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The study of evaluation indexes for yellow peach climate quality and its meteorological factor model can provide technical support to ensure high-quality production and facilitate rural revitalization. Taking "Jinxiu" variety of yellow peach as the research object, based on the yellow peach quality observations from 221-1300 m altitude and temperature and rainfall data from 13 meteorological stations near orchards at the middle section of Luoxiao Mountains and the west side of Xuefeng Mountain during 2019-2023, a climatic quality evaluation index for yellow peach and meteorological factorial regression model for its quality elements are constructed by using the methods of weighted summation, Pearson's correlation, regression analysis and multiple covariance analysis, and examined with independent samples. Effects of different altitudes and harvest dates on the climatic quality ratings of yellow peaches are further investigated based on the constructed model. Results show that the main meteorological influencing factors for yellow peach soluble solids content (SS) is the average air temperature 80 d before harvest, for titratable acid content (AT) is the total rainfall 40 d before harvest, and for fruit shape index (IS) are the average air temperature from 1 May to 10 June, total rainfall from 1 May to 10 June, the average air temperature 10 d before harvest and total rainfall 10 d before harvest. Mean absolute error between the simulated and measured values of SS, AT, and IS of validation samples is 0.397%, 0.093%, and 0.010, respectively, and the root mean square error is 0.072%, 0.014%, and 0.001, respectively, and r is 0.649 (p=0.05), 0.718 (p=0.01), and 0.957 (p=0.01), respectively. The simulated quality ratings for 75% of validation samples match the actual climatic quality ratings, while 25% differs by 1 level. Simulation based on the constructed model reveals that the total frequency of superior and excellent quality in the study area shows an increasing and then decreasing trend with both the elevation and the harvesting period, among which the best quality is found in the mid-high elevation areas of 600-820 m or the harvest from 31 July to 10 August. Fruits harvested in high elevation areas above 1300 m or harvested from 21 August to 31 August appear to have a high frequency of lower quality.

BackgroundHeavier body mass index (BMI) is the most established predictor of earlier age at puberty. However, it is unknown whether the timing of the childhood switch to heavier BMI (age at BMI rebound) also matters for puberty.MethodsIn the LEGACY Girls Study (n = 1040), a longitudinal cohort enriched with girls with a family history of breast cancer, we collected paediatric growth chart data from 852 girls and assessed pubertal development every 6 months. Using constrained splines, we interpolated individual growth curves and then predicted BMI at ages 2, 4, 6, 8 and 9 years for 591 girls. We defined age at BMI rebound as the age at the lowest BMI between ages 2 and 8 years and assessed its association with onset of thelarche, pubarche and menarche using Weibull survival models.ResultsThe median age at BMI rebound was 5.3 years (interquartile range: 3.6-6.7 years). A 1-year increase in age at BMI rebound was associated with delayed thelarche (HR = 0.90; 95% CI = 0.83-0.97) and menarche (HR = 0.86; 95% CI = 0.79-0.94). The magnitude of these associations remained after adjusting for weight between birth and 2 years, was stronger after adjusting for BMI at age 9, and was stronger in a subset of girls with clinically assessed breast development.ConclusionsEarlier BMI rebound is associated with earlier pubertal timing. Our observation that BMI rebound may be a driver of pubertal timing in girls with and without a family history of breast cancer provides insight into how growth and pubertal timing are associated with breast cancer risk.

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset. [ABSTRACT FROM AUTHOR]

Environmental barrier coatings are critically needed in the future to safeguard SiC-based ceramic matrix composites (SiC CMCs) used in gas turbines. Element doping of rare earth monosilicates could further improve the properties of the coating. The crystal structure, elastic properties, and resistance to water vapor corrosion of Yb2SiO5 and YbRESiO5 (where RE = Sc, Y, Eu, Gd, Ho, Tm, Lu) were examined in this study using first-principles calculations. When RE is Yb, the material is Yb2SiO5. Based on the outcomes of the calculation, we prepared YbRESiO5 (RE = Sc, Yb, Eu) and studied the thermodynamic properties. The findings show that YbReSiO5's resistance to water vapor corrosion is as follows: YbLuSiO5 < YbEuSiO5 < YbGdSiO5 < YbYSiO5 < Yb2SiO5 < YbTmSiO5 < YbHoSiO5 < YbScSiO5. YbScSiO5 has a lower unit cell volume, average Re-O bond length, and thermal expansion coefficient than Yb2SiO5, while YbEuSiO5 has the reverse pattern. Moreover, of the eight materials, YbScSiO5 has the greatest elastic modulus and lattice distortion. After doping with Eu, YbEuSiO5 exhibits a decrease in thermal conductivity by nearly thirty percent compared to Yb2SiO5, due to the formation of oxygen vacancies. The development of environmental barrier coating materials may benefit from these discoveries. [ABSTRACT FROM AUTHOR]

