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Abstract Peroxyacetyl nitrate (PAN) is an important photochemical product and affects ozone (O3) formation in the troposphere. Yet, the long-term observation of PAN remains scarce, limiting the full understanding of its impacts on photochemical pollution. Here, we observed PAN from 2018 to 2022 in urban Fuzhou, Southeastern China. We found that, in contrast to upward trend of O3, PAN concentrations shown a significant decreasing trend at an average rate of −0.07 ppb/year. NO2, CO, UVB, and T contributed to the decreasing trend of PAN according to Machine learning analyses, while the effect of O3-represented atmospheric oxidation capacity on PAN was fluctuating from year to year. Chemical box model revealed active PA production and depletion in Fuzhou. Thus, despite the decreasing PAN concentration, PAN chemistry effectively promoted O3 formation by rising ROx levels, leading to increases of 2.18%–58.4% in net O3 production rate in different years. Our results provide valuable insights into the evolution of photochemical pollution in urban environments.

Abstract Tumor cells can survive when detached from the extracellular matrix (ECM) or lose cell–cell connections, a phenomenon known as anoikis-resistance (AR). AR is closely associated with tumor cell metastasis and recurrence, enabling tumor cells to disseminate, migrate, and invade after detachment. To address this issue, a novel intervention method combining intraoperative hemostasis with multifunctional nanozyme driven-enhanced chemodynamic therapy (ECDT) has been proposed, which holds the potential to weaken the AR capability of tumor cells and suppress tumor recurrence. Here, a nanocomposite containing a dendritic mesoporous nanoframework with Cu2+ was developed using an anion-assisted approach after surface PEG grafting and glucose oxidase (GOx) anchoring (DMSN-Cu@GOx/PEG). DMSN-Cu@GOx/PEG was further encapsulated in a thermal-sensitive hydrogel (H@DMSN-Cu@GOx/PEG). DMSN-Cu@GOx/PEG utilizes its high peroxidase (POD) activity to elevate intracellular ROS levels, thereby weakening the AR capability of bladder cancer cells. Additionally, through its excellent catalase (CAT) activity, DMSN-Cu@GOx/PEG converts the high level of hydrogen peroxide (H2O2) catalyzed by intracellular GOx into oxygen (O2), effectively alleviating tumor hypoxia, downregulating hypoxia-inducible factor-1α (HIF-1α) expression, inhibiting epithelial-mesenchymal transition (EMT) processes, and ultimately suppressing the migration and invasion of bladder cancer cells. Interestingly, in vivo results showed that the thermosensitive hydrogel H@DMSN-Cu@GOx/PEG could rapidly gel at body temperature, forming a gel film on wounds to eliminate residual tumor tissue after tumor resection surgery. Importantly, H@DMSN-Cu@GOx/PEG exhibited excellent hemostatic capabilities, effectively enhancing tissue coagulation during post-tumor resection surgery and mitigating the risk of cancer cell dissemination and recurrence due to surgical bleeding. Such hydrogels undoubtedly possess strong surgical application. Our developed novel nanosystem and hydrogel can inhibit the AR capability of tumor cells and prevent recurrence post-surgery. This study represents the first report of using dendritic mesoporous silica-based nanoreactors for inhibiting the AR capability of bladder cancer cells and suppressing tumor recurrence post-surgery, providing a new avenue for developing strategies to impede tumor recurrence after surgery. Graphical Abstract

ObjectiveAiming at the urgent need for research on vulnerability analysis methods for ship targets, a vulnerability analysis method for supply ship targets under anti-ship missile strikes is proposed.MethodsTaking a typical supply ship target as the research object, anti-ship missiles are selected as the strike weapon, and the structure-activity relationship of the target is analyzed. The dynamic response of a supply ship under the action of anti-ship missile internal explosion load is obtained through numerical simulation. The damage and damage criteria of key components under the action of anti-ship missile internal explosion load are studied, and the function damage degree of the supply ship is obtained. The target vulnerability of the supply ship is analyzed, and the attack method that causes the maximum damage to its navigation function is determined.ResultsA vulnerability analysis method flow is formed which includes an analysis of target structure-activity relationships, damage modes of key components, damage criteria and degree of target functional damage. Based on the vulnerability distribution information, the target should be targeted at the location where non-redundant components are concentrated, achieving the goal of causing maximum range damage. ConclusionThe findings of this study can provide technical support for future research on ship target vulnerability.

