Model Assessment Research Articles

Impact of Prevalent Dietary Pattern on Serum Vitamin B12 Status and Its Association With Inflammation Among Reproductive Age Women.

Association between vitamin B12 deficiency (VB12D) and dietary patterns being well documented has bearing on obstetrics and neonatal outcomes. However, relationship between VB12D and serum inflammatory markers (IMs), particularly in vegetarian diet and Polycystic ovary syndrome (PCOS), remains elusive. This cross-sectional study assesses VB12D and IMs among reproductive age women consuming different diets. Nonvegetarian (PCOS, n = 104; healthy, n = 148) and vegetarian women (PCOS n = 112; healthy, n = 186) are for evaluated clinical, biochemical, hormonal assessment, inflammatory, and four vitamin B 12 (VB12) markers. VB12D is defined by Fedosov's wellness quotient (4cB12). Using 4cB12, prevalence of VB12D is discerned in 54.4% (PCOS: 72.1%; healthy 36.5%) and 93.4% (PCOS: 95.9%; healthy: 91.9%) among nonvegetarians and vegetarians, respectively. Vegetarian PCOS women depict lowest median (interquartile range [IQR]) of serum B12 76.2(72.6) pgmL-1, holotranscobalamine (HTC) 37.9(11.3) and highest homocysteine (HCY) 40.32(6.0) µmolL-1, methylmalonic acid (MMA) 352.26(156.7) nmolL-1 with highest Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and IMs (Monocyte chemoattractant protein 1 (MCP-1), High sensitivity C-reactive protein (hs-CRP), Tumour Necrosis Factor alpha (TNF-α)and Interleukin 6 (IL-6)). Significant correlation of serum hs-CRP, TNF-α, and IL-6 with VB12 markers is observed. The VB12D is rampant among reproductive age women that gets exacerbated by PCOS or vegetarian diet. It is directly correlated with magnitude of proinflammatory markers. The results carry substantial implications for public health policies aimed at improving preconception maternal VB12 status for better future pregnancy and offspring outcomes.

Association between insulin resistance and multiple sclerosis: a systematic review and meta-analysis.

There is increasing evidence of metabolic perturbations in multiple sclerosis (MS) patients, and insulin is an important parameter that has controversial effects on neurological disease. Therefore, this systematic review and meta-analysis study aimed to explore the association between insulin resistance (IR) and MS as well as insulin levels and MS.Three electronic databases, including Medline, Scopus, and the Web of Science, were examined up to 26 May 2023 for observational studies. Two independent reviewers assessed the studies according to a pre-specified protocol. Random-effects model using a Restricted-maximum Likelihood (REML) estimator was used to meta-analyze the association between IR [assessed by Homeostatic Model Assessment (HOMA-IR)], insulin and MS.Eighteen datasets from 2012 to 2022 were included in this meta-analysis. The standardized mean difference (SMD) for comparison IR and insulin between MS and healthy control group as outcomes 1 and 2 were 0.78 and 0.72 respectively. Furthermore, for outcome 1, we observed a greater effect size in studies that recruited different types of MS (Mix) (SMD: 1.09) than in those that included only relapsing-remitting MS (RRMS) (SMD: 0.59).The meta-analysis revealed a significant association between IR, insulin and MS, with stronger associations in studies that recruited mixed patients. However, high heterogeneity has been observed in the present study. Therefore, more studies are needed to confirm the association between these parameters and MS.

Association between being large for gestational age and cardiovascular metabolic health in children conceived from assisted reproductive technology: a prospective cohort study.

To the best of our knowledge, no study has investigated the potential joint effect of large for gestational age (LGA) and assisted reproductive technology (ART) on the long-term health of children. This was a prospective cohort study that recruited children whose parents had received ART treatment in the Center for Reproductive Medicine, Shandong Provincial Hospital, affiliated to Shandong University, between January 2006 and December 2017. Linear mixed model was used to compare the main outcomes. The mediation model was used to evaluate the intermediary effect of body mass index (BMI). 4138 (29.5%) children born LGA and 9910 (70.5%) children born appropriate for gestational age (AGA) were included in the present study. The offspring ranged from 0.4 to 9.9years. LGAs conceived through ART were shown to have higher BMI, blood pressure, fasting blood glucose, fasting insulin, and homeostatic model assessment of insulin resistance values, even after controlling for all covariates. The odds of overweight and insulin resistance are also higher in LGA subjects. After adjusting for all covariates, LGAs conceived through ART had BMI and BMI z-scores that were 0.48kg/m2 and 0.34 units greater than those of AGAs, respectively. The effect of LGA on BMI was identified as early as infancy and remained consistently significant throughout pre-puberty. Compared to AGA, LGA children conceived from ART were associated with increased cardiovascular-metabolic events, which appeared as early as infancy and with no recovery by pre-puberty.

