World Networks Research Articles

Is there a SPOT for remote pediatric stroke evaluation in the world of telestroke networks? A pilot Feasibility Study of Performing the PedNIHSS Over Televideo

Is there a SPOT for remote pediatric stroke evaluation in the world of telestroke networks? A pilot Feasibility Study of Performing the PedNIHSS Over Televideo

Dialectic of Predisposition and Ability in Human Professional Activity

In the following scientific study, we consider a logical model, which for the first time brings us closer to the integrated perception of barriers and obstacles existing in the memory environment, "refracting" human predispositions and transforming them into skills. The area of human orientation in the world of changing opportunities, as well as in the solution of the "predisposition-ability" conflict at the stage of the formation of new professions in an intelligent society of cognitive and innovation perspective, defined by the criteria of technological development, digitalisation and transition to a virtual world of communication networks and the Internet, is justified. From a scientific point of view, however, it is possible to grasp the characteristics of the memory environment and to search for an "exhaustive list of predispositions" as well as an "exhaustive list of characteristics of each memory environment unit". However, this usually requires years of research with a single person. From the practical point of view, this is unacceptable, because in modern circumstances such recommendations are required, which allow to predict professional activity in a few hours, and in the future to allow a person to learn to navigate independently in conditions of changes in the environment of his life and activity. The correlation of the activity approach developments within the subject's life development, as well as the model of predisposition realisation mentioned by M. Achtnich as holistic and optimal, maintained by the specific-historical conditions of the figure, have mostly determined the key problem, to which this article is devoted. The purpose of this scientific exploration is the philosophical understanding of predisposition and skill dialectic in human professional activity (on the example of models by Martin Achtnich and Gregory Popov). Moreover, the tasks of academician G.Popov largely echo the original formulation of P. Drucker's smart approach, whose paradigm is now being applied to reflect on the smart approach to any areas of activity, defined by the speed of environmental change, the rapid circulation of information, and the circumstances of uncertainty. The recommendations of G.Popov, discussed in the following article, and the concept of P. Drucker do not conflict, mutually complementing each other.

Practical Exploration of Data Security Application in Automotive Industry and Work Suggestions

As an important intelligent terminal in the new network world, intelligent networked vehicles have a large amount of data interactions with devices such as mobile, vehicle, roadside, and cloud servers. After large-scale commercial application, if it is not effectively controlled, it will bring great data security risks. China is gradually improving the data security regulatory system, and how to better manage automotive data security is a brand-new challenge for the automotive industry.

Extensions and Detailed Analysis of Synergy Between Traditional Classification and Classification Based on Negative Features in Deep Convolutional Neural Networks

In recent times, deep convolutional neural networks became an irreplaceable tool for pattern recognition in many different machine learning applications, especially in image classification. On the other hand, these models are often used in critical systems which are the reason for new and recent research regarding their robustness and reliability. One of the most important issues for these models is their susceptibility to different adversarial attacks. In our previous work Milošević and Racković (Neural Network World. 2019;29(4):221–34), and Milošević and Racković (Neural Comput Applic. 2021;33:7593–602), the new type of learning applicable to all the convolutional neural networks was introduced: the classification based on the negative features and the synergy of traditional and those newly introduced network models. In the case of partial inputs/image occlusion, it was shown that our new method creates models that are more robust and perform better when compared to traditional models of the same architecture. In this paper, some extensions of the earlier proposed synergy are given by introducing negatively trained features and additional synergy between four independent neural network models. A detailed analysis of the robustness of the newly proposed model is performed on EMNIST and CIFAR-10 image classification data sets in the case of the selected input occlusions and adversarial attacks. The newly proposed neural network architecture improves the robustness of the neural network and increases its resistance to various types of input damage and adversarial attacks.

