List Of Nodes Research Articles

Women's Solo Travel and Gender Equality: Insights from Labuan Bajo According to the SDG

Objective: This study aims to analyze the tourism network of Labuan Bajo, focusing on identifying key destinations and their roles within the network, particularly for women solo travelers. The objective is to enhance tourism management by understanding these dynamics. Theoretical Framework: This research is underpinned by tourist and gender studies, which provide a foundation for examining the connectivity and influence of tourist destinations, as well as the specific needs and patterns of woman solo travelers. Method: The methodology involves creating edge and node lists from TripAdvisor reviews, followed by network construction using centrality measures—degree, betweenness, and eigen centrality. Network visualization techniques are applied to interpret relationships between destinations. Data was collected from January 1, 2020, to September 1, 2023, with 2,843 reviews analyzed. Results and Discussion: The results reveal that Padar Island is the most central and influential destination, serving as a key hub for woman solo travelers. Other significant destinations include Loh Liang National Park, Kanawa Island,Kalong Island, and Cunca Wulang Waterfall. Rangko Cave, however, shows the lowest centrality measurements. The network's division into clusters highlights distinct patterns in woman solo travel behavior and preferences. These findings suggest strategic areas for investment and improvement to enhance tourist experiences and promote women's equality in travel. Research Implications: The study's findings provide practical insights for tourism management, suggesting targeted investments and infrastructure development in high-centrality areas. Improving safety, accessibility, and amenities at key hubs can create a more inclusive environment for woman solo travelers. Originality/Value: This study contributes to the literature by combining network analysis with gender-focused tourism research, offering a novel approach to understanding and improving tourism dynamics in emerging destinations. The insights gained can influence sustainable tourism development and gender equality in travel.

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

Competitive Influence Maximization in Trust-Based Social Networks With Deep Q-Learning

Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which describes the phenomena of multiple actors competing for resources within the same infrastructure, can be solved with a greedy approach selecting the seed nodes utilizing the influence strength between nodes. Recently, deep reinforcement learning methods were applied for estimating the influence strength. We train a controller with reinforcement learning for selecting a node list of given length as the initial seed set for the information spread. Our experiments show that deep Q-learning methods are suitable to analyze the competitive influence maximization on trust and distrust based social networks. Keywords: influence maximization, reinforcement learning, Q-learning.

A Time-sensitive Traffic Scheduling Algorithm for Industrial Network of Power Grid

Industrial Internet communications in power scenarios are highly time-sensitive and require low network latency and high stability. Therefore, in order to ensure the smooth operation of the automated production line, we propose a time-sensitive traffic scheduling (TsTS) algorithm combined with SRv6 technology based on the application background of industrial and civil converged networks. The method is: through the previous hop node sensing of network congestion Link congestion, and then adjust the detour path for time-sensitive traffic through Dijkstra’s algorithm. Moreover, in order to effectively reduce the link overhead of SRH and prevent bandwidth waste caused by large IPv6 addresses, we also propose a node list compression algorithm for detour paths. The core method is to put only necessary nodes into SRH, that is, Sensitive traffic can be forwarded with the help of ordinary routing. Experiments show that this method performs well in terms of delay and link utilization.

Trapping Sets Search Using the Method of Mixed Integer Linear Programming with a Priori List of Variable Nodes

