bluuebunny/crossref_metadata_2025 huggingface dataset:
Repulsive chemotaxis and predator evasion in predator–prey models with diffusion and prey-taxis
Authors: Mishra Purnedu , Wrzosek Dariusz
Published: November 2021

The role of predator evasion mediated by chemical signaling is studied in a diffusive prey–predator model when prey-taxis is taken into account (model A) or not (model B) with taxis strength coefficients [Formula: see text] and [Formula: see text], respectively. In the kinetic part of the models, it is assumed that the rate of prey consumption includes functional responses of Holling, Beddington–DeAngelis or Crowley–Martin. Existence of global-in-time classical solutions to model A is proved in space dimension [Formula: see text] while to model B for any [Formula: see text]. The Crowley–Martin response combined with bounded rate of signal production precludes blow-up of solution in model A for [Formula: see text]. Local and global stability of a constant coexistence steady state which is stable for the corresponding ordinary differential equation (ODE) and purely diffusive model are studied along with mechanism of Hopf bifurcation for model B when [Formula: see text] exceeds some critical value. In model A, it is shown that prey-taxis may destabilize the coexistence steady state provided [Formula: see text] and [Formula: see text] are big enough. Numerical simulation depicts emergence of complex space-time patterns for both models and indicates existence of solutions to model A which blow-up in finite time for [Formula: see text].
A discrete Weber inequality on three-dimensional hybrid spaces with application to the HHO approximation of magnetostatics
Authors: Chave Florent , Di Pietro Daniele A. , Lemaire Simon
Published: November 2021

We prove a discrete version of the first Weber inequality on three-dimensional hybrid spaces spanned by vectors of polynomials attached to the elements and faces of a polyhedral mesh. We then introduce two Hybrid High-Order methods for the approximation of the magnetostatics model, in both its (first-order) field and (second-order) vector potential formulations. These methods are applicable on general polyhedral meshes, and allow for arbitrary orders of approximation. Leveraging the previously established discrete Weber inequality, we perform a comprehensive analysis of the two methods. We finally validate them on a set of test-cases.
Phase-Field Modeling of a Single Horizontal Fluid-Driven Fracture Propagation in Spatially Variable Rock Mass
Authors: Chen Fuyong , Zhou Shuwei , Zhuang Xiaoying , Zhang Wengang , Wu Renjie
Published: November 2021

Hydraulic fracture propagation directly affects the recovery rate of resources when hydraulic fracturing techniques are applied to exploiting unconventional oil and gas resources. Rock mass is the main engineering medium of hydraulic fractures and is generally considered to be of considerable spatial variability in physical and mechanical properties. Understanding the irregular propagation mechanism of hydraulic fracture in spatial heterogeneity rock mass is essential and beneficial to assess the recovery rate of oil or gas resources. This work develops a random phase-field method (RPFM) to simulate the irregular propagation of hydraulic fracture in spatially variable rock mass. The spatial variability of elastic modulus is characterized by the random field theory. Utilizing the advantages in modeling complex crack patterns and crack kinematics, the phase-field method (PFM) is used to predict the fracture propagation. Various anisotropic random fields of elastic modulus with different coefficients of variance and scales of fluctuation are generated via the Cholesky decomposition method. The random fields are subsequently implemented into COMSOL Multiphysics and combined with the PFM to investigate the hydraulic fracture propagation. This study investigates the influence of spatial variability of elastic modulus on the peak fluid pressure, fracture length, fracture area and fracture shape. It reveals that the spatial variability of elastic modulus has a significant influence on the propagation of hydraulic fractures, and the results provide a preliminary reference for hydraulic fracturing design with consideration of spatial variability of rock mass.
Multi-Objective Optimization of Laminated Functionally Graded Carbon Nanotube-Reinforced Composite Plates Using Deep Feedforward Neural Networks-NSGAII Algorithm
Authors: Ly Duy K. , Truong Tam T. , Nguyen-Thoi T.
Published: November 2021

