasma incompatible group B platelet transfusion
Authors: Bienz Marc , Gupta Akash , Goldstein Jenette , Kennedy James , Lin Yulia 
Published: November 2022

AbstractObjectivesTo report a rare case of acute hemolytic transfusion reaction (AHTR) following an ABO plasma incompatible group B platelet transfusion.BackgroundAHTR is an uncommon, but potentially fatal event. Most reported cases of platelet transfusions associated with AHTR involve group O donors.Case ReportA 34‐year‐old man, recipient of a group AB haploidentical haematopoietic stem cell transplantation (HSCT), had received re‐induction chemotherapy for leukaemia relapse. A group B whole blood‐derived buffy coat platelet pool was transfused. He developed rigours/fever, profound hemolytic anaemia, and hemodynamic instability. Serological investigations revealed AHTR from passive transfer of high titre anti‐A (256 for IgM and 8192 for IgG).DiscussionThis case highlights the potential risks associated with ABO‐mismatched transfusions, and the complexity associated with transfusing HSCT recipients and red cell transfusion recipients with dual populations of circulating red cells. The literature on minor ABO plasma incompatible transfusions, challenges in establishing local policies to limit the risks of AHTR and risk mitigation strategies are discussed.ConclusionClinicians must maintain a high level of suspicion for AHTR after ABO plasma incompatible platelet transfusions. Patients must be aware of the risks of AHTR, and early recognition and diagnosis of this complication may be lifesaving.
An analysis of data sources and study registries used in systematic reviews
Authors: Nick Jan M. , Sarpy Nancy L. 
Published: November 2022

AbstractBackgroundReporting standards for data sources in systematic reviews (SRs) have been developed, yet research shows varying compliance in the methods section. When this happens, replication of search results is difficult and creates ambiguous and biased data sources.AimsThis study captured author practices in choosing English and non‐English‐language databases, listing all the databases searched, and incorporating study registries as part of the search strategy.MethodsUsing an analytic, cross‐sectional, study design, volunteer data collectors (n = 107) searched one of two assigned English language platforms for SRs on specified health conditions. All the data sources found in the methods section of each SR were documented and analyzed for patterns using bibliographic techniques.ResultsThe final sample size of the SRs reviewed was N = 199. The mean number of data sources seen in the SRs was 3.9 (SD 2), with a range of 1–10. Eighteen records (9%) used a single data source to conduct the SRs. Four leading language platforms were seen in the SRs: English (100% of occurrences), up to 8% used Chinese data sources, and 4% included Spanish or Portuguese. The four most frequently used data sources were: (1) Medline (98%), (2) Embase (65%), (3) Cochrane Library (56%), and (4) Web of Science (33%). The percentage of SRs listing study registries was 30%.Linking Evidence to ActionStrategies to reduce bias and increase the rigor and reliability of SRs include comprehensive search practices by exploring non‐English‐language databases, using multiple data sources, and searching study registries. By following PRISMA‐S guidelines to report data sources correctly, reproducibility can be accomplished.
Gravity Balancing of a Two-Degree-of-Freedom Parallel Robotic Platform With Variable Payloads
Authors: Nguyen Vu Linh 
Published: November 2022

Abstract
This paper presents a gravity-balancing method for a two-degree-of-freedom (2-DoF) parallel robotic platform with variable payloads. The robotic platform is constructed with a parallelogram-based five-bar parallel mechanism, and it is gravity balanced by adopting a pair of gear-spring units (GSUs). Each GSU is formed by a geared seven-link mechanism with a tension spring. The parameters of the GSUs are realized from an analytical solution for approximate gravity balancing. The significance of the proposed method is that it can work with a range of payloads via energy-free adjustment and enables a choice in spring stiffness. In this paper, theoretical and practical models are provided to demonstrate the effectiveness of the proposed method. The simulation results showed that the actuator torque of a robotic platform with different payloads could be reduced by 98.2% with gravity balancing. Experimental tests with a prototype were conducted in which the measured driving force of the robotic platform along a trajectory showed a reduction of 87.1%.
Optimizing Internal Energy Streams in Micro Gas Turbines in Cogeneration toward Flexible Heat-to-Power Ratio—Global Thermodynamic Performance Assessment and Specific Case Studies
Authors: De Paepe Ward , Clymans Tom 
Published: November 2022

