bluuebunny/crossref_metadata_2025 huggingface dataset:
Aspirin treatment prevents inflammation in experimental bifurcation aneurysms in New Zealand White rabbits
Authors: Wanderer Stefan , Grüter Basil Erwin , Strange Fabio , Boillat Gwendoline , Sivanrupan Sivani , Rey Jeannine , von Gunten Michael , Remonda Luca , Widmer Hans Rudolf , Casoni Daniela , Andereggen Lukas , Fandino Javier , Marbacher Serge
Published: March 2021

Background

Aneurysm wall degeneration is linked to growth and rupture. To address the effect of aspirin (ASA) on aneurysm formation under various wall conditions, this issue was analyzed in a novel rabbit bifurcation model.

Methods

Bifurcation aneurysms created in 45 New Zealand White rabbits were randomized to vital (n=15), decellularized (n=13), or elastase-degraded (n=17) wall groups; each group was assigned to a study arm with or without ASA. At follow-up 28 days later, aneurysms were evaluated for patency, growth, and wall inflammation at macroscopic and histological levels.

Results

36 rabbits survived to follow-up at the end of the trial. None of the aneurysms had ruptured. Patency was visualized in all aneurysms by intraoperative fluorescence angiography and confirmed in 33 (92%) of 36 aneurysms by MRI/MRA. Aneurysm size was significantly increased in the vital (without ASA) and elastase-degraded (with and without ASA) groups. Aneurysm thrombosis was considered complete in three (50%) of six decellularized aneurysms without ASA by MRI/MRA. Locoregional inflammation of the aneurysm complex was significantly reduced in histological analysis among all groups treated with ASA.

Conclusion

ASA intake prevented inflammation of both the periadventitial tissue and aneurysm wall, irrespective of initial wall condition. Although ASA prevented significant growth in aneurysms with vital walls, this preventive effect did not have an important role in elastase-degraded pouches. In possible translation to the clinical situation, ASA might exert a potential preventive effect during early phases of aneurysm formation in patients with healthy vessels but not in those with highly degenerative aneurysm walls.
The local-filament pattern in the anomalous transparency of the Universe for energetic gamma rays
Authors: Troitsky Sergey
Published: March 2021

The propagation length of high-energy photons through the Universe is limited by$$e^{+}e^{-}$$e+e-pair production on the extragalactic background radiation. Previous studies reported discrepancies between predicted and observed attenuation, suggesting explanations in terms of new physics. However, these effects are dominated by a limited number of observed sources, while many do not show any discrepancy. Here, we consider the distribution in the sky of these apparently anomalous objects, selected in two very different approaches: the study of unphysical hardenings at distance-dependent energies in deabsorbed spectra of TeV blazars, and the observation of ultra-high-energy air showers from the directions of BL Lac type objects. In both cases, directions to the anomalous sources follow the projected local distribution of galaxies: all the distant sources, contributing to the anomalies, are seen through the local filament. This matches the prediction of the proposed earlier explanation of the anomalies based on mixing of photons with axion-like particles in the filament’s magnetic field. For ultra-high energies, this axion interpretation may be tested by the search of primary gamma rays.
Theoretical study of the $$D^0 \rightarrow K^- \pi ^+ \eta $$ reaction
Authors: Toledo Genaro , Ikeno Natsumi , Oset Eulogio
Published: March 2021

We develop a model to study the $$D^0 \rightarrow K^- \pi ^+ \eta $$

D
0

→

K
-

π
+

η

weak decay, starting with the color favored external emission and Cabibbo favored mode at the quark level. A less favored internal emission decay mode is also studied as a source of small corrections. Some pairs of quarks are allowed to hadronize producing two pseudoscalar mesons, which posteriorly are allowed to interact to finally provide the $$K^- \pi ^+ \eta $$

K
-

π
+

η

state. The chiral unitary approach is used to take into account the final state interaction of pairs of mesons, which has as a consequence the production of the $$\kappa $$
κ
($$K^*_0(700)$$

K
0
∗

(
700
)

) and the $$a_0(980)$$

a
0

(
980
)

resonances, well visible in the invariant mass distributions. We also introduce the $$\bar{K}^{*0} \eta $$