This paper proposes a novel and efficient DNN-NSGAII approach which is an integration of the deep feedforward neural network (DNN) and the nondominated sorting genetic algorithm II (NSGAII) to solve multi-objective optimization (MOO) problems of laminated functionally graded carbon nanotube-reinforced composite (FG-CNTRC) quadrilateral plates. The core idea of the proposed approach is to use the DNN as an analyzer to evaluate the objective and constraint functions instead of using the time-consuming finite element analysis methods (FEAMs) during the optimization process, while the NSGAII is employed to find a set of Pareto-optimal solutions of the MOO problems. Accordingly, the proposed DNN-NSGAII remarkably saves the computational cost, but still provides a high accurate optimal solution. The precision, efficiency, and capability of the proposed method are demonstrated through two different MOO problems of the FG-CNTRC quadrilateral plates. Obtained results of the DNN-NSGAII are compared with those of other methods to investigate the reliability and efficiency of proposed method. Moreover, the effects of various boundary conditions and carbon nanotube (CNT) distributions on the Pareto-optimal solutions of MOO problems are also examined.
Coordinated Time-Stepping Method for Coupled DEM–FEM–MBD Algorithm
Authors: Liang Shaomin , Ji Shunying
Published: November 2021

The coordination of time steps directly affects the accuracy and efficiency of the calculation results in the simulation of the interaction between particles and structures. In this paper, a coordinated time-stepping method for coupling is proposed. The discrete element method (DEM) is used to establish the particle model, and the finite element method (FEM) and multibody dynamics (FEM-MBD) are applied to establish the structural model. The time step of the coupling algorithm between particles and a structure is discussed. The calculation efficiency and accuracy of the above methods are verified by an impact model, particle free falling model and electric pendulum model. Finally, the factors affecting the calculation efficiency of the coupling algorithm are discussed. The results show the following: compared with the theoretical solution, the calculation results of the coordinated time-stepping method are accurate. The multiple relationships of time steps between DEM and FEM-MBD are defined as communication coefficients, and the selection of a communication coefficient of 10 or 50 can effectively improve the calculation efficiency. When the communication coefficient continues to increase, the improvement of the calculation efficiency is not obvious. The number of structural elements has a great impact on the calculation efficiency, the number of particles in a certain range has a small impact on the calculation efficiency, and the time step of the DEM has a great influence on the calculation efficiency. The coordination time step method can improve the efficiency of the calculation while ensuring the accuracy of the calculation.
A dynamical version of the SYK model and theq-Brownian motion
Authors: Pluma Miguel , Speicher Roland
Published: November 2021

We extend recent results on the asymptotic eigenvalue distribution of the SYK model to the multivariate case and relate the limit of a dynamical version of the SYK model with the q-Brownian motion, a non-commutative deformation of classical Brownian motion. Furthermore, we extend the results for fluctuations to the multivariate setting and treat also higher correlation functions. The structure of our results for the sparse SYK random matrices resembles the formulas for higher order freeness for ordinary GUE random matrices.
Creativity in CS1: A Literature Review
Authors: Sharmin Sadia
Published: November 2021

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored.

To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.
UrduAI: Writeprints for Urdu Authorship Identification
Authors: Sarwar Raheem , Hassan Saeed-Ul
Published: November 2021

The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. However, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces and when the number of candidate authors increases. Consequently, these solutions are inapplicable to real-world cases. Moreover, due to the unavailability of reliable POS taggers or sentence segmenters, all existing authorship identification studies on Urdu text are limited to the word n-grams features only. To overcome these limitations, we formulate a stylometric feature space, which is not limited to the word n-grams feature only. Based on this feature space, we use an authorship identification solution that transforms each text sample into a point set, retrieves candidate text samples, and relies on the nearest neighbors classifier to predict the original author of the anonymous text sample. To evaluate our solution, we create a significantly larger corpus than existing studies and conduct several experimental studies that show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works.
Through (Tracking) Their Eyes: Abstraction and Complexity in Program Comprehension
Authors: Kather Philipp , Duran Rodrigo , Vahrenhold Jan
Published: November 2021

Previous studies on writing and understanding programs presented evidence that programmers beyond a novice stage utilize plans or plan-like structures. Other studies on code composition showed that learners have difficulties with writing, reading, and debugging code where interacting plans are merged into a short piece of code. In this article, we focus on the question of how different code-composition strategies and the familiarity with code affect program comprehension on a more abstract, i.e., algorithmic level. Using an eye-tracking setup, we explored how advanced students comprehend programs and their underlying algorithms written in either a merged or abutted (sequenced) composition of code blocks of varying familiarity. The effects of familiarity and code composition were studied both isolated and in combination. Our analysis of the quantitative data adds to our understanding of the behavior reported in previous studies and the effects of plans and their composition on the programs’ difficulty. Using this data along with retrospective interviews, we analyze students’ reading patterns and provide support that subjects were able to form mental models of program execution during task performance. Furthermore, our results suggest that subjects are able to retrieve and create schemata when the program is composed of familiar templates, which may improve their performance; we found indicators for a higher element-interactivity for programs with a merged code composition compared to abutted code composition.
The Role of Citizen Science in Promoting Health Equity
Authors: Rosas Lisa G. , Rodriguez Espinosa Patricia , Montes Jimenez Felipe , King Abby C.
Published: November 2021

