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Despite the extensive use of next-generation sequencing (NGS) of RNA, simultaneous direct sequencing and quantitative mapping of multiple RNA nucleotide modifications remains challenging. Mass spectrometry (MS)-based sequencing can directly sequence all RNA modifications without being limited to specific ones, but it requires a perfect MS ladder that few tRNAs can provide. Here, we describe an MS ladder complementation sequencing approach (MLC-Seq) that circumvents the perfect ladder requirement, allowing de novo MS sequencing of full-length heterogeneous cellular tRNAs with multiple nucleotide modifications at single-nucleotide precision. Unlike NGS-based methods, which lose RNA modification information, MLC-Seq preserves RNA sequence diversity and modification information, revealing new detailed stoichiometric tRNA modification profiles and their changes upon treatment with the dealkylating enzyme AlkB. It can also be combined with reference sequences to provide quantitative analysis of diverse tRNAs and modifications in total tRNA samples. MLC-Seq enables systematic, quantitative, and site-specific mapping of RNA modifications, revealing the truly complete informational content of tRNA.

Electrosorption (ES) is a research frontier in electrochemical separation, with proven potential applications in desalination, wastewater treatment, and selective resource extraction. However, due to the limited adsorption capacity of film electrodes, ES requires short circuiting or circuit reversal, accompanied by a solution switch between the feed solution and receiving solution, to sustain desalination over many charge–discharge cycles. In previously reported studies, solution switches have been commonly ignored to simplify experimental procedures, and their impacts on separation performance are thus not well understood. This study aims to provide a quantitative analysis of the impacts of mixing due to a solution switch on the performance of ES-based desalination. A numerical model of ES has been employed to evaluate the adverse effects of the solution switch on the desalination performance in three commonly used operation modes. The analysis reveals that the impacts of mixing due to solution-switch are more severe with a larger concentration difference between the desalinated water and the brine and provides insights into the effectiveness of increasing electrode loading or specific capacity in mitigating the detrimental impacts of mixing. Even with state-of-the-art systems, producing freshwater from seawater or even brackish water with medium-to-high salinity is practically challenging due to the presence of solution switch.

Animal experiments are important in trauma-related studies because they simulate in vivo effects. Rodents are a good choice for preparing trauma models; however, contractile healing in rodents results in a healing pattern that differs considerably from that in humans. Therefore, this study developed a new rodent model that avoids contractile healing of the skin around the wound using an anticontraction ring, and the skin in the wound’s center remains intact and acts as a source for epithelialized diffusion healing. Cell proliferation, migration, revascularization, and collagen secretion did not differ between the novel and conventional full-skin defect trauma models. However, the healing rate at various stages significantly differed between the 2 groups owing to differences in the healing patterns. And without effective treatment, the experimental group cannot heal. The stabilities of the novel and conventional methods were good regardless of operator or batch. In summary, this new animal trauma model provides a stable experimental environment similar to that in humans, which may promote trauma-related research.

CFIRL is a long noncoding RNA (lncRNA), we previously identified as the most significantly upregulated lncRNA in the failing hearts of patients with dilated cardiomyopathy (DCM). In this study, we determined the function of CFIRL and its role in DCM. Real-time polymerase chain reaction and in situhybridization assays revealed that CFIRL was primarily localized in the nucleus of cardiac fibroblasts and robustly increased in failing hearts. Global knockdown or fibroblast-specific knockout of CFIRL attenuated transverse aortic constriction (TAC)-induced cardiac dysfunction and fibrosis in vivo. Overexpression of CFIRL in vitropromoted fibroblast proliferation and aggravated angiotensin II-induced differentiation to myofibroblasts. CFIRL knockdown attenuated these effects. Mechanistically, RNA pull-down assay and gene expression profiling revealed that CFIRL recruited ENO1, a newly identified noncanonical transcriptional factor, to activate IL-6 transcription. IL-6 exerted a paracrine effect on cardiomyocytes to promote cardiac hypertrophy, which can be prevented by CFIRL knockdown. These findings uncover the critical role of CFIRL, a fibroblast-associated lncRNA, in heart failure by facilitating crosstalk between fibroblasts and cardiomyocytes. CFIRL knockdown might be a potent strategy to prevent cardiac remodeling in heart failure, particularly in DCM.

With the increasing awareness of environmental protection, the problem of heavy metal pollution in soil has received greater attention. The bottom vacuum leaching remediation method (BVM) is an emerging soil remediation technology that has demonstrated enhanced efficiency and effectiveness in treating soils with low permeability. This experiment employed three soil column models of varying heights to examine the patterns of heavy metal migration and distribution, by conducting a series of experimental studies that investigated changes in soil columns and leaching agents, as well as the distribution of water content, heavy metals, and particle size after the leaching process. The experimental results indicate a correlation between the height of the soil column and the leaching speed, with higher columns exhibiting slower leaching rates. However, the final removal rate is not necessarily the lowest. Particle size analysis reveals that the decomposition of large particles into smaller particles, facilitated by the action of the leaching agent, significantly influences the removal efficiency. The combined effect of particle decomposition and vacuum action demonstrates a pattern of increasing and then decreasing the distribution of heavy metals, forming a distinct zone of heavy metal accumulation.

Repetitive implant-related infections (IRIs) are devastating complications in orthopedic surgery, threatening implant survival and even the life of the host. Biofilms conceal bacterial-associated antigens (BAAs) and result in a "cold tumor"-like immune silent microenvironment, allowing the persistence of IRIs. To address this challenge, an iron-based covalent organic framed nanoadjuvant doped with curcumin and platinum (CFCP) was designed in the present study to achieve efficient treatment of IRIs by inducing a systemic immune response. Specifically, enhanced sonodynamic therapy (SDT) from CFCP combined with iron ion metabolic interference increased the release of bacterial-associated double-stranded DNA (dsDNA). Immunogenic dsDNA promoted dendritic cell (DC) maturation through activation of the stimulator of interferon gene (STING) and amplified the immune stimulation of neutrophils via interferon-β (IFN-β). At the same time, enhanced BAA presentation aroused humoral immunity in B and T cells, creating long-term resistance to repetitive infections. Encouragingly, CFCP served as neoadjuvant immunotherapy for sustained antibacterial protection on implants and was expected to guide clinical IRI treatment and relapse prevention.

