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The key performance indicators (KPIs) of the sixth generation (6G) will increase by orders of magnitude compared to the fifth generation (5G), promising extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency (SE) and $\mu \text {s}$ -level latency. Cell-free massive MIMO (CF-mMIMO) with rich spatial dimension resources is expected to be a key architecture to realize $\text {TK}\mu $ extreme connectivity, but the existing research has not yet given a compact and closed-form approximation to describe the relationship between the spatial dimension and system performance, which makes it difficult to evaluate the KPIs of $\text {TK}\mu $ intuitively. This paper derives explicit closed-form expressions for the relationship between system performance and system configuration parameters for finite blocklength CF-mMIMO systems and analyzes the relationship between system performance and spatial dimensions. Based on this, we perform parameter selection and performance evaluation of specific implementations in the three $\text {TK}\mu $ KPIs in CF-mMIMO systems. Both theoretical analysis and simulation results show that increasing the spatial degree of freedom (DoF) and deploying antennas more dispersedly can realize latency reduction while guaranteeing the system performance, and the joint collaboration of multi-users and multiple access points (APs) with large DoFs can achieve a continuous increase in SE and data rate.

To improve wireless connectivity, reconfigurable intelligent surface (RIS) offers an energy-efficient solution in millimeter wave (mmWave) multi-input multi-output (MIMO) systems. However, the achievable spectrum efficiency (SE) has been limited by challenges associated with channel estimation (CE) and hybrid beamforming design. To address these issues, we propose an efficient data transmission (DT) scheme with two-stage compressed sensing (CS)-based CE by exploiting the sparse angular domain channel structure and pruning insignificant components. Simulation results demonstrate that the proposed method achieves reduction in pilot overhead and complexity of CE, and higher SE of DT via iterative optimization.

This article investigates the efficient model predictive load frequency control problem for multiarea power systems, where the measured state is transmitted under a new dynamic event-triggered mechanism (DETM) with hybrid variables. By applying $H_{2}$/$H_{\infty }$ performance index, a DETM-based efficient model predictive control (EMPC) method is presented. This EMPC issue is described as a “min-max” optimization problem (OP) with hard constraints on system state. By utilizing a Lyapunov function with internal dynamic variable, an offline auxiliary OP with constraints of matrix inequalities is put forward to optimize the feedback gain and the weighting matrix of the DETM. Another offline OP is also proposed to maximize the size of initial feasible region (IFR). To steer the augmented state in IFR into the terminal constraint set and improve the control performance, a sequence of admissible control is implemented whose perturbation parameters are designed by solving an online OP. A case study on a three-area power system demonstrates the validity and superiority of the designed DETM and dynamic event-based EMPC algorithm.

The stability and ${\mathcal {H}}_\infty$ filtering for delayed discrete-time T-S fuzzy systems are studied in this article. The primary objective is to obtain less conservative and more effective analysis and design methods by exploring a combination of the characteristics of T-S fuzzy systems and the delay-dependent methods. First, as the first step of the Lyapunov–Krasovskii functional (LKF) method, a membership-dependent (MD) LKF with delay-product-type term is established to contain more delay and membership function information. Then, to obtain the negative definite condition of the forward difference of the constructed functional, an MD-matrix-separation-based inequality is developed to obtain tighter estimations for the augmented summation terms and an MD-variable-augmented-based free-weighting matrix method is proposed to avoid the generation of delay-dependent nonlinear terms. Based on the abovementioned methods, a less conservative stability criterion and an ${\mathcal {H}}_\infty$ fuzzy filter design method are proposed. Finally, the merits of the proposed methods are verified via two examples.

Context: Running is one of the most popular sports worldwide. However, controversies exist regarding how running affects runner’s intervertebral discs (IVD).Objective: The purpose of this study was to systematically review studies that evaluated IVD morphology or composition changes in response to running exercise, to determine the impact of running exercise on IVD.Data Sources: A systematic literature search was performed for 4 major databases: PubMed, Cochrane, Embase, and Web of Science.Study Selection: Inclusion criteria were as follows: (1) healthy people without known IVD disease or major complications such as tuberculosis (IVD degeneration or low back pain are considered as minor complications); (2) subjects performed 1-time or regular running exercises; (3) pre and post comparison of runners or comparison between runners and healthy control subjects; (4) direct or indirect IVD morphology or composition measured; (5) IVD assessed before and after either acute or chronic running exercise, or compared cross-sectionally between runners and controls. Exclusion criteria were as follows: (1) reviews, editorials, letters or abstracts only; (2) animal studies; (3) subjects performed exercise other than running.Study Design: Systematic review.Level of Evidence: Level 3.Data Extraction: The extracted data included study design and primary outcomes of the included studies. The Newcastle-Ottawa scale (NOS) was used to evaluate study quality and risk of bias.Results: A total of 13 studies with 632 participants were included in the final analysis; 4 studies measured IVD changes using stature or spinal height, and the other 9 measured IVD changes using magnetic resonance imaging; 6 studies found that running acutely and negatively impacts IVD; 3 out of 5 cross-sectional studies found that IVD parameters are better for runners than controls; 1 longitudinal study found no significant difference in IVD before and after training for marathon in runners; 1 longitudinal study found no significant difference in changes of IVD between runners and controls after 15 years of follow-up.Conclusion: Negative changes in IVD exist for a short period of time after running, which may be due to the temporary compression pushing water content out of the disc. Cross-sectional studies suggest that long-term running exerts a mild positive effect on IVD; however, this inference has not been confirmed by high-quality longitudinal studies.

The future power systems with renewable energy sources (RESs) and electric vehicles (EVs) are usually subject to strong uncertainties in system parameters, communication network, and disturbances, which may severely degrade the frequency control performance. This article proposes a performance enhancing load frequency control (LFC) scheme for future power systems with strong uncertainties based on the Kalman-filter (KF) control compensation method. First, the LFC of a multiarea interconnected power system (MAIPS) with RESs and EVs is conceptualized as a linear stochastic and discrete model. Then, KF is introduced to evaluate the unmeasurable states of the LFC, so as to design an additional KF-based state feedback control law. The KF-based control loop serves as compensation for the original controller of the LFC system. Next, an optimal compensation control gain of the KF-based control loop is derived based on a differential optimal calculation method to enhance the control performance of the LFC of MAIPS under strong uncertainties. Finally, simulation results are conducted on a typical three-area LFC systems that integrate RESs and EVs, which demonstrate that the proposed control strategy can provide better control performance and robustness than the original LFC strategy when the systems are subject to strong uncertainties.

