Evolution and Governance of the Structure of Marine Economic Networks in China’s Coastal Areas: Based on Sea-related A-share Listed Companies

Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linkages,the importance of marine economic net-work research is beginning to emerge.The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development.Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China,we use social network analysis(SNA)to discuss the characteristics of its evolution as of 2010,2015,and 2020 and its governance.The following results were obtained.1)In terms of topological characteristics,the scale of the marine economic network in China’s coastal areas has accelerated and expan-ded,and the connections have become increasingly close;thus,this development has complex network characteristics.2)In terms of spatial structure,the intensity of the connection fluctuates and does not form stable development support;the group structure gradually becomes clear,but the overall pattern is fragmented;there are spatial differences in marine economic agglomeration radiation;the radi-ation effect of the eastern marine economic circle is obvious;and the polarization effect of northern and southern marine economic circles is significant.On this basis,we construct a framework for the governance of a marine economic network with the market,the government,and industry as the three governing bodies.By clarifying the driving factors and building objectives of marine economic network construction,this study aims to foster the high-quality development of China’s marine economy.

Evolution of economic linkage network of the cities and counties on the northern slope of the Tianshan Mountains,China

The exchanges between cities and counties in the northern slope economic belt of Tianshan Mountains(NSEBTM)are increasingly frequent and the economic linkages are increasingly close,but the spatial distribution of economic development and linkages among the cities and counties within NSEBTM is uneven.Therefore,it is of great significance to study the evolution of spatial-temporal pattern of the economic linkage network of cities and counties on NSEBTM to promote the coordinated and integrated development of the regional economy on NSEBTM.In this study,we used the modified gravity model and social network analysis method to analyze the spatio-temporal evolution characteristics of the economic linkage network structure of cities and counties on NSEBTM in 2000,2010,and 2020.The results showed that the comprehensive development quality level of cities and counties on NSEBTM increased from 2000 to 2020,its growth rate also increased,and its gap between cities and counties continued expanding.Both the spatial distribution patterns of the comprehensive development quality level of cities and counties on NSEBTM in 2000 and 2010 were presented as“high in the middle and low at both ends”,while the spatial distribution pattern of 2020 was exhibited as“high value and low value staggered”.The total amount of external economic linkages of cities and counties on NSEBTM showed an obvious upward trend,and its gap between cities and counties continued expanding,presenting a pattern of“a strong middle section and weak ends”.The direction of economic linkages of NSEBTM existed obvious central orientation and geographical proximity.The density of economic linkage network of NSEBTM increased from 2000 to 2020,and the structure of economic linkage network changed from single-core structure centered with Urumqi City to multicore structure centered with Urumqi City,Karamay City,Shihezi City,and Changji City,shifting from unbalanced development to balanced development.In the future,we should accelerate the construction of urban agglomeration on NSEBTM,cultivate a modern Urumqi metropolitan area,improve comprehensive development quality of the cities and counties at the eastern and western ends,strengthen the intensity of economic linkages between cities and counties,optimize the economic linkage network,and promote the coordinated and integrated development of regional economy.

Local Trade Networks among Farmers and Traders

Both farmers and traders benefit from trade networking, which is crucial for the local economy. Therefore, it is crucial to understand how these networks operate, and how they can be managed more effectively. Throughout this study, we examine the economic networks formed between farmers and traders through the trade of food products. These networks are analyzed from the perspective of their structure and the factors that influence their development. Using data from 18 farmers and 15 traders, we applied exponential random graph models. The results of our study showed that connectivity, Popularity Spread, activity spread, good transportation systems, and high yields all affected the development of networks. Therefore, farmers’ productivity and high market demand can contribute to local food-crop trade. The network was not affected by reciprocity, open markets, proximity to locations, or trade experience of actors. Policy makers should consider these five factors when formulating policies for local food-crop trade. Additionally, local actors should be encouraged to use these factors to improve their network development. However, it is important to note that these factors alone cannot guarantee success. Policy makers and actors must also consider other factors such as legal frameworks, economic policies, and resource availability. Our approach can be used in future research to determine how traders and farmers can enhance productivity and profit in West Africa. This study addresses a research gap by examining factors influencing local food trade in a developing country.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

Drainage Network and Lineament Analysis:An Approach for Potwar Plateau(Northern Pakistan)

Drainage responds rapidly to tectonic changes and thus it is a potential parameter for teetonogeomorphological analysis. Drainage network of Potwar is a good geological record of movement, displacements, regional uplifts and erosion of the tectonic units. This study focuses on utilizing drainage network extracted from Shuttle Radar Digital Elevation Data （SRTM-DEM） in order to constrain the structure of the Potwar Plateau. SWAN syncline divides Potwar into northern Potwar deformed zone （NPDZ） and southern Potwar platform zone （SPPZ）. We extracted the drainage network from DEM and analyzed 112 streams using stream power law. Spatial distribution of concavity and steepness indices were used to prepare uplift rate map for the area. DEM was further utilized to extract lineaments to study the mutual relationship between lineaments and drainage patterns. We compared the local correlation between the extracted lineaments and drainage network of the area that gives us quantitative information and shows promising prospects. The streams in the NPDZ indicate high steepness values as compared to the streams in the SPPZ. The spatial distribution of geomorphic parameters distinctive deformation and uplift rates suggest the among eastern, central and western parts. The local correlation between drainage network and lineaments from DEM is strongly positive in the area within I km of radius.

