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Abstract Two-dimensional magnets have been discovered recently as a new class of quantum matter exhibiting a broad wealth of exotic phenomena, including notably various topological excitations rooted in emergent exchange couplings between the localized magnetic moments. By analyzing the anisotropies in the single-ion magnetization and two-body exchange couplings obtained from first-principles calculations, we reveal coexistence of both giant Dzyaloshinskii–Moriya interaction and strong anisotropic XXZ-type biquadratic coupling in a recently predicted monolayer CrMnI6 magnet. The former is induced by the spontaneous in-plane inversion symmetry breaking in the bipartite system, the latter is inherently tied to the distinct high-spin state of the Mn sublattice, while the large magnitudes of both stem from the significant spin-orbit coupling. Next, we use atomistic magnetics simulations to demonstrate the vital role of Dzyaloshinskii–Moriya interaction in harboring topological bimeronic excitations, and show that the biquadratic coupling favors a Berezinskii–Kosterlitz–Thouless-like transition as the system reduces its temperature from the paramagnetic phase. These findings substantially enrich our understanding of the microscopic couplings in 2D magnets, with appealing application potentials.

Infant drowning has occurred frequently in swimming pools recent years, which motivates the research on automatic real-time detection of the accident. Unlike youths or adults, swimming infants are small in terms of size and motion range, and unable to send out distress signals in emergencies, which exerts negative effects on the detection of drowning. Aiming at this problem, a new step is initialized towards detecting infant drowning automatically and efficiently based on video surveillance. Diverse live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets. A part of the datasets is downscaled or enlarged to enhance generalization ability of the model. On this basis, advantages of Faster R-CNN and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in real-world. Supervised learning experiments are carried out, model test results show that mean Average Precision (mAP) of either Faster R-CNN or YOLOv5s of the series of YOLOv5 can be over 89%; the former can process merely 6 frames of videos per second with the precision of only 62.04%, while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%. The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance, which is of great application value to reduce the accidents in swimming pools.

As a globally invasive quarantine pest, the cotton mealybug, Phenacoccus solenopsis, is spreading rapidly, posing serious threats against agricultural and forestry production and biosecurity. In recent years, the niche conservatism hypothesis has been widely debated, which is particularly evident in invasive biology research. Identifying the niche dynamics of P. solenopsis, as well as assessing its global invasion risk, is of both theoretical and practical importance. Based on 462 occurrence points and 19 bioclimatic variables, we used n-dimensional hypervolume analysis to quantify the multidimensional climatic niche of this pest in both its native and invasive ranges. We examined niche conservatism and further optimized the MaxEnt model parameters to predict the global invasion risk of P. solenopsis under both current and future climate conditions. Our findings indicated that the niche hypervolume of this pest in invasive ranges was significantly larger than that in its native ranges, with 99.45% of the niche differentiation contributed by niche expansion, with the remaining less than 1% explained by space replacement. Niche expansion was most evident in Oceania and Eurasia. The area under the receiver operating characteristic curve (0.83) and true skill statistic (0.62) indicated the model’s robust performance. The areas of suitable habitats for P. solenopsis are increasing significantly and the northward spread is obvious in future climate change scenarios. North Africa, northern China, Mediterranean regions, and northern Europe had an increased invasion risk of P. solenopsis. This study provided scientific support for the early warning and control of P. solenopsis.

Maintaining temperature profile in a solid oxide fuel cell (SOFC) is one of the essential issues that need to be addressed during the load tracking. In this paper, a novel control scheme with three-loop structure is developed to mitigate SOFC temperature profile variation. This scheme can simply implemented by three variable-speed blowers. The first blower is an anode recirculation blower used to maintain anode inlet temperature, the second one is a cathode recirculation blower used to maintain cathode inlet temperature, and the last one is an air blower used to maintain cathode outlet temperature. Compared with other simple schemes with only one-loop or two-loop structure, the scheme with three-loop structure achieves the best performance in mitigating SOFC internal temperature profile variation and guaranteeing a safe margin on operation limits. With a 10% system load step drop, the three-loop control scheme presents the best performance in rapidity and stability, with a much smaller overshoot, a much shorter settling time, and the smallest ITAE criterion. The SOFC temperature profile variation is also smallest (deviating within 4.7 K). Time-dependent temperature gradient is much less than 3.0 K/min. Temperature gradient is significant less than 10 K/cm. Steam to carbon ratio is also kept more than 2.0. Furthermore, the three-loop control scheme can perform high system efficiency, which reaches 53.9% at the 90% load. As a result, the three-loop SOFC temperature control scheme is robust in safe and efficient operation.

