Evaluation and Assessment of the ABATE Framework to Enhance Implicit Bias Training for Virtual Interviews in Medical Schools.

Introduction: The influence of implicit biases in virtual interviews must be addressed to ensure equity within the admissions process. ABATE is a mnemonic framework of five specific categories of implicit bias (affinity-based, backdrop-based, appearance-based, technology and media-based, and enunciation-based biases) that should be anticipated and mitigated for faculty, staff, health professionals, and medical students who conduct virtual interviews at medical schools. Methods: A 60-minute workshop was developed to educate medical school admissions interviewers about the ABATE model and strategies to mitigate implicit bias during virtual interviews. Four workshops were held over 1 year totaling 217 individual attendees. The workshops were evaluated using a single-group, pre-post questionnaire designed with the Kirkpatrick evaluation model. Results: Attendees reported that they found the ABATE workshop useful and relevant to improving their ability to minimize implicit bias during virtual interviews. Significant improvements were found in attendee reactions to the utility of implicit bias training (M pre = 2.6, M post = 3.1, p = .002). Significant changes were also reported in attendees' attitudes about interviewing confidence (M pre = 3.0, M post = 3.2, p = .04), bias awareness (M pre = 3.0, M post = 3.4, p = .002), and identifying and applying bias mitigation solutions (M pre = 2.5, M post = 3.0, p = .003). Knowledge specific to backdrop-based biases also significantly increased (M pre = 3.2, M post = 3.4, p = .04). Discussion: The ABATE workshop demonstrates promise in mitigating implicit bias in virtual medical school interviews.

Epidemiological evolution and economic impact of the COVID-19 pandemic in the European Union and worldwide and effects of control strategies on them: An ecological study.

OBJECTIVE: To evaluate the epidemiological evolution and economic impact of COVID-19 pandemic in the European Union (EU) and worldwide, and the effects of control strategies on them. MATERIAL AND METHODS: We collected incidence, mortality, and gross domestic product (GDP) data between the first quarter of 2020 and of 2023. Then, we reviewed the effectiveness of the mitigation and zero-COVID control strategies. The statistical analysis was done calculating the incidence rate ratio (IRR) of two rates and its 95% confidence interval (CI). RESULTS: In the EU, COVID-19 presented six epidemic waves. The sixth one at the beginning of 2022 was the biggest. Globally, the biggest wave occurred at the beginning of 2023. Highest mortality rates were observed in the EU during 2020-2021 and globally at the beginning of 2021. In mitigation countries, mortality was much higher than in zero-COVID countries (IRR=6.82 [95% CI: 6.14-7.60]; p<0.001). A GDP reduction was observed worldwide, except in Asia. None of the eight zero-COVID countries presented a GDP growth percentage lower than the EU percentage in 2020, and 3/8 in 2022 (p=0.054). COVID-19 pandemic caused epidemic waves with high mortality rates and a negative impact on GDP. CONCLUSION: The zero-COVID strategy was more effective in avoiding mortality and potentially had a lower impact on GDP in the first pandemic year.

Green production of apples delivers environmental and economic benefits in China.

Sustainable alternative farming systems are gaining popularity worldwide because of the negative effects of conventional agriculture on global climate change and the environmental degradation caused by intensive use of synthetic inputs. The green farming system in China is an integrated production strategy that focuses on reducing chemical fertilizer use while increasing organic manure inputs. Despite their rapid growth as more sustainable systems over the past decades, green farming systems have not been systematically evaluated to date. We used apple production as a representative case to assess the sustainability of green farming systems. Across major apple-producing regions in China, green farming reduced the application of chemical fertilizer nitrogen (N) by 46.8% (from 412 to 219 kg ha-1) and increased that of manure N by 33.1% (from 171 to 227 kg ha-1) on average compared with conventional systems enhancing N use efficiency by 7.27-20.27% and reducing N losses by 8.92%-11.56%. It also slightly lowered yield by 4.34%-13.8% in four provinces. Soil fertility was improved in green orchards through increases in soil organic matter, total N, and available major nutrients. Our cradle-to-farm-gate life-cycle assessment revealed that green farming helped to mitigate greenhouse gas emissions by an average of 12.6%, potentially contributing to a reduction of 165 239 t CO2 eq annually in major apple-producing areas. In addition, green farming achieved 39.3% higher profitability ($7180 ha-1 year-1) at the farmer level. Our study demonstrates the potential of green production of apples for the development of sustainable agriculture in China. These findings advance our understanding of sustainable alternative farming systems and offer perspectives for the sustainable development of global agriculture.

