Search

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. [ABSTRACT FROM AUTHOR]

Gain-dissipative Ising machines (GIMs) are dedicated devices that can rapidly solve combinatorial optimization problems. The noise intensity in traditional GIMs should be significantly smaller than its saturated fixed-point amplitude, indicating a lower noise margin. To overcome the existing limit, this work proposes an overdamped bistability-based GIM (OBGIM). Numerical test on uncoupled spin network show that the OBGIM has a different bifurcation dynamics from that of the traditional GIM. Moreover, the domain clustering dynamics on non-frustrated network proves that the overdamped bistability enables the GIM to suppress noise-induced random spin-state switching effectively; thus, it can function normally in an environment with a relatively large noise level. Besides, some prevalent frustrated graphs from the SuiteSparse Matrix Collection were adopted as MAXCUT benchmarks. The results show that the OBGIM can induce stochastic resonance phenomenon when solving difficult benchmarks. Compared with the traditional GIM, this characteristic makes the OBGIM achieve comparable solution accuracy in larger noise environment, thus achieving strong noise robustness. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a promising clinical decision support system (CDSS) in the medical field owing to its advanced text analysis capabilities and interactive design. However, ChatGPT primarily focuses on learning text semantics rather than learning complex data structures and conducting real-time data analysis, which typically necessitate the development of intelligent CDSS employing specialized machine learning algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids in algorithm design for intelligent CDSS at the textual level. In this study, besides discussing the types of CDSS and their relationship with ChatGPT, we mainly investigate the benefits and drawbacks of employing ChatGPT as an auxiliary design tool for intelligent CDSS. Our findings indicate that by collaborating with human expertise, ChatGPT has the potential to revolutionize the development of robust and effective intelligent CDSS. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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-1ß-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. [ABSTRACT FROM AUTHOR]

As one of the most popular multiple criteria decision‐making (MCDM) methods, the stochastic multicriteria acceptability analysis (SMAA) is powerful in dealing with the uncertain preference information of decision makers. However, how to find compromise solutions for MCDM problems remains an open question in the SMAA family. A compromise solution refers to an alternative that is the closest to the ideal solution. In this paper, we propose an improved SMAA method, called the SMAA with compromise solutions (SMAA‐CO), to determine compromise solutions for MCDM problems with the uncertain preferences of decision makers and conflicting criteria. The SMAA‐CO uses nonmonotonic additive value functions to depict decision makers' ideal values for reference points. We apply the proposed method to capture multiple decision makers' uncertain preference information. An illustrative example concerning supplier selection in the pulp and paper industry is used to show the applicability of the proposed method. Some managerial insights are given. [ABSTRACT FROM AUTHOR]

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 T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe. [ABSTRACT FROM AUTHOR]

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 T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe. [ABSTRACT FROM AUTHOR]

Machinery fault diagnosis is an attractive but challenging task, especially for low-speed conditions. Therefore, a new discriminative approach that introduces robust principal component analysis (RPCA) and multikernel to deep neural networks is proposed to perform intelligent fault diagnosis. First, RPCA is applied to extract fault signals from extreme background noise based on its sensitivity to grossly corrupted data. Second, two cascaded multikernel principal component analysis stages with additional robustness to distortions in feature extraction are used to enhance the energy of spectrum symptom and overcome the tricky issues of low-speed machinery. Especially, the multikernel is introduced into the basic PCA filters to learn the data-adapting convolution filter and gain additional robustness to nonlinearity in the signal. Finally, the proposed method is demonstrated on signals from laboratory tests (with a slightly damaged defect in a bearing) and structural fault data, outperforming those of traditional machine learning and classical deep learning methods. Moreover, hidden information of the network is visualized to analyze the reasons for its high performance. [ABSTRACT FROM AUTHOR]

Raw vibration signals poorly perform in industrial bearing fault diagnosis because impulse features are damped and masked by disturbances and noises. Fault diagnosis is more challenging due to weak features. This work presents a signal filtering and fault characteristic enhancement method based on reconstruction adaptive determinate stationary subspace filtering (Rad-SSF) and enhanced third-order spectrum to address the above problems. In particular, Rad-SSF reconstructs an adaptive self-determined, decomposed vibration signal trajectory matrix to obtain non-stationary signals. Then, the filtered signal with the best fault characteristics is extracted according to kurtosis. A 1.5-dimensional third-order energy spectrum is performed to enhance the fault characteristics by strengthening the fundamental frequency and eliminating non-coupling harmonics. Finally, the dominant frequency in the spectrum is contrasted to recognize fault diagnosis, referring to theoretical fault characteristic frequency. The feasibility and effectiveness of the proposed method are demonstrated by simulation and engineering signals under different conditions. [ABSTRACT FROM AUTHOR]

Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this study, the influence of common‐colored noise on the performance of GIMs is numerically investigated. The results of a domain clustering dynamics analysis reveal that red noise can better suppress the generation of the noise‐induced irregular temporary domain. Furthermore, several prevalent MAXCUT problem topologies, including the Moebius ladder, random Moebius ladder, and 2D random lattice, are adopted as test benchmarks. The results reveal that GIMs influenced by white, blue, and violet noise perform better at low‐intensity noise condition. In contrast, pink and red noise‐injected GIMs demonstrate higher performances when applied to MAXCUT topologies with both ferromagnetic and antiferromagnetic connections under larger noise intensity conditions. This indicates that the noise dispersion can be used as an additional hyperparameter to optimize the performance of GIMs. [ABSTRACT FROM AUTHOR]

Polyethylene glycol (PEG) is a well-known water retention agent in biomedical products, the hydration efficiency of which is affected by its molecular weight. Using a broadband dielectric spectroscopy (100 MHz–18 THz), the hydration state of PEG aqueous solutions with various molecular weights was quantitatively evaluated. As the molecular weight increases, the restriction strength of hydration water increases in potency, while the number of hydration water molecules decreases. Owing to the opposite changes in hydration number and restriction strength, the measured collective hydrogen bond (HB) strength shows negligible molecular weight dependence. PEG with larger Mw produces a more heterogenous HB network. The internal folding and twining caused by the growth of the PEG chain obstruct the proper exposure of hydrophilic part of the monomer producing less hydration waters. The evaluation result supports an application of PEGs with low molecular weight in contact lens package solutions. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are closed back‐splicing products of precursor mRNA in eukaryotes. Compared with linear mRNAs, circRNAs have a special structure and stable expression. A large number of studies have provided different regulatory mechanisms of circRNAs in tumors. Challenges exist in understanding the control of circRNAs because of their sequence overlap with linear mRNA. Here, we survey the most recent progress regarding the regulation of circRNA biogenesis by RNA‐binding proteins, one of the vital functional proteins. Furthermore, substantial circRNAs exert compelling biological roles by acting as protein sponges, by being translated themselves or regulating posttranslational modifications of proteins. This review will help further explore more types of functional proteins that interact with circRNA in cancer and reveal other unknown mechanisms of circRNA regulation. [ABSTRACT FROM AUTHOR]

Low-speed bearing fault signal is weak and overwhelmed by background noise, and it is ineffective in diagnosing bearing faults. In this study, an improved statistical filtering and convolution neural network (CNN) low-speed bearing fault diagnosis method is presented. Statistical filtering is a convenient and effective signal-filtering method that can extract fault signals from strong background noise original signals. The improved method can deal with drawbacks in selecting the standard distinction index of statistical filtering algorithm. CNN is used to extract signal features automatically and build the diagnosis model for fault classification. An engineering experiment is performed with different condition-bearing fault signals to test this presented method, and the results show that the improved statistical filter and CNN are effective in diagnosing low-speed bearing signals. Comparison with traditional methods proves that this study has superior performance in low-speed bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

A method of magnetocardiography (MCG) measurement using an overdamped bistable model based stochastic resonance (SR) technique for advancing biomagnetic sensors is proposed. To determine the system parameters for SR, an evolutionary algorithm combined with the coherent detection (CD) method is applied. We acquire and process an animal MCG at room temperature, detected by a commercially available optically pumped magnetometer. A comparison with a lowpass filter and the traditional CD method verified the ability of the proposed method to obtain a higher signal-to-noise ratio and peak value of the MCG, whilst retaining sufficient dynamic information. [ABSTRACT FROM AUTHOR]