Promoting Geoheritage in the Aspiring Geopark of Taburno-Camposauro (Southern Apennines, Italy) with Innovative Tools

In the inland areas of Campania (Italy), the Taburno-Camposauro Regional Natural Park covers almost 137 square kilometres. It well represents, in the reliefs that give it its name, the southern segment of the Apennine chain. In fact, the rock outcrops, landscape features and surface and ground water make it possible to reconstruct the geological evolution of this area. Nonetheless, it is possible to understand how the history of man, who has frequented these places since ancient times, has developed by taking advantage of the resources offered by this territory. Among these resources, it is believed that the characteristics of the geological heritage spread throughout the Park can also be an opportunity to attract not only researchers, but also significant tourist flows. To this end, not only has the procedure been initiated to be included in the world network of Geoparks, but efforts have also been made to promote the most representative geological sites using the latest communication tools (e.g., social media). Besides these, numerous initiatives aimed at schools and national tourism agencies were developed. Promotion found particular emphasis with the realisation of an art installation by a well-known author in a water catchment system. In fact, this installation triggered an artistic vein around the beauty of the sites, manifested by videos and photo exhibitions and even forms of entertainment. These events have increased interest in the geological heritage, as evidenced by the increase in visitors observed by a specific analysis of the performance of social media posts, as well as frequent visitors to the geoheritage elements of the Park.

Climate Change and the Struggle for Climate Justice

Climate change is one of the greatest global challenges. The phenomenon and actions taken to address it have thrown up some contentious issues. One of such issues is climate justice. This paper analyses the bases for climate justice and the attempts at achieving it. The basic sources of data were the publications/documents of the United Nations Framework Convention on Climate Change (UNFCCC) secretariat and the Intergovernmental Panel on Climate Change (IPCC). Information was also obtained from the publications and websites of the climate negotiation blocs, such as G77 + China and Alliance of Small Island States (AOSIS), and several Civil Society Organisations (CSOs) such as Climate Action Network, Oxfam, GreenPeace and Third World Network. The climate justice struggle has been necessitated mainly by two driving forces. These are the experiences of the impact of climate change and the environmental legal principles that have emerged from environmental and climate change challenges. In spite of the fact that climate change and its impact are global, vulnerabilities vary across regions and socio-economic groups. The disproportionate impact of climate change is one of the drivers of the struggle for climate justice. The environmental/climatic legal principles that have formed bases for the climate justice struggle derive largely from UNFCCC, the Rio Declaration and the Paris Agreement. They are, to an extent, related to the Principle of Disproportionate Climate Burden. The principles include Common But Differentiated Responsibilities, Polluter Pays, No-Harm (to others) and Beneficiary Pays. Strategies employed in the struggle for climate justice include advocacy, focused negotiations at CoPs, protests/strikes, and litigation. Although, the developing countries have been the leaders in the struggle for climate justice, CSOs have been particularly prominent. The demand for increased financing of climate mitigation and adaptation has been characteristic. The struggle has resulted in more climate action. However, the ultimate focus must be the achievement of net zero emissions.

Enhancing the global and local robustness of networks: A network motif-based approach

In the real world, both natural and artificial networks, may experience degradation or failure due to faults or external perturbations, which may negatively impact people's lives. Therefore, optimizing network robustness to maintain functionality under such conditions is a crucial research area. With the deepening of network robustness research, more and more studies have found that the local robustness of networks is related to network motifs. Considering the optimization of the robustness based on the global network properties (global robustness) and local robustness of the network, this paper proposes a motif-based edge rewiring algorithm (MBER), which includes the Edge Selection Algorithm, Edge Rewiring Algorithm, Simplified Calculation Algorithm for the Number of Major Network Motifs and Adjusted Nondominated Sorting Algorithm. We optimize four synthetic networks and four real-world networks using MBER and four other optimization algorithms, respectively. The results demonstrate that the MBER algorithm is more effective in enhancing the global and local robustness of networks. Additionally, we analyse the time complexity of MBER and four other optimization algorithms, revealing that the MBER algorithm exhibits the highest time complexity due to its consideration of local robustness. This paper can provide a reference for enhancing the robustness of real-world networks more comprehensively and enriches the research on network robustness enhancement.

