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Abstract The demand for lightweight antennas in 5 G/6 G communication, wearables, and aerospace applications is rapidly growing. However, standard manufacturing techniques are limited in structural complexity and easy integration of multiple material classes. Here we introduce charge programmed multi-material additive manufacturing platform, offering unparalleled flexibility in antenna design and the capability for rapid printing of intricate antenna structures that are unprecedented or necessitate a series of fabrication routes. Demonstrating its potential, we present a transmitarray antenna composed of an interconnected, multi-layered array of dielectric/conductive S-ring unit cells, reducing 94% mass of conventional antenna configurations. A fully printed circular polarized transmitarray system fed by a source and a Risley prism antenna system operating at 19 GHz both show close alignment between testing results and numerical simulations. This printing method establishes a universal platform, propelling discovery of new antenna designs and enabling data-driven design and optimizations where rapid production of antenna designs is crucial.

Anisomycin (compound 1), a multifunctional pyrrolidine antibiotic, primarily inhibits protein biosynthesis by binding to the ribosome. Upon binding to the ribosome, the para-phenol moiety of anisomycin inserts completely into the hydrophobic crevice of the A-site and blocks the access of the incoming aminoacyl-tRNAs, disrupting peptide bond formation. Hence, the para-methoxyphenyl group serves as a starting point for developing novel anisomycin analogs with potent antifungal and insecticidal properties. However, the activation and condensation mechanism of phenylpyruvic acid has not yet been elucidated. In this study, genetic manipulations of aniP and its homologue siAniP confirmed their indispensable role in 1 biosynthesis. Bioinformatics analysis suggested that AniP and siAniP function as transketolase. siAniP was found to catalyzed condensation between 4-hydroxyphenylpyruvic acid (3) and glyceraldehyde (GA), initiating pyrrolidine synthesis. siAniP was specific for aromatic keto acids and tolerant of aliphatic and aromatic aldehydes, and was able to catalyze the asymmetric intermolecular condensation of two keto acids, leading to the formation of 24 α-hydroxy ketone. To the best of our knowledge, siAniP is the first TK that catalyzes the transfer of a C2 ketol and symmetrical intermolecular coupling using aromatic keto acids as donor substrates. Structural analysis, docking model construction, and site-directed mutagenesis identified that I220, H275, R322 and W391 were crucial for substrate binding. Moreover, sequence similarity network (SSN)-based genome neighborhood network (GNN) analyses of AniP suggested the widespread occurrence of the AniP-like-mediated reaction in the biosynthesis of 1 and its analogs, particularly in the assembly of benzylpyrrolidine. These findings not only expand the repertoire of TKs but also provide a potent biocatalyst that could be used for the structural innovation of 1 and its derivatives.

Abstract Microfluidic impedance flow cytometry has been widely used in leukocyte differential and counting, but it faces a bottleneck due to the trade-off between impedance detection throughput and sensitivity. In this study, a microfluidic impedance flow cytometer based on a virtual constriction microchannel was reported, in which the virtual constriction microchannel was constructed by crossflow of conductive sample and insulated sheath fluids with underneath micro-electrodes for impedance measurements. Compared to conventional mechanical constriction microchannels, this virtual counterpart could effectively avoid direct physical contact between cells and the microchannel walls to maintain high throughputs, and significantly reduce the volume of the impedance detection region for sensitivity improvements. Using the developed microfluidic impedance flow cytometer, impedance pulses of three leukemia cell lines, K562, Jurkat, and HL-60, were detected, achieving a 99.8% differentiation accuracy through the use of a recurrent neural network. Furthermore, impedance pulses of four white blood cell subpopulations (neutrophils, eosinophils, monocytes, and lymphocytes) from three donors were detected, achieving a classification accuracy of ≥99.2%. A classification network model was established based on purified white blood cell and applied to impedance pulses of two white blood cell mixtures, resulting in proportional distributions of four leukocyte subpopulations within theoretical ranges. These results indicated that the developed microfluidic impedance flow cytometer based on the virtual constriction microchannel could achieve both high detection throughput and high sensitivity, showing great potentials for clinical diagnostics and blood analysis.

Federated Learning (FL) has recently attracted a lot of attention due to its ability to train a machine learning model using data from multiple clients without divulging their privacy. However, the training data across clients can be very heterogeneous in terms of quality, amount, occurrences of specific features, etc. In this paper, we demonstrate how the server can observe data heterogeneity by mining gradient trajectories that the clients compute from a two-dimensional mapping of high-dimensional gradients computed by each client from its bottom layer. Based on these ideas, we propose a new clustered federated learning with gradient trajectory method, called CFLGT, which dynamically clusters clients together based on the gradient trajectories. We analyze CFLGT both theoretically and experimentally to show that it overcomes several drawbacks of mainstream Clustered Federated Learning (CFL) methods and outperforms other baselines.

The burgeoning demand for zirconium, driven by the rapid development of smart devices, low-carbon energy technologies, and other emerging industries, underscores the importance of understanding the dynamics of its global trade network. However, the evolutionary patterns of the international zircon ore trade network and its resilience to disruptions remain unclear. This study constructs the international zircon ore trade network from 2013 to 2022, analyzes its structural evolution at both the network and node levels, and evaluates its robustness in 2022 using five attack strategies: random node removal, random edge removal, edge degradation, targeted removal based on node degree, and targeted removal based on node betweenness centrality. Our findings reveal that: (1) the international zircon ore trade network exhibits a shift in the import market towards Asia, with the export market dominated by Spain, the United States, and Brazil. China plays a crucial role as a bridge connecting various countries, while Japan exerts significant influence within the network; (2) the network is particularly vulnerable to targeted attacks based on node degree and betweenness centrality, highlighting the potential for significant disruption following the removal of key nodes. This study provides valuable insights for ensuring the stable and sustainable supply and consumption of zirconium resources, informing the development of targeted policies for countries and relevant industries.

The recent retreat of glaciers in High Mountain Asia is a major issue for downstream communities. Similarly, glaciers in the Astore Basin are melting, causing glacial lakes to expand faster, new lakes to form, and increasing the risk of glacial lakes outburst floods (GLOFs). The present study uses Landsat data from 1993 to 2021 to explore seasonal and decadal changes in glacier lakes, which are validated using an in situ differential Global Positioning System (dGPS). During the ablation period (June - October) of 2021, we observed a five-fold increase (18 to 100) in the number of glacier lakes, as well as a six-fold increase (0.62 to 3.86 km2) in lakes larger than 0.01 km2. Over the last decade, from 2011 to 2020, the number of potentially dangerous glacial lakes (PDGLs) has doubled. GLOF risk must be reduced through continual monitoring of these lakes. Prioritizing the deployment of GLOF monitoring and early warning systems, as well as sustainable water management practices, is critical for mitigation and adaptation measures in mountainous regions.

