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Understanding the reconstruction of surface sites is crucial for gaining insights into the true active sites and catalytic mechanisms. While extensive research has been conducted on reconstruction behaviors of atomically dispersed metallic catalytic sites, limited attention has been paid to non-metallic ones despite their potential catalytic activity comparable or even superior to their noble-metal counterpart. Herein, we report a carbonaceous, atomically dispersed non-metallic selenium catalyst that displayed exceptional catalytic activity in the hydrazine oxidation reaction (HzOR) in alkaline media, outperforming the noble-metal Pt catalysts. In situ X-ray absorption spectroscopy (XAS) and Fourier transform infrared spectroscopy revealed that the pristine SeC4 site pre-adsorbs an ∗OH ligand, followed by HzOR occurring on the other side of the OH–SeC4. Theoretical calculations proposed that the pre-adsorbed ∗OH group pulls electrons from the Se site, resulting in a more positively charged Se and a higher polarity of Se–C bonds, thereby enhancing surface reactivity toward HzO/R.

The true promise of MXene as a practical supercapacitor electrode hinges on the simultaneous advancement of its three-dimensional (3D) assembly and the engineering of its nanoscopic architecture, two critical factors for facilitating mass transport and enhancing an electrode’s charge-storage performance. Herein, we present a straightforward strategy to engineer robust 3D freestanding MXene (Ti3C2Tx) hydrogels with hierarchically porous structures. The tetraamminezinc(II) complex cation ([Zn(NH3)4]2+) is selected to electrostatically assemble colloidal MXene nanosheets into a 3D interconnected hydrogel framework, followed by a mild oxidative acid-etching process to create nanoholes on the MXene surface. These hierarchically porous, conductive holey-MXene frameworks facilitate 3D transport of both electrons and electrolyte ions to deliver an excellent specific capacitance of 359.2 F g–1 at 10 mV s–1 and superb capacitance retention of 79% at 5000 mV s–1, representing a 42.2% and 15.3% improvement over pristine MXene hydrogel, respectively. Even at a commercial-standard mass loading of 10.1 mg cm–2, it maintains an impressive capacitance retention of 52% at 1000 mV s–1. This rational design of an electrode by engineering nanoholes on MXene nanosheets within a 3D porous framework dictates a significant step forward toward the practical use of MXene and other 2D materials in electrochemical energy storage systems.

Given the abundant solar light available on our planet, it is promising to develop an advanced fabric capable of simultaneously providing personal thermal management and facilitating clean water production in an energy-efficient manner. In this study, we present the fabrication of a photothermally active, biodegradable composite cloth composed of titanium carbide MXene and cellulose, achieved through an electrospinning method. This composite cloth exhibits favorable attributes, including chemical stability, mechanical performance, structural flexibility, and wettability. Notably, our 0.1-mm-thick composite cloth (RC/MXene IV) raises the temperature of simulated skin by 5.6 degrees C when compared to a commercially available cotton cloth, which is five times thicker under identical ambient conditions. Remarkably, the composite cloth (RC/MXene V) demonstrates heightened solar light capture efficiency (87.7%) when in a wet state instead of a dry state. Consequently, this cloth functions exceptionally well as a high-performance steam generator, boasting a superior water evaporation rate of 1.34 kg m(-2) h(-1) under one-sun irradiation (equivalent to 1000 W m(-2)). Moreover, it maintains its performance excellence in solar desalination processes. The multifunctionality of these cloths opens doors to a diverse array of outdoor applications, including solar-driven water evaporation and personal heating, thereby enriching the scope of integrated functionalities for textiles.

Fallowing is an important strategy for enhancing soil health, water harvesting and crop yields, thus improving sustainability and reducing production risks in dryland farming systems in Australia. However, accurate data regarding the location, frequency, extent, and duration of fallow land is not readily available at high spatio-temporal resolutions before and during a cropping season. As a result, continental maps depicting seasonal dynamics of fallow and cropping lands and their responses to climate change and human activities remain missing. This study set out to develop an automated approach to discriminate fallow areas from cropping areas within Australian broadacre cropping region. This was done by applying a mathematical algorithm of turning point detection technique to a curve fitted vegetation index time series from 2016 to 2022, derived from high spatial (10 m) and temporal (5-days) resolution Sentinel-2 data. Specifically, we aimed to derive: (i) 5-day revisit fallow/cropping map layers; (ii) key attributes of the fallow and cropping periods (e.g., start, end, duration); (iii) cropping intensity and area. The proposed approach yielded a high and significant overall accuracy, surpassing 92% with substantial balanced accuracy (>0.9) against >3000 fields for individual winter and summer seasons across Australia. In addition, this study's temporal and geographical outputs, encompassing seasonal attributes, cropping intensity and cropping areas, robustly enhanced the understanding of seasonal cropping dynamics and historical shifts in dryland farming practices. Findings derived from this analysis aligned well with extreme climatic events and changes in crop activities adopted in response to such events. Future research will aim to extract key seasonal dynamics attributes influenced by cropping practices, enhancing the conversion of high-resolution earth observation data into actionable industry knowledge.

Developing a desirable ethanol dehydrogenation process necessitates a highly efficient and selective catalyst with low cost. Herein, we show that the "complex active site" consisting of atomically dispersed Au atoms with the neighboring oxygen vacancies (Vo) and undercoordinated cation on oxide supports can be prepared and display unique catalytic properties for ethanol dehydrogenation. The "complex active site" Au-Vo-Zr3+ on Au1/ZrO2 exhibits the highest H2 production rate, with above 37,964 mol H2 per mol Au per hour (385 g H2 g-1 Au h-1) at 350 oC, which is 3.32, 2.94 and 15 times higher than Au1/CeO2, Au1/TiO2, and Au1/Al2O3, respectively. Combining experimental and theoretical studies, we demonstrate the structural sensitivity of these complex sites by assessing their selectivity and activity in ethanol dehydrogenation. Our study sheds new light on the design and development of cost-effective and highly efficient catalysts for ethanol dehydrogenation. Fundamentally, atomic-level catalyst design by colocalizing catalytically active metal atoms forming a structure-sensitive "complex site", is a crucial way to advance from heterogeneous catalysis to molecular catalysis. Our study advanced the understanding of the structure sensitivity of the active site in atomically dispersed catalysts.