Monitoring and assessment of CO2 and NO2 in schools within the Sentinel Schools Network of Catalonia during the COVID-19 era.

In response to the global impact of the COVID-19 pandemic, international and national authorities, including those in Catalonia (Spain), recognized the crucial need to ensure proper ventilation in classrooms, emphasizing the importance of safe and healthy indoor environments for face-to-face learning. The present work, conducted within the COVID-19 Sentinel Schools Network of Catalonia (CSSNC) framework, aimed to monitor carbon dioxide (CO2) and nitrogen dioxide (NO2) concentrations in 23 schools, ensuring a comprehensive sample regarding educational levels, daily scholar schedules, and classroom typologies distributed across the four provinces of Catalonia. The research spanned three study periods: March and April 2021, October 2021 to January 2022, and March to June 2022. Briefly, 28%, 25%, and 37% of classrooms surpassed the 700 parts per million (ppm) CO2 limit in each study period, respectively. Generally, CO2 averages were lower in preschool classrooms (mean ± SD = 486 ± 106 ppm), while high school classrooms displayed the highest CO2 concentrations (mean ± SD = 710 ± 253 ppm). Moreover, classrooms in towns (<30000 inhabitants) exhibited higher CO2 levels as compared to classrooms from schools located in cities. As for NO2, the highest averages were obtained in urban areas, particularly in the Barcelona metropolitan area (e.g. mean indoor levels of 24.56 µg m-3 as compared to 11.05 µg m-3 in towns). In addition, the Indoor/Outdoor ratio (I/O ratio) in towns was the lowest (0.60). These results, together with the higher concentration of CO2 indoors, could indicate poorer ventilation in town schools. The results of this study are anticipated to contribute to implementing evidence-based measures to improve indoor air quality (IAQ) in educational settings.

Cost-effectiveness of erosion mitigation to meet water clarity targets in the Manawatu-Whanganui Region of New Zealand.

Soil erosion is a significant environmental issue worldwide. It affects water quality, biodiversity, and land productivity. New Zealand government agencies and regional councils work to mitigate soil erosion through policies, management programmes, and funding for soil conservation projects. Information about cost-effectiveness is crucial for planning, targeting, and implementing erosion mitigation to achieve improvements in sediment-related water quality. While there is a good understanding of the costs of erosion mitigation measures, there is a dearth of literature on their cost-effectiveness in reducing sediment loads and improving water quality at the catchment level. In this study, we estimate the cost-effectiveness of erosion mitigation measures in meeting visual water clarity targets. The analysis utilizes the spatially explicit SedNetNZ erosion process and sediment budget modelling in the Manawatu-Whanganui Region and region-specific mitigation costs. The erosion mitigation measures considered in the analysis include afforestation, bush retirement, riparian retirement, space-planted trees, and gully tree planting. We modelled two scenarios with on-farm erosion mitigation implemented across the region from 2021 to 2100, resulting in a 48% and 60% reduction of total sediment load. We estimate the marginal costs to achieve the visual national bottom line for water clarity, as assessed by the length of waterways that meet the clarity targets. We also estimate the marginal costs of improving average water clarity, which can be linked with non-market valuation studies when conducting a cost-benefit analysis. We find that gully tree planting and space-planted trees are the most cost-effective mitigation measures and that riparian retirement is the least cost-effective. Moreover, cost-effectiveness is highly dependent on current land use and the biophysical features of the landscape. Our estimates can be used in cost-benefit analysis to plan and prioritize soil erosion mitigation at the catchment and regional levels.

Health systems challenges, mitigation strategies and adaptations to maintain essential health services during the COVID-19 pandemic: learnings from the six geopolitical regions in Nigeria.

