World Networks Research Articles

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.

Biosphere Reserves as model regions for transdisciplinarity? A literature review

AbstractThe World Network of Biosphere Reserves promotes learning sites for sustainable development, designated under the UNESCO Man and the Biosphere programme (MAB). The programme aims to strengthen biodiversity conservation, economic development and capacity building. Scientific research in and about Biosphere Reserves is expected to support these objectives. In response to a strong focus on natural sciences and conservation issues, calls for transdisciplinary approaches emanated from science and the newest MAB Lima Action Plan. Yet, the extent and contributions of transdisciplinary research in Biosphere Reserves remains unexplored. This study provides a comprehensive and systematic screening of 3304 scientific publications in and about Biosphere Reserves published since 1975. Research within Biosphere Reserves spans a broad spectrum, encompassing social to political to ecological investigations, with a focus on natural sciences and studies conducted mainly in Europe and Asia. We identified an emerging field of transdisciplinary science in research, represented in 336 publications. Most transdisciplinary studies were conducted in Mexican and Indian Biosphere Reserves. While transdisciplinary research provided insights about participation, management and governance in Biosphere Reserves, its transformative potential could be enhanced, notably through stronger forms of participation of non-academic actors in research processes. Our review suggests strengthening knowledge co-creation about transformative solutions and interventions addressing deep leverage points. Scientific research could thereby enhance the role of Biosphere Reserves as model regions for sustainability transformations.

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.

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.

Education of Value Attitude to Family among Adolescents in the World of Social Networks

The article reveals the problems of family education in the digital society. The “Childfree” movement, which emerged in Western Europe on the basis of the feminist movement, promotes the ideas of freedom and independence from the family. Supporters of the anti-family movement in Korea (“Sampo Generation”) advocate voluntary childlessness. These movements pose the strongest threat not only to the institution of the family, but also to the entire human society, denying traditional spiritual and moral values. The negative impact of social networks is manifested in the fact that barriers of communication between parents and children are created. It is proposed to develop a model of integration of educational interactions of teachers, children and their parents on the Internet. The school is considered as a social practice that relies on the infrastructure that includes material and social resources, including social networks. The benefits of social media and gadgets should be competently utilized. The content of social networks should be filled with strong Russian traditions of family education. The purpose of the article is to substantiate the relevance of the problem of education of value relations to the family in adolescents in the world of social networks. The research methodology is based on the poly-subject strategy of upbringing. The scientific literature was analyzed using the apperception method. Methodology and research methods. The development of the problem of education of value relations to the family in adolescents in the digital society is based on a poly-subject strategy of education. The scientific literature was analyzed using the apperception method. The result of the study is the formulation of scientific and pedagogical tasks related to the justification, development and testing of the model of digital educational environment of the school that promotes the education of value relations to the family in adolescents, providing pedagogization of the Internet environment. Such a model of digital educational environment of the school will be a means of pedagogization of social networks at the level of prevention, protection and management. Pedagogization of social networks should be evaluated with the help of informational, normative, activity, diagnostic indicators.

Mechanistic and Statistical Analysis of Possible Defense Mechanisms for Probabilistic Failures in Robustness, Stability and Sustainability of Static and Dynamic Complex Networks

Complex networks are ubiquitous even in the form of defense networks in many fields such as ecological immune systems, biological immune systems, brain networks, air defense systems, national security, cyber security, peace and conflict studies, and so on. In the real world, complex networks exist in both static and dynamic forms. By understanding probabilistic failures and their possible defense mechanisms in static and dynamic complex networks, scientists and researchers can make decisions in developing and implementing methodologies of many fields as we can say, from the fields of biology to fields of technology. In this research, static and dynamic scale-free and small world networks have been generated as complex networks with network generation algorithms. Besides that, probabilistic failures and their possible defense mechanisms have also been generated and designed for previously generated complex networks. On these generated probabilistic failures and their possible defense mechanism, several mechanistic and statistical analysis methods have been applied. To generate these static and dynamic complex networks and their probabilistic failures and possible defense mechanism, two modified network generation algorithms along with several network measurements algorithms have been implemented where parameters have been chosen to produce possible types of static and dynamic scale-free and small world networks, and their corresponding probabilistic failures and possible defense mechanisms. Besides analyzing probabilistic failures and possible defense mechanisms for generated complex networks; the robustness, stability and sustainability of those networks have been studied and analyzed for probabilistic failures and their possible defense mechanisms.

