Accumulation of heavy metals in two crop seeds due to soil contamination as determined by neutron activation analysis techniques

Samples of two crop seeds (corn and sesame) grown on different contaminated soil sources were collected and prepared for neutron activation analysis. Soil contamination sources were organic waste compost applied to soil amendments namely, sewage sludge(Bs) and municipal solid waste (MSW) at 4%, 6% and 8% respectively. The non destructive NAA technique was used to determine some trace elements accumulation in plant seeds. Results revealed that increasing rate of tested organic waste resulted in dramatic increase in tested heavy metals levels in seeds(i.e., Fe, Zn, Co, Cr, Sc and Hg). Sesame seeds showed higher affinity to accumulate trace elements than corn grains in most tested elements. Moreover, MSW addition enhanced the accumulation of tested metals in seeds more than Bs compost.

Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

This paper presents a procedure of sing le gear tooth analysis for early detection and diagnosis of gear faults. The objec tive of this procedure is to develop a method for more sensitive detection of th e incipient faults and locating the faults in the gear. The main idea of the sin gle gear tooth analysis is that the vibration signals collected with a high samp ling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of i ndividual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the locat ion of a failed tooth can be indicated in terms of the position of the segment t hat deviates from the normal segments. An experimental investigation verified th e advantages of the single gear tooth analysis.

Optimization of Baosteel's digital automatic analysis technique for maceral specification

Baosteel developed a digital automatic analysis technique for maceral specification in 2002. This analysis system combines digital image processing, graphics, databases, expert systems, artificial intelligence and other advanced technologies. After 6 years of application in coke production, the system proved itself successful in coal quality testing and coal blending guidance on maceral. However,during this long process, some inadequacies were found that impacted the precision and accuracy of the analysis. So ,in 2008 Baosteel began to work on improving the coal maceral analysis system. The improvements included the following：further upgrading and enhancing the analysis performance of microscopic images ;extending the gray levels to increase the reflectance measurement accuracy 64 times;changing the focus method and effectively eliminating the interference of halo. In addition, an improved image recognition method was adopted to make the extraction of vitrinite more accurate and a new model of coal constituent algorithm was added which can accurately determine the composition of maceral （exinite, vitrinite,inertinite）. Since these improvements were completed, the system has achieved higher automation, speed and accuracy, collected more information and performed more accurate maceral analysis for coke production. Meanwhile, the improved system has provided a reliable analytical basis for the further study on the relationship between coke quality and coal blending.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.

Arc-length technique for nonlinear finite element analysis

Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.

Nonlinear Dynamic Analysis of Deepwater Drilling Risers Subjected to Random Loads

Excited by ocean currents, random wave and vessel motion, deepwater drilling risers exhibit significant dynamic response. In time domain, a method is proposed to calculate the nonlinear dynmnic response of deepwater drilling risers subjected to random wave and dynamic large displacement vessel motion boundary condition. Structural and functional loads, external and intemal pressure, free surfaee effect of irregular wave, hydrodynamic forees induced by current and wave, as well as wave and low frequency （drift） motion of the drilling vessel are all accounted for. An example is presented which illustrates the application of the proposed method. The study shows that long term drift motion of the vessel has profound effect on the envelopes of bending stress and lateral displacement, as well as the range of lower flex joint angle of the deepwater riser. It can also be concluded that vessel motion is the principal dynamic loading of nonlinear dynamic response for the deepwater risers rather than wave force.

Analysis of the geomorphology surrounding the Chang'e-3 landing site

Chang＇e-3 （CE-3） landed on the Mare Imbrium basin in the east part of Sinus Iridum （19.51°W, 44.12°N）, which was China＇s first soft landing on the Moon and it started collecting data on the lunar surface environment. To better understand the environment of this region, this paper utilizes the available high-resolution topography data, image data and geological data to carry out a detailed analysis and research on the area surrounding the landing site （Sinus Iridum and 45 km×70 km of the landing area） as well as on the topography, landform, geology and lunar dust of the area surrounding the landing site. A general topographic analysis of the surrounding area is based on a digital elevation model and digital elevation model data acquired by Chang＇e-2 that have high resolution; the geology analysis is based on lunar geological data published by USGS; the study on topographic factors and distribution of craters and rocks in the surrounding area covering 4km^4km or even smaller is based on images from the CE-3 landing camera and images from the topographic camera; an analysis is done of the effect of the CE-3 engine plume on the lunar surface by comparing images before and after the landing using data from the landing camera. A comprehensive analysis of the results shows that the landing site and its surrounding area are identified as typical lunar mare with flat topography. They are suitable for maneuvers by the rover, and are rich in geological phenomena and scientific targets, making it an ideal site for exploration.

Undrained Stability Analysis of Three-Dimensional Rectangular Trapdoor in Clay

A new collapse model of the trapdoors,three-dimensional rectangular trapdoor(3DRT),is presented for ground surface collapse.Undrained stability of 3DRT is examined with the upper bound method of plasticity limit analysis theory.The soil where the trapdoors are located is assumed to be a perfectly plastic model with a Tresca yield criterion.Block analysis technique is employed to investigate the collapse of 3DRT.The model is divided into five different block types and added up to ten rigid blocks.According to the law of conservation of energy,the critical stability ratios of 3DRT are obtained through a search proceeding.The results of upper bound solution for 3DRT are given,and three trapdoor models with depth various are discussed during the application in the stability analysis of square trapdoors.The critical stability ratios can be used in the design of underground excavation and support force.

