Fault diagnosis of electro-mechanical actuator based on WPD-STFT time-frequency entropy and PNN

Electro-mechanical actuators (EMAs) are increasingly being used as critical actuation devices of the aircraft. It will cause serious accidents once the fault of EMAs occurs, thus the fault diagnosis of EMAs is essential to maintain the normal operation of aircraft. In this paper, a method based on WPD-STFT time-frequency entropy and PNN is proposed to achieve fault diagnosis of EMAs by processing the vibration signals collected by the accelerometer installed in the EMAs. Firstly, the vibration signals are decomposed by wavelet packet to obtain the signal components of different frequency bands, the signal components are subjected to STFT and spectrograms are obtained. Then, time-frequency entropy is calculated and combined with principal component analysis (PCA) for dimension reduction as the feature vector. Finally, the probabilistic neural network (PNN) classifier is introduced to classify the fault modes. The experimental result shows that this method can accomplish the accurate fault diagnosis of EMAs. Moreover, the performance of the proposed WPD-STFT time-frequency entropy method has an advantage over that of WPD-PCA method or STFT combined with mass-moment entropy method for feature extraction

Drug Delivery Applications of Coaxial Electrospun Nanofibres in Cancer Therapy

Cancer is one of the most serious health problems and the second leading cause of death worldwide, and with an ageing and growing population, problems related to cancer will continue. In the battle against cancer, many therapies and anticancer drugs have been developed. Chemotherapy and relevant drugs are widely used in clinical practice; however, their applications are always accompanied by severe side effects. In recent years, the drug delivery system has been improved by nanotechnology to reduce the adverse effects of the delivered drugs. Among the different candidates, core–sheath nanofibres prepared by coaxial electrospinning are outstanding due to their unique properties, including their large surface area, high encapsulation efficiency, good mechanical property, multidrug loading capacity, and ability to govern drug release kinetics. Therefore, encapsulating drugs in coaxial electrospun nanofibres is a desirable method for controlled and sustained drug release. This review summarises the drug delivery applications of coaxial electrospun nanofibres with different structures and drugs for various cancer treatments

Deconvolution approach for floating wind turbines

Green renewable energy is produced by floating offshore wind turbines (FOWT), a crucial component of the modern offshore wind energy industry. It is a safety concern to accurately evaluate excessive weights while the FOWT operates in adverse weather conditions. Under certain water conditions, dangerous structural bending moments may result in operational concerns. Using commercial FAST software, the study's hydrodynamic ambient wave loads were calculated and converted into FOWT structural loads. This article suggests a Monte Carlo-based engineering technique that, depending on simulations or observations, is computationally effective for predicting extreme statistics of either the load or the response process. The innovative deconvolution technique has been thoroughly explained. The suggested approach effectively uses the entire set of data to produce a clear but accurate estimate for severe response values and fatigue life. In this study, estimated extreme values obtained using a novel deconvolution approach were compared to identical values produced using the modified Weibull technique. It is expected that the enhanced new de-convolution methodology may offer a dependable and correct forecast of severe structural loads based on the overall performance of the advised de-convolution approach due to environmental wave loading.publishedVersio

Double surface imaging designs with unconstrained object to image mapping under rotational symmetry

In this work, we present a novel imaging design formed by two optical surfaces with rotational symmetry. In these designs, both object and image shapes are given but mapping from object to image is obtained through the design process. In the examples considered, the image from a planar object surface is virtual and located at infinity and is seen from a known pupil, which can emulate a human eye. The differential equation method is used to provide single optical surface imaging designs by considering the local properties of the imaging surface and the wavefronts. In the first introductory part, both the rotational symmetrical and the freeform single surface imaging designs are presented using the differential equation method. In these designs, not only the mapping is obtained in the design process, but also the shape of the object is found. In the second part, the method is extended to two surface designs with rotational symmetry and the astigmatism of the image has been studied. By adding one more optical surface to the system, the shape of the rotational symmetrical object can be designed while controlling the tangential rays and sagittal rays simultaneously. As a result, designs without astigmatism (at the small pupil limit) on a planar object surface have been obtained. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Single freeform surface imaging design with unconstrained object to image mapping

An imaging design approach which is free of third-order astigmatism for one freeform optical surface and the image is presented in this paper. A set of differential equations is derived from generalized ray tracing. The solution of the above derived equations provides the anastigmatic freeform optical surface, the image surface and the object to image mapping. The obtained design can be used as a good starting point for optimization. As an example, a reflective freeform surface is designed for a single reflective Head Mounted Display (HMD). This example has a 3 mm pupil, 15mm eye clearance, 24-degree diagonal full field of view, and the final design yields an average MTF of 62.6% across 17 field points

A Case Study of Mass Transport during the East-West Oscillation of the Asian Summer Monsoon Anticyclone