• W90 and Mg alloy were firstly bonded by ultrasonic-assisted soldering at 250 °C. • Bonding region at W90/Sn interface increased with ultrasonic time increased. • The addition of Al in Sn formed Al 4 W at the W90/solder interface. A double-layered W/Mg structure is expected to be a new generation of nuclear radiation shielding material. The tungsten heavy alloy (W90) and AZ31B Mg alloy were firstly bonded by ultrasonic-assisted soldering using pure Sn and Sn-Al filler metal in an atmospheric environment. The influence of ultrasonication time on the microstructure and mechanical properties of the joint was investigated. The typical microstructure of the W90/Sn/Mg joint was W90/Mg 2 Sn + Sn/Mg 2 Sn layer/Mg. As the ultrasonication time increased from 2 s to 10 s, the joint width reduced and the thickness of the Mg 2 Sn layer increased. The shear strength of the joint firstly increased, then flattened, and finally decreased. The joint strength reached the maximum value of 10.5 MPa. The fracture position of the joint changed from the W90/filler metal interface to the Mg 2 Sn layer. The addition of Al in Sn resulted in the formation of the Al 4 W phase at the W/Sn-1Al interface. The W/filler metal interface changed from the semi-coherent interface to the coherent interface and the joint strength increased. As the ultrasonication time was 6 s, the shear strength W90/Sn-1Al/Mg joint reached the maximum value of 24.6 MPa and the joint fractured at two positions: W90/filler metal interface and filler metal. With the further increase of ultrasonication time, the joint strength decreased and the joint fractured in the Mg 2 Sn layer. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Urea combined with nitrification inhibitors (NI) can significantly reduce nitrous oxide emissions (N2O) compared to urea. However, there is a lack of evidence for higher agronomic benefits and better environmental benefits, i.e., the ability to minimize N2O emissions and improve nitrogen (N) use efficiency (NUE) while maintaining high yield under N application rates lower than conventional N application rates. Methods: A 3-year in situ fertilization experiment was conducted under film mulching conditions in the Loess Plateau region with low rainfall. Three treatments of conventional N fertilization (CK), reduced N fertilization (RN), and RN with NI (RN + NI) were used to quantify soil N2O emissions, plant N uptake, NUE, and grain yield. Results: RN and RN + NI treatments significantly reduced N2O emissions by 17.29–29.15% and 56.00–60.51%, respectively, and significantly increased NUE by 10.79–21.27% and 17.11–25.70%, respectively, compared with CK treatment. However, there was no significant difference in average plant N uptake (178.96–191.37 kg N ha−1) and grain yield (7.84–8.14 t ha−1) among CK, RN, and RN + NI treatments. Furthermore, higher available soil water storage at sowing and large rainfall events during the first month after fertilization would enhance the effect of NI on inhibiting N2O emissions, since the efficiency of NI in reducing N2O emissions depends on the level of water-filled pore space. Conclusions: Therefore, reducing N application combined with NI can be used as an effective N fertilizer management measure, which is helpful to achieve agriculture sustainable development in areas similar to the climate model of the Loess Plateau.

Lithium-ion capacitors (LICs) combining the advantages of lithium-ion batteries and supercapacitors are considered a promising next-generation energy storage device. However, the sluggish kinetics of battery-type anode cannot match the capacitor-type cathode, restricting the development of LICs. Herein, hierarchical carbon framework (HCF) anode material composed of 0D carbon nanocage bridged with 2D graphene network are developed via a template-confined synthesis process. The HCF with nanocage structure reduces the Li+transport path and benefits the rapid Li+migration, while 2D graphene network can promote the electron interconnecting of carbon nanocages. In addition, the doped N atoms in HCF facilitate to the adsorption of ions and enhance the pseudo contribution, thus accelerate the kinetics of the anode. The HCF anode delivers high specific capacity, remarkable rate capability. The LIC pouch-cell based on HCF anode and active HCF (a-HCF) cathode can provide a high energy density of 162 Wh kg−1and a superior power density of 15.8 kW kg−1, as well as a long cycling life exceeding 15,000 cycles. This study demonstrates that the well-defined design of hierarchical carbon framework by incorporating 0D carbon nanocages and 2D graphene network is an effective strategy to promote LIC anode kinetics and hence boost the LIC electrochemical performance.

Clenbuterol (CLB) as an illegal feed additive may cause a great security risk to food safety. However, convenient and efficient detection means for CLB in practical application remain a formidable challenge. Herein, a stable Eu-based organic framework {[H2N(CH3)2]2[Eu2(ttca)2]·H2O}n(compound 1) (H4ttca = [1,1′:2′,1″-terphenyl]-4,4′,4″,5′-tetracarboxylic acid) has been harvested, exhibiting excellent chemical stability and thermal stability. Luminescence investigation reveals that compound 1can sensitively and selectively detect CLB without being affected by different components from simulated serum and urine (limit detection: 22.7 nM). Furthermore, sensor 1can also be applicable to CLB recognition in real swine feeds, presenting excellent anti-interference performance. The good cyclicity of compound 1endows CLB determination with many advantages: low cost, high stability, and simplicity. Importantly, in view of the indication of the luminescence color (red to blue), test membranes were fabricated and employed for convenient and fast CLB detection, providing a valuable scheme for the visual monitoring of CLB in meat products. This work enriches rare earth metal compounds and luminescence sensor portfolios and breaks the concentration record (nM) for detecting CLB compared with reported complex materials, providing an effective monitoring platform for CLB visually.

