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Federated learning (FL) is a prominent paradigm of 6G edge intelligence (EI), which mitigates privacy breaches and high communication pressure caused by conventional centralized model training in the artificial intelligence of things (AIoT). The execution of multimodal federated perception (MFP) services comprises three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately competitive on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is vital to the MFP networks. To address the above issues, this article explores service-oriented resource management with integrated sensing, communication, and computing (ISCC). Specifically, employing the incentive mechanism of the MFP service market, the resources management problem is defined as a social welfare maximization problem, where the concept of "expanding resources" and "reducing costs" is used to enhance learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

The future vehicle‐to‐everything (V2X) scenarios greatly call for reliable and efficient communication links between roadside units (RSUs) and vehicles. Integrated sensing and communication (ISAC) represents one of the key candidate technologies for 6G, which provides new solutions for this problem. In this paper, the beam tracking design for V2X communication is investigated by exploiting the ISAC technique, where the RSU uses the echoes of the ISAC signals to estimate the motion parameters of the vehicles. Compared with traditional feedback‐based beam tracking, the proposed method can reduce the overall signaling costs and improve communication robustness. Furthermore, to achieve high accuracy and low latency for beam alignment, a Trajectory Adaptive Unscented Kalman Filter (TA‐UKF) beam tracking algorithm is proposed, which obtains the optimal state transition model following the change of the vehicle state. Considering practical application scenarios, an ISAC‐based adaptive beam coverage (ISAC‐AC) scheme is further developed for the beam tracking of extended targets, which can achieve the beam misalignment of vehicles in practical scenarios. Numerical results demonstrate that the proposed beam tracking and coverage strategies outperform baseline approaches. [ABSTRACT FROM AUTHOR]

Modern communication and computing technology is the basic support of the industrial intelligent systems (IIS). As a key component of IIS, the smart port is essential to be offered low‐complexity and high‐reliability communication service, especially for driverless engineering vehicles. However, it is combined and nonconvex to find the optimal association between vehicles and the road side units (RSUs). Besides, due to the mobility of vehicles and the severe path loss of mmWave links, beam switching and reassociation between vehicles and RSUs are required frequently, which brings a great challenge to the communication for the IIS. A low complexity closed‐loop strategy based on distributed cooperation for mmWave communication in IIS is proposed in this study, in which user association and beam tracking with the assistance of beam pools is proposed. Many‐to‐many user association is established based on distributed multiagent reinforcement learning, where the vehicle can independently select the set of serving RSUs based on the local observation without information exchange with others, reducing the signaling overhead and computational complexity while improving system throughput. Furthermore, multipoint‐cooperation soft switching of beams based on beam tracking improves the reliability of mmWave communication with the smaller training cost. Extensive analysis and simulation results demonstrate that the proposed solution significantly reduces the complexity of the mmWave communication while improving the throughput and stability in IIS. [ABSTRACT FROM AUTHOR]

The arrival of big data and the Internet of Things (IoT) era greatly promotes innovative in-network computing techniques, where the edge-cloud continuum becomes a feasible paradigm in handling multi-dimensional resources such as computing, storage, and communication. In this article, an energy constrained unmanned aerial vehicle (UAV)-aided mobile edge-cloud continuum framework is introduced, where the offloaded tasks from ground IoT devices can be cooperatively executed by UAVs acts as an edge server and cloud server connected to a ground base station (GBS), which can be seen as an access point. Specifically, a UAV is powered by the laser beam transmitted from a GBS, and can further charge IoT devices wirelessly. Here, an interesting task offloading and energy allocation problem is investigated by maximizing the long-term reward subject to executed task size and execution delay, under constraints such as energy causality, task causality, and cache causality. A federated deep reinforcement learning (FDRL) framework is proposed to learn the joint task offloading and energy allocation decision while reducing the training cost and preventing privacy leakage of DRL training. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to three baseline schemes. [ABSTRACT FROM AUTHOR]

