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Abstract Rechargeable aqueous zinc-metal batteries, considered as the possible post-lithium-ion battery technology for large-scale energy storage, face severe challenges such as dendrite growth and hydrogen evolution side reaction (HER) on Zn negative electrode. Herein, a three-dimensional Cu-In alloy interface is developed through a facile potential co-replacement route to realize uniform Zn nucleation and HER anticatalytic effect simultaneously. Both theoretical calculations and experimental results demonstrate that this bifunctional Cu-In alloy interface inherits the merits of low Zn-nucleation overpotential and high HER overpotential from individual copper and indium constituents, respectively. Moreover, the dynamical self-reconstruction during cycling leads to an HER-anticatalytic and zincophilic gradient hierarchical structure, enabling highly reversible Zn chemistry with dendrite-free Zn (002) deposition and inhibited HER. Moreover, the improved interface stability featured by negligible pH fluctuations in the diffusion layer and suppressed by-product formation is evidenced by in-situ scanning probe technology, Raman spectroscopy, and electrochemical gas chromatography. Consequently, the lifespan of the CuIn@Zn symmetric cell is extended to more than one year with a voltage hysteresis of 6 mV. Importantly, the CuIn@Zn negative electrode is also successfully coupled with high-loading iodine positive electrode to fabricate Ah-level (1.1 Ah) laminated pouch cell, which exhibits a capacity retention of 67.9% after 1700 cycles.

Abstract Drilled cores provide first‐hand data for deposit research. The element compositions at different depths of cores are an important basis for evaluating the reserves of mineral deposits. Therefore, a dataset containing a complete drill core element content has great value on practicality. In this study, a pXRF analyser was used to measure the element contents in eight cores of the Xuancheng Qiaomaishan copper–sulphur deposit ZK16 + 02, ZK16 + 03, ZK16 + 04, ZK1804, ZK18 + 02, ZK1403, ZK1602, and ZK1604, and a geochemical dataset was obtained. The dataset includes Cu, S, Fe, W, Mg, Al, Si, As, Mn, Co, Ni, Zn, Se, Ti, V, Cr, Ca, Cl, P, Sr, Y, Zr, Nb, Mo, Pb, Th, U, and other 27 major and trace elements. Based on this dataset, the vertical variation pattern of main and trace element content in drill cores can be examined, the distributions of elements in full hole or exploration line profile can be drawn, and element correlation analysis and principal component analysis can be performed.

Abstract The integration of hippocampal oscillations during non-rapid eye movement (NREM) sleep is crucial for memory consolidation. However, how cardinal sleep oscillations bind across various subfields of the human hippocampus to promote information transfer and synaptic plasticity remains unclear. Using human intracranial recordings from 25 epilepsy patients, we find that hippocampal subfields, including DG/CA3, CA1, and SUB, all exhibit significant delta and spindle power during NREM sleep. The DG/CA3 displays strong coupling between delta and ripple oscillations with all the other hippocampal subfields. In contrast, the regions of CA1 and SUB exhibit more precise coordination, characterized by event-level triple coupling between delta, spindle, and ripple oscillations. Furthermore, we demonstrate that the synaptic plasticity within the hippocampal circuit, as indexed by delta-wave slope, is linearly modulated by spindle power. In contrast, ripples act as a binary switch that triggers a sudden increase in delta-wave slope. Overall, these results suggest that different subfields of the hippocampus regulate one another through diverse layers of sleep oscillation synchronization, collectively facilitating information processing and synaptic plasticity during NREM sleep.

Abstract Photoelectrochemical (PEC) cell is an ideal platform for organic transformation because of its green benefits and minimal energy consumption. As an emerging methodology, the reaction types of photoelectrocatalytic organic synthesis (PECOS) are limited to simple oxidation and C–H activation at current stage. Metal catalysis for the construction of C(sp2)–N bonds has not been touched yet in PECOS. We introduce here a PEC method that successfully engages Ni catalysis for the mild production of aniline derivatives. Experimental and computational investigations elucidate that the addition of photoanode-generated amine radical to Ni catalyst avoids the sluggish nucleophilic attack, enabling the reaction to proceed at an ultra-low potential (–0.4 V vs. Ag/AgNO3) and preventing the overoxidation of products in conventional electrochemical synthesis. This synergistic catalysis strategy exhibits good functional group tolerance and wide substrate scope on both aryl halides and amines, by which some important natural products and pharmaceutical chemicals have been successfully modified.

Abstract Tungsten trioxide (WO3) has been recognized as the most promising photocatalyst for highly selective oxidation of methane (CH4) to formaldehyde (HCHO), but the origin of catalytic activity and the reaction manner remain controversial. Here, we take {001} and {110} facets dominated WO3 as the model photocatalysts. Distinctly, {001} facet can readily achieve 100% selectivity of HCHO via the active site mechanism whereas {110} facet hardly guarantees a high selectivity of HCHO along with many intermediate products via the radical way. In situ diffuse reflectance infrared Fourier transform spectroscopy, electron paramagnetic resonance and theoretical calculations confirm that the competitive chemical adsorption between CH4 and H2O and the different CH4 activation routes on WO3 surface are responsible for diverse CH4 oxidation pathways. The microscopic mechanism elucidation provides the guidance for designing high performance photocatalysts for selective CH4 oxidation.

