bluuebunny/crossref_metadata_2025_split huggingface dataset:
The shock of COVID‐19 and the shock of rapid vaccine development on the value of senior life settlement contracts
Authors: Ortiz Carlos E. , Stone Charles A. , Zissu Anne 
Published: October 2023

AbstractIn this paper, we develop a model that can capture how COVID‐19 and the subsequent rapid vaccine development against COVID‐19 impacts the value of pools of senior life settlements. The pandemic unexpectedly boosted the mortality rates of senior citizens who had prior diagnoses of certain health conditions. Our model accounts for the existence and concentration of these COVID‐19 comorbidities in portfolios of senior life settlements. It is the concentration of assets linked to the mortality rates of a group who is at elevated risk to COVID‐19 and who is also the primary beneficiaries of the COVID‐19 vaccine that we examine. We illustrate how the shock of the pandemic increases the value of senior life settlements and how the accelerated development and distribution of COVID‐19 vaccines moderated this increase. Our model is general enough to simulate the impact on other financial contracts that are linked to individual mortality rates. These would include life insurance contracts, annuities, and health insurance policies.
Product mix adjustments and import competition in Vietnam's manufacturing industries
Authors: Nguyen Tien Dzung , Nguyen The Kien , Vu Xuan Nam 
Published: October 2023

AbstractIn this article, we make use of Vietnam's annual enterprise surveys to investigate the product selection and product mix responses to import competition in manufacturing firms. We estimate the effect of imports from Vietnam's free trade agreement partners on firms' product scope and sales concentration, controlling for firms and industry characteristics and the conditions in export markets. The empirical results consistently indicate that Vietnam's manufacturing firms narrow the product scope and concentrate on better selling products in response to rising import competition. Given the dominant presence of quality sorting in the manufacturing industries, our analysis suggests an aggregate quality improvement as firms reallocate sales towards high‐quality products in responses to competition.
The impact of foreign demand shocks on organisational hierarchies
Authors: Bonilla Santiago , Polanec Sašo 
Published: October 2023

AbstractAccording to the theory of knowledge‐based hierarchies, a sufficiently large change in firm size should induce firms to change their organisational structure by adding or dropping organisational layers. In open economies, one of the main determinants of firm size is the volume of foreign sales, and its changes may also trigger reorganisations. However, changes in foreign sales are not only driven by foreign demand shocks alone, but also by firm characteristics like productivity and quality, which in theory confound both foreign sales and organisational structure. We construct a set of Bartik‐type measures of exogenous foreign demand shocks, and test whether firms respond to these shocks by changing their hierarchical structure. For this purpose, we combine transaction‐level trade data and employer–employee data for a set of Slovenian manufacturing firms that were engaged in exporting during the 1998–2011 period. While we find a positive relationship between changes in actual exports growth and number of organisational layers, we find no evidence that exogenous foreign demand shocks lead to adding or dropping organisational layers.
Intensity‐modulated radiotherapy and chemotherapy for canine right atrial tumors: A retrospective study of seven dogs
Authors: Moirano Steven , Turek Michelle , Sanchez Diana , Vail David , Van Asselt Nate , Lawrence Jessica , Forrest Lisa 
Published: October 2023

AbstractMost primary cardiac tumors in dogs are located in the right atrium/atrial appendage, with hemangiosarcoma being the most common. The aims of this retrospective, case series were to describe outcomes for seven dogs with right atrial tumors treated with hypofractionated intensity‐modulated radiotherapy and concurrent vinblastine and propranolol. One dog had a complete response, four dogs had partial responses and two dogs had stable disease after treatment. Effusions resolved in all dogs. Median progression‐free survival was 290 days. Five dogs died from metastatic disease, one dog from unrelated neoplasia, and one dog is alive. Median overall survival was 326 days. Three dogs with confirmed hemangiosarcoma survived 244, 326, and 445 days. Two dogs developed clinically significant, but nonfatal, cardiac arrhythmias. One dog that received three courses of radiation had subclinical myocardial and arterial fibrosis at necropsy. Hypofractionated chemoradiotherapy was well tolerated and may provide clinical benefit in dogs with right atrial tumors.
Probabilistic Fatigue Data Analysis Using Physics-Guided Mixture Density Networks
Authors: Chen Jie , Liu Yongming 
Published: October 2023