Combination of terrestrial reference frames based on space geodetic techniques in SHAO:methodology and main issues

Based on years of input from the four geodetic techniques （SLR, GPS, VLBI and DORIS）, the strategies of the combination were studied in SHAO to generate a new global terrestrial reference frame as the material realization of the ITRS defined in IERS Conventions. The main input includes the time series of weekly solutions （or fortnightly for SLR 1983-1993） of observational data for satellite techniques and session-wise normal equations for VLBI. The set of estimated unknowns includes 3- dimensional Cartesian coordinates at the reference epoch 2005.0 of the stations distributed globally and their rates as well as the time series of consistent Earth Orientation Parameters （EOPs） at the same epochs as the input. Besides the final solution, namely SOL-2, generated by using all the inputs before 2015.0 obtained from short-term observation processing, another reference solution, namely SOL- 1, was also computed by using the input before 2009.0 based on the same combination of procedures for the purpose of comparison with ITRF2008 and DTRF2008 and for evaluating the effect of the latest six more years of data on the combined results. The estimated accuracy of the x-component and y-component of the SOL- 1 TRF-origin was better than 0.1 mm at epoch 2005.0 and better than 0.3 mm yr- 1 in time evolution, either compared with ITRF2008 or DTRF2008. However, the z-component of the translation parameters from SOL-1 to ITRF2008 and DTRF2008 were 3.4 mm and -1.0 ram, respectively. It seems that the z-component of the SOL-1 TRF-origin was much closer to the one in DTRF2008 than the one in ITRF2008. The translation parameters from SOL-2 to ITRF2014 were 2.2, -1.8 and 0.9 mm in the x-, y- and z-components respectively with rates smaller than 0.4 mmyr-1. Similarly, the scale factor transformed from SOL-1 to DTRF2008 was much smaller than that to ITRF2008. The scale parameter from SOL-2 to ITRF2014 was -0.31 ppb with a rate lower than 0.01 ppb yr-1. The external precision （WRMS） compared with IERS EOP 08 C04 of the combined EOP series was smaller than 0.06 mas for the polar motions, smaller than 0.01 ms for the UT1-UTC and smaller than 0.02 ms for the LODs. The precision of the EOPs in SOL-2 was slightly higher than that of SOL-1.

Wavelet Analysis of Space Solar Telescope Images

The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

A fast Stokes inversion technique based on quadratic regression

Stokes inversion calculation is a key process in resolving polarization information on radiation from the Sun and obtaining the associated vector magnetic fields. Even in the cases of simple local thermo- dynamic equilibrium （LTE） and where the Milne-Eddington approximation is valid, the inversion problem may not be easy to solve. The initial values for the iterations are important in handling the case with mul- tiple minima. In this paper, we develop a fast inversion technique without iterations. The time taken for computation is only 1/100 the time that the iterative algorithm takes. In addition, it can provide available initial values even in cases with lower spectral resolutions. This strategy is useful for a filter-type Stokes spectrograph, such as SDO/HMI and the developed two-dimensional real-time spectrograph （2DS）.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments,such as low intelligence and poor comfort perfor-mance in the driving process.The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions.In this paper,in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are pro-posed.Especially,the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition,and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve.By using low-order interpolation curve splicing,the planning computation is reduced,and the real-time performance of planning is improved,com-pared with one-segment curve fitting method.Furthermore,the comfort performance of the planned path is reflected intuitively by the curvature information of the path.Finally,the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim.The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

In this study, the author will investigate and utilize advanced machine learning models related to two different methodologies to determine the best and most effective way to predict individuals with heart failure and cardiovascular diseases. The first methodology involves a list of classification machine learning algorithms, and the second methodology involves the use of a deep learning algorithm known as MLP or Multilayer Perceptrons. Globally, hospitals are dealing with cases related to cardiovascular diseases and heart failure as they are major causes of death, not only for overweight individuals but also for those who do not adopt a healthy diet and lifestyle. Often, heart failures and cardiovascular diseases can be caused by many factors, including cardiomyopathy, high blood pressure, coronary heart disease, and heart inflammation [1]. Other factors, such as irregular shocks or stress, can also contribute to heart failure or a heart attack. While these events cannot be predicted, continuous data from patients’ health can help doctors predict heart failure. Therefore, this data-driven research utilizes advanced machine learning and deep learning techniques to better analyze and manipulate the data, providing doctors with informative decision-making tools regarding a person’s likelihood of experiencing heart failure. In this paper, the author employed advanced data preprocessing and cleaning techniques. Additionally, the dataset underwent testing using two different methodologies to determine the most effective machine-learning technique for producing optimal predictions. The first methodology involved employing a list of supervised classification machine learning algorithms, including Naïve Bayes (NB), KNN, logistic regression, and the SVM algorithm. The second methodology utilized a deep learning (DL) algorithm known as Multilayer Perceptrons (MLPs). This algorithm provided the author with the flexibility to experiment with different layer sizes and activation functions, such as ReLU, logistic (sigmoid), and Tanh. Both methodologies produced optimal models with high-level accuracy rates. The first methodology involves a list of supervised machine learning algorithms, including KNN, SVM, Adaboost, Logistic Regression, Naive Bayes, and Decision Tree algorithms. They achieved accuracy rates of 86%, 89%, 89%, 81%, 79%, and 99%, respectively. The author clearly explained that Decision Tree algorithm is not suitable for the dataset at hand due to overfitting issues. Therefore, it was discarded as an optimal model to be used. However, the latter methodology (Neural Network) demonstrated the most stable and optimal accuracy, achieving over 87% accuracy while adapting well to real-life situations and requiring low computing power overall. A performance assessment and evaluation were carried out based on a confusion matrix report to demonstrate feasibility and performance. The author concluded that the performance of the model in real-life situations can advance not only the medical field of science but also mathematical concepts. Additionally, the advanced preprocessing approach behind the model can provide value to the Data Science community. The model can be further developed by employing various optimization techniques to handle even larger datasets related to heart failures. Furthermore, different neural network algorithms can be tested to explore alternative approaches and yield different results.

