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Copper (Cu) is a common heavy metal and a hazardous environmental pollutant. Emerging epidemiological evidence suggests that Cu exposure is associated with female infertility, especially ovarian dysfunction. However, the mechanisms underlying ovarian toxicity remain poorly understood. Granulosa cells play crucial roles in follicle development and are the main target cells of environmental pollutants for ovarian toxicity. In this study, we investigated the effects of Cu exposure on human granulosa (KGN) cells by using cell biology and metabolomics methods, and explored the molecular mechanisms of Cu-induced cytotoxicity. We found that Cu reduced cell viability in a dose- and time-dependent manner. Then, metabolomic analyses led to the identification of 279, 368 and 466 differentially expressed metabolites (DEMs) in KGN cells exposed to 10, 60 and 240 μM Cu, respectively. Pathway enrichment analysis revealed that high Cu led to disturbances of glutathione metabolism, nucleotide metabolism, glycerophospholipid and ether lipid metabolism. Using cell biological assays, we found that exposure to high Cu significantly decreased the GSH/GSSG ratio and altered the activities of the antioxidant enzymes SOD and CAT. Exposure to high Cu significantly increased the level of mitochondrial ROS. These findings further supported the results revealed by metabolomic analysis and provided clues for elucidating the mechanism by which Cu interferes with the development of ovarian follicles.

Objective: To evaluate the effect of subcutaneous teriparatide therapy on fracture healing rate and change in bone mass density in osteoporotic hip fractures. Method: The meta-analysis was done from September to December 2022, and comprise literature search on Wanfang, CNKI, VIP, PubMed, Embase, Cochrane Library, and Web of Science databases from the establishment of the respective database till December 2022. The relevant journals of the library of Macao University of Science and Technology, China, were manually searched for randomised controlled trials of teriparatide in the treatment of osteoporotic hip fractures. The shortlisted studies were subjectd to Cochrane Risk of Bias tool and the Jadad Rating Scale. Meta-analysis was done using the RevMan 5.4 software provided by the Cochrane Collaboration Network. Fracture healing rate and bone mineral density were the primary outcome measures, while mortality, adverse events, malformations, complications, subsequent fractures, timed-up-and-go test, visual analogue scale score, and procollagen type I N-terminal propeptide were the secondary outcome measures. Results: Of the 1,094 articles retrieved, 8(0.7%) randomised controlled trials were analysed. There were 744 patients; 372(50%) in the teriparatide group and 372(50%) in the control group. Fracture healing rate was not significantly different (p=0.82), while bone mineral density was significantly different between the groups (p0.05), while subsequent fractures, timed-up-and-go score, visual analogue scale score and procollagen type I N-terminal propeptide were significantly different between the groups (p

Person reidentification (ReID) is a challenging task of finding a target pedestrian in a gallery set collected from multiple nonoverlapping camera views. Recently, state-of-the-art ReID performance has been achieved via an end-to-end trainable deep neural network framework, which integrates convolution feature extraction, similarity learning and reranking into a joint optimization framework. In such a framework, the similarity is learned via an embedding network, the reranking is conducted with a random walk, and the whole framework is optimized with a cross-entropy-based verification loss. Unfortunately, the embedding net is difficult to train well because their two-dimensional outputs mutually interfere each other when using the conventional random walk. In addition, the supervision information has not been fully exploited during the training phase due to the binary nature of the verification loss. In this paper, we propose a novel approach, called group-shuffling dual random walks with label smoothing (GSDRWLS), in which random walks are performed separately on two channels-one for positive verification and one for negative verification-and the binary verification labels are properly modified with an adaptive label smoothing technique before feeding into the verification loss in order to train the overall network effectively and to avoid the overfitting problem. Extensive experiments conducted on three large benchmark datasets, including CUHK03, Market-1501 and DukeMTMC, confirm the superior performance of our proposal.

In this article, we consider a cognitive unmanned aerial vehicle network (CUAVN), which consists of a leading unmanned aerial vehicle (LUAV) and a group of following unmanned aerial vehicles (FUAVs), and where the FUAVs fly irregularly from a data collection point to the next data collection point and transmit collected data to the LUAV. Our goal is to maximize the achievable throughput of the CUAVN by jointly adapting the FUAVs sensing duration, the trajectory of the FUAVs, and the transmit power of the FUAVs under the constraints of maximum interference, maximum UAV speed, UAV collision avoidance and the total flying periods. In the challenging scenarios, the original non-convex problem is addressed by iteratively optimizing three decoupled subproblems: sensing duration optimization, FUAVs trajectory optimization, and FUAVs power allocation optimization. Since there are highly non-convex objective function and constraints in the FUAV trajectory optimization subproblem, we convert it into a more tractable convex problem by utilizing difference-of-convex (DC) functions. Furthermore, a three dimensional (3D) spatial-temporal sensing scheme based on UAVs is introduced into cognitive UAV systems with multiple primary transmit-receive pairs (PTRs) to further improve spectrum efficiency. Simulation results show that the achievable throughput of the FUAVs with the proposed scheme rises by approximately 53% compared with the FUAV fixed trajectory flight scheme and by approximately 58% in comparison with the only time-dimension spectrum sensing scheme.

