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This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.

According to the urgent low latency and the heavy computation tasks demands required for sixth‐generation (6G) wireless networks, the authors introduce the conventional resource allocation algorithms, including the game theory, artificial‐intelligence (AI) methods, and matching theory enabled framework, in which the multi‐access edge computing (MEC) scheme collaborative with the cloud platform to serve the primary users (PUs) and cognitive users (CUs) for next generation multiple access (NGMA). The proposed framework allows both the PUs and CUs to offload their computation tasks in a 6G‐enabled cognitive radio (CR) networks, so called cloud‐assisted CR‐MEC networks. In particular, the fundamentals of this conceived networks based on NGMA are first introduced. Hence, a number of methods based on the resource allocation algorithms are proposed in order to improve the quality of service for the mobile users, and reduce their transmission latency as well as the energy consumptions. Moreover, the motivations, challenges, and representative models for these conventional algorithms are described for integrated‐intelligent communication and computing aided NGMA networks. Furthermore, the open issues and future research directions for this conceived networks are summarised. The computational complexity of convolution methods is extremely high for real‐time computation works, hence the resource allocation would be a challenge for considering the latency‐critical application tasks. For the sake of tackling this problem, algorithm in terms of AI method might be considered as the solutions. The advantage of implementing the AI algorithm is training a learning model for the sake of getting the complex connections between the future task mobility and past task mobilities. In particular, AI method could learn online by interacting with the environment by comparing with the conventional algorithms, such as the convex optimization as well as the game theory method.

With the development of integrated sensing and communication (ISAC) systems, waveform design is currently attracting extensive attention. At the same time, subcarrier superposition can lead to the high peak-to-average power ratio (PAPR) problem in orthogonal frequency-division multiplexing (OFDM). Therefore, in this article, we investigate the low PAPR waveform design for OFDM-based ISAC systems. A weighted optimization problem with the constraint of zero integrated sidelobe level (ISL) is formulated with the aim of flexibly balancing between PAPR and communication performance and an alternating direction method of multipliers (ADMMs)-based algorithm is proposed to address this issue. Moreover, the nonlinear power amplifier is also considered to demonstrate the impact of PAPR on ISAC systems. Simulation results demonstrate that the proposed algorithm can effectively reduce the PAPR and achieve a performance trade-off between PAPR and communication performance.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

Federated learning (FL) embraces the concepts of targeted data gathering and training, and it can reduce many of the systemic privacy costs and hazards associated with traditional machine learning frameworks. However, with the low latency requirements of the sixth generation (6G) wireless communication networks and the Internet of Things (IoT) networks, the convergence delay of FL dramatically influences the overall system performance. In order to solve this urgent and challenging problem, in this paper, a joint client scheduling and wireless resource allocation algorithm is proposed, named SCSBA, which considers system heterogeneity, client heterogeneity, and the fairness of client participation to reduce the latency resulting from the heterogeneous communication conditions and computation capabilities among clients with the non identically independently distributed (Non-IID) data distributions. Specifically, the Stackelberg leader-follower game is first formulated in which the server decides the price of the single quota of participating in the FL process every communication round and the clients decide whether to participate in FL. Then the equilibrium solution of the game is derived and proved. In addition, a bandwidth allocation algorithm based on the covariance matrix adaptation evolutionary strategy (CMA-ES) is designed to minimize the time delay of each communication round. The simulation results verify the effectiveness of the proposed strategy for reducing the time latency of FL processes with heterogeneous clients, i.e., FedAvg and FedOpt.

This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intra-link errors. Based on the theoretical basis for cooperative communications with side information and the turbo principle, we design a joint source-channel coding algorithm to iteratively exchange the extrinsic information for enhancing the decoding gains at the destination. Surprisingly, simulation results indicate that even in bad channel conditions, SF relaying can still effectively improve the recovered information quality.

The primary objective of this article is to establish an analytical framework for evaluating the rate-distortion and the outage probability performance in Internet-of-Things (IoT) systems based on lossy cooperative wireless communications. Two correlated sources transmit information through fading multiple access channels (MACs) with the assistance of a bit-flipping (BF) helper. To begin with, we derive a closed-form expression of the inner bound on the achievable rate-distortion region. To reduce computational complexity, we then propose an approximate method for calculating the outage probability based on the lossy source-channel separation theorem. Moreover, Monte-Carlo methods are adopted to evaluate the outage probability in MAC and orthogonal transmission schemes. Theoretically, approximate results are also compared with the exact results obtained by Monte-Carlo methods. It is shown that the gap between the approximate and exact performance curves decreases as the distortion requirement becomes smaller. Especially when the distortion requirement reduces to zero, the approximated outage probability is exactly the same as the results obtained by Monte-Carlo methods. In addition, we also present performance comparisons in terms of the outage probability between Rayleigh and Nakagami-m fading, and between BF helper and optimal helper.

