bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage
Authors: Wei Xinpeng , Han Daoru , Du Xiaoping 
Published: January 2021

Abstract
The average lifetime or the mean time to failure (MTTF) of a product is an important metric to measure the product reliability. Current methods of evaluating the MTTF are mainly based on statistics or data. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate the MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict the MTTF in the design stage by means of a physics-based Gaussian process (GP) method. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). The proposed adaptive supervised training method with the Gaussian process regression can quickly predict the MTTF with a reduced number of physical model calls. The proposed method can enable continually improved design by changing design variables until reliability measures, including the MTTF, are satisfied. The effectiveness of the method is demonstrated by three examples.
Deposition Pattern Analysis on a Fouled Multistage Test Compressor
Authors: Suman Alessio , Vulpio Alessandro , Casari Nicola , Pinelli Michele , Kurz Rainer , Brun Klaus 
Published: January 2021

Abstract
Compressor fouling is one of the main causes of gas turbine performance degradation. Microsized particles adhere to the blade surfaces increasing the surface roughness and modifying the airfoil shape. In this work, the contamination of the Allison 250 C18 multistage compressor engine with four sorts of micrometric dust has been provided. The tests were performed changing the relative humidity (RH) at the compressor inlet and the unit rotational speed. After each test, a photographic inspection of the internal fouled parts has been realized and the digital pictures have been analyzed employing an image processing package. The deposit's build-up of stator vanes and rotor blades has been postprocessed and the most affected regions of each compressor stage have been highlighted. Besides, a numerical simulation of the machine has been performed. The numerical flow field has been used to highlight the blade regions, which show the most favorable conditions for particle deposition. A theoretical model has been applied to the flow field to simulate the particle deposition. The combination of the deposition model with the results of the numerical simulations gives the chance to better understand the experimentally founded deposition patterns. Those results have been finally compared to the pictures of the patterns. The possibility to detect and measure the deposition patterns on a rotating test rig and the comparison with models and experiments gave the possibility to assess in detail the particle deposition phenomenon on a multistage axial compressor flow path.
Flame Transfer Functions and Dynamics of a Closely Confined Premixed Bluff Body Stabilized Flame With Swirl
Authors: Nygård Håkon T. , Worth Nicholas A. 
Published: January 2021

Abstract
The flame transfer function (FTF) and flame dynamics of a highly swirled, closely confined, premixed flame is studied over a wide range of equivalence ratios and bulk velocities at a fixed perturbation level at the dump plane. The operating conditions are varied to examine the ratio of flame height to velocity in scaling the FTF. The enclosure geometry is kept constant, resulting in strong flame-wall interactions for some operating conditions due to varying flame height. The resulting effect on the FTF due to changes in the “effective flame confinement” can therefore be studied. For sufficiently high equivalence ratio, and the resulting sufficiently small effective confinement, modulations of the FTF are observed due to interference of the perturbations created at the swirler and at the dump plane. The small length scales and high velocities result in modulations centered at high frequencies and spanning a wide range of frequencies compared to previous studies of similar phenomena. A critical point was reached for increasing effective confinement, where the modulations are suppressed. This is linked to a temporal shift in the heat release rate where the flame impinges on the combustion chamber walls. The shift reduced the expected level of interference, demonstrating effective confinement is important for the FTF response. Additionally, a distributed time lag (DTL) model with two time lags is successfully applied to the FTFs, providing a simple method to capture the two dominant time scales in the problem, recreate the FTF, and examine the effect of effective confinement.
Characteristics of Band Gap and Low-Frequency Wave Propagation of Mechanically Tunable Phononic Crystals With Scatterers in Periodic Porous Elastomeric Matrices
Authors: Ning Shaowu , Chu Dongyang , Yang Fengyuan , Jiang Heng , Liu Zhanli , Zhuang Zhuo 
Published: January 2021

