bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of the COVID-19 pandemic on the sexual and mental health of adolescent and adult men who have sex with men and transgender women participating in two PrEP cohort studies in Brazil: COBra study protocol
Authors: Ferraz Dulce , Dourado Inês , Zucchi Eliana Miura , Mabire Xavier , Magno Laio , Grangeiro Alexandre D , Couto Márcia Thereza , Ferguson Laura , Westin Mateus , Alves dos Santos Lorruan , Préau Marie
Published: April 2021

Introduction

The COVID-19 pandemic and its control measures have impacted health and healthcare provision in various levels. Physical distancing measures, for instance, may affect sexual health, impacting access to HIV prevention supplies and changing sexual behaviour, as well as mental health, increasing feelings of unsafety and weakening community support ties. These effects can be worsened among socially marginalised groups, such as men who have sex with men (MSM) and transgender women (TGW). Brazil is among the countries most affected by COVID-19 in the world, where control measures have been inconsistently implemented. We aim to investigate the effects of the COVID-19 pandemic on the sexual and mental health of adolescent and adult MSM and TGW in Brazil.

Methods

Convergent mixed-method prospective cohort study, nested in two ongoing HIV pre-exposure prophylaxis (PrEP) cohorts in Brazil, named PrEP1519 and Combina. Participants will be invited to answer, at baseline and after 6 months, a questionnaire about the effects of the COVID-19 pandemic on sexual behaviour, HIV prevention and mental health. Data on HIV infection and sexually transmitted infections (STI) will be collected as part of routine follow-up from the cohorts. Main outcome measures (HIV infection, STI and depression symptoms) will be observed within 12 months after baseline. Sample size is estimated at 426 participants. Complementarily, 50 participants will be invited to in-depth interviews through video calls or interactive voice response, and 20 will be invited to chronicle their lives during the pandemic through digital diaries. Triangulation will be done across qualitative methods and with the quantitative data.

Ethics and dissemination

The study was approved by Research Ethics Committees from the Brazilian Universities coordinating the study. Findings will be published in scientific journals and presented at meetings. Informative flyers will be elaborated to communicate study findings to participants and key stakeholders.
Clinical translation of mitochondrial replacement therapy in Canada: a qualitative study of stakeholders’ attitudes
Authors: Noohi Forough , Li Miranda , Joly Yann
Published: April 2021

Mitochondrial replacement therapy (MRT) in Canada is considered a criminal offense according to article 5(1)(f) of the Assisted Human Reproduction Act (AHRA) (2004). The Act prohibits any practice that modifies the genome of “a human being or in vitro embryo such that the alteration is capable of being transmitted to descendants.” We carried out 32 semi-structured interviews with clinicians, researchers, patient groups, egg donors, and members of the public to explore their attitudes toward the clinical implementation of MRT in Canada. Our interview guide was informed by the socio-ethical, legal, and scientific literature of MRT. We used a thematic analysis to identify and analyze emerging themes and sub-themes. Our findings were divided into five broad themes: ( i) an outdated criminal ban, ( ii) motives for using MRT, ( iii) terminology, ( iv) practical and theoretical risks and benefits, and ( v) the feasibility of clinical translation in Canada. Although the public and stakeholders’ views on the feasibility of foreseeable translation of MRT in Canadian clinics varied, there was consensus on conducting an overdue review of the current AHRA ban on MRT.
Interior estimates of harmonic heat flow
Authors: Liu Xiangao , Liu Zixuan , Wang Kui
Published: April 2021

Motivated by Giaquinta and Hildebrandt’s regularity result for harmonic mappings [M. Giaquinta and S. Hildebrandt, A priori estimates for harmonic mappings, J. Reine Angew. Math. 1982(336) (1982) 124–164, Theorems 3 and 4], we show a [Formula: see text]-regularity result of the harmonic flow between two Riemannian manifolds when the image is in a regular geodesic ball. The proof is based on De Giorgi–Moser’s iteration and Schauder estimate.
Flavor mixing of quarks and a new texture
Authors: Fritzsch Harald
Published: April 2021

