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The integration of ferroelectricity, ferromagnetism, and half-metallicity in two-dimensional (2D) materials is pivotal for advancing spintronic device technologies. However, the progress in identifying such materials is limited, and we here propose a compelling approach by constructing asymmetry structures (Janus) based on known 2D magnets, namely, the Janus tetragonal Mn2BN monolayer as a promising polar half-metal. The asymmetric arrangement of B and N atoms, coupled with comparable atomic sizes and evident electronegativity, ensures structural stability and inherent polarization, while the dominance of Mn atoms governs magnetism. The robust ferromagnetic order stems from a strong super-exchange interaction, evident in the significant hybridization between Mn d and B/N p orbitals. The Mn2BN monolayer exhibits a wide spin bandgap (1.09 eV), a substantial electric polarization (9.15 μC cm−2), and a sizable magnetic anisotropic energy (238 μeV/Mn) and maintains stable ferromagnetic order to ∼800 K. These properties position it as a promising candidate for next-generation multifunctional devices in spintronics. [ABSTRACT FROM AUTHOR]

The carbon nano-peapod is a representative structure with interlayer van der Waals (vdW) interactions, in which encapsulated fullerene molecules play a critical role in modulating the transport properties of the carbon nanotubes (CNTs). In particular, their influence on the thermal transport characteristics has been the focal point of considerable attention. In this study, we trained an accurate machine learning potential for fullerene-encapsulated CNTs based on the efficient NEP model to investigate their thermal properties. Using equilibrium molecular dynamics simulation along with the spectral decomposition method for thermal conductivity, we find that the thermal conductivity of fullerene-encapsulated CNTs is roughly 55 % lower than that of empty CNTs, aligning with experimental observations for CNT bundles with fullerene encapsulation [Kodama et al., Nat. Mater. 16, 892 (2017)]. The research suggests that weak vdW interactions between both the fullerene and CNTs, as well as between fullerene molecules themselves, hinder phonon propagation. The encapsulated fullerene contributes to an increase in phonon scattering within the CNTs, ultimately leading to a reduction in thermal conductivity. We utilized machine learning potential to investigate the structure of fullerene-encapsulated CNTs and their heat transport property. This approach provides valuable insights for performance research of complex systems featuring interlayer vdW interactions. [ABSTRACT FROM AUTHOR]

This paper proposes the generalized maintenance policies for a computing system when the copula functions model the interdependency. In this paper, the dependence between the components has been modeled by three copula functions. We deal with random maintenance policies for repairable parallel systems with n dependent or independent components. Firstly, we discuss the maintenance first and last policies for parallel systems composed of dependent components. We consider the random maintenance policy for parallel systems that are dependent and attempt to find the optimal maintenance time by considering the copula framework. The expected cost rate is considered as objective function to minimize. For each policy, the optimal maintenance time that minimizes the mean cost rate is analyzed and discussed numerically. And we study the effects of model parameters on the optimal maintenance policy and compare the copula. The findings of the paper extend the results in the literature from system with independent components to dependent components. Numerical results are presented for components lifetime follow Weilbull distribution. Therefore manager can use the results to formulate the optimal decision problem of their parallel systems with dependent components. [ABSTRACT FROM AUTHOR]

In this paper, extended preventive replacement models for series and parallel system with n independent non-identical components are proposed. It is assumed that the system suffers from two types of failure. One is repairable (type-I) failure, at that time the system can be rectified by minimal repair. Another is non-repairable (type-II) failure, then the whole system is replaced. In the proposed models, the system is replaced at the planned time, at random working time, or at the time when type-II failure occurs, with options whichever occurs first or whichever occurs last. The average cost rate (ACR) function and the failure rate function (FRF) of the series and parallel system under the different cases are obtained respectively. Moreover, the optimal preventive replacement time of models based on minimization of the ACR function is obtained theoretically. Numerical examples are presented to evaluate the cost of the system and verify the performance of our results. [ABSTRACT FROM AUTHOR]

In this paper, a new single component repairable system model with a repairman is proposed. Assume that the successive working time interval of the component and the successive repair time interval after repair is described by the extended geometric process. The repairman has multiple vacation when the component is working, and component is repaired delayed with a given probability when it fails. The component will work again when it repaired. Under the assumption, the explicit expression of the long-run average cost rate function of the system based on the failure number of the component is derived. Numerical cases are designed to illustrate the long-run average cost rate function of the proposed model. Finally, sensitive analysis of parameters is carried out. [ABSTRACT FROM AUTHOR]

High-speed railway (HSR) has demonstrated tremendous growth worldwide, and is currently attaining a maximum velocity of 575 km/h. Such a high speed makes mobile wireless communications extremely challenging for HSR since the handover (HO) rate increases with speed, resulting in a high call dropping probability. By employing a dedicated distributed antenna system (DAS) along with the two-hop network architecture, in this paper, we propose a frequency switch (FSW) scheme to mitigate the persistent HO issue in conventional HSR wireless communication systems. The proposed scheme significantly alleviates interruption time associated with the traditional HO process, providing an effective scheme when the train moves across small cells covered by remote antenna units (RAUs) belonging to the same central unit (CU). In order to further reduce the HO command failure probability, an enhanced fast predictive HO mechanism is proposed by starting the HO process in advance, when the train moves from one RAU to the next one controlled by the adjacent CU. Analytical results show that compared to the traditional HO scheme, the proposed schemes can improve the system performance substantially by delivering ultra-reliable low-latency communications with a reduced HO/FSW rate. [ABSTRACT FROM AUTHOR]

Strong background noise and complicated interfering signatures when implementing vibration-based monitoring make it difficult to extract the weak diagnostic features due to incipient faults in a multistage gearbox. This can be more challenging when multiple faults coexist. This paper proposes an effective approach to extract multi-fault features of a wind turbine gearbox based on an integration of minimum entropy deconvolution (MED) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). By using simulated periodic transient signals with different noise to signal ratios (SNR), it evaluates the outstanding performance of MED in noise suppression and reveals the deficient in extract multiple impulses. On the other hand, MOMEDA can performs better in extracting multiple pulses but not robust to noise influences. To compromise the merits of them, therefore the diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals. Experimental verification based on vibrations from a wind turbine gearbox test bed shows that the approach allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox. [ABSTRACT FROM AUTHOR]