Abstract
The characteristics of passive responses and fixed band gaps of phononic crystals (PnCs) limit their possible applications. For overcoming this shortcoming, a class of tunable PnCs comprised multiple scatterers and soft periodic porous elastomeric matrices are designed to manipulate the band structures and directionality of wave propagation through the applied deformation. During deformation, some tunable factors such as the coupling effect of scatterer and hole in the matrix, geometric and material nonlinearities, and the rearrangement of scatterer are activated by deformation to tune the dynamic responses of PnCs. The roles of these tunable factors in the manipulation of dynamic responses of PnCs are investigated in detail. The numerical results indicate that the tunability of the dynamic characteristic of PnCs is the result of the comprehensive function of these tunable factors mentioned earlier. The strong coupling effect between the hole in the matrix and the scatterer contributes to the formation of band gaps. The geometric nonlinearity of matrix and rearrangement of scatterer induced by deformation can simultaneously tune the band gaps and the directionality of wave propagation. However, the matrix’s material nonlinearity only adjusts the band gaps of PnCs and does not affect the directionality of wave propagation in them. The research extends our understanding of the formation mechanism of band gaps of PnCs and provides an excellent opportunity for the design of the optimized tunable PnCs and acoustic metamaterials (AMMs).
Efficient Parametric Optimization for Expensive Single Objective Problems
Authors: Weaver-Rosen Jonathan M. , Malak Richard J. 
Published: January 2021

Abstract
Parametric optimization solves optimization problems as a function of uncontrollable or unknown parameters. Such an approach allows an engineer to gather more information than traditional optimization procedures during design. Existing methods for parametric optimization of computationally or monetarily expensive functions can be too time-consuming or impractical to solve. Therefore, new methods for the parametric optimization of expensive functions need to be explored. This work proposes a novel algorithm that leverages the advantages of two existing optimization algorithms. This new algorithm is called the efficient parametric optimization (EPO) algorithm. EPO enables adaptive sampling of a high-fidelity design space using an inexpensive low-fidelity response surface model. Such an approach largely reduces the required number of expensive high-fidelity computations. The proposed method is benchmarked using analytic test problems and used to evaluate a case study requiring finite element analysis. Results show that EPO performs as well as or better than the existing alternative, Predictive Parameterized Pareto Genetic Algorithm (P3GA), for these problems given an allowable number of function evaluations.
Investigation of Printing Parameters of Additive Manufacturing Process for Sustainability Using Design of Experiments
Authors: Khalid Marwan , Peng Qingjin 
Published: January 2021

Abstract
Additive manufacturing (AM) offers many advantages to make objects compared to traditional subtractive manufacturing methods. For example, complex geometries can be easily fabricated, and lightweight parts can be formed while maintaining the parts strength for the low carbon footprint, low material consumption and waste. But there are some areas for AM to improve in sustainability, reliability, productivity, robustness, material diversity, and part quality. Life-cycle assessment studies have identified that the AM printing stage has a big impact on the life-cycle sustainability of 3D printed products. AM building parameters can be properly selected to improve the sustainability of AM. This paper explores the fused deposition modeling (FDM) process parameters for sustainability to reduce the process energy and material consumption. Investigated parameters include the printing layer height, number of shells, material infilling percentage, infilling type, and building orientation. Taguchi design of experiments approach and statistical analysis tools are used to find optimal parameter settings to improve the sustainability of the FDM process. Models formulated in this research can be easily extended to other AM processes.
Investigating Trajectory-Based Combustion Control Using a Controlled Trajectory Rapid Compression and Expansion Machine
Authors: Tripathi Abhinav , Sun Zongxuan 
Published: January 2021

Abstract
This study presents a systematic framework for the real-time implementation of a new combustion control strategy—trajectory-based combustion control—for the operation of free piston engines (FPEs). The free piston engine is an alternative architecture of internal combustion (IC) engines, which does not have a mechanical crankshaft and hence allows extreme operational flexibility in terms of piston trajectory. The key idea of trajectory-based combustion control is to modulate the autoignition dynamics by tailoring the pressure and temperature history of the fuel–air mixture inside the combustion chamber, using the piston trajectory as the control input, for the optimal operation of the free piston engine. Here, we present the experimental investigation of trajectory-based combustion control using a novel instrument called controlled trajectory rapid compression and expansion machine (CT-RCEM) that can be used for studying a single combustion cycle of an internal combustion engine with precisely controlled initial and boundary conditions. The effect of the shape of the piston trajectory on the combustion phasing, combustion efficiency, and the indicated thermal efficiency has been found to be significant. The experimental results indicate that the trajectory-based combustion control is an effective strategy for combustion phasing control for FPE operation.
Image-Based Surface Defect Detection Using Deep Learning: A Review
Authors: Bhatt Prahar M. , Malhan Rishi K. , Rajendran Pradeep , Shah Brual C. , Thakar Shantanu , Yoon Yeo Jung , Gupta Satyandra K. 
Published: January 2021

