bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new hybrid analytical model for electromagnetic analysis of wound rotor induction motors
Authors: Rezaee‐Alam Farhad , Hosseini Mojtaba , Rezaeealam Behrooz 
Published: May 2022

AbstractAccurate modeling of slotting and magnetic saturation effects has been two main challenges for analytical modeling of electric machines. In this article, first, different techniques such as sub‐domain (S‐D) model, winding function theory, magnetic equivalent circuit (MEC) model, and conformal mapping (CM) method are introduced to analytical model and analysis of electric machines. These techniques are also compared in terms of their accuracy, and computational complexity. Second, with respect to the comparison results, a new hybrid analytical model is presented for electromagnetic modeling of wound rotor induction motors, which consider the modeling accuracy of slotting and magnetic saturation effect, simultaneously. In this way, the air‐gap region is modeled by using S‐D model, and the iron parts are considered by using MEC model assisted by CMs. In final, the analytical results are verified by comparing with corresponding results obtained through finite element method and experiment set‐up.
Natural Dual‐Crosslinked Self‐Healing Hydrogels for In Situ Wound Healing
Authors: Wang Yushi , Xiong Xueru , Lin Caihong , Huang Wen , Chen Yunhua , Wang Lin , Liu Sa , Jia Yong‐Guang 
Published: May 2022

AbstractPoly(l‐glutamic acid) (PLGA), a polypeptide prepared by bacteria fermentation, has broad applications in tissue engineering. A natural host–guest pair of β‐cyclodextrin (β‐CD) and bile acid molecules are attached covalently onto the PLGA backbones separately, and host–guest crosslinked self‐healing hydrogels can be easily formed by mixing these polymer solutions. Inspired by the adhesives of mussel foot proteins, a dopamine methyl acrylamide derivative is introduced into this system to prepare a PLGA‐based dual‐network hydrogel in situ through the combination of host–guest crosslinking and the photo‐crosslinking of the dopamine derivative. The mechanical properties of the hydrogels can be tuned by altering the molar ratios of β‐CD and bile acid as well as the content of dopamine methyl acrylamide derivative. The dynamic inclusion complexation of β‐CD and bile acid allows the hydrogel to self‐heal rapidly under ambient atmosphere and their mechanical properties can recover to original levels within 1 min as evidenced in rheological results. The dual‐network hydrogels exhibit stronger adhesion behavior to pig skin than the host–guest crosslinked hydrogels in vitro. The adhesion of the dual‐network hydrogels to rat whole cortical incision has promoted faster wound healing and tissue regeneration than that in sutured treatments in vivo.
Recent Advances in 2D Material/Conducting Polymer Composites for Thermoelectric Energy Conversion
Authors: Wang Yeye , Bai Zuzhi , Guo Yong , Liu Congcong , Jiang Qinglin , Jiang Fengxing , Yang Jiaji , Ding Wenjun , Liu Peipei , Xu Jingkun 
Published: May 2022

AbstractTwo‐dimensional material (2DM)/conducting polymer (CP) composites with excellent electrical transport behavior, functional multidimensional structure, and good flexibility have attracted widespread attention as potential composite thermoelectric (TE) materials in the field of energy conversion. Appropriate preparation strategies determine the interface microstructure of 2DM/CP composites, which would influence the optimization degree of TE performance and practical applications. This review describes the recent progress in preparing advanced 2DM/CP composites by different design strategies for improving TE performance. In addition, some typical 2DM/CP composites applied to TE generators are also presented. Finally, other challenges and opportunities toward high‐performance 2DM/CP composites are discussed, including precise structural regulation, an in‐depth understanding of the interfacial transport mechanism, and the development of practical 2DM/CP‐based flexible TE devices.
Fabrication of Microcellular Injection‐Molded Polypropylene with Super High Expansion Ratio by Precision Mold Opening Technology
Authors: Gao Zhiyong , Zhao Haibin , Zhai Dongjie , Li Qing , Guo Yu , Wang Guilong , Zhao Guoqun 
Published: May 2022