Abstract Due to its unbounded and orthogonal modes, the orbital angular momentum (OAM) is regarded as a key optical degree of freedom (DoF) for future information processing with ultra-high capacity and speed. Although the manipulation of OAM based on metasurfaces has brought about great achievements in various fields, such manipulation currently remains at single-DoF level, which means the multiplexed manipulation of OAM with other optical DoFs is still lacking, greatly hampering the application of OAM beams and advancement of metasurfaces. In order to overcome this challenge, we propose the idea of multiplexed coherent pixel (MCP) for metasurfaces. This approach enables the manipulation of arbitrary complex-amplitude under incident lights of both plane and OAM waves, on the basis of which we have realized the multiplexed DoF control of OAM and wavelength. As a result, the MCP method expands the types of incident lights which can be simultaneously responded by metasurfaces, enriches the information processing capability of metasurfaces, and creates applications of information encryption and OAM demultiplexer. Our findings not only provide means for the design of high-security and high-capacity metasurfaces, but also raise the control and application level of OAM, offering great potential for multifunctional nanophotonic devices in the future.

Abstract Nonlinear nanophotonic devices have shown great potential for on‐chip information processing, quantum source, 3D microfabrication, greatly promoting the developments of integrated optics, quantum science, nanoscience and technologies, etc. To promote the applications of nonlinear nanodevices, improving the nonlinear efficiency, expanding the spectra region of nonlinear response and reducing device thickness are three key issues. Herein, this study focuses on the nonlinear effect of third‐harmonic generation (THG), and present a thin Si meta‐sructure to improve the THG efficiency in the ultraviolet (UV) region. The measured THG efficiency is up to 10−5 at an emission wavelength of 309 nm. Also, the THG nanosystem is only 100 nm in thickness, which is two–five times thinner than previous all‐dielectric nanosystems applied in THG studies. These findings not only present a powerful thin meta‐structure with highly efficient THG emission in UV region, but also provide a constructive avenue for further understanding the light–matter interactions at subwavelength scales, guiding the design and fabricating of advanced photonic devices in future.

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.

Abstract Nowadays a unit quaternion is widely employed to represent the three-dimensional (3D) rotation matrix and then applied to the 3D similarity coordinate transformation. A unit dual quaternion can describe not only the 3D rotation matrix but also the translation vector meanwhile. Thus it is of great potentiality to the 3D coordinate transformation. The paper constructs the 3D similarity coordinate transformation model based on the unit dual quaternion in the sense of errors-in-variables (EIV). By means of linearization by Taylor's formula, Lagrangian extremum principle with constraints, and iterative numerical technique, the Dual Quaternion Algorithm (DQA) of 3D coordinate transformation in weighted total least squares (WTLS) is proposed. The algorithm is capable to not only compute the transformation parameters but also estimate the full precision information of computed parameters. Two numerical experiments involving an actual geodetic datum transformation case and a simulated case from surface fitting are demonstrated. The results indicate that DQA is not sensitive to the initial values of parameters, and obtains the consistent values of transformation parameters with the quaternion algorithm (QA), regardless of the size of the rotation angles and no matter whether the relative errors of coordinates (pseudo-observations) are small or large. Moreover, the DQA is advantageous to the QA. The key advantage is the improvement of estimated precisions of transformation parameters, i.e. the average decrease percent of standard deviations is 18.28%, and biggest decrease percent is 99.36% for the scaled quaternion and translations in the geodetic datum transformation case. Another advantage is the DQA implements the computation and precision estimation of traditional seven transformation parameters (which still are frequent used yet) from dual quaternion, and even could perform the computation and precision estimation of the scaled quaternion. Graphical Abstract

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user’s MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.

Root rot is a major disease that causes decline of alfalfa production, and Fusarium is a major pathogen associated with root rot. In this study, 13 Fusarium isolates were obtained from alfalfa with root rot in Gansu Province, the major alfalfa production region in China. The isolates were characterized by molecular genotyping (ITS, TEF 1-α and RPB2 fragments) and identified as six species, which included the F. acuminatum, F. incarnatum, F. oxysporum, F. proliferatum, F. redolens, and F. solani. We found that their morphology varied significantly at both the macro- and micro-levels, even for those from the same species. We developed a low cost and fast pathogenicity test and revealed that all isolates were pathogenic to alfalfa with typical root rot symptoms such as leaf yellowing and brown lesions on the root and stem. However, the virulence of the isolates differed. We also found that the conidia of all isolates germinated as early as 24 hours post inoculation (hpi), while hyphae colonized the root extensively and invaded the xylem vessel by 48 hpi. Together our results reveal that different virulent Fusarium isolates use a similar invasion strategy in alfalfa. This natural plant-fungus pathosystem is intriguing and warrants further examination, particularly with regard to efforts aimed at mitigating the impact of multiple similar vascular pathogens on infected alfalfa plants.

