Prediction of College Employment Rate Based on Big Data Analysis

This paper uses big data technology to predict the employment rate of colleges and universities. In this paper, combined with the current rental price, daily life consumption, and college students’ personal interests and hobbies consumption and other indicators, the individual is simulated by big data, and the individual is associated by using the AI-driven edge fog computing service optimization algorithm to form a cluster, so as to realize the prediction from element to neural network cluster by using edge computing. In addition, this paper takes the employment data of colleges and universities in Hunan province from June 2020 to May 2021 as the research sample to test the prediction model and makes a comparative analysis with the CNN model and LSTM model. The edge fog computing model in this paper has more analytical indexes as tuples compared to the CNN model, so the results show that the prediction accuracy can reach 83.25%. In this case, there is little difference between the two models of data processing and predictive efficiency. Compared with the LSTM based classification prediction model, this model is edge computing, which greatly improves the data quality of model and data parameters, and the calculation efficiency can be increased by 45%–65%. Therefore, the use of big data technology can provide a reference for the research direction of higher education.

How do the factors associated with frailty change with sex? An exploratory network analysis

Background It is important to study multiple social, physical and psychosocial factors associated with frailty in populations characterized by social and health disparities, such as men and women. Methods This was a cross-sectional population-based study with older adults ≥65 years from the FIBRA (Frailty in Brazilian Older Adults) 2008-2009 study. We carried out a comparative analysis of the factors associated with the frailty phenotype in older men (N=706) and women (N=1.251) using networks based on mixed graphical models (MGM) according to sex. Results In the male network, frailty was most strongly associated with years of schooling, overall satisfaction with life and falls; in the female network, the syndrome was associated with satisfaction with problem solving, depression and diabetes in addition to years of schooling. Furthermore, permutation tests showed that the networks for males and females were statistically different in terms of their structure, the global strength of the relationships and the strength of the relationships between frailty and diabetes; frailty and falls; frailty and depression; frailty and overall satisfaction with life; and frailty and satisfaction with problem solving (p<0.05). The walktrap network cluster detection algorithm revealed that in men, frailty was in a physical and social dimension while in women the syndrome was in a cardiometabolic and psychosocial dimension. Conclusions Network analysis showed that different factors are associated with frailty for each sex. The findings suggest that different strategies for dealing with frailty should be adopted for men and women so that care and prevention efforts can be directed appropriately.

Efficiency of cluster schemes implementation for discrete dynamic programming for systems of support of dispatching technological processes in transport systems

Within the framework of a computationally complex canonical scheduling problem, formulated by an optimization model for one-processor servicing of a finite deterministic flow of objects, a scheme of computational process of an algorithm of discrete dynamic programming in cluster implementation is considered. Variants of balancing of computational subtasks over network cluster array are investigated, purposed to reduce the volume and intensity of intranetwork interaction. It has been established that for practical improvement of efficiency of cluster algorithm, it is required not to increase the uniformity of distribution of subtasks among the cluster nodes, but to minimize the network traffic between the cluster nodes. Balancing options are proposed that allow to significantly increase localization of data in network computing. Experimental results are analytically confirmed, showing the scaling limits of implementation of discrete dynamic programming algorithms on a cluster architecture. The method for choosing the number of computational nodes and dimension of the problem being solved, which provide a threefold reduction in overhead costs for network exchange, is shown. The results obtained make it possible to objectively substantiate the choice of methodological and algorithmic approaches when choosing computer tools developing architectural and technological solutions for dispatching systems support in inland water transport.

Worldwide Trends in Bilingual Education Research: A Half-Century Overview

Despite the wealth of studies on bilingual education, there is a dearth of meta-research on the worldwide development and trends of this area of investigation over the past few decades. The occupation of this gap allows scholars to take stock of current states of research, get overviews of the contributions made to the field, foresee future research trends, and identify research needs and gaps that may be addressed in future investigation. This study analyses the evolution and trends of bilingual education research during a 50-year period (1969–2018) from a bibliometric perspective. The results show a steady increase in the number of publications, and was exponential in the last decade, mainly in the form of research articles, which makes bilingual education a truly consolidated and increasingly evolving research field. The US is the leading country with respect to the number of publications, affiliations, and sponsors, followed, primarily, by some other North American (e.g., Canada), European (e.g., UK and Spain), and Asian (e.g., China) countries, as well as Australia. There is a large research network cluster led by the US involving intercontinental interaction among institutions from Europe, Asia, and, to a lesser extent, South America. However, a scant level of internationalisation of scholars publishing works on bilingual education was observed, with most author collaboration being limited to different US institutions. The most influential authors belong to institutions from the US, Canada, Spain, and Israel, and, to a lesser extent, Australia. The main research topics in the field depend on the contexts and include regulations of language institutions, bilingual education models, language skills, pedagogical strategies, education levels, and ages, among others. These results may contribute to the identification of new research needs and therefore, to the development of future directions in bilingual education research.

