Discrete interferences optimum beamformer in correlated signal and interfering noise

This paper introduces a significant special situation where the noise is a collection of D-plane interference signals and the correlated noise of D+1 is less than the number of array components. An optimal beamforming processor based on the minimum variance distortionless response (MVDR) generates and combines appropriate statistics for the D+1 model. Instead of the original space of the N-dimensional problem, the interference signal subspace is reduced to D+1. Typical antenna arrays in many modern communication networks absorb waves generated from multiple point sources. An analytical formula was derived to improve the signal to interference and noise ratio (SINR) obtained from the steering errors of the two beamformers. The proposed MVDR processor-based beamforming does not enforce general constraints. Therefore, it can also be used in systems where the steering vector is compromised by gain. Simulation results show that the output of the proposed beamformer based on the MVDR processor is usually close to the ideal state within a wide range of signal-to-noise ratio and signal-to-interference ratio. The MVDR processor-based beamformer has been experimentally evaluated. The proposed processor-based MVDR system significantly improves performance for large interference white noise ratio (INR) in the sidelobe region and provide an appropriate beam pattern.

Super-resolution imaging of negative-refractive graded-index photonic crystal flat lens

Photonic crystal (PC) not only breaks through the diffraction limit of traditional lenses but also can realize super-resolution imaging. Improving the resolution is the key task of PC imaging. The main work of this paper is to use a graded-index Photonic crystal (GPC) flat lens to improve the image resolution. An air-hole type two-dimensional (2D) GPC structure based on silicon medium is proposed in this paper. Numerical simulations through RSoft reveal that when the medium in the imaging area is air, the full width at half maximum (FWHM) value of a single image reaches 0.362λ. According to the Rayleigh criterion, the images of two point sources 0.57λ apart can also be distinguished. In the imaging system composed of cedar oil and GPC flat lens, the FWHM value of a single image reaches 0.34λ. In addition, the images of multiple point sources 0.49λ apart can still be distinguished.

Turning C1-gases to Isobutanol Towards a Great Environmental and Economic Sustainability via Innovative Biological Routes: Two Birds With One Stone

BackgroundThe dramatic increase in emissions of greenhouse gases (GHGs) has led to an irreversible effect on the ecosystem, which in turn caused significant harm to human beings and other species. Exploring innovative and effective approaches to neutralizing GHGs is urgently needed. Considering the advancement of synthetic biology and the bioconversion process, C1-utilizing cell factories (CUCFs) have been modified to be able to effectively convert C1-gases includes biogas, natural gas, and carbon dioxide (CO2) into chemicals or fuels via biological routes, which greatly facilitates the inedible carbon sources used in biomanufacturing, increases the potential value of GHGs and meanwhile reduces the GHG emissions. Process design and resultsEven though the current experimental results are satisfactory in lab-scale research, the evaluation of economic feasibility as well as applications of CUCFs in industrial-scale still need to be analyzed. This study designed three scenarios of CUCFs-based conversion of biogas, natural gas, and CO2 into isobutanol, the detailed techno-economic analyses of these scenarios were conducted with the comparisons of capital cost, operating cost, and minimum isobutanol selling price (MISP). Results revealed that direct bio-conversion of CO2 by CUCFs into isobutanol exhibited the best economic performance with a MISP of $1.38/kg isobutanol. The single sensitivity analysis showed that the gas utilization rate, flow rate, and CO2 cost are the three most significant economic-driving forces on MISP of CO2-derived biological isobutanol. Multiple-point sensitivity analysis presented that the MISP for the long-term case can be as low as 0.99 $/kg with using ideal targets. ConclusionsOur findings provide a comprehensive assessment of bio-conversion of C1-gases via CUCFs to isobutanol in terms of the bioprocess design, mass/energy calculation, capital investment, operating expense, sensitivity analysis, and environmental impact. It is expected that this study may lead to the paradigm shift in isobutanol synthesis with C1-gases as substrates.