Maternal vaginal/rectal colonization of group B streptococcus (GBS) is a main risk for neonatal invasive infection. Efficient determination of GBS colonization in pregnant women is crucial. This study aimed to investigate the prevalence of GBS carriage and evaluate the diagnostic performance of six methodologies for GBS screening conducted in China, including blood agar plate, liquid chromogenic medium, and loop-mediated isothermal amplification (LAMP) without pre-enrichment, chromogenic agar plate with pre-enrichment, and GBS antigen detection without and with pre-enrichment in comparison with the standard reference method (Lim broth-enriched subculture with plating on 5% sheep blood agar). Vaginal/rectal swabs were collected from 1,281 pregnant women at 35–37 weeks of gestation. Of them, 309 were taken in triplicate, one for Lim broth-enriched subculture, one for blood agar plate, and the third for GBS antigen detection (Reagent W); 177 were acquired in duplicate, one for Lim broth-enriched subculture and the other for GBS antigen detection (Reagent H); 502 were obtained in duplicate, one for Lim broth-enriched subculture and the other for liquid chromogenic medium; 158 were collected in duplicate, one for Lim broth-enriched subculture and the other for LAMP; and 135 were inoculated in Lim broth-enriched for GBS antigen detection (Reagent W) and subculture with chromogenic agar plate and 5% blood agar plate. The overall prevalence of GBS carriage was 10.1% (130/1,281, 95% CI: 8.5–12.1%) according to the standard reference method. Compared with the standard reference method, the LAMP had excellent performance of sensitivity (100%, 95%CI: 83.4–100%), specificity (94%, 95%CI: 88.1–97.1%), and Yoden index (0.940); as well as the blood agar plate with sensitivity (81.5%, 95%CI: 61.3–93.0%), specificity (100%, 95%CI: 98.3–100.0%), and Yoden index (0.815). The other four methods were not sufficient to reach the threshold in terms of sensitivity or specificity compared to the standard reference method. Furthermore, for LAMP, results can be obtained within 0.5–1 h, while for blood agar plate, which needed 24–48 h, and further identification was required. Our data suggested that the performance of LAMP was highly comparable to the standard Lim broth-enriched subculture and LAMP is considered as an alternative for fast and accurate GBS screening. [ABSTRACT FROM AUTHOR]

Group B Streptococcus (GBS) is an important etiological agent of maternal and neonatal infections as well as postpartum women and individuals with impaired immunity. We developed and evaluated a rapid classification method for sequence types (STs) of GBS based on statistic models with Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF/MS). Whole-cell lysates MALDI-TOF/MS analysis was performed on 235 well-characterized GBS isolates from neonatal invasive infections in a multi-center study in China between 2015 and 2017. Mass spectra belonging to major STs (ST10, ST12, ST17, ST19, ST23) were selected for model generation and validation. Recognition and cross validation values were calculated by Genetic Algorithm-K Nearest Neighbor (GA-KNN), Supervised Neural Network (SNN), QuickClassifier (QC) to select models with the best performance for validation of diagnostic efficiency. Informative peaks were further screened through peak statistical analysis, ST subtyping MSP peak data and mass spectrum visualization. For major STs, the ML models generated by GA-KNN algorithms attained highest cross validation values in comparison to SNN and QC algorithms. GA-KNN models of ST10, ST17, and ST12/ST19 had good diagnostic efficiency, with high sensitivity (95–100%), specificity (91.46%–99.23%), accuracy (92.79–99.29%), positive prediction value (PPV, 80%–92.68%), negative prediction value (NPV, 94.32%–99.23%). Peak markers were firstly identified for ST10 (m/z 6250, 3125, 6891) and ST17 strains (m/z 2956, 5912, 7735, 5218). Statistical models for rapid GBS ST subtyping using MALDI-TOF/MS spectrometry contributes to easier epidemical molecular monitoring of GBS infection diseases. [ABSTRACT FROM AUTHOR]