This paper concentrates on the real-time reachable set synthesis of heterogeneous nonlinear singular multiagent systems with uncertain parameters based on adaptive fault-tolerant control under non-zero initial conditions. A proportional and differential-like adaptive fault-tolerant control protocol is designed to respond to this problem, and a closed-loop singular multiagent system is built through this control protocol. Then a Lyapunov function suitable for the system is constructed, and it is proved that when there is a fault in the system, the error system will ultimately converge to the neighborhood near the origin. In addition, using the free-weighting matrices strategy and particular decoupling method to solve the linear matrix inequalities, the real-time reachable set boundary of the closed-loop singular multiagent control system and the gains of the designed controller are obtained. Finally, the efficacy of the presented strategy is demonstrated by practical examples of multiagent support systems and the DC motor models. Note to Practitioners—Because of the complex and changeable characteristics of a modern engineering system, it can not be operated by a single module, so the research of multiagent systems has become the mainstream trend of modern system research. In the actual project, the safety problem must be addressed. Therefore, our main research content is how to make the machine or system model run within a more accurate security range. In practical applications, some random and unpredictable events often occur, which may lead to failures in the system and make the system unable to operate properly. These behaviors may lead to unknown security problems. When we study security issues, we need to consider the occurrence of this situation. Therefore, when a fault occurs in the system, ensuring the system’s normal operation within the safe range is one of the key topics that need to be studied. Unlike ordinary systems, singular systems can more widely describe the actual system model. Therefore, the study of singular systems is indispensable in actual engineering system research. Based on the above analysis, the real-time reachable set synthesis of heterogeneous nonlinear singular multiagent systems with faults is studied, and the consensus protocol of the proportional and differential-like is designed.

The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). This work is motivated by a cervical spine motion prediction problem, with the goal of predicting the cervical spine motion of different subjects using the measurements of “exterior features”. The joint distribution of the exterior features for each subject may vary with one’s distinct BMI, sex and other characteristics; the sample size of this problem is limited due to the high experimental cost. The proposed method is well suited for transfer learning problems with limited training samples. The learning problem is formulated as a dynamic programming model and the latter is then solved by an efficient greedy algorithm. Numerical studies show that the proposed method outperforms prevailing transfer learning methods. The proposed method also achieves high prediction accuracy for the cervical spine motion problem. Note to Practitioners—This paper is motivated by the problem of using human body exterior features (e.g., contours, surface markers) acquired by computer vision or other measurement means to determine the underlying physics or mechanics. The goal is to build a subject-specific model to predict cervical spine motion based on surface-based measurements. Conventional supervised learning and transfer learning methods fail to give an accurate prediction due to the subject-to-subject variation and/or the small sample size of the training data. This paper proposes a novel transfer learning method called renewing iterative self-labeling domain adaptation to modify the possible error of the pseudo labels and thus make the training process more stable. The proposed method improves the accuracy by over 35% on the traditional transfer learning methods. This method is applicable to limited-sample problems with different training and testing input distributions, and is particularly suitable for time-series data.

Time-sensitive networking (TSN) can offer deterministic low-latency communication, making it a critical solution for high-level autonomous vehicle's in-vehicle network. The deterministic transmission of TSN relies on TSN traffic scheduling. To ensure real-time transmission performance and vehicle functional safety, in-vehicle TSN scheduling aims to reduce end-to-end delay. Despite the promising potential of graph neural networks and deep reinforcement learning (DRL) in navigating complex TSN scheduling environments, its application has predominantly been limited to enhancing schedulability without a targeted focus on minimizing delays. This article introduces a DRL in-vehicle TSN scheduling framework based on the graph attention network (GAT). The scheduling problem is abstracted as a delay optimization problem and mapped to a Markov decision process (MDP), which is solved using the proximal policy optimization (PPO) algorithm. The GAT with attention mechanism is incorporated to extract critical information to enhance feature extraction and improve scheduling accuracy. This GAT-based PPO method can achieve high-precision offline scheduling through training, producing low-delay scheduling results. Simulation results demonstrate that the proposed method improves offline scheduling performance compared to other DRL-based scheduling methods. Leveraging the trained neural network, the proposed method can also deliver high robustness in online scheduling under link failure scenarios. It can produce a scheduling solution in just 3.8 s, and the scheduling results for all failure scenarios surpass those of rule-based benchmarking methods.

For synthetic aperture radar (SAR) imaging with big data, traditional fast Fourier transform (FFT)-based imaging algorithms are no longer fast enough for some real-time applications, such as ship monitoring and early warning. The imaging scene of ships in a calm sea background presents block sparse feature. In this article, considering the block sparse feature, we propose a low computational complexity imaging algorithm. First, we introduce a sublinear complexity method called 2-D band-limited sparse Fourier transform (2D-BLSFT) to accelerate 2D-FFT. Then, $\omega {K}$ -BLSFT algorithm for SAR imaging is designed based on $\omega {K}$ algorithm and 2D-BLSFT. Due to the sublinear computational and sample complexity of 2D-BLSFT, the computational complexity in 2-D inverse FFT (2D-IFFT) step and Stolt mapping step is reduced. Furthermore, the robust processing is studied considering the nonideal block sparse feature in practical scenes. Numerical simulations confirm that 2D-BLSFT outperforms the state-of-the-art sparse FFT (SFFT) algorithms. Moreover, experimental results based on simulation data and GF-3 spaceborne SAR data verify that the imaging performance of $\omega {K}$ -BLSFT algorithm surpasses that of existing SFFT-based algorithms.

