Model Assessment Research Articles

Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy

Abstract. We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades to advance understanding of Earth system change and its impacts on society and the environment and thereby support international climate policy. We go on to recommend a number of priority research areas for the coming decade, a timescale that encompasses a number of newly starting international modelling activities, as well as the IPCC Seventh Assessment Report (AR7) and the second UNFCCC Global Stocktake. Progress in these priority areas will significantly advance our understanding of Earth system change and its impacts, increasing the quality and utility of science support to climate policy. We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris climate targets – for example, pathways that overshoot 1.5 or 2 °C global warming, before returning to these levels at some later date. Earth system models need to be capable of thoroughly assessing such warming overshoots – in particular, the efficacy of mitigation measures, such as negative CO2 emissions, in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 or 2 °C above pre-industrial temperatures is also required. We recommend Earth system models run overshoot scenarios in CO2-emission mode to more fully represent coupled climate–carbon-cycle feedbacks and, wherever possible, interactively simulate other key Earth system phenomena at risk of rapid change during overshoot. Regional downscaling and impact models should use forcing data from these simulations, so impact and regional climate projections cover a more complete range of potential responses to a warming overshoot. An accurate simulation of the observed, historical record remains a fundamental requirement of models, as does accurate simulation of key metrics, such as the effective climate sensitivity and the transient climate response to cumulative carbon emissions. For adaptation, a key demand is improved guidance on potential changes in climate extremes and the modes of variability these extremes develop within. Such improvements will most likely be realized through a combination of increased model resolution, improvement of key model parameterizations, and enhanced representation of important Earth system processes, combined with targeted use of new artificial intelligence (AI) and machine learning (ML) techniques. We propose a deeper collaboration across such efforts over the coming decade. With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend an increased focus on high-impact–low-likelihood (HILL) outcomes – in particular, the risk and consequences of exceeding critical tipping points during a warming overshoot and the potential impacts arising from this. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly as a result of climate mitigation actions, it is important that spatially detailed, disaggregated information used to generate future scenarios in integrated assessment models be available for use in impact models. Conversely, there is a need to develop methods that enable potential societal responses to projected Earth system change to be incorporated into scenario development. The new models, simulations, data, and scientific advances proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable by modelling communities across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy.

Risk assessment method for civil aircraft approach and landing at high plateau based on QAR data

Civil aircraft approach and landing at high altitude is a high-risk stage of plateau flight. In order to effectively implement the risk identification and grade judgment of approach and landing at high altitude, an LSTM-DNN deep learning risk assessment method based on variable fuzzy recognition with weights removed is proposed. This method is first based on the high-altitude flight data recorded by the quick access recorder (QAR), and draws on the civil aircraft flight quality monitoring project specification (FOQA) consultation notice and industry QAR monitoring standards. Combined with the indicator importance analysis and Delphi expert survey, five key monitoring items are extracted as the risk assessment indicators of civil aircraft approach and landing at high altitude, including large heading change during landing, low track, large descent rate during 2000-1000 ft approach, vertical acceleration at touchdown, and large descent rate during 500-50ft approach. Then, in order to overcome the subjective bias of the evaluation indicator weight, the weight of the evaluation indicator is determined by the weight removal method, and the risk grade membership function is constructed based on the variable fuzzy recognition method. Finally, a risk assessment model for civil aircraft approach and landing at high altitude based on LSTM-DNN is established. Taking the Chengdu-Lhasa approach and landing segment as an example, the QAR data was extracted to train and test the risk assessment model, and the results were compared with those of Logistic multivariate regression, support vector machine (SVM) and other evaluation methods. The results show that the average recognition rate of the LSTM-DNN deep learning model based on variable fuzzy recognition with weights removed reaches 94.18%, and the highest can reach 94.79%, which verifies the objective effectiveness of the risk assessment method.