Lead-free ferroelectric thin films with excellent performances are desired for environmentally friendly micro-electromechanical system (MEMS) devices. In this work, 0.938(Bi 0.5 Na 0.5 )TiO 3 -0.062BaTiO 3 -0.5%Mn (BNBTM) thin film with enhanced ferroelectric property was deposited on SrTiO 3 (STO) single-crystal substrate via pulsed laser deposition. (La 0.6 Sr 0.4 )CoO 3 (LSCO) was selected as both electrode and buffer layer due to its excellent electrical conductivity and good lattice matching with BNBTM and STO. XRD pattern showed both LSCO and BNBTM to exhibit pure perovskite structure and (100) orientation. Saturated hysteresis loop was obtained with the remnant polarization P r of 22 μC/cm 2 and the average coercive field E c of 9.1 kV/mm. The excellent ferroelectric property makes it a promising candidate for environmentally friendly MEMS devices. [ABSTRACT FROM AUTHOR]

In this paper, a new repairable k-out-of-n system model is proposed, in which the deviation cost of the system is taken into account. Firstly, the standard k-out-of-n system is replaced at planned time or at the failure time. Next, replacement policies are studied at planned time or periodic time when k is a constant. Thirdly, replacement policies are studied at planned time or periodic time when k is a random number, respectively. Finally, replacement policies are planned at the completion of random working time. The expected cost rate functions and its optimum replacement policies are derived analytically for each model. Meanwhile, optimum number of units is designed. The analytical solution of replacement policies for each model is discussed. Numerical examples are given to demonstrate our results. [ABSTRACT FROM AUTHOR]

Owing to their wide distribution, easy production, and resistance to degradation, microplastics (MPs) represent a globally emerging group of pollutants of concern. Furthermore, their decomposition can result in the generation of nanoplastics (NPs), which cause further environmental issues. Currently, the impact of the combination of these plastics with other organic pollutants on crop growth remains poorly investigated. In this study, a hydroponic experiment was conducted for seven days to evaluate the effects of 50 nm, 50 mg/L polystyrene (PS), and 1 mg/L phenanthrene (Phe) on the growth of rice plants. The results revealed that both Phe and PS inhibited growth and improved the antioxidant potential of rice. Relative to Phe alone, exposure to a combination of PS and Phe reduced Phe accumulation in the roots and shoots by 67.73% and 36.84%, respectively, and decreased the pressure on the antioxidant system. Exposure to Phe alone destroyed the photosynthetic system of rice plant leaves, whereas a combination of PS and Phe alleviated this damage. Gene Ontology (GO) analysis of the rice transcriptomes revealed that detoxification genes and phenylalanine metabolism were suppressed under exposure to Phe, which consequently diminished the antioxidant capacity and polysaccharide synthesis in rice plants. Kyoto Encyclopaedia of Genes and Genomes (KEGG) transcriptome analysis revealed that the combined presence of both PS and Phe improved photosynthesis and energy metabolism and alleviated the toxic effects of Phe by altering the carbon fixation pathway and hormone signal transduction in rice plants. The combination of PS and Phe also prevented Phe-associated damage to rice growth. These findings improve our understanding of the effects of MP/NPs and polycyclic aromatic hydrocarbons on crops. [Display omitted] • Exposure to Phe and PS destroyed the photosynthetic system of rice plant leaves. • The combination of PS and Phe reduced the damage to rice growth. • The combination of PS and Phe affected the carbon fixation pathway of rice. • Hormone signal transmission may alleviate the damage caused by PS and Phe. [ABSTRACT FROM AUTHOR]

Abstract: In late phase clinical trials, a large number of investigative centers are needed for recruiting sufficient number of patients in a timely manner. The quality of investigative centers involved in multi-center clinical studies can be crucial for the success of the trials. If ‘high quality’ centers can be identified from completed studies, we may use these centers in future trials to improve the chance of success. We explored the possibility of evaluating clinical centers based on clinically rated scores, the extent of protocol violations and patient non-compliance from several double-blinded, multi-center, placebo-controlled studies of aprepitant. To evaluate patient non-compliance on protocol-scheduled visits, a generalized estimating equations (GEE) approach was used to estimate the visit compliance scores for the investigative centers. A new rank score that combines these factors was derived using principal component analysis. The rank score was also compared to the original clinical rated overall score. [Copyright &y& Elsevier]

To summarize safety data such as clinical adverse experiences in clinical trials with a moderate to long-term follow-up, we may use a measurement which accounts for the potential differences in the follow-up duration between treatment groups. The incidence rate, which uses the total person-time follow-up in a treatment group as the denominator, is one of these measures. When treatment comparisons are based on the difference of the incidence rates, it is of interest to construct confidence intervals for the rate differences. In this paper, we first discuss the assumptions and scenarios in which the exposure adjusted incidence rate may be appropriate. Then we review several methods of calculating confidence intervals for the difference of two incidence rates assuming that the number of events come from a Poisson distribution. The methods considered include Wald's method, the two-by-two table method, the Miettinen and Nurminen (MN) method, and the conditional MN method. For all but the MN method, explicit confidence intervals can be obtained. For the MN method, some numerical iterations are required. The properties of these methods are evaluated by simulations. The results show that the MN method outperforms the other three in terms of the coverage of the confidence interval, especially when the rates and therefore the number of events are small. Lastly, data from a clinical study are used to demonstrate the application of the methods. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the small area estimation literature, the sampling error variances are customarily assumed to be known or to depend on a finite number of parameters. We consider the empirical best linear unbiased predictor (EBLUP) obtained by using the individual directly estimated variance for each small area. An approximation for the mean squared error (MSE) of the EBLUP that recognizes the impact on the predictors of estimation of the variance components is derived. Simulation studies show that the theoretical expressions are good approximations for the MSE of the predictors unless the between-area variance component is very small (relative to the within-area variance). An improved estimator of the MSE is developed that has smaller overestimation than the original estimator when the between-area variance component is small. The robustness of the MSE estimator is studied and predictors for nonnormal sampling errors are proposed. An example from the National Resources Inventory that motivated the development of the theory is described. [ABSTRACT FROM AUTHOR]