You Can’t Get There from Here: Mobility Networks and the Housing Choice Voucher Program

The Housing Choice Voucher (HCV) program represents the largest subsidized housing program in the United States. While families with vouchers can, in theory, lease any housing of reasonable quality renting below a rent ceiling, the empirical evidence suggests that they rarely use their vouchers to move to lower poverty neighborhoods. This paper examines the question of how spatial boundaries impact the residential possibilities of HCV subsidized families, both the visible boundaries of Public Housing Authority (PHA) catchment areas and the invisible boundaries of racial and economic segregation. I use administrative data supplied by the Department of Housing and Urban Development, which includes all moves by HCV families between 2005 and 2015 in the Baltimore, MD, Cleveland, OH, and Dallas, TX, metropolitan areas. Using a Louvain method of network cluster detection, I subdivide each metro into distinct mobility clusters—sets of census tracts within which voucher holders move but between which they rarely do. I find that the empirical mobility clusters at the metropolitan level are highly defined by PHA’s catchment areas. Even though families are technically allowed to “port” their voucher from one PHA catchment area to another, such behavior is rare. Within the PHA catchment areas, HCV mobility clusters are defined by patterns of race, income, and history. These findings suggest that patterns of racial and economic segregation seem to partially define the mobility clusters within PHA catchment areas, but not across them.

An Energy-Efficient Mobile Sink based Clustering using Simulated Annealing for Traversing

Over recent decades, both scientific and commercial societies have been seeing the progress of wireless sensor networks (WSNs). Clustering is the most common form of growing WSN lifetime. The optimal number of cluster heads (CHs) & structure of clusters are the main problems in clustering techniques. The paper focuses on an efficient CH preference mechanism that rotates CH between nodes amid a greater energy level than others. Original energy, residual energy as well as the optimum value of CHs is assumed to be used by the algo for the choice of the next category of IoT-capable network cluster heads including ecosystem control, smart cities, or devices. The updated version of K-medium algo k-means++. Meanwhile, Simulated Annealing is implemented as the shortest path tree for mobile nodes which is constructed to establish the connection between the nodes for finding the shortest and secure path for data transmission hence resulting in faster data sending and receiving process. Keywords: WSN, CH selection, Residual energy (RE), Network Lifetime, Energy-efficient (EE)

Resource tokenization for crowdfunding of wireless networks

This paper presents a novel blockchain-based spectrum tokenization method used to crowdsource wireless network deployment projects. Crowdsourcing is a method of financing certain projects and ideas through the funds collected by individuals or businesses in an open marketplace. The method presented in this paper finances the wireless network deployment projects belonging to service providers or governments. The method tokenizes proposed novel wireless resource units, and sells these units to investors. A new Value Unit Per User (VUPU) resource unit is introduced with a new pricing scheme depending on a load of a base station. A novel Proof of Data Load (PoDLO) consensus algorithm is proposed which is used to verify data and traffic load of a base station. Device Diversity Factor (DDF) and Subscriber Unique Permanent Identifier (SUPI) Factor (SUF) are proposed new ways to determine the value of a base station and a network cluster.

The influence of public health information sharing on social media during the early COVID-19 outbreak

PurposeSocial media have become famous platform to search and share the COVID-19-related information. The objective of this research is to bridge the gap by proposing the effects of network cluster and transmitter activity on information sharing process.Design/methodology/approachData were collected by using Facebook application, which was available for 14 days (May 1–14) in 2020. These data were analyzed to determine the influence of the network cluster and transmitter activity.FindingsThe results showed that network cluster is positively related to transmitter activity on social media. In addition, transmitter activity partially mediated the effect of network cluster on the extent of information liked and shared. That is, transmitter activity can affect COVID-19-related information sharing on Facebook, and the activity effect is plausible and should become stronger as social network become denser.Originality/valueThis study has contributed to the knowledge of health information sharing in social media and has generated new opportunities for research into the role of network cluster. As social media is firmly entrenched in society, researches that improve the experience or quality for users is potentially impactful.

Sailfish optimizer algorithm (SFO) for optimized clustering in wireless sensor network (WSN)

Purpose Inspired optimization algorithms respond to numerous scientific and engineering difficulties based on its flexibility and simplicity. Such algorithms are valid for optimization difficulties devoid of structural alterations. Design/methodology/approach This paper presents a nature-inspired optimization algorithm, named Sailfish optimizer (SFO) stimulated using sailfish group. Monetary custom of energy is a dangerous problem on wireless sensor network (WSN). Findings Network cluster is an effective method of reducing node power consumption and increasing network life. An algorithm for selecting cluster head (CHs) based on enhanced cuckoo search was proposed. But this algorithm uses a novel encoding system and wellness work. It integrates a few problems. To overthrow this method many metaheuristic-based CH selection algorithms are presented. To avoid this problem, this paper proposed the SFO algorithm based energy-efficient CH selection of WSN. Originality/value The proposed SFO algorithm based energy-efficient algorithm is used for discovering the CHs ideal situation. The simulations under delay, delratio, drop, energy, network lifetime, overhead and throughput are carried out.