Network Quality Research Articles

Efficient Pipeline Conflict Resolution for Layered QC-LDPC Decoders in OFDM-PON

Efficient Pipeline Conflict Resolution for Layered QC-LDPC Decoders in OFDM-PON

Regulation of the mitochondrial integrated stress response and ATF4 by acute contractile activity in mouse muscle

In the context of energetically demanding conditions, such as exercise, skeletal muscle relies on mitochondria to support its metabolic requirements. Maintenance of mitochondria and their bioenergetic availability is thus critical to sustain the metabolic flexibility of muscle. The quality of the organelle network is highly regulated by the coordination of mitochondrial quality control (MQC) processes. One MQC pathway that has garnered recent interest is the integrated stress response (ISR), which is activated in response to a variety of stimuli. The transcription factor ATF4, denoted as the main effector of this response pathway, serves to ameliorate cellular stress by upregulating a plethora of cytoprotective genes, such as CHOP and ATF5. Current literature has shown that the ISR is activated upon mitochondrial stress/perturbations to restore optimal organelle function and health. However, the precise mechanism that constitutes the activation of ISR and thus the response of ATF4 following acute exercise-induced stress is unknown. Therefore, our objective is to determine the extent of ISR/ATF4 induction in vivo and to test whether this pathway is required for the maintenance of muscle function. To investigate this, a mouse in situ hindlimb protocol was utilized to acutely stimulate muscles at 0.25-1 tetani/per second for 9 mins, followed by a 1-hour recovery period. The kinases, CAMKIIα and JNK2, were robustly phosphorylated/activated 6-fold immediately following the protocol. The initial stage of ISR activation, reflected in the ratio of phosphorylated to total-eIF2α protein levels, was also increased in response to acute contractile activity, evident in the recovery phase. Downstream of ISR activation, the total protein expression of ATF4 remained unchanged, however, contractile activity induced an increase in the localization of ATF4 to the nucleus. Robust 2-fold increases in the mRNA expression of ATF4 and CHOP were also observed and enhanced following the recovery period. Changes in ATF4 mRNA appeared to be independent of transcriptional activation, as assessed using an ATF4 promoter-reporter plasmid. An in vitro cell free mRNA decay assay revealed an increase in ATF4 mRNA stability post-stimulation, possibly as a result of cellular compartmental changes in the RNA binding proteins, HuR and CUGBP1. RNA sequencing analyses also confirmed the observed increase in related ISR genes following an exhaustive bout of exercise. These data suggest that acute contractile activity is indeed suffcient to induce mitochondrial stress and activate the ISR, corresponding to the induction of ATF4. Future work will reveal the underlying mitochondrial signal responsible for activating this pathway, as well as the downstream consequences for the regulation of MQC processes in maintaining mitochondrial homeostasis. This work was supported by funds from the Canadian Institutes for Health Research (CIHR). Victoria C. Sanfrancesco is the recipient of the CIHR Frederick Banting and Charles Best Canada Graduate Scholarship-Master's (CGS-M). David A. Hood is the holder of a Canadian Research Chair in Cell Physiology. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks

NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks

Protocol for the development of the Chatbot Assessment Reporting Tool (CHART) for clinical advice

IntroductionLarge language model (LLM)-linked chatbots are being increasingly applied in healthcare due to their impressive functionality and public availability. Studies have assessed the ability of LLM-linked chatbots to provide accurate...

Active Filters for Standard-Compliant Power Quality in Electrical Networks

The article is devoted to the analysis of three circuits of modular multilevel converters (MMCS) with cascade connection of single-phase bridge and half-bridge voltage converters, each of which operates in pulse width modulation (PWM) mode. The main feature of active filters based on MMS is the possibility of selective high-speed harmonic filtering in on–line mode: AF allows the suppression of any given set of harmonics, including the negative harmonic of the fundamental frequency (reverse sequence), with a given degree of suppression - to zero or a normalized value - by an appropriate task in the control program. For the practical implementation of MMS in solving problems of normalization of electricity quality indicators, the so-called DSB control algorithm of MMS was developed, based on the traditional "classical" theory of automatic control and the theory of three-phase circuits. A comparative assessment of the use of various MMC schemes for symmetry and reactive power compensation has been carried out. It is shown that the problem of voltage symmetry using MMS is a special one, requiring the use of special algorithms to solve it, ensuring the reactive nature of branches of MMS circuits.