AbstractIn this study, microcellular injection molding with the combination of the precision mold opening and rapid heat cycle mold technology is conducted to fabricate the 30% glass fiber reinforced polypropylene (PP/GF30) foam. By optimizing supercritical fluid, mold temperature, mold opening distance, and other process conditions, the injection‐molded PP/GF30 foam with a super high expansion ratio, density of 0.32 g cm−3, and porosity of 75%, is fabricated. The crystallization and rheological properties of the PP/GF30 samples are tested to determine the suitable injection molding parameters. The results show that glass fiber promotes the crystallization of polypropylene (PP) while improving its storage modulus and complex viscosity. The fiber distribution and orientation as well as the cellular structure are investigated by scanning electronic microscopy (SEM) and X‐ray computed tomography. Due to the high mold temperature, the shear effect and melt cavity filling are greatly changed. The oriented fibers in the core layer are randomly moved by the cell growth and presented as a scaffold to support the cell wall structure. The glass fiber increases the PP melt stiffness and avoids cell collapse so that the cellular structure with small cell size and even cell distribution is fabricated. The compression properties, surface roughness, and thermal conductivity are examined.
A Facile Way of Enhancing Thermally and Electrically Conductive Properties of Epoxy/Ni/GNP Composites via Construction of a Hybrid Filler Network with a Magnetic Field Orientation Technique
Authors: Yang Bin , Jia Ning , Wang Xiaohong , Pan Yang , Luo Mengning , Chen Xinya , Xia Ru , Zheng Zhengzhi , Qian Jiasheng , Ke Yuchao , Su Lifen , Fang Yirong 
Published: May 2022

AbstractIn this work, epoxy (EP) resin composites with “point‐surface” heterostructures constructed by hybrid fillers of Ni particles and graphene nanosheets (GNPs) are prepared via a blending method under magnetic field. Effects of filler content and magnetic orientation on dielectric, thermally and electrically conductive, and thermomechanical properties of the resultant composites are investigated. The findings disclose that the synergistic effect of hybrid fillers and the magnetic orientation result in the formation of a complex heterogeneous network structure, significantly improving both thermal and electrical conductivities. From the temperature distribution analysis, it is the construction of the heterogeneous network that improves the thermally conductive and thermomechanical properties. Sample Y4 (containing 28 wt% Ni and 2 wt% GNP) displays an average heating rate (AHR) of 152.4 °C min−1and has an out‐of‐plane thermal conductivity of 0.930 W m−1 K−1(267.6% higher than that of neat EP). The infrared thermal imaging (ITI) together with SEM observations further confirms that the Ni chains in situ formed under a magnetic field are responsible for the improved composite's thermal conductivity and thermal management capability, which make it potentially applicable for 3D packaging materials in power‐intensive electronic components.
Tough and Strong Waterborne Polyurethane Network Combined with Sub‐Nanoscaled Calcium Phosphate Oligomers for Protective Coating
Authors: Xi Panyi , Luo Jing , Chen Weichao , Jiang Yijun 
Published: May 2022

AbstractThe optimal design of high wear‐resistance and durability of coatings using organic–inorganic interpenetrating networks has been a long‐term goal of researchers. Although some progress has been made in the modification of waterborne polyurethanes (WPU), there are still significant challenges in fabricating more durable polyurethane‐based protective coatings from the perspective of structural design at a molecular level. Here, a sub‐nanoscaled (<1 nm) inorganic calcium phosphate oligomer (CPO) is used to carry out organic–inorganic hybrid cross‐linking in a WPU network and precisely regulate the structure of the macromolecular network at the molecular level. The herein‐produced CPO‐WPU hybrid coating shows excellent wear resistance and water resistance. Taber abrasion tests demonstrate that the CPO‐WPU coating can be capable of 15 500 turns at a load of 750 g, 2.2 times that of pure WPU coating, and the water‐resistance of CPO‐WPU coating is significantly improved compared with WPU coating due to its well‐integrated organic–inorganic network. Overall, the CPO‐WPU coating material significantly improves the mechanical strength, wear‐resistance, and water‐resistance, compared with the original WPU coating and other modified polyurethane. This hybrid coating can provide a potential application in paper‐making, textiles, and furniture due to its unique organic–inorganic network, low cost, and green process.
Convective‐scale numerical weather prediction and warnings over Lake Victoria: Part II—Can model output support severe weather warning decision‐making?
Authors: Mittermaier Marion , Landman Stephanie , Csima Gabriella , Goodman Steven 
Published: May 2022

