One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense

Bank Credit Strategy Model Based on AHP-Fuzzy Comprehensive Evaluation

Credit risk control and credit strategy formulation of medium and micro enterprises have always been important strategic issues faced by commercial banks. Banks usually make corporate loan policies based on the credit degree, the information of trading bills and the relationship of supply-demand chain of the enterprise. In this paper, we established the AHP-Fuzzy comprehensive evaluation model for quantifying enterprise credit risk. Based on the relevant data of 123 enterprises with credit records, the credit strategy is formulated according to the three indicators of enterprise strength, enterprise reputation and stability of supply-demand relationship. This paper also combines the credit reputation, credit risk and supply and demand stability rating in order to establish the bank credit strategic planning model to decide whether to lend or not and the lending order. The conclusion shows that, under the condition of constant total loan amount, the enterprises with the highest credit rating should be given priority. Then, combined with the change of customer turnover rate with interest rate, we take the bank's maximize expected income as objective to calculate the optimal loan interest rate of different customer groups

Research progress in the relationship between gut microbia and its metabolites and gestational diabetes mellitus

The global incidence of gestational diabetes mellitus (GDM) continues to rise in recent years. Research has shown that GDM can increase the risk of adverse pregnancy outcomes in pregnant women and lead to malignant intergenerational circulation. The etiology of GDM is complex and the pathogenesis has not been fully elucidated. Maternal dietary assessment and guidance is the first-line method for managing GDM in clinical practice. Reasonable diet plays an important role in gut microbia and its metabolites during pregnancy, and the dysfunction of gut microbia is closely related to the occurrence of metabolic diseases. It has been shown that gut microbial metabolites such as short-chain fatty acids (SCFAs), trimethylamine oxide (TMAO) and bile acids are strongly influenced by diet and play an important role in metabolic disorders related to insulin resistance (such as GDM). Progress has been made in the prevention and treatment of metabolic diseases by improving gut microbia through medical nutrition therapy, which provides a new direction for the control of GDM. The status quo of GDM, the characteristics and alteration of gut microbia in pregnant women with GDM, the GDM-related gut microbial metabolites, and the feasible prevention and treatment of GDM by targeting gut microbia and its metabolites are reviewed

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.Comment: 14 page

Residential Wood Burning and Vehicle Emissions as Major Sources of Environmentally Persistent Free Radicals in Fairbanks, Alaska.

Environmentally persistent free radicals (EPFRs) play an important role in aerosol effects on air quality and public health, but their atmospheric abundance and sources are poorly understood. We measured EPFRs contained in PM2.5 collected in Fairbanks, Alaska, in winter 2022. We find that EPFR concentrations were enhanced during surface-based inversion and correlate strongly with incomplete combustion markers, including carbon monoxide and elemental carbon (R2 > 0.75). EPFRs exhibit moderately good correlations with PAHs, biomass burning organic aerosols, and potassium (R2 > 0.4). We also observe strong correlations of EPFRs with hydrocarbon-like organic aerosols, Fe and Ti (R2 > 0.6), and single-particle mass spectrometry measurements reveal internal mixing of PAHs, with potassium and iron. These results suggest that residential wood burning and vehicle tailpipes are major sources of EPFRs and nontailpipe emissions, such as brake wear and road dust, may contribute to the stabilization of EPFRs. Exposure to the observed EPFR concentrations (18 ± 12 pmol m-3) would be equivalent to smoking ∼0.4-1 cigarette daily. Very strong correlations (R2 > 0.8) of EPFR with hydroxyl radical formation in surrogate lung fluid indicate that exposure to EPFRs may induce oxidative stress in the human respiratory tract

Atomic magnetometry using a metasurface polarizing beamsplitter in silicon on sapphire

We demonstrate atomic magnetometry using a metasurface polarizing beamsplitter fabricated on a silicon-on-sapphire (SOS) platform. The metasurface splits a beam that is near-resonant with the rubidium atoms (795 nm) into orthogonal linear polarizations, enabling measurement of magnetically sensitive circular birefringence in a rubidium vapor through balanced polarimetry. We incorporated the metasurface into an atomic magnetometer based on nonlinear magneto-optical rotation and measured sub-nanotesla sensitivity, which is limited by low-frequency technical noise and transmission loss through the metasurface. To our knowledge, this work represents the first demonstration of SOS nanophotonics for atom-based sensing and paves the way for highly integrated, miniaturized atomic sensors with enhanced sensitivity and portability

Hypermatrix factors for string and membrane junctions

The adjoint representations of the Lie algebras of the classical groups SU(n), SO(n), and Sp(n) are, respectively, tensor, antisymmetric, and symmetric products of two vector spaces, and hence are matrix representations. We consider the analogous products of three vector spaces and study when they appear as summands in Lie algebra decompositions. The Z3-grading of the exceptional Lie algebras provide such summands and provides representations of classical groups on hypermatrices. The main natural application is a formal study of three-junctions of strings and membranes. Generalizations are also considered.Comment: 25 pages, 4 figures, presentation improved, minor correction

Validation of the plasma-wall self-organization model for density limit in ECRH-assisted start-up of Ohmic discharges on J-TEXT

A recently developed plasma-wall self-organization (PWSO) model predicts a significantly enhanced density limit, which may be attainable in tokamaks with ECRH-assisted ohmic startup and sufficiently high initial neutral density. Experiments have been conducted on J-TEXT to validate such a density limit scenario based on this model. Experimental results demonstrate that increasing the pre-filled gas pressure or ECRH power during the startup phase can effectively enhance plasma purity and raise the density limit at the flat-top. Despite the dominant carbon fraction in the wall material, some discharges approach the edge of the density-free regime of the 1D model of PWSO.Comment: 17 pages, 8 figure

Warming-induced shifts in alpine soil microbiome: An ecosystem-scale study with environmental context-dependent insights

10 páginas.- 5 figuras.- referencias.- Supplementary data to this article can be found online at https://doi. org/10.1016/j.envres.2024.119206Climate warming is a pressing global issue with substantial impacts on soil health and function. However, the influence of environmental context on the responses of soil microorganisms to warming remains largely elusive, particularly in alpine ecosystems. This study examined the responses of the soil microbiome to in situ experimental warming across three elevations (3850 m, 4100 m, and 4250 m) in the meadow of Gongga Mountain, eastern Tibetan Plateau. Our findings demonstrate that soil microbial diversity is highly resilient to warming, with significant impacts observed only at specific elevations. Furthermore, the influence of warming on the composition of the soil microbial community is also elevation-dependent, underscoring the importance of local environmental context in shaping microbial evolution in alpine soils under climate warming. Notably, we identified soil moisture at 3850 m and carbon-to-nitrogen ratio at 4250 m as indirect predictors regulating the responses of microbial diversity to warming at specific elevations. These findings underscore the paramount importance of considering pre-existing environmental conditions in predicting the response of alpine soil microbiomes to climate warming. Our study provides novel insights into the intricate interactions between climate warming, soil microbiome, and environmental context in alpine ecosystems, illuminating the complex mechanisms governing soil microbial ecology in these fragile and sensitive environments.This study was funded by the Science and Technology Research Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (IMHE-ZYTS-07), and the Youth Innovation Promotion Association, Chinese Academy of Sciences (2023391). M.D.B. acknowledges support from TED2021-130908B–C41/AEI/10.13039/501100011033/Unión European NextGenerationEU/PRTR and from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033.Peer reviewe