Fluoroquinolone (FQ)-resistant Group B Streptococcus (GBS) has been reported with considerable cross-resistance, worsening the crisis of multidrug-resistant (MDR) GBS in clinical settings. However, national epidemiological data on FQ-resistant GBS in mainland China have not been well-characterized. This study aimed to determine the prevalence of FQ resistance among GBS from neonatal invasive infections and maternal colonization in northern and southern China, to investigate the serotyping, multilocus sequence typing, and antibiotic cross-resistance, and to characterize the mutations in gyrA and parC genes in quinolone resistance-determining region (QRDR). In order to provide a comprehensive view of the location and structure of resistance genes, whole-genome sequencing on III/ST19 MDR isolates were performed. Among 426 GBS, 138 (32.4%) were FQ resistant, with higher prevalence in northern China than in southern China in both neonates (57.8%, 37/64 vs. 21.7%, 39/180) and pregnant women (50.9%, 29/57 vs. 26.4%, 33/125). Serotypes were distributed as III (48.5%), Ib (39.9%), V (6.5%), and Ia (5.1%). Sequence types were mainly ST19 (53.6%) and ST10 (39.1%), followed by ST12 (1.4%), ST17 (1.4%), ST23 (1.4%), and 0.7% each of ST27, ST188, ST197, and ST597. ST19 isolates were more prevalent in southern China than in northern China in both neonates (64.1%, 25/39 vs. 27.0%, 10/37) and pregnant women (81.8%, 27/33 vs. 41.4%, 12/29), whereas ST10 isolates were more common in northern China than in southern China in both neonates (64.9%, 24/37 vs. 20.5%, 8/39) and pregnant women (58.6%, 17/29 vs. 15.2%, 5/33). Serotype III isolates were mainly ST19 (89.6%, 60/67), while Ib isolates were largely ST10 (94.5%, 52/55). Sequencing data revealed several mutations in QRDR, including Ser81Leu in gyrA (99.2%, 130/131), Ser79Phe or Tyr in parC (76.2%, 48/63), and a previously unreported Ile218Thr and Ile219Phe double mutation pattern (49.2%, 31/63) in parC. ST10 isolates were associated with Ser79Phe (84%, 21/25), while ST19 isolates were limited to Ser79Tyr (95.7%, 22/23). A new integrative and conjugative element (ICE) harboring tetM and gyrA genes was identified in a III/ST19 isolate. This study investigates the molecular characteristics of FQ-resistant GBS in northern and southern China, emphasizing the need for continuous surveillance geographically and further research to characterize the mechanisms of ICE transfer. [ABSTRACT FROM AUTHOR]

Cellular vehicular networks constitute an important part of future vehicular communication systems. However, they pose challenges to fulfill vehicles with demands for the transmission rates of various network services while achieving satisfactory costs and energy consumption. The cloud radio access network architecture is considered as a green wireless access network framework that reduces the deployment costs of base stations while achieving resource sharing and dynamic scheduling to lower energy consumption, and thus will greatly improve spectrum efficiency. in this article, we first propose a cloud vehicular radio access network (C-VRAN) architecture to enable rapid network innovation for green vehicular communications. Specifically, we propose a vehicular network framework consisting of centralized processing, collaborative radio, real-time cloud computing, and data compression functions. Then, the design and the potential benefits of C-VRAN are presented in detail. Moreover, an effective data compression approach for the proposed architecture is proposed to meet low-distortion compression for cellular vehicular network data, thereby improving fiber resource utilization. Finally, we briefly discuss key challenges and solutions for future green vehicular communications. [ABSTRACT FROM AUTHOR]

Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people's work and life in the future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as baseline method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time. [ABSTRACT FROM AUTHOR]

Next-generation (5G) vehicular networks will support various network applications, leading to specific requirements and challenges for wireless access technologies. This trend has motivated the development of the long-term evolution-vehicle (LTE-V) network, a 5G cellular-based vehicular technology. Due to the limited bandwidth for vehicular communications, it is important to efficiently utilize slim spectrum resources in vehicular networks. In this paper, we introduce a cloud radio access network (C-RAN)-based vehicular network architecture, named C-VRAN, which facilitates efficient management and centralized processing of vehicular networks. Furthermore, we propose a discrete cosine transform (DCT)-based data compression scheme for C-VRAN to enhance the effective data rate of the fronthaul network. This scheme first uses DCT to perform time-frequency conversion of LTE-V I/Q data and then utilizes the Lloyd-Max algorithm to quantify data in the frequency domain before finally selecting an appropriate coding scheme to achieve better performance. Simulation results show that the proposed scheme can achieve 3 times compression ratio within 1% error vector amplitude distortion, and it also has strong independence and versatility, allowing it to be used as a standalone module for the current LTE-V system. [ABSTRACT FROM AUTHOR]

