Number Of Connections Research Articles

Along- and across-strike variation of damage zone parameters in the Kornos-Aghios Ioannis normal fault, Lemnos Island, Greece

The Kornos-Aghios Ioannis Fault (KAIF) is an extensional fault system that deformed Lower Miocene volcanic rocks and Middle Eocene to Lower Miocene turbidite sandstones at shallow depth (<1 km). The fault system is exposed for about 10 km and consists of two major NW-SE-striking segments connected by an E-W-striking one. This structural architecture provides the opportunity to study the variation of damage zone parameters (i.e., damage zone width, fracture density, attitude of deformation structures, and fracture connectivity) in wall, tip and intersecting damage zones. The >122 m wide tip damage zone has a width greater than the wall and intersecting damage zones (72.6 and 50.9m, respectively) and exhibits the highest average fracture density recognized along the entire KAIF. In tip and intersecting damage zones, damage structures exhibit a larger azimuthal variability and hence a greater fracture connectivity (average number of connections per branch (CB) 1.60 and 1.53, respectively) compared to wall damage zones (average CB value 0.88). Accordingly, significant along-strike variations of damage zone parameters occur in the absence of a clear dependence on fault displacement. Particularly, fault intersections and tip regions represent areas of enhanced secondary permeability potentially controlling fluid circulation in the subsurface.

Spatiotemporal characteristics and obstacle factors identification of agricultural drought disaster risk: A case study across Anhui Province, China

Identifying the spatiotemporal characteristics and obstacle factors of the agricultural drought disaster risk is essential to reduce grain losses and ensure food security. However, due to the high uncertainty in drought disaster risk system, there are few effective risk evaluation and diagnosis methods. In this study, the uncertain information contained in sample data was mined and utilized, and a calculation method of the dynamic difference degree coefficients for the five-element connection number was proposed. Then, a quantitative risk evaluation and diagnosis model was established. Furthermore, the spatiotemporal characteristics and key obstacle factors of the agricultural drought disaster risk across Anhui Province, China from 2008 to 2017 were identified. The results indicated that the risk grades of the connection number values of North Anhui, Central Anhui, and South Anhui in 2008 were 0.359 (high risk), 0.072 (medium risk), and 0.097 (medium risk), respectively, and those in 2017 were 0.103 (medium risk), 0.196 (medium risk), and 0.368 (low risk), respectively. Thus, there were salient spatiotemporal characteristics in which the risk decreased from north to south and tended to decrease over time. In addition, the long-term high risk in North Anhui was due to the severe drought hazard, low forest cover, high exposure to drought, and low drought resistance capacity. The worse risk status in Central Anhui was caused by the small precipitation and water resources amount, low relative humidity, and large agricultural population. The factors inhibiting risk reduction in South Anhui were the large paddy field areas, low reservoir regulation and storage, and fewer water-saving irrigation areas. In summary, this method is effective and exhibits high sensitivity to risk change, and it can be applied to other grain production areas. The results can provide a scientific basis for formulating the targeted measures of agricultural drought disaster risk control in Anhui Province.

Disruption and recovery of the US domestic airline networks during the COVID-19 pandemic

The COVID-19 pandemic has had serious impacts on the airline industry. Ensuring that aviation policies in emergent situations both guarantee network connectivity and maintain competition among airlines is crucial in these circumstances. To this end, we aimed to understand the network dynamics of individual airlines. In this study, we quantitatively reveal the day-to-day dynamics of these US domestic airline networks, comprising 17 airlines, from January 2019 to December 2021. Specifically, we applied a framework for analyzing temporal networks, in which the network structure changes over time. We found that, first, even though the number of nodes and edges returned to pre-pandemic levels around July 2021, the structure of the entire US domestic airline network remained altered. We also found that the network dynamics varied significantly from airline to airline. Full-service carriers were less flexible in changing their network structure and suffered higher revenue losses. On the contrary, most regional carriers completely shifted to a new structure, which may have contributed to reducing their revenue losses. Low-cost carriers were characterized by more pronounced differences between airlines and drastically changed their network structure immediately after the declaration of a national emergency. Finally, we also examined the recovery process and found that the flights connecting airports that are more central and share more common neighbors, and those connecting airports with larger numbers of connections tend to recover earlier for most of the airlines.

