Scheme For Networks Research Articles

A Diversified Recommendation Scheme for Wireless Content Caching Networks

A Diversified Recommendation Scheme for Wireless Content Caching Networks

SAKMS: A Secure Authentication and Key Management Scheme for IETF 6TiSCH Industrial Wireless Networks Based on Improved Elliptic-Curve Cryptography

SAKMS: A Secure Authentication and Key Management Scheme for IETF 6TiSCH Industrial Wireless Networks Based on Improved Elliptic-Curve Cryptography

Design of Coded Caching Scheme for MAMISO Networks With Improved DoF

Design of Coded Caching Scheme for MAMISO Networks With Improved DoF

Energy Efficient Transmission Strategy for Mobile Edge Computing Network in UAV-Based Patrol Inspection System

Energy Efficient Transmission Strategy for Mobile Edge Computing Network in UAV-Based Patrol Inspection System

Long-term upgrade strategies in multiband and multifiber optical transport networks

Flexible-grid optical transport networks (OTNs) are currently undergoing a massive scale upgrade to bandwidth variable transceivers (BVTs) to cater to the exponential Internet traffic growth. Network operators seeking to plan future capacity in a scalable manner can also add additional bands of operations or additional fibers in the OTN. We propose two strategies for planning multiband upgrades in OTNs, which use an integer linear programming (ILP) solution and a heuristic solution with a reinforcement learning (RL)-based extension. Network simulation studies on three differently sized OTNs show that the heuristic and its RL-based extension perform extremely close to the optimal ILP-based solution while reducing execution times drastically. Finally, a techno-economic study based on what we believe to be a novel cost model highlights the cost-per-bit-per-second savings through carefully planned upgrades.

Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks

Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks

Targeted isolation of antiviral cinnamoylphloroglucinol-terpene adducts from Cleistocalyx operculatus by building blocks-based molecular networking approach

The building blocks-based molecular network (BBMN) strategy was applied to the phytochemical investigation of Cleistocalyx operculatus, leading to the targeted isolation of eighteen novel cinnamoylphloroglucinol-terpene adducts (CPTAs) with diverse skeleton types (cleistoperones A–R, 1–18). Their structures including absolute configurations were determined by extensive spectroscopic methods, quantum chemical calculations, and single-crystal X-ray crystallographic experiments. Cleistoperone A (1), consisting of a cinnamoylphloroglucinol motif and two linear monoterpene moieties, represents an unprecedented macrocyclic CPTA, whose densely functionalized tricyclo[15.3.1.03,8]heneicosane bridge ring skeleton contains an enolizable β,β′-triketone system and two different kinds of stereogenic elements (including five point and three planar chiralities). Cleistoperones B and C (2 and 3) are two new skeletal CPTAs with an unusual coupling pattern between the (nor)monoterpene moiety and the cinnamoyl chain of the cinnamoylphloroglucinol unit. Cleistoperone D (4) possesses an unprecedented cage-like 6/6/6/4/6-fused heteropentacyclic scaffold. The plausible biosynthetic pathways for 1–18 were also proposed. Notably, compounds 1, 4, 7, 8, and 18 exhibited significant antiviral activity against respiratory syncytial virus (RSV). The most potent one, cleistoperone A (1) with IC50 value of 1.71 ± 0.61 μmol/L, could effectively inhibit virus replication via affecting the Akt/mTOR/p70S6K signaling pathway.

Adaptive Reliable Cluster Head Selection Strategy for Multi-Hop Multimedia Wireless Sensor Networks

Adaptive Reliable Cluster Head Selection Strategy for Multi-Hop Multimedia Wireless Sensor Networks

A comparative cost assessment of coalescing epidemic control strategies in heterogeneous social-contact networks

