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Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

In this article, an adaptive fuzzy tracking control scheme is developed for a class of uncertain switched nonlinear systems with input saturations and full-state constraints. First to surmount the design difficulty with respect to a saturation nonlinearity controller, a nonlinear smooth function approximating the nondifferential saturation function is introduced to establish a standard switched adaptive tracking control strategy based on the mean-value theorem and the input compensation technique. Then, invoking fuzzy-logic systems (FLSs), a novel analysis method of average dwell time for switched nonlinear systems with full-state constraints is proposed by using an artful logarithmic inequality. Furthermore, the designed adaptive controller can ensure that all the states of uncertain switched nonlinear systems are not to violate the set constraint bounds by employing barrier Lyapunov functions (BLFs), and that the system output tracking error can converge to a desired neighborhood of the origin within a suitable compact set. Finally, the simulation results are given to demonstrate the validity of the presented approach.

Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

Due to the influence of filters and multipath channels, the received signal of the current symbol will be affected by the interference of the previous symbol and the next symbol, the interference will reduce the performance of channel estimation, especially when the SF is relatively low, which will affect the system throughput rate. In this paper, based on BPSK chirp spread spectrum modulation system, we first describe the channel estimation of matched filtering and FFT, and derive the influence of ISI on the channel estimation of the current pilot symbol. Then we proposed the improved channel estimation based on the estimated time delay spread and transmitted signals. Finally, we simulate the MSE performance of channel estimation under different channels and different transmitted signals. The simulation results show that the MSE performance of the improved channel estimation can be improved under different channel when the SF is low, and higher throughput can be obtained.

SAPO-34, SAPO-5/34 based catalysts doped with Cu, Ce as active components were synthesized via a one-pot hydrothermal method by using different amounts of additive (α-cellulose), and their catalytic activities were measured for selective catalytic reduction (SCR) of NO with NH3. The synthesized Cu–Ce co-doped products switch from cubic SAPO-34, to flower-like aggregated SAPO-5/34, hybrid crystal SAPO-5/34, and finally to spherical aggregated SAPO-34 with the increase of α-cellulose amount. The Cu–Ce co-doped SAPO-5/34 hybrid crystal structure catalysts with 0.75 mol ratios of C/P (Cu–Ce/SP-0.75) exhibit excellent NH3-SCR activity with higher than 90% NOx conversion in the temperature range of 180–450 °C, at WHSV of 20000 mL/(g·h). Furthermore, the catalyst displays outstanding sulfur resistance and NOx conversion maintains above 90% at 200–450 °C after adding 100 ppm of SO2. The characteristic results suggest that the high deNOx performance of Cu–Ce/SP-0.75 is due to the enhanced accessibility, abundant activity species, excellent redox property and high adsorptive and activated capacity for NH3.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the location of warden is uncertain. In this paper, we propose a covert communication scheme against a warden with uncertain locations, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the limit of covert outage probability (the probability of the transmission found by the warden). First, we analyze the monotonicity of the covert outage probability to obtain the optimal location for the warden. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. First, we derive the transmit-to-jamming power ratio limit from the maximum allowed covert outage probability. With this constraint, the connection probability is maximized over the transmit-to-jamming power ratio for a fixed transmission rate. Since the connection probability and the transmission rate are coupled, the bisection method is applied to maximize the connectivity throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Purpose Best value procurement (BVP) has been recognized for some time as offering significant opportunities to advance process excellence in the construction sector. As an innovative approach to strategic procurement, BVP has attracted attention from the New Zealand (NZ) Government. It has similarly been found that the most substantial benefit of this modified approach to procurement is in value creation and innovative organizational processes through identifying “best value.” Yet to date, there is a lack of robust evidence as to how BVP can exactly influence construction innovation. Accordingly, this paper aims to explore how to improve BVP implementation to promote construction innovation and what are the values to be achieved in BVP mega projects from the view of innovation. Design/methodology/approach Semi-structured interviews with 22 participants, including project managers, procurement specialists, engineers and general managers from three organization types, were conducted to explore BVP implementation in a range of mega construction projects in NZ. Findings Barriers to BVP implementation and value innovation have been identified in this paper. Data analysis suggested traditional mindset in the procurement process, market constraints, mistrusts and fuzzy definition of BVP are the challenges for BVP implementation; BVP cultivates organizational competition because of diverse collaboration models and value attitudes; and BVP considers more values from the whole supply chain. To promote innovative construction, existing BVP should consider adopting progressive enhancements toward updating procurement guidance, encouraging effective communications, collaborating and promoting changes in stakeholders’ mindset. Practical implications Identified barriers to BVP implementation set up a platform for framing guidance, which could provide an effective approach as it enables a better understanding of what BVP means to NZ and what needs to be overcome. Taking this into account, similar small size markets around the world would be able to consider the applicability of BVP for innovative improvements. Originality/value This paper provides insights into value concepts in project procurement. It theoretically and practically possesses originality in linking BVP to innovative construction. The study of BVP and its application further reveals the importance of establishing a distinct regulation and fostering organizational competition from value aspects to achieve construction innovation.