New Skills and Knowledge for Digital Entrepreneurs in the Age of Artificial Intelligence

Digital technologies are becoming increasingly complex and integrated, leading to significant transformations in society and the economy. The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education. Recognizing that the relationship between technology and business is two-way and becoming stronger, revealing that well-prepared employees are a guarantee of success and prosperity of companies in various fields, we try to focus on the main groups of qualities, skills and basic knowledge of students in the age of artificial intelligence. The development of the Internet, expansion of connectivity through social networks, the advent of AI, 3D printing, and immersive technologies like Augmented Reality, and Virtual Reality, require new knowledge and skills, leading to new challenges in education. Qualified personnel in this modern world must have solid professional training and systemic thinking (knowledge, skills, accumulated information), developed cognitive abilities, and personal skills based on collecting and analyzing large amounts of diverse information from heterogeneous sources. Questions arise: how can multiple information sources be combined effectively, and how can the fusion of multiple sources provide additional information to support decision-making processes? Combining information obtained from the real world makes the results heterogeneous and more informative. It follows the need to develop machine learning methods to extract relevant information from increasingly complex data sets. The goal is to improve the accuracy of the applied classification algorithms by combining predictions from multiple models, as well as obtaining a more stable final classification evaluation, effective handling of noisy data, adaptation to changing conditions, and improving stability when solving problems. On the other hand, how to ensure that the enormous potential of artificial intelligence, virtual reality, connection with the physical world, machine learning, and pervasive networks of people and machines will be fully used to improve the quality of life and contribute to the building of stable societies. Changes must be subordinated to policy and investments for reliable artificial intelligence and based on an ethical and human-centered approach. All of these should be established as a fundamental principle of training in Higher education which imposes the need for transformation in modern education.

Exploring the Implementation of UNESCO’s MAB Program in South Africa: A Case Study of the Cape Winelands Biosphere Reserve

The Man and the Biosphere Program (MAB) responds to challenges of the Anthropocene through an explicit social-ecological approach. Implemented as a world network of biosphere reserves, MAB aims to increase [eco]system sustainability and resilience globally, via individual model sites for learning and sustainable development. This research provides an in-depth case study of MAB implementation in South Africa using the Cape Winelands Biosphere Reserve (CWBR), established in 2007 when a key MAB guiding policy, the Madrid Action Plan came into effect. The study utilized semi-structured in-depth interviews with strategic and operational management, and document analysis. The CWBR prioritizes their role as a landscape coordinator, a driver of socio-economic development and site in which humans derive benefits from healthy natural environments. The CWBR have adopted a non-profit organization cooperative governance model in support of this vision, fulfilling the socio-economic development function primarily through successful international partnerships. Challenges faced include a perceived lack of sufficient government support, limited stakeholder awareness and insufficient resources for project implementation. Over reliance on the pillar of their model, the chief executive officer in the current governance form, is an instrument in their effectiveness, yet carries significant risk. These are learnings useful for other biosphere reserves translating an international designation for a local context.

Energy criterion for synchronization of neuron populations

Energy regulates the neuron activities, including inter-neuronal communication, biorhythm, and brain function. In this paper, we propose the energy criterion for the synchronization of neuron populations, and neurons tend to be synchronous if the energy variance approaches zero. Furthermore, we investigate the energy distribution of neuron population. The individual energy groups, two or three groups, expand until they merge into a uniform energy group with the increasing of the coupling intensity. Then, the uniform energy group tends to be a Normal distribution when the neuron population is synchronized. The energy criterion and energy distribution for synchronization are confirmed in the chain network, small world network and the scale free network composed by the FithzHugh-Nagumo (FHN) neurons. These results advance the understanding of the role played by the energy in neuron populations.

The equitable non-split domination number of graphs

Finding a group of dominant servers is a necessary step towards optimising service provisioning to all clients in the complex world of server-client networks. This crucial decision rests on the non-split dominance number in the graph architecture. We present the idea of an equitable non-split domination number in graphs to address the possible problem of unequal task distribution among servers. When the vertices of a dominating set, Dens, have the same colour in an equitable colouring scheme, the set is said to be equitable. The induced subgraph ⟨V −D⟩ also maintains its connectivity. In particular, the application of equitable non-split domination numbers to graphs like the Triangular Grid, Antiprism, Wheel, and Complete Graphs is explored in this study.