Impact of physical activity levels on the association between air pollution exposures and glycemic indicators in older individuals

BackgroundAir pollution may exacerbate diabetes-related indicators; however, the longitudinal associations between air pollutant concentrations and glycemic markers remain unclear. In this prospective cohort study, we examined the longitudinal associations between air pollution and glycemic indicators among older individuals with normoglycemia at baseline and determined whether these associations differed according to changes in physical activity levels.MethodsOverall, 1,856 participants (mean age, 70.9 years) underwent baseline and 4-year follow-up surveys. We used linear mixed-effect models to examine the associations between previous 1-year exposures to air pollutants and glycemic indicators. We further investigated associations between previous 5-year exposures to air pollutants and glycemic indicators after the inverse probability of treatment weighting (IPTW). We explored effect modifications by the level of physical activity maintenance and changes in metabolic equivalent of task (METs) for physical activity.ResultsLevels of particulate matter with aerodynamic diameters ≤ 10 μm (PM10) and ≤ 2.5 μm, and nitrogen dioxide (NO2) were significantly associated with increased fasting blood glucose, Hemoglobin A1c, insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) values. After IPTW, the associations remained significant for PM10 and NO2. The positive associations of NO2 with insulin and HOMA-IR remained significant in the maintained inactive group, but not in the maintained moderate-to-vigorous active group. The positive associations of PM10 or NO2 with insulin and HOMA-IR remained significant in the group with increased METs, but not in those with decreased METs. In the post-hoc analysis of non-linear relationships between an increase in METs and glycemic indicators, insulin and HOMA-IR remarkably increased in the higher PM10 and NO2 exposure group from the point of 12,000 and 13,500 METs-min/week increase, respectively.ConclusionsWe demonstrated longitudinal associations between air pollution exposures and increased insulin resistance in older individuals. Maintaining moderate-to-vigorous physical activity may mitigate the adverse effects of air pollution on insulin resistance. In older individuals dwelling in highly polluted areas, an increase of less than 12,000 METs-min/week may be beneficial for insulin resistance.

Association of circulating adipokines with metabolic measures among people with HIV: Moderating effects of alcohol use.

People with HIV (PWH) are at increased risk for cardiometabolic comorbidities. We have reported that lifetime alcohol use among people with HIV (PWH) is associated with increased risk for metabolic syndrome. Dysfunctional adipose tissue and altered circulating adipokines mediate metabolic dysregulation. The objective of this study was to determine the associations of circulating adipokine concentration with metabolic measures, and the moderating effects of lifetime and recent alcohol use in PWH. This is a cross-sectional analysis of data from 357 PWH at their baseline visit of the longitudinal New Orleans Alcohol and HIV (NOAH) study. The concentrations of four circulating adipokines (adiponectin, leptin, resistin, and fatty acid-binding protein 4 [FABP4]) and their associations with five metabolic measures (triglycerides, cholesterol, Hemoglobin A1c, Homeostatic Model Assessment for Insulin Resistance, and metabolic syndrome) were examined. Higher circulating adiponectin was associated with increased odds of normal triglyceride, cholesterol, and Hemoglobin A1c levels. Increased leptin and FABP4 concentrations were associated with decreased odds of normal triglyceride and cholesterol levels. Increased leptin and FABP4 concentrations were associated with increased odds of insulin resistance and meeting criteria for metabolic syndrome. Increased circulating resistin concentration was associated with decreased odds of normal triglyceride levels and increased odds of meeting criteria for metabolic syndrome. Additionally, among PWH with increased lifetime alcohol use, higher adiponectin concentration was associated with decreased odds of meeting criteria for metabolic syndrome. These data suggest the interplay between adiponectin, leptin, FABP4, and resistin may contribute to metabolic stability among PWH. Moreover, lifetime, but not recent, alcohol use moderates the relationship between adipokines and metabolic measures. These data highlight the relevance of functional adipose tissue mass and associated circulating adipokine levels in maintaining metabolic homeostasis, and its moderation by lifetime alcohol consumption.

Design, Implementation, and Analysis of an Assessment and Accreditation Model to Evaluate a Digital Competence Framework for Health Professionals: Mixed Methods Study.