ObjectiveThis paper studies the damage modes of targets simultaneously subjected to multiple damage elements by analyzing the damage characteristics of a cylindrical shell structure subject to a double underwater explosion (UNDEX). MethodsFirst, a finite element model of the cylindrical shell is established, then the arbitrary Lagrangian−Eulerian (ALE) method is used to investigate its damage characteristics when subject to UNDEX with single or dual explosion sources, and the damage effects are evaluated in terms of the structural fracture area, maximum vertical displacement and extent of plastic strain area, and the 500 kg TNT charge is calculated and analyzed. ResultsThe results show that when the standoff distance of dual explosion source is 1 m, the damage to the outer cylindrical shell is more serious when the standoff distance of dual explosion sources is equal to that of the single explosion source, but the damage to the inner cylindrical shell is less than that of the single explosion source. At the same time, with the increase in standoff distance of the two TNT charges, especially when the standoff distance is greater than 3 m, the damage effects on the cylindrical shell structure become weaker. ConclusionsBy calculating the damage to a cylindrical shell structure by UNDEX under different initial parameters, the general rules of damage to cylindrical shell structures by single and dual explosion sources are given, which can provide references for enhancing the power of underwater weapons.

SUMMARY: Avian influenza viruses (AIV) of the H3N8 subtype pose a significant threat to both the poultry industry and public health. This study aimed to develop and validate a highly specific polyclonal antibody targeting the neuraminidase (NA) protein from a novel H3N8 AIV, which exhibits tri-basic hemagglutinin cleavage sites and shares genetic proximity to recent human isolates. The NA gene of H3N8 AIV was cloned and introduced into E. coli BL21 and Rosetta competent cells to induce the recombinant protein expression. Optimization procedures, including IPTG concentration, time, and temperature, were implemented to enhance protein expression efficiency. Polyclonal antibodies were generated and validated through western blotting, indirect immunofluorescence assay (IFA), and indirect ELISA. As a result, the pET-32a-NA (N8) vector was successfully constructed. The expression of recombinant NA protein with a size of approximately 70 KDa was obtained and then optimized with a final IPTG concentration of 0.6 mM, at 27°C for 14 h. Western blotting and IFA analysis demonstrated that the prepared polyclonal antibody effectively and specifically bound to NA(N8) protein. The titer of the polyclonal antibodies reached 1:409600 by indirect ELISA. These results indicate the potential of these antibodies for the development of detection assays and biological studies required for H3N8 AIVs.

Abstract Channel state information (CSI), which is crucial for resource allocation and system performance in time division duplex (TDD) massive multiple‐input multiple‐output (MIMO) systems, is difficult to predict because of the channel's time‐varying nature. To overcome this limitation, a scheme for channel prediction combined with deep learning (DL) is proposed. The system uses a deep neural network (DNN) to interpolate channel estimates from a few received pilot signals and a recurrent neural network (RNN) to train through the current time and the recent historical channel estimates to predict the CSI while the channel is constantly varying. In the end, a mixed neural network of RNN and DNN, is called MRDNN. In addition, the proposed DL‐based method does not rely on the relevant feature information about the channel, such as internal characteristics and parameters of the channel itself or channel statistical information, which improves its effectiveness in practical applications. The results of the simulation show that the MRDNN‐based method is better than the existing methods, like traditional AR method and NL Kalman method, and also can be effective in improving the quality of channel prediction and the performance of the system under the dynamic change scenario of low mobility.

