bluuebunny/crossref_metadata_2025_split huggingface dataset:
Furcation involvement and tooth loss: A registry‐based retrospective cohort study
Authors: Trullenque‐Eriksson Anna , Tomasi Cristiano , Petzold Max , Berglundh Tord , Derks Jan 
Published: November 2022

AbstractAimThis registry‐based retrospective cohort study aimed to evaluate the impact of furcation status on the risk for molar loss.Materials and MethodsSubjects with and without furcation involvement (FI) in 2010/2011 were identified in a nationwide registry in Sweden (age‐ and gender‐matched sample: 381,450 subjects; 2,374,883 molars). Data on dental and periodontal status were extracted for the subsequent 10‐year period. Impact of FI (at baseline or detected during follow‐up) on molar loss (i.e., tooth extraction) was evaluated through multilevel logistic regression and survival analyses.ResultsFI had a significant impact on molar loss. FI degrees 2 and 3 resulted in adjusted risk ratios of 1.67 (95% confidence interval [CI] 1.63–1.71) and 3.30 (95% CI 3.18–3.43), respectively. Following the first detection of deep FI (degrees 2–3), estimated survival decreased by 4% at 5 years and 8% at 10 years. In addition to FI, endodontic status and probing depth were relevant risk factors for molar loss.ConclusionsFurcation status had a clinically relevant impact on the risk for molar loss. Following first detection of deep FI, however, the decline in molar survival was minor.
Using supply chain databases in academic research: A methodological critique
Authors: Culot Giovanna , Podrecca Matteo , Nassimbeni Guido , Orzes Guido , Sartor Marco 
Published: November 2022

AbstractThis article outlines the main methodological implications of using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain for academic purposes. These databases provide secondary data on buyer–supplier relationships that have been publicly disclosed. Despite the growing use of these databases in supply chain management (SCM) research, several potential validity and reliability issues have not been systematically and openly addressed. This article thus expounds on challenges of using these databases that are caused by (1) inconsistency between data, SCM constructs, and research questions (data fit); (2) errors caused by the databases' classifications and assumptions (data accuracy); and (3) limitations due to the inclusion of only publicly disclosed buyer–supplier relationships involving specific focal firms (data representativeness). The analysis is based on a review of previous studies using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain, publicly available materials, interviews with information service providers, and the direct experience of the authors. Some solutions draw upon established methodological literature on the use of secondary data. The article concludes by providing summary guidelines and urging SCM researchers toward greater methodological transparency when using these databases.
The role of bacterial membrane vesicles in antibiotic resistance
Authors: MacNair Craig R. , Tan Man‐Wah 
Published: November 2022

AbstractBacterial survival during antibiotic exposure is a complex and multifaceted phenomenon. On top of antibiotic resistance genes, biofilm formation, and persister tolerance, bacterial membrane vesicles (MVs) provide a layer of protection that has been largely overlooked. MVs are spherical nanoparticles composed of lipid membranes and are common to Gram‐positive and Gram‐negative bacteria. Although the importance of MVs in bacterial pathogenesis and virulence factor transport has been firmly established, a growing body of work now identifies MVs as key contributors to bacterial survival during antibiotic exposure. Herein, we highlight the ability of MVs to reduce antibiotic efficacy and transmit resistance elements. We also discuss the potential of targeting MV production as an unconventional therapeutic approach.
Preadolescent‐versus adolescent‐onset immunoglobulin A vasculitis: The impact of age on prognosis
Authors: Coşkun Serkan , Güngörer Vildan , Ekici Tekin Zahide , Çelikel Elif , Kurt Tuba , Tekgöz Nilüfer , Sezer Müge , Karagöl Cüneyt , Kaplan Melike Mehveş , Polat Merve Cansu , Öner Nimet , Acar Banu Çelikel 
Published: November 2022

