bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence Evaluation of the Waste Slag Above Tunnel on the Safety of the Structure of the High-Speed Railway Tunnel
Authors: Ma Weigong , He Weibo
Published: December 2020

A high-speed railway loess tunnel being built is in the Shallow-Buried Section because the construction party violates the design, and the slag discarded at the top of the tunnel is just down-pressing the main line. In order to ensure that the stability of tunnel structure does not affect the safe operation in the later period, a stratum structure model is established based on non-destructive testing and on-site tunnel monitoring measurement. The parameters of surrounding rock are determined by displacement back analysis method, and the influence of waste slag on the safety of tunnel structure is checked by the parameters obtained from back analysis. Through the comparative analysis of the stress of tunnel lining under two working conditions, combined with relevant codes and design documents, it is considered that the slag has a great influence on the stress of tunnel lining structure, but the sectional strength and maximum crack width of eccentrically compressed components of tunnel lining meet the design requirements, and the structure is within the safety range.
Research on the Automatic Detection Method of Tunnel Clearance Based on Point Cloud Data
Authors: Han Jun , Chen Guodong , Liu Tao , Yang Qian
Published: December 2020

Due to the deformation of the tunnel and the abnormal outburst of internal facilities, the existing railway tunnel line needs to be inspected regularly. However, the existing detection methods have some shortcomings, such as large measurement interference, low efficiency, discontinuity of section, and independence with the track structure. Therefore, an automatic detection method of tunnel space clearance based on point cloud data is proposed. By fitting the central axis of the tunnel, the extraction can be realized at any position of the tunnel. The coordinate system of tunnel gauge detection based on rail top surface is established, and different types of tunnel gauge frames are introduced. The improved ray algorithm method is used to realize automatic detection and analysis of various tunnel types. Field experiments on existing railway tunnels show that the method can accurately obtain the limit point and size of the tunnel. The cross-section of transgression is obtained. It can meet the requirements of tunnel detection accuracy and has great practicability in tunnel disease detection.
An Automatic Spherical Targets Detection Method with Multiple Geometrical Constraints
Authors: Xie Fugui , Zhang Tonggang , Zhong Dan , Kan Yuhui
Published: December 2020

Spherical targets are used extensively in the registration and coordinate transformation of the railway point cloud. Thus, it is necessary to accurately detect the spherical targets from the railway point cloud. This paper proposes an automatic spherical targets detection method with multiple geometrical constraints. In this method, possible spherical points are extracted by the improved three points filter method. And possible spherical points are refined according to neighborhood height difference and curvature. Then, the refined possible spherical points are spatially clustered by the Euclidean clustering method and the potential target point clouds can be extracted by constructing the spherical neighborhood according to the cluster centroid. Finally, the ratio constrained random sample consensus (RC-RANSAC) method is proposed in this paper, based on the RANSAC method, to detect the spherical targets in the potential target point clouds. The point cloud scanned from the high-speed railway is taken as experimental data. The spherical targets in the point cloud are detected by this method. The experimental results show that the proposed method can detect the spherical target with and without the background in radial direction.
Research on Gearbox Fault Diagnosis Based on PCA and AGA-BP Neural Network
Authors: Xie Fengyun , Dong Jiankun , Yan Shaoshi , Jiang Yongqi , Fu Yu
Published: December 2020

As the key component of high-speed train bogie, the fault characteristics of gearbox are mainly reflected in its vibration signal. The vibration signals collected in the process of gearbox fault diagnosis are usually complex and changeable, and have strong randomness and contingency. A gearbox fault diagnosis method based on multi feature extraction, principal component analysis (PCA) and adaptive genetic algorithm is proposed to optimize the back propagation neural network analysis. The original vibration data in the gearbox fault diagnosis experiment published by Jiangsu qianpeng Diagnostic Engineering Co., Ltd. is extracted by multi eigenvalues. The feature set is reduced by PCA. The selected principal components are diagnosed and analyzed by AGA-BP neural network. The final diagnosis result is that the root mean square error (MSE) of AGA-BP neural network is 0.0116, and the recognition rate of gearbox fault is 100%.
Designing “Companion Artifacts”: The Relational Construction of Culture and Technology in Social Robotics
Authors: Šabanović Selma
Published: December 2020

Social robots are designed to coexist with people and learn through their interactions. We, in turn, are expected to develop ways of behaving, communicating, and organizing that support robots. Inspired by this co-evolving relationship, this talk will explore social robots as “companion artifacts”, focusing critical attention on how our concepts of self, cultural practices, social organizations, and sociotechnical infrastructures are co-constructed with existing and imagined social robots. I discuss how “Japanese culture” is repeatedly assembled in relation to social robots, what it means to “domesticate” robotic technologies, and how community-based methods can incorporate diverse sociocultural values into social robotics.
What Robots Represent… and Why It Matters
Authors: Sparrow Robert
Published: December 2020