Hydrogen can alleviate the increasing environmental pollution and has good development prospects in power generation due to its high calorific value and low environmental impact. The previously designed hydrogen-fired combined cycle ignored water recycling, which led to an inefficient application of hydrogen and the wastage of water. This paper proposes the concept of a hydrogen-fired combined cycle with water recovery to reuse the condensed water as an industrial heat supply. It was applied to an F-class combined cycle power plant. The results demonstrate that the efficiency of hydrogen-fired combined cycles with and without water recovery increased by 1.92% and 1.35%, respectively, compared to that of the natural-gas-fired combined cycle under full working conditions. In addition, an economic comparison of the three cycles was conducted. The levelized cost of energy of the hydrogen-fired combined cycle with water recovery will be 52.22% lower than that of the natural-gas-fired combined cycle in 2050. This comparative study suggested that water recovery supplementation could improve the gas turbine efficiency. The proposed hydrogen-fired combined cycle with water recovery would provide both environmental and economic benefits.

Turbine blades are crucial components exposed to harsh conditions, such as high temperatures, high pressures, and high rotational speeds. It is of great significance to accurately predict the life of blades for reducing maintenance cost and improving the reliability of gas turbine systems. A rapid and accurate blade life assessment method holds significant importance in the maintenance plan of gas turbine engines. In this paper, a novel on-line remaining useful life (RUL) prediction method for high-temperature blades is proposed based on 3D reconstruction technology and data-driven surrogate mode. Firstly, the 3D reconstruction technology was employed to establish the geometric model of real turbine blades, and the fluid–thermal–solid analysis under actual operational conditions was carried out in ANSYS software. Six checkpoints were selected to estimate the RUL according to the stress–strain distribution of the blade surface. The maximum equivalent stress was 1481.51 MPa and the highest temperature was 1393.42 K. Moreover, the fatigue-creep lifetime was calculated according to the parameters of the selected checkpoints. The RUL error between the simulation model and commercial software (Control and Engine Health Management (CEHM)) was less than 0.986%. Secondly, different data-driven surrogate models (BP, DNN, and LSTM algorithms) were developed according to the results from numerical simulation. The maximum relative errors of BP, DNN, and LSTM models were 0.030%, 0.019%, and 0.014%. LSTM demonstrated the best performance in predicting the RUL of turbine blades with time-series characteristics. Finally, the LSTM model was utilized for predicting the RUL within a gas turbine real operational process that involved five start–stop cycles.

In this paper, we consider a slot-controlled coded compressed sensing protocol for unsourced massive random access (URA) that concatenates a shared patterned Reed–Muller (PRM) inner codebook to an outer error-correction code. Due to the limitations of the geometry-based decoding algorithm in single-sequence settings and due to the message interference that may result in decreased decoding performance under multi-sequence circumstances, a list PRM projection algorithm and an iterative list PRM projection algorithm are proposed to supplant the signal detector associated with the inner PRM sequences in this paper. In detail, we first propose an enhanced path-saving algorithm, called list PRM projection detection, for use in single-user scenarios that maintains multiple candidates during the first few layers so as to remedy the risk of spreading errors. On this basis, we further propose an iterative list PRM projection algorithm for use in multi-user scenarios. The vectors for PRM codes and channel coefficients are jointly detected in an iterative manner, which offers significant improvements regarding the convergence rate for signal recovery. Furthermore, the performances of the proposed algorithms are analyzed mathematically, and we verify that the theoretical simulations are consistent with the numerical simulations. Finally, we concatenate the inner PRM codes that employ iterative list detection in two practical error-correction outer codes. According to the simulation results, we conclude that the packetized URA with the proposed iterative list projection detection works better than benchmarks in terms of the number of active users it can support in each slot and the amount of energy needed per bit to meet an expected error probability.