Continuous Dependence on the Initial Functions and Stability Properties in Hyers–Ulam–Rassias Sense for Neutral Fractional Systems with Distributed Delays

We study several stability properties on a finite or infinite interval of inhomogeneous linear neutral fractional systems with distributed delays and Caputo-type derivatives. First, a continuous dependence of the solutions of the corresponding initial problem on the initial functions is established. Then, with the obtained result, we apply our approach based on the integral representation of the solutions instead on some fixed-point theorems and derive sufficient conditions for Hyers–Ulam and Hyers–Ulam–Rassias stability of the investigated systems. A number of connections between each of the Hyers–Ulam, Hyers–Ulam–Rassias, and finite-time Lyapunov stability and the continuous dependence of the solutions on the initial functions are established. Some results for stability of the corresponding nonlinear perturbed homogeneous fractional linear neutral systems are obtained, too.

Hubs and Bottlenecks in Protein-Protein Interaction Networks.

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Traffic-based adaptive bandwidth adjustment for flexible OTN connectivity in optical networks.

In conventional optical transport networks, the service form is the fixed bandwidth connectivity, which is not flexible for carrying bursting traffic. To support the time-varying traffic in an efficient way, researchers are studying the optical service units for building the more flexible optical transport network (OTN) connectivity, which is capable of dynamic hitless bandwidth adjustment. To better utilize the benefits of flexible connectivity, network operators need efficient algorithms to adjust the flexible connectivity bandwidth, especially in the network with a massive number of connections. In this paper, based on max-min fair bandwidth allocation criteria, we propose two traffic-based adaptive bandwidth adjustment algorithms to make bandwidth adjustment decisions, with the aim to improve bandwidth effectiveness. Simulation results show that the proposed algorithms can improve bandwidth utilization by up to 16%. Additionally, under high load conditions, it can reduce the loss in traffic bitrate by a maximum of 10%.

How Much Noise Suffices for Privacy of Multiagent Systems?

As multi-agent systems always involve a large number of nodes and connections, it is crucial to study the privacy preserving problem with minimal “corrupted” noise in the framework of differential privacy or noise-perturbation schemes. We first show that a moderate amount of noise is sufficient to ensure privacy as long as the minimal observability subspace of the considered system is blurred by noise. Based on this, it is shown that blurring more than half of the sensors can provide a desirable level of privacy protection using a node-based privacy preserving mechanism. By formulating the problem of the minimal amount of noise injected into an optimization framework, we give conditions on the tradeoff between privacy preservation and the amount of the noise. To further reduce the amount of the injected noise, an edge-based privacy preserving mechanism is devised. It is found that less than half of the sensors blurred by noise still enable us to solve the privacy preserving problem if more constraints on the communication topology are imposed. Finally, some discussions and comparisons are conducted to demonstrate the effectiveness of our proposed privacy preserving strategies.

Error Vector Magnitude as a Performance Standard for Antennas in the Millimeter-Wave Era: Part 1: Metric comparisons and measurement concepts

Millimeter-wave communication systems will rely on the large-scale deployment of wideband digital modulation schemes and highly integrated phased arrays to significantly expand the number of simultaneous connections and enable multigigabit per second wireless links. The quality of a digitally modulated signal transmitted and received by millimeter-wave phased arrays depends not only on radiated power and directivity, but also the wideband frequency response of phased array elements and linearity of embedded radio-frequency (RF) transceivers. Error vector magnitude (EVM) is a measure of the amplitude and phase distortion in digitally modulated signals that accounts for all of these effects, and it is increasingly used to characterize the system-level performance of over-the-air (OTA) links between antennas and phased arrays. This article examines the merits of adopting EVM as a performance standard for antennas and phased arrays by comparing it with other commonly applied OTA metrics and reviewing EVM measurement methods and concepts applied to time and frequency domain analysis. The sequel to this article [1] will provide a comprehensive survey of the various techniques applied by representative researchers to model, simulate, and measure EVM for OTA links between antennas and phased arrays, and reflect on open challenges for further research.