High‐entropy materials have drawn much attention as battery materials due to their distinctive properties. Lithiated high‐entropy oxide (Li0.33(MgCoNiCuZn)0.67O, LiHEO) exhibits both high lithium‐ion and electronic conductivity, making it a potential coating material for layered Ni‐rich oxide cathodes (Li1+x(Ni1−y−zCoyMnz)1−xO2, NCM or NMC) in conventional Li‐ion battery cells; however, high‐temperature synthesis limits its application. Therefore, a photonic curing strategy is used for synthesizing LiHEO and the non‐lithiated form (denoted as high‐entropy oxide [HEO]), and nanoscale coatings are successfully produced on LiNi0.85Co0.1Mn0.05O2 (NCM851005) particles. To one's knowledge, this is the first report on particle coating with high‐entropy materials using photonic curing. NCM851005 with LiHEO‐modified surface shows good cycling stability, with a capacity retention of 97% at 1 C rate after 200 cycles. The improvement in electrochemical performance is attributed to the conformal coating that prevents structural changes caused by the reaction between cathode material and liquid electrolyte. Compared to bare NCM851005, the coated material shows a significantly reduced tendency for intergranular cracking, successfully preventing electrolyte penetration and suppressing side reactions. Overall, photonic curing presents a novel cost‐ and energy‐efficient synthesis and coating procedure that paves the way for surface modification of any heat‐sensitive material for a wide range of applications.

Abstract On-surface synthesis provides tools to prepare low-dimensional supramolecular structures. Traditionally, reactive radicals are a class of single-electron species, serving as exceptional electron-withdrawing groups. On metal surfaces, however, such species are affected by conduction band screening effects that may even quench their unpaired electron characteristics. As a result, radicals are expected to be less active, and reactions catalyzed by surface-stabilized radicals are rarely reported. Herein, we describe a class of inter-molecular radical transfer reactions on metal surfaces. With the assistance of aryl halide precursors, the coupling of terminal alkynes is steered from non-dehydrogenated to dehydrogenated products, resulting in alkynyl-Ag-alkynyl bonds. Dehalogenated molecules are fully passivated by detached hydrogen atoms. The reaction mechanism is unraveled by various surface-sensitive technologies and density functional theory calculations. Moreover, we reveal the universality of this mechanism on metal surfaces. Our studies enrich the on-surface synthesis toolbox and develop a pathway for producing low-dimensional organic materials.

Effectively classifying each pixel in an image is an important research topic in semantic segmentation. The Existing methods typically require the network to directly generate a feature map of the same size as the original image and classify each pixel, which makes it difficult for the network to fully leverage the representations from the backbone. To handle this challenge, this paper proposes a method named mask classification combined with an adaptive dilated convolution network (MCDCNet). Firstly, a Vision Transformer (ViT)-based module is employed to capture contextual features as the backbone. Secondly, the Spatial Extraction Module (SEM) is proposed to extract multi-scale spatial information through adaptive dilated convolution while preserving the original feature size. This spatial information is then integrated into the corresponding contextual features to enhance the representation. Finally, a novel inference process is proposed that incorporates the instance activation map (IAM)-based decoder for semantic segmentation, thereby enhancing the network’s capability to capture and comprehend semantic features. The experimental results demonstrate that our network significantly outperforms other per-pixel classification networks across several semantic segmentation datasets. In particular, on Cityscapes, MCDCNet achieves 80.3 mIoU with 11.8 M Params, demonstrating that the network is able to deliver a strong segmentation performance while maintaining a relatively low parameter count.

Electroencephalography (EEG) can reflect changes in brain activity under different states. The electrical signals of the brain are observed to exhibit varying amplitudes and frequencies. These variations are closely linked to different states of consciousness, influencing the internal and external behaviors, emotions, and learning performance of humans. The assessment of personal level of attention, which refers to the ability to consciously focus on something, can also be facilitated by these signals. Research on brain attention aids in the understanding of the mechanisms underlying human cognition and behavior. Based on the characteristics of EEG signals, this research identifies the most effective method for detecting brain attention by adapting various preprocessing and machine learning techniques. The results of our analysis on a publicly available dataset indicate that KNN with the feature importance feature extraction method performed the best, achieving 99.56% accuracy, 99.67% recall, and 99.44% precision with a rapid training time.

Abstract In this paper, a composite pressure-sensitive mechanism combining diaphragm bending and volume compression was developed for resonant pressure microsensors to achieve high-pressure measurements with excellent accuracy. The composite mechanism was explained, and the sensor structure was designed based on theoretical analysis and finite element simulation. An all-silicon resonant high-pressure microsensor with multiple miniaturized cavities and dual resonators was developed, where dual resonators positioned in two resonant cavities with suitably different widths are used to perform opposite characteristics in pressure and the same characteristics at different temperatures, which can improve pressure sensitivities and realize temperature self-compensation by differential frequency output. The microsensor was fabricated by microfabrication, and the experimental results showed that the sensor had an accuracy of ±0.015% full scale (FS) in a pressure range of 0.1~100 MPa and a temperature range of −10~50 °C. The pressure sensitivity of the differential frequency was 261.10 Hz/MPa (~2523 ppm/MPa) at a temperature of 20 °C, and the temperature sensitivities of the dual resonators were −1.54 Hz/°C (~−14.5 ppm/°C) and −1.57 Hz/°C (~−15.6 ppm/°C) at a pressure of 2 MPa. The differential output had an outstanding stability within ±0.02 Hz under constant temperature and pressure. Thus, this research provides a convenient solution for high-pressure measurements because of its advantages, namely, large range, excellent accuracy and stability.

Stabilizing the CMOS-compatible low-temperature Au-free GaN Ohmic contact is a critical work that determines the performance and yield of GaN power HEMTs in mass production. The instability of this contact has been puzzling the industry and academia for years. In this work, an overlooked factor, fluorine injection, is unambiguously verified to widely exist during dielectric etching and can easily destroy the low-temperature GaN Ohmic contact formation. The injection depth is confirmed to be over 30 nm with a fluorine peak concentration of 1023 at/cm3 in vicinity of the surface. Traditional method of partial AlGaN recessing with a pretty tiny processing window is proven unfriendly for production and vulnerable to the fluorine injection. Two methods to get rid of the fluorine are proposed. The first one is to over-etch the AlGaN barrier to the GaN channel to fully remove the fluorine ions. The second is to deposit an etch-stop blocking layer of Al2O3, which is also compatible with CMOS process.

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG. Our method leverages large language models (LLMs) to conduct causal analysis on textual data related to equipment faults derived from sensor networks, thereby significantly enhancing the accuracy and efficiency of fault diagnosis. This paper details a series of experiments that validate the effectiveness of the KG-ICL method in accurately diagnosing fault causes and locations of industrial equipment systems. By leveraging LLMs and structured knowledge, our approach offers a robust tool for condition monitoring and fault management, thereby improving the reliability and efficiency of operations in industrial sectors.

Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed. Firstly, a constraint mechanism is introduced that uses labels to control the backbone of the network during training to enhance the consistency of the unchanged regions and the differences between the changed regions in the extracted dual-temporal images. Secondly, a Cross-Spatial-Channel Attention (CSCA) module is proposed, which realizes the interaction of valid information between dual-temporal feature maps through channels and spatial attention and uses multi-level information for more accurate detection. The verification results show that compared with advanced parallel methods, CINet achieved the highest F1 scores on all six widely used remote sensing benchmark datasets, reaching a maximum of 92.00 (on LEVIR-CD dataset). These results highlight the excellent ability of CINet to detect changes in various practical scenarios, demonstrating the effectiveness and feasibility of the proposed constraint enhancement and CSCA module.