The flavor mixing of the quarks is described by the CKM matrix, which is parametrized by three mixing angles and one phase parameter. We discuss a new texture for the two mass matrices of the six quarks. The three flavor mixing angles can be calculated — they are functions of the ratios of the quark masses. The third mixing angle is given by the CKM matrix element [Formula: see text]. We find: [Formula: see text]. The calculated mixing angles agree with the mixing angles, measured in many experiments.
Bifurcation and Chaos of Spontaneous Oscillations of Hair Bundles in Auditory Hair Cells
Authors: Cao Ben , Gu Huaguang , Bai Jiawei , Wu Fuqiang
Published: April 2021

Various spontaneous oscillations and Hopf bifurcation have been observed in hair bundles of auditory hair cells, which play very important roles in the auditory function. In the present paper, the bifurcations and chaos of spontaneous oscillations of hair bundles are investigated in a theoretical model to explain the experimental observations. Firstly, the equivalent negative stiffness and symmetrical characteristic of the model are acquired. The model exhibits coexisting attractors symmetrical to each other or an attractor with symmetry by itself. The attractors include stable focus, stable periodic oscillations, and chaotic oscillations. Secondly, except for the well-known subcritical and supercritical Hopf bifurcations from the stable focus to period-1 limit cycle, the complex bifurcations of spontaneous oscillation patterns such as period-doubling bifurcation cascade to chaos and intermittency between periodic limit cycles and chaos, are observed. Various chaotic oscillations are distinguished. Lastly, a complex bifurcation process containing multiple modes of oscillations and bifurcations mentioned above is obtained, which provides the relationships between different spontaneous oscillation patterns. The results present not only the well-known Hopf bifurcation, but also the various spontaneous oscillations including periodic and chaotic patterns, which are consistent with the recent experimental results. The complex bifurcation process presents a global view of the nonlinear dynamics of complex spontaneous oscillations of hair bundles, which is very important for the auditory function.
Identification of the Gray–Scott Model via Deterministic Learning
Authors: Dong Xunde , Wang Cong
Published: April 2021

Gray–Scott model is one of the most well-known reaction–diffusion models which has a wealth of spatiotemporal chaos behavior. It is commonly used to study spatiotemporal chaos. In the paper, a novel method is proposed for the identification of the Gray–Scott model via deterministic learning and interpolation. The method mainly consists of two phases: the local identification phase and the global identification phase. Local identification is achieved using the finite difference method and deterministic learning. Based on the local identification results, the interpolation method is employed to obtain global identification. Numerical experiments show the feasibility and effectiveness of the proposed method.
Bifurcations in a Predator–Prey Model of Leslie-Type with Simplified Holling Type IV Functional Response
Authors: Zhang Jun , Su Juan
Published: April 2021

In this paper, we complete the remaining investigation of local bifurcations in a predator–prey model of Leslie-type with simplified Holling type IV functional response. The system has at most three equilibria, and local bifurcations were completely investigated in the cases of one and three equilibria, but in the case of two equilibria the previous study was only on a fixed parameter. We extend the study in the case of two equilibria for all parameters, and find that the system exhibits Hopf bifurcations of codimensions 1 and 2, and Bogdanov–Takens bifurcations of codimensions 2 and 3. Previous results and our research show that the codimension of local bifurcations is at most 3, and both focus type and cusp type Bogdanov–Takens bifurcations of codimension 3 can occur.
Polynomial Vector Fields on the Clifford Torus
Authors: Llibre Jaume , Murza Adrian C.
Published: April 2021

First, we characterize all the polynomial vector fields in [Formula: see text] which have the Clifford torus as an invariant surface. Then we study the number of invariant meridians and parallels that such polynomial vector fields can have on the Clifford torus as a function of the degree of these vector fields.
Influences of Embedding Parameters and Segment Sizes in Recursive Characteristics Analysis on Coefficients of Friction
Authors: Sun Guodong , Zhang Chao , Zhu Hua , Lang Shihui
Published: April 2021