Recently, the traditional rear and side view mirrors have been started to be exchanged with a digital version. The aim of this study was to investigate the difference in driving performance between traditional rear-view mirrors and digital rear view mirrors which is called Camera Monitor System (CMS) in the vehicle industry. Here, two different types were investigated: CMS without or with Augmented Reality (AR) Information. The user test was conducted in a virtual environment, with four driving scenarios defined for testing. The user test results revealed that the participants' driving performance using CMS (only cameras and 2D displays without augmented information) did not improve over traditional mirrors.

Inhomogeneous lithium-ion (Li+) deposition is one of the most crucial problems, which severely deteriorates the performance of solid-state lithium metal batteries (LMBs). Herein, we discovered that covalent organic framework (COF-1) with periodically arranged boron-oxygen dipole lithiophilic sites could directionally guide Li+ even deposition in asymmetric solid polymer electrolytes. This in situ prepared 3D cross-linked network Poly(ACMO-MBA) hybrid electrolyte simultaneously delivers outstanding ionic conductivity (1.02 × 10−3 S cm−1 at 30 °C) and excellent mechanical property (3.5 MPa). The defined nanosized channel in COF-1 selectively conducts Li+ increasing Li+ transference number to 0.67. Besides, The COF-1 layer and Poly(ACMO-MBA) also participate in forming a boron-rich and nitrogen-rich solid electrolyte interface to further improve the interfacial stability. The Li‖Li symmetric cell exhibits remarkable cyclic stability over 1000 h. The Li‖NCM523 full cell also delivers an outstanding lifespan over 400 cycles. Moreover, the Li‖LiFePO4 full cell stably cycles with a capacity retention of 85% after 500 cycles. the Li‖LiFePO4 pouch full exhibits excellent safety performance under pierced and cut conditions. This work thereby further broadens and complements the application of COF materials in polymer electrolyte for dendrite-free and high-energy-density solid-state LMBs.

Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.

Hemoglobin hydrolysate is derived from the enzymatic degradation of hemoglobin. This work aimed to evaluate whether hemoglobin hydrolysate promotes the absorption of non-heme iron and the safety of absorbed iron in mice by analyzing the iron binding content, iron circulation, and liver homeostasis. We found that hemoglobin hydrolysate promoted the absorption of non-heme iron with high efficiency in duodenum by spontaneously binding non-heme iron during digestion, and increased hepatic iron content by up-regulating divalent metal transporter 1, zinc transporter 14, but hepatic iron content only increased at 3 weeks. Duodenal iron entered the blood through ferroportin without restriction at 3 weeks, and excessive iron entered the liver and then affected the hepatocyte membranes permeability and lipid synthesis through oxidative stress. With the prolongation of dietary intervention, the up-regulated hepcidin acted on the ferroportin to restrict excess iron from entering the blood, and then the hepatic homeostasis recovered. In addition, hemoglobin hydrolysate enhanced the hepatic antioxidant capacity. Taken together, hemoglobin hydrolysate has a strong ability to promote the absorption of non-heme iron in vivo, and the absorbed iron is relatively safe due to the regulation of hepcidin.

Yu Qi,1– 3,* Meiyan Zhou,1– 3,* Wenting Zheng,1,2,* Yaqi Dong,1,2 Weihua Li,2,4 Long Wang,1,2 Haijun Xu,1,2 Miao Zhang,1,5 Dunpeng Yang,1,5 Liwei Wang,1– 3 Hai Zhou1– 3 1The Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 2Department of Anesthesiology, Xuzhou Central Hospital, Xuzhou, Jiangsu, People’s Republic of China; 3Jiangsu Province Key Laboratory of Anesthesiology Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 4College of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 5Department of Cardiothoracic Surgery, Xuzhou Central Hospital, Xuzhou, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hai Zhou; Liwei Wang, The Xuzhou Clinical College of Xuzhou Medical University, Department of Anesthesiology, Xuzhou Central Hospital, Xuzhou, Jiangsu, People’s Republic of China, Email zhouhai339339@163.com; doctorlww@sina.comPurpose: Postoperative nausea and vomiting (PONV) frequently occur in patients after surgery. In this study, the authors investigated whether perioperative S-ketamine infusion could decrease the incidence of PONV in patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy.Patients and Methods: This prospective, randomized, double-blinded, controlled study was conducted a total of 420 patients from September 2021 to May 2023 at Xuzhou Central Hospital in China, who underwent elective VATS lobectomy under general anesthesia with tracheal intubation. The patients were randomly assigned to either the S-ketamine group or the control group. The S-ketamine group received a bolus injection of 0.5 mg/kg S-ketamine and an intraoperative continuous infusion of S-ketamine at a rate of 0.25 mg/kg/h. The control group received an equivalent volume of saline. All patients were equipped with patient-controlled intravenous analgesia (PCIA)

This study introduces a novel approach utilizing Gaussian process model predictive control (MPC) to stabilize the output voltage of a polymer electrolyte fuel cell (PEFC) system by simultaneously regulating hydrogen and airflow rates. Two Gaussian process models are developed to capture PEFC dynamics, taking into account constraints including hydrogen pressure and input change rates, thereby aiding in mitigating errors inherent to PEFC predictive control. The dynamic performance of the physical model and Gaussian process MPC in constraint handling and system inputs is compared and analyzed. Simulation outcomes demonstrate that the proposed Gaussian process MPC effectively maintains the voltage at the target 48 V while adhering to safety constraints, even amidst workload disturbances ranging from 110-120 A. In comparison to traditional MPC using detailed system models, Gaussian process MPC exhibits a 43\% higher overshoot and 25\% slower response time. Nonetheless, it offers the advantage of not requiring the underlying true system model and needing less system information.