Few-layer chemical vapor deposition (CVD) graphene was successively wet-transferred on a magnetron sputtering Ag/TiO2 film to develop a recyclable surface-enhanced Raman scattering (SERS) substrate with high sensitivity, stability, and reproducibility owing to the passivation of graphene, the SERS function of Ag and the photocatalytic activity of TiO2. The detection limit of rhodamine 6G (R6G) for the graphene/Ag/TiO2 nanocomposite could be lowered to 10−11 M, and its enhancement factor was 3.75 × 106. Moreover, a good linear response was observed between Raman intensity and R6G concentration with a fitted curve (R2 = 0.98), demonstrating that the prepared substrate can estimate the concentration of probe molecules for practical testing applications. Importantly, the homogeneity of the substrate structure and the passivation protection of the graphene layer resulted in excellent reproducibility (relative standard deviation less than 9%) and stability (the substrate maintained good SERS activity after 30 days of exposure to air) of the SERS substrate. Besides, the photocatalytic properties of TiO2 could eliminate the Raman signal of methylene blue (MB) efficiently and make this substrate recyclable. After being reused five times, its excellent SERS performance was still retained. Thus, the successful preparation of this composite structure opens up new avenues for the construction of high-performance multilayer composite SERS substrate, which is expected to become a new type of ultra-sensitive, highly stable, and reusable analytical tool. [ABSTRACT FROM AUTHOR]

• The ballistic limits of CFRP and GLARE laminated structures are determined. • The damage modes of composite laminated structures are clarified. • Cohesive force damage morphology is closely related to the fiber laying direction. • Damage mechanism of composite laminated structures is revealed in high-impact load. Composite laminated structures are often subjected to high-speed impact from external objects in high impact environments, resulting in unpredictable forms of damage and even uncontrollable damage modes. To ensure the safety and reliability of these structures, research was conducted on the anti-penetration response and response mechanism of laminated structures made of different composite materials. Using carbon fiber reinforced plastic (CFRP) laminated structures and glass fiber/epoxy resin composite aluminum alloy (GLARE) laminated structures as research objects, a high-impact experimental setup was constructed based on a first-stage light gas gun to explore the relationship between the energy absorption characteristics and impact energy of composite laminates. Combined with the ballistic limit equations, the ballistic limit values of the composite laminated structures were determined, and the ballistic limits of the 2 mm thick CFRP laminated structure and the 2 mm thick GLARE laminated structure were 138.3 m/s and 215.6 m/s, respectively. The damage modes of CFRP laminated structure are mainly fiber fracture on the impact surface and fiber delamination, fiber tensile fracture and fiber bundle splitting on the back surface, while the damage modes of GLARE laminated structure are mainly plastic deformation, internal fiber fracture and crack extension at the penetration. At the same time, the cohesion unit is introduced to carry out numerical simulation research, which shows that the damage pore morphology of the cohesion unit between layers of composite laminated structures are closely related to their fiber layups, and the damage mode of cohesion unit between layers of composite laminated structures have been investigated, which reveals the damage mechanism in high-impact environments. The research results provide theoretical basis and common technical support for reverse design of composite laminated structures suitable for high impact environments. [ABSTRACT FROM AUTHOR]

3D face reconstruction and dense alignment play a key role in digital humans. Learning-based approaches have been developed by employing cascaded convolutional neural networks for feature extraction and spatial-semantic relationship construction. However, these networks often struggle with accurately capturing the complex relationships among various facial components and attributes, particularly in unconstrained environments. Furthermore, the mappings modeled by convolutional weights are at low levels, which are usually implicit and local, and lack global discrimination. In this paper, we propose a global and instance-level perceived relationship matrices-based network (PRMNet) to recover high-fidelity 3D faces and perform accurate dense alignment in unconstrained environments. Specifically, a Key Information Extraction Module (KIEM) extracts crucial features from global and local feature banks. This reduces reasoning costs while maintaining high perceptual quality. Learnable global and instance-level perceived feature relationship matrices are then integrated into the Feature Relationship Reasoning Module (FRRM). This calibration combines key information from both the global macroscopic and sample-specific microscopic views, allowing accurate construction of spatial-semantic relationships to harvest both global discrimination and local relevance. Finally, we introduce a Spatial Reasoning and Guidance Module (SRGM), designed to recalibrate the joint weight responses of feature extraction and its enhancement paths using various attention mechanisms, thereby further enhancing global discrimination ability. Extensive quantitative and qualitative experiments on the benchmark datasets show that our PRMNet outperforms the state-of-the-art. Codes and all resources will be publicly available at https://github.com/Ray-tju/PRMNet. [ABSTRACT FROM AUTHOR]

The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm (P < 0.05), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm (P < 0.05). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion. [ABSTRACT FROM AUTHOR]

This paper studies the channel estimation performance in massive multiple-input multiple-output (MIMO) systems with Ricean fading through considering the relative channel estimation error (RCEE) metric. The closed-form expressions of the probability distribution function (PDF) and cumulative distribution function of RCEE are derived, according to which the PDFs of RCEE with Rayleigh fading and pure line-of-sight (LoS) propagation are obtained as special cases. Through analyzing RCEE, it notes that as the number of base-station antennas M becomes unbounded, RCEE approaches a given constant. Moreover, the asymptotic analysis proves that, in high M-regime, the increasing rate of the RCEE scales in the order of √M for Rayleigh fading and pure LoS propagation, while the scaling order with general Ricean fading is capped by √M. All the above mentioned results are verified through Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Under the strong noise environment, the composite fault signal of gearbox is weak, which makes it difficult to extract fault features. For this problem, based on noise-assisted method, we propose a novel method called Modified Singular Spectrum Decomposition (MSSD). Singular Spectrum Decomposition (SSD) has many advantages such as high decomposition precision and strong ability to restrain mode mixing, etc. However, the ability of SSD to extract a weak signal is not ideal, the decomposition results usually contain a lot of redundant noise and mode mixing caused by intermittency, which is also a troubling problem. In order to improve the decomposition efficiency and make up for the defects of SSD, the new method MSSD adds an adaptive and particular noise in every SSD decomposition stage for each trial, and in addition, whenever the input signal is decomposed to obtain an intrinsic module function (IMF), a unique residual is obtained. After multiple decomposition, the average value of the residual is used as input to the next stage, until the residual cannot continue to decompose, which means that the residual component has, at most, one extreme value. Finally, analyzing simulated signals to explain the advantages of MSSD compared to ensemble empirical mode decomposition (EEMD) and complete ensemble local mean decomposition with adaptive noise (CEEMDAN). In order to further prove the effectiveness of MSSD, this new method, MSSD, is applied to the fault diagnosis of an engineering gearbox test stand in an actual engineer project case. The final results show that MSSD can extract more fault feature information, and mode mixing has been improved and suffers less interference compared to SSD. [ABSTRACT FROM AUTHOR]