Cooperative communication technology has become a research hotspot in wireless sensor networks (WSNs) in recent years, and will become one of the key technologies for improving spectrum utilization in wireless communication systems in the future. It leverages cooperation among multiple relay nodes in the wireless network to realize path transmission sharing, thereby improving the system throughput. In this paper, we model the process of cooperative communications with relay selection in WSNs as a Markov decision process and propose DQ-RSS, a deep-reinforcement-learning-based relay selection scheme, in WSNs. In DQ-RSS, a deep-Q-network (DQN) is trained according to the outage probability and mutual information, and the optimal relay is selected from a plurality of relay nodes without the need for a network model or prior data. More specifically, we use DQN to process high-dimensional state spaces and accelerate the learning rate. We compare DQ-RSS with the Q-learning-based relay selection scheme and evaluate the network performance on the basis of three aspects: outage probability, system capacity, and energy consumption. Simulation results indicate that DQ-RSS can achieve better performance on these elements and save the convergence time compared with existing schemes. [ABSTRACT FROM AUTHOR]

The performance of orthogonal frequency division multiplexing (OFDM) based wireless vehicular communication systems is faced with the great challenge of impulsive noise (IN), which could limit the application of OFDM in ultra-reliable low-latency communication scenarios. In this paper, the challenge of IN elimination for OFDM-based wireless systems is efficiently overcome by the proposed sparse learning algorithms and probabilistic framework inspired by the emerging machine learning theories. For the first time, the sparse machine learning theory is introduced to IN recovery and elimination. Exploiting the measurement vector of IN observed from the reserved null sub-carriers as the input, a novel sparse machine learning based algorithm of sparse cross-entropy minimization is proposed, in which the probability distribution of the IN support is iteratively updated by minimizing the loss function, i.e. the cross-entropy. The proposed algorithm is able to effectively and efficiently learn the sparse pattern and converge to the accurate distribution of IN support. To facilitate an accelerated and even more efficient learning process, regularization is imposed on the loss function by adding a weighting parameter in favor of the accurate distribution. The computer simulation results confirm that the proposed scheme outperforms conventional methods while utilizing fewer spectrum resources over wireless vehicular channels. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system's access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means. [ABSTRACT FROM AUTHOR]

Small cells are beneficial for both users and operators to improve communication quality. The femtocell is one type of small cell used for indoor coverage. Enterprise femtocell installation inevitably produces intralayer and interlayer interference management issues. Enterprise femtocell optimization and indoor propagation modeling are built in this work based on the communications environment characteristics. Q-learning-based distributed and hybrid power control strategies are then proposed. The throughput, energy efficiency, and user experience satisfaction with conventional scheduling methods are compared. The simulation results show that distributed Q-learning performs better local optimization while hybrid Q-learning enhances global performance. [ABSTRACT FROM PUBLISHER]

Machine-type communication is undergoing ubiquitous development by connecting with trillions of intelligent devices and providing a broad range of applications through all of our lives. MTC can access different networks, which is similar to a fusion of heterogeneous networks, including LTE, WiFi, ZigBee, and so on. However, security and privacy issues still bring about a great challenge for the development of MTC, particularly secure transmission under heterogeneous networks. This article first presents an overview of the MTC architecture and its evolution toward LTE, security threats, and solutions. Then, as 5G systems have been designed to support a diverse set of communications, we propose a scalable cross-layer authentication solution based on MTC devices? hardware fingerprints toward an LTE heterogeneous network. [ABSTRACT FROM PUBLISHER]

Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity’s impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm’s robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail. [ABSTRACT FROM AUTHOR]

