bluuebunny/crossref_metadata_2025 huggingface dataset:
Robustness of majorana zero-energy state
Authors: Wang Zheng-Chuan
Published: April 2022

Based on the principle of linearized stability proposed by Lyapunov, we investigate the robustness of Majorana zero-energy state (MZES), which plays an important role in topological quantum computation. We show that the MZES is not robust enough against the external perturbations, because mathematically it is critically stable instead of being asymptotically stable, and only the states with asymptotic stability can be regarded as robust, therefore, the MZES cannot be used to carry quantum information in topological quantum computation. Our study is different from previous works that usually make the numerical tests by some special perturbations, whereas our analytical derivation is suitable for arbitrary perturbations. As an example, we demonstrate this by the stability analysis of MZES in the spin-orbit-coupled semiconductor/superconductor junction.
Deep Learning for Covid-19 Screening Using Chest X-Rays in 2020: A Systematic Review
Authors: Santosh KC , Ghosh Supriti , GhoshRoy Debasmita
Published: April 2022

Artificial Intelligence (AI) has promoted countless contributions in the field of healthcare and medical imaging. In this paper, we thoroughly analyze peer-reviewed research findings/articles on AI-guided tools for Covid-19 analysis/screening using chest X-ray images in the year 2020. We discuss on how far deep learning algorithms help in decision-making. We identify/address data collections, methodical contributions, promising methods, and challenges. However, a fair comparison is not trivial as dataset sizes vary over time, throughout the year 2020. Even though their unprecedented efforts in building AI-guided tools to detect, localize, and segment Covid-19 cases are limited to education and training, we elaborate on their strengths and possible weaknesses when we consider the need of cross-population train/test models. In total, with search keywords: (Covid-19 OR Coronavirus) AND chest x-ray AND deep learning AND artificial intelligence AND medical imaging in both PubMed Central Repository and Web of Science, we systematically reviewed 58 research articles and performed meta-analysis.
Mixed-Voltage I/O Buffer Using NMOS Blocking Considering Gate Oxide Reliability
Authors: Nedalgi Dharmaray , Siddamal Saroja V.
Published: April 2022

This paper presents a [Formula: see text] tolerant I/O buffer with low voltage (VDD) devices. A novel bootstrap circuit for mixed voltage I/O buffer is proposed to solve the unwanted leakage paths and gate oxide reliability issues. The proposed circuit is designed using 1.8[Formula: see text]V thick gate devices in 22-nm FinFET technology with 1.8[Formula: see text]V signaling and tolerant to 3.3[Formula: see text]V. The structure can be used in any CMOS technology for [Formula: see text] tolerant I/O buffer.
A Novel Method for Identifying Microservices by Considering Quality Expectations and Deployment Constraints
Authors: Li Jianan , Xu Hanchuan , Xu Xiaofei , Wang Zhongjie
Published: April 2022

Nowadays, microservice architecture has become a dominant software development and deployment paradigm. Decomposing a system into loosely coupled, highly cohesive, and fine-grained microservices while meeting various technical constraints and implementing business capabilities is particularly important for microservice system (MS) designers. When an MS has a large number of functionalities and complex interconnections, it is a big challenge to identify microservices solely based on the experience of MS designers. We propose a structured and automated microservice identification method to decompose a system into appropriate microservices for this challenge. We model a system as unified modeling language (UML) class and sequence diagrams. In the identification phase, we take into account not only the traditional coupling-related criteria but also the quality expectation and deployment constraints, both of which have not yet been fully concerned in previous studies. Based on the criteria, a microservice identification algorithm using the clustering technique is designed. A case study of elderly care services illustrates the identification process. Experiments are conducted to evaluate and compare the proposed method against state-of-the-art methods. Results indicate that the proposed method significantly outperforms those compared from the literature.
Parallel complexity for nilpotent groups
Authors: Myasnikov Alexei , Weiß Armin
Published: April 2022

Recently, Macdonald et al. showed that many algorithmic problems for finitely generated nilpotent groups including computation of normal forms, the subgroup membership problem, the conjugacy problem, and computation of subgroup presentations can be done in [Formula: see text]. Here, we follow their approach and show that all these problems are complete for the uniform circuit class [Formula: see text] — even if an [Formula: see text]-generated nilpotent group of class at most [Formula: see text] is part of the input but [Formula: see text] and [Formula: see text] are fixed constants. In particular, unary encoded systems of a bounded number of linear equations over the integers can be solved in [Formula: see text]. In order to solve these problems in [Formula: see text], we show that the unary version of the extended gcd problem (compute greatest common divisors and express them as linear combinations) is in [Formula: see text]. Moreover, if we allow a certain binary representation of the inputs, then the word problem and computation of normal forms is still in uniform [Formula: see text], while all the other problems we examine are shown to be [Formula: see text]-Turing-reducible to the binary extended gcd problem.
Computable paradoxical decompositions
Authors: Duda Karol , Ivanov (Iwanow) Aleksander
Published: April 2022