The outcome of an epidemic is contingent upon the mitigating control strategies deployed by policymakers. The deployment of control strategies is constrained by the cost of economic, social, and technological factors, which often depend on regional conditions. Our model facilitates a comparative assessment of costs between two interconnected subpopulations within a heterogeneous social-contact network, which may deploy either aligned or divergent control strategies, as is frequently the case for neighboring countries. Depending on their respective asymmetric resources, each subpopulation can deploy a contact confinement strategy, a vaccination strategy, or a hybrid of both. The multi-objective goal of minimizing both the epidemic burden and the economic cost from policy implementation and productivity losses is compounded using a scaled scalarization method. Due to the inter-dependence of policy outcomes, the optimal strategy is derived from the Nash equilibrium in a 2-player compounded pay-off matrix of the total cost. Our network-based model implements a probabilistic Susceptible-Exposed-Infectious-Recovered-Dead dynamics. It also includes the effects of ego-network support and waning immunity modulated by re-exposure to viral contaminants (‘SEIRSD’). Robust outcomes of repeated network simulations with COVID-19 parameters are examined. Our method aims to equip policymakers in a negotiation process with a comparative cost matrix for divergent mitigation policies and to identify the optimal Nash-based strategy profile.

City-scale modelling of road thermal and hydrologic characteristics and failure mechanisms: Case study of Montreal

This study focuses on understanding and quantifying projected changes to road thermal and hydrologic characteristics that are associated with road failure, for the City of Montreal, Canada. To this end, city-scale modelling using a land surface model with realistic representation of urban regions, including roads, enabled by the super-resolution of 250 m, for the current 2001–2020 and future 2041–2060 periods are used. Analysis for the cold season suggests potential reduction in failures associated with thermal characteristics such as daily freeze-thaw and partial thaws, with a projected decrease of 12.5% and 2 %, respectively. Higher potential for hydrologic characteristics-related failures are likely, given projected increases in road/surface runoff in the 12–15% range. For the warm season, projected increases in thermal characteristics such as daily maximum and minimum temperatures (4–4.5 °C), and hydrological characteristics (25 %), suggest increased potential for road failure from rutting and drainage issues. Analysis performed separately for the highways and local roads reveal important spatial variability of the considered characteristics and associated failures, which can be helpful in prioritizing road sections for detailed analysis and in the development of sustainable adaptation strategies for the Montreal road network and enhancing the city's resilience to future climate change.

Adaptive neural network finite‐time control for fractional‐order nonlinear systems with external disturbance

AbstractThis paper is concerned with the finite‐time tracking control problem of fractional‐order nonlinear systems (FONSs) with uncertainty and external disturbance. A novel design scheme of the adaptive neural network finite‐time controller (ANNFTC) is developed by utilizing the theory of finite‐time stability and fractional‐order dynamic surface control (DSC) scheme combined with backstepping method. Radial basis function neural networks (RBF NNs) are employed to estimate the unknown nonlinear function. Furthermore, an auxiliary function is introduced to approximate the unknown upper bounds of the approximation error in RBF NNs and external disturbance. The ANNFTC ensures the finite‐time boundedness of all signals in FONSs and enhances the system output's tracking performance. The effectiveness of the proposed approach is demonstrated through a simulation example, providing empirical evidence to support the theoretical framework presented in this paper.

Efficient and quantum‐secure authenticated key exchange scheme for mobile satellite communication networks

SummaryThe mobile satellite communication (MSC) system is a vital communication method in which mobile users and network control centers connect via satellites. Since the satellite's communication is wireless, communication security is a critical factor to ensure accountable communication. Key exchange and authentication (KEA) mechanism is widely used to achieve data security for data transmitted in an insecure or open channel. Different authentication systems are suggested in the last many years to establish safe communication, whereas most existing security protocols are based on factorization or discrete logarithm. However, these systems are no longer reliable by Shor's algorithm as any discrete logarithm and factorization can be resolved in polynomial time on quantum computers. Thus, developing a quantum secure KEA protocol for MSC systems is necessary. In this direction, recently a ring learning with an error‐based KEA technique is proposed to ensure a quantum‐safe environment. This scheme is quantum‐safe and satisfies desirable security attributes but has low efficiency in computation and communication. Moreover, establishing a secure session requires six communications among involved entities. As a result, replay attack detection at an early stage is not possible for the central authority (server), which could delay the server response, and the adversary gets the advantage of drawing a denial of service scenario for authorized entities. We propose a lattice‐based KEA protocol for the MSC system to improve computation, communication efficiency, and early‐stage replay attack detection. The security analysis of the proposed scheme is presented in the random oracle model. Calculation of performance is also presented to observe advantages in computation and communication overhead.