Purpose of research is to develop a new high-speed method for searching trappin sets in graph codes, ensuring the completeness of the search.Methods. There are two approaches to finding trappin sets. The first, based on the Monte Carlo method with a biased probability estimation using Importance Sampling, involves the use of a decoder. The advantage of this approach is its high performance. The disadvantages are the dependence on decoder parameters and channel characteristics and the finite probability of missing trappin sets. The second approach is based on the use of linear programming methods. The advantage of this approach is the completeness of the resulting list of trappin sets, due to its independence from the decoder parameters and channel characteristics. The disadvantage of this approach is its high computational complexity. In the article, within the framework of the second approach, a new method for searching trappin sets with less computational complexity is proposed. The method involves solving a mixed integer linear programming problem using an a priori list of code vertices participating in the shortest cycles in the code graph. Results. Using the proposed method, a search for trappin sets was performed for several low-density codes. For this purpose, the mathematical linear programming package IBM CPLEX version 12.8 was used, which was run on 32 threads of a 16-core AMD Ryzen 3950X processor with 32GB of RAM (DDR4). In the Margulis code (2640, 1320), using the proposed method, the trappin set TS(6,6) was found in a time of 0.53 s. The speedup provided by the method proposed in the paper compared to the Velazquez-Subramani method is 8252.415 times. Thanks to the high speed and completeness of the search, trappin sets were found for the first time TS(62,16) and TS(52,14) in the Margulis code (4896, 2474 ).Conclusion. The paper proposes a new method for searching trapping sets by solving a mixed integer linear programming problem with an a priori list of code. The method is fast and provides completeness of the search.

Developing a method for the detection and identification of rootstock blockchain network nodes

The object of this study is the protocol for detecting nodes in the Rootstock blockchain network and crawling tools. Node discovery protocols are the foundation of any decentralized peer-to-peer network. In blockchain systems, full nodes store and maintain a complete copy of all transactions performed in the network. However, they do not store information about all other nodes, such as their IDs or IP addresses. Each node usually maintains an incomplete list of nodes to which it connects to exchange blockchain data. In decentralized networks, nodes join and leave the network and their IP addresses can change, making it impractical to maintain a complete, up-to-date list of all nodes. Therefore, the only way to get a list of all nodes in the network is to poll each node sequentially. The developed method involves sending specially formed messages to nodes to obtain their neighbors. The graph search algorithm is used to traverse all received neighboring nodes. This allows one to consistently detect all network nodes. Identifying the desired sequence of messages requires a preliminary analysis of the node software RSKj in the part of node discovery protocol. Effectiveness of the proposed method was verified using the developed software and an experiment in the main network. 6 verification nodes were deployed in different physical locations and at different times. All test nodes were detected in less than 10 minutes. The developed method found 222 nodes that have 209 unique IP addresses. Results of this study show how to perform analysis of node discovery protocol. They provide the means to obtain information about the available nodes of the Rootstock blockchain system, enabling the analysis of both the blockchain network in general and individual node

Adaptation and Creation of Psycho-Opera Scripts Based on Emotional Calculation - An Example from Verdi’s Opera Macbeth

Abstract This study effectively associates the evaluation object tree with attribute nodes by constructing an innovative tree-structured emotion dictionary. Utilizing the TF-IDF algorithm, this paper meticulously classifies the emotional features in opera librettos and adds a list of inspirational word nodes. Further, a basic framework of weighted decision matrix is designed, and the data in the emotion score matrix is normalized by Softmax method, to derive the weight allocation coefficients, according to which the results of weight coefficient allocation of the opera libretto are calculated. In particular, for the adaptation of the famous opera Macbeth, this paper proposes three significant principles to ensure that the emotional Expression of the libretto matches the creative intent. The adapted libretto was subjected to emotion calculation and effect analysis, and the empirical study showed that in the first six minutes of the adaptation, there were two periods characterized by extremely significant emotional tension, in which the emotional tension exceeded 7.5 points in both cases. In the specific dynamic analysis, about 60% of the probability emotional value of clip 2 is located in the first quadrant, which coincides with the expected creative goal. In the correlation analysis of the script adaptation, the correlation between emotional attachment and the audience’s willingness to watch the play is as high as 0.8963, indicating that the adaptation is effective. This study not only innovates the method of emotion analysis of opera libretto in theory, but also provides practical guidance for opera libretto adaptation in practice, which helps to enhance the artistic infectivity of opera works and the audience’s viewing experience.