Numerous harmonics and electromagnetic interference (EMI) noise are prevalent in inverter system, potentially compromising output power quality. Harmonic and EMI filters serve as crucial components for mitigating harmonic distortion and EMI noise in grid-connected inverter. Nonetheless, these filters significantly increase the overall weight and volume of the inverter system, so there is a need to reduce their size and weight. This article uses planar magnetic integration technology to integrate the inductors of the LCL filter, the inductors of the passive EMI filter and the sampling coil of the active EMI filter into a single magnetic core unit through reasonable design of the PCB windings. In addition, capacitance integration is facilitated by the insertion of dielectric material between planar coil layers. This innovative approach effectively reduces the size and weight of the filter, consequently elevating the power density of the inverter system. The planar magnetic integrated LCL-EMI hybrid filter is analyzed using a single-phase Gallium-Nitride (GaN) inverter simulation and experimental platform, demonstrating that the design scheme reduces the size and weight of the filter while ensuring harmonic and EMI suppression. The feasibility of the planar magnetic integration scheme is thereby verified.

With the fast development of multi-agent reinforcement learning, communication among agents has become a new research hotspot for its significant role in promoting the cooperation of automated devices. However, in real-world scenarios, agents such as unmanned vehicles and robots are likely to suffer from communication resource constraints, making designing efficient communication protocols essential. In this paper, we propose to quantize messages and reduce discrete entropy to achieve effective multi-agent communication under bandwidth limits. Achieving this goal requires solving two challenges: The first one is that the gradients of discrete entropy remain zero except for several discontinuous points wherein the gradients are undefined, making it hard to reduce discrete entropy via gradient-based training. To overcome it, we design Surrogate Entropy Minimization (SEM) scheme and confirm its effectiveness theoretically. The second challenge is maximizing cooperation performance under a given bandwidth limit. We model it as a constrained optimization problem and design Soft Barrier Method (SBM). Our proposed scheme is evaluated alongside four other methods in six environment settings and five different bandwidth limits, and demonstrates outstanding performance. Specifically, it manages to reduce bandwidth consumption by up to 90% with little or no loss of cooperation performance.

Although numerous miRNAs have been discovered, their functions in the different subcellular organelles have remained obscure. In this study, we found that miR-665 was enriched in the nucleus of cardiomyocytes, and then investigated the underlying role of nuclear miR-665 in heart failure. RNA fluorescence in situhybridization assays in human heart tissue sections and primary cardiomyocytes showed that miR-665 was localized in the nucleus of cardiomyocytes. Increased expression of nuclear miR-665 was observed not only in the cardiomyocytes isolated from the heart of mice treated in vivoby transverse aortic constriction (TAC), but also in phenylephrine (PE)-treated cultured cardiomyocytes in vitro. To further explore the role of miR-665 in heart failure, a type 9 recombinant adeno-associated virus (rAAV) system was employed to manipulate the expression of miR-665 in mice. Overexpression of miR-665 aggravated TAC-induced cardiac dysfunction, while down-expression of miR-665 showed opposite effects. Bioinformatic prediction and biological validation confirmed that the PTEN(phosphatase and tensin homolog) gene was one of the targets of miR-665 in the nucleus. Furthermore, restoring PTENexpression significantly eliminated the destructive effects of miR-665 over-expression in TAC-induced cardiac dysfunction. Our data showed that nuclear miR-665 aggravates heart failure via inhibiting PTENexpression, which provided a therapeutic approach for heart failure.

The impact of underwater bubbles on solid surfaces with a gradient wettability is important for fundamental science and technological applications. Despite progress in the translational motion of underwater bubbles on asymmetric surface, a fundamental dynamic understanding of asymmetrical bounce behavior of the underwater bubble needs to be further improved. Herein, we investigate asymmetrical bounce of underwater bubbles after impacting the wettability gradient copper mesh surface. Asymmetrical bounce of the underwater bubble on the wettability gradient mesh (WGM) surface is composed by asymmetrical spread in the spread process and asymmetrical recoil in the recoil process, which induce the unidirectional movement behavior. Additionally, effective bubble-size manipulation can be easily implemented due to different volumes of bubbles remaining at different locations of WGM surface. We envision that this work will provide a new understanding of asymmetrical bounce behavior of the underwater bubble on the wettability gradient surface, and propose a novel and feasible strategy for constructing the underwater bubble manipulation system.

In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization methods, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost.

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In the soils of western Jilin Province in northeastern China, some significant gaps have been observed between the fraction of the soil existing as clay-size particles (<0.002 mm) and the amount attributable to crystalline clay minerals, and that the relative proportions of crystalline clay minerals to the total clay-size fraction (CP) apparently varies with latitude. The purpose of the present study was to identify the reason for this discrepancy and to explain the dependence on latitude. The grain sizes and mineral compositions of the whole soils from western Jilin Province, China, were analyzed by laser particle-size analysis (LPSA) and X-ray diffraction (XRD), and the <0.002 mm particle-size fraction was analyzed by XRD and X-ray fluorescence (XRF). The results confirmed that the percentage gaps between the clay fraction and clay minerals increased with increasing latitude. The theoretical illite percentage calculated from K2O content was compared with the illite percentage measured by XRD, and the results suggested that the measured illite accounted for only a small proportion of the theoretical illite. Structures of some special minerals below the identification threshold of XRD was suggested to be the reason for the percent gaps. The grain size and mineral crystallization both changed with latitude: the soil particle size and the CPdecreased. In addition, clay minerals were more sensitive to climate than particle sizes were, and the CPof clay minerals in the soils within 0~180 cm depth all decreased with increasing latitude; however, the grain size showed patterns with latitude only in relatively shallow soil layers. The present study provides a reference and error analysis for the testing of clay minerals in alpine regions, and more suitable methods may be considered for development of clay-mineral testing in future studies.

In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) back to the base station (BS). Though deep learning approaches have made a hit in the CSI feedback problem, whether they can remain excellent in actual environments needs to be further investigated. In this letter, we point out that the real-time dataset in application often has the domain gap from the training dataset caused by the time delay. To bridge the gap, we propose bubble-shift (B-S) data augmentation, which attempts to offset performance degradation by changing the delay and remaining the channel information as much as possible. Moreover, random-generation (R-G) data augmentation is especially proposed for outdoor scenarios due to the complex distribution of its channels. It generalizes the characteristics of the channel matrix and alleviates the over-fitting problem. Simulation results show that the proposed data augmentation boosts the robustness of networks in both indoor and outdoor environments.

