bluuebunny/crossref_metadata_2025 huggingface dataset:
Grid-based Genetic Operators for Graphical Layout Generation
Authors: Shiripour Morteza , Dayama Niraj Ramesh , Oulasvirta Antti
Published: May 2021

Graphical user interfaces (GUIs) have gained primacy among the means of interacting with computing systems, thanks to the way they leverage human perceptual and motor capabilities. However, the design of GUIs has mostly been a manual activity. To design a GUI, the designer must select its visual, spatial, textual, and interaction properties such that the combination strikes a balance among the relevant human factors. While emerging computational-design techniques have addressed some problems related to grid layouts, no general approach has been proposed that can also produce good and complete results covering color-related decisions and other nonlinear design objectives. Evolutionary algorithms are promising and demonstrate good handling of similar problems in other conditions, genetic operators, depending on how they are designed. But even these approaches struggle with elements' overlap and hence produce too many infeasible candidate solutions. This paper presents a new approach based on grid-based genetic operators demonstrated in a non-dominated sorting genetic algorithm (NSGA-III) setting. The operators use grid lines for element positions in a novel manner to satisfy overlap-related constraints and intrinsically improve the alignment of elements. This approach can be used for crossovers and mutations. Its core benefit is that all the solutions generated satisfy the no-overlap requirement and represent well-formed layouts. The new operators permit using genetic algorithms for increasingly realistic task instances, responding to more design objectives than could be considered before. Specifically, we address grid quality, alignment, selection time, clutter minimization, saliency control, color harmony, and grouping of elements.
Guided Exploration
Authors: Weninger Markus , Gander Elias , Mössenböck Hanspeter
Published: May 2021

Many monitoring tools that help developers in analyzing the run-time behavior of their applications share a common shortcoming: they require their users to have a fair amount of experience in monitoring applications to understand the used terminology and the available analysis features. Consequently, novice users who lack this knowledge often struggle to use these tools efficiently. In this paper, we introduce the guided exploration (GE) method that aims to make interactive monitoring tools easier to use and learn. In general, tools that implement GE should provide four support operations on each analysis step: they should automatically (1) detect and (2) highlight the most important information on the screen, (3) explain why it is important, and (4) suggest which next steps are appropriate. This way, tools guide users through their analysis processes, helping them to explore the root cause of a problem. At the same time, users learn the capabilities of the tool and how to use them efficiently. We show how GE can be implemented in new monitoring tools as well as how it can be integrated into existing ones. To demonstrate GE's feasibility and usefulness, we present how we extended the memory monitoring tool AntTracks to provided guided exploration support during memory leak analysis and memory churn analysis. We use these guidances in two user scenarios to inspect and improve the memory behavior of the monitored applications. We hope that our contribution will help usability researchers and developers in making monitoring tools more novice-friendly by improving their usability and learnability.
M[eye]cro
Authors: Wambecke Jérémy , Goguey Alix , Nigay Laurence , Dargent Lauren , Hauret Daniel , Lafon Stéphanie , de Visme Jean-Samuel Louis
Published: May 2021

We present M[eye]cro an interaction technique to select on-screen objects and navigate menus through the synergistic use of eye-gaze and thumb-to-finger microgestures. Thumb-to-finger microgestures are gestures performed with the thumb of a hand onto the fingers of the same hand. The active body of research on microgestures highlights expected properties including speed, availability and eye-free interaction. Such properties make microgestures a good candidate for multitasking. However, while praised, the state-of-the-art hypothesis stating that microgestures could be beneficial for multitasking has never been quantitatively verified. We study and compare M[eye]cro to a baseline, i.e., a technique based on physical controllers, in a cockpit-based context. This context allows us to design a controlled experiment involving multitasking with low- and high-priority tasks in parallel. Our results show that performances of the two techniques are similar when participants only perform the selection task. However, M[eye]cro tends to yield better time performance when participants additionally need to treat high-priority tasks in parallel. Results also show that M[eye]cro induces less fatigue and is mostly preferred.
Molecular HCI
Authors: Pruszko Laura , Coutrix Céline , Laurillau Yann , Piranda Benoît , Bourgeois Julien
Published: May 2021