Fatigue mitigation of wind turbine system using multiple point model predictive control

<span lang="EN-US">A multi-point model predictive control (MPMPC) is widely used for many applications, including wind energy system (WES), notably enhanced power characteristics and oscillation regulation. In this work, MPMPC is adapted to condense the fatigue load of the WES and improve the lifetime of the turbine assembly. The lifetime examination is carried out by considering the three chief parameters: basic lifetime until failure, short-time damage equivalent loads (DELs), and lifetime DELs. The simulation study is performed for two cases: blade root bending moments and tower top bending. Further, fatigue load examination is demonstrated to analyze the effectiveness of the proposed controller. The observed results show that the lifetime analysis of the wind turbine system displayed more excellent characteristics, i.e., 49.50% greater than MPC. Also, the fatigue load mitigation showed greater magnitude due to the control action of the proposed controller, about 37.38% grander than MPC. Therefore, the attained outcomes exhibit outstanding performance compared with conventional controllers.</span>

Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure

The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution.

A time-division correction method for adaptive optics system

An adaptive optics system can measure and compensate the wavefront distortion caused by dynamic disturbance in real time. It is usually used for astronomical observation and other occasions. According to the current technology, it is only suitable for small field of view optical system or point target, but not for ground extended target detection. In order to solve this problem, a correction method is proposed: firstly, the sub aperture image of wavefront sensor is divided into several sub regions, each sub region corresponds to a certain light direction or field angle range; secondly, calculate the offset of the image feature points in each sub region, and an image with good correction effect in a field of view in this direction is obtained; the last step is to measure each sub region one by one and combine these images into a full frame image. Through comparison, it is found that the method proposed in this paper is essentially to divide the extended target into multiple point light sources for correction.

mmCSM-NA: accurately predicting effects of single and multiple mutations on protein–nucleic acid binding affinity

While protein–nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these interactions a challenge. Consequently, most approaches to understand the effects of missense mutations on protein-nucleic acid affinity have focused on single-point mutations and have presented a limited performance on independent data sets. To overcome this, we have curated the largest dataset of experimentally measured effects of mutations on nucleic acid binding affinity to date, encompassing 856 single-point mutations and 141 multiple-point mutations across 155 experimentally solved complexes. This was used in combination with an optimized version of our graph-based signatures to develop mmCSM-NA (http://biosig.unimelb.edu.au/mmcsm_na), the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities. mmCSM-NA obtained a Pearson's correlation of up to 0.67 (RMSE of 1.06 Kcal/mol) on single-point mutations under cross-validation, and up to 0.65 on independent non-redundant datasets of multiple-point mutations (RMSE of 1.12 kcal/mol), outperforming similar tools. mmCSM-NA is freely available as an easy-to-use web-server and API. We believe it will be an invaluable tool to shed light on the role of mutations affecting protein–nucleic acid interactions in diseases.

Facies and Petrophysical Modeling of Triassic Chang 6 Tight Sandstone Reservoir, Heshui Oil Field, Ordos Basin, China

Tight sandstone reservoirs are widely distributed worldwide. The Upper Triassic Chang 6 member of the Yanchang Formation is characterized by low permeability and porosity. The facies model offers a unique approach for understanding the characteristics of various environments also heterogeneity, scale, and control of physical processes. The role of subsurface facies features and petrophysical properties was unclear. Notable insufficient research has been conducted based on facies and petrophysical modeling and that demands to refine the role of reservoir properties. To tackle this problem, a reservoir model is to be estimated using various combinations of property modeling algorithms for discrete (facies) and continuous (petrophysical) properties. Chang 6 member consists of three main facies, i.e., channel, lobe main body, and lobe margin facies. The current research is aimed at comparing the applicability and competitiveness of various facies and petrophysical modeling methods. Further, well-log data was utilized to interpret unique facies and petrophysical models to better understand the reservoir architecture. Methods for facies modeling include indicator kriging, multiple-point geostatistics, surface-based method, and sequential indicator simulation. Overall, the indicator kriging method preserved the local variability and accuracy, but some facies are smoothed out. The surface-based method showed far better results by showing the ability to reproduce the geometry, extent, connectivity, and facies association. The multiple-point geostatistics (MPG) model accurately presented the facies profiles, contacts, geometry, and geomorphological features. Sequential indicator simulation (SIS) honored the facies spatial distribution and input statistical parameters. The porosity model built using sequential Gaussian simulation (SGS) showed low porosity (74% values &lt;2%). Gaussian random function simulation (GRFS) models showed very low average porosity (8%-10%) and low permeability (less than 0.1 mD). These methods indicate that Chang 6 member is a typical unconventional tight sandstone reservoir with ultralow values of petrophysical properties.