The day-ahead demand response (DR) potential forecasting can provide reference information for load aggregators (LAs) to participate in bidding offers in the electricity market and reduce decision-making risks. However, most of the current DR potential forecasting methods have the following two problems: 1) lack of consideration of spatial correlation between different customers; 2) lack of consideration of causal relationships between input feature nodes. When there are drastic changes in DR potential, critical information may not be utilized effectively, resulting in poor forecasting accuracy. Based on this, this article proposes a directed graph structure based day-ahead demand response potential forecasting model considering the dynamic spatial-temporal correlation. Firstly, the residential customers are clustered according to the DR potential pattern. Secondly, the important feature nodes affecting the DR potential of LAs are extracted, and a directed edge structure is established by analyzing the causal relationships between different clusters of nodes, and the Pearson correlation coefficient (PCC) is used to characterize the dynamic spatio-temporal correlations between similar feature nodes to establish a directed graph structure. Finally, the directed graph structure is used to train an online forecasting model. Case study shows that the proposed model can tap into the dynamic spatio-temporal correlation properties of residential customers and improve the forecasting accuracy of DR potential for LAs.

For massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) compression and feedback are crucial for enhancing system performance. Deep learning (DL)-based methods have been designed and proven to perform well in this task. However, the distribution of CSI in real-world communication systems may differ from the one observed during model training, which can undermine the effectiveness of DL-based methods due to their limited generalization ability. Several methods have been proposed to facilitate online training and enable network adaptation to unknown scenarios. Nevertheless, the knowledge learned from previous scenarios is often forgotten, leading to performance degradation when encountering a previous scenario again. In this letter, we propose a novel continuous learning-based CSI feedback approach, which can effectively address the challenge of catastrophic forgetting and ensure consistent high performances across all historical scenarios, thereby enhancing the generalization capability of the model.

Automatic modulation recognition (AMR) has received widespread attention as a crucial aspect of non-cooperative communication. Despite this, large carrier frequency offsets (CFOs) and sample rate offsets (SROs) caused by inaccurate parameter estimation at the receiver are harmful to the recognition accuracy, which is still to be addressed. In this letter, we focus on intelligent modulation recognition tasks under such offsets. A novel transformer-based method named TransGroupNet is designed that can extract deep features of signals from the instantaneous amplitude, phase, and frequency (APF) domain. In addition, lightweight group convolution layers are added ahead of the transformer blocks for better feature preprocessing. Simulations demonstrate that the proposed TransGroupNet achieves better recognition accuracy under large offsets compared with the previous state-of-the-art methods, even though these methods adopt correction modules (CM) to address such offsets.

• The CNTs coated with various Ni-based NiM nanoalloys are CVD grown on the graphene nanoscrolls to form unique carbon material scaffold. • The electrons of encapsulated NiM nanoalloys can traverse through the CNT to the outmost loading (CoP) by electron traversing effect. • Due to the varying d-electron orbital characteristic of NiMs, the encapsulated various nims can weaken the interactions of the outmost CoP with H* and push the downshift of the d-band center to different degrees to optimize the HER activity. • The catalytic performance of the outmost CoP can be optimized by changing the encapsulated metal nanoparticles in the unique carbon material scaffold, which is a novel d-band center control engineering strategy for HER. Attaining a highly efficient and inexpensive electrocatalyst is significant for the hydrogen evolution reaction (HER) but still challenging nowadays. The transition-metal phosphides (TMPs) catalysts with platinum-like electronic structures are a potential candidate for the HER, but those are prone to be strongly bound with hydrogen intermediates (H*), resulting in sluggish HER kinetics. Herein we report a unique hybrid structure of CoP anchored on graphene nanoscrolls@carbon nano tubes (CNTs) scaffold (Ni M @C-CoP) encapsulating various Ni M (M =Zn, Mo, Ni, Co) bimetal nanoalloy via chemical vapor deposition (CVD) growth of CNT on graphene nanoscrolls followed by the impregnation of cobalt precursors and phosphorization for efficiently electrocatalytic hydrogen evolution. CoP nanoparticles mainly scattered at the tip of CNT branches which exhibited the analogical ''Three-layer core-shell'' structures. Experiments and density functional theory(DFT) calculations consistently disclose that the encapsulated various NiMs can offer different numbers of electrons to weaken the interactions of outmost CoP with H* and push the downshift of the d -band center to different degrees as well as stabilize the outmost CoP nanoparticles to gain catalytic stability via the electron traversing effect. The electrocatalytic HER activity can be maximumly enhanced with low overpotentials of 78 mV (alkaline) and 89 mV (acidic) at a current density of 10 mA/cm2 and sustained at least 24 h especially for NiZn@C-CoP catalyst. This novel system is distinct from conventional three-layer heterostructure, providing a specially thought of d -band center control engineering strategy for the design of heterogeneous catalysts and expanding to other electrocatalysts, energy storage, sensing, and other applications. Herein we report a unique hybrid structure of CoP anchored on graphene nanoscrolls@CNTs scaffold (NiM@C-CoP) encapsulating varying NiM (M=Zn, Mo, Ni, Co) bimetal nanoalloy for efficiently electrocatalytic hydrogen evolution. Experiments and density functional theory calculations consistently disclose that the encapsulated NiMs can weaken the interactions of outmost CoP with H* and push the downshift of the d-band center to different degrees via the electron traversing effect. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, deep learning aided methods have been developed for error correction coding with quantitative constraints. However, previous studies only focus on additive white Gaussian noise (AWGN) channels, which is not sufficient for actual communication environments. In this article, we propose a novel autoencoder aided error correction coding scheme for low-resolution reception under time-varying channels. Based on the symbol extension of the proposed autoencoder and the faster-than-Nyquist (FTN) technology, pilot-free transmission can be realized without adding additional bandwidth. The transformer block is introduced to lighten and improve the decoder. Additionally, two kinds of preamplification techniques are applied for further performance boosting. Simulations show that the proposed method can achieve better performance compared with the traditional methods at high signal-to-noise ratio (SNR) under different time-varying channels without quantization. Moreover, it outperforms the previous state-of-the-art ECCNet and can achieve remarkable transmission performance even under time-varying low-resolution reception scenarios.