Performance disparities across sub-populations are known to exist in deep learning-based vision recognition models, but previous work has largely addressed such fairness concerns assuming knowledge of sensitive attribute labels. To overcome this reliance, previous strategies have involved separate learning structures to expose and adjust for disparities. In this work, we explore a new paradigm that does not require sensitive attribute labels, and evades the need for extra training by leveraging the vision-language model, CLIP, as a rich knowledge source to infer sensitive information. We present sample clustering based on similarity derived from image and attribute-specified language embeddings and assess their correspondence to true attribute distribution. We train a target model by re-sampling and augmenting under-performed clusters. Extensive experiments on multiple benchmark bias datasets show clear fairness gains of the model over existing baselines, which indicate that CLIP can extract discriminative sensitive information prompted by language, and used to promote model fairness.

This paper introduces GAgent: an Gripping Agent designed for open-world environments that provides advanced cognitive abilities via VLM agents and flexible grasping abilities with variable stiffness soft grippers. GAgent comprises three primary components - Prompt Engineer module, Visual-Language Model (VLM) core and Workflow module. These three modules enhance gripper success rates by recognizing objects and materials and accurately estimating grasp area even under challenging lighting conditions. As part of creativity, researchers also created a bionic hybrid soft gripper with variable stiffness capable of gripping heavy loads while still gently engaging objects. This intelligent agent, featuring VLM-based cognitive processing with bionic design, shows promise as it could potentially benefit UAVs in various scenarios.

Chinese Spell Checking (CSC) is a widely used technology, which plays a vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approaches relying on BERT architecture achieve excellent performance. However, limited by the scale of the foundation model, BERT-based method does not work well in few-shot scenarios, showing certain limitations in practical applications. In this paper, we explore using an in-context learning method named RS-LLM (Rich Semantic based LLMs) to introduce large language models (LLMs) as the foundation model. Besides, we study the impact of introducing various Chinese rich semantic information in our framework. We found that by introducing a small number of specific Chinese rich semantic structures, LLMs achieve better performance than the BERT-based model on few-shot CSC task. Furthermore, we conduct experiments on multiple datasets, and the experimental results verified the superiority of our proposed framework.

New data sources, and artificial intelligence (AI) methods to extract information from them are becoming plentiful, and relevant to decision making in many societal applications. An important example is street view imagery, available in over 100 countries, and considered for applications such as assessing built environment aspects in relation to community health outcomes. Relevant to such uses, important examples of bias in the use of AI are evident when decision-making based on data fails to account for the robustness of the data, or predictions are based on spurious correlations. To study this risk, we utilize 2.02 million GSV images along with health, demographic, and socioeconomic data from New York City. Initially, we demonstrate that built environment characteristics inferred from GSV labels at the intra-city level may exhibit inadequate alignment with the ground truth. We also find that the average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, as measured through GSV. Finally, using a causal framework which accounts for these mediators of environmental impacts on health, we find that altering 10% of samples in the two lowest tertiles would result in a 4.17 (95% CI 3.84 to 4.55) or 17.2 (95% CI 14.4 to 21.3) times bigger decrease on the prevalence of obesity or diabetes, than the same proportional intervention on the number of crosswalks by census tract. This work illustrates important issues of robustness and model specification for informing effective allocation of interventions using new data sources.

[$^{18}$F]fluorodeoxyglucose (FDG) positron emission tomography (PET) has emerged as a crucial tool in identifying the epileptic focus, especially in cases where magnetic resonance imaging (MRI) diagnosis yields indeterminate results. FDG PET can provide the metabolic information of glucose and help identify abnormal areas that are not easily found through MRI. However, the effectiveness of FDG PET-based assessment and diagnosis depends on the selection of a healthy control group. The healthy control group typically consists of healthy individuals similar to epilepsy patients in terms of age, gender, and other aspects for providing normal FDG PET data, which will be used as a reference for enhancing the accuracy and reliability of the epilepsy diagnosis. However, significant challenges arise when a healthy PET control group is unattainable. Yaakub \emph{et al.} have previously introduced a Pix2PixGAN-based method for MRI to PET translation. This method used paired MRI and FDG PET scans from healthy individuals for training, and produced pseudo normal FDG PET images from patient MRIs that are subsequently used for lesion detection. However, this approach requires a large amount of high-quality, paired MRI and PET images from healthy control subjects, which may not always be available. In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization. Two deep learning methods, CycleGAN and SynDiff, were employed, and we found that diffusion-based method achieved improved performance in accurately localizing the epileptic focus., Comment: SPIE Medical Imaging 2024

To tackle the scarcity and privacy issues associated with domain-specific datasets, the integration of federated learning in conjunction with fine-tuning has emerged as a practical solution. However, our findings reveal that federated learning has the risk of skewing fine-tuning features and compromising the out-of-distribution robustness of the model. By introducing three robustness indicators and conducting experiments across diverse robust datasets, we elucidate these phenomena by scrutinizing the diversity, transferability, and deviation within the model feature space. To mitigate the negative impact of federated learning on model robustness, we introduce GNP, a \underline{G}eneral \underline{N}oisy \underline{P}rojection-based robust algorithm, ensuring no deterioration of accuracy on the target distribution. Specifically, the key strategy for enhancing model robustness entails the transfer of robustness from the pre-trained model to the fine-tuned model, coupled with adding a small amount of Gaussian noise to augment the representative capacity of the model. Comprehensive experimental results demonstrate that our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods and confronting different levels of data heterogeneity., Comment: 12 pages, 10 figures

Machine learning model bias can arise from dataset composition: sensitive features correlated to the learning target disturb the model decision rule and lead to performance differences along the features. Existing de-biasing work captures prominent and delicate image features which are traceable in model latent space, like colors of digits or background of animals. However, using the latent space is not sufficient to understand all dataset feature correlations. In this work, we propose a framework to extract feature clusters in a dataset based on image descriptions, allowing us to capture both subtle and coarse features of the images. The feature co-occurrence pattern is formulated and correlation is measured, utilizing a human-in-the-loop for examination. The analyzed features and correlations are human-interpretable, so we name the method Common-Sense Bias Discovery (CSBD). Having exposed sensitive correlations in a dataset, we demonstrate that downstream model bias can be mitigated by adjusting image sampling weights, without requiring a sensitive group label supervision. Experiments show that our method discovers novel biases on multiple classification tasks for two benchmark image datasets, and the intervention outperforms state-of-the-art unsupervised bias mitigation methods.

