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Development of large-scale alkaline seawater electrolysis requires robust and corrosion-resistant anodes. Here we propose engineering NiFe layered double hydroxide (LDH)-based anodes by incorporating a series of anions into the LDH interlayers. The most optimal NiFe LDH anode with intercalated phosphates demonstrates stable operation at a high current density of 1.0 A cm −2 for over 1000 hours in a 2 W-scale alkaline seawater electrolyzer (ASWE). Fundamental studies indicate that the basicity, indicated by p K a values, of the intercalated anions in NiFe LDH governs its oxygen evolution reaction activity and corrosion resistance. Highly basic anions (i.e., phosphates) securely anchor Fe sites and facilitate proton transfer to boost both durability and activity. Notably, we demonstrate the proof-of-concept for the NiFe anode in an industrial 1 kW-scale ASWE stack (1,081.2 cm 2 anode area in total). This unit achieves a stable operating current density of 0.5 A cm −2 at about 2.0 V, twice that of the commercial alkaline pure water electrolyzer, contributing to an economically competitive hydrogen production cost of US$ 1.96 kg H2 −1 . Large-scale alkaline seawater electrolysis demands robust anodes for efficient hydrogen production. Here, the authors report a NiFe layered double hydroxide anode with intercalated phosphates, achieving stable performance at 1.0 A cm −2 for over 1,000 hours, offering improved durability and activity.

This study aimed to preliminarily develop machine learning (ML) models capable of predicting the risk of device-associated infection and 30-day outcomes following invasive device procedures in intensive care unit (ICU) patients. The study utilized data from 8574 ICU patients who underwent invasive procedures, sourced from the Medical Information Mart for Intensive Care (MIMIC)-IV version 2.2 database. Patients were allocated into training and validation datasets in a 7:3 ratio. Seven ML models were employed for predicting device-associated infections, while five models were used for predicting 30-day survival outcomes. Model performance was primarily evaluated using the receiver operating characteristic (ROC) curve for infection prediction and the survival model’s concordance index (C-index). Top-performing models progressively reduced the number of variables based on their importance, thereby optimizing practical utility. The inclusion of all variables demonstrated that extreme gradient boosting (XGBoost) and extra survival trees (EST) models yielded superior discriminatory performance. Notably, when restricted to the top 10 variables, both models maintained performance levels comparable to when all variables were included. In the validation cohort, the XGBoost model, with the top 10 variables, achieved an area under the curve (AUC) of 0.810 (95% CI 0.808–0.812), an area under the precision-recall curve (AUPRC) of 0.226 (95% CI 0.222–0.230), and a Brier score (BS) of 0.053 (95% CI 0.053–0.054). The EST model, with the top 10 variables, reported a C-index of 0.756 (95% CI 0.754–0.757), a time-dependent AUC of 0.769 (95% CI 0.763–0.775), and an integrated Brier score (IBS) of 0.087 (95% CI 0.087–0.087). Both models are accessible via a web application. The internally evaluated XGBoost and EST models demonstrated exceptional predictive accuracy for device-associated infection risks and 30-day survival outcomes post-invasive procedures in ICU patients. Further validation is required to confirm the clinical utility of these two models in future studies.

This article investigated the potential of fly ash (FA)/blast furnace slag- (BFS-) based geopolymer as a novel backfilling material. The effects of NaOH concentration and FA/BFS mass ratio were explored through XRD, FTIR, and TG-DTG analyses. The results indicated that the reaction products and strengths of geopolymer depended on the NaOH concentration and types of source materials. Slump, final setting time, and setting ratio increased as a function of FA content. However, the increase in FA content reduced the compressive strength and microstructure of the backfilling material (BM) due to the lower reactivity than BFS. Microstructure analysis reveals that the matrix tends to be denser with the BFS content and NaOH concentration increase.

In this study, we investigated the mechanism of photosynthesis and physiological function of blueberry leaves under low temperature stress (4-6°C) by exogenous hydrogen sulfide (H S) by spraying leaves with 0.5 mmol·L NaHS (H S donor) and 200 μmol·L hypotaurine (Hypotaurine, H S scavenger). The results showed that chlorophyll and carotenoid content in blueberry leaves decreased under low temperature stress, and the photochemical activities of photosystem II (PSII) and photosystem I (PSI) were also inhibited. Low temperature stress can reduce photosynthetic carbon assimilation capacity by inhibiting stomatal conductance ( ) of blueberry leaves, and non-stomatal factors also play a limiting role at the 5 day of low temperature stress. Low temperature stress leads to the accumulation of Pro and H O in blueberry leaves and increases membrane peroxidation. Spraying leaves with NaHS, a donor of exogenous H S, could alleviate the degradation of chlorophyll and carotenoids in blueberry leaves caused by low temperature and reduce the photoinhibition of PSII and PSI. The main reason for the enhancement of photochemical activity of PSII was that exogenous H S promoted the electron transfer from to on PSII acceptor side under low temperature stress. In addition, it promoted the accumulation of osmotic regulator proline under low temperature stress and significantly alleviated membrane peroxidation. H S scavengers (Hypotaurine) aggravated photoinhibition and the degree of oxidative damage under low temperature stress. Improving photosynthetic capacity as well as alleviating photosynthetic inhibition and oxidative stress with exogenous H S is possible in blueberry seedlings under low temperature stress.

