Author: Latest Results
Childhood-Onset Systemic Lupus Erythematosus (cSLE): An International Perspective
Abstract
Purpose of Review
Childhood-onset systemic lupus erythematosus (cSLE) is a severe and potentially life-threatening chronic autoimmune disease. cSLE is more aggressive and has poorer outcomes than adult-onset disease. The global burden of cSLE is poorly understood, with most publications on cSLE originating from high-resourced settings. The reports from less resourced settings indicate high morbidity and mortality in these populations.
Recent Findings
In this article, we review the disparities in global access to rheumatology care and research for patients with cSLE. We highlight recent cSLE advances from all regions of the globe. We describe current obstacles to cSLE clinical care and research in all settings. Finally, we propose a path forward for high quality, equitable and accessible care to individuals with cSLE everywhere.
Summary
Individuals with cSLE are at risk for morbidity and death, yet patients worldwide face challenges to adequate access to care and research. Sustained, collaborative efforts are needed to create pathways to improve care and outcomes for these patients.
A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China
Abstract
Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.
UAV Databased Temperature Patterns Analysis with Carbon Emission Detection Using Deep Neural Network
Abstract
Unmanned aerial vehicle (UAV) imaging methods have drawn a lot of interest lately from academics and industry professionals as an affordable option for agro-environmental uses. To improve the UAV capabilities of diverse applications, machine learning (ML) methods are specifically applied to UAV-based remote sensing data. Spatiotemporal properties were analysed and the city-level carbon emissions statistics were estimated. This research proposes novel technique in UAV-based climate temperature pattern analysis and carbon emission detection utilizing the deep learning (DL) model. Here the input is collected as UAV-based weather data which is processed for noise removal and smoothening. Processed data is extracted and classified utilizing Gaussian belief deep neural network and spatial convolutional Q-swarm colony metaheuristic optimization. A metaheuristic optimisation algorithm uses gradient information-free iterative evaluation of the objective function in order to identify a global optimum. Experimental analysis has been carried out in terms of detection accuracy, average precision, F-1 score, recall and AUC for various UAV-based weather dataset. The proposed technique attained mean average precision was 92%, recall was 94%, AUC was 87%, detection accuracy was 96% and F1-score was 93%. This work shows that remotely sensed data can be used to support more advanced evaluations that are more successful, particularly in areas with extensive selective logging and diverse forest conditions, and to help quantify carbon emissions from selective logging using conventional methodologies.
Extracting paleoweather from paleoclimate through a deep learning reconstruction of Last Millennium atmospheric blocking
Abstract
Projected changes in atmospheric blocking and associated extreme weather are marked by considerable uncertainties. While paleoclimate records could help reduce these uncertainties, their low temporal resolution makes extracting synoptic-scale signals challenging. Here, a deep learning model is developed to infer summertime blocking frequency from tree-ring-based gridded reconstructions of Northern Hemisphere surface temperature over the Last Millennium. The model, despite not directly incorporating paleoclimate proxies or their locations, is implicitly constrained by them. The reconstructions highlight the tropical Pacific’s strong influence on blocking variability at interannual-to-centennial time scales. A weakened tropical Pacific zonal temperature gradient during the Little Ice Age correlates with a hemispherically reduced -yet more variable interannually- blocking frequency and altered regional patterns. This deep learning approach offers a pathway for extracting paleoweather signals from paleoclimate records that enables improved understanding of blocking response to external forcing and constraining of model projections of blocking under climate change scenarios.
Assessing Climate Change Impacts on Rainfall-Runoff in Northern Iraq: A Case Study of Kirkuk Governorate, a Semi-Arid Region
Abstract
Located in the climatically vulnerable Middle East, Iraq is projected to be highly susceptible to climate change impacts. Evaluating these effects on water resources requires an analysis of key hydrological factors, including rainfall and associated runoff. The Soil Conservation Service Curve Number (SCS-CN) method remains the foremost runoff modelling approach because it relies on the curve number (CN), derived from land use, soil type, and other parameters. In this chapter, the SCS-CN model is integrated with geographic information systems (GIS) and Long Aston Research Station Weather Generation (LARS-WG) to estimate rainfall-runoff under climate change projections for the Kirkuk governorate in Iraq. The SCS-CN model inputs of hydrological soil properties, rainfall data, and potential retention were obtained through GIS analysis. The results from 1992 to 2022 showed an average annual runoff of a maximum runoff of 290 mm in 1992 and a minimum of 184 mm in 2021. Under future climate change projections, the estimated annual average runoff is predicted to decline to 110 mm by 2055, with a maximum of 280 mm by 2025. These findings indicate substantial climate change impacts on future rainfall, the associated surface runoff, water availability, and water resources in the Kirkuk governorate. Integrating established hydrologic models, such as SCS-CN with GIS, can enhance climate change impact assessments for improved water resource planning.
