bluuebunny/crossref_metadata_2025 huggingface dataset:
Designing Robust Models for Behaviour Prediction Using Sparse Data from Mobile Sensing
Authors: Kucukozer-Cavdar Seyma , Taskaya-Temizel Tugba , Mehrotra Abhinav , Musolesi Mirco , Tino Peter
Published: July 2021

Understanding in which circumstances office workers take rest breaks is important for delivering effective mobile notifications and make inferences about their daily lifestyle, e.g., whether they are active and/or have a sedentary life. Previous studies designed for office workers show the effectiveness of rest breaks for preventing work-related conditions. In this article, we propose a hybrid personalised model involving a kernel density estimation model and a generalised linear mixed model to model office workers’ available moments for rest breaks during working hours. We adopt the experience-based sampling method through which we collected office workers’ responses regarding their availability through a mobile application with contextual information extracted by means of the mobile phone sensors. The experiment lasted 10 workdays and involved 19 office workers with a total of 528 responses. Our results show that time, location, ringer mode, and activity are effective features for predicting office workers’ availability. Our method can address sparse sample issues for building individual predictive behavioural models based on limited and unbalanced data. In particular, the proposed method can be considered as a potential solution to the “cold-start problem,” i.e., the negative impact of the lack of individual data when a new application is installed.
Falsification of Hybrid Systems Using Adaptive Probabilistic Search
Authors: Ernst Gidon , Sedwards Sean , Zhang Zhenya , Hasuo Ichiro
Published: July 2021

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.
Replication of Computational Results Report for “Doping Tests for Cyber-Physical Systems”
Authors: Grossmann Gerrit
Published: July 2021

The article
Doping Tests for Cyber-Physical Systems
is accompanied by a prototype implementation in Python 2.7. The artifact (i.e., code and observational data) is hosted on a publicly available repository. The article contains comprehensive documentation in the appendix, and running the code is straightforward. The article
Doping Tests for Cyber-Physical Systems
can thus receive the
Artifacts Evaluated—Reusable
badge.
Towards Interconnected Blockchains
Authors: Lohachab Ankur , Garg Saurabh , Kang Byeong , Amin Muhammad Bilal , Lee Junmin , Chen Shiping , Xu Xiwei
Published: July 2021

Unprecedented attention towards blockchain technology is serving as a game-changer in fostering the development of blockchain-enabled distinctive frameworks. However, fragmentation unleashed by its underlying concepts hinders different stakeholders from effectively utilizing blockchain-supported services, resulting in the obstruction of its wide-scale adoption. To explore synergies among the isolated frameworks requires comprehensively studying inter-blockchain communication approaches. These approaches broadly come under the umbrella of Blockchain Interoperability (BI) notion, as it can facilitate a novel paradigm of an integrated blockchain ecosystem that connects state-of-the-art disparate blockchains. Currently, there is a lack of studies that comprehensively review BI, which works as a stumbling block in its development. Therefore, this article aims to articulate potential of BI by reviewing it from diverse perspectives. Beginning with a glance of blockchain architecture fundamentals, this article discusses its associated platforms, taxonomy, and consensus mechanisms. Subsequently, it argues about BI’s requirement by exemplifying its potential opportunities and application areas. Concerning BI, an architecture seems to be a missing link. Hence, this article introduces a layered architecture for the effective development of protocols and methods for interoperable blockchains. Furthermore, this article proposes an in-depth BI research taxonomy and provides an insight into the state-of-the-art projects. Finally, it determines possible open challenges and future research in the domain.
Benchmarking Quantum Computers and the Impact of Quantum Noise
Authors: Resch Salonik , Karpuzcu Ulya R.
Published: July 2021

Benchmarking is how the performance of a computing system is determined. Surprisingly, even for classical computers this is not a straightforward process. One must choose the appropriate benchmark and metrics to extract meaningful results. Different benchmarks test the system in different ways, and each individual metric may or may not be of interest. Choosing the appropriate approach is tricky. The situation is even more open ended for quantum computers, where there is a wider range of hardware, fewer established guidelines, and additional complicating factors. Notably, quantum noise significantly impacts performance and is difficult to model accurately. Here, we discuss benchmarking of quantum computers from a computer architecture perspective and provide numerical simulations highlighting challenges that suggest caution.
Beyond Multimedia Authoring
Authors: Mattos Douglas Paulo De , Muchaluat-Saade Débora C. , Ghinea Gheorghita
Published: July 2021

