Model Assessment Research Articles

Reasoning in Institutional Discourse – The Development of a Sustainable Impact Assessment Model

Reasoning in Institutional Discourse – The Development of a Sustainable Impact Assessment Model

Systematic initial embodied carbon assessment of concrete modular high-rise residential buildings: a case in Hong Kong

Reducing carbon emissions from buildings is essential to achieving carbon neutrality. Modular construction (MC) has been adopted as an innovative approach to improve construction sustainability. However, detailed embodied carbon (EC) assessment of concrete modular high-rise residential buildings is lacking. This study aims to systematically assess the cradle-to-end-of-construction EC of concrete modular high-rise residential buildings. A multilevel EC assessment model was proposed and validated to assess the EC at different spatiotemporal levels. A concrete modular high-rise staff quarters building was selected for case study. Its cradle-to-end-of-construction EC was quantified as 569.3 kgCO2e/m2 with around 80% from material production. The structural modules with structural walls generated more EC than the nonstructural modules. The residential area comprising the modules had higher EC emissions than the communal area. The case building generated 14.6% less cradle-to-site EC from the production and transportation of structural materials but 20% more cradle-to-end-of-construction EC compared with two prefabricated panelized building counterparts, respectively. The cradle-to-end-of-construction EC of the case building was reduced by 8.3% by eliminating the nonstructural module floor and by 16.7% by further decreasing the nonstructural module walls. The results show that concrete MC reduces the EC at the construction stage but does not necessarily achieve a lower initial EC than its conventional counterpart. Resolving the double-panel issues can effectively mitigate the EC of modular buildings. These findings demonstrate the effectiveness of the proposed multilevel EC assessment model and provide valuable insights for future research on the systematic EC assessment and reduction of modular buildings.

Few shot intelligent assessment method for compaction quality of earthwork considering uncertainty

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices.

Environmental Impacts in Water Resources Surrounding World Surfing Reserves: A Case Study in the Madre Watershed, Brazil

In order to promote the adoption of an ecosystem-based approach in environmental management processes, integrated assessment studies can be conducted to link human activities and observed impacts on natural environments. Analytical models such as DPSIR are used, based on the monitoring of environmental indicators, to improve planning and management processes for water resources at multiple scales. World Surfing Reserves (WSRs) are institutions that have been utilizing this environmental assessment model to successfully implement environmental management strategies and instruments in coastal zones. Building upon the adaptive co-management model developed in the 6th WSR of Bahía de Todos Santos (Mexico), which incorporates DPSIR analysis and the use of GIS (remote sensing) tools in environmental assessment, this research develops a protocol for integrated environmental assessment, focusing on water resource management, through a case study conducted in the 9th WSR of Guarda do Embaú, Santa Catarina, Brazil. It is understood that this protocol can be utilized in other surfing reserves, especially in estuarine regions, to identify priority aspects for water resource management through: (i) water quality indicators, (ii) institutional environmental monitoring reports, and (iii) georeferenced statistical data (GIS). Based on these data, integrated analyses were conducted to determine which polluting agents pose a greater risk to the sustainable management of water resources surrounding the 9th WSR. Among the observed results, it was found that multiple pressure factors pose high environmental risk to the water resource stocks, mainly due to the discharge of urban and agricultural effluents into rivers, as well as deforestation in protected areas directly associated with the conservation of water reservoirs. As a result of these factors, a moderate risk of eutrophication, water scarcity, unavailability of recreational water use, disease spread, and sedimentation was observed, alongside a high risk of biodiversity loss, habitat loss, and unavailability of drinking water. Due to the risks of the mentioned impacts, the aspects identified in this study require management at the appropriate scale for processes occurring in the watershed, in order to gradually mitigate the effects of polluting activities.

