Age Differences in Stress and Coping: Problem-Focused Strategies Mediate the Relationship between Age and Positive Affect

The present study examined the different types of stressors experienced by adults of different ages, their coping strategies, and positive/negative affect. A mediation hypothesis of coping strategies was tested on the relationships between age and positive/negative affect. One-hundred and ninety-six community-dwelling adults (age range 18-89 years old) reported the most stressful situation they experienced in the past month and coping strategies. Levels of positive and negative affect in the past month were also measured. Content analysis revealed age differences in different types of stressors adults reported. Three types of coping strategies were found: problem-focused, positive emotion-focused, and negative emotion-focused coping. Older adults were less likely than younger adults to use problem-focused coping and reported lower levels of positive affect. Path analysis supported the mediation hypothesis, showing that problem-focused coping mediated the relationship between age and positive affect. Implications are discussed on the importance of promoting problem-focused coping among older adults

Non-volatile heterogeneous III-V/Si photonics via optical charge-trap memory

We demonstrate, for the first time, non-volatile charge-trap flash memory (CTM) co-located with heterogeneous III-V/Si photonics. The wafer-bonded III-V/Si CTM cell facilitates non-volatile optical functionality for a variety of devices such as Mach-Zehnder Interferometers (MZIs), asymmetric MZI lattice filters, and ring resonator filters. The MZI CTM exhibits full write/erase operation (100 cycles with 500 states) with wavelength shifts of $\Delta\lambda_{non-volatile} = 1.16 nm$ ($\Delta n_{eff,non-volatile} ~ 2.5 \times 10^{-4}$) and a dynamic power consumption $<$ 20 pW (limited by measurement). Multi-bit write operation (2 bits) is also demonstrated and verified over a time duration of 24 hours and most likely beyond. The cascaded 2nd order ring resonator CTM filter exhibited an improved ER of ~ 7.11 dB compared to the MZI and wavelength shifts of $\Delta\lambda_{non-volatile} = 0.041 nm$ ($\Delta n_{eff, non-volatile} = 1.5 \times 10^{-4}$) with similar pW-level dynamic power consumption as the MZI CTM. The ability to co-locate photonic computing elements and non-volatile memory provides an attractive path towards eliminating the von-Neumann bottleneck

Drivers and assemblies of soil eukaryotic microbes among different soil habitat types in a semi-arid mountain in China

The effects of environmental and species structure on soil eukaryotic microbes inhabiting semi-arid mountains remain unclear. Furthermore, whether community assembly differs in a variety of soil habitat types, for example, artificial forest, artificial bush, farmland, and natural grassland, is not well understood. Here, we explored species diversity and composition of soil eukaryotic microbes south of the Taihang Mountains (mid-western region of China) using Illumina sequencing of the 18S rRNA gene (V4) region on the MiSeq platform. The results suggest that the forest soil habitat type improved the diversity and abundance of soil eukaryotic microbes that will benefit the restoration of degraded soil. The SAR (Stramenopiles, Alveolates, Rhizaria) supergroup and Metazoa were the dominant soil eukaryotic microbial groups at the phylum level. About 26% of all operational taxonomic units were common among the different soil habitat types. The O-elements, water content, soil organic matter, and elevation significantly influenced the abundance of soil eukaryote communities (P < 0.05). Our findings provide some reference for the effectiveness of local ecological restoration and the establishment of a soil eukaryotic microbe resource databases in a semi-arid area

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu

RadOnc-GPT: A Large Language Model for Radiation Oncology

This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation oncology patient records and clinical notes from the Mayo Clinic in Arizona. The model employs instruction tuning on three key tasks - generating radiotherapy treatment regimens, determining optimal radiation modalities, and providing diagnostic descriptions/ICD codes based on patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT outputs to general large language model outputs showed that RadOnc-GPT generated outputs with significantly improved clarity, specificity, and clinical relevance. The study demonstrated the potential of using large language models fine-tuned using domain-specific knowledge like RadOnc-GPT to achieve transformational capabilities in highly specialized healthcare fields such as radiation oncology