ABSTRACT: Since 2021, a novel strain of goose reovirus (GRV) has emerged within the goose farming industry in Guangdong province, China. This particular viral variant is distinguished by the presence of white necrotic foci primarily localized in the liver and spleen, leading to substantial economic losses for the poultry industry. However, the etiology, prevalence and genomic characteristics of the causative agent have not been thoroughly investigated. In this study, we conducted an epidemiological inquiry employing suspected GRV samples collected from May 2021 to September 2022. The macroscopic pathological and histopathological lesions associated with GRV-infected clinical specimens were examined. Moreover, we successfully isolated the GRV strain and elucidated the complete genome sequence of the isolate GD21/88. Through phylogenetic and recombination analysis, we unveiled that the GRV strains represent a novel variant resulting from multiple reassortment events. Specifically, the μNS, λC, and σNS genes of GRV were found to have originated from chicken reovirus, while the σA gene of GRV exhibited a higher degree of similarity with a novel duck reovirus. The remaining genes of GRV were traced back to Muscovy duck reovirus. Collectively, our findings underscore the significance of GRV as a pathogenic agent impacting the goose farming industry. The insights gleaned from this study contribute to a more comprehensive understanding of the epidemiology of GRV in Southern China and shed light on the genetic reassortment events exhibited by the virus.

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space–air–ground–sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics, and unique problems of NS-COM network. Afterward, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management, and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring, and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite-to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output and holographic multiple-input multiple-output, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, stratospheric-satellite-based radar detection and imaging, NS-COM-assisted enhanced global navigation system, NS-COM-assisted intelligent unmanned system, and free-space optical communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.

In this paper, we present a double-intelligent reflecting surfaces (IRS)-assisted multiuser secure system where the inter-IRS channel is considered. In particular, we maximize the weighted sum secrecy rate of the system by jointly optimizing the beamforming vector for transmitted signal and artificial noise at the base station (BS) and the cooperative phase shifts of two IRSs, under the constraints of transmission power at the BS and the unit-modulus phase shift of IRSs. To tackle the nonconvexity of the optimization problem, we first convert the objective function to its concave lower bound by utilizing a novel successive convex approximation technique, then solve the transformed problem iteratively by applying an alternating optimization method. The Lagrange dual method, Karush–Kuhn–Tucker conditions, and alternating direction method of multipliers are applied to develop a low-complexity solution for each subproblem. Finally, simulation results are provided to verify the advantages of the cooperative double-IRS scheme in comparison with the benchmark schemes.

ABSTRACTInfluenza A viruses (IAVs) pose a serious risk to both human and animal health. IAVs’ receptor binding characteristics account for a major portion of their host range and tissue tropism. While the function of neuraminidase (NA) in promoting the release of progeny virus is well-known, its role in the virus entry process remains poorly understood. Studies have suggested that certain subtypes of NA can act as receptor-binding proteins, either alone or in conjunction with haemagglutinin (HA). An important distinction is that NA from the avian influenza virus have a second sialic acid-binding site (2SBS) that is preserved in avian strains but missing in human or swine strains. Those observations suggest that the 2SBS may play a key role in the adaptation of the avian influenza virus to mammalian hosts. In this review, we provide an update of the recent research advances in the receptor-binding role of NA and highlight its underestimated importance during the early stages of the IAV life cycle. By doing so, we aim to provide new insights into the mechanisms underlying IAV host adaptation and pathogenesis.