Shape-changing User Interfaces attract growing interest in Human-Computer Interaction. Modular robotics offer a great opportunity for their implementation. However, the current theoretical and technical advances of modular robotics are fragmented and little centered on the user. To unify existing work and center future research on the user, we perform a systematic literature review enabling us to build a unifying space for the design of modular shape-changing user interfaces.Our aim is to bridge the gap between HCI and robotics. We relate properties of different domains and identify inconsistencies to structure the design space. Towards this aim, we conduct a thorough cross-disciplinary survey to propose: 1) a set of design properties at the scale of the interface (macro-scale) and at the scale of the modules (micro-scale) and 2) the impact of these properties on each other. This paper can be used to describe and compare existing modular shape-changing UIs and generate new design ideas by building upon knowledge from robotics and HCI.
Odin's Helmet
Authors: Hoppe Matthias , Oskina Daria , Schmidt Albrecht , Kosch Thomas
Published: May 2021

Virtual Reality (VR) experiences have massively improved in the mediation of feedback. However, the simulation of forces is still limited. This paper presents Odin's Helmet, a head-worn device to simulate g-forces that act on the human head in real-life situations. Odin's Helmet uses four head-mounted propellers as actuators to simulate g-forces through pushing and pulling the user's head while being immersed in VR. Odin's Helmet's goal is to increase presence and manipulate the user's perception of the otolith organ in the vestibular system. The user's perception will be tricked to experience a sensation of self-movement in VR. A technical evaluation shows Odin's Helmet's applicability to apply perceivable g-forces to the user's head. We conclude with future use cases of Odin's Helmet, such as redirected walking by controlling the user's head orientation, attention guidance, and wind simulations through Odin's Helmet.
Task Modelling for Interactive System Design
Authors: Bowen Judy , Dittmar Anke , Weyers Benjamin
Published: May 2021

Task models have been used for decades in interactive system design and there are several mature modelling approaches with corresponding tool support. However, in our own work, we have also experienced their limitations, especially in situations where task models are partial ancillary models and not primary artifacts. This was one of the motivations for this paper, which presents a systematic examination of literature to better understand the current place of task models in the continual evolution of user-centred software development practices. While overview work in this domain typically focuses on the analysis of representative task modelling notations and/or tools and relies on foundation papers, we apply a mixed top-down and bottom-up approach to identify relevant themes and trends in the use of task models over the last twenty-years. The paper identifies and discusses dominant patterns of use as well as gaps. It provides a comprehensive framing of both past and present trends in task modelling and supports those who want to incorporate task modelling in their own work. From this we identify areas of research that should receive greater attention in order to address future considerations.
Introduction to The Special Section on Bias and Fairness in AI
Authors: Calders Toon , Ntoutsi Eirini , Pechenizkiy Mykola , Rosenhahn Bodo , Ruggieri Salvatore
Published: May 2021

Fairness in Artificial Intelligence rightfully receives a lot of attention these days. Many life-impacting decisions are being partially automated, including health-care resource planning decisions, insurance and credit risk predictions, recidivism predictions, etc. Much of work appearing on this topic within the Data Mining, Machine Learning and Artificial Intelligence community is focused on technological aspects. Nevertheless, fairness is much wider than this as it lies at the intersection of philosophy, ethics, legislation, and practical perspectives. Therefore, to fill this gap and bring together scholars of these disciplines working on fairness, the first workshop on Bias and Fairness in AI was held online on September 18, 2020 at the ECML-PKDD 2020 conference. This special section includes six articles presenting different perspectives on bias and fairness from different angles.
On the Applicability of Machine Learning Fairness Notions
Authors: Makhlouf Karima , Zhioua Sami , Palamidessi Catuscia
Published: May 2021

Machine Learning (ML) based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that ML predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policy makers to navigate the relatively large catalogue of ML fairness notions.
Ethical Adversaries
Authors: Delobelle Pieter , Temple Paul , Perrouin Gilles , Frénay Benoit , Heymans Patrick , Berendt Bettina
Published: May 2021

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature - including COMPAS - where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We investigated the trade-offs between these targets in terms of model hyperparameters and also illustrated our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.
Blind Spots in AI
Authors: van Leeuwen Cora , Smets Annelien , Jacobs An , Ballon Pieter
Published: May 2021

Decisions support systems (DSS) are used more and more to offer right information at the right time. Serendipity has been pro- posed to ensure that the experience is broad and engaging. However, only designing for serendipity might not be enough to avoid historical discrimination affecting your DSS. For this reason we argue to include equity when designing for serendipity.
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
Authors: Draws Tim , Tintarev Nava , Gadiraju Ujwal
Published: May 2021

