Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that remains difficult to cure. However, early screening and timely intervention can significantly slow its progression. Traditional AD detection methods are plagued by high misdiagnosis rates, low hardware integration, and lack of diagnostic diversity. To address these challenges, this paper proposes a configurable System-on-Chip (SoC) design based on a multimodal fusion Artificial Neural Network (ANN) for high-precision diagnosis. The proposed design integrates Electroencephalogram (EEG) and Magnetic Resonance Imaging (MRI) signals. First, a discretized reverse training method was employed to compress the features of the MRI images and reduce the input dimensionality. Second, intra-layer parallel computation and inter-layer pipeline scheduling were implemented to enhance the computational throughput. Finally, a dynamic configuration strategy for Processing Elements (PE) was introduced to optimize the hardware resource utilization. The proposed design achieves a six-fold improvement in throughput and provides multiple diagnostic approaches for AD. In conclusion, this work provides an efficient and scalable hardware solution for the early screening and dynamic monitoring of AD, which is expected to promote the development of portable and intelligent AD diagnostic devices and has good prospects for clinical transformation and application.

Ray-tracing through numerical weather models (NWMs) is one of the most accurate methods for determining slant tropospheric delays (STDs) in microwave remote sensing. However, the massive data volumes of high-resolution NWMs create substantial I/O operations, limiting large-scale ray-tracing on general hardware. This constraint has historically necessitated parameterized tropospheric delay models, which are disseminated as standardized products (e.g., zenith delays with mapping functions and horizontal gradients). Recently, the AI-driven VAE-former algorithm revolutionized NWM compression, achieving >470:1 ratios by compressing 37 pressure level, 0.25°×0.25° ERA5 data into files smaller than surface-only VMF3 products (1°×1° resolution). This breakthrough challenges the conventional reliance on parameterized models as the sole practical solution. We quantified discrepancies in tropospheric delay parameters between original ERA5 and VAEformer-compressed CRA5 data across 2022, evaluating compression fidelity on global grids and against in-situ zenith tropospheric delay (ZTD) estimates. Results show global average precision loss from compression is10 mm). Our findings demonstrate CRA5 as a reliable ERA5 substitute, with compression-induced inaccuracies being negligible for most microwave-based remote sensing applications. This work underscores that parameterized delay modeling is no longer the exclusive pathway, enabling efficient local computation of high-precision STDs without through mapping functions and gradients.

The pseudolite positioning system can enhance Global Navigation Satellite System (GNSS) by improving satellite geometry and providing independent services in environments where GNSS is unavailable. This paper investigates a GNSS/Pseudolite integrated positioning system and verifies its positioning service performance in urban canyon environments. The experimental results indicate that in challenging urban canyon environments, the number of visible satellites for the GNSS significantly decreases, leading to a substantial decline in positioning accuracy. The GNSS/Pseudolite integrated system demonstrates its advantages. Even in favorable experimental conditions, compared to using GNSS PPP alone, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by 10% and 3D accuracy by 12%. When the obstruction is severe, such as when the cut-off elevation angle is 50°, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by ${7 2. 5 \%}$ and 3D accuracy by 79.2% compared to GNSS PPP. In challenging urban canyon environments, the GNSS/Pseudolite integrated system can still provide high-precision positioning services.

Accurate tropospheric delay forecasts are imperative for microwave-based remote sensing techniques, playing a pivotal role in early warning and forecasting of natural disasters such as tsunamis, heavy rains, and hurricanes. Nevertheless, conventional methods for forecasting tropospheric delays entail substantial computational resources and high network transmission speeds, thereby restricting their real-time applicability in remote sensing operations. In this study, we introduce a novel approach to derive forecasted tropospheric delays using artificial intelligence (AI) weather forecast foundation models (FMs), exemplified by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We assess the accuracy of these forecasts on a global scale employing fifth-generation ECMWF atmospheric re-analysis of the global climate (ERA5) (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5), ground-based Global Navigation Satellite System (GNSS), and in situ radiosonde (RS) measurements as reference data. Our results show that the FM-based scheme outperforms traditional methods in both forecast accuracy and length, with the ability to provide high-accuracy tropospheric delay parameters locally for 15-day forecasts at any location within minutes. Furthermore, the FM scheme still maintains accuracy better than empirical models when forecasting up to ten days in advance. This research demonstrates the potential of AI weather forecast FMs in delivering high-precision tropospheric delay medium-range forecasts and improvements for real-time remote sensing applications.

SUMMARY: With the advance of next-generation sequencing technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the linked region (the region linked to the causal mutation) and an interactive Shiny web application for visualizing the results. We deeply sequenced a sorghum male-sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked sorghum F2 populations and 1 rice F2 population and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that the BSAseq workflow will be broadly applicable to the identification of causal mutations for many phenotypes of interest. AVAILABILITY AND IMPLEMENTATION: BSAseq is freely available on https://www.sciapps.org/page/bsa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.