In urban dynamic environment, most of the existing works on LiDAR simultaneous localization and mapping (SLAM) are based on static scene assumption and are greatly affected by dynamic obstacles. In order to solve this problem, this article is based on fast LiDAR odometry and mapping (F-LOAM) and adopts the FA-RANSAC algorithm, improved ScanContext algorithm, and global optimization to propose a robust and fast LiDAR odometry and mapping (RF-LOAM). First, the region-growing algorithm is used to cluster the fan-shaped grids. Then, we propose the FA-RANSAC algorithm based on feature information and adaptive threshold for dynamic object removal and extract the static edge and planar feature points for the first distortion compensation. Afterward, the estimated pose is calculated by the static feature points and is used to perform the second distortion compensation. Then, the height difference and adaptive distance threshold are used to improve the accuracy of ScanContext, and the efficiency of ScanContext is improved by deleting the loop closure historical matching frames and simplifying the feature matching. Finally, global optimization is used for keyframe. The experimental tests are carried out on the KITTI datasets, Urbanloco datasets, and our Extracted dataset. The results show that compared with the state-of-the-art SLAM methods, our method can not only accurately complete dynamic object removal and loop closure detection but also achieve more robust and faster localization and mapping in urban dynamic scenes.

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as a promising paradigm for dealing with the massive amounts of data generated by intelligent devices. Nevertheless, although FL has natural advantages in data security protection, eavesdroppers can also deduce the raw data according to the shared parameters. Existing works mainly focused on encrypting the content of uploaded parameters, but we believe that it can improve security further by hiding the presence of parameter updating. Therefore, in this paper, we conceive a UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise (AN) to interfere with the eavesdropping of unintended users. To strike a balance between the security level and the training cost (including time overhead and energy consumption), we propose a distributed proximal policy optimization-based strategy for the sake of jointly optimizing the trajectory and AN transmitting power of the UAV, the CPU frequency, the transmitting power and the bandwidth allocation of the participated devices, as well as the needed accuracy of the local model. Furthermore, a series of experiments have been conducted to validate the effectiveness of our proposed scheme.

The objective of this study was to investigate the effects of a back exoskeleton on joint kinematics and kinetics during assisted manual handling tasks using subject-specific musculoskeletal biodynamic models and model-based analyses. We constructed these musculoskeletal models using OpenSim (Delp et al., 2007), incorporating optical motion capture, ground reaction forces (GRFs) measurements, and humanexoskeleton interactive force input. Our long-term goal is to enable digital modeling and simulation that can aid in the design and development of more effective exoskeletons and safer manual handling practices.

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.

Mineral nutrients spatiotemporally participate in the biosynthesis and accumulation of storage biopolymers, which directly determines the harvested grain yield and quality. Optimizing fertilizer nutrient availability improves the grain yield, but quality aspects are often underestimated. We hypothesize that extensive mineral nutrients have significant effects on the biosynthesis, content, and composition of storage proteins, ultimately determining physicochemical properties and food quality, particularly in the context of climate change. To investigate this, we hierarchized 16 plant mineral nutrients and developed a novel climate–nutrient–crop model to address the fundamental question of the roles of protein and starch in grain-based food quality. Finally, we recommend increasing the added value of mineral nutrients as a socioeconomic strategy to enhance agro-food profitability, promote environmental sustainability, and improve climate resilience.

Lacto-N-biose (LNB) is a member of the human milk oligosaccharide (HMO) family and is synthesized via an enzymatic reaction in vitro with N-acetylglucosamine (GlcNAc) and cofactors. In this study, LNB was synthesized using a cell factory for the first time. First, three modules were constructed in Kluyveromyces lactisfor producing LNB from lactose and GlcNAc without the addition of cofactors. Second, a de novo pathway was constructed in K. lactisfor producing LNB from lactose without adding GlcNAc. Finally, a transcriptional switch was introduced into K. lactisto reprogram its metabolic network for improving the flux from GlcNAc-6-P to GlcNAc in the de novo pathway. Subsequently, a final LNB yield of 10.41 g/L, similar to the salvage pathway yield, was achieved through the de novo pathway. The engineered K. lactisprovides a promising technology platform for the industrial scale production of LNB.

Flight data anomaly detection plays an imperative role in the safety and maintenance of unmanned aerial vehicles (UAVs). It has attracted extensive attention from researchers. However, the problems related to the difficulty in obtaining abnormal data, low model accuracy, and high calculation cost have led to severe challenges with respect to its practical applications. Hence, in this study, firstly, several UAV flight data simulation softwares are presented based on a brief presentation of the basic concepts of anomalies, the contents of UAV flight data, and the public datasets for flight data anomaly detection. Then, anomaly detection technologies for UAV flight data are comprehensively reviewed, including knowledge-based, model-based, and data-driven methods. Next, UAV flight data anomaly detection applications are briefly described and analyzed. Finally, the future trends and directions of UAV flight data anomaly detection are summarized and prospected, which aims to provide references for the following research.