The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewpoint diversity is not trivial to assess. In this paper, we use existing and novel ranking fairness metrics to evaluate viewpoint diversity in search result rankings. We conduct a controlled simulation study that shows how ranking fairness metrics can be used for viewpoint diversity, how their outcome should be interpreted, and which metric is most suitable depending on the situation. This paper lays out important groundwork for future research to measure and assess viewpoint diversity in real search result rankings.
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
Authors: Yang Fan , Liu Ninghao , Du Mengnan , Hu Xia
Published: May 2021

With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios. Current interpretation techniques mainly focus on the feature attribution perspective, which are limited in indicating why and how particular explanations are related to the prediction. To this end, an intriguing class of explanations, named counterfactuals, has been developed to further explore the "what-if" circumstances for interpretation, and enables the reasoning capability on black-box models. However, generating counterfactuals for raw data instances (i.e., text and image) is still in the early stage due to its challenges on high data dimensionality and unsemantic raw features. In this paper, we design a framework to generate counterfactuals specifically for raw data instances with the proposed Attribute-Informed Perturbation (AIP). By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Instead of directly modifying instances in the data space, we iteratively optimize the constructed attributeinformed latent space, where features are more robust and semantic. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework, and show the superiority over other alternatives. Besides, we also introduce some practical applications based on our framework, indicating its potential beyond the model interpretability aspect.
Adversarial Attacks and Defenses
Authors: Liu Ninghao , Du Mengnan , Guo Ruocheng , Liu Huan , Hu Xia
Published: May 2021

Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. Techniques to protect models against adversarial input are called adversarial defense methods. Although many approaches have been proposed to study adversarial attacks and defenses in different scenarios, an intriguing and crucial challenge remains that how to really understand model vulnerability? Inspired by the saying that "if you know yourself and your enemy, you need not fear the battles", we may tackle the challenge above after interpreting machine learning models to open the black-boxes. The goal of model interpretation, or interpretable machine learning, is to extract human-understandable terms for the working mechanism of models. Recently, some approaches start incorporating interpretation into the exploration of adversarial attacks and defenses. Meanwhile, we also observe that many existing methods of adversarial attacks and defenses, although not explicitly claimed, can be understood from the perspective of interpretation. In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation. We categorize interpretation into two types, feature-level interpretation, and model-level interpretation. For each type of interpretation, we elaborate on how it could be used for adversarial attacks and defenses. We then briefly illustrate additional correlations between interpretation and adversaries. Finally, we discuss the challenges and future directions for tackling adversary issues with interpretation.
A Bus Signal Priority Model at Oversaturated Intersection under Stochastic Demand
Authors: Hao Bin-bin , Lv Bin , Chen Qixiang
Published: May 2021

This paper focuses on the optimization of bus signal priority with the consideration of the stochastic traffic demand. Based on a situation of the variability of traffic composition, the phase clearance reliability (PCR) value of each phase and traffic composition ratio is introduced to reflect the traffic condition at intersection. Then, a bus signal priority optimization model is proposed with the purpose of the maximum of the total vehicular departure as the optimization goal. In order to obtain the optimal solution, an improved algorithm is designed by introducing the PCR value search strategy. Finally, two cases’ study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the model can not only clear the queue under the condition of continuous dynamic traffic flow but also reduce the vehicle queuing and passenger delay.
Compact Ultrawide Band Metamaterial-Inspired Split Ring Resonator Structure Loaded Band Notched Antenna
Authors: Jairath Kapil , Singh Navdeep , Jagota Vishal , Shabaz Mohammad
Published: May 2021

A novel compact size ultrawide band planar antenna with band notched characteristics is present. The band rejection characteristic is achieved by loading a pair of metamaterial inspired rectangular split ring resonator (SRR) near the feed line and by etching the SRR slots on a radiating patch. The simulated and measured results reveal that the proposed antenna exhibits the impedance bandwidth over the ultrawide band (UWB) frequency range from 3.1 to 14 GHz with the voltage standing wave ratio less than 2 except for band stop bands at 3.29 to 3.7 GHz (WiMAX band), 3.7 to 4.10 GHz (C-band), 5.1 to 5.9 GHz (WLAN band), and 7.06 to 7.76 GHz (downlink X-band satellite communication), respectively. The proposed antenna fabricated on low-cost FR-4 substrate has compact size of 24 × 20 × 1.6 mm3. The simulation results are compared with measured results and demonstrate good agreement with stable gain over pass bands. The proposed antenna also exhibits dipole-like radiation pattern in E-plane and omni-directional pattern in H-plane. These results led to conclusion that the presented antenna is a suitable candidate for ultrawide band UWB applications with desired band notch characteristics.
Study on the Intentional Choice Mechanism of Course Selection Based on Swarm Intelligence Algorithm
Authors: Zhang Dawei , Wang Daling
Published: May 2021

