• Energy Research
• 2025-2025close
• Open Access close
• Closed Access close
• Open Source close
• Embargo close
• US close
• DE close
• BE close

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.

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.

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.

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.

Abstract Background By exceeding planetary environmental boundaries, multiple global crises have become imminent in the 21st century. The healthcare system is a contributor to the climate crisis, accounting for approximately 5% of greenhouse gas emissions in Western countries. In anaesthetic clinics, desflurane, a highly potent greenhouse gas and volatile anaesthetic with no compelling indications, accounts for up to two thirds of total emissions. Its use can be drastically reduced using simple measures. In the present study, we investigated whether a relevant and timely reduction in use could be achieved by dismounting desflurane vaporisers and providing information to the team without restricting its use. Methods The study was conducted in a German university hospital with approximately 1250 beds, over a 12-month period between 2021 and 2022, with a comparison to the corresponding periods of the previous years up to 2017. The interventions were, first, the removal of desflurane vaporisers, and second, staff education on the climate impact of volatile anaesthetics. The primary outcome variable was the reduction of hypnotic-related emissions in CO2 equivalents per anaesthetic procedure. Results Prospective data collection and interventions were conducted from 28 March 2021 to 27 March 2022. The amount of CO2 equivalent emissions per procedure in the form of volatile anaesthetics was reduced by 86% compared with the year before the interventions (p < 0.001). Interestingly, there was already a 52.1% reduction in the year before the procedure (p < 0.001). There were no significant changes in the use of sevoflurane or propofol. Hypnotic-related costs decreased by €14,549, whereas extubation time did not change significantly. Conclusions Removal of desflurane vaporisers and staff training can quickly and significantly reduce the emissions of an anaesthesia department in a large German teaching hospital. This may also reduce the costs. Trial registration The trial was registered with the German Clinical Trials Register, identifier DRKS00024973 on 12/04/2021.