Atmospheric and Oceanic Sciences

To investigate the age and growth characteristics of the pointhead flounder (Cleisthenes pinetorum) in the West Sea (Yellow Sea) of Korea, samples were collected from bottom trawl vessels throughout 2019. A total of 1116 individuals (1015 females and 101 males) were analyzed. Because specimens were obtained from commercial landings, only fish of approximately 20 cm or larger were available due to marketability constraints. The body weight (BW) − total length (TL) relationships were BW = 0.00001TL3.3443 (R2 = 0.9279) for females and BW = 0.000002TL3.2659 (R2 = 0.9347) for males. The observed sex ratio (male:female = 1:10) was strongly female-biased; however, this likely reflected the underrepresentation of smaller males in commercial catches rather than the natural population structure. Females also exhibited larger body lengths than males. Otoliths were generally round, with a slightly elongated anterior region, and measurements were taken along the longest axis from the core to the margin. The relationship between TL and otolith radius (R) was expressed as TL = 8.0342R + 5.6218 (R2 = 0.8408). Growth equations were estimated for both sexes; however, because older males were poorly represented, the male von Bertalanffy growth function was fitted with the asymptotic length fixed to 43.3 cm (110% of the observed maximum male TL). Annuli were formed annually in November, and the spawning season was identified as September–November, suggesting a close association between annulus formation and the spawning period. The von Bertalanffy growth equations were Lt = 57.66(1 − exp[−0.1865(t + 0.46)]) for females and Lt = 43.3(1 − exp[−0.2679(t + 0.5490)]) for males.

BACKGROUND: COVID-19 remains a leading infectious cause of death and hospitalisation globally. Coinfections with SARS-CoV-2 and other respiratory pathogens may result in more severe illness, however the prevalence of coinfection in Australia is unknown. AIMS: This Australian study aimed to determine the prevalence and microbiology of respiratory and bloodstream coinfections, antimicrobial use, and outcomes in hospitalised patients with moderate to severe COVID-19. METHODS: This was a retrospective cohort study of adult patients with moderate to severe COVID-19, admitted at the Sunshine Coast University Hospital from February to July 2022. Data regarding patient characteristics, comorbidities, microbiological results, hospital length of stay, intensive care unit admission, and mortality were compared between the coinfection and no-coinfection groups. Logistic regression analysis was performed to identify factors associated with coinfection. RESULTS: Coinfection was documented in 23 (12%) of the 190 patients admitted with moderate-severe COVID-19. Bacterial infections were the most common (54% of coinfection episodes), followed by fungal (32%), and viral (14%). Antibiotics were prescribed for 74% of patients, for a median duration of 6 days (IQR 4-8 days). Patients with coinfection had a median length of stay of 9 days (IQR: 4-19.5) compared to 6 days in the no-coinfection group (IQR: 3-9; p = 0.047). There was no mortality difference between the two groups. Patients admitted to intensive care had higher odds of coinfection compared to patients not admitted to intensive care (OR 3.39, 95% CI 1.19-9.66, p = 0.02). Severe COVID-19 and Aboriginal and Torres Strait Islander descent were also associated with coinfection. The causal nature of these relationships requires further interrogation. CONCLUSIONS: The prevalence of respiratory and bloodstream coinfection was low in our cohort of hospitalised COVID-19 patients. Despite non-standardised microbiological testing, antibiotic use was disproportionately high. Further work is required to define risk factors and improve diagnosis of COVID-19-associated coinfection, to better inform antimicrobial stewardship.

To recognize handwritten tax invoice text and manage tax invoices, a handwritten tax invoice recognition method based on improved differentiable binary network and convolutional recurrent neural network (CRNN) is proposed. This method first achieves bill text detection and localization through a differentiable binary network that integrates feature pyramid enhancement modules (FPEM). An improved CRNN is used to achieve bill text content recognition. In terms of text detection, the proposed module has an average detection precision of 89.1%, a recall rate of 86.4%, and an average frame rate of 63.5 frames per second. Compared with the Text Detection model based on Segformer and Enhanced Feature Pyramid and the Improved Fourier Contour Embedding Network, the precision has increased by 4.9% and 3.5%, respectively, and the frame rate has increased by 20.5 and 11.7 frames per second. In terms of text recognition, the improved CRNN has an average recognition precision of 95.6%, a recall rate of 94.2%, an average frame rate of 56.5 frames per second, and an F1-Score of 0.94. Compared with the Dual-Stream Network Fusing Spatial and Frequency Domain Feature and the Multi-modal Network based on Visual Attention and Semantic Perception, the precision has been improved by 4.9% and 3.0%, respectively, and the F1-Score has been improved by 0.07 and 0.04. This method performs excellently in processing complex handwritten tax receipts, with outstanding recognition precision and robustness, significantly reducing tax workload, and improving tax declaration and management efficiency.