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified. [ABSTRACT FROM AUTHOR]

Both contact mechanics and kinematics are important for the pre-clinical evaluation of total knee replacements prior to wear simulation. The study has been focused on developing finite element contact models to predict converged contact pressures and kinematics for two different tibio-femoral designs, the frontally flat and dished bearings. The flat bearing led to high contact pressures due to edge effect, and comparable anterior-posterior displacement and internal-external rotation of tibial inserts during gait cycle. The study shows that the computational accuracy and efficiency should be addressed with respect to varied bearing geometries and operating conditions. • A frontally flat-on-flat bearing was investigated by comparing with dished bearing. • Computational prediction of contact mechanics and kinematics of total knee joint implants prior to wear simulation. • Finite element contact models for gait simulation. • Elevated contact pressures due to edge effect predicted through substantially refined FE contact model. • Provide foundations for further wear simulation. [ABSTRACT FROM AUTHOR]

• Propose a modified gearbox composite fault diagnosis method based on MOMEDA. • Propose kurtosis spectral entropy as the objective function of the grid search algorithm. • Optimized the two parameters of cycle and filter size in MOMEDA. • Combined modal function method divides multi-periodic shock signals into different CIMFs. In a strong noise environment, vibration signals are easily submerged by noise. In recent years, many scholars have studied a large number of noise reduction methods. In 2017, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault diagnosis of gearbox. Although MOMEDA overcomes Maximum correlation kurtosis deconvolution (MCKD) defects and it can extract continuous impulse signal, but it still has the following problems: 1) It can only extract single periodic pulse. If we want to extract the characteristics of multiple periodic pulse signals, we need to further update the algorithm; 2) In a strong noise environment, MOMEDA can also search for a fixed periodic signal, but most of the information is false component, it is easy to cause misdiagnosis, therefore, the signal needs to be preprocessed; 3) The accuracy of MOMEDA noise reduction is affected by the search interval and filter size, and McDondald did not reasonably explain them, so an adaptive selection method is needed. Considering these problems. Firstly the article preprocesses the composite fault with ensemble empirical mode decomposition (EEMD) and then reconstructs the intrinsic mode function with the same time scale. Further, proposing kurtosis spectral entropy as the objective function, the grid search method is used to search the filter length of MOMEDA, and the reconstructed intrinsic mode function is further denominated by MOMEDA. Finally, the proposed method is used to search the complex fault pulse signals in strong noise environment. It proves its reliability. [ABSTRACT FROM AUTHOR]

The usefulness of meta-analysis has been recognized in the evaluation of drug safety, as a single trial usually yields few adverse events and offers limited information. For rare events, conventional meta-analysis methods may yield an invalid inference, as they often rely on large sample theories and require empirical corrections for zero events. These problems motivate research in developing exact methods, including Tian et al.'s method of combining confidence intervals (2009, Biostatistics, 10, 275-281) and Liu et al.'s method of combining p-value functions (2014, JASA, 109, 1450-1465). This article shows that these two exact methods can be unified under the framework of combining confidence distributions (CDs). Furthermore, we show that the CD method generalizes Tian et al.'s method in several aspects. Given that the CD framework also subsumes the Mantel-Haenszel and Peto methods, we conclude that the CD method offers a general framework for meta-analysis of rare events. We illustrate the CD framework using two real data sets collected for the safety analysis of diabetes drugs. [ABSTRACT FROM AUTHOR]

The effects of electrical pulse width and output irradiance on the inactivation effect of intense pulse light (IPL) are studied in this paper. The measured radiant efficiency of pulsed xenon lamp can be more than 50%, and its irradiance can reach levels 100-times greater than that of a low-pressure mercury lamp. Staphylococcus aureus is used in inactivation experiments. When the irradiance and dose are both constant, there is no significant difference in inactivation efficiency when the pulse width is changed. However, a narrow pulse width corresponding to high irradiance at the same single-pulsed dose displays better inactivation effect. Experimental results are compared between the xenon lamp and low-pressure mercury lamp. The reduction factor (RF) value of the xenon lamp is more than 1.0 higher under the condition of both the same dose and irradiance. In order to achieve the same RF value, the dose of continuous-wave light must be at least three-times greater than that of pulsed light. The spectral action of the pulsed light is also studied. It is confirmed that UVC plays a major role across the whole spectrum. The experimental results show that extreme high-pulsed irradiance presents the main contributing factor behind the excellent bactericidal effect of IPL. [ABSTRACT FROM AUTHOR]