K

¯

∗
0

η

production in a phenomenological way and show that the s-wave pseudoscalar interaction together with this vector excitation mode are sufficient to provide a fair reproduction of the experimental data. The model provides the relative weight of the $$a_0(980)$$

a
0

(
980
)

to the $$\kappa $$
κ
excitation, and their strength is clearly visible in the low energy part of the $$K \pi $$

K
π

spectrum.
An Effective Hyperparameter Optimization Algorithm for DNN to Predict Passengers at a Metro Station
Authors: Tsai Chun-Wei , Fang Zhi-Yan
Published: March 2021

As one of the public transportation systems, metro is certainly an indispensable part in urban areas of a metropolis today. Several successful results have shown that deep learning technologies might provide an effective way to predict the number of passengers at a metro station. However, most information systems based on deep learning technologies are usually designed and tuned manually by using domain knowledge and trial-and-error; thus, how to find out a set of suitable hyperparameters for a deep neural network (DNN) has become a critical research issue. To deal with the problem of hyperparameter setting for a DNN in solving the prediction of passengers at a metro station, a novel metaheuristic algorithm called search economics for hyperparameter optimization is presented to improve the accuracy rate of such a prediction system. The basic idea of the proposed algorithm is to divide the solution space into a set of subspaces first and then assign a different number of search agents to each subspace based on the “potential of each subspace.” The potential is estimated based on the objective values of the searched solutions, the objective values of the probe solutions, and the computation time invested in each subspace. The proposed method is compared with Bayesian, random forest, support vector regression, DNN, and DNN with different hyperparameter search algorithms, namely, grid search, simulated annealing, and particle swarm optimization. The simulation results using the data provided by the government of Taipei city, Taiwan, indicate that the proposed method outperforms all the other forecasting methods compared in this article in terms of the mean absolute percentage error.
Multi-level Chunk-based Constituent-to-Dependency Treebank Transformation for Tibetan Dependency Parsing
Authors: Shi Shumin , Luo Dan , Wu Xing , Long Congjun , Huang Heyan
Published: March 2021

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.
A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia
Authors: Sun Yuan , Chen Andong , Chen Chaofan , Xia Tianci , Zhao Xiaobing
Published: March 2021

Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.
Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones
Authors: Shi Dai , Tao Dan , Wang Jiangtao , Yao Muyan , Wang Zhibo , Chen Houjin , Helal Sumi
Published: March 2021

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.
Ray Tracing-based Light Energy Prediction for Indoor Batteryless Sensors
Authors: Kim Daeyong , Ahn Junick , Shin Jun , Cha Hojung
Published: March 2021

Light energy harvesting is a valuable technique for batteryless sensors located indoors. A key challenge is finding the right locations to deploy sensors to provide sufficient harvesting capability. A trial-and-error approach or energy prediction method is used as the solution, but existing schemes are either time-consuming or employing a naïve prediction mechanism primarily developed for outdoor environments. In this paper, we propose a light energy prediction technique, called Solacle, which accounts for various factors in indoor light harvesting to provide accuracy at any given location. Exploiting the ray tracing technique, Solacle estimates the illuminance and the luminous efficacy of light sources to predict the harvesting capability, by considering the spatiotemporal characteristics of the surrounding environment. To this end, we defined the optical properties of a space, and devised an optimization approach, specifically a gradient-free-based scheme, to acquire adequate values for the combination of optical properties. We implemented the system and evaluated its efficacy in controlled and real environments. The experiment results show that the proposed approach delivers a significant improvement over previous work in light energy prediction of indoor space.
Beneficial Neglect
Authors: Kim Minhyung , Kim Inyeop , Lee Uichin
Published: March 2021

Smartphone notifications inform users of events to prompt timely engagement. However, there are growing concerns about messaging behaviors in schools because frequent in-class checking and responding may hinder students' learning. This study analyzed a large-scale smartphone usage dataset collected from 81 first-year college students for 14 weeks. We quantified notification arrival and checking patterns in classrooms and analyzed how notification handling behaviors were correlated with students' smartphone usage and academic performance. Our results showed that receiving messages and frequent checking occur during class. Frequent checking behaviors were positively related to longer app usage, while willful neglect of incoming messages was positively related to overall academic performance. Our work demonstrates that problematic behaviors exist related to instant messaging in learning contexts and recommends further study to devise effective intervention mechanisms.
Ok Google, What Am I Doing?
Authors: Adaimi Rebecca , Yong Howard , Thomaz Edison
Published: March 2021