An Efficient and Secure Blockchain Consensus Protocol for Internet of Vehicles

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

Preprocessing for segment routing optimization

AbstractIn this article we introduce a preprocessing technique to solve the Segment Routing Traffic Engineering Problem optimally using significantly fewer computational resources than previously introduced methods. Segment routing is a recently developed interior gateway routing protocol to be used on top of existing protocols that introduces more flexibility in traffic engineering. In practice, segment routing allows to deviate traffic from its original path by specifying a list of intermediate nodes or links, called segments, to visit before going to its destination. The issue we tackle in this article is that the number of segment paths scales exponentially with the maximum number of segments allowed leading to scalability issues in mathematical formulations. This article introduces the notion of dominated segment paths, these are paths that can be eliminated from the solution space when searching for an optimal solution. We propose a dynamic programming algorithm eliminating dominated paths for any number of segments. Numerical results show that respectively 50%, 90%, and 97% of paths are dominated when considering up to 2, 3, and 4 segments on benchmark network topologies.

Social media dissemination of a novel digital health approach: a social network analysis of the TeleCheckAF project

Abstract Funding Acknowledgements Type of funding sources: None. Background TeleCheck-AF is a novel remote on-demand mobile health (mHealth) infrastructure supporting teleconsultations. During the COVID-19 pandemic, social media (SoMe) communication on Twitter contributed to the rapid dissemination and implementation of this health care innovation into 41 European hospitals by using the hashtag #TeleCheckAF. Purpose To (1) analyze the SoMe Twitter network behind #TeleCheckAF, (2) describe the communities and behavioral groups within the network and (3) to determine the opinion leaders and influencers. Methods Publicly available data on actors and interactions within the #TeleCheckAF SoMe network was collected by web-scraping from the platform Twitter. This included all public tweets that used the hashtag #TeleCheckAF including their meta-data. Based on this, a node list (Twitter accounts) and an edge list (ties between accounts) as well as a social network were created. The position of an actor in the network and an actor’s opinion leadership was analyzed with the help of centrality measures (i.e. degree centrality, closeness centrality, betweenness centrality). Communities, or groups of nodes that were highly connected to each other, were further assessed by a modularity algorithm. Behavioral groups were analyzed based on participation in the healthcare innovation TeleCheck-AF (active participation) and dissemination of #TeleCheckAF on Twitter. Three behavioral groups were defined: Diffusers (dissemination but no active participation), adopters (no dissemination but active participation) and communicative adopters (dissemination and active participation). Results The network consisted of 413 086 nodes and 636 502 edges. 287 actors within the SoMe Twitter network behind #TeleCheckAF, who were either diffusers, adopters or communicative adopters, were further analyzed. Twenty-two different communities and 20 opinion leaders were identified. Peaks in Twitter activity could be temporally related to (inter-,)national virtual conferences, where results on TeleCheck-AF were presented. Two official European Society of Cardiology (ESC) Twitter handles and two Twitter handles of ESC SoMe supervisors and ESC Journal SoMe editors were among the group of opinion leaders reflecting the effect of the ESC SoMe promotion strategy. Overall, not only professionals directly involved in the TeleCheck-AF project, but also different other stakeholders such as allied health professionals or health technology companies were represented within the network. Conclusion The SoMe network behind #TeleCheckAF showed a wide dissemination involving a broad spectrum of healthcare professionals and was accelerated by the official ESC Twitter handles. Social network analysis created a helpful insight into the mechanisms behind innovation dissemination within social media. Future research could further analyze the difssemination process using e.g. hazard modeling and investigate the evolution of the network and behaviors over time.