Although digital health is essential for improving health care, its adoption remains slow due to the lack of literacy in this area. Therefore, it is crucial for health professionals to acquire digital skills and for a digital competence assessment and accreditation model to be implemented to make advances in this field. This study had two objectives: (1) to create a specific map of digital competences for health professionals and (2) to define and test a digital competence assessment and accreditation model for health professionals. We took an iterative mixed methods approach, which included a review of the gray literature and consultation with local experts. We used the arithmetic mean and SD in descriptive statistics, P values in hypothesis testing and subgroup comparisons, the greatest lower bound in test diagnosis, and the discrimination index in study instrument analysis. The assessment model designed in accordance with the competence content defined in the map of digital competences and based on scenarios had excellent internal consistency overall (greatest lower bound=0.91). Although most study participants (110/122, 90.2%) reported an intermediate self-perceived digital competence level, we found that the vast majority would not attain a level-2 Accreditation of Competence in Information and Communication Technologies. Knowing the digital competence level of health professionals based on a defined competence framework should enable such professionals to be trained and updated to meet real needs in their specific professional contexts and, consequently, take full advantage of the potential of digital technologies. These results have informed the Health Plan for Catalonia 2021-2025, thus laying the foundations for creating and offering specific training to assess and certify the digital competence of such professionals.

An Assessment Model of the Algarve as a Sustainable Tourism Destination: A Conceptual Framework

ABSTRACT Sustainable tourism assessment models emphasize the need for holistic, integrated evaluations of tourism destinations, particularly in mature and mass tourism areas. While best practices can be shared, each destination requires a tailored approach, considering its unique features like infrastructure and natural assets. Research shows that sustainable tourism should involve continuous evaluation rather than a one-time assessment. This article develops a model for the ongoing review of sustainable tourism in the Algarve, a famous sun, sea, and sand destination in Portugal. Using qualitative and quantitative methods, the model was designed by analyzing existing frameworks and their relevant dimensions. A questionnaire was then administered to local stakeholders to validate and refine the selected indicators, which were ultimately used to create a framework for monitoring sustainable tourism in the region.

“ESG score” vs. “ESG rating”: a conceptual model for the sustainability assessment and self-assessment of European SMEs

This paper aims to contribute to the ongoing discourse regarding the distinctions and application of scoring and rating systems by presenting a conceptual model designed to assess and self-assess small and medium-sized enterprises (hereinafter also “SMEs”) on their sustainability transition. Indeed, SMEs operate in very different economic contexts and have simplified organizational and governance structures. These characteristics can be effectively captured through this conceptual model based on a customized questionnaire tailored to the specificities of SMEs. Following an analysis of existing literature and regulatory frameworks, a conceptual model is proposed that includes a questionnaire that, unlike commonly proposed industry questionnaires, is designed to generate an Environmental, Social and Governance (hereinafter also “ESG”) Score complemented by a forward-looking perspective. This model can be useful for SMEs, as it allows them to self-assess their strengths and weaknesses in the sustainability transition process, highlighting specific needs and suggesting actions to improve their sustainability transition. It also allows banks to make a more accurate assessment of the sustainability of their customers, facilitating the redefinition of green and social credit products according to the needs of SMEs. Additionally, it also supports supply chain leaders in ensuring a sustainable supply chain by facilitating the sustainability assessment of SMEs. This is so that everything complies with the new European Union (hereinafter also “EU”) regulations. Finally, the questionnaire is currently being tested at a local bank and has been proposed to SME suppliers in a specific supply chain. It will be possible to make changes to the questionnaire based on the feedback received during the administration phase.

Development of an Oral Epithelial Ex Vivo Organ Culture Model for Biocompatibility and Permeability Assessment of Biomaterials.

