Constraining the Excessive Aggregation of Non-Fullerene Acceptor Molecules Enables Organic Solar Modules with the Efficiency >16.

Translating high-performance organic solar cell (OSC) materials from spin-coating to scalable processing is imperative for advancing organic photovoltaics. For bridging the gap between laboratory research and industrialization, it is essential to understand the structural formation dynamics within the photoactive layer during printing processes. In this study, two typical printing-compatible solvents in the doctor-blading process are employed to explore the intricate mechanisms governing the thin-film formation in the state-of-the-art photovoltaic system PM6:L8-BO.

Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.

In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plethora of LDCT denoising methods have been proposed.

Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising.

Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality. Previously, many supervised or unsupervised deep learning-based LDCT denoising algorithms have been proposed. Unsupervised LDCT denoising algorithms are more practical than supervised ones since they do not need paired samples.

Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants.

Due to the inefficiency of multiple binary images encryption, a parallel binary image encryption framework based on the typical variants of spiking neural networks, spiking neural P (SNP) systems is proposed in this paper. More specifically, the two basic units in the proposed image cryptosystem, the permutation unit and the diffusion unit, are designed through SNP systems with multiple channels and polarizations (SNP-MCP systems), and SNP systems with astrocyte-like control (SNP-ALC systems), respectively. Different from the serial computing of the traditional image permutation/diffusion unit, SNP-MCP-based permutation/SNP-ALC-based diffusion unit can realize parallel computing through the parallel use of rules inside the neurons.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed.

Activatable ultrasmall gold nanorods for "off-on" fluorescence imaging-guided photothermal therapy.

Sensitive and specific fluorescence imaging-guided photothermal therapy (PTT) with high-efficiency is of essential importance and is still a challenge for nanotheranostics. To address these issues, we developed activatable ultrasmall gold nanorods (AUGNRs) to realize "off-on" switched fluorescence imaging-guided efficient PTT. Herein, the GNRs with an ultrasmall small size (∼4 nm) were set as the PTT platform due to their distinct absorption-dominant characteristics, generating an enhanced photothermal conversion efficiency.

Prognostic and clinicopathological value of Gli-1 expression in gastric cancer: A meta-analysis.

Glioma associated oncogene-1 (Gli-1) is considered as a strong positive activator of downstream target genes of hedgehog signal pathway in mammalians. However, its diagnostic and prognostic value in gastric cancer remains unclear and controversial. Therefore, a quantitative meta-analysis was conducted to determine the clinical value of Gli-1 in gastric cancer patients.

Relating Doses of Contrast Agent Administered to TIC and Semi-Quantitative Parameters on DCE-MRI: Based on a Murine Breast Tumor Model.

Objective: To explore the changes in the time-signal intensity curve(TIC) type and semi-quantitative parameters of dynamic contrast-enhanced(DCE)imaging in relation to variations in the contrast agent(CA) dosage in the Walker 256 murine breast tumor model, and to determine the appropriate parameters for the evaluation ofneoadjuvantchemotherapy(NAC)response.

Materials And Methods: Walker 256 breast tumor models were established in 21 rats, which were randomly divided into three groups of7rats each. Routine scanning and DCE-magnetic resonance imaging (MRI) of the rats were performed using a 7T MR scanner.