As the preferred technology in the current energy storage field, lithium-ion batteries cannot completely eliminate the occurrence of thermal runaway (TR) accidents. It is of significant importance to employ real-time monitoring and warning methods to perceive the battery’s safety status promptly and address potential safety hazards. Currently, the monitoring and warning of lithium-ion battery TR heavily rely on the judgment of single parameters, leading to a high false alarm rate. The application of multi-parameter early warning methods based on data fusion remains underutilized. To address this issue, the evaluation of lithium-ion battery safety status was conducted using the cloud model to characterize fuzziness and Dempster–Shafer (DS) evidence theory for evidence fusion, comprehensively assessing the TR risk level. The research determined warning threshold ranges and risk levels by monitoring voltage, temperature, and gas indicators during lithium-ion battery overcharge TR experiments. Subsequently, a multi-parameter fusion approach combining cloud model and DS evidence theory was utilized to confirm the risk status of the battery at any given moment. This method takes into account the fuzziness and uncertainty among multiple parameters, enabling an objective assessment of the TR risk level of lithium-ion batteries.

The detection and identification of defects in transmission lines using computer vision techniques is essential for maintaining the safety and reliability of power supply systems. However, existing training methods for transmission line defect detection models predominantly rely on single-node training, potentially limiting the enhancement of detection accuracy. To tackle this issue, this paper proposes a server-side adaptive parameter aggregation algorithm based on multi-method fusion (SAPAA-MMF) and formulates the corresponding objective function. Within the federated learning framework proposed in this paper, each client executes distributed synchronous training in alignment with the fundamental process of federated learning. The hierarchical difference between the global model, aggregated using the improved joint mean algorithm, and the global model from the previous iteration is computed and utilized as the pseudo-gradient for the adaptive aggregation algorithm. This enables the adaptive aggregation to produce a new global model with improved performance. To evaluate the potential of SAPAA-MMF, comprehensive experiments were conducted on five datasets, involving comparisons with several algorithms. The experimental results are analyzed independently for both the server and client sides. The findings indicate that SAPAA-MMF outperforms existing federated learning algorithms on both the server and client sides.

Abstract Background Cortical and subcortical microstructural modifications are critical to understanding the pathogenic changes in frontotemporal lobar degeneration (FTLD) subtypes. In this study, we investigated cortical and subcortical microstructure underlying cognitive and language impairments across behavioral variant of frontotemporal dementia (bvFTD), semantic variant of primary progressive aphasia (svPPA), and nonfluent variant of primary progressive aphasia (nfvPPA) subtypes. Methods The current study characterized 170 individuals with 3 T MRI structural and diffusion-weighted imaging sequences as portion of the Frontotemporal Lobar Degeneration Neuroimaging Initiative study: 41 bvFTD, 35 nfvPPA, 34 svPPA, and 60 age-matched cognitively unimpaired controls. To determine the severity of the disease, clinical dementia rating plus national Alzheimer’s coordinating center behavior and language domains sum of boxes scores were used; other clinical measures, including the Boston naming test and verbal fluency test, were also evaluated. We computed surface-based cortical thickness and cortical and subcortical microstructural metrics using tract-based spatial statistics and explored their relationships with clinical and cognitive assessments. Results Compared with controls, those with FTLD showed substantial cortical mean diffusivity alterations extending outside the regions with cortical thinning. Tract-based spatial statistics revealed that anomalies in subcortical white matter diffusion were widely distributed across the frontotemporal and parietal areas. Patients with bvFTD, nfvPPA, and svPPA exhibited distinct patterns of cortical and subcortical microstructural abnormalities, which appeared to correlate with disease severity, and separate dimensions of language functions. Conclusions Our findings imply that cortical and subcortical microstructures may serve as sensitive biomarkers for the investigation of neurodegeneration-associated microstructural alterations in FTLD subtypes. Graphical Abstract Flowchart of the study design (see materials and methods for detailed description).

Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022.

Abstract Considering that a unit dual quaternion can describe elegantly the rigid transformation including rotation and translation, the point-wise weighted 3D coordinate transformation using a unit dual quaternion is formulated. The constructed transformation model by a unit dual quaternion does not need differential process to eliminate the three translation parameters, while traditional models do. Based on the Lagrangian extremum law, the analytical dual quaternion algorithm (ADQA) of the point-wise weighted 3D coordinate transformation is proved existed and derived in detail. Four numerical cases, including geodetic datum transformation, the registration of LIDAR point clouds, and two simulated cases, are studied. This study shows that ADQA is valid as well as the modified procrustes algorithm (MPA) and the orthonormal matrix algorithm (OMA). ADQA is suitable for the 3D coordinate transformation with point-wise weight and no matter rotation angles are small or big. In addition, the results also indicate that if the distribution of common points degrades from 3D or 2D space to 1D space, the solvable correct transformation parameters decrease. In other words, all common points should not be located on a line. From the perspective of improving the transformation accuracy, high accurate control points (with small errors in the coordinates) should be chosen, and it is preferred to decrease the rotation angles as much as possible. Graphical Abstract