AbstractBackgroundThis study aimed to investigate the characteristics of patients with preadolescent‐ and adolescent‐onset immunoglobulin A vasculitis (IgAV) and to determine whether age affects IgAV outcomes in adolescents.MethodsDemographic, clinical, and laboratory data of 333 patients diagnosed with IgAV at the Department of Pediatric Rheumatology, University of Health Sciences, Ankara City Hospital, were evaluated retrospectively. The patients were classified into two groups: preadolescents (<10 years) and adolescents (10–19 years). Subgroup analyses were also performed by grouping the adolescent patients into early, middle, and late adolescent groups.ResultsOf the 333 patients, 219 (65.8%) and 114 (34.2%) were preadolescents and adolescents. Palpable purpura, renal, joint, and gastrointestinal (GI) tract involvement were detected in 333 (100%), 78 (23.4%), 79 (23.7%), and 124 (37.2%) patients, respectively; testicular involvement was observed in 25 (13.3%) of 187 male patients. The frequency of renal involvement was significantly higher in the adolescent group than in the preadolescent group at the time of diagnosis (p = 0.030). Notably, joint involvement was significantly higher in the adolescent group (p = 0.001). The need for aggressive therapy was significantly higher in the adolescent group than in the preadolescent group (p = 0.003). There was no significant difference in clinical data, demographic characteristics, and laboratory findings between the adolescent subgroups (p > 0.05).ConclusionsImmunoglobulin A vasculitis can occur at any age but the disease prognosis appears to worsen with age. The present study reported that joint involvement, kidney involvement, and the need for more aggressive treatment were higher in the adolescent group than in the preadolescent group.
Attention‐affect interactions and triarchic psychopathy: New electrophysiological insights from the late positive potential
Authors: Ribes‐Guardiola Pablo , Ventura‐Bort Carlos , Poy Rosario , Segarra Pilar , Branchadell Victoria , Moltó Javier 
Published: November 2022

AbstractOne of the most prominent characteristics of psychopathy is a reduced processing of emotionally relevant information. However, it is still unclear how attentional mechanisms may modulate this deficit. The current study aimed to examine the impact of attentional focus on emotion processing in relation to the triarchic constructs of boldness, meanness, and disinhibition. Participants performed two tasks in which pleasant, neutral, and unpleasant framed pictures were presented. In the first task, participants were required to indicate the color of the frame (alternative‐focus task), whereas in the second task they were instructed to indicate the emotional category of the image (affect‐focus task). The Late Positive Potential (LPP) was used as an index of sustained engagement of attention to affective material. Confirming a successful task manipulation, we observed reduced LPP amplitudes, particularly for affective relevant material, in the alternative‐focus task compared to the affect‐focus task. Most interestingly, our results evidenced that trait meanness scores were associated with blunted elaborative processing of affective material (both appetitive and aversive) when this information was task‐relevant (affect‐focus task), but not when it was task‐irrelevant (alternative‐focus task). These findings indicate that high mean individuals are characterized by blunted elaborative processing of affective stimuli when their motivational relevance is determined in a top‐down manner (i.e., when it is task‐relevant). Our results highlight the need for further studying of the bottom‐up and top‐down dynamics of emotional attention in psychopathy.
On the linearized system of equations for the condensate–normal fluid interaction at very low temperature
Authors: Escobedo M. 
Published: November 2022

AbstractThe linearization around one of its equilibrium of a system that describes the correlations between the superfluid component and the normal fluid part of a condensed Bose gas in the approximation of very low temperature and small condensate density is studied. A simple and transparent argument gives a necessary and sufficient condition for the existence of global solutions satisfying the conservation of the total number of particles and energy. The global solutions describe the evolution in time of the density of the thermal cloud and, unlike in previous work, that of the condensate's density. Their convergence to a suitable stationary state is also shown and rates of convergence for the normal fluid and superfluid components are obtained.
Detection and Characterization of Fatigue Cracks in Butt Welds of Offshore Structures Using the Eddy Current Method
Authors: Gansel René , Maier Hans Jürgen , Barton Sebastian 
Published: November 2022