Purpose: We developed an augmentation technique for PCL reconstruction with independent internal brace reinforcement and evaluated the functional outcome after PCL reconstruction employing autologous hamstrings augmented with an internal brace system for patients with isolated or combined grade 3 posterior instability who were treated with this technique. Methods: From January 2016 to January 2018, patients with isolated or combined grade 3 PCL tears who underwent single-bundle PCL reconstruction using autologous hamstrings augmented with independent internal braces were studied. The function of the operated knee was evaluated according to the International Knee Documentation Committee (IKDC) score, Lysholm score, and Tegner activity score. The patients were asked the level of returned to their previous sport. Posterior knee laxity was examined with a KT-1000 arthrometer, and data on range of motion (ROM), re-operation, and other complications were collected. Results: A total of 33 consecutive patients who received single-bundle PCL reconstruction using autologous hamstrings augmented with independent internal braces with a minimum two years follow-up were included in this study. Two patients had undergone this procedure during the study period and were not included in this study (one had combined bone fractures, and one patient had previous meniscus surgery). Thirty-one patients were available for final analysis. The mean follow-up was 45.35 ± 10.88 months (range 29–66 months). The average IKDC subjective knee evaluation scores from 51.65 ± 12.35 to 84.52 ± 6.42, the Lysholm score from 53.90 ± 11.86 to 85.68 ± 4.99, and the Tegner score from 2.81 ± 0.79 to 6.71 ± 1.83 (P < 0.05 for all). The mean total posterior side-to-side difference in knee laxity, assessed using a KT-1000 arthrometer, decreased from 12.13 ± 2.66 mm pre-operatively to 1.87 ± 0.56 mm post-operatively at 70° (P < 0.05). Most patients (29/31) had normal or near normal knee ROM post-operatively; two patients revealed a 6–15° loss of knee flexion compared with the contralateral knee. Twenty-nine patients (93.55%) returned to a normal daily exercise level. Twenty-three patients (74.19%) returned to competitive sports with high-level sports (Tegner score of 6 or above; eleven patients (35.48%) reported to be on the same level as well as the Tegner level); six patients (19.35%) returned to recreational sports (Tegner score of 4 or 5). Two patients had Tegner scores of 2 and 3, indicating poor function level. No patient needed PCL revision surgery during the follow-up period. Conclusion: Single-bundle PCL reconstruction with internal brace augmentation for PCL injury exhibited satisfactory posterior stability and clinical outcomes in patients with isolated or combined grade 3 PCL injuries at a minimum two year follow-up. [ABSTRACT FROM AUTHOR]

Melasma is a common skin disease induced by an increase in the content of melanin in the skin, which also causes serious physical and mental harm to patients. In this research, a novel peptide (Nigrocin-OA27) from Odorrana andersonii is shown to exert a whitening effect on C57 mice pigmentation model. The peptide also demonstrated non-toxic and antioxidant capacity, and can significantly reduce melanin content in B16 cells. Topical application effectively delivered Nigrocin-OA27 to skin's epidermal and dermal layers and exhibited significant preventive and whitening effects on the UVB-induced ear pigmentation model in C57 mice. The whitening mechanism of Nigrocin-OA27 may be related to reduced levels of the microphthalmia-associated transcription factor and the key enzyme for melanogenesis-tyrosinase (TYR). Nigrocin-OA27 also inhibited the catalytic activity by adhering to the active core of TYR, thereby reducing melanin formation and deposition. In conclusion, Nigrocin-OA27 may be a potentially effective external agent to treat melasma by inhibiting aberrant skin melanin synthesis. • A novel peptide Nigrocin-OA27 from amphibians exerted a whitening effect. • Nigrocin-OA27 can deliver to the epidermal and dermal layers of the skin. • The whitening mechanism may be related to the reduce of TYR and MITF levels. [ABSTRACT FROM AUTHOR]

• Bearing capacity and energy absorption characteristics of the optimized AlSi10Mg structure are significantly enhanced. • Finite element calculation model used in this paper accurately describes the mechanical behavior of AlSi10Mg structure. • Fracture features of AlSi10Mg porous structure indicate a ductile as well as a brittle failure under compressive load. • Normalized energy absorption helps designers select the one structure most suitable for a given application. In order to correctly understand the mechanical behavior, damage mode and damage mechanism of AlSi10Mg optimized structure under compressive load, a series of experimental and simulation studies were carried out. The stress-strain response, bearing capacity, energy absorption characteristics, damage mode and mechanism of the structure are analyzed and discussed. The results show that AlSi10Mg porous structure has obvious strain hardening effect, but no obvious strain rate effect. The volume fraction of AlSi10Mg porous structure increased from 9.43% to 22.15%, and its bearing strength increased from 22.34 MPa to 50.98 MPa. The optimized design structure is complex in this paper, which eliminates undesirable failure modes, and the benefit of high specific energy absorption is retained. The experimental and simulation results show that shear failure is the main cause of structural damage, and the normal of the failure section and the load action direction are roughly inclined at an angle of 45° ∼ 55°. The research results can provide an important reference for the optimal design of porous metal structure. [ABSTRACT FROM AUTHOR]

The research on shock wave/turbulent boundary-layer interactions is mainly limited to calorically perfect gases; little has been reported on the thermochemical non-equilibrium (real gas) effect. This effect is prominent at conditions of high Mach and Reynolds numbers. In this work, a household parallel solver for hypersonic thermochemical non-equilibrium flows with a Reynolds-averaged Navier–Stokes turbulence model is developed in which the coupling of turbulence with vibration and chemistry occurs under a gradient-law assumption. The thermal non-equilibrium is based on Park's two-temperature model, and the chemical non-equilibrium is based on Gupta's 11-species model. The method proposed in this paper is first validated using experimental data, including cases of a laminar cylinder flow at a high-enthalpy condition, a supersonic flat-plate turbulent boundary layer flow, a hypersonic transition flow, and a hypersonic compression corner flow at a low-enthalpy condition. This approach is then applied to assess the hypersonic flow characteristics past the 34° compression corner at a flight height of 30 km. Results show that the joint effects of turbulence and thermochemical non-equilibrium have a significant impact on the flow field organization, wall data, and separation length of the shock wave/boundary-layer interaction. Furthermore, the mechanism of the neck region accompanied by maximum heat flux, wall pressure and skin friction in both laminar and turbulent cases is well-interpreted. This study can be used as a reference tool for the aerodynamic design of future hypersonic vehicles accounting for multi-physics effects. • A parallel thermochemical non-equilibrium solver coupled SST turbulence model is developed and validated. • The real gas effect on hypersonic shock wave/turbulent boundary-layer interaction is assessed via different gas models. • The effect of Mach number on hypersonic shock wave/turbulent boundary layer interaction is analyzed. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of components is a crucial aspect of mechanical systems prognostic and health management. However, during the online RUL prediction of components, the existing method cannot use the prediction results for feedback and optimize the prediction model. To address this issue, this paper proposes a framework for RUL prediction with adaptive dynamic feedback. First, a feedback multi-feature fusion (FMFF) method is designed. It introduces a series of non-exceeding threshold positive constants, that is fraction thresholds (FT), to perform division of the degradation state dimension. Besides, the first hitting time (FHT) from the current moment to FT is the fraction remaining useful life (FRUL). It is used to construct and update the fusion factors. Then, the feedback multi-model selection (FMMS) method is proposed for adaptive feedback and to select the degradation model based on the FRUL prediction results in different stages. The probability density function of RUL based on the FHT is calculated based on the FMMS method to realize the RUL prediction of the component. Finally, the effectiveness and superiority of the proposed framework are validated based on XJTU-SY dataset and run-to-failure test. [ABSTRACT FROM AUTHOR]