CHEABC-QCRP: A novel QoS-aware cluster routing protocol for industrial IoT

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

Critical Analysis of Active Filter for Harmonic Reduction under Varying Load

In this paper, we give the main focus on the proliferation of non-linear loads in power systems, the mitigation of harmonics has become a critical concern to ensure stable and efficient operation. This study presents an analysis of an advanced control active filter (ACAF) designed for harmonic reduction under varying load conditions. The ACAF is a sophisticated solution that employs advanced control algorithms to effectively suppress harmonics and maintain power quality. The research investigates the performance of the ACAF under varying load conditions, which represent real-world scenarios where power consumption fluctuates. A comprehensive analysis is conducted to evaluate the filter's ability to adapt and maintain harmonic suppression efficiency across different load profiles. The study considers various load scenarios, including steady-state operation, transient load changes, and dynamic load fluctuations. The findings of this research contribute to the understanding of advanced control active filters in practical power systems with non-linear loads and varying operating conditions. The analysis provides valuable insights into the design, optimization, and implementation of ACAFs for harmonic mitigation, ultimately improving power quality and system reliability in modern electrical networks. A classified list of 38 publications on this topic is also given for the quick reference.

Analysis of the Evolution of Water Quality in Tanks According to the Connections and Operation Mode: Application to the Most Suitable Design

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age as a simple indicator of water quality in the network is of great interest in both the design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper first analyses the factors with the biggest influence on the tank’s water age to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet are presented. Also, real tank behaviour is characterised through field measurements and Epanet simulations in order to determine the best-fitting mixing model.

Analysis of the State of Highways of Regional Importance in the Russian Federation

The purpose of the article is to analyze the regional road network of the Russian Federation, to identify the main factors affecting its condition depending on the federal district. Recently, investments have improved the situation in the field of repair and construction of regional roads, but the condition of regional roads remains unsatisfactory — about half of all regional roads do not meet regulatory requirements. Based on the analysis of the regional road network, it is necessary to make a decision on the investment of a particular road facility. The article examines the relationship between factors such as the percentage of regional highways that do not meet regulatory requirements and the individual rating of the standard of living. The question is also raised about identifying the most problematic places in the regional road network based on mathematical statistics methods, building dependencies of the share of bad regional roads on the length of federal roads, on the road climate zone, on the gross regional product per capita, on population, on the density of the road network and other factors. The main research methods are correlation and regression analysis. As a result of the study, a statistical analysis of the quality of the highway network of regional significance in the federal districts of the Russian Federation for 2007–2022 was carried out, the main patterns and dependencies of the percentage of roads that do not meet regulatory requirements on the density of the road network, gross regional product, and road climate zone were revealed. Conclusions are obtained that at the moment the state of regional highways and the gross regional product are poorly correlated values. On the contrary, the condition of regional roads strongly correlates with the index of quality of life in the region and the length of federal roads. In the first case, there is a strong positive correlation, in the second — a strong negative one.

An efficient provably secure authentication and key agreement protocol for satellite communication networks

AbstractThe leverage of satellite resources to establish a communication network offers a wide range of benefits, including the capability to support news gathering, broadcasting, and aeronautical and maritime tracking. The unique aspects of the satellite‐linked terrestrial network provide high‐speed services, dependable and consistent network quality, and comprehensive geographic coverage of remote regions. Unfortunately, these structural features also render the network vulnerable to unauthorized intrusion, potentially leading to significant disruptions. Consequently, the implementation of authentication measures presents an appealing solution for enhancing the overall service quality of this network. Recently, Kumar et al. presented an authentication and key agreement scheme for satellite communications. Strikingly, their scheme proves inadequate in safeguarding against several malicious attacks and reveals certain design weaknesses. In this article, we present a reliable and secure authentication protocol that takes advantage of the complexities inherent in the elliptic curve discrete logarithm problem. We assess the protocol's effectiveness against various types of attacks using formal proof, Burrows–Abadi–Needham logic, informal descriptive proof analysis, and the verification tool SCYTHER. Furthermore, we compare the computational, communication, and storage overhead of our proposed protocol to an existing one, demonstrating its efficiency and superiority.