We cross-check the Hubble Space Telescope (HST) Cepheid/Type Ia supernova (SN Ia) distance ladder, which yields the most precise local H _0 , against early James Webb Space Telescope (JWST) subsamples (∼1/4 of the HST sample) from SH0ES and CCHP, calibrated only with NGC 4258. We find HST Cepheid distances agree well (∼1 σ ) with all combinations of methods, samples, and telescopes. The comparisons explicitly include the measurement uncertainty of each method in NGC 4258, an oft-neglected but dominant term. Mean differences are ∼0.03 mag, far smaller than the 0.18 mag “Hubble tension.” Combining all measures produces the strongest constraint yet on the linearity of HST Cepheid distances, 0.994 ±0.010, ruling out distance-dependent bias or offset as the source of the tension at ∼7 σ . However, current JWST subsamples produce large sampling differences in H _0 whose size and direction we can directly estimate from the full HST set. We show that Δ H _0 ∼ 2.5 km s ^−1 Mpc ^−1 between the CCHP JWST program and the full HST sample is entirely consistent with differences in sample selection. We combine all JWST samples into a new distance-limited set of 16 SNe Ia at D ≤ 25 Mpc. Using JWST Cepheids, JAGB, and tip of the red giant branch, we find 73.4 ± 2.1, 72.2 ± 2.2, and 72.1 ± 2.2 km s ^−1 Mpc ^−1 , respectively. Explicitly accounting for common supernovae, the three-method JWST result is H _0 = 72.6 ± 2.0, similar to H _0 = 72.8 expected from HST Cepheids in the same galaxies. The small JWST sample trivially lowers the Hubble tension significance due to small-sample statistics and is not yet competitive with the HST set (42 SNe Ia and 4 anchors), which yields 73.2 ± 0.9. Still, the joint JWST sample provides important cross-checks that the HST data pass.

The Hubble Tension, a >5 σ discrepancy between direct and indirect measurements of the Hubble constant ( H _0 ), has persisted for a decade and motivated intense scrutiny of the paths used to infer H _0 . Comparing independently derived distances for a set of galaxies with different standard candles, such as the tip of the red giant branch (TRGB) and Cepheid variables, can test for systematics in the middle rung of the distance ladder. The I band is the preferred filter for measuring the TRGB due to constancy with color, a result of low sensitivity to population differences in age and metallicity supported by stellar models. We use James Webb Space Telescope (JWST) observations with the maser host NGC 4258 as our geometric anchor to measure I -band (F090W versus F090W − F150W) TRGB distances to eight hosts of 10 Type Ia supernovae (SNe Ia) within 28 Mpc: NGC 1448, NGC 1559, NGC 2525, NGC 3370, NGC 3447, NGC 5584, NGC 5643, and NGC 5861. We compare these with Hubble Space Telescope (HST) Cepheid-based relative distance moduli for the same galaxies and anchor. We find no evidence of a difference between their weighted means, 0.01 ± 0.04 (stat) ± 0.04 (sys) mag. We produce 14 variants of the TRGB analysis, altering the smoothing level and color range used to measure the tips to explore their impact. For some hosts, this changes the identification of the strongest peak, but this causes little change to the sample mean difference, producing a full range of 0.00–0.02 mag, all consistent at 1 σ with no difference. The result matches past comparisons of I -band TRGB and Cepheids when both use HST. SNe and anchor samples observed with JWST are too small to yield a measure of H _0 that is competitive with the HST sample of 42 SNe Ia and 4 anchors; however, they already provide a vital systematic cross-check to HST measurements of the distance ladder.

The tip of the red giant branch (TRGB) allows for the measurement of precise and accurate distances to nearby galaxies based on the brightest ascent of low-mass red giant branch stars before they undergo the helium flash. With the advent of JWST, there is great promise to utilize the technique to measure galaxy distances out to at least 50 Mpc, significantly further than the Hubble Space Telescope's (HST's) reach of 20 Mpc. However, with any standard candle, it is first necessary to provide an absolute reference. Here, we use Cycle 1 data to provide an absolute calibration in the F090W filter. F090W is most similar to the F814W filter commonly used for TRGB measurements with HST, which had been adopted by the community due to its minimal dependence on the underlying metallicities and ages of stars. The imaging we use was taken in the outskirts of NGC 4258, which has a direct geometrical distance measurement from the Keplerian motion of its water megamaser. Utilizing several measurement techniques, we find ${M}_{\mathrm{TRGB}}^{{\rm{F}}090{\rm{W}}}$ = −4.362 ± 0.033 (stat) ± 0.045 (sys) mag (Vega) for the metal-poor TRGB. We also perform measurements of the TRGB in two Type Ia supernova hosts, NGC 1559 and NGC 5584. We find good agreement between our TRGB distances and previous determinations of distances to these galaxies from Cepheids (Δ = 0.01 ± 0.06 mag), with these differences being too small to explain the Hubble tension (∼0.17 mag). In addition, we showcase the serendipitous discovery of a faint dwarf galaxy near NGC 5584.