Improved Hybrid Routing Protocol (IHRP) in MANETs Based on Situation Based Adaptive Routing

Without the need of a fixed foundation or base station, the Mobile Ad hoc Network creates its own wireless network. One of the most troublesome aspects of Mobile Ad hoc Network (MANET) is the occurrence of unexpected loss of network connectivity. As a result of this problem, packets continue to drop, and we must restore the connection by sending Route Request (RREQ) and Route Reply (RREP). As a result, network performance will suffer yet another setback. We used the scenario routing technique to combine the Dream Multipath Routing (DMR), Ad hoc on-demand multipath distance vector (AOMDV), Optimized link-state routing (OLSR), and Ad-hoc on Demand Vector (AODV) routing protocols to build the IHRP routing protocol in this work. According to previous studies, (AODV) is more suited when node motion is high. The purpose of DREAM Multipath Routing (DMR) is to maintain node mobility and location information coordinated. Route packet flooding is prevented by computing the expected chance of node relocation. The number of mobile nodes in the wireless network fluctuates, and the DMR operates on each one individually. In the network, each node maintains a list of nearby nodes and their current locations. Using the AOMDV routing protocol is effective for load balancing and preventing congestion on the network. OLSR is a good fit for networks that priorities link reliability above other considerations when routing traffic. When using the aforementioned (DMR, AODV, AOMDV, and OLSR) protocols to create the IHRP routing protocol, we are capable of better regulation of network behavior and perform. In the case of 100 nodes, data is sent for analysis for The Improved Hybrid Routing Protocol (IHRP), Zone Routing Protocol (ZRP), AOMDV, AODV, and OLSR routing protocols. For data send, the performance of IHRP, ZRP, AOMDV, AODV, and OLSR is 11513, 10240, 10225, 10558, and 9184, respectively, and for 50 nodes, the performance of IHRP, ZRP, AOMDV, AODV, and OLSR is 11151, 9807, 9636, 9586, and 7470, respectively. Thus, with 100 and 50 nodes, the Improved Hybrid Routing Protocol (IHRP) outperforms the AOMDV, ZRP, AODV, and OLSR routing protocols. In the case of 100 nodes and 50 nodes, the data receive analysis for IHRP, ZRP, AOMDV, AODV, and OLSR routing protocols is 11513, 10240, 10225, 10558, and 9184, respectively, and 9367, 8714, 8370, 6730, and 7298, respectively, So IHRP also receives data faster than ZRP, AOMDV, AODV, and OLSR routing protocols. The IHRP outperforms AOMDV, AODV, ZRP, and OLSR routing protocols in terms of data transmit, receive, data drop, PDR, throughput, E-E latency, and NRL.

TBMOR: A lightweight trust-based model for secure routing of opportunistic networks

Opportunistic networks are self-organizing dynamic networks that use the communication opportunities presented by node movement to enable data delivery. Due to the indirect nature of inter-node connections and dynamic topology, opportunistic networks are vulnerable to malicious attacks, which poses a great challenge to their security. In this paper, we first propose a trust-based model for the secure routing of opportunistic networks (TBMOR), which incorporates forwarding positive degree (FP) and node activity degree (NA) into the calculation of trust value, evaluates nodes in the network by a dynamic combination of direct trust and indirect trust, and then optimizes the next node list using trust values and pruning strategies. Secondly, this paper extends the network life cycle by dynamically setting up energy thresholds to reduce network redundancy by dynamically partitioning the number of replicas using trust values.Simulation results show that compared to other routers, TBMOR can effectively resist malicious attacks, has a higher message delivery rate and lower transmission latency than other routers.

The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem

Given two pick-up and delivery points, the best path chosen does not necessarily follow the criteria of minimum travel time or generalized minimum cost evaluated with a deterministic approach. Given a criterion, the perceived cost is not deterministic for many reasons (congestion, incomplete information on the state of the system, inexact prediction of the system state, etc.). The same consideration applies to the best-chosen route, assuming that the route is an ordered list of network nodes to visit. The paths and routes perceived and chosen (drivers or companies) could follow different criteria (i.e., minizmum congested travel time for the path and minimum monetary cost for the route). In this context, the paths chosen between two pick-up and delivery points, studied with the path choice problem (PCP), influence the best route, studied with the vehicle routing problem (VRP). This paper reports some considerations on the importance of modelling the path choice behavior in the VRP; the influence of the PCP on the VRP is studied. The considerations are supported by a numerical example in a small network in which the results obtained by adopting the deterministic or probabilistic models for the PCP are compared. To validate the reported thesis, the models are applied in a small test system, and it allows the reader to follow the numerical results step by step.