The methods of recurrence plots (RPs) and recurrence quantification analysis (RQA) have been used to investigate the tribosystem. The morphology of RPs and RQA measures are strongly dependent on the embedding parameters of the recursive matrix and the segment sizes of the time-series. To improve the calculation accuracy of recursive characteristics analysis, the influences of the embedding parameters and segment sizes on the morphology of RPs and RQA measures have been studied in this letter. Three kinds of theoretical chaotic time-series and measured coefficient of friction (COF) signals during the running-in process were chosen as research objects, and the morphology of RPs and RQA measures were obtained using CRP toolbox afterward. The results indicate that no embedding was actually needed if the data sets are to be qualitatively analyzed using RPs and RQA. Additionally, the morphology of RPs and RQA measures are sensitive to the segment sizes for theoretical chaotic time-series, while the RQA measures of COF signal in the steady-state period are rather stable due to its self-similarity. Finally, according to the guidelines of the parameter settings, the dynamical evolution of measured COF signals during the running-in process have been investigated. It is indicated that recursive characteristics of COF signals could reveal the tribological behaviors’ evolution and conduct the running-in status identification. The results in this paper are significant for improving the calculation accuracy and saving computational time when using the method of recursive characteristics analysis on the tribological behaviors.
The Role of Time-Dependent Phase Space Structures in Reaction Dynamics and the No-Recrossing Property of Dividing Surfaces
Authors: Mandell Cate , Wiggins Stephen
Published: April 2021

We analyze benchmark models for reaction dynamics associated with a time-dependent saddle point. Our model allows us to incorporate time dependence of a general form, subject to an exponential growth restriction. Under these conditions, we analytically compute the time-dependent normally hyperbolic invariant manifold; its time-dependent stable and unstable manifolds; and a time-dependent dividing surface that has the no-recrossing property. Consideration of the time dependence of these phase space structures is necessary in order to precisely capture reacting and nonreacting trajectories. Moreover, we show that a time-dependent dividing surface is necessary in order to eliminate recrossing in the time-dependent setting. In other words, if the dividing surface is not time-dependent, recrossing may occur.
Locating and Stabilizing Unstable Periodic Orbits Embedded in the Horseshoe Map
Authors: Miino Yuu , Ito Daisuke , Ueta Tetsushi , Kawakami Hiroshi
Published: April 2021

Based on the theory of symbolic dynamical systems, we propose a novel computation method to locate and stabilize the unstable periodic points (UPPs) in a two-dimensional dynamical system with a Smale horseshoe. This method directly implies a new framework for controlling chaos. By introducing the subset based correspondence between a planar dynamical system and a symbolic dynamical system, we locate regions sectioned by stable and unstable manifolds comprehensively and identify the specified region containing a UPP with the particular period. Then Newton’s method compensates the accurate location of the UPP with the regional information as an initial estimation. On the other hand, the external force control (EFC) is known as an effective method to stabilize the UPPs. By applying the EFC to the located UPPs, robust controlling chaos is realized. In this framework, we never use ad hoc approaches to find target UPPs in the given chaotic set. Moreover, the method can stabilize UPPs with the specified period regardless of the situation where the targeted chaotic set is attractive. As illustrative numerical experiments, we locate and stabilize UPPs and the corresponding unstable periodic orbits in a horseshoe structure of the Duffing equation. In spite of the strong instability of UPPs, the controlled orbit is robust and the control input retains being tiny in magnitude.
Geochronology of Himalayan shear zones: unravelling the timing of thrusting from structurally complex fault rocks
Authors: Montemagni Chiara , Villa Igor M.
Published: April 2021

Dating structurally complex fault rocks often results in internally inconsistent ages, as several mineral generations are intergrown at scales

≪

10 µm and are almost always altered to various degrees. First, electron probe microanalysis is necessary to assess both inventory and spatial distribution of minerals and their retrogression or alteration phases. We then used
40
Ar/
39
Ar step-heating combining two independent indicators that allow the discrimination of coexisting mica generations from each other: (1) mica stoichiometry, which is proxied by
39
Ar concentration in combination with
37
Ar/
39
Ar and
38
Ar/
39
Ar (Ca/K and Cl/K) ratios; (2) furnace temperature at which the degassing peak accompanying dehydration and structural collapse is observed. As dehydration rates depend on average bond strength in the crystal structure, it is predicted and observed that the temperature of the differential Ar release peak is variable among different minerals. We observe that the Ca/Cl/K signatures of pure micas coincide with the Ar release peak. The Main Central Thrust zone in the Garhwal Himalaya records a protracted history. Foliation of the Vaikrita Thrust formed at 15–8 Ma, followed by static decompression at 7 Ma; foliation of the structurally lower Munsiari Thrust formed around 5 Ma. Our elaborate and time-consuming petrochronological procedure should become routine whenever analysing polydeformed metamorphic rocks.