Abstract
A Physics-guided Mixture Density Network (PgMDN) model is proposed for uncertainty quantification of fatigue data analysis in this paper. It integrates a Mixture Density Network for probabilistic modeling and physics knowledge as regularizations. This model can handle arbitrary distribution of data (e.g., strongly non-Gaussian, multi-mode, and truncated distributions). The physics knowledge from parameters and their partial derivatives is used as equality/inequality constraints. The training of the physics-guided machine learning is formulated as a constrained optimization problem. To train the neural network with the commonly used backpropagation algorithm, the constrained optimization problem is transformed to an unconstrained one using a dynamic penalty function algorithm. With the physics constraints, the required training data sized can be reduced and the overfitting problem can be mitigated. The PgMDN is applied for fatigue stress-life curve estimation for multiple data sets. Some discussions are given to illustrate the effectiveness of incorporating the physics knowledge, the improvement of the dynamic penalty function method compared with the static method, and the benefits achieved from the distribution mixture compared with a single Gaussian distribution.
Topology Morphing in Lattice Structures Through Tensile Buckling
Authors: Sundararaman Venkatesh , McHale Ciarán , P. O’Donnell Matthew , V. Chenchiah Isaac , Weaver Paul M. 
Published: October 2023

Abstract
Incorporating, by design, tensile buckling into the macroscopic response of lattice structures offers a novel approach for adaptive (meta-)material/structure development. In this study, we explore the potential of utilizing the simultaneous tensile buckling of adjacent cells to induce a transformation in lattice topology. Unit cells are passively transformed from rectangle-like to triangle/pentagon-like unit cells, with an associated change in the effective macroscopic properties. This approach provides a new route to elastically tailor the non-linear response of (meta-) materials/structures. The paper explores the behavior of such a system through finite element analysis. The results identify: i) that the initial lattice internal topology (rectangular) is dominated by membrane effects, ii) a negative region of stiffness is associated with the transformation phase, and iii) once formed, the new topology (triangular/pentagonal) exhibits positive stiffness in both compression and tension.
A Machine Learning Framework for In-Situ Microstructural and Kinetics Analysis of Thermoplastic Composites While Processing Under a Polarized Microscope
Authors: Li Shuangshan , Wynn Mathew , Zobeiry Navid 
Published: October 2023

Abstract
There has been a significant increase in thermoplastic composite applications in aerospace in recent years. Semi-crystalline thermoplastics like polyetheretherketone (PEEK) nucleate and grow crystalline structures called spherulites. Addition of carbon fibers forms a crystal structure called transcrystallinity. Processing parameters such as thermal uniformity, dwell time, and temperature history influence crystallinity and crystal morphology in thermoplastic composites. To investigate crystal morphology, neat PEEK and PEEK-carbon fiber composite samples are melted and cooled down while observed under a polarizing light microscope with a heating stage. A classification machine learning model based on the YOLOv3 algorithm is developed and applied to microscopy images to determine spherulite size and count in real-time. A segmentation model based on the U-Net algorithm is used to separate spherulites that have grown and impinged. The impingement of these crystal structures creates boundaries that are difficult to detect in the micrograph. The U-Net model is also used to identify transcrystalline regions that nucleate from carbon fibers. The use of these models in situ provides a unique real-time perspective of the kinetics of different crystals that form in varying processing parameters, part geometries, and reinforcement types.
Self-Propagation Deployable Space Structures Based on Bistable Mechanism
Authors: Li Jiaan , Mu Ruinan , Huo Keyan , Zhang Lu , Zhao Haifeng 
Published: October 2023