Automatic Sparse Connectivity Learning for Neural Networks.

Since sparse neural networks usually contain many zero weights, these unnecessary network connections can potentially be eliminated without degrading network performance. Therefore, well-designed sparse neural networks have the potential to significantly reduce the number of floating-point operations (FLOPs) and computational resources. In this work, we propose a new automatic pruning method--sparse connectivity learning (SCL). Specifically, a weight is reparameterized as an elementwise multiplication of a trainable weight variable and a binary mask. Thus, network connectivity is fully described by the binary mask, which is modulated by a unit step function. We theoretically prove the fundamental principle of using a straight-through estimator (STE) for network pruning. This principle is that the proxy gradients of STE should be positive, ensuring that mask variables converge at their minima. After finding Leaky ReLU, Softplus, and identity STEs can satisfy this principle, we propose to adopt identity STE in SCL for discrete mask relaxation. We find that mask gradients of different features are very unbalanced; hence, we propose to normalize mask gradients of each feature to optimize mask variable training. In order to automatically train sparse masks, we include the total number of network connections as a regularization term in our objective function. As SCL does not require pruning criteria or hyperparameters defined by designers for network layers, the network is explored in a larger hypothesis space to achieve optimized sparse connectivity for the best performance. SCL overcomes the limitations of existing automatic pruning methods. Experimental results demonstrate that SCL can automatically learn and select important network connections for various baseline network structures. Deep learning models trained by SCL outperform the state-of-the-art human-designed and automatic pruning methods in sparsity, accuracy, and FLOPs reduction.

The software platform for evaluation of the effectiveness of structural model aggregation of complex systems

Complex systems have a large dimension, consist of a large number of elements and connections between them. Networks are used to represent complex systems. Due to the high dimension of modern systems, researchers evaluate proposed solutions using generated networks. The high dimension of the system leads to problems in modeling and control. To solve these problems, methods for reducing the dimension of complex systems are required. Aggregation of the structural model of the system consists in combining elements of the system into subsystems and as a result dimension of the system and computational complexity are reduced. The subject matter of research is a software platform for evaluation of the effectiveness of structural model aggregation of complex systems. The goal of the work is to develop the software platform for evaluation of the effectiveness of structural model aggregation of complex systems. The relevance of the work lies in the fact that using the software platform it is possible to make the structural model aggregation of systems with a large number of elements and it is also possible to evaluate the effectiveness of structural model aggregation of the system. The following tasks were solved in the work: development of the software platform which consists of a system generator module, a system aggregation module, a maximum flow searching module and a statistical data processing module; structural model aggregation of the system using the software platform; evaluation of the effectiveness of structural model aggregation of the system using the software platform. As a result of the work the software platform was created, the structural model aggregation of the system was made and the effectiveness of structural model aggregation of the system using the software platform was evaluated. The studies allow us to conclude: using the software platform it is possible to generate a system, make the structural model aggregation of the system and solve the problem of the maximum flow searching; the software platform also allows to evaluate the effectiveness of structural model aggregation of the system; the value of the maximum flow is the same at two levels of the system, so in this case the structural model aggregation of the system is made while preserving the correctness of its parameters.