Conversational assistants in the form of stand-alone devices such as Amazon Echo and Google Home have become popular and embraced by millions of people. By serving as a natural interface to services ranging from home automation to media players, conversational assistants help people perform many tasks with ease, such as setting timers, playing music and managing to-do lists. While these systems offer useful capabilities, they are largely passive and unaware of the human behavioral context in which they are used. In this work, we explore how off-the-shelf conversational assistants can be enhanced with acoustic-based human activity recognition by leveraging the short interval after a voice command is given to the device. Since always-on audio recording can pose privacy concerns, our method is unique in that it does not require capturing and analyzing any audio other than the speech-based interactions between people and their conversational assistants. In particular, we leverage background environmental sounds present in these short duration voice-based interactions to recognize activities of daily living. We conducted a study with 14 participants in 3 different locations in their own homes. We showed that our method can recognize 19 different activities of daily living with average precision of 84.85% and average recall of 85.67% in a leave-one-participant-out performance evaluation with 30-second audio clips bound by the voice interactions.
EarDynamic
Authors: Wang Zi , Tan Sheng , Zhang Linghan , Ren Yili , Wang Zhi , Yang Jie
Published: March 2021

Biometric-based authentication is gaining increasing attention for wearables and mobile applications. Meanwhile, the growing adoption of sensors in wearables also provides opportunities to capture novel wearable biometrics. In this work, we propose EarDynamic, an ear canal deformation based user authentication using in-ear wearables. EarDynamic provides continuous and passive user authentication and is transparent to users. It leverages ear canal deformation that combines the unique static geometry and dynamic motions of the ear canal when the user is speaking for authentication. It utilizes an acoustic sensing approach to capture the ear canal deformation with the built-in microphone and speaker of the in-ear wearable. Specifically, it first emits well-designed inaudible beep signals and records the reflected signals from the ear canal. It then analyzes the reflected signals and extracts fine-grained acoustic features that correspond to the ear canal deformation for user authentication. Our extensive experimental evaluation shows that EarDynamic can achieve a recall of 97.38% and an F1 score of 96.84%. Results also show that our system works well under different noisy environments with various daily activities.
DronePrint
Authors: Kolamunna Harini , Dahanayaka Thilini , Li Junye , Seneviratne Suranga , Thilakaratne Kanchana , Zomaya Albert Y. , Seneviratne Aruna
Published: March 2021

With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.
LSVP
Authors: Ram Ashwin , Zhao Shengdong
Published: March 2021

The ubiquity of mobile phones allows video content to be watched on the go. However, users' current on-the-go video learning experience on phones is encumbered by issues of toggling and managing attention between the video and surroundings, as informed by our initial qualitative study. To alleviate this, we explore how combining the emergent smart glasses (Optical Head-Mounted Display or OHMD) platform with a redesigned video presentation style can better distribute users' attention between learning and walking tasks. We evaluated three presentation techniques: highlighting, sequentiality, and data persistence to find that combining sequentiality and data persistence is highly effective, yielding a 56% higher immediate recall score compared to a static video presentation. We also compared the OHMD against smartphones to delineate the advantages of either platform for on-the-go video learning in the context of everyday mobility tasks. We found that OHMDs improved users' 7-day delayed recall scores by 17% while still allowing 5.6% faster walking speed, especially during complex mobility tasks. Based on the findings, we introduce Layered Serial Visual Presentation (LSVP) style, which incorporates sequentiality, strict data persistence, and transparent background, among other properties, for future OHMD-based on-the-go video learning.
Structural Mechanisms for Replicating DNA in Eukaryotes
Authors: Attali Ilan , Botchan Michael R. , Berger James M.
Published: March 2021

The faithful and timely copying of DNA by molecular machines known as replisomes depends on a disparate suite of enzymes and scaffolding factors working together in a highly orchestrated manner. Large, dynamic protein–nucleic acid assemblies that selectively morph between distinct conformations and compositional states underpin this critical cellular process. In this article, we discuss recent progress outlining the physical basis of replisome construction and progression in eukaryotes.
Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes
Authors: Leggieri Patrick A. , Liu Yiyi , Hayes Madeline , Connors Bryce , Seppälä Susanna , O'Malley Michelle A. , Venturelli Ophelia S.
Published: March 2021

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.
The Yin and Yang of Histone Marks in Transcription
Authors: Talbert Paul B. , Henikoff Steven
Published: March 2021