METS-IR/HOMA-IR and MAFLD in U.S. adults: dose–response correlation and the effect mediated by physical activity

ObjectivesMetabolic dysfunction-associated fatty liver disease (MAFLD), a globally prevalent disease, is closely linked to insulin resistance (IR). Physical activity (PA) is closely linked to both MAFLD and IR. We aim to explore the dose–response relationship between metabolic score for IR (METS-IR)/homeostasis model assessment of IR (HOMA-IR) and MAFLD, and investigate the relationship between PA, IR and MAFLD.MethodsParticipants from the NHANES study were included in this cross-section study. Logistic regression and the receiver operating characteristic were used to assess the predictive performance of METS-IR/HOMA-IR for MAFLD. Restrictive cubic splines were performed to visualize their dose–response relationship. Decision tree analysis was used to identify high-risk populations of MAFLD. PA’s mediating effect in the association between METS-IR/HOMA-IR and MAFLD was also examined.ResultsOf all 1,313 participants, 693 had MAFLD (52.78%). There were a positive association between METS-IR (OR = 1.162, 95% CI = 1.126–1.199) and HOMA-IR (OR = 1.630, 95% CI = 1.431–1.856) and MAFLD risk. The AUCs of the METS-IR and HOMA-IR were 0.831 (0.809, 0.853) and 0.767 (0.741, 0.791), respectively, with significantly different predictive performance (P < 0.001). Adding METS-IR/HOMA-IR to the basic model greatly improved the statistical significance for MAFLD. Five high-risk subgroups were identified for MAFLD. PA mediated about 0.81% and 0.78% (indirect effect/total effect) in the association between METS-IR/HOMA-IR and MAFLD.ConclusionsMAFLD risk might be predicted by METS-IR/HOMA-IR, among which METS-IR performed better. And PA mediated the association between them. More attention should be paid to the therapeutic effect of lifestyle changes on MAFLD.Highlights1. Positive associations were found between METS-IR and HOMA-IR and MAFLD risk.2. METS-IR has better predictive performance for MAFLD risk than HOMA-IR.3.Two high-risk subgroups were identified for MAFLD by METS-IR: individuals with METS-IR ≥ 40; Hispanic black individuals with 34 ≤ METS-IR < 40 and aged ≥ 46.4. In the significant association between METS-IR/HOMA-IR and MAFLD, about 0.81% and 0.78% (indirect effect/total effect), respectively, were mediated by physical activity.Graphical

Long-term insulin resistance is associated with frailty, frailty progression, and cardiovascular disease.

Insulin resistance and diabetes are associated with an increased risk of frailty, and frailty is associated with cardiovascular disease and premature mortality. We aim to investigate the impact of long-term insulin resistance trajectories on future frailty and cardiovascular risk among young adults. In total, 3168 participants with a 30-year follow-up period. The baseline period covered the first 15years as the exposure period. Insulin resistance was determined using the homeostasis model assessment for insulin resistance (HOMA-IR), and three trajectories (low, moderate, and high) were constructed. The subsequent 15years constituted the event accrual period. Frailty was assessed using a deficit accumulation mode, and cardiovascular outcomes were obtained from the 15-year event accrual period. The mean age of all 3168 participants was 41.0 (37.0-43.0) years, with 1750 (55.2%) being women. Participants in the high level of insulin resistance trajectory had an increased prevalence of frailty (OR: 1.55, 95% CI: 1.05-2.30, P=0.028). Although no statistically significant associations were observed after full adjustment, single-factor analysis indicated association between the moderate and high trajectories and frailty progression. Additionally, participants with high level of insulin resistance trajectory were associated with an increased risk of cardiovascular disease, coronary heart disease, and stroke. A notable correlation between HOMA-IR trajectory and cardiovascular diseases was still discernible within the subgroup where the frailty index ≥0.12 (HR: 2.12, 95% CI: 1.17-3.83, P=0.013) (P for interaction >0.05). Long-term high level of insulin resistance is associated with high prevalence of frailty, and an increased risk of cardiovascular events. Emphasizing the importance of early prevention and intervention for abnormal glucose metabolism in young adults to prevent frailty and cardiovascular disease.

Gingipain from Porphyromonas gingivalis causes insulin resistance by degrading insulin receptors through direct proteolytic effects