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students' proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it's hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel \underline{Dis}entangling Heterogeneous \underline{K}nowledge \underline{C}ognitive \underline{D}iagnosis framework on untested knowledge(DisKCD). Specifically, we leverage course grades, exercise questions, and resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students' proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students' proficiency on UKCs. Our anonymous code is available at https://anonymous.4open.science/r/DisKCD.

In the past three years, working with the Pacific Salmon Foundation and various First Nations groups, we have established Starlink-empowered wild salmon monitoring sites in remote Northern British Columbia, Canada. We report our experiences with the network services in these challenging environments, including deep woods and deep valleys, that lack infrastructural support with some close to Starlink's service boundary at the far north. We assess the portability and mobility of the satellite dishes and the quality of existing network access in underdeveloped countries that Starlink expects to cover. Our experiences suggest that network access based on LEO satellite constellations holds promise but faces hurdles such as energy supply constraints and environmental factors like temperature, precipitation, and solar storms. The presence of wildlife and respecting local residents' culture and heritage pose further complications. We envision several technical solutions addressing the challenges and believe that further regulations will be necessary., Comment: 8 pages, 6 figures

We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine learning models on edge devices near sensors such that decisions can be made instantaneously, rather than first having to transmit incoming data to servers. To enable deployment on edge devices with limited storage and computational capabilities, the full-precision parameters in standard models can be quantized to use fewer bits. The resulting quantized models are then calibrated using back-propagation and full training data to ensure accuracy. This one-time calibration works for deployments in static environments. However, model deployment in dynamic edge environments call for continual calibration to adaptively adjust quantized models to fit new incoming data, which may have different distributions. The first difficulty in enabling continual calibration on the edge is that the full training data may be too large and thus not always available on edge devices. The second difficulty is that the use of back-propagation on the edge for repeated calibration is too expensive. We propose QCore to enable continual calibration on the edge. First, it compresses the full training data into a small subset to enable effective calibration of quantized models with different bit-widths. We also propose means of updating the subset when new streaming data arrives to reflect changes in the environment, while not forgetting earlier training data. Second, we propose a small bit-flipping network that works with the subset to update quantized model parameters, thus enabling efficient continual calibration without back-propagation. An experimental study, conducted with real-world data in a continual learning setting, offers insight into the properties of QCore and shows that it is capable of outperforming strong baseline methods., Comment: 15 pages. An extended version of "QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models" accepted at PVLDB 2024

The water oxidation reaction in photosystem II (PS II) produces most of the molecular oxygen in the atmosphere, which sustains life on Earth, and in this process releases four electrons and four protons that drive the downstream process of CO2 fixation in the photosynthetic apparatus. The catalytic center of PS II is an oxygen-bridged Mn4Ca complex (Mn4CaO5) which is progressively oxidized upon the absorption of light by the chloro­phyll of the PS II reaction center, and the accumulation of four oxidative equivalents in the catalytic center results in the oxidation of two waters to di­oxy­gen in the last step. The recent emergence of X-ray free-electron lasers (XFELs) with intense femtosecond X-ray pulses has opened up opportunities to visualize this reaction in PS II as it proceeds through the catalytic cycle. In this review, we summarize our recent studies of the catalytic reaction in PS II by following the structural changes along the reaction pathway via room-temperature X-ray crystallography using XFELs. The evolution of the electron density changes at the Mn complex reveals notable structural changes, including the insertion of OX from a new water molecule, which disappears on completion of the reaction, implicating it in the O—O bond formation reaction. We were also able to follow the structural dynamics of the protein coordinating with the catalytic complex and of channels within the protein that are important for substrate and product transport, revealing well orchestrated conformational changes in response to the electronic changes at the Mn4Ca cluster.

Ever since the discovery that Mn was required for oxygen evolution in plants by Pirson in 1937 and the period-four oscillation in flash-induced oxygen evolution by Joliot and Kok in the 1970s, understanding of this process has advanced enormously using state-of-the-art methods. The most recent in this series of innovative techniques was the introduction of X-ray free-electron lasers (XFELs) a decade ago, which led to another quantum leap in the understanding in this field, by enabling operando X-ray structural and X-ray spectroscopy studies at room temperature. This review summarizes the current understanding of the structure of Photosystem II (PS II) and its catalytic centre, the Mn4CaO5 complex, in the intermediate Si (i = 0–4)-states of the Kok cycle, obtained using XFELs.

In electrocatalytic CO2 reduction reaction (CO2RR), metal-free Se-based multi-doping carbon has become a new focus. In this paper, a series of metal-free Se, B, P, N doped carbon-based catalysts (Se-BP-N-C) were prepared by a simple and scalable pyrolysis method. The electrocatalytic CO2 reduction results show that, the main product on Se-BP-N-C catalyst is CO, and there are no other by-products except H2. Specifically, the optimal one prepared at 1050 °C (named Se-BP-N-C (1050)), can selectively reduce CO2 into CO with Faradaic efficiency (FE) up to 96.2% (at − 0.5 V), and maintains long-term stability after continuous electrolysis for 10 h in a flow cell (current density remained 82.7%). This study would provide a good reference for designing advanced efficient metal-free catalysts in electrocatalysis.

Controlled synthesis of polymer-based porous membranes via innovative methods is of considerable interest, yet it remains a challenge. Herein, we established a general approach to fabricate porous polyelectrolyte composite membranes (PPCMs) from poly-(ionic liquid) (PIL) and MXene via an ice-assisted method. This process enabled the formation of a uniformly distributed macroporous structure within the membrane. The unique characteristics of the as-produced composite membranes display significant light-to-heat conversion and excellent performance for solar-driven water vapor generation. This facile synthetic strategy breaks new ground for developing composite porous membranes as high-performance solar steam generators for clean water production.