For increasing Internet of Things (IoT) devices, 6G wireless technology aims for ubiquitous communications in which positioning services are necessary. Private spatial keyword-based positioning service is promising in 6G-oriented IoT since it positions users based on spatial locations and textual keywords while protecting user privacy. However, due to economic benefits or malicious attacks, positioning service providers may return erroneous or incomplete results, which cause tremendous economic damage and security threats, e.g., always assigning a selective driver for the specific car-hailing user. A technical challenge for extending existing private schemes to enable users to verify the correctness and completeness of positioning results is the distinctive positioning paradigm between compared spatial locations and matched textual keywords. This paper proposes a private and verifiable spatial keyword positioning scheme named VSpatial in 6G-oriented IoT. VSpatial enables users to verify the correctness and completeness of spatial keyword-based positioning results while preserving user privacy. The main inspiration for addressing the technical challenge is converting both spatial locations and textual keywords into an internal status, i.e., adapting comparison and matching to existence judging by multiple cryptographic tools, such as hierarchical cube and pseudorandom function. Based on this inspiration, we design a novel private authenticated data structure (named PVTree), and then propose two constructions of VSpatial, i.e., VSpatial-S and VSpatial-D, to suit static and dynamic environments, respectively. The core idea for adapting VSpatial-S to VSpatial-D is transferring one whole PVTree into multiple exponential-size partitions. Security analysis proves the security and verifiability of VSpatial. Theoretical and experimental evaluations show that VSpatial achieves faster-than-linear positioning efficiency and linear verification overhead.

Federated Learning (FL) has achieved state-of-the-art performance in training a global model in a decentralized and privacy-preserving manner. Many recent works have demonstrated that incentive mechanism is of paramount importance for the success of FL. Existing incentives to FL either neglect communication efficiency, or consider communication efficiency but design the incentive mechanisms using non-cooperative games under complete information assumption, or study incentive mechanism under incomplete information but only apply to the sequential interaction setting. We shed light on this problem from the cooperative perspective and propose an incentive mechanism for communication-efficient FL based on the Nash bargaining theory. Specially, we formulate our incentive mechanism as a one-to-many concurrent bargaining game among the aggregator and clients, and systematically analyze the Nash bargaining solution (NBS, game equilibrium) to design the incentive mechanism. It should be noted that the existing sequential bargaining is not suitable for incentivizing FL due to high (exponential) time complexity, which deteriorates the straggler problem in FL. Our formulated bargaining game is challenging due to the NP-hardness. We propose a probabilistic greedy-based client selection algorithm and derive an analytical payment solution as an approximate NBS. We prove the convergence guarantee of our incentive mechanism for communication-efficient FL. Finally, we conduct experiments over real-world datasets to evaluate the performance of our incentive mechanism.

An efficient asymmetric tandem reaction of indene-based dienes with isoxazole-5-ones catalyzed by chiral cinchona alkaloid-derived thioureas has been established, constructing a diverse array of novel enantioenriched spirodihydrofluorene compounds bearing isoxazolone moieties. More importantly, the spirodihydrofluorene frameworks can be effortlessly transformed into an atropisomeric fluorene skeleton bearing oxime groups. This work not only addresses the challenges in the construction of enantioenriched spirodihydrofluorene frameworks but also provides a powerful strategy for structurally novel fluorenylamine-ketoximes containing axial elements.

Due to insufficient scalability, the existing consortium chain cannot meet the requirements of low latency, high throughput, and high security when applied to Internet of Vehicles (IoV) data sharing. Therefore, we propose a two-layer consensus algorithm based on the primary-secondary consortium chain - Weighted Raft and Byzantine Fault Tolerance (WRBFT). The intra-group consensus of the WRBFT algorithm adopts weighted Raft, and the best node is selected as the primary node to lead the intra-group consensus by comprehensively evaluating the signal-to-noise ratio (SNR), data processing capacity and storage capacity of the nodes. The inter-group consensus adopts practical Byzantine fault tolerance (PBFT) based on BLS aggregate signature with nonlinear coefficients to ensure that the inter-group consensus can tolerate 1/3 of Byzantine nodes. At the same time, the verifiable random function (VRF) is used to select the primary node of the inter-group consensus to ensure the randomness of the primary node. A large number of experimental results show that the proposed WRBFT algorithm reduces delay, and improves throughput and system security.

The focus of this article is on maintaining the stability and stabilization of Takagi–Sugeno (T–S) fuzzy systems under conditions of time-delay and sampled-data control. The objective is to introduce stability analysis and synthesis methods that are less conservative. First, a novel sampling point-dependent looped-functional with augmented integral terms is constructed. Then, we propose a matrix-separation-based inequality to obtain a compact estimation for the augmented integral term. Stability and stabilization results with reduced conservatism are obtained based on the above innovative method. The superiority and efficacy of the proposed methods are ultimately demonstrated through the presentation of three examples.

Decentralized identity (DID) systems conforming to the World Wide Web Consortium (W3C) Decentralized Identifiers (DIDs) and Verifiable Credentials Data Model recommendations have recently attracted attention due to their better autonomy, interoperability, and openness design. However, those W3C recommendations lack a design for addressing the single point of failure (SPOF) and identity revocation, which could seriously compromise the robustness and practicality of DID systems. To remedy these limitations, we propose FutureDID, a DID system that enables multiple parties to jointly issue credentials and efficiently revoke DID identities, providing a robust and practical DID system. FutureDID is designed with a multi-party credential issuing mechanism based on distributed key generation technology, which transforms trust from a single entity to distributed committees and facilitates authentication between issuers, making it more resistant to SPOF. Moreover, the underlying blockchain system is built on a chameleon hash function to ensure tamper-proof and enable efficient identity revocation. We have implemented a prototype system using FISCO BCOS and conducted extensive evaluations to demonstrate the effectiveness and practicality of our system. Our evaluations have shown that FutureDID provides a significant improvement in efficiency, achieving at least a 60 $\times$× efficiency improvement in identity revocation compared to state-of-the-art systems.

