Guidelines for data collection and monitoring for asset management of New Zealand road bridges

Publisher PD

A risk and criticality-based approach to bridge performance data collection and monitoring

Peer reviewedPostprin

Detection and characterisation of pollutant assets with AI and EO to prioritise green investments: the geoasset framework

Detailed and complete data on physical assets are required in order to adequately assess environment-related risk and impact exposure and the diffusion of these risks and impacts through the financial system. Investors need to know where the physical assets (e.g., power plant, factory, farm) are located of companies in their portfolios, and what their polluting characteristics are. This is essential to manage these environment-related risks and to channel investments to more sustainable alternatives. At present, data on physical assets is typically incomplete, inaccurate, or not released in a timely manner. As a result, key stakeholders including asset owners, asset managers, regulators and policymakers are frequently forced to make crucial decisions with incomplete information. Accurate and comprehensive global asset-level databases are a prerequisite for meaningful innovation in green and digital finance. They provide the link between the financial system and the “real economy” and allows the wealth of EO datasets and insights that we have available to be made actionable for sustainable finance decision making. We created a framework to derive a global database of pollutant plants, such as cement, iron, and steel, which represent about 15% of the global CO2 emissions. Our solution makes use of state-of-the-art deep learning architectures coupled with Earth observation data

Global database of cement production assets and upstream suppliers.

Acknowledgements: This work was supported by the Children’s Investment Fund Foundation (CIFF).Funder: Children's Investment Fund Foundation (CIFF); doi: https://doi.org/10.13039/100010409Funder: Children's Investment Fund Foundation (CIFF)Cement producers and their investors are navigating evolving risks and opportunities as the sector's climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company's greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics

Global database of cement production assets and upstream suppliers

Acknowledgements: This work was supported by the Children’s Investment Fund Foundation (CIFF).Funder: Children's Investment Fund Foundation (CIFF); doi: https://doi.org/10.13039/100010409Funder: Children's Investment Fund Foundation (CIFF)Cement producers and their investors are navigating evolving risks and opportunities as the sector’s climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company’s greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics

Global database of cement production assets and upstream suppliers

Abstract Cement producers and their investors are navigating evolving risks and opportunities as the sector’s climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company’s greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics

Dose-intensive response-based chemotherapy and radiation therapy for children and adolescents with newly diagnosed intermediate-risk hodgkin lymphoma: a report from the Children\u27s Oncology Group Study AHOD0031

PURPOSE: The Children\u27s Oncology Group study AHOD0031, a randomized phase III study, was designed to evaluate the role of early chemotherapy response in tailoring subsequent therapy in pediatric intermediate-risk Hodgkin lymphoma. To avoid treatment-associated risks that compromise long-term health and to maintain high cure rates, dose-intensive chemotherapy with limited cumulative doses was used. PATIENTS AND METHODS: Patients received two cycles of doxorubicin, bleomycin, vincristine, etoposide, cyclophosphamide, and prednisone (ABVE-PC) followed by response evaluation. Rapid early responders (RERs) received two additional ABVE-PC cycles, followed by complete response (CR) evaluation. RERs with CR were randomly assigned to involved-field radiotherapy (IFRT) or no additional therapy; RERs with less than CR were nonrandomly assigned to IFRT. Slow early responders (SERs) were randomly assigned to receive two additional ABVE-PC cycles with or without two cycles of dexamethasone, etoposide, cisplatin, and cytarabine (DECA). All SERs were assigned to receive IFRT. RESULTS: Among 1,712 eligible patients, 4-year event-free survival (EFS) was 85.0%: 86.9% for RERs and 77.4% for SERs (P \u3c .001). Four-year overall survival was 97.8%: 98.5% for RERs and 95.3% for SERs (P \u3c .001). Four-year EFS was 87.9% versus 84.3% (P = .11) for RERs with CR who were randomly assigned to IFRT versus no IFRT, and 86.7% versus 87.3% (P = .87) for RERs with positron emission tomography (PET) -negative results at response assessment. Four-year EFS was 79.3% versus 75.2% (P = .11) for SERs who were randomly assigned to DECA versus no DECA, and 70.7% versus 54.6% (P = .05) for SERs with PET-positive results at response assessment. CONCLUSION: This trial demonstrated that early response assessment supported therapeutic titration (omitting radiotherapy in RERs with CR; augmenting chemotherapy in SERs with PET-positive disease). Strategies directed toward improved response assessment and risk stratification may enhance tailoring of treatment to patient characteristics and response