An accurate gas turbine performance model is important for system performance evaluation. To minimize the simulated performance error, an adaption method using coefficients of scaling factors is applied to tune the component characteristic maps and make the gas turbine model meet measurements of a few randomly sampled points. However, the field data are non-homogeneously distributed. In this situation, randomly selecting a few sampling may lead to the inappropriate correction of the component characteristic maps and lower the prediction accuracy of model. Firstly, the coefficients of scaling factors are introduced to construct the performance adaption optimization problem of the gas turbine model. Secondly, the k-means clustering algorithm is applied to divide the 146 field data points into 10 different categories, and then the points closest to the cluster centers are selected to form the sampling set. Thirdly, a particle swarm optimization algorithm is used to search the optimal scaling factors. As a result, the model error decreases from 2.947% to 0.610%. Finally, the proposed method is validated with the remaining field data of a real E-class gas turbine. The average predicted error is 0.466%. Compared with the performance results obtained by random sample, the model based on cluster sampling shows a better accuracy.

Abstract Background According to the Global Burden of Disease Study 2017, smoking is one of the leading four risk factors contributing to deaths in China. We aimed to evaluate the associations of smoking with all-cause mortality in a Chinese rural population. Methods Male participants over age 45 (n = 5367) from a large familial aggregation study in rural China, were included in the current analyses. A total of 528 former smokers and 3849 current smokers accounted for 10 and 71.7% of the cohort, respectively. Generalized Estimating Equations were used to evaluate the association between baseline smoking status and mortality, adjusting for pertinent covariates. Results There were 579 recorded deaths during the 15-year follow-up. Current smokers (odds ratio [OR],1.60; 95% CI,1.23–2.08) had higher all-cause mortality risks than nonsmokers. Relative to nonsmokers, current smokers of more than 40 pack-years ([OR],1.85; 95% CI,1.33–2.56) had a higher all-cause mortality risk. Compared to nonsmokers, current smokers who started smoking before age 20 ([OR],1.91; 95% CI,1.43–2.54) had a higher all-cause mortality risk, and former smokers in the lower pack-year group who quit after age 41 (median) ([OR],3.19; 95% CI,1.83–5.56) also had a higher risk of death after adjustment. Furthermore, former smokers who were also former drinkers had the highest significant risk of mortality than never smokers or drinkers. (P for interaction = 0.034). Conclusions This study provides evidence that current smokers and former smokers have a higher mortality risk than nonsmokers and would benefit from cessation at a younger age.

We propose a novel slot-pattern-control based coded compressed sensing for unsourced random access with an outer A-channel code capable of correcting t errors. Specifically, an RM extension code called patterned Reed–Muller (PRM) code is proposed. We demonstrate the high spectral efficiency due to its enormous sequence space and prove the geometry property in the complex domain that enhances the reliability and efficiency of detection. Accordingly, a projective decoder based on its geometry theorem is also proposed. Next, the “patterned” property of the PRM code, which partitions the binary vector space into several subspaces, is further extended as the primary principle for designing a slot control criterion that reduces the number of simultaneous transmissions in each slot. The factors affecting the chance of sequence collisions are identified. Finally, the proposed scheme is implemented in two practical outer A-channel codes: (i) the t-tree code and (ii) the Reed–Solomon code with Guruswami–Sudan list decoding, and the optimal setups are determined to minimize SNR by optimizing the inner and outer codes jointly. In comparison with the existing counterpart, our simulation results confirm that the proposed scheme compares favorably with benchmark schemes regarding the energy-per-bit requirement to meet a target error probability as well as the number of accommodated active users in the system.