Densenet Model for Binary Glaucoma Classification Performance Assessment with Texture Feature

Densenet Model for Binary Glaucoma Classification Performance Assessment with Texture Feature

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Positive association of the anti-aging protein α-Klotho with insulin resistance and its inverse L-shaped relationship with glycaemic control in the middle-aged and elderly population.

α-Klotho has been linked to insulin resistance (IR) in basic research. However, experimental evidence is inconsistent, and there is a lack of data from human research. This study seeks to elucidate the association of α-Klotho with IR in a nationwide, multiracial population. A total of 5289 participants aged 40-79 years were included in the National Health and Nutrition Examination Survey (NHANES) spanning 2007-2016. Serum α-Klotho was measured using enzyme-linked immunosorbent assays (ELISA), and IR was evaluated by the homeostatic model assessment of insulin resistance (HOMA-IR). Weighted multivariate logistic and linear regression analysis, subgroup analysis stratified by demographic characteristics, medical condition or obesity status, and sensitivity analysis using propensity score matching (PSM) were performed. Restricted cubic splines (RCS) were performed to explore the nonlinear relationship. In the fully adjusted logistic regression model, a significant positive association was observed between log-transformed α-Klotho and IR (OR = 3.63, 95% CI: 1.56, 8.45), particularly in males or nonobese individuals (Pinteraction < 0.05). In the linear regression model, log10(α-Klotho) was associated with fasting blood glucose (FBG, β = 1.25, 95% CI: 0.74, 1.76) and glycosylated hemoglobin (HbA1c, β = 0.49, 95% CI: 0.20, 0.77). RCS revealed an inverse L-shaped dose-response relationship of α-Klotho with FBG and HbA1c (Pnonlinear <0.05). Beyond the inflection point of log10(α-Klotho) at 2.79, β coefficients sharply rose for these glycaemic control indicators. The study provides clinical evidence supporting a positive association between α-Klotho and IR. Moreover, the inverse L-shaped relationship suggests that α-Klotho should reach a certain level to predict glycaemic changes effectively.

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: results from a prospective cohort study.

The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while β-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis.

Ruptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge. In this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data. Following rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made. Our study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.

Comparison of numerical calculations and ALOHA modeling in consequence assessment of chlorine gas emissions from ethylene dichloride reactors.

Incidents involving chemical storage tanks in the petrochemical industry are significant events with severe consequences. Within the petrochemical industry, EDC is a sector that produces ethylene dichloride through the reaction of chlorine and ethylene. The present research was conducted to evaluate the consequences of chlorine gas released from the EDC reactor in a petrochemical industry in southern Iran. Data regarding reactor specifications were obtained from the factory's technical office, while climatic data was acquired from the Meteorological Organization. The consequences of chlorine gas release from the reactor were assessed in four predefined scenarios using numerical calculation methods and modeling with the ALOHA software. The numerical calculation method involved thermodynamic fluid path analysis, discharge coefficient calculations, and wind speed impact analysis. The hazard radius was determined based on the ERPG1-2-3 index. Results showed that in the scenario of chlorine gas release from EDC reactors, according to the ALOHA model, an increase in wind speed from 3 to 7m/h led to an expanded dispersion radius. At a radius of 700m from the reactor, the maximum outdoor concentration reached 3.12ppm, decreasing to 2.27ppm at 800m and further to 1.53ppm at 1000m. The comparison of numerical calculations and modeling using the ALOHA software indicates the desirable conformity of the results with each other. The R2 coefficient for evaluating the conformity of the results was 0.9964, indicating the desired efficiency of the model in evaluating the consequences of the release of toxic gasses from the EDC tank. The results of this research can be useful in designing the site and emergency response plan.

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

Application of 3D laser scanning technology for mapping and accuracy assessment of the point cloud model for the Great Achievement Palace heritage building

Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building.

Risk assessment model based on nucleotide metabolism-related genes highlights SLC27A2 as a potential therapeutic target in breast cancer.