In this work, we demonstrated the epitaxial growth of a gallium nitride (GaN) buffer structure on 200 mm SOI (silicon-on-insulator) substrates. This epitaxial layer is grown using a reversed stepped superlattice buffer (RSSL), which is composed of two superlattice (SL) layers with different Al component ratios stacked in reverse order. The upper layer, with a higher Al component ratio, introduces tensile stress instead of accumulative compressive stress and reduces the in situ curvature of the wafer, thereby achieving a well-controlled wafer bow ≤ ±50 µm for a 3.3 µm thick buffer. Thanks to the compliant SOI substrate, good crystal quality of the grown GaN layers was obtained, and a breakdown voltage of 750 V for a 3.3 µm thick GaN buffer was achieved. The breakdown field strength of the epitaxial GaN buffer layer on the SOI substrate is estimated to be ~2.27 MV/cm, which is higher than the breakdown field strength of the GaN-on-Si epitaxial buffer layer. This RSSL buffer also demonstrated a low buffer dispersion of less than 10%, which is good enough for the further processing of device and circuit fabrication. A D-mode GaN HEMT was fabricated on this RSSL buffer, which showed a good on/off ratio of ~109 and a breakdown voltage of 450 V.

Rapid assessment of physiological status is a precondition for addressing biological stress in trees so that they may recover. Environmental stress can cause water deficit in plants, while terahertz (THz) spectroscopy is sensitive to changes in aqueous solutions within organisms. This has given the THz sensor a competitive edge for evaluating plant phenotypes, especially under similar environmental stress, if there are existing differences in the corresponding THz information. In this study, we utilized THz technology in association with traditional weighing methods to explore physiological changes in citrus leaves under different temperature, duration, and stress treatment conditions. It was found that the higher the temperature and the longer the exposure duration, the more severe the reduction in the relative absorption coefficient. There was a positive correlation between the trends and the increase in the ion permeability of cells. In addition, based on the effective medium theory, THz spectral information can be transformed into information on free water and bound water in the leaves. Under different treatment conditions, water content shows different trends and degrees of change on the time scale, and accuracy was verified by traditional weighing methods. These findings revealed that characteristics of THz information can serve as a simple and clear indicator for judging a plant’s physiological status.

Salinity is an essential factor of lake water environments and aquatic systems. It is also sensitive to climatic changes and human activities based on concentration variations of solved minerals. However, there are few consecutively temporal studies on lake salinity variations on the Tibetan Plateau because the harsh environmental conditions make it difficult to carry out in-situ observations for several lakes. In this study, we constructed a remote sensing retrieval model for lake salinity based on 87 in-situ lake investigations; moreover, interannual lake salinity and associated variations from 152 lakes larger than 50 km2 were analyzed on the Tibetan Plateau. A significant decreasing trend in lake salinity was observed between 2000 and 2019 (p

Abstract In this paper, a novel simulation-based evolutionary method is presented for designing parameter-free MEMS structures with maximum degrees of freedom. This novel design method enabled semiautomatic structure evolution by weighing the attributes of each segment of the structure and yielded an optimal design after multiple iterations. The proposed method was utilized to optimize the pressure-sensitive diaphragm of a piezoresistive pressure sensor (PPS). Finite element method (FEM) simulations revealed that, in comparison to conventional diaphragms without islands and with square islands, the optimized diaphragm increased the stress by 10% and 16% and reduced the nonlinearity by 57% and 77%, respectively. These improvements demonstrate the value of this method. Characterization of the fabricated PPS revealed a high sensitivity of 8.8 mV V−1 MPa−1 and a low nonlinearity of 0.058% FS at 20 °C, indicating excellent sensor performance.

Carbazomycins (1–8) are a subgroup of carbazole derivatives that contain oxygen at the C3 and C4 positions and an unusual asymmetric substitution pattern. Several of these compounds exhibit antifungal and antioxidant activities. To date, no systematic biosynthetic studies have been conducted on carbazomycins. In this study, carbazomycins A and B (1 and 2) were isolated from Streptomyces luteosporeus NRRL 2401 using a one-strain-many-compound (OSMAC)-guided natural product mining screen. A biosynthetic gene cluster (BGC) was identified, and possible biosynthetic pathways for 1 and 2 were proposed. The in vivo genetic manipulation of the O-methyltransferase-encoding gene cbzMT proved indispensable for 1 and 2 biosynthesis. Size exclusion chromatography indicated that CbzMT was active as a dimer. In vitro biochemical assays confirmed that CbzMT could repeatedly act on the hydroxyl groups at C3 and C4, producing monomethylated 2 and dimethylated 1. Monomethylated carbazomycin B (2) is not easily methylated; however, CbzMT seemingly prefers the dimethylation of the dihydroxyl substrate (12) to 1, even with a low conversion efficiency. These findings not only improve the understanding of carbazomycin biosynthesis but also expand the inventory of OMT-catalyzing iterative methylations on different acceptor sites, paving the way for engineering biocatalysts to synthesize new active carbazomycin derivatives.

In recent years, Aksu apple has become popular with consumers because of its unique texture and taste. At present, maintaining quality during storage is the key problem with the apples in the Aksu region. 1-Methylcyclopropene (1-MCP) can delay fruit senescence, so is widely used in fruit preservation. However, many factors affect the preservation effect of 1-MCP. The effects of 1-MCP concentration (0 µL·L−1, 1 µL·L−1, 3 µL·L−1, 5 µL·L−1, and 8 µL·L−1) and postharvest application time (0, 1 and 2 d after harvest) on the quality of stored apple were studied. It was found that 1 µL·L−1 1-MCP was more beneficial in improving the quality of stored apples, reduced the respiration intensity and decay rate, increased the fruit firmness and total soluble solid content, and reduced the relative content of ester volatile aroma components. In addition, 1-MCP treatment applied at different postharvest times also affected the sensory quality and flavor of apples. The effect of 1-MCP treatment immediately after harvest was better.

The fractal theory can effectively describe the complexity and multi-scale of concrete under impact load and provide a scientific basis for evaluating concrete’s impact resistance. Therefore, based on the fractal theory, this study carried out the fragmentation size analysis by weighing the quality of SFRRAC fragments, disclosed the distribution characteristics of impact fragmentation size of steel fiber reinforced recycled aggregate concrete (SFRRAC) specimens under different recycled coarse aggregate (RCA) replacement ratio, different steel fiber (SF) contents and different impact pressures. The results indicate that the fractal dimension can describe the degree of fragmentation of the specimen. The greater the fractal dimension, the more the amount of fragmentation of the specimen subjected to impact load, the lesser the fragmentation size, and the greater the degree of fragmentation. Under the impact load, the fractal dimension of SFRRAC is between 1.36 and 2.28. As the impact pressure increases, the energy consumption increases, and the fractal dimension decreases. With the growth in replacement ratio, the fractal dimension gradually increases, and the energy consumption is negatively correlated with the fractal dimension. Along with the growth of SF content, the energy consumption gradually increases, and the fractal dimension continuously decreases. A new metric angle is provided to explore the inherent law between the impact-crushing characteristics of SFRRAC and the dynamic load, thereby offering foundational support for the application of SFRRAC in practical engineering.