In this paper, a highly active surface enhanced Raman scattering (SERS) substrate based on a three-dimensional (3D) dendritic silver nanostructure was constructed in a microfluidic channel by a one-step electrodisplacement reaction for in situ detection of fluid media. The finite difference time domain (FDTD) method was used to simulate the mechanism of multiple surface plasmon resonance of dendritic silver structures. The results showed that the existence of secondary branches in silver dendrites could produce stronger local electric field, which verified the effectiveness of the SERS substrate. In addition, the in situ analysis performance of the microfluid-SERS detection system was investigated in different concentrations of rhodamine 6G (R6G), with a detection limit up to 10−10 mol L−1. The successful practice of this method opened a new way for the fabrication of high-performance microfluidic-SERS integrated sensor systems, and is expected to become a powerful analytical tool in the field of ultra-sensitive and rapid in situ analysis. [ABSTRACT FROM AUTHOR]

To explore the relationship between the cutting vibration and the cutting load of a single pick, this paper studied a new method for a single pick cutting rock load identification. This paper improved the low accuracy problem of the regularization method in the inverse process of frequency response function in the traditional load identification method by introducing a filter operator. By combining the inverse pseudoexcitation method and the improved regularization method, the identification of the load dependent on the vibration signal was realized. A single pick cutting rock test equipment was built, which could simulate the actual working conditions of pick cutting rock in the underground or tunnel. By changing cutting speed, cutting angle, cutting spacing, and cutting depth of the single pick, the change trends of real cutting load and identification load were obtained. The load identification method proposed in this paper was consistent with the change trend of the real load under the single pick cutting state. Therefore, the method had good recognition accuracy and the maximum load recognition error was 17.35%. Compared with other traditional load identification methods, the identification error was reduced by a maximum of 1.98%. This method can identify the cutting load of single pick and modify the morbidity problem of frequency response function matrix. The method has a better recognition effect on the cutting load of the pick than the traditional recognition methods. The research could benefit the design of the cutting system and the arrangement of the pick on the coal mine or tunneling machinery. [ABSTRACT FROM AUTHOR]

In the industrial process, the safety and reliability of the mechanical system determine the quality of the product, and whether small faults can be diagnosed in time is the key to ensuring the safe operation of the system and restraining the deterioration of faults. In recent years, the data-driven fault diagnosis has attracted widespread attention in academia. However, the traditional data-driven fault diagnosis methods rely on the features extracted from expert systems, so that the effect of fault diagnosis is entirely reliant on how well the expert system can extract the features. This paper proposes a new fault diagnosis method based on AlexNet Convolutional neural network (CNN) from a data-driven perspective. Firstly, a new method for converting time-domain vibration signal into RGB image based on erosion operation (EOSTI) is proposed. Initially converted three-dimensional (3-D) images have relatively close structural elements and are difficult to identify. For such defects, the target separated RGB image is generated. Secondly, explore the classification accuracy of AlexNet to make it more suitable for fault classification of different bearing datasets. Finally, the proposed method which is tested on two datasets, including coal washing machine dataset, maintenance fault dataset, has achieved prediction accuracy of 99.43 % and 99.67 %, respectively. The results have been compared with other methods. The comparisons show the effectiveness and accuracy of the proposed approach. The result shows that this method is feasible in engineering practice. [ABSTRACT FROM AUTHOR]

Due to the discrepancy of the components themselves, operating conditions and loads, the bearings show different degradation processes, and a single fixed model cannot describe different degradation processes accurately. In order to solve the adaptive prediction model for component degradation, a hybrid prediction technique is proposed in this paper, firstly, the Theil-sen estimator (TSE) is proposed as the health indicator of bearing operation, and it has been validated that the TSE has good trendability, monotonicity, and robustness. Based on the TSE, in order to identify the time to start prediction (TSP) accurately, the idea of deviation degree is proposed to reflect the difference between local and global degradation trend. Then, the RUL prediction is performed by matching the Wiener model suitable for the prediction task through the model adaptive algorithm. Finally, the effectiveness of the proposed method is verified by XJTU-SY bearing dataset and bearing accelerated degradation experiments. [ABSTRACT FROM AUTHOR]

• A proportion-weighted method is proposed to construct a new health indicator. • A two-step outlier detection strategy is proposed to solve clustering boundary. • A staged RUL prediction model is proposed to match models in different stages. • RUL prediction with the XJTU-SY bearing datasets and the self-test datasets. Remaining useful life (RUL) prediction is an important prognostic task in mechanical maintenance. However, a single health indicator cannot provide complete degradation information and clearly divide the degradation process, which reduces the accuracy of predicting the RUL of key components. Therefore, an adaptive staged RUL prediction method based on feature fusion is proposed. Firstly, a proportion-weighted method is proposed, which integrates various degradation information of rolling bearings to construct an evaluative health indicator (EHI). Secondly, aiming at the problem of clustering boundary ambiguity in different degradation stages, a two-step outlier detection strategy is proposed to solve the boundary point state interweaving problem between stages. Then, a staged RUL prediction model is proposed to match the model of the corresponding stage adaptively and dynamically to accurately predict RUL. Finally, two different sets of experiments are carried out to verify the accuracy and superiority of the proposed method in the RUL prediction. [ABSTRACT FROM AUTHOR]

