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High-quality meteorological data are essential for climate monitoring and renewable energy applications. ERA5-Land, a newly released high-resolution reanalysis dataset, provides a wide range of meteorological variables, but its accuracy remains a concern. This study evaluated the performance of ERA5-Land in the Lower Jinsha River Basin, the largest clean energy base in China, focusing on precipitation, wind speed, air temperature, and solar radiation. A statistical bias correction procedure was developed, combining month-specific regression fitting with daily and hourly adjustments. Results indicated that air temperature estimates agreed best with ground observations, with a coefficient of determination (R 2 ) exceeding 0.87 and percent bias (Pbias) below 15%, followed by solar radiation. Precipitation and wind speed, in contrast, exhibited larger uncertainties (R 2  < 0.31, Pbias up to 67.76%). After applying the statistical bias correction, systematic biases were largely eliminated across all examined variables. Absolute errors decreased by more than 10%, and temporal consistency also improved moderately, especially for wind speed and solar radiation, where R 2 increased by 29.5% and 25.8%, respectively. The corrected dataset captured basin-wide climatic variations from 1980 to 2019, including decreasing precipitation, increasing temperature and solar radiation, and the spatial heterogeneity changes in wind speed. Overall, this study contributes to better knowledge of ERA5-Land uncertainties in multiple meteorological variables and provides a practical statistical correction framework, which can serve as a reference for data-scarce regions with similar climatic and geographical conditions and clean energy development contexts.

Autoimmune diseases are characterized by vast alterations in immune responses, but the pathogenesis remains sophisticated and yet to be fully elucidated. Multiple mechanisms regulating cell differentiation, maturation, and death are critical, among which mitochondria-related cellular organelle functions have recently gained accumulating attention. Mitochondria, as a highly preserved organelle in eukaryotes, have crucial roles in the cellular response to both exogenous and endogenous stress beyond their fundamental functions in chemical energy conversion. In this review, we aim to summarize recent findings on the function of mitochondria in the innate immune response and its aberrancy in autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus, etc., mainly focusing on its direct impact on cellular metabolism and its machinery on regulating immune response signaling pathways. More importantly, we summarize the status quo of potential therapeutic targets found in the mitochondrial regulation in the setting of autoimmune diseases and wish to shed light on future studies.

Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation resource requirements, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data while accounting for site-level heterogeneity. Fed-GLMM can be applied to both federated and centralized research networks to enable privacy-preserving data integration and improve computational efficiency. By communicating a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted to the pooled dataset. We demonstrate the performance of Fed-GLMM in numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities.

Adverse events (AEs) are a significant concern for healthcare systems. However, it is difficult to evaluate their influence because of the complexity of various medical services. This study aimed to assess the influence of AEs on the outcomes of hospitalized patients using a diagnosis-related group (DRG) database. We conducted a case–control study of hospitalized patients at a multi-district tertiary hospital with 2200 beds in China, using data from a DRG database. An AE refers to an unintended physical injury caused or contributed to by medical care that requires additional hospitalization, monitoring, treatment, or even death. Relative weight (RW), a specific indicator of DRG, was used to measure the difficulty of diagnosis and treatment, disease severity, and medical resources utilized. The primary outcomes were hospital length of stay (LOS) and hospitalization costs. The secondary outcome was discharge to home. This study applied DRG-based matching, Hodges–Lehmann estimate, regression analysis, and subgroup analysis to evaluate the influence of AEs on outcomes. Two sensitivity analyses by excluding short LOS and changing adjustment factors were performed to assess the robustness of the results. We identified 2690 hospitalized patients who had been divided into 329 DRGs, including 1345 patients who experienced AEs (case group) and 1345 DRG-matched normal controls. The Hodges–Lehmann estimate and generalized linear regression analysis showed AEs led to prolonged LOS (unadjusted difference, 7 days, 95% confidence interval [CI] 6–8 days; adjusted difference, 8.31 days, 95% CI 7.16–9.52 days) and excess hospitalization costs (unadjusted difference, $2186.40, 95% CI: $1836.87-$2559.16; adjusted difference, $2822.67, 95% CI: $2351.25-$3334.88). Logistic regression analysis showed AEs were associated with lower odds of discharge to home (unadjusted odds ratio [OR] 0.66, 95% CI 0.54–0.82; adjusted OR 0.75, 95% CI 0.61–0.93). The subgroup analyses showed that the results for each subgroup were largely consistent. LOS and hospitalization costs increased significantly after AEs in complex diseases (RW ≥ 2) and in relation to high degrees of harm subgroups (moderate harm and above groups). Similar results were obtained in sensitivity analyses. The burden of AEs, especially those related to complex diseases and severe harm, is significant in China. The DRG database serves as a valuable source of information that can be utilized for the evaluation and management of AEs.