This letter proposes a transfer learning model for automatic modulation recognition (AMR) with only a few modulated signal samples. The transfer model is trained with the audio signal UrbanSound8K as the source domain, and then fine-tuned with a few modulated signal samples as the target domain. For improving the classification performance, the signal-to-noise ratio (SNR) is utilized as a feature to facilitate the classification of signals. Simulation results indicate that the transfer model has a significant superiority in terms of classification accuracy.

With the popularity of mobile devices with global positioning system (GPS), transportation network company (TNC) service has become an indispensable option of people's daily commute. However, it also provides opportunities for malicious parties to compromise TNC users’ location privacy. There are great challenges to preserve TNC users’ location privacy while improving the revenue of TNC and its quality of service (QoS). To address this issue, we propose a novel scheme to schedule the TNC vehicles while preserving the TNC users’ location differential privacy. Briefly, we add high dimensional Laplace noises to guarantee the TNC users’ geo-indistinguishability. Due to the differential private obfuscation, the demand for TNC vehicles in an area becomes uncertain. Thus, we employ the data-driven approach to characterize users’ demand uncertainty, formulate the TNC's revenue maximization problem into risk-averse stochastic programming, and provide corresponding feasible solutions. Using the released public data of Didi Chuxing, we conduct extensive simulations to evaluate the performance of the proposed scheduling scheme and compare the results under different $\zeta$ζ-structure metrics. The results show that the proposed scheme can efficiently schedule the TNC vehicles, maximize the TNC's revenue and provide a better service for TNC users while protecting the TNC users’ location privacy.

In this article, we address the problem of waveform design for bistatic multiple-input multiple-output dual-function radar-communication system with focuses on both the multi-user interference based symbol error rate (SER) of the communication side and the Cramér-Rao lower bounds (CRLBs) for direction-of-departure (DOD) and direction-of-arrival (DOA) of target from the radar side. Particularly, we minimize the maximal ratio of the error distances from desired symbols to the distances from undesired symbols, referred to as “discriminant” distances, to enhance the separability of received symbols, which in turn improves the SER. While we derive two CRLB constraints on DOD and DOA separately as the semi-definite programming formulations to avoid complex computation from fractional constraints via constructing two special positive semi-definite matrices. Moreover, the aforementioned “distance-similarity”-based metric is extended to the “direction-similarity”-based metric. Numerical results demonstrate the effectiveness of our proposed communication metrics and design solutions in terms of SER performance and robustness against non-linear phase-noise compared with the competing methods.

Intelligent reflecting surface (IRS) is applied in 5G and 6G to improve communication performance by reconfiguring signal propagation environments while maintaining low computational complexity and high energy efficiency. This paper proposes a novel methodology of joint active and passive beamforming optimization for wireless communications with the aid of multiple IRSs. In the considered system, the IRSs are deployed to assist the communication link from a multi-antenna access point (AP) to single-antenna users. We maximize the received signal to noise ratio (SNR) and sum rate of the multi-IRS-assisted communication system for single user and multiple users respectively, by solving a joint optimization problem of the active beamforming at the AP and the reflection beamforming at the IRSs. Specifically, we develop a mechanism of joint beamforming optimization for multi-IRS-assisted communications based on covariance matrix adaptation evolution strategy (CMA-ES), namely the BO-CMA algorithm, to jointly consider both the AP active beamforming and IRS reflection beamforming in the dynamic signal propagation environment. Furthermore, simulation results are provided to show that the BO-CMA algorithm is capable of significantly outperforming the benchmark schemes. Consequently, the solution based on the BO-CMA algorithm can dynamically rebuild the signal propagation environment, and improve the received SNR and the sum rate respectively for multi-IRS-assisted one user and multiple users communications.

This study assessed the efficacy, safety, pharmacokinetics (PK), and immunogenicity profiles of a denosumab biosimilar (LY06006) in Chinese postmenopausal osteoporotic women with a high risk of fracture.

This letter proposes a multi-agent deep reinforcement learning (MADRL) framework for resource allocation in air-ground integrated multi-access edge computing (MEC) networks, where unmanned aerial vehicles (UAVs) provide computing services in addition to ground-computing access points (GCAPs). For maximizing the computation efficiency, the resource allocation problem is formulated as the mixed-integer programming problems. Then, we develop a cooperative deep deterministic policy gradient (CODDPG) algorithm to solve the problem via an observable Markov game. The simulation results demonstrate that the proposed algorithm outperforms centralized reinforcement learning in terms of the computation efficiency.