The mulsemedia (Multiple Sensorial Media (MulSeMedia)) concept has been explored to provide users with new sensations using other senses beyond sight and hearing. The demand for producing such applications has motivated various studies in the mulsemedia authoring phase. To encourage researchers to explore new solutions for enhancing the mulsemedia authoring, this survey article reviews several mulsemedia authoring tools and proposals for representing sensory effects and their characteristics. The article also outlines a set of desirable features for mulsemedia authoring tools. Additionally, a multimedia background is discussed to support the proposed study in the mulsemedia field. Open challenges and future directions regarding the mulsemedia authoring phase are also discussed.
Deep Learning for Medical Anomaly Detection – A Survey
Authors: Fernando Tharindu , Gammulle Harshala , Denman Simon , Sridharan Sridha , Fookes Clinton
Published: July 2021

Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.
Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering
Authors: Banerjee Pratyay , Pal Kuntal Kumar , Devarakonda Murthy , Baral Chitta
Published: July 2021

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e.,B,I,Oonly) compared to multiple entity-classes in traditional NER (i.e.,Bentity1,Bentity2,Ientity1,I,O), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.
Two-dimensional Stroke Gesture Recognition
Authors: Magrofuoco Nathan , Roselli Paolo , Vanderdonckt Jean
Published: July 2021

The expansion of touch-sensitive technologies, ranging from smartwatches to wall screens, triggered a wider use of gesture-based user interfaces and encouraged researchers to invent recognizers that are fast and accurate for end-users while being simple enough for practitioners. Since the pioneering work on two-dimensional (2D) stroke gesture recognition based on feature extraction and classification, numerous approaches and techniques have been introduced to classify uni- and multi-stroke gestures, satisfying various properties of articulation-, rotation-, scale-, and translation-invariance. As the domain abounds in different recognizers, it becomes difficult for the practitioner to choose the right recognizer, depending on the application and for the researcher to understand the state-of-the-art. To address these needs, a targeted literature review identified 16 significant 2D stroke gesture recognizers that were submitted to a descriptive analysis discussing their algorithm, performance, and properties, and a comparative analysis discussing their similarities and differences. Finally, some opportunities for expanding 2D stroke gesture recognition are drawn from these analyses.
A Survey on Session-based Recommender Systems
Authors: Wang Shoujin , Cao Longbing , Wang Yan , Sheng Quan Z. , Orgun Mehmet A. , Lian Defu
Published: July 2021

Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.
ASSETS 2020 UX panel report
Authors: Roper Abi , Mascetti Sergio
Published: July 2021

This contribution describes the organization and the outcomes of the User Experience panel at the ASSETS 2020 virtual conference. The panel was the first one in the history of ASSETS to be conducted as a virtual event, which required a number of changes, both in terms of panellist and attendee involvement. Adapting to this new situation required new solutions that we believe can be useful for other future events - be they virtual, physical or hybrid. We believe the experiences reported also provide helpful insights for reflection on future work in the domain of accessible computing.
The center for research and education on accessible technology and experiences (CREATE)
Authors: Mankoff Jennifer , Wobbrock Jacob O.
Published: July 2021

In an era of rapidly evolving technology and increasing interconnection, full participation in society depends on the successful use of technology. Thus, to ensure equity and participation for people with disabilities, technology must be accessible - we must create and adapt interactive systems to improve access to technology and to the world at large. The University of Washington Center for Research and Education on Accessible Technology and Experiences (CREATE) is dedicated to propelling accessible technology research and education from incremental improvements to paradigm-shifting breakthroughs that enable greater inclusion and participation for people of all abilities.
What happens to misunderstandings of biomedical concepts across a medical curriculum?
Authors: Badenhorst Elmi , Mamede Silvia , Abrahams Amaal , Bugarith Kishor , Cilliers Francois , Gordon Chivaugn , Gunston Geney , Zweigenthal Virginia , Schmidt Henk G.
Published: July 2021