In this brief, a class of sector bound-dependent matrix-separation-based inequality is proposed that can utilize the properties of nonlinear functions to overcome the weaknesses of existing estimation methods. Then, an expanded class of Lyapunov–Krasovskii functional is constructed to include some integral terms of the sector constraint of nonlinear functions. By applying the improved inequality, the stability criterion of genetic regulatory networks (GRNs) with stochastic perturbations and time-varying delay is established. The validity of presented criterion and the advancement of proposed method are analysed based on two examples.

China pledged to achieve carbon neutrality by 2060 to combat global climate change, yet the resulting multi-aspect domestic impacts are not fully analysed due to an incomplete understanding of the underlying anthropogenic–natural interactions. Building an integrated cross-disciplinary modelling framework that can capture the feedbacks of changing aerosols on meteorology, here we highlight the amplified air quality, human health and renewable energy self-reinforcing synergies of China’s carbon neutral target in comparison to the baseline in 2015 and 2060. We find that owing to emissions reduction and more favourable meteorological conditions caused by less aerosol, achieving China’s carbon neutrality target in 2060 reduces national population-weighted PM2.5concentrations and associated premature deaths by ~39 μg m−3and 1.13 (95% confidence interval: 0.97–1.29) million while boosting provincial solar (wind) power performance by up to ~10% (~6%) with mostly decreased resource variability in comparison to the 2060 baseline. Enhanced renewable performance along with low-carbon energy transition may provide additional opportunities to address the remaining air pollution and associated human health damages upon achieving carbon neutrality. Our results highlight that global developing and polluting countries’ pledge for carbon neutrality can produce important positive feedbacks between aerosols mitigation, air quality improvement and enhanced renewable energy, which can be amplified via weakened aerosol–meteorology interactions and better atmospheric dispersion.

Grape berries often crack near the proximal end, which may be related to water absorption and their cellular anatomical structure. To study the relationship between water absorption, cell anatomical structures, and berry cracking near the proximal end, 49 varieties were selected. Eighteen were prone to cracking near the proximal end, while 31 were resistant. An in vitro soaking experiment on ripe berries measured the difference in berry-cracking degrees among different varieties. In vitro staining was used to trace water absorption and paraffin sections were prepared to observe and analyze the structural parameters of different tissues. Results showed that the cracking rate and water uptake of the crack-prone berries were significantly higher than those of the crack-resistant berries. Fruit prone to cracking was characterized by a thinner cuticle, epidermis, and sub-epidermis. After staining, it was found that dye absorption was limited to the berry near the proximal end. Other cell size parameters may also lead to cracking near the proximal end. By tracing water transport and analyzing differences in cell structure characteristics among varieties, we speculated that the vascular bundle xylem water transport repression and differences in cell anatomical structures may have led to berry cracking near the proximal end. The reasons for berry cracking near the proximal end were preliminarily explained, providing theoretical support for further screening of crack-resistant varieties.

Pink spinel anorthosite (PSA) and pink spinel troctolite (PST) are two lunar lithologies known to contain Mg-rich spinel. PSA rich in spinel and lacking mafic minerals, was detected by the visible and near-infrared reflectance spectroscopy. PST clasts were found in returned lunar samples and meteorites. NWA 13191 is a recently approved lunar meteorite that contains a large amount of spinel-bearing clasts and provides an opportunity to discuss its origin. Sixty-four spinel-bearing clasts were studied in this research. These clasts are dominated by anorthitic feldspars (20.8–80.9 vol%, An90.9–96.8), mafic-rich and aluminum-rich glass (14.7–72.1 vol%) quenched from a melt, and spinels (0.19–5.18 vol%). Forty-nine of these clasts appear to have unusually low modal abundances of mafic silicates (avg. olivine ± pyroxene, 1.87 vol%), which distinguishes them from known spinel-bearing lunar samples (e.g., PST). The spinel compositions (avg. Mg# = 90.6, Al# = 97.4) and mafic minerals contents are basically consistent with those of PSA. The absorption characteristics of glass in the reflection spectrum are not obvious, so it is not clear if the PSA contains melt. The simulated crystallization experiment clearly shows that it contains a large amount of melt at the spinel crystallization stage. These phenomena provide experimental and sample evidence for the existence of glass in the lunar spinel-bearing lithologies. NWA 13191 records the highest known bulk Mg# (avg. 89.8), and the spinel records the highest Al# (98.8) and Mg# (93.1) of lunar samples to date. The chemical properties of spinel-bearing clasts in NWA 13191 are consistent with the slightly REE-enriched and alkali-poor Mg-suite rocks, such as PST, magnesian anorthosites (MANs), and olivine-enriched Mg-suite rocks. These phenomena and previous simulated crystallization experiments indicate that a Mg-Al-rich melt may be produced by impact melting of Mg-rich anorthosite precursors. The spinel is a metastable crystallization product along with plagioclase and vitric melt near the Moon’s surface. This realization provides observational evidence for previous simulated crystallization experiments and theoretical speculations.

Detecting proteins in ultralow concentrations in complex media is important for many applications but often relies on complicated techniques. Herein, a single-molecule protein analyzer with the potential for high-throughput applications is reported. Gold-coated magnetic nanoparticles with DNA-labeled antibodies were used for target recognition and separation. The immunocomplex was loaded into microdroplets generated with centrifugation. Immuno-PCR amplification of the DNA enabled the quantification of proteins at the level of single molecules. As an example, ultrasensitive detection of α-synuclein, a biomarker for neurodegenerative diseases, is achieved. The limit of detection was determined to be ∼50 aM in buffer and ∼170 aM in serum. The method exhibited high specificity and could be used to analyze post-translational modifications such as protein phosphorylation. This study will inspire wider studies on single-molecule protein detection, especially in disease diagnostics, biomarker discovery, and drug development.

In this letter, an efficient approximate expectation propagation (EPA) based iterative detection and decoding (IDD) scheme named EPA-IDD is first proposed for LDPC-coded massive MIMO systems. EPA is applied in IDD to bypass the variance updates in the inner loops, which reduces matrix inversions, simplifies extrinsic messages, and effectively enhance the convergence performance of IDD. In addition, a partial resetting scheme is proposed to efficiently adopt the decoder output into the EPA detector. A Neumann series based approximation called wNSA-EPA-IDD is further developed to reduce complexity. Numerical results show that the proposed EPA-IDD schemes outperform the state-of-the-art (SOA) double EP (DEP) in various LDPC-coded MIMO scenarios. Complexity analysis is presented to validate the improved efficiency of the proposed algorithms.