In the present study, a customized device (Epi-ExPer) was designed and fabricated to facilitate an epithelial organ culture, allowing for controlled exposure to exogenous chemical stimuli and accommodating the evaluation of permeation of the tissue after treatment. The Epi-ExPer system was fabricated using a stereolithography (SLA)-based additive manufacturing (AM) method. Human and porcine oral epithelial mucosa tissues were inserted into the device and exposed to resinous monomers commonly released by dental restorative materials. The effect of these xenobiotics on the morphology, viability, permeability, and expression of relevant markers of the oral epithelium was evaluated. Tissue culture could be performed with the desired orientation of air-liquid interface (ALI) conditions, and exposure to xenobiotics was undertaken in a spatially guarded and reproducible manner. Among the selected monomers, HEMA and TEGDMA reduced tissue viability at high concentrations, while tissue permeability was increased by the latter. Xenobiotics affected the histological image by introducing the vacuolar degeneration of epithelial cells and increasing the expression of panCytokeratin (pCK). Epi-ExPer device offers a simple, precise, and reproducible study system to evaluate interactions of oral mucosa with external stimuli, providing a biocompatibility and permeability assessment tool aiming to an enhanced in vitro/ex vivo-to-in vivo extrapolation (IVIVE) that complies with European Union (EU) and Food and Durg Administration (FDI) policies.

Technology is never neutral: Robodebt and a human rights analysis of automated decision-making on welfare recipients

ABSTRACT Government agencies have embraced automated decision-making systems, particularly in welfare, despite the risk of mass-replicated erroneous decision-making creating systemic issues. This article uses Robodebt as an Australian case study with global ramifications to argue that government agency use of erroneous automated decision-making systems in welfare raises human rights concerns. First, this article examines the shortcomings of administrative law in a digital welfare context. Second, the article closely analyses the International Covenant on Civil and Political Rights and International Covenant on Economic, Social and Cultural Rights and identifies several human rights concerns raised by Robodebt. This article then examines ex-ante impact assessment models and concludes by recommending an obligation on government agencies to conduct a human rights impact assessment before deploying an automated decision-making system. By demonstrating the human rights implications on welfare recipients arising from automated decision-making systems, this article justifies the use of international human rights law as a complementary framework.

The impact of environmental microplastics as emerging carcinogens: A public health concern

Microplastics, small plastic particles less than 5 millimeters in size, have become common in the environment and occur in air, water, and food sources. Possible effects on human health, particularly as carcinogens, have become a growing concern. This review aims to investigate the mechanisms by which microplastics may contribute to cancer development and the implications for public health. A comprehensive literature review was conducted, focusing on studies from 2010 to 2024. The data were collected from various sources, including Google Scholar, PubMed, Web of Science and Scopus. The key areas examined include biological mechanisms of carcinogenesis, human exposure pathways, and risk assessment models. Microplastics can induce carcinogenesis through multiple pathways, including oxidative stress, inflammation, and cellular damage pathways. Research both in vivo and in vitro has demonstrated that microplastics can cause DNA damage and disrupt cellular processes, potentially leading to cancer. Human exposure occurs primarily through ingestion, inhalation, and dermal contact in the absence of data, and epidemiological studies indicate a link between increased levels of microplastic exposure and a greater risk of cancer. Microplastics represent a coming class of environmental carcinogens with significant public health implications. More investigations are needed to completely explain their carcinogenic mechanisms and to develop effective strategies to reduce human exposure. Public health policies must evolve to mitigate the risks connected with microplastic pollution, protecting future generations from its potential health impacts.

Maternal Broccoli Powder Intake Ameliorates Insulin Resistance and Inflammation via AMPK/mTOR Pathway in the Livers of High-Fructose-Fed Male Rat Offspring Exposed to Maternal Protein Restriction.