Supplementary material:
Complete data for the studied samples are available at
https://doi.org/10.6084/m9.figshare.c.5357212

Thematic collection:
This article is part of the Isotopic dating of deformation collection available at:
https://www.lyellcollection.org/cc/isotopic-dating-of-deformation
Robust Ordinal Regression: User Credit Grading with Triplet Loss-Based Sampling
Authors: Zhang Jing , Guo Jiaqi , Ren Yonggong
Published: April 2021

With the development of social media sites, user credit grading, which served as an important and fashionable problem, has attracted substantial attention from a slew of developers and operators of mobile applications. In particular, multi-grades of user credit aimed to achieve (1) anomaly detection and risk early warning and (2) personalized information and service recommendation for privileged users. The above two goals still remained as up-to-date challenges. To these ends, in this article, we propose a novel regression-based method. Technically speaking, we define three natural ordered categories including
BlockList
,
GeneralList
, and
AllowList
according to users’ registration and behavior information, which preserve both the global hierarchical relationship of user credit and the local coincident features of users, and hence formulate user credit grading as the ordinal regression problem. Our method is inspired by KDLOR (
kernel discriminant learning for ordinal regression
), which is an effective and efficient model to solve ordinal regression by mapping high-dimension samples to the discriminant region with supervised conditions. However, the performance of KDLOR is fragile to the extreme imbalanced distribution of users. To address this problem, we propose a robust sampling model to balance distribution and avoid overfit or underfit learning, which induces the triplet metric constraint to obtain hard negative samples that well represent the latent ordered class information. A step further, another salient problem lies in ambiguous samples that are noises or located in the classification boundary to impede optimized mapping and embedding. To this problem, we improve sampling by identifying and evading noises in triplets to obtain hard negative samples to enhance robustness and effectiveness for ordinal regression. We organized training and testing datasets for user credit grading by selecting limited items from real-life huge tables of users in the mobile application, which are used in similar problems; moreover, we theoretically and empirically demonstrate the advantages of the proposed model over established datasets.
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion
Authors: Wang Yang
Published: April 2021

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each other. This fact motivated a lot of research attention on fusing the multi-modal feature spaces to comprehensively characterize the data objects. Most of the existing state-of-the-arts focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance over their counterparts with single modal. Recently, deep neural networks have been exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces. In this article, we provide a substantial overview of the existing state-of-the-arts in the field of multi-modal data analytics from shallow to deep spaces. Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition, and fusion over multi-modal spaces. Finally, we share our viewpoints regarding some future directions in this field.
Gaussian Mixture Model Clustering with Incomplete Data
Authors: Zhang Yi , Li Miaomiao , Wang Siwei , Dai Sisi , Luo Lei , Zhu En , Xu Huiying , Zhu Xinzhong , Yao Chaoyun , Zhou Haoran
Published: April 2021

Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.
Exploring Image Enhancement for Salient Object Detection in Low Light Images
Authors: Xu Xin , Wang Shiqin , Wang Zheng , Zhang Xiaolong , Hu Ruimin
Published: April 2021

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.
RICA-MD: A Refined ICA Algorithm for Motion Detection
Authors: Zhang Chao , Wu Xiaopei , Lu Jianchao , Zheng Xi , Jolfaei Alireza , Sheng Quan Z. , Yu Dongjin
Published: April 2021

With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the diverse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.
A Weakly Supervised Semantic Segmentation Network by Aggregating Seed Cues: The Multi-Object Proposal Generation Perspective
Authors: Xiao Junsheng , Xu Huahu , Gao Honghao , Bian Minjie , Li Yang
Published: April 2021

Weakly supervised semantic segmentation under image-level annotations is effectiveness for real-world applications. The small and sparse discriminative regions obtained from an image classification network that are typically used as the important initial location of semantic segmentation also form the bottleneck. Although deep convolutional neural networks (DCNNs) have exhibited promising performances for single-label image classification tasks, images of the real-world usually contain multiple categories, which is still an open problem. So, the problem of obtaining high-confidence discriminative regions from multi-label classification networks remains unsolved. To solve this problem, this article proposes an innovative three-step framework within the perspective of multi-object proposal generation. First, an image is divided into candidate boxes using the object proposal method. The candidate boxes are sent to a single-classification network to obtain the discriminative regions. Second, the discriminative regions are aggregated to obtain a high-confidence seed map. Third, the seed cues grow on the feature maps of high-level semantics produced by a backbone segmentation network. Experiments are carried out on the PASCAL VOC 2012 dataset to verify the effectiveness of our approach, which is shown to outperform other baseline image segmentation methods.
Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM
Authors: Lu Huimin , Yang Rui , Deng Zhenrong , Zhang Yonglin , Gao Guangwei , Lan Rushi
Published: April 2021