Nucleosomes wrap DNA and impede access for the machinery of transcription. The core histones that constitute nucleosomes are subject to a diversity of posttranslational modifications, or marks, that impact the transcription of genes. Their functions have sometimes been difficult to infer because the enzymes that write and read them are complex, multifunctional proteins. Here, we examine the evidence for the functions of marks and argue that the major marks perform a fairly small number of roles in either promoting transcription or preventing it. Acetylations and phosphorylations on the histone core disrupt histone–DNA contacts and/or destabilize nucleosomes to promote transcription. Ubiquitylations stimulate methylations that provide a scaffold for either the formation of silencing complexes or resistance to those complexes, and carry a memory of the transcriptional state. Tail phosphorylations deconstruct silencing complexes in particular contexts. We speculate that these fairly simple roles form the basis of transcriptional regulation by histone marks.
Review—Recent Advances in Biogenic Silver Nanoparticles & NanoComposite Based Plasmonic-Colorimetric and Electrochemical Sensors
Authors: Wani Irshad Ahmad
Published: March 2021

The unique opto-electronic properties coupled with small surface of silver nanoparticles have opened the door for their use in sensing applications. Silver nanoparticles are extensively used for identification of various health and environmentally hazardous pollutants such as heavy metal ions and other harmful organic chemicals with extremely low detection limits. In recent years, the toxicity of the silver nanoparticles has further been reduced using green synthesis methods. This review, therefore, present current advances in the sensing applications of green synthesized silver nanoparticles and nanocomposites. This paper reports exclusively the synthesis of silver nanoparticles using reductants derived from biological systems especially plant extracts and other bio-safe materials. Various characterization techniques employed to ascertain the phase purity and morphological features of the silver nanoparticles have been discussed. Applications of the green synthesized silver nanoparticles for colorimetric and electrochemical sensing of various pollutants such as heavy metal ions H2O2, NH3, nitrite ions, sulfide ions, kanamycin, nitrobenzene, biomolecules such as nucleic acids, aminoamides etc have been deliberated at length. This article will act as a guide to the reader about the recent advances in colorimetric, optical, and electrochemical sensing properties of green synthesized silver nanoparticles and nanocomposites.
The Application of K Value Adaptive VMD Method in Pipeline Leakage Location
Authors: Liu R. F. , Yang M. J. , Sun C. Q. , Zeng S.
Published: March 2021

The research about online monitoring and leakage automatic location of water distribution networks (WDN) has a wide range of applications that include water resource protection, monitoring, and allocation. Variational mode decomposition (VMD) and cross-correlation (CC) based leakage location is a popular and effective method in WDN. However, the value of K intrinsic mode functions (IMFs) based on VMD decomposition needs to be determined artificially, which affects the separation effect of signal frequency band characteristics directly. Hence, this work proposes an adaptive method to determine the parameter K of leakage vibration signal’s IMFs, which will be applied to automatic leakage location in WDN. Firstly, the number of saddle points in the frequency domain envelope of the sampled signal in different step sizes is calculated. The parameter K is determined according to the curvature change of the number of saddle points and the sampled signal. Finally, the selective IMFs are reconstituted into a new signal, which can determine a leak position using CC based time-delay estimation (TDE). To verify the effectiveness of the proposed algorithm, the different methods based on EMD and Fast ICA are compared. The experimental results demonstrate that the proposed parameter K value adaptive VMD (KVA-VMD) decomposition method is more suitable for leakage location in WDN.
Crataegus pinnatifida Bunge Inhibits RANKL-Induced Osteoclast Differentiation in RAW 264.7 Cells and Prevents Bone Loss in an Ovariectomized Rat Model
Authors: Kim Minsun , Kim MinBeom , Kim Jae-Hyun , Hong SooYeon , Kim Dong Hee , Kim Sangwoo , Kim Eun-Young , Jung Hyuk-Sang , Sohn Youngjoo
Published: March 2021

