bluuebunny/crossref_metadata_2025 huggingface dataset:
A gradient-based framework for 3D print appearance optimization
Authors: Nindel Thomas Klaus , Iser Tomáš , Rittig Tobias , Wilkie Alexander , Křivánek Jaroslav
Published: July 2021

In full-color inkjet 3D printing, a key problem is determining the material configuration for the millions of voxels that a printed object is made of. The goal is a configuration that minimises the difference between desired target appearance and the result of the printing process. So far, the techniques used to find such a configuration have relied on domain-specific methods or heuristic optimization, which allowed only a limited level of control over the resulting appearance.

We propose to use differentiable volume rendering in a continuous material-mixture space, which leads to a framework that can be used as a general tool for optimising inkjet 3D printouts. We demonstrate the technical feasibility of this approach, and use it to attain fine control over the fabricated appearance, and high levels of faithfulness to the specified target.
A mathematical foundation for foundation paper pieceable quilts
Authors: Leake Mackenzie , Bernstein Gilbert , Davis Abe , Agrawala Maneesh
Published: July 2021

Foundation paper piecing is a popular technique for constructing fabric patchwork quilts using printed paper patterns. But, the construction process imposes constraints on the geometry of the pattern and the order in which the fabric pieces are attached to the quilt. Manually designing foundation paper pieceable patterns that meet all of these constraints is challenging. In this work we mathematically formalize the foundation paper piecing process and use this formalization to develop an algorithm that can automatically check if an input pattern geometry is foundation paper pieceable. Our key insight is that we can represent the geometric pattern design using a certain type of
dual hypergraph
where nodes represent faces and hyperedges represent
seams
connecting two or more nodes. We show that determining whether the pattern is paper pieceable is equivalent to checking whether this hypergraph is acyclic, and if it is acyclic, we can apply a
leaf-plucking
algorithm to the hypergraph to generate viable sewing orders for the pattern geometry. We implement this algorithm in a design tool that allows quilt designers to focus on producing the geometric design of their pattern and let the tool handle the tedious task of determining whether the pattern is foundation paper pieceable.
Solid-fluid interaction with surface-tension-dominant contact
Authors: Ruan Liangwang , Liu Jinyuan , Zhu Bo , Sueda Shinjiro , Wang Bin , Chen Baoquan
Published: July 2021

We propose a novel three-way coupling method to model the contact interaction between solid and fluid driven by strong surface tension. At the heart of our physical model is a thin liquid membrane that simultaneously couples to both the liquid volume and the rigid objects, facilitating accurate momentum transfer, collision processing, and surface tension calculation. This model is implemented numerically under a hybrid Eulerian-Lagrangian framework where the membrane is modelled as a simplicial mesh and the liquid volume is simulated on a background Cartesian grid. We devise a monolithic solver to solve the interactions among the three systems of liquid, solid, and membrane. We demonstrate the efficacy of our method through an array of rigid-fluid contact simulations dominated by strong surface tension, which enables the faithful modeling of a host of new surface-tension-dominant phenomena including: objects with higher density than water that remains afloat; 'Cheerios effect' where floating objects attract one another; and surface tension weakening effect caused by surface-active constituents.
Designing actuation systems for animatronic figures via globally optimal discrete search
Authors: Huber Simon , Poranne Roi , Coros Stelian
Published: July 2021

We present an algorithmic approach to designing animatronic figures - expressive robotic characters whose movements are driven by a large number of actuators. The input to our design system provides a high-level specification of the space of motions the character should be able to perform. The output consists of a fully functional mechatronic blueprint. We cast the design task as a search problem in a vast combinatorial space of possible solutions. To find an optimal design in this space, we propose an efficient best-first search algorithm that is guided by an admissible heuristic. The objectives guiding the search process demand that the design remains free of singularities and self-collisions at any point in the high-dimensional space of motions the character is expected to be able to execute. To identify worst-case self-collision scenarios for multi degree-of-freedom closed-loop mechanisms, we additionally develop an elegant technique inspired by the concept of adversarial attacks. We demonstrate the efficacy of our approach by creating designs for several animatronic figures of varying complexity.
Constrained projective dynamics
Authors: Kee Min Hyung , Um Kiwon , Jeong Wooseok , Han Junghyun
Published: July 2021

