Blue Cross and Blue Shield of Kansas City

With the global adoption of electric vehicles accelerating, the need for advancements in lithium-ion battery (LIB) technology to support long driving ranges and enhanced safety is paramount. Current innovations often involve incorporating silicon into traditional LIB cells or transitioning to lithium metal cells with liquid or gel electrolytes. While these strategies aim to achieve high energy densities, they present significant safety challenges due to the flammability of the electrolytes and the potential for thermal runaway.Solid-state batteries (SSBs) offer a promising alternative by integrating non-flammable electrolytes, improving both energy density and safety. While lithium metal anodes in SSBs provide high energy density, the risk of dendrite formation and consequent cell shorting remains a critical concern. To address this, silicon-based anodes paired with solid electrolytes emerge as a safer alternative, delivering high energy density without compromising reliability.Blue Current’s research focuses on advancing SSBs with silicon anodes through three performance vectors. First, achieving the highest silicon content maximizes energy density. Second, ensuring excellent cycling performance at low operational pressures minimizes additional packaging requirements. Third, maintaining high thermal stability of battery materials ensures safety under extreme conditions. This presentation will highlight the cycling performance of SSBs featuring NMC cathodes and silicon-based anodes under low-pressure operation, showcasing their potential for high-efficiency, safer energy storage solutions.

Sustainable tourism plays a pivotal role in achieving the United Nations Sustainable Development Goals (SDGs), particularly in emerging economies such as Indonesia. This study conducts a systematic literature review (SLR) to evaluate the extent to which Indonesia’s public tourism policies align with the SDGs framework. Using the PICOC model and PRISMA guidelines, 51 peer-reviewed articles published between 2014 and 2024 were analysed. The findings reveal that while regulatory, economic, and communication instruments have been widely adopted, persistent challenges such as governance fragmentation, inadequate stakeholder engagement, technological underutilisation, and limited institutional capacity undermine their effectiveness. The study highlights partial alignment with key SDGs, notably Goals 8, 11, 12, 13, and 17, but also identifies critical gaps in policy integration and monitoring. It concludes by recommending strengthened cross-sectoral coordination, participatory policy-making, technological innovation, and capacity building as pathways toward enhancing the strategic alignment of tourism governance with sustainable development objectives in Indonesia.

For many years, unplanned equipment downtime has wreaked havoc on productivity and is an expensive way to run an operational business; yet when we look at the manufacturing of today, virtually all industries are impacted. However, conventional maintenance approaches, like reactive and preventive, do not provide an effective solution for tackling these issues. Through predictive maintenance, it is possible to anticipate equipment failures as well as optimize maintenance schedules. This study will explore the integration of predictive maintenance capabilities within the QAD Enterprise Resource Planning (ERP) system, using machine learning algorithms to predict equipment failures. Our approach helps reduce unnecessary downtime and maximizes overall equipment effectiveness using real-time data from such Internet of Things (IoT) sensors and advanced predictive analytics. The methodology includes the collection of data from IoTs, data pre-processing, feature engineering, and employing Machine Learning models for predictive maintenance. The result of these key findings is that this predicts a substantial decrease in unplanned downtime and maintenance costs, which validates the ability to include predictive maintenance in QAD ERP. Finally, the study concludes by demonstrating the potential to create more resilient and efficient manufacturing operations by combining machine learning-powered predictive maintenance with enterprise resource planning (ERP) systems.

This report evaluates a test of the Marine Growth Sampling Tool (MGST), a remotely operated vehicle (ROV)mounted system developed to collect biofouling samples from offshore energy structures.The MGST was designed as an alternative to diver-based sampling, which has become increasingly impractical in Dutch offshore wind farms (OWFs) due to stricter safety regulations and higher costs.Effective monitoring of biofouling communities on OWF turbine foundations is essential for understanding ecological impacts, such as changes in biofouling composition and nutrient cycling.The MGST was tested in November 2023 at the Hollandse Kust Zuid offshore wind farm, using the damaged E04 turbine foundation.The test aimed to assess the MGST's operational performance, including depth limitations, coating damage to turbine foundations, and sample quality.The MGST, mounted on a Saab SeaEye Tiger ROV, scraped biofouling from turbine foundations at depths between 4.5 and 20 meters.Three scraper types (metal, green plastic, and blue plastic) were tested.Over two days, the MGST successfully collected 21 biofouling samples.The tool demonstrated high precision, with only a 1% average deviation between programmed and actual sampled areas.There were no significant differences in sample quality, species richness, or abundance among the three tested scraper types, though the plastic scrapers wore down more quickly than the metal scraper.The test also evaluated the potential damage to the turbine foundation's protective coating by quantification of particles present in the biofouling samples.Noting that all samples were collected in an MGST net with a mesh size 0.5 mm, in half of the samples no coating particles were found, and in the remainder the maximum total size of coating particles found in a sample was small, at 0.005% of the sampled area.No coating damage was observed on video.This indicates that the MGST causes negligible harm to turbine coatings, making it suitable for upscaling in long-term monitoring applications.Taxonomic analysis revealed a diverse range of species dominated by amphipods and anemones, and the sample quality was found to be high, like that of diver-collected samples.A comparison with the international BISAR biofouling dataset confirmed that the MGST samples fell within expected ranges for species richness and abundance.Assessment of the impact of processing biofouling samples to higher taxonomic levels to reduce costs, showed that geographic patterns in the BISAR data which were visible at species level, became invisible already at genus level.It is recommended to process samples at the lowest possible taxonomic level possible, ideally on species level.This will increase the reusability of the biofouling data for scientific studies and to answer questions that currently have not been asked yet.We conclude that the MGST is a viable alternative to diver-based sampling, offering a safer method for monitoring biofouling communities in OWFs.Further tests are needed to evaluate the efficiency of sampling harder structures, such as mussels, as well as thicker biofouling layers.Future comparative studies between MGST and diver-collected samples are also suggested to validate the tool's broader applicability.

This study aimed to evaluate the association of mental health and substance use disorders on the risk of adverse infant outcomes overall and by race/ethnicity and payer. We used birth certificates (2017-2022; n = 125,071) linked with state-wide insurance claims (2016-2022; n = 7,583,488) to assess the risk of an adverse infant outcome (i.e., prematurity [< 37 weeks gestation] or low birthweight [< 2,500g]) associated with "any mental health" or "any substance use" disorder overall, by race/ethnicity, and by payer using diagnoses during the 9 months of pregnancy. We additionally evaluated seven specific mental health conditions and four specific substance use disorders. The rate of having an adverse infant outcome was 13.4%. Approximately 21.5% of birthing individuals had a mental health condition, and 8.7% had a substance use disorder. We found increased adjusted risk of an adverse infant outcome associated with having a mental health condition overall (aRR: 1.28; 95%CI: 1.23-1.32) and for all racial/ethnic groups and payers. We additionally found increased risk associated with substance use disorder overall (aRR: 1.32; 95%CI: 1.25-1.40) and for White, Black, privately-covered, and Medicaid-covered individuals. There was increased risk associated with six of seven mental health and three of four substance use disorders. Given the risk of adverse infant outcomes associated with mental health and substance use disorders across racial/ethnic groups and payers, our findings highlight the critical importance of policies and clinical guidelines that support early identification and treatment of a broad spectrum of mental health and substance use disorders throughout the perinatal period.

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