Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.
With the large-scale commercial launch of fifth generation (5G) mobile network, the development of new services and applications catering to the year 2030, along with the deep convergence of information, communication, and data technologies (ICDT), and the lessons and experiences from 5G practice will drive the evolution of the next generation of mobile networks. This article surveys the history and driving forces of the evolution of the mobile network architecture and proposes a logical function architecture for sixth generation (6G) mobile network. The proposed 6G network architecture is termed SOLIDS (related to the following basic features: soft, on-demand fulfillment, lite, native intelligence, digital twin, and native security), which can support self-generation, self-healing, self-evolution, and self-immunity without human involvement and address the primary issues in the legacy 5G network (e.g., high cost, high power consumption, and highly complicated operation and maintenance), significantly well.
The sixth generation (6G) mobile networks will reshape the world by offering instant, efficient, and intelligent hyper-connectivity, as envisioned by the previously proposed Ubiquitous-X 6G networks. Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks, calling for revolutionary theories and technological innovations. To this end, we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network (WePCN) vision for the Ubiquitous-X 6G network. In particular, we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework, namely semantic base, and then establishing an intelligent and efficient semantic communication (IE-SC) network architecture. In the IE-SC architecture, a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer, network protocol layer, and application-intent layer via semantic information flows. The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G. It features a lower bandwidth requirement, less redundancy, and more accurate intent identification. We also present a brief review of recent advances in semantic communications and highlight potential use cases, complemented by a range of open challenges for 6G.
The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks (6G). Due to data heterogeneity and dynamics, decentralized data management (DM) is desirable for achieving transparent data operations across network domains, and blockchain can be a promising solution. However, the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency, efficiency, and privacy requirements in decentralized blockchain-based DM. In this paper, we propose blockchain solutions to address the challenge. First, we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures. Second, we investigate the authentication and authorization requirements for DM stakeholders. Third, we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing. Subsequently, we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives. Finally, we conclude this paper and discuss future research directions.
In current wireless communication and electronic systems, digital signals and electromagnetic (EM) radiation are processed by different modules. Here, we propose a mechanism to fuse the modulation of digital signals and the manipulation of EM radiation on a single programmable metasurface. The programmable metasurface consists of massive subwavelength-scale digital coding elements. A set of digital states of all elements forms simultaneous digital information roles for modulation and the wave-control sequence code of the programmable metasurface. By designing digital coding sequences in the spatial and temporal domains, the digital information and far-field patterns of the programmable metasurface can be programmed simultaneously and instantly in desired ways. For the experimental demonstration of the mechanism, we present a programmable wireless communication system. The same system can realize transmissions of digital information in single-channel modes with beam-steerable capability and multichannel modes with multiple independent information. The measured results show the excellent performance of the programmable system. This work provides excellent prospects for applications in fifth- or sixth-generation wireless communications and modern intelligent platforms for unmanned aircrafts and vehicles.
In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) continues to increase. As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures, including satellites and unmanned aerial vehicles (UAVs). Thus, we can build a non-terrestrial network (NTN) using a cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN must be empowered using mobile edge computing (MEC) while providing oasisoriented on-demand coverage for devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN, which makes it difficult to coordinate the resources. In this study, we propose a process-oriented framework to design communication and MEC systems in a time-division manner. In this framework, large-scale channel state information (CSI) is used to characterize the complex propagation environment at an affordable cost, where a nonconvex latency minimization problem is formulated. Subsequently, the approximated problem is provided, and it can be decomposed into sub-problems. These sub-problems are then solved iteratively. The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms, implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources, and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.