While there are many definitions of citizen science, the term usually refers to the participation of the general public in the scientific process in collaboration with professional scientists. Citizen scientists have been engaged to promote health equity, especially in the areas of environmental contaminant exposures, physical activity, and healthy eating. Citizen scientists commonly come from communities experiencing health inequities and have collected data using a range of strategies and technologies, such as air sensors, water quality kits, and mobile applications. On the basis of our review, and to advance the field of citizen science to address health equity, we recommend ( a) expanding the focus on topics important for health equity, ( b) increasing the diversity of people serving as citizen scientists, ( c) increasing the integration of citizen scientists in additional research phases, ( d) continuing to leverage emerging technologies that enable citizen scientists to collect data relevant for health equity, and ( e) strengthening the rigor of methods to evaluate impacts on health equity.
Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process
Authors: Liu Zaisheng , Ni Fei , Li Rongpeng , Zhang Honggang , Liu Chang , Zhang Jiefang , Xie Songyun
Published: November 2021

The neuropsychological characteristics inside the brain are still not sufficiently understood in previous Gestalt psychological analyses. In particular, the extraction and analysis of human brain consciousness information itself have not received enough attention for the time being. In this paper, we aim to investigate the features of EEG signals from different conscious thoughts. Specifically, we try to extract the physiologically meaningful features of the brain responding to different contours and shapes in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG). The experimental results show that more brain regions in the frontal lobe are involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images. Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) is significantly different from that evoked by the ordered Gestalt (GST) images in several frequency bands, which indicate that the human cognition of the shape and contour of images can be separated to some extent through topological analysis. This implies the feasibility to digitize the neural signals while preserving the whole and local features of the original signals, which are further verified by our extensive experiments. In general, this paper evaluates and quantifies cognitively related neural correlates by persistent homology features of EEG signals, which provides an approach to realizing the digitization of neural signals. Preliminary verification of the analyzability of human consciousness signals provides reliable research ideas and directions for the realization of feature extraction and analysis of human brain consciousness cognition.
Intelligent Environment Platform for Industrial Clusters Based on Cloud Computing Technology
Authors: Dai Wensheng
Published: November 2021

With the development of science and technology and the improvement of industrialization, the development of more and more industries has interacted to form many industrial clusters. For the healthy development and safety of various industries, environmental quality monitoring and management in industrial clusters is of utmost importance. Currently, domestic and foreign enterprises and related departments are vigorously developing smart environment platforms. The purpose of this paper is to design an intelligent environment platform for industrial clusters based on cloud computing technology. This article first uses the methods of literature research and network investigation to collect relevant literature and research results and organizes statistics on the collected information. Then, through case analysis to study the needs and overview of the construction of smart environment platforms in industrial clusters, it uses the wireless smart sensing technology of the Internet of Things and the information interaction technology of the Internet to provide data to cloud computing through mobile terminals, and cloud computing provides various types on demand data service. Then, according to the demand analysis of the intelligent environment platform, the functional modules and operation procedures of the intelligent environment platform are designed. The main monitoring content is water quality testing, air quality testing, garbage disposal and soil quality testing, etc. Finally, this article processes, analyzes, and predicts the detected sampled data through cloud computing. It can also locate and track abnormal data. Through the curve fitting of the data, the environmental conditions in the area can be predicted. Experiments show that the prediction accuracy rate is as high as 93.8%, which plays an important role in monitoring and preventing environmental pollution.
ALK-Positive Anaplastic Large Cell Lymphoma: A Diagnostic Dilemma for the Otolaryngologist in a Resource Poor Setting
Authors: Figaro Nicholas , Ramoutar Rickhi , Arozarena Rodolfo , Meyers Dawn , Juman Solaiman
Published: November 2021