Evaluating Internet Network Quality: A Comprehensive Analysis by the PNUP Directorate of Building

This study raises the problem of the condition of the building network of the Directorate of PNUP which has an irregular cabling structure so that if a network installation is carried out by connecting to the nearest switch without regard to cable paths, the cables are not on the path according to the irregular distribution of IP networks and bandwidth. The purpose of this study is to enhance the quality of the internet network in the PNUP Directorate building, compare the network performance before and after revitalizing fiber optic cables, and determine the internet bandwidth requirements in the PNUP Directorate building. The method employed is assessing internet network performance using Quality of Service parameter, by evaluating when the implementation that has been carried out is deemed sufficient, the evaluation is carried out based on the test results of the Quality of Service parameter. The user speed test is performed on public IP, followed by data recording and capture using tilnet tools. The values for delay, throughput, packet loss, and jitter are then processed and calculated. The outcome of this research demonstrates that network revitalization has successfully increased bandwidth usage, particularly in data transmission (Tx). Additional capacity and increased network performance after the revitalization allow for greater and more efficient use of bandwidth in transmitting data.

Service-aware real-time slicing for virtualized beyond 5G networks

Edge Intelligence is expected to play a vital role in the evolution of 5G networks, empowering them with the capability to make real-time decisions regarding various allocations related to their management and service provisioning to end-users. This shift facilitates the transition from a network-aware approach, where applications are developed to manage network quality fluctuations, to a service-aware network that self-adjusts based on the hosted applications. In this paper, we design and implement a service-aware network managed from the network edge. We utilize and assess various Machine Learning models to classify cellular network traffic flows in the backhaul, aiming to predict their future impact on network load. Leveraging these predictions, the network can proactively and autonomously reallocate slices in the Radio Access Network via programmable APIs, ensuring the demands of the traffic-generating applications are met. The approach integrates innovative MLOps methodologies for distributed and online training, enabling continuous model refinement and adaptation to evolving network dynamics. Our framework was tested in a real-world environment with realistic traffic scenarios, and the results were evaluated in real-time, down to a granularity of 10ms. Our findings indicate that the network can swiftly adjust to traffic, providing users with slices tailored to their application needs. Notably, our experiments show that under the studied settings, the users experienced up to 4 times lower latency (jitter) and nearly 4 times higher throughput when interacting with various applications, compared to the standard non-AI/ML unit. Furthermore, our dynamic scheme significantly optimizes resource allocation, ensuring energy efficiency by avoiding over- and under-provisioning of resources.

Estimating the composition ratios of network services carried in mixed traffic

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity.

The procoagulant signature of cancer cells drives fibrin network formation in tumor microenvironment and impacts its quality. Implications in cancer cell migration and the resistance to anticancer agents

IntroductionCancer cells induce hypercoagulability in the tumoral microenvironment by expressing Tissue Factor (TF). We aimed to study the impact of the procoagulant signature of cancer cells on the quality and structure of fibrin network. We also studied the impact of fibrin clot shield (FCS) on the efficiency of anticancer agents and the migration of cancer cells. Materials and methodsPancreatic cancer cells BXPC3 and breast cancer cells MDA-MB231 and MCF7, were cultured in the presence of normal Platelet Poor Plasma (PPP), diluted 10 % in conditioning media. Their potential to induce thrombin generation and their fibrinolytic activity were assessed. The structure of fibrin network was analyzed with Scanning Electron Microscopy (SEM). Cancer cells' mobility with fibrin clot and their interactions with fibrin were observed. Cancer cells were treated with paclitaxel (PTX) or 4-hydroxy-tamoxifen (4OHTam) in the presence or absence of FCS. ResultsCancer cells, in presence of PPP, induced fibrin network formation. High TF-expressing cancer cells (BXPC3 and MDA-MB23 cells), led to dense fibrin network with fine fibers. Low TF expressing cells MCF7 led to thick fibers. Exogenous TF enhanced the density of fibrin network formed by MCF7 cells. Cancer cells through their inherent profibrinolytic potential migrated within the fiber scaffold. The BXPC3 and MCF7 cells moved in clusters whereas the MDA-MB231 cells moved individually within the fibrin network. FCS decreased the efficiency of PTX and 4OHTam on the viability of cancer cells. ConclusionsThe procoagulant signature of cancer cells is determinant for the quality and structure of fibrin network in the microenvironment. Original SEM images show the architecture of “bird's nest”-like fibrin network being in touch with the cell membranes and surrounding cancer cells. Fibrin network constructed by triggering thrombin generation by cancer cells, provides a scaffold for cell migration. Fibrin clot shields protect cancer cells against PTX and 4OHTam.