Abstract
Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques are useful in solving a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways of classifying various efforts in literature for surface defect detection using deep learning techniques. These three ways are based on defect detection context, learning techniques, and defect localization and classification method respectively. This article also identifies future research directions based on the trends in the deep learning area.
An Integrated Framework for Optimal Design of Complex Mechanical Products
Authors: Xue Deyi , Imaniyan David 
Published: January 2021

Abstract
The presently achieved research results are not effective for the design of complex mechanical products when various methods and tools in different schemes have to be employed at different design stages. A new integrated framework for the optimal design of complex mechanical products is introduced in this research considering modeling, simulation, and optimization aspects. First, a hybrid scheme is developed for the integrated modeling of complex mechanical products. In this hybrid scheme, descriptions of a generic product are modeled by an and-or tree. Feasible design candidates are created from the and-or tree through tree-based search. Geometric descriptions in a design candidate are associated with a computer-aided design (CAD) system. Second, a hybrid simulation method is developed for the evaluation of different product aspects with different simulation tools which are integrated through the hybrid modeling scheme. Simulations with geometric descriptions are conducted by analysis functions of the CAD system. Simulations with non-geometric descriptions are conducted by the knowledge-based systems. Third, a hybrid optimization method is developed to identify the optimal design of the complex mechanical product. For each design candidate, parameter optimization is conducted to obtain the optimal parameter values. The optimal design solution is identified from all design candidates through configuration optimization. A prototype system has been implemented for conceptual design and detailed design of complex mechanical products.
Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review
Authors: He Bin , Liu Long , Zhang Dong 
Published: January 2021

AbstractAs a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of the physical model-based one, it includes a prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of the digital twin-driven virtual model-based one, it includes non-deep learning methods and deep learning methods. Non-deep learning methods include the wiener process, gamma process, hidden Markov model (HMM), regression-based model, and proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN). It mainly summarizes the performance degradation and life test of various models in gear and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.
A multidimensional framework for characterizing the citation impact of scientific publications
Authors: Bu Yi , Waltman Ludo , Huang Yong 
Published: January 2021

AbstractThe citation impact of a scientific publication is usually seen as a one-dimensional concept. We introduce a multidimensional framework for characterizing the citation impact of a publication. In addition to the level of citation impact, quantified by the number of citations received by a publication, we also conceptualize and operationalize the depth and breadth and the dependence and independence of the citation impact of a publication. The proposed framework distinguishes between publications that have a deep citation impact, typically in a relatively narrow research area, and publications that have a broad citation impact, probably covering a wider area of research. It also makes a distinction between publications that are strongly dependent on earlier work and publications that make a more independent scientific contribution. We use our multidimensional citation impact framework to report basic descriptive statistics on the citation impact of highly cited publications in all scientific disciplines. In addition, we present a detailed case study focusing on the field of scientometrics. The proposed citation impact framework provides a more in-depth understanding of the citation impact of a publication than a traditional one-dimensional perspective.
The metabolic reprogramming in acute myeloid leukemia patients depends on their genotype and is a prognostic marker
Authors: Lo Presti Caroline , Fauvelle Florence , Jacob Marie-Christine , Mondet Julie , Mossuz Pascal 
Published: January 2021

Abstract
Leukemic cells display some alterations in metabolic pathways, which play a role in leukemogenesis and in patients’ prognosis. To evaluate the characteristics and the impact of this metabolic reprogramming, we explore the bone marrow samples from 54 de novo acute myeloid leukemia (AML) patients, using an untargeted metabolomics approach based on proton high-resolution magic angle spinning-nuclear magnetic resonance. The spectra obtained were subjected to multivariate statistical analysis to find specific metabolome alterations and biomarkers correlated to clinical features. We found that patients display a large diversity of metabolic profiles, according to the different AML cytologic subtypes and molecular statuses. The link between metabolism and molecular status was particularly strong for the oncometabolite 2-hydroxyglutarate (2-HG), whose intracellular production is directly linked to the presence of isocitrate dehydrogenase mutations. Moreover, patients’ prognosis was strongly impacted by several metabolites, such as 2-HG that appeared as a good prognostic biomarker in our cohort. Conversely, deregulations in phospholipid metabolism had a negative impact on prognosis through 2 main metabolites (phosphocholine and phosphoethanolamine), which could be potential aggressiveness biomarkers. Finally, we highlighted an overexpression of glutathione and alanine in chemoresistant patients. Overall, our results demonstrate that different metabolic pathways could be activated in leukemic cells according to their phenotype and maturation levels. This confirms that metabolic reprogramming strongly influences prognosis of patients and underscores a particular role of certain metabolites and associated pathways in AML prognosis, suggesting common mechanisms developed by leukemic cells to maintain their aggressiveness even after well-conducted induction chemotherapy.
Transjugular intrahepatic portosystemic shunt for hepatic sinusoidal obstruction syndrome associated with consumption of Gynura segetum
Authors: Zhang Lijie , Li Qing , Makamure Joyman , Zhao Dan , Liu Ziyi , Zheng Chuansheng , Liang Bin 
Published: January 2021