One thing that distinguishes robots from other machines is the extent to which they operate at the level of meaning and not just mechanism. In this presentation, I will suggest that the embodied nature of robots means that they sometimes function as icons and thus convey meanings in a different manner to other media forms. Moreover, the representational content of social robots sometimes implicates their designers in difficult ethical dilemmas. In order to try to address these, engineers need to think more consciously about the politics of the meanings their robots rely on and convey.
RoboLabs: Building Surfaces of Intelligibility
Authors: Mayer Henning
Published: December 2020

Whether it is humanoid robots or chatbots: their thinking is on the surface and their promises are on the deep end. Superficial expression and activity data are used to make internal emotions, motives and attitudes available for development—mostly based on technologies that are not introspective, i.e. that do not know any impressions or experiential qualities. This paper translates this ambivalence into an analytics of the interconnectivity of intelligence types. Humans, but also machines, become visible here only as designs of the coupling of various intelligences—as carriers that have to be arranged in such a way that different types of intelligence can offer each other their need for complementation. An ethnographic case study draws on these theoretical considerations by comparing two situations of a) statistically and algorithmically modeling future users and b) repairing robot motions in a playful way. In both scenarios, algorithmic and hermeneutical intelligences complement each other by constitutively different modes of interoperability.
Applying Integrative Social Robotics: The Case of Silbot
Authors: Madsen Christoffer , Madsen Sebastian , Mørk Mikkel
Published: December 2020

This paper aims to clarify how “Integrative Social Robotics” (ISR) can solve the triple gridlock of description, evaluation and regulation (D-E-R gridlock) in relation to the concrete case of Silbot. It is indicated how the five principles of ISR can mitigate the ‘strained collaboration’ of Silbot, described by Jeon et al. (2020). The paper arrives at a series of questions that can be used in the research, design and development (RDD) process with the intention to have developers critically reflect on the RDD process along the lines of the ISR framework.
Nudging by Social Robots
Authors: Rodogno Raffaele
Published: December 2020

In this paper, I consider whether social robots can nudge us in standard ways, i.e., by influencing choice/behavior in predictable ways, and the ethical issues attached to that. I then argue that social robots can also nudge us in non-standard ways, by influencing in predictable ways cognitive and affective states as opposed to simple choice/behavior, and consider the ethical issues attached to that. Before tackling the issues specific to nudging and its ethics in connection to social robots, I introduce nudging and its ethics more in general.
Ethical Nudging of Users While They Interact with Robots
Authors: Sullins John P. , Dougherty Sean
Published: December 2020

We describe the kinds of robotic nudging systems for which we need to have ethical standards developed. Then we will discuss the potential for moral harm that ill designed robotic nudging systems can cause. This is followed by an ethical argument to justify certain kinds of permissible robot nudges. Finally, we will discuss the ongoing effort to build actionable standards that designers of these systems can use to help ensure that they are building robotic systems to the highest ethical standards.
Understanding Autonomous Driving as Institutional Activity: Opening New Ways to React to Discriminatory Concerns in Autonomous Driving
Authors: Dietrich Manuel
Published: December 2020

We will elaborate on why it is appropriate to be concerned about structural discrimination induced by future autonomous vehicles—not only in the context of crash-optimization. In order to understand discrimination effects and potential strategies to reduce them, a novel conceptual perspective on how to frame autonomous driving is introduced. According to it, autonomous driving is considered an institutional activity. This perspective enables us to connect to theories about institutional justice—and open new ways to react to discriminatory concerns. To illustrate this direction, we sketch how to apply institutional justice to an autonomous highway driving system.
Anthropomorphism in Social Robotics: Simondon and the Human in Technology
Authors: Rantala Juho
Published: December 2020

Anthropomorphism is a complex phenomenon that arises from human interaction with other entities and the environment. The phenomenon is thought to be desirable in social robots, enhancing their functionality and sociality. On the other hand, strict anthropomorphism can limit the possible capabilities of robots. Following Gilbert Simondon’s analysis of technology as inherently human, we can create a philosophical description of the foundation on which to begin studying anthropomorphism and, on the other hand, frame practical research in light of this description, thus leading to a more robust understanding of anthropomorphism as a phenomenon.
Context-Awareness for Social Robots
Authors: Frijns Helena A. , Schürer Oliver
Published: December 2020

In the present work, we provide a short literature review of three different ways of approaching the topics of context and context-awareness and relate these to developments in Human-Robot Interaction (HRI) and social robotics. We distinguish an engineering approach to context-awareness, the study of social context in human-centred design, and a view of context as a cognitive component in interaction. We propose a revised definition of context to capture these three views and discuss implications.
The Social Uncanniness of Robotic Companions
Authors: Hoffman Guy
Published: December 2020