BACKGROUND: The COVID-19 pandemic control strategies disrupted the smooth delivery of essential health services (EHS) globally. Limited evidence exists on the health systems lens approach to analyzing the challenges encountered in maintaining EHS during the COVID-19 pandemic. This study aimed to identify the health system challenges encountered and document the mitigation strategies and adaptations made across geopolitical zones (GPZs) in Nigeria. METHODS: The national qualitative survey of key actors across the six GPZs in Nigeria involved ten states and the Federal Capital Territory (FCT) which were selected based on resilience, COVID-19 burden and security considerations. A pre-tested key informant guide was used to collect data on service utilization, changes in service utilization, reasons for changes in primary health centres' (PHCs) service volumes, challenges experienced by health facilities in maintaining EHS, mitigation strategies implemented and adaptations to service delivery. Emerging sub-themes were categorized under the appropriate pillars of the health system. RESULTS: A total of 22 respondents were interviewed. The challenges experienced in maintaining EHS cut across the pillars of the health systems including: Human resources shortage, shortages in the supply of personal protective equipments, fear of contracting COVID-19 among health workers misconception, ignorance, socio-cultural issues, lockdown/transportation and lack of equipment/waiting area (. The mitigation strategies included improved political will to fund health service projects, leading to improved accessibility, affordability, and supply of consumables. The health workforce was motivated by employing, redeploying, training, and incentivizing. Service delivery was reorganized by rescheduling appointments and prioritizing some EHS such as maternal and childcare. Sustainable systems adaptations included IPC and telehealth infrastructure, training and capacity building, virtual meetings and community groups set up for sensitization and engagement. CONCLUSION: The mitigation strategies and adaptations implemented were important contributors to EHS recovery especially in the high resilience LGAs and have implications for future epidemic preparedness plans.

How public environmental appeals affect the collaborative governance in pollution and carbon reduction: Evidence from spatial effects across Chinese cities.

China has always adhered to the strategy of sustainable development. It is prevalent the public want a good living environment, which requires local governments and businesses to enhance their environmental governance capabilities. Using the panel data from Chinese cities from 2012 to 2019 and econometrics models, we examine the impact mechanisms of public environmental appeals (PEA) on efficiency of collaborative governance in pollution reduction and carbon mitigation (GPC). Results indicate that there is a positive spatial clustering of GPC across cities, with high-high clustering is notably concentrated in the southern regions of China and low-low clustering is prevalent in the northern regions. Spatial econometrics model results reveal that the stronger PEA, the higher GPC. The result of mechanism analysis shows the mediation of environmentally friendly technological innovation is crucial. Subsequent inquiry uncovers that the digital economy positively moderates the impact of PEA on GPC. The Belt and Road policy region exhibits heightened sensitivity to PEA, thereby enhancing the positive impact of PEA on GPC.

Mitigating glyphosate levels in surface waters: Long-term assessment in an agricultural catchment in Belgium.

The increasing concern over pesticide pollution in water bodies underscores the need for effective mitigation strategies to support the transition towards sustainable agriculture. This study assesses the effectiveness of landscape mitigation strategies, specifically vegetative buffer strips, in reducing glyphosate loads at the catchment scale under realistic conditions. Conducted over six years (2014-2019) in a small agricultural region in Belgium, our research involved the analysis of 732 water samples from two monitoring stations, differentiated by baseflow and event-driven sampling, and before (baseline) and after the implementation of mitigation measures. The results indicated a decline in both the number and intensity of point source losses over the years. Additionally, there was a general decrease in load intensity; however, the confluence of varying weather conditions (notably dry years during the mitigation period) and management practices (the introduction of buffer strips) posed challenges for a statistically robust evaluation of each contributing factor. A reduction of loads was measured when comparing mitigation with baseline, although this reduction is not statistically significant. Glyphosate loads during rainfall events correlated with a rainfall index and runoff ratio. Overall, focusing the mitigation strategy on runoff and erosion was a valid approach. Nevertheless, challenges remain, as evidenced by the continuous presence of glyphosate in baseflow conditions, highlighting the complex dynamics of pesticide transport. The study concludes that while progress has been made towards reducing pesticide pollution, the complexity of interacting factors necessitates further research. Future directions should focus on enhancing farmer engagement in mitigation programs and developing experiments with more intense data collection that help to assess underlying dynamics of pesticide pollution and the impact of mitigation strategies in more detail, contributing towards the goal of reducing pesticide pollution in water bodies.

Improving Equity in Deep Learning Medical Applications with the Gerchberg-Saxton Algorithm.