In industrial sensor networks, complex industrial environments may be encountered leading to a mix of signals of different types. Complicated interference caused by mixed signals on industrial equipments may significantly degrade the classification rate of signals, which may result in a long training time in order to extract features. In addition, with limited channel resources, it is difficult to make the global optimal decision in industrial distributed wireless sensor networks. To address this problem, a signal classification method using feature fusion is proposed for industrial Internet of Things in this article. In the proposed method, the received signals of nodes are processed by frequency reduction and sampling pretreatment, based on which intelligent representations of signals are obtained. Using federated learning, the data samples are trained with the feature fusion network. Moreover, the trained deep learning network is used on each sensor node to classify signals, the results of which will be transmitted to aggregation center. In the aggregation center, the improved evidence theory method is used to aggregate the recognition results of each sensor node to achieve the final classification. Simulation shows that the proposed method has excellent classification performances. Notably, it is not required for the proposed method to transmit signals from nodes to the aggregation center, which could effectively protect the privacy of industrial information.

This paper concentrates on a fixed-time consensus issue for nonstrict nonlinear uncertain multi-agent systems (MASs) with state constraints and input delay. In comparison with previous works, the topic of full state constraints and input delay is first embodied in nonstrict MASs in a fixed time. A semi-global practical fixed-time stability (SPFTS) is employed to handle the consensus problem in this note. The radial basis function neural networks (RBFNNs) are developed to counteract unknown items in each agent. Pade approximation approach is introduced to cope with input delay. By using the backstepping technique, adaptive virtual controllers, adaption laws and the actual consensus controller are devised. And the rest followers can converge to a specified trajectory built by the leader in fixed time. Finally, a practical example is employed to test the correctness for the proposed control protocol.

PurposeIn the construction sector, the knowledge-based process outgrows its emphasis on technological aspects. Yet, there is a lack of applied studies showing how a procurement system (PS) could be selected in the digital age. In particular, there is a radical need to establish an innovative process to visualise novel PS decision. Therefore, this paper aims to present a knowledge visualised framework for aiding construction PS decision-making.Design/methodology/approachThis paper describes the construction of process innovation. The framework (process) is supported by four influential decision supporting methods (mean utility values, analytic hierarchy process, fuzzy set theory and Delphi method) and computer programming (Matlab).FindingsThere are four stages of this framework: (1) uniform rating for decision alternatives; (2) group decision for determining the decision attribute; (3) determining the final choice; (4) reporting the cognitive computing process. Supported by individual and groups decision dynamics, this framework emphasises how the dashboard aided innovative approach enables the induction of understanding, cognitive computing for decision-making and how the information would precisely be represented, which are vital requirements of modern construction.Originality/valueThe contribution of this paper presents two leverage points that support the modern PS decision. Firstly, this paper provides a holistic view of the decision supporting methods on the basis of how a suitable PS would be systematically sought. Based on the existing studies, this paper upgrades into a visualised knowledge decision supporting process. It helps the participants understand and improve their cognitive learning. Secondly, this framework allows the participants to have a view of the individual and group decisions. It sheds light on the development of the collaborative decision-making process.

In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain. There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained. The simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers’ pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost.

Developing supported catalyst and elucidating the catalyst activation mechanism are of significant importance for large scale synthesis of single-walled carbon nanotubes (SWNTs). In the work, we designed an alumina-supported iron (Fe–Al2O3) catalyst for catalytic growth of carbon nanotubes by CO chemical vapor deposition (CVD). In the temperature range of 800–1000 °C, the prepared powder catalyst only affords the growth of carbon fibers or multi-walled carbon nanotubes. In contrast, when placing the powder catalyst on flat SiO2 substrates, such as SiO2/Si and quartz, SWNTs were observed to grow at the catalyst-substrate interface, highlighting the contributions of the SiO2 substrate in SWNT synthesis. Systematic characterizations on both the catalysts and carbon nanotubes revealed that the SiO2 substrate promotes the reduction of iron oxide in the adjacent Fe–Al2O3 catalyst, facilitating the generation of small Fe particles at the catalyst-substrate interface, which act as the active phase for the subsequent growth of SWNTs. This work opens a new avenue for growing SWNTs from supported catalyst and sheds light on enhancing SWNT growth efficiency by tuning catalyst reduction behaviors.

This paper investigates a finite-time consensus issue for non-affine pure-feedback multi-agent systems with dead-zone input. Compared with the existing results on multi-agent systems, finite-time consensus problem of non-affine multi-agent systems is proposed for the first time. Based on the backsteppting technique, adaptive finite-time consensus control scheme is presented. With the help of this strategy, adaptive virtual variables, adaptive laws and the actual controller are designed to guarantee that the consensus errors converge to a small scale of the origin in finite time. Finally, a practical example is applied to verify the feasibility of the proposed method.