Fe3C nanoparticles hold promise as catalysts and nanozymes, but their low activity and complex preparation have hindered their use. Herein, this study presents a synthetic alternative toward efficient, durable, and recyclable, Fe3C-nanoparticle-encapsulated nitrogen-doped hierarchically porous carbon membranes (Fe3C/N–C). By employing a simple one-step synthetic method, we utilized wood as a renewable and environmentally friendly carbon precursor, coupled with poly(ionic liquids) as a nitrogen and iron source. This innovative strategy offers sustainable, high-performance catalysts with improved stability and reusability. The Fe3C/N–C exhibits an outstanding peroxidase-like catalytic activity toward the oxidation of 3,3′,5,5′-tetramethylbenzidine in the presence of hydrogen peroxide, which stems from well-dispersed, small Fe3C nanoparticles jointly with the structurally unique micro-/macroporous N–C membrane. Owing to the remarkable catalytic activity for mimicking peroxidase, an efficient and sensitive colorimetric method for detecting ascorbic acid over a broad concentration range with a low limit of detection (~2.64 µM), as well as superior selectivity, and anti-interference capability has been developed. This study offers a widely adaptable and sustainable way to synthesize an Fe3C/N–C membrane as an easy-to-handle, convenient, and recoverable biomimetic enzyme with excellent catalytic performance, providing a convenient and sensitive colorimetric technique for potential applications in medicine, biosensing, and environmental fields.

Two-dimensional (2D) MXene-based lamellar membranes play transformative roles in membrane filtration technology. Their practical use in water treatment is however hindered by several hurdles, e.g., unfavorable swelling due to weak interactions between adjacent MXene nanosheets, tortuous diffusion pathways of layered stacking, and the intrinsic aquatic oxidation-prone nature of MXene. Herein, nanoporous 2D/2D heterostructure membranes are elaborately constructed via solution-phase assembly of oppositely charged MXene and modified layered double hydroxide (MLDH) nanosheets. As a multifunctional component, positively charged holey MLDH nanosheets were first tailor-made to serve simultaneously as a binder, spacer and surface-modifier; next they were intercalated into negatively charged MXene lamella to enhance structural stability and mass transfer of membranes. As a result, the as-prepared MLDH@MXene heterostructure membranes successfully break the persistent trade-off between high permeability and selectivity while mitigating the common drawbacks in 2D MXene-based lamellar membranes, e.g., swelling issues, restacking problems, and vulnerable chemical stability. Noticeably, at an operating pressure of 4 bar and a feed solution of 100 ppm of Congo red, the heterostructure membranes enable a threefold jump in permeability (332.7 +/- 20 L m(-2) h(-1 )bar(-1)) when compared to the pristine MXene membrane (119.3 +/- 18 L m(-2 )h(-1) bar(-1)), and better operational stability without compromising the rejection.

Two-dimensional (2D) membrane-based ion separation technology has been increasingly explored to address the problem of lithium resource shortage, yet it remains a sound challenge to design 2D membranes of high selectivity and permeability for ion separation applications. Zeolitic imidazolate framework functionalized modified layered double hydroxide (ZIF-8@MLDH) composite membranes with high lithium-ion (Li+) permeability and excellent operational stability were obtained in this work by in situ depositing functional ZIF-8 nanoparticles into the nanopores acting as framework defects in MLDH membranes. The defect-rich framework amplified the permeability of Li+, and the site-selective growth of ZIF-8 in the framework defects bettered its selectivity. Specifically speaking, the ZIF-8@MLDH membranes featured a high permeation rate of Li+ up to 1.73 mol m−2 h−1 and a desirable selectivity of Li+/Mg2+ up to 31.9. Simulations supported that the simultaneously enhanced selectivity and permeability of Li+ are attributed to changes in the type of mass transfer channels and the difference in the dehydration capacity of hydrated metal cations when they pass through nanochannels of ZIF-8. This study will inspire the ongoing research of high-performance 2D membranes through the engineering of defects.

Ever since the discovery that Mn was required for oxygen evolution in plants by Pirson in 1937 and the period-four oscillation in flash-induced oxygen evolution by Joliot and Kok in the 1970s, understanding of this process has advanced enormously using state-of-the-art methods. The most recent in this series of innovative techniques was the introduction of X-ray free-electron lasers (XFELs) a decade ago, which led to another quantum leap in the understanding in this field, by enabling operando X-ray structural and X-ray spectroscopy studies at room temperature. This review summarizes the current understanding of the structure of Photosystem II (PS II) and its catalytic centre, the Mn4 CaO5 complex, in the intermediate Si (i = 0-4)-states of the Kok cycle, obtained using XFELs.

Miao Zhang, Jing Liu, Yu-Chen Pan, Hui-Li Liu, Jing Shu, Xiao-Ming Wu, Yang Yang Department of Pediatric Gynecology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, 100045, ChinaCorrespondence: Jing Liu, Department of Pediatric Gynecology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, 100045, People’s Republic of China, Tel +86 010 59616889, Email liujing2893@126.comObjective: The aim of this study was to analyze the prevalence of vaginal flora and drug resistance in bacterial vaginitis among girls.Methods: A total of 3099 girls (0– 10 years old) with vaginitis who visited the Beijing Children’s Hospital from January 2020 to December 2021 were included in the present study. The clinical data, results of bacterial culture of vaginal secretions, and drug sensitivity reports of the subjects were collected and analyzed.Results: Of the 3099 girls with vaginitis, 399 girls had a positive bacterial culture of vaginal secretions. Nineteen types of bacteria were cultured from the vaginal secretions of these 399 girls, with a total of 419 strains. The top three infective bacteria were Haemophilus influenzae (127 strains, 30.31%), Staphylococcus aureus (66 strains, 15.75%), and Streptococcus agalactiae (32 strains, 7.64%). Additionally, 20 girls were simultaneously infected with two types of bacteria. Staphylococcus aureus, Group G Streptococcus, Haemophilus parainfluenzae, and Pseudomonas aeruginosa more frequently occurred in mixed infections. The number and bacterial detection rate among school-age girls were higher than those of preschool-age girls. We found seasonal variation in infection rates, and vaginitis among girls was higher in summer. Recurrence of vaginitis in girls was not related to the type of pathogenic bacteria in the infection. Drug sensitivity analyses showed that the resistance rates of clindamycin and erythromycin were general