AbstractThe greater Lake Victoria region was the focus of the ‘HIGHWeather impact lAke sYstem’ project, with the aim of developing an early warning capability for hazardous weather. Strong thunderstorm outflows can cause boats to capsize and lead to drowning. Lightning is also a hazard to fishermen, and over land flash flooding from heavy rainfall is a significant hazard. Between March and October 2019, 16 regional warnings were issued, and 14 days had reported impacts. Only one of the issued warnings aligned with any reported impacts. The issued warnings were verified using three different methodologies and found to have a very low frequency bias, confirming that too few warnings are being issued, especially for precipitation. The skill of the warnings based on the probability of detection and symmetric extremal dependency index was also found to be low. Potential warnings days are derived from individual and multivariate combinations of numerical weather prediction forecasts of mean wind speed, precipitation and a new lightning diagnostic from the 4.4 km Tropical Africa configuration of the Met Office Unified Model. The methodology identified 5–7 times the issued number of warnings, including 50% of days with reported impacts and 25% of days with issued warnings. Overall, 70%–80% of the identified potential warning days appear to be justified, suggesting that for this period not enough warnings were issued. It is shown that a daily aggregate of the lightning diagnostic is a skilful addition to any guidance supporting a decision‐making process.
Corrigendum to “Existence of solutions for a quasilinear elliptic system with local nonlinearity on ℝN$$ {\mathbb{R}}&amp;#x0005E;N $$” [Math. Meth. Appl. Sci., 2021, 44(17): 13186–13212]
Authors: Zhang Xingyong , Liu Cuiling 
Published: May 2022

We would like to correct the range of
in the condition (F2) of Theorem 1.1 and an error (i.e., the proof of inequality (3.16)) in the proof of Lemma 3.4 in [Math. Meth. Appl. Sci., 2021, 44(17): 13186–13212].
Ultralow‐parameter denoising: Trainable bilateral filter layers in computed tomography
Authors: Wagner Fabian , Thies Mareike , Gu Mingxuan , Huang Yixing , Pechmann Sabrina , Patwari Mayank , Ploner Stefan , Aust Oliver , Uderhardt Stefan , Schett Georg , Christiansen Silke , Maier Andreas 
Published: May 2022

AbstractBackgroundComputed tomography (CT) is widely used as an imaging tool to visualize three‐dimensional structures with expressive bone‐soft tissue contrast. However, CT resolution can be severely degraded through low‐dose acquisitions, highlighting the importance of effective denoising algorithms.PurposeMost data‐driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state‐of‐the‐art performance helps to minimize radiation dose while maintaining data integrity.MethodsThis work presents an open‐source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data‐driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image‐to‐image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design.ResultsAlthough only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state‐of‐the‐art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x‐ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal‐to‐noise ratio values of 33.17 and 43.07 on the respective data sets.ConclusionsDue to the extremely low number of trainable parameters with well‐defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning‐based denoising architectures.
Association of diaphragm thickness and echogenicity with age, sex, and body mass index in healthy subjects
Authors: van Doorn Jeroen L. M. , Wijntjes Juerd , Saris Christiaan G. J. , Ottenheijm Coen A. C. , van Alfen Nens , Doorduin Jonne 
Published: May 2022