A large number of new data-consuming applications are emerging, and many of them involve mobile users. In the next generation of wireless communication systems, device-to-device (D2D) communication is introduced as a new paradigm to offload the increasing traffic to the user equipment. Before the traffic transmission, D2D discovery and access procedure is the first important step which needs to be completed. In this paper, our goal is to design a device discovery and access scheme for the fifth generation cellular networks. We first present two types of device discovery and access procedures. Then we provide performance analysis based on the Markov process model. In addition, we present numerical simulation on the Vienna Matlab platform. The simulation results demonstrate the viability of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The IEEE 802.16 mesh network is a promising next generation wireless backbone network. In the network, the allocation of minislots is handled by centralized scheduling and distributed scheduling, which are independently exercised. However, the standard does not specify how the frame can be partitioned among its centralized and distributed schedulers. Through efficient partitioning that dynamically adapts the partitioning based on demand, network can support more user applications. Although a dynamic frame partitioning scheme to use Markov model has been studied, the dynamic frame partitioning method has not been fully investigated. This paper proposes two novel and general dynamic frame partitioning scheme for IEEE 802.16 mesh networks so that the minislot allocation can be more flexible and the utilization is increased. The two schemes respectively use GM(1,1)-Markov model and Grey-Verhulst-Markov model to predict efficient partitions for future frames according to the minislot utilization in current frames. Our study indicates that the two proposed schemes outperform the scheme of using Markov model. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multiple antenna technology for wireless communications is becoming mature and wide ranging in the wireless broadband network. Many spectrum usage measurement reports have shown that inaccurate spectrum sensing will waste spectrum resources. A Multiple Antenna Asynchronous Cognitive MAC (MAAC-MAC) protocol is proposed through introducing a multiple antenna architecture and power control mechanism into the hardware-constrained cognitive MAC (MHC-MAC) protocol with Asynchronous-Assembly Line Mode in cognitive radio network. In the spectrum sensing stage, the protocol makes use of multiple antennas to perform spectrum sensing and positioning, aimed at improving spectrum sensing and detection performance. During the negotiation phase, the CU (Cognitive User) access power control mechanism fully utilizes the licensed band without interfering PU (Primary User) and also increases the transmission rate and system throughput. It may be used for improved positioning and location-based services both indoors and outdoors for IT convergence. [ABSTRACT FROM AUTHOR]

his paper proposes a time-frequency-based fingerprinting identification approach by extracting the transient characterizations regarding highly integrated wireless local area network (WLAN) cards. The transient energy envelope is derived from the slice of spectrogram in the time-frequency domain. Then this transient response is fitted to a polynomial under least square criteria, and the polynomial coefficients are regarded as the feature vector. A data acquisition system has been set up to capture IEEE 802.11b Wi-Fi (wireless fidelity) signals. The results exhibit an approving distinctiveness of up to 94% to classify different manufactories of WLAN cards and of 78.4% for the WLAN cards of the same manufactory. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artifcial neural network (ANN) is proposed for perceptual image quality assessment. The quality prediction is based on image features such as EPSNR, blocking, and blur. Training and testing of the ANN are performed with the mean opinion scores (MOS) provided by the Laboratory for Image and Video Engineering (LIVE). It is shown that the proposed image quality assessment model is capable of predicting MOS of the five types’ image distortions. [Copyright &y& Elsevier]

The caching‐enabled mobile edge computing (MEC) system becomes increasingly crucial to the future long‐term evolution vehicle‐to‐infrastructure (LTE‐V2I) network, especially for fast delivery of popular content of delay‐sensitive applications in the backhaul capacity–limited vehicular networks. Most related studies mainly focus on computation offloading strategy design and coding caching, while the deployment strategy design of caching capacities for high‐speed vehicles has been overlooked. In this paper, we investigate the deployment problem in LTE‐V2I networks with particular consideration on high‐speed vehicles. First, in a one‐way scenario, a joint optimization framework is formulated to minimize the cache size of the MEC system, meanwhile maximizing the average downloading percentage, where the MEC system's backhaul capacities, vehicle speeds, content popularity distributions, and sides of the highway are taken into account. The impacts of LTE‐V2I system parameters, eg, vehicle speed, on the objective function are revealed through the one‐way optimization. Then, the two‐way optimization is formulated and solved by developing an iterative optimization approach based on the aforementioned one‐way scenario. Particularly, the optimal caching allocation between two ways is derived. Simulation results validate the effectiveness of the proposed approaches even with low backhaul capacities and high vehicle speed. [ABSTRACT FROM AUTHOR]