Unsourced multiple access (UMA) is the technology for massive, low-power, and uncoordinated Internet-of-Things in the 6G wireless system, improving connectivity and energy efficiency on guaranteed reliability. The multi-user coding scheme design is a critical problem for UMA. This paper proposes a UMA coding scheme based on the T-Fold IRSA (irregular repetition slotted Aloha) paradigm by using joint Intra/inter-slot code design and optimization. Our scheme adopts interleave-division multiple access (IDMA) to enhance the intra-slot coding gain and the low-complexity joint intra/inter-slot SIC (successive interference cancellation) decoder structure to recover multi-user payloads. Based on the error event decomposition and density evolution analysis, we build a joint intra/inter-slot coding parameter optimization algorithm to minimize the SNR (signal-to-noise ratio) requirement at an expected system packet loss rate. Numerical results indicate that the proposed scheme achieves energy efficiency gain by balancing the intra/inter-slot coding gain while maintaining relatively low implementation complexity.

Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been proposed accordingly. In this paper, we present a new block algorithm based on CoSaMP, called block compressive sampling matching pursuit (BlockCoSaMP). Compared with CoSaMP algorithm, the proposed algorithm shows improved performance when sparse signal is presented in block form. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the performance of the CS algorithm, and Block restricted isometry property (Block RIP) is the extension of traditional RIP. Based on the Block RIP, we derive the sufficient condition to guarantee the convergence of Block-CoSaMP algorithm. In addition, the number of required iterations is obtained. Finally, simulation experiments show that with the increase of block length, the performance of Block-CoSaMP algorithm approaches to that of block subspace pursuit (Block-SP) algorithm. When the block length and sparsity are small, the performance of Block-CoSaMP algorithm is better than that of the CoSaMP, l2/l1 norm and block orthogonal matching pursuit (BOMP) algorithms. Especially, when compared with CoSaMP and l2/l1 norm algorithms, the proposed algorithm exhibits more obvious performance gain.

Wireless powered communication networks are promising in the next generation wireless communication systems since they can prolong the communication time of users. However, the wireless powered performance is limited due to the low energy harvesting efficiency caused by channel fading. In order to tackle this issue, rotary-wing unmanned aerial vehicle (UAV) enabled wireless powered communication network (WPCN) is first proposed to provide energy harvesting and information transmission to multiple ground users. Then, the energy consumption minimization problem for the UAV-enabled WPCN is formulated subject to the speed, initial and final positions of the UAV, the energy harvesting causality and user scheduling constraints. Finally, an efficient algorithm by employing the path discretization and successive convex approximation techniques is proposed to solve the challenging non-convex problem. Simulation results show that our proposed scheme can efficiently balance the energy harvesting phase and transmission phase based on the optimal trajectory design, moreover, it outperforms other benchmark schemes in terms of the energy consumption.

In the above article [1], the ordinate unit of Figure 9 was labeled incorrectly.

In this paper, a novel wireless energy harvesting cooperative jammer (EH-CJ)-aided transmission scheme, which can enhance the security for cooperative cognitive radio networks (CCRNs), is proposed. The EH-CJ first harvests energy from ambient radio frequency (RF) signals transmitted from the primary transmitter (PT). This energy is then used to transmit jamming noise (JN), so that the performance of eavesdroppers' receptions is degraded, and also to deliver additional power to the secondary energy receiver (ER). In this way, the proposed scheme can enhance the security and extend the lifetime for CCRNs. For this system, we formulate an optimization problem for maximizing the secrecy rate of the secondary system under transmit power constraints at the secondary transmitter as well as the EH-CJ, minimum harvested energy at the secondary ER and information rate at the primary receiver (PR). Since this optimization problem is not convex, we propose a two-stage optimization algorithm based upon the Charnes-Cooper transformation and rank-one relaxation to solve it. Computer simulation results have demonstrated that the proposed EH-CJ-aided scheme achieves better secrecy rate performance, as compared to an isotropic JN scheme as well as compared to an equivalent scheme which does not use the aid of EH-CJ.

In this paper, an ordered successive interference cancellation algorithm is proposed to realize efficient multi-user detection in interleave-division multiple access. In the proposed algorithm, the multiuser detection is implemented in the frequency domain and the bit detection results are obtained by de-spreading the chip detection results. Afterwards, the bit detection results are spread and interleaved to obtain regeneration soft chip results. The regeneration soft chip results are combined with the priori information provided by the channel decoder to obtain soft decision feedback symbols. In the proposed algorithm, the soft decision feedback symbols of detected users are exploited to cancel their interference to the subsequent un-detected users. In addition, the user detection order is optimized according to the signalto-interference-plus-noise-ratio. The simulation results show that the proposed algorithm can achieve better performance in both single-input-single-output and multiple-input-multiple-output scenario.