Social robots offer a promise of relatedness without the limitations of human ability and availability. However, the idea of robotic companionship can also cause unease, a phenomenon I coin the Social Uncanniness of robotic companions. This sense of unease could be attributed to the fact that social robots touch on deeply rooted psychological concerns, including our need to be unique individuals that cannot easily be replaced or replicated; our ambivalent relationship with natural decay, including our own; and a fear that by relating to robots we may lose our ability to relate appropriately to other humans.
The Rights of (Social) Robots
Authors: Gunkel David J.
Published: December 2020

A number of recent publications have examined and advanced the concept of robot rights. These investigations have been largely theoretical and speculative. This paper seeks to move the debate about the moral and legal standing of social robots out of the realm of theory. It does so by investigating what rights a social robot would need to have in order to facilitate responsible integration of these technologies into our world. The analysis, therefore, seeks to formulate practical guidance for developing an intelligent and executable plan for culturally sustainable social robots.
Between Luther and Buddhism: Scandinavian Creation Theology and Robophilosophy
Authors: Ess Charles
Published: December 2020

The computational turn leads to a robo-philosophy that uses computational and robotic technologies as testbeds for philosophical questions such as the nature of being human. Robo-theology extends these approaches and interests via religious robots that further evoke questions of the mind-body, Creator-creation, and faith-reason relationships. As part of a recent agenda for non-dualistic approaches in robo-theology, Scandinavian Creation Theology (SCT) contributes a more optimistic conception of human nature and correlative non-dualistic accounts that more fully resonate with Eastern approaches. SCT is further fruitful for central issues in robo-theology such as distributed ethical agency and responsibility, love, sex, and trust.
Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging
Authors: Park Yae Won ,   Alzheimer’s Disease Neuroimaging Initiative, Choi Dongmin , Park Mina , Ahn Sung Jun , Ahn Sung Soo , Suh Sang Hyun , Lee Seung-Koo
Published: December 2020

Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. Conclusion: Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
tRNA-Derived Fragments in Alzheimer’s Disease: Implications for New Disease Biomarkers and Neuropathological Mechanisms
Authors: Wu Wenzhe , Lee Inhan , Spratt Heidi , Fang Xiang , Bao Xiaoyong
Published: December 2020

Background: Alzheimer’s disease (AD) is the most common type of dementia caused by irreversible neurodegeneration, with the onset mechanisms elusive. tRNA-derived RNA fragments (tRFs), a recently discovered family of small non-coding RNAs (sncRNAs), have been found to associate with many human diseases, including infectious, metabolic, and neurological diseases. However, whether tRFs play a role in human AD development is not known. Objective: This study aimed to explore whether tRFs are involved in human AD. Methods: Thirty-four postmortem human hippocampus samples were used. The expression of Drosha, Dicer, and angiogenin (ANG), three ribonucleases responsible for the biogenesis of sncRNAs, was determined by qRT-PCR and western blot. The tRFs in the hippocampus was detected by qRT-PCR or northern blot. We also used qRT-PCR to quantify NOP2/Sun RNA methyltransferase 2 (NSun2) and polyadenylation factor I subunit 1 (CLP1), two tRNA modification enzymes. Results: tRFs derived from a subset of tRNAs are significantly altered in the hippocampus of AD patients. The expression change of some tRFs showed age- and disease stage-dependent. ANG is significantly enhanced in AD, suggesting its role in inducing tRFs in AD. The expression of NSun2 in AD patients younger than 65 was significantly decreased. According to a previous report supporting NSun2-mediated tRNA methylation modification making tRNA less susceptible to ANG-mediated cleavage, our results suggested that the decrease in NSun2 may make tRNAs less methylated and subsequently enhanced tRF production from ANG-mediated tRNA cleavage. Conclusion: Our studies demonstrated for the first time the involvement of tRFs in human AD.
Computational Evaluation of Interaction Between Curcumin Derivatives and Amyloid-β Monomers and Fibrils: Relevance to Alzheimer’s Disease
Authors: Orjuela Adrian , Lakey-Beitia Johant , Mojica-Flores Randy , Hegde Muralidhar L. , Lans Isaias , Alí-Torres Jorge , Rao K.S.
Published: December 2020