The coupling relationship between the deformation field, the diffusion field, and the seepage field is an important factor in fluid transport mechanisms in the long-term coalbed methane (CBM) exploitation process. A mathematical model of gas–water two-phase fluid–structure coupling in a double-porosity medium in coal reservoirs is established in this paper. Taking Hancheng Block, a typical production block in Qinshui Basin, as the geological background critical desorption pressure, reservoir permeability anisotropy is considered in the model. COMSOL Multiphysics (COMSOL_6.0) was used to create the model. The accuracy and rationality of the model were verified by comparing field production data with the results of the simulation. Using the simulation, the influence law of various reservoir geological characteristics parameters (Langmuir strain constant, ratio of critical desorption pressure to reservoir pressure of coal seam (CDPRP), elastic modulus, initial water saturation, Langmuir pressure, etc.) on CBM productivity, reservoir pressure, and permeability ratio was discussed, and a thorough analysis of the factors affecting productivity was obtained using the orthogonal test method. The findings of this study indicate that the change in permeability is the result of the superposition effect of many factors. Different stages of drainage have different primary regulating factors. Rock skeleton stress has a consequence on coal matrix permeability in the early drainage stage, and coal matrix shrinkage is primarily impacted in the later drainage stage. Besides the initial water saturation, other reservoir geological parameters (e.g., CDPRP, Langmuir volume, Langmuir strain constant, elastic modulus) have a strong relationship with productivity. When the value of coal geological parameters increases, the degree of productivity release is higher (as the initial water saturation increases, the production decreases correspondingly). Different coal and rock parameters have varying levels of impact on the drainage stage of CBM wells. The influences of the CDPRP, Langmuir volume, Langmuir strain constant, and elastic modulus on gas production are mainly concentrated in the initial and intermediate drainage stages and begin to fall off during the last drainage stage. Per the multi-factor analysis, the main coal–rock parameters affecting the productivity release are the Langmuir strain constant, followed by the CDPRP and other parameters. The analysis findings can offer theoretical guidance for CBM well selection and layer selection and enhance the block’s overall CBM development level. The improved productivity prediction model for CBM, which is based on fluid–structure coupling theory, can offer a new technical benchmark for CBM well productivity prediction.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

This article highlights challenges associated with securing visible light communication (VLC) systems by using physical layer security (PLS) techniques. Motivated by the achievements in PLS studies for radio frequency (RF) communication, many PLS techniques for VLC systems were also rigorously investigated by tailoring the RF techniques to the VLC environment. However, careful consideration of the inherent differences between RF and VLC systems is still needed. By disregarding these differences, an eavesdropper could be given an opportunity to wiretap the VLC systems, even when PLS techniques are employed to protect them. Crucially, the fact that it is often not possible to know the number and locations of eavesdroppers in real VLC systems may allow eavesdroppers to devise various cooperative eavesdropping methods. By examining a few examples of the possible eavesdropper threats that can occur in VLC systems, this article offers novel insights into the vulnerabilities of state-of-the-art PLS schemes for VLC systems. Although the focus of the paper is mostly on these weaknesses, some potential solutions are also briefly proposed with a view to stimulating discourse in the community.

In this paper, we propose a lightweight and adaptable trust mechanism for the issue of trust evaluation among Internet of Things devices, considering challenges such as limited device resources and trust attacks. Firstly, we propose a trust evaluation approach based on Bayesian statistics and Jøsang’s belief model to quantify a device’s trustworthiness, where evaluators can freely initialize and update trust data with feedback from multiple sources, avoiding the bias of a single message source. It balances the accuracy of estimations and algorithm complexity. Secondly, considering that a trust estimation should reflect a device’s latest status, we propose a forgetting algorithm to ensure that trust estimations can sensitively perceive changes in device status. Compared with conventional methods, it can automatically set its parameters to gain good performance. Finally, to prevent trust attacks from misleading evaluators, we propose a tango algorithm to curb trust attacks and a hypothesis testing-based trust attack detection mechanism. We corroborate the proposed trust mechanism’s performance with simulation, whose results indicate that even if challenged by many colluding attackers that can exploit different trust attacks in combination, it can produce relatively accurate trust estimations, gradually exclude attackers, and quickly restore trust estimations for normal devices.

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems.

In this paper, a machine-to-machine (M2M) communication system is proposed with joint M2M and cellular user equipment (CUE) device selection schemes to decrease the outage probability of the system. The machine devices and CUEs are positioned randomly according to a binomial point process (BPP), and two novel ordering metrics are proposed for the joint selection scheme: one based on the locations of the M2M devices and the other based on instantaneous channel gains. The simulation results confirm that the proposed selection scheme attains a significant reduction in the outage probability for M2M networks while limiting the interference to the base station (BS) by a delimited threshold. A hybrid-duplex BS is employed to switch between a half-duplex (HD) and a full-duplex (FD) to attain the best performance corresponding to various levels of residual self-interference. The closed-form formulas of the outage probability are derived for each of these ordering policies corresponding to different path loss exponents, and the analytical results are verified through Monte Carlo simulations. The proposed model and its related analysis is given in this paper lead the way for further work in the 5G Internet of Things (IoT) area.