Research on the extent and nature of commonly misunderstood fundamental biomedical concepts across a medical curriculum is scarce. These misunderstandings could point toward robust misconceptions. We examined first whether common misunderstandings persist throughout a medical curriculum, followed by a fine-grained analysis to identify their nature. We designed and administered a 2-tier test to 987 medical students across our curriculum, with 8 questions covering the respiratory and cardiovascular systems, cell division, and homeostatic processes. Proportions of incorrect responses were computed. Four questions where misunderstandings persisted were further qualitatively analyzed. A one-way ANOVA showed the proportion of incorrect responses decreased significantly by students’ academic year [ F(6, 986) = 96.05, P < 0.001]. While novices and end-of -first-year students showed similar proportion of incorrect responses ( P > 0.05), incorrect responses decreased significantly between first years and second years ( P < 0.001). Thereafter, the proportion of incorrect responses remained stable from second to final year ( P > 0.05), with ∼35% of incorrect responses. Five questions showed no decrease of incorrect responses between second and final years, with two questions where final year students performed marginally better than novices. A Chi-square analysis, with Bonferroni post hoc test, showed certain misunderstandings appeared frequently across the curriculum. The qualitative analysis of the open-ended questions yielded 15 categories of common misunderstandings of fundamental biomedical concepts in all years of training. If educators become aware of commonly misunderstood biomedical concepts, preventative measures could be taken to prevent robust misconceptions.
Three-Dimensional Finite Element Analysis of L4-5 Degenerative Lumbar Disc Traction under Different Pushing Heights
Authors: Ding Huaili , Liao Lijun , Yan Peichun , Zhao Xiaolin , Li Min
Published: July 2021

Objective. To study and analyze the changes of intervertebral foramen height and area of the degenerative L4-5 intervertebral disc under different pushing heights by the finite element method. Methods. CT and MRI images of T12-S1 segments were obtained from a healthy volunteer who met the inclusion criteria. A DR machine was used to capture images of the lumbar lateral section before and after simultaneous pushing of the L4 and L5 spinous processes by manipulation called Daogaijinbei, and the measurement showed that the displacement changes of L4 and L5 were both approximately 10 cm, so the pushing height was set at 0–10 cm. A three-dimensional finite element model of the entire normal lumbar spine was established using Mimics 16.0, Geomagic Studio 2014, Hypermesh 13.0, MSC.Patran 2012, and so on. The disc height and nucleus area of the lumbar disc of the normal entire lumbar disc model were adjusted to establish models of the L4-5 disc with mild, moderate, and severe degeneration. Changes of disc height and area of the L4-5 degenerative intervertebral disc under different pushing heights were calculated. Results. The size of the L4-5 intervertebral foramen was analyzed from the height and area of the intervertebral foramen, and the results showed the following: (1) as for the normal lumbar disc and a lumbar of the L4-5 disc with mild and moderate degeneration, the height of the L4-5 intervertebral foramen and its area both increased during pushing between 0 and 8 cm. After the pushing height reached 8 cm, the height and area of the L4-5 intervertebral foramen gradually became stable; (2) as for the L4-5 disc with severe degeneration, during the process of pushing, the height and area of the L4-5 intervertebral foramen increased slightly, but this change was not obvious. Conclusions. After the spinal manipulation, the sizes of the L4-5 intervertebral foramen of the L4-5 disc with mild and moderate degeneration were significantly larger than those before pushing; in contrast, the size of L4-5 intervertebral foramen of the L4-5 disc with severe lumbar degeneration was not significantly changed.
Systematic Review of Visual Motor Integration in Children with Developmental Disabilities
Authors: Carsone Blair , Green Katherine , Torrence William , Henry Bridgett
Published: July 2021

Original research articles regarding visual motor integration skills in children with developmental disabilities and the impact of occupational therapy were identified, appraised, and synthesized. Twenty-four articles were chosen for this review. Themes were noted during the critique of articles. Three themes emerged: “age,” “gender,” and “diagnosis.” Regarding the impact on visual motor integration, there was strong evidence for age, moderate evidence for gender, and strong evidence for diagnosis. Future research investigating visual motor integration in children should control for age and diagnosis.
Integration of Indigenous Knowledge into Adult Learning Material Development and the Facilitation Methodology
Authors: Assefa Yalalem
Published: July 2021

Connecting indigenous knowledge systems and practices with adult education programs has priceless value of promoting and transferring indigenous perspectives from generation to generation. Indeed, education is the surest path to ensuring social continuity when it ought to be based on the real-life experiences of learners and what their immediate environment and social realities entail. This demands the development of relevant adult learning materials and the utilization of participatory facilitation methodologies. Considering this in mind, this study was aimed to explore the integration of indigenous knowledge into adult education practices. In doing so, a case-study design was employed. The study sample was composed of eighteen experts, seven adult education literacy center coordinators, and seventy adult learners. Interview and FGD were considered the major data-gathering tools. Also, thematic analysis was the center of this study’s data analysis. As a result, the finding indicated that even though adult learners have diverse learning experiences, narrow emphasis has been given to the development of learning concepts and objectives in adult learning materials. The application of appropriate facilitation methods through participating experienced learners remains symbolic. To enhance the positive inclusion of indigenous knowledge into adult learning, learning material and its facilitation environment must be conducive and adequately be portraited. Therefore, the objective of this study is to explore the existing practice of indigenous knowledge integration into adult education in North Wollo, from the perspectives of learning material development and utilization of the facilitation methodology.
A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection
Authors: Xie Yalong , Li Aiping , Gao Liqun , Liu Ziniu
Published: July 2021

Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state‐of‐the‐art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.
Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re‐Identification
Authors: Guo Jifeng , Sun Wenbo , Pang Zhiqi , Fei Yuxiao , Chen Yu
Published: July 2021

The current unsupervised domain adaptation person re‐identification (re‐ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re‐ID. At present, the unsupervised domain adaptation person re‐ID method based on pseudolabels has obtained state‐of‐the‐art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re‐ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K‐reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https://github.com/sunburst792/SMCC-method/tree/master.
Novel Network Public Opinion Prediction and Guidance Model Based on “S-Curve”: Taking the Loss of Contact with “Malaysia Airlines”
Authors: Liu Xiangdong , Cao Axiao , Li Chuyang
Published: July 2021

It is of great significance for the government to control the network public opinion in time and maintain social stability to predict the network public opinion in emergency. This paper proposes a novel improvement method to “S-curve” theory in the context of big data and establishes three novel network public opinion prediction models. These models take into account the proliferation trend of initial and follow-up network public opinion over a long period of time when emergencies are formed and the objective environment suddenly changes, based on the information diffusion model conforming to the traditional “S-curve” theory. The novel improvement and establishment allow our model to have more accurate predictions than other scholars’ models that mainly study the first network public opinion in a shorter period of time. And it is more applicable to real social conditions, in line with the public’s cognition of reality, and provides more reference for the government to take preventive and corresponding positive guiding measures in advance. To better establish the model, we obtained the 24-day Weibo data associated with the incident of “Malaysia Airlines” loss of contact from big data for model establishment, public opinion prediction, and comprehensive evaluation. The result innovatively shows that, in addition to the initial public opinion that is worthy of attention, the follow-up public opinion is also noteworthy and proves that our model has more practical value.
A Unified Model of Oil/Water Two-Phase Flow through the Complex Pipeline
Authors: Gao Qingchun , Wang Zhiming , Zeng Quanshu
Published: July 2021

Oil-water two-phase flow through the complex pipeline, consisting of varying pipes and fittings in series or parallel, is commonly encountered in the petroleum industry. However, the majority of the current study is mainly limited to single constant-radius pipe. In this paper, a unified model of oil-water two-phase flow in the complex pipeline is developed based on the combination of pipe serial-parallel theory, flow pattern transformation criterion, two-fluid model, and homogenous model. A case is present to verify the unified model and compare with CFD results. The results show that the proposed unified model can achieve excellent performance in predicting both the flow distributions and pressure drops of oil-water two-phase flow in the complex pipeline. Compared with CFD results for water volumetric fractions ranging from 0% to 100%, the highest absolute percentage error of the proposed model is 14.4% and the average is 9.8%.
Antiproliferative Activities of Methanolic Extract and Fractions of Tetrapleura Tetraptera Fruit
Authors: Aikins Anastasia Rosebud , Birikorang Peggy Afua , Chama Mary , Dotse Eunice , Anning Abigail , Appiah-Opong Regina
Published: July 2021

Most of the current cancer chemotherapeutics are associated with harsh and undesirable side effects, including toxicity and chemoresistance, driving the need for safer and more effective alternatives. In this study, the antiproliferative activities of the methanolic extract of Tetrapleura tetraptera fruits and nine different fractions (C1–C9) from the column chromatographic separation of the extract against leukemia (Jurkat) and human breast cancer (MCF-7) cell lines were investigated using a tetrazolium-based colorimetric assay. Phytochemical screening of the extract and fractions found alkaloids, carbohydrates, flavonoids, glycosides, phenols, saponins, steroids, tannins, and terpenoids in the methanolic extract. Most of the fractions exhibited antiproliferative activity (>100 μg/mL) with the Jurkat cells being more susceptible than the MCF-7 cells. Four of the collected fractions C4, C3, C5, and C2 had good selective indices in decreasing order of activity, in the case of Jurkat cells. Liquid chromatography-mass spectrometry analysis of all samples (except for C4 and C9) revealed that C1, C2, C3, and C5 each had a single component. More importantly, fractions C2, C3, and C5, which were selective to Jurkat cells, also had the same retention time of 1.846 min. Fractions C6 and C8 had two components, with C7 having four components. This study serves as a basis for further work to isolate and characterize potential anticancer agents from the fractions of extracts of T. tetraptera fruits.
Analysis of Energy Dissipation Characteristics of Damaged Sandstone under Impact Load
Authors: Wang Feng , Wang Haibo , Xu Ying , Cheng Bing , Wang Qianqian
Published: July 2021

