In this research, the covariance estimates were used to estimate the population mean in the stratified random sampling and combined regression estimates. were compared by employing the robust variance-covariance matrices estimates with combined regression estimates by employing the traditional variance-covariance matrices estimates when estimating the regression parameter, through the two efficiency criteria (RE) and mean squared error (MSE). We found that robust estimates significantly improved the quality of combined regression estimates by reducing the effect of outliers using robust covariance and covariance matrices estimates (MCD, MVE) when estimating the regression parameter. In addition, the results of the simulation study proved

Massive multiple-input multiple-output (MaMi) systems have attracted much research attention during the last few years. This is because MaMi systems are able to achieve a remarkable improvement in data rate and thus meet the immensely ongoing traffic demands required by the future wireless networks. To date, the downlink training sequence (DTS) for the frequency division duplex (FDD) MaMi communications systems have been designed based on the idealistic assumption of white noise environments. However, it is essential and more practical to consider the colored noise environments when designing an efficient DTS for channel estimation. To this end, this paper proposes a new DTS design by exploring the joint use of spatial channel and n

Despite extensive investigations, an effective treatment for sepsis remains elusive and a better understanding of the inflammatory response to infection is required to identify potential new targets for therapy. In this study we have used RNAi technology to show, for the first time, that the inducible lysophosphatidylcholine acyltransferase 2 (LPCAT2) plays a key role in macrophage inflammatory gene expression in response to stimulation with bacterial ligands. Using siRNA- or shRNA-mediated knockdown, we demonstrate that, in contrast to the constitutive LPCAT1, LPCAT2 is required for macrophage cytokine gene expression and release in response to TLR4 and TLR2 ligand stimulation but not for TLR-independent stimuli. In addition, cells transfe

G-system composed of three isolates G3 ( Bacillus),G12 ( Arthrobacter )and G27 ( Brevibacterium) was used to detect the mutagenicity of the anticancer drug, cyclophosphamide (CP) under conditions similar to that used for standard mutagen, Nitrosoguanidine (NTG). The CP effected the survival fraction of isolates after treatment for 15 mins using gradual increasing concentrations, but at less extent comparing to NTG. The mutagenic effect of CP was at higher level than that of NTG when using streptomycin as a genetic marker, but the situation was reversed when using rifampicin resistant as a report marker. The latter effect appeared upon recording the mutagen efficiency (ie., number of induced mutants/microgram of mutagen). Measuring the R

Soil fertility is a crucial factor in measuring soil quality, it indicates the extent to which soil can support plant life. Soil fertility is measured by the amount of macro and micronutrients, pH, etc. Soil nutrients are depleted after each harvest and therefore must be added. To maintain soil nutrient levels, fertilizer is added to the soil. Adding fertilizer in the precise amount is a matter of great importance because excess or insufficient application can harm plant life and reduce productivity. The use of modern technology is a solution to this problem. Although automated techniques for sowing, weeding, crop harvesting, etc. have been proposed and implemented, none of the techniques are aimed to maintaining soil fertility. The study a

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k