A Review of the Impact of Cybersecurity in High-risk Medical Devices And In-vitro Medical Devices All Over The World

In modern healthcare systems, medical devices are playing a major role which involves personalized medical devices which improve the patient’s lifestyle as they can be remotely monitored and their data are transmissible. Due to these data transmissions, the number of connections to the existing computer networks is increased. Being interoperable and interconnected these personalized medical devices provide great benefits like improved sensing capabilities and actuating capabilities. But the problem with high connectivity computer networks is that it exposes medical device to high cybersecurity vulnerabilities. The main targets are the pacemakers and institutions like hospitals and clinics. Hackers can easily hack medical devices and change prescriptions. So a cybersecurity breach can leak a patient’s sensitive and confidential data and risk the patient’s life. To prevent these multifaceted problems from happening these problems must be viewed from a systematic perspective and requires governance, technical controls, regulation, and standards.

ON RAINBOW ANTIMAGIC COLORING OF SNAIL GRAPH(S_n ), COCONUT ROOT GRAPH (Cr_(n,m) ), FAN STALK GRAPH (Kt_n ) AND THE LOTUS GRAPH(Lo_n )

Rainbow antimagic coloring is a combination of antimagic labeling and rainbow coloring. Antimagic labeling is labeling of each vertex of the graph with a different label, so that each the sum of the vertices in the graph has a different weight. Rainbow coloring is part of the rainbow-connected edge coloring, where each graph has a rainbow path. A rainbow path in a graph is formed if two vertices on the graph do not have the same color. If the given color on each edge is different, for example in the function it is colored with a weight , it is called rainbow antimagic coloring. Rainbow antimagic coloring has a condition that every two vertices on a graph cannot have the same rainbow path. The minimum number of colors from rainbow antimagic coloring is called the rainbow antimagic connection number, denoted by In this study, we analyze the rainbow antimagic connection number of snail graph , coconut root graph , fan stalk graph and lotus graph .

Total Rainbow Connection Number of Corona Product of Book Graph(Bn) and Pencil Graf(Pcm)

Let G be a simple and finite graph. Rainbow connection and total rainbow connection c are set c : G → {1,2,. . . , k} where k is the minimal color on graph G. A rainbow connection number(rc) is a pattern by giving different colors to the connection edges (E(G)) so that a rainbow path is formed. The total rainbow connection number (trc) is a payment pattern by giving color to vertices (V(G)) and edges (E(G)) in graph G so that a total rainbow path is formed. This article discusses rainbow connection numbers (rc) and total rainbow connection numbers (trc) in the corona graph of book graph (Bn) and pencil graph (Pcm). The results obtained are rc(Bn ⨀ Pcm) = 2n+3 and trc(Bn ⨀ Pcm) = 4n+5, 3 ≤ n ≤ 5.

Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation

Accurate load forecasting is important to mitigate the negative impact of Electric vehicle integration into the existing grid. Previous studies mostly focus on individual or aggregated levels without specifying the impact of accuracy due to the selection of different spatial levels and lack the integration of uncertainty estimation in the forecasting models. To address these issues, this study compares the predictive performance of a Random Forest and Artificial Neural Networks at different spatial levels with 15-min resolution data across case studies (i) with 2 Electric Vehicles charging poles and 3 users, (ii) with 75 charging poles, 8 charging rails and 70 users. The outcome shows that forecasting the Electric Vehicle load of smaller case studies will require the presence or calendar information of users. Whereas in case studies with more than 10 charging piles, the features “previous week's power”, “hour of the day” and the “number of connections” can achieve similar results. The results also showed that the aggregated forecasting was more accurate than individual charging piles. Moreover, the uncertainty plot generated for a 90% prediction interval showed that the uncertainty estimates were more reliable for the case study with large numbers of Electric Vehicles.

