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Modern enterprises grapple with complex financial data and multidimensional risk interdependencies in their operations. Machine learning offers transformative potential for tax risk assessment and smart auditing solutions. This research analyzes 3,232 tax records from regional manufacturing and service sectors (2021–2023) to evaluate three predictive models: SVM, XGBoost, and Random Forest. Results demonstrate Random Forest’s superior performance, achieving 92.00% (manufacturing) and 93.39% (service) accuracy – substantially outperforming XGBoost and SVM (85–90%). Key manufacturing risk indicators follow a “high tax-high volatility-high scrutiny” pattern, with tax burden rate (0.129 weight), profit fluctuation (0.100), and audit frequency (0.091) being most predictive. Service sector risks manifest as “volatility-declaration-tax burden” dynamics, where profit volatility (0.142) emerges as the strongest predictor. These findings both validate machine learning’s efficacy in tax analysis and equip regulators with intelligent risk management tools.

Temperature is one of the most fundamental physical properties to characterize various physical, chemical, and biological processes. Even a slight change in temperature could have an impact on the status or dynamics of a system. Thus, there is a great need for high-precision and large-dynamic-range temperature measurements. Conventional temperature sensors encounter difficulties in high-precision thermal sensing on the submicron scale. Recently, optical whispering-gallery mode (WGM) sensors have shown promise for many sensing applications, such as thermal sensing, magnetic detection, and biosensing. However, despite their superior sensitivity, the conventional sensing method for WGM resonators relies on tracking the changes in a single mode, which limits the dynamic range constrained by the laser source that has to be fine-tuned in a timely manner to follow the selected mode during the measurement. Moreover, we cannot derive the actual temperature from the spectrum directly but rather derive a relative temperature change. Here, we demonstrate an optical WGM barcode technique involving simultaneous monitoring of the patterns of multiple modes that can provide a direct temperature readout from the spectrum. The measurement relies on the patterns of multiple modes in the WGM spectrum instead of the changes of a particular mode. It can provide us with more information than the single-mode spectrum, such as the precise measurement of actual temperatures. Leveraging the high sensitivity of WGMs and eliminating the need to monitor particular modes, this work lays the foundation for developing a high-performance temperature sensor with not only superior sensitivity but also a broad dynamic range.Temperature measurement: optical barcodes from whispering-gallery sensorsExtremely precise measurement of temperature using devices known as whispering-gallery mode (WGM) sensors can be greatly improved using a technique that simultaneously monitors different modes, or patterns, of the optical signals. WGM sensors rely on the sustained circulation of light within closed concave microstructures—discs, rings, or spheres—similar to the movement of sound waves in whispering galleries such as the dome of St. Paul’s Cathedral in London. Jie Liao and Lan Yang at Washington University in St. Louis, Missouri, USA, developed procedures to analyse the effect of temperature on the collective patterns of light signals in WGM sensors. The results are converted into optical barcodes which indicate the temperature directly. This multimode system overcomes significant limitations imposed by the restricted range and less direct monitoring methods of existing single-mode sensors.

Optical whispering-gallery-mode microresonators with ultrahigh quality factors and small mode volumes have played an important role in modern physics. They have been demonstrated as a diverse platform for a wide range of applications in photonics, such as nonlinear optics, optomechanics, quantum optics, and information processing. Thermal behaviors induced by power build-up in the resonators or environmental perturbations are ubiquitous in high-quality-factor whispering-gallery-mode resonators and have played an important role in their operation for various applications. In this review, we discuss the mechanisms of laser-field-induced thermal nonlinear effects, including thermal bistability and thermal oscillation. With the help of the thermal bistability effect, optothermal spectroscopy and optical nonreciprocity have been demonstrated. By tuning the temperature of the environment, the resonant mode frequency will shift, which can also be used for thermal sensing/tuning applications. The thermal locking technique and thermal imaging mechanisms are discussed briefly. Finally, we review some techniques employed to achieve thermal stability in a high-quality-factor resonator system.Optical microresonators: Investigating light’s tiny whispersOptical whispering-gallery mode microresonators can trap light in a highly confined volume, similar to the behavior of sound waves in the famous whispering gallery of St Paul’s Cathedral, London, and are being used for a diverse variety of optical studies and applications. Xuefeng Jiang and Lan Yang at Washington University in St. Louis, Missouri, USA, review the optothermal behaviors induced by power build-up in the resonators or environmental perturbations in these systems. Several techniques have been developed with the help of optothermal behaviors, such as optothermal spectroscopy, thermal tuning, thermal locking, thermal imaging, etc. Temperature fluctuations affect the resonant frequencies of the light they contain, which can be used for thermal sensing applications, but must also be controlled to avoid problems caused by heat fluctuations when the microresonators are used in other applications.