This paper proposes a novel energy-momentum conserving integration method. Adopting Projective Dynamics, the proposed method extends its unconstrained minimization for time integration into the constrained form with the position-based energy-momentum constraints. This resolves the well-known problem of unwanted dissipation of energy and momenta without compromising the real-time performance and simulation stability. The proposed method also enables users to directly control the energy and momenta so as to easily create the vivid deformable and global motions they want, which is a fascinating feature for many real-time applications such as virtual/augmented reality and games.
Eliminating topological errors in neural network rotation estimation using self-selecting ensembles
Authors: Xiang Sitao
Published: July 2021

Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.
Fire in paradise
Authors: Hädrich Torsten , Banuti Daniel T. , Pałubicki Wojtek , Pirk Sören , Michels Dominik L.
Published: July 2021

Resulting from changing climatic conditions, wildfires have become an existential threat across various countries around the world. The complex dynamics paired with their often rapid progression renders wildfires an often disastrous natural phenomenon that is difficult to predict and to counteract. In this paper we present a novel method for simulating wildfires with the goal to realistically capture the combustion process of individual trees and the resulting propagation of fires at the scale of forests. We rely on a state-of-the-art modeling approach for large-scale ecosystems that enables us to represent each plant as a detailed 3D geometric model. We introduce a novel mathematical formulation for the combustion process of plants - also considering effects such as heat transfer, char insulation, and mass loss - as well as for the propagation of fire through the entire ecosystem. Compared to other wildfire simulations which employ geometric representations of plants such as cones or cylinders, our detailed 3D tree models enable us to simulate the interplay of geometric variations of branching structures and the dynamics of fire and wood combustion. Our simulation runs at interactive rates and thereby provides a convenient way to explore different conditions that affect wildfires, ranging from terrain elevation profiles and ecosystem compositions to various measures against wildfires, such as cutting down trees as firebreaks, the application of fire retardant, or the simulation of rain.
A Latent Variable Augmentation Method for Image Categorization with Insufficient Training Samples
Authors: Lin Luyue , Zheng Xin , Liu Bo , Chen Wei , Xiao Yanshan
Published: July 2021

Over the past few years, we have made great progress in image categorization based on convolutional neural networks (CNNs). These CNNs are always trained based on a large-scale image data set; however, people may only have limited training samples for training CNN in the real-world applications. To solve this problem, one intuition is augmenting training samples. In this article, we propose an algorithm called Lavagan (
La
tent
V
ariables
A
ugmentation Method based on
G
enerative
A
dversarial
N
ets) to improve the performance of CNN with insufficient training samples. The proposed Lavagan method is mainly composed of two tasks. The first task is that we augment a number latent variables (LVs) from a set of adaptive and constrained LVs distributions. In the second task, we take the augmented LVs into the training procedure of the image classifier. By taking these two tasks into account, we propose a uniform objective function to incorporate the two tasks into the learning. We then put forward an alternative two-play minimization game to minimize this uniform loss function such that we can obtain the predictive classifier. Moreover, based on Hoeffding’s Inequality and Chernoff Bounding method, we analyze the feasibility and efficiency of the proposed Lavagan method, which manifests that the LV augmentation method is able to improve the performance of Lavagan with insufficient training samples. Finally, the experiment has shown that the proposed Lavagan method is able to deliver more accurate performance than the existing state-of-the-art methods.
MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble
Authors: Lin Yaojin , Hu Qinghua , Liu Jinghua , Zhu Xingquan , Wu Xindong
Published: July 2021

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm,
mu
lti-
l
abel-specific
f
eature space
e
nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.
MCS+
: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks
Authors: Laishram Ricky , Wendt Jeremy D. , Soundarajan Sucheta
Published: July 2021

In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose
MCS+
, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that
MCS+
consistently outperforms the best baseline—the similarity of the sample that
MCS+
generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of
MCS+
to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.
Unsupervised Subspace Extraction via Deep Kernelized Clustering
Authors: Na Gyoung S. , Chang Hyunju
Published: July 2021

Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor (
USE
), which does not require any side information and rigid assumptions on data. Furthermore,
USE
can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of
USE
is well justified mathematically, and we also empirically demonstrate the effectiveness of
USE
for several benchmark datasets.
Applied AI matters: AI4Code
Authors: Talamadupula Kartik
Published: July 2021

The marriage of Artificial Intelligence (AI) techniques to problems surrounding the generation, maintenance, and use of source code has come to the fore in recent years as an important AI application area1. A large chunk of this recent attention can be attributed to contemporaneous advancements in Natural Language Processing (NLP) techniques and sub-fields. The naturalness hypothesis, which states that "software is a form of human communication" and that code exhibits patterns that are similar to (human) natural languages (Devanbu, 2015; Hindle, Barr, Gabel, Su, & Devanbu, 2016), has allowed for the application of many of these NLP advances to code-centric usecases. This development has contributed to a spate of work in the community --- much of it captured in a survey by Allamanis, Barr, Devanbu, and Sutton (2018) that focuses on classifying these approaches by the type of probabilistic model applied to source code.

This increase in the variety of AI techniques applied to source code has found various manifestations in the industry at large. Code and software form the backbone that underpins almost all modern technical advancements: it is thus natural that breakthroughs in this area should reflect in the emergence of real world deployments.
The Exposome in the Era of the Quantified Self
Authors: Zhang Xinyue , Gao Peng , Snyder Michael P.
Published: July 2021

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.
Transitional Justice and Property: Inextricably Linked
Authors: Alviar García Helena
Published: July 2021

This article analyzes the different ways in which transitional justice has dealt with demands over property restitution and redistribution. To do this, it presents a review of academic literature regarding how to define reparation, the justifications for restitution, and the debate regarding property redistribution as a part of peace negotiations. The article ends with a synthesis of the different critiques raised to the ways in which restitution and redistribution of property have been legally structured. These critiques include foregrounding neoliberalism (as an economic ideal and a governance project) in transitional justice, unveiling gender biases as well as demands for more comprehensive redistribution in the aftermath of civil war.
Population Models, Not Analyses, of Human Neuroscience Measurements
Authors: Gardner Justin L. , Merriam Elisha P.
Published: July 2021

Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements.
Influence of Psychological Nursing Intervention on Psychological State, Treatment Compliance, and Immune Function of Postoperative Patients with Rectal Cancer
Authors: Sun Xicai , Zhong Weifeng , Lu Jingjing , Zhuang Wenzhen
Published: July 2021

In order to explore the clinical effect of psychological nursing intervention on postoperative chemotherapy for rectal cancer, 120 cases of rectal cancer patients were selected as the research subjects. The control group received conventional nursing treatment after operation, and the research group received comprehensive psychological nursing intervention on this basis. The self-rating anxiety scale (SAS) scores, self-rating depression scale (SDS) scores, hope level scores, nursing satisfaction, mental state changes, treatment compliance, and immune function of two groups were analyzed and compared. There was no significant difference between the two groups of patients in the preoperative SAS, SDS, and hope level scale scores. After the intervention, postoperative SAS and SDS scores and CD8+ value of the research group were significantly lower than those of the control group. In contrast, the postoperative hope level score, treatment compliance, and postoperative CD4+/CD8+ of the research group were significantly higher, and the nursing satisfaction was better than that of the control group. The application of psychological nursing intervention in postoperative chemotherapy for patients with rectal cancer can effectively relieve anxiety and depression of patients, promote patients to establish a healthy and coordinated mental state, improve treatment compliance, improve immune function, and promote disease recovery.
Deep Learning for Detection of Colonic Polyps from Computed Tomography Colonoscopy Images Combined with Colonoscopy
Authors: Guo Xiangyan , Gao Hui , Sun Xiaofang , Li Surong
Published: July 2021