Before rock burst, coal, and gas outburst dynamic load, rock mass in geotechnical engineering has been an indifferent degree of damage. The dissipation energy of rock mass under dynamic load reflects the difficulty of rock breaking. In view of the energy dissipation of damaged rock mass under dynamic load, the cyclic loading and unloading test is carried out to make sandstone in different damage states, and the damage degree of sandstone is characterized by the change of longitudinal wave velocity before and after cyclic loading and unloading. Then, the rock with different damage degrees is tested by adopting the split Hopkinson pressure bar (SHPB). Finally, the energy dissipation characteristics of damaged rock under impact load are analyzed. The results show that the damage factor of sandstone increases with the increase of the upper limit of stress after cyclic static loading. The dynamic strength and peak strain of damaged sandstone increase with the increase of impact pressure and decrease with the increase of damage degree. With the increase of damage degree of sandstone, the reflection energy and dissipation energy of sandstone increase, while the transmission energy decreases.
Computed Tomography Angiography under Deep Learning in the Treatment of Atherosclerosis with Rapamycin
Authors: Ji Fuguang , Zhou Shuai , Bi Zhangshuan
Published: July 2021

The clinical characteristics and vascular computed tomography (CT) imaging characteristics of patients were explored so as to assist clinicians in diagnosing patients with atherosclerosis. 316 patients with atherosclerosis who were hospitalized for emergency treatment were treated with rapamycin (RAPA) in the hospital. A group of manually delineated left ventricular myocardia (LVM) on the patient’s coronary computed tomography angiography (CCTA) were selected as the region of interest for imaging features extracted. The CCTA images of 80% of patients were randomly selected for training, and those of 20% of patients were used for verification. The correlation matrix method was used to remove redundant image omics features under different correlation thresholds. In the validation set, CCTA diagnostic parameters were about 40 times higher than the manually segmented data. The average dice similarity coefficient was 91.6%. The proposed method also produced a very small centroid distance (mean 1.058 mm, standard deviation 1.245 mm) and volume difference (mean 1.640), with a segmentation time of about 1.45 ± 0.51 s, compared to about 744.8 ± 117.49 s for physician manual segmentation. Therefore, the deep learning model effectively segmented the atherosclerotic lesion area, measured and assisted the diagnosis of future atherosclerosis clinical cases, improved medical efficiency, and accurately identified the patient’s lesion area. It had great application potential in helping diagnosis and curative effect analysis of atherosclerosis.
Convergence Analysis of Schwarz Waveform Relaxation for Nonlocal Diffusion Problems
Authors: Li Ke , Guo Dali , Zhao Yunxiang
Published: July 2021

Diffusion equations with Riemann–Liouville fractional derivatives are Volterra integro-partial differential equations with weakly singular kernels and present fundamental challenges for numerical computation. In this paper, we make a convergence analysis of the Schwarz waveform relaxation (SWR) algorithms with Robin transmission conditions (TCs) for these problems. We focus on deriving good choice of the parameter involved in the Robin TCs, at the continuous and fully discretized level. Particularly, at the space-time continuous level, we show that the derived Robin parameter is much better than the one predicted by the well-understood equioscillation principle. At the fully discretized level, the problem of determining a good Robin parameter is studied in the convolution quadrature framework, which permits us to precisely capture the effects of different temporal discretization methods on the convergence rate of the SWR algorithms. The results obtained in this paper will be preliminary preparations for our further study of the SWR algorithms for integro-partial differential equations.
Targeting the EZH2-PPAR Axis Is a Potential Therapeutic Pathway for Pancreatic Cancer
Authors: Hu Jilong , Zheng Zhinan , Lei Jia , Cao Yuxin , Li Qiyun , Zheng Zhi , Chen Chuanjun
Published: July 2021