Most of the existing fingerprint identification techniques are unable to distinguish different wireless transmitters, whose emitted signals are highly attenuated, long-distance propagating, and of strong similarity to their transient waveforms. Therefore, this paper proposes a new method to identify different wireless transmitters based on compressed sensing. A data acquisition system is designed to capture the wireless transmitter signals. Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. The results show that the proposed method is more efficient for the identification of wireless transmitters with similar transient waveforms. [ABSTRACT FROM AUTHOR]

Background/purposes: The continuously increasing carbapenem resistance within Enterobacterales and Pseudomonas poses a threat to public health, nevertheless, the molecular characteristics of which in southern China still remain limited. And carbapenemase identification is a key factor in effective early therapy of carbapenem-resistant bacteria infections. We aimed to determine the molecular characteristics of these pathogens and compare commercial combined disc tests (CDTs) with the modified carbapenem inactivation method (mCIM) and EDTA-CIM (eCIM) in detecting and distinguishing carbapenemases using whole genome sequencing (WGS)., Methods: A total of 78 Enterobacterales , 30 Pseudomonas were obtained from two tertiary hospitals in southern China. Susceptibility tests were conducted using an automated VITEK2 compact system with confirmation via the Kirby-Bauer method. The WGS was conducted on all clinical isolates and the molecular characteristics were analyzed by screening the whole genome sequences. CDTs with or without cloxacillin, mCIM, and eCIM, were performed and compared by taking WGS results as the benchmark., Results: A total of 103 carbapenem non-susceptible and 5 carbapenem susceptible bacteria were determined, with Klebsiella pneumoniae (42.7%), Pseudomonas aeruginosa (23.3%) and Escherichia coli (18.4%) being most prevalent. Carbapenemase genes were detected in 58 (56.3%) of the 103 carbapenem-non-susceptible clinical isolates, including 46 NDM, 6 KPC, 3 IMP, 1 IPM+VIM,1NDM+KPC, and 1 OXA-181. Carbapenemase-producing isolates were detected more frequently in Enterobacterales (76.3%). Among K. pneumoniae , the major sequence types were st307 and st11, while among E. coli and P. aeruginosa , the most prevalent ones were st410 and st242 respectively. For carbapenemase detection in Enterobacterales , the mCIM method achieved 100.00% (95% CI, 92.13-100.00%) sensitivity and 94.44% (70.63-99.71%) specificity (kappa, 0.96); for Pseudomonas , detection sensitivity was 100% (5.46-100.00%), and 100% (84.50-100.00%) specificity (kappa, 0.65). Commercial CDT carbapenemase detection sensitivity for Enterobacterales was 96.49% (86.84-99.39%), and 95.24% (74.13-99.75%) specificity (kappa, 0.90); for Pseudomonas , carbapenemase detection sensitivity was 100.00% (5.46-100.00%) and 37.93% (21.30-57.64%) specificity (kappa, 0.04). When cloxacillin testing was added, CDT specificity reached 84.61% (64.27-94.95%)., Conclusion: The molecular epidemiology of carbapenem-non-susceptible isolates from pediatric patients in Southern China exhibited distinctive characteristics. Both the mCIM-eCIM combination and CDT methods effectively detected and differentiated carbapenemases among Enterobacterales isolates, and the former performed better than CDT among Pseudomonas ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liang, Chen, Liang, Li, Cai, Lai, Zhong, Xie, Huang, Gao and Long.)