BACKGROUND: Patient experience is increasingly used as a public quality metric, but its relationship to hospital safety grades remains incompletely characterized because patient reported experience and technical safety measures capture different dimensions of care. METHODS: We performed a retrospective cross-sectional analysis of publicly available hospital level data from 2023. Leapfrog Hospital Safety Grades were linked with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) patient experience scores. The linked dataset included 1,442 U.S. hospitals; 1,390 hospitals with letter grades A through F were included in grade stratified and regression analyses. HCAHPS domains were analyzed on a 1-5 scale, with higher scores indicating more favorable patient reported experience. One way ANOVA compared mean HCAHPS scores across Leapfrog grades. Multivariable logistic regression with hospital size adjustment evaluated HCAHPS domains associated with Leapfrog Grade A versus grades B through F. RESULTS: Mean HCAHPS domain scores declined across most domains as Leapfrog grades worsened from A to F. In multivariable analysis, nurse communication was associated with higher odds of receiving a Grade A safety rating (odds ratio [OR] 1.54, 95% confidence interval [CI] 1.11 to 2.13; p = 0.01), as was the hospital recommendation score (OR 1.54, 95% CI 1.15 to 2.06; p = 0.004). Doctor communication showed an inverse conditional association (OR 0.75, 95% CI 0.59 to 0.97; p = 0.03). Other HCAHPS domains were not independently associated with Grade A after adjustment. CONCLUSIONS: Hospitals with higher Leapfrog safety grades generally had more favorable patient reported experience scores, and nurse communication and hospital recommendation were the most clinically interpretable independent predictors of Grade A status. The inverse association for doctor communication should be interpreted cautiously because HCAHPS domains are correlated and the analysis used hospital level cross-sectional data. These findings support the complementary role of patient experience in hospital quality assessment but do not establish causality.

In the version of this article initially published, a reference was missing from the Fig. 3 legend and is now cited as ref. 53 in the description of panel a : “The dust-flux records from ODP Site 849 (blue) 51 and 17PC (yellow) 52,53 .” The reference (Jacobel, A. W., McManus, J. F., Anderson, R. F. & Winckler, G. Climate-related response of dust flux to the central equatorial Pacific over the past 150 kyr. Earth Planet. Sci. Lett . https://doi.org/10.1016/j.epsl.2016.09.042 (2017)) has been added to the HTML and PDF versions of the article.

Although Large Language Models (LLMs) have shown potential in industrial applications, they encounter significant hurdles in vertical scenarios like elevator maintenance, including hallucinations, lack of domain specificity, and an inability to interpret numerical physical states. To bridge this semantic-physical gap, this paper proposes a Unified Style-Aware Chain-of-Thought (SA-CoT) framework tailored for Small Language Models (SLMs). The novelty of our approach lies in two aspects: first, we construct a robust instruction dataset using a style-aware augmentation strategy to simulate diverse real-world user behaviors and noise; second, we innovate by textualizing raw sensor data, enabling the fine-tuned 4B-parameter SLM to generate high-dimensional embeddings for downstream numerical analysis. Experiments demonstrate a dual breakthrough: in generative diagnosis, the SA-CoT framework consistently outperforms general models, achieving a 5.6-fold improvement in BLEU-4 scores compared to GPT-4o. Furthermore, its embeddings capture physical features more effectively than traditional baselines, yielding highly competitive accuracy in Alarm Type Classification and Vibration Magnitude Regression. These results suggest that domain-aligned SLMs offer a robust and cost-effective framework for autonomous predictive maintenance, indicating that knowledge density plays a more critical role than parameter scale in specialized industrial applications.