We study stars in the J-regions of the asymptotic giant branch (JAGB) of near-infrared color–magnitude diagrams in the maser host NGC 4258 and four hosts of six Type Ia supernovae (SNe Ia): NGC 1448, NGC 1559, NGC 5584, and NGC 5643. These clumps of stars are readily apparent near 1.0 < F150W − F277W < 1.5 and m _F150W = 22–25 mag with James Webb Space Telescope NIRCam photometry. Various methods have been proposed to assign an apparent reference magnitude to this recently proposed standard candle, including the mode, median, sigma-clipped mean, or a modeled luminosity function parameter. We test the consistency of these by measuring intrahost variations, finding differences of up to ∼0.2 mag that significantly exceed statistical uncertainties. Brightness differences appear intrinsic, and are further amplified by the nonuniform shape of the JAGB luminosity function, also apparent in the LMC and SMC. We follow a “many methods” approach to measure consistently JAGB magnitudes and distance moduli to the SN Ia host sample calibrated by NGC 4258. We find broad agreement with distance moduli measured from Cepheids, tip of the red giant branch, and Miras. However, the SN host mean distance modulus estimated via the JAGB method necessary to estimate H _0 differs by ∼0.19 mag among the above definitions, the result of different levels of luminosity function asymmetry. The methods yield a full range of 71−78 km s ^−1 Mpc ^−1 , i.e., a fiducial result of H _0 = 74.7 ± 2.1(stat) ± 2.3(sys, ±3.1 if combined in quadrature) km s ^−1 Mpc ^−1 , with systematic errors limited by the differences in methods. Future work may seek to standardize and refine this promising tool further, making it more competitive with established distance indicators.

BackgroundNeuroimaging studies have identified aberrant activity patterns in multiple brain regions in functional dyspepsia (FD) patients. However, due to the differences in study design, these previous findings are inconsistent, and the underlying neuropathological characteristics of FD remain unclear.MethodsEight databases were systematically searched for literature from inception to October 2022 with the keywords “Functional dyspepsia” and “Neuroimaging.” Thereafter, the anisotropic effect size signed the differential mapping (AES-SDM) approach that was applied to meta-analyze the aberrant brain activity pattern of FD patients.ResultsA total of 11 articles with 260 FD patients and 202 healthy controls (HCs) were included. The AES-SDM meta-analysis demonstrated that FD patients manifested increased activity in the bilateral insula, left anterior cingulate gyrus, bilateral thalamus, right precentral gyrus, left supplementary motor area, right putamen, and left rectus gyrus and decreased functional activity in the right cerebellum compared to the HCs. Sensitivity analysis showed that all these above regions were highly reproducible, and no significant publication bias was detected.ConclusionThe current study demonstrated that FD patients had significantly abnormal activity patterns in several brain regions involved in visceral sensation perception, pain modulation, and emotion regulation, which provided an integrated insight into the neuropathological characteristics of FD.

Abstract In order to train an end‐to‐end model for better performance in distinguishing superclasses and make the model more sensitive to inter‐class differences, the authors propose a Feature Context Network (FCNet), a novel ensemble framework for image retrieval based on clustering (EFC) and metric learning. In the authors' approach, the EFC framework consists of one common feature extractor and multiple learning branches. This tree‐like structure not only promotes the stability of the training process but also keeps the diversity by using multiple branches. Each branch is learnt by an independent random re‐labelling operation that distributes original classes into superclasses. In such a way, the framework thus can accelerate the training process. To make the EFC framework focus on the different attributes across the classes, the authors utilize k‐means clustering to divide the training set into subsets and train the model sequentially with those subsets. Meanwhile, to reduce the impact of over‐fitting problems, the authors design a branch weight structure during training. Further, FCNet fusions different scales of the neighbourhood information and consequently makes the feature map learn richer information with the different receptive fields. Simultaneously, it can reduce the layer of the network and the number of model parameters. Extensive experiments demonstrate that this approach outperforms the tested representative methods on CARS‐196, CUB‐200‐2011 datasets.

Based on the comparison of basic geological conditions and enrichment characteristics of shale oil plays, the heterogeneity of source and reservoir conditions and differential enrichment of medium-high maturity continental shale oil plays in China have been confirmed. (1) Compared with the homogeneous geological settings and wide distribution of marine shale oil strata in North America, the continental medium and high maturity shale oil plays in China are significantly different in geological conditions generally; continental multi-cyclic tectonic evolution forms multiple types of lake basins in multi-stages, providing sites for large-scale development of continental shale oil, and giving rise to large scale high-quality source rocks, multiple types of reservoirs, and diverse source-reservoir combinations with significant heterogeneity. (2) The differences in sedimentary water environments lead to the heterogeneity in lithology, lithofacies, and organic material types of source rocks; the differences in material source supply and sedimentary facies belt result in reservoirs of different lithologies, including argillaceous and transition rocks, and tight siltstone, and complex source-reservoir combination types. (3) The heterogeneity of the source rock controls the differentiation of hydrocarbon generation and expulsion, the diverse reservoir types make reservoir performance different and the source-reservoir configurations complex, and these two factors ultimately make the shale oil enrichment patterns different. Among them, the hydrocarbon generation and expulsion capacity of high-quality source rocks affect the degree of shale oil enrichment. Freshwater hydrocarbon source rocks with TOC larger than 2.5% and saline hydrocarbon source rocks with TOC of 2% to 10% have a high content of retained hydrocarbons and are favorable. (4) High-abundance organic shale is the basis for the enrichment of shale oil inside the source. In addition to being retained in shale, liquid hydrocarbons migrate along laminae, diagenetic fractures, and thin sandy layers, and then accumulate in laminae of argillaceous siltstone, siltstone, and argillaceous dolomite, and dolomitic siltstone suites, etc. with low organic matter abundance in the shale strata, resulting in differences in enrichment pattern.