Despite impressive progress in the research of all-conjugated diblock copolymers, there is a quite limited investigation into triblock counterparts. Moreover, regarding the block sequence, which is peculiar to triblock copolymers, its effect on their crystallization and microphase separation is still poorly understood. Herein, we design and synthesize a family of all-conjugated triblock copolymers comprising poly(3-butylthiophene) (P3BT), poly(3-hexylselenophene) (P3HS), and poly(3-dodecylthiophene) (P3DDT) of various block sequences, i.e., P3BT-b-P3DDT-b-P3HS, P3BT-b-P3HS-b-P3DDT, and P3HS-b-P3BT-b-P3DDT, elucidate the impact of the block sequence on their crystalline structures in conjunction by utilizing various solvents and thermal annealing, and investigate the charge transport properties of these different crystalline structures. Remarkably, the block sequence with the P3DDT block located in the central position (P3BT-b-P3DDT-b-P3HS) and faster solvent evaporation favor the cocrystals, while the P3DDT block situated at terminals (P3BT-b-P3HS-b-P3DDT and P3HS-b-P3BT-b-P3DDT), slower evaporation of the solvent, and heating lead to microphase separation over cocrystallization. Intriguingly, three different microphase-separated structures, which are correlated to the block sequence, are observed after high-temperature annealing (240 °C). Moreover, these various crystalline structures of triblock copolymers influence their charge mobilities in organic field-effect transistors (OFETs) closely. Thus, this work comprehensively elucidates the effect of the block sequence (intrinsic factor), solvent, and heating temperature (extrinsic factors) on the crystalline structure and electrical properties of conjugated triblock copolymers.

The microscopic explanation of the elongation variation in a Fe-0.5 wt.% Si alloy during electrically assisted forming (EAF) was investigated. Specimens were electrically stretched, measured with electron backscattered diffraction (EBSD). Results show that there is a positive linear relationship between material elongation and the ratio of maximum special grain boundary cluster size and grain size, while the maximum special grain boundary cluster size is closely related to the fraction of special grain boundaries and triple junction distribution. The effect of electric current on the fraction and characteristic distribution of special grain boundaries is significant and can be attributed to that electric current has an influence on dislocations and then recovery and grain growth. Cooling condition has a complex effect on special grain boundaries and grain size. The distribution of special grain boundaries in triple junctions during EAF does not conform to random distribution. The variation of material elongation during EAF is attributed to the change of microcrack propagation behavior caused by the change of special grain boundary cluster size and grain size.

Nucleic acid amplification enhancers suitable for use in a recombinase-aided amplification (RAA) assay were studied for the first time, and amplification of a long-fragment (509 bp) was initially explored.

This study mainly studied the coagulation–flocculation behavior and floc properties of composite bioflocculant (PAFC-CBF) to treat low turbidity surface water. Results showed that compared with PAFC alone, PAFC-CBF could improve turbidity removal efficiency from 87.50% up to 92.14%, and effectively enhanced the removal effect of UV254and TOC. Moreover, the distribution of [Al + Fe] species in PAFC-CBF showed that the content of [Al + Fe]bwas the best when mass ratios of m(PAFC):m(CBF) = 20 and pH was 7. In addition, CBF contributed to the increased growth rate of flocs and flocs size. Moreover, PAFC-CBF formed denser and more porous flocs with higher recovery factor, which was easy to recovery and re-growth after breakage. And the coagulation–flocculation synergistic effect of PAFC-CBF was achieved by combining adsorption charge neutralization and adsorption bridging.

BackgroundThe relationship between maternal education and adverse pregnancy outcomes is well documented. However, limited research has investigated maternal educational disparities in adverse pregnancy outcomes in China. This study examined maternal educational inequalities associated with adverse pregnancy outcomes in rural China.MethodsWe conducted a population-based cohort study using participants enrolled in the National Free Preconception Health Examination Project in Yunnan province from 2010 to 2018. The primary outcome was stillbirth, and the secondary outcome was adverse pregnancy outcomes, defined as a composite event of stillbirth, preterm birth or low birth weight. The study was restricted to singleton births at 20–42 weeks’ gestation. Univariate and multivariate log-binomial regression models were performed to estimate crude risk ratios (RRs) and confounding-adjusted RRs (ARRs) for stillbirth and adverse pregnancy outcomes according to maternal education level.ResultsA total of 197 722 singleton births were included in the study. Compared with mid-educated women, low-educated women were at a significantly increased risk of stillbirth (ARR, 1.20; 95% CI, 1.05 to 1.38) and adverse pregnancy outcomes (ARR, 1.11; 95% CI, 1.07 to 1.16). However, the risk of stillbirth (ARR, 1.16; 95% CI, 1.01 to 1.35) was significantly higher for high-educated women compared with mid-educated women.ConclusionCompared with women with medium education level, women with lower education level were more likely to experience adverse pregnancy outcomes, including stillbirth, and women with higher education level were more likely to experience stillbirth.

The intrinsic electric field in piezoelectric materials strongly controls the recombination dynamics, and choosing a specific piezoelectric material in designing a photocatalyst is of great interest. Herein, uniform BiOBr single-crystal micro–nanosheets with 2–8 μm side length and 100–300 nm thickness were suitably prepared using a hydrothermal method after optimizing the fabrication parameters. A near single-domain structure with reversible polarization switching was detected along with a typical butterfly-shape piezoelectric hysteresis loop, showing over 150 pm displacement under a ∼37 V bias voltage. Photoelectric characterization displayed almost a three times difference for the photoelectric anisotropy along and perpendicular to the plane, whereas the photoelectrochemical measurement indicated a low charge transfer resistance and a correspondingly efficient photoelectrochemical response under 365 nm laser, which are attributed to the significant increase in surface potential observed using Kelvin probe force microscopy and the potential internal field related to the piezoelectricity.