Bearing fault impulses are easily submerged by background noise, resulting in the inconspicuous fault feature and affecting the accuracy of the fault diagnosis. This paper presents a novel method for bearing the fault feature enhancement and diagnosis based on the statistical filtering and the 1.5-dimensional symmetric difference analytic energy operator (1.5D-SDAEO) to solve the problem. 1). The statistical filtering is used to filter the background noise under the standard distinction index. 2). The 1.5D-SDAEO is used to enhance the signal impulse, suppress the residual noise, and improve the SNR. 3). The dominant frequency in the energy spectrum is compared with the rolling bearing fault characteristic frequency to the fault diagnosis. The feasibility and the superiority of the presented method are verified by the simulation, engineering, and comparison experiments. All results show that the presented method can effectively enhance the fault feature and accurately diagnose the rolling bearing fault. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A novel method of intelligent diagnosis is proposed for structural faults in low-speed rotating machinery. It uses a hybrid scheme to automatically identify health states in a complex mechanical system by combining an improved mode decomposition approach, a Gramian angular summation field, and a convolutional neural network. The proposed method is tested with a data set affected by noise to evaluate its performance and generalizability that are both essential in fault diagnosis. Experimental results show that the proposed scheme is superior to traditional machine learning methods. It not only provides a more efficient and widely applicable way to learn intrinsic fault signatures, but it also has enhanced feature learning capability to improve precision and generalizability. Finally, the reasons for the method’s high performance are analyzed to determine the best general features for an adaptive classifier that is generalizable to diverse operating conditions. [ABSTRACT FROM AUTHOR]

Improvement of the food value of rice straw is urgently required in rice crop growing areas to mitigate pollution caused by rice straw burning and enhance the supply of high-quality forages for ruminants. The aims of the present study were to compare the effects of fresh corn Stover and rice straw co-fermented with probiotics and enzymes on rumen fermentation and establish the feasibility of increasing the rice straw content in ruminant diets and, by extension, reducing air pollution caused by burning rice straw. Twenty Simmental hybrid beef cattle were randomly allotted to two groups with ten cattle per group. They were fed diets based either on rice straw co-fermented with probiotics and enzymes or fresh corn Stover for 90 days. Rumen fluid was sampled with an esophageal tube vacuum pump device from each animal on the mornings of days 30, 60, and 90. Bacterial diversity was evaluated by sequencing the V4–V5 region of the 16S rRNA gene. Metabolomes were analyzed by gas chromatography/time-of-flight mass spectrometry (GC–TOF/MS). Compared to cattle fed fresh corn Stover, those fed rice straw co-fermented with probiotics and enzymes had higher (P < 0.05) levels of acetic acid and propionate in rumen liquid at d 60 and d 90 respectively, higher (P < 0.05) abundances of the phyla Bacteroidetes and Fibrobacteres and the genera Ruminococcus, Saccharofermentans, Pseudobutyrivibrio, Treponema, Lachnoclostridium, and Ruminobacter, and higher (P < 0.05) concentrations of metabolites involved in metabolisms of amino acid, carbohydrate, and cofactors and vitamins. Relative to fresh corn Stover, rice straw co-fermented with probiotics and enzymes resulted in higher VFA concentrations, numbers of complex carbohydrate-decomposing and H2-utilizing bacteria, and feed energy conversion efficiency in the rumen. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Thermally conductive and electrically insulating h-BN/MVQ and h-BN/SiCw/MVQ composites were prepared by internal mixing and two roll mixing, using 1-dimensional (1D) silicon carbide whiskers (SiCw) and 2-dimensional (2D) hexagonal boron nitride flakes (h-BN) as fillers, and methyl vinyl silicone rubber (MVQ) as polymer matrix. Surface modification of 1 D SiCw and 2 D h-BN was characterized by Fourier transform infrared spectroscopy (FT-IR) and contact angle analysis. Morphology, thermal conductivity and dielectric constant of h-BN/MVQ and h-BN/SiCw/MVQ were studied. The results indicated that surface modified h-BN and SiCw could be uniformly distributed in MVQ matrix. At the same volume filler loading, thermal conductivity of h-BN/SiCw/MVQ ternary composite was higher than that of h-BN/MVQ binary composite. When part of 2 D h-BN was instead of 1 D SiCw, there was synergistically enhanced effect between 1 D SiCw and 2 D h-BN for thermal conductivity. When volume ratio of h-BN/SiCw was 8/2, thermal conductivity of h-BN/SiCw/MVQ was higher than that of other ratio. Thermal conductivity of h-BN/SiCw/MVQ composite with different volume ratio was linearly fit with Agari' model. [ABSTRACT FROM AUTHOR]