Background: The most important hallmark in the neuropathology of Alzheimer’s disease (AD) is the formation of amyloid-β (Aβ) fibrils due to the misfolding/aggregation of the Aβ peptide. Preventing or reverting the aggregation process has been an active area of research. Naturally occurring products are a potential source of molecules that may be able to inhibit Aβ42 peptide aggregation. Recently, we and others reported the anti-aggregating properties of curcumin and some of its derivatives in vitro, presenting an important therapeutic avenue by enhancing these properties. Objective: To computationally assess the interaction between Aβ peptide and a set of curcumin derivatives previously explored in experimental assays. Methods: The interactions of ten ligands with Aβ monomers were studied by combining molecular dynamics and molecular docking simulations. We present the in silico evaluation of the interaction between these derivatives and the Aβ42 peptide, both in the monomeric and fibril forms. Results: The results show that a single substitution in curcumin could significantly enhance the interaction between the derivatives and the Aβ42 monomers when compared to a double substitution. In addition, the molecular docking simulations showed that the interaction between the curcumin derivatives and the Aβ42 monomers occur in a region critical for peptide aggregation. Conclusion: Results showed that a single substitution in curcumin improved the interaction of the ligands with the Aβ monomer more so than a double substitution. Our molecular docking studies thus provide important insights for further developing/validating novel curcumin-derived molecules with high therapeutic potential for AD.
Education Exerts Different Effects on Cognition in Individuals with Subjective Cognitive Decline and Cognitive Impairment: A Population-Based Study
Authors: Chen Guanqun , Zhao Mingyan , Yang Kun , Lin Hua , Han Chunlei , Wang Xiaoni , Han Ying
Published: December 2020

Background: Education plays a potential important effect on the prevalence and incidence of dementia. However, most of the evidence based on convenience sampling. Objective: To explore effects of education on cognition in individuals with subjective cognitive decline (SCD) and cognitive impairment (CI) from a population-based study. Methods: We examined the effect of education on cognition among individuals with SCD (n = 451) and CI (n = 280) from a population-based study. A series of neuropsychological tests of memory, executive, language, and general cognitive function were used to assess the participants. Results: Multiple regression analyses revealed that education has a positive effect on cognition in both SCD and CI group in the population-based research. Further stratification study showed that the beneficial effect of education remains in the SCD group regardless of the education level, especially in the SCD participants with a low education level. However, that effect of education exists in the CI group with a low education level and disappears in the high education level. Conclusion: These results from a population-based sample suggest that high educational attainment may delay cognitive decline in the individuals with SCD regardless of high or low educational level, and high education only predicts cognition in those in the low educational level in CI group.
Innovation of enterprise financial management based on machine learning and artificial intelligence technology
Authors: Yubo Cao
Published: December 2020

With the development of economic globalization, the competition between companies is increasing and becoming a norm. As one of the main value-added tools, financial management has greatly improved its position in business management. Traditional financial management is difficult to keep up with the pace of modern company management, which to a large extent hinders the effective development of enterprises. Therefore, under the current macroeconomic background, the necessity of studying financial management innovation has become more urgent. In this context, seeking innovation is not only a problem for enterprises, but also an important strategic goal of economic development and the concept of national modern enterprise development. Many studies have been carried out in the field of technological innovation, and few have focused on innovation in financial management. Exploratory research on the factors that affect the choice of financial management mode and route planning is important both in reality and in theory. It can help enterprises to gain greater competitive advantage through innovative financial management and improve their operating efficiency and production quality. This paper is based on learning. A research on the innovation of enterprise financial management is carried out on machine and artificial intelligence technology.
Correlation between agricultural parameters and spectral vegetation index of rape based on artificial intelligence
Authors: Tang Hongyuan , Liao Guiping
Published: December 2020

At present, people’s living standard and consumption level are increasing day by day, the demand for edible oil crops is increasing. As the main source of oil crops, rape needs to use more accurate management and cultivation techniques, change backward technology, increase overall yield, and ensure the supply of oil crops. By means of artificial intelligence, the spectral segments of plants were analyzed to determine the species of light and the nutrients produced by plants, the monitoring of development and the prediction of crop yield. According to the spectral information of plant absorption and reflection, the chlorophyll level contained in plants can provide data support for rape planting and management, and monitor the real-time status of its growth and development. Mastering the nutrition and demand of rape in the whole process of growth and reproduction is very beneficial to the management and yield improvement of rape. And can adjust the cultivation method in time, play the advantage of hyperspectral technology.
Simulation of English classroom effectiveness based on human-computer interaction and facial identification
Authors: Hu Liang , Zeng Qi , Wu Xinli , Lv Zhaofang
Published: December 2020

