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Abstract Gain‐dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low‐temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point amplitude, which may result in increased power consumption to suppress noise‐induced spin state switching. To enhance the noise robustness of GIM, in this study a GIM based on a topologically defective lattice potential (TDLP) is proposed. Numerical simulations demonstrate that the TDLP‐based GIM can accurately simulate the bifurcation spin evolution in the Ising model. Furthermore, through the MAXCUT benchmark based on G‐set graphs, the optimal performance of TDLP‐based GIM is shown to surpass that of traditional GIMs. Additionally, the proposed TDLP‐based GIM successfully solves the MAXCUT benchmark and domain clustering dynamics benchmark based on G‐set graphs when the noise intensity exceeds its saturation fixed‐point amplitude. This indicates that the proposed system provides a promising architecture for breaking the small noise constraints required by traditional GIMs.

In actual engineering production, bearings typically operate in harsh environments. The fault features of bearing vibration signals are often submerged by background noise, making it difficult to extract the fault signal features and impacting the accuracy of fault diagnosis. To address this problem, this paper proposes a bearing fault diagnosis method based on the Savitzky-Golay Gramian Angular Field (GAF) with fault feature enhancement combined with ResNet. First, the acquired vibration signals are segmented, and the segmented signals are subjected to Butterworth high-pass filtering to obtain the high-frequency components of the signals that contain fault information. Secondly, the extracted high-frequency components are boosted by the S-enhancement algorithm for fault features. The boosted signals are then filtered by Savitzky-Golay to achieve data smoothing aggregation enhancement. Subsequently, the feature-enhanced GAF graphs are obtained using the transformation method. Finally, bearing fault diagnosis is performed using the Glamian Angle field diagram as input to the ResNet18 model. To verify the feasibility of the proposed method, experiments were conducted using Case Western Reserve University (CWRU) bearing fault dataset and bearing fault dataset of laboratory experimental platform. The experimental results showed that the fault diagnosis accuracy were 99.28% and 100%, respectively. The results validated the feasibility of the proposed method. Through comparative experiments with the Symmetric Dot Pattern (SDP) method, the traditional GAF method and the Recurrence Plots (RP) method, the results demonstrate that the proposed method has high diagnostic accuracy, proved the effectiveness of the method.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with pathological features of β-amyloid (Aβ) and hyperphosphorylated tau protein accumulation in the brain, often accompanied by cognitive decline. So far, our understanding of the extent and role of adaptive immune responses in AD has been quite limited. T cells, as essential members of the adaptive immune system, exhibit quantitative and functional abnormalities in the brains of AD patients. Dysfunction of the blood-brain barrier (BBB) in AD is considered one of the factors leading to T cell infiltration. Moreover, the degree of neuronal loss in AD is correlated with the quantity of T cells. We first describe the differentiation and subset functions of peripheral T cells in AD patients and provide an overview of the key findings related to BBB dysfunction and how T cells infiltrate the brain parenchyma through the BBB. Furthermore, we emphasize the risk factors associated with AD, including Aβ, Tau protein, microglial cells, apolipoprotein E (ApoE), and neuroinflammation. We discuss their regulation of T cell activation and proliferation, as well as the connection between T cells, neurodegeneration, and cognitive decline. Understanding the innate immune response is crucial for providing comprehensive personalized therapeutic strategies for AD.

Accurately detecting the depolarization QRS complex in the ventricles is a fundamental requirement for cardiovascular disease detection using electrocardiography (ECG). In contrast to traditional signal enhancement algorithms, emerging neural network approaches have shown promise for QRS detection because of their generalizability on complex data. However, the inevitable noise present during ECG recording leads to a decrease in the performance of neural networks. To enhance the robustness and performance of neural network-based QRS detectors, we propose a simulated degeneration unit (SDU)-assisted convolutional neural network (CNN). An SDU simulates the physical degeneration process of interfering optical pulses, which can effectively suppress in-band noise. Through comprehensive performance evaluations on three open-source databases, the SDU-enhanced CNN-based approach demonstrated better performance in detecting QRS complexes than other recently reported QRS detectors. Furthermore, real-world noise injection tests indicate that the optimal noise robustness boundary for the CNN equipped with SDU is 167–300% higher than that for the CNN without SDU.

Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University’s (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology.

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.

Abstract Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted Lu3Fe5O12 with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance.

Abstract Spin waves (SWs), an ultra-low power magnetic excitation in ferro or antiferromagnetic media, have tremendous potential as transport less data carriers for post-CMOS technology using their wave interference properties. The concept of magnon interference originates from optical interference, resulting in a historical taboo of maintaining an identical wavevector for magnon interference-based devices. This makes the attainment of on-chip design reconfigurability challenging owing to the difficulty in phase tuning via external fields. Breaking the taboo, this study explores a novel technique to systematically control magnon interference using asymmetric wavevectors from two different SW modes (magnetostatic surface SWs and backward volume magnetostatic SWs) in a microstructured yttrium iron garnet crossbar. Using this system, we demonstrate phase reconfigurability in the interference pattern by modulating the thermal landscape, modifying the dispersion of the interfering SW modes. Thus, we manifest that such a tunable interference can be used to implement reconfigurable logic gates operating between the XNOR and XOR modes by using symmetric and asymmetric interference, respectively.

Bearing failures are the most frequent causes of breakdowns in rotating machinery. Different levels of severity in these failures exhibit distinct fault characteristics in the vibration signal. This paper presents a bearing fault diagnosis method that considers different severity levels, involving the selection of statistics detection index symptom parameter and the application of canonical discriminant analysis (CDA). Initially, kurtosis is employed to detect abnormalities in the bearing. Subsequently, statistical analysis theory is utilized to extract efficient symptom parameters from the time domain and frequency domain vibration signals. As a statistical analysis method, CDA can discriminate between different signals by maximizing the between-group difference and minimizing the inter-group difference. By analyzing the distribution of CDA canonical scores, bearing faults can be intuitively diagnosed. The proposed method is validated using vibration signals obtained from an experimental bench with three different bearing conditions (normal, inner race fault, outer race fault) exhibiting varying severity levels. The results demonstrate the effectiveness and feasibility of diagnosing faults under different severity levels.

Adipose-derived stem cells (ADSCs) are employed as a promising alternative in treating cartilage injury. Regulating the inflammatory “fingerprint” of ADSCs to improve their anti-inflammatory properties could enhance therapy efficiency. Herein, a novel injectable decorin/gellan gum hydrogel combined with ADSCs encapsulation for arthritis cartilage treatment is proposed. Decorin/gellan gum hydrogel was prepared according to the previous manufacturing protocol. The liquid–solid form transition of gellan gum hydrogel is perfectly suitable for intra-articular injection. Decorin-enriched matrix showing an immunomodulatory ability to enhance ADSCs anti-inflammatory phenotype under inflammation microenvironment by regulating autophagy signaling. This decorin/gellan gum/ADSCs hydrogel efficiently reverses interleukin-1β-induced cellular injury in chondrocytes. Through a mono-iodoacetate-induced arthritis mice model, the synergistic therapeutic effect of this ADSCs-loaded hydrogel, including inflammation attenuation and cartilage protection, is demonstrated. These results make the decorin/gellan gum hydrogel laden with ADSCs an ideal candidate for treating inflammatory joint disorders.

ObjectivesTo address the problem of the fault diagnosis of ship propulsion shafting bearings, this paper proposes a visual diagnosis method based on a holographic symmetrical dot pattern (SDP) and similarity recognition. MethodsFirst, the bearing vibration signals are collected in three directions to comprehensively monitor the non-stationary changes in the time-domain and frequency-domain caused by the regular impact of the bearing faults. Second, based on SDP, multiple one-dimensional time-domain signals and spectrums are merged into a two-dimensional image to amplify the difference between different state signals. Finally, a simple bearing diagnosis is performed based on the similarity recognition method. ResultsThe results of engineering experiments show that this method can achieve the effective graphic fusion of multiple signals, fully display the characteristics of equipment signals and accurately diagnose faults. ConclusionsThe results of this study can provide valuable references for the visual fault diagnosis of ship propulsion shafting bearings.

Logical stochastic resonance (LSR) system is a physical system capable of performing robust reconfigurable logical operations in the presence of background noise using specific nonlinearities. Traditional LSR systems are typically based on polynomial nonlinearities, which make them suitable for implementation by electronic components. However, there has been little research on LSR systems based on hyperbolic nonlinearities which have the potential to be realized directly using the physical properties of materials. Inspired by the kink-bearing potential related to phonon nonlinear excitation, we propose an LSR system based on a hyperbolic one-site lattice (HOL) for the first time in this study. The performance of the HOL-based LSR (HOL-LSR) system is investigated under both noise-free and noisy conditions. Moreover, assisted by a bionic optimizer, we compare the performance of the proposed HOL-LSR and traditional quadstable LSR (QLSR) system under the influence of Gaussian white noise and Lévy pulse. The results demonstrate the existence of parameter-induced LSR effects in the HOL-LSR system. In addition, with only Gaussian white noise, the performance of the HOL-LSR and QLSR systems is comparable. However, when the background noise contains pulse components, the robustness of the HOL-LSR system is significantly stronger than that of the QLSR system. These results prove the superiority of HOL-LSR system over traditional LSR and encourage the construction of LSR systems directly based on material with hyperbolic property.

Abstract Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/Y3Fe5O12 bilayer structure. We demonstrated that the CoFeB/Y3Fe5O12 bilayer structure can create a sharp rejection band in its spin-wave transmission spectra. The lowest point of this strong rejection band allows the detection of a small frequency shift owing to the external magnetic field variation. Experimental observations revealed that such a bilayer magnetic sensor exhibits 20 MHz frequency shifts upon the application of an external magnetic field of 0.5 mT. Considering the lowest full width half maximum, which is about 2 MHz, a sensitivity of 10–2 mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10–6 T (µT) has been demonstrated using the sharp edge of the rejection band of the CoFeB/Y3Fe5O12 bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y3Fe5O12 and Y3Fe5O12 has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of $${10}^{-9}$$ 10 - 9 T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe.

Logical stochastic resonance (LSR) is a paradigm to realize reconfigurable robust Boolean operations using specific nonlinearity in the presence of background noise. The stable-state number of the traditional LSR is less than four, which restricts its upper limit in the heavy noise floor. To further improve the noise robustness and output quality of LSR, we proposed quadstable nonlinearity based LSR system for the first time in this work. Using the parameters determined by the ant lion optimizer, we compare the performance of proposed quadstable LSR (QLSR) and traditional tristable LSR (TLSR) as reconfigurable logic gates in the heavy noise floor. The results show that in the heavy noise floor with noise intensity fluctuation, the maximum noise intensity that the QLSR can withstand is significantly higher than the that of TLSR. When the heavy noise floor contains pulses, the pulse robustness of QLSR and TLSR is comparable. Nevertheless, the quantitative indexes reveal that larger stable-state numbers endow the QLSR a stronger ability to extract noise energy for enhancing logic information; thus, the output quality of QLSR is better than that of TLSR.

To obtain a high-precision bearing fault diagnosis of different speeds under intense background noise, a novel method called state-space principal component tracking filtering (SPCTF) is proposed for fault diagnosis. SPCTF can recover the fault information through the sequence of “Prediction – Measurement – Correction - Optimal estimation - Principal component extraction” from the polluted raw signals with noise. Firstly, the switch Kalman filter (SKF) is utilized to establish a dynamic filter model for time-series signals. And then, a feature tracking matrix is generated from the principal component features extracted from the optimal estimated state matrix of each time in the model. Thirdly, the principal component analysis extracts the effective fault feature from redundant information generated in the feature tracking matrix. Finally, the fault characteristic frequency is analyzed with the envelope spectrum to achieve a high-precision fault bearing diagnosis. The proposed method is demonstrated on signals from simulation signals and laboratory tests (several speeds), outperforming traditional methods.

Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.

Abstract Lemon is a fruit with delicious taste and attractive flavor, which has been widely accepted by people over the world. Dry lemon slice is a classical processed product of lemon. It is readily to be browning during storage. Which chemicals are responsible for the browning behavior remains unclear. Therefore, in this work, nuclear magnetic resonance spectroscopy is used to analyze the metabolites changes in dry lemon slice during storage. Nineteen metabolites were identified and their related levels were statistically calculated by principal component analysis. The first principal components were represented by glutamic acid, α‐amino‐butyric acid, γ‐amino‐butyric acid, alanine, limonoid, citric acid, glutamine, and phosphocholine. The changes of browning degree during storage and its correlations with the levels of polyphenol, ascorbic acid, amino acid nitrogen, and 5‐hydroxymethylfurfural were analyzed. The results indicated that Maillard reaction and oxidation of phenolics were two main processes contributing to the nonenzymatic browning during storage.

Superparamagnetic tunnel junctions (SMTJs) are spintronic nanodevices that can oscillate spontaneously under the influence of thermal noise. Studies have proposed that SMTJs can be modeled as magnetic neurons by subjecting them to electrical Gaussian white noise and can be subsequently synchronized under subthreshold driving. Considering the non-Gaussian background noise in industrial applications, this study investigated the influence of levy noise on SMTJ subthreshold synchronization. The results revealed that levy noise with weak pulse characteristics can be used to promote the subthreshold synchronization of the SMTJs, whereas levy noise with enhanced pulse characteristics cannot be used. Under the same conditions, enhanced levy noise asymmetry is advantageous for achieving SMTJ synchronization under subthreshold driving.

Urban green spaces (UGSs) improve the quality of life of urban inhabitants. With the acceleration of urbanization and changes in traffic networks, it remains unclear whether changes in the distribution of UGSs can satisfy the needs of all inhabitants and offer equal services to inhabitants from different socioeconomic backgrounds. This study addresses this issue by analyzing dynamic changes in UGS accessibility in 2012, 2016, and 2020 for inhabitants of the central urban area of Fuzhou in China at the community level. The study introduces multiple transportation modes for an accessibility estimation based on a framework using the two-step floating catchment area method and examines the dynamic changes in community deprivation of UGS accessibility using Kernel regularized least squares, a machine learning algorithm. The results demonstrate that spatial disparities of UGS accessibility exist among the multi-transport modes and vary with time. Communities with high accessibility to UGSs by walking are scattered around the urban area; for accessibility by cycling, the high accessibility regions expand and surround the regions with low accessibility in the core urban areas, forming a semi-enclosed spatial pattern. However, the core urban spatial orientation of UGS accessibility by public transit demonstrates a reverse trend to the above two modes. The spatial pattern of UGS accessibility also varies over time, and the growth rate of accessibility slightly declined during the study period. Furthermore, the increase in UGS accessibility tended to slow from 2016–2020 compared with 2012–2016, and the trend toward equality was also erratic. The degree of deprivation for communities first weakened and was then aggravated, corresponding to the slowdown in the growth rate of accessibility, leading to the persistence existence of social inequality. Moreover, significant deprivation mainly exists among less educated people or those using the cycling and integrated travel modes. Although public transport is developing, deprived communities, such as communities with large proportion of older people, have experienced a decline in access to UGSs by public transport. Based on these findings, the study proposes a policy framework for the balanced distribution of UGSs as part of urbanization.

Colorectal cancer (CRC) is one of the most common cancers worldwide. Recent studies have shown that long non-coding RNAs (lncRNAs) are involved in tumorigenesis and the development of CRC. By constructing a differential lncRNA expression profile, we screened gene chips and found that DNAJC3-AS1 was highly expressed in CRC tissues and was associated with poor prognosis in patients with CRC. Further, we proved through assays such as wound healing, colony formation, and Cell Counting Kit-8 (CCK8) that interfering with DNAJC3-AS1 could reduce the proliferation, migration, and invasion of CRC cells. Mechanically, we found that DNAJC3-AS1 regulates fatty acid synthase to promote the progression of CRC via the epidermal growth factor receptor/phosphatidylinositol 3-kinase/protein kinase B/nuclear factor κB signaling pathway. Therefore, DNAJC3-AS1 may be a new target for the diagnosis and therapy of CRC.

Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms.

To precisely diagnose the rotating machinery structural faults, especially structural faults under low rotating speeds, a novel scheme based on combination of empirical mode decomposition (EMD), sample entropy, and deep belief network (DBN) is proposed in this paper. EMD can decompose a signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios (SNRs) and sample entropy is performed to extract the signals that carry fault information with high SNR. The extracted fault signal is reconstructed into a new vibration signal that will carry abundant fault information. DBN has strong feature extraction and classification performance. It is suitably performed to build the diagnosis model based on the reconstructed signal. The effectiveness of the proposed method is validated by structural faults signal and the comparative experiments (BPNN, CNN, time-domain signal only, frequency-domain signal only). The results show that the diagnosis accuracy of the proposed method is between 99% and 100%, the BPNN is less than 25%, and the CNN is between 70% and 95%, which means the verified, proposed method has a superior performance to diagnose the structural fault.

Highly dispersed Mn⁻Ce binary metal oxides supported on carbon nanofibers (MnOx⁻CeO2/CNFs(OX)) were prepared for Hg0 removal from coal-fired flue gas. The loading value of the well-dispersed MnOx⁻CeO2 was much higher than those of many other reported supports, indicating that more active sites were loaded on the carbon nanofibers. In the present study, 30 wt % metal oxides (15 wt % MnOx and 15 wt % CeO2) were successfully deposited on the carbon nanofibers, and the sorbent yielded the highest Hg0 removal efficiency (>90%) within 120⁻220 °C under a N2/O2 atmosphere. An increase in the amount of highly dispersed metal oxides provided abundant active species for efficient Hg0 removal, such as active oxygen species and Mn4+ cations. Meanwhile, the carbon nanofiber framework provides the pathway for charge transfer during the heterogeneous Hg0 capture reaction processes. Under a N2+6%O2 atmosphere, a majority of Hg0 was removed via chemisorption reactions. The effects of flue gas composition were also investigated. O2 replenished the active oxygen species on the surface and thus greatly promoted the Hg0 removal efficiency. SO2 had an inhibitory effect on Hg0 removal, but NO facilitated Hg0 capture performance. Overall, carbon nanofibers seems to be a good candidate for the support and MnOx⁻CeO2/CNFs(OX) may be promising for Hg0 removal from coal-fired flue gas.

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.

Power Hardware in the Loop (PHIL) systems are used to test a power system with the help of combined software and hardware. Generally, to construct a PHIL system, a switched-mode power amplifier that has a stable performance is used, because of their large, linear signal control-to-output characteristics. However, the fundamental limitations of a switch-mode power amplifier (PA) are the dynamic performance and output bandwidth. In this paper, a compound controller has been used for the rectifier part of a PA, which can ensure the stability of a PA under transient or fault operating conditions. Moreover, a compound controller, which involves a feed-forward controller, a proportional controller and a repetitive controller, is proposed in the inverter part of a PA, and it can be used for PHIL applications. Experimental results are obtained under various operating conditions, such as transient responses under load step change, and output voltage bandwidth testing for a PHIL system, it is concluded that a proposed switched-mode power amplifier is useful for the PHIL system.

Beam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper. It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). The inputs of GRNN include the number of punching, the maximum load, the minimum load, the effective stroke, and the computational pump efficiency, while the outputs are the electric power consumption and the oil yield. Experimental results show that there is good overlap between model estimations and unseen data. Then sixty-one sets of optimum parameters are found based on the obtained model. Also, the results show that, under the optimum parameters, more than 5.34% oil yield is obtained and more than 3.75% of electric power consumption is saved.