With the proliferation of mobile access services and wireless devices, spectrum resources are increasingly becoming scarce. Industrial wireless sensor networks may have to share frequency bands with other systems and suffer from considerable interference. To address that, industrial cognitive radio networks (ICRNs) were developed for effective spectrum sharing, where signal classification is a fundamental and important technology, especially for industrial wireless devices, which need to identify suspicious transmissions. In this article, a novel framework of signal intelligent classification is proposed based on deep learning networks in ICRNs. In the proposed framework, wireless signals will be preprocessed first by Choi–Williams distribution time–frequency analysis and represented by two-dimensional time–frequency images. Then, features of wireless signals are extracted through stack hybrid autoencoders (SHAEs). To accommodate general cases, we design multiple signal classification methods, including unsupervised, semisupervised, and supervised methods, which are processed by Softmax function, semisupervised linear discriminant function, and Fisher discriminant function, respectively. Finally, simulation studies are conducted and the corresponding simulation results show that the proposed framework is able to learn hierarchical features accurately and achieve excellent signal classification performance. Moreover, it can effectively overcome the negative impact caused by feature parameters uncertainty.

Doppler suppression techniques with angle-domain Doppler compensation target reducing the time fluctuations of time-varying channel for high-mobility massive MIMO communications. However, the existing Doppler suppression solutions suffer from high peak-to-average power ratio (PAPR). In this letter, we propose a PAPR reduction scheme for Doppler suppression, where an optimization problem is formulated to minimize the Doppler spread under a constraint of tolerable PAPR level. We further develop a suboptimal solution which decomposes the original joint optimization problem into two steps. First, an iterative Fourier technique is employed and the idea of basis expansion model (BEM) is proposed to reduce the computational complexity. Then, the antenna weighting vector is optimized which takes into consideration both Doppler spread and PAPR. Simulation results are provided to demonstrate that the proposed scheme can substantially outperform the existing schemes.

This article is concerned with the fuzzy adaptive tracking control problem for a class of uncertain nonaffine nonlinear systems with full state constraints and event-triggered inputs. First, based on the mean-value theorem and the input compensation method, the control input signal of nonaffine nonlinear term is successfully decoupled, and the remained compound nonlinear function can be approximated by fuzzy logic systems. Then, an improved event-triggered control strategy including the fixed threshold, the event-trigger control input, and the decreasing function of tracking errors, is given to update the actual control input. Correspondingly, the computation burden of the communication procedure is effectively alleviated. In addition, the developed control scheme guarantees that all the states of the closed-loop systems are not to violate the predefined compact sets by constructing barrier Lyapunov functions. Also, the system output tracking error can converge to a desired neighborhood of the origin. Finally, the validity of the proposed approach is demonstrated by the simulation results.

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the three-dimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

Identity-based attacks serve as the basis of an intruder’s attempt to launch security infringements in mobile health monitoring scenarios. Wireless channel perturbations due to the presence of human body are a relative phenomenon depending heavily on the subject’s dielectric properties. A new body-centric signature authorization (B-CSAI) approach based on time-frequency domain characteristics was proposed. This method utilizes multiple millimeter wave bands of 27–28 GHz, 29–30 GHz, and 31–32 GHz, thereby enhancing the security in body-centric communications exploiting benefits of subject specific channel signature. The proposed bornprint method is based on the intrinsic identity-related time-frequency domain information, which generated by the user’s natural hand motion signature and resulting creeping waves and space waves. It can meet the unconditional keyless authorization requirements. A detailed measurement campaign considering radiation efficiency ( $\bar {\eta }({\mathrm{ dB}}) = -25.8, -24.7, -26.4$ ), path-loss exponent, and shadowing factor in three millimeter wave bands, using six human subjects confirm the usability and efficiency of the proposed approach. This also shows that there is a wide space for realizing security from physical mechanisms.

Interest in the highly efficient energy hub (EH) model has been growing despite the high computational requirements of planning for a multi-energy, multi-device operation. To address both the device size limitation and the multi-scenario issue, we propose a new solution methodology for solving the EH planning problem. In the method, the decision variables are device sizes. First, a dimension reduction technique is proposed to address the curse of dimensionality based on the correlation of unknown variables such as the capacities of different devices in an EH. Second, to avoid local convergence, a solution method called the variable-sized unimodal searching (VUS) approach is proposed to assure a global optimal planning scheme for the one-dimensional non-convex optimization model obtained from the preceding dimension reduction process. The case study indicates that the proposed approach has a higher computing efficiency than the Benders decomposition (BD) algorithm to deal with a scenario-based stochastic planning problem with a large number of scenarios. Thus, the effectiveness of the EH planning approach is verified.