Enhancer of zeste homolog 2 (EZH2) is abnormally highly expressed in pancreatic cancer (PC). However, it is not ideal to treat PC by inhibiting EZH2. This study reported that the combined use of pan-peroxisome proliferator-activated receptor (PPAR) agonist could significantly improve the anti-PC effect of EZH2 inhibitor. In vitro, PC cell lines PANC-1 and AsPC-1 were cultured, and MTT and flow cytometry were performed to observe the effects of pan-PPAR agonist bezafibrate and EZH2 selective inhibitor GSK126 on cell viability and apoptosis. In vivo, CDXs of PANC-1 and AsPC-1 were established to observe the effects of bezafibrate and GSK126 on bearing tumors. Western blotting was performed to detect the protein expressions of H3K27me3, β-catenin, p-β-catenin, cyclin D1, c-Myc, and cleaved caspase 3 in vitro and in vivo. The results showed that bezafibrate significantly improved the effects of GSK126 on proliferation inhibition and apoptosis promotion in vitro and the growth suppression of CDX tumors in vivo. It also significantly enhanced the effects of GSK126 on upregulating the expression level of p-β-catenin and that of cleaved caspase 3 in vitro and in vivo. In parallel, downregulation of the expression levels of H3K27me3, β-catenin, cyclin D1, and c-Myc was also observed in vitro or in vivo. These results suggest that the combination of bezafibrate and GSK126 has synergistic effects on PC, and the molecular mechanism may be related to the enhanced inhibition of the Wnt/β-catenin signaling pathway. We believe that targeting the EZH2-PPAR axis is a potential therapeutic pathway for PC.
Potential Novel Therapies for Neurodevelopmental Diseases Targeting Oxidative Stress
Authors: Curpan Alexandrina S. , Luca Alina-Costina , Ciobica Alin
Published: July 2021

Neurodevelopmental disorders are a category of diseases that is not yet fully understood. Due to their common traits and pathways, often it is difficult to differentiate between them based on their symptoms only. A series of hypotheses are trying to define their etiology, such as neuroinflammation, neurodegeneration, and immunology, but none have managed to explain their multifactorial manifestation. One feature that may link all theories is that of oxidative stress, with a redox imbalance as well as several other markers of oxidative damage (on lipids, proteins, and nucleic acids) being observed in both postmortem samples of the brain of patients with schizophrenia and autism spectrum disorders. However, the implication of oxidative stress in pathology is still distrustfully looked upon. For this purpose, in the current paper, we were interested in reviewing the implications of oxidative stress in these disorders as well as the impact of N‐acetylcysteine on the oxidative status with a focus on the glutathione level and N‐methyl‐D‐aspartate receptor. We were also interested in finding papers targeting the use of antioxidant properties of different plant extracts.
Phenolic Compound Analysis and Pharmacological Screening of Vitex agnus-castus Functional Parts
Authors: Berrani Assia , Marmouzi Ilias , Bouyahya Abdelhakim , Kharbach Mourad , El Hamdani Maha , El Jemli Meryem , Lrhorfi Aicha , Zouarhi Meryem , Faouzi My El Abbes , Bengueddour Rachid
Published: July 2021

Vitex agnus-castus is a medicinal plant of the Verbenaceae family, widely used in traditional medicine. This study is aimed at investigating the functional variability of phenolic compounds in different parts (leaves, stems, flowers, roots, and seeds) of Vitex agnus-castus methanolic extracts and at assessing their in vitro antidiabetic, antioxidant, and antibacterial activities. The results of HPLC‐DAD‐QTOF‐MS indicated the presence of 25 phenolic compounds with a remarkable variability between plant parts; high levels were registered in chlorogenic, vanillic, 3,4‐dihydroxybenzoic, and 3‐hydroxybenzoic acids; hesperidin; and luteolin. V. agnus castus fruits and stems presented higher antioxidant activities. The extracts inhibited the growth of five pathogenic bacteria with MIC values documented between 7.81 and 31.25 mg/mL. In vitro antihyperglycemic effect revealed higher effect in flowers (2921.84 μg/mL) and seeds (2992.75 μg/mL) against α‐glucosidase and of leaves (2156.80 μg/mL) and roots (2357.30 μg/mL) against α‐amylase. The findings of this showed that V. agnus castus is a promising source for antidiabetic bioactive compounds. However, further investigations regarding the evaluation of in vivo antidiabetic effects of these compounds are needed.
Establishing an AI Model on Data Sensing and Prediction for Smart Home Environment Control Based on LabVIEW
Authors: Yao Kai-Chao , Huang Wei-Tzer , Wu Cheng-Chun , Chen Teng-Yu
Published: July 2021