A variety of factors affect English classroom teaching, which prevents teachers from effectively grasping students’ learning status and learning situation. In particular, classroom management is more difficult during online teaching. In order to improve the effectiveness of English classroom teaching, based on the human-computer interaction algorithm and facial identification algorithm, this paper effectively recognizes the human-computer interaction process and classroom learning status of students in online teaching, and eliminates the image background according to the actual teaching needs. Moreover, by extracting and fusing the time sequence information and spatial information of the motion in the video, a spatiotemporal feature image capable of expressing a dynamic typical feature is obtained. In addition, this paper uses system algorithms to judge the status and feed back the identification results to the teacher’s teaching terminal equipment, which is convenient for timely teaching adjustment. Finally, this paper analyzes the effectiveness of the algorithm through simulation experiments. The research results show that the algorithm constructed in this paper has good performance.
Mental health diagnosis of college students based on facial recognition and neural network
Authors: Liu Nan , Liu Haihong , Liu Haining
Published: December 2020

In recent years, college campus incidents caused by mental health problems have been increasing year by year, and college students’ mental health problems have become the focus of attention of schools, society and parents. Based on this, this paper proposes a facial emotion recognition method for college students. By using moving target detection, target classification, target tracking, and a series of image preprocessing techniques, this method achieves intelligent monitoring of the area where college students are located and can automatically alert when a potentially dangerous target is found. Moreover, this method uses a combination of shape features and motion features to select and extract feature quantities. In addition, the method calculates the similarity between the target and candidate target corresponding sub-models, and according to the ability of each feature to distinguish between the target and the background, monitors the student’s mental health in real time and prevents various problems from occurring. Through experimental research, we can see that the model constructed in this paper has good performance.
Test of English vocabulary recognition based on natural language processing and corpus system
Authors: Longjiang Duan
Published: December 2020

English vocabulary recognition has certain applications in both learning and life. The existing English vocabulary recognition model is limited by a variety of factors, which will result in a more complicated recognition process and a low recognition accuracy. In order to improve the effect of English vocabulary recognition, based on natural language processing algorithms and corpus systems, this paper proposes a multi-feature fusion adaptive kernel-related filter tracking algorithm for the problems of kernel-related filtering algorithms. Moreover, based on the KCF algorithm, this paper improves the algorithm from three parts: feature fusion, adaptive change of update rate, and scale detection. In addition, this paper explores whether the vocabulary recognition of different rhythms will affect the reaction time and accuracy of the second language vocabulary recognition when the test subjects are in the experimental conditions with similar characters and different voices. The research results show that the model constructed in this paper performs well in the recognition of English words.
Quality evaluation of English pronunciation based on artificial emotion recognition and gaussian mixture model
Authors: Gang Zhang
Published: December 2020

At present, the posterior probability measure widely used in English speech recognition has the situation that the posterior probability measure of different phonemes cannot be consistent to measure the pronunciation quality of the phoneme and the acoustic modeling method of voice recognition is inconsistent with the evaluation target. Therefore, in order to improve the evaluation effect of English pronunciation quality in colleges and universities, this article is based on artificial emotion recognition and high-speed hybrid model to analyze and filter various clutters that affect speech quality to improve students’ English speech recognition. Moreover, this article uses the characteristics of the clutter and the target in the data to conform to different distributions and based on the clutter distribution characteristics obtained by statistics, this article realizes the suppression of the clutter to improve the target detection performance. In addition, the method proposed in this paper solves the limitations of the clutter suppression technology in the traditional voice detection system and improves the target detection performance. In order to study the pronunciation quality evaluation effect of this model and its effect in English teaching, this paper designs a controlled experiment to analyze the model’s performance. The research results show that the model constructed in this paper has good performance.
UAV swarm autonomous control based on Internet of Things and artificial intelligence algorithms
Authors: Wang Xinhua , Chen Guanyu , Gong Huajun , Jiang Ju
Published: December 2020

At present, the UAV swarm positioning solution has the problems of poor positioning accuracy and instability. Therefore, it is necessary to design a sliding mode formation controller to realize formation. This study analyzes the self-service control strategy of the UAV swarm and establishes the behavior-based formation control strategy as the main research point of this article. This paper combines the Internet of Things and artificial intelligence algorithms to build an autonomous control model for the UAV swarm and designs the UAV formation control law from the disturbed and undisturbed conditions respectively. With reference to the basic architecture of the Internet of Things, this study imitates ZigBee’s self-organizing network to propose an adaptive networking scheme based on the Internet of Things by using the AP+STA working mode of the Internet of Things module in the node device. The results of the experiment show that the positioning accuracy of the UAV is high, which can meet the needs of cluster flight. Based on the Z-axis coordinates of the UAV, the accuracy of the laser distance measurement and the barometer value is significantly improved, the root mean square error is reduced, and the positioning result is significantly better than the data of direct traditional positioning.
A novel approach for the next software release using a binary artificial algae algorithm
Authors: Pirozmand Poria , Ebrahimnejad Ali , Alrezaamiri Hamidreza , Motameni Homayun
Published: December 2020

