Characterizing workload correlations in multi processor hard real-time systems

Rate transient analysis methods for water-producing gas wells in tight reservoirs with mobile water

Beamsteering for a phased array antenna generally causes the sidelobe level deterioration due to a variety of random and spatially correlated errors. Conventional array error analysis method is based on probability central limit theorem, and can be calculated by the probability statistic approach. In the paper, this reaserch presents a new estimation method of the phased array patterns by using numerical simulated model. Different from the conventional statistical analysis method, each unit error of the phased array antenna is taken into account rigorously. The element patterns with mutual coupling effects are researched by an active element pattern technique. The whole implemented process is not complicated calculous manipulation. The error samples of the phased array antenna are produced with normal distribution and corresponding array pattern simulated results are discussed. In order to validating the proposed method, an experimental phased array antenna is designed and machined, and the measured results indicate the proposed method is valid.

The microgrid contains inverter-type distributed generation (DG), and its fault characteristics are directly affected by the control strategies of DG. Therefore, the conventional fault analysis method of the distribution network is not suitable for the microgrid. Under fault conditions, the distributed generation has more complex fault characteristics due to its different control methods. Based on the output characteristic of inverter-type DG with constant power (PQ) control and V/F control, the constraint equation was obtained to construct an equivalent model of DG obstacle characteristic analysis. Then the equivalent models of microgrid under islanding operation and grid-connected operation modes are established, and the steady-state response is solved. Finally, the power grid simulation software DIgSILENT is applied to build a microgrid model containing inverter-type DGs which is constituted with PQ control and V/F control. By comparing with the conventional fault analysis method, the adaptability, effectiveness and accuracy of the proposed method are verified under different fault conditions as well as multiple control strategies, which provides an accurate criterion for microgrid protection method.

In order to increase the performance of underwater target line spectrum extraction, an adaptive polarization filtering line spectrum extraction method is researched in the paper. By using of the polarization characteristics of underwater target signals, the polarization characteristics of underwater target line spectrum signal is analyzed via singular value decomposition (SVD) method in frequency domain. Firstly, the line spectrum signal is presented by the eigenimage corresponding to the maximum singular value to increase the signal-to-noise ratio (SNR) of the signal. Then the polarization parameters are extracted according to the polarization characteristics of the line spectrum signals. And the line spectrum signal is filtered by constructing the polarization filtering function. The simulation results show that the output SNR of the proposed method are larger than that of conventional FFT analysis method when the SNR of the signal is larger than -9dB, and the detection probability of the proposed method is 100% when the SNR of the signal is -1 dB, while the detection probability of the conventional FFT analysis method is 27.73%. It shows that the proposed method provides a new approach for underwater line spectrum signal extraction techniques.

This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods.

This review paper reports recent progress in spectral analysis methods for resolution enhancement and band assignments in the near infrared (NIR) region. Spectra in the NIR region are inherently rich with information on the physical and chemical properties of molecules. However, it is not always straightforward to analyse the spectra because an NIR spectrum consists of a number of overlapped bands due to overtones and combination modes. An NIR spectrum may be analysed by conventional spectral analysis methods, chemometrics or two-dimensional correlation spectroscopy. The following conventional methods are currently utilised to analyse NIR spectra: (a) derivatives, (b) difference spectroscopy, (c) Fourier self-deconvolution and (d) curve fitting. The derivative method is powerful in separating superimposed bands and correcting for a baseline slope. Conventional experimental methods for spectral analysis, such as isotope exchange and measurement of polarisation spectra, are also valid in the NIR region. Chemometrics is very useful for extracting information from NIR spectra. Among a variety of chemometrics methods, multiple linear regression, principal component analysis, principal component regression and partial least squares regression are most often used for qualitative and quantitative analysis. Recently, chemometrics has been used for resolution enhancement of NIR spectra. Particularly, loadings plots or regression coefficients are useful for separating overlapped bands and for making band assignments. Notable recent advances in the analysis of NIR spectroscopy are the development or introduction of new spectral analysis methods such as two-dimensional (2D) correlation spectroscopy and self-modelling curve resolution methods (SMCR). 2D correlation analysis enables enhancement of apparent spectral resolution by spreading spectral peaks over a second dimension. SMCR allows one to resolve the experimental matrix into concentration profiles and pure spectra of the involved species without prior knowledge of any of these features.

Evaluation of an automated method of percent reactive antibody determination

The plant-available pools of calcium, magnesium and potassium are assumed to be stored in the soil as exchangeable cations adsorbed on the cation exchange complex. In numerous forest ecosystems, despite very low plant-available pools, elevated forest productivities are sustained. We hypothesize that trees access nutrient sources in the soil that are currently unaccounted by conventional soil analysis methods. We carried out an isotopic dilution assay to quantify the plant-available pools of calcium, magnesium and potassium and trace the soil phases that support these pools in 143 individual soil samples covering 3 climatic zones and 5 different soil types. For 81%, 87% and 90% of the soil samples (respectively for Ca, Mg and K), the plant-available pools measured by isotopic dilution were greater than the conventional exchangeable pool. This additional pool is most likely supported by secondary non-crystalline mineral phases in interaction with soil organic matter and represents in many cases (respectively 43%, 27% and 47% of the soil samples) a substantial amount of plant-available nutrient cations (50% greater than the conventional exchangeable pools) that is likely to play an essential role in the biogeochemical functioning of forest ecosystems, in particular when the resources of Ca, Mg and K are low.