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A model predictive feedback control strategy based on time-varying efficiency is investigated and applied to a hydraulic hub-motor auxiliary system (HHMAS) in this paper. Adding HHMAS to a traditional heavy commercial vehicle can improve fuel economy and traction performance on roads with low adhesion coefficients. However, the hydraulic drive system experiences serious disturbance imposed by time-varying parameters and external conditions. Model predictive feedback control based on time-varying efficiency offers a solution for HHMAS to cope with the disadvantage of the hydraulic drive system and improve the environmental adaptability of the vehicle controller. In this study, the control law of hydraulic variable pump (HVP) target displacement is established based on temperature compensation in consideration of the influence of multiple factors on pump target displacement. For coordinated power distribution of HHMAS, the minimum wheel speed difference and the reduction in system impact are regarded as optimal control targets in adjusting the engine torque and HVP displacement and designing the model predictive controller. Simulation results show that the proposed model predictive control method can reduce the speed difference between front and rear wheels by up to 64% and can achieve the wheel speed following effect faster than the traditional proportional-integral-derivative algorithm. Given that the control parameters do not need to be calibrated in the proposed method, the calibration time is saved, and the actual development process of the hydraulic hub-motor driving vehicle is remarkably improved.

The stress–strain curves of a bainite steel have been measured with hot compression experiments at temperatures of 1173, 1273, 1373, 1473 K and strain rates of 0.01, 0.1, 1, 10 s−1on a thermo-mechanical simulator (Gleeble-1500). The processing maps, activation energy maps, and the overlap regions of dynamic recrystallization zones and flow stability domains have been obtained by utilizing the stress–strain data experimented. The feasible process windows have been determined according to a new criterion that these windows are in the overlap regions where the activation energy isoline is the sparsest with its variation no more than 1%. Moreover, the optimum temperature and strain rate for a given strain have been ascertained by the largest power dissipation efficiency within the feasible process windows.

Unmanned Aerial Vehicles (UAVs) enabled Aerial Base Stations (UABSs) have been studied widely in future communications. However, there are a series of challenges such as interference management, trajectory design and resource allocation in the scenarios of multi-UAV networks. Besides, different performances among UABSs increase complexity and bring many challenges. In this paper, the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated. In order to satisfy the signal to interference plus noise power ratio of users, each UABS needs to adjust its position and transmission power. Based on the interactions among multiple communication links, a non-cooperative Mean-Field-Type Game (MFTG) is proposed to model the joint optimization problem. Then, a Nash equilibrium solution is solved by two steps: first, the users in the given area are clustered to get the initial deployment of the UABSs; second, the Mean-Field Q (MFQ)-learning algorithm is proposed to solve the discrete MFTG problem. Finally, the effectiveness of the approach is verified through the simulations, which simplifies the solution process and effectively reduces the energy consumption of each UABS.

Mitochondrial DNA (mtDNA) variants influence the risk of late-onset human diseases, but the reasons for this are poorly understood. Undertaking a hypothesis-free analysis of 5,689 blood-derived biomarkers with mtDNA variants in 16,220 healthy donors, here we show that variants defining mtDNA haplogroups Uk and H4 modulate the level of circulating N-formylmethionine (fMet), which initiates mitochondrial protein translation. In human cytoplasmic hybrid (cybrid) lines, fMet modulated both mitochondrial and cytosolic proteins on multiple levels, through transcription, post-translational modification and proteolysis by an N-degron pathway, abolishing known differences between mtDNA haplogroups. In a further 11,966 individuals, fMet levels contributed to all-cause mortality and the disease risk of several common cardiovascular disorders. Together, these findings indicate that fMet plays a key role in common age-related disease through pleiotropic effects on cell proteostasis.

Conjugated rod–rod block copolymers (BCPs) are important semiconducting materials because they combine the unique microphase-separation characteristics of BCPs with the remarkable optoelectronic properties of conjugated polymers. The ability to tailor the two fundamental phase transitions (microphase separation and crystallization) in BCPs could enable efficient control over their physical and optoelectronic properties. Herein, a set of poly(3-butylthiophene)-block-poly(3-dodecylthiophene) (P3BT-b-P3DDT) BCPs with controlled block ratios are synthesized and the interplay between their microphase separation and cocrystallization is explored by tuning both the intrinsic (i.e., block ratios) and extrinsic factors (i.e., solvent and thermal annealing temperatures). An increased P3BT content, slower solvent evaporation, and higher thermal annealing temperatures favor microphase separation in P3BT-b-P3DDT. Furthermore, the relationship between various P3BT-b-P3DDT crystalline structures and their charge-transport properties is scrutinized. This work elucidates how P3BT-b-P3DDT BCPs undergo microphase separation and crystallization and how these processes can be tailored, strengthening our fundamental understanding of conjugated rod–rod BCP systems.