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU

, BLEU

, METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.
SPGAN: Face Forgery Using Spoofing Generative Adversarial Networks
Authors: Li Yidong , Liu Wenhua , Jin Yi , Cao Yuanzhouhan
Published: April 2021

Current face spoof detection schemes mainly rely on physiological cues such as eye blinking, mouth movements, and micro-expression changes, or textural attributes of the face images [9]. But none of these methods represent a viable mechanism for makeup-induced spoofing, especially since makeup has been widely used. Compared with face alteration techniques such as plastic surgery, makeup is non-permanent and cost efficient, which makes makeup-induced spoofing become a realistic threat to the integrity of a face recognition system. To solve this problem, we propose a generative model to construct spoofing face images (confusing face images) for improving the accuracy and robustness of automatic face recognition. Our network structure is composed of two separate parts, with one using inter-attention mechanism to obtain interested face region, and another using intra-attention to translate imitation style with preserving imitation style-excluding details. These two attention mechanisms can precisely learn imitation style, where inter-attention pays more attention to imitation regions of image and intra-attention learns face attributes with long distance in image. To effectively discriminate generated images, we introduce an imitation style discriminator. Our model (SPGAN) generates face images that transfer the imitation style from target to subject image and preserve the imitation-excluding features. Experimental results demonstrate the performance of our model in improving quality of imitated face images.
Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush
Authors: Zhang Rong , Pan Jinghu , Lai Jianbo
Published: April 2021

With the advent of big data, the use of network data to characterize travel has gradually become a trend. Tencent Migration big data can fully, dynamically, immediately, and visually record the trajectories of population migrations with location‐based service technology. Here, the daily population flow data of 346 cities during the Spring Festival travel rush in China were combined with different travel modes to measure the spatial structure and spatial patterns of an intercity trip network of Chinese residents. These data were then used for a comprehensive depiction of the complex relationships between the population flows of cities. The results showed that there were obvious differences in the characteristics of urban networks from the perspective of different modes of travel. The intercity flow of aviation trips showed a core‐periphery structure with national hub cities as the core distribution. Trips by train showed a core‐periphery structure with cities along the national railway artery as the core. This gradually decreased toward hinterland cities. Moreover, the intercity flow of highway trips indicated a spatial pattern of strong local aggregation that matched the population scale.
Model-Based Hardware-Software Codesign of ECT Digital Processing Unit
Authors: Allam Atef , Deabes Wael
Published: April 2021

Image reconstruction algorithm and its controller constitute the main modules of the electrical capacitance tomography (ECT) system; in order to achieve the trade-off between the attainable performance and the flexibility of the image reconstruction and control design of the ECT system, hardware-software codesign of a digital processing unit (DPU) targeting FPGA system-on-chip (SoC) is presented. Design and implementation of software and hardware components of the ECT-DPU and their integration and verification based on the model-based design (MBD) paradigm are proposed. The inner-product of large vectors constitutes the core of the majority of these ECT image reconstruction algorithms. Full parallel implementation of large vector multiplication on FPGA consumes a huge number of resources and incurs long combinational path delay. The proposed MBD of the ECT-DPU tackles this problem by crafting a parametric segmented parallel inner-product architecture so as to work as the shared hardware core unit for the parallel matrix multiplication in the image reconstruction and control of the ECT system. This allowed the parameterized core unit to be configured at system-level to tackle large matrices with the segment length working as a design degree of freedom. It allows the trade-off between performance and resource usage and determines the level of computation parallelism. Using MBD with the proposed segmented architecture, the system design can be flexibly tailored to the designer specifications to fulfill the required performance while meeting the resources constraint. In the linear-back projection image reconstruction algorithm, the segmentation scheme has exhibited high resource saving of 43% and 71% for a small degradation in a frame rate of 3% and 14%, respectively.
GE11 Peptide Conjugated Liposomes for EGFR-Targeted and Chemophotothermal Combined Anticancer Therapy
Authors: Huang Xueqin , Chen Lingzhi , Zhang Yuping , Zhou Suyan , Cai Huai-Hong , Li Ting , Jin Hua , Cai Jiye , Zhou Haibo , Pi Jiang
Published: April 2021