Abstract
With the increasing demand for large-scale space structures, such as solar power satellites and space telescopes, new deployment mechanisms are needed to improve the construction efficiency and simplify the maintenance in orbit as well. A self-propagation chain structure, which consists of bistable units, is considered as a potential choice for deployable structures recently.
A self-propagation chain structure is proposed, and it is applied to the deployable need of the radial rib antenna. Firstly, a curved rib comprising bistable units in series is designed to realize the shape transformation from the folded (curved) state into the deployed (straight) one. Then, the kinematics and dynamics equations governing the motion are derived to investigate the propagation mechanism. The influence of structural parameters of each unit to the deploying process are studied comprehensively. Finally, a deployable radial rib antenna driven by a self-deployed mechanism is realized by utilizing the spiral chain as the rib. The results demonstrate that the proposed deployable structure has the advantages to preserve the deployment stability and shape accuracy. This work provides a novel idea to design the deployable structures with self-propagating mechanism in space.
Are There Cases in Which Classical Beam Theories Fail to Compute the Fundamental Frequency?
Authors: Carrera Erasmo , Augello Riccardo , Petrolo Marco 
Published: October 2023

Abstract
Beam structures are widely used in many engineering applications. Well-known examples are aircraft wings and helicopter rotor blades in aerospace engineering, and concrete beams in civil constructions. When dealing with this kind of slender structures, one-dimensional (1D) models are the primary engineers‘ choice. These models are based on the Euler-Bernoulli and Timoshenko theories. The former does not account for transverse shear effects on cross-section deformations. The latter provides a model which, at best, foresees a constant shear-deformation distribution on the cross-section. As a result, they both consider the cross-section of the structure as rigid. This strong approximation has basically no effect when dealing with compact cross-sections. But when the cross-section has a more complicated geometry and has thin-walled portions, the approximation has a strong influence on the modelling capability of classical theories.
In this work, those aspects are underlined with practical examples. Computationally heavier two-dimensional (2D) and three-dimensional (3D) models are used to model the same structures to build reference results, and they are compared with those obtained with classical 1D models. The attention is given to the modal characteristics and, in particular, to the first frequency. The capability of the classical models to catch the first frequency is here related to geometrical properties of the cross-section.
Studies on the Processing and Characterization of Fiber Reinforced/Epoxy-Nanoclay
Authors: Mogiligidda Rajashekar , Hosur Mahesh 
Published: October 2023

Abstract
In recent years, advent of nanomaterials has opened new avenues for design of structural fiber reinforced polymeric (FRP) composites that are stronger, lighter, and more durable than the conventional FRP composites. The current study is to develop and characterize next generation lightweight, ultra-strong FRP composites through the incorporation of multifunctional nanoparticles in polymers. In order to determine which type of nanoclay works well with the resin system chosen, four different types of nanoclay were tried and the one which performed the best was used to further fabricate nanophased glass and carbon epoxy composites. In order to determine which among the four different types of nanoclay would yield better mechanical properties, nanophased epoxy resin systems with 1 and 2 wt.% of nanoclay were used. The results show that Kaolin Clay nanoclay performed better when compared to the other 3 types: 35–45 wt.% Dimethly diaalkyl, Stearyl ammonium 25–30 wt.% and 15–35 wt.% Octadeylamine. There is about 2.88% increase in the strength and about 6.7% increase in the strength for the 1 wt.% and 2 wt.% Kaolin nanoclay respectively, when compared to just resin sample. All the other nanoclay yielded a decrease in the strength. This indicated that Kaolin Clay dispersed and bonded better with epoxy. Carbon and glass fabric epoxy laminated plates were then manufactured using the VARTM method with Kaolin nanoclay and tested for their flexural properties. There is a significant increase in the properties of composites with about 20.88% increase in the strength for 1 wt.% and 17.94% for 2 wt.% for the carbon/epoxy nanocomposites. For the glass/epoxy nanocomposites, the corresponding increase and in the strength is by 27.64% for 1 wt.% and 16.21% for 2 wt.% with Kaolin nanoclay. Additionally, increased strain to failure indicates higher strain energy for the nanophased systems.
Machine Learning-Aided Cohesive Zone Modeling of Fatigue Delamination
Authors: Zhang Liang , Liu Xin , Tian Su , Gao Zhenyuan , Yu Wenbin 
Published: October 2023