In software incremental development methodology, the product develops in several releases. In each release, one set of the requirements is suggested for development. The development team must select a subset of the proposed requirements for development in the next release such that by consideration the limitation of the problem provides the highest satisfaction to the customers and the lowest cost to the company. This problem is known as the next release problem. In complex projects where the number of requirements is high, development teams cannot choose an optimized subset of the requirements by traditional methods, so an intelligent algorithm is required to help in the decision-making process. The main contributions of this study are fivefold: (1) The customer satisfaction and the cost of every requirement are determined by use of fuzzy numbers because of the possible changing of the customers’ priorities during the product development period; (2) An improved approximate approach is suggested for summing fuzzy numbers of different kinds, (3) A new metaheuristic algorithm namely the Binary Artificial Algae Algorithm is used for choosing an optimized subset of requirements, (4) Experiments performed on two fuzzy datasets confirm that the resulted subsets from the suggested algorithm are free of human mistake and can be a great guidance to development teams in making decisions.
Reliability and mobility aware task offloading strategy and scheduling algorithm in wisdom medical scenario
Authors: Qi Ping , Shu Hong , Zhu Qiang
Published: December 2020

Computation offloading is a key computing paradigm used in mobile edge computing. The principle of computation offloading is to leverage powerful infrastructures to augment the computing capability of less powerful devices. However, the most existing computation offloading algorithms assume that the mobile device is not moving, and these algorithms do not take into account the reliability of task execution. In this paper, we firstly present the formalized description of the workflow, the wireless signal, the wisdom medical scenario and the moving path. Then, inspired by the Bayesian cognitive model, a trust evaluation model is presented to reduce the probability of failure for task execution based on the reliable behaviors of multiply computation resources. According to the location and the velocity of the mobile device, the execution time and the energy consumption model based on the moving path are constructed, task deferred execution and task migration are introduced to guarantee the service continuity. On this basis, considering the whole scheduling process from a global viewpoint, the genetic algorithm is used to solve the energy consumption optimization problem with the constraint of response time. Experimental results show that the proposed algorithm optimizes the workflow under the mobile edge environment by increasing 20.4% of successful execution probability and decreasing 21.5% of energy consumption compared with traditional optimization algorithms.
Vestibular function assessment of Susac syndrome patients by the video head impulse test and cervical vestibular-evoked myogenic potentials
Authors: Oron Yahav , Handzel Ophir , Habot-Wilner Zohar , Regev Keren , Karni Arnon , Zur Dina , Baraquet Dana , Goldstein Michaella , Elkayam Ori , Ungar Omer J.
Published: December 2020

BACKGROUND: Susac syndrome (retino-cochleo-cerebral vasculopathy, SuS) is an autoimmune endotheliopathy characterized by the clinical triad of encephalopathy, branch retinal artery occlusions and sensorineural hearing loss. In contrast to data regarding auditory function, data measuring vestibular function is sparse and the cervical vestibular-evoked myogenic potentials (cVEMPs). OBJECTIVE: To determine whether the video head impulse test (vHIT) can serve as a confirmatory assessment of vestibulocochlear dysfunction in cases of suspected SuS. METHODS: Seven patients diagnosed with SuS underwent pure tone audiometry, a word recognition test, cVEMPs and the vHIT. RESULTS: Five patients were diagnosed with definite SuS, and two with probable SuS. Two patients were asymptomatic for hearing loss or tinnitus, and no sensorineural hearing loss was detected by audiograms. Four patients complained of tinnitus, and three patients reported experiencing vertigo. Three patients had abnormal cVEMPs results. All seven patients’ vHIT results were normal, except for patient #2, who was one of the three who complained of vertigo. The calculated gain of her left anterior semicircular canal was 0.5, without saccades. CONCLUSIONS: This is the first study to describe the results of the vHIT and cVEMPs among a group of patients with SuS. The results suggest that the vHIT should not be the only exam used to assess the function of the vestibular system of SuS patients.
Cross-hole ERT Configuration Assessment for Boulder Detection: A Full-scale Physical Model Test
Authors: Nie Lichao , Shen Junfeng , Zhou Pengfei , Liu Zhengyu , Pang Yonghao , Zhou Wei , Chen Andong
Published: December 2020