How to actively target tumor sites manipulating the controllable release of the encapsulated anticancer drugs and photosensitizers for synergistic anticancer therapy remains a big challenge. In this study, a cancer cell-targeted, near-infrared (NIR) light-triggered and anticancer drug loaded liposome system (LPs) was developed for synergistic cancer therapy. Photosensitizer indocyanine green (ICG) and chemotherapy drug Curcumin (CUR) were coencapsulated into the liposomes, followed by the surface conjugation of GE11 peptide for epidermal growth factor receptor (EGFR) targeting on the cancer cell surface. Strictly controlled by NIR light, GE11 peptide modified and CUR/ICG-loaded LPs (GE11-CUR/ICG-LPs) could introduce hyperthermia in EGFR overexpressed A549 cancer cells for photothermal therapy, which could also trigger the increased release of CUR for enhanced cancer cell inhibition. GE11-CUR/ICG-LPs synergized photochemotherapy could induce reactive oxygen species (ROS) generation and cytoskeleton disruption to activate stronger apoptotic signaling events than the photothermal therapy or chemotherapy alone by regulating Bax/Bcl-2 and PI3K/AKT pathways. This EGFR-targeted drug-delivery nanosystem with NIR sensitivity may potentially serve in more effective anticancer therapeutics with reduced off-target effects.
JRL‐YOLO: A Novel Jump‐Join Repetitious Learning Structure for Real‐Time Dangerous Object Detection
Authors: Zeng Yiliang , Zhang Lihao , Zhao Jiahong , Lan Jinhui , Li Biao
Published: April 2021

Campus security incidents occur from time to time, which seriously affect the public security. In recent years, the rapid development of artificial intelligence has brought technical support for campus intelligent security. In order to quickly recognize and locate dangerous targets on campus, an improved YOLOv3‐Tiny model is proposed for dangerous target detection. Since the biggest advantage of this model is that it can achieve higher precision with very fewer parameters than YOLOv3‐Tiny, it is one of the Tinier‐YOLO models. In this paper, the dangerous targets include dangerous objects and dangerous actions. The main contributions of this work include the following: firstly, the detection of dangerous objects and dangerous actions is integrated into one model, and the model can achieve higher accuracy with fewer parameters. Secondly, to solve the problem of insufficient YOLOv3‐Tiny target detection, a jump‐join repetitious learning (JRL) structure is proposed, combined with the spatial pyramid pooling (SPP), which serves as the new backbone network of YOLOv3‐Tiny and can accelerate the speed of feature extraction while integrating features of different scales. Finally, the soft‐NMS and DIoU‐NMS algorithm are combined to effectively reduce the missing detection when two targets are too close. Experimental tests on self‐made datasets of dangerous targets show that the average MAP value of the JRL‐YOLO algorithm is 85.03%, which increases by 3.22 percent compared with YOLOv3‐Tiny. On the VOC2007 dataset, the proposed method has a 9.29 percent increase in detection accuracy compared to that using YOLOv3‐Tiny and a 2.38 percent increase compared to that employing YOLOv4‐Tiny, respectively. These results all evidence the great improvement in detection accuracy brought by the proposed method. Moreover, when testing the dataset of dangerous targets, the model size of JRL‐YOLO is 5.84 M, which is about one‐fifth of the size of YOLOv3‐Tiny (33.1 M) and one‐third of the size of YOLOv4‐Tiny (22.4 M), separately.
Colouterine and Jejunouterine Fistula Secondary to Chronic Diverticulitis
Authors: Galanis Ilias , Floros Georgios , Theodoropoulos Christophoros , Metaxa Myriam , Theodoropoulos Panagiotis , Tsintavis Panagiotis , Bartziotas Dimitrios , Giannos Georgios , Stylianidis Georgios , Papadopoulos Georgios
Published: April 2021