This article investigates the problem of dynamic event-triggered finite-time $H_{\infty }$ state estimation for a class of discrete-time nonlinear two-time-scale Markov jump complex networks. A hybrid adjusting variables-dependent dynamic event-triggered mechanism (DETM) is proposed to regulate the releases of measurement outputs of a node to a remote state estimator. Such a DETM contains both an additive dynamically adjusting variable (DAV) and a multiplicative adaptively adjusting variable. The aim is to design a DETM-based mode-dependent state estimator, which guarantees that the resultant error dynamics is stochastically finite-time bounded with $H_{\infty }$ performance. By constructing a mode-dependent Lyapunov function with multiple DAVs and a singular perturbation parameter associated with time scales, a matrix-inequalities-based sufficient condition is derived, the feasible solutions of which facilitate the design of the parameters of the state estimator. The validity of the designed state estimator and the superiority of the devised DETM are verified by two examples. It is verified that the devised DETM is capable of saving network resources and simultaneously improving the estimation performance.

This article investigates the stability of delayed neural networks with large delays. Unlike previous studies, the original large delay is separated into several parts. Then, the delayed neural network is viewed as the switched system with one stable and multiple unstable subsystems. To effectively guarantee the stability of the considered system, the type-dependent average dwell time (ADT) is proposed to handle switches between any two sequences. Besides, multiple Lyapunov functions (MLFs) are employed to establish stability conditions. Adding more delayed state vectors increases the allowable maximum delay bound (AMDB), reducing the conservatism of stability criteria. A general form of the global exponential stability condition is put forward. Finally, a numerical example illustrates the effectiveness, and superiority of our method over the existing one.

The synchronization control for delayed neural networks (DNNs) via a sampled-data controller considering communication delay is studied by input delay approach. Although few scholars have put forward the coexistence of transmission delay and communication delay in this problem, no report has clarified the interaction between transmission delay and communication delay. Also, the time-squared terms are underutilized. Thus, a novel augmented Lyapunov functional, which consists of a mixed-delay-based augmented part and a time-squared two-sided looped part, is proposed to fill this gap. In the mixed-delay-based augmented part, not only the information of transmission delay and communication delay themselves, but also the interaction between those two delays is considered. Time-dependent quadratic terms as well as the sampling integral states are introduced in the two-sided looped part, so that more characteristic information of the sampling pattern is encompassed and the relationship of the states at the sampling instant is enhanced. Then, this novel augmented functional is applied to the synchronization control of DNNs. A less conservative synchronization criterion is obtained in the form of linear matrix inequalities. A numerical example illustrates the validity and superiority of the presented synchronization criterion.

This research investigates the stability of discrete-time neural networks (DNNs) with a time-varying delay by using the Lyapunov–Krasovskii functional (LKF) method. Recent researches acquired some less conservatism stability criteria for time-varying delayed systems via some augmented LKFs. However, the forward difference of such LKFs resulted in high-degree time-varying delay-dependent polynomials. This research aims to develop some augmented state-related vectors and the corresponding extended free-weighting matrices zero equations to avoid the appearance of such high-degree polynomials and help to provide more freedom for the estimation results. Besides, an augmented delay-product-type LKF is also established for ameliorating the stability conditions of the time-varying delayed DNNs. Then, based on the above methods and Jensen’s summation inequality, the auxiliary-function-based summation inequality, and the reciprocally convex matrix inequality, some less conservatism stability criteria for time-varying delayed DNNs are formulated. The validity of the proposed time-varying delay-dependent stability criteria is illustrated by two numerical examples.

Grapevine breeding is hindered by a limited understanding of the genetic basis of complex agronomic traits. This study constructs a graph-based pangenome reference (Grapepan v.1.0) from 18 newly generated phased telomere-to-telomere assemblies and 11 published assemblies. Using Grapepan v.1.0, we build a variation map with 9,105,787 short variations and 236,449 structural variations (SVs) from the resequencing data of 466 grapevine cultivars. Integrating SVs into a genome-wide association study, we map 148 quantitative trait loci for 29 agronomic traits (50.7% newly identified), with 12 traits significantly contributed by SVs. The estimated heritability improves by 22.78% on average when including SVs. We discovered quantitative trait locus regions under divergent artificial selection in metabolism and berry development between wine and table grapes, respectively. Moreover, significant genetic correlations were detected among the 29 traits. Under a polygenic model, we conducted genomic predictions for each trait. In general, our study facilitates the breeding of superior cultivars via the genomic selection of multiple traits.

Background: Congenital anomalies of the kidney and urinary tract (CAKUT) are among the most common causes of kidney diseases in children. Previous studies on CAKUT etiologies have been predominantly focused on non-modifiable genetic risk factors. The existing nongenetic studies are limited by lack of comprehensive investigation of potentially modifiable risk factors and the inability to distinguish among various phenotypes of CAKUT. Therefore, this study aimed to comprehensively evaluate both maternal and fetal risk factors of CAKUT, sorted by disease phenotype. Methods: A prospective birth cohort study was conducted among 10,179 women who delivered a singleton live newborn in Lanzhou, China, between 2010 and 2012. Face-to-face interviews were conducted among the participants within 1–3 days after delivery using standard questionnaires to collect information on maternal demographics and characteristics. All newborns underwent postnatal renal ultrasonographic screening during their routine 1-month checkup. Clinical data, including birth outcomes and maternal complications, were confirmed by reviewing their medical records. Maternal and fetal risk factors were compared in children with and without CAKUT. Multivariable logistic regression analysis was performed to identify independent risk factors of CAKUT and their phenotypes, respectively. Results: A total of 489 (4.8%) cases of CAKUT were identified. Logistic regression revealed that maternal overweight (pre-pregnancy), gestational diabetes, preterm birth, and low birth weight were independent risk factors for CAKUT. Maternal overweight increased the risk of vesicoureteral reflux (VUR, odds ratio (OR) = 1.441, 95% confidence interval (CI) 1.010–2.057) and posterior urethral valves (PUV, OR = 1.868, 95% CI 1.074–3.249). Gestational diabetes increased the risk of ureteropelvic junction obstruction (UPJO, OR = 1.269; 95% CI 1.044–1.543) and posterior urethral valves (OR = 1.794; 95% CI 1.302–2.474). Preterm birth increased the risk of ureteropelvic junction obstruction (OR = 1.056; 95% CI 1.004–1.111). Conclusions: Our study identified various risk factors associated with different CAKUT phenotypes, stressing the importance of separate analyses for each phenotype. Our findings may provide helpful guidance on developing targeted and effective CAKUT prevention programs in the future. Graphical abstract:

Pseudomonas aeruginosa(PA) is one of the most common multidrug-resistant pathogens found in clinics, often manifesting as biofilms. However, due to the emergence of superbugs in hospitals and the overuse of antibiotics, the prevention and treatment of PA infections have become increasingly challenging. Utilizing DNA nanostructures for packaging and delivering antibiotics presents an intervention strategy with significant potential. Nevertheless, construction of functional DNA nanostructures with multiple functionalities and enhanced stability in physiological settings remains challenging. In this study, the authors propose a magnesium-free assembly method that utilizes tobramycin (Tob) as a mediator to assemble DNA nanostructures, allowing for the functionalization of DNA nanostructures by combining DNA and antibiotics. Additionally, our study incorporates maleimide-modified DNA into the nanostructures to act as a targeting moiety specifically directed towards the pili of PA. The targeting ability of the constructed functional DNA nanostructure significantly improves the local concentration of Tob, thereby reducing the side effects of antibiotics. Our results demonstrate the successful construction of a maleimide-decorated Tob/DNA nanotube (NTTob-Mal) for the treatment of PA-infected lung inflammation. The stability and biocompatibility of NTTob-Mal are confirmed, highlighting its potential for clinical applications. Furthermore, its specificity in recognizing and adhering to PA has been validated. In vitroexperiments have shown its efficacy in inhibiting PA biofilm formation, and in a murine model, NTTob-Mal has exhibited significant therapeutic effectiveness against PA-induced pneumonia. In summary, the proposed antibiotic drug-mediated DNA nanostructure assembly approach holds promise as a novel strategy for targeted treatment of PA infections.

In spatial crowdsourcing, location-based task recommendation schemes are widely used to match appropriate workers in desired geographic areas with relevant tasks from data requesters. To ensure data confidentiality, various privacy-preserving location-based task recommendation schemes have been proposed, as cloud servers behave semi-honestly. However, existing schemes reveal access patterns, and the dimension of the geographic query increases significantly when additional information beyond locations is used to filter appropriate workers. To address the above challenges, this article proposes two efficient and privacy-preserving location-based task recommendation (EPTR) schemes that support high-dimensional queries and access pattern privacy protection. First, we propose a basic EPTR scheme (EPTR-I) that utilizes randomizable matrix multiplication and public position intersection test (PPIT) to achieve linear search complexity and full access pattern privacy protection. Then, we explore the trade-off between efficiency and security and develop a tree-based EPTR scheme (EPTR-II) to achieve sub-linear search complexity. Security analysis demonstrates that both schemes protect the confidentiality of worker locations, requester queries, and query results and achieve different security properties on access pattern assurance. Extensive performance evaluation shows that both EPTR schemes are efficient in terms of computational cost, with EPTR-II being $10^{3}\times$103× faster than the state-of-the-art scheme in task recommendation.

Energy efficiency (EE) is currently one of the primary concerns in wireless communication systems. In future 6G systems, achieving high energy-efficient communication with T-bit transmission rate requirements will heavily depend on resource and power parameters. This paper proposes a novel energy efficiency model that incorporates wireless resources and power for a multi-user multiple-input-multiple-output (MIMO) wireless communication system. Firstly, we analyze the equivalency between frequency and spatial dimensions and introduce a two-dimensional resource domain. Additionally, we propose an energy efficiency model incorporating the wireless resource and power. Using the model, we analyze the interaction between the two parameters aiming at the maximize energy efficiency and present an energy-efficient algorithm. Furthermore, We propose two methods for judging high energy efficiency communication of each user based on the model. Finally, our numerical results illustrate the performance of energy efficiency for each user and the advantages and disadvantages of each measurement method in the multi-user system.

When faced with challenging antenna configurations or high-order modulations in realistic propagation environments, the Belief Propagation (BP) MIMO detector outperforms its linear counterparts. To mitigate the error floor issue and lower the complexity, a revised BP detector, named the Belief-selective Propagation (BsP) detector, has recently emerged by selectively utilizing trusted incoming messages for updates. Despite those promising potentials, the straightforward hardware implementation of the BsP detector still suffers from high complexity and necessitates further optimization. To bridge the gap between the BsP algorithm and implementation, this paper introduces an approximate but implementation-friendly BsP detector called aBsP, based on which the very first BsP hardware is proposed. Two unexplored features: approximate initialization and simplified message updates save the complexity (more than 84%) with acceptable performance penalization. Multi-level optimization techniques involving group-layered message updating, approximate arithmetic circuits, and hybrid-precise quantization are developed to boost the hardware efficiency. A $128\times 8\,\,256$ -QAM aBsP MIMO detector ASIC in 40 nm CMOS occupies an area of 0.68 mm2 and reaches a throughput of 790.52 Mbps. Benchmarking with the recent arts, this work achieves $1.08\times $ area efficiency and $3.34\times $ gate efficiency.

The upcoming 6G Non-Terrestrial Networks (NTN) technology is expected to enable comprehensive connectivity of the Internet of Things (IoT) across various environments. The presence of a large number of IoT devices operating in diverse scenarios, along with the associated vast amounts of data, poses challenges in terms of security and privacy for the 6G NTN IoT networks. Therefore, blockchain technology is considered a key solution for enhancing network security and simplifying management in 6G networks. While IoT devices usually adopt heterogeneous blockchain platforms in different scenarios, cross-chain identity authentication technology is a prerequisite for secure interactions. However, traditional authentication techniques, such as PKI-based solutions, face challenges in handling authentication within 6G Non-Terrestrial Networks, since techniques heavily rely on trusted third-party certificate authorities (CAs) to issue credentials across blockchains. In response to these challenges, we propose a decentralized cross-chain identity authentication scheme for 6G NTN IoT networks called IoTAuth. We design a novel authentication mechanism with user autonomy, combining threshold signatures and DID technology to transform the process of identity credential issuance into a collaborative issuance by multiple nodes under the 6G NTN IoT networks. Furthermore, the scheme realizes cross-chain identity authentication without a centralized manner via an identity conversion mechanism based on relay-chain technology, which further guarantees security in the 6G NTN IoT networks context. The experimental results illustrate the effectiveness and practicality of the scheme. Finally, we present comprehensive future directions.