This article reports the fine-tuning of the optical resonance wavelength (ORW) of surface-micromachined optical ultrasound transducer (SMOUT) arrays to enable ultrasound data readout with non-tunable interrogation light sources for photoacoustic computed tomography (PACT). Permanent ORW tuning is achieved by material deposition onto or subtraction from the top diaphragm of each element with sub-nanometer resolution. For demonstration, a SMOUT array is first fabricated, and its ORW is tuned for readout with an 808 nm laser diode (LD). Experiments are conducted to characterize the optical and acoustic performances of the elements within the center region of the SMOUT array. Two-dimensional and three-dimensional PACT (photoacoustic computed tomography) is also performed to evaluate the imaging performance of the ORW-tuned SMOUT array. The results show that the ORW tuning does not degrade the optical, acoustic, and overall imaging performances of the SMOUT elements. As a result, the fine-tuning method enables new SMOUT-based PACT systems that are low cost, compact, powerful, and even higher speed, with parallel readout capability.

We demonstrated a widely tunable Tm-doped mode-locked all-fiber laser, with the widest tunable range of 136 nm, from 1842 to 1978 nm. Nonlinear polarization evolution (NPE) technique is employed to enable mode-locking and the wavelength-tunable operation. The widely tunable range attributes to the NPE-induced transmission modulation and bidirectional pumping mechanism. Such kind of tunable mode-locked laser can find various applications in optical communications, spectroscopy, time-resolved measurement, and among others.

The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OHSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OHSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OHSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OHSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.

The purpose of this work is to investigate the effect of different initial temperatures on the combustion characteristics of RDX materials. In the experiments, the electric heating plate below the RDX samples was controlled so that the initial temperatures were set to be 298, 323, 373, 423, and 473 K (below the material melting point), respectively. Three optical diagnostic methods were employed to capture the ignition process, flame thermal radiation, and NO distribution in the flame at different conditions. The results show that the increase in initial temperature can improve the reaction rate, shorten the ignition delay time, and increase the flame combustion intensity and speed, because of the earlier evaporation and pyrolysis process in the RDX samples. Increasing the initial temperature also enhances the thermal effect of the flame, which is related to the generation and consumption of NO to a certain extent.

BackgroundAerosol-generating procedures such as oesophagogastroduodenoscopy (OGD) result in infectious particles being exhaled by patients. This substantially increases the medical staff’s risk of occupational exposure to pathogenic particles via airway inhalation and facial mucosal deposition. Infectious particles are regarded as a key route of transmission of SARS-CoV-2 and, thus, represents a major risk factor for medical staff during the ongoing COVID-19 pandemic. There is a need for quantitative evidence on medical staff’s risk of multiroute exposure to infectious particles exhaled by patients during OGD to enable the development of practical, feasible and economical methods of risk-reduction for use in OGD and related procedures. This randomised controlled trial (RCT)—Personal protective EquiPment intervention TrIal for oesophagogastroDuodEnoscopy (PEPTIDE)—aims to establish a state-of-the-art protocol for quantifying the multiroute exposure of medical staff to infectious particles exhaled by patients during real OGD procedures.Method and analysisPEPTIDE will be a prospective, two-arm, RCT using quantitative methods and will be conducted at a tertiary hospital in China. It will enrol 130 participants (65 per group) aged over 18. The intervention will be an anthropomorphic model with realistic respiratory-related morphology and respiratory function that simulates a medical staff member. This model will be used either without or with a surgical mask, depending on the group allocation of a participant, and will be placed beside the participants as they undergo an OGD procedure. The primary outcome will be the anthropomorphic model’s airway dosage of the participants’ exhaled infectious particles with or without a surgical mask, and the secondary outcome will be the anthropomorphic model’s non-surgical mask-covered facial mucosa dosage of the participants’ exhaled infectious particles. Analyses will be performed in accordance with the type of data collected (categorical or quantitative data) using SPSS (V.26.0) and RStudio (V.1.3.959).Ethics and disseminationEthical approval for this RCT was obtained from the Ethics Committee of Peking Union Medical College Hospital (ZS-3377). All of the potential participants who agree to participate will provide their written informed consent before they are enrolled. The results will be disseminated through presentations at national and international conferences and publications in peer-reviewed journals.Trial registration numberNCT05321056.

The coal pulverizing system is an important auxiliary system in thermal power generation systems. The working condition of a coal pulverizing system may directly affect the safety and economy of power generation. Prognostics and health management is an effective approach to ensure the reliability of coal pulverizing systems. As the coal pulverizing system is a typical dynamic and nonlinear high-dimensional system, it is difficult to construct accurate mathematical models used for anomaly detection. In this paper, a novel data-driven integrated framework for anomaly detection of the coal pulverizing system is proposed. A neural network model based on gated recurrent unit (GRU) networks, a type of recurrent neural network (RNN), is constructed to describe the temporal characteristics of high-dimensional data and predict the system condition value. Then, aiming at the prediction error, a novel unsupervised clustering algorithm for anomaly detection is proposed. The proposed framework is validated by a real case study from an industrial coal pulverizing system. The results show that the proposed framework can detect the anomaly successfully.