The objective of this study was to investigate the diagnosis of colonic polyps (CP) through the computed tomography (CT) images combined with colonoscopy based on Fourier central slice theorem algorithm. In this study, 86 patients with CP admitted to hospital were selected as research objects. CT imaging and colonoscopy were applied to diagnose the patients based on the algorithm of Fourier central slice theorem. The results showed that the diagnostic detection rates of CP and colon cancer (CC) were 88.2% and 94.2%, respectively. The occurrence site of CP was the sigmoid and ascending colon. 38 patients were positive for serosal invasion of CP while 42 patients were negative for serosal invasion of CP, and there were no statistical differences (

P
>
0.05

). The lesion positions of remaining 6 cases were hard to find and could not be detected accurately. Besides, the diagnostic accuracy of preoperative and postoperative stages III and IV was all 100.00%. The combination of CT imaging and colonoscopy was employed to diagnose CP, which was found to be able to accurately locate the lesions, to effectively evaluate the tumor stage before and after surgery, and to have a good diagnostic efficacy in detecting tumor serosal layer.
Recommendation Model Based on Semantic Features and a Knowledge Graph
Authors: Liu Yudong , Chen Wen
Published: July 2021

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.
Convergence Theorems for m‐Coordinatewise Negatively Associated Random Vectors in Hilbert Spaces
Authors: Shi Lyurong
Published: July 2021

In this study, some new results on convergence properties for m‐coordinatewise negatively associated random vectors in Hilbert space are investigated. The weak law of large numbers, strong law of large numbers, complete convergence, and complete moment convergence for linear process of H‐valued m‐coordinatewise negatively associated random vectors with random coefficients are established. These results improve and generalise some corresponding ones in the literature.
Survival from Maternal Cardiac Arrest Complicating Coronavirus Disease 2019
Authors: Shields Andrea D. , Byrne John J. , Munisteri Meghan , Wood Michael
Published: July 2021

Introduction. Coronavirus disease 2019 (COVID-19) has been linked to significant cardiovascular complications such as cardiac arrest, which are associated with a poor prognosis in adults. Little is known about the cardiac complications, specifically cardiac arrest, of COVID-19 during pregnancy and postpartum periods. Case. We present a case of survival and full neurological recovery after maternal cardiac arrest associated with COVID-19 in a postpartum female. Her postpartum course was also associated with seizures attributed to posterior reversible encephalopathy syndrome. After 19 days in the hospital, she was discharged home neurologically intact. Conclusion. More information is needed to determine the range of short- and long-term cardiac complications that may be associated with COVID-19 during pregnancy and postpartum. Additionally, pregnant patients with COVID-19 may be more likely to survive cardiac arrest compared to the general population.
Backpropagation Neural Network Algorithm-Based Color Doppler Ultrasound Detection of Gestational Diabetes Mellitus and Perinatal Outcomes
Authors: Zhang Xiaoqing , Lou Yinsu , Hu Sunhao , Zhu Dan
Published: July 2021

In this work, the related risk factors and perinatal outcomes of pregnant women with gestational diabetes mellitus (GDM) were analyzed based on color Doppler ultrasound (CDU) diagnosis. Backpropagation (BP) algorithm-based CDU imaging algorithm (BPC) was constructed in this study and applied in CDU images of 80 pregnant women with GDM. Besides, amplitude and phase estimation (APES) and low-complexity adaptive beam (LCA) algorithms were introduced for comparison with BPC in turn. It was found that Dice similarity coefficient (96.44%), sensitivity (95.45%), and specificity (91.56%) of BPC were greater than those of APES (83.97%, 85.84%, 78.45%) and LCA (84.74%, 86.29%, 82.35%), while its running time (6.44 ± 1.39 s) was shorter than that of APES (11.87 ± 2.41 s) and LCA (13.76 ± 1.54 s) (

P
<
0.05

). Pregnant women in the experimental group (group B) were older than those in the control group (group A) (

P
<
0.05

). The pulsatility index (PI) and renal artery resistance index (RI) of fetuses in group B (0.95 ± 0.15) were higher than those of group A (0.57 ± 0.24) (

P
<
0.05

). In addition, pregnancy age, family history of hypertension, and abortion history were positively correlated with GDM (