Around half of all Australian adolescents combine their final years of secondary education with a part-time job and this may be creating pressure that impacts their mental health. However, there is virtually no national population-based research investigating how work-study conflicts for young people might adversely affect mental health. This study used data from Wave 7 of the Longitudinal Study of Australian Children (LSAC), including 996 adolescents (16–17 years, 44% males) who were working and studying concurrently in secondary school. The extent of work-study conflict was measured using a four-item scale. Depression and anxiety levels were assessed using the Short Mood and Feelings Questionnaire (SMFQ) and the Spence Children's Anxiety Scale (SCAS). Associations were tested using Linear Regression, adjusting for a range of covariates (including psychosocial job quality and prior wave 6 depression/anxiety). Half of all students reported experiencing at least one indicator of work-study conflict. After adjusting for covariates (including prior depression/anxiety), work-study conflicts were significantly associated with greater depression (β =.33, p < 0.001) and anxiety (β =.26, p < 0.001). Many young Australians are having difficulties balancing their work and study commitments in their final secondary school years, and this is associated with greater depression and anxiety. [ABSTRACT FROM AUTHOR]

Reconstructing fine‐grained, detailed spatial structures from time‐evolving coarse‐scale geophysical fields has been a long‐standing challenge. Current deep learning approaches addressing this issue generally require massive fine‐scale fields as supervision, which is often unavailable due to limitations in existing observational systems and the scarcity of widespread high‐precision sensors. Here, we present AdaptDeep, a self‐supervised framework for refined reconstruction of geophysical fields via domain adaptation from the coarse‐scale source domain to the fine‐scale target domain. This method incorporates two pretext tasks, cropped field reconstruction and temporal augmentation‐assisted contrastive learning, to leverage spatial and temporal correlations in the target domain. A global propagation structure is proposed in the feature extraction network to leverage bidirectional information for enhanced long‐range dependencies and robustness against estimation errors. In experiments, AdaptDeep correctly identifies local, fine structures and significantly recovers 81.2% detailed information in sea surface temperature fields. Plain Language Summary: In recent years, deep learning has greatly contributed to the field of meteorology and we perform an investigation into the use of convolutional neural networks, to tackle the issue of reconstructing precise spatial structures from coarse‐scale geophysical fields that evolves over time. Right now, techniques using deep learning need highly detailed "ground truth" data to guide them, but this kind of data is often hard to get. This is particularly true in less economically developed areas where there's a lack of high‐quality sensors. To address these concerns, we've developed AdaptDeep, which teaches itself how to reconstruct geophysical fields at a finer scale. It does this by mimicking the process of turning coarse‐scale source data into finer‐scale target data. The backbone of AdaptDeep is a neural network, which uses information from all directions and a global context to increase its understanding of long‐range dependencies and reduce estimation errors. We put AdaptDeep to the test, and found that it could accurately pick out local, detailed structures, recovering an impressive 81.2% of detailed information in sea surface temperature fields. This shows that AdaptDeep could be a powerful new tool in helping us to understand our environment in more detail. Key Points: A self‐supervised framework is presented for refined reconstruction of geophysical fields without high‐resolution fields as ground truthTwo pretext tasks are designed and incorporated into the model to leverage spatial and temporal correlations in the target domainThe feature extraction network employs a global propagation structure to exploit global information [ABSTRACT FROM AUTHOR]