We prove a computable version of Hall’s Harem theorem and apply it to computable versions of Tarski’s alternative theorem.
Bicategories for TQFTs with defects with structure
Authors: Lee I. J. , Yetter D. N.
Published: April 2022

We provide a description of adequate categorical data to give a Turaev–Viro type state-sum construct of invariants of 3-manifolds with a system of defects, generalizing the Dijkgraaf–Witten type invariants of our earlier work. We term the defects in our construction defects-with-structure because algebraic data associated to them is in general richer than a module category over the spherical fusion category from which the theory is constructed when no defect is present.
Experimental Study of the Aerodynamic Characteristics of a Suspended Monorail Vehicle-Bridge System Under Crosswinds
Authors: Zou Yunfeng , Liu Zhipeng , Shi Kang , Song Jun , He Xuhui , Liu Qingkuan
Published: April 2022

Using wind tunnel tests, the aerodynamic characteristics of a suspended monorail transport (SMT) are systematically investigated under crosswinds for the first time. Two practical vehicle–bridge combination forms (single-line and double-line) are considered. As a contrast, single vehicle and bridge models are also studied to further reveal their aerodynamic characteristics. The mean and root mean square (RMS) wind pressure coefficients, aerodynamic coefficients and power spectrum density (PSD) distributions are used to numerically investigate the effects. The paper confirms that: (1) the mean wind pressure coefficients of the single vehicle and bridge models are close to unity at the center point of the windward side and gradually decrease from the center point to both sides. The maximum value of the RMS wind pressure coefficients mainly occurs at the top or bottom surface of the testing models due to the effect of vortex shedding; (2) the aerodynamic coefficients and the mean and RMS wind pressure coefficients are significantly increased for both the vehicle and bridge models when considering the aerodynamic coupling effects; (3) the aerodynamic characteristics of the bridge can be significantly affected by the location of the running vehicle, but the fluid flow can maintain relative stability during the meeting of the two vehicles.
eRxNet: A Pipeline of Convolutional Neural Networks for Tuberculosis Screening
Authors: Griffin Terence , Chen Qilei , Sun Xinzi , Wang Dechun , Brunette Maria J. , Cao Yu , Liu Benyuan
Published: April 2022

Tuberculosis (TB) is a contagious disease affecting millions of people annually worldwide. Treatment of this disease and reduction in local epidemics can be improved markedly by increasing the speed and efficiency of screening and diagnosis. eRxNet is a pipeline of convolutional neural networks designed to provide healthcare professionals with detailed and accurate analysis of chest X-rays (CXRs) for TB screening. The pipeline combines whole image classification, object detection (bounding boxes), and instance segmentation (polygonal masks) to provide data analysis at varying levels of detail. In order to construct a high performing system, a comparison of different CNN architectures applied to these tasks is presented. Images from two large TB datasets, UML-Peru and TBX11K, were used for training and evaluation of the models. Combining the two datasets required the development of a preprocessing stage which includes lung segmentation and image enhancement. We show that the resulting four-stage pipeline of CNNs, using a combination of DenseNet, Faster R-CNN, and Mask R-CNN, has sufficiently strong performance to be a useful tool for TB screening.
On weak metric dimension of digraphs
Authors: Feng Min , Wang Kaishun , Yang Yuefeng
Published: April 2022

Using the two-way distance, we introduce the concept of the weak metric dimension of a strongly connected digraph [Formula: see text]. We first establish lower and upper bounds for the number of arcs in [Formula: see text] by using the diameter and weak metric dimension of [Formula: see text], and characterize all digraphs attaining the lower or upper bound. Then we study a digraph with weak metric dimension 1 and classify all vertex-transitive digraphs having weak metric dimension 1. Finally, all digraphs of order n with weak metric dimension [Formula: see text] or [Formula: see text] are determined.
On the chromatic number of (P5,dart)-free graphs
Authors: Xu Weilun , Zhang Xia
Published: April 2022

For a graph G, [Formula: see text], [Formula: see text] represent the clique number and the chromatic number of G, respectively. A hereditary family [Formula: see text] of graphs is called [Formula: see text]-bounded with [Formula: see text]-binding function [Formula: see text] if [Formula: see text] for all [Formula: see text]. A result of Schiermeyer shows that the class of [Formula: see text]-free graphs has a [Formula: see text]-binding function [Formula: see text] [L. Esperet, L. Lemoine, F. Maffray and G. Morel, The chromatic number of [Formula: see text]-free graphs, Discrete Math. 313 (2013) 743–754]. In this paper, we prove that the class of [Formula: see text]-free graphs has a [Formula: see text]-binding function [Formula: see text]. For the class of [Formula: see text]-free graphs, we give a [Formula: see text]-binding function [Formula: see text].
Informed Trading in Dark Pools: Fair-Access Dark Venue vs. Restricted-Access Dark Venues
Authors: Nguyen Nguyet
Published: April 2022