Periodontitis is a critical risk factor for the occurrence and development of diabetes. Porphyromonas gingivalis may participate in insulin resistance (IR) caused by periodontal inflammation, but the functional role and specific mechanisms of P. gingivalis in IR remain unclear. In the present study, clinical samples were analysed to determine the statistical correlation between P. gingivalis and IR occurrence. Through culturing of hepatocytes, myocytes, and adipocytes, and feeding mice P. gingivalis orally, the functional correlation between P. gingivalis and IR occurrence was further studied both in vitro and in vivo. Clinical data suggested that the amount of P. gingivalis isolated was correlated with the Homeostatic Model Assessment for IR score. In vitro studies suggested that coculture with P. gingivalis decreased glucose uptake and insulin receptor (INSR) protein expression in hepatocytes, myocytes, and adipocytes. Mice fed P. gingivalis tended to undergo IR. P. gingivalis was detectable in the liver, skeletal muscle, and adipose tissue of experimental mice. The distribution sites of gingipain coincided with the downregulation of INSR. Gingipain proteolysed the functional insulin-binding region of INSR. Coculture with P. gingivalis significantly decreased the INSR–insulin binding ability. Knocking out gingipain from P. gingivalis alleviated the negative effects of P. gingivalis on IR in vivo. Taken together, these findings indicate that distantly migrated P. gingivalis may directly proteolytically degrade INSR through gingipain, thereby leading to IR. The results provide a new strategy for preventing diabetes by targeting periodontal pathogens and provide new ideas for exploring novel mechanisms by which periodontal inflammation affects the systemic metabolic state.

Premature pubarche in Prader-Willi syndrome: Risk factors and consequences.

Children with Prader-Willi Syndrome (PWS) may develop premature pubarche (PP). We investigated the frequency of PP, and its potential precursors and sequelae, in PWS. A chart review of children with PWS treated at our institution between 1990 and 2021 was performed. PP was defined as Tanner stage 2 (TS2) pubic hair in girls <8 and boys <9 years old. Demographic, anthropometric, and laboratory data were collected to assess predisposing factors and consequences of PP in comparison to patients with PWS who had normal pubarche (NP). Analysis included 43 children with PWS, 23 (53.5%) with PP and 20 (46.5%) with NP. Median age at pubarche was 7.0 years in PP group and 10.0 years in NP group. Age at pubarche was not correlated with age of recombinant human growth hormone (rhGH) initiation, body mass index (BMI) z-score, or homeostasis model assessment of insulin resistance (HOMA-IR) at pubarche. BMI z-score at pubarche was modestly correlated with degree of pubarchal BA advancement (p = 0.033). Those with PP were more likely to have a lower high-density lipoprotein (HDL) (1.05 mmol/L vs. 1.41 mmol/L in the NP group, p = 0.041). The difference between target and final height did not differ between groups (p = 0.507). PP is common in PWS but does not compromise final height in comparison to the NP group. Obesity and insulin resistance were not associated with PP in children with PWS, contrary to what has been seen in obese children without PWS.

Understanding the role of the complement system in insulin resistance and metabolic syndrome in patients with rheumatoid arthritis.

The complement system has been associated with the etiopathogenesis of rheumatoid arthritis (RA). Insulin resistance (IR) and metabolic syndrome are prevalent among RA patients. The aim of this study was to explore the relationship between a comprehensive evaluation of the complement system and IR, as well as metabolic syndrome, in RA patients. 339 non-diabetic patients with RA were recruited. Functional assays of the three complement pathways were assessed. Additionally, serum levels of individual components of the complement system were measured: C1q (classical), lectin (lectin), C2, C4, and C4b (classicallectin), factor D and properdin (alternative), C3 and C3a (common), C5, C5a, and C9 (terminal), as well as regulators factor I and C1-inhibitor. IR and beta cell function indices were calculated using the homeostatic model assessment (HOMA). Criteria for metabolic syndrome were applied. Multivariable linear regression analysis was performed to investigate the association between the complement system and IR in RA patients. Many elements of the upstream and common complement pathways, but not the functional tests of the three routes, correlated positively with higher levels of IR and beta cell function. However, after multivariable adjustment for factors associated with IR, these relationships were lost. Conversely, the presence of metabolic syndrome in patients with RA maintained a relationship with higher levels of C1q, C4, C3, properdin and factor I after adjusting for confounders. There is a positive correlation between the complement system and metabolic syndrome among non-diabetic patients with RA. This association is independent of traditional IR factors.

Insight into the Predictive Power of Surrogate Diagnostic Indices for Identifying Individuals with Metabolic Syndrome.