Research on the Dilemma Privacy Invasion based on the Human in the Data Age

Since entering the information age, the emergence of the Internet has greatly extended human awareness and brought about a great degree of impact and influence on the material and spiritual world of mankind. What also ensues are many ethical problems in the era of big data, such as the infringement of personal privacy, data discrimination, the gradual dissolution of human individuality, the loss of human subjective values, and the manipulation of free will, etc.. A series of problems that need to be critiqued and solved. As a practical philosophy directly pointing to the self-generation and creation of individuals, how should ethics examine the impact of this new network world on human beings in the era of big data? It has become a necessity to reflect on and criticize the question of the value of human existence in the current big data environment under new tools, new technologies and new perspectives.

A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks

Link prediction in social network is an important topic due to its applications like finding collaborations and recommending friends. Among existing link prediction methods, similarity-based approaches are found to be most effective since they examine the number of common neighbours (CN). Current work presents a novel link prediction algorithm based on particle swarm optimization (PSO) and implemented on four real world datasets namely, Zachary’s karate club (ZKC), bottlenose dolphin network (BDN), college football network (CFN) and Krebs’ books on American politics (KBAP). It consists of three experiments: i) to find the measures on existing methods and compare them with our proposed algorithm; ii) to find the measured values of the existing methods along with our proposed one to determine future links among nodes that have no CN; and iii) to find the measures of the methods to determine future links among nodes having same number of CN. In experiment 1, our proposed approach achieved 75.88%, 78.34%, 82.63% and 78.36% accuracy for ZKC, BDN, CFN, and KBAP respectively. These results beat the performances of traditional algorithms. In experiment 2, the accuracies are found as 75.53%, 74.25%, 81.63% and 78.34% respectively. In experiment 3, accuracies are detected as 72.75%, 81.53%, 78.35% and 75.13% respectively.

Amplifying Generative AI's Impact on Creative Content: Maximizing Neural Network Potential

In a world increasingly driven by technology, the field of creative content production stands on the initiative of a revolution. This paper explores the cutting-edge advancements in Generative Artificial Intelligence (GAI) & its profound impact on creative content creation. By leveraging the power of neural networks, GAI has the potential to transform how we generate, consume, and interact with creative works across various mediums. The paper begins by explaining the core principles of GAI, simplifying complex concepts for readers. It explores the fascinating world of neural networks, demonstrating how these intricate systems learn and replicate creative patterns and styles similar to human thinking. But the fascination doesn't end there. As the story progresses, the paper reveals the diverse benefits of GAI for creative professionals and enthusiasts alike. From transforming content creation processes to enabling personalized experiences and simplifying automation, the possibilities seem limitless. However, every technological wonder comes with its own set of challenges and considerations. This paper navigates through the ethical maze surrounding GAI, addressing concerns such as prejudices, copyright issues, and the intricate balance between human and artificial creativity. It advocates for responsible and moral practices in the use of GAI, ensuring that this potent tool is employed for the betterment of society. Yet, the most compelling feature lies in the introduction of a groundbreaking innovation: the "Investigate" algorithm. This algorithm, a culmination of advanced techniques including reinforcement learning and evolutionary optimization, promises to enhance GAI's impact by expanding the boundaries of neural network potential even further. It intrigues readers with the promising potential of a future where AI continuously improves itself, leading to the creation of increasingly sophisticated and innovative creative content. In conclusion, this paper calls upon readers to embark on a journey of discovery and innovation, inviting them to explore further into the realm of GAI's impact on creative content production. Through the examination of neural network potential and the moral considerations that come with it, we stand on the threshold of a new era in creativity and innovation.