Online sequential extreme learning machine (OS-ELM) has become a popular online learning strategy for single-hidden layer feedforward neural networks, and complex-valued signals are ubiquitous in real applications. In order to cater for complex-valued signals, especially for noncircular signals, in this paper we extend OS-ELM to complex domain, and propose two augmented online sequential complex ELM models by incorporating the conjugates of the network input and the hidden layer respectively. In this way, the proposed models are equipped with the capability to capture the complete second-order statistics of noncircular signals, which results in the enhanced generalization ability. The corresponding regularized models are derived to avoid the possible overfitting problem. By exploiting the algebra structure resulted from the augmented architecture, several approaches to reducing the computational complexity are also proposed. Simulation results validate the efficiency of the proposed algorithms.

This study proposes a construction method of deterministic measurement matrix based on a correlation criterion. On this basis, the authors investigated the massive multiple‐input and multiple‐output (MIMO) channel estimation algorithm using the complementary sequence as measurement matrix and obtained the compressed sensing (CS) signal model through the analysis on traditional massive MIMO channels. In this model, the measurement matrix plays the role of the pilot sequence in the traditional models. Then, the complementary sequence was determined as the measurement matrix for channel estimation. Meanwhile, the regularised orthogonal matching pursuit (ROMP), which realises accurate signal recovery through reselection of support set, was employed. Finally, the CS‐based channel estimation model was analysed, and the channel estimation results of different measurement matrices were compared through simulations. The results show that the complementary sequence outperforms the other two sequences when serving as a measurement matrix; the ROMP‐based channel estimation surpassed the orthogonal matching pursuit and matching pursuit algorithms in both computing accuracy and runtime.

In this paper, we demonstrate the impact of channel aging on the spectral- and energy-efficiency performance of the multi-pair two-way massive MIMO relay (MTW-MMR) systems, where massive antennas are deployed at the relay and the amplify-and-forward (AF) protocol is operated. By assuming the maximum-ratio combining/maximum-ratio transmission (MRC/MRT) or zero-forcing reception/ zero-forcing transmission (ZFR/ZFT) relaying processing, we analyze the spectral efficiency (SE) and energy efficiency (EE) of the MTW-MMR systems experiencing both imperfect channel estimation and channel aging based on the four proposed power-scaling schemes. Furthermore, we compare the performance of the full-duplex (FD)-assisted MTW-MMR systems with that of the half-duplex (HD)-assisted MTW-MMR systems, showing that the FD mode outperforms the HD mode with the interference level not exceeding -15 dB. Our studies show that channel aging may significantly degrade the achievable performance in terms of SE and EE. However, the inter-pair interference caused by the other user pairs and the self-interference can be effectively mitigated by the MRC/MRT processing or ZFR/ZFT processing at the relay.

As an efficient training strategy for single hidden layer neural networks, extreme learning machine, and its variants have been widely used due to its fast learning speed and superior generalization performance. However, when used for quaternion signal processing, extreme learning machine cannot make full use of the cross-channel correlation and quaternion statistics and thus often provides only suboptimal solutions. To this end, in this paper, we extend the extreme learning machine to quaternion domain and then propose two augmented quaternion extreme learning machine models for quaternion signal processing. These two models incorporate the involutions of the network input and hidden nodes, respectively, and can fully capture the second-order statistics of quaternion signals. In order to overcome the possible overfitting problem, two corresponding regularized augmented algorithms are also derived with the help of the generalized HR calculus. The superiority of the proposed models is verified by the simulation results.

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a fault diagnosis ontology model is built to support the automatic reasoning of the maintenance strategy from the mapped fault feature criteria individual. To enhance the ability of selecting a proper maintenance strategy, the ontology model represents more abundant knowledge from several sources, such as fault criteria analysis, physical structure analysis, FMECA (failure mode, effects, and criticality analysis), and the maintenance logic decision tool. The availability of the proposed hybrid driven method is verified by the field fault data from a real GE LM2500 PLUS gas turbine unit. The results indicate that the hybrid driven method is effective in detecting the path fault in advance. Furthermore, diversified fault information, such as fault effects, fault criticality, fault consequence, and fault detectability, could be provided to support selecting a proper maintenance strategy. It is proven that the data-knowledge hybrid driven method can improve the capability of the gas path fault detection, fault analysis, and maintenance strategy selection.