With the passage of time and recent advances in science and information technology, the development in the area of course selection based on some defined criteria has made the choice of the mechanism easy and effective. In this paper, the approach is based on an innovative perspective, and swarm intelligence was introduced for the intentional choice mechanism of course selection. The study has considered the course selection in English as an example. Swarm intelligence algorithm and integrative course selection were combined with the recommendation algorithm and the intent of course selection in English course to discuss the relevant decision mechanism. Firstly, the comprehensive selection intentional recommendation algorithm and PSO algorithm were introduced, and the algorithm was initialized. Secondly, the operation process was described in detail, and the application process was analyzed. Then, it was introduced into the English course elective process. Finally, the experimental results of the study came with the conclusion through the test that the PSO algorithm has a higher degree of accuracy and can better judge individual behaviors, which contributes to the establishment of choice-choice mechanism. The effectiveness of the study was demonstrated through experiments.
Distribution Characteristics and Formation Mechanism of Surface Crack Induced by Extrathick Near Horizontal Seam Mining: An Example from the Datong Coal Field, China
Authors: Yan Yueguan , Yan Weitao , Dai Huayang , Guo Junting
Published: May 2021

Under the mining condition of extrathick coal seam, the overburden moves violently, the surface is destroyed seriously, and the discontinuous deformation such as cracks is fully developed. Taking Datong mining area as an example, this paper studies and analyzes the development characteristics and generation mechanism of surface cracks in loose layer covered area and bedrock exposed area by means of field measurement and similar model experiment. In the bedrock exposed area, there is no main crack. The width, length, number, and step drop of surface cracks are relatively small. There are many cracks with the same scale and distributed evenly and parallel. However, in the loose covered area, the main cracks are mainly located above the open‐off cut and the stoppage line side that is deviated from the working face. There are many secondary cracks around the main crack, with a large crack density and a few cracks in the outer edge of the main crack. Through research, it is found that three mining faces belong to the typical three‐zone mining mode and the main fracture is consistent with the O‐ring fracture distribution theory. This research’s results can provide theoretical and technical support for the follow‐up land remediation and ecological restoration.
Natural Products with Inhibitory Activity against Human Immunodeficiency Virus Type 1
Authors: Serna-Arbeláez Maria S. , Florez-Sampedro Laura , Orozco Lina P. , Ramírez Katherin , Galeano Elkin , Zapata Wildeman
Published: May 2021

Infections caused by human immunodeficiency virus (HIV) are considered one of the main public health problems worldwide. Antiretroviral therapy (ART) is the current modality of treatment for HIV-1 infection. It comprises the combined use of several drugs and can decrease the viral load and increase the CD4+ T cell count in patients with HIV-1 infection, thereby proving to be an effective modality. This therapy significantly decreases the rate of morbidity and mortality owing to acquired immunodeficiency syndrome (AIDS) and prolongs and improves the quality of life of infected patients. However, nonadherence to ART may increase viral resistance to antiretroviral drugs and transmission of drug-resistant strains of HIV. Therefore, it is necessary to continue research for compounds with anti-HIV-1 activity, exhibiting a potential for the development of an alternative or complementary therapy to ART with low cost and fewer side effects. Natural products and their derivatives represent an excellent option owing to their therapeutic potential against HIV. Currently, the derivatives of natural products available as anti-HIV-1 agents include zidovudine, an arabinonucleoside derivative of the Caribbean marine sponge (Tectitethya crypta), which inhibits the reverse transcriptase of the virus. This was the first antiviral agent approved for treatment of HIV infection. Additionally, bevirimat (isolated from Syzygium claviflorum) and calanolide A (isolated from Calophyllum sp.) are inhibitors of viral maturation and reverse transcription process, respectively. In the present review, we aimed to describe the wide repertoire of natural compounds exhibiting anti-HIV-1 activity that can be considered for designing new therapeutic strategies to curb the HIV pandemic.
Energy-Efficient Routing Protocol Based on Zone for Heterogeneous Wireless Sensor Networks
Authors: Yanfei Jia , Guangda Chen , Liquan Zhao
Published: May 2021