In this study, the authors aimed to realize a smart home using an AI model that can be integrated with the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) application to realize environment control. The collected input data were outdoor temperature, indoor temperature, humidity, illumination, and indoor person count. The output control decisions included control of air conditioners, dehumidifiers, power curtains, and lights. An artificial neural network was utilized to process the input data for machine learning for the objective of achieving a comfortable environment. In addition, the control decision predictions made by this AI model were analyzed for model loss and model accuracy. This study implemented the model. Specifically, LabVIEW was used to design the sensing component, data display, and control interface, and Python was used to establish the intelligent model. Moreover, by using the web publishing tool built into LabVIEW, remote sensing and control were fulfilled in this implementation.
Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images
Authors: Pan Yanyan , Zhang Huiping , Yang Jinsuo , Guo Jing , Yang Zhiguo , Wang Jianbing , Song Ge
Published: July 2021

This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network (Conv.Net) in the diagnosis of strokes. Specifically, four automatic segmentation algorithms were proposed to segment multimodal MRI images of stroke patients. The segmentation effects were evaluated factoring into DICE, accuracy, sensitivity, and segmentation distance coefficient. It was found that although two-dimensional (2D) full convolutional neural network-based segmentation algorithm can locate and segment the lesion, its accuracy was low; the three-dimensional one exhibited higher accuracy, with various objective indicators improved, and the segmentation accuracy of the training set and the test set was 0.93 and 0.79, respectively, meeting the needs of automatic diagnosis. The asymmetric 3D residual U-Net network had good convergence and high segmentation accuracy, and the 3D deep residual network proposed on its basis had good segmentation coefficients, which can not only ensure segmentation accuracy but also avoid network degradation problems. In conclusion, the Conv.Net model can accurately segment the foci of patients with ischemic stroke and is suggested in clinic.
Numerical Investigation of the Time‐Fractional Whitham–Broer–Kaup Equation Involving without Singular Kernel Operators
Authors: Nonlaopon Kamsing , Naeem Muhammad , Zidan Ahmed M. , Shah Rasool , Alsanad Ahmed , Gumaei Abdu
Published: July 2021

This paper aims to implement an analytical method, known as the Laplace homotopy perturbation transform technique, for the result of fractional‐order Whitham–Broer–Kaup equations. The technique is a mixture of the Laplace transformation and homotopy perturbation technique. Fractional derivatives with Mittag‐Leffler and exponential laws in sense of Caputo are considered. Moreover, this paper aims to show the Whitham–Broer–Kaup equations with both derivatives to see their difference in a real‐world problem. The efficiency of both operators is confirmed by the outcome of the actual results of the Whitham–Broer–Kaup equations. Some problems have been presented to compare the solutions achieved with both fractional‐order derivatives.
Numerical Steepest Descent Method for Hankel Type of Hypersingular Oscillatory Integrals in Electromagnetic Scattering Problems
Authors: Wu Qinghua , Sun Mengjun
Published: July 2021

We present a fast and accurate numerical scheme for approximating hypersingular integrals with highly oscillatory Hankel kernels. The main idea is to first change the integration path by Cauchy’s theorem, transform the original integral into an integral on

a
,
+
∞

, and then use the generalized Gauss Laguerre integral formula to calculate the corresponding integral. This method has the advantages of high-efficiency, fast convergence speed. Numerical examples show the effect of this method.
SEMA4D Knockdown Attenuates β‐Catenin‐Dependent Tumor Progression in Colorectal Cancer
Authors: Rezaeepoor Mahsa , Rashidi Golnaz , Pourjafar Mona , Mohammadi Chiman , Solgi Ghasem , Najafi Rezvan
Published: July 2021

Semaphorin 4D (SEMA4D), a protein originally demonstrated to regulate the immune system and axonal growth cone collapse in the developing central nervous system, is overexpressed in various human malignancies, including colorectal cancer (CRC). This investigation was undertaken to examine the effects of SEMA4D silencing on the biological properties of the CRC cell line. SW48 cells were transfected with a siRNA‐targeting SEMA4D. The mRNA expression of underlying pro‐ and antiapoptotic proteins including Bax, Bcl‐2, P53, and caspase‐3, cancer stem cell (CSC) markers, epithelial‐mesenchymal transition (EMT) markers, MMP‐2, and MMP‐9 was examined using qRT‐PCR. Further, the protein expression of E‐cadherin and β‐catenin was confirmed by Western blot. SW48 cell migration and MMP activity were detected using scratch and zymography analysis, respectively. Finally, the apoptosis rate was assessed via the flowcytometry test. SEMA4D knock‐down was associated with a considerable suppression of in vitro cell viability, EMT‐related genes, CSC markers, β‐catenin signaling pathway, sphere‐forming, cell migration, and MMP‐2 activity as well as induction of apoptosis. This study identifies the inhibitory effects of SEMA4D gene silencing on tumor progression. Thereby, this might conclude a possible alternative to cancer therapy by targeting several prominent pathways involved in cancer through SEMA4D suppression.
GC‐MS‐Based Serum Metabolomic Investigations on the Ameliorative Effects of Polysaccharide from Turpiniae folium in Hyperlipidemia Rats
Authors: Yang Xiao-lian , Li Li , Zhang Tao-fu , Deng Jing , Lin Xiu-lian , Li Ya-mei , Xia Bo-hou , Lin Li-mei
Published: July 2021