Prior empirical studies find that dark pools are, on average, associated with uninformed order flow. The “exemption from fair-access requirement” has been conjectured as a necessary condition for dark venues to segment uninformed order flow. This study presents direct evidence contrasting a dark venue that offers equal access to all market participants to other dark pools which have the ability to subjectively exclude order flow. Using the period leading up to surprise corporate earnings news, I document robust evidence of informed trading taking place in the fair access dark venue. I do not find such evidence in other dark venues.
Simulation of hydrogen diffusion in pipeline steel under pre-cracks and sour environment
Authors: Asadipoor M. , Asadipour M. , Kadkhodapour J. , Anaraki A. Pourkamali
Published: April 2022

Purpose: In this work, the influences of different field operating parameters (pH and partial pressure of H2S) and crack types (isolated and cluster cracks) are investigated on the distribution of hydrogen concentration in pipeline steel. Design/methodology/approach: A numerical simulation based on the finite element method was performed using the COMSOL Multiphysics software to achieve the objectives. Findings: Hydrogen concentration and total hydrogen flux are increased when pH decreases and [Formula: see text] increases. Besides, the crack flanks are the most appropriate areas for hydrogen diffusion. Accordingly, the areas including step-wise cracking absorb more atomic hydrogen, and the space between the upper surface of the crack and the outer wall is no longer protected. Furthermore, the computational results reveal how a blister on the top of a cluster crack can be crucial by providing enough area for hydrogen to diffuse, and the area between the crack top surface and the outer wall could no longer be protected from hydrogen flux.

Originality: Evaluation of hydrogen concentration in different areas of isolated and cluster cracks under different field operating conditions (pH and [Formula: see text] is not yet understood, which is discussed in this study.
Donnan equilibrium revisited: Coupling between ion concentrations, osmotic pressure, and donnan potential
Authors: Chen Gang
Published: April 2022

This paper discusses the little-known fact that Donnan’s equilibrium criteria established over 100 years ago neglected the coupling between ion concentrations and the osmotic pressure. Such coupling can be treated based on general thermodynamic considerations including the solvent equilibrium, leading to a membrane potential that consists of not only the classical Donnan potential term but also an additional term due to the osmotic pressure, and the existence of a membrane potential even when the impermeable species are not charged. This coupled treatment conflicts with the well-established Poisson–Boltzmann equation and Nernst–Planck equation, but is consistent with the extension of these equations including the solvent effects by Freise and Schlogl, enables us to view the electrical double layer equilibrium as Donnan equilibrium.
A New Word Mining Method Based on Fast-text Model
Authors: Li Luozheng , Song Peipei , Zhang Dan , Zhao Dongyan
Published: April 2022

It is a challenging task to automatically excavate new words from huge amounts of domain-specific corpus. This paper proposed a new phrase mining method based on a fast-text model. With existing terminology and iterative computing framework, this method can extract high-quality domain terms from a given domain-specific corpus. Experiments on geographic corpus show that mining results of this method are more accurate compared with that of the traditional unsupervised mining method, with an accuracy of 68.6% in the top 500 candidates. Meanwhile, the scoring system is more reasonable and effective. In general, this new word mining method can effectively utilize external knowledge and save costs of manual labeling. It is more practical and important to the construction and expansion of the glossary.
Is the Language Change Gradual or Abrupt?
Authors: Li Yan
Published: April 2022

Whether language change is abrupt or gradual has been debated by many scholars. Taking Hanzhong dialect as the research object, this paper holds that language change is not just gradual but also abrupt, and proves the gradualness from five points and abruptness from four points. Gradualness and abruptness are mainly caused by language contact and immigration. In the long history, Hanzhong has experienced countless natural and man-made disasters such as war and famine. People’s frequent movements inevitably result in language contact and frequent migration further intensify language evolution.
V-Gas: Generating High Gas Consumption Inputs to Avoid Out-of-Gas Vulnerability
Authors: Ma Fuchen , Ren Meng , Ying Fu , Sun Wanting , Song Houbing , Shi Heyuan , Jiang Yu , Li Huizhong
Published: April 2022

Out-of-gas errors occur when smart contract programs are provided with inputs that cause excessive gas consumption and which will be easily exploited to perform Denial-of-Service attacks. Various approaches have been proposed to estimate the gas limit of a function in smart contracts to avoid such error. However, underestimation often occurs when the contract is complex In this work, we propose V-Gas, which automatically generates inputs that maximize the gas cost and reduce underestimation. V-Gas is designed based on static analysis and feedback-directed mutational fuzz testing. First, V-Gas builds the gas weighted control flow graph of functions in smart contracts. Then, V-Gas develops gas consumption guided selection and mutation strategies to generate the input that maximize the gas consumption.