The coronavirus disease 2019 (COVID-19) pandemic is challenging the current public health emergency response systems (PHERSs) of many countries. Although environmental factors, such as those influencing the survival of viruses and their transmission between species including humans, play important roles in PHERSs, little attention has been given to these factors. This study describes and elucidates the roles of environmental factors in future PHERSs. To improve countries' capability to respond to public health emergencies associated with viral infections such as the COVID-19 pandemic, a number of environmental factors should be considered before, during, and after the responses to such emergencies. More specifically, to prevent pandemic outbreaks, we should strengthen environmental and wildlife protection, conduct detailed viral surveillance in animals and hotspots, and improve early-warning systems. During the pandemic, we must study the impacts of environmental factors on viral behaviors, develop control measures to minimize secondary environmental risks, and conduct timely assessments of viral risks and secondary environmental effects with a view to reducing the impacts of the pandemic on human health and on ecosystems. After the pandemic, we should further strengthen surveillance for viruses and the prevention of viral spread, maintain control measures for minimizing secondary environmental risks, develop our capability to scientifically predict pandemics and resurgences, and prepare for the next unexpected resurgence. Meanwhile, we should restore the normal life and production of the public based on the ″One Health″ concept, that views global human and environmental health as inextricably linked. Our recommendations are essential for improving nations' capability to respond to global public health emergencies.
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.
The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), Acute Physiology and Chronic Health Evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931–0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.
The transmission of coronavirus disease 2019 (COVID-19) has presented challenges for the control of the indoor environment of isolation wards. Scientific air distribution design and operation management are crucial to ensure the environmental safety of medical staff. This paper proposes the application of adaptive wall-based attachment ventilation and evaluates this air supply mode based on contaminants dispersion, removal efficiency, thermal comfort, and operating expense. Adaptive wall-based attachment ventilation provides a direct supply of fresh air to the occupied zone. In comparison with a ceiling air supply or upper sidewall air supply, adaptive wall-based attachment ventilation results in a 15%–47% lower average concentration of contaminants, for a continual release of contaminants at the same air changes per hour (ACH; 10 h–1). The contaminant removal efficiency of complete mixing ventilation cannot exceed 1.0. For adaptive wall-based attachment ventilation, the contaminant removal efficiency is an exponential function of the ACH. Compared with the ceiling air supply mode or upper sidewall air supply mode, adaptive wall-based attachment ventilation achieves a similar thermal comfort level (predicted mean vote (PMV) of –0.1–0.4; draught rate of 2.5%–6.7%) and a similar performance in removing contaminants, but has a lower ACH and uses less energy.
Multifunctional fillers are greatly required for dental resin composites (DRCs). In this work, a spray dryer with a three-fluid nozzle was applied for the first time to construct high-performance complex nanoparticle clusters (CNCs) consisting of different functional nanofillers for dental restoration. The application of a three-fluid nozzle can effectively avoid the aggregation of different nanoparticles with opposite zeta potentials before the spray drying process in order to construct regularly shaped CNCs. For a SiO2–ZrO2 binary system, the SiO2–ZrO2 CNCs constructed using a three-fluid nozzle maintained their excellent mechanical properties ((133.3 ± 4.7) MPa, (8.8 ± 0.5) GPa, (371.1 ± 13.3) MPa, and (64.5 ± 0.7) HV for flexural strength, flexural modulus, compressive strength, and hardness of DRCs, respectively), despite the introduction of ZrO2 nanoparticles, whereas their counterparts constructed using a two-fluid nozzle showed significantly decreased mechanical properties. Furthermore, heat treatment of the SiO2–ZrO2 CNCs significantly improved the mechanical properties and radiopacity of the DRCs. The DRCs containing over 10% mass fraction ZrO2 nanoparticles can meet the requirement for radiopaque fillers. More importantly, this method can be expanded to ternary or quaternary systems. DRCs filled with SiO2–ZrO2–ZnO CNCs with a ratio of 56:10:4 displayed high antibacterial activity (antibacterial ratio > 99%) in addition to excellent mechanical properties and radiopacity. Thus, the three-fluid nozzle spray drying technique holds great potential for the efficient construction of multifunctional cluster fillers for DRCs.
The hydropower sector is currently experiencing several technological developments. New technologies and sustainable practices are emerging to make hydropower more flexible and eco-friendly. Novel materials have also been recently developed to increase performance, durability, and reliability; however, no systematic discussions can be found in the literature. Therefore, in this paper, novel materials for hydropower applications are presented, and their performance, advantages, and limitations are discussed. For example, composites can reduce the weight of steel equipment by 50% to 80%, polymers and superhydrophobic materials can reduce head losses by 4% to 20%, and novel bearing materials can reduce bearing wear by 6%. These improvements determine higher efficiencies, longer life span, waste reduction, and maintenance needs, although the initial cost of some materials is not yet competitive with respect to the costs of traditional materials. The novel materials are described here based on the following categories: novel materials for turbines, dams and waterways, bearings, seals, and ocean hydropower.