In heterogeneous wireless sensor networks, sensor nodes are randomly distributed in some regions. In some applications, they may be randomly distributed in different regions. Besides, nodes with the same type have almost the same probability to be selected as cluster head. The cluster head will consume much more energy to receive and transmit data than the other nodes. If nodes with little residual energy have been elected as cluster heads, it will affect the efficiency of the network due to its early death. An improved energy-efficient routing protocol is proposed for heterogeneous wireless sensor networks. Firstly, it supposes that the different types of nodes are distributed in different zones. Secondly, by improving the threshold, nodes with large residual energy have a greater possibility of becoming cluster heads. In the end, it designs a mixed data transmission method. The cluster heads of supper nodes and advance nodes directly transmit data to the base station. The normal nodes adopt single hops and multiple hops mixed methods to transmit data. This can minimize the energy of the communication from cluster head to base station. Simulation results show that this algorithm has achieved a longer lifetime for the wireless sensor network than stable election protocol and threshold-sensitive stable election protocol algorithm.
Papilledema as the Sole Manifestation of Neuroborreliosis
Authors: May Kaitlyn , Upadhyayula Shankar
Published: May 2021

Lyme disease is reportable, and approximately 30,000 cases/year are notified to the local and state health departments. However, other estimates based on insurance records suggest there are close to 476,000 cases/year that are diagnosed and treated. In addition to a large burden of illness, areas where Lyme disease is common are expanding. Therefore, clinicians should be aware of uncommon presentations of this condition. We describe the case of a 5-year-old girl who presented with papilledema as an isolated manifestation of Lyme disease. Of note, her ocular symptoms were intermittent and worse when tired. In endemic areas, Lyme disease must be considered in the differential diagnosis for patients presenting with isolated ophthalmic findings even outside the usual Lyme season.
Clinical Evaluation and Treatment of Patients with Postconcussion Syndrome
Authors: Renga Vijay
Published: May 2021

Postconcussion syndrome (PCS) is a complex set of symptoms occurring in a small percentage of patients following concussion. The condition is characterized by headaches, dizziness, cognitive difficulties, somatosensory issues, and a variety of other symptoms with varying durations. There is a lack of objective markers and standard treatment protocols. With the complexity created by premorbid conditions, psychosomatic issues, secondary gains, and litigations, providers often find themselves in a tough situation in the care of these patients. This article combines literature review and clinical insights with a focus on the underlying pathophysiology of PCS to provide a roadmap for evaluating and treating this condition.
Dynamic Analysis and Degenerate Hopf Bifurcation‐Based Feedback Control of a Conservative Chaotic System and Its Circuit Simulation
Authors: Zhang Xiaojuan , Chen Mingshu , Wang Yang , Tian Huaigu , Wang Zhen
Published: May 2021

A novel conservative chaotic system with no equilibrium is investigated in this study. Various dynamics such as the conservativeness, coexistence, symmetry, and invariance are presented. Furthermore, a partial‐state feedback control scheme is proposed, and the stable domain of control parameters is analyzed based on the degenerate Hopf bifurcation. In order to verify the numerical simulation analysis, an analog circuit is designed. The simulation results show that the output of the analog circuit system can reproduce the numerical simulation results and verify the correctness of the theoretical analysis.
Serum Soluble ST2 Correlated with Symptom Severity and Clinical Response of Sublingual Immunotherapy for House Dust Mite-Induced Allergic Rhinitis Patients
Authors: Zhu Kang , Xia Cui , Chen Jingguo , Yu Chao , Gao Tianxi , Yan Jing , Shao Na , Zhu Pin , Sun Bin , Ren Xiaoyong , Zhang Yanni
Published: May 2021

Background. Suppressor of tumorigenicity 2 (ST2) is a key biomarker in inflammation and cardiovascular diseases, but limited data is available on its role in allergic rhinitis (AR). Objective. The aim of this study is to explore the role of serum soluble ST2 (sST2) in evaluating disease severity and predicting the efficacy of sublingual immunotherapy (SLIT) in house dust mite- (HDM-) induced AR patients. Methods. Eighty healthy controls (HC group) and 160 HDM-induced AR patients, including 40 mild patients (MAR group) and 120 moderate-severe patients (MSAR group), were recruited in this study. Serum was collected from all participants and levels of sST2 were determined by ELISA and the relationship between sST2 levels and disease severity was assessed. In the MSAR group, 109 patients received 3 years of SLIT, and the relationship between serum levels of sST2 and efficacy of SLIT was exampled. Results. Serum sST2 levels were increased in HDM-induced AR patients compared to the HC group (