Empirical Insights into 5G Deployments in Highway Operational Environments and Comparative Performance with 4G

Despite the promising benefits, the integration of 5G, particularly through Non-Standalone (NSA) architectures that rely on existing 4G infrastructures, introduces challenges in maintaining optimal radio network performance and service quality. This study evaluates 4G and 5G radio network performance through empirical field trials across highway operational scenarios, a domain less scrutinized compared with urban environments. By conducting simultaneous measurements and comparing all available networks, this research focuses on dissecting the performance of critical quality indicators to gauge the intricacies of radio network behavior in high-speed travel conditions. Our findings illuminate distinct behavioral patterns, highlighting the unique challenges and optimization opportunities in these scenarios. Variability in signal strength across the highway routes underscores the influence of geography and infrastructure on coverage, while the relative stability in signal quality suggests the networks’ capability to maintain signal quality amid fluctuating strength. Interference results indicate effective management of signal interference, crucial for high-quality links, whereas latency and throughput metrics highlight a lag behind anticipated goals for reduced latency but promising data rates. This study not only showcases the variances in network quality and performance, thus pinpointing areas for operator-specific enhancements, but also emphasizes the comparison between the robustness of 4G infrastructures and the challenges in optimizing 5G networks.

Secure Virtual Machine Migration and Host Overload Detection Using Modified Pelican Optimization with Variable Load Mean Function

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system.

An efficient minimum cost battery charging and discharging scheduling scheme for electric vehicle using hybrid energy valley-pathfinder algorithm

ABSTRACT Emission pollution is one of the major issues in many growing cities. To overcome the problem and to prevent pollution, “Electric Vehicle (EV)” is one of the best solutions. Moreover, it has both economic and environmental benefits. To increase network reliability and quality, distribution companies contribute effective assistance. The major challenge met by this network is choosing an applicable connecting point for the bi-directional operation in a distribution network. Other issues the network faces are the performance and quality of the distribution network, cost reduction, and geographic location. The system’s stability is affected by connecting to the grid for charging EVs. Therefore, the optimal charging process is needed for the EVs. This paper proposes developing an optimal charging system without damaging the stability of the power system. This optimization is carried out by combining two algorithms and developing a new algorithm called “Final Position of Energy Valley and Pathfinder Algorithm (FP-EVPA).” This algorithm minimizes overall system operating costs. Also, it helps to reduce pollution emissions, grid losses, environmental pollution, nodal offset voltage, peak valley difference of load, charging, and operating costs. The optimized method maintains the quality of the power grid and system stability. It is also helpful for the users for minimal charging costs. The empirical findings of the model are better than 0.19%, 0.2%, 0.18%, and 0.07% of GEO, EOO, EVO, and PFA algorithms in terms of median. The developed FP-EVPA was compared with other traditional algorithms with different test functions to examine the effective achievement of the developed method.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network’s physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem’s assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics.

Optimizing fog device deployment for maximal network connectivity and edge coverage using metaheuristic algorithm

Fog computing emerged to address the limitations and challenges of traditional Cloud computing, particularly in handling real-time, heterogeneous, and latency-sensitive applications. However, the spread of Fog computing devices across the network introduces various challenges, especially concerning device connectivity and ensuring sufficient coverage to fulfil users’ requests. To maintain network operability, Fog Device Deployment (FDD) must effectively consider two crucial factors: connectivity and coverage. Network connectivity relies on FDD, determining the physical network topology, while coverage determines the accessibility of the Internet of Things (IoT) or edge devices. Both these objectives significantly impact the network performance and guarantee the network's Quality of Service (QoS). However, determining an optimal FDD method that reduces computation and communication overhead, and provides high network connectivity and coverage, is challenging. In this work, we propose an FDD algorithm that effectively connects the Fog devices for internal communication and covers maximum edge devices to entertain the requests. Firstly, FDD is formulated as a multi-objective optimization problem and then, an emerging metaheuristic Jaya Algorithm (JA) is applied to optimize the multi-objective function. The suitability of the JA, for the FDD problem, is substantiated by its rapid convergence and better computational complexity when contrasted with other contemporary population-based algorithms. In conclusion, the performance of the proposed method is assessed across a spectrum of benchmark-generated instances, each reflecting distinct Fog scenarios. The experimental outcomes showcase the proposed method's remarkable promise, especially when compared against state-of-the-art methodologies.