The problem of the reachable set (RS) control of sliding mode control (SMC) for a class of singular systems with or without time-varying delay under zero initial conditions is studied. The purpose is to get an RS boundary containing all states of the system by designing an SMC. Firstly, singular systems with or without time-varying delay are decomposed into slow and fast subsystems by using the decomposition approach. Then, the augmented Lyapunov functional is built utilizing the decomposed state vector. The SMC is designed based on the exponential reaching criterion, resulting in the corresponding closed-loop control system (CLCS) construction. As a consequence, an RS criterion is constructed by employing the inequality scaling approach and the free-weighting matrix in conjunction with the linear matrix inequality (LMI). Finally, the validity and primacy of the results are provided by two numerical and practical examples. [ABSTRACT FROM AUTHOR]

To understand how malignant tumors develop, we tracked cell membrane, nuclear membrane, spindle, and cell cycle dynamics in polyploid giant cancer cells (PGCCs) during the formation of high-grade serous carcinoma organoids using long-term time-lapse imaging. Single cells underwent traditional mitosis to generate tissue with uniform nuclear size, while others formed PGCCs via asymmetric mitosis, endoreplication, multipolar endomitosis, nuclear fusion, and karyokinesis without cytokinesis. PGCCs underwent restitution multipolar endomitosis, nuclear fragmentation, and micronuclei formation to increase nuclear contents and heterogeneity. At the cellular level, the development of PGCCs was associated with forming transient intracellular cells, termed fecundity cells. The fecundity cells can be decellularized to facilitate nuclear fusion and synchronized with other nuclei for subsequent nuclear replication. PGCCs can undergo several rounds of entosis to form complex tissue structures, termed fecundity structures. The formation of PGCCs via multiple modes of nuclear replication in the absence of cytokinesis leads to an increase in the nuclear-to-cytoplasmic (N/C) ratio and intracellular cell reproduction, which is remarkably similar to the mode of nuclear division during pre-embryogenesis. Our data support that PGCCs may represent a central regulator in malignant histogenesis, intratumoral heterogeneity, immune escape, and macroevolution via the de-repression of suppressed pre-embryogenic program in somatic cells.

• The CNTs are CVD grown on the surface and inner space of graphene nanoscrolls to form unique caterpillar-like structure. • The caterpillar-like carbon materials as scaffold greatly improve the catalytic activity of active materials (MoSe 2). • The encapsulated ni nanoparticles of unique caterpillar-like structure can optimize the electron structure of the outmost loading (MoSe 2) by electron traversing effect. • Electron traversing effect and doping effect can be synergistically applied in elevating catalytic performance. Hydrogen is a clean and flexible energy carrier that has the promising to satisfy urgent demands of the energy crisis and environmental protection. Electrochemical hydrogen evolution reaction (HER), a critical half-reaction in water splitting, is one of the greenest and most common methods to obtain high-purity hydrogen. Designing preeminent activity and stability electrocatalysts for hydrogen precipitation reaction (HER) to reduce energy consumption is of great essential. 3D carbon-based materials have attracted widespread concern as the potential scaffolds of highly active and durable electrocatalysts for HER. To boost the HER activity and prolong the lifespan of electrocatalysts, multifarious 3D carbon architectures make an appearance to be engineered for accelerating electronic/mass transfer and maximizing the exposure of active sites. Herein, we designed and fabricated high-performance electrocatalysts based on a special caterpillar-like 3D graphene nanoscrolls@CNTs (GNS@CNTs) scaffold decorated with Co-doped MoSe 2 nanosheets for HER. In the caterpillar-like hierarchical structure, CNTs were seamlessly co-bonded and dilated the interlayer and outer spacing of GNS through CVD growth technology, and nickel nanoparticles were covered by the CNTs tips. Taking advantage of the plentiful hierarchical pore, larger specific surface area, and higher chemical stability of the caterpillar-like structure, the catalysts exhibited enhanced electrocatalytic properties than some existing data reported. Density functional theory calculations showed that the encapsulated nickel nanoparticle could tune the electronic structure of the outer anchored Co-doped MoSe 2 and optimize its ∆ G of H* adsorption by electron traversing effect and doping effect. These indicate that caterpillar-like GNS@CNT is an ideal scaffold for anchoring actives substance and is suitable for high-efficient HER. This study provides new insights for designing hierarchical carbon composite nanostructures for catalysts, sensors, energy materials, and other applications. [ABSTRACT FROM AUTHOR]

Edge computing has been envisioned as a key enabler to provide computation-intensive and delay-sensitive services in the future Internet of Things systems. By offloading the computational tasks to the edge server, both the service latency and energy consumption can be reduced. Since devices may request various types of computing services, caching appropriate services in the edge server to immediately provide computing resources can improve the quality of service. Nevertheless, it brings new challenges to jointly optimize the resource allocation, where the timeliness of caching and offloading operations are different. In this article, we first formulate the collaborative service caching and computation offloading as a dual-timescale resource allocation problem to minimize the costs of latency and energy consumption. Under this framework, a novel scheme based on hierarchical deep reinforcement learning is proposed to output collaborative caching and computing actions. Specifically, the proposed approach contains the service caching policy and the device computing policy with hierarchical action–value functions, which allows a flexible configuration of caching timescales. The simulation results demonstrate that the proposed policy outperforms the existing schemes on convergence performance and various parameters.

In this paper, the problem of real-time reachable set synthesis for a type of Takagi–Sugeno fuzzy uncertain singular systems founded on an adaptive event-triggered strategy is discussed. Firstly, based on the adaptive event-triggered strategy, a proportional and differential feedback controller is designed to transform the singular system into a normal system to ensure the admissibility of the system. Secondly, combined with Lyapunov stability theory, the sufficient conditions of reachable set boundary are given in the form of linear matrix inequality, and the stability of closed-loop control system is verified. In addition, the required real-time reachable set boundary data and controller gains are calculated using the matrix decoupling approach. Finally, an actual DC motor model is used in the simulation part to verify the efficacy of the presented approach.