With the objectives of achieving “peak carbon” and “carbon neutrality”, accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of vehicles at both temporal and spatial scales from a macro perspective. Therefore, focusing on the quantitative model of vehicle micro energy consumption and road meso energy consumption, this paper reviewed and summarized the energy consumption model of road traffic in terms of data collection, quantification accuracy, and scope of application. Based on this analysis, this paper identifies the challenges of the current road traffic energy consumption model. Finally, we look forward to future research directions for studying quantitative models of energy consumption from road transportation.

Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.

Heterogeneous nucleation plays a critical role in the phase transition of water, which can cause damage in various systems. Here, we report that heterogeneous nucleation can be inhibited by utilizing hydrogel coatings to isolate solid surfaces and water. Hydrogels, which contain over 90% water when fully swelled, exhibit a high degree of similarity to water. Due to this similarity, there is a great energy barrier for heterogeneous nucleation along the water–hydrogel interface. Additionally, hydrogel coatings, which possess polymer networks, exhibit higher fracture energy and more robust adhesion to solid surfaces compared to water. This high fracture and adhesion energy acts as a deterrent for fracture nucleation within the hydrogel or along the hydrogel–solid interface. With a hydrogel layer approximately 100 μm thick, the boiling temperature of water under atmospheric pressure can be raised from 100 to 108 °C. Notably, hydrogel coatings also result in remarkable reductions in cavitation pressure on multiple solid surfaces. We have demonstrated the efficacy of hydrogel coatings in preventing damages resulting from acceleration-induced cavitation. Hydrogel coatings have the potential to alter the energy landscape of heterogeneous nucleation on the water–solid interface, making them an exciting avenue for innovation in heat transfer and fluidic systems.

COVID-19 has become a global pandemic and there is an urgent call for developing drugs against the virus (SARS-CoV-2). The 3C-like protease (3CLpro) of SARS-CoV-2 is a preferred target for broad spectrum anti-coronavirus drug discovery. We studied the anti-SARS-CoV-2 activity of S. baicalensis and its ingredients. We found that the ethanol extract of S. baicalensis and its major component, baicalein, inhibit SARS-CoV-2 3CLpro activity in vitro with IC50’s of 8.52 µg/ml and 0.39 µM, respectively. Both of them inhibit the replication of SARS-CoV-2 in Vero cells with EC50’s of 0.74 µg/ml and 2.9 µM, respectively. While baicalein is mainly active at the viral post-entry stage, the ethanol extract also inhibits viral entry. We further identified four baicalein analogues from other herbs that inhibit SARS-CoV-2 3CLpro activity at µM concentration. All the active compounds and the S. baicalensis extract also inhibit the SARS-CoV 3CLpro, demonstrating their potential as broad-spectrum anti-coronavirus drugs.

Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., speed, flow) on road segments using partially observed data, plays an essential role in intelligent transportation systems. Generally, traffic estimation problems can be divided into two categories: model-driven approaches and data-driven approaches. The model-driven method is commonly used to solve TSE efficiently and calibrate the parameters of these models. The data-driven method requires a large amount of historical observed traffic data in order to improve performance accurately. In order to combine the advantages of model-driven and data-driven methods, this paper proposed a hybrid framework incorporating the traffic flow model into deep learning (TFMDL) modeling that contains both model-driven and data-driven components. This paper focuses on highway TSE with observed data from loop detectors. We build a hybrid cost function to adjust the weights of model-driven and data-driven proportions. We then evaluate the proposed framework using the open-access performance measurement system (PMS) dataset on a corridor of US I-405 in Los Angeles, California. The experimental results show the advantages of the proposed TFMDL approach in performing better than several benchmark models in terms of estimation accuracy and data efficiency.

Volumetric magnetic resonance imaging (MRI) atrophy is a hallmark of Rasmussen’s encephalitis (RE). Here, we aim to investigate voxel-wise gray matter (GM) atrophy in RE, and its associations with glucose hypometabolism and neurotransmitter distribution utilizing MRI and PET data. In this study, fifteen RE patients and fourteen MRI normal subjects were included in this study. Voxel-wise GM volume and glucose metabolic uptake were evaluated using structural MRI and FDG-PET images, respectively. Spatial Spearman’s correlation was performed between GM atrophy of RE with FDG uptake alterations, and neurotransmitter distributions provided in the JuSpace toolbox. Compared with the control group, RE patients displayed extensive GM volume loss not only in the ipsilateral hemisphere, but also in the frontal lobe, basal ganglia, and cerebellum in the contralateral hemisphere. Within the RE group, the insular and temporal cortices exhibited significantly more GM atrophy on the ipsilesional than the contralesional side. FDG-PET data revealed significant hypometabolism in areas surrounding the insular cortices in the ipsilesional hemisphere. RE-related GM volumetric atrophy was spatially correlated with hypomebolism in FDG uptake, and with spatial distribution of the dopaminergic and serotonergic neurotransmitter systems. The spatial concordance of morphological changes with metabolic abnormalities suggest FDG-PET offers potential value for RE diagnosis. The GM alterations associated with neurotransmitter distribution map could provide novel insight in understanding the neuropathological mechanisms and clinical feature of RE.