An adaptive charging scheme for large-scale wireless rechargeable sensor networks inspired by deep Q-network

An adaptive charging scheme for large-scale wireless rechargeable sensor networks inspired by deep Q-network

Learning in Deep Radial Basis Function Networks.

Learning in neural networks with locally-tuned neuron models such as radial Basis Function (RBF) networks is often seen as instable, in particular when multi-layered architectures are used. Furthermore, universal approximation theorems for single-layered RBF networks are very well established; therefore, deeper architectures are theoretically not required. Consequently, RBFs are mostly used in a single-layered manner. However, deep neural networks have proven their effectiveness on many different tasks. In this paper, we show that deeper RBF architectures with multiple radial basis function layers can be designed together with efficient learning schemes. We introduce an initialization scheme for deep RBF networks based on k-means clustering and covariance estimation. We further show how to make use of convolutions to speed up the calculation of the Mahalanobis distance in a partially connected way, which is similar to the convolutional neural networks (CNNs). Finally, we evaluate our approach on image classification as well as speech emotion recognition tasks. Our results show that deep RBF networks perform very well, with comparable results to other deep neural network types, such as CNNs.

Research on the Network Communication Value and Strategies of Chinese Traditional Festival Culture

In the contemporary context of globalization and digitization, the communication of Chinese traditional festival culture not only involves the inheritance and promotion of culture but also pertains to the cultural identity and self-confidence of the Chinese nation. Starting from the perspective of the values conveyed, this paper emphasizes the role of traditional festival culture in strengthening the shaping of social ideology, maintaining a balance between the local and global aspects of culture, and highlighting its significance in the dialectical interaction with modern technology in the era of the internet. Subsequently, the paper proposes strategies for innovating content, utilizing advanced technology, enhancing interaction with audiences, and promoting cross-disciplinary integration to effectively propagate Chinese traditional festival culture in the online environment. Simultaneously, considering the impact of social transformation and external cultural influences, this study systematically analyzes potential challenges in the communication process and puts forth corresponding measures to promote the construction of cultural self-confidence in China through the dissemination of traditional festival culture.

An implementation analysis of a quality improvement project to reduce cesarean section in Brazilian private hospitals

BackgroundBrazil has one of the highest prevalence of cesarean sections in the world. The private health system is responsible for carrying out most of these surgical procedures. A quality improvement project called Adequate Childbirth Project (“Projeto Parto Adequado”- PPA) was developed to identify models of care for labor and childbirth, which place value on vaginal birth and reduce the frequency of cesarean sections without a clinical indication. This research aims to evaluate the implementation of PPA in private hospitals in Brazil.MethodEvaluative hospital-based survey, carried out in 2017, in 12 private hospitals, including 4,322 women. We used a Bayesian network strategy to develop a theoretical model for implementation analysis. We estimated and compared the degree of implementation of two major driving components of PPA—“Participation of women” and “Reorganization of care” – among the 12 hospitals and according to type of hospital (belonging to a health insurance company or not). To assess whether the degree of implementation was correlated with the rate of vaginal birth data we used the Bayesian Network and compared the difference between the group “Exposed to the PPA model of care” and the group “Standard of care model”.ResultsPPA had a low degree of implementation in both components “Reorganization of Care” (0.17 – 0.32) and “Participation of Women” (0.21 – 0.34). The combined implementation score was 0.39–0.64 and was higher in hospitals that belonged to a health insurance company. The vaginal birth rate was higher in hospitals with a higher degree of implementation of PPA.ConclusionThe degree of implementation of PPA was low, which reflects the difficulties in changing childbirth care practices. Nevertheless, PPA increased vaginal birth rates in private hospitals with higher implementation scores. PPA is an ongoing quality improvement project and these results demonstrate the need for changes in the involvement of women and the care offered by the provider.