P
<
0.001

), and the concentrations were higher in the MSAR group than in the MAR group and HC group (all

P
<
0.05

). Moreover, sST2 levels positively correlated with the total nasal symptom score (TNSS), visual analogue scale (VAS), and specific IgE levels (

P
<
0.05

). Seventy-eight MSAR patients accomplished SLIT, and they were divided into an effective group (

n
=
40

) and an ineffective group (

n
=
38

). The serum sST2 levels in the effective group were lower than those in the ineffective group (

P
<
0.001

). In addition, patients in the effective group levels exhibited significantly lower sST2 levels post-SLIT than pre-SLIT (

P
<
0.001

), but no statistic difference was observed in the ineffective group (

P
>
0.05

). Receiver operating characteristic (ROC) curve showed promising accuracy for predicting clinical efficacy of SLIT in AR patients (

area
 
under
 
the
 
curve
=
0.839

,

P
<
0.001

). Conclusion. Serum sST2 is a potential biomarker for assessing disease severity and may serve as a sensitive biomarker for predicting the therapeutic response of SLIT in HDM-induced AR patients.
Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection
Authors: Li Meiman , Xie Wenfu
Published: May 2021

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.
Hubei’s Core Response Policies in the Early Stage of COVID‐19
Authors: Zhang Yuyao , Shi Leiyu , Chen Haiqian , Wang Xiaohan , Sun Gang
Published: May 2021

Background. This study is aimed at confirming the effectiveness of nonpharmaceutical interventions during the COVID‐19 outbreak in Hubei, China. Methods. The data are all from the epidemic information released by the National Health Commission of the People’s Republic of China and the Health Commission of Hubei Province. We used the multivariable linear regression by the SPSS 19.0 software: the cumulative number of confirmed cases, the cumulative number of cured cases, and the number of daily severe cases were taken as dependent variables, and the six policies, including the Joint Prevention and Control Mechanism of the State Council, lockdown Wuhan city, the first‐level response to public health emergencies, the expansion of medical insurance coverage to suspected patients, mobile cabin hospitals, and counterpart assistance in Hubei province, were gradually entered into multiple linear regression models as independent variables. Results. The factors influencing the cumulative number of diagnosed cases ranged from large to small: mobile cabin hospitals and the expansion of medical insurance coverage to suspected patients. The factors influencing the cumulative number of cured cases ranged from large to small: counterpart support medical teams in Hubei province and mobile cabin hospitals. The factors influencing the number of daily severe cases ranged from large to small: mobile cabin hospitals and the expansion of medical insurance coverage to suspected patients. Conclusion. The mobile cabin hospital is a major reason for the successfully defeating COVID‐19 in China. As COVID‐19 pandemic spreads globally, the mobile cabin hospital is a successful experience in formulating policies to defeat COVID‐19 for other countries in the outbreak phase.
Green Synthesis of Selenium Nanoparticles by Cyanobacterium Spirulina platensis (abdf2224): Cultivation Condition Quality Controls
Authors: Alipour Shohreh , Kalari Sara , Morowvat Mohammad Hossein , Sabahi Zahra , Dehshahri Ali
Published: May 2021

Selenium nanoparticles (SeNPs) are well‐known bioactive compounds. Various chemical and biological methods have been applied to SeNP synthesis. Spirulina platensis is a widely used blue‐green microalgae in various industries. In this study, the biosynthesis of SeNPs using sodium selenite and Spirulina platens has been developed. The SeNP synthesis was performed at different cultivation condition including pH and illumination schedule variation. The SeNPs were characterized by FT‐IR, XRD, size, and zeta potential measurements, and the antioxidant activities of selected SeNPs were evaluated by DPPH and FRAP assays. FT‐IR analysis showed the production of SeNPs. The 12 h dark/12 h light cycles and continuous light exposure at pH 5 led to the production of stable SeNPs with sizes of 145 ± 6 and 171 ± 13 nm, respectively. Antioxidant activity of selected SeNPs was higher than sodium selenite. It seems that green synthesis is a safe method to produce SeNPs as well as a convenient method to scale‐up this production.
Data Mining Analysis of Gene Prognostic Markers of Metastatic Skin Cancer Based on the Elastic Network Method
Authors: Liu Gang , Li Chen , Wei Wenhao , Li Wentao , Zhen Haiyan
Published: June 2021

