With the widespread use of indoor positioning technology, the need for high-precision positioning services is rising; nevertheless, there are several challenges, such as the difficulty of simulating the distribution of interior location data and the enormous inaccuracy of probability computation. As a result, this paper proposes three different neural network model comparisons for indoor location based on WiFi fingerprint - indoor location algorithm based on improved back propagation neural network model, RSSI indoor location algorithm based on neural network angle change, and RSSI indoor location algorithm based on depth neural network angle change - to raise accurately predict indoor location coordinates. Changing the action range of the activation function in the standard back-propagation neural network model achieves the goal of accurately predicting location coordinates. The revised back-propagation neural network model has strong stability and enhances indoor positioning accuracy based on experimental comparisons of loss rate (loss), accuracy rate (acc), and cumulative distribution function (CDF).

Boronate affinity molecularly imprinted inverse opal particles were developed for the multiplex label-free detection of glycoproteins with high sensitivity and specificity.

Cleft palate (CP) is the second most common congenital birth defect. The etiology of CP is complicated, with involvement of various genetic and environmental factors. To investigate the gene regulatory mechanisms, we designed a powerful regulatory analytical approach to identify the conserved regulatory networks in humans and mice, from which we identified critical microRNAs (miRNAs), target genes and regulatory motifs (miRNA–TF–gene) related to CP. Using our manually curated genes and miRNAs with evidence in CP in humans and mice, we constructed miRNA and transcription factor (TF) co-regulation networks for both humans and mice. A consensus regulatory loop (miR17/miR20a–FOXE1–PDGFRA) and eight miRNAs (miR-140, miR-17, miR-18a, miR-19a, miR-19b, miR-20a, miR-451a and miR-92a) were discovered in both humans and mice. The role of miR-140, which had the strongest association with CP, was investigated in both human and mouse palate cells. The overexpression of miR-140-5p, but not miR-140-3p, significantly inhibited cell proliferation. We further examined whether miR-140 overexpression could suppress the expression of its predicted target genes (BMP2, FGF9, PAX9 and PDGFRA). Our results indicated that miR-140-5p overexpression suppressed the expression of BMP2 and FGF9 in cultured human palate cells and Fgf9 and Pdgfra in cultured mouse palate cells. In summary, our conserved miRNA–TF–gene regulatory network approach is effective in detecting consensus miRNAs, motifs, and regulatory mechanisms in human and mouse CP.

Infrared small target detection (IRSTD) is a critical yet challenging task due to low target-background contrast, minimal target texture, and high noise levels. While data-driven methods have significantly advanced performance, persistent challenges remain. Traditional pooling schemes in multi-scale networks often sacrifice crucial details necessary for detecting small targets, and existing attention mechanisms struggle to balance performance with computational efficiency. To address these challenges, we propose the Hybrid Feature Mining Network (HFMNet), a lightweight and efficient model designed to enhance feature representation for IRSTD. First, to reduce detail loss caused by conventional pooling—which can amplify noise (e.g., max pooling) or suppress contrast (e.g., average pooling)—we introduce the Cross-Pooling Shallow Enhancement Module (CSEM). By integrating diverse pooling strategies across parallel shallow streams, CSEM preserves fine details, improves localization, and prevents target loss in deeper layers. Second, for efficient local-global context modeling, we propose the Hybrid Feature Mining Module (HFMM). This module combines local and global attention by decomposing large 2D convolutions into coupled 1D convolutions and incorporating dilated convolutions, broadening the receptive field while reducing complexity. Coupled with the Vision State Space Module (VSSM) for global context modeling, HFMM effectively integrates local and global information to enhance detection performance. The effectiveness of our HFMNet has been evaluated on four benchmark datasets. In comparison to other SOTA methods, the Pd reaches 92.52% on the IRSTD-1K dataset and 99.08% on the NUAA-SIRST dataset, with only 7.09M parameters, making it a promising lightweight solution for IRSTD applications. The code will be released at https://github.com/Fortuneteller6/HFMNet.