Profile of Absolute Light Utilization Efficiency Within Leaves of Euonymus japonicus

Absolute light utilization efficiency across leaf section of Euonymus japonicus T. was calculated based on the measurements of photoacoustic technique (PA technique) and microscopic fiber optic probe. This new method was based on the principal of depth analysis by PA technique and the differential analysis of light gradients across leaf section by micro-optical probe technique. The depth analysis was shown by a sample of PA scan light absorption spectra. Results showed that the tissue layers between palisade tissue and spongy tissue used the smallest proportion of incident light energy for photochemical reactions (about 0.026% incident light energy of 660 nm light), while in tissue layer more close to the adaxial surface of leaf or the abaxial surface of leaf, the efficiency of utilization of light energy tended to be improved, e. g. 0.092% for tissue layers close to adaxial surface; 0.036% for tissue layers close to abaxial surface. The results that different leaf tissue layers utilized different proportion of incident light energy for photochemical reaction directly prove the hypothesis put forward by Han and Vogelmann.

Estimating Tropical Cyclone Precipitation from Station Observations

In this paper, an objective technique for estimating the tropical cyclone （TC） precipitation from station observations is proposed. Based on a comparison between the Original Objective Method （OOM） and the Expert Subjective Method （ESM）, the Objective Synoptic Analysis Technique （OSAT） for partitioning TC precipitation was developed by analyzing the western North Pacific （WNP） TC historical track and the daily precipitation datasets. Being an objective way of the ESM, OSAT overcomes the main problems in OOM, by changing two fixed parameters in OOM, the thresholds for the distance of the absolute TC precipitation （D0） and the TC size （D1）, into variable parameters.Case verification for OSAT was also carried out by applying CMORPH （Climate Prediction Center MORPHing technique） daily precipitation measurements, which is NOAA＇s combined satellite precipitation measurement system. This indicates that OSAT is capable of distinguishing simultaneous TC precipitation rain-belts from those associated with different TCs or with middle-latitude weather systems.

Calpain mediated cisplatin-induced ototoxicity in mice

Ototoxic drug-induced apoptosis of inner ear cells has been shown to be associated with calpain expression. Cisplatin has severe ototoxicity, and can induce cochlear cell apoptosis. This study assumed that cisplatin activated calpain expression in apoptotic cochlear cells. A mouse model of cisplatin-induced ototoxicity was established by intraperitoneal injection with cisplatin （2.5, 3.5, 4.5, 5.5 mg/kg）. Immunofluorescence staining, image analysis and western blotting were used to detect the expression of calpain 1 and calpain 2 in the mouse cochlea. At the same time, the auditory brainstem response was measured to observe the change in hearing. Results revealed that after intraperitoneal injection with cisplatin for 5 days, the auditory brainstem response threshold shifts increased in mice. Calpain 1 and calpain 2 expression significantly increased in outer hair cells, the spiral ganglion and stria vascularis. Calpain 2 protein expression markedly increased with an increased dose of cisplatin. Results suggested that calpain 1 and calpain 2 mediated cisplatin-induced ototoxicity in BALB/c mice. During this process, calpain 2 plays a leading role.

Optical fiber positioning based on four-quadrant detector with Gaussian fitting method

With the development of large-scale spectral surveys, fiber positioning technology has been developing rapidly. Because of the performance advantages of a four-quadrant（4Q） detector, a fiber positioning and real-time monitoring system based on the 4Q detector is proposed. The detection accuracy of this system is directly determined by the precision of the center of the spot. A Gaussian fitting algorithm based on the 4Q detector is studied and applied in the fiber positioning process to improve the calculated accuracy of the spot center. The relationship between the center position of the incident spot and the detector output signal is deduced. An experimental platform is built to complete the simulated experiment. Then we use the Gaussian fitting method to process experimental data, compare the fitting value with the theoretical one and calculate the corresponding error.