Efficient Load Balancing of Resources for Different Cloud Service Providers in Cloud Computing

In distributed computing cloud computing is an emerging technology which provides pay per model as per user demand or requirement. Cloud has a collection of virtual machines which facilities both computational and storage requirement. Scheduling and Load balancing are the main challenges in the cloud computing on which we are emphasizing. Scheduling is the process to control the order of work going to be performed by computer system. Load balancing has an important role in the performance in cloud computing. Better load balancing will make cloud computing more efficient and will also increase user satisfaction. It provides a way to handle several inquiries residing inside cloud computing environment set. Complete balancing acquires two tasks, one is resource provisioning/resource allocation and task scheduling throughout the system. In the proposed research paper, we are presenting a hybrid algorithm created by FCFS and Round Robin algorithms. As the Round Robin is the easiest algorithm that's why it is frequently used and the first preference for implementing easy schedulers. The Round Robin algorithm only requires a list of nodes. In the proposed solution we have eliminated the drawbacks of simple Round Robin algorithm by introducing assignment of time slices to different processes depending upon priorities.

СИСТЕМАТИЗАЦІЯ ПІДХОДІВ ДО ІННОВАЦІЙНИХ ЗМІН У СТРУКТУРІ ПІДПРИЄМСТВА ПІД ЧАС РЕЛОКАЦІЇ

Political and economic disturbances in the world economy require new approaches to economic activity. The relocation of enterprises creates conditions for innovative transformation. Relocation takes place in conditions of total resource limitation and uncertainty of the external environment. The owner decides the feasibility of relocation, determination of the list of assets available and necessary for relocation, etc. He must choose the best methods and tools for optimizing business processes, form a list of assets to move and rank it. The purpose of the study is to identify business process optimization tools; conveying to entrepreneurs tools and methods for the restoration of relocated enterprises. The main methods of research are the methods of planning and organization of the experiment – in terms of determining the initial situation and input factors; methods of dynamic analysis (index method) – when choosing action algorithms; methods of conscious logic (method of elimination) – when processing the obtained results and determining the final result. The proposed system of actions of the entrepreneur regarding the optimization of business processes after relocation. Ascertain the expediency of using reengineering tools and a value approach to optimizing the structural changes of the enterprise. The importance rating of the enterprise's business nodes is formed on the basis of the value approach. The rating system of the enterprise's business nodes will allow managers to consciously make changes to the business model of their economic activity. The logic of organizing the business processes of an economic entity is radically changed as a result of the application of methods of chain transformations based on index methods and methods of conscious logic. The resulting ranking list of business nodes will help entrepreneurs to form effective business models for entering new business systems and effective functioning in them. Such tools and approaches should help enterprises actively recover after relocation in conditions of total restrictions on all groups of resources.

Medley: A Membership Service for IoT Networks

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wireless ad-hoc network. Our failure detector, named Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead. We adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. This adaptation is non-trivial. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. We also provide optimizations to achieve time-bounded detection, as well as to reduce tail detection times. Via analysis, simulation, and Raspberry Pi deployments, we show that Medley can simultaneously optimize detection time and communication traffic.

A Technique of NLAODV algorithm to get Routes of Nodes-List in Mobile Ad-hoc Network (MANET)

A Mobile Ad-hoc Network (MANET) is a group of nodes that connect to each other wirelessly without the use of a central server or a traditional network structure. In MANETs, data packets are sent through wireless channels to keep communication going. Ad-hoc On-Demand Distance Vector Routing is a MANET-related reactive routing technology that generates a path of nodes to a destination by broadcasting packets of route request across the network. In this kind of Routing protocol, a connection loss causes the source to send control (RREQ) on the network, causing network congestion and performance deterioration. This study offers a Node List (NLAODV) Node List Ad-hoc On-Demand Distance Vector routing method that includes links and path-nodes to identify if any node on the network is involved in the route discovery process for sending control packets from wireless source node to the wireless destination node. Simulation findings reveal that the proposed NLAODV algorithm minimizes flood Packets and get best network as it’s not necessary for all nodes to be present in route discovery.