Deep learning (DL) has gained prominence in healthcare for its ability to facilitate early diagnosis, treatment identification with associated prognosis, and varying patient outcome predictions. However, because of highly variable medical practices and unsystematic data collection approaches, DL can unfortunately exacerbate biases and distort estimates. For example, the presence of sampling bias poses a significant challenge to the efficacy and generalizability of any statistical model. Even with DL approaches, selection bias can lead to inconsistent, suboptimal, or inaccurate model results, especially for underrepresented populations. Therefore, without addressing bias, wider implementation of DL approaches can potentially cause unintended harm. In this paper, we studied a novel method for bias reduction that leverages the frequency domain transformation via the Gerchberg-Saxton and corresponding impact on the outcome from a racio-ethnic bias perspective.

Exploring the climate change mitigation potential and regional distribution of agrivoltaics with geodata-based farm economic modelling and life cycle assessment.

Tackling climate change remains a critical challenge for society. Achieving climate neutrality requires a massive expansion of renewable energies such as wind and photovoltaics (PV). Agriculture plays a key role in this context, especially as the expansion of ground-mounted PV systems often leads to land-use conflicts. Agrivoltaics (AV), which combines agricultural and electricity production, can be a solution, but the synergies are particularly dependent on local agronomic conditions. There is also a knowledge gap in how AV expansion impacts greenhouse gas (GHG) emissions at the landscape level and how it contributes to regional emission reduction targets. In this study, we analysed the economic and climate change mitigation impacts of AV expansion pathways in the German state Baden-Württemberg using an integrated land use model and life cycle assessment under the assumption of general rentability of electricity production by AV. We found that implementing AV on 1%-5% of the regions's arable and grassland area reduced the total agricultural gross margin by a maximum of approximately 0.5%. Concurrently, AV implementation reduced GHG emissions by about 1.2 million to 5.9 million metric tons of CO2 equivalent (Mt CO2-eq). Even if this reduction is almost exclusively accounted for in the energy sector, in absolute terms it amounts to more than the current GHG emissions from Baden-Württemberg's agricultural sector (about 4.4 Mt CO2-eq in 2021). In the 5% expansion scenario, almost 90% of the installations were installed on grassland, but this share dropped to 72% when considering landscape quality constraints. Although we found considerable regional disparity, our findings still suggest that AV is an essential component for regional emission reduction targets. These results are particularly relevant for policymakers in spatial planning, agricultural and energy policy.

Cost, market, and policy constraints on mitigating climate change through afforestation in China.

Afforestation is a promising nature-based climate solution for mitigating climate change, but it is subject to a complex web of biophysical, cost-benefit, market, and policy processes. Although its biophysical feasibility has been established, the cost, market, and policy constraints that affect climate change mitigation through afforestation are still unclear. Here, we estimate such cost, market, and policy constraints on the basis of biophysical feasibility. Our findings reveal that implementation costs are a more relevant constraint than opportunity costs on mitigating climate change through afforestation. The China Certified Emission Reduction market currently provides only a 0.308 % incentive for climate change mitigation through afforestation, due to market access constraints. The current market prices of China Certified Emission Reduction, China Carbon Emissions Trading Exchange, and Nature Based Carbon Offset in Voluntary Carbon Market constrain 88.15 %, 87.95 %, and 85.75 % of CO2 removal actions through afforestation, compared to the carbon price scenario (US$62.97 tCO2-1) of the EU Emissions Trading System. Moreover, land policy under the scenarios of prohibiting conversion of cultivated land to forest and forest restoration in degraded areas exhibit 8.87-29.59 % and 65.16-74.10 % constraints, respectively, on mitigating climate change through afforestation compared to land-use freedom conversion scenarios from 2020 to 2060. Thus, enhancing the incentive price of CO2 removal, addressing the market access barrier, strengthening cooperation between global carbon markets, and exploring carbon-neutral and food multi-oriented land policies can be valuable sources of mitigation efforts over the next 40 years.

Assessment of seasonal variations in particulate matter accumulation and elemental composition in urban tree species.

Outdoor air pollution in urban areas, especially particulate matter (PM), is harmful to human health. Urban trees and shrubs provide crucial ecosystem services such as air pollution mitigation by acting as natural filters. However, urban greenery comprises a particular biodiversity, and different plant species vary in their capacity to accumulate PM. Twenty-two plant species were analyzed and selected according to their leaf traits, the different fractions of PM accumulated on the leaves (large - PML, coarse - PMC, and fine - PMF) and their chemical composition. The study was conducted in four city zones: urban traffic (UT), urban background (UB), industrial (IND), and rural (RUR), comparing winter (W) and summer (S) seasons. The average PM levels in the air and accumulated on the leaves were higher in W than in S season. During both seasons, the highest PM accumulated on the leaves was recorded at the UT zone. Nine species were selected as the most suitable for accumulating PML, seven as the most efficient for accumulating PMC, and six for accumulating PMF. The leaf area and leaf roundness were correlated negatively with PM accumulation. The evergreen species L. nobilis was indicated as suitable for dealing with air pollution based on PM10 and PM2.5 values recorded in the air. Regarding the PM element and metal composition, L. nobilis, Photinia x fraseri, Olea europaea, Quercus ilex and Nerium oleander were selected as species with notable elements and metal accumulation. In summary, the study identified species with higher PM accumulation capacity and assessed the seasonal PM accumulation patterns in different city zones, providing insights into the species interactions with PM and their potential for monitoring and coping with air pollution.

Wasted Food, Wasted Resources? A Critical Review of Environmental Impact Analysis of Food Loss and Waste Generation and Treatment.

Food loss and waste (FLW) comes with significant environmental impacts and thus prevents a sustainable food system transition. Here we conducted a systematic review of 174 screened studies that assessed the environmental impacts of FLW generation and treatment. We found that the embodied impacts of FLW along the supply chain and impacts from FLW treatment received equal attention, but few studies have included both. The reviewed studies show narrow geographical (mostly conducted in industrialized countries) and food supply chain (mostly focused on the consumption stage) coverage. Life cycle analysis (LCA), material flow analysis (MFA), or their combination are the most commonly used to quantify FLW related environmental impacts. More method standardization, integration, and innovation and better FLW data with regional and stage resolution from a first-hand source are badly needed. Among the various proposed mitigation strategies covering technology, economy, behavior, and policy aspects, process optimization and waste management options are the most discussed. Our review calls for a more holistic environmental impact assessment of FLW generation and treatment and analysis of the trade-offs among different environmental impact categories and between supply chain stages, which would better inform relevant policy on effective environmental impact mitigation strategies toward sustainable food systems.

Nature-based solutions potential for flood risk reduction under extreme rainfall events.

Climate change will substantially increase extreme rainfall events, especially in the Tropics, enhancing flood risks. Such imminent risks require climate adaptation strategies to endure extreme rainfall and increase drainage systems. Here, we evaluate the potential of nature-based solutions by using an ecosystem service modeling approach, evaluating the impact of extreme rainfall events on flood risks in a large urban area and with a real-world land recovery plan. We evaluate the cost-effectiveness of four different land recovery scenarios and associated co-benefits, based on a gradient increase in area recovered and cost of implementation. Although the scenarios reveal increasing flood risk reduction and co-benefits along with greater proportion of land recovery, the most cost-effective scenario was the one with an intermediate land recovery where 30% of the study area would be reforested. We emphasize the striking benefits of nature-based solutions for flood risk reduction in cities, considering landscape scale and stakeholders' needs.

Consideration of Climate Change on Environmental Impact Assessment in Tanzania: Challenges and Prospects.

The potential of the environmental impact assessment (EIA) process to respond to climate change impacts of development projects can only be realized with the support of policies, regulations, and actors' engagement. While considering climate change in EIA has become partly mandatory through the EU revised Directive in Europe, African countries are still lagging. This paper assesses Tanzanian policies, laws, regulations, and EIA reports to uncover consideration of climate change impacts, adaptation, and mitigation measures, drawing from the transformational role of EIA. The methodology integrates content analysis, interpretive policy analysis, and discourse analysis. The analyses draw from environmental policy, three regulatory documents and three EIA reports in Tanzania using a multi-cases study design. The aim was to understand how considering Climate Change issues in EIA has played out in practice. Results reveal less consideration of climate change issues in EIA. The policy, laws, and regulations do not guide when and how the EIA process should consider climate change-related impacts mitigation and adaptation. The practice of EIA in the country is utterly procedural in line with regulations provisions. Consequently, environmental impact statements only profile the climatology of the study area without conducting a deeper analysis of the historical and future climate to enhance the resilience of proposed projects. The weakness exposed in the laws and regulations contributes to the challenges of responding to the impacts of climate change through the EIA process. It is possible to address climate change issues throughout the project life cycle, including design, approval, implementation, monitoring, and auditing, provided the policy and regulations guide how and when the EIA process should consider climate change issues. Additionally, increasing stakeholders' awareness and participation can enhance the EIA process's potential to respond to the impacts of climate change.

