A software-defined networking approach for handover management with real-time video in WLANs

To achieve high performance and reliability in video streaming over wireless local area networks （WLANs）, one must jointly consider both optimized association to access points （APs） and handover management based on dynamic scanning of alternate APs. In this article, we propose a new architecture within the software-defined networking （SDN） framework, which allows stations to be connected to several APs simultaneously and to switch fast between them. We evaluate our system in a real-time testbed and demonstrate that our SDN-based handover mechanism significantly reduces the number and duration of video freeze events and allows for smaller playout buffers.

A Study on the Evolution of Mechanism of Infectious Disease Analysis Model of Event Using Micro-Blog Platform

After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.

Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.

New Characteristics of Active Life Expectancy of the Elderly in China

Life expectancy of the elderly is a significant problem in China, and it changes not only the health care, but also the pension. This study used tracking data from the Chinese Urban and Rural Elderly Population Survey to calculate the age-specific Active Life Expectancy (ALE) of the Chinese elderly population aged 60 years and over. For analysis, this population was divided into different sub-populations according to gender, census register and region. The main conclusions of our study are as follows: 1) The quality of life for elderly males may be greater than that for elderly females;2) There were significant differences in Active Life Expectancy (ALE) and Inactive Life Expectancy (ILE) between urban and rural elderly;3) The differences in ALE between the eastern, central and western regions of China were not significant;and 4) The increased remaining life expectancy of the elderly was mainly attributed to the extended ALE in the lower age group and the expanded ILE in the higher age group. This study expands the knowledge of Chinese elderly’s life expectancy in different health status.

Structural Dimensions Analysis of Service Employees' Innovation Behavior Based on Structural Equation Model

In order to know about the essence of service employees' innovation behavior, the service employees' innovation behavior scale based on their unique characteristics is designed. Data were collected from high-star hospitalities. Through the issuance and the collection of questionnaires, the scale is verified to have good reliability and validity by SPSS software analysis. Meanwhile,the structural equation model( SEM) is suited for testing structural dimensions of service employees' innovation behavior. The results showed that service employees' innovation behavior could be decomposed into a four-dimensional structure,namely innovation orientation, ideas generation, innovation implementation and innovation practice. In addition,there are significant differences in service employees' innovation behavior about subjects' gender,position,education background and years of working.

Dimensional Measurement of Complete-connective Network under the Condition of Particle’s Fission and Growth at a Constant Rate

We construct a complete-connective regular network based on Self-replication Space and the structural principles of cantor set and Koch curve. A new definition of dimension is proposed in the paper, and we also investigate a simplified method to calculate the dimension of two regular networks. By the study results, we can get a extension: the formation of Euclidean space may be built by the process of the Big Bang's continuously growing at a constant rate of three times.

Verification of option parity relations in domestic warrants market of China

This paper screens five pairs of call walTants/put warrants with the same listing date and final exercise date from domestic delisted wattant, and collects and processes relevant statistic in warrant market and stock market. Because inconformity of strike price between call warrants/put warrants in domestic warrant market, and regarding the strike price of put warrants as standard, this paper takes advantage of BSM formula recalculates the price of call warrants, and carries out verification of option parity relations by regression analysis and Wilcoxon＇s Sign Rank Test. From a theoretical point of view, homogenous call warrants/put warrants should satisfy the parity relations. However, due to the lack of short sales mechanism in domestic warrant market and stock market, the empirical results indicate that domestic warrant market can＇ t meet the option parity relations.

Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have varying levels of understanding when it comes to securing an online application.Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws.Many published articles focused on these attacks’binary classification.This article described a novel deep-learning approach for detecting SQL injection and XSS attacks.The datasets for SQL injection and XSS payloads are combined into a single dataset.The dataset is labeledmanually into three labels,each representing a kind of attack.This work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a vector.Our model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload dataset.The payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and normal.The outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious payloads.BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.

Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial institutions.One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine attacks.This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy.The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity solution.The research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical information.The results suggest that detecting the submarine early increases the likelihood of averting a collision.The dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access efficiency.Securing communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the task.The swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its peers.This,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other UAVs.The dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

PIECEWISE LINEAR NCP FUNCTION FOR QP FREE FEASIBLE METHOD

In this paper, a QP-free feasible method with piecewise NCP functions is proposed for nonlinear inequality constrained optimization problems. The new NCP functions are piecewise linear-rational, regular pseudo-smooth and have nice properties. This method is based on the solutions of linear systems of equation reformulation of KKT optimality conditions, by using the piecewise NCP functions. This method is implementable and globally convergent without assuming the strict complementarity condition, the isolatedness of accumulation points. Purr thermore, the gradients of active constraints are not requested to be linearly independent. The submatrix which may be obtained by quasi-Newton methods, is not requested to be uniformly positive definite. Preliminary numerical results indicate that this new QP-free method is quite promising.

An improved proximal-based decomposition method for structured monotone variational inequalities

The proximal-based decomposition method was originally proposed by Chen and Teboulle （Math. Programming, 1994, 64：81-101 for solving corrvex minimization problems. This paper extends it to solving monotone variational inequalities associated with separable structures with the improvements that the restrictive assumptions on the involved parameters are much relaxed, and thus makes it practical to solve the subproblems easily. Without additional assumptions, global convergence of the new method is proved under the same mild assumptions on the problem＇s data as the original method.

Optimal restructuring strategies under various dynamic factors

Corporate restructuring was identified as a new industrial force that has great impact on economic values and that therefore has become central in daily financial decision making. This article investigates the optimal restructuring strategies under different dynamic factors and their numerous impacts on firm value. The concept of quasi-leverage is introduced and valuation models are built for corporate debt and equity under imperfect market conditions. The model’s input variables include the quasi-leverage and other firm-specific parameters, the output variables include multiple corporate security values. The restruc- turing cost is formulated in the form of exponential function, which allows us to observe the sensitivity of the variation in security values. The unified model and its analytical solution developed in this research allow us to examine the continuous changes of security values by dynamically changing the coupon rates, riskless interest rate, bankruptcy cost, quasi-leverage, personal tax rate, corporate taxes rate, transaction cost, firm risk, etc., so that the solutions provide useful guidance for financing and restructuring decisions.

Applying BP neural network to detect conveyor belt fire with multi-sensors

A kind of feed forward neural network with three layers was applied to detect conveyor belt fire faster. And backward propagation (BP) algorithm was used to train the network parameters. The appropriate parameters and architecture of network were ob- tained after training with 81 pair of data. Matlab was used to simulate and the experi- ment result shows training time is least and error reduces most rapidly when ten neu- rons were in hidden layer and momentum coefficient is equal to 0.95. Temperature, rate of temperature change, dense of carbon monoxide and rate of carbon monoxide dense change were considered as four parameters to detect the PVC belt fire in this paper. It is indicated that the network can give alarm as fire takes place about 350 s. The network can effectively detect the fire at the early stage of conveyor belt fire. At the same time, the reliability of alarm can be increased and the anti-interference capability can be en- hanced when using this network.

Intelligent data analytics is here to change engineering management

A great deal of scientific research in the world aims at discovering the facts about the world so that we understand it better and find solutions to problems. Data enabling technology plays an important role in modern scientific discovery and technologic advancement. The importance of good information was long recognized by prominent leaders such as Sun Tzu and Napoleon. Factual data enables managers to measure, to understand their businesses, and to directly translate that knowledge into improved decision making and performance. This position paper argues that data analytics is ready to change engineering management in the following areas: 1) by making relevant historical data available to the manager at the time when it's needed; 2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful data from multiple sources to support quantitative decision-making. Considering the unique need for engineering management, the paper proposes researchable topics in the two broad areas of data acquisition and data analytics. The purpose of the paper is to provoke discussion from peers and to encourage research activity.

Prisoners' Dilemma Supergame on Rectangle Lattice

In this paper a class of large supergames, i.e., infinitely repeated games played by many players are studied. The players located on the vertex set of planar rectangle lattice play several basic games with his neighbors. The basic game is two-person prisoners’ dilemma game with asymmetric payoffs. Under the conditions of the pre-specified updating rules and the transition probabilities, the relevant stochastic process of strategy evolution forms a Markovian process. The simulation results about the long-run behavior are provided.