Flexibility analysis and design of heat exchanger network for syngas-to-methanol process

The heat exchanger network(HEN)in a syngas-to-methanol process was designed and optimized based on pinch technology under stable operating conditions to balance the energy consumption and economic gain.In actual industrial processes,fluctuations in production inevitably affect the stable operation of HENs.A flexibility analysis of the HEN was carried out to minimize such disturbances using the downstream paths method.The results show that two-third of the downstream paths cannot meet flexibility requirements,indicating that the HEN does not have enough flexibility to accommodate the disturbances in actual production.A flexible HEN was then designed with the method of dividing and subsequent merging of streams,which led to 13.89%and 20.82%reductions in energy consumption and total cost,respectively.Owing to the sufficient area margin and additional alternative heat exchangers,the flexible HEN was able to resist interference and maintain production stability and safety,with the total cost increasing by just 4.08%.

Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

Economic analysis of spider web airline networks

The distinct network organization, management, service and operating characteristics of US Southwest Airlines are key elements of its success compared with other airlines. As a network organization type, the spider web airline network has received more attention. In this paper, we analyzed the relation between the spider web airline network and spider web, and the structure of spider web airline network, built the assignment model of the spider web airline network,and investigated the economics concerned.

A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing research methods show poor performance.AQN3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the performance.The proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)phases.In DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered path.Next,it is tested with the trained IDS.The IDS encompasses‘3’steps:pre-processing,matrix reduction,and classification.In pre-processing,the data is organized in a clear format.Then,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix values.Next,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal data.In an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing algorithms.The proposed work attains an enhanced outcome than the prevailing methods.

Greek Political Language during the Economic Crisis—A Network Analytic Approach

In this paper, we seek to analyze pre-electoral political language in Greece with the use of Social Network Analysis. For this analysis, we collected data from the pre-elections speeches of five political leaders from the 20th of September 2015 Greek general elections. We proceed to form, analyze and compare networks of words with an emphasis on financial vocabulary. Findings can provide interesting insights into how political leaders structure their speeches, evaluate important issues and use economic terms and political rhetoric, while different structural patterns can reveal the differences between political parties. Finally, we check whether the overall networks follow the general rules of real-life networks by belonging to the small-world or scale-free categories.

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines.Given the advantage of obtaining accurate diagnosis results,multi-sensor fusion has long been studied in the fault diagnosis field.However,existing studies suffer from two weaknesses.First,the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types.Second,the localization for multi-source faults is seldom investigated,although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable.This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations(MSRs).First,an MSR model is developed to learn MSRs automatically and further obtain fault recognition results.Second,centrality measures are employed to analyze the MSR graphs learned by the MSR model,and fault sources are therefore determined.The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump.Results show the proposed method’s validity in diagnosing fault types and sources.

Economic Benefits and Environmental Costs of China＇s Exports： A Comparison with the USA Based on Network Analysis

This＇ paper decomposes economic benefits （value-added） and environmental costs （CO2） of exports according to their sources, and maps the global value network （GVN） and the global emissions network （GEN） for China＇s exports during 1995-2009 from national, sectoral and national sectoral perspectives. A comparison is conducted between China and the USA. National GVN and GEN show that shares of value-added and CO2 emissions from China in its GVN and GEN both decreased first then increased after 2006, while shares from the USA in its GVN and GEN generally decreased. Seetoral GVN and GEN show that among China＇s exports, ＂electrical and optical equipment＂ and ＂electricity, gas and water supply＂ were, respectively, the sectors that obtained the most value-added and emitted the most CO2. National-sectoral G VN and GEN for China exhibited reciprocal and disassortative patterns, and in-strengths and out-strengths of GVN and GEN for China ＇s exports were mainly captured by several domestic country-sector pairs.

Step-wise synthesis of work exchange networks involving heat integration based on the transshipment model

Due to the deterioration of serious energy dilemma,energy-conservation and emission–reduction have been the strategic target in the past decades,thus people have identified the vital importance of higher energy efficiency and the influence of lower carbon development.Since work exchange network is a significant part of energy recovery system,its optima design will have dramatically significant effect on energy consumption reduction in chemical process system.With an extension of the developed transshipment model in isothermal process,a novel step-wise methodology for synthesis of direct work exchange network(WEN)in adiabatic process involving heat integration is first proposed in this paper,where a nonlinear programming(NLP)model is formulated by regarding the minimum utility consumption as objective function and optimizing the initial WEN in accordance with the presented matching rules to get the optimized WEN configuration at first.Furthermore,we focus on the work exchange network synthesis with heat integration to attain the minimal total annual cost(TAC)with the introduction of heat-exchange equipment that is achieved by the following strategies in sequence:introducing heat-exchange equipment directly,adjusting the work quantity of the adjacent utility compressors or expanders,and approximating upper/lower pressure limits consequently to obtain considerable cost savings of expanders or compressors and work utility.Finally,a case taken from the literature is studied to illustrate the feasibility and effectiveness of the proposed method.