Identification of Suitable Locations in a Small Water Supply Network for the Placement of Water Quality Sensors Based on Different Criteria under Demand-Driven Conditions

Drinking water quality monitoring in real time is of utmost importance to ensure public health. Although water utilities, following the related legislative framework, monitor drinking water quality through samplings, the likelihood of detecting contaminants in consumers’ taps is low, depending on the scale of the monitoring programme. Additionally, even if the monitoring frequency is high, there is a time delay since sampling and analysis processes take some time. The selection of suitable locations for the installation of online water quality sensors is a hard task for a water utility due to the complexity of the water distribution system, the limitations of certain network junctions which are not easily accessible, and the computational burden involved. This topic has been extensively studied in recent years and sophisticated methods have been developed using optimization techniques. However, small water utilities do not have the means to implement such tools. This paper applies a methodology to identify the suitable junctions for the installation of online water quality sensors based on different objectives and under demand-driven conditions. This paper utilizes the hydraulic simulation model of a standard network to set up the water quality simulation model. A thorough analysis of various contamination scenarios takes place with different injection nodes and at different starting injection times for 24 h. The latter relates to the contaminant’s spread due to varying water demand. After a thorough analysis of 816 scenarios, a prioritized list of the most suitable nodes for the installation of the sensors is available for each optimization objective. Comparing the prioritized list of nodes achieved from each single or multi-objective function, the detection probability is almost the same. The analysis revealed that, due to varying water demand conditions, the ranking of the proposed nodes suitable for the installation of water quality monitoring sensors differs. Thus, varying hourly water demand should be part of analyses seeking to get reliable results.

A technique for designing nano-scale circuits using a fuzzy logic and nature-inspired fish swarm optimization algorithm

Recent breakthroughs in quantum processing software and hardware have shown substantial development toward operational results. We anticipate quantum computing to have significant applications in some domains ranging from machine learning and optimization to drug development in the future. Quantum computing and designing have gotten a lot of interest from investigators. Physical design is one of the two main stages in designing nano-scale circuits that receive a list of circuit nodes as input and produce the final arrangement in a particular technology. However, it has encountered several design issues and execution limits in the long term. Moreover, designing nano-scale circuits is an NP-hard problem in terms of cost, delay, and energy consumption. This paper has provided a new method for solving the optimal design of nano-scale circuits using a fuzzy-based Fish Swarm Algorithm (FSA). The FSA represents fish, hunting, and fish behavior to get the best results. It has a quick integration speed, a powerful global search capacity, and a significant shortcoming. In our proposed method, the fuzzy observer is responsible for detecting the system's state within the defined area and maintaining the state within it. The proposed method has been evaluated against some benchmarks, and cost, delay, energy, and area characteristics have been reduced. Simulation outcomes demonstrated the suggested method's correct performance in terms of "energy," "cost", "area", and "delay" where the suggested technique outperforms others (GA, PSO, FSA and ACO).

Food webs for three burn severities after wildfire in the Eldorado National Forest, California

Wildfire dynamics are changing around the world and understanding their effects on ecological communities and landscapes is urgent and important. We report detailed food webs for unburned, low-to-moderate and high severity burned habitats three years post-fire in the Eldorado National Forest, California. The cumulative cross-habitat food web contains 3,084 ontogenetic stages (nodes) or plant parts comprising 849 species (including 107 primary producers, 634 invertebrates, 94 vertebrates). There were 178,655 trophic interactions between these nodes. We provide information on taxonomy, body size, biomass density and trophic interactions under each of the three burn conditions. We detail 19 sampling methods deployed across 27 sites (nine in each burn condition) used to estimate the richness, body size, abundance and biomass density estimates in the node lists. We provide the R code and raw data to estimate summarized node densities and assign trophic links.