We use ERA-Interim reanalysis, MLS observations, and a trajectory model to examine the chemical transport and tracers distribution in the Upper Troposphere and Lower Stratosphere (UTLS) associated with an east-west oscillation case of the anticyclone in 2016. The results show that the spatial distribution of water vapor (H2O) was more consistent with the location of the anticyclone than carbon monoxide (CO) at 100 hPa, and an independent relative high concentration center was only found in H2O field. At 215 hPa, although the anticyclone center also migrated from the Tibetan Mode (TM) to the Iranian Mode (IM), the relative high concentration centers of both tracers were always colocated with regions where upward motion was strong in the UTLS. When the anticyclone migrated from the TM, air within the anticyclone over Tibetan Plateau may transport both westward and eastward but was always within the UTLS. The relative high concentration of tropospheric tracers within the anticyclone in the IM was from the east and transported by the westward propagation of the anticyclone rather than being lifted from surface directly. Air within the relative high geopotential height centers over Western Pacific was partly from the main anticyclone and partly from lower levels

Vehicle Routing Problem in Cold Chain Logistics: a Joint Distribution Model with Carbon Trading Mechanisms

Fierce competition and the mandate for green development have driven cold chain logistics companies to minimize total distribution costs and carbon emissions to gain a competitive advantage and achieve sustainable development. However, the cold chain logistics literature considers carbon trading mechanisms in sharing economy, namely the joint distribution, is limited. Our research builds a Joint Distribution-Green Vehicle Routing Problem (JD-GVRP) model, in which cold chain logistics companies collaborate among each other to deliver cold chain commodities by considering carbon tax policy. Based on the real business data from four cold chain companies and 28 customers, a simulated annealing (SA) algorithm is applied to optimize the model. The results indicate that joint distribution is an effective way to reduce total costs and carbon emissions when compared with the single distribution. The total cost is positively correlated with the carbon price, while the carbon emissions vary differently when the carbon price increases. In addition, carbon quotas have no effect on the delivery path. This research expands cold chain logistics literature by linking it with joint distribution and carbon trading mechanisms. Moreover, this research suggests that cold chain logistics companies could enhance delivery efficiency, reduce the business cost, and improve competitiveness by reinforcing the collaboration at the industry level. Furthermore, the government should advocate the mode of joint distribution and formulate an effective carbon trading policy to better utilize social and industrial resources to achieve the balanced economic and environmental benefits

sTarPicker: A Method for Efficient Prediction of Bacterial sRNA Targets Based on a Two-Step Model for Hybridization

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/

Anastigmatic imaging with unconstrained object to image mapping

Anastigmatic imaging of an object to an image surfaces without the point-to-point mapping prescription and using a single optical surface is analyzed in 2D and 3D geometries (free-form and rotational-symmetric). Several design techniques are shown

Enhancing remediation potential of heavy metal contaminated soils through synergistic application of microbial inoculants and legumes

Soil microorganisms play a crucial role in remediating contaminated soils in modern ecosystems. However, the potential of combining microorganisms with legumes to enhance the remediation of heavy metal-contaminated soils remains unexplored. To investigate this, we isolated and purified a highly efficient cadmium and lead-tolerant strain. Through soil-cultivated pot experiments with two leguminous plants (Robinia pseudoacacia L. and Sophora xanthantha), we studied the effects of applying this microbial agent on plant nutrient uptake of soil nutrients, heavy metal accumulation, and the dynamics of heavy metal content. Additionally, we examined the response characteristics of inter-root microbial and bacterial communities. The results demonstrated that microorganisms screened from heavy metal-contaminated soil environments exhibited strong survival and adaptability in heavy metal solutions. The use of the Serratia marcescens WZ14 strain-phytoremediation significantly increased the soil’s ammonium nitrogen (AN) and organic carbon (OC) contents compared to monoculture. In addition, the lead (Pb) and cadmium (Cd) contents of the soil significantly decreased after combined remediation than those of the soil before potting. However, the remediation effects on Pb- and Cd-contaminated soils differed between the two legumes following the Serratia marcescens WZ14 inoculation. The combined restoration altered the composition of the plant inter-rhizosphere bacterial community, with the increase in the relative abundance of both Proteobacteria and Firmicutes. Overall, the combined remediation using the tolerant strain WZ14 with legumes proved advantageous. It effectively reduced the heavy metal content of the soil, minimized the risk of heavy metal migration, and enhanced heavy metal uptake, accumulation, and translocation in the legumes of S. xanthantha and R. pseudoacacia. Additionally, it improved the adaptability and resistance of both legumes, leading to an overall improvement in the soil’s environmental quality. These studies can offer primary data and technical support for remediating and treating Cd and Pb in soils, as well as rehabilitating mining sites