This study aimed to assess the diagnostic capability of insulin surrogate measurements in identifying individuals with metabolic syndrome (MetS) and propose applicable indices derived from fasting values, particularly in large study populations. Data were collected from the datasets of the Surveillance of Risk Factors of NCDs in Iran Study (STEPS). MetS was defined based on the National Cholesterol Education Program (NCEP) criteria. Various insulin surrogate indices, including Homeostasis Model Assessment (HOMA), Quantitative Insulin Sensitivity Check Index (QUICKI), Fasting glucose to insulin ratio (FGIR), Reynaud, Reciprocal insulin, McAuley, Metabolic Score for Insulin Resistance (METS-IR), Triglyceride-glucose index (TyG), TG/ HDL-C, TG/ BMI, and TG/ WC ratio were assessed. Receiver Operating Characteristic (ROC) curves were used to assess pathologic conditions and determine the optimal cut-off through the highest score of the Youden index. Also, Area Under the Curve (AUC) values were established for each index totally and according to sex, age, and BMI differences. The study population consisted of 373 individuals (49.9% women; 75.1% middle age, 39.1% obese, and 27.3% overweight), of whom 117 (31.4%) had MetS. The METS-IR (AUC: 0.856; 95% CI: 0.817-0.895), TG/ HDL-C (AUC: 0.820; 95% CI: 0.775-0.886), TyG (AUC: 0.808; 95% CI: 0.759-0.857), and McAuley (AUC: 0.804; 95% CI: 0.757-0.852) indices provided the greatest AUC respectively for detection of MetS. The values of AUC for all the indices were higher in men than women. This trend was consistent after data stratification based on BMI categories, middle age, and senile individuals. The present study indicated that indices of insulin, including METS-IR, TG/HDLC, TyG, and McAuley, have an equal or better capacity in determining the risk of MetS than HOMA-IR, are capable of identifying individuals with MetS and may provide a simple approach for identifying populations at risk of insulin resistance.

Unleashing the role of knowledge absorptive capacity and relationship quality: exploring the outcomes of digital supply chain transformation in new norms

PurposeThis study aims to explore the potential role of supply chain digital transformation on collaborative knowledge creation, supply chain innovation, and value co-creation in new norms. It also examines the impact of collaborative knowledge creation and supply chain innovation on value co-creation. Furthermore, the study examines the impact of collaborative knowledge creation on supply chain innovation. Finally, it investigates the possible mediating role of knowledge absorptive capacity and relationship quality in shaping these interactions.Design/methodology/approachTo establish the empirical part of this study, the collection of data involved distributing a questionnaire to 247 managers working in manufacturing companies. The measurement model assessment and hypothesis testing were performed employing the PLS-SEM approach.FindingsThe findings indicate that supply chain digital transformation significantly impacts collaborative knowledge creation, supply chain innovation, and value co-creation. This study also confirms the significant impact of collaborative knowledge creation on supply chain innovation and value co-creation. Furthermore, it reveals that knowledge absorptive capacity mediates the impact of supply chain digital transformation on collaborative knowledge creation. It also shows that the impact of collaborative knowledge creation on supply chain innovation and value co-creation is mediated by relationship quality among participants.Originality/valueThe findings of this study make significant contributions to academic theory, existing literature, and the scholarly community within the realms of supply chain management, innovation, knowledge management, and value co-creation. It also offers practical implications for managers to strategically navigate the evolving norms of supply chain management. Companies can use these insights to improve their innovation processes and knowledge management, while policymakers can consider the study's findings when developing supportive frameworks for the manufacturing sector.

Predicting metformin efficacy in improving insulin sensitivity among women with polycystic ovary syndrome and insulin resistance: a machine learning study

ObjectiveMetformin is clinically effective in treating polycystic ovary syndrome (PCOS) with insulin resistance (IR), while its efficacy varies among individuals. This study aims to develop a machine learning model to predict the efficacy of metformin in improving insulin sensitivity among women with PCOS and IR. MethodsThis is a retrospective analysis of a multicenter, randomized controlled trial involving 114 women diagnosed with PCOS and IR. All women received metformin treatment for 4 months. We incorporated 27 baseline clinical variables of the women into the construction of our machine learning model. We firstly compared four commonly used feature selection methods to screen valuable clinical variables. Then we used the valuable variables as inputs to evaluate the performance of five machine learning models, including k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (Xgboost), in predicting the efficacy of metformin. ResultsAmong the five machine learning models, SVM performed the best with an area under the receiver operating characteristic curve (AUC) of 0.781 (95% confidence interval [CI]: 0.772-0.791). The key predictive variables identified were homeostasis model assessment of insulin resistance (HOMA-IR), body mass index (BMI), and low-density lipoprotein cholesterol (LDL-C). ConclusionThe developed machine learning model could be applied to to predict the efficacy of metformin in improving insulin sensitivity among women with PCOS and IR. The result could help doctors evaluate the efficacy of metformin in advance, optimize treatment plans, and thereby enhance overall clinical outcomes.