For evaluation, we implement V-Gas based on js-evm, a widely used Ethereum virtual machine written in Javascript, and conduct experiments on 736 real-world transactions recorded on Ethereum. A total of 44.02% of the transactions would have out-of-gas errors based on the estimation results given by solc, meaning that the recorded real gas consumption for those transactions is larger than the gas limit estimated by solc. In comparison, V-Gas could reduce the underestimation ratio to 13.86%. To evaluate the performance of feedback-directed engine in V-Gas, we implemented other directed fuzzing engines and compared their performance with that of V-Gas. The results showed that V-Gas generates the same or higher gas estimation value on 97.8% of the transactions with less time, usually within 5 minutes. Furthermore, V-Gas has exposed 25 previously unknown out-of-gas vulnerabilities in widely used smart contracts, 6 of which have been assigned unique CVE identifiers in the U.S. National Vulnerability Database.
Comparing Two Safe Distance Maintenance Algorithms for a Gaze-Controlled HRI Involving Users with SSMI
Authors: Krishna Sharma Vinay , Murthy L. R. D. , Biswas Pradipta
Published: April 2022

People with severe speech and motor impairment often find it difficult to manipulate physical objects due to spasticity and have familiarity with eye pointing based communication. This article presents a novel eye gaze controlled augmented reality human-robot interface that maintains a safe distance of the robot from the operator. We used a bespoke appearance-based eye gaze tracking algorithm and compared two different safe distance maintenance algorithms. We undertook simulation studies followed by a user trial involving end users. Users with severe speech and motor impairment could bring the robotic arm at any designated point within its working envelope in less than 3 minutes.
Negative Insurance Claim Generation Using Distance Pooling on Positive Diagnosis-Procedure Bipartite Graphs
Authors: Haque Md Enamul , Tozal Mehmet Engin
Published: April 2022

Negative samples in health and medical insurance domain refer to fraudulent or erroneous insurance claims that may include inconsistent diagnosis-procedure relations with respect to a medical coding system. Unfortunately, only a few datasets are publicly available for research in health insurance domain, yet none reports any negative claims. However, negative claims are essential not only to develop new machine learning approaches but also to test and validate automated artificial intelligence systems deployed by insurance providers. In this study, we introduce a synthetic negative claim generation procedure based on the bipartite graph representations of positive claims. Our empirical results demonstrate promising outcomes that will improve the development and evaluation processes of machine learning approaches in healthcare, where negative samples are required, but not available. Moreover, the proposed scheme can be applied to other domains, where bipartite graph representations are meaningful and negative samples are lacking.
How Economic Development Influences the Environment
Authors: Jayachandran Seema
Published: April 2022

Reducing global poverty and addressing climate change and other environmental crises are among the most important challenges facing humanity today. This review discusses one way in which these problems are intertwined: how economic development affects the environment. I synthesize recent micro-empirical research on the environmental effects of economic development in low- and middle-income countries. The studies that I discuss identify the causal effects of specific aspects of economic development, such as greater household purchasing power, expanded access to credit, more secure property rights, technological progress, and stronger regulatory capacity. I conclude by outlining some gaps in the literature.
Exothermic Formation Reactions as Local Heat Sources
Authors: Arlington Shane Q. , Fritz Gregory M. , Weihs Timothy P.
Published: April 2022

This review focuses on the properties of reactive materials (RMs) that enable exothermic formation reactions and their application as local heat sources. We examine how the heat produced by these formation reactions can enable a range of useful functions including bonding, sealing, ignition, signaling, and built-in degradation. We begin by describing the chemistries, geometries, microstructures, and fabrication of RMs. We then explore the magnitude and measurement of their stored chemical energies and the rates and mechanisms by which the stored energy can be released to generate useful heat. The majority of the review focuses on how the released heat can be modeled and used to perform a range of functions.
Long Noncoding RNAs That Function in Nutrition: Lnc-ing Nutritional Cues to Metabolic Pathways
Authors: Lovell Claudia D. , Anguera Montserrat C.
Published: April 2022