Grasslands have been increasingly impacted by human activities, gradually becoming one of the most threatened ecosystems globally. Advanced geographic information technology and remote sensing techniques allow for a fresh perspective on studying the response of the grassland degradation index ( G D I ) to climate change. This study utilized remote sensing image data of grasslands to calculate the vegetation coverage and derive the G D I for five grassland regions of China from 2001 to 2019. The results indicate that the national degradation status of grasslands remained at a level of mild degradation. The increasing trend of the G D I in some regions was effectively inhibited by regional climate change, especially in the Northeastern and Northern Plain–Mountain–Hill Grassland regions, where the G D I showed a continuous decreasing trend. G D I was strongly correlated with atmospheric pressure, precipitation, temperature, and wind speed. In the arid northern region, the increasing precipitation and decreasing temperatures predominantly contributed to the depressed G D I . In the Qinghai–Tibetan Plateau Grassland region, the instability of the G D I is attributed to fluctuating atmospheric pressure, with a correlation coefficient ranging from 0.5 to 0.8. Our findings underscore the importance of meteorological factors to evaluate and forecast grassland ecosystem stability. This understanding is vital for developing informed conservation and management strategies to address current and future climate challenges. [ABSTRACT FROM AUTHOR]

CoCrAlYSi-hBN coating was fabricated using the high-velocity oxygen fuel (HVOF) process under several processing conditions， and the effects of process parameters, gas flow and spray distance, on the microstructure and properties of the coating were investigated in the present paper. The microstructure, hardness, bonding strength and chemical composition of the coating was researched by using scanning electronmicroscopy (SEM), microhardness tester, adhesive strength measuring and energy dispersive spectroscopy (EDS). The results show that with the increasing gas flow, the porosity and h-BN content of the coating are reduced, and the hardness is improved; However, the bonding strength increases first and then decreases due to excessive energy leading serious oxidation. With the increase of spraying distance, the oxide inclusions are increased, which causes the hardness of coatings increase and the bonding strength decrease. In addition, when the spraying distance is 225 mm, the hardness and bonding strength of the coating are the highest due to the low porosity and boron nitride content.

Background The use of human umbilical cord mesenchymal stem cells (UC-MSCs) has shown promise in improving the pathophysiological characteristics of rats with chronic obstructive pulmonary disease (COPD). However, more research is needed to understand the exact mechanism behind their therapeutic effects and their impact on lung microbiota. Methods To investigate this, rats were randomly assigned to one of 3 groups: Control, COPD + vehicle, and COPD + UC-MSCs group. Lung function changes after UC-MSCs therapy were evaluated weekly for 6 weeks. Additionally, lactate dehydrogenase (LDH), TNF (tumor necrosis factor)-α, IL (interleukin)-6, and IL-1β level in bronchoalveolar lavage fluid (BALF) were analyzed. Arterial blood gas and weight were recorded. Hematoxylin and eosin (HE) staining was used to examine lung pathology, while changes in the lung microbiota were evaluated through 16S rRNA sequencing. Results The administration of UC-MSCs in rats led to a progressive amelioration of COPD, as demonstrated by enhanced lung function and reduced inflammatory response. UC-MSCs treatment significantly altered the structure and diversity of the lung microbiota, effectively preventing microbiota dysbiosis. This was achieved by increasing the abundance of Bacteroidetes and reducing the levels of Proteobacteria. Additionally, treatment with UC-MSCs reduced the activation of pathways associated with COPD, including microbial metabolism, ABC transporters, and Quorum sensing. The group of UC-MSCs showed increased metabolic pathways, such as amino acid biosynthesis, purine metabolism, starch and sucrose metabolism, and biosynthesis of secondary metabolites, compared to the COPD group. Conclusions The use of UC-MSCs was found to reduce inflammation and improve lung function in rats with COPD. The mechanism may be related to the lung microbiota, as UC-MSCs improved the communities of lung microbiota and regulated dysregulated metabolic pathways. [ABSTRACT FROM AUTHOR]

Triplet‐triplet annihilation upconversion (TTA‐UC) has the potential to enhance photoredox catalysis yield. It includes a sensitizer and an annihilator. Efficient and stable annihilators are essential for photoredox catalysis, yet only a few examples are reported. Herein, we designed four novel pyrene annihilators (1, 2, 3 and 4) via introducing aryl‐alkynyl groups onto pyrene to systematically modulate their singlet and triplet energies. Coupled with platinum octaethylporphyrin (PtOEP), the TTA‐UC efficiency is enhanced gradually as the number of aryl‐alkynyl group increases. When combining 4 with palladium tetraphenyl‐tetrabenzoporphyrin (PdTPTBP), we achieved the highest red‐to‐green upconversion efficiency (22.4±0.3 %) (out of a 50 % maximum) so far. Then, this pair was used to activate photooxidation of aryl boronic acid under red light (630 nm), which achieved a great improved reaction yield compared to that activated by green light directly. The results not only provide a design strategy for efficient annihilators, but also show the advantage of applying TTA‐UC into improving the photoredox catalysis yield. [ABSTRACT FROM AUTHOR]