AbstractIntroduction/AimsDiaphragm ultrasound is increasingly used in the diagnosis of diaphragm dysfunction and to guide respiratory management in patients with neuromuscular disorders and those who are critically ill. However, the association between diaphragm ultrasound variables and demographic factors like age, sex, and body mass index (BMI) are understudied. Such relationships are important for correct interpretation of normative values and comparison with selected patients groups. The aim of this study was to determine the associations between diaphragm ultrasound variables and subject characteristics.MethodsB‐mode ultrasound was used to image the diaphragm at the zone of apposition in 83 healthy subjects. Diaphragm thickness at resting end‐expiration (Tend‐exp), diaphragm thickness at maximal end‐inspiration (Tmax‐insp), diaphragm thickening ratio (Tmax‐insp/Tend‐exp), and diaphragm echogenicity were measured. Multivariate linear regression was used to explore the associations between diaphragm ultrasound variables and subject characteristics.Results
Tend‐exp, Tmax‐insp, and thickening ratio do not change with age whereas diaphragm echogenicity increases with age. The thickening ratio had a weak negative association with BMI, while Tend‐exp was positively associated with BMI. Men had a larger Tend‐exp and Tmax‐insp than women (Tend‐exp 1.6 ± 0.5 and 1.4 ± 0.3 mm; p = .011, Tmax‐insp 3.8 ± 1.0 and 3.2 ± 0.9 mm; p = .004), but similar thickening ratios.DiscussionDiaphragm thickness, thickening, and echogenicity measured with ultrasound are associated with factors such as age, BMI, and sex. Therefore, subject characteristics should be considered when interpreting diaphragm ultrasound measurements. In the absence of normative values, matched control groups are a prerequisite for research and in clinical practice.
Structural topology optimization of elastoplastic continuum under shakedown theory
Authors: Li Kai , Cheng Gengdong 
Published: May 2022

AbstractThis article presents an elastoplastic continuum topology optimization method for shakedown under the SIMP‐based framework for the first time, aiming to maximize shakedown load‐carrying capacity under variable repeated loading. In contrast to most elastoplastic topology optimizations which have path‐dependent properties, the shakedown topology optimization only needs loading vertices of the loading domain rather than any complete loading history. Based on Melan's lower bound theorem, the gradient‐based topology optimization framework synthesizing the shakedown analysis and sensitivity analysis is developed to maximize the shakedown multiplier. Path‐independent sensitivity related to the specified density interpolation model is derived analytically for the first time in conjunction with the adjoint method. Besides, a primal‐dual algorithm of shakedown analysis is implemented for computing the shakedown load limit of structures and corresponding adjoint variables needed in the sensitivity analysis. In addition to the density interpolation scheme of material Young's module, the yield strength is also interpolated to overcome numerical difficulties. Several numerical examples demonstrate the effectiveness of the proposed method, which indicates the shakedown load‐carrying performance enhancements.
Double dynamic bonds tough hydrogel with high self‐healing properties based on acylhydrazone bonds and borate bonds
Authors: Chen Lijun , Wang Sui , Guo Zhiyong , Hu Yufang 
Published: May 2022

AbstractHydrogels with excellent self‐healing properties have specific applications in smart polymer materials, but they often do not have excellent mechanical properties, or the preparation process of hydrogels is too complicated. Here, we prepared a multi‐functional intelligent hydrogel using a simple water bath one‐pot method through the synergistic effect of dual dynamic cross‐linked agents, including acylhydrazone bonds (the first cross‐linked agent) and borate ester bonds (the second cross‐linked agent), at the same time hydrogen bonds provided physical interaction. The hydrogel exhibited excellent self‐healing ability (up to 91%) and extraordinary mechanical strength (tensile stress 173.1405 kPa and hysteresis energy of 1.8659 MJ m−3). The research used chemical cross‐linked (acylhydrazone bonds and borate ester bonds) and physical cross‐linked (hydrogen bonds). The prepared hydrogel had the dual advantages of excellent self‐healing performance and high mechanical strength. The hydrogel can be used in smart materials, such as working pressure tissues and sensing materials that require a certain degree of mechanical strength and self‐healing properties, and also provides a design strategy for preparing hydrogels with high mechanical strength and excellent self‐healing properties.
Analysis of nonlinear mechanical behavior of resin materials at low and medium strain rates
Authors: Jianlin Zhong , Rui Ren , Ziruo Tang , Jie Ren , Changfang Zhao 
Published: May 2022