Recently, increasing personal data has been stored in blockchain databases, ensuring data integrity by consensus. Although transparent and immutable blockchains are mainly adopted, the need to deploy preferences on which users can read and edit the data is growing in importance. Based on chameleon hashes, recent blockchains support editability governance but can hardly prevent data breaches because the data is readable to all participants in plaintexts. This motivates us to propose NANO, the first permissioned blockchain database that provides downward compatible readability and editability governance (i.e., users who can edit the data can also read the data). Two challenges are protecting policy privacy and efficiently revoking malicious users (e.g., users who abuse their editability privileges). The punchline is leveraging Newton's interpolation formula-based secret sharing to hide policies into polynomial parameters and govern the distribution of data decryption keys and chameleon hash trapdoors. Inspired by proxy re-encryption, NANO integrates unique user symbols into user keys, achieving linear user revocation overhead. Security analysis proves that NANO provides comprehensive privacy preservation under the chosen-ciphertext attack. Experiments on the FISCO blockchain platform demonstrate that compared with state-of-the-art related solutions, NANO achieves a 7× improvement on average regarding computational costs, gas consumption, and communication overhead.

Taking into account the infinite distributed delays and reaction-diffusions, this article investigates the global exponential synchronization problem of a class of memristor-based competitive neural networks (MCNNs) with different time scales. Based on the Lyapunov–Krasovskii functional and inequality approach, an adaptive control approach is proposed to ensure the exponential synchronization of the addressed drive-response networks. The closed-loop system is a discontinuous and delayed partial differential system in a cascade form, involving the spatial diffusion, the infinite distributed delays, the parametric adaptive law, the state-dependent switching parameters, and the variable structure controllers. By combining the theories of nonsmooth analysis, partial differential equation (PDE) and adaptive control, we present a new analytical method for rigorously deriving the synchronization of the states of the complex system. The derived $m$ -norm ( $m \geq 2$ )-based synchronization criteria are easily verified and the theoretical results are easily extended to memristor-based neural networks (NNs) without different time scales and reaction-diffusions. Finally, numerical simulations are presented to verify the effectiveness of the theoretical results.

In this paper, a multiple-input multiple-output (MIMO) single carrier frequency division multiple access (SC-FDMA) detector is proposed, named simplified generalized approximate message passing (Sim-GAMP), which is derived from the existing low-complexity GAMP (LC-GAMP). To improve its BER performance, Sim-GAMP is further developed by means of deep learning (DL) techniques, which is called DL-GAMP in this work. Specifically, the layers of DL-GAMP are constructed by unfolding iterations of Sim-GAMP and introducing learnable parameters, thus contributing to better BER performance. Numerical results demonstrate that DL-GAMP can mitigate the performance loss of Sim-GAMP and present good robustness in mismatched cases. Compared with state-of-the-art (SOA) MIMO SC-FDMA detectors, the proposed DL-GAMP enjoys advances in both BER performance (up to $\mathbf {0.83}$ dB gain at BER$=\mathbf {10^{-3}}$) and computational complexity ($\mathbf {66.44\%}$ reduction at most).

In this letter, we present a novel approach to improve belief propagation (BP) decoding performance by sparsifying the Tanner graph. Our proposed method first constructs a low-weight parity-check (LWPC) matrix with increased rows based on the original parity-check matrix. Then, all 4-cycles in the LWPC matrix are eliminated by adding auxiliary variable nodes and corresponding parity-check rows, resulting in the generalized LWPC (G-LWPC) matrix. Compared to the original matrix, the sparsity is greatly improved without altering the encoding constraints. Simulation results show that BP decoding based on the G-LWPC matrix is particularly effective for short code lengths. For a (128, 20) polar code with 24-bit cyclic redundancy check, our proposed G-LWPC matrix reduces the density from 17.4% to 1.3%, and with 10 iterations, BP decoding can achieve the performance of successive cancellation list decoding with a list size of 32.

With the wide deployment of computing capabilities and artificial intelligence (AI) techniques, Internet of intelligence is envisioned as a natural tendency in future networks. This technological revolution will foster abundant computing services with new requirements, such as distributed large AI model training and on-the-fly image rendering, which require dynamic collaboration of multi-dimensional resources from both the communication and computation perspectives. In this article, we introduce a communication and computation integrated network architecture, named (Com)2 Net. Enormous computing traffic can be scheduled across space-air-ground domain, end-edge-cloud domain, and multi-data center domain, which facilitates ubiquitous connectivity and collaborative computation, thus supporting diverse advanced computing services. Furthermore, an intelligent resource adaption scheme is proposed to dynamically orchestrate multi-dimensional resources for massive concurrent computing tasks, in which quantum genetic algorithms are utilized to make the best joint decision for inter-domain traffic scheduling and intra-domain resource allocation. Finally, we present a case study, and discuss open research issues that are fundamental for efficient collaborative computation in (Com)2 Net.

Reconfigurable intelligent surface (RIS) is a revolutionary technology that can be applied in millimeter wave (mmWave) communications to reduce the high power consumption and propagation loss. However, channel estimation (CE) is challenging due to the large number of passive RIS elements without signal processing abilities. In this paper, the uplink CE for RIS-assisted mmWave multi-input multi-output (MIMO) systems is formulated as a sparse signal recovery problem in a novel way. Then, the beam pattern and reflection pattern design based on the compressed sensing (CS) theory are proposed to guarantee the efficient CE. Simulation results demonstrate that, for various CS-based CE algorithms, the proposed patterns can reduce more than 50% pilot overhead at 0 dB signal-to-noise ratio (SNR) while maintaining the same accuracy of CE compared with the existing patterns.