Abstract
Although the simultaneous production of heat and power, the so-called combined heat and power (CHP), is from a thermodynamic point of view still the most efficient energy conversion method, cogeneration units have nowadays problems to position themselves in the current and future energy market. The increasing renewable energy penetration requires CHP units to become more flexible, especially on their currently fixed heat-to-power ratio. Within this framework, micro-gas turbines (mGTs), as small-scale decentralized cogeneration units, offer opportunities. Since they use the recuperated Brayton cycle, they offer the theoretic option to adjust the internal heat streams to provide a flexible heat-to-power ratio as well as the unique feature of a tunable outlet temperature, making the unit feasible/interesting for a larger range of applications having a combined heat and power demand. Hence, in this paper, we assessed the impact of the use of a recuperator bypass for enhanced operational flexibility of mGT. In a first step, the optimal pathway for the recuperator bypass, i.e., cold or hot side bypass, is selected for a typical mGT, the Turbec T100 (currently commercially available as the AE-T100), considering both thermodynamics as well as technological feasibility. Moreover, the potential performance impact on the electrical and total efficiency is calculated as well as on the total available thermal power. In a second step, the specific performance of the option of using a recuperator bypass is assessed for two specific cases: flexible heat-to-power ratio at low temperature and high temperature, i.e., steam generation, cogeneration. Thermodynamic simulations show that the impact on the electric efficiency remains rather limited (maximal 6% absolute efficiency reduction for a 40% bypass ratio), while the available thermal energy and exergy increase significantly: up to 60% increase for thermal power and even 115% increase in the exergy content of the flue gases. Moreover, there is no distinct difference between cold or hot bypass, leaving the selection of the optimal bypass route a pure technical choice. Finally, considering the specific cases studied, simulation results show that heat-to-power ratio could be increased by more than 50% for all power outputs for the low temperature CHP applications, even resulting in a global efficiency increase, while for the high temperature case, recuperator bypass allows for a significant increase in steam production, at total efficiencies comparable to the separate production (i.e., boiler and grid), clearly highlighting the benefits and potential of a recuperator bypass.
One-Dimensional Plasma Model of Electrical Discharge Machining in Deionized Water for Prediction of Plasma Characteristics Along the Interelectrode Gap
Authors: Tanveer Asif , Kapoor Shiv G. , Mujumdar Soham , Curreli Davide 
Published: November 2022

Abstract
Electrical discharge machining (EDM) is a nonconventional machining process that involves the formation of a plasma in an interelectrode gap filled with a dielectric that melts and vaporizes the electrodes (tool and workpiece) when a voltage is applied across them. This work models the plasma discharge in the EDM process to describe plasma characteristics such as electron density, heavy species densities, plasma potential, and plasma temperature using chemical kinetics, fluid flow, and heat transfer mechanisms in a 1D domain in the direction of the gap. The 1D domain allows the model to utilize surface reactions on the electrode walls necessary for sustaining the plasma reactions. The domain also provides a perspective of the plasma characteristics near the workpiece. Temperature results are compared with the experimental data obtained from spectrometer measurements. Additionally, an estimate of the plasma diameter is made and compared with actual high-speed camera images. The effect of EDM parameters such as supply voltage and interelectrode gap size on plasma characteristics is studied. The model predicts the incumbent heat flux on the workpiece electrode for small gaps which could have applications in the development of melt-pool models of EDM. Finally, the model provides a physics-based understanding of the mechanisms of plasma generation in the EDM process.
Axial Wall Displacement at the Common Carotid Artery is Associated With the Lamb Waves
Authors: Hao Zhili , Rahman MD Mahfuzur , Jutlah Lauren B. L. , Athaide Chloe E. , Au Jason S. 
Published: November 2022