Despite recent progress in tailoring the molecular packing of donor–acceptor (D–A) copolymers in the solid state, studies on their solution-state aggregation and the dependence of the solid-state packing on solution aggregates remain to be clearly understood. Herein, we outline the relationship between the solution aggregates and solid-state crystal orentation in a specific class of D–A copolymers, i.e., 5,6-difluoro-4,7-bis(thiophen-2-yl)-2,1,3-benzothiadiazole (FBTT) units connected to bithiophene (2T) or thienothiophene (TT), by adjusting both the intrinsic (i.e., backbone planarity) and extrinsic factors (i.e., solvent) and investigate the influence of these structures with different crystal orientations on the organic field-effect transistor (OFET) performance. Remarkably, both PFBTT-2T and PFBTT-TT weakly aggregate in good solvents, forming face-on crystals in solid-state films. In contrast, they aggregate more strongly in relatively poor solvents and form edge-on crystals in films. When thin films are produced from the same solvent chlorobenzene (CB), PFBTT-2T with reduced planarity adopts face-on orientation, whereas PFBTT-TT with greater planarity adopts edge-on orientation. Moreover, the charge transport properties of both polymers strongly correlate with their various crystalline orientations. This work comprehensively elucidates how FBT-based polymers aggregate and pack in the solution and solid states and how these processes and electrical properties can be controlled by tuning the solvent quality and molecular structure.

In this letter, a learning-aided resource allocation scheme based on the constrained Markov decision process (CMDP) is proposed to improve the average network energy efficiency (EE) with the constrained quality of service (QoS) in the pattern division multiple access (PDMA)-based simultaneous wireless information and power transfer (SWIPT) system. In order to solve the formulated CMDP resource allocation problem, the Lagrange duality is adopted to transform CMDP into an unconstrained Markov decision process (MDP). Due to the instability of the practical system, the Deep Q Network (DQN)-based CMDP scheme is proposed to obtain the optimal solution. The simulation results verify the proposed scheme converges faster than the benchmark in terms of increasing average network EE.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, aiming to obtain higher classification accuracy, DL requires numerous training samples. In order to solve this problem, it is a challenge to study how to efficiently use DL for AMR in the case of few samples. In this letter, inspired by the capsule network (CapsNet), we propose a new network structure named AMR-CapsNet to achieve higher classification accuracy of modulation signals with fewer samples, and further analyze the adaptability of DL models in the case of few samples. The simulation results demonstrate that when 3% of the dataset is used to train and the signal-to-noise ratio (SNR) is greater than 2 dB, the overall classification accuracy of the AMR-CapsNet is greater than 80%. Compared with convolutional neural network (CNN), the classification accuracy is improved by 20%.

The cocrystallization method that combines various constituents into cocrystals yields the newly formed materials with significantly enhanced charge transport properties. However, this strategy has not been greatly utilized in all-conjugated block copolymers (BCPs). Herein, we scrutinize the relationship between cocrystals and charge mobilities in all-conjugated BCPs (i.e., poly(3-butylthiophene)-block-poly(3-hexylthiophene); denoted P3BT-b-P3HT) by tuning their molecular weights and thermal annealing process. All the rod–rod BCPs form cocrystals with high charge mobilities than P3BT and P3HT homopolymers and P3BT/P3HT blend, imparting the cocrystal-facilitated charge transport because of the synergy of two conjugated components. Upon 150 °C treatment, their crystallinities increase and their charge mobilities at 15k, 18k, and 28k increase slightly. In contrast, P3BT-b-P3HT-12k shows decreased charge mobilities. It is due to the preferential increase of crystal size and order through the π–π stacking direction in the former while through the alkyl stacking direction in the latter. Intriguingly, when these P3BT-b-P3HT cocrystals experience two-step thermal treatment, P3BT-b-P3HT-12k retains its cocrystalline structure, while microphase separation of P3BT and P3HT occurs in P3BT-b-P3HT-15k, 18k, and 28k with different degrees. All P3BT-b-P3HT BCPs exhibit decreased charge mobilities. This study demonstrates the cocrystallization-promoted charge mobility in all-conjugated BCPs, which may facilitate their application in a wide range of optoelectronic devices.

In order to improve the energy efficiency (EE) in the underlay cognitive radio (CR)networks, a power allocation strategy based on an actor-critic reinforcement learning is proposed, where a cluster of cognitive users (CUs) can simultaneously access to the same primary spectrum band under the interference constraints of the primary user (PU), by employing the non-orthogonal multiple access (NOMA) technique. In the proposed scheme, the optimization of the power allocation is formulated as a non-convex optimization problem. Additionally, the power allocation for different CUs is based on the actor-critic reinforcement learning model, in which the weighted data rate is set as the reward function, and the generated action strategy (i.e. the power allocation) is iteratively criticized and updated. Both the CU's spectral efficiency and the PU's interference constrains are considered in the training of the actor-critic reinforcement learning. Furthermore, the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions. According to the simulation results, we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method, while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

Herein, we report a metal-free and step-economic synthesis of phenanthridines from 2-biarylmethanamines under mild conditions. The reaction involves iodine-supported intramolecular C–H amination and oxidation of 5,6-dihydrophenanthridine under air and benign visible light. The mechanism study reveals that visible light plays a key role in both these steps.