Automatic discovery of stateful variables in network protocol software based on replay analysis

Network protocol software is usually characterized by complicated functions and a vast state space.In this type of program,a massive number of stateful variables that are used to represent the evolution of the states and store some information about the sessions are prone to potentialflaws caused by violations of protocol specification requirements and program logic.Discovering such variables is significant in discovering and exploiting vulnerabilities in protocol software,and still needs massive manual verifications.In this paper,we propose a novel method that could automatically discover the use of stateful variables in network protocol software.The core idea is that a stateful variable features information of the communication entities and the software states,so it will exist in the form of a global or static variable during program execution.Based on recording and replaying a protocol program’s execution,varieties of variables in the life cycle can be tracked with the technique of dynamic instrument.We draw up some rules from multiple dimensions by taking full advantage of the existing vulnerability knowledge to determine whether the data stored in critical memory areas have stateful characteristics.We also implement a prototype system that can discover stateful variables automatically and then perform it on nine programs in Pro FuzzBench and two complex real-world software programs.With the help of available open-source code,the evaluation results show that the average true positive rate(TPR)can reach 82%and the average precision can be approximately up to 96%.

An economic tie network-structure analysis of urban agglomeration in the middle reaches of Changjiang River based on SNA

Due to its great strategic significance in integrating regional coordinated development and enhancing the rise of Central China, urban agglomeration in the middle reaches of Changjiang （Yangtze） River has attracted much attention from both theoretical and practical aspects. Such research into the area＇s economic network structure is beneficial for the formation of an urban- and regional-development strategy. This paper constructs an economic tie model based on a modified gravitation model. Subsequently, referring to social network analysis, the paper empirically studies the network density, network centrality, subgroups and structural holes of the middle reaches of Changjiang River＇s urban agglomeration economic network. The findings are fourfold： （1） an economic network of urban agglomeration in the middle reaches of Changjiang River has been formed, and economic ties between the cities in this network are comparatively dense; （2） the urban agglomeration in the middle reaches of Changjiang River can be divided into four significant subgroups, with each subgroup having its own obvious economic communications, while there is less economic-behavioral heterogeneity among subgroups - this is especially true for the two subgroups that exist in the Poyang Lake Ecological Economic Zone; （3） an economy pattern driven by the central cities of Wuhan, Changsha and Nanchang has emerged in the urban agglomeration of the middle reaches of Changjiang River, while these three capital cities have exerted great radiation abilities to their surrounding cities, the latter are less able to absorb resources from the former （4） the Wuhan Metropolitan Areas and the Poyang Lake Ecological Economic Zone have more structural holes than the Ring of Changsha, Zhuzhou and the Xiangtan City Clusters, meaning that cities at the periphery of these two areas are easily constrained by central cities. The Ring of Changsha, Zhuzhou and the Xiangtan City Clusters have fewer structural holes; thus, the cities in this area will not face as many constraints as those in the other two areas.

Codimensional matrix pairing perspective of BYY harmony learning:hierarchy of bilinear systems,joint decomposition of data-covariance,and applications of network biology

One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang(BYY)harmony learning from a perspective of problem solving,parameter learning,and model selection.In a complementary role,the paper provides further insights from another perspective that a co-dimensional matrix pair(shortly co-dim matrix pair)forms a building unit and a hierarchy of such building units sets up the BYY system.The BYY harmony learning is re-examined via exploring the nature of a co-dim matrix pair,which leads to improved learning performance with refined model selection criteria and a modified mechanism that coordinates automatic model selection and sparse learning.Besides updating typical algorithms of factor analysis(FA),binary FA(BFA),binary matrix factorization(BMF),and nonnegative matrix factorization(NMF)to share such a mechanism,we are also led to(a)a new parametrization that embeds a de-noise nature to Gaussian mixture and local FA(LFA);(b)an alternative formulation of graph Laplacian based linear manifold learning;(c)a codecomposition of data and covariance for learning regularization and data integration;and(d)a co-dim matrix pair based generalization of temporal FA and state space model.Moreover,with help of a co-dim matrix pair in Hadamard product,we are led to a semi-supervised formation for regression analysis and a semi-blind learning formation for temporal FA and state space model.Furthermore,we address that these advances provide with new tools for network biology studies,including learning transcriptional regulatory,Protein-Protein Interaction network alignment,and network integration.

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.

Economic Assessment of Planned Projects in Highway Network Planning

This paper presents a method of economic assessment for planned projects in the process of highway network planning. Economic assessment method is being done on the basis of cost benefit analysis, and the cost and benefit calculation methods are discussed respectively.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.