Abstract
As compared with its radial wall displacement, axial wall displacement at the common carotid artery (CCA) carries independent clinical values, but its physical mechanisms are unclear. This study aims to investigate whether axial wall displacement at the CCA is solely from Young waves. A pulse wave propagation theory is utilized to identify two types of waves, Young waves, and Lamb waves, in an artery, and identifies two sources for axial wall displacement, wall shear stress and radial wall displacement gradient with a factor of the difference between axial and circumferential initial tension, which reveals the influence of axial initial tension on the waveform of axial wall displacement. Theoretical expressions are derived for calculating the waveforms of axial wall displacement and its two sources in the Young waves. With the measured pulsatile pressure and blood velocity at the CA of three healthy adults as the inputs, the waveforms of axial wall displacement in the Young waves are calculated at different values of axial initial tension and are found to greatly differ from their measured counterparts. As such, the Lamb waves may contribute to axial wall displacement at the CCA and the associated physical and physiological implications are discussed. Given the clinical values of axial wall displacement at the CCA, the Lamb waves may play a non-negligible role in determining arterial health and needs to be further studied for a comprehensive assessment of arterial wall mechanics.
Combining Feature Learning and Transfer Learning in Balancing Anomaly Detection for Gas Turbine Engine Vibration Analysis
Authors: Xie Jiarui , Schmidt Katherine , Budeanu Nausica , Letendre Vincent , Zhao Yaoyao Fiona 
Published: November 2022

Abstract
Rotor imbalance is a vital measure that indicates the health state of a gas turbine (GT). Abnormal balancing patterns will lead to excessive vibration and gradually compromise structural integrity. This paper presents the construction of anomaly detection (AD) models that recognize abnormal balancing patterns for two aeroderivative GTs, AGT-A and AGT-B, from Siemens Energy. Such a diagnostic tool can predict at an early stage whether a high vibration would occur during the vibration test and avoid engine reject for re-balance. Machine learning (ML) algorithms have been extensively utilized to conduct GT design space exploration and condition monitoring. However, ML has not been implemented to improve the efficiency of GT manufacturing processes, mainly due to data scarcity. The authors propose a combined feature learning and transfer learning technique to leverage the data resources of GT manufacturing processes. The physical and operational similarities between GTs belonging to the same series imply the transferability of features between models. The normal balancing patterns of the data-rich AGT-A were first learned by a sparse autoencoder to detect balancing anomalies. Then, the learned features were used to initialize the balancing AD model for the data-poor AGT-B. The test accuracy of the AGT-B AD model was increased from 75% to 92% with transfer learning. The presented methodology can facilitate and enable various data-driven analysis tasks for the manufacturing processes of original equipment manufacturers.
Multi-Scale Topology Optimization With Neural Network-Assisted Optimizer
Authors: Rastegarzadeh Sina , Wang Jun , Huang Jida 
Published: November 2022

Abstract
High-resolution structural designs attracts researchers to multi-scale topology optimizations (TO) paradigms. With the advances of machine learning (ML) methods, the integration of ML with TO has been attempted in many works. However, most works employ ML in a data-driven paradigm, which requires abundant training data. The generalization ability of such a data-driven paradigm is also ambiguous. This research aims to utilize the machine learning techniques as an optimizer for multi-scale structural design problems to address the connectivity issues of adjacent microstructures, a common problem in the multi-scale structure design. First, parameterized cellular materials (PCM) are utilized to develop a multi-scale parameterized TO problem. Then the problem is reformulated into a single unconstrained objective function using the penalty method and parameterized into a neural network (NN) that optimizes its weights and biases. The optimized network acts as a continuous model all over the design domain with the cellular material parameter as its response. This approach does not need to eliminate the elements with the intermediate densities, unlike density-based TO frameworks (e.g., SIMP). Using the NN-assisted optimizer, to handle the connectivity issue, the optimized NN can be discretized to a higher resolution, eliminating the need to use an interpolation filter. The performance of the proposed framework is significantly enhanced compared to the previously published method.
Hierarchical Surrogate Modeling With Multiple Order Partially Observed Information
Authors: Xu Yanwen , Wang Pingfeng 
Published: November 2022