The 7050 aluminum alloy orifice plate is extruded and strengthened by the cold expansion process of slotted bushing holes. A three-dimensional finite element model was established for cold expansion strengthening of holes, and the residual stress distribution of 7050 aluminum alloy orifice plates is studied after cold expansion. The influence of different relative extrusion amounts and different sample thicknesses on the residual stress around the sample hole was analyzed, and the relationship between the extrusion amounts and the thicknesses of the substrate and the residual stress was obtained by analysis. The research results indicate that the cold expansion process of slotted bushing holes can enhance the residual stress distribution around the hole and form a strong residual stress layer. As the amounts of extrusion increase, the residual stress tends to increase, reaching the maximum when the amounts of extrusion are 4%. The residual stress on the surface of the extrusion inlet increases with the increase in the thickness of the substrate. Within the range of 3%-4% of the extrusion amounts, with the continuous increase of the extrusion amounts, the fatigue life has been substantially enhanced, and the number of fatigue life is more than 3 times that of the empty plate fatigue test.

The tip of the red giant branch provides a luminous standard candle for calibrating distance ladders that reach Type Ia supernova (SN Ia) hosts. However, recent work reveals that tip measurements vary at the ∼0.1 mag level for different stellar populations and locations within a host, which may lead to inconsistencies along the distance ladder. We pursue a calibration of the tip using 11 Hubble Space Telescope fields around the maser host, NGC 4258, that is consistent with SN Ia hosts by standardizing tip measurements via their contrast ratios. We find F814W-band tips that exhibit a full 0.3 mag range and 0.1 mag dispersion. We do not find any correlation between H i column density and the apparent tip to 0.04 ± 0.03 mag/cm ^−2 . We search for a tip–contrast relation (TCR) and measure the TCR within the fields of NGC 4258 of −0.015 ± 0.008 mag/ R , where R is the contrast ratio. This value is consistent with the TCR originally discovered in the GHOSTS sample of −0.023 ± 0.005 mag/ R . Combining these measurements, we find a global TCR of −0.021 ± 0.004 mag/ R and a calibration of ${M}_{I}^{\mathrm{TRGB}}=-4.025\pm 0.035-(R-4)\times 0.021$ mag. We also use stellar models to simulate single age and metallicity stellar populations with [Fe/H] from −2.0 to −0.7 and ages from 3 to 12 Gyr and reconstruct the global TCR found here to a factor of ∼2. This work is combined in a companion analysis with tip measurements of nearby SN Ia hosts to measure H _0 .

We present a low-dispersion period–luminosity relation (PL) based on 154 Cepheids in Messier 33 (M33) with Hubble Space Telescope (HST) photometry from the PHATTER survey. Using high-quality ground-based light curves, we recover Cepheid phases and amplitudes for multi-epoch HST data and we perform template fitting to derive intensity-averaged mean magnitudes. HST observations in the SH0ES near-infrared Wesenheit system significantly reduce the effect of crowding relative to ground-based data, as seen in the final PL scatter of σ = 0.11 mag. We adopt the absolute calibration of the PL based on HST observations in the Large Magellanic Cloud and a distance derived using late-type detached eclipsing binaries to obtain a distance modulus for M33 of μ = 24.622 ± 0.030 mag ( d = 840 ± 11 kpc), a best-to-date precision of 1.3%. We find very good agreement with past Cepheid-based measurements. Several tip of the red giant branch estimates bracket our result while disagreeing with each other. Finally, we show that the flux contribution from star clusters hosting Cepheids in M33 does not impact the distance measurement and we find only ∼3.7% of the sample is located in (or nearby) young clusters. M33 offers one of the best sites for the cross-calibration of many primary distance indicators. Thus, a precise independent geometric determination of its distance would provide a valuable new anchor to measure the Hubble constant.

The tip of the red giant branch (TRGB) is a standard candle that can be used to help refine the determination of the Hubble constant. Gaia Data Release 3 (DR3) provides synthetic photometry constructed from low-resolution BP/RP spectra for Milky Way field stars that can be used to directly calibrate the luminosity of the TRGB in the Johnson–Cousins I band, where the TRGB is least sensitive to metallicity. We calibrate the TRGB luminosity using a two-dimensional maximum likelihood algorithm with field stars and Gaia synthetic photometry and parallaxes. For a high-contrast and low-contrast break (characterized by the values of the contrast parameter R or the magnitude of the break β ), we find M ^TRGB _I = −4.02 and −3.92 mag respectively, or a midpoint of −3.970 ${}_{-0.024}^{+0.042}$ (sys) ± 0.062 (stat) mag. This measurement improves upon the TRGB measurement from Li et al., as the higher precision photometry based on Gaia DR3 allows us to constrain two additional free parameters of the luminosity function. We also investigate the possibility of using Gaia DR3 synthetic photometry to calibrate the TRGB luminosity with ω Centauri, but find evidence of blending within the inner region for cluster member photometry that precludes accurate calibration with Gaia DR3 photometry. We instead provide an updated TRGB measurement of m ^TRGB _I = 9.82 ± 0.04 mag in ω Centauri using ground-based photometry from the most recent version of the database described in Stetson et al., which gives M ^TRGB _I = −3.97 ± 0.04 (stat) ± 0.10 (sys) mag when tied to the Gaia EDR3 parallax distance from the consensus of Vasiliev & Baumgardt, Soltis et al., and Maíz Apellániz.