This paper is concerned with the problem of adaptive asymptotic tracking fault-tolerant control (FTC) for uncertain nonlinear systems with actuator faults and event-triggered inputs. Firstly, fault-tolerant controller is designed to effectively compensate the unknown actuator failures by constructing the proper parameter updated laws. Then, the event-triggered strategy based on the relative threshold method is applied to the proposed adaptive fault-tolerant controller. Furthermore, it is proved that all the closed-loop signals are uniformly bounded and the tracking error can asymptotically converge to zero. Finally, the simulation results are given to demonstrate the effectiveness of the presented control scheme.

Interest in electricity-gas demand response (EGDR) programs has been growing, especially with respect to Energy Hub (EH) systems, which are typically large industrial multi-energy users involving electric, heating and cooling loads. A key component in modeling EGDR is the electricity-shifting curve (ESC), which is usually obtained empirically. However, rigorous and systematic approaches to obtaining and understanding the ESC are lacking. In this article, a quantitative modeling approach is proposed based on the aggregated utility curve of multi-energy demands such as electricity, heating and cooling loads. First, an ESC based on consumer psychology and behavior is adopted. Second, utility curves of different loads (electricity, heating and cooling) are aggregated to a single utility curve, which is then combined with the theory of consumer choice to develop the ESC with regard to the price ratio of different energy sources. Third, various factors affecting the shifting curves are identified and then analyzed. Based on the case studies, generalized guidelines for improving the ESC are provided. Additionally, a multi-energy user with a higher heating-to-electricity ratio (HER) is verified as having better performance in obtaining a broader shifting area, and a multi-energy user with lower HER has a larger shifting amount in common range of price ratio. In summary, this work presents a quantitative and systematic approach to modeling the ESC in an EH and to understanding EGDR behaviors which can be used to improve EH response flexibility.

For future wireless networks, enormous numbers of interconnections are required, creating a multi-hop topology and leading to a great challenge on data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, it is still an open problem on the implementation of CoMAC in multi-hop wireless networks considering fading channel and resource allocation. In this paper, we propose multi-layer CoMAC (ML-CoMAC) by combining CoMAC and orthogonal communication to compute functions in the multi-hop network. Firstly, to make the multi-hop network more tractable, we reorganize it into a hierarchical network with multiple layers that consists of subgroups and groups. Then, in the hierarchical network, the implementation of ML-CoMAC is given by computing and communicating subgroup and group functions over layers, where CoMAC is applied to compute each subgroup function and orthogonal communication is adopted for each group to obtain the group function. The general computation rate is derived and the performance is further improved through time allocation and power control. The closed-form solutions to optimization problems are obtained, which suggests that orthogonal communication and existing CoMAC schemes are generalized.\ud

As a critical component in perovskite solar cells (PSCs), hole-transporting materials (HTMs) have been extensively explored. To develop efficient dopant-free HTMs for PSCs, a decent hole mobility (>10-3 cm2 V-1 s-1) is critically essential, which is, however, seldom reported. In this work, we introduce two novel donor-acceptor (D-A) type conjugated polymers (PDTPC-1 and PDTPC-2) with narrow bandgap unit, i.e., fused dithienopicenocarbazole (DTPC), as the donor building block and benzo[c][1,2,5]thiadiazole derivatives as the acceptors. The highly planar and strong electron-donating DTPC endows the polymers with superior hole mobility up to ∼4 × 10-3 cm2 V-1 s-1. Because of the better energy alignment with perovskite and excellent film-forming property, PSCs with PDTPC-1 as HTM show an appreciably enhanced PCE of ∼17% in dopant-free PSCs along with improved device stability as opposed to PDTPC-2. Our work revealed for the first time that the introduction of narrow bandgap DTPC in D-A polymers could achieve remarkably high hole mobility in the pristine form, favoring the application in dopant-free PSCs.

The electric vehicle with passive hybridization of fuel cells and supercapacitors leads to lower cost and compactness due to the absence of dc–dc converters. This article models such a vehicle and evaluates the energy efficiency of its powertrain system. The powertrain component losses, as functions of electric machine torque, speed and dc-link voltage, are modeled with a high level of detail, which are verified against available test data. Compared to a pure fuel cell system, the fuel cell efficiency is higher when supercapacitors are introduced under pulse current load, and it is higher at lower current amplitude. As the pulse current frequency increases, the fuel cell efficiency also increases due to higher proportional current from the high-efficiency supercapacitors. A multiplicity of drive cycles is selected, divided into a low, middle, and high-speed category to analyze the powertrain efficiency. The total powertrain energy efficiency varies between 53%–71% during propulsion for the studied drive cycles, whereas it is higher during braking ranging from 84% to 94%. The differences are closely related to the speed, acceleration, and dc-link voltage levels. The lower powertrain efficiency causes higher hydrogen consumption, leading to a reduced fuel cell efficiency at high speed, high acceleration, and low dc-link voltage.