P
<
0.05

). In conclusion, BPC could not only improve diagnosis accuracy in fetuses’ CDU images but also shorten calculation time. Pregnancy age, family history of hypertension, and abortion history were the related risk factors for GDM in pregnant women.
Low‐Intensity Exercise Routine for a Long Period of Time Prevents Osteosarcopenic Obesity in Sedentary Old Female Rats, by Decreasing Inflammation and Oxidative Stress and Increasing GDF‐11
Authors: Mena-Montes Beatriz , Hernández-Álvarez David , Pedraza-Vázquez Gibrán , Toledo-Pérez Rafael , Librado-Osorio Raúl , García-Álvarez Jorge Antonio , Alarcón-Aguilar Adriana , Lazzarini-Lechuga Roberto , Rosas-Carrasco Oscar , Königsberg Mina , López-Diazguerrero Norma Edith , Luna-López Armando
Published: July 2021

The loss of skeletal muscle mass and strength is known as sarcopenia; it is characterized as a progressive and generalized muscle disorder associated with aging. This deterioration can seriously compromise the elderly’s health and reduce their quality of life. In addition to age, there are other factors that induce muscle mass loss, among which are sedentary lifestyle, chronic diseases, inflammation, and obesity. In recent years, a new clinical condition has been observed in older adults that affects their physical capacities and quality of life, which is known as osteosarcopenic obesity (OSO). Osteoporosis, sarcopenia, and obesity coexist in this condition. Physical exercise and nutritional management are the most widely used interventions for the treatment and prevention of sarcopenia. However, in older adults, physical exercise and protein intake do not have the same outcomes observed in younger people. Here, we used a low‐intensity exercise routine for a long period of time (LIERLT) in order to delay the OSO appearance related to sedentarism and aging in female Wistar rats. The LIERLT routine consisted of walking at 15 m/min for 30 min, five days a week for 20 months. To evaluate the effects of the LIERLT routine, body composition was determined using DXA‐scan, additionally, biochemical parameters, inflammatory profile, oxidative protein damage, redox state, and serum concentration of GDF‐11 at different ages were evaluated (4, 8, 12, 18, 22, and 24 months). Our results show that the LIERLT routine delays OSO phenotype in old 24‐month‐old rats, in a mechanism involving the decrease in the inflammatory state and oxidative stress. GDF‐11 was evaluated as a protein related to muscle repair and regeneration; interestingly, rats that perform the LIERLT increased their GDF‐11 levels.
The Effect of Garlic Tablets on the Endometriosis-Related Pains: A Randomized Placebo-Controlled Clinical Trial
Authors: Amirsalari Sudabeh , Behboodi Moghadam Zahra , Taghizadeh Ziba , Jafar Abadi Mina Naghi , Sabaghzadeh Irani Parichehr , Goodarzi Saied , Ranjbar Hadi
Published: July 2021

Endometriosis is a common chronic inflammatory disease. Garlic contains components that have antiproliferative, anti-inflammatory, and antioxidative effects. The current study aimed to evaluate the effectiveness of garlic on endometriosis symptoms. This was a randomized placebo-controlled triple-blind clinical trial. A convenience sample of 60 women was randomly allocated into two groups. The intervention group received usual care supplemented with 400 mg garlic tablets, and the placebo group received identical placebo tablets. A four-part Visual Analogue Scale (VAS) was used to measure the severity of pains. The pains were measured on four occasions (before the intervention and on one-, two-, and three-month follow-ups). Data were analyzed using the t-test, chi-square, repeated measures ANOVA, and ANCOVA by SPSS 16. The overall severity of pain reduced from 6.51 ± 0.86 to 1.83 ± 1.25 in the intervention group (p < 0.05). It increased from 6.41 ± 1.12 to 6.65 ± 1.37 in the control group (p = 0.02). The repeated measures ANOVA showed that there is a significant difference in the change of pain scores between intervention and control groups (p < 0.001, np2 = 0.572). Garlic extract can reduce pelvic and back pain, dysmenorrhea, and dyspareunia which are important symptoms of endometriosis.
Aortic Blunt Trauma Analysis during a Frontal Impact
Authors: Grave-Capistrán Mario Alberto , Prieto-Vázquez Arturo Yishai , Torres-SanMiguel Christopher René
Published: July 2021