The incidence of allergic diseases continues to rise. Cross-sectional and longitudinal studies have indicated that allergic diseases occur in a time-based order: from atopic dermatitis and food allergy in infancy to gradual development into allergic asthma and allergic rhinitis in childhood. This phenomenon is defined as the \"atopic march\". Some scholars have suggested that the atopic march does not progress completely in a temporal pattern with genetic and environmental factors. Also, the mechanisms underlying the atopic march are incompletely understood. Nevertheless, the concept of the atopic march provides a new perspective for the mechanistic research, prediction, prevention, and treatment of atopic diseases. Here, we review the epidemiology, related diseases, mechanistic studies, and treatment strategies for the atopic march.

The Yellow River Economic Belt, where the degree of digital economy development is uneven, is the first research object used in this study. It then suggests a way to measure the degree of digital economy development and carbon emissions in order to address the problem of effectively controlling carbon emissions in the rapidly developing digital economy. Finally, a genetic method is presented to further enhance the backpropagation neural network model’s update process, which was improved utilizing the particle swarm optimization technique. According to the findings, this research identified three primary elements: digital industrialization, digital finance, and digital ecological environment. According to the findings, this research identified three primary elements: digital industrialization, digital finance, and digital ecological environment. With the use of digital technology, the digital ecological environment fosters a peaceful coexistence between people and the natural world. In addition to encouraging the advancement of digital technology, it may also help to integrate digital transformation and green development. The use of digital technology in ecological environment governance can assist accomplish sustainable development goals, improve resource allocation, and encourage intelligent and green production and life. In order to change conventional financial service models, the financial sector known as “digital finance” makes use of digital technologies and data components. It has the potential to be very important in encouraging industrial upgrading and propelling the growth of new industries. Additionally, the whole credit structure of the industrial chain may be improved by digital credit and risk management, which will support the economic structure’s optimization. The use of digital technology to a variety of sectors, encouraging their digital transformation and modernization, is known as digital industrialization. It is a key component of a contemporary industrial system that may drive new industries and formats, support the intelligent and information-based transformation of established industries, and improve the economic structure. At the same time, the associated carbon emissions dropped by 0.0439 units for every unit rise in the study area’s digital economy’s degree of growth. The region’s overall population, energy consumption, sophisticated industrial structure, and industrial structure rationalization all positively promote carbon emissions, whilst other variables have the opposite impact. The final study approach had the highest predictive performance, with a high goodness of fit of 0.9936 and an average absolute error of 16.971. The aforementioned study results demonstrate that the methodology can effectively evaluate the level of carbon emissions and the development of the digital economy across different regions and provide targeted solutions to lower carbon emissions in line with local conditions, thus fostering the vibrancy of the digital economy.

Tuning optical microcavities to exceptional points enhances their ability to sense nanoscale objects, owing to the topological features of exceptional points. Exceptional points, exceptional optics Recent insights into open (non-Hermitian) physical systems have led to a new range of optical systems in which, counter-intuitively, loss is introduced. By careful tuning of loss and gain, certain degeneracies called 'exceptional points' emerge, which have intriguing properties that can be harnessed, for example, in new types of lasers, one-way optical waveguides and topological effects. Two papers in this issue demonstrate the high sensitivity of such non-Hermitian degeneracies to external perturbations, which can be used for precision sensing and detection. Weijian Chen et al . report sensing of nanoparticles with exceptional points generated in a silicon dioxide micro-toroid resonator. Hossein Hodaei et al . generated a higher-order exceptional point by coupling three micro-rings made from a semiconductor laser material. This third-order exceptional point has an even higher, cube-root (rather than square-root) dependence on perturbations. The two papers together provide a new route to ultraprecise chip-based sensing systems. Sensors play an important part in many aspects of daily life such as infrared sensors in home security systems, particle sensors for environmental monitoring and motion sensors in mobile phones. High-quality optical microcavities are prime candidates for sensing applications because of their ability to enhance light–matter interactions in a very confined volume. Examples of such devices include mechanical transducers 1 , magnetometers 2 , single-particle absorption spectrometers 3 , and microcavity sensors for sizing single particles 4 and detecting nanometre-scale objects such as single nanoparticles and atomic ions 5 , 6 , 7 . Traditionally, a very small perturbation near an optical microcavity introduces either a change in the linewidth or a frequency shift or splitting of a resonance that is proportional to the strength of the perturbation. Here we demonstrate an alternative sensing scheme, by which the sensitivity of microcavities can be enhanced when operated at non-Hermitian spectral degeneracies known as exceptional points 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . In our experiments, we use two nanoscale scatterers to tune a whispering-gallery-mode micro-toroid cavity, in which light propagates along a concave surface by continuous total internal reflection, in a precise and controlled manner to exceptional points 12 , 13 . A target nanoscale object that subsequently enters the evanescent field of the cavity perturbs the system from its exceptional point, leading to frequency splitting. Owing to the complex-square-root topology near an exceptional point, this frequency splitting scales as the square root of the perturbation strength and is therefore larger (for sufficiently small perturbations) than the splitting observed in traditional non-exceptional-point sensing schemes. Our demonstration of exceptional-point-enhanced sensitivity paves the way for sensors with unprecedented sensitivity.