Anaplastic large cell lymphoma is a rare subtype of non-Hodgkin’s lymphoma. The morphological diversity with which its anaplastic cells confer make the diagnosis of this hematological entity extremely challenging to the pathologist in a resource poor setting. We present a case of a 35-year-old male with a nasopharyngeal mass and cervical lymphadenopathy and the adversities faced by out otolaryngology department with obtaining the diagnosis of ALK-positive anaplastic large cell lymphoma.
Financial Agglomeration, Energy Efficiency, and Sustainable Development of China’s Regional Economy: Evidence from Provincial Panel Data
Authors: Liu Haiman , Long Jiancheng , Shen Zunhuan
Published: November 2021

Ecological deterioration, air pollution, and resource depletion have shrouded the vast regions of China, raising widespread concerns about the sustainable development of the domestic economy. Although financial agglomeration has become a pivotal approach for China to realize green transformation, there is a lack of evidence against the causal correlation between financial agglomeration and sustainable development of the regional economy. To fill this gap, using the data of 29 provincial capital cities in China spanning from 2009 to 2019 and adopting individual time bidirectional fixed effect model, IV-GMM approach, and alternative modeling techniques, this paper investigates the impact of financial agglomeration on sustainable development of the regional economy for the first time. The results indicate that there is a significant positive correlation between financial agglomeration and the sustainable development of the regional economy. Financial agglomeration facilitates the improvement of regional energy efficiency, and the latter further mediates the relationship between financial agglomeration and sustainable development of the regional economy. In addition, the empirical results also demonstrate that the higher the economic policy uncertainty, the weaker the positive relationship between financial agglomeration and energy efficiency. The present study is of great significance for China to implement energy-saving and emission-reduction tasks and achieve sustainable urban construction.
Accurate Perception of Rock Strata Movement for Environmental Protection in Coal Mining: Taking Thick and Hard Roof Cooperative Control as an Example
Authors: Zhao Tong , Liu Changyou , Yetilmezsoy Kaan , Bahramian Majid , Gong Peilin
Published: November 2021

Accurate perception of the key stratum instability can improve the safety of coal mining and also provide a basis for alleviating overlying rock strata destruction and environmental disturbance. To efficiently evaluate the instability of the key stratum and its threat to safe mining and environmental protection, the fracture characteristics and weakening mechanisms were studied through physical simulation, theoretical analysis, and field measurement. A scheme and the parameters of confined blasting in water‐filled deep hole presplit technology (CBWDHPT) for thick and hard roof (THR) weakening were proposed. Research studies showed that, after the THR fractured into large blocks, the subsequent sliding instability induced serious support‐crushing accidents; however, increasing the support strength could only provide limited control. Confined water and infiltrated modified rock mass functioned as the transfer load medium of the explosives, and the CBWDHPT fully utilized high explosion energy to break rocks. Consequently, the collapse and filling of the immediate roof and low‐positioned THR, as well as the timely cutting off the middle‐positioned THR, could be realized, which alleviated the migration space of THR blocks, overlying strata destruction, and earth‐surface step subsidence. Finally, the environmentally friendly strategy (including the CBWDHPT and hydraulic support optimization) for overlying rock strata protection was proposed. In the industrial test, the THR was broken into blocks of different sizes after utilizing the CBWDHPT, and the support working resistance was significantly decreased. It was concluded that the environmentally friendly strategy could effectively reduce the risk of overlying rock strata destruction.
An Easy-to-Integrate IP Design of AHB Slave Bus Interface for the Security Chip of IoT
Authors: Yuan Conggui , Zheng Xin , Rao Bo , Cai Shuting
Published: November 2021

Information security is fundamental to the Internet of things (IoT) devices, in which security chip is an important means. This paper proposes an Advanced High-performance Bus Slave Control IP (AHB-SIP), which applies to cryptographic accelerators in IoT security chips. Composed by four types of function registers and AHB Interface Control Logic (AICL), AHB-SIP has a simple and easy-to-use structure. The System on Chip (SoC) design can be realized by quickly converting the nonstandard interface of the security module to the AHB slave interface. AHB-SIP is applied to the security accelerators of SM2, SM3, and SM4 and random number generator (RNG). Combined with a low-power embedded CPU, TIMER, UART, SPI, IIC, and other communication interfaces, a configurable SoC can be integrated. Moreover, SMIC 110 nm technology is employed to tape out the SoC on a silicon chip. The area of AHB-SIP is 0.072 mm2, only occupying 6‰ of the chip (3.45