One out of two idiopathic infertile men has pathologic sperm DNA fragmentation values: Potential implications for clinical practice.

To investigate the distribution of sperm DNA fragmentation (SDF) values and their association with clinical and seminal parameters in idiopathic infertile men. Data from 3224 primary infertile men (belonging to couples having failed to conceive a pregnancy within 12 months) who underwent a thorough diagnostic work-up were analysed. A SDF value ≥ 30% (according to Sperm Chromatin Structure Assay) was considered pathologic. We excluded: (1) men with genetic abnormalities; (2) men with history of cryptorchidism; (3) men with biochemical hypogonadism; (4) men with clinical varicocele; and (5) men with other possible known aetiological factors. Descriptive statistics and logistic regression analyses were used to describe the whole cohort. Of all, 792 (23%) men with at least one abnormal WHO semen parameter but without any identified aetiologic factor for infertility, were considered as idiopathic infertile men. Of 792, 418 (52.7%) men had SDF≥30%. Men with pathologic SDF were older (p = .02), had higher Follicle-stimulating hormone(FSH) (p = .04) but lower total testosterone (p = .03) values than those with SDF<30%. The homoeostatic model assessment index for insulin resistance (HOMA-IR) was higher in men with SDF≥30% (p = .01). Idiopathic infertile men with SDF≥30% presented with lower sperm concentration (p < .001) and lower progressive sperm motility (p < .01) than those with SDF < 30%. Logistic regression analysis revealed that older age (OR: 1.1, p = .02) and higher HOMA-IR score (OR: 1.8, p = .03) were associated with SDF ≥ 30%, after accounting for FSH and sperm concentration values. Approximately half of infertile men categorized as idiopathic had pathologic SDF values. Idiopathic infertile men with pathologic SDF showed worse clinical, hormonaland semen parameters than those with normal SDF values. These results suggest that including SDF testing could be clinically relevant over the real-life management work-up of infertile men.

Is there any concern of insulin resistance and metabolic dysfunctions with antiseizure medications? A prospective comparative study of valproate vs. levetiracetam

PurposeTo evaluate the incidence of insulin resistance and its association with change in serum anti-seizure medication (ASM) level and their pharmacokinetic, body composition and metabolic hormones after six months of levetiracetam (LEV) exposure in persons with epilepsy (PWE) in comparison to valproate (VPA). MethodsThis prospective-longitudinal study included clinically diagnosed PWE on VPA or LEV monotherapy (for<3 months). At enrolment, body weight/composition, BMI were measured and blood samples were collected for assessing metabolic dysfunctions by estimation of serum insulin, insulin resistance [in terms of Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)], leptin, adiponectin, lipid profile along with ASMs level. Subjects were followed up for six months and all the above parameters were reassessed. ResultsA total of 150 PWE were screened based on inclusion and exclusion criteria, and 105 number of subjects were enrolled (n=35 in VPA and n=70 in LEV group). Out of them, 92 subjects (n=32 in VPA; n=60 in LEV) completed six months follow-up. After six months, serum insulin level increased significantly in VPA group compared to baseline p<0.001). Insulin resistance (HOMA-IR>2.5) was observed in 14.28% of PWE in VPA group. Significantly higher percentage-change in body-weight (p=0.003), leptin and decreased adiponectin were found in VPA-group compared to baseline ((p=0.003, 0.02, 0.001, <0.001, respectively). These changes were independent of serum level or pharmacokinetic of VPA. On the other hand, no such changes were observed in LEV-group despite increased serum LEV level and altered pharmacokinetic parameters after six months. ConclusionSix months treatment with VPA resulted in insulin resistance and metabolic dysfunctions in PWE. These alterations were not correlated with change in VPA serum level. These changes were not observed in LEV therapy suggesting its better safety profile. This may be considered while prescribing the ASM like VPA and LEV in adult patients with obesity or insulin resistance and diabetes.

A Single-group, Clinical Interventional Study on the Effect of Iron Deficiