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Due to climate change, the Northwesterner Gilgit Baltistan's, Ghizer district is highly susceptible to glacial lake outburst floods (GLOFs). Nearly 24 GLOFs have occurred in this area in the last ∼200 years, demonstrating the growing recurrent nature of these incidents. Taking this into account, the assessment of risks associated with GLOFs was investigated in this study. All regional glacial lakes were identified in the first phase, and changes between 2000 and 2023 were mapped using moderate-resolution satellite images (Landsat). To map built-up and agricultural areas, Landsat's lower resolution limited its use in such complex topography. Therefore, Sentinel-2 data were used, and images from 2016 to 2023 were classified using a random forest classifier. A total of 617 glacial lakes covering ∼31.67 km2 of the area were mapped in 2023. Since 2000, ∼88 glacial lakes have appeared, showing an increasing trend in the number of lakes. In the second phase, categorization and susceptibility to GLOFs were assessed using multicriteria decision analysis. The grass GIS tool, r.avaflow, was used to generate GLOFs simulations based on friction, density, release area, travel time, and two travel time scenarios, i.e., 1800 and 3600 s, for four high-weighted glacial lakes. Results showed that the glacial lake near Darkut village, Yaseen Valley, poses a significant threat to downstream communities. In contrast, two other lakes in Gupis Valley will have a moderate effect on the infrastructure and agriculture. The glacial lake of Punyal Valley poses no significant threat.

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.

India is anticipated to grow its total energy consumption and CO(2) emissions by more than any other country over the next two decades. India will have to attract around $400 billion in financing to realize its 500 GW target of renewable energy by 2030. Given complex renewable energy sector risks, rapidly scaling-up risk-friendly private equity financing will be critical to achieve India's target. This research seeks to answer questions regarding the motivation, perceptions, strategies, and investment behavior of private equity investors in the Indian renewable energy sector. The answers to these questions presented herein have been distilled from primary research interviews with 40 executive-level sector practitioners and literature analysis. This research finds that global-macro forces and sector-specific appeal are attracting varied investors to the sector. These investors primarily deploy capital in existing developer platforms, creating control developer platforms, and Infrastructure Investment Trusts. Critical investment criteria and value creation strategies of these investors are comprehensively discussed. Emerging investment opportunities to create new renewable energy sector value by partnering with companies across the power sector value chain are also presented. This research concludes that despite significant sector risks, investors remain confident they can achieve outsized risk-adjusted returns relative to most other global infrastructure assets. This optimism stems from the confidence of investors in their own ability to manage risk-return dynamics through judicious investment selection and management strategies, the sector's uniquely large demand growth and market size, and the Indian central government's perceived strong commitment to finding creative solutions to chronic sector issues. Lessons from the investment insights, themes and factor analyses discussed herein can be drawn upon in evaluating renewable energy investments and policymaking worldwide.

Universal access to energy is a global priority, increasingly delivered through grid-tied and off-grid infrastructure. However, energy policies frequently conflate universal access with extending and subsidising networked electricity, resulting in technology-dominated approaches. Rapid urbanisation in the global south has outstripped infrastructure capacity, where urban dwellers’ access to affordable, reliable, and sustainable forms of energy are precarious. This failure to reflect human needs and societal expectations alongside technical considerations is threatening the sustainability of urban energy transitions. This paper draws from qualitative data with low-income urban dwellers and municipal policymakers to critically examine South Africa’s energy access policies. We demonstrate how prioritising ‘electricity for all’ via grid connections fails to deliver universal access to affordable energy. First, the state’s emphasis on extending and subsidising networked electricity prioritises proximity to grid connections rather than access to energy services, and permanently excludes households living in un-serviceable structures/settlements. Second, limited community participation produces a policy that ignores low-income households’ urban practices and creates perverse incentives to distort energy consumption. We argue that delivering an urban energy transition that is economically feasible, locally appropriate and socially desirable requires policy expansion beyond physical delivery, working with targeted communities on policy development, knowledge exchange, and capacity building.