Misinformation and higher-order evidence
Abstract
This paper uses computational methods to simultaneously investigate the epistemological effects of misinformation on communities of rational agents, while also contributing to the philosophical debate on ‘higher-order’ evidence (i.e. evidence that bears on the quality and/or import of one’s evidence). Modelling communities as networks of individuals, each with a degree of belief in a given target proposition, it simulates the introduction of unreliable mis- and disinformants, and records the epistemological consequences for these communities. First, using small, artificial networks, it compares the effects, when agents who are aware of the prevalence of mis- or disinformation in their communities, either deny the import of this higher-order evidence, or attempt to accommodate it by distrusting the information in their environment. Second, deploying simulations on a large(r) real-world network, it observes the impact of increasing levels of misinformation on trusting agents, as well as of more minimal, but structurally targeted, unreliability. Comparing the two information processing strategies in an artificial context, it finds that there is a (familiar) trade-off between accuracy (in arriving at a correct consensus) and efficiency (in doing so in a timely manner). And in a more realistic setting, community confidence in the truth is seen to be depressed in the presence of even minimal levels of misinformation.
Child and Adolescent Engagement with Climate Change on Social Media and Impacts on Mental Health: a Narrative Review
Abstract
Purpose of the Review
We describe the existing literature which explores the relationship between engagement with climate change on social media and child and adolescent mental health and well-being.
Recent Findings
Children and adolescents use social media to gather information about climate change, build community with like-minded peers, and get involved in collective climate action. Climate anxiety can motivate young people to seek out climate-related information on social media.
Summary
Social media has benefits to child and adolescent mental health in the context of the climate crisis as a tool to promote awareness, social support, and climate-related civic engagement. However, social media can spread misinformation and increase child and adolescent exposure to negative climate change messaging, thereby increasing distress. Clinicians and young people alike recognize the advantages of social media for promoting child and adolescent resilience in response to climate change and offer suggestions for how to reduce potential harm.
Assessment of future climate change over the north-west region of Bangladesh using SDSM and CanESM2 under RCP scenarios
Abstract
The frequency of extreme hydrologic events such as floods, storm surges, droughts, heat waves, extreme precipitation, and other similar occurrences has been increasing in Bangladesh due to the impact of climate change. Therefore, the assessment of changes in future climates is essential for climate-induced risk management in the country to safeguard natural resources and human lives. The main purpose of the current study is to assess the trend of maximum temperature (Tmax), minimum temperature (Tmin), and precipitation for the north-west region of Bangladesh in seasonal and annual scales for three future periods, including 2025–2050, 2051–2080, and 2081–2100, respectively. In order to achieve this goal, a large-scale atmospheric dataset obtained from the well-known general circulation model (GCM), CanESM2, is downscaled to finer scales at the local level using the widely used statistical downscaling model (SDSM). The downscaling of local climate variables is carried out using daily observed climate data under three representative concentration pathways (RCP) scenarios, including RCP2.6, RCP4.5, and RCP8.5, respectively. Correlation matrices with p-values have been utilized to select the most suitable predictors from NCEP/NCAR reanalysis data. Both the calibration (0.87 < R2 < 0.98, 0.87 < EV < 0.99, 19.24 > SE < 0.12) and validation findings demonstrate that the model performs satisfactorily. The bias correction approach is also adopted to achieve more consistent results. Seasonally, the mean seasonal temperature and precipitation are projected to rise in all seasons (except winter for precipitation). Annually, Tmax and Tmin have grown by 0.49 °C and 1.36 °C, respectively, whereas precipitation has increased by 49% up to the next century considering the RCP8.5 scenario (worst case). Overall, the outcome of the current study is expected to be supportive to policymakers and water managers in planning climate-resilient agricultural and infrastructure development activities for managing climate-induced disastrous events in the north-west region of Bangladesh.
Biosensor in Climate Change and Water Rise Analysis Based on Diverse Biological Ecosystems Using Machine Learning Model
Abstract
The stability of environmental conditions around the world has been threatened by climate change in the last few decades. Sea levels are rising more quickly than ever before due to a combination of factors including temperature increases, changes in precipitation patterns, and glacier melting. We describe a machine learning strategy to design coastal sea level fluctuation as well as related uncertainty across a range of timescales by utilising important ocean temperature estimations as proxies for regional thermosteric sea level component. The aim of this research is to propose novel method in climate change with water level rise analysis using biosensor with machine learning techniques in diverse biological ecosystems. Here, the input is collected as biosensor-based climate analysis with water level rise analysis dataset and processed for noise removal, normalisation, and smoothening. Then, climate data analysis is carried out utilising fuzzy adversarial encoder (FAE) model and the water level rise analysis is carried out using recurrent transfer AlexNet neural network (RTAlexNetNN). The classified output shows climate change analysis with water level rise modelling. The experimental analysis has been carried out for various climate data and water rise data in terms of random accuracy, specificity, MSE, F-measure, and normalised cross-correlation. The proposed technique obtained 98% random accuracy, 96% normalised cross-correlation, 94% specificity, 58% MSE, and 92% F-measure.