Radiomics offers a novel strategy for the differential diagnosis, prognosis evaluation, and prediction of treatment responses in breast cancer. Studies have explored radiomic signatures from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM), but the diagnostic accuracy varies widely. To evaluate this performance, we conducted a meta-analysis performing a comprehensive literature search across databases including PubMed, EMBASE, SCOPUS, Web of Science (WOS), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and the Chinese BioMedical Literature Database (CBM) until March 31, 2024. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC) were calculated. Twenty-four eligible studies encompassing 5588 breast cancer patients were included in the meta-analysis. The meta-analysis yielded a pooled sensitivity of 0.81 (95% confidence interval [CI]: 0.77-0.84), specificity of 0.85 (95%CI: 0.81-0.87), PLR of 5.24 (95%CI: 4.32-6.34), NLR of 0.23 (95%CI: 0.19-0.27), DOR of 23.16 (95%CI: 17.20-31.19), and AUC of 0.90 (95%CI: 0.87-0.92), indicating good diagnostic performance. Significant heterogeneity was observed in analyses of sensitivity (I2 = 74.64%) and specificity (I2 = 83.18%). Spearman's correlation coefficient suggested no significant threshold effect (P = 0.538). Meta-regression and subgroup analyses identified several potential heterogeneity sources, including data source, integration of clinical factors and peritumor features, MRI equipment, magnetic field strength, lesion segmentation, and modeling methods. In conclusion, DCE-MRI radiomic models exhibit good diagnostic performance in predicting ALNM and SLNM in breast cancer. This non-invasive and effective tool holds potential for the preoperative diagnosis of lymph node metastasis in breast cancer patients.

Lake ecosystems on the Tibetan Plateau (TP) are highly susceptible to external input owing to their fragile environment. However, less study has been done to focus the direct influences of anthropogenic activities on the lake sediments by human activities. In this study, we performed the first large-scale investigation to use the fecal stanol proxies in lake surface sediments to assess the impact of human and herbivorous feces on TP lakes. The 75 investigated lakes were divided into three types (I, II, and III) according to cluster analysis of the fecal stanol ratios R1′ and R2′. Fecal stanols in type I lake surface sediments originate from a mixture of herbivorous and human feces; fecal stanols in type II lake surface sediments primarily originate from herbivorous feces with minimal human feces. In contrast, fecal stanols in type III lake surface sediments are mainly derived from herbivorous feces (coprostanol concentration > 100 ng/g), a mixed source of herbivorous feces and cholesterol reduction (coprostanol concentration of 10–100 ng/g), and in situ reduction of cholesterol (coprostanol concentration

In the realm of visual working memory research, the retro-cue paradigm helps us study retro-cue effects such as retro-cue benefit (RCB) and retro-cue cost (RCC). RCB reflects better performance with cued items, while RCC indicates poorer performance with uncued items. Despite consistent evidence for RCB, it’s still uncertain whether it remains when previously uncued items are cued afterward. Additionally, research findings have been inconsistent. This study combines prior experiments by controlling the proportion of cue types and the number of memory items. Besides, using a CDA index to assess the status of items after the cue appeared. Results showed better performance under the double-cue condition (involving two cues pointing inconsistently with only the second cue being valid) compared to the neutral-cue condition, and better performance under the single-cue condition compared to double-cue. EEG data revealed that after the appearance of the second cue in the double-cue condition, there was a significant increase in CDA wave amplitude compared to the single-cue condition. Behavior results suggests that RCB occurs under double-cue but to a lesser extent than the single-cue. And EEG outcomes indicates that individuals did not remove the uncued item from their visual working memory after the first cue. Instead, they kept it in a passive state and then shifted it to an active state after the appearance of the second cue.

Though high entropy oxides have been explored as possible conversion-type negative electrodes for Li-ion batteries, the roles of the different elements remain unclear. Here the authors determine the behavior of each element during electrochemical cycling and connect it to the nanoscale structure.

[Objective] The land use carbon emissions in Jiujiang City, Jiangxi Province were quantitatively measured, and its temporal and spatial evolution characteristics, and the risk of land use carbon emissions were determined in order to provide a scientific reference for the construction of green and low-carbon land use methods in Jiujiang City. [Methods] Land use carbon emissions, and their temporal and spatial variation characteristics in Jiujiang City from 2000 to 2020 were measured by the carbon emission coefficient method. The carbon emission risk of each county was identified by the carbon emission risk index based on grid perspective. The factors influencing land use carbon emissions were analyzed based on the logarithmic mean divisia index (LMDI) model. [Results] Net carbon emissions from land use in Jiujiang City have been increasing at an average annual rate of 13.75% during 2000—2020. Construction land was the main carbon source, accounting for more than 90% of the carbon emissions, whereas forest land was the main carbon sink. Additionally, net carbon emissions in Jiujiang City presented a spatial distribution pattern of "high in northeast and low in southwest" . Wuning County and Xiushui County have good forest coverage and have always been carbon sinks. Lianxi District, Xunyang District, Hukou County, and Ruichang City, with more construction land, had the largest net carbon emissions and accounted for more than 95% of carbon emissions in Jiujiang City. Moreover, the carbon emission risk from land use in Jiujiang City was generally low, and showed a distribution pattern of "high in northeast and low in southwest" . High carbon emission risk areas were Lianxi District, Xunyang District, and Chaisang District, all along the Yangtze River. Economic development level was the main factor increasing carbon emissions, while energy consumption intensity was the key factor curbing carbon emissions. [Conclusion] Carbon emissions from land use have increased significantly during 2000—2020. New carbon source land use should be controlled, land use structure should be optimized, a low-carbon, green-energy utilization system should be actively constructed, and "Jiujiang model" construction of green and low-carbon development should be promoted in the Yangtze River Economic Belt.

Abstract Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

The Tibetan Plateau (TP) is often referred to as the “Asian Water Tower”, as it plays a crucial role in supplying water for ∼1.9 billion people. Lakes in this arid mountain region have a wide salinity gradient and are sensitive to the complex interactions between climate and the water cycle, affecting aquatic ecosystems and species distributions. Here, we analyzed relationships between measured environmental variables and the spatial distribution of surface sediment diatom assemblages from 45 lakes located throughout the TP with the intention of developing a diatom-inference model for salinity. Diatom assemblage composition and diversity differed among lakes from the western (W), southern (S), northern (N), and southeastern (E) regions of the TP. The most common taxa observed in the lake set, Pantocsekiella ocellata, Nitzschia palea, and Staurosira venter, tended to have widespread distributions across the TP. In general, diatom diversity and richness tended to be highest in lakes with salinity concentrations between 0.1 and 10 g/L and were lowest in high salinity lakes (>20 g/L) of the N region. Canonical correspondence analysis (CCA) identified lake water salinity and maximum lake depth as explaining a statistically significant portion of diatom assemblage variation. Weighted-averaging (WA) was used to develop a diatom-based salinity inference model (R2jack = 0.71, RMSEPjack = 0.28) based on diatom optima from freshwater to meso-hyposaline lakes of the TP. Diatom-salinity reconstructions and changes in diatom community composition can potentially be used to track long-term patterns in climate and environmental conditions across the highly complex mountain landscape of the TP.