Amorphous indium–gallium–zinc oxide (a-IGZO) thin-film transistors (TFTs) with high mobility are highly desirable to achieve high-speed operation in active-matrix displays and large-area sensors. Only a few works provide potential solutions. In this letter, we have embedded carbon nanotubes (CNTs) into sputtered a-IGZO film to form a-IGZO/CNT/a-IGZO (ACA) sandwiched channel. In the ACA channel, the CNT percolation networks connected by the a-IGZO film work as high-speed carrier paths to enable faster transport of carriers during the ON state, while it does not degrade the leakage performance during the OFF state. The type and the embedding location of the CNT percolation network are critical to determine the ACA device performance, which is analyzed and verified by experiment and simulation. The optimum ACA design has approximately doubled the mobility and the ON-current density of the TFT. The design owns relatively better uniformity and provides a high-speed TFT solution for the advanced electronics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Vulcanizing forming mould is considered to be one of the key elements to determine the quality of compound rubber bearings, to make up for the shortcomings of empirical design methods in the design of vulcanizing forming mould. A new approach is put forward to achieve the optimal design of temperature field by integrating the genetic algorithm (GA) and finite element analysis (FEM). Firstly, the mathematical model of the temperature difference of the inner surface of the mould is established according to the principle for linear superposition of steady state heat transfer. Then different optimization schemes are designed as to the specific working conditions. Subsequently, the GA is used to optimize the structure and power distribution of the electric heating pipe as two variables. Finally, the FEM takes over the job to complete the analysis on the design results. The results show that the optimal design results are in agreement with the numerical simulation results, by which the effectiveness of the method is proved. [ABSTRACT FROM AUTHOR]

Fault feature extracted from fault signals plays a vital role in fault detection and diagnosis. However, in the early stage of defect, the fault signals (amplitudes and energy) are very weak and overwhelmed by large noise, and it is very difficult to extract fault feature effectively. To handle this problem, in this paper, an automatic filtering method based on an improved genetic algorithm (IGA) for extracting fault signals is presented. This method improves the binary coding generation of GA, so that each binary value matches a frequency of a fault signal frequency spectrum. Based on designed “statistical information evaluation function” as IGA fitness function, the similarity between normal state signal (noise signal) and IGA extracted noise signal is evaluated, and IGA employs genetic operations to search the optimal noise binary string. Negating noise binary string can obtain optimal fault signal binary string. It will then be multiplied by abnormal state vibration signal frequency spectrum, to yield a new frequency spectrum that only has fault components. This proposed method does not need to seek center frequency and bandwidth, and can automatically remove noise. The effective and advanced performance of the proposed method is evaluated by three different rolling bearing defects (outer race defect, inner race defect, and roller defect) in rolling bearing on rotating machine experimental bench and gearbox. Compared with rolling bearing fault characteristic frequency and results of denoising methods of bandpass filtering and wavelet decomposition, the proposed method can automatically filter noise and effectively extract fault signal. It proves that this method is feasible, effective, and advanced. [ABSTRACT FROM PUBLISHER]

A specially designed, large-scale municipal solid waste (MSW) settlement apparatus was used to conduct experiments in this study. The suction for unsaturated waste in different layers also was measured. The test results show that the deeper the sample, the more saturated it will become during testing, although the initial state of the overall sample is almost the same. The pore gas pressure was measured for different depths of the sample. It is shown that the pore gas pressure varies in different layers. Two sets of experiments were conducted to make the comparisons. For one test, water was added to the sample; for the other test, no water was added. Significant differences were observed in the quantity of gas and leachate produced during contrastive tests. A previous theoretical model was updated and extended to be suitable to new boundary conditions with a liquid supply. [ABSTRACT FROM AUTHOR]

The work proves the reddening noise can suppress random spin switching, so that the Ising machine can better find the ground state. B Quantum Analog Annealing of Gain-Dissipative Ising Machine Driven by Colored Gaussian Noise b In article number 2100497, Zhiqiang Liao, Hiroyasu Yamahara, and co-workers utilize noise dispersion as a hyperparameter to modulate the performance of a gain-dissipative Ising machine. The cover depicts the superior performance of red-noise-driven Ising machine in domain clustering dynamics. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]