Abstract
Understanding the input and output relationship of a complex engineering system is an essential task that attracts widespread interests in engineering design fields. To investigate the system performance, surrogate models can be developed based upon a finite set of input-output sample points, and then used to replace expensive black box type performance function and reduce the cost on function evaluations for system design optimization. The finite set of sample points could be obtained from multiple information sources such as experiments with different tests or simulation using different order of computer models. There is a pressing need for an efficient surrogate modeling method that can comprehensively utilize all available information, both fully and partially observed information (POI) collected from sources with different fidelities and dimensionalities. This paper proposes a multi-order system modeling method for partially observed information (MOSM-POI), which takes account of the POI structure and sparseness and uses multiple reduced order models to assist the understanding of the high-dimensional complex system. The Bayesian Gaussian process latent variable model (BGP-LVM) was employed to incorporate POI and a new framework was developed to cope with the high sparseness POI. The numerical experiments demonstrated that the proposed MOSM-POI method provides an accurate solution to take advantage of partially observed information from the multi-order system in developing surrogate models for complex systems.
Medial Axis Transformation Based Design and Process Planning Methodology for Discrete Multi-Material Additive Manufacturing
Authors: Manoharan Madhanagopal , Naidu Potnuru Hema Praneetha , Joy Midhun , Kumaraguru Senthilkumaran 
Published: November 2022

Abstract
Additive Manufacturing (AM) eases the fabrication of multifunctional simultaneous printing of multi-materials, which is quite cumbersome to fabricate in traditional manufacturing processes. Heterogeneous objects are entities engineered with multiple materials, and their emergence with the advent of AM is sporadic. This paper presents the novel layer decomposition methodology by utilizing Medial Axis Transformation (MAT) to generate discrete volumes for multi-material printing. Further, these discrete geometries are printed using multi-material addons, such as Palette Pro for material extrusion-based Additive Manufacturing systems. Tensile testing is performed to analyze the effectiveness and characteristics of the layer decomposition strategy. However, fabrication of the generated heterogeneous objects utilizes a computational model that maps the geometry of the object with the material composition. This methodology can also be applicable to model compositional heterogeneity for part fabrication using a metal-based Direct Energy Deposition (DED) process.
Automated CAD Modelling of Cam Profiles and FEA Fatigue Analysis of Cam-Follower Mechanisms
Authors: Theril Krishnajith , Zhang Shengyong 
Published: November 2022

Abstract
This paper illustrates a method to automate the 3D designing process of Simple harmonic motion (SHM) cam profile in CATIA CAD package. In a more user-friendly approach, a graphical user interface (GUI) is developed, where the users can enter the cam parameters and obtain 3D CAD model as direct result. It is observed that 90% time will be reduced by using this automated designing and also chance of errors also reduces. Due to the high follower load impacting on cam profile, high contact stresses induced on the component surfaces due to the asperity contacts cause the cam or roller surfaces to be subjected to high risk of contact fatigue failure. Present work focuses on comparing fatigue life of SHM and Cycloidal cam functions. The fatigue life is assessed using finite element analysis (FEA) in ANSYS Workbench. We have considered follower as flat-faced and frictionless contact between cam and follower in this study. After developing the 3D model of SHM and cycloidal cam profile through automated method, both the models were then imported to ANSYS for fatigue analysis under the same loading conditions. It has been found that cycloidal function has more fatigue life than SHM function.
Fault Detection and Diagnosis for a Hydraulic Press by Use of a Mixed Domain Database
Authors: Makansi Faried , Schmitz Katharina 
Published: November 2022

Abstract
An automated detection and diagnosis of machine and process faults is a core element of modern maintenance strategies. Data-driven methods, often summarized by the term machine learning, potentially provide manifold advantages in this area compared to conventional approaches. However, the necessity of a comprehensive database, which covers a variety of operation and fault cases, poses a remaining challenge for the application of data-driven methods. One way to mitigate the issue of costly data acquisition is to merge datasets from different, but related systems and applications to a larger dataset. In this contribution, the mixing of data obtained from a hydraulic press and a corresponding simulation model is investigated. For this, datasets comprising regular and faulty machine operation are generated in both domains. Using different mixing ratios of real and simulated data-instances, models for fault classification are derived and evaluated. The results show that an instance-based transfer from the simulated domain to the real domain can help reducing the amount of training data required from the physical machine. The mixing ratio appears to be decisive for a beneficial effect of mixing data from the two domains.