In this article, multiple-input multiple-output (MIMO) single-carrier frequency division multiple access (SC- FDMA) detection algorithms, including low-complexity approxi- mate message passing (LC-AMP) and frequency domain initialization (FDI) benefitted LC-AMP (FDI-LC-AMP), are proposed. The detailed derivation of LC-AMP algorithm starting from approximate message passing (AMP) is provided. Adaptive nearest-neighbor approximation (ANNA) and modified message damping strategy are illustrated. Additionally, FDI is adopted to improve the convergence performance of LC-AMP. The simulation results show that the proposed LC-AMP/FDI-LC-AMP with ANNA exhibit superior performance compared to other state-of-the-art (SOA) MIMO SC-FDMA detectors with and without channel encoding. The trade-off between complexity and performance is also discussed. Benefitting from FDI and ANNA, FDI-LC-AMP with ANNA can obtain 22.9%−44.0% reduction in complexity compared to the SOAs while performing better.

This article concentrates on the fixed-time synchronization (FxTS) problem for fuzzy complex dynamical networks (CDNs) with reaction-diffusion terms and multiple weights. First, a novel fixed-time convergence method is proposed, which relaxes the constraint on the derivative of the constructed Lyapunov functional and incorporates some of the existing results as special cases. Then, an intermittent pinning control scheme is designed to make the state trajectories of all nodes of fuzzy CDNs converge to the equilibrium point within a fixed time, which greatly reduces the control cost. On the basis of the newly presented convergence method and control scheme, several easy-to-verify criteria in the form of linear matrix inequalities are provided to guarantee the FxTS for fuzzy CDNs, and a more accurate estimation of the settling time is obtained. Finally, two examples are given to clarify the correctness of the established theoretical results.

Due to its high parallelism, belief propagation (BP) decoding is amenable to high-throughput applications and thus represents a promising solution for the ultra-high peak data rate required by future communication systems. To bridge the performance gap compared to the widely used successive cancellation list (SCL) decoding algorithm, BP list (BPL) decoding for polar codes extends candidate codeword exploration via multiple permuted factor graphs (PFGs) to improve the error-correcting performance of BP decoding. However, it is a significant challenge to design a unified and flexible BPL hardware architecture that supports various PFGs and code configurations. In this paper, we present the first VLSI implementation of a BPL decoder for polar codes that overcomes this implementation challenge with a hardware-friendly algorithm for on-the-fly flexible permutations. First, we introduce a sequential generation (SG) algorithm to obtain a near-optimal PFG set. Additionally, we demonstrate that any permutation can be decomposed into a combination of multiple fixed routings, and design a low-complexity permutation network to generate graphs in an on-the-fly fashion. Our BPL decoder has a low decoding latency by executing decoding and permutation generation in parallel and supports arbitrary list sizes without area overhead. Experimental results based on $28$nm FD-SOI technology show that for length-$\boldsymbol{1024}$ polar codes with a code rate of one-half, our BPL decoder with $\boldsymbol{32}$ PFGs exhibits similar error-correcting performance to SCL with a list size of $4$ and achieves an average throughput of $\boldsymbol{25.63}$ Gbps and an area efficiency of $\boldsymbol{29.46}$ Gbps/mm$\boldsymbol{{}^{2}}$, which is $\boldsymbol{1.82\times}$ and $\boldsymbol{4.33\times}$ faster than the state-of-the-art BP flip and SCL decoders, respectively.

Expectation propagation (EP) achieves promising performance under various antenna configurations and modulations in massive multiple-input multiple-output (MIMO) detection. Among existing EP variants, the improved EP (IEP) achieves the best bit-error-rate (BER) performance, but at the expense of slower convergence rate. In this correspondence, a novel power EP (PEP) detector is proposed to further enhance the efficiency of EP by balancing the convergence rate and BER performance with two variance-control factors derived from minimizing the $\alpha$-divergence in the moment matching. Numerical simulation results are illustrated to show that the proposed PEP can outperform state-of-the-art EP-based detectors in various MIMO scenarios.

Expectation propagation (EP) has recently been considered with iterative detection and decoding (IDD) to enhance the bit error rate (BER) performance for coded massive Multiple-input Multiple-output (M-MIMO) system. However, current state-of-the-art (SOA) EP-based IDD schemes like double-EP (DEP) are still far from realization due to unaffordable latency brought by their serial structure and large computational complexity. To relieve this issue, in this article, an efficient EP-based parallel IDD method is proposed for coded M-MIMO. First, by investigating the factor graph (FG)-based message passing in EP-based IDD, a double non-resetting framework named EP-dNRe is proposed to improve the IDD efficiency. Based on this framework, a novel parallel EP-based IDD scheme named EP-based parallel detection and decoding (PDD-EP) is further proposed. After proper initialization, the parallel loops of PDD-EP can execute the EP detection and decoding modules simultaneously, which brings latency reduction and improved performance-complexity trade-off. Simulation results and complexity analysis are presented to confirm the efficiency of the proposed PDD-EP. Particularly, both DEP with double non-resetting (DEP-dNRe) and PDD-EP can greatly reduce the required number of iterations to reach the same performance as the SOA DEP. Furthermore, the proposed PDD-EP can attain the same BER as DEP-dNRe with about 33.1% less complexity, and outperforms DEP-dNRe up to 0.2 dB by similar complexity in various LDPC-coded M-MIMO systems.

In this article, several improved stability criteria for time-varying delayed neural networks (DNNs) are proposed. A degree-dependent polynomial-based reciprocally convex matrix inequality (RCMI) is proposed for obtaining less conservative stability criteria. Unlike previous RCMIs, the matrix inequality in this article produces a polynomial of any degree in the time-varying delay, which helps to reduce conservatism. In addition, to reduce the computational complexity caused by dealing with the negative definite of the high-degree terms, an improved lemma is presented. Applying the above matrix inequalities and improved negative definiteness condition helps to generate a more relaxed stability criterion for analyzing time-varying DNNs. Two examples are provided to illustrate this statement.

Despite decades pursuing efficient hardware design for signal processing based on linear algebra, traditional hardware description languages (HDLs)-based design workflows remain challenging and time-consuming. High-level synthesis (HLS) provides an easier approach but still requires thorough designs of basic modules concerning linear transformations to achieve acceptable hardware efficiency. To simplify the HLS workflow, we propose the FLAMES library, which provides efficient ready-to-use linear transformation modules. Users can implement algorithms with significantly higher code-writing efficiency via the FLAMES library. We demonstrate its effectiveness by implementing the orthogonal matching pursuit list (OMPL) algorithm for compressed sensing in FPGA, achieving $1.56 \times $ and $1.12 \times $ throughput/slice compared with traditional HLS for the sequential and parallel architecture, respectively.