Sub-optimal prenatal conditions such as maternal undernutrition during pregnancy and lactation posit high risks of adult metabolic diseases. High fructose intake causes insulin resistance and liver inflammation contributing to metabolic diseases. However, food-based preventive measure for these metabolic diseases in the offspring is under-researched. This study aims to investigate the effect of maternal broccoli powder (BP) intake during lactation on insulin resistance and liver inflammation in high-fructose-diet-fed adult male offspring exposed to maternal protein restriction. Pregnant Wistar rats are provided normal protein (NP) or low protein (LP) diets and 0% or 0.74% BP-containing NP diets and 0% or 0.74% BP-containing LP diet during lactation. At weaning, offspring receiving water (W) or 10% fructose solution (Fr) are assigned into six groups: NP/NP/W, NP/NP/Fr, NP/NPBP/Fr, LP/LP/W, LP/LP/Fr, and LP/LPBP/Fr. At week 13, plasma insulin, macrophage infiltration, activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) phosphorylation, and autophagy flux markers are examined. LP/LPBP/Fr shows lower insulin levels and Homeostatic model assessment for insulin resistance (HOMA-IR) values than LP/LP/Fr. Liver macrophage infiltration are decreased in LP/LPBP/Fr. LP/LPBP/Fr exhibits upregulated AMPK phosphorylation, downregulated mTOR phosphorylation, and increased Microtubule-associated protein1A/1B-light chain 3B-II (LC3B-II) levels. Maternal BP intake during lactation ameliorates insulin resistance and inflammation in the livers of adult offspring on a high-fructose diet from LP mothers.

Effects of Different Photoperiods on Growth Performance, Glucose Metabolism, Acetylcholine, and Its Relative Acetylcholine Receptor Modulation in Broiler Chickens.

Photoperiods are crucial environmental factors in the growth and health of modern intensive broiler chicken production. To date, the effects of different photoperiods on glucose metabolism, acetylcholine (ACh), and its relative acetylcholine receptor modulation in broilers remain elusive. Herein, we aimed to identify the effects of different photoperiods on regulating glucose metabolism, ACh, nicotinic acetylcholine receptor alpha 4 (α4 nAChR) mRNA, and M3 muscarinic acetylcholine receptor (M3 mAChR) modulation in broilers. A total of 216 healthy 5-day-old Arbor Acres (AA) male broilers was randomly assigned to 12L:12D, 18L:6D, and 24L:0D photoperiods for 4 weeks. The results show that, compared with the 12L:12D photoperiod, the 18L:6D and 24L:0D photoperiods significantly increase the average daily gain (ADG) and average daily feed intake (ADFI) of broilers (p < 0.05). However, the feed efficiency (FE) of broilers significantly decreased in the 18L:6D and 24L:0D photoperiods (p < 0.05). Moreover, compared with the 12L:12D photoperiod, the ACh concentrations and α4 nAChR mRNA expression levels in the hypothalamus and medulla oblongata of broilers significantly increased (p < 0.05); M3 mAChR mRNA expression levels in cecum significantly reduced in the 18L:6D photoperiod and the 24L:0D photoperiod (p < 0.05). Compared with the 12L:12D photoperiod, the serum glucose (GLU), serum insulin (INS), serum triglyceride (TG) levels, and homeostasis model assessment of insulin resistance (HOMA-IR) of broilers significantly enhanced in the 18L:6D and 24L:0D photoperiods (p < 0.05). Our results indicate that extending the photoperiod can promote the growth rate, ACh expression, and α4 nAChR mRNA expression of broilers while reducing the feed efficiency, inhibiting M3 mAChR mRNA expression, and inducing glucose metabolism disorders in broilers.

Low-calorie, high-protein diets, regardless of protein source, improve glucose metabolism and cardiometabolic profiles in subjects with prediabetes or type 2 diabetes and overweight or obesity.