Fistulae between the colon or the small intestine and the uterus are extremely rare as the uterus is a thick, muscular organ. Here, we present the case of a 74-year-old female presenting to our surgical department because of fecal vaginal discharge for the past few months, which proved to be caused by a combined colouterine and jejunouterine fistula due to chronic diverticulitis. Total abdominal hysterectomy with bilateral oophorectomy with en bloc resection of part of the jejunum and the sigmoid colon and primary anastomoses were performed. This case represents an unusual type of diverticulitis complication and aims to point out the diagnostic and therapeutic issues of such a rare medical condition.
Speculum Observation and Trajectory Measurement in Gas Extraction Drilling: A Case Study of Changling Coal Mine
Authors: Zhao Enbiao , Li Kangnan , Yang Xin , Deng Nan
Published: April 2021

Coal will still be China’s basic energy for quite a long time. With the increase of mining depth, gas content and pressure also increase. The problems of gas emission and overrun affect the safety and efficient production of coal resource to a certain extent. In this work, the field test of gas drainage borehole peeping and trajectory measurement in coal seam of Changling coal mine are carried out. These coal seams include C5b coal seam, upper adjacent C5a coal seam, C6a coal seams, C6c in lower adjacent strata, and C5b coal seam in high-level borehole. The view of gas drainage borehole peeping and trajectory measurement in the working seam, upper adjacent layer, lower adjacent layer, and high position are obtained. It is found that the hole collapses at the position of about 20 m in both adjacent strata and high-level boreholes, and there are a lot of cracks in the high-level boreholes before 12 m. The deviation distance of high-level borehole is large, and the actual vertical deviation of upper adjacent layer is small. Finally, the strategies to prevent the deviation of drilling construction are put forward. It includes four aspects: ensuring the reliability of drilling equipment, reasonably controlling the drilling length, standardizing the drilling, and reasonably selecting the drilling process parameters.
Analysis of Lateral Displacement Law of Deep Foundation Pit Support in Soft Soil Based on Improved MSD Method
Authors: Lei Gang , Gong Xiaonan
Published: April 2021

Foundation pit envelope and foundation pit excavation solution design is a multidisciplinary problem that could be linked to a series of safety issues in the geotechnical engineering of an actual construction project. Moreover, the construction of large deep foundation pit in soft soil often faces greater risks and challenges as the support structure deforms more easily and unpredictably. In order to improve the deformation prediction of deep foundation pit engineering precision and efficiency and to ensure that the construction of deep foundation pit engineering is safe and efficient, in this article the traditional MSD (mobilizable strength design) theory research and analysis, combined with the Jinan formation characteristics of a tunnel in JInan, and new parameters were introduced to the original MSD method theory: the wall itself within the bending strain energy U and support compression elastic potential energy of V. A new law of conservation of energy is constructed, and finally, an optimized MSD method has been proposed, this method is shown in the article. Finally, the results of foundation pit deformation calculation were compared among the optimized MSD method, finite element calculation method, and field monitoring data analysis method, so as to demonstrate the reliability of the prediction system of optimized MSD method and finite element analysis method. The results show that, by optimizing the MSD calculation method, the horizontal displacement of the retaining wall varies depending on the excavation depth of the foundation pit and the form of internal support with the overall peak value of displacement between 0∼0.2% H (H being the excavation depth); the deformation of retaining wall increases gradually with the increase of excavation depth of the foundation pit, and the peak position of deformation gradually moves down with the excavation of foundation pit. The trend of these changes is consistent with the results of the finite element method and field data analysis method, proving that the optimized MSD method is reliable in predicting the deformation of the foundation pit under specific stratum conditions.
Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks
Authors: Bangash Javed Iqbal , Khan Abdul Waheed , Khan Asfandyar , Khan Atif , Uddin M. Irfan , Hua Qiaozhi
Published: April 2021