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

Aroma analysis of horticultural produce is an emerging field in which both flavor producing and malodorous compounds are detected from within a complex sample matrix. Qualitative and quantitative information is desired to monitor produce ripeness and provide quality control over processed products such as juices, preserves and canned products. Conventional analysis methods such as purge and trap and gas chromatography–mass spectrometry provide much of this information but are laborious and time consuming. Faster techniques are required when large numbers of samples are being analyzed and when rapid feedback to the produce harvester is required. Solid-phase microextraction (SPME) has recently been shown to significantly reduce the sampling times required by more conventional methods. The use of fast gas chromatographic techniques along with the recently commercialized time-of-flight mass spectrometer has also significantly reduced the separation and analysis times. We have combined SPME with gas chromatography–time-of-flight mass spectrometry as a rapid and quantitative tool for the analysis of flavor volatiles in apples and tomatoes. The sampling and analysis processes provide significant improvements to sample throughput, with analysis times taking only 2–6 minutes. The linear response of this system to butylacetate, ethyl-2-methylbutanoate and hexylacetate ranges from ppb to ppm levels, and the identification of unknown flavor compounds is possible even in the presence of other co-eluting compounds. The SPME technique is able to investigate volatiles changes in apple cuticle and tissues, which open the new possibility for flavor biochemistry research.

Investigation of the problems with using gas adsorption to probe catalyst pore structure evolution during coking

This paper presents the experimental results of static vertical load tests on a model building frame with geotextile as plinth beam supported by pile groups embedded in cohesionless soil (sand). The experimental results have been compared with those obtained from the nonlinear FEA and conventional method of analysis. The results revealed that the conventional method of analysis gives a shear force of about 53%, bending moment at the top of the column about 17% and at the base of the column about 50–98% higher than that by the nonlinear FEA for the frame with geotextile as plinth beam.

A power system is a recognized complex network system. The simulation process of power system production simulation is a complex physical system accurate solution process, and conventional analytical methods are difficult to effectively achieve simulation goals. Therefore, complex system modeling and simulation methods must be adopted to achieve objective simulation of the operation of the power system. This paper applies the production simulation technology to the scale assessment of flexibility demand, clarifies the technical principle of the production simulation simulator, optimizes the production simulation model, proposes the unit combination and economic dispatch considering safety constraints, and finally builds the production simulation model of the Beijing-Tianjin-Tangshan power grid, and finally builds the production simulation model of the Beijing-Tianjin-Tangshan power grid based on the simulation data in 2021, and conducts 8760 hours of production simulation time series simulation for the whole year of 2025. It provides theoretical guidance and technical support for the research of demand-side regulation ability.

Performance-based framework for evaluating the flexural response of precast concrete wall panels to blast loading

Herpesviruses employ extensive bidirectional transcription of overlapping genes to overcome length constraints on their gene product repertoire. As a consequence, many lytic transcripts cannot be measured individually by reverse transcription-quantitative PCR (RT-qPCR) or conventional RNA sequencing (RNA-seq) analysis. A. G. Bruce, S. Barcy, T. DiMaio, E. Gan, et al. (Pathogens 6:11, 2017, https://doi.org/10.3390/pathogens6010011) have proposed an approximation method using unique coding sequences (UCDS) to estimate lytic gene abundance from Kaposi's sarcoma-associated herpesvirus (KSHV) RNA-seq data. Although UCDS has been widely employed, its accuracy, to our knowledge, has never been rigorously validated for any herpesvirus. In this study, we use cap analysis of gene expression sequencing (CAGE-seq) as a gold-standard to determine the accuracy of UCDS for estimating Epstein-Barr virus (EBV) lytic gene expression levels from RNA-seq data. We also introduce the Unique TranScript (UTS) method, which, like UCDS, estimates transcript abundance from changes in mean RNA-seq read depth. UTS is distinguished by its use of empirically determined 5' and 3' transcript ends rather than coding sequence annotations. Compared to conventional read assignment, both UCDS and UTS improved the accuracy of quantitation of overlapping genes, with UTS giving the most-accurate results. The UTS method discards fewer reads and may be advantageous for experiments with less sequencing depth. UTS is compatible with any aligner and, unlike isoform-aware alignment methods, can be implemented on a laptop computer. Our findings demonstrate that the accuracy achieved by complex and expensive techniques such as CAGE-seq can be approximated using conventional short-read RNA-seq data when read assignment methods address transcript overlap. Although our study focuses on EBV transcription, the UTS method should be applicable across all herpesviruses as well as to other genomes with extensively overlapping transcriptomes. IMPORTANCE Many viruses employ extensively overlapping transcript structures. This complexity makes it difficult to quantify gene expression by using conventional methods, including RNA-seq. Although high-throughput techniques that overcome these limitations exist, they are complex, expensive, and scarce in the herpesvirus literature relative to short-read RNA-seq. Here, using Epstein-Barr virus (EBV) as a model, we demonstrate that conventional RNA-seq analysis methods fail to accurately quantify the abundances of many overlapping transcripts. We further show that the previously described Unique CoDing Sequence (UCDS) method and our Unique TranScript (UTS) method greatly improve the accuracy of EBV lytic gene measurements obtained from RNA-seq data. The UTS method has the advantages of discarding fewer reads and being implementable on a laptop computer. Although this study focuses on EBV, the UCDS and UTS methods should be applicable across herpesviruses and for other viruses that make extensive use of overlapping transcription.