AbstractCommonly used thermoset/thermoplastic continuous fiber‐reinforced composites have an isotropic failure strain of no more than 0.1 and deformation under gas pressure impact loading in the low to medium strain rate range (). In order to accurately grasp the load carrying capacity of the composite material under this condition and predict the damage failure behavior, experimental analysis and finite element study of the nonlinear mechanical behavior of the resin before damage under low and medium strain rate loading are carried out in this article. Based on the linear function of compressive stress and logarithmic strain rate of the resin material, the elastic–plastic principal structure related to the strain rate of the resin material is established. The mechanical behavior of the resin material at low and medium strain rates was obtained by writing the VUMAT subroutine for the numerical finite element simulation of the test conditions, and the good agreement with the experimental results proved the accuracy of the prediction of the mechanical behavior of the resin material at low and medium strain rates by this intrinsic model.
Flammable hazards of BESS failures
Authors: Morones Aníbal 
Published: May 2022

AbstractUnder certain conditions, Lithium‐ion (Li‐ion) batteries can produce flammable and/or explosive atmospheres and pose related risks. Battery Energy Storage Systems are composed of large quantities of Li‐ion batteries. This article uses basic concepts of combustion to describe the flammable hazards associated with BESS failures.
Introduction of an artificial neural network–based method for concentration‐time predictions
Authors: Bräm Dominic Stefan , Parrott Neil , Hutchinson Lucy , Steiert Bernhard 
Published: May 2022

AbstractPharmacometrics and the application of population pharmacokinetic (PK) modeling play a crucial role in clinical pharmacology. These methods, which describe data with well‐defined equations and estimate physiologically interpretable parameters, have not changed substantially during the past decades. Although the methods have proven their usefulness, they are often resource intensive and require a high level of expertise. We investigated whether a method based on artificial neural networks (ANNs) may provide an alternative approach for the prediction of concentration‐time curve to supplement the gold standard methods. In this work, we used simulated data to overcome the requirement for a large clinical training data set, implemented a pharmacologically reasonable network architecture to improve extrapolation to different dosing schemes, and used transfer learning to quickly adapt the predictions to new patient groups. We demonstrate that ANNs are able to learn the shape of concentration‐time curves and make individual predictions based on a short sequence of PK measurements. Furthermore, an ANN trained on simulated data was applied to real clinical data and was demonstrated to extrapolate to different dosing schemes. We also adapted the ANN trained on simulated healthy subjects to simulated hepatic impaired patients through transfer learning. In summary, we demonstrate how ANNs could be leveraged in a PK workflow to efficiently make individual concentration‐time predictions, and we discuss the current limitations and advantages of such an ANN‐based method.
Dynamical effects of inflation in ensemble‐based data assimilation under the presence of model error
Authors: Scheffler Guillermo , Carrassi Alberto , Ruiz Juan , Pulido Manuel 
Published: May 2022

AbstractCovariance inflation is one of the necessary tools enabling the success of ensemble Kalman filters (EnKFs) in high‐dimensional spaces and in the presence of model error. Inflation maintains the ensemble variance to a sufficiently large value, counteracting the variance damping at analysis times and its underestimation arising from model and sampling errors. In this work, we investigate the effect of inflation on the dynamics of the EnKF ensemble. When the focus is on the recursive full cycle forecast–analysis–forecast, an apparently counterintuitive effect of multiplicative inflation appears in the span of the ensemble in the EnKF. In particular, we demostrate that multiplicative inflation changes the alignment of ensemble anomalies on to weakly stable backward Lyapunov vectors. Whereas the ensemble is expected to collapse on to the subspace corresponding to the unstable portions of the Lyapunov spectrum, the use of multiplicative inflation contributes to the retention of anomalies beyond that subspace. Given that the presence of model error implies that the analysis error is no longer fully confined in the local unstable subspace, this feature of multiplicative inflation is of paramount importance for optimal filtering. We propose hybrid schemes, whereby additive perturbations complement multiplicative inflation by suitably increasing the dimension of the subspace spanned by the ensemble. The use of hybrid schemes improves analysis root‐mean‐squared error in the Lorenz 96 model compared with the use of multiplicative inflation alone, emphasizing the role of model dynamics when designing additive inflation schemes.