Abstract
A reliable assessment of the actual condition of wind turbines is essential for their continuous operation, or if an extension of their service life is considered. To be able to make valid statements about the condition of the welded structure, an inspection concept for the reliable detection of fatigue cracks and damage on welds of the offshore tower and foundation structures is essential. In the present study, five different eddy current differential sensors were evaluated on cyclically fatigued DY butt welds under tensile load with a focus on the signal-to-noise ratio (SNR). Based on previous evaluations, two sensors were selected for semi-automatic weld testing. It is shown that due to the weld crossing necessary for the detection of fatigue cracks, air coils arranged parallel to the test surface have the highest SNR. This must be contrasted with the potential for crack depth determination. In this context, coils with different arrangements with respect to the test surface were analyzed. It is shown that groove depths can be differentiated based on the imaginary part of the measurement signal for groove depths of up to 8 mm, and actual fatigue cracks in welds with a crack depth of 0.5 mm were detected.
Machine Learning-Based Fault Detection and Diagnosis of Internal Combustion Engines Using an Optical Crank Angle Encoder
Authors: Geraei Hosna , Seddik Essam , Neame Ghabi , Huangfu Elliot (Yixin) , Habibi Saeid 
Published: November 2022

Abstract
Fault Detection and Diagnosis (FDD) in internal combustion engines is an important tool for better performance, safety, reliability, and instrument to reduce maintenance costs. Early detection of engine faults can help avoid abnormal event progression to failure. This study is carried out to develop two FDD algorithms to detect and diagnose internal combustion engine faults using an optical crank angle encoder. Experiments were carried out on a 2018 Ford Gen 3, 5.0L, V8, Coyote engine to achieve these goals. The engine head was modified to access the combustion chamber of specific cylinders for in-cylinder pressure measurement and, subsequently, combustion analysis. During this project, three engine faults were introduced: EGR valve failure, cylinder leakage, and spark plug degradation. In the first method, Fast Fourier Transform (FFT) is applied to the data collected using the optical crank angle encoder. FFT converts the crank angle domain data to the frequency domain. Then, the data dimension is reduced using Principal Component Analysis (PCA). The dataset with reduced dimensions is used as Multi-layer Perceptron (MLP) inputs. 10-fold cross-validation is used to determine the number of hidden layers in the MLP. The MLP model detects and diagnoses severities of cylinder leaks and EGR faults with a relatively high success rate (92%). The second method developed a classification model using the Random Forest (RF) classifier and Curve Descriptive (CD) Features. The performance of the MLP model and the Curve Descriptive features with Random Forest (CD-RF) models for detecting and diagnosing misfire faults are compared. Results show that the MLP model and CD-RF model accuracy for classifying misfire faults are 86.67% and 88,89%, respectively.
Exploring the Oxy-Fuel Combustion in Spark-Ignition Engines for Future Clean Powerplants
Authors: Serrano José Ramón , Martín Díaz Jaime , Gomez-Soriano Josep , Raggi Rodrigo 
Published: November 2022

Abstract
In the context of the CO2 challenge, oxy-fuel combustion in internal combustion engines (OFC-ICE) arises as a promising technology for carbon capture and almost zero-NOx solutions. Although the literature shows some experimental and theoretical works on OFC-ICE, there is a lack of systematic studies dealing with dilution strategies or where non-synthetic EGR is used. Using a combination of 0D-1D and CFD modeling and experimental measurements, dilution with oxygen (λ &gt; 1) and real EGR in a single-cylinder spark-ignition OFC-ICE is here assessed, considering thermo-mechanical limitations and knocking. Results show that an EGR strategy is more appropriate than O2 dilution. A slightly poor mixture near stoichiometric conditions, with EGR rates around 70%, reduces NOx more than 99%, and CO and uHC up to 90% with respect to the conventional ICE. It is concluded that OFC-ICE has a lower knocking propensity, thus allowing to increase the compression ratio to partially compensate for the expected efficiency diminution to about 4 percentage points.
Application of Modal Decomposition Techniques to Characterize the Internal Nozzle Flow of a Medium-Duty Diesel Injector Operating With Gasoline-Like Fuels
Authors: Asztalos Katherine J. , Torelli Roberto , Pei Yuanjiang , Zhang Yu , Tao Feng , Garg Rajesh , Langenderfer David , Moon Chi Young , Sforzo Brandon A. , Powell Christopher F. 
Published: November 2022