In Southeast China, the presence of boulders significantly threatens subway construction projects. Undetected boulders could negatively impact the efficiency and safety of subway construction methods that use shield and tunnel-boring machines. Therefore, boulder detection is a necessary step before construction begins. Recently, the cross-hole electrical resistivity tomography (ERT) method has gained attention as a potential boulder-detection approach in subway construction projects. However, in this method, the detection effect of each electrode configuration still needs to be assessed. In this study, a full-scale physical model test was conducted to determine the optimal electrode configuration for the cross-hole ERT method. Additionally, a sensitivity analysis of three types of electrode configurations, namely, bipole–bipole, dipole–dipole, and pole–tripole, was conducted. The results showed that the detection ability of the bipole–bipole electrode configuration was satisfactory, with very good boulder-detection resolution and sensitivity. Conversely, the resolution of the dipole–dipole electrode configuration, with relatively low signal strength, decreased as boulder distribution became more complex. Although the pole–tripole electrode configuration showed acceptable resolution, the boulder-detection results had many artifacts. Thus, the full-scale physical model test was conducted to thoroughly investigate the cross-hole ERT electrode configurations and determine the influence of boulder distribution on the measurements, with a view on achieving a more efficient application of this method in the field.
Investigation of High School Students' Peer Bullying Levels in Terms of Various Variables
Authors: ALKAN Hilal , ARSLAN Nihan
Published: December 2020

In this study, it was aimed to examine the peer bullying levels of high school students in terms of various variables. The universe of the research consists of high school students in Istanbul. The research sample consists of 384 students. Descriptive analysis method was used in the research. In the study 'Information Form' and 'Peer Bullying Scale' were used as data collection tools. In the analysis of the data, Independent Groups t-Test, One Way Analysis of Variance were used. Within the scope of the research findings, It was determined that peer bullying differs significantly according to gender. It has been determined that male participants exhibit more bullying behaviors than female participants. When peer bullying scores were examined according to school type, it was determined that there was no significant difference. The research findings are discussed in the context of the literature.
Bir “Göçmen” ve Bir “Yerli”: Özdamar ve Tanpınar’da Vatan Kavramı
Authors: KARAKAYA Dursun
Published: December 2020

Bu çalışmada; iki Türk yazarın, eserlerinde ve benliklerinde oluşturdukları “vatan” kavramı ele alınmıştır. İlk yazar olarak Almanya’da yaşayan ve Almanca yazan Emine Sevgi Özdamar, ikinci yazar olarak ise Ahmet Hamdi Tanpınar seçilmiştir. 1901 yılında doğan, ilk gençlik yılları savaş içinde geçen ve yeni kurulan bir ülkenin ilk aydınlarından olan Ahmet Hamdi Tanpınar ile 1946 yılında 2. Dünya Savaşı’nın hemen ardından doğup ülkesindeki kaos ortamını yaşayan, göz altına alınan ve 19 yaşında Almanya’ya giden Emine Sevgi Özdamar için vatan ne anlam ifade etmektedir? Yazarların “Der Hof im Spiegel” ve “Beş Şehir” adlı eselerinde bu kavramın (vatanın) onlar için ne anlama geldiği araştırılırken, aralarındaki kuşak ve üslup farklılıkları, kimliklerini ve bakış açılarını oluşturan etkenler dikkate alınmıştır. Tüm bu etkenler ışığında Özdamar ve Tanpınar’ın, vatan kavramını nasıl algıladıkları açıklılığa kavuşturulmaya çalışılmıştır.
ОСОБЕННОСТИ РАСЧЕТА И РЕГУЛИРОВАНИЯ ДВУХКОНТУРНОГО ТУРБОРЕАКТИВНОГО ДВИГАТЕЛЯ С ФОРСАЖНОЙ КАМЕРОЙ СГОРАНИЯ В НАРУЖНОМ КОНТУРЕ НА ПРЯМОТОЧНЫХ РЕЖИМАХ РАБОТЫ
Authors: Кислов Олег Владимирович , Шевченко Михаил Анатольевич
Published: December 2020

A promising direction in aviation is the creation of anaircraft for supersonic cruise speeds (Mach 3...4). It is known that ramjet engines are more preferable for Mach numbers larger 3. However, they do not have starting thrust and uneconomical at subsonic flight speeds. At the same time, at subsonic flight speeds, turbofan engines are the most expedient. The combination of the positive properties of turbofan engines at subsonic speeds and a ramjet engines at supersonic speeds is possible by using duct-burning turbofan engine, which can operate at the ramjet mode with the blocked gas turbine duct at supersonic flight conditions. At this mode, duct-burning turbofan engine turns into ramjet engine, which, however, has special features due to the presence of fan in front of the combustion chamber, which operates in turbine mode or in zero power mode and also because of the outlet jet, which has annular shape, flows out from the duct causes the appearance of bottom drag. The presence of bottom drag requires both the development of a mathematical model for its calculation and taking into account its influence on the choice of the control law for the nozzle outlet area. The article presents a mathematical model of the working process of duct-burning turbofan engine at ramjet mode, taking into account the presence of fan in the flow path and bottom drug. Using the developed mathematical model, the regularities of changes in the internal and effective thrust, as well as the specific fuel consumption, depending on the relative fuel consumption and the critical section of the nozzle at a given altitude and flight speed are established. The critical section of the nozzle is the main regulating factor, and the relative fuel consumption is related to the main regulating factor - the fuel consumption. These patterns are useful for choosing a control program.There is such a combination of regulating factors whichprovides two extremes in the regularities of trust and specific fuel consumption changes: the mode of minimum specific fuel consumption and the mode of maximum thrust. In addition, the influence of gas underexpansion in the nozzle on the thrust-economic parameters of the engine and the required area of the nozzle outlet section were estimated. The obtained regularities are advisable to use when engine control program is chosen.
КОМБІНОВАНА МЕТРИКА ВІЗУАЛЬНОЇ ЯКОСТІ ЗОБРАЖЕНЬ ДИСТАНЦІЙНОГО ЗОНДУВАННЯ НА ОСНОВІ НЕЙРОННОЇ МЕРЕЖІ
Authors: Еремеев Олег Игоревич , Лукин Владимир Васильевич , Okarma Krzysztof
Published: December 2020