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government's support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system. [ABSTRACT FROM AUTHOR]

Magnetohydrodynamic (MHD) control of hypersonic laminar separation flows is investigated in this paper. A series of numerical simulations over various geometry configurations, namely, a compression corner and a double wedge ramp hypersonic inlet, have been conducted by application of an external electromagnetic field. Results show that the performance of MHD separation flow control is mainly determined by flow acceleration of the Lorentz force directed in the streamwise direction. The Joule heating term always brings negative effects on the MHD separation flow control and increased the static pressure locally, where the electromagnetic field is applied. With an external electromagnetic field applied, the low velocity fluid in the boundary layer can be accelerated. Moreover, there exists a best location for the MHD zone to be applied and completely eliminate the separation of the flow from the surface. [ABSTRACT FROM AUTHOR]

Under progressive Type-II censoring, inference of stress-strength reliability (SSR) is studied for a general family of lower truncated distributions. When the lifetime models of the strength and stress variables have arbitrary and common parameters, maximum likelihood and pivotal quantities based generalized estimators of SSR are established, respectively. Confidence intervals are constructed based on generalized pivotal quantities and bootstrap technique under different parameter cases as well. In addition, to compare the equivalence of the strength and stress parameters, likelihood ratio testing of interested parameters is provided as a complementary. Simulation studies and two real-life data examples are provided to investigate the performance of proposed methods. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the tribological properties of the WC/TiC-Co substrate under different loading conditions under three impact abrasive wear conditions. Design/methodology/approach: The three body collisional wear behavior of Co alloy with WC and TiC at three impact energy was studied from 1 to 3 J. Meanwhile, the microstructure, hardness, phase transformation and wear behavior of these specimens were investigated by scanning electron microscopy, Rockwell hardness (HRV), EDS and impact wear tester. The resulting wear rate was quantified by electronic balance measurements under different pressures. Findings: The specific wear rate increases with the increase of the nonlinearity of the impact energy and the increase in the content of WC or TiC. The effect of TiC on wear rate is greater than that of WC, but the hardness is smaller. The wear characteristics of the samples are mainly characterized by three kinds of behavior, such as cutting wear, abrasive wear and strain fatigue wear. The WC-Co with fewer TiC samples suffered heavier abrasive wear than the more TiC samples under both low and high impact energy and underwent fewer strain fatigue wears under high impact energy. Originality/value: The experimental results show that the wear resistance of the Co alloy is improved effectively and the excellent impact wear performance is achieved. The results can be used in cutting tools such as coal mine cutting machines or other fields. [ABSTRACT FROM AUTHOR]

Composite nanofiber meshes of well-aligned polyacrylonitrile (PAN)/FeCo nanofibers containing nanoparticles (NPs) were successfully fabricated by a magnetic-field-assisted electrospinning technology, which was confirmed to be a favorable method for the preparation of aligned composite nanofibers in this article. Meanwhile, FeCo NPs, with a particle size of approximately 60 nm, were synthesized using a hydrothermal route. The nanocomposite fibers were prepared by an electrospun solution of PAN containing 0, 2, 4, and 6 wt% NPs. The as-spun nanofibers were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometer. Both the diameters and the degree of alignment of the composite nanofibers decreased with the increase in voltage and increased with the increase in FeCo content. The composite nanofibers exhibited superior ordered performance, with the highest alignment value being 97%. Due to the highly ordered alignment structures, the composite nanofiber meshes showed large anisotropic magnetic property. In particular, the saturation magnetization of the composite nanofiber films in the parallel and perpendicular directions of the fiber axis were 42 emu/g and 19.5 emu/g, respectively. Meanwhile, the remanence also exhibited distinction in different directions (parallel: 2.01 emu/g; perpendicular: 0.86 emu/g). [ABSTRACT FROM AUTHOR]

Nanoplastics (NPs) are an emerging threat to higher plants in terrestrial ecosystems. However, the molecular of NP-related phytotoxicity remains unclear. In the present study, rice seedlings were exposed to polystyrene (PS, 50 nm) NPs at 0, 50, 100, and 200 mg/L under hydroponic conditions to investigate the induced physiological indices and transcriptional mechanisms. We found that 50, 100, and 200 mg/L PS significantly reduced root (53.05%, 49.61%, and 57.58%, respectively) and shoot (54.63%, 61.56%, and 62.64%, respectively) biomass as compared with the control seedlings. The activities of antioxidant enzymes, including catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and ascorbate peroxidase (APX), were significantly activated in all PS treatment groups, indicating that PS inhibited plant growth and induced oxidative stress. Transcriptome analyses showed that PS modulated the expression of the genes involved in cell detoxification, active oxygen metabolism, mitogen-activated protein kinase (MAPK), and plant hormone transduction pathways. Our study provides new insights into phytotoxicity by demonstrating the potential underlying toxicity of PS NPs in higher plants. [Display omitted] • PS affects physiological and molecular indices in rice. • ROS signaling may be a key pathway for the adverse responses to PS exposure. • High-concentration PS may affect rice growth via transporter channel proteins. • PS was taken up through the root tip. [ABSTRACT FROM AUTHOR]

Check valve is an important part of the range hood. In this paper, a novel structure of check valve is presented to promote the opening performance. The numerical model of the improved valve is established, and a comparative analysis between the conventional valve and the improved valve is presented. The flow velocity and the pressure difference of the valves are investigated. Mainly, the opening performance of the valves are compared and discussed. The results show that as the air flux increases, the disk net force of improved valve increase much faster than that of the conventional valve. When the disk is working inside the spherical chamber region, the difference of the disk net force between the improved check and the conventional check becomes more significant as the opening angle increases. The results indicate that the improved valve has a better opening performance compared with the conventional valve. [ABSTRACT FROM AUTHOR]