Abstract
Improving the efficiency of internal combustion engines can result in lower operating costs and enable reduced total cost of vehicle ownership. On the other hand, it is of utmost importance that the development of engine technologies is also directed toward curbing climate-altering and harmful emissions. One such technology is gasoline compression ignition, which can offer increased fuel efficiency and reduced emissions and criteria pollutants. By means of modal analysis methods, this study focuses on characterizing the internal flow behavior of a production, eight-hole, medium-duty diesel injector operating with gasoline-like fuels under typical diesel injection conditions. High-resolution tomographic reconstructions of the injector tip geometry, along with needle motion measurements, were obtained via X-ray measurements. This information was combined with a previously validated computational setup to perform a series of in-nozzle flow computational fluid dynamics (CFD) simulations. The CFD results were analyzed to identify the connection between the injector’s global behavior and relevant flowfields such as velocity and vorticity. The results obtained with the X-ray geometry were then compared against those obtained with a nominal geometry to investigate the effect of the injector’s geometric features on its behavior. Variations to the K-factor and inlet ellipticity of the orifices were explored, and these parameters’ ability to reduce in-orifice cavitation was assessed by analyzing the predicted in-orifice fuel vapor volume fraction. Relatively large and persistent flow structures arising within the injector sac were identified as the main driver for orifice-to-orifice variability, as their location and size impacted the amount of fuel delivered by each orifice. Assessing the extent to which these energetic structures impact the injector performance is of interest; thus, it is desirable to investigate the most energetically significant flowfield structures as a function of modifications to specific features of the injector geometry. Modal decomposition tools, such as space-only proper orthogonal decomposition (POD) and spectral proper orthogonal decomposition (SPOD) were applied to gain insight into dominant coherent structures that persist over the early phase of the needle opening event. It was found that the geometrical modifications applied resulted in variations of the modal energy content, as well as in the leading dominant spatial mode features. Additionally, it was found that the dominant frequency associated with the most significant energetic content was largely invariant to the geometrical modifications, with the difference between realistic and modified geometries being mostly found in the modal energy content.
Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks
Authors: Almeldein Ahmed , Van Dam Noah 
Published: November 2022

Abstract
Detailed chemical kinetics calculations can be very computationally expensive, and so various approaches have been used to speed up combustion calculations. Deep neural networks (DNNs) are one promising approach that has seen significant development recently. Standard DNNs, however, do not necessarily follow physical constraints such as conservation of mass. Physics Informed Neural Networks (PINNs) are a class of neural networks that have physical laws embedded within the training process to create networks that follow those physical laws. A new PINN-based DNN approach to chemical kinetics modeling has been developed to make sure mass fraction predictions adhere to the conservation of atomic species. The approach also utilizes a mixture-of-experts (MOE) architecture where the data is distributed on multiple sub-networks followed by a softmax selective layer. The MOE architecture allows the different sub-networks to specialize in different thermochemical regimes, such as early stage ignition reactions or post-flame equilibrium chemistry, then the softmax layer smoothly transitions between the sub-network predictions. This modeling approach was applied to the prediction of methane-air combustion using the GRI-Mech 3.0 as the reference mechanism. The training database was composed of data from 0D ignition delay simulations under initial conditions of 0.2–50 bar pressure, 500–2000 K temperature, an equivalence ratio between 0 and 2, and an N2-dilution percentage of up to 50%. A wide variety of network sizes and architectures of between 3 and 20 sub-networks and 6,600 to 77,000 neurons were tested. The resulting networks were able to predict 0D combustion simulations with similar accuracy and atomic mass conservation as standard kinetics solvers while having a 10–50× speedup in online evaluation time using CPUs, and on average over 200× when using a GPU.
Investigation on the Effects of Passive Pre-Chamber Ignition System and Geometry on Engine Knock Intensity
Authors: Di Sabatino Francesco , Martinez-Hernandiz Pablo Jose , Novella Rosa Ricardo , Ekoto Isaac 
Published: November 2022