Reconfigurable intelligent surface (RIS) has been proposed as a potential solution to improve the coverage and spectrum efficiency for future wireless communication. However, the privacy of users' data is often ignored in previous works, such as the user's location information during channel estimation. In this paper, we propose a privacy-preserving design paradigm combining federated learning (FL) with RIS in the mmWave communication system. Based on FL, the local models are trained and encrypted using the private data managed on each local device. Following this, a global model is generated by aggregating them at the central server. The optimal model is trained for establishing the mapping function between channel state information (CSI) and RIS' configuration matrix in order to maximize the achievable rate of the received signal. Simulation results demonstrate that the proposed scheme can effectively approach to the theoretical value generated by centralized machine learning (ML), while protecting user' privacy.

The mycelial pellet is a microbial aggregator formed under certain conditions. Besides the ability to secrete metabolites and adsorb a large amount of pollutants, the mycelial pellet has the characteristics of high biological activity, fast sedimentation speed, and easy solid–liquid separation. The technology of the mycelial pellet has been considered to be a promising technology for biomass utilization. To date, the mycelial pellet has been extensively studied for the production of various valuable products. Applications of mycelial pellets for microalgal biomass harvesting and wastewater treatment have also received more attention in recent years. Nevertheless, there are still some difficulties in the practical application of mycelial pellet technology. This review focuses on the characteristics, formation mechanism, formation conditions, and application research of mycelial pellets. This study provides much knowledge and information on the application technology of mycelial pellets in water pollution control, microalgae biomass harvesting, and industrial fermentation production. Problems existing in the scientific research and the main development trends of mycelial pellets are presented.

In this letter, we investigate the information security problem for non-orthogonal multiple access (NOMA) networks against an external eavesdropper. Considering the overhead of the channel state information (CSI) feedback, we utilize the quantized CSI during the secure NOMA transmission, and derive the secrecy rate for each receiver by adopting the successive interference cancellation. In order to optimally allocate the transmit power, an optimization problem is formulated such that the minimum secrecy rate of the receivers is maximized. For the proposed scheme, we also analyze the secrecy outage and transmission outage probabilities, and their closed-form expressions are derived. In addition, the security-reliability tradeoff is analyzed. Numerical results are demonstrated to verify the analytical results.

Despite recent impressive advances in synthesis of all-conjugated diblock copolymers via facile quasi-living Grignard metathesis (GRIM) polymerization, it remains challenging to achieve well-defined all-conjugated triblock copolymers of interest. Herein, we report the judicious design and synthesis of a series of all-conjugated triblock copoly(3-alkylthiophene)s consisting of poly(3-butylthiophene) (P3BT), poly(3-hexylthiophene) (P3HT), poly(3-octylthiophene) (P3OT), or poly(3-dodecylthiophene) (P3DDT) in all 12 possible combinations. The effects of block sequences and the length of alkyl side chains on their cocrystallization and microphase-separated structures are investigated. Moreover, the correlation between different crystalline structures and charge mobilities in organic field-effect transistors (OFETs) is scrutinized. These rationally synthesized triblock copoly(3-alkylthiophene)s self-assemble into cocrystals with an edge-on orientation in as-cast and 150 °C-annealed samples. Remarkably, the combinations with the shortest alkyl side chains placed as the central block (i.e., P3HT-b-P3BT-b-P3OT, P3HT-b-P3BT-b-P3DDT, P3OT-b-P3BT-b-P3DDT, and P3OT-b-P3HT-b-P3DDT) exhibit higher film crystallinity and charge mobilities than the other two combinations where the blocks with shortest alkyl side chains are located at two terminals. Intriguingly, upon thermal annealing near the melting temperature of these combinations, P3BT-b-P3OT-b-P3HT, P3BT-b-P3DDT-b-P3OT, and P3HT-b-P3DDT-b-P3OT can retain their cocrystalline structure while the other combinations are found to microphase separate. Finally, the all-conjugated triblock copoly(3-alkylthiophene)s with proper block sequence demonstrate stable charge mobilities at high temperatures and good flexibility in OFET devices. Taken together, this study elucidates that the block sequence is of key importance on control of the crystallization and charge transport behavior of poly(3-alkylthiophene)-based triblock copolymers.

In order to improve the physical layer security of the device-to-device (D2D) cellular network, we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning. Due to the mobility of users, using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication. Therefore, in this paper, we utilize the Echo State Network (ESN) to select the transmit antenna and the Long Short-Term Memory (LSTM) to establish the D2D pair. The simulation results show that the LSTM-based and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.

Visible light-mediated cascade remote oxyfluoroalkylation of alkenes under mild conditions is developed for the first time. The key point of this transformation is the incorporation of alkene fluoroalkylation-initiated remote benzyl C–H bond activation via a 1,5-H shift in a highly controlled site-selective manner and Kornblum reaction with dimethyl sulfoxide as the oxidant. With this method, a broad array of fluoroalkyl groups were introduced into a double bond to produce 1,6-fluoroalkylated ketones at room temperature.

