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In practical radio transmissions, bit error and channel capture are two dominating factors that affect wireless network performance. Previous models have omitted the interaction between bit error and channel capture. We present a homogeneous-network performance-prediction model for a Rayleigh fading channel that incorporates both the capture effect and transmission error into a 3-D Markov Chain. We accurately characterise the interaction between packet error and capture by incorporating them both into the model of the receiver operations. We show how the model can be solved efficiently. The model provides quality of service measures, including packet delay and loss, which are difficult to achieve with other models. Simulation results confirm that our 3-D model accurately predicts the performance for practical SNRs and receiver sensitivities. We demonstrate that our model can be directly applied to call admission control of Voice over IP service with a QoS guarantee in a WiFi network. The model is then extended to heterogeneous networks, where different stations have different packet arrival rates and packet sizes. [ABSTRACT FROM AUTHOR]

Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value. [ABSTRACT FROM AUTHOR]

Some samples of semi-dry flue gas desulfurization (FGD) ash were taken from sinter gas of a steel factory. Scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses were employed to identify the samples in order to investigate their physical and chemical characteristics. The results show that semi-dry FGD ash from a steel factory is stable under atmospheric conditions. It has irregular shape, a smooth surface and loose construction. The size of FGD ash particles is around 0.5—25 µm, the average size is about 5 µm and the median diameter is 4.18 µm. Semi-dry FGD ash from a steel factory consists of CaSO3, CaSO4, CaCO3, some amorphous vitreous material and unburned carbon. An experimental method was found to study the oxidation characteristics of ash. A prediction model of the oxidation efficiency was obtained based on response surface methodology. The results show that not only the temperature, but also gas:solid ratio, play an important role in influencing the oxidation efficiency. The interactions of the gas:solid ratio with temperature play an essential role. An improved response surface model was obtained which can be helpful to describe the degree of oxidation efficiency of semi-dry FGD ash. [ABSTRACT FROM PUBLISHER]

Smart home is an emerging form of the Internet of Things (IoT), enabling people to enjoy a convenient and intelligent life. The data generated by smart home devices are transmitted through the public channel, which is not secure enough, so the secret data in smart home are easily intercepted by malicious adversaries. In order to solve this problem, this paper proposes a smart home privacy protection method combining DES encryption and the improved Least Significant Bit (LSB) information hiding algorithm, changing the practice of directly exposing smart home secret information to the Internet, first, using Data Encryption Standard (DES) encryption to encrypt the smart home information and second, the improved LSB information hiding algorithm is used to hide the ciphertext, so that the adversary cannot detect the smart home secret information. The goal of the scheme is to provide a double protection for the secure transmission of the smart home secret information. If an attacker wants to carry out an attack, it has to break through at least two defense lines, which seems impossible to do. Experiment results show that the improved LSB algorithm is more robust than the existing algorithms, and it is very safe. Therefore, the scheme proposed in this paper is very practical for protecting the smart home secret information. [ABSTRACT FROM AUTHOR]

Integrating millimeter Wave (mmWave) and long-term evolution (LTE) sub-6 GHz bands to provide wireless services for high-speed rails (HSRs) is a promising solution to balance the ever-rising service demand and operational cost. In this paper, we optimize the network interface selection of the mmWave and LTE interfaces for HSR communications, to minimize the operational cost while satisfying the service requests of passengers. The network interface selection problem is formulated as a finite-horizon discrete-time Markov decision process (MDP) problem. Based on the file requests and associated deadlines of a passenger and the train schedule, we solve the MDP problem ahead-of-time by using dynamic programming (DP) and achieve optimal interface selection for each waypoint along the railways. Further, by exploiting the subadditivity and superadditivity of the considered problem, we reveal that an optimal switch of interfaces is only triggered at the moment when the remaining undelivered data or the elapsed time reaches their specific thresholds. Based on the thresholds, the optimal interface selection can be directly achieved for every waypoint, hence eliminating the need for DP and substantially reducing the computational complexity. The proposed algorithms are evaluated with practical train schedules. Simulation results show that the proposed algorithms can substantially reduce the operational cost by 35% in our considered simulation setting, as compared to existing alternatives. [ABSTRACT FROM AUTHOR]

The distributed nature of Federated Learning (FL) introduces security vulnerabilities and issues related to the heterogeneous distribution of data. Traditional FL aggregation algorithms often mitigate security risks by excluding outliers, which compromises the diversity of shared information. In this paper, we introduce a novel filtering-and-voting framework that adeptly navigates the challenges posed by non-iid training data and malicious attacks on FL. The proposed framework integrates a filtering layer for defensive measures against the intrusion of malicious models and a voting layer to harness valuable contributions from diverse participants. Moreover, by employing Deep Reinforcement Learning (DRL) for dynamic aggregation weight adjustment, we ensure the optimized aggregation of participant data, enhancing the diversity of information used for aggregation and improving the performance of the global model. Experimental results demonstrate that the proposed framework presents superior accuracy over traditional and contemporary FL aggregation methods as diverse models are utilized. It also shows robust resistance against malicious poisoning attacks. • A novel filtering-and-voting framework for secure and optimized performance in FL. • Analytical evaluation with a convergence bound confirms the framework's convergence. • DRL-based weight selection enhances learning from unique knowledge in non-iid data. [ABSTRACT FROM AUTHOR]