The aorta is the largest artery of the human body, and it is considered in the continuous medium mechanics as a hyperelastic material for its biological properties. The thoracic aorta is directly affected in vehicular collision events by compression generated between the ribcage and the three-point seatbelt tension producing injuries in the artery wall. A three-dimensional model of the thoracic aorta was constructed from digital tomographic images considering the ascending aorta, the aortic arch, and the descending aorta. The model obtained presents acceptable characteristics such as a length of 222.8 mm and an ascending aortic diameter of 22.7 mm, 22.7 mm in the aortic arch, and 16.09 mm in the descending aorta. A 150 ms time numerical simulation was developed through the finite element method (MEF), and the model was analyzed simulating a compression load on the artery at its front location. Boundary conditions were considered by selecting specific nodes in the model, such as the points where the artery is held in the thorax with other elements. In addition, displacement nodes were considered to establish a natural behavior of the artery. The outcomes show significant displacements in the artery wall. The most affected areas are the aortic arch and descending aorta, whose displacements reach 14 mm from their original position. Based on the abbreviated injury scale (AIS), the degree of injury to the aorta in this collision event is estimated, an AIS 2 with a moderate severity index and required medical attention.
Target Groups for a Short Dexamethasone Course among Critically Ill COVID-19 Patients
Authors: Oganesyan Armen , Menzulin Ruslan , Surovoy Yury , Nikiforchin Andrei , Zykov Kirill
Published: July 2021

Introduction. Corticosteroids are one of the most promising therapeutic agents for critically ill patients with coronavirus disease 2019 (COVID-19). Despite emerging data, assessed populations and regimens vary, and there are patient subgroups whose response to steroids remains unclear. We aimed to evaluate the outcomes of COVID-19 patients admitted to the intensive care unit (ICU) and treated with a short dexamethasone course to determine which patient categories derive the highest benefit. Methods. A retrospective cohort study was conducted using a prospectively collected single-center ICU database (April 1–October 1, 2020). Adult COVID-19 patients were assigned to dexamethasone (12 mg × 3 days) and usual care groups. Patient, management, and outcome data were extracted. The primary outcome was the 28-day ICU mortality. Subgroup analysis was performed to assess the impact of dexamethasone on mortality in patients with invasive mechanical ventilation (IMV). Results. Of 233 patients, 220 (median age: 65 years, 38% female) were included: 83 patients received dexamethasone and 137 received usual care. Overall, 28 (33.7%) and 54 (39.4%) patients in the dexamethasone and usual care groups, respectively, died within 28 days since ICU admission (rate ratio (RR) 0.86; 95% confidence interval (95% CI): 0.59–1.23;p=0.405). In the IMV cohort, dexamethasone did not decrease the 28-day mortality compared with usual care (47.5% vs. 62.0%; RR 0.78; 95% CI: 0.57–1.09;p=0.107). A subgroup analysis revealed significantly lower 28-day mortality in IMV patients <65 years receiving dexamethasone vs. usual care (22.6% vs. 48.5%; RR 0.47; 95% CI: 0.22–0.98;p=0.043), which was not seen in IMV patients ≥65 years (75.0% vs. 71.1%; RR 1.06; 95% CI: 0.79–1.42;p=0.719). Patients ≥65 years experienced hyperglycemia, bacterial infection, and septic shock significantly more often than younger patients who received dexamethasone (p=0.002,p=0.025, andp<0.001, respectively). Conclusions. A 3-day dexamethasone course is not associated with lower 28-day mortality in critically ill COVID-19 patients, either in the entire ICU cohort or in the IMV. Dexamethasone may significantly reduce the 28-day mortality in IMV patients <65 years, but not in the older IMV subgroup. Dexamethasone administration in patients ≥65 years is associated with a significantly higher rate of adverse events than that in younger patients
Subspace Learning and Joint Distribution Adaptation for Unsupervised Cross-Database Microexpression Recognition
Authors: Zhang Yanliang , Liu Ying , Li Geng , Peng Hongxing
Published: July 2021