With rapid population growth and increased water pollution, the global water situation is deteriorating. Due to this risk, it is now crucial to efficiently remove organic contaminants from water. Metal-organic frameworks (MOFs), due to their unique qualities including adjustable porosity, large pore volume, and excellent adsorption and regeneration properties have recently gained significant scientific interest in the field of water treatment. The knowledge mapping, classification, synthesis, and design of MOFs are described in this review of the literature in order to clarify the advancement of MOFs in the removal of organic contaminants from aqueous solutions. To generate fresh concepts for the use of MOFs in the treatment of organic pollutants, the mechanism of adsorption of organic pollutants in MOFs is described, the application trends of MOFs for the treatment of organic pollutants are reviewed, and the bottleneck issues and application prospects of MOFs are compiled.

A transition-metal-free and environmentally friendly synthesis method for bromobenzothiazines through tandem C–H amination/bromination was reported. This reaction contains both intramolecular C–H amination and site-selective electrophilic bromination of arenes with NaBr as the bromo source, PhI(OAc)2or K2S2O8as the oxidant, and H2O as the only solvent.

Glucagon like peptide-1 (GLP-1) is produced to induce postprandial insulin secretion. Liraglutide, a full agonist of the GLP-1 receptor, has a protective effect on weight gain in obese subjects. Brown adipose tissue plays a major role in the control of energy balance and is known to be involved in the weight loss regulated by liraglutide. The putative anti-obesity properties of liraglutide and the cell signaling pathways involved were examined. Four groups of C57/BL6 mice fed with chow or HFHS diet were injected with either liraglutide or vehicle for four weeks. Western blotting was used to analyze protein expression. Liraglutide significantly attenuated the weight gain in mice fed with HFHS diet and was associated with significant reductions of epididymal fat and inguinal fat mass. Furthermore, liraglutide significantly upregulated the expression of brown adipose-specific markers in perigonadal fat in association with upregulation of AMPK-SIRT-1-PGC1-α cell signaling. However, elevation of brown fat markers in skeletal muscle was only observed in HFHS diet fed mice after liraglutide treatment, and AMPK-SIRT-1 cell signaling is not involved in this process. the anti-obesity effect of liraglutide occurs through adaptive thermogenesis and may act through different cell signaling pathways in fat and skeletal muscle tissue. Liraglutide induces beige fat development partially through the AMPK-SIRT-1-PGC1-α cell signaling pathway. Therefore, liraglutide is a potential medication for obesity prevention and in targeting pre-diabetics.

ABSTRACTDenitrifying bacterial communities play an important role in nitrogen removal. The diversity, abundance and distribution of denitrifiers can be applied in eutrophication assessment for the bay ecosystems. In this study, the composition and variation of cytochrome cd1-type nitrite reductase gene (nirS)-type denitrifiers in the sediments of Haizhou Bay were investigated by using a PCR-based cloning approach. A total of 156 valid sequences were grouped into 76 OTUs based on 97% sequence similarity. The nirSsequences were identified with their closest-matched sequences from sediments of estuary and eutrophied bay, indicating similarity of the denitrifying bacterial communities in similar coastal environments. Canonical correspondence analysis indicated that nitrogen nutrient and dissolved oxygen were the major contributing parameters for the diversity and spatial distribution of denitrifying bacteria of Haizhou Bay.

The stretchable conductive materials with adjustable electromagnetic interference (EMI) shielding performance and other multifunctional integrated applications are remarkably desirable and challenging in flexible electronics. However, the existing stretchable materials often suffer from the decay of EMI shielding performance during the stretching process, which limits their practical applications. Meanwhile, it remains a great challenge to construct materials with ordered porous frameworks as integrated shielding devices. Incorporating porous structure will generate abundant interior surfaces/interfaces, facilitating multiple reflections of incident electromagnetic waves (EMWs). Herein, we employ ultrasound method to modify liquid metal (LM) with carbon nanotubes (CNTs), and further utilize an UV in-situ polymerization method to fabricate CNT@LM/polyacrylamide/gelatin (LMCPG) dual network hydrogel elastomer. The LMCPG hydrogel elastomer possesses high strength and durability, and its tensile strength reaches 117 kPa at 1033% strain owing to the high strength of the rigid network and the beneficial deformation ability of the flexible network. Deformable CNT@LM droplets elongate randomly during stretching process, which facilitates establishing more conductive paths. The EMI shielding capacity of the stretched elastomer can be controlled, achieving an intelligent on-off behavior, which exhibits a 211% increase at 200% strain in EMI shielding effectiveness (SE) compared with the undeformed state. Moreover, the LMCPG elastomer shows a significant triboelectric performance, which can provide power supply for flexible electronic devices, achieving an uninterrupted monitoring of the human body motion by devices. Therefore, as a flexible electronic material, it can offer a facile solution to achieve intelligent multifunctional integrated EMI shielding materials.