This paper studies a relay-assisted wireless-powered communication network (WPCN), where a hybrid relay node (HRN) broadcasts radio-frequency energy to energy-harvesting users and forwards these users' data to a base station in amplify-and-forward (AF) or decode-and-forward (DF) fashions. The joint time duration and power allocation optimization problems for both AF and DF relay-assisted WPCNs are investigated to maximize the energy efficiency of HRN, which are challenging to solve due to the nonconvexity and strong coupling of variables. For the DF relay-assisted WPCN, we propose an optimal time duration and power allocation algorithm by decomposing the original problem into three subproblems, i.e., time duration allocation subproblem for users' information transfer, time duration allocation subproblem for HRN's energy and information transfer, and HRN's power allocation subproblem. For the AF rely-assisted WPCN, we propose an iterative algorithm to optimize the transmit power and energy transfer time of HRN, by proving the quasi-concavity of the objective function where the optimal time duration allocation has been derived. The simulation results verify that the proposed algorithms can render significant energy efficiency gains, e.g., the performance gains of the proposed algorithms are about 300% and 150% compared with the existing schemes for the AF relay-assisted WPCN and DF relay-assisted WPCN, respectively, when the noise power is relatively large.

Gaussian approximation (GA) is widely used to construct polar codes. However, when the code length is long, the subchannel selection inaccuracy due to the calculation error of conventional approximate GA (AGA), which uses a two-segment approximation function, results in a catastrophic performance loss. In this paper, new principles to design the GA approximation functions for polar codes are proposed. First, we introduce the concepts of polarization violation set (PVS) and polarization reversal set (PRS) to explain the essential reasons that the conventional AGA scheme cannot work well for the long-length polar code construction. In fact, these two sets will lead to the rank error of subsequent subchannels, which means that the orders of subchannels are misaligned, which is a severe problem for polar code construction. Second, we propose a new metric, named cumulative-logarithmic error (CLE), to quantitatively evaluate the remainder approximation error of AGA in the logarithm. We derive the upper bound of CLE to simplify its calculation. Finally, guided by PVS, PRS, and CLE bound analysis, we propose new construction rules based on a multi-segment approximation function, which obviously improve the calculation accuracy of AGA so as to ensure the excellent performance of polar codes especially for the long code lengths. Numerical and simulation results indicate that the proposed AGA schemes are critical to constructing high-performance polar codes.

In this paper, the energy-efficient massive MIMO design problem is investigated in both single-cell and multi-cell scenarios with the aim of selecting appropriate parameters to maximize the overall system energy utilization. To this end, a more realistic parameter selection model is established by considering the channel estimation error and pilot contamination (PC). In the proposed model, we not only optimize the number of antennas, the number of users, and the transmission power to balance the improved radiated energy efficiency and the increased circuit power consumption, but also optimize the length of pilot sequences to make a desired trade-off between improving channel estimation accuracy and reserving more resources for data transmission. We first focus on the single-cell scenario, and strictly prove the quasi-concavity of the objective function in each dimension after suitable variable transformation, based on which an alternative optimization plus bisection searching (AO-BS) algorithm is proposed to solve the problem quickly. Then, the research is extended into the multi-cell scenario by increasingly considering PC and multi-cell interference. Since PC is related to the relative pilot length, i.e., the ratio between the length of pilot sequences and the number of users, the formulated problem is difficult to deal with directly. For effective solving, we divide the original problem into multiple subproblems according to the setting of pilot reusing factor, and employ the proposed AO-BS algorithm to solve each subproblem. Through numerical simulations, it is observed that the proposed AO-BS algorithm converges rapidly and is almost able to achieve the globally optimal solution. Meanwhile, from the perspective of energy efficiency maximization, massive MIMO is more preferable in the case of large cell coverage, and selecting a large relative pilot length is beneficial, always resulting in higher energy utilization in both single-cell and multi-cell scenarios.

An energy-efficient layered video multicast (LVM) scheme for “ bandwidth-hungry ” video services is studied in OFDM-based cognitive radio (CR) systems, where the video data is encoded into a base layer and several enhancement layers with the former intended for all subscribers to guarantee the basic quality of reconstructed video and the latter aiming at the quality improvement for the promising users with good channel conditions. Moreover, in order to balance user experience maximization and power consumption minimization, a novel performance metric energy utility (EU) is proposed to measure the sum achieved quality of reconstructed video at all subscribers when unit transmit power is consumed. Our objective is to maximize the system EU by jointly optimizing the intersession/interlayer subcarrier assignment and subsequent power allocation. For this purpose, we first perform subcarrier assignment for base layer and enhancement layers using greedy algorithm and then present an optimal power allocation algorithm to maximize the achievable EU using fractional programming. Simulation results show that the proposed algorithms can adaptively capture the state variations of licensed spectrum and dynamically adjust the video transmission to exploit the scarce spectrum and energy resources adequately. Meanwhile, the system EU obtained in our algorithms is greatly improved over traditional spectrum efficiency (SE) and energy efficiency (EE) optimization models.