Maintaining human thermal comfort in the cold outdoors is crucial for diverse outdoor activities, e.g., sports and recreation, healthcare, and special occupations. To date, advanced clothes are employed to collect solar energy as a heat source to stand cold climates, while their dull dark photothermal coating may hinder pragmatism in outdoor environments and visual sense considering fashion. Herein, tailor-made white webs with strong photothermal effect are proposed. With the embedding of cesium–tungsten bronze (CsxWO3) nanoparticles (NPs) as additive inside nylon nanofibers, these webs are capable of drawing both near-infrared (NIR) and ultraviolet (UV) light in sunlight for heating. Their exceptional photothermal conversion capability enables 2.5–10.5 °C greater warmth than that of a commercial sweatshirt of six times greater thickness under different climates. Remarkably, this smart fabric can increase its photothermal conversion efficiency in a wet state. It is optimal for fast sweat or water evaporation at human comfort temperature (38.5 °C) under sunlight, and its role in thermoregulation is equally important to avoid excess heat loss in wilderness survival. Obviously, this smart web with considerable merits of shape retention, softness, safety, breathability, washability, and on-demand coloration provides a revolutionary solution to realize energy-saving outdoor thermoregulation and simultaneously satisfy the needs of fashion and aesthetics.

Herein, we established a synthetic route towards MXene/poly(ionic liquid) (PIL) composite porous membranes as a new platform of solar-thermal conversion materials. These membranes were made by a base-triggered ionic crosslinking process between a cationic PIL and a weak polyacid in solution in the presence of dispersed MXene nanosheets. A three-dimensionally interconnected porous architecture was formed with MXene nanosheets uniformly distributed within it. The unique characteristics of the as-produced composite membranes displays significant light-to-heat conversion and excellent performance for solar-driven water vapor generation. This facile synthetic strategy opens a new avenue for developing composite porous membranes as solar absorbers for the solar-driven water production from natural resources.

Background: As a promising green energy conversion device, direct ethylene glycol fuel cells (DEGFC) have been widely studied. But high-cost of their catalysts has severely limited large-scale commercial application. Developing economic and effective anodic electrocatalyst is an urgent work presently. Methods: Herein, an efficient high entropy alloy catalyst (HEA, penta-element) was prepared. Then, their catalytic performance and properties were authenticated by some electrochemical and physical methods. Importantly, it was also applied to a real DEGFC stack. Significant findings: Notably, the ethylene glycol oxidation current density (0.65 A mg−1PtPdAu) on as-prepared HEA is three times than that of commercial Pt/C (0.22 A mg−1Pt) with good long-term durability. Moreover, the HEA-equipped DEGFC obtains an open circuit potential of 0.58 V, which delivers 2 times larger peak power density (17.63 mW cm−2) than that of commercial Pt/C (7.37 mW cm−2). This work would be a good reference to developing other advanced HEA materials in electrocatalytic fields.

Carbon nanotubes (CNTs) have received considerable attention for their excellent thermal and electrical conductivity as well as scalable production. However, CNT dispersions are prone to settling and have a short shelf time, especially under high concentration, which significantly hinders their further processing and increases transportation costs. Here, we report a highly concentrated CNT dough enabled by ionic liquid crystal (ILC) as auxiliaries. Benefiting from the temperature-controlled physical transformation of the ILC, the CNTs of the powder state are successfully transferred to highly processable dough with excellent electrical conductivity, flame retardancy, and outstanding redispersibility even after 180 days of storage. In particular, the CNT dough exhibits excellent self-healing properties and good reshapable capability. Various bulk form CNT derived from the ILC armored CNT dough are realized by facile processing technique. Hybrid nanocomposite papers with ANF nanofiber exhibited excellent photothermal conversion and Joule heating properties. The redispersible CNT doughs presented here promise to revolutionize traditional CNT powder and dispersions as the primary raw material for building CNT-based architectures and facilitate the large-scale application of CNTs.

Noble metals are the most commonly used electrocatalysts, but due to the high-cost and scarcity, improving their utilization has become a hot topic. As the Pt-based high-entropy alloy (HEA) can greatly increase the activity of catalyst and increase the utilization of noble metal, herein, a HEA with promising performance in ethylene glycol oxidation reaction (EGOR) is developed. The EGOR results show that, the onset potential of PtPdAuNiCo/C is 0.55 V, which is 20 mV lower than Pt/C (0.57 V) reference. Besides, the PtPdAuNiCo/C exhibits a high activity of 0.482 A mg−1PtPdAu, which is 2.18 times of Pt/C (0.221 A mg−1Pt) reference. And the current retention rate of PtPdAuNiCo/C (81.3%) is also higher than Pt/C (73.0%) reference in 500-cycle stability test. When as-obtained PtPdAuNiCo/C assembled into a direct ethylene glycol fuel cell, it exhibits a high-power density of 8.38 mW cm−2. It is 1.40 times than that of Pt/C (6.00 mW cm−2) reference. This work would be a good reference to HEA materials application on electrocatalysis in future.