Nanopore detection is a label-free detection method designed to analyze single molecules by comparing specific translocation events with high signal-to-noise ratios. However, it is still challenging to understand the influences of structural flexibility of 100 nm DNA origami on nanopore translocations. Here, we used solid-state nanopores to characterize the translocation of "nunchaku" origami structures, the flexibility of which can be regulated by introducing specific DNA strands and streptavidin protein. The structural changes can result in significant variations in the translocation signals and distributions. It is anticipated that such a method of the flexible DNA origami translocation through a solid-state nanopore will find further applications in molecular detection as well as biosensing.

Although the use of coordinated direct and relay transmission (CDRT) in non-orthogonal multiple access (NOMA) can extend the coverage, its duplicated transmission reduces the spectrum efficiency (SE) of NOMA. To improve the SE, we propose a spectrum-efficient scheme for NOMA-based CDRT over Nakagami- $m$ fading channels. In this scheme, the base station (BS) connects with a cell-center user (CCU) directly while communicating with a cell-edge user (CEU) via a relay and the CCU. Then, the relay and the CCU use network coding to process and retransmit the signals sent by the BS first and the CEU later. Finally, the BS and the relay simultaneously broadcast downlink signals. We derive the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE using both perfect and imperfect successive interference cancellation (SIC). Simulations verify the correctness of our theoretical analysis and the superiority of the proposed scheme in SE and EE.

Unmanned aerial vehicle (UAV) can be combined with non-orthogonal multiple access (NOMA) to achieve better performance. However, jointly optimizing the location, transmit power and decoding order for NOMA-UAV networks remains difficult, due to the change of decoding order as a result of UAV mobility. In this letter, a low-complexity scheme is proposed to maximize the sum rate of NOMA-UAV networks via updating decoding order, which can be decomposed into two steps. First, the joint location and power optimization can be divided into two non-convex sub-problems, which are further approximated via successive convex optimization. Then, the decoding order is updated according to the optimized UAV location. An iterative algorithm is proposed to execute the two steps alternately. In addition, the asymptotic performance is analyzed. Simulation results demonstrate the effectiveness of the proposed scheme.\ud

6G IoT networks aim for providing significantly higher data rates and extremely lower latency. However, due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices (IoDs) deployed, 6G IoT networks face two critical challenges, i.e., energy limitation and severe signal attenuation. Simultaneous wireless information and power transfer (SWIPT) and cooperative relaying provide effective ways to address these two challenges. In this paper, we investigate the energy self-sustainability (ESS) of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks. In the proposed strategy, the transmission process is equally divided into two phases. Specifically, in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy, respectively. In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy. We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation. Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.

In traditional power grid, the direction of power flow of transformers is usually fixed in one direction. However, with the integration of the renewable energy sources, such as wind farms, the power flow in the grid becomes fluctuating and uncertain, which may cause the reverse power flow passing the transformer. For example, excessive reverse power flow may lead to the increase of transformer power loss and challenge the thermal stability of the transformer. This paper investigates the impact of wind energy integration on the existing power system infrastructure, such as distribution transformers. Probabilistic power flow (PPF) method is used for the analysis. Firstly, a sampling method based on Halton sequence is applied for PPF calculation. Then, to ensure the accuracy of probabilistic power flow calculation, the correlation between wind speeds of different wind farms is considered. A 30-Bus system is used for the probabilistic analysis of the transformer reverse power flow with different level of wind penetration. The results show that the reverse power flow of transformer may occur before 100% wind penetration is achieved. The higher the total wind power capacity in the grid causes the greater probability of transformer reverse power flow. Also, the location of wind farms will also affect the probability of transformer reverse power flow.

In this work, an ultrasensitive aptasensor for the detection of Mucin 1 (MUC1) was presented based on the target-induced catalytic hairpin assembly combined with excellent mimic peroxidase performance of PtPd bimetallic nanoparticles (PtPdNPs). Traditionally, the cyclic reuse of target protein was achieved by protein conversion with enzyme cleavage or polymerization, which is costly and complex. However, in this work, it can be performed by simple strand displacement. In addition, PtPdNPs, a mimic peroxidase, was used a probe to catalyze the oxidation of tetramethylbenzidine (TMB) by H2O2, leading to the electrochemical signal amplification. With this ingenious design, the prepared aptasensor for MUC1 detection showed a favorable linear response from 100 fg mL−1 to 1 ng mL−1 and a relatively low detection limit of 16 fg mL−1. The proposed biosensor possessed acceptable stability, selectivity and reproducibility for MUC1 assay. Additionally, the fabricated aptasensor has been successfully applied to detect MUC1 in serum samples with satisfactory results. This new strategy supplied one efficient approach to improve signal amplification, which also open an avenue for sensitivity enhancement in targets detection.