Modern pollen distribution and its relationship to vegetation and climate in the lower Yarlung Zangbo River are investigated to provide a reference for paleoenvironmental reconstructions and to improve the understanding of pollen dispersal patterns in mountainous regions. Twenty-eight modern samples collected in moss polsters from 800 and 5400 m a.s.l. are used for palynological analysis. Our results indicate that palynological assemblages and indicator species from moss polsters can reflect the vertical distribution of vegetation belts. Abies, Picea and Pinus pollen from the upper forests and shrub meadow elements (Ericaceae, Cyperaceae and Polygonum) show strong abilities to be transported upward, whereas pollen (e.g. Macaranga, Alnus and Rutaceae) and spore (e.g. Athyrium, Cyatheaceae, Dicranopteris, Pteris) taxa from the lower evergreen forests show weak abilities to be transported upward. An unusual finding is that Tsuga pollen from the lower coniferous forest tends to be transported downslope, probably due to the intense spring rainfall and associated surface runoff in the study area. Due to its potential impact on the geographic range shifts of plants, pollen dispersal may be an additional factor to be considered in studies of vegetation change in the southeastern Tibetan Plateau, such as the projection of Abies or Picea treeline under climate warming. Furthermore, the redundancy analysis shows that precipitation and wind speed are the main factors influencing pollen distribution in the study area.

In order to study the effect of abrasive arrangement pattern on grinding the surface of structured grooves, grinding wheels with three different abrasive orderly arrangements, namely phyllotactic pattern, staggered pattern and array pattern, were used to grind a flat surface. Firstly, the mathematical models of the three abrasive arrangements were established. Secondly, the parameters of grinding wheels were designed according to characters of drag reduction on structured groove surface. And the MATLAB was used to simulate the grinding process, the results of which were compared with the theoretically calculated values. Finally, the reliability of the model and simulation was proved by grinding experiment. The results show that all the three orderly abraive arrangements can be used to grind the micro-groove surface at a grinding depth of 0.050 0 mm, while the axial distances of adjacent abrasive rows are 0.04 mm (phyllotaxis pattern), 0.40 mm (staggered pattern) and 0.80 mm (array pattern), respectively. The groove surfaces ground by the grinding wheels with array abrasive or staggered abrasive are more stable, but the groove parameter ratios cannot meet the requirement of 0.2～1.0. By using the grinding wheel with phyllotactic arranged abrasive, the machined grooves meet the requirements on feature parameters of drag-reduction surface.

In the last decades, the low-energy adaptive clustering hierarchy (LEACH) protocol has become one of the most prominent protocols in wireless sensor networks (WSNs). Based on different application scenarios, researchers have come up with various modifications of the LEACH protocol, which are classified as clustering protocols. Nevertheless, in current clustering protocols, sensor nodes usually propagate messages over a large scale for cluster formation and discovering an optimal inter-cluster route, which consumes a large amount of energy. In this study, we introduce a Query-Driven Clustering (QDC) protocol to improve the energy efficiency in WSNs. The proposed QDC includes four sequential parts. Firstly, a size-balanced network partitioning algorithm is to split large-scale WSN into several small sub-networks with the help of 5G infrastructure. Secondly, an estimation-based mechanism aims to keep energy consumed in centralized sub-network information maintenance at a low level. Thirdly, in each sub-network, centralized query-driven clustering algorithms are introduced for clustering the query targets, which saves the high energy consumption for clustering nodes on a global scale. Lastly, as for inter-cluster routing in each sub-network, an energy-efficient and load-balanced routing scheme is developed. Through extensive experiments, QDC exhibits much higher energy efficiency and thus enhances the network lifetime, compared with other protocols in WSNs.

Abstract Dissolved organic matter (DOM) composition in lakes is controlled by multiple environmental drivers, but the relative importance of various drivers remains poorly understood at the continental scale. Here, we established a model resolving possible influencing pathways of climate, land cover, societal development, and water retention time of lakes on the quantity and quality of chromophoric DOM (CDOM) from 182 lakes spanning across strong climatic and economic gradients in China. Our results indicate that land cover and societal development both exhibit positive direct effects on lake CDOM quantity, highlighting the significant role of well‐vegetated soils and anthropogenic activities as sources of lake DOM on a continental scale. Climate has two strong but opposite effects—a warming and wet climate facilitates soil OM production and export, while it also enhances CDOM in‐lake transformation. Three proxies are proposed as indicators of the magnitude of biogeochemical drivers influencing lake DOM across different ecoclimatic zones, including fluorescent DOM/DOC indicating economic activity, percentages of a degraded fluorescent DOM component indicating solar irradiation, and percent tyrosine‐like DOM reflecting DOM processing time within the watershed and lake. Collectively, our findings indicate that the effects of climate change and rapid societal development will result in increased loadings of terrestrial and anthropogenic DOM into lakes and drive higher rates of within‐lake processing of DOM. Consequently, lakes will play a more important role as an “active pipe” in mediating the flux and transformation of organic carbon, intensifying the coupling between terrestrial and aquatic carbon cycles on a continental scale.

To increase the surface roughness of basalt fiber (BF) and enhance the bonding properties with the concrete matrix, three modified solutions were prepared to treat the BF surface. The BF surface was modified by the best mixture-ratio solution after SEM and AFM tests. Modified basalt fiber (MBF) and nano-silica (NS) were used as reinforcing materials to improve the RC. The MBF and NS contents were used as the mix ratio control parameter. Ten recycled concrete beams were designed. Through a four-point flexural test, the destruction characteristics, deflection, cracking load, maximum bearing capacity, and strain of RC beams were obtained. The effects of replacement rate, MBF content, and NS content on the flexural properties of the RC beams were obtained. The results showed that in comparison to modification by other solutions, the surface roughness of BF was greater with modification by 2.91% KH550 and 5% NS mixed solution. The failure characteristics were similar for natural aggregate concrete beams and RC beams reinforced with MBF and NS; all underwent four stages of ordinary concrete damage. The natural aggregate concrete beams had greater flexural strength and flexural resistance than RC beams. However, the MBF and NS, added separately or together, improved the dehiscence load, peak load, ductility, and tensile strain of the RC beams. The current standards can be used to calculate the cracking moment and ultimate bearing capacity of MBF and NS reinforced RC beams; their effect was significant.

Abstract This paper presents a micromachined electrochemical angular accelerometer with highly integrated sensitive microelectrodes. Theoretical analyses and numerical simulations were conducted to model the angular accelerometer with key geometrical parameters (e.g., electrode spacing, via spacing and via size) optimized. Highly integrated sensitive microelectrodes were manufactured based on microfabrication and assembled to form MEMS-based electrochemical angular accelerometers. Device characterization was conducted, locating a sensitivity of 80 V/(rad/s2), a bandwidth of 0.01–18 Hz and a noise level of 3.98 × 10−8 (rad/s2)/√Hz. In comparison to a previously reported electrochemical angular microaccelerometer, a significant improvement in sensitivity (80 V/(rad/s2) vs. 10 V/(rad/s2)) was achieved due to the new structure of sensitive microelectrodes. These results indicated the potential of the developed MEMS-based electrochemical angular accelerometer in seismology, including natural disaster monitoring and resource exploration.