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3.45 mm2), and the power consumption of encryption modules combined with AHB-SIP is lower than that combined with AXI interface, which is decreased up to 61.0% and is ideal for the application of IoT.
Effects of Sesame Consumption on Inflammatory Biomarkers in Humans: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Rafiee Shabnam , Faryabi Roghaye , Yargholi Alireza , Zareian Mohammad Ali , Hawkins Jessie , Shivappa Nitin , Shirbeigi Laila
Published: November 2021

Objectives. Existing evidence produces conflicting findings regarding the effect of sesame intake on inflammatory biomarkers; this knowledge gap has yet to be met through systematic review and meta-analysis. This meta-analysis of randomized, controlled clinical trials (RCTs) was conducted to evaluate the effects of sesame consumption on markers of inflammation in humans. Methods. PubMed, Scopus, and the Cochrane Database of Systematic Reviews were searched through August 2020 to identify relevant papers for inclusion. Using the random-effects model, data were evaluated as weighted mean differences (WMD) with 95% confidence intervals (CI). Cochrane’s Q and I-squared (I2) tests were used to identify within-studies heterogeneity. Results. Seven RCTs with 310 participants (157 intervention and 153 control) were included in the meta-analysis. Sesame consumption reduced serum level interleukin-6 (IL-6) (WMD − 0.90; 95% CI (−1.71, −0.09), I2 = 80.4%) compared to the control group. However, sesame intake had no significant effects on C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) compared to the control group. Subgroup analysis identified a reduction in serum CRP, TNF-α, and IL-6 concentration among studies with participants who had a higher level of these biomarkers at baseline, those which used sesamin capsules, and those with a bigger sample size, those conducted in Asia, and studies on females. Conclusion. Sesame consumption reduced serum levels of IL-6 but did not affect CRP and TNF-α in humans. Additional trials should be conducted utilizing a larger and longer treatment duration, along with studies using different sesame formulations (capsule, oil, and seed) and conducting on participants with varied health conditions.
A Real‐Time and Long‐Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT‐Based Multimedia Communication Systems
Authors: Ren Hanchi , Hu Yi , Myint San Hlaing , Hou Kun , Zhang Xiuyu , Zuo Min , Zhang Chi , Zhang Qingchuan , Li Haipeng
Published: November 2021

The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning‐based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C‐OF, which achieves an extremely fast, stable, and long‐term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT‐based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C‐OF outperforms both face detection and object tracking methods.
Three-Dimensional Numerical Modeling of a Coastal Highway Bridge under Stokes Waves
Authors: Rajabi Meysam , Heydari Fahimeh , Ghassemi Hassan , Ketabdari Mohammad Javad , Ghafari Hamidreza
Published: November 2021

This article investigated the effect of structural flexibility on a coastal highway bridge subjected to Stokes waves through a three-dimensional numerical model. Wave-bridge interaction modeling was performed by an FSI model with the coupling of finite element and finite volume methods. An experimental model validated the FSI numerical analysis. Eventually, the overall results of hydrodynamic and structural analyses are presented and discussed. The results illustrate that the structural flexibility significantly increases the initial shock of the wave force on the flexible bridge. In contrast, the fixed bridge tolerates the least forces in the initial shock of the wave force. Then, by adding a wedge-shaped part to the bridge structure, an attempt was made to reduce the initial shock of the wave force to the structure. The results showed the wedge-shaped part with an angle of 30° reduces the initial shock of wave forces down to 50% for horizontal force and 43% for vertical force on the flexible structure.
CT Imaging in the Diagnosis of Lung Injury of Organophosphorus Poisoning and Analysis of Its Correlation with Procalcitonin and C-Reactive Protein Levels
Authors: Sun Wenwen , Wang Dongliang , Yan Xianliang
Published: November 2021

This study focused on the application value of CT images of patients with organophosphorus poisoning lung injury. Specifically, 50 patients diagnosed with organophosphorus pesticide poisoning lung injury in the hospital were selected as research subjects. They all had chest CT examinations. Patients were classified according to different degrees of poisoning, and different groups were compared for the levels of C-reactive protein (CRP) and procalcitonin (PCT) in the serum. The results showed that, of the 50 patients, 25 patients had mild organophosphorus poisoning, accounting for 50%, 14 patients had moderate poisoning, accounting for 28%, and 11 patients suffered from severe organophosphorus poisoning. It was found that, in AOPP patients, cascade effect was exerted by stimulating the secretion of inflammatory mediators such as CRP, causing systemic inflammatory response syndrome, and multiorgan failure appeared in the early stage of the disease. After the histogram equalization treatment, the image was clearer than that processed by the other three methods. In terms of CT image smoothing, the effect of the Gaussian filtering was better than the mean filtering effect, and the effect of the median filtering was better than that of the Gaussian filtering. Therefore, the structure of the lungs became clearer, and the lung texture was also clearly displayed, and CT images after histogram equalization have an important value in the diagnosis of lung injury caused by organophosphorus poisoning.
Sports Event Level Measurement Indicator System Using Multisensor Information Fusion
Authors: Yu Weiwei , Xing Jinming
Published: November 2021

In view of the imperfection of the measurement index of the level of sports events, the coverage rate of the measurement index is low and the stability is poor. Therefore, this paper puts forward a sports event level measurement index system based on multisensor information fusion. First, the simulated annealing algorithm is used to cluster the grouped sensors and fuse the optimal Bayesian estimation of compatible sensors. Second, the relative entropy measure method is used to expand the compatibility measure of the sensor information in the group, and the optimal Bayesian estimation value of the consistent measure test is obtained. The outliers are eliminated, the optimal fusion value is obtained by the overall weighted statistical fusion, and the alternative measure index system is constructed. Finally, analytic hierarchy process (AHP) is used to calculate the weight of each alternative index, so as to achieve the final measurement index. The results show that the standard deviation of clustering average energy consumption is low, and the energy loss is small. The system can effectively construct the measurement index, and the index coverage rate is as high as 95%.
ECMO for Metabolic Crisis in a Patient with Mitochondrial Disease
Authors: Sharma Sonal , Deerman Clifford , Andreae Michael H. , Myler Conrad
Published: November 2021

Patients with mitochondrial disease exhibit disrupted pyruvate oxidation, resulting in intraoperative and perioperative physiologic derangements. Increased enzymatic conversion of pyruvate via lactate dehydrogenase during periods of fasting or stress can lead to metabolic decompensation, with rapid development of fatal lactic acidosis. We describe the intraoperative management and postoperative critical care of a patient with mitochondrial disease who presented for repair of esophageal perforation following repair of a paraesophageal hernia. His surgery was complicated by the development of metabolic crisis and severe lactic acidosis which became resistant to conventional therapy before ultimately resolving with the initiation of venoarterial extracorporeal membrane oxygenation (VA-ECMO).
Estrogen Receptor: A Paradigm for Targeted Therapy
Authors: Damodaran Senthil , Hortobagyi Gabriel N.
Published: November 2021

Nearly two-thirds of breast cancers overexpress estrogen receptors, and endocrine therapy is considered the backbone of systemic therapy both in early and advanced settings. While this is now widely recognized in clinical practice, this is the culmination of outstanding contribution of many investigators and patients. Indubitably, estrogen receptor targeting has had the most impact among targeted therapies and has significantly affected patient survival. In this commentary, we revisit a landmark article published in Cancer Research in 1977 by Knight and colleagues, which laid the groundwork for the use of estrogen receptors in prognostication and adjuvant treatment selection, as well as some of the key breakthroughs in estrogen receptor biology that span more than a century.

See related article by Knight and colleagues, Cancer Res 1977;37:4669–71
Association of Homologous Recombination–DNA Damage Response Gene Mutations with Immune Biomarkers in Gastroesophageal Cancers
Authors: Cerniglia Michael , Xiu Joanne , Grothey Axel , Pishvaian Michael J. , Baca Yasmine , Hwang Jimmy J. , Marshall John L. , VanderWalde Ari M. , Shields Anthony F. , Lenz Heinz-Josef , Korn W. Michael , Salem Mohamed , Philip Philip A. , Goldberg Richard M. , Zeng Jia , Kim Sunnie S.
Published: November 2021