High spectrum efficiency (SE) requirement and massive connections are the main challenges for the fifth generation (5G) and beyond 5G (B5G) wireless networks, especially for the case when Internet of Things (IoT) devices are located in a disaster area. Non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-aided network is emerging as a promising technique to overcome the above challenges. In this paper, an emergency communications framework of NOMA-based UAV-aided networks is established, where the disasters scenarios can be divided into three broad categories that have named emergency areas, wide areas and dense areas. First, a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas. Then, a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas. Furthermore, a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas. Simulation results are provided to validate the effectiveness of the above three schemes. Finally, potential research directions and challenges are also highlighted and discussed.

The efficient spectrum utilization of non-orthogonal multiple access (NOMA) and flexible mobility of unmanned aerial vehicle (UAV) enable NOMA UAV to become a prospective approach for future wireless networks. However, its secure transmission remains a great challenge. in this article, we summarize several potential solutions to mitigate adversarial eavesdropping toward aerial-ground transmission in NOMA-UAV networks, and propose three effective security schemes accordingly. First, a power allocation scheme is proposed at the UAV to ensure the privacy of a specific user with the sum transmission rate of others maximized. A closed-form solution is derived. Then, beamforming is leveraged to provide efficient privacy protection for multiple users in the network via jointly optimizing the precoding vectors to avoid eavesdropping and to maximize the spectrum efficiency. Finally, we perform artificial jamming together with beamforming to protect all the users in the network. Numerical results are provided to evaluate the superiority of the proposed schemes. Some future research issues and challenges of this direction are also pointed out.

Data aggregation is a promising technology in the Internet-of-Things (IoT) network for a wide range of applications, e.g., environmental monitoring, traffic control, and real-time surveillance. In order to meet the high requirement of transmission rate for data aggregation, we investigate the transceiver optimization to improve the spectral efficiency. As a tradeoff between the system complexity and performance, hybrid transceivers are adopted for data aggregation in the IoT network. We first present the optimal structures of digital precoders and unconstrained analog transceivers to maximize the spectral efficiency. Then, we propose two different kinds of iterative algorithms to optimize the analog transceivers under nonconvex unit-modulus constraints. The first algorithm is based on the framework of the alternating direction method of multipliers (ADMM). The second one is the steepest descent (SD) algorithm based on the Riemannian geometry, which has lower computational complexity than the first one. For both algorithms, closed-form solutions are derived in each iteration. Finally, numerical results demonstrate that the performance of the proposed algorithms in the hybrid transceiver design is very close to the fully digital solution but with less hardware complexity and power consumption.

Cellular traffic prediction plays an important role in network management and resource utilization. However, due to the high nonlinearity and dynamic spatial-temporal correlation, it is challenging to obtain the traffic prediction accurately. In this letter, a spatial-temporal attention-convolution network is proposed to predict the citywide cellular traffic. Considering the temporal correlation of the cellular traffic, the traffic data is modeled by the hourly, daily and weekly traffic components independently. In each traffic component, the spatial-temporal attention module is designed to capture the dynamic spatial-temporal correlation of cellular traffic; the spatial-temporal convolution module utilizes the graph convolution and standard convolution to obtain the spatial and temporal features simultaneously. By considering the external factors, the prediction of final mobile traffic is obtained. Experiment results indicate that the proposed approach outperforms the existing prediction methods on the real-world cellular traffic datasets.

This paper focuses on a class of nonlinear switching multi-agent systems about event-triggered adaptive tracking control. Compared with existing results, the event-triggered mechanism is first employed in nonlinear switched multi-agent systems with unknown parameters, and it can reduce the amount of computation and the frequency of the controller updates. By utilizing the backstepping technology, adaptive laws are designed to analyze the unknown parameters. In the sequel, all signals of multi-agent systems are bounded, the tracking errors converge to a small neighborhood of the origin. Finally, simulation results verify the effectiveness of the proposed scheme.

With the popularity of community question answering (CQA) sites, the research on identifying the expert users in online communities attracted increasing attention. We present a novel expert ranking algorithm based on the quality of user posts and the authority of user in community, and the similarity between the knowledge tags of users and questions in CQA sites is adopted in our scheme. Experimental results show that our scheme has better performance and accuracy under the same background with an amount of data samples.

Energy-limited devices and connectivity in complicated environments are two main challenges for Internet of Things (IoT)-enabled mobile networks, especially when IoT devices are distributed in a disaster area. Unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) is emerging as a promising technique to tackle the above problems. In this article, we establish an emergency communications framework of UAV-enabled SWIPT for IoT networks, where the disaster scenarios are classified into three cases, namely, dense areas, wide areas and emergency areas. First, to realize wireless power transfer for IoT devices in dense areas, a UAV-enabled wireless power transfer system is considered where a UAV acts as a wireless charger and delivers energy to a set of energy receivers. Then, a joint trajectory planning and resource scheduling scheme for a multi-UAVs system is discussed to provide wireless services for IoT devices in wide areas. Furthermore, an intelligent prediction mechanism is designed to predict service requirements (i.e., data transmission and battery charging) of the devices in emergency areas, and accordingly, a dynamic path planning scheme is established to improve the energy efficiency (EE) of the system. Simulation results demonstrate the effectiveness of the above schemes. Finally, potential research directions and challenges are also discussed.