This paper investigates a cooperative uplink system where two users wish to send messages to a base station with the help of a buffer-aided relay. Transmission modes in terms of both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) are considered. For the considered system, the probability for the user-to-relay channels to successfully perform NOMA is first theoretically derived in two scenarios of dynamic and fixed power controls (PCs) at users. Then, an efficient buffer-aided hybrid NOMA/OMA based mode selection (MS) scheme is proposed, which adaptively switches between the NOMA and OMA transmission modes according to the instantaneous strength of wireless channels and the buffer state. The state transmission matrix probabilities of the corresponding Markov chain is also derived, and the performance of the proposed hybrid NOMA/OMA scheme is analyzed with closed-form expressions, in terms of sum throughput, outage probability, diversity gain, and average packet delay. For both the dynamic and fixed PCs, the proposed scheme is proved to achieve a diversity gain of two when the buffer size is not smaller than three, which means that fixed PC will not lead to a loss of diversity gain.

Sparse code multiple access (SCMA) is a promising candidate air interface of next-generation mobile networks. In this paper, we focus on a downlink SCMA system where a transmitter sends confidential messages to multiple users in the presence of external eavesdroppers. Consequently, we develop a novel secure transmission approach over physical layer based on a highly structured SCMA codebook design. In our proposed scheme, we rotate the base constellations (BCs) with random angles by extracting channel phases from the channel state information. By employing randomized constellation rotation, the security of downlink SCMA can be ensured. In addition, a tight SCMA upper bound is introduced to guide the design of the encrypted codebook. As a result, we propose an approach to avoid the significant error rate performance loss caused by using codebooks that are designed using our method. The proposed upper-bound-aided codebook design scheme can select relatively good codebooks with low complexity. By combining SCMA codebook design and secure communication, our scheme ensures security for massive quantities of users with low encrypted and decrypted complexity at the cost of transmission rate and possible error rate performance loss. Moreover, the proposed scheme can achieve robustness against channel estimation errors. Analyses and Monte Carlo simulations confirm the effectiveness of our scheme.

This paper proposes a new iterative frequency domain equalization (FDE) algorithm for multiple-input multiple-output (MIMO)-frequency division multiplexing (GFDM) systems. This new FDE scheme is capable of enhancing the system fidelity by considering the complete frequency-domain second order description of the received signal. In addition, a new nulling filter design is also proposed for MIMO-GFDM systems to remove the residual interference, which further improves the system fidelity compared with the traditional scheme. Simulation results are presented to verify the effectiveness and efficiency of the proposed FDE algorithm.

In this article, the sum secure degrees-of-freedom (SDoF) of the multiple-input multiple-output (MIMO) X channel with confidential messages (XCCM) and arbitrary antenna configurations is studied, where there is no channel state information (CSI) at two transmitters and only delayed CSI at a multiple-antenna, full-duplex, and decode-and-forward relay. We aim at establishing the sum-SDoF lower and upper bounds. For the sum-SDoF lower bound, we design three relay-aided transmission schemes, namely, the relay-aided jamming scheme, the relay-aided jamming and one-receiver interference alignment scheme, and the relay-aided jamming and two-receiver interference alignment scheme, each corresponding to one case of antenna configurations. Moreover, the security and decoding of each scheme are analyzed. The sum-SDoF upper bound is proposed by means of the existing SDoF region of two-user MIMO broadcast channel with confidential messages (BCCM) and delayed channel state information at the transmitter (CSIT). As a result, the sum-SDoF lower and upper bounds are derived, and the sum-SDoF is characterized when the relay has sufficiently large antennas. Furthermore, even assuming no CSI at two transmitters, our results show that a multiple-antenna full-duplex relay with delayed CSI can elevate the sum-SDoF of the MIMO XCCM. This is corroborated by the fact that the derived sum-SDoF lower bound can be greater than the sum-SDoF of the MIMO XCCM with output feedback and delayed CSIT.