The channel state information (CSI) needs to be fed back from the user equipment (UE) to the base station (BS) in frequency division duplexing (FDD) multiple-input multiple-output (MIMO) system. Recently, neural networks are widely applied to CSI compressed feedback since the original overhead is too large for the massive MIMO system. Notably, lightweight feedback networks attract special attention due to their practicality of deployment. However, the feedback accuracy is likely to be harmed by the network compression. In this letter, a cost free distillation technique named codeword mimic (CM) is proposed to train better feedback networks with the practical lightweight encoder. A mimic-explore training strategy with a special distillation scheduler is designed to enhance the CM learning. Experiments show that the proposed CM learning outperforms the previous state-of-the-art feedback distillation method, boosting the performance of the lightweight feedback network without any extra inference cost.

Internal waves (IWs) are a common characteristic of oceans and serve a crucial role in transmitting energies between large-scale tides and small-scale mixing. This study developed a deep-learning-based method for extracting IW signatures on multiple satellite imagery from synthetic aperture radar (SAR) and optical sensors in sun-synchronous and geostationary orbits with varying spatial resolution. We collected 1115 satellite images, including 116 Environmental Satellite (ENVISAT) advanced SAR (ASAR), 839 MODerate-resolution Imaging Spectroradiometer (MODIS), and 160 Himawari-8 Advanced Himawari Imager (AHI) images with clear IW signatures in the South China Sea (SCS), Sulu Sea, and Celebes Sea for model training. Considering the distinct IW characteristics under different imaging mechanisms, the specially tailored IW extraction network (IWE-Net) leverages three modifications to improve the accuracy and robustness: online data augmentation, squeeze and excitation blocks, and Matthews correlation coefficient loss. The overall mean precision, recall, and F1-score of the IWE-Net model are 85.75%, 85.67%, and 85.71%, respectively, demonstrating that the model is accurate for IW signature extraction. We also proved the transferability of our method to sea areas worldwide, long-term periods, and Sentinel satellite sensors completely independent of the model training. Globally, the numbers of IW images and extracted pixels show an obvious tidal-related double-peak distribution. Furthermore, we processed 15461 MODIS images in the northeastern SCS to present a holistic IW distribution map over the past 22 years. An unreported IW silent zone caused by drastic topography changes has been discovered, indicating the great potential of deep learning in information retrieval from remote sensing imagery.

An online trajectory planning method for collision avoidance is proposed to improve vehicle driving safety and comfort simultaneously. The collision-free trajectory for autonomous driving is formulated as a nonlinear optimization problem. A novel approximate convex optimization approach is developed for the online optimal trajectory in both longitudinal and lateral directions. First, a dual variable is used to model the non-convex collision-free constraint for driving safety and is calculated by solving a dual problem of the relative distance between vehicles. Second, the trajectory is further optimized in a model predictive control framework considering the safety. It realizes continuous-time and dynamic feasible motion with collision avoidance. The geometry of object vehicles is described by polygons instead of circles or ellipses in traditional methods. In order to avoid aggressive maneuver in the longitudinal and lateral directions for driving comfort, rates of the acceleration and the steering angle are restricted. The final formulated optimization problem is convex, which can be solved by using quadratic programming solvers and is computationally efficient for online application. Simulation results show that this approach can obtain similar driving performance compared to a state-of-the-art nonlinear optimization method. Furthermore, various driving scenarios are tested to evaluate the robustness and the ability for handling complex driving tasks.

In this article, a phased array antenna skin is proposed for conformally assembling scenarios and wide scanning applications. Planar inverted F antenna (PIFA) is used as a radiation element of the proposed array due to its low profile and omnidirectional radiation pattern. The PIFA element is conformally assembled in a wing of an aircraft and is designed and fabricated with the technology of composite material, which makes the PIFA element load bearing. The endfire beam synthesis is also investigated, and the proposed array supports wide-angle beam scanning in both forward and backward regions. An array of 240 PIFA elements are designed, simulated, fabricated, and experimentally tested. The fabricated sample is able to bear an overload of 1 g in the mechanical test, and all the electronic experiment results agree well with the simulation data.

Light scattered or radiated from a material carries valuable information on the said material. Such information can be uncovered by measuring the light field at different angles and frequencies. However, this technique typically requires a large optical apparatus, hampering the widespread use of angle-resolved spectroscopy beyond the lab. Here we demonstrate compact angle-resolved spectral imaging by combining a tunable metasurface-based spectrometer array and a metalens. With this approach, even with a miniaturized spectrometer footprint of only 4 × 4 μm2, we demonstrate a wavelength accuracy of 0.17 nm, spectral resolution of 0.4 nm and a linear dynamic range of 149 dB. Moreover, our spectrometer has a detection limit of 1.2 fJ, and can be patterned to an array for spectral imaging. Placing such a spectrometer array directly at the back focal plane of a metalens, we achieve an angular resolution of 4.88 × 10−3rad. Our angle-resolved spectrometers empowered by metalenses can be employed towards enhancing advanced optical imaging and spectral analysis applications.

With the increasing size of datasets in wideband spectrum sensing, high-resolution radar imaging and high-definition multimedia, real-time computation, and sample storage have become a big challenge. In the above-mentioned applications, signals are usually band limited and sparse, which has block sparse features in the frequency domain. In this article, we consider the block sparse feature and propose fast band-limited sparse signal reconstruction algorithms for big data processing. We first give the optimization strategy to reduce the complexity of reconstruction and propose the optimization theory for band-limited sparse signals with rigorous mathematical proofs. Then, two versions of band-limited sparse Fourier transform (BLSFT), i.e., BLSFT V1 and BLSFT V2, are designed based on the optimization theory. Both of the proposed algorithms are universal algorithms with sublinear computational and sample complexity. Numerical simulations confirm that BLSFT is superior to state-of-the-art algorithms in complexity and robustness. Finally, we discuss the application of BLSFT in real-time spectrum sensing of frequency hopping signals and demonstrate its feasibility via real-world experiments.