Hyperlipidemia, a typical metabolic disorder syndrome, can cause various cardiovascular diseases. The polysaccharides were found to have enormous potential in the therapy of hyperlipidemia. This study was aimed at evaluating the ameliorative effects of polysaccharide from Turpiniae folium (TFP) in rats with hyperlipidemia. A serum metabolomic method based on gas chromatography‐mass spectrometry (GC‐MS) was used to explore the detailed mechanism of TFP in rats with hyperlipidemia. The oxidative stress indicators, biochemical indexes, and inflammatory factors in serum and histopathological changes in the liver were also evaluated after 10‐week oral administration of TFP in rats with high‐fat diet‐induced hyperlipidemia. TFP significantly relieved oxidative stress, inflammation, and liver histopathology and reduced blood lipid levels. Multivariate statistical approaches such as principal component analysis and orthogonal projection to latent structure square‐discriminant analysis revealed clear separations of metabolic profiles among the control, HFD, and HFD+TFP groups, indicating a moderating effect of TFP on the metabolic disorders in rats with hyperlipidemia. Seven metabolites in serum, involved in glycine, serine, and threonine metabolism and aminoacyl‐tRNA biosynthesis, were selected as potential biomarkers in rats with hyperlipidemia and regulated by TFP administration. It was concluded that TFP had remarkable potential for treating hyperlipidemia. These findings provided evidence for further understanding of the mechanism of action of TFP on hyperlipidemia.
Characterizations of Bacterial Vaginosis among HIV-Positive and HIV-Negative Women in Rural Eastern Cape Province, South Africa
Authors: Apalata Teke , Nojaholo Sandisiwe , Seipone Ikanyeng D. , Nxasana Ntombizodumo
Published: July 2021

Bacterial vaginosis (BV) is extremely common among the African population and is associated with the transmission and acquisition of human immunodeficiency virus (HIV) infection. The objective of this study was to determine the prevalence and characteristics of BV among HIV-infected and -uninfected women in rural Eastern Cape province of South Africa. A descriptive cross-sectional study was conducted between September 2017 and March 2018 on women aged 18 years and above (n = 100), attending Nelson Mandela Academic Hospital and Ngangelizwe Community Health Centre with signs and symptoms suggestive of vaginal infection. High vaginal swabs were collected, and BV was diagnosed using Nugent’s score. The prevalence rate of BV was 70% irrespective of HIV status. Of the 61 HIV-infected patients, 49 (80.3%) and 12 (19.7%) were BV positive and BV negative, respectively; whilst of the 39 HIV-uninfected women, 21 (53.8%) and 18 (46.2%) were BV positive and BV negative, respectively (OR = 3.5; CI: 1.4–8.5;

p
=
0.005

). Women aged above 35 years were highly likely to develop BV (

p
=
0.049

). The presence of Mobiluncus species (>25 per high microscopic field) was significantly associated with BV among HIV-infected patients (

p
=
0.030

). A recent history of antibiotic use (≤3 months) was significantly associated with BV among HIV-negative patients (

p
=
0.044

). This study shows that BV is more prevalent among HIV-positive women than their HIV-negative counterparts, and its occurrence is higher among those aged above 35 years. The predominance of Mobiluncus species in the vagina microbiota of HIV-infected women might play a significant role in the development of BV. These findings suggest that the treatment of BV could restore normal flora and reduce susceptibility to and transmission of HIV.
Long-Term Outcomes of Thermal Ablation for Benign Thyroid Nodules: The Issue of Regrowth
Authors: Sim Jung Suk , Baek Jung Hwan
Published: July 2021

Thermal ablation (TA) for benign thyroid nodules (BTNs) is widely accepted as an effective and safe alternative to surgery. However, studies on the long-term outcomes of TA have reported problems with nodule regrowth and symptom recurrence, which have raised the need for adequate control of regrowth. Therefore, a more complete TA with a longer-lasting treatment effect may be required. In this study, we review and discuss long-term outcomes and regrowth of BTNs following TA and evaluate factors affecting the long-term outcomes. We also discuss the management of regrowth based on long-term outcomes.