T helper 17 (Th17) cells are a subset of CD4 + T helper cells involved in the inflammatory response in autoimmunity. Th17 cells secrete Th17 specific cytokines, such as IL-17A and IL17-F, which are governed by the master transcription factor RoRγt. However, the epigenetic mechanism regulating Th17 cell function is still not fully understood. Here, we reveal that deletion of RNA 5-methylcytosine (m 5 C) methyltransferase Nsun2 in mouse CD4 + T cells specifically inhibits Th17 cell differentiation and alleviates Th17 cell-induced colitis pathogenesis. Mechanistically, RoRγt can recruit Nsun2 to chromatin regions of their targets, including Il17a and Il17f , leading to the transcription-coupled m 5 C formation and consequently enhanced mRNA stability. Our study demonstrates a m 5 C mediated cell intrinsic function in Th17 cells and suggests Nsun2 as a potential therapeutic target for autoimmune disease. Th17 cells produce a range of characteristic Th17 type cytokines and express transcription factors governed by epigenetic regulation to engage the Th17 programme. Here the authors implicate the RNA 5- methylcytosine (m 5 C) methyltransferase Nsun2 in Th17 cells and the promotion of colitis in a murine model.

Orthodontic tooth movement (OTM) is primarily driven by alveolar bone remodeling, wherein tension-induced osteogenesis plays a central role. Importin7 (IPO7) has been identified as a highly mechanoresponsive nuclear transport receptor (NTR). However, its role in regulating tension-induced osteogenesis during OTM and its underlying mechanism remains elusive. In the present study, cyclic tensile strain and a rat OTM model were used to investigate the role of IPO7. The results revealed that IPO7 was significantly expressed both in vitro and in vivo following exposure to mechanical stretch. Moreover, IPO7 knockdown inhibited tension-induced osteogenesis in BMSCs. Upon mechanical force stimulation, IPO7 translocated from the cytoplasm to the nucleus. Furthermore, immunoprecipitation (IP) coupled with mass spectrometry (MS) was performed and demonstrated that RUNX2 was one of the IPO7-interacting proteins related to osteogenesis. Although RUNX2 has been established to translocate to the nucleus to facilitate osteogenesis, whether it is IPO7 that regulate RUNX2 nuclear input during tension-induced osteogenesis remains to be determined. Then, the interaction between IPO7 and RUNX2 was further validated via co-immunoprecipitation (co-IP) and colocalization assays in BMSCs using immunofluorescence. Taken together, this study demonstrates that IPO7 promotes tension-induced osteogenesis by regulating the nucleoplasmic localization of RUNX2. Thus, targeting IPO7 may represent a prospective therapeutic strategy for enhancing alveolar bone remodeling and the efficacy of orthodontic treatment.

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

Objective This study examines how marriage‐cohabitation gaps in household specialization (labor supply and earnings) vary across institutional contexts for same‐sex couples (SSCs) and different‐sex couples (DSCs) in Canada. Background Prior research suggests that marriage‐cohabitation gaps are smaller in contexts where cohabitation is more prevalent, but it has overlooked how legal protections (at the contextual level) and gender composition (at the couple level) moderate this association. As a result, little is known about whether differences in household specialization stem from heightened gendered expectations attached to marriage or stronger legal protections for married couples. This study posits that marriage‐cohabitation gaps will be larger in contexts where legal protections for cohabitors are less marriage‐like. Methods Using the 2006 and 2016 Canadian Census and the 2011 National Household Survey, I estimate ordinal and fractional logit models to examine marriage‐cohabitation gaps in specialization among all couples (N = 2,788,055) and couples with young children (N = 826,305). Results Among DSCs, marriage‐cohabitation gaps were larger in Québec than in English Canada vis‐à‐vis earnings but not labor supply. Patterns among SSCs were more heterogeneous: gaps in labor supply were larger in English Canada for female couples but larger in Québec for male couples. Gaps in earnings were generally larger in Québec, with few exceptions. However, DSCs consistently specialized more than SSCs. Conclusion While existing research suggests marriage‐cohabitation gaps in household specialization are largely explained by the prevalence of cohabitation, my results indicate that legal protections (at the contextual level) and gender composition (at the couple level) play a more decisive role.