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The single-stage object detection technology represented by YOLOv8 has significant optimizations in the backbone network, but fails to efficiently integrate contextual information in the neck network, leading to missed and false detections in small object detection. Additionally, the large number of algorithm parameters and high computational complexity make it unsuitable for end-to-end industrial deployment. To address these issues, this paper introduce the BiFormer attention mechanism based on the Transformer structure to enhance the detection performance for small objects and improve the algorithm's accuracy. At the same time introduce the GSConv module to reduce the algorithm size while ensuring no adverse impact on its performance, balancing the increase in computational and parametric costs brought by BiFormer. An object detection algorithm named Bi-YOLO is designed to achieve a balance between lightweight and algorithm performance. Experimental results show that compared to YOLOv8n, the Bi-YOLO object detection algorithm improves algorithm accuracy by 4.6%, increases the small object detection accuracy on the DOTA dataset by 2.3%, and reduces the number of parameters by 12.5%. Bi-YOLO effectively achieves a balance between algorithm lightweight and performance, providing a new approach for end-to-end industrial deployment. [ABSTRACT FROM AUTHOR]

Since the invention of the microphone by Barina in 1876, there have been numerous applications of audio processing, such as phonographs, broadcasting stations, and public address systems, which merely capture and amplify sound and play it back. Nowadays, audio processing involves analysis and noise-filtering techniques. There are various methods for noise filtering, each employing unique algorithms, but they all require two or more microphones for signal processing and analysis. For instance, on mobile phones, two microphones located in different positions are utilized for active noise cancellation (one for primary audio capture and the other for capturing ambient noise). However, a drawback is that when the sound source is distant, it may lead to poor audio capture. To capture sound from distant sources, alternative methods, like blind signal separation and beamforming, are necessary. This paper proposes employing a beamforming algorithm with two microphones to enhance speech and implementing this algorithm on an embedded system. However, prior to beamforming, it is imperative to accurately detect the direction of the sound source to process and analyze the audio from that direction. [ABSTRACT FROM AUTHOR]

Aims: The dysregulation of TGF‐β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF‐β signaling in glioma is currently unclear. Methods: Data on glioma's RNA‐seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF‐methylation‐gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA. Results: In glioma, a TGF‐β signaling‐related 15‐lncRNA signature was constructed, including AC010173.1, HOXA‐AS2, AC074286.1, AL592424.1, DRAIC, HOXC13‐AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC‐AS1, AGAP2‐AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP‐1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2‐AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF‐κB and ERK 1/2 in the TGF‐β signaling pathway. Conclusions: A prognostic lncRNA signature of TGF‐β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2‐AS1 plays an important role in glioma progression. [ABSTRACT FROM AUTHOR]

For the hierarchical normal and inverse gamma model, we calculate the Bayes posterior estimator of the variance parameter of the normal distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the variance parameter under the squared error loss function and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the variance parameter of the normal distribution with a conjugate inverse gamma prior by two methods. In numerical simulations, we have illustrated five aspects: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data; the numerical comparisons of the Bayes posterior estimators and the PESLs of the oracle, moment, and MLE methods; and the plots of the marginal densities for various hyperparameters. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we utilize the %bodyfat data of 250 men of various ages to illustrate our theoretical studies. [ABSTRACT FROM AUTHOR]