As a future energy system, the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services. However, this efficient and reliable service relies on collecting and analyzing users' electricity consumption data frequently, which induces various security and privacy threats. To address these challenges, we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA. Specifically, we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain, which provides strong support for achieving efficient and secure data collection in smart grids. Subsequently, we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption, batch aggregation signature and anonymous authentication. In particular, the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation. Finally, the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.
The low ionic conductivity of solid-state electrolytes (SSEs) and the inferior interfacial reliability between SSEs and solid-state electrodes are two urgent challenges hindering the application of solid-state sodium batteries (SSSBs). Herein, sodium (Na) super ionic conductor (NASICON)-type SSEs with a nominal composition of Na3+2xZr2–xMgxSi2PO12 were synthesized using a facile two-step solid-state method, among which Na3.3Zr1.85Mg0.15Si2PO12 (x = 0.15, NZSP-Mg0.15) showed the highest ionic conductivity of 3.54 mS∙cm–1 at 25 °C. By means of a thorough investigation, it was verified that the composition of the grain boundary plays a crucial role in determining the total ionic conductivity of NASICON. Furthermore, due to a lack of examination in the literature regarding whether NASICON can provide enough anodic electrochemical stability to enable high-voltage SSSBs, we first adopted a high-voltage Na3(VOPO4)2F (NVOPF) cathode to verify its compatibility with the optimized NZSP-Mg0.15 SSE. By comparing the electrochemical performance of cells with different configurations (low-voltage cathode vs high-voltage cathode, liquid electrolytes vs SSEs), along with an X-ray photoelectron spectroscopy (XPS) evaluation of the after-cycled NZSP-Mg0.15, it was demonstrated that the NASICON SSEs are not stable enough under high voltage, suggesting the importance of investigating the interface between the NASICON SSEs and high-voltage cathodes. Furthermore, by coating NZSP-Mg0.15 NASICON powder onto a polyethylene (PE) separator (PE@NASICON), a 2.42 A∙h non-aqueous Na-ion cell of carbon|PE@NASICON|NaNi2/9Cu1/9Fe1/3Mn1/3O2 was found to deliver an excellent cycling performance with an 88% capacity retention after 2000 cycles, thereby demonstrating the high reliability of a separator coated with NASICON-type SSEs.
Due to sexual dimorphism in the growth of certain cultured fish species, the production of monosex fishes is desirable for the aquaculture industry. Nowadays, the most widely practiced technique available for the mass production of monosex fish populations is sex steroid-induced sex reversal. Here, a novel strategy for the successful production of all-female (AF) common carp (Cyprinus carpio L.), to take advantage of the sexual dimorphism in growth documented in this species, has been developed using genetic engineering via single gene-targeting manipulation without any exogenous hormone treatments. Male and female heterozygous cyp17a1-deficient common carp were first obtained using the clustered regularly interspaced short palindromic repeats/CRISPR-associated endonuclease 9 (CRISPR/Cas9) technique. An all-male phenotype for homozygous cyp17a1-deficient carp, regardless of the individuals' sex-determination genotypes (XY or XX), has been observed. A male-specific DNA marker newly identified in our laboratory was used to screen the neomale carp population with the XX genotype from the cyp17a1-deficient carp. These neomale carp develop a normal testis structure with normal spermatogenesis and sperm capacity. The neomale common carp were then mated with wild-type (WT) females (cyp17a1+/+ XX genotype) using artificial fertilization. All the AF offspring sample fish from the neomale-WT female mating were confirmed as having the cyp17a1+/− XX genotype, and normal development of gonads to ovaries was observed in 100.00% of this group at eight month post-fertilization (mpf). A total of 1000 carp fingerlings, 500 from the WT male and female and 500 from the neomale and WT female mating, were mixed and reared in the same pond. The average body weight of cyp17a1+/− XX females was higher by 6.60% (8 mpf) and 32.66% (12 mpf) than that of the control common carp. Our study demonstrates the first successful production of a monosex teleost population with the advantages of sexual dimorphism in growth using genetic manipulation targeting a single locus.