Optimization model for urban ecological network connectivity considering geospatial constraints

ABSTRACT Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

Securing the Socio-Cyber World: Multiorder Attribute Node Association Classification for Manipulated Media

With the rapid development of information technology, social network has become an indispensable part of daily life. People have been able to get news from all over the world through social networks for a long time. People spend more time online than they do in real life. However, the information we get in the world of social network is not purely benign. Due to the development of artificial intelligence technology, more and more tampered media information appears in social networks, some for entertainment, while others become the dark side of social networks, of which the most harmful is to people in the media tamper. For fake news and misinformation caused by media tampering, we need to trace the source and clearly distinguish the truth from the manipulated. This article proposes an image media forgery classification method of multiorder attribute nodes. First, we use different methods to extract the edge, texture, grayscale, and color attributes of the image. Second, according to the characteristics of different attributes, we calculate the first-order entropy of edge attributes, the second-order entropy of texture attributes, local entropy of grayscale, and color properties. Finally, we represent each image with some nodes and build a graph convolutional network (GCN) to classify real and fake images. Experimental results on mainstream media manipulation datasets show that our method is the state-of-the-art compared with similar methods.

Dependence and Alienation: A Study of Online Social Anxiety Among College Students Under the New Media Perspective

With the rapid change of Internet technology, new media has been deeply integrated into people s daily life and become an indispensable and important element, especially for the group of college students, new media is the main platform for them to obtain information and communicate and interact. However, in the virtual world of online social networking, the anonymity and virtuality of online social networking make them prone to uncertainty and insecurity in their interactions, and college students are faced with unprecedented social pressure and anxiety, which can easily make them fall into self-doubt and anxiety. Therefore, this study explores the causes of college students online social anxiety under the threshold of new media and the influence of social media dependence on social media self-presentation, with a view to alleviating college students group online social anxiety.

Research on the Reversal Phenomenon of Online Public Opinion in the Post-Truth Era

"Post-truth" is full of negative effects of the current network public opinion - ignoring the truth, disregarding the facts, dissolving the authority, position first, coupled with the frequent occurrence of global emergencies and hotspot events, the netizens wrapped up in group emotions have to join the public opinion of the network world carnival, this phenomenon also provides an environment for rumors to grow secretly, seriously undermining the harmony and stability of the Internet ecosystem. This phenomenon also provides an environment for network rumors to grow secretly, which seriously undermines the harmony and stability of the Internet ecology. In this paper, we take the "fat cat" incident as the starting point to analyze the impact, evolutionary causes and countermeasures of the popular public opinion events.

Co-Ability and embodied data: blurring the lines between human and nonhuman entities in an interconnected world

This article explores the dynamic interplay between human and non-human entities, focusing on how embodied data representation is distributed. It examines how predictive coding, which utilizes preconceived knowledge, interacts with tangible experiences to shape our understanding of the world. Emphasizing this, I propose the concept of co-Ability as a deep underlying explanatory framework for understanding adaptive behaviors within a networked world. A non-verbal dialog between humans and a data-saturated environment is analyzed through an action-oriented perspective and the predictive coding framework in cognition, utilizing digital craft and rapid prototyping. This transformative approach augments human interaction with digital landscapes through tangible prototypes, bridging physical experience with abstract information, and identifying potential ways to conceptualize data materially. The article discusses the various aspects of connectivity among network agents and the evolving nature of these connections as they adapt to real-world conditions and dynamic shifts in data, highlighting that information exchange in an interconnected network is more than bilateral; it generates ripple effects that extend beyond immediate connections. These reciprocal exchanges simultaneously alter both the digital and analog domains, with data constantly bifurcating into multiple pathways and outcomes. A significant challenge addressed in this article is the question of how to frame information materially, inviting further exploration.