The aim was to study the effect of two low-calorie, high-protein (HP) diets, with most of the protein coming from animal or plant sources, on glycaemic and other cardiometabolic outcomes in subjects with overweight or obesity and glucose metabolism disorders. A total of 117 participants aged >18 years with body mass index over 27.5 kg/m2 and prediabetes or type 2 diabetes mellitus (T2DM) were randomized to one of two HP low-calorie diets (35% of total calories from protein), in which 75% of the protein was from either plant-based sources (HPP) or animal sources (HPA). For both diets, 30% and 35% of the total calories were from fat and carbohydrates, respectively. The dietary intervention lasted 6 months. Both diets improved body composition to a similar extent, including weight loss (-8.05 ± 5.12 kg for the HPA diet and -7.70 ± 5.47 kg for the HPP diet at 6 months) and fat mass, mainly visceral fat. Both diets had a similar beneficial effect on glucose metabolism, including fasting glucose, insulin, homeostasis model assessment of insulin resistance index and glycated haemoglobin. Other biochemical parameters, including lipid profiles, liver enzymes, adipokines and inflammatory biomarkers, similarly improved in both groups. Fasting incretins, mainly glucagon-like peptide 1, decreased significantly in both groups, and this effect correlated with weight loss. Low-calorie HP diets improved body composition, glucose metabolism and other cardiometabolic outcomes, regardless of protein source (either animal or plant sources), in outpatients with prediabetes or T2DM. The clinical trial was registered in ClinicalTrials.gov (identifier: NCT05456347) https://clinicaltrials.gov/study/NCT05456347?term=NCT05456347&rank=1.

Histomorphological scoring of murine colitis models: A practical guide for the evaluation of colitis and colitis-associated cancer

Background and aimsHistomorphology is a powerful and cost-efficient tool for evaluating inflammatory and neoplastic conditions. Inflammatory bowel disease (IBD) is a widespread condition with globally rising incidences, and a lot of research is done to better understand the pathogenesis of IBD and to identify potential therapeutic approaches. However, standardized and reproducible scores for the histomorphological evaluation of murine IBD models are lacking. Therefore, we aimed to develop an easy-to-use and reproducible score for standardized assessment of colitis and associated cancer models. MethodsIn this study, samples from three different colitis models with and without associated cancer formation were analyzed to develop a universal, robust, and reproducible score for the grading of murine colitis models using the following three parameters: 1. Extent of leucocyte infiltration, 2. Tissue damage, 3. Architectural disruption of the mucosa. ResultsA scoring system was established for different kinds of colitis models (genetically induced enterocolitis, genetically induced metabolic injury, and chemically induced colitis-associated cancer) and all stages of the disease, from mild inflammatory changes to severe inflammation with neoplastic changes as the extreme extent of IBD. The scoring scheme is easy to use, can easily be learned, and proves to have a high interrater reliability. ConclusionsWe propose a robust histological scoring system for the assessment of murine colitis and colitis-associated cancer models, giving more researchers access to conclusive and reliable histological assessment.

Exploring Saffron's Therapeutic Potential: Insights on Phytochemistry, Bioactivity, and Clinical Implications.

Saffron, derived from the Crocus sativus plant, has been revered for centuries for its culinary, medicinal, and cultural significance. This review provides a comprehensive overview of saffron's chemical constituents and phytochemistry, elucidating its rich profile of bioactive compounds. Emphasis is placed on exploring the bio-accessibility, bioavailability, and bioactivity of saffron's phytochemicals, laying the foundation for understanding its pharmaceutical significance. The pharmaceutical importance of saffron and its phytochemicals is thoroughly examined, focusing on their diverse therapeutic properties. These include anticancer, antidiabetic, antioxidant, antimicrobial, anti-inflammatory, antinociceptive, anticonvulsant, antidepressant, learning and memory enhancement, cardiovascular, and antihypertensive properties. Such multifaceted pharmacological activities underscore saffron's potential as a valuable medicinal resource. Clinical studies investigating the efficacy and safety of saffron in various health conditions are synthesized, providing insights into its clinical applications. Moreover, toxicity assessments in animal models, encompassing acute, subacute, subchronic, and developmental toxicity, are discussed to delineate the safety profile of saffron and its bioactive constituents. Finally, recent advances and future perspectives in saffron research are highlighted, underscoring emerging trends and potential avenues for further exploration. This review serves as a comprehensive resource for researchers, clinicians, and stakeholders interested in harnessing the therapeutic potential of saffron while ensuring its safe and effective utilization in healthcare settings.