Symmetry-breaking charge separation (SB-CS), which could convert a singlet exciton into a radical anion and cation pair, has the potential to be applied in organic photoelectric devices; however, it is critical to achieve a fast CS rate and slow charge recombination (CR) simultaneously for its application. Thus, we planned to optimize the CS and CR process via molecular engineering and synthesized three isomeric 9,10-bis((4-hexylphenyl)ethynyl) anthracene (BPEA) dimers linked by the phenylene spacer in ortho-(o-dimer), meta-(m-dimer), and para-arrangements (p-dimer) and systematically studied their SB-CS dynamics using ultrafast transient absorption (fs-TA) spectroscopy. The fs-TA experiments confirmed that these three dimers all underwent the SB-CS process in polar solvents, but with different SB-CS dynamics. The o-dimer displayed the fastest CS rate (∼8 ps), which was attributed to its relatively strong electronic coupling (V) and large CS driving force. Meanwhile, it also showed the slowest CR rate (∼20 ns) due to the small CR driving force. Hence, the ratio of its rates of CS over CR reached an unprecedently high value of ∼2500 in DMF. The result of this study provides some insights into developing novel SB-CS materials with a fast CS rate and slow CR rate for their application. [ABSTRACT FROM AUTHOR]

BACKGROUND: The anesthesia machine serves as a vital piece of lifesaving equipment. OBJECTIVE: To analyze incidents of failures in the Primus anesthesia machine and address these malfunctions to reduce recurrence of failure, save maintenance costs, enhance safety, and improve overall efficiency. METHODS: We conducted an analysis on the records pertaining to the maintenance and parts replacement of the Primus anesthesia machines used in the Department of Anaesthesiology at Shanghai Chest Hospital over the past two years to identify the most common causes of failure. This included an assessment of the damaged parts and degree of damage, as well as a review of factors that caused the fault. RESULTS: The main cause of the faults in the anesthesia machine was found to be air leakage and excessive humidity in the central air supply of the medical crane. The logistics department was instructed to increase inspections to check and ensure the quality of the central gas supply and ensure gas safety. CONCLUSION: Summarizing the methods for dealing with anesthesia machine faults can save hospitals a lot of money, ensure normal hospital and department maintenance, and provide a reference to repair such faults. The use of Internet of Things platform technology can continuously develop the direction of digitalization, automation, and intelligent management in each stage of the "whole life cycle" of anesthesia machine equipment. [ABSTRACT FROM AUTHOR]

In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures, including longitudinal study designs, may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inflation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. To this end, we propose a Zero-Inflated Poisson Gamma (ZIPG) model framework to address these aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, we provide a flexible ZIPG model framework by connecting both the mean abundance and the variability of abundances to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides significant insights into distinguished differential variability and mean abundance. for this article are available online. [ABSTRACT FROM AUTHOR]

Singlet fission (SF), which can convert one singlet exciton into two triplet excitons, has the potential to enhance the solar energy conversion efficiency in devices such as photovoltaic cells and processes such as photocatalysis. Recently, intramolecular SF (iSF), which is observed in covalently linked molecular oligomers, has attracted significant research interest due to its unique advantages in revealing the SF mechanism and practical applications. In the case of exothermal chromophores (their singlet state energy (ES1) is more than twice their triplet state energy (ET1), such as pentacene), efficient iSF is easily achieved. However, efficient iSF is limited in endothermic/isothermal chromophores (ES1 ≤ 2ET1, such as tetracene). Compared to exothermal systems, the energy loss in the SF of endothermic/isothermal systems is much lower, which is more favorable for enhancing the solar energy conversion efficiency. Therefore, achieving efficient iSF in endothermic/isothermal systems is necessary. Unfortunately, the design strategy for efficient iSF based on endothermic/isothermal chromophores is not clear. Herein, we summarize the SF studies on covalently linked tetracene oligomers and analyze the key influencing factors on iSF in these systems. Additionally, we review the harvesting of the two formed triplets from the iSF of tetracene oligomers. It is hoped that the results of this review will provide some new insights into the design of highly efficient iSF materials based on endothermic/isothermic chromophores for practical applications. [ABSTRACT FROM AUTHOR]