Long noncoding RNAs (lncRNAs) are sensitive to changing environments and play key roles in health and disease. Emerging evidence indicates that lncRNAs regulate gene expression to shape metabolic processes in response to changing nutritional cues. Here we review various lncRNAs sensitive to fasting, feeding, and high-fat diet in key metabolic tissues (liver, adipose, and muscle), highlighting regulatory mechanisms that trigger expression changes of lncRNAs themselves, and how these lncRNAs regulate gene expression of key metabolic genes in specific cell types or across tissues. Determining how lncRNAs respond to changes in nutrition is critical for our understanding of the complex downstream cascades following dietary changes and can shape how we treat metabolic disease. Furthermore, investigating sex biases that might influence lncRNA-regulated responses will likely reveal contributions toward the observed disparities between the sexes in metabolic diseases.
A Systematic Review of Autonomous Emergency Braking System: Impact Factor, Technology, and Performance Evaluation
Authors: Yang Lan , Yang Yipeng , Wu Guoyuan , Zhao Xiangmo , Fang Shan , Liao Xishun , Wang Runmin , Zhang Mengxiao
Published: April 2022

In order to track the research progress of AEB-related technologies, this paper makes a systematic analysis and research on the impact factors, key technologies, and effect evaluation of AEB. First, the paper deeply analyzes the three levels of factors affecting the performance of AEB, which are vehicle factors, driver factors, and environmental factors. Second, the paper deeply studies the technical status of the three subsystems of environment perception, decision-making, and control execution. Particularly, the performance of Mazda, Honda, NHTSA, Berkeley, and Seungwuk Moon are compared and analyzed based on MATLAB. Third, the paper summarizes the current AEB virtual test methods, closed field test methods, and its test sites. Three classic evaluation methods in the world, including the AEB test evaluation standards of ENCAP, IIHS, and i-Vista are analyzed. Finally, the paper prospects the specific research directions, including the protection of vulnerable road users, target detection method, collision avoidance strategy, complex scenarios application, and application of emerging technologies.
Plant-Derived Iron Nanoparticles for Removal of Heavy Metals
Authors: Thilakan Deepika , Patankar Jaie , Khadtare Srushti , Wagh Nilesh S. , Lakkakula Jaya , El-Hady Khalid Mohamed , Islam Saiful , Islam Md. Rabiul , Khan Mohd Shahnawaz , Alafaleq Nouf Omar , Tarique Mohammad
Published: April 2022

Nanoparticle synthesis has seen exponential development recently as its characteristics of high surface area, high rate of adsorption, and easy, cost-effective synthesis have been exploited for the purpose of ground water purification via the removal of organic and inorganic compounds, along with the removal of heavy metals and microbes. The synthesis of Zero-Valent Iron Nanoparticles (ZVI NPs) by green methods has proved to be environmentally friendly in many ways as it employs the use of naturally occurring plant extracts. These nanoparticles have large surface areas and efficiently remove heavy metals. The reducing potential of these ZVI NPs is mostly −0.44 V, thus allowing them to reduce heavy metal compounds such as cadmium, lead, zinc, copper, and arsenic present in wastewater. Irradiated nanoparticles have also exhibited antimicrobial resistance and adsorption. It is also observed that nanoparticles show a higher rate of efficacy at a lower pH. The adsorbent, which is ZVI NPs in this case, when present in large doses reduces heavy metal compounds rapidly and effectively.
A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification
Authors: Thapar Puneet , Rakhra Manik , Cazzato Gerardo , Hossain Md Shamim
Published: April 2022

Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.
Flattening of New Media Design Based on Deep Reinforcement Learning
Authors: Zhu Yuan
Published: April 2022

In order to strengthen the flat design of new media, this paper proposes the research on the flat design of new media based on deep reinforcement learning. Firstly, this paper introduces the basic principle of deep belief network and lists the flat design methods of new media animation. An algorithm based on deep reinforcement learning is designed, which can gradually fill the missing area image. A set of repair strategy is designed through the new media animation image repair algorithm. Taking animation facial expression recognition Based on deep learning as an example, this paper expounds the theme through the combination of theory and practice. In the Jaffe database and Cohn Kanade database, three new media animations with different resolutions of

16
×
16

,

32
×
32

, and

64
×
64

are taken as examples, and DBN (Deep Confidence Network) method is compared with other five common classification methods. DBN has the highest identified value. It should be noted that the accuracy of the DBN method is

23.79
%

,

4.31
%

, and

4.80
%

higher than that of MLP under

16
×
16

,

32
×
32

, and

64
×
64

, respectively. The recognition performance of DBNs is almost the highest. Although the classical SVM method has achieved 98.11% recognition rate on

32
×
32

image, which is higher than the DBN method, the fluctuation of the SVM method is relatively large, and the recognition rate on

16
×
16

image has declined greatly. Relatively speaking, the recognition rate of the DBN method is relatively stable.
Oceanic Radionuclide Dispersion Method Investigation for Nonfixed Source from Marine Reactor Accident
Authors: Guo Dingqing , Wang Jinkai , Ge Daochuan , Chen Chunhua , Chen Liwei
Published: April 2022