The prevalence of homologous recombination–DNA damage response (HR-DDR) genetic alterations is of therapeutic interest in gastroesophageal cancers. This study is a comprehensive assessment of HR-DDR mutation prevalence across gastroesophageal adenocarcinomas and squamous cell carcinomas. Here we investigate the association of HR-DDR mutations with known predictors for immune-checkpoint inhibition [deficiency in mismatch-repair (dMMRP), tumor mutational burden (TMB), and programmed death ligand 1 (PD-L1)]. We confirmed HR-DDR mutations are present in a subset of gastroesophageal adenocarcinomas (23%) and gastroesophageal squamous cell carcinomas (20%). Biomarker expression of dMMRP (18% vs. 1%) and TMB-high with a cutoff of ≥10 mt/MB (27% vs. 9%) was significantly more prevalent in the DDR-mutated cohort compared with the non-DDR-mutated cohort. Mean combined positive score for PD-L1 in the total adenocarcinoma cohort was significantly higher in the DDR-mutated cohort compared with the non–DDR-mutated cohort (10.1 vs. 5.8). We demonstrated that alterations in ARID1A, BRCA2, PTEN, and ATM are correlated with dMMRP, TMB-high, and increased PD-L1 expression in gastroesophageal adenocarcinomas. Our findings show that a subset of gastroesophageal tumors harbor HR-DDR mutations correlated with established immune biomarkers. By better understanding the relationship between HR-DDR mutations and immune biomarkers, we may be able to develop better immunotherapy combination strategies to target these tumors.
Combination Therapy of Hepatocellular Carcinoma by GPC3-Targeted Bispecific Antibody and Irinotecan is Potent in Suppressing Tumor Growth in Mice
Authors: Chen Xin , Chen Yanmin , Liang Rong , Xiang Lanxin , Li Jingwen , Zhu Yuankui , He Huixia , Huang Le , Zuo Dianbao , Li Weihang , Liang Xinjun , Dong Shuang , Hu Sheng , Ho Mitchell , Feng Mingqian
Published: November 2021

Hepatocellular carcinoma (HCC) is a world leading cause of cancer-related mortality, and currently no curative treatment for advanced HCC is available. Glypican-3 (GPC3) is an attractive target for HCC immunotherapy. This study explored the efficacy of six GPC3-targeted bispecific antibodies, alone or in combination with chemotherapeutic drug Irinotecan, for the treatment of HCC. The bispecific antibodies were constructed using three different structures, knob-into-hole (KH), scFv-scFv-hFc, and scFv-hFc-scFv, where CD3-targeting mAb OKT3 (scFv) was paired with two representative GPC3 mAbs hYP7 (scFv) and HN3 (VH only) that target different epitopes. The In vitro cell killing assay revealed that all bispecific antibodies efficiently killed GPC3 positive cancer cells, with hYP7-KH, hYP7-OKT3-hFc, and HN3-KH being most potent. In vivo xenograft mouse studies demonstrated that all bispecific antibodies suppressed tumor growth similarly, with hYP7-OKT3-hFc performing slightly better. Combination of hYP7-OKT3-hFc with Irinotecan dramatically improved the efficacy and arrested tumor growth of HepG2, Hep3B, and G1 in xenograft mice. Our results demonstrated that the cell surface proximal bispecific antibody hYP7-OKT3-hFc was superior in terms of potency and the GPC3-targeted bispecific antibody combined with Irinotecan was much potent to control HCC growth.
HoloMentor: A Novel Mixed Reality Surgical Anatomy Curriculum for Robot-Assisted Radical Prostatectomy
Authors: Papalois Zoe-Athena , Aydın Abdullatif , Khan Azhar , Mazaris Evangelos , Rathnasamy Muthusamy Anand Sivaprakash , Dor Frank J.M.F. , Dasgupta Prokar , Ahmed Kamran
Published: November 2021