Logical network synchronization is an important research direction in the field of computer science and engineering, involving the consistency of time and states among multiple nodes in a network. With the rapid development and widespread application of modern networks, the study of logical network synchronization has become increasingly significant. The purpose of this brief is to explore the problem of solving logic network synchronization based on state-circulated control. Firstly, we introduce the general synchronization problem in logical networks and provide its necessary and sufficient conditions. Building upon this foundation, we address the more complex synchronization problem in asynchronous logical networks. Additionally, this brief investigates the synchronization problem in logical networks with impulsive disturbances.

This article focuses on dynamic event-triggered mechanism (DETM)-based model predictive control (MPC) for T-S fuzzy systems. A hybrid dynamic variables-dependent DETM is carefully devised, which includes a multiplicative dynamic variable and an additive dynamic variable. The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem (OP). To facilitate the co-design of the MPC controller and the weighting matrix of the DETM, an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant (RPI) set that contain the membership functions and the hybrid dynamic variables. A dynamic event-triggered fuzzy MPC algorithm is developed accordingly, whose recursive feasibility is analysed by employing the RPI set. With the designed controller, the involved fuzzy system is ensured to be asymptotically stable. Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

The detection of ships encompasses an abundance of applications within the domains of fishery management, marine rescue operations, and maritime monitoring. In recent years, a multitude of detectors based on deep learning have been utilized for the purpose of ship detection using synthetic aperture radar (SAR) images. However, disturbed by the strong scattering background on land and the influence of the SAR target scale, the existing detectors face great challenges in detecting inshore small ships. To solve this problem, this article proposes an oriented ship detection network for SAR images based on soft threshold and context information. First, a soft-threshold quantization module (STQM) based on the soft threshold function is designed to alleviate the interference of background noise on the feature map. Second, a local and global context fusion module (LGCFM) is designed to capture the contextual information of the target to enhance the detection of small targets. Third, the inclusion of the center loss in the loss function serves to impose additional constraints on the center coordinates and shape of the oriented bounding box. This is done to achieve a more balanced distribution of loss contributions across the various variables and to mitigate the target’s susceptibility to variations in the shape of the ground-truth bounding box. Finally, the proposed network is tested on the publicly available mini-rotated-high-resolution SAR images dataset (MR-HRSID) and Rotated SAR Ship Detection Dataset (R-SSDD) datasets. The results from experiments demonstrate that our approach attains state-of-the-art detection capabilities for inshore ships and small targets, while also effectively mitigating interference from terrestrial noises.

Metaverse provides human-centric immersive communication experiences where humans can teleport across different virtual landscapes and build real-time communications via digital identities with others in the same landscape. Despite great benefits, a natural question in human-centric metaverse communications is how to secure digital content among humans. In this regard, blockchain has been widely applied due to its distinct features (e.g., decentralization, transparency, and immutability). Unfortunately, the inherent properties of the blockchain also hinder humans from further deploying preferences to flexibly govern the digital content (i.e., who can read and who can edit), limiting human-centric communication abilities. Some redactable blockchain-based solutions have been proposed, but most of them suffer from the issues of data and preference leakage. To address the issues, we propose a privacy-preserving identity-based data governance (IDRG) scheme for blockchain-empowered human-centric metaverse communications. Combining digital identities, IDRG cryptographically allows humans to govern readability and editability with the right downward compatibility (i.e., humans with editability are endowed with readability) while protecting policy privacy. Specifically, IDRG leverages the polynomial function technique to break through the bottleneck of the traditional identity-based encryption technique (i.e., a policy only contains a user) to achieve a policy for multiple users. Subsequently, the optimized policies are utilized to enrich chameleon hash-based redactable blockchains for comprehensive rights governance. Further, IDRG supports user accountability and revocation by combining the proxy re-encryption technique. Security analysis proves the security of IDRG under the chosen-ciphertext attack. Experiments on the FISCO blockchain platform demonstrate that IDRG requires approximately 0.1 s to process an encryption request, 0.01 s for a reading request, and 1 s for an editing request. Overall, IDRG achieves a $3\times $ reduction in computational costs compared with state-of-the-art solutions.

The event-triggered model predictive control (MPC) problem is addressed for polytopic uncertain systems. A new dynamic event-triggered mechanism (DETM) with a bounded dynamic variable and a time-varying threshold is proposed to manage measurement data packet releases. The dynamic output-feedback MPC issue is detailed as a “min–max” optimization problem (OP) with an objective function over an infinite horizon, where the hard constraint on the predictive control is required. By applying a Lyapunov-like function containing the bounded dynamic variable, an auxiliary OP constrained by several matrix inequalities is proposed, and the design methods of the output-feedback gains are provided if this auxiliary OP is feasible. The designed MPC controller ensures that the closed-loop system is input-to-state practically stable. Two examples including an event-triggered DC motor are given to illustrate the validity of the developed MPC algorithm. Simulation results verify that the proposed DETM has advantages over some existing triggering mechanisms in decreasing the consumption of resources while meeting the required performance.

It is supposed that ultra-reliable low latency communication (uRLLC) would continue to evolve in the future sixth generation (6G) network, to provide enhanced capability towards extreme connectivity, with the aid of well established multiple-input multiple-output (MIMO) technology. Since the latency constraint can be represented equivalently by the blocklength of a codeword, channel coding theory at a finite blocklength plays an important role in theoretic analysis of uRLLC. Based on Polyanskiy’s and Yang’s asymptotic results on maximal achievable rate, we first derive the proximate closed-form expressions for the expectation and variance of channel dispersion. Then, the upper bound of average maximal achievable rate is obtained for massive MIMO systems under ideal independent and identically distributed fading channels. Since almost all the fundamental parameters, including the spatial degree-of-freedom (DoF), are considered, this expression can be viewed as a performance bound of the spatiotemporal two-dimension channel coding to some extent. Moreover, it is shown by simulation and analysis, as the DoF goes to infinity, MIMO systems reveal a nature of deterministic transmission, since the average maximal achievable coding rate per antenna can be achieved at each transmission. In this case, the inversely proportional law observed therein implies that the blocklength in the time domain can be further shortened at the expense of spatial DoF. This exchangeability of space and time, to support a given coding rate, paves a solid and feasible road for us to further reduce latency in 6G uRLLC.