Radionuclide dispersion model, which is of critical importance to the emergency response of severe nuclear accident, is used to estimate the consequences arising from accidental or routine releases and to predict areas of high contamination. It is difficult to evaluate the radioactive consequence accurately and rapidly for the accidental release of radionuclides from marine reactor because of the complex mobility feature in the sea. Based on CFD method, a finite-volume, three-dimensional regional oceanic dispersion model was developed in this paper to simulate the dispersion of radionuclides originating from marine reactor. The simulated dose variation of 137Cs presented good agreement with the monitoring data of marine radioactive pollution caused by Fukushima Dai-ichi nuclear accident, which demonstrated the validity of the method. A severe accident scenario of marine reactor was simulated and analyzed, which indicates that the regional oceanic dispersion model can provide dose assessment for nuclear emergency response.
Real-Time Information Exchange Strategy for Large Data Volumes Based on IoT
Authors: Du Jin
Published: April 2022

In this paper, we study and analyse the real-time information exchange strategy of big data in the Internet of Things (IoT) and propose a primitive sensory data storage method (TSBPS) based on spatial-temporal chunking preprocessing, which substantially improves the speed of near real-time storage and writing of microsensory data through spatial-temporal prechunking, data compression, cache batch writing, and other techniques. The model is based on the idea of partitioning, which divides the storage and query of perceptual data into the microperceptual data layer and the perceptual data layer. The microaware data layer mainly studies the storage optimization and query optimization of raw sensory data and cleaned valid data; the aware data is the aggregation and statistics of microaware data, and the aware data layer mainly studies the storage optimization and query optimization of aware data. By arranging multiple wireless sensors at key monitoring points to collect corresponding data, building the core data service backend of the system, defining multifunctional servers, and constructing an optimal database model, we effectively solve the parameter collection and classification aggregation processing of different devices. To address the requirement of reliable and secure transmission in the process, we design a highly concurrent and high-performance TCP-based socket two-layer transmission framework and introduce the asymmetric encryption method (RSA) and data integrity verification method to design a transmission protocol that is both reliable and secure. The integration of big data and IoT is bound to bring the intelligence of human society to a new level with unlimited development prospects.
Fuzzy-Swarm Intelligence-Based Short-Term Load Forecasting Model as a Solution to Power Quality Issues Existing in Microgrid System
Authors: Teferra Demsew Mitiku , Ngoo Livingstone M. H. , Nyakoe George N.
Published: April 2022

Load demand is highly stochastic and uncertain. This is because it was highly influenced by a number of variables like load type, weather conditions, time of day, the seasonality factor, economic constraints, and other randomness effects. The loads are categorized as holiday loads (national and religious), weekdays, and weekend days. The nonlinearity and uncertain characteristics of electrical load in a microgrid are one of the major sources of power quality problems in a microgrid system, and they can be handled using an accurate load forecast model. The fuzzy load prediction model can effectively handle these nonlinearity and uncertainty characteristics to have an accurate load forecast, but the main challenge with this model is its inability to accommodate a large volume of historical load and weather information when the membership function of the input and output fuzzy variables and the number of the fuzzy rules are tremendous. The swarm intelligence load forecast model based on particle swarm optimization algorithms can improve the limitations of the fuzzy system and increase its forecasting performance. The parameters of time, temperature, historical load, and error correction factors are considered as the Fuzzy and Fuzzy-PSO model input variables, while the forecasted industrial load is the only output variable. The Gaussian membership function is considered for both the input and output fuzzy variables. A 3-year historical hourly load data of an Ethiopian industrial system is used to train and validate both prediction models. The mean absolute percentage error (MAPE) is used to evaluate the performance of these prediction models. The Fuzzy-PSO load prediction model shows results that have superior performance to the fuzzy-alone load prediction results.
Human Health Activity Recognition Algorithm in Wireless Sensor Networks Based on Metric Learning
Authors: Sun Dejie , Zhang Jie , Zhang Shuai , Li Xin , Wang Hangong
Published: April 2022

Wireless sensor network is an ad hoc network with sensing capability. Usually, a large number of sensor nodes are randomly deployed in an unreachable environment or complex area for data collection and transmission, which can realize the perception and monitoring of the target area or specific objects and transmit the obtained data to the remote end of the system. Human health activity recognition algorithm is a hot topic in the field of computer. Based on the small sample problem and the linear indivisibility of real samples encountered in metric learning, this paper proposes a human activity recognition algorithm for wireless sensor networks. Human activity recognition algorithm for wireless sensor networks uses human activity recognition algorithm to solve the singularity of intraclass divergence matrix, so as to reduce the impact of small sample problem. The algorithm maps two different feature spaces to the high-dimensional linearly separable kernel space through the corresponding kernel function, calculates the distance between samples in the two projected feature subspaces to obtain two distance measurement functions, and finally linearly combines them with weights to obtain the final distance measurement function.
A Multiobjective Allocation Method for High-Quality Higher Education Resources Based on Cellular Genetic Algorithm
Authors: He Tianshu
Published: April 2022