Elevated Serum Syndecan-1 Levels are Associated with Obesity-Related Complications in Children

Background: Syndecan-1 plays a crucial role in cell differentiation, growth, adhesion, wound healing, and inflammatory processes. It has been linked to obesity, particularly overweight and chronic low-grade inflammation. Objectives: This study aimed to compare serum syndecan-1 levels between obese children and a healthy control group and to investigate the relationship between serum syndecan-1 levels and renal and cardiovascular complications associated with obesity in children. Methods: We assessed 30 obese and 30 healthy children aged 8 - 16 years. Blood samples were collected from both groups to measure serum aspartate aminotransferase (AST), syndecan-1, fasting blood glucose (FBG), alanine aminotransferase (ALT), creatinine, and urea concentrations. In the obese group, high-density lipoprotein cholesterol (HDL-C), insulin, triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels were also measured, along with homeostasis model assessment of insulin resistance (HOMA-IR). Results: The mean serum syndecan-1 levels in the obese and control groups were 15.65 ± 10.01 ng/mL and 6.99 ± 3.79 ng/mL, respectively, with a significantly higher level observed in the obese group (P &lt; 0.005). Significant differences were also found in BMI, BMI SDS, creatinine, AST, and ALT levels between the two groups (P &lt; 0.05), with all parameters being higher in the obese group. A significant positive linear association was identified between syndecan-1 levels and creatinine, TG, and BMI SDS (r = 0.711, r = 0.630, r = 0.682, P &lt; 0.01). Additionally, a strong negative correlation was found between syndecan-1 and HDL-C levels (r = -0.609, P &lt; 0.001). No significant linear relationship was detected between syndecan-1 levels and LDL-C, ALT, AST, glucose, urea, insulin levels, or HOMA-IR measurements (P &gt; 0.05). Linear regression analysis revealed that serum creatinine levels and BMI SDS were significantly linked to changes in syndecan-1 levels. Conclusions: Syndecan-1 is elevated in obese children due to inflammation and may serve as an early biomarker for endothelial damage. It can be useful in detecting obesity-related renal damage and cardiovascular risk in children.

Method for Enhancing AI Accuracy in Pressure Injury Detection Using Real and Synthetic Datasets

Pressure injuries pose significant health risks, especially for the elderly, immobile individuals, and those with sensory impairments. These injuries can rapidly become chronic, making initial diagnosis important. Due to the difficulty of transporting patients from local health facilities to higher-level general hospitals for treatment, it is essential to utilize telemedicine tools, such as chatbots, to ensure rapid initial diagnosis. Recent advances in artificial intelligence have demonstrated potential for medical imaging and disease classification. Ongoing research in the field of dermatological diseases focuses on disease classification. However, the assessment accuracy of artificial intelligence is often limited by unequal class distributions and insufficient dataset quantities. In this study, we aim to enhance the accuracy of artificial intelligence models by generating synthetic datasets. Specifically, we focused on training models for Pressure Injury assessment using both real and synthetic datasets. We used PI data at a domestic medical university. As part of our supplementary research, we established a chatbot system to facilitate the assessment of pressure injuries. Using both constructed and synthetic data, we achieved a top-1 accuracy of 92.03%. The experimental results demonstrate that combining real and synthetic data significantly improves model accuracy. These findings suggest that synthetic datasets can be effectively utilized to address the limitations of small-scale datasets in medical applications. Future research should explore the use of diverse synthetic data generation methods and validate model performance on a variety of datasets to enhance the generalization and robustness of AI models for Pressure Injury assessment.

Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.

Designing the Urban Smart Futures Agenda for Lancaster, UK

The smart city concept has garnered a lot of interest; however, it often falls short when it comes to providing clarity on the benefits it can offer. Discussing smartness in the context of cities and their inhabitants requires the involvement of a wide range of stakeholders in decision-making. Similarly, the decision-making process is often unclear and can lack integrity. For this reason, in this research, we clarify this process and establish a smart agenda for urban areas. Specifically, this study focusses on the existing research in truly smart cities (where liveability is at the heart of decision-making). The research team implemented the assessment model (SMART) during a facilitated workshop under COVID-19 restrictions. Taking societal, environmental, health, economic, and governance liveability perspectives into account, the results yielded a set of recommendations for designing the smart urban agenda, which can support cities that aspire to become smart.