Abstract A new electrochemical angular microaccelerometer with integrated sensitive electrodes perpendicular to flow channels was developed in this paper. Based on a liquid inertial mass, an incoming angular acceleration was translated into varied concentrations of reactive ions around sensitive microelectrodes, generating a detection current. Key structural parameters of the sensitive microelectrodes were designed and compared based on theoretical analysis and numerical simulations. An angular microaccelerometer incorporating sensitive microelectrodes was then fabricated, assembled and characterized, producing a sensitivity of 338 V/(rad/s2), a −3 dB bandwidth of 0.01–10 Hz and a noise level of 4.67 × 10−8 (rad/s2)/Hz1/2 @ 1 Hz. These performances were better than their commercial counterparts based on traditional electrodes and previously reported microaccelerometers based on microsensitive electrodes in parallel with flow channels, which can be applied to measure rotational accelerations in earthquakes and buildings.

This paper presents a MEMS electrochemical angular accelerometer with a silicon-based four-electrode structure, which was made of thousands of interconnected microchannels for electrolyte flow, anodes uniformly coated on structure surfaces and cathodes located on the sidewalls of flow holes. From the perspective of device fabrication, in this study, the previously reported multi-piece assembly was simplified into single-piece integrative manufacturing, effectively addressing the problems of complex assembly and manual alignment. From the perspective of the sensitive structure, in this study, the silicon-based four-electrode structure featuring with complete insulation layers between anodes and cathodes can enable fast electrochemical reactions with improved sensitivities. Numerical simulations were conducted to optimize the geometrical parameters of the silicon-based four-electrode structure, where increases in fluid resistance and cathode area were found to expand working bandwidths and improve device sensitivity, respectively. Then, the silicon-based four-electrode structure was fabricated by conventional MEMS processes, mainly composed of wafer-level bonding and wafer-level etching. As to device characterization, the MEMS electrochemical angular accelerometer with the silicon-based four-electrode structure exhibited a maximum sensitivity of 1458 V/(rad/s2) at 0.01 Hz and a minimum noise level of −164 dB at 1 Hz. Compared with previously reported electrochemical angular accelerometers, the angular accelerometer developed in this study offered higher sensitivities and lower noise levels, indicating strong potential for applications in the field of rotational seismology.

Introduction: As the golden approach of single-cell analysis, fluorescent flow cytometry can estimate single-cell proteins with high throughputs, which, however, cannot translate fluorescent intensities into protein numbers.Methods: This study reported a fluorescent flow cytometry based on constrictional microchannels for quantitative measurements of single-cell fluorescent levels and the recurrent neural network for data analysis of fluorescent profiles for high-accuracy cell-type classification.Results: As a demonstration, fluorescent profiles (e.g., FITC labeled β-actin antibody, PE labeled EpCAM antibody and PerCP labeled β-tubulin antibody) of individual A549 and CAL 27 cells were firstly measured and translated into protein numbers of 0.56 ± 0.43 × 104, 1.78 ± 1.06 × 106 and 8.11 ± 4.89 × 104 of A549 cells (ncell = 10232), and 3.47 ± 2.45 × 104, 2.65 ± 1.19 × 106 and 8.61 ± 5.25 × 104 of CAL 27 cells (ncell = 16376) based on the equivalent model of the constrictional microchannel. Then, the feedforward neural network was used to process these single-cell protein expressions, producing a classification accuracy of 92.0% for A549 vs. CAL 27 cells. In order to further increase the classification accuracies, as a key subtype of the recurrent neural network, the long short-term memory (LSTM) neural network was adopted to process fluorescent pulses sampled in constrictional microchannels directly, producing a classification accuracy of 95.5% for A549 vs. CAL 27 cells after optimization.Discussion: This fluorescent flow cytometry based on constrictional microchannels and recurrent neural network can function as an enabling tool of single-cell analysis and contribute to the development of quantitative cell biology.

ObjectiveThis study aims to compare the value of a gadolinium contrast-enhanced 1.5-T three-dimensional (3D) steady-state free precession (SSFP) sequence with that of a noncontrast 3D SSFP sequence for magnetic resonance coronary angiography in a pediatric population.Materials and methodsSeventy-nine patients from 1 month to 18 years old participated in this study. A 3D SSFP coronary MRA at 1.5-T was applied before and after gadolinium-diethylenetriaminepentaaceticacid (DTPA) injection. The detection rates of coronary arteries and side branches were assessed by McNemar's χ2 test. The image quality, vessel length, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the coronary arteries were analyzed by the Wilcoxon signed-rank test. The intra- and interobserver agreements were evaluated with a weighted kappa test or an intraclass correlation efficient test.ResultsA contrast-enhanced scan detected more coronary arteries than a noncontrast-enhanced scan in patients under 2 years old (P 0.05). The contrast-enhanced 3D SSFP protocol detected longer lengths for the left anterior descending coronary artery in children younger than 2 years and the left circumflex coronary artery (LCX) in children younger than 5 years (P

Abstract Recent climate and environment over the Tibetan Plateau (TP) have undergone significant changes, dominated by variations in the Westerlies and the Indian summer monsoon. However, long‐term shifts in atmospheric circulation during the transitional seasons are still lacking. Here we investigate the modern distribution of Tsuga pollen over the central‐western TP and confirm it as an indicator of variable atmospheric circulation in spring. By combining our Tsuga record from Taro Co with existing records in the Tsuga pollen source area, we suggest that a potential particle transport pathway from the southern slope of the Himalayas to the interior of the plateau appeared in the spring of the late Holocene. Our results show that the springtime atmospheric circulation over the southwestern TP during the early and late Holocene is closely related to the substantial remnants of ice sheets at northern high latitudes and the frequency of El Niño events, respectively.

Novel Ag3PO4/Bi2WO6 catalysts with enhanced visible-light performance were synthesized using a hydrothermal method and characterized to investigate their morphology, microscopic structure, and binding energies. Photoluminescence spectrum (PL) and electrochemical impedance spectroscopy (EIS) data demonstrate that the formed Ag3PO4/Bi2WO6 heterojunction effectively promotes hole (h+)–electron (e−) separation and transfer efficiency, resulting in the enhancement of photocatalytic activity. Ag3PO4/Bi2WO6 displays higher photocatalytic activity than pure Bi2WO6 or Ag3PO4 alone. Photogenerated holes (h+), ·O2−, and ·OH were found to be the main active species for the degradation of malachite green (MG), methylene blue (MB), and Rhodamine B (RhB). The DFT calculation explains the photostability of Ag3PO4/Bi2WO6 from the perspective of electronic structure. The bandgap of Ag3PO4/Bi2WO6 between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) is 1.41 eV, compared with that of Ag3PO4 at 0.91 eV and Bi2WO6 at 2.59 eV. Ag–O–Bi hybridization and the wide HOMO–LUMO bandgap lead to difficulty in electron transfer. As a consequence, Ag+ is difficult to obtain electrons and difficult to convert into Ag0, which makes the catalyst stable.