Mitigating air pollution benefits multiple sustainable development goals in China.

Achieving the United nations 2030 Sustainable Development Goals (SDGs) remains a significant challenge, necessitating urgent and prioritized strategies. Among the various challenges, air pollution continues to pose one of the most substantial threats to the SDGs due to its widespread adverse effects on human health and ecosystems. However, the connections between air pollution and the SDGs have often been overlooked. This study reveals that out of the 169 SDG targets, 71 are adversely impacted by air pollution, while only 6 show potential positive effects. In China, two major atmospheric nitrogen pollutants, ammonia and nitrogen oxides, resulted in an economic loss of 400 billion United States Dollar (USD) in 2020, which could be reduced by 33% and 34% by 2030, respectively. It would enhance the progress towards SDGs in China by 14%, directly contributing to the achievement of SDGs 1 to 6 and 11 to 15. This improvement is estimated to yield overall benefits totaling 119 billion USD, exceeded the total implementation cost of 82 billion USD with ammonia as the preferential mitigation target. This study underscores the importance of robust scientific evidence in integrated policies aimed at aligning improvements in environmental quality with the priorities of sustainable development.

High lipoprotein(a): Actionable strategies for risk assessment and mitigation.

High levels of lipoprotein(a) [Lp(a)] are causal for atherosclerotic cardiovascular disease (ASCVD). Lp(a) is the most prevalent inherited dyslipidemia and strongest genetic ASCVD risk factor. This risk persists in the presence of at target, guideline-recommended, LDL-C levels and adherence to lifestyle modifications. Epidemiological and genetic evidence supporting its causal role in ASCVD and calcific aortic stenosis continues to accumulate, although various facets regarding Lp(a) biology (genetics, pathophysiology, and expression across race/ethnic groups) are not yet fully understood. The evolving nature of clinical guidelines and consensus statements recommending universal measurements of Lp(a) and the scientific data supporting its role in multiple disease states reinforce the clinical merit to start population screening for Lp(a) now. There is a current gap in the implementation of recommendations for primary and secondary cardiovascular disease (CVD) prevention in those with high Lp(a), in part due to a lack of protocols for management strategies. Importantly, targeted apolipoprotein(a) [apo(a)]-lowering therapies that reduce Lp(a) levels in patients with high Lp(a) are in phase 3 clinical development. This review focuses on the identification and clinical management of patients with high Lp(a). Specifically, we highlight the clinical value of measuring Lp(a) and its use in determining Lp(a)-associated CVD risk by providing actionable guidance, based on scientific knowledge, that can be utilized now to mitigate risk caused by high Lp(a).

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

WITHDRAWN: A preliminary assessment on spatial prioritization for integrated urban flood management based on source tracking method.

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Achieving Carbon Mitigation with Economic Benefits through High-Resolution Analysis of Renewable Resource Integration in Global Coastal Cities.

Coastal regions, home to more than half of the global population and contributing over 50% to the global economy, possess vast renewable resources, such as seawater and solar energy. The effective utilization of these resources, through the seawater-cooled district cooling system (SWDCS), seawater toilet flushing (SWTF), and rooftop solar photovoltaic system (RTPV), has the potential to significantly reduce carbon emissions. However, implementing these technologies in different geographic contexts to achieve the desired carbon and economic outcomes at the city level lacks a clear roadmap. To address this challenge, we comprehensively analyzed 12 coastal megacities worldwide by integrating geospatial building data. Our study evaluated the potential energy savings, carbon mitigation, and levelized carbon abatement costs (LCACs) from a life cycle perspective. The results revealed that using seawater and solar energy within urban boundaries can reduce electricity consumption from 1 to 24% across these cities. The spatial distribution of the LCAC for seawater-based systems exhibited more variation compared to the RTPV. By applying specific LCAC thresholds ranging from 0 to 225 USD/tCO2e, all cities could achieve both carbon reductions and economic benefits. These thresholds resulted in up to 80 million tonnes of carbon emission reductions and 5 billion USD of economic benefits, respectively. Our study provides valuable insights into integrating renewable resource systems, enabling coastal cities to achieve carbon and economic advantages at the city scale simultaneously.