In order to improve the rate and reversible capacity of lithium-sulfur (Li-S) battery, a reagent of dithiothreitol (DTT) was utilized to check the dissolution and shuttle of long-chain lithium polysulfides (LiPSs) by cutting the disulfide bond (–S–S– bonds) in them. The slurry of DTT-doped multi-walled carbon nanotubes (MWCNTs) was coated on the surface of sulfur cathode as a shield to slice the long-chain LiPSs to short-chain ones for checking the dissolution and migration of LiPSs to lithium anode. The morphology and structure of the electrodes were observed by scanning electron microscopy (SEM). The electrochemical performance was tested by galvanostatic charge-discharge, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The initial discharge capacity of S-DTT- carbon nanotube paper (CNTP) electrode reached 1670 and 949 mAh/g at 0.05 and 2 C respectively with a coulombic efficiency of over 99%. The electrode maintained a reversible specific capacity of 949 mAh/g after 45 cycles at 2 C. This suggested that the DTT-doped MWCNT coating can restrain shuttle effect and improve the rate and capacity of Li-S battery. The S-DTT-CNTP electrode not only accommodates the volume expansion but also provides stable electronics and ions channels.

Rolling bearings are essential transmission and support components in aircraft engines, playing a critical role in ensuring their safe and stable operation. Rolling bearing faults have a significant impact and should not be ignored. The effective diagnosis of bearing faults has always been a critical requirement for ensuring reliable operation. With the increasing demands of modern manufacturing to reduce costs and improve quality, the development of advanced bearing fault detection methods has become indispensable. This paper presents the brief review of recent trends in research on bearing failure modes, bearing fault detection and diagnosis methods, and development trends and prospects. This article provides a comprehensive review of the existing fault diagnosis methods for rolling bearings in four aspects: the integration of advanced sensor technology and advanced data processing technology, multimodal fusion, intelligent detection, and real-time monitoring. It discusses methods based on vibration analysis, acoustic methods, current-based methods, electromagnetic methods, infrared methods, radiographic methods, visual methods, and intelligent detection methods. This study reveals that the application of intelligent detection technology, multimodal fusion detection technology, and real-time online monitoring technology has achieved favorable results. In the future, bearing fault detection will develop in a more intelligent, integrated, and real-time direction.

The multispectral thermal imager is a cutting-edge tool for the real-time measurement of high-temperature and transient-temperature fields. It can achieve high-precision temperature distribution measurements in complex scenes by acquiring multi-spectral radiation information. However, the existing research mainly focuses on the temperature measurement function, and there is little exploration of its potential application. In this paper, the method of measuring the distance of a high-temperature target based on a multispectral thermal imager is proposed and verified for the first time, which solves the problem that the traditional ranging model cannot be accurately measured in high-temperature environments. By constructing the theoretical model of the multi-aperture optical splitting system, the internal relationship between temperature field pixel migration and target distance is analyzed, and the feasibility and applicability of the method is verified by experiments. The results show that the multispectral thermal imager can overcome the interference of high-temperature radiation to traditional ranging technology, realize high-precision temperature measurement, and obtain the target distance information simultaneously. The measurement error of 300 m is 12.09%, which can be applied to the real-time measurement of the flame temperature field.

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website.

This paper introduces an innovative differential sampling technique for calibrating AC waveforms, leveraging a commercially available 16-bit digital-to-analog converter (DAC) as the reference standard. The novelty of this approach lies in its enhanced stability over traditional direct sampling methods, especially as the frequency of the AC waveform increases. Notably, this technique provides a cost-effective sampler alternative to the differential sampling methods that rely on a programmable Josephson voltage standard (PJVS). A critical aspect of this methodology is the precise measurement of the DAC’s output voltage, for which a static measurement strategy is adopted to utilize the exceptional linearity and transfer accuracy of the Keysight 3458A (Santa Rosa, CA, USA) in its standard DCV mode. The differential sampling method has demonstrated good accuracy, achieving a near 1 µV/V agreement with a pulse-driven AC Josephson voltage standard (ACJVS) across a 40 Hz to 200 Hz frequency range. The method attained an expanded uncertainty (k = 2) of 1 part in 106 while measuring a 0.707107 VRMS sine wave at 50 Hz, showcasing its efficacy in precise AC waveform calibration.