A closed-loop Supply Chain Network Equilibrium Model with Fuzzy Demand under Production Capacity Constraints and Price Rigidities

A closed-loop supply chain network equilibrium problem is examined,which consists of mutual competitive manufacturers with production capacity constraints and retailers with fuzzy market demands,mutual competitive collectors as well as a binding price ceiling of the commodities.By utilizing the credibility measure of fuzzy event,variational inequality and Lagrange dual theory,the optimum behaviors of manufactures,retailers,collectors and consumers are described.Therefore,the supply chain network equilibrium model is established.Numerical examples were given to illustrate the impact of production capacity constraints and price ceiling on the network equilibrium pattern.The results show that the shortage of commodities in consumer market will be more serious,the wholesale price of product and purchase price of waste products rise,the profits of retailers decrease,the profits of manufacturers and collectors increase as the government gradually reduce the binding price ceiling on competitive markets,and the trend will be more obvious in the existence of production capacity constraints at same time.

Machine learning better predicts colonoscopy duration

BACKGROUND The use of machine learning(ML)to predict colonoscopy procedure duration has not been examined.AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.METHODS Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected.ML models predicting procedure duration were developed using data available at time of scheduling.The top performing model was compared against historical practice.Models were evaluated based on accuracy(prediction–actual time)±5,10,and 15 min.RESULTS ML outperformed historical practice with 77.1%to 68.9%,87.3%to 79.6%,and 92.1%to 86.8%accuracy at 5,10 and 15 min thresholds.CONCLUSION The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.

Agriculture Technology as a Tool to Influence Youth Farming in Ghana

The extent of engagement of the youth going into farming is steadily seen as the modern-day panacea for sustainability and extenuation of food insecurity. Yet, this plodding-shift of the youth has opened new doors for a different problem that needs efficient agricultural resolution. The study examines the correlation between youth farming and the various factors that influence the youth to go into agriculture using data obtained from the Ghanaian youth. We applied Explanatory Factor Analysis (EFA) of the paradigms, with principal component factoring. The results justified using Confirmatory Factor Analysis (CFA) in AMOS and R. The results showed that technology outperforms factors such as motivation, economics, and government policies to significantly influence the youth towards farming. Attitude and knowledge were also found to have an indirect influence on youth farming. The paper contributes to the theoretical influence of technology on the youth and on the practicality of agricultural farming.

A DYNAMIC ALLOCATION MODEL FOR MEDICAL RESOURCES IN THE CONTROL OF INFLUENZA DIFFUSION

In this paper, we develop a unique time-varying forecasting model for dynamic demand of medical resources based on a susceptible-exposed-infected-recovered （SEIR） influenza diffusion model. In this forecasting mechanism, medical resources allocated in the early period will take effect in subduing the spread of influenza and thus impact the demand in the later period. We adopt a discrete time-space network to describe the medical resources allocation process following a hypothetical influenza outbreak in a region. The entire medical resources allocation process is constructed as a multi-stage integer programming problem. At each stage, we solve a cost minimization sub-problem subject to the time-varying demand. The corresponding optimal allocation result is then used as an input to the control process of influenza spread, which in turn determines the demand for the next stage. In addition, we present a comparison between the proposed model and an empirical model. Our results could help decision makers prepare for a pandemic, including how to allocate limited resources dynamically.

BACKBONE OF TECHNOLOGY EVOLUTION IN THE MODERN ERA AUTOMOBILE INDUSTRY: AN ANALYSIS BY THE PATENTS CITATION NETWORK

In the automobile industry, especially in its modem era, large amount of technologies have been generated to produce automobiles. The technological evolution in this industry is formed by complicated effects of the emergence of some milestone inventions and interaction, integration, and succession among diverse technologies. It＇s a big challenge to sort out crucial inventions and technologies progresses that mainly form this industry＇s technological evolution. We use patent citation data and apply network analytical techniques to reveal characteristics of the ＂backbone＂ in the automobile industry＇s technological evolution. We employ three algorithms respectively to explore the main path of the technological evolution, the most important subnetwork which outlines the main characteristics of the industry＇s technological evolution, and the most important technological inventions （act as authorities and hubs of the technological evolution） in the industry. Main results are reported in detail by tables, figures and interpretations to disclose the most influential technologically developing path, pivotal transfers in technological trajectories, and important technological convergences and divergences over time, of the modem era automobile industry.