An Efficient Fault-Tolerant Vehicular Data Forwarding Scheme for Timely and Reliable Data Transmission

With the increasing demands of various applications on vehicles, such as road condition sensing, traffic management, location-based services, and others both academic researchers and automotive industries pay much attention to Vehicular Networks. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the service requests from a large number of vehicular users. Further, network faults disrupt communication among vehicles, which affects the reliability and timely data delivery in vehicular networks. In this paper, for timely and reliable data transmission, an efficient data forwarding scheme for vehicular networks is proposed. Also, the paper addresses three types of network faults along with the data forwarding issue by selecting an optimal route with a higher probability of connectivity and lower experienced delay. The proposed scheme detects the faults dynamically and takes necessary actions to recover from the fault. Extensive simulation has been performed to evaluate the proposed scheme. Compared to the existing AODV- D approach and SRCP scheme, the proposed scheme attains better performance in terms of packet delivery ratio, end-to-end delay, and control overhead.

Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach: effects of layer thickness, infill rate, and building direction

Abstract This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas.

Label reusing based graph neural network for unbalanced classification of personalized driver genes in cancer

It is a big challenge to identify personalized driver genes (PDGs) for understanding tumor heterogeneity of cancer individual patients. From the perspective of machine learning, identifying PDGs is an inherent class imbalance issue due to the fewer known driver genes than most passenger ones. However, existing machine learning based methods including unsupervised and supervised learning based methods ignore the importance of limited well-established cancer tissue specific driver genes(CSDGs) for this class imbalance issue. Here we converted the PDG prediction issue to a semi-supervised classification task and a novel method (namely PersonalizedGNN) was developed to identify PDGs by using graph attention neural network and label reuse strategy in personalized gene interaction network (PGIN). PersonalizedGNN effectively utilizes the structure information of PGIN and the limited well-established CSDG information for achieving promising performance. Using the breast cancer and lung cancer datasets from The Cancer Genome Atlas, we validated our method and compared it with other advanced methods. PersonalizedGNN showed outstanding potential in identifying cancer driver genes in terms of prediction precision. Furthermore, we could discover subtype-specific de novo cancer driver genes and in vitro cell-based assays for a novel driver gene FZD7 in lung squamous cell carcinoma cells further validated the PersonalizedGNN. In summary, PersonalizedGNN offers a new effective perspective for discovering PDGs by considering information of prior known CSDGs in PGIN which help researchers understand tumor heterogeneity of cancer individual patients.

Exploring the chemical diversity of sesquiterpenes from the rarely studied south China sea soft coral Sinularia tumulosa assisted by molecular networking strategy

Molecular networking strategy-based prioritization of the isolation of the rarely studied soft coral Sinularia tumulosa yielded 14 sesquiterpenes. These isolated constituents consisted of nine different types of carbon frameworks, namely asteriscane, humulane, capillosane, seco-asteriscane, guaiane, dumortane, cadinane, farnesane, and benzofarnesane. Among them, situmulosaols A–C (1, 3 and 4) were previously undescribed ones, whose structures with absolute configurations were established by the combination of extensive spectral data analyses, quantum mechanical-nuclear magnetic resonance and time-dependent density functional theory electronic circular dichroism calculations, the Snatzke's method, and the modified Mosher's method. Notably, situmulosaol C (4) was the second member of capillosane-type sesquiterpenes. The plausible biogenetic relationships of these skeletally different sesquiterpenes were proposed. All sesquiterpenoids were evaluated for their antibacterial, cytotoxic and anti-inflammatory effects. The bioassay results showed compound 14 exhibited significant antibacterial activities against a variety of fish and human pathogenic bacteria with MIC90 values ranging from 3.6 to 33.8 μg/mL. Moreover, moderate cytotoxic effects against HEL cells for components 13 and 14 and moderate inhibitory effect on lipopolysaccharide-induced inflammatory responses in RAW264.7 cells for substance 13 were also observed.