Skin cancer is a typical cancer tumor, which occurs all over the world and has a relatively high recurrence rate, including metastatic tumors that occur in other tissues and metastases to the skin, thus jeopardizing the personal life satisfaction and soundness of patients. Due to individual differences, the traditional treatment methods cannot adapt to every patient accurately, so it is difficult to achieve the desired treatment effect for each individual. Nowadays, with the development of gene chip, many new therapies based on gene are more targeted and flexible for the treatment of skin cancer patients. Therefore, it is necessary to mine and analyze appropriate gene biomarkers according to patients' genes. Because of the high cost of gene chip technology and the large number of human genes, there are few samples of gene data and high dimensions. It is a key problem to mine effective genetic biomarkers from the sample data. In this paper, we firstly performed the preliminary analysis using the difference expression analysis and proportional hazards model, then used the elastic network method to reduce the range of genetic data selection, and screened 26 gene prognostic markers closely related to the recurrence of metastatic skin cancer. Finally, the 26 gene biomarkers were analyzed by functional analysis and verified using a test sample. Research findings have shown that the obtained genetic markers have certain value in the clinical prognostic treatment of metastatic skin cancer.
On the Hybrid Power Mean of Two-Term Exponential Sums and Cubic Gauss Sums
Authors: Cao Shaofan , Wang Tingting
Published: May 2021

In this paper, an interesting third-order linear recurrence formula is presented by using elementary and analytic methods. This formula is concerned with the calculating problem of the hybrid power mean of a certain two-term exponential sums and the cubic Gauss sums. As an application of this result, some exact computational formulas for one kind hybrid power mean of trigonometric sums are obtained.
Carbon Stock and Soil Characteristics under Expansive Shrubs in the Dry Afromontane Forest in Northern Ethiopia
Authors: Haile Mebrahtu , Birhane Emiru , Mekonen Rannestad Meley , S. Adaramola Muyiwa
Published: May 2021

Increased presence of expansive plant species could bring about various ecological influences on biomass carbon, soil organic carbon, and the physical and chemical properties of the soils. However, their impacts on these ecological parameters could differ due to a wide range of life forms, plant communities of the invaded ecosystems, and abiotic conditions. This work was conducted to examine the impacts of Cadia purpurea and Tarchonanthus camphoratus cover on carbon stock in vegetation and soil and soil physicochemical properties in Desa’a forest, northern Ethiopia. Vegetation and soil data were collected from a total of 150 sampling plots (size 20 m × 20 m) from uninvaded and invaded vegetation conditions. The soil samples were collected from topsoil (0–15 and 15–30 cm) of the uninvaded and invaded vegetation conditions. The statistical difference in carbon stock and soil characteristics

P
<
0.05

of both invaded and uninvaded vegetation conditions were tested using an independent t-test using an R-software. The mean above- and below-ground biomass carbon stocks of the uninvaded vegetation condition (17.62 Mg·C/ha and 4.14 Mg·C/ha, respectively) were found to be significantly higher than those of the invaded vegetation condition (4.73 Mg·C/ha and 1.11 Mg·C/ha, respectively). The mean soil organic carbons (SOC) were significantly higher

P
<
0.01

in the uninvaded (122.83 Mg·C/ha) than in the invaded (90.13 Mg·C/ha) vegetation condition. The total carbon stock estimates were significantly higher

P
<
0.01

in the uninvaded vegetation condition (144.59 Mg·C/ha) than in the invaded vegetation condition (95.97 Mg·C/ha). Furthermore, the result revealed that most of the soil characteristics were significantly lower

P

<

0.05

under the expansive shrubs invaded vegetation conditions except for significantly high sand content

P
<
0.05

. Silt, nitrogen, phosphorus, calcium, copper, and zinc did not significantly change with the cover of the expansive shrubs. Our results suggest that increased presence of the expansive species decreased carbon trapping and affected most of the soil nutrients within the forest. Hence, to enhance the carbon storage potential and to maintain the soil nutrient status of the forest, proper conservation, monitoring, and management of the existing PNV and controlling a further expansion of the expansive shrubs are required. Further studies will be required on the factors responsible for the difference in carbon stocks and soil nutrients in each vegetation condition in addition to the impacts of the expansive shrubs expansion.
Association of Cardiovascular Autonomic Neuropathy and Distal Symmetric Polyneuropathy with All-Cause Mortality: A Retrospective Cohort Study
Authors: Vági Orsolya E. , Svébis Márk M. , Domján Beatrix A. , Körei Anna E. , Istenes Ildikó , Putz Zsuzsanna , Mészáros Szilvia , Hajdú Noémi , Békeffy Magdolna , Tesfaye Solomon , Kempler Péter , Horváth Viktor J. , Tabák Adam G.
Published: May 2021