As the gold-standard method for single-cell analysis, flow cytometry enables high-throughput and multiple-parameter characterization of individual biological cells. This review highlights the demands for clinical flow cytometry in laboratory hematology (e.g., diagnoses of minimal residual disease and various types of leukemia), summarizes state-of-the-art clinical flow cytometers (e.g., FACSLyricTM by Becton Dickinson, DxFLEX by Beckman Coulter), then considers innovative technical improvements in flow cytometry (including quantitative, spectral, and imaging approaches) to address the limitations of clinical flow cytometry in hematology diagnosis. Finally, driven by these clinical demands, future developments in clinical flow cytometry are suggested.

Abstract Dwelling in a variety of aquatic habitats, one of the most abundant groups of microcrustaceans, ostracodes, are widely used indicator organisms in paleolimnological research. Typically, they are identified via traditional methods using morphological features but this may be excessively time‐consuming and prone to inter‐investigator variation. DNA barcoding and metabarcoding have become important tools for specimen identification, with a great impact in the field of taxonomy, (paleo‐)ecology and evolution. Despite its potential, metabarcoding has been rarely used to analyze the community structure of ostracodes. Here, we evaluate the performance of a metabarcoding approach for ostracode identification in surface sediment samples collected from Lake Nam Co on the Tibetan Plateau. We tested six different primer pairs amplifying fragments of three different genes, and compared their success in inferring ostracode communities, coupled with morphological identification of ostracodes from the same sediment samples. In total, depending on the primer pair used, seven to nineteen ostracode amplicon sequence variants (ASVs) were identified. Via microscopy, eight morphospecies were identified. We found considerable differences between primer pairs in yielding ostracode sequences via metabarcoding. In general, the highest proportions of ostracode reads and ASVs were found with primers amplifying fragments of the 18S rRNA gene, whereas primers for COI gene had the highest in silico amplification success and highest sequencing depth per sample but only contained

In order to lessen the effect of La-doped on the gas-sensing response of SnO2 powders, different La-doped SnO2 powders were prepared by sol-gel method, and the prepared SnO2 and La-doped SnO2 powders were characterized and analyzed by Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), scanning electron microscope (SEM), specific surface area and pore size tester (BET) and X-ray diffractometer (XRD). The gas-sensing response of the samples were tested. The results show that La doping reduces the grain size of SnO2. With the increase of La doping, the gas-sensing response of La-doped SnO2 powders to ethanol increases first and then decreases. When the molar ratio of La/Sn is 0.25, the gas-sensing response of La-doped SnO2 powder to ethanol is the highest, 43.5% higher than that of pure SnO2 to ethanol.

The thermodynamics of many lakes around the globe are shifting under a warming climate, affecting nutrients and oxygen transportation within the lake and altering lake biota. However, long-term variation in lake heat and water balance is not well known, particularly for regions like the Tibetan Plateau. This study investigates the long-term (1963–2019) variation in the heat balance of a large lake in the Tibetan Plateau (Nam Co) by combining the strengths of modeling and remote sensing. Remotely sensed lake surface water temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Along Track Scanning Radiometer Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) are used to calibrate and validate a conceptual model (air2water) and a thermodynamic model (LAKE) for the studied lake, for which in situ observation is limited. The results demonstrate that remotely sensed lake surface water temperature can serve as a valuable surrogate for in situ observations, facilitating effective calibration and validation of lake models. Compared with the MODIS-based lake surface water temperature (LSWT) for the period 2000–2019, the correlation coefficient and root mean square error (RMSE) of the LAKE model are 0.8 and 4.2 °C, respectively, while those of the air2water model are 0.9 and 2.66 °C, respectively. Based on modeling, we found that the water temperature of Nam Co increased significantly (p < 0.05) during the period of 1963–2019, corresponding to a warming climate. The rate of water temperature increase is highest at the surface layer (0.41 °C/10a). This warming trend is more noticeable in June and November. From 1963 to 2019, net radiation flux increased at a rate of 0.5 W/m2/10a. The increase in net radiation is primarily responsible for the warming of the lake water, while its impact on changes in lake evaporation is comparatively minor. The approaches developed in this study demonstrate the flexibility of incorporating remote sensing observations into modeling. The results on long-term changes in heat balance could be valuable for a systematic understanding of lake warming in response to a changing climate in the Tibetan Plateau.

A large number of different-sized lakes exist in the inland area of the Tibetan Plateau (TP), which are examples of the important connection between the atmosphere and hydrosphere through the analysis of lake surface convergence and evaporation processes. The evaporation level changes that occur in middle–large-sized lakes (surface area > 50 km2) in the area directly influence the regional mass and energy balance values, atmospheric boundary layer heat and humidity structures, and weather processes occurring in the lower-reach areas. The studies conducted in the literature at present, concerning lake evaporation processes, generally overlook the differences in lake heat storage behavior due to the reduced amount of data in the literature concerning lake bathymetry. According to the in situ bathymetric data obtained for 68 middle–large-sized lakes in the inner basin of the TP, in this study, we calculated their heat storage (G) change values by using the different vertical-depth water-temperature-change integral method, and we established a regression equation for the heat storage and lake surface net radiation values for 68 lakes. The evaporation rates of 134 middle–large-sized lakes larger than 50 km2 in the inland are of the TP were calculated by obtaining the G regression result and adopting it into the Penman model, as well as estimating the evaporation losses of theses 134 lakes from 2002 to 2018. The result shows that the annual average evaporation rate for these lakes is 927.39 mm/year, with an insignificant upward trend (0.10 mm/year). This method achieved good accuracy compared with the Bowen ratio method, which estimates the evaporation rate during the ice-free season, with a high correlation coefficient (R) value of 0.95 and least root mean square error (RMSE) value of 61 mm. The annual mean evaporation rate can be divided into the southern and northern lake groups along a 34°N line with a difference of 314.41 mm/year. The annual average evaporation volume of these lakes was 25.02 km3 and showed an upward trend of 0.35 km3/year. Among them, the annual average evaporation volume contribution ratio of level-1 lakes (50 km2 ≤ lake’s area < 100 km2, 61 lakes) was 14.04%, showing an upward trend, and the contribution of level-3 lakes (lake’s area ≥ 500 km2, 10 lakes) was 41.50%, showing a downward trend. There were no obvious changes in the level-2 lakes (100 km2 ≤ lake’s area < 500 km2, 63 lakes), which maintained at the same level in approximately 44.46%. Air temperature is the most important factor affecting the evaporation rate of lakes, while the lake surface area is the main factor affecting lake evaporation volume. Our study, considering the actual lake heat storage value, provides a useful reference for further improving lake water budget balance values and watershed hydrologic features in the inland closed lakes located in the TP.