The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional |$q$| -parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended |$q$| -parallel bilinear Diffie–Hellman exponent (E |$q$| -PBDHE) assumption and prove that the proposed approach provides 'indistinguishably chosen-plaintext attacks secure' data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. This paper expands the control domain to a large area with multiple intersections. In particular, a Multi-intersection Vehicular Cooperative Control (MiVeCC) is proposed to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Under the framework, a two-stage reinforcement learning is implemented to obtain the optimal policy for vehicle control. To support RL in the proposed framework, a multi-vehicle state representation method and a safety-oriented value representation method are designed. The former structures the collected vehicle information, and the latter evaluates the traffic condition. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with benchmark methods. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc networks (VANET), effective trust establishment with authentication is an important requirement. Trust management among communicating vehicles is significant for secure message transmission; however, very less contributions have been made towards evaluating the trustworthiness of the node. This research work intends to introduce a new trust management system in VANET with two major phases: secured message transmission and node trustability prediction. The security assured message passing is carried out by incorporating the privacy preservation model under the data sanitization process. The key used for the sanitization process is optimally tuned by a new hybrid algorithm termed Sea Lion Explored-Whale Optimization Algorithm, which is the combination of Whale Optimization Algorithm and Sea Lion Optimization Algorithm, respectively. The blockchain technology is assisted to handle the key generated by the nodes. Subsequently, the trustability of the node is evaluated under novel specifics "two-level evaluation process" with a rule-based and machine learning-based evaluation process. Finally, the performance of the proposed model is verified and proved over other conventional methods for certain measures. [ABSTRACT FROM AUTHOR]

• We reveal the intrinsic relation between the unknown frequency-domain representation of general band-limited graph signals and the graph Fourier transform basis. • A new analytical expression is derived to determine the graph Fourier transform basis of observed band-limited signals. • Given the graph Fourier transform basis and the statistical knowledge of the graph Laplacian, the estimation of the eigenvalues of the graph Laplacian is convex and efficiently solved using the alternating direction method of multipliers. A graph provides an effective means to represent the statistical dependence or similarity among signals observed at different vertices. A critical challenge is to excavate graphs underlying observed signals, because of non-convex problem structure and associated high computational requirements. This paper presents a new graph learning technique that is able to efficiently infer the graph structure underlying observed graph signals. The key idea is that we reveal the intrinsic relation between the frequency-domain representation of general band-limited graph signals, and the graph Fourier transform (GFT) basis. Accordingly, we derive a new closed-form analytic expression for the GFT basis, which depends deterministically on the observed signals (as opposed to being solved numerically and approximately in the literature). Given the GFT basis, the estimation of the graph Laplacian, more explicitly, its eigenvalues, is convex and efficiently solved using the alternating direction method of multipliers (ADMM). Simulations based on synthetic data and experiments based on public weather and brain signal datasets show that the new technique outperforms the state of the art in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Heart failure (HF) with left ventricular diastolic dysfunction is a growing global concern. This study evaluated myocardial oxidized nicotinamide adenine dinucleotide (NAD + ) levels in human systolic and diastolic HF and in a murine model of HF with preserved ejection fraction, exploring NAD + repletion as therapy. We quantified myocardial NAD + and nicotinamide phosphoribosyltransferase levels, assessing restoration with nicotinamide riboside (NR). Findings show significant NAD + and nicotinamide phosphoribosyltransferase depletion in human diastolic HF myocardium, but NR successfully restored NAD + levels. In murine HF with preserved ejection fraction, NR as preventive and therapeutic intervention improved metabolic and antioxidant profiles. This study underscores NAD + repletion's potential in diastolic HF management., Competing Interests: This work was supported by the Sydney Medical School Foundation (Dr O’Sullivan); Sydney Cardiovascular Fellowship (Dr O’Sullivan), NSW Health Early-Mid Career Fellowship and Clinician-Scientist Awards (Dr O’Sullivan; DOH1003 and DOH1006), National Heart Foundation Future Leader Fellowship (Dr O’Sullivan; NHF104853), National Heart Foundation Future Leader Fellowship (Dr Koay; NHF107180), NSW Cardiovascular Collaborative Grant (Dr Koay), and philanthropic support from The University of Sydney donors (Dr Lal). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

Currently, about 300 million people worldwide are affected by asthma. Most of these sufferers inhale immunosuppressants (ie corticosteroids) and beta-adrenergic receptor agonists for their asthma treatment. However, about 5%-10% of patients of asthma have poor response to such treatment. Investigation of kinase signaling pathway and nuclear transcription factor as a target molecule in the treatment of allergic asthma has been the concern of scholars home and abroad. This paper reviewed inhibitors of kinase signaling pathway and nuclear transcription factors for the treatment of asthma.