Optimizing the allocation of higher education resources and improving the utilization efficiency of educational resources are of great significance to further promoting the balanced development of higher education quality. In order to rationalize and maximize the efficiency of the allocation of innovation and entrepreneurship education resources in colleges and universities and build an evaluation index system for the input and output of educational resources, this paper proposes to use the improved cytogenetic algorithm educational resources for resource allocation and utilization. The adaptive constraint processing technology is combined with the cytogenetic algorithm to avoid the algorithm falling into local optimum. Our method has achieved good results on high-dimensional functions. The utilization efficiency and allocation efficiency of innovation and entrepreneurship education resources in colleges and universities have increased by 21.33% and 18.92%, respectively, and tend to be in a balanced state, which can optimize the allocation of educational resources.
An Exploratory Pledging Framework of Crowdfunding for Utilities Tunnel Engineering Project
Authors: Sun Feng , Liu Cheng
Published: April 2022

This paper gives an exploratory mathematical framework for crowdfunding pledging of utilities tunnel engineering project. For two typical types of “All-or-Nothing” and “Keep-it-All” platforms, it analyzes the crowdfunding process how to operate on the platforms for the project. There are four types of crowdfunding modes, and a utilities tunnel project can flexibly choose one or several crowdfunding types analyzed in this paper to improve project construction according to the status of the project. Debt crowdfunding should design rational interest payment mechanism and Macaulay duration, and equity crowdfunding should consider the backers may get dividend and capital revenue for a utilities tunnel project. Simulation analyses of debt crowdfunding and equity crowdfunding give some cases for project fulfillment. The founder of reward crowdfunding can give free or low price energy products to the backers, while the backers of donation crowdfunding are completely philanthropic to supply the pledged capital.
Dissecting and analyzing the Subclonal Mutations Associated with Poor Prognosis in Diffuse Glioma
Authors: Bai Ming , Wang Xiaolong , Zhang Huixue , Wang Jianjian , Lyaysan Gaysina , Xu Si , Tian Kuo , Wang Tianfeng , Li Jie , Wang Na , Lu Xiaoyu , Zhang Xiaoming , Wang Lihua
Published: April 2022

The prognostic and therapeutic implications in diffuse gliomas are still challenging. In this study, we first performed an integrative framework to infer the clonal status of mutations in glioblastomas (GBMs) and low‐grade gliomas (LGGs) by using exome sequencing data from TCGA and observed both clonal and subclonal mutations for most mutant genes. Based on the clonal status of a given gene, we systematically investigated its prognostic value in GBM and LGG, respectively. Focusing on the subclonal mutations, our results showed that they were more likely to contribute to the poor prognosis, which could be hardly figured out without considering clonal status. These risk subclonal mutations were associated with some specific genomic features, such as genomic instability and intratumor heterogeneity, and their accumulation could enhance the prognostic value. By analyzing the regulatory mechanisms underlying the risk subclonal mutations, we found that the subclonal mutations of AHNAK and AHNAK2 in GBM and those of NF1 and PTEN in LGG could influence some important molecules and functions associated with glioma progression. Furthermore, we dissected the role of risk subclonal mutations in tumor evolution and found that advanced subclonal mutations showed poorer overall survival. Our study revealed the importance of clonal status in prognosis analysis, highlighting the role of the subclonal mutation in glioma prognosis.
Research and Application of Integrated Presplitting Blasting in Nonpillar Mining Technology
Authors: Liu Xiaoyu , Mei Guodong , Xie Xuyang , Wang Sha , He Manchao
Published: April 2022

This article introduces a technology of integrated presplitting blasting, which can be applied to a new nonpillar coal mining method (i.e., Gob-side Entry Retaining by Roof Cutting (GERRC)) under the condition of thick and hard roofs. First, the new nonpillar coal mining method emerging in China is explained. Then, the concept of integrated presplitting blasting was put forward, and the mechanism of the technology was explained by the method of numerical simulation. Through a mining case with a thick hard limestone roof, the relevant design of nonpillar mining and the design parameters of integrated presplitting blasting are explained in detail. After field practice verification, from the construction process and test results, as well as combined with underground pressure observation and analysis, it shows that technology and design are reasonable. This research supplements the mining technology system of coal mining engineering and the proposed technologies and methods have strong applicability, especially for related coal mines nonpillar mining and safe and efficient mining.
The Flight Mechanism of a Bird-like Flapping Wing Robot at a Low Reynolds Number
Authors: Ding Changtao , Yao Xiating , Liu Chengyao
Published: April 2022