The wide distribution of images of remote sensing (RS) of the Earth in various application areas makes it important to ensure the high quality of such images, which is important to identify necessary information. The complexity of the systems and the impact of various physical processes cause a significant number of distortions that lead to image corruption and possible loss of information. The use of processing methods that should reduce the impact of such factors requires control of their work, which uses quantitative indicators of visual quality. The article considers the task of creating a combined visual quality metric based on an artificial neural network (ANN), which provides high accuracy of visual quality assessment and stability of work on the noise characteristic of the RS. The problem of analysis of RS distortions is considered and the approach of using the database of test images TID2013 for verification on typical RS distortions is offered. The analysis of well-known visual quality metrics and their suitability for the estimation of such images is carried out. According to its results, it was determined that the best metrics provide the accuracy of image quality assessment for RS tasks at the level of 0.93 according to Spearman's rank-order correlation coefficient with subjective estimates of the TID2013 image database. The joint application of existing quality metrics allows eliminating the shortcomings of each of them and increasing the overall efficiency, so the article considers the problems and defines the requirements for creating a combined metric involving a neural network. A method of limiting the number of involved quality metrics with the involvement of Lasso regularization is proposed, which allows determining the most informative features (quality metrics) and simplifying the procedure of selection and reduction of their number. A study was conducted on the influence of the metric selection criterion and quantity on the accuracy of the combined metric. The influence of the structure of the neural network, the number of hidden layers, and the number of neurons in them are also analyzed. Based on the obtained results, the best implementation of ANN was selected, which with the involvement of 16 visual quality metrics allows achieving the accuracy of visual quality assessment at 0.97 according to Spearman's correlation with subjective estimates of the TID2013 database.
História Natural de Lesões Precursoras do Câncer do Colo do Útero: Estudo Exploratório de uma Coorte de Mulheres do Rio de Janeiro - RJ, Brasil
Authors: Silva Ilce Ferreira da , Koifman Rosalina Jorge , Koifman Sergio , Mattos Inês Echenique
Published: December 2020

Introdução: Neoplasia de Itraepitelial Cervical (NIC ) quando não tratada progride para câncer, persiste como NIC ou regride para um tecido normal. Objetivo: Determinar o padrão de evolução natural das lesões precursoras do câncer cervical e avaliar o papel de cofatores sobre o risco de desenvolvimento dessa neoplasia numa coorte de mulheres do Rio de Janeiro, Brasil. Método: Foi feito um estudo exploratório em 227 mulheres com NIC referidas a partir do Centro de Patologia Cervical, de 1998 2000. A evolução das NIC foi verificada em pacientes que foram deixadas sem tratamento por apresentarem limitações clínicas, que foram seguidas por dois anos com exames citológicos. Resultados citológicos foram classificados como: Lesão de Baixo Grau (LBG), Lesão de Alto Grau (LA G). Probabilidade e risco de regressão, persistência e progressão das lesões pré-cancerosas foram determinados usando test (L-R:95%) de Kaplan Meier e regressão de Cox proporcional. Resultados: A probabilidade de regressão da lesão foi: 46% (LBG), 72,8% (LAG). As LAG apresentaram maior probabilidadde de progressão para câncer (7,1%) e persistência (43,1%) do que LBG. Mulheres <30 anos eram mais propensas a regredir (79,9%) do que as mulheres mais velhas. Mulheres≥50 eram mais prováveis de apresentar lesão persistente (43,3%) do que as mulheres mais jovens (31,8%). Conclusão: Mulheres < 30 anos no diagnóstico de LBG eram mais propensas a regredir do que as mulheres mais velhas. Mulheres com≥50 anos com HSIL eram mais sujeitas a ter lesões persistentes e progredirem, em comparação as mais jovens.