With abundant resources and multiple electron transfer, V2O3is a potential anode material for sodium ion batteries (SIBs). However, dramatic volume changes and slow reaction kinetics during the charge/discharge process still limit its application. Herein, porous yolk-shell V2O3/nitrogen-doped carbon (V2O3/NC) spheres are produced by a simple solvothermal method combined with subsequent carbonization. In this hierarchical structure, ultra-fine nanoparticles as primary building unit facilitate ion/electron transport; unique yolk-shell structure relieves the volume strain improving the structure integrity; nitrogen-doped carbon enhances electrochemical conductivity; moreover, rational mesoporous structure accelerates electrolyte penetration and high specific surface area supplies abundant reaction sites. Based on the above merits, yolk-shell V2O3/NC spheres exhibit satisfactory sodium ion storage performance (129.5 mAh g−1at 5.0 A g−1after 12,000 ultra-long cycles). What's more, an assembled V2O3/NC//Na3V2(PO4)3full cell displays outstanding cycle performance (46 mAh g−1after 300 cycles at 0.2 A g−1), demonstrating its potential value for practical applications.

The P4P architecture is mainly composed of appTracker, iTracker and peer. The single appTracker manages sharing resources in the different ISP domains. Every peer registers with appTracker when joining the network, and then requests resources from appTracker. In this architecture, there is too much workload for the single appTracker, thus, the bottleneck problem often appears when the scale of the network is enlarged in this centralised structure. An improved design of P4P based on distributed tracker is proposed to solve the overload problem of single appTracker server. In the improved P4P system, a distributed tracker overlay network replaces the appTracker to manage the resources in the different ISP domains. The functions of iTracker arranged by ISP are extended and the info interfaces of the iTracker are designed in detail in order to realise sharing the resources among the different iTrackers. The experiments prove that the P4P framework based on the distributed tracker can solve the server bottleneck problem and improve the scalability and stability while maintaining the characteristics of locality and transmission capacity.

Wireless sensor network (WSN) has proposed requirements more than just data collection and transmission. Different types of traffics call for different quality of service (QoS) demands. However, for the nature of uncertainty of wireless link and unpredictability of traffic transmission, it is very hard to distribute resources to different traffics precisely ahead of time. Based on the cascade self-tuning control, this study proposes the cascade self-tuning architecture for FD-MAC (CSFD-MAC) architecture. Taking the advantage of feedback theory, CSFD-MAC decouples contention window (CW) adaption and active time (AT) adaption into two separated cascading control loops. CW controller aims for the absolute node-to-node delay to a fixed value and AT controller guarantees the relative delay ratio. The software simulations illustrate the validity of controller design and the hardware experiments demonstrate the ability of CSFD-MAC in the absolute delay control as well as the proportional delay differentiation. More important, CSFD-MAC which takes AT as a controlled variable, not only has a much more superior performance in the metrics of delay and throughput compared with other QoS-aware MAC, but also has the advantage of efficient power management.

To recover the spectral efficiency (SE) loss caused by the half-duplex mode, this letter proposes a denoise-and-forward two-path successive relaying (DNF-TPSR) scheme, in which channel state information is not required. With differential binary phase-shift keying (DBPSK) modulation, the mixed signals received at the relay are mapped into a denoised symbol, and the destination uses the denoised symbol received in the previous time slot to cancel the inter-relay interference in the current time slot. We derive the end-to-end bit error rate (BER) and the diversity gain, which are validated by the simulation results. Although DNF-TPSR could not achieve the BER of half-duplex relay selection scheme, the SE is significantly improved by our proposed DNF-TPSR. Moreover, the BER and the SE of DNF-TPSR outperform that of direct transmission and full-duplex relaying with residual self-interference exceeding a certain level.

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas, such as mountainous areas. Therefore, the aerial networks are the key framework for the future communications, in which the aerial vehicle could act as the access point. Additionally, Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial networks. In this paper, we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing, power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks, namely CR-MEC aerial networks. Explicitly, this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation sub-problems. In the conceived CR-MEC aerial networks, we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory, aiming to minimize the system’s energy consumption. At the first step of UPCOMA, multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user pairs. Moreover, the stable user pairs would match to a high altitude platform who act as the base station, which is for appropriately allocating Transmission Time Slots (TSs) at the second step of UPCOMA. Additionally, a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual deadlines. Furthermore, the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low complexity. Meanwhile, the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

Information freshness is a key factor for Internet-of-Things (IoT) to make appropriate decisions and operations. This paper proposes an analytical framework for evaluating the timeliness performance of the IoT system based on Unmanned Aerial Vehicle (UAV) lossy communications. The performance analysis consists of the outage probability analysis and the Age-of-Information (AoI) analysis with outages. To begin with, we solve a lossy coding problem formulated from the UAV communication system, and derive a closed-form expression of the outage probability based on Shannon’s lossy source-channel separation theorem. Then, we characterize the Peak AoI (PAoI) for the considered system, and further minimize the PAoI by deriving the optimal rate for generating information. Moreover, we analyze the system performance through theoretical calculations and simulations. The results indicate that the optimal server utilization ratio is always no larger than 0.5. In practical applications, we can utilize the proposed analytical framework to determine the system parameters which guarantee the timeliness performance of UAV lossy communications.