Solving an energy resource management problem with a novel multi-objective evolutionary reinforcement learning method

Microgrids have become popular candidates for integrating diverse energy sources into the power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is a type of Unit Commitment problem, where a player operates a microgrid with diverse renewable generators integrated with an external supplier. Calculating the economic dispatch of each committed unit on a planning horizon is an NP-hard problem, and therefore, finding an exact solution is difficult. This paper presents a multi-objective solution to the ERM problem from the perspective of battery operation and external supplier dispatch. First, a novel multi-objective decision problem modeling is proposed that considers three objectives: cost, greenhouse gas emissions, and battery degradation. This framework involves a learning agent that controls the depth of discharge of a Lithium-Ion battery. To address the proposed problem, a new multi-objective algorithm called Multi-Objective Evolutionary Policy Search (MEPS) is introduced. The proposed algorithm uses NeuroEvolution of Augmenting Topologies structure to evolve artificial neural networks for estimating action-preference values considering multi-objective rewards. The MEPS performance is evaluated on both standard and newly-proposed benchmark problems, using the hypervolume as the evaluation metric. When compared to standard deep reinforcement learning, results showed that MEPS provides cost-effective, environmentally friendly, and efficient energy storage management solutions. Furthermore, MEPS effectively solves the proposed ERM problem by finding neural networks with a small number of nodes and connections, which are suitable for use in embedded control systems. Overall, MEPS proved to be a promising multi-objective approach in the transition to clean energy resources.

An opportunistic energy‐efficient dynamic self‐configuration clustering algorithm in WSN‐based IoT networks

SummaryThe demand for the Internet of Things (IoT) has significantly increased in the current scenario; specific sectors that require IoT include industrial automation, home control, health care applications, military and surveillance applications, habitat monitoring, and nanoscopic sensor applications. The use of optimal wireless sensor networks (WSNs) in transmission techniques has resulted in their involvement. A WSN is made up of thousands of randomly distributed sensor nodes that sense and transmit environmental data such as temperature, pressure, humidity, light, and sound. One of the most important requirements when using these sensor nodes is energy. As a result, it has become a major area of research in recent years; additionally, several design techniques and protocols have been presented in the last decade, particularly for IoT‐based applications. As a result, the systemization of an energy‐optimized WSN in dynamic functional conditions with automatic self‐configuration of sensor nodes is a critical goal. This paper proposed an opportunistic energy‐efficient dynamic self‐configuration routing (OEDSR) algorithm for IoT‐based applications. Initially, the optimal route to the base station (BS) is calculated by using the residual energy and mobility factor of the sensor nodes obtained through a routing tree model based on graph theory. To reduce the number of connections, an optimal path is determined based on dynamic cluster generation through a hierarchical tree architecture. Finally, the network‐related parameters, such as throughput, delay and packet delivery ratio (PDR), are compared with the peer existing routing protocols to depict the efficiency of the OEDSR protocol.

Evaluation Model and Application of the Implementation Effectiveness of the River Chief System (RCS)—Taking Henan Province as an Example

To scientifically evaluate the implementation of the River Chief System (RCS), accelerate the overall improvement of the water ecological environment, and promote the sustainable development of river and lake functions, this study selects 26 evaluation indicators from six aspects, including the effectiveness of organization and management, the effectiveness of water resources protection, the effectiveness of water environment management, the effectiveness of water pollution prevention and control, the effectiveness of water ecological restoration, and the effectiveness of the management of the waterside shoreline, and establishes an evaluation system for the effectiveness of the implementation of the RCS. Among the 26 indicators, data for the qualitative indicators mainly come from a series of statistical yearbooks and RCS reports, while data for the quantitative indicators are obtained through scoring by more than 20 experts and calculating the average. The CRITIC objective weighting method is improved from three aspects of comparison intensity, correlation coefficient, and degree of variation, and the subjective weighting of indicators is carried out using the AHP 1–5 scale method. The optimal linear combination of subjective and objective weighting results is obtained using the combination weighting method with game theory, which is auxiliary to the set pair analysis. Considering the “certainty” and “uncertainty” in the evaluation process, the four-element connection number model of set pair analysis is established to evaluate the implementation effect of the RCS in Henan Province from 2018 to 2021. The results show that the implementation effect of the RCS in Henan Province improves year by year and reaches excellent in 2019. The results of this study can be used as a reference for evaluating the work of the RCS in other regions and can also provide a reference for the study of evaluation problems in other fields.

