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The system of disclosure for public companies no longer meets the needs of investors and other stakeholders. Largely put in place by the Securities and Exchange Commission in 1982, the principles underlying the system have failed to keep pace with shifts in the market and dramatic changes in technology. The system requires a paradigm shift and fundamental alterations in the principles underlying the approach to disclosure. The shift must include the integration of comparative data, the expansion of the categories subject to mandatory disclosure, and the disaggregation of financial statements. Failure to update the system of disclosure will result in investors increasingly relying on sources of information outside of the periodic reporting process, reducing the importance of required disclosure and the role of the Securities and Exchange Commission.

The power transformer is a critical primary device in the power grid, and the verification of its winding mechanical stability is of paramount importance in ensuring the safe and stable operation of the power grid. In the conventional numerical calculation methods for verifying the mechanical stability of power transformer windings, the influence of temperature variations at the winding hot spots on winding mechanical stability has not been taken into account. In reality, factors such as the transformer’s operating load rate, ambient temperature, and the duration of short-circuit fault currents passing through will affect the mechanical stability margin of the transformer windings. Under conditions such as winding aging, deformation, or other reasons, the transformer windings may become unstable due to material parameter degradation, leading to insufficient mechanical stability margin. This paper analyzes the mechanical stability of power transformer windings considering the impact of the temperature field. Initially, a numerical model for calculating short-circuit currents in transformers was established to compute the short-circuit current under three-phase short-circuit-to-ground conditions as an excitation. Subsequently, a 3D electromagnetic force finite element calculation model was developed to determine the electromagnetic forces experienced under this condition. The results of the calculated electromagnetic forces were then used in a numerical calculation method to assess the mechanical stability of the windings. Furthermore, a 3D transformer electromagnetic–thermal flow finite element model was created to calculate the steady-state temperature rise under various operating conditions of the transformer. This model is validated through transformer temperature rise tests, and transient temperature rises under different operating conditions are calculated. The obtained data are fitted using the nonlinear least squares method to derive a fitting function for the winding hot spot temperature concerning load rate, ambient temperature, and short-circuit time. Taking into consideration the influence of temperature on the yield strength and modulus of elasticity of transformer winding materials, the variation in mechanical stability margin of transformer windings due to temperature effects is analyzed. Additionally, the operating domain for preventing the transformer from becoming unstable under three-phase short-circuit impacts is calculated for different degrees of material parameter degradation. This method provides an effective reference for transformer design and operation, demonstrating clear practical value.

Abstract The Primordial Inflation Explorer (PIXIE) is an Explorer-class mission concept to measure the energy spectrum and linear polarization of the cosmic microwave background (CMB). A single cryogenic Fourier transform spectrometer compares the sky to an external blackbody calibration target, measuring the Stokes I, Q, U parameters to levels ∼200 Jy/sr in each 2.65° diameter beam over the full sky, in each of 300 frequency channels from 28 GHz to 6 THz. With sensitivity over 1000 times greater than COBE/FIRAS, PIXIE opens a broad discovery space for the origin, contents, and evolution of the universe. Measurements of small distortions from a CMB blackbody spectrum provide a robust determination of the mean electron pressure and temperature in the universe while constraining processes including dissipation of primordial density perturbations, black holes, and the decay or annihilation of dark matter. Full-sky maps of linear polarization measure the optical depth to reionization at nearly the cosmic variance limit and constrain models of primordial inflation. Spectra with sub-percent absolute calibration spanning microwave to far-IR wavelengths provide a legacy data set for analyses including line intensity mapping of extragalactic emission and the cosmic infrared background amplitude and anisotropy. We describe the PIXIE instrument sensitivity, foreground subtraction, and anticipated science return from both the baseline 2-year mission and a potential extended mission.

Industrial heat sources (IHSs) are major contributors to energy consumption and environmental pollution, making their accurate detection crucial for supporting industrial restructuring and emission reduction strategies. However, existing models either focus on single-class detection under complex backgrounds or handle multiclass tasks for simple targets, leaving a gap in effective multiclass detection for complex scenarios. To address this, we propose a novel multiclass IHS detection model based on the YOLOv8-FC framework, underpinned by the multiclass IHS training dataset constructed from optical remote sensing images and point-of-interest (POI) data firstly. This dataset incorporates five categories: cement plants, coke plants, coal mining areas, oil and gas refineries, and steel plants. The proposed YOLOv8-FC model integrates the FasterNet backbone and a Coordinate Attention (CA) module, significantly enhancing feature extraction, detection precision, and operational speed. Experimental results demonstrate the model’s robust performance, achieving a precision rate of 92.3% and a recall rate of 95.6% in detecting IHS objects across diverse backgrounds. When applied in the Beijing–Tianjin–Hebei (BTH) region, YOLOv8-FC successfully identified 429 IHS objects, with detailed category-specific results providing valuable insights into industrial distribution. It shows that our proposed multiclass IHS detection model with the novel YOLOv8-FC approach could effectively and simultaneously detect IHS categories under complex backgrounds. The IHS datasets derived from the BTH region can support regional industrial restructuring and optimization schemes.

Google Earth Engine (GEE) is a cloud-based platform revolutionizing geospatial analysis by providing access to vast satellite datasets and computational capabilities for monitoring environmental and societal issues. It incorporates machine learning (ML) techniques and algorithms as part of its tools for analyzing and processing large geospatial data. This review explores the diverse applications of GEE in monitoring and mitigating greenhouse gas emissions and uptakes. GEE is a cloud-based platform built on Google’s infrastructure for analyzing and visualizing large-scale geospatial datasets. It offers large datasets for monitoring greenhouse gas (GHG) emissions and understanding their environmental impact. By leveraging GEE’s capabilities, researchers have developed tools and algorithms to analyze remotely sensed data and accurately quantify GHG emissions and uptakes. This review examines progress and trends in GEE applications, focusing on monitoring carbon dioxide (CO2), methane (CH4), and nitrous oxide/nitrogen dioxide (N2O/NO2) emissions. It discusses the integration of GEE with different machine learning methods and the challenges and opportunities in optimizing algorithms and ensuring data interoperability. Furthermore, it highlights GEE’s role in pinpointing emission hotspots, as demonstrated in studies monitoring uptakes. By providing insights into GEE’s capabilities for precise monitoring and mapping of GHGs, this review aims to advance environmental research and decision-making processes in mitigating climate change.

With advancements in cable manufacturing processes, the physical parameters of certain cable conductors fall outside of the scope specified by the IEC60287-1-1 standard, and the alternating current (AC) resistance calculated using the IEC standard may lead to reliability issues in the thermal evaluation of cable lines. Therefore, conducting an AC resistance test on cable conductors becomes critical for the thermal evaluation of cable lines. The source of error in the existing AC resistance test was analyzed first. It was found that the characteristics of the source used in the test lead to an error between the test value and the actual value of AC resistance. Moreover, an optimized AC resistance testing method based on active power was proposed to decrease the error. The accuracy of the method was also demonstrated. Finally, AC resistance tests were conducted on cable conductors with different cross-sectional areas, segmental methods, and oxidation methods by using the proposed method. The test results are also thoroughly discussed.

Trace amounts of consumed alcohol are detectable within sweat and insensible perspiration. However, the relationship between ingested and transdermally emitted alcohol is complex, varying across environmental conditions and involving a degree of lag. As such, the feasibility of real-time drinking detection across diverse environments has been unclear. In the current research we revisit sensor performance using new tools, exploring the accuracy of a new generation of rapid-sampling transdermal biosensor for contemporaneous drinking detection across diverse environments via machine learning.Regular drinkers (N = 100) attended three laboratory sessions involving the experimental manipulation of alcohol dose, rate of consumption, and environmental dosing conditions. Participants further supplied breath alcohol concentration (BAC) readings in the field over 14 days. Participants wore compact wrist sensors capable of rapid sampling (20sec intervals). Transdermal sensor data was translated into alcohol use estimates using machine learning, integrating only transdermal data collected prior to the point of BAC assessment.A total of 5.39 million transdermal readings (28,615hours) and 12,699 BAC readings were collected for this research. Models indicated strong transdermal sensor accuracy for real-time drinking detection across both laboratory and field contexts (AUROC, 0.966, 95 % CI, 0.956-0.972; Sensitivity, 89.8 %; Specificity, 90.6 %). Models aimed at differentiating high-risk (≥0.08 %) drinking levels yielded intermediate (AUROC, 0.738; 95 % CI, 0.698-0.777; only drinking episodes) to strong (AUROC, 0.941, 95 % CI, 0.929-0.954; all data) accuracy levels.Results indicate a range of useful future applications for transdermal alcohol sensors including long-term health tracking, medical monitoring, and just-in-time relapse prevention.

ABSTRACTCarbon‐interstitial compounds of precious metal alloys (Ci‐PMA) have attracted increased attention as effective catalytic materials, but their precise and controllable synthesis remains significant challenges. Herein, we have established a universal approach for the straightforward synthesis of supported Ci‐platinum group metal‐indium alloys (M3InCx, M = Pt, Pd, Ni, x = 0.5 or 1). The control experiment results indicate that the C atoms in Pt3InC0.5 come from the solvent. Furthermore, 0.2 wt.% Pt3InC0.5/SiO2 exhibits excellent catalytic performance for aqueous phase reforming (APR) of methanol (CH3OH) to produce hydrogen, with productivity and turnover frequency of 310.0 −1mol·kgcat·h−1 and 30 126 h−1 at 200°C, which are 1.7 times greater than those of Pt3In/SiO2. The infrared results of CH3OH adsorption reveal that the substantially better performance for APR of CH3OH of Pt3InC0.5/SiO2 than Pt3In/SiO2 is due to its significantly enhanced CH bond dissociation ability. This study not only provides a straightforward and universal approach for the controlled synthesis of Ci‐PMA but also stimulates fundamental research into Ci‐PMA for catalysis and other applications.