Objectives: To evaluate the performance of Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectra (MALDI-TOF MS) for automated classification of GBS (Group B Streptococcus ) into five major CCs (clonal complexes) during routine GBS identification., Methods: MALDI-TOF MS of 167 GBS strains belonging to five major CCs (CC10, CC12, CC17, CC19, CC23) were grouped into a reference set ( n = 67) and a validation set ( n = 100) for the creation and evaluation with GBS CCs subtyping main spectrum (MSP) and MSP-M using MALDI BioTyper and ClinProTools. GBS CCs subtyping MSPs-M was generated by resetting the discriminative peaks of GBS CCs subtyping MSP according to the informative peaks from the optimal classification model of five major CCs and the contribution of each peak to the model created by ClinProTools., Results: The PPV for the GBS CCs subtyping MSP-M was greater than the subtyping MSP for CC10 (99.21% vs. 93.65%), but similar for CC12 (79.55% vs. 81.06%), CC17 (93.55% vs. 94.09%), and CC19 (92.59% vs. 95.37%), and lower for CC23 (66.67% vs. 83.33%)., Conclusion: MALDI-TOF MS could be a promising tool for the automated categorization of GBS into 5 CCs by both CCs subtyping MSP and MSP-M, GBS CCs subtyping MSP-M is preferred for the accurate prediction of CCs with highly discriminative peaks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Huang, Gao, Zhong, Xie, Liang, Ji and Liu.)

The oxacillin- and cefoxitin-susceptible mecA -positive Staphylococcus aureus is a novel "stealth" methicillin-resistant S. aureus (MRSA) type. Here, we sequenced the whole genome of two oxacillin- and cefoxitin-susceptible mecA -positive MRSA isolates from breast abscesses in a lactating woman and a nasal swab of a healthy student in Guangzhou for investigating the mechanism underlying its occurrence. The reversion of these isolates was selected by exposure to sub-MICs of cefoxitin with or without mupirocin. The mecA expression of both parental strains and their revertants was determined, and the whole genome of the revertants was sequenced. Comparative whole-genome analyses performed for both strains revealed that mecA of the clinical strain was mutated by a single-bp insertion at the 262nd position in the tandem repeat region of the gene, and this mutation that led to the formation of a premature stop codon. The colonizing strain was mutated by a novel G-to-A base substitution in the second promoter region (-35 bp) of mecA . The mecA expression level of strain 697 revertant was 37 times higher than that of the parental strain. Although the mecA expression level was even higher for parental strain 199 compared with that for its revertant, its cDNA sequence contained a single-bp insertion. Collectively, both the missense and single substitution mutations of the second promoter of mecA could render MRSA isolates as "stealth" MRSA, thereby emphasizing the importance of combining phenotype tests with mecA or penicillin-binding protein 2a detection for the identification of MRSA. IMPORTANCE The oxacillin- and cefoxitin-susceptible mecA -positive Staphylococcus aureus is a novel type of "stealth" methicillin-resistant S. aureus (MRSA), which is difficult to be detected using conventional methods. To investigate the genomic basis of their occurrence, we sequenced the whole genome of two previously recovered oxacillin- and cefoxitin-susceptible mecA -positive MRSA isolates from breast abscesses in a lactating woman and a nasal swab of a healthy student in Guangzhou. Complete SCC mec structure was absent except for mecA in clinical isolate 199. Additionally, a novel single-base pair insertion was observed in the clinical strain, which resulted in premature termination and a frameshift mutation. The colonizing isolate 697 had a Scc- mec -type IVa, and the second promoter region (-35 bp) of mecA was mutated by a novel G-to-A base substitution. The reversion of oxacillin- and cefoxitin-susceptible mecA -positive S. aureus to resistant MRSA isolates was selected by exposure to subminimum inhibitory cefoxitin with or without mupirocin.

As a kind of biocompatible material with long history, silk fibroin is one of the ideal platforms for on-skin and implantable electronic devices, especially for self-powered systems. In this work, to solve the intrinsic brittleness as well as poor chemical stability of pure silk fibroin film, mesoscopic doping of regenerated silk fibroin is introduced to promote the secondary structure transformation, resulting in huge improvement in mechanical flexibility (∼250% stretchable and 1000 bending cycles) and chemical stability (endure 100 °C and 3-11 pH). Based on such doped silk film (SF), a flexible, stretchable and fully bioabsorbable triboelectric nanogenerator (TENG) is developed to harvest biomechanical energy in vitro or in vivo for intelligent wireless communication, for example, such TENG can be attached on the fingers to intelligently control the electrochromic function of rearview mirrors, in which the transmittance can be easily adjusted by changing contact force or area. This robust TENG shows great potential application in intelligent vehicle, smart home and health care systems.