Abstract
Background
To evaluate the efficacy and safety of transjugular intrahepatic portosystemic shunt (TIPS) on hepatic sinusoidal obstruction syndrome (HSOS) associated with consumption of Gynura segetum (GS).

Methods
We retrospectively reviewed 9 consecutive patients with GS-related HSOS who were refractory to supportive treatment and underwent TIPS at our institution between January 2014 and September 2019. The patients were evaluated for safety and efficacy, including TIPS complications and changes in portosystemic pressure gradient (PPG), ascites, total bilirubin, liver size and portal vein diameter.

Results
TIPS procedures were performed successfully in the 9 patients, and no technically-related complications due to the TIPS procedure were recorded. The PPG was improved by TIPS in all patients (mean PPG before TIPS, 30.4 ± 5.2 vs. 13.0 ± 4.1 mm Hg post-TIPS, P = 0.008). One patient who was lost to follow-up, whereas the remaining 8 patients survived with a median follow-up period of 12 months (range 5–39 months). Although the total bilirubin was significantly increased 5–7 days after TIPS compared with that before the procedure (3.57 ± 1.58 vs. 4.82 ± 2.06 mg/dl, P = 0.017), it returned to baseline levels at 1-month follow-up (3.53 ± 2.72 vs. 4.82 ± 2.06 mg/dl, P = 0.401). The patients experienced complete resolution or noticeable reduction of ascites (P < 0.001), significant reduction of liver size (16.7 ± 2.2 vs. 13.7 ± 1.7 cm, P = 0.018), and significant enlargement of the portal trunk (10.7 ± 2.5 vs. 13.4 ± 2.4 mm, P = 0.017) after TIPS compared to the pre-TIPS state.

Conclusion
TIPS may offer a potentially useful treatment for the GS-related HSOS.
Unemployment rate, opioids misuse and other substance abuse: quasi-experimental evidence from treatment admissions data
Authors: Azagba Sunday , Shan Lingpeng , Qeadan Fares , Wolfson Mark 
Published: January 2021

Abstract
Background
The relationship between economic conditions and substance abuse is unclear, with few studies reporting drug-specific substance abuse. The present study examined the association between economic conditions and drug-specific substance abuse admissions.

Methods
State annual administrative data were drawn from the 1993–2016 Treatment Episode Data Set. The outcome variable was state-level aggregate number of treatment admissions for six categories of primary substance abuse (alcohol, marijuana/hashish, opiates, cocaine, stimulants, and other drugs). Additionally, we used a broader outcome for the number of treatment admissions, including primary, secondary, and tertiary diagnoses. We used a quasi-experimental approach -difference-in-difference model- to estimate the association between changes in economic conditions and substance abuse treatment admissions, adjusting for state characteristics. In addition, we performed two additional analyses to investigate (1) whether economic conditions have an asymmetric effect on the number of substance use admissions during economic downturns and upturns, and (2) the moderation effects of economic recessions (2001, 2008–09) on the relationship between economic conditions and substance use treatment.

Results
The baseline model showed that unemployment rate was significantly associated with substance abuse treatment admissions. A unit increase in state unemployment rate was associated with a 9% increase in treatment admissions for opiates (β = 0.087, p < .001). Similar results were found for other substance abuse treatment admissions (cocaine (β = 0.081, p < .001), alcohol (β = 0.050, p < .001), marijuana (β = 0.036, p < .01), and other drugs (β = 0.095, p < .001). Unemployment rate was negatively associated with treatment admissions for stimulants (β = − 0.081, p < .001). The relationship between unemployment rate and opioids treatment admissions was not statistically significant in models that adjusted for state fixed effects and allowed for a state- unique time trend. We found that the association between state unemployment rates and annual substance abuse admissions has the same direction during economic downturns and upturns. During the economic recession, the negative association between unemployment rate and treatment admissions for stimulants was weakened.

Conclusion
These findings suggest that economic hardship may have increased substance abuse. Treatment for substance use of certain drugs and alcohol should remain a priority even during economic downturns.