An application of complex networks on predicting the behavior of infectious disease on campus

Networks are widely used in understanding the spread of infectious disease. Universities and colleges are characterized by frequent social gatherings and extensive interpersonal communication, making them ideal applications of complex networks. In this paper, a compound model consisting of a homogeneous (small world) network and a heterogeneous (scale free) network is established, the spreading characteristics of epidemics are analyzed by using the mean‐field (MF) and the heterogeneous mean‐field (HMF) approaches, and the effects of various factors such as the total node number (N), the randomly reconnection probability (p), the initial proportion of infected node (p0), the average degree (k), and the shared nodes (s) are simulated by numerical calculations. The simulation results are in consistent with theoretical predictions, indicating that the randomness effect decreases with increasing k and N and is small when k (k = 8) and N (N = 200) are relatively large; the randomness effect is more significant in the scale free (SF) network than in the small world (SW) network, and the disease spread faster in the SF network; shared nodes can weakly increase the disease spread, but when the shared nodes are involved in an infected and an uninfected network, it can induce the disease outbreak in the uninfected network.

Analysis and Comparison of Fourier and Wavelet Transforms: Application for the Study of Seismic Source Parameters, Case of the 2001 Arequipa Earthquake

The purpose of this study is to apply the spectral analysis technique to the aftershocks of the 2001 Arequipa earthquake (Mw=8.4), based on the Fourier and Wavelet transforms.The data corresponds to the world network (IRIS), which has a station called NNA that is located in Peru. Together, 10 records corresponding to replicas registered during a period of 10 days after the main event have been analyzed. The methodology used in this study was based on the SAC software that stands for "Seismic Analysis Code". As a result of the Fourier transform, the fracture radius values obtained are in the range of 0.21 to 1.15km, the stress drop values for the analyzed aftershocks are between 0.60 to 38.08Mpa, the Energy Magnitude is from 3.36 to 4.42Me; for Wavelet: the frequency content which presents the values between 0.18 to 0.30 Hz, The maximum radiated energy between 25.70 to 40.80, the values obtained are in the range of 92.18 to 146.12s. The analysis and comparison of the transforms showed that with the Fourier transform different parameters can be determined by applying spectral analysis, however, with the wavelet transform the maximum radiated energy was determined This study contributes to the literature by finding these parameters of both transforms as it is of vital importance to determine the zones of energy accumulation that would cause earthquakes of great magnitude in the future.

Explainable decomposition of nested dense subgraphs

Discovering dense regions in a graph is a popular tool for analyzing graphs. While useful, analyzing such decompositions may be difficult without additional information. Fortunately, many real-world networks have additional information, namely node labels. In this paper we focus on finding decompositions that have dense inner subgraphs and that can be explained using labels. More formally, we construct a binary tree T with labels on non-leaves that we use to partition the nodes in the input graph. To measure the quality of the tree, we model the edges in the shell and the cross edges to the inner shells as a Bernoulli variable. We reward the decompositions with the dense regions by requiring that the model parameters are non-increasing. We show that our problem is NP-hard, even inapproximable if we constrain the size of the tree. Consequently, we propose a greedy algorithm that iteratively finds the best split and applies it to the current tree. We demonstrate how we can efficiently compute the best split by maintaining certain counters. Our experiments show that our algorithm can process networks with over million edges in few minutes. Moreover, we show that the algorithm can find the ground truth in synthetic data and produces interpretable decompositions when applied to real world networks.

Control of cascading failures using protective measures

Cascading failures, triggered by a local perturbation, can be catastrophic and cause irreparable damages in a wide area. Hence, blocking the devastating cascades is an important issue in real world networks. One of the ways to control the cascade is to use protective me‌asures, so that the agents decide to be protected against failure. Here, we consider a coevolution of the linear threshold mo‌del for the spread of cascading failures and a decision-making game based on the perceived risk of failure. Protected agents are less vulnerable to failure and in return the size of the cascade affects the agent’s decision to get insured. We find at what range of protection efficiency and cost of failure, the global cascades stop. Also we observe that in some range of protection efficiency, a bistable region emerges for the size of cascade and the prevalence of protected agents. Moreover, we show how savings or the ability of agents to repair can prevent cascades from occurring.