Abstract
The objective of this paper is to develop a machine learning-aided cohesive zone model (CZM) for fatigue delamination in composite structures. The so-called string-based CZM can handle pure and mixed fatigue delamination. Its solid thermodynamic foundation enables it to handle spectrum loading sequences well. An implicit integration scheme for this CZM is developed for improved accuracy and to generate needed training data. A conditional recurrent neural network (RNN) model can solve mixed sequential and time-invariant data problems. The time-invariant data are first input into a feed-forward neural network to predict the initial state of an RNN model. The RNN model will take the state and sequential data to recurrently predict the time series outputs. The conditional RNN model is trained to take the place of computationally costly finite element analysis (FEA) and then used for interface parameter calibration. The Dakota toolkit (a general-purpose optimizer), along with the trained conditional RNN model, can parameterize, automate, and accelerate model calibration. The trial-and-error process is then accomplished with Dakota and parameterized and automated with Python scripts and accelerate global optimum search with surrogate models. The present CZM is validated by calibrating its associated interface parameters from a series of constant amplitude double cantilever beam (DCB) tests on unidirectional E-glass fiber/E722 composite beams. It may be modified to accommodate other types of fracture or interfacial debonding.
Numerical and Experimental Shock Study on a Logging-While-Drilling Mockup Assembly
Authors: Song Fei , Olivio Amanda , Li Ke 
Published: October 2023

Abstract
To determine formation properties surrounding the wellbore, it is necessary to identify the hydrocarbon-producing zones and to obtain an improved understanding of the subsurface before initiating production. Logging while drilling is an effective modern technique in which the physical formation properties are recorded during drilling operations. Timely logging-while-drilling (LWD) data are generally transmitted to the surface through mud pulses. These data can also be used to guide well placement so that the wellbore remains within the zone of interest or in the most productive portion of a reservoir. In practice, the LWD tools are often placed in the bottomhole assembly at the lowest portion of the drillstring.
LWD tools can operate in harsh environments during drilling operations. The increasingly aggressive drilling conditions subject the tools to varying degrees of shock and vibration, which have become the leading cause of mechanical failures for drilling and measurement tools. Furthermore, the aggressive shock and vibration loads can be transmitted from the drill collar to the internal components, which frequently results in premature failures of the expensive electronics or other measurement devices. These failures can have a major impact on operators and service companies, costing millions of dollars in repairs and nonproductive hours of rig time.
In this paper, a high-fidelity 3D finite element analysis (FEA) model that can describe contact interactions between the collar, the chassis, and the mounted electronic board is developed for an LWD mockup assembly. A shock test system of the same (i.e., device under test held by shock arms) instrumented with multiple accelerometers is also presented to mimic such dynamics that the tool could experience while drilling in a controlled laboratory environment. Favorable agreement is observed between experimental measurements and the FEA model predictions, which validates the developed FEA model. The validated numerical model can be employed for virtual qualification of newly developed drilling and measurement tools and for post-job root cause analysis of tool failures.
Dynamic Analysis of Variable Thickness Shells in Aerospace Applications via CUF Adaptive Finite Elements
Authors: Cinefra Maria , Moruzzi Martino Carlo 
Published: October 2023