Background. People with diabetic cardiovascular autonomic neuropathy (CAN) have increased cardiovascular mortality. However, the association between distal symmetric polyneuropathy (DSPN) or CAN with all-cause mortality is much less investigated. Thus, we set out to examine the effect of CAN and DSPN on all-cause mortality in a well-phenotyped cohort. Methods. All diabetes cases (

n
=
1,347

) from the catchment area of a secondary diabetes care centre who had medical examination including neuropathy assessment between 1997 and 2016 were followed up for all-cause mortality in the NHS Hungary reimbursement database until 2018. We investigated the association of CAN (Ewing tests) and DSPN (Neurometer) with all-cause mortality using Cox models stratified by diabetes type. Results. Altogether,

n
=
131
/
1,011

persons with type 1/type 2 diabetes were included. Of the participants, 53%/43% were male, mean age was

46
±
12
/
64
±
10

years, diabetes duration was

13
±
10
/
7
±
8

years, 42%/29% had CAN, and 39%/37% had DSPN. During the

9
±
5
/
8
±
5

-year follow-up,

n
=
28
/
494

participants died. In fully adjusted models, participants with type 1 diabetes patients with versus without DSPN had an increased mortality (HR 2.99, 95% CI 1.4-8.63), while no association with CAN was observed. In type 2 diabetes, both DSPN and CAN independently increased mortality (HR 1.32, 95% CI: 1.07-1.64, and HR 1.44, 95% CI: 1.17-1.76). Conclusions. Our results are compatible with an increased risk of mortality in people with type 1 diabetes and DSPN. Furthermore, we report a similarly strong association between DSPN and CAN and all-cause mortality in type 2 diabetes mellitus.
Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model
Authors: Lu Peng , Zhang Yabin , Zhou Bing , Zhang Hongpo , Chen Liwei , Lin Yusong , Mao Xiaobo , Gao Yang , Xi Hao
Published: May 2021

In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians’ confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.
Andrographolide Inhibition of Th17-Regulated Cytokines and JAK1/STAT3 Signaling in OVA-Stimulated Asthma in Mice
Authors: Yu Qian , Shi YaJie , Shu Chang , Ding XuChun , Zhu ShiPing , Shen ZhouPing , Lou YaFang
Published: May 2021

Asthma has long been considered a disease of airway inflammation. The excessive or prolonged production of inflammatory mediators can result in airway remodeling and severe clinical syndromes such as dyspnea or even apnea. Therefore, pharmaceutical intervention is required to restrain the excessive release of such inflammatory mediators in control of asthma. Novel therapeutics and mechanistic insight are sought for the management of this chronic inflammatory disease. Andrographolide (AG) is a type of diterpenoid ester compound and is reported to demonstrate multiple properties such as antioxidation and anti-inflammation. However, the anti-inflammatory capacity of AG by regulating immunologic function in airway of asthma has not been fully studied to date. Therefore, this study investigates whether AG is capable of suppressing the inflammatory response of asthma in OVA-stimulated mice and the mechanism by which this is achieved. Animals were randomly divided into 4 groups: control group, OVA model group, OVA + AG (0.1 mg/ml) group, and OVA + dimethylsulfoxide (DMSO) group. The serum, BALF, and lung tissue of the mice were collected separately for the administration of ELISA, rt-PCR, western blot and pathological section and staining. We found that AG attenuated the OVA-induced production of IL-6, IL-17A, IL-17F, and RORγt; inhibited the OVA-mediated phosphorylation of JAK 1 and STAT3; and alleviated airway remodeling and the neutrophil infiltration of lung tissue. We conclude that AG inhibits the inflammatory response of asthma in OVA-stimulated mice by blocking the activation of Th17-regulated cytokines and the JAK1/STAT3 signaling pathway.
A Sports Training Video Classification Model Based on Deep Learning
Authors: Xu Yunjun
Published: May 2021

A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.