Microexpression recognition has been widely favored by researchers due to its many potential applications, such as business negotiation and lie detection. Cross-database microexpression recognition is more challenging and attractive than normal microexpression recognition because the training and testing samples come from different databases. The ensuing challenge is that the feature distributions between training and testing samples differ too much. As a result, the performance of current well-performing microexpression recognition methods often fails to achieve the desired effect. In this paper, we overcome this problem by introducing Subspace Learning and Joint Distribution Adaptation (SLJDA) by projecting the source and target domains into the subspace and later reducing the distance between them and then minimizing the distance between the marginal and conditional probability distributions of the data between the source domain and the target domain. To evaluate its performance, a large number of cross-database experiments are performed in the SMIC database and CASMEII database. The experimental results show the superiority of the method compared with existing microexpression recognition methods.
Synchronous Remote Rendering for VR
Authors: Kelkkanen Viktor , Fiedler Markus , Lindero David
Published: July 2021

Remote rendering for VR is a technology that enables high-quality VR on low-powered devices. This is realized by offloading heavy computation and rendering to high-powered servers that stream VR as video to the clients. This article focuses on one specific issue in remote rendering when imperfect frame timing between client and server may cause recurring frame drops. We propose a system design that executes synchronously and eliminates the aforementioned problem. The design is presented, and an implementation is tested using various networks and hardware. The design cannot drop frames due to synchronization issues but may on the other hand stall if temporal disturbances occur, e.g., due to network delay spikes or loss. However, experiments confirm that such events can remain rare given an appropriate environment. For example, remote rendering on an intranet at 90 fps with a server located approximately 50 km away yielded just 0.002% stalled frames while rendering with extra latency corresponding to the duration of exactly one frame (11.1 ms at 90 fps). In a LAN without extra latency setting, i.e., with latency equal to locally rendered VR, 0.009% stalls were observed while using a wired Ethernet connection and 0.058% stalls when using 5 GHz wireless IEEE 802.11 ac.
Reactivation of Acute Retinal Necrosis following SARS-CoV-2 Infection
Authors: Gonzalez Monica P. , Rios Radames , Pappaterra Mariella , Hernandez Miguel , Toledo Allison , Santos Carmen , Emanuelli Andres , Kurup Shree K. , Oliver Armando L.
Published: July 2021

Purpose. To report on a case of reactivation of acute retinal necrosis following SARS-CoV-2 infection. Methods. Observational case report. Observations. A 32-year-old female with a distant history of left retinal detachment secondary to necrotizing herpetic retinitis complained of right-eye vision loss, pain, redness, and photophobia. An ophthalmological examination revealed findings consistent with acute retinal necrosis of the right eye. A polymerase chain reaction (PCR) analysis of the right vitreous was positive for herpes simplex virus type 2 (HSV-2). A coronavirus disease 2019 (COVID-19) screening test using reverse transcriptase- (RT-) PCR was positive for SARS-CoV-2 RNA. Conclusions. Our case suggests that COVID-19 may cause a latent HSV infection to reactivate, causing contralateral involvement in patients with a prior history of HSV-associated acute retinal necrosis.
Influence of the Power Generation Capacity of the Structural Parameters of a Piezoelectric Bimorph
Authors: Tian Xiaochao , Zhu Jinzhi , Wang Zhicong , Wang Hu , Sun Yuze , Wang HaiGang , Zhang Sida , Yang Zhigang
Published: July 2021

With the popularization of integrated circuits, MEMS, and portable electronic devices, chemical batteries have many disadvantages as the main energy supply method, such as large size, high quality, and limited energy supply life, requiring regular replacement, resulting in waste of materials, environmental pollution, and other issues. From the above reasons, energy harvesting technology plays an important role in improving the efficiency and life of electronic equipment. In order to explore the influence of the bimorph piezoelectric vibrator’s structural parameters on the power generation capacity, this paper establishes a cantilever beam rectangular bimorph piezoelectric vibrator power generation model, derives the mathematical expression of the bimorph piezoelectric vibrator power generation, and determines the parameter factors that affect the power generation effect. Using MATLAB simulation analysis to obtain the influence relationship curve of system output voltage and structural parameters, the experiment tests the influence law of output voltage and thickness ratio, width‐to‐length ratio, and Young’s modulus ratio; the test results are consistent with the theoretical analysis, verifying the theory and the correctness of the analysis. The results show that when the thickness ratio is 0.58 and the width‐to‐length ratio is 1, the power generation effect of the piezoelectric vibrator is the best to reach 14.5V; the power generation capacity of the transducer is inversely proportional to Young’s modulus ratio. This research provides a new idea for the popularization of integrated circuits, MEMS, and portable electronic devices.
Active Fault‐Tolerant/Active Passive Intrusion‐Tolerant H∞ Cooperative Control of Discrete NCS under the Background of Big Data
Authors: Jun Wang , Xiao-li Meng
Published: July 2021