This paper investigates a full duplex wireless-powered two way communication networks, where two hybrid access points (HAPs) and a number of amplify and forward relays both operate in full duplex scenario. We use time switching (TS) and static power splitting (SPS) schemes with two way full duplex wireless-powered networks as a benchmark. Then, the new time division duplexing static power splitting (TDD SPS) and the full duplex static power splitting (FDSPS) schemes as well as a simple relay selection strategy are proposed to improve the system performance. For TS, SPS, and FDSPS, the best relay harvests energy using the received RF signal from HAPs and uses harvested energy to transmit signal to each HAP at the same frequency and time, therefore only partial self-interference (SI) cancellation needs to be considered in the FDSPS case. For the proposed TDD SPS, the best relay harvests the energy from the HAP and its self-interference. Then, we derive closed-form expressions for the throughput and outage probability for delay limited transmissions over Rayleigh fading channels. Simulation results are presented to evaluate the effectiveness of the proposed scheme with different system key parameters, such as time allocation, power splitting ratio, and residual SI.

In this paper, we analyze the outage performance of different multicarrier relay selection schemes for two-hop orthogonal frequency-division multiplexing (OFDM) systems in a Poisson field of relays. In particular, special emphasis is placed on decode-and-forward relay systems, equipped with bulk and per-subcarrier selection schemes, respectively. The exact expressions for outage probability are derived in integrals for general cases. In addition, asymptotic expressions for outage probability in the high signal-to-noise ratio region in the finite circle relay distribution region are determined in closed forms for both relay selection schemes. In addition, the outage probabilities for free space in the infinite relay distribution region are derived in closed forms. Meanwhile, a series of important properties related to cooperative systems in random networks are investigated, including diversity, outage probability ratio of two selection schemes, and optimization of the number of subcarriers in terms of system throughput. All analyses are numerically verified by simulations. Finally, a framework for analyzing the outage performance of the OFDM systems with spatially random relays is constructed, which can be easily modified to analyze other similar cases with different forwarding protocols, location distributions, and/or channel conditions.

Hydraulic fracturing is one of the important measures to increase production of oil and gas reservoirs. Pressure after the assessment of the existing technology is usually divided into direct and indirect diagnosis of hydraulic fracture. The diversity of evaluation results is due to the uncertainty and immaturity of the technology itself. Up to now, we have not found an economical and accurate hydraulic fracture evaluation method in the development and application of fracturing technology. In this paper, the pressure treatment technology after the comprehensive evaluation of boundary conditions is studied. By introducing the support vector regression theory, an evaluation model and a solution for correcting fracturing parameters are proposed. Fracturing parameters include fracture length, fracture height, fracture width, integrated fracturing fluid leakage coefficient, fracture conductivity, fracture closure pressure, and so on. For the optimization of various parameters, objective and scientific comprehensive evaluation results can be obtained by selecting different kernel functions. The results show that the model and method based on support vector machine are effective and practical.

In time-division duplex (TDD) distributed largescale multiple input multiple output (DM-MIMO) systems, the traditional downlink channel precoding method is used to resist inter-user interference (IUI). However, when the Channel State Information (CSI) is incomplete, the performance loss is serious, not only the bit error rate is high, but also the complexity of the traditional precoding algorithm is high. In order to solve these problems, this paper proposes an adaptive precoding framework based on deep learning (DL) for joint training and split application deployment. First, we train a channel emulator deep neural network (CE-DNN) to learn and simulate the transmission process of the wireless communication channel. Then, we concatenate an untrained precoding DNN (P-DNN) with a trained CE-DNN and retrain the cascaded neural network to converge. The last step is to obtain the P-DNN, namely the adaptive precoding network, by dismantling the joint trained network. Simulation results show that, when CSI is imperfect, the proposed method is compared with Tomlinson-Harashima precoding (THP) and block diagonalization (BD) precoding. The proposed method has a lower mean square error (MSE) and higher spectrum efficiency, as well as a bit error rate (BER) performance close to the THP. The source codes and the neural network codes are available on request. [ABSTRACT FROM AUTHOR]