There are individual differences in rehabilitation after cochlear implantation that can be explained by brain plasticity. However, from the perspective of brain networks, the effect of implantation age on brain plasticity is unclear. The present study investigated electroencephalography functional networks in the resting state, including eyes-closed and eyes-open conditions, in 31 children with early cochlear implantation, 24 children with late cochlear implantation, and 29 children with normal hearing. Resting-state functional connectivity was measured with phase lag index, and we investigated the connectivity between the sensory regions for each frequency band. Network topology was examined using minimum spanning tree to obtain the network backbone characteristics. The results showed stronger connectivity between auditory and visual regions but reduced global network efficiency in children with late cochlear implantation in the theta and alpha bands. Significant correlations were observed between functional backbone characteristics and speech perception scores in children with cochlear implantation. Collectively, these results reveal an important effect of implantation age on the extent of brain plasticity from a network perspective and indicate that characteristics of the brain network can reflect the extent of rehabilitation of children with cochlear implantation.

Background: Assessment of renal lesions and deficiency accurately remains critical in the diagnosis of congenital anomalies of the kidneys and urinary tracts (CAKUT) in children. Advanced imaging such as Magnetic resonance Imaging (MRI) and Diffusion weighted Imaging (DWI) allows structural and functional insufficiency to be detected. Currently, radiomics machine learning models are being explored as full-automated diagnostic tools. We aimed to develop a machine learning integrated radiomics model to predict renal anomalies and deficiency in children.Methods: A retrospective study of 280 children with MRI/DWI were enrolled between 2018 and 202 at a children’s hospital. A total of 1,037 radiomics features were extracted from the DWI images of each participant, which were divided into training set and test set (8:2 split). Using 5-fold cross-validated method, multiple machine learning algorithms were employed to predict renal lesions and deficiency when compared with the radiologist’s diagnosis based on DWI, 99mTc-labeled dimercaptosuccinic acid (DMSA) SPECT cortical renal scintigraphy or 99mTc-labeled diethylenetriamine pentaacetate (DTPA) renal scan.Results: For detecting the kidney lesions, the LASSO + Random Forest algorithm outperformed other classifiers with an accuracy of 0.750 (95% confidence interval, 0.734–0.766) and area under the curve (AUC) of 0.765 (95% confidence interval, 0.700–0.831). The performance of classifiers did not show a significant difference (p > 0.05) in detecting bilateral or unilateral kidney lesions by DWI scanning. The classifiers performed significantly better in bilateral kidney deficit than in unilateral kidney deficit (p < 0.05). We next built prediction models for renal deficiency using the radiomics signature and clinical features compared to renal scintigraphy. The ensemble model had a high-test accuracy of 80.9% ± 4.2% and a sensitivity of 91.7% ± 7.1% with a moderate calibration.Conclusion: An ensemble model integrated with DWI-radiomic and clinical features can be utilized to predict renal lesions and deficiency in children with CAKUT.

High-resolution multi-proxy records from two lakes on the southern Tibetan Plateau, Nam Co and Tangra Yumco, are used to infer long-term variations in the Asian monsoon system with a novel set of ostracod shell chemistry proxies. We track the moisture evolution since the Last Glacial Maximum using the trace element, rare earth element (REE) and stable isotope composition of ostracod shells. The sediment records covering the past 18.8 cal. ka BP and 17.4 cal. ka BP, respectively, demonstrate the suitability of REEs as indicators of weathering intensity and thus hydrological changes and moisture sources in the catchment. In Nam Co, high concentrations of light REEs between 14 and 13 cal. ka BP suggest an increased drainage from the glaciated Nyainqêngtanglha Mountains in the south, pointing to meltwater input. REEs in ostracod shells therefore provide additional information on water sources critical for the interpretation of stable isotope records. Mg/Ca, Sr/Ca, and Ba/Ca ratios reflect salinity and thus changes in effective moisture. Asynchronous behavior of Mg/Ca, Sr/Ca, and Ba/Ca ratios are controlled by changes in dominance of precipitating carbonate minerals in the lake. Synchronous behavior reflects calcite precipitation, indicating low-Mg/Ca warm-wet conditions. Constantly low Sr/Ca ratios reflect aragonite precipitation, indicating high-Mg dry conditions. Increased Sr/Ca and Ba/Ca relative to Mg/Ca ratios show monohydrocalcite precipitation, indicating high-Mg/Ca cold-dry conditions. Furthermore, Fe/Ca, Mn/Ca and U/Ca ratios in ostracods reflect changes in oxygen saturation in lake bottom waters controlled by lake level and microbial activity. The paleoclimate histories reconstructed from Nam Co and Tangra Yumco show high similarity throughout the late Quaternary. We identified two major dry periods, corresponding to Heinrich 1 and the Younger Dryas, followed by strengthening in Indian summer monsoon precipitation. The early Holocene is characterized by a moisture maximum, reflecting abundant water supply by a strong ISM. A time-delayed shift to dry conditions occurred at 2.6 cal. ka BP at Tangra Yumco, and at 2 cal. ka BP at Nam Co, resulting in decreasing lake levels, caused by weakened monsoon intensity due to a southeastward migration of the ISM-Westerly boundary with an estimated velocity of approximately 600 m per year.

Understanding climate and monsoonal dynamics on the Tibetan Plateau is crucial, as recent hydrological changes, evidenced by rising lake levels, will be accelerated by current global warming and may alter aquatic habitats and species inventories. This study combines chironomid assemblages with sedimentological, mineralogical and geochemical data of a short sediment core (37.5 cm) from the high-altitude (> 4,733 m asl), saline (9 g L-1) and shallow (~5 m water depth) Shen Co, located in the southern part of the central Tibetan Plateau. The predominantly littoral, species-poor (10 chironomid morphotypes) chironomid assemblages are dominated by salt-tolerant taxa, that are highly sensitive to lake level fluctuations and macrophyte vegetation dynamics, making them ideally suited for tracking lake level changes over time. Results indicate a period (from ca. 1830 to 1921 CE) of drier conditions with low runoff and high evaporation rates in the Shen Co catchment, as indicated by a dominance of low-Mg calcite and dolomite and increased Ca/Fe and Sr/Rb ratios. This resulted in a decline in lake levels, an increase in salinity and the periodic occurrence of desiccation events at the sampling site. The first chironomid morphotype to appear after the dry period is Acricotopus indet. morphotype incurvatus, which indicate still low (

Modern neural networks [e.g., Deep Neural Networks (DNNs)] have recently gained increasing attention for visible image classification tasks. Their success mainly results from capabilities in learning a complex feature mapping of inputs (i.e., feature representation) that carries images manifold structure relevant to the task. Despite the current popularity of these techniques, they are training-costly with Back-propagation (BP) based iteration rules. Here, we advocate a lightweight feature representation framework termed as Guided Random Projection (GRP), which is closely related to the classical random neural networks and randomization-based kernel machines. Specifically, we present an efficient optimization method that explicitly learns the distribution of random hidden weights instead of time-consuming fine-tuning or task-independent randomization configurations. Further, we also report the detailed mechanisms of the GRP with subspace theories. Experiments were conducted on visible image classification benchmarks to evaluate our claims. It shows that the proposed method achieves reasonable accuracy improvement (more than 2%) with moderate training cost (seconds level) compared with other randomization methods.