In order to correctly understand the bearing capacity, failure mode and failure mechanism of AlSi10Mg porous optimized structures at different strain rates under compressive load, a series of experimental and simulative studies were carried out. Through quasi-static and modified Split Hopkinson Pressure Bar (SHPB) experiments, the damage failure modes of the AlSi10Mg porous structures with three different volume fractions are obtained at different strain rates. The stress-strain curves of AlSi10Mg porous structures obtained from the experimental results at different high strain rates show that its mechanical behavior is not sensitive to strain rates. Combined with meso-structure analysis and high-precision numerical simulation, it is found that the shear failure of AlSi10Mg porous structures due to relative dislocation along the inclined section under axial compressive load is the most direct cause of failure, which reveals its failure mechanism. Combining experimental research and theoretical analysis, the energy absorption characteristics and influencing factors of AlSi10Mg porous structures are determined at different strain rates. At the same time, the prediction models of dynamic elastic modulus and dynamic compressive strength of AlSi10Mg porous structures are constructed, which accurately describe the variation law between dynamic elastic modulus, dynamic compressive strength and structural volume fractions. These research works in this paper will provide an important reference for the structure optimal design of lightweight, strong energy absorption and impact resistance. [ABSTRACT FROM AUTHOR]

• A MILP model for parallel machine scheduling and maintenance is proposed. • Job-machine release and maintenance duration/interval flexibility are considered. • A RUL prediction method is proposed to get online machine reliability. • Online machine reliability is used to guide all maintenance activities. The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel machine scheduling problem. Based on this, a corresponding mixed integer programming model is constructed to minimize the makespan and the processing loss beyond the machine RUL threshold, where a discrete teaching and learning based optimization algorithm is applied to solve this NP-hard problem, and a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines. In addition, this paper demonstrates that the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

• First, a new POMP algorithm is proposed, which can efficiently reconstruct the original signal. • Second, the convolutional neural network (CNN) is designed for high-dimensional signals, so that the one-dimensional signal is converted to three-dimensional image for further training.. • Third, the multiscale algorithm is applied to the CNN architecture, and MIRCNN is established by adding inverted residual learning. It can extract the different features between fault signals of variable load bearings, improving the ability to identify faults. • Finally, experimental results on two rolling bearing test beds with different bearing types and operating conditions and compared with existing state-of-the-art methods to prove the effectiveness and accuracy of the proposed method. In industrial production, it is particularly important to diagnose the bearing fault in time under variable loads. The intelligent diagnosis method has strong robustness without human intervention, but it needs a lot of raw data. However, large amounts of data storage is relatively difficult and slow transmission speed. Meanwhile, under different loads, the same fault feature has no significant difference in the process of bearing degradation. To address these problems, this article proposes a new multiscale inverted residual convolutional neural network (MIRCNN) method for fault diagnosis of variable load bearing. Firstly, a semi tensor product compressed sensing (CS) method based on parallel orthogonal matching pursuit (POMP) is proposed. The vibration signal is reconstructed with the proposed method to solve the problems of difficult data storage and slow transmission speed. Then, the convolutional neural network (CNN) is designed for high-dimensional signals, so that the one-dimensional signal is converted to three-dimensional image for further training. Finally, the multiscale algorithm is applied to the CNN architecture, and MIRCNN is established by adding inverted residual learning. It can extract the different features between fault signals of variable load bearings, improving the ability to identify faults. Experimental results on two rolling bearing test beds with different bearing types and operating conditions and compared with existing state-of-the-art methods to prove the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

• This paper proposes an adaptive maximum cyclostationary blind deconvolution (AMCBD) to diagnose bearing faults. • This paper presents the performance efficiency ratio (PER) index. This index is proposed after considering the noise reduction effect and time cost of deconvolution, which can evaluate the cost performance of deconvolution. • On the basis of PER, combined with the equal-step search strategy, an adaptive strategy of filter length is proposed. • This paper proposes a cyclic frequency estimation method based on autocorrelation function of morphological envelope. Blind deconvolution has been proved to be an effective method for fault detection since it can recover periodic impulses from mixed fault signals convoluted by noise and periodic impulses. As a new blind deconvolution technique, maximum cyclostationarity blind deconvolution (CYCBD) has great advantages over minimum entropy deconvolution (MED), maximum correlation kurtosis deconvolution (MCKD), and optimal minimum entropy deconvolution (MOMEDA) in processing bearing fault signals. However, CYCBD has the following two defects: Cyclic frequency needs to be determined in advance; The filter length of CYCBD affects its ability to recover impulses. The ability of CYCBD to recover the impulse will increase with the increase of filter length, but the signal will be distorted if the length is too large. Besides, the time cost will increase significantly after the length is increased. In this paper, an optimization strategy of CYCBD parameters is proposed, and then an adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed. Firstly, aiming at the determination of cyclic frequency, this paper proposes a cyclic frequency set estimation method based on autocorrelation function of morphological envelope, and the validity of the method is verified by simulation and experiment. Secondly, for the second problem, after considering the performance and time cost of CYCBD, the performance efficiency ratio index is proposed. Then, the equal-step search strategy is used to adaptively select the filter length. Finally, the effectiveness of the method is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

Electric bicycle main frame is the most principal structure, connecting and supporting other various components, while bearing a variety of forces and moments. In this paper the topology optimization technology is applied to generate robust electric bicycle main frame by optimizing the material distribution subject to the constraints and dynamic loads. Geometric, mechanical and finite element models, as well as a flexible coupling dynamic model are constructed. Validity and accuracy of these models are investigated through real-life testing. By applying typical road excitation, dynamic loads of all key points are extracted. A set of forces data is extracted every 0.5 s during the whole simulation, including peak values of these forces. In order to obtain appropriate topology optimization results, the values of two crucial parameters, volume fraction and minimum member size, are discussed respectively. Then the topology optimization of multi-load case is implemented with the objective of minimizing the set of weighted compliances resulting from individual load cases. Results illustrate that element density distribution of the model is optimized with manufacturing constraints of minimum member size control and extrusion constraint. Consequently, the better frame form design of the electric bicycle is obtained. Modal analysis for the original and refined models is performed respectively to evaluate the structure stiffness. The results indicate that this optimization program is effective enough to develop a new electric bicycle frame as a reference for manufacturers. [ABSTRACT FROM AUTHOR]

The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method. [ABSTRACT FROM AUTHOR]