Web Server Load Balancing Mechanism with Least Connection Algorithm and Multi-Agent System

Demands for information over the Internet massively increase through the continuous expansion of website applications. Therefore, generating powerful and efficient server architecture for web servers is a must to satisfy Internet users and avoid the overloaded system. The research focuses on developing a new mechanism for load balancing to distribute incoming HTTP requests in website applications by combining the Least Connection algorithm and Multi-Agent System (LC-MAS). The proposed mechanism distributes the request based on load condition and the fewest number of active connections. The research applies virtualization technology to build servers on this proposed mechanism. The architecture is built inside a physical server with Proxmox as virtualization management and Linux Debian 7.11 as an operating system. Then, the research is tested in two scenarios (LCMAS and LC) using 500, 1,000, and 1,500 requests. The performance of this proposed mechanism is measured through the values of average response time, throughput, and error percentage. The results show that the proposed mechanism (LC-MAS) distributes the workload more equally than LC, with an average response time for 1,500 requests of 1338.8 milliseconds, 20.07% error, and 125 transactions per second. The LC-MAS makes the website application performance much better when the request increases. The LC-MAS helps in the utilization of system resources and improves system robustness.

Global trade network patterns are coupled to fisheries sustainability.

The rapid development of seafood trade networks alongside the decline in biomass of many marine populations raises important questions about the role of global trade in fisheries sustainability. Mounting empirical and theoretical evidence shows the importance of trade development on commercially exploited species. However, there is limited understanding of how the development of trade networks, such as differences in connectivity and duration, affects fisheries sustainability. In a global analysis of over 400,000 bilateral trade flows and stock status estimates for 876 exploited fish and marine invertebrates from 223 territories, we reveal patterns between seafood trade network indicators and fisheries sustainability using a dynamic panel regression analysis. We found that fragmented networks with strong connectivity within a group of countries and weaker links between those groups (modularity) are associated with higher relative biomass. From 1995 to 2015, modularity fluctuated, and the number of trade connections (degree) increased. Unlike previous studies, we found no relationship between the number or duration of trade connections and fisheries sustainability. Our results highlight the need to jointly investigate fisheries and trade. Improved coordination and partnerships between fisheries authorities and trade organizations present opportunities to foster more sustainable fisheries.

The effect of novel 3R,6R-bis (4-hydroxy benzyl) piperazine-2,5-dione (BHBPPD) derivatives on the expression of caspases in gastric cancer: A molecular docking and dynamics simulation

2,5-Diketopiperazines show remarkable pharmaceutical potential. Growing cancer cases drive novel drug research, focusing on synthesizing an anticancer agent for gastric cells.The investigation centered on the effects of 3R,6R-bis (4-hydroxy benzyl) piperazine-2,5-dione (BHBPPD) on gastric cancer cells, with a specific emphasis on its role in inducing apoptosis. The study involved measuring of critical caspase expression levels, pivotal components of the apoptotic pathway. Utilizing geo-analysis, the research delved into the examination of the GSE4651 and GSE10262 datasets of to gastric cancer cells, intending to identify shared factors linked to the caspase activation pathway.Employing molecular docking techniques, the binding energy of the BHBPPD compound with the identified factors was calculated, facilitating the determination of its primary target. Subsequent simulations were carried out for CYCS and TNFRSF10B, which displayed the highest number of connections. The integration of bioinformatics analysis revealed that BHBPPD exhibited the most favorable binding energy towards TNFRSF10B and CYCS, thereby enhancing the structural stability of these proteins. Notably, this augmented strength of TNFRSF10B resulted in heightened interactions with other pertinent factors, consequently leading to an increased propensity for apoptosis.These findings collectively highlight the potential of the synthesized product, BHBPPD, as a good candidate for targeted anticancer therapy. These findings collectively underline the potential of the synthesized derivative, BHBPPD, as a promising candidate for targeted anticancer therapy.