The quality of sports and leisure has received the attention of local sports departments. The construction of sports parks has become a new hot spot to enhance the image of the city and develop sports. Therefore, this paper studies the coupled and synergistic development of the sports industry and urban park ecology. First, the current situation of urban sports parks in China is analyzed. Then the affiliation numerical method was introduced into the evaluation of park ecology. Finally, we calculate the acquisition index and evaluation and use it to analyze the ecological and environmental effects of plant communities. The results showed that in Manduhai Park, Hohhot, the temperature change of plant communities first rose and then slowly decreased, with the highest temperature at 4 pm and the lowest at 8 pm, and the cooling rate was 3.34%~5.05%. The relative humidity was highest at 8:00 a.m. and lowest at 4:00 p.m., with a humidification rate of 1.82% to 12.18%. Wind speed is the highest at 12:00 noon and the lowest in the morning and evening; the human body feels the best comfort in the morning at 8:00, and feels uncomfortable around 12:00 and 16:00, with a variation range of 71.77±2.73 to 73.19±2.54; the research found that 42% of the visitors chose leisure and sports activities; followed by 39% for entertainment and games, and 12% for cultural activities again. Thirty-one percent of the visitors were satisfied with the leisure and sports facilities, 31% thought they were average, 28% reached only basic satisfaction, and 10% were not satisfied with the leisure and sports facilities.

Although massive progress in power conversion efficiencies (PCEs) has been made on organic solar cells (OSCs), the challenge remains to scale up high-performance OSCs that are compatible with industrial production conditions, such as post-treatment-insensitive, non-halogenated solvent, large-area printing, processing under atmospheric environment. In this study, two A-D-A-D-A type oligomers, named 5BDDBDT-F and 5BDDBDT-Cl, were designed and synthesized to be incorporated into binary OSCs. Ex situ and in situ characterizations were performed to reveal the underlying working mechanisms. The two-phase transitions: liquid-to-liquid and liquid-to-solid can be synergistically tailored to promote the molecular arrangement of the binary blend. Benefiting from the enhanced crystallinity, the oligomer can act as a bridging linker to rehabilitate the interfacial defects to reduce the trap-assisted recombination losses and thus the non-radiative recombination losses. As a result, using non-halogenated solvent (o-xylene), the best-performing OSCs with PCEs of 18.32% with a VOC of 0.850 V, an FF of 79.15% were achieved based on the PM6:5BDDBDT-F:BTP-eC9 ternary OSCs, and 18.43% with a VOC of 0.854 V, an FF of 79.29% were obtained based on PM6:5BDDBDT-Cl:BTP-eC9 ternary OSCs without any post-processing. More importantly, it is noteworthy that the large-area (1 cm2) blade coated devices based on PM6:5BDDBDT-F:BTP-eC9 and PM6:5BDDBDT-Cl:BTP-eC9, processed using non-halogenated solvent in an open-air environment without thermal-annealing treatment, obtained high PCE values of 17.11% and 17.06%, respectively, which are among the highest PCE values of blade-coated OSCs in open air. This study demonstrated an efficient strategy of controlling crystallization kinetics to improve the material crystallinity, as an effective guideline for achieving high photovoltaic performance in printing ternary OSCs, pushing the OSCs towards future manufacturing. Two novel oligomers boost the 1 cm2 devic

To improve the performance of mobile web services, a new transport protocol, QUIC, has been recently proposed as a substitute for TCP. However, with pros and cons of QUIC, it is challenging to decide whether and when to use QUIC in large-scale real-world mobile web services. Complex temporal correlation of network conditions, high user heterogeneity in a nationwide deployment, implementation diversity of QUIC variants limited, and resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans, an adaptive transport protocol selection mechanism, to switch transport protocols for mobile web services online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, adopts an online learning method to keep pace with implementation variation, and switches transport protocols at the request level to reach high performance with acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of Baidu. Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 25.8% on average compared to the usage of a single protocol.

Purpose Claudin 18.2 (CLDN18.2) is a reliable target for lesion detection and could have clinical implications for epithelial tumors, especially digestive system neoplasms. However, there is no predictive technology for accurate whole-body mapping of CLDN18.2 expression in patients. This study assessed the safety of the I-124-18B10(10L) tracer and the feasibility of mapping whole-body CLDN18.2 expression using PET functional imaging. Methods The I-124-18B10(10L) probe was synthesized manually, and preclinical experiments including binding affinity and specific targeting ability were conducted after testing in vitro model cells. Patients with pathologically confirmed digestive system neoplasms were enrolled in an ongoing, open-label, single-arm, first-in-human (FiH) phase 0 trial (NCT04883970). I-124-18B10(10L) PET/CT or PET/MR and F-18-FDG PET were performed within one week. Results I-124-18B10(10L) was successfully constructed with an over 95% radiochemical yield. The results of preclinical experiments showed that it had high stability in saline and high affinity in CLDN18.2 overexpressing cells (Kd = 4.11 nM). Seventeen patients, including 12 with gastric cancers, 4 with pancreatic cancers, and 1 with cholangiocarcinoma were enrolled. I-124-18B10(10L) displayed high uptake in the spleen and liver, and slight uptake in the bone marrow, lung, stomach and pancreas. The tracer uptake SUVmax in tumor lesions ranged from 0.4 to 19.5. Compared with that in lesions that had been treated with CLDN18.2-targeted therapy, I-124-18B10(10L) uptake was significantly higher in lesions that had not. Regional I-124-18B10(10L) PET/MR in two patients showed high tracer uptake in metastatic lymph nodes. Conclusions I-124-18B10(10L) was successfully prepared and exhibited a high binding affinity and CLDN18.2 specificity in preclinical studies. As an FiH CLDN18.2 PET tracer, I-124-18B10(10L) was shown to be safe with acceptable dosimetry and to clearly reveal most lesions overexpressing

Aerodynamic performance evaluation is an important part of the aircraft aerodynamic design optimization process; however, traditional methods are costly and time-consuming. Despite the fact that various machine learning methods can achieve high accuracy, their application in engineering is still difficult due to their poor generalization performance and "black box" nature. In this paper, a knowledge-embedded meta learning model, which fully integrates data with the theoretical knowledge of the lift curve, is developed to obtain the lift coefficients of an arbitrary supercritical airfoil under various angle of attacks. In the proposed model, a primary network is responsible for representing the relationship between the lift and angle of attack, while the geometry information is encoded into a hyper network to predict the unknown parameters involved in the primary network. Specifically, three models with different architectures are trained to provide various interpretations. Compared to the ordinary neural network, our proposed model can exhibit better generalization capability with competitive prediction accuracy. Afterward, interpretable analysis is performed based on the Integrated Gradients and Saliency methods. Results show that the proposed model can tend to assess the influence of airfoil geometry to the physical characteristics. Furthermore, the exceptions and shortcomings caused by the proposed model are analysed and discussed in detail.

Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy is often achieved by ensemble learning where results are synthesized from multiple base models. This characteristic implies that ensemble learning needs substantial computing resources, preventing their use in resource-limited environments, such as in edge devices. To extend the applicability of ensemble learning, we propose the LightTS framework that compresses large ensembles into lightweight models while ensuring competitive accuracy. First, we propose adaptive ensemble distillation that assigns adaptive weights to different base models such that their varying classification capabilities contribute purposefully to the training of the lightweight model. Second, we propose means of identifying Pareto optimal settings w.r.t. model accuracy and model size, thus enabling users with a space budget to select the most accurate lightweight model. We report on experiments using 128 real-world time series sets and different types of base models that justify key decisions in the design of LightTS and provide evidence that LightTS is able to outperform competitors., Comment: 15 pages. An extended version of "LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation" accepted at SIGMOD 2023

Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with relieving human efforts. However, existing graph NAS methods mainly work under the homophily assumption and overlook another important graph property, i.e., heterophily, which exists widely in various real-world applications. To date, automated heterophilic graph learning with NAS is still a research blank to be filled in. Due to the complexity and variety of heterophilic graphs, the critical challenge of heterophilic graph NAS mainly lies in developing the heterophily-specific search space and strategy. Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities. Specifically, Auto-HeG incorporates heterophily into all stages of automatic heterophilic graph learning, including search space design, supernet training, and architecture selection. Through the diverse message-passing scheme with joint micro-level and macro-level designs, we first build a comprehensive heterophilic GNN search space, enabling Auto-HeG to integrate complex and various heterophily of graphs. With a progressive supernet training strategy, we dynamically shrink the initial search space according to layer-wise variation of heterophily, resulting in a compact and efficient supernet. Taking a heterophily-aware distance criterion as the guidance, we conduct heterophilic architecture selection in the leave-one-out pattern, so that specialized and expressive heterophilic GNN architectures can be derived. Extensive experiments illustrate the superiority of Auto-HeG in developing excellent heterophilic GNNs to human-designed models and graph NAS models., Comment: Accepted by Proceedings of the ACM Web Conference 2023 (WWW '23)

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks. However, existing graph condensation methods rely on the joint optimization of nodes and structures in the condensed graph, and overlook critical issues in effectiveness and generalization ability. In this paper, we advocate a new Structure-Free Graph Condensation paradigm, named SFGC, to distill a large-scale graph into a small-scale graph node set without explicit graph structures, i.e., graph-free data. Our idea is to implicitly encode topology structure information into the node attributes in the synthesized graph-free data, whose topology is reduced to an identity matrix. Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data. Through training trajectory meta-matching, SFGC aligns the long-term GNN learning behaviors between the large-scale graph and the condensed small-scale graph-free data, ensuring comprehensive and compact transfer of informative knowledge to the graph-free data. Afterward, the underlying condensed graph-free data would be dynamically evaluated with the graph neural feature score, which is a closed-form metric for ensuring the excellent expressiveness of the condensed graph-free data. Extensive experiments verify the superiority of SFGC across different condensation ratios., Comment: Accepted by NeurIPS 2023

Transonic buffet is a flow instability phenomenon that arises from the interaction between the shock wave and the separated boundary layer. This flow phenomenon is considered to be highly detrimental during flight and poses a significant risk to the structural strength and fatigue life of aircraft. Up to now, there has been a lack of an accurate, efficient, and intuitive metric to predict buffet and impose a feasible constraint on aerodynamic design. In this paper, a Physics-Assisted Variational Autoencoder (PAVAE) is proposed to identify dominant features of transonic buffet, which combines unsupervised reduced-order modeling with additional physical information embedded via a buffet classifier. Specifically, four models with various weights adjusting the contribution of the classifier are trained, so as to investigate the impact of buffet information on the latent space. Statistical results reveal that buffet state can be determined exactly with just one latent space when a proper weight of classifier is chosen. The dominant latent space further reveals a strong relevance with the key flow features located in the boundary layers downstream of shock. Based on this identification, the displacement thickness at 80% chordwise location is proposed as a metric for buffet prediction. This metric achieves an accuracy of 98.5% in buffet state classification, which is more reliable than the existing separation metric used in design. The proposed method integrates the benefits of feature extraction, flow reconstruction, and buffet prediction into a unified framework, demonstrating its potential in low-dimensional representations of high-dimensional flow data and interpreting the "black box" neural network.

Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-light images is of paramount importance for practical applications in the computer vision domain.To effectively address these challenges, we present a novel low-light image enhancement model, termed Spatial Consistency Retinex Network (SCRNet), which leverages the Retinex-based structure and is guided by the principle of spatial consistency.Specifically, our proposed model incorporates three levels of consistency: channel level, semantic level, and texture level, inspired by the principle of spatial consistency.These levels of consistency enable our model to adaptively enhance image features, ensuring more accurate and visually pleasing results.Extensive experimental evaluations on various low-light image datasets demonstrate that our proposed SCRNet outshines existing state-of-the-art methods, highlighting the potential of SCRNet as an effective solution for enhancing low-light images.

In this study, with respect to wave energy generation technology, a new scheme is proposed for oscillating-buoy type wave energy conversion. A three-dimensional model of a power generation device is established based on SolidWorks, and a three-dimensional viscous numerical pool is setup using STAR-CCM+ to facilitate the simulation analysis of the device. The hydrodynamic performance and energy capture characteristics of the device were examined using theoretical analysis and numerical simulations. The regularities of parameters, such as motion response, force, and output power of the device, were analysed under four wave environments with different time periods (T) and wave heights (H). The analysis and conclusions can be utilised as a reference for studying the hydrodynamic performance of wave power generation devices.