Breast cancer (BC) is the most prevalent malignant tumor worldwide among women, with the highest incidence rate. The mechanisms underlying nucleotide metabolism on biological functions in BC remain incompletely elucidated. MATERIALSAND METHODS: We harnessed differentially expressed nucleotide metabolism-related genes from The Cancer Genome Atlas-BRCA, constructing a prognostic risk model through univariate Cox regression and LASSO regression analyses. A validation set and the GSE7390 dataset were used to validate the risk model. Clinical relevance, survival and prognosis, immune infiltration, functional enrichment, and drug sensitivity analyses were conducted. Our findings identified four signature genes (DCTPP1, IFNG, SLC27A2, and MYH3) as nucleotide metabolism-related prognostic genes. Subsequently, patients were stratified into high- and low-risk groups, revealing the risk model's independence as a prognostic factor. Nomogram calibration underscored superior prediction accuracy. Gene Set Variation Analysis (GSVA) uncovered activated pathways in low-risk cohorts and mobilized pathways in high-risk cohorts. Distinctions in immune cells were noted between risk cohorts. Subsequent experiments validated that reducing SLC27A2 expression in BC cell lines or using the SLC27A2 inhibitor, Lipofermata, effectively inhibited tumor growth. We pinpointed four nucleotide metabolism-related prognostic genes, demonstrating promising accuracy as a risk prediction tool for patients with BC. SLC27A2 appears to be a potential therapeutic target for BC among these genes.

Development of a maturity assessment model for sustainable tourism

Development of a maturity assessment model for sustainable tourism

DARE-ISC model for prediction of 1-year ischaemic stroke risk in the general population and atrial fibrillation patients: a Danish nationwide cohort study.

To develop a risk assessment model (DAnish REgister Ischaemic Stroke Classifier, DARE-ISC) for predicting 1-year primary ischaemic stroke/systemic embolism (SE) in the general population. Secondly, to validate the accuracy DARE-ISC in atrial fibrillation (AF) patients where well-established models and risk scores exist. Retrospective cohort study. DARE-ISC was developed using gradient boosting decision trees with information from 375 covariates including baseline information on relevant diagnoses, demographic characteristics, registered health-services, lifestyle-related covariates, hereditary stroke components, drug prescriptions and stress proxies. Danish nationwide registries. All Danish individuals aged ≥18 from 2010 to 2017 (n=35 519 348 person-years). The model was trained on the 2010-2016 cohorts with validation in the 2017 cohort. Model optimisation and validation were performed through comparison of the area under the receiver operating characteristic curve (AUC) and average precision scores. Additionally, the relative importance of the model covariates was derived using SHAP values. DARE-ISC had an AUC (95% CI) of 0.874 (0.871 to 0.876) in the general population. In AF patients, DARE-ISC was superior to the GARFIELD-AF risk model and CHA2DS2-VASc score with AUC of 0.779 (95% CI 0.75 to 0.806), 0.704 (95% CI 0.674 to 0.732) and 0.681 (95% CI 0.652 to 0.709), respectively. Furthermore, in AF patients, DARE-ISC had an average threefold and fourfold higher ratio of correctly identified strokes compared with the GARFIELD-AF risk model and CHA2DS2-VASc score, as indicated by average precision scores of 0.119, 0.041 and 0.034, respectively. DARE-ISC had a very high stroke prediction accuracy in the general population and was superior to the GARFIELD-AF risk model and CHA2DS2-VASc score for predicting ischaemic stroke/SE in AF patients.

Integrating portable X-ray fluorescence site survey and ArcGIS models for rapid risk assessment and mitigation strategies at an abandoned arsenic mine site: a case study

ABSTRACT Australia’s metalliferous abandoned mine sites (MAMSs), pose tangible threats to the environment and human health. To address these concerns, our study utilised state-of-the-art handheld XRF technology to conduct a real-time assessment of the Mole River arsenic mine site. The data revealed notably elevated levels of arsenic and manganese, with the southeast corner of the site identified as a contaminant hotspot. We used a tiered risk assessment approach to compare the detected contaminant concentrations to the Australian health investigation levels (tier 1). This led us to a broader examination of erosion vulnerabilities and the potential migration of contaminants (tier 2). Further, a hydrological assessment (tier 3) identified significant erosion in southern regions, indicating the potential for contaminants to be transported off-site through surface water runoff to Sam’s Creek and Mole River. The proximity of a reservoir to these runoff pathways brought forth additional challenges, especially during heavy rainfall events. Subsequent laboratory analysis of water samples reinforced our findings, as they confirmed heightened arsenic concentrations in Mole River downstream, accentuating the potential risks to ecosystems and human health. By integrating the XRF contour map and erosion assessment with the RUSLE model, valuable insights are gained into critical hotspots with high contamination and erosion potential. By directing rehabilitation efforts towards critical hotspots, resources can be allocated more efficiently and cost-effectively.

Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid ultrasound morphology.

Our primary aim was to identify radiomic ultrasound features that can distinguish benign from malignant adnexal masses with solid ultrasound morphology, and primary invasive from metastatic solid ovarian masses, and to develop ultrasound-based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. Our secondary aim was to compare the diagnostic performance of our radiomics models with that of the ADNEX model and subjective assessment by an experienced ultrasound examiner. This is a retrospective observational single center study. Patients with a histological diagnosis of an adnexal tumor with solid morphology at preoperative ultrasound examination performed between 2014 and 2021 were included. The patient cohort was split into training and validation sets with a ratio of 70:30 and with the same proportion of benign and malignant (borderline, primary invasive and metastatic) tumors in the two subsets. The extracted radiomic features belonged to two different families: intensity-based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine-tuned using 5-fold cross-validation over the training set, and tested on the held-out validation set. The variables used in model building were patient's age, and those radiomic features that were statistically significantly different between benign and malignant adnexal masses (Wilcoxon-Mann-Whitney Test with Benjamini-Hochberg correction for multiple comparisons) and assessed as not redundant based on the Pearson correlation coefficient. We describe discriminative ability as area under the receiver operating characteristics curve (AUC) and classification performance as sensitivity and specificity. 326 patients were identified and 775 preoperative ultrasound images were analyzed. 68 radiomic features were extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 features were selected for inclusion in model building. The same 52 radiomic features differed statistically significantly between benign, primary invasive malignant and metastatic tumors. However, the values of the features manifested overlap between primary malignant and metastatic tumors and did not differ statistically significantly between them. In the validation set, 25/98 tumors (25.5%) were benign, 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastases). In the validation set, a model including only radiomics features had an AUC of 0.80, and 78% sensitivity and 76% specificity at its optimal risk of malignancy cutoff (68% based on Youden's index). The corresponding results for a model including age and radiomics features were 0.79, 86% and 56% (cutoff 60% based on Youden's method), while those of the ADNEX model were 0.88, 99% and 64% (at 20% malignancy cutoff). Subjective assessment had sensitivity 99% and specificity 72%. Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are promising enough to justify continued development of radiomics analysis of ultrasound images of adnexal masses. This article is protected by copyright. All rights reserved.

A Systematic Review of Simulation in Burn Care: Education, Assessment, and Management.

Assessment and management of burns require nuanced, timely interventions in high-stake settings, creating challenges for trainees. Simulation-based education has become increasingly popular in surgical and nonsurgical subspecialties to supplement training without compromising patient safety. This study aimed to systematically review the literature on existing burn management-related simulations. A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles describing burn-specific surgical and nonsurgical simulation models were included. The model type, study description, simulated skills, assessment methods, fidelity, cost, and outcomes were collected. Of 3,472 articles, 31 met inclusion criteria. The majority of simulations were high-fidelity (n=17, 54.8%). Most were immersive (n=17, 54.8%) and used synthetic benchtop models (n=13, 41.9%), whereas none were augmented/virtual reality. Simulations of acute and early surgical intervention techniques (n=16, 51.6%) and burn wound assessments (n=15, 48.4%) were the most common, whereas burn reconstruction was the least common (n=3, 9.7%). Technical skills were taught more often (n=29, 93.5%) than non-technical skills (n=15, 48.4%). Subjective assessments (n=18, 58.1%) were used more often than objective assessments (n=23, 74.2%). Of the studies that reported costs, 91.7% (n=11) reported low costs. This review identified the need to expand burn simulator options, especially for burn reconstruction, and highlighted the paucity of animal, cadavers, and augmented/virtual reality models. Developing validated, accessible burn simulations to supplement training may improve education, patient safety, and outcomes.

Are vehicle lifespan caps an effective and efficient method for reducing US light-duty vehicle fleet GHG emissions?