Objectives: The disruption to surgical training and medical education caused by the global COVID-19 pandemic highlighted the need for realistic, reliable, and engaging educational opportunities available outside of the operating theatre and accessible for trainees of all levels. This article presents the design and development of a virtual reality curriculum which simulates the surgical mentorship experience outside of the operating theatre, with a focus on surgical anatomy and surgical decision-making. Method: This was a multi-institutional study between London’s King’s College and Imperial College. The index procedure selected for the module was robotic radical prostatectomy. For each stage of the surgical procedure, subject-matter experts (N = 3) at King’s College London, identified (1) the critical surgical-decision making points, (2) critical anatomical landmarks, and (3) tips and techniques for overcoming intraoperative challenges. Content validity was determined by an independent panel of subject-matter experts (N = 8) at Imperial College, London, using Fleiss’ kappa statistic. The experts’ teaching points were combined with operative footage and illustrative animations, and projected onto a virtual reality headset. The module was piloted to surgical science students (N = 15). Quantitative analysis compared participants’ confidence regarding their anatomical knowledge before and after taking the module. Qualitative data were gathered from students regarding their views on using the virtual reality model. Results: Multi-rater agreement between experts was above the 70.0% threshold for all steps of the procedure. Seventy-three percentage of pilot study participants “agreed” or “strongly agreed” that they achieved a better understanding of surgical anatomy and the rationale behind each procedural step. This was reflected in an increase in the median knowledge score after trialing the curriculum (p < 0.001). 100% of subject-matter experts and 93.3% of participants “agreed” or “strongly agreed” that virtual mentorship would be useful for future surgical training. Conclusions: This study demonstrated that virtual surgical mentorship could be a feasible and cost-effective alternative to traditional training methods with the potential to improve technical skills, such as operative proficiency and nontechnical skills such as decision-making and situational judgement.
Impact of the COVID-19 Pandemic on Cardiovascular Science: Anticipating Problems and Potential Solutions: A Presidential Advisory From the American Heart Association
Authors: McNally Elizabeth M. , Elkind Mitchell S.V. , Benjamin Ivor J. , Chung Mina K. , Dillon Glenn H. , Hernandez Adrian F. , Ibeh Chinwe , Lloyd-Jones Donald M. , McCullough Louise D. , Wold Loren E. , Wright Davene R. , Wu Joseph C. ,   on behalf of the American Heart Association
Published: November 2021

The coronavirus disease 2019 (COVID-19) pandemic has had worldwide repercussions for health care and research. In spring 2020, most non–COVID-19 research was halted, hindering research across the spectrum from laboratory-based experimental science to clinical research. Through the second half of 2020 and the first half of 2021, biomedical research, including cardiovascular science, only gradually restarted, with many restrictions on onsite activities, limited clinical research participation, and the challenges associated with working from home and caregiver responsibilities. Compounding these impediments, much of the global biomedical research infrastructure was redirected toward vaccine testing and deployment. This redirection of supply chains, personnel, and equipment has additionally hampered restoration of normal research activity. Transition to virtual interactions offset some of these limitations but did not adequately replace the need for scientific exchange and collaboration. Here, we outline key steps to reinvigorate biomedical research, including a call for increased support from the National Institutes of Health. We also call on academic institutions, publishers, reviewers, and supervisors to consider the impact of COVID-19 when assessing productivity, recognizing that the pandemic did not affect all equally. We identify trainees and junior investigators, especially those with caregiving roles, as most at risk of being lost from the biomedical workforce and identify steps to reduce the loss of these key investigators. Although the global pandemic highlighted the power of biomedical science to define, treat, and protect against threats to human health, significant investment in the biomedical workforce is required to maintain and promote well-being.
Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning
Authors: Hao J. , Liao W. , Zhang Y.L. , Peng J. , Zhao Z. , Chen Z. , Zhou B.W. , Feng Y. , Fang B. , Liu Z.Z. , Zhao Z.H.
Published: November 2021

Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.
Dancing on My Own: Parasocial Love, Romantic Loneliness, and Imagined Interaction
Authors: Hu Mu , Zhang Bingqing , Shen Yinghe , Guo Jing , Wang Shuwen
Published: November 2021

The present study examined the relationships between people's romantic loneliness, parasocial love (PSL), and imagined interaction (II) with media figures they viewed as romantic partners. Two hundred and twenty four college students in a Chinese university participated in the survey study. Romantic loneliness was negatively related to PSL moderated by gender. The negative relationship only emerged in males but not in females. People who had II with the personae had stronger PSL in contrast those who didn’t. PSL was positively related to II's attributes of frequency and valence and functions of compensation and relationship maintenance. Frequency of II negatively mediated the relationship between romantic loneliness and PSL.
Factors’ Affect on the Viettel Corporation Brand, Vietnam
Authors: Xuan Binh Ngo , Thi Ngoc Nguyen , Thi Thuy Truong , Thi Nhung Nguyen
Published: November 2021

Apparently, brand is now gradually becoming an important intangible asset of the business. It is really a sharp and useful tool to help businesses compete and develop sustainably. Today, many customers choose to buy goods because that product is affixed with a famous brand. Therefore, the brand has brought great value to the business. The article focusses on analysing the factors affecting the expansion of Viettel corporation brand from the perspective of customers, thereby proposing recommendations to help Viettel, in particular, and Vietnamese businesses, in general, to expand their brands successfully.