Osteoporosis is characterized by a decrease in bone microarchitecture with an increased risk of fracture. Long-term use of primary treatments, such as bisphosphonates and selective estrogen receptor modulators, results in various side effects. Therefore, it is necessary to develop alternative therapeutics derived from natural products. Crataegus pinnatifida Bunge (CPB) is a dried fruit used to treat diet-induced indigestion, loss of appetite, and diarrhea. However, research into the effects of CPB on osteoclast differentiation and osteoporosis is still limited. In vitro experiments were conducted to examine the effects of CPB on RANKL-induced osteoclast differentiation in RAW 264.7 cells. Moreover, we investigated the effects of CPB on bone loss in the femoral head in an ovariectomized rat model using microcomputed tomography. In vitro, tartrate-resistant acid phosphatase (TRAP) staining results showed the number of TRAP-positive cells, and TRAP activity significantly decreased following CPB treatment. CPB also significantly decreased pit formation. Furthermore, CPB inhibited osteoclast differentiation by suppressing NFATc1, and c-Fos expression. Moreover, CPB treatment inhibited osteoclast-related genes, such as Nfatc1, Ca2, Acp5, mmp9, CtsK, Oscar, and Atp6v0d2. In vivo, bone mineral density and structure model index were improved by administration of CPB. In conclusion, CPB prevented osteoclast differentiation in vitro and prevented bone loss in vivo. Therefore, CPB could be a potential alternative medicine for bone diseases, such as osteoporosis.
Effect of Cobble Content on the Shear Behaviour of Sand‐Cobble Mixtures
Authors: Wang Chuang , Dong Jinyu , Huang Zhiquan , Zhou Jianjun , Yang Jihong
Published: March 2021

The sand and cobble stratum is a kind of mechanically unstable stratum. Shield machine often encounter problems such as difficulty in excavation, cutterhead wear, and poor slag discharge of the spiral dumper while constructing in this kind of stratum. Considering the complexly and variety of the material composition and structure of this stratum, the sand and cobble stratum in China, Chengdu Subway Line 7, Chadianzi‐Yipintianxia Station, was selected to conduct indoor large‐scale direct shear tests to systematically study the effects of cobble content (CC) on the shear strength and shear properties of sand and cobble soil. The test results showed that the shear strength and angle of internal friction of sand and cobble soil nonlinearly increased with CC, and the shear strength and angle of internal friction slightly increased when CC was less than 40%. The shear strength and angle of internal friction of sand and cobble soil significantly increased after CC reaching 40%. The shear stress‐shear displacement curve has three stages, including the elastic deformation stage, yield stage, and hardening stage. The CC had a control effect on the strength and deformation characteristics of sand and cobble soil. The shear stress‐displacement curve of sand and cobble soils with CCs of 20% and 80% can be fitted as an exponential model, while the shear stress shear displacement curves of sand and cobble soils with CCs of 40% and 60% are hyperbolic. For sand and cobble soil with same CC, the larger the vertical stress is, the larger the normal displacement is.
Effect of Static Rotor Eccentricity on End Winding Forces and Vibration Wearing
Authors: Jiang Hong-Chun , Tang Gui-Ji , He Yu-Ling , Sun Kai , Li Wei-Jun , Cheng Lun
Published: March 2021

In order to study the vibration wearing regularity and the strength failure point of stator end windings before and after static rotor eccentricity, the three-dimensional electromagnetic forces and the subsequent mechanical responses are studied in this paper. The electromagnetic force, stress, and deformation on the end winding of the QFSN-600-2YHG turbo-generator are calculated by the finite element method (FEM) through an electromagnetic-structure coupling. The radial vibration characteristics of the winding are verified by experiments. It shows that the vibration wearing in the same layer is more serious than that between two neighboring layers. For different layers, the interphase coils endure a larger wearing risk than the innerphase coils. Inside the same phase, the last coil along the rotating direction has the highest risk of insulation wearing. The occurrence of static rotor eccentricity will significantly increase the electromagnetic forces and the vibration amplitudes on some coils. The end-phase coil which is close to the minimum air-gap point is the most dangerous one due to the lasting overstresses and the intensified deformations.
[Retracted] Branch‐and‐Reduction Algorithm for Indefinite Quadratic Programming Problem
Authors: Qiu Yongjian , Zhu Yuming , Yin Jingben
Published: March 2021