The flight mechanism of a bird-like flapping wing robot at a low Reynolds number was studied in this study for improving the robot performances. Both the physical model and the kinematic model were first established. The dynamic model of the robot at a low Reynolds number was built with the RANS (Reynolds-averaged Navier-Stokes) equations and the Spalart-Allmaras turbulence model. The flight experiments were carried out and the results were discussed. Lift and drag coefficient curves show that it generates upward lift and forward thrust in the phase that the wing flaps downwards, the rate of the coefficient curves is the biggest when the flapping direction changes. Pressure contours indicate that small vortexes with high pressure values appear at the wing edges. There are four velocity vortex groups in total at the front and back of the wing in the velocity contours. Some methods for improving the robot flight efficiency and the robot strength as well as the stitching position of the robot skin have been obtained from the above results. The methods provide the important guidance for the stable flights of the flapping wing robot with the high efficiency.
Multisource Deep Transfer Learning Based on Balanced Distribution Adaptation
Authors: Gao Peng , Li Jingmei , Zhao Guodong , Ding Changhong
Published: April 2022

The current traditional unsupervised transfer learning assumes that the sample is collected from a single domain. From the aspect of practical application, the sample from a single-source domain is often not enough. In most cases, we usually collect labeled data from multiple domains. In recent years, multisource unsupervised transfer learning with deep learning has focused on aligning in the common feature space and then seeking to minimize the distribution difference between the source and target domains, such as marginal distribution, conditional distribution, or both. Moreover, conditional distribution and marginal distribution are often treated equally, which will lead to poor performance in practical applications. The existing algorithms that consider balanced distribution are often based on a single-source domain. To solve the above-mentioned problems, we propose a multisource transfer learning algorithm based on distribution adaptation. This algorithm considers adjusting the weights of two distributions to solve the problem of distribution adaptation in multisource transfer learning. A large number of experiments have shown that our method MTLBDA has achieved significant results in popular image classification datasets such as Office-31.
Application of Free Skin Flap Transplantation in Skin Malignant Tumor Resection
Authors: Zhou Yue , Xu Weili , Hou Jue , Hou Chunsheng , Zhang Jufang
Published: April 2022

One of the most important surgical approaches for restoring cutaneous abnormalities caused by tumor resection is the insertion of a skin flap. Flap transplantation has been used to cover and fill different deficiencies after tumor removal. However, distal necrosis is among the most prevalent postsurgical consequences for skin flaps, particularly when treating large regions. Blood circulation failure causes flap necrosis, which may lead to serious problems, particularly in patients undergoing body cavity reconstruction following tumor excision. ADSCs (adipose-derived stem cells) are a kind of stem cell separated from the adipose tissue that has multilineage differentiation, simple availability, high proliferation capability, and self-renewal capabilities. This paper uses human adipose-derived stem cell (ADSC) therapy for skin flap transplantation in skin malignant tumor resection. ADSC-based therapy is an applicable technique for assisting flap transplantation. The isolation of ADSC is performed using different trypsin concentrations, and then, the population doubling time is determined. The isolated ADSCs are differentiated and then employed for the skin flap model. The performance of the suggested method is analyzed using various assays. The usage of adipose-derived stem cells to boost the vitality of the skin flaps proved successful.
Robot Remote Monitoring and Fault Diagnosis Based on Industrial Internet of Things
Authors: Zhou Guanghong , Zhuang Erxing , Hu Junping
Published: April 2022

In order to realize the remote monitoring of robots, a remote monitoring platform for industrial robots is designed based on the browser/server (B/S) architecture. Through this platform, users can check the real-time running parameters and running status of robots in any place with network. The Industrial Internet of Things scheme of remote platform system is proposed to adopt three-layer structure: on-site “perception layer,” information “transmission layer,” and remote data service center. The data acquisition controller of the whole system and the core part of the sensor layer is designed. The data acquisition controller adopts the embedded platform design, which can be directly connected with the control cabinet of the industrial robot to read the running status of the robot in real time, monitor the alarm and warning data, and it is transmitted to the local server and remote service center in the first time. At the same time, the robot can receive the control command of the server for remote debugging and fault maintenance. Aiming at the data model of industrial machinery parts, a fault prediction method based on BP neural network algorithm is proposed. According to the needs of the target algorithm and the analysis of the measurement results, an attempt is made to obtain a more feasible fault diagnosis and early warning method. Through the remote monitoring system, fault early warning and corresponding troubleshooting methods are realized.