Three different alumina/aluminum bi-layer armors having different striking faces, i.e., monolithic alumina, mosaic alumina, and mosaic alumina enhanced by aluminum honeycomb, were fabricated and tested under the impact of the flat projectile. The ballistic performance of each armor type was also investigated using three-dimensional (3D) finite element (FE) simulations. Upon validating the FE simulation results with experimental measurements, the ballistic limit velocity and failure mechanisms for each type of armor, as well as the influence of ceramic tile size, impact position, border effect, and inter tile gap width were quantified. For the enhanced mosaic armor, the metallic honeycomb lattice performed as a cellular skeleton to confine the ceramic tiles and fragments, leading to enhanced ballistic resistance. Besides, the honeycomb enhanced mosaic armor was also found to have much improved multi-hit ballistic resistance in comparison with monolithic and mosaic alumina. The honeycomb preserved the structural integrity of the mosaic armor so that a high level of residual ballistic resistance remained even after impact. With the extended reliability calculation method, single shot ballistic data were used to estimate the performance of the honeycomb enhanced mosaic armor under multiple projectile impacts.

Bioinspired and biohybrid micromotors represent a revolution in microrobotic research and are playing an increasingly important role in biomedical applications. In particular, biological micromotors that are multifunctional and can perform complex tasks are in great demand. Here, we report living and multifunctional micromotors based on single cells (green microalgae: Chlamydomonas reinhardtii) that are controlled by optical force. The micromotor's locomotion can be carefully controlled in a variety of biological media including cell culture medium, saliva, human serum, plasma, blood, and bone marrow fluid. It exhibits the capabilities to perform multiple tasks, in particular, indirect manipulation of biological targets and disruption of biological aggregates including in vitro blood clots. These micromotors can also act as elements in reconfigurable motor arrays where they efficiently work collaboratively and synchronously. This work provides new possibilities for many in vitro biomedical applications including target manipulation, cargo delivery and release, and biological aggregate removal.

In this paper, the adaptive event-triggered tracking control of uncertain nonlinear systems with dead-zone inputs is developed based on fuzzy approximation technique. Firstly, a robust fuzzy state ...

Non-orthogonal multiple access (NOMA) aided unmanned aerial vehicle (UAV) is becoming a promising technique for future wireless networks. However, its security remains a great challenge due to the line-of-sight in UAV communications and high transmit power for weak users in NOMA. Thus, in this paper, we propose a power allocation (PA) scheme for NOMA-UAV networks with circular trajectory, to maximize the sum rate of common users while guaranteeing the security for a specific user. To achieve this, we consider three cases based on the distance from the UAV to the secure user. Specifically, the lowest transmit power is assigned to the secure user in each time slot to guarantee its security, with the remaining power allocated to common users to maximize their sum rate. Due to the non-convexity of the problem, we analyze its monotonicity and derive the closed-form solutions for these three cases. To further improve the transmission rate of the secure user, we also derive the upper bound for its decoding threshold, and analyze the linear relationship between the secure decoding threshold and the sum rate of common users. Simulation results are demonstrated to evaluate the effectiveness of the proposed secure PA scheme in NOMA-UAV networks.

Non-orthogonal multiple access (NOMA) is one of the most significant technologies to meet the demand of high spectral efficiency (SE) in the fifth generation (5G) cellular networks. The utilization of simultaneous wireless information and power transfer (SWIPT) contributes to prolonging the battery life of the mobile users (MUs) and enhancing the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). In this paper, we study the achievable data rate maximization problem for the downlink multi-carrier NOMA (MC-NOMA) network with power splitting (PS)-based SWIPT, in which power allocation and PS control are jointly optimized with the limitation of available power budget as well as the requirement for EH. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of the coupled variables and the inter-user interference. To cope with the problem, a decoupled approach is developed, in which the power allocation and PS control are separated and the corresponding sub-problems are respectively solved through Lagrangian duality method. Furthermore, an alternative approach based on deep learning is proposed, which is capable of effectively obtaining the approximate optimal solution according to the empirical data. Simulation results confirm the effectiveness of the proposed schemes, and demonstrate the superiority of the combination of PS-based SWIPT with MC-NOMA over SWIPT-aided single-carrier NOMA (SC-NOMA) and SWIPT-aided orthogonal multiple access (OMA).