Abstract
The effects of passive pre-chamber (PC) geometry and nozzle pattern as well as the use of either conventional spark or non-equilibrium plasma PC ignition system on knocking events were studied in an optically-accessible single-cylinder gasoline research engine. The equivalence ratio of the charge in the main chamber (MC) was maintained equal to 0.94 at a constant engine speed of 1300 rpm, and at constant engine load of 3.5 bar indicated mean effective pressure for all operating conditions. MC pressure profiles were collected and analyzed to infer the amplitude and the frequency of pressure oscillations that resulted in knocking events. The combustion process in the MC was investigated utilizing high-speed excited methylidyne radical (CH*) chemiluminescence images. The collected results highlighted that PC volume and nozzle pattern substantially affected the knock intensity (KI), while the use of the non-equilibrium plasma ignition system exhibited lower KI compared to PC equipped with a conventional inductive ignition system. It was also identified that knocking events were likely not generated by conventional end gas auto-ignition, but by jet-related phenomena, as well as jet-flame wall quenching. The relation between these phenomena and PC geometry, nozzle pattern, as well as ignition system has been also highlighted and discussed.
A Study on the Oil Film Thickness Between the Lower Rail of Oil Control Ring and Lower Flank of Oil Control Ring Groove of an Engine
Authors: Miura Ken , Ito Akemi , Nakamura Yuta , Yamada Rina , Nishibe Koichi , Usui Miyuki , Iijima Naoki 
Published: November 2022

Abstract
As friction reduction of an engine sometimes causes an increase in lubricating oil consumption, the requirement of reducing oil consumption has become crucial recently. The oil used for lubrication around a piston travels into the combustion chamber via piston ring’ gap, side and back, and sliding surface. This study focused on the oil flow via the side of the lower rail of the three-piece type oil control ring (OCR). Oil film thickness was measured by the laser induced fluorescence method. Oil film thickness between the lower rail and the lower flank of the ring groove at the outer and the inner side was successfully measured. The waveform of the oil film thickness was compared to the wave forms of friction force, inertial force, pressure under the OCR and pressure at the third land. The waveform of oil film thickness was well explained by those forces. Furthermore, it was also found that the shape of the lower rail remarkably affected the oil film thickness.
Predicting Combustion Variability Using Machine Learning From the Flow Field Data at Spark Timing for a Gasoline Direct Injection Engine
Authors: Probst Daniel , Attal Nitesh , Mandhapati Raju , Avanessian Oshin 
Published: November 2022

Abstract
Machine learning was used to predict three combustion metrics based on the flow field data at spark angle (SA), obtained from large eddy simulations (LES). These metrics were peak cylinder pressure (PCP), crank angle of PCP (CA of PCP), and indicated mean effective pressure (IMEP). The ML model was evaluated for use as a surrogate for detailed combustion model in studies of cycle-to-cycle variation (CCV). The computational time for combustion predictions (from SA to exhaust valve opening (EVO)) was 15 hours on 24 cores while the trained ML model was able to make predictions in 3 seconds (a speed up factor of 7200). The ML model was trained on a single core in 45 minutes using 88 LES simulation cycles. As the LES simulation data at SA has high fidelity (in this case approximately 260,000 computational cells) a workflow to filter the data to a reasonably sized feature set for use in ML was developed. Subvolumes were defined and the Pearson Correlation Coefficient (PCC) was used to evaluate the features. An ensemble ML approach was employed, which combines the predictions of multiple base learners to improve the final prediction. The ML model predictions for the three combustion metrics showed good correlation with the CFD simulations.