Realizability problem of distance-edge-monitoring numbers

Let $G$ be a graph with vertex set $V(G)$ and edge set $E(G)$. For a set $M$ of vertices and an edge $e$ of a graph $G$, let $P(M, e)$ be the set of pairs $(x, y)$ with a vertex $x$ of $M$ and a vertex $y$ of $V(G)$ such that $d_{G}(x, y)\neq d_{G-e}(x, y)$. %For a vertex $x$, let $EM(x)$ be the set of edges $e$ such that there exists a vertex $v$ in $G$ with $(x, v)\in P(\{x\}, e)$. If $e \in EM(x)$, then we say that $e$ can be monitored by the vertex $x$. Given a vertex $x$, an edge $e$ is said to be monitored by $x$ if there exists a vertex $v$ in $G$ such that $(x, v)\in P(\{x\}, e)$, and the collection of such edges is $EM(x)$. A set $M$ of vertices of a graph $G$ is distance-edge-monitoring (DEM for short) set if every edge $e$ of $G$ is monitored by some vertex of $M$, that is, the set $P(M, e)$ is nonempty. The DEM number $\operatorname{dem}(G)$ of a graph $G$ is defined as the smallest size of such a set in $G$. The vertices of $M$ represent distance probes in a network modeled by $G$; when the edge $e$ fails, the distance from $x$ to $y$ increases, and thus we are able to detect the failure. In this paper, we first give some bounds or exact values of line graphs of trees, grids, complete bipartite graphs, and obtain the exact values of DEM numbers for some graphs and their line graphs, including the friendship and wheel graphs. Next, for each $n, m>1$, we obtain that there exists a graph $G_{n,m}$ such that $\operatorname{dem}(G_{n,m})=n$ and $\operatorname{dem}(L(G_{n,m}))=4 \ or \ 2n+t$, for each integer $t\geq 0$. In the end, the DEM number for the line graph of a small world network (DURT) is given.

ZSGEEHCP: Zonal Stable Gateway Based Energy Efficient Heterogeneous Clustering Protocol For WSN- Based IoT.

In future most of the devices on the earth connected to the internet for making world of intelligent networks. The internet of things (IoT) and Industrial IoT play a very significant role for implementing such kind of smart networks. So, in that case, the transmission of packets of data sent to the base station from sensor nodes with minimum energy consumption in a homogeneous or heterogeneous environment to hike the stability of the network appears as a big challenge for IoT-based networks. But, such kind of problem can be overcome by using a proper clustering algorithm. In this work, different kinds of classical clustering algorithms are studied precisely. Most of the traditional clustering algorithm protocols presume that all the nodes have the same amount of energy, they are unable to fully exploit the presence of node heterogeneity and the same problem will be faced with IoT-based networks. In this specific piece of work, we recommend a gateway based heterogeneous energy-based clustering algorithm which is crucial for many wireless sensor networks along with IoT-based applications. In this concept geographical area is divided into three zone, Advanced, Intermediate and normal zone. Here, advance nodes send data through gateway to the base station while intermediate node use clustering algorithm for transferring data and normal nodes communicate data to the base station directly. ZSGHCP is driven by the mounded election protocol probabilities of each node elected as a cluster head depending on the rest of the energy. By simulation, we show that ZSGHCP invariably extends the stability time as compared to current clustering procedures. Finally, we investigate the sensitivity of our ZSGHCP protocol to network heterogeneity characteristics that capture energy imbalance, throughput, and no of alive nodes. We construct that ZSGHCP payout extends the durability region for excessive values of additional energy brought by extra higher-powered nodes.