With light duty vehicles (LDVs) responsible for 17% of annual US greenhouse gas (GHG) emissions, integrating emerging GHG-reducing technologies into the fleet is essential. However, the slow rate of vehicle turnover presents a significant barrier to the market penetration of new technologies, with adoption delayed by the low number of vehicles needing replacement each year. A strategy of accelerated vehicle turnover through a vehicle lifespan cap could potentially mitigate this limit. While older studies reach differing conclusions on their effectiveness, two newer studies that incorporate life cycle assessment find that accelerated turnover strategies can be effective if coupled with high levels of electric vehicle deployment. We seek to determine whether a vehicle lifespan cap strategy can be an effective and efficient (cost-effective) method for reducing US LDV fleet GHG emissions. We augment the capabilities of the Fleet Life Cycle Assessment and Material Flow Estimation (FLAME) fleet life cycle assessment model, integrating vehicle lifespan caps and comprehensive calculations of cost along with sensitivity analysis for electric vehicle survival curves and battery degradation. The augmented FLAME model is used to analyse the impact of vehicle lifespan caps of varying lengths on a suite of scenarios, including a business as usual (BAU) scenario and eight scenarios modelling different technology improvement assumptions. This work confirms that vehicle lifespan caps have limited effectiveness in reducing GHG emissions under a BAU scenario but show potential to meaningfully reduce GHG emissions in a scenario with accelerated deployment of electric vehicles. However, abatement costs are high, exceeding 2020 USD 1000/tCO2eq under baseline assumptions, but falling within the range of current estimates of the social cost of carbon under more optimistic assumptions. Overall, vehicle lifespan caps must be carefully considered as they accelerate both the benefits and costs of new vehicle technologies, and are best positioned as part of a larger integrated strategy for tackling transportation GHG emissions.

Small-sample Linear Profile Error Uncertainty Assessment Based On Grey System

Abstract The uncertainty assessment of the profile error of the cam profile, as defined in the national standard method, is difficult to carry out under conditions of small sample size and absence of probability distribution assumptions. This paper proposes a small-sample assessment model for the uncertainty of the profile error based on grey system. Firstly, the coordinate transformation is conducted using Vector Alignment Method（VAM）to reduce systematic errors, and the Non-Uniform Rational B-splines(NURBS)curve interpolation is utilized to fit the cam profile curve and perform error assessment. Subsequently, based on the error assessment results, Grey Information Measurement Model (GIMM) for the uncertainty of the profile error in small samples is established. This model employs Grey Relational Analysis (GRA) to eliminate outliers and evaluates the uncertainty of the profile error by solving grey correlation coefficients. Maximum-Minimum Information Measure Method (MIMM) is used to assess the optimal sample size. Finally, numerical experiments and experimental tests were conducted on the uncertainty of camshaft profile error in automobiles. A total of 15 sets of profile data were compared with Guide to the Representation of Uncertainty in Measurement(GUM) and Monte Carlo Method (MCM) under different sample sizes. The results showed that GIMM achieved evaluation with only 8 sets of data samples under small sample and poor information conditions, with an uncertainty of 0.6338, compared to 0.6346 for GUM and 0.6391 for MCM. The acceptance rate of GIMM reached 95.2%. This model outperforms other methods, providing a simplified and reliable assessment of cam profile error uncertainty.

Environmental, Energy, and Techno-Economic Assessment of Waste-to-Energy Incineration

Waste-to-energy (WtE) incineration is a feasible way to respond to both the municipal solid waste management and renewable energy challenges, but few studies have been carried out on its environmental and economic impact in fast-developing southeastern Asian countries. To fill such a research gap, this study innovatively conducted a holistic assessment of WtE incineration application potential in Java Island, Indonesia. Here, we have established a life cycle assessment model for WtE incineration in Java, and have estimated the environmental impact, electricity generation potential, and techno-economic feasibility of implementing incineration by 2025. We have revealed that global warming potential, terrestrial ecotoxicity potential, eutrophication potential, and acidification potential are the major environmental impacts stemming from incineration activities. Moreover, we have estimated that promoting incineration in Java could reduce CO2 emissions by 41% on average. The electricity generated from incineration could contribute to 3.72% of Indonesia’s renewable energy target for the electricity grid mix by 2025. The cumulative energy production potential from incineration is estimated to reach 2,316,523 MWh/year in 2025 and will increase by 14.3% in 2050. The techno-economic assessment of incineration implementation in Java cities has been enumerated as feasible. The levelized cost of electricity from incineration (0.044 USD/kWh) is competitive with the current Indonesian electricity price (0.069 USD/kWh). Policies of minimizing incineration pollution, providing financial support guarantees, and overcoming social barriers have been proposed to facilitate the application of WtE incineration.