The merger of wireless sensor technologies, pervasive computing, and biomedical engineering has resulted in the emergence of wireless body sensor network (WBSN). WBSNs assist human beings in various monitoring applications such as health-care, entertainment, rehabilitation systems, and sports. Life-critical health-care applications of WBSNs consider both reliability and delay as major Quality of Service (QoS) parameters. In addition to the common limitations and challenges of wireless sensor networks (WSNs), WBSNs pose distinct constraints due to the behavior and chemistry of the human body. The biomedical sensor nodes (BMSNs) adopt multihop communication while reporting the heterogeneous natured physiological parameters to the nearby base station also called local coordinator. Routing in WBSNs becomes a challenging job due to the necessary QoS considerations, overheated in-body BMSNs, and high and dynamic path loss. To the best of our knowledge, none of the existing routing protocols integrate the aforementioned issues in their designs. In this research work, a multiconstraint-aware routing mechanism (modular-based) is proposed which considers the QoS parameters, dynamic and high path loss, and the overheated nodes issue. Two types of network frameworks, with and without relay/forwarder nodes, are being used. The data packets containing physiological parameters of the human body are categorized into delay-constrained, reliability-constrained, critical (both delay- and reliability-constrained), and nonconstrained data packets. NS-2 is being used to carry out the simulations of the proposed mechanism. The simulation results reveal that the proposed mechanism has improved the QoS-aware routing for WBSNs by adopting the proposed multiconstraint-aware strategy.
[Retracted] Clinical Efficacy and Safety of “Three‐Dimensional Balanced Manipulation” in the Treatment of Cervical Spondylotic Radiculopathy by Finite Element Analysis
Authors: Cao Shengnan , Chen Yuanzhen , Zhang Feng , Sun Shifei , Wang Congan , Hou Guangjian , Wang Dandan , Sun Guodong , Shi Bin
Published: April 2021

Cervical spondylotic radiculopathy (CSR) is the most commonly encountered cervical spine disorder. Cervical manipulation has been demonstrated as an effective therapy for patients. However, the mechanisms of manipulations have not been elucidated. A total of 120 cervical spondylotic radiculopathy patients were divided into the “three‐dimensional balanced manipulation” treatment group (TBM group) and control group randomly. The control group was treated with traditional massage; the TBM treatment group was treated with “three‐dimensional balanced manipulation” based on traditional massage. The symptoms and clinical efficacy of the patients were compared before and after treatment for one month. A three‐dimensional finite element model was established. The mechanical parameters were imported to simulate TBM, and finite element analysis was performed. The results showed that the total effective rate was significantly higher in the TBM group compared with the control group. The biomechanical analysis showed the vertebral body stress was mainly distributed in the C3/4 spinous processes; the deformation mainly concentrated in the anterior processes of the C3 vertebral body. The intervertebral disc stress in the C3~C7 segment was mainly distributed in the anterior part of the C3/4 intervertebral disc, and the deformation extends to the posterior part of the C3/4 nucleus pulposus. In summary, these data are suggesting that TBM was effective in CSR treatment. The results of the finite element model and biomechanical analysis provide an important foundation for effectively avoiding iatrogenic injuries and improving the effect of TBM in the treatment of CSR patients.
New Results in Controlled Metric Type Spaces
Authors: Abuloha Muhib , Rizk Doaa , Abodayeh Kamaleldin , Mlaiki Nabil , Abdeljawad Thabet
Published: April 2021

In this paper, a new class of functions denoted by

Ψ

ν

is introduced which we use to prove new interesting fixed point results in controlled metric type spaces. Also, we present examples to illustrate our work.
Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE‐TCN Transfer Learning
Authors: Shi Huaitao , Shang Yajun , Zhang Xiaochen , Tang Yinghan
Published: April 2021

In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE‐TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU‐SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings.
Effect of Superficial Scratch Damage on Tension Properties of Carbon/Epoxy Plain Weave Laminates
Authors: Duan Miaomiao , Yue Zhufeng , Song Qianguang
Published: April 2021

The effect of scratch damage on the tension properties of carbon fiber plain weave laminates has been studied in detail using digital image correlation (DIC) and acoustic emission (AE). A range of scratch lengths was machined onto different laminates. The bearing capacity of the laminates was then compared with that of unaltered samples. The strain field distributions near the scratches were measured and analyzed as a function of scratch length with DIC. Initiation and propagation of damage were monitored during the tensile tests using AE. Failure sites and morphologies were observed and analyzed. The results show that superficial scratches have little effect on the strength of plain weave laminates when the scratch length is less than 80% of the specimen width. Scratches affect the distribution of strain near the scratch but not far away from the scratch or at the back face of the sample. Not all samples broke from the scratch site but instead broke from the free edge of the sample or close to the gripping region.
A Computationally Efficient User Model for Effective Content Adaptation Based on Domain‐Wise Learning Style Preferences: A Web‐Based Approach
Authors: Pan Dong , Hussain Anwar , Nazir Shah , Khan Sulaiman
Published: April 2021

In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain‐wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain‐wise learning style preferences for better content adaptation. We have developed a web‐based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style‐based model, and the experimental results show that personalization based on incorporating discipline‐wise learning style variations becomes more effective.