Purpose Most source recording device identification models for Web media forensics are based on a single feature to complete the identification task and often have the disadvantages of long time and poor accuracy. The purpose of this paper is to propose a new method for end-to-end network source identification of multi-feature fusion devices. Design/methodology/approach This paper proposes an efficient multi-feature fusion source recording device identification method based on end-to-end and attention mechanism, so as to achieve efficient and convenient identification of recording devices of Web media forensics. Findings The authors conducted sufficient experiments to prove the effectiveness of the models that they have proposed. The experiments show that the end-to-end system is improved by 7.1% compared to the baseline i-vector system, compared to the authors’ previous system, the accuracy is improved by 0.4%, and the training time is reduced by 50%. Research limitations/implications With the development of Web media forensics and internet technology, the use of Web media as evidence is increasing. Among them, it is particularly important to study the authenticity and accuracy of Web media audio. Originality/value This paper aims to promote the development of source recording device identification and provide effective technology for Web media forensics and judicial record evidence that need to apply device source identification technology.

A dual active-bridge (DAB) dc–dc converter is widely used in bidirectional high-power conversion, such as renewable energy system or train traction system. The open-circuit fault of DAB may threaten the system safety operation and should be eliminated in time. In this article, each fault mode of DAB is analyzed in detail. The transient operations and the load-carrying capacity are carefully analyzed. Based on the analysis, a DAB fault-tolerant method called “primary-side lower power secondary-side bypass arm” (PLP-SBA) is proposed. When the fault occurs in the primary side, the power of DAB is cut down, and when the fault occurs in the secondary side, the entire fault bridge arm is bypassed. The PLP-SBA method can ensure a certain load-carrying capacity after an open-circuit fault. A 5-kW DAB experimental platform is built to verify the correctness and effectiveness of the DAB mode analysis and PLP-SBA method.

Combination of unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) is deemed as an promising solution to achieving massive connectivity in future wireless networks. In this paper, a UAV-aided NOMA scheme is proposed to achieve simultaneous wireless information and power transfer (SWIPT) and guarantee the secure transmission for ground passive receivers (PRs), in which the nonlinear energy harvesting model is applied. Each time frame is divided into two phases. In the first phase, the received power at each PR is maximized to achieve rapid charging. In the second phase, SWIPT is performed via NOMA with the remaining energy at each PR, and artificial jamming is generated at UAV together with the NOMA information to guarantee the security. The throughput of PRs is maximized, with the highest received jamming power cancelled at each PR via successive interference cancellation (SIC). This disrupts the eavesdropping effectively by jamming without affecting the legitimate transmission. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and then propose iterative algorithms to solve them. Simulation results are presented to show the effectiveness of the proposed scheme.

In this article, the adaptive fault-tolerant tracking control problem of nonaffine stochastic nonlinear systems with actuator failures and full-state constraints is studied. To surmount the design difficulty from nonaffine nonlinear term with multi-input and single-output (MISO) faulty modes, a novel nonlinear fault compensation function with adjustable parameter factor is first introduced to establish a standard adaptive fault-tolerant control (AFTC) strategy based on the mean-value theorem. Then, the remaining nonlinear function, including the partial loss of effectiveness, outage, and stuck cases, together with the constructed compound nonlinear function can be approximated by using the suitable fuzzy-logic system (FLS). Moreover, it is shown that all the states of nonaffine stochastic nonlinear systems are not violating the preset constraint bounds by employing the barrier Lyapunov functions (BLFs). Also, the given adaptive controller can guarantee all the closed-loop signals are uniformly ultimately bounded (UUB) in probability in the sense of fourth-moment within the appropriate compact sets. Finally, two simulation examples are given to demonstrate the validity of the proposed method.

As the Internet of Things (IoT) is maturing and acquires its social flavor, the Social IoT enables smart devices to build inter-thing social networks without human intervention. As a new form of smart devices, unmanned aerial vehicles (UAVs) are finding their way into IoT applications. The integrated Social Internet of Flying Things (SIoFT) can provide the social-aware UAV-assisted services. However, the broadcast nature of air-to-ground (A2G) channels makes them vulnerable to being eavesdropped by terrestrial malicious users due to their strong line-of-sight (LoS) links. In this paper, we investigate to ensure the security of A2G communications when the location information of multiple potential eavesdroppers cannot be perfectly estimated. Following the “no pain no gain” principle, the terrestrial users who reuse the UAV cellular spectrum will act as friendly jammers to realize “win-win” situation. Hence, joint trajectory design, power control, and channel allocation optimization problem is formulated to maximize the average secrecy rate of UAVs in worst case. In the first stage, we utilize the block coordinate descent method and successive convex optimization method to solve the trajectory design and power control problems in an iterative manner. In the second stage, we convert the user pairing problem into a popular matching problem with externalities. Two distributed algorithms are proposed to maintain the popular matching under dynamics. Moreover, we conduct detailed analysis of the popularity, convergence, and computational complexity. Simulation results demonstrate the superiority of our proposed method in terms of different performance metrics.

In this paper, an event-trigger-based adaptive funnel control problem is considered for a class of pure-feedback nonlinear systems. Due to the nonaffine variables existed in the virtual controls of...