Abstract
New technologies and tools lead to design structures with increasingly more complex and disruptive geometries. In the field of thin structures, such as plates and shells, it is necessary to have numerical analysis tools that take into account these innovations and the use of advanced materials, such as composites, metamaterials or sandwich plates, or in general muti-layer materials. Within the Carrera’s Unified Formulation (CUF) framework, this article proposes a formulation based on adaptive finite elements that can study complex geometries, as variable thickness plates or with edges not orthogonal to the midsurface, while retaining the advantages of CUF theories for the study of multi-layer structures, both in terms of accuracy and reduced computational cost. The adaptability of these new elements concerns both the geometry and the possibility of using different structural models within the same element. Two case studies are presented in the following paper in order to demonstrate the new formulation: a plate with a topology optimized thickness and a semi-cylinder with a sinusoidal thickness.
Physics-Informed Multitask Learning for Material Development
Authors: Wu Yulun , Li Yumeng 
Published: October 2023

Abstract
Accurately predicting the materials’ responses, such as strain and energy, under certain loading conditions is crucial for developing fundamental structure-property relationships and facilitating material design. However, this process can be computationally expensive and challenging, especially for heterogeneous material systems with a large design space, where physics-based repetitive numerical simulations may be required. Furthermore, conducting physical experiments over such a large design space can be both time-consuming and costly. To address these challenges, convolutional neural networks (CNNs) have become increasingly popular as a computationally feasible way to make high-fidelity predictions for various materials, based on simulation results or experimental data. CNNs are particularly useful for materials with complex microstructures that are difficult to characterize or quantify, especially when suitable descriptors are not available. However, these models often suffer from poor transferability and reduced robustness due to limited training data. To overcome this challenge, we propose using multi-task learning (MTL) to provide deep learning models with more knowledge of material behaviors. MTL can improve the performance of multiple related learning tasks by leveraging useful information among them. In this paper, we explore a novel MTL model to improve the fidelity of predictions for both displacement and strain energy. We verify the ability of our proposed model by using the Material MNIST dataset as a case study, where one task is to predict strain energy based on different material microstructures. We demonstrate that, through MTL, the CNN model can achieve a better understanding of materials’ responses and achieve more robust performance in predicting responses for previously unseen material structures.
Free Vibration Analysis of Rotating Rayleigh Cantilever Beam Using P-Type Finite Element Method
Authors: Dhar Dipannoy , Sarkar Korak 
Published: October 2023

Abstract
Rotating beams are widely used in various mechanical structures like propeller blades, turbines etc. In this paper, we investigate the free vibration of rotating non-uniform Rayleigh cantilever beam. Rayleigh beam theory accounts for rotary inertia which is ignored in Euler beam theory (EBT). p-FEM has been incorporated for analysing the free vibration of non-uniform beam. Various natural frequencies and mode shapes have been achieved using p-FEM code and compared with benchmark solutions. Convergence study has been conducted in order to prove superiority of p-FEM over h-FEM. CAD simulation has also been analysed in this paper for better insight.
Electronic Conduction Mechanism in Van Der Waals Flake Thin Film
Authors: Roy Ajit K. , Lee Jonghoon , Nepal Dhriti , Ferguson John 
Published: October 2023

Abstract
Despite the wide sample by sample variation, the electrical conduction mechanism of van der Waals flake thin film is characterized by the variable range hopping over wide range of temperature and subsequent transition into an Arrhenius type conduction at higher temperature. Using the reduced activation energy analysis and a multi-channel conduction model, we discuss how to characterize the nature of the Arrhenius type conduction at high temperature between the nearest neighbor hopping and the band conduction. Also, we examine how those conduction mechanisms are related to the microscopic structure in the electronic density of states of the sample. For the graphene flake thin film sample we synthesized, Mott VRH and band conduction are identified.
Complete genome sequence of
Agrobacterium fabrum
ARqua1
Authors: Lan Bo , Zhang Qian , Yin Kangquan 
Published: October 2023

ABSTRACT

We report the complete genome of
Agrobacterium fabrum
ARqua1 generated from Oxford Nanopore and Illumina sequencing. The genome of ARqua1 has a total length of 5,714,310 bp, comprising a circular chromosome, a linear chromosome, and two plasmids. In total, 5,446 genes were predicted, of which 5,288 were annotated.