A second-order sliding mode control (SOSMC) with a fractional module using adaptive fuzzy controller is developed for an active power filter (APF). A second-order sliding surface using a fractional module which can decrease the discontinuities and chattering is designed to make the system work stably and simplify the design process. In addition, a fuzzy logic control is utilized to estimate the parameter uncertainties. Simulation and experimental discussion illustrated that the designed fractional SOSMC with adaptive fuzzy controller is valid in satisfactorily eliminating harmonic, showing good robustness and stability compared with an integer order one.

The increasing use of nonlinear loads entails serious electrical energy quality problems in power grids. Active power filter could lead to significant improvement in harmonic compensation performance. In this paper, an adaptive second-order sliding mode fuzzy control based on linear feedback strategy for a three-phase active power filter is proposed to eliminate harmonics. This scheme is constituted by adaptive controller, second-order sliding mode controller, fuzzy controller, and linear feedback controller. The linear feedback controller can compensate the system’s nonlinearities and make the new system have a linear behavior between the input and output. Moreover, the second-order sliding mode controller could transfer the discontinuities in the controller and reduce system chattering. Simulation results illustrate the proposed controller has better performance compared with fuzzy control and adaptive sliding mode fuzzy control.

Because of the heavy use of electrical equipment, harmonic pollution is becoming more and more serious, which brings potential safety risks to our lives. In view of the fact that active power filters can effectively eliminate harmonics, an adaptive second-order sliding mode fuzzy control based on the backstepping method is proposed to eliminate harmonics in this article. The second-order sliding mode control can eliminate chattering in the system and results in a smoother controller. The uncertainties in the system are approximated by a fuzzy control system, and the adaptive laws are designed to ensure the stability of the system. Backstepping control can improve the stability and robustness of closed-loop systems. Simulation results show that the proposed method has better tracking effect, strong robustness, and stability and can effectively eliminate harmonics.

Abstract High‐throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome‐scale interaction mapping. Here, we report Barcode Fusion Genetics‐Yeast Two‐Hybrid (BFG‐Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG‐Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein‐pair barcodes that can be quantified via next‐generation sequencing. We applied BFG‐Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG‐Y2H increases the efficiency of protein matrix screening, with quality that is on par with state‐of‐the‐art Y2H methods.

This paper mainly discusses an adaptive fuzzy sliding mode control problem for a microgyroscope system via a global fast terminal sliding mode approach. The fabrication imperfections, the time varying system parameters, and the external disturbances are taken into consideration synchronously, and the corresponding dynamic model is established. A global fast terminal sliding mode control scheme is designed to guarantee that the system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state surface. Furthermore, an adaptive fuzzy control is introduced to approximate the model uncertainties and external disturbances. The chattering caused by the sliding mode control is weakened as well. Both the stability analysis and the tracking performance of the gyroscope system are proved by the Lyapunov stability theory. The effectiveness of the invoked control schemes for microgyroscope system is illustrated through the simulation results.

The main objective of this paper is to design a dynamic surface adaptive fuzzy controller for a three-phase active power filter (APF). In order to deal with the nonlinearity of the APF, a fuzzy strategy combining an adaptive backstepping method is proposed where fuzzy controller is mainly responsible for the elimination of nonlinearities. Moreover, by adopting the dynamic surface method, the number of input variables of the fuzzy system in modeling could be reduced; therefore, the controller and design parameters can be implemented without complex calculation. The Lyapunov stability of closed-loop system with the fuzzy control, sliding control, and dynamic surface control could be assured. The simulation results prove that the dynamic surface adaptive fuzzy method has excellent performance in stability, robustness, adaptability, and total harmonic distortion (THD), which is confirmed to suppress harmonics effectively.

The development of the thermoelectric (TE) power generation is rapid, and the applications have extensively been studied. The principle is based on the Seebeck effect, in which the temperature difference between hot and cold sides of the TE material converts to electrical energy. In this paper, a design is proposed to convert the thermal energy between indoor and outdoor of a board to electrical energy by the thermoelectric generator (TEG). Furthermore, the electrical energy generated is charged to supercapacitors as a battery or a power supply to the loads (e.g., lights) of the house. Besides the experimental work, a thermal model and an electrical model of the TEG have been proposed. To study the power generation performance in terms of materials, the simulation of the conversion efficiency of the TE board using materials with different thermal conductance have also been conducted. It was found that, using graphene as the thermally conductive material, the conversion efficiency was enhanced by 1.6% and 1.7%, when the temperature difference was 15 °C and 40 °C, respectively.