For the linear discrete networked control system (NCS) which may suffer DoS attack on both sides of the controller, when the actuator has time‐varying failure, the intelligent sensor unit uses wireless sensors to collect data. According to the large amount of data collected, the active fault tolerance/active passive capacity of linear discrete NCS under the discrete event‐triggered communication mechanism (DETCS) is studied. The problem of cooperative controller design is discussed. Firstly, a linear discrete NCS model integrating DETCS, actuator fault, and network attack is established. Then, based on the idea of integral sliding mode control, an active fault‐tolerant/attack active passive intrusion‐tolerant cooperative controller is designed, and the actuator attack side network attack and sensor side network attack are extended to the state to obtain a new state vector. Then, an adaptive Kalman filter estimator (AKF) estimates the fault and attack information and then adjusts the initial fault‐tolerant/intrusion‐tolerant cooperative controller in real time according to the estimated information obtained by the adaptive Kalman filter estimator; finally, the MATLAB simulation example is used to verify the improvement of system performance by the designed control law and the saving of network resources by the introduction of DETCS.
Intelligent Control System in Desert Areas Based on Photovoltaic Microgrid Power Supply
Authors: Li Jianjun , Ji Gaoqing
Published: July 2021

With the advent of the global energy crisis, the use of sustainable green energy has become more and more widespread and the utilization rate of photovoltaic industry in high-altitude desert areas is getting higher and higher. This article mainly studies the intelligent control system in desert area based on photovoltaic microgrid power supply. The system uses shielded twisted pair to transmit signals, and electrostatic interference and electromagnetic induction interference are effectively suppressed. After ensuring that the encoder feedback is correct and the motor movement direction is consistent with the command direction and the safety of the movement stroke, the PID parameters of the motor are tuned in PAMCturningpro. For the performance test of this system, the design and implementation of the test plan should be carried out according to the concurrent support target and response time target proposed in the performance analysis of the system. Concurrent users use the LoadRunner tool to create the customer test machine to simulate the concurrent access and operation of the system. In each iteration process, the test results are recorded in the same way as the benchmark test and finally the system can still achieve the expected performance when the actual concurrency reaches the limit of 5007. The results show that the intelligent control system in this paper has the advantages of low cost, convenient operation, perfect function, high precision, stable and reliable operation, etc. and has good practical value.
Analysis of Tunnel Lining Failure Mechanism under the Action of Active Fault
Authors: Ma Sujian , Zhang Liang , Wang Dong , Tan XinRong , Li Sifeng , Liu Yang
Published: July 2021

The underground structure that crosses the active fault will cause more serious damage under the dislocation of the active fault. Relying on an actual tunnel in the southwest mountainous area to establish a three‐dimensional finite element model, the failure mechanism of the tunnel under strike‐slip and thrust fault dislocation is revealed from the lining deformation, stress distribution, and plastic zone distribution, and the results show that the damage range of the lining distributes in the area of the fracture and the damage effect is greatly affected by the movement amount of the active fault. The lining damage under the active fault dislocation is mainly tensile damage, while the lining under the thrust fault dislocation shows compression damage on both sides of the fracture when there is a fracture with a large dip angle. The development range of plastic zone is positively correlated with the dip angle of the fracture and the amount of movement, and the development range is negatively correlated with the dip angle of the fracture and positively correlated with the amount of dislocation. The plastic zone range can be predicted, and the key monitoring range can be set according to the movement form of the active fault, the dip angle of the fracture zone, and the amount of fault movement.