This paper presents a rectangular branch‐and‐reduction algorithm for globally solving indefinite quadratic programming problem (IQPP), which has a wide application in engineering design and optimization. In this algorithm, first of all, we convert the IQPP into an equivalent bilinear optimization problem (EBOP). Next, a novel linearizing technique is presented for deriving the linear relaxation programs problem (LRPP) of the EBOP, which can be used to obtain the lower bound of the global optimal value to the EBOP. To obtain a global optimal solution of the EBOP, the main computational task of the proposed algorithm involves the solutions of a sequence of LRPP. Moreover, the global convergent property of the algorithm is proved, and numerical experiments demonstrate the higher computational performance of the algorithm.
Cediranib Induces Apoptosis, G1 Phase Cell Cycle Arrest, and Autophagy in Non‐Small‐Cell Lung Cancer Cell A549 In Vitro
Authors: Guo Menghuan , Liu Zhiyuan , Si Jing , Zhang Jinhua , Zhao Jin , Guo Zhong , Xie Yi , Zhang Hong , Gan Lu
Published: March 2021

Lung cancer remains the leading cause of cancer death worldwide. Late diagnosis, chemoresistance, and metastasis are the main reasons for the high mortality rate of lung cancer. Therefore, the development of other treatments is urgent. Cediranib (CED), a vascular endothelial growth factor receptor (VEGFR) kinase inhibitor, shows promising antitumour activities in various cancers including lung cancer. Here, we explored the effects and the underlying molecular mechanism of CED on non‐small‐cell lung cancer (NSCLC) cell line A549 cells in vitro. Our results show that CED could inhibit A549 cell proliferation and cloning formation. Meanwhile, G1 phase cell cycle arrest was also found, as featured by the increased proportion of G1 phase cells as well as the reduction of G1 phase relative proteins CDK4/cyclin D1 and CDK2/cyclin E. Moreover, the ratio of LC3‐II/LC3‐I was elevated significantly in CED‐treated groups compared with the controls. Furthermore, the expression of p‐Akt, p‐P38, p‐Erk1/2, and p‐mTOR proteins was decreased obviously in the treatment groups. These results suggest that CED could induce apoptosis and G1 phase cell cycle arrest in A549 cells. Meanwhile, CED may induce autophagy through MAPK/Erk1/2 and Akt/mTOR signal pathway in A549 cells.
Distributed Virtual Environment Basketball Equipment Embedded Systems’ Research and Development
Authors: Zhou Yang
Published: March 2021

With the development of social economy and the improvement of material level, people are paying more and more attention to their own health, and they are also constantly improving their fitness awareness. As far as the current sports equipment is concerned, the boring exercise methods can no longer satisfy people’s pursuit of higher quality fitness exercises. The embedding of the distributed virtual environment can incorporate its perceptual, immersive, and interactive characteristics, which increases the realization of multiuser interactive behavior in the field of sports equipment in the virtual scene. Therefore, this paper proposes the research and development of basketball sports equipment embedded system based on distributed virtual environment. First of all, this article adopts the literature method to learn the application of distributed virtual environment and embedded system in depth and, secondly, designs the embedded system architecture of basketball sports equipment suitable for distributed virtual environment and the software and hardware framework of the embedded system. Finally, the performance, delay comparison, system optimization test, and other aspects of the embedded system are analyzed. The average delay of interactive functions of embedded systems based on distributed virtual environment is 1.4 min, which saves 4 min compared with traditional interactive methods. And, the throughput rate of the system has also been greatly improved. The research on the embedded system of sports equipment based on the distributed virtual environment proposed in this paper will be beneficial to the development of society and economy, and it can also help more people to enrich the way of sports and enhance the fun of sports.
Ulam–Hyers Stability of Caputo-Type Fractional Stochastic Differential Equations with Time Delays
Authors: Wang Xue , Luo Danfeng , Luo Zhiguo , Zada Akbar
Published: March 2021

In this paper, we study a class of Caputo-type fractional stochastic differential equations (FSDEs) with time delays. Under some new criteria, we get the existence and uniqueness of solutions to FSDEs by Carath

e
´

odory approximation. Furthermore, with the help of H

o
¨

lder’s inequality, Jensen’s inequality, It

o
^

isometry, and Gronwall’s inequality, the Ulam–Hyers stability of the considered system is investigated by using Lipschitz condition and non-Lipschitz condition, respectively. As an application, we give two representative examples to show the validity of our theories.
Effect of a Rapid Tooling Technique in a 3D Printed Part for Developing an EDM Electrode
Authors: Alamro Turki , Yunus Mohammed , Alfattani Rami , Alnaser Ibrahim A.
Published: March 2021

The role of rapid tooling (RT) in additive manufacturing (AM) seems essential in improving and spreading out the vista of manufacturing proficiency. In this article, attempts were made to discover the feasibility and the accomplishments of the RT electrode in the field of electro-discharge machining (EDM). Fused deposition modeling (FDM) is one of the AM processes adopted to fabricate the EDM electrode prototype by coating with copper. The copper is deposited on FDM-built ABS plastic component for about 1 mm through thick electroplating. The copper-coated FDM (CCF) and solid copper (SC) electrodes are used to conduct experiments on a die-sinking EDM machine using tool alloy steel as a workpiece. The CCF polymer electrode can be efficiently used in EDM operations as the build time of any complex shape was substantially reduced. However, the material removal rate (MRR) is far less than that of the SC electrode. It is recommended that the CCF electrode is used for semifinishing and finishing operations in which MRR happens to be less. However, CCF can get spoiled as high temperatures are generated on the machining tool, and the plastic core hardly sustains such high temperatures.
LncRNA NKILA Promotes Cardiomyocytes Apoptosis by Targeting miR22-3p-TXNIP Signal Axis to Inhibit Proliferation, Migration, and Invasion of Cardiomyocytes under High Glucose-Induced Condition
Authors: Zhao Yan , Ming Wenjuan , Ma Xiaohuan , Zheng Yuchan , Yan Yanli
Published: March 2021

This study aims to investigate the molecular mechanism of LncRNA NKILA underlying promoting cardiomyocytes apoptosis and relevant diabetic cardiomyopathy. We utilized high concentration of glucose to induce human cardiomyocytes cell line AC16 to imitate diabetic cardiomyopathy. And then, we performed high-throughput big data analysis, RT-PCR, and western blot assays to evaluate the expression levels of associated mRNA and protein. Cell apoptosis was tested by Annexin V-FITC. The proliferation, migration, and invasion of AC16 cells were examined by CCK8 assay, colony formation assay, EdU assay, wound healing test, and transwell chamber assay. We utilized statistical analysis and luciferase activity assay to analyze the interaction of relevant genes. LncRNA NKILA was highly expressed in AC16 cells induced by high glucose and inhibited AC16 cell proliferation, migration, and invasion by inducing cell apoptosis. Luciferase activity assay demonstrated that LncRNA NKILA binds to miR22-3p. The influence of LncRNA NKILA on AC16 cell proliferation, migration, and invasion could be reversed by miR22-3p. Luciferase activity assay demonstrated that TXNIP was a target of miR-22-3p in AC16 cells, and all the effects of TXNIP on AC16 cell proliferation, migration, and invasion could be abolished by miR22-3p. These results provided comprehensive data about a novel molecular mechanism of LncRNA NKILA promoting cardiomyocytes apoptosis: LncRNA NKILA performed its function in AC16 cells under high glucose-induced condition by targeting mir-22-3p-TXNIP signal axis, which indicated that LncRNA NKILA may play a crucial role in diabetic cardiomyopathy.
Nonlinear Forced Vibration of Bidirectional Functionally Graded Porous Material Beam
Authors: Wu Jianqiang , Chen Lunting , Wu Ruixian , Chen Xiaochao
Published: March 2021

The nonlinear forced vibration of bidirectional functionally graded porous material beams where the material components gradient change in both thickness and axial directions are studied in this study. Combining von Karman’s geometric nonlinearity and first‐order shear deformation theory, the governing equations describing the coupled deformations are formulated as a system of nonlinear partial differential equations. Utilizing the Galerkin method, the formulated continuous model is transformed to a coupled nonlinear ordinary differential dynamic system. By accomplishing bifurcation calculation for periodic response of the discrete system using pseudoarclength technique, the vibration response curves are obtained by extracting the max‐min amplitude of periodic motions. To highlight the effect of nonlinearity, the linear and nonlinear dynamic responses of beam are demonstrated. It is found that the periodic motion of beam may undergo cyclic‐fold bifurcation. Numerical results are presented to examine the effects of the system parameters, e.g., gradient indexes, porosity, damping coefficients, and aspect ratio.
Photobiomodulation Therapy Ameliorates Glutamatergic Dysfunction in Mice with Chronic Unpredictable Mild Stress‐Induced Depression
Authors: Zhang Di , Shen Qi , Wu Xiaolei , Xing Da
Published: March 2021

Accumulating evidence indicates that dysfunction of the glutamatergic neurotransmission has been widely involved in the pathophysiology and treatment of depression. Photobiomodulation therapy (PBMT) has been demonstrated to regulate neuronal function both in vitro and in vivo. Herein, we aim to investigate whether the antidepressant phenotype of PBMT is associated with the improvement of glutamatergic dysfunction and to explore the mechanisms involved. Results showed that PBMT decreased extracellular glutamate levels via upregulation of glutamate transporter‐1 (GLT‐1) and rescued astrocyte loss in the cerebral cortex and hippocampus, which also alleviated dendritic atrophy and upregulated the expression of AMPA receptors on the postsynaptic membrane, ultimately exhibiting behaviorally significant antidepressant effects in mice exposed to chronic unpredictable mild stress (CUMS). Notably, PBMT also obtained similar antidepressant effects in a depressive mouse model subcutaneously injected with corticosterone (CORT). Evidence from in vitro mechanistic experiments demonstrated that PBMT treatment significantly increased both the GLT‐1 mRNA and protein levels via the Akt/NF‐κB signaling pathway. NF‐κB‐regulated transcription was in an Akt‐dependent manner, while inhibition of Akt attenuated the DNA‐binding efficiency of NF‐κB to the GLT‐1 promoter. Importantly, in vitro, we further found that PKA activation was responsible for phosphorylation and surface levels of AMPA receptors induced by PBMT, which is likely to rescue excitatory synaptic transmission. Taken together, our research suggests that PBMT as a feasible therapeutic approach has great potential value to control the progression of depression.
Thymoquinone Ameliorates Lung Inflammation and Pathological Changes Observed in Lipopolysaccharide-Induced Lung Injury
Authors: Boskabady Mostafa , Khazdair Mohammad Reza , Bargi Rahimeh , Saadat Saeideh , Memarzia Arghavan , Mohammadian Roshan Nema , Hosseini Mahmoud , Askari Vahid Reza , Boskabady Mohammad Hossein
Published: March 2021

Anti-inflammatory, antioxidant, and immunomodulatory effects of thymoquinone (TQ) have been shown. The effects of TQ on lipopolysaccharide- (LPS-) induced inflammation and pathological changes in rats’ lung were investigated in this study. Four groups of rats included (1) control (saline treated); (2) LPS (treated with 1 mg/kg/day i.p. for two weeks); and (3 and 4) 5 or 10 mg/kg TQ i.p. 30 min prior to LPS administration. Total and differential WBC counts in the blood and bronchoalveolar fluid (BALF), TGF-β1, INF-γ, PGE2, and IL-4 levels in the BALF and pathological changes of the lung were evaluated. Total WBC count and eosinophil, neutrophil, and monocyte percentage were increased, but the lymphocyte percentage was reduced in the blood and BALF. The BALF levels of PGE2, TGF-β1, and INF-γ were also increased, but IL-4 level was reduced due to LPS administration. LPS also induced pathological insults in the lung of rats (P<0.05toP<0.001for all changes in LPS-exposed animals). Treatment with TQ showed a significant improvement in all changes induced by LPS (P<0.05toP<0.05). TQ showed a protective effect on LPS-induced lung inflammation and pathological changes in rats which suggested a therapeutic potential for TQ on lung injury.
Multibody Nonrigid Structure from Motion Segmentation Based on Sparse Subspace Clustering
Authors: Huang Wenqing , Hu Qingfeng , Wang Yaming , Jiang Mingfeng
Published: March 2021

Sparse subspace clustering (SSC) is one of the latest methods of dividing data points into subspace joints, which has a strong theoretical guarantee. However, affine matrix learning is not very effective for segmenting multibody nonrigid structure from motion. To improve the segmentation performance and efficiency of the SSC algorithm in segmenting multiple nonrigid motions, we propose an algorithm that deploys the hierarchical clustering to discover the inner connection of data and represents the entire sequence using some of trajectories (in this paper, we refer to these trajectories as the set of anchor trajectories). Only the corresponding positions of the anchor trajectories have nonzero weights. Furthermore, in order to improve the affinity coefficient and strong connection between trajectories in the same subspace, we optimise the weight matrix by integrating the multilayer graphs and good neighbors. The experiments prove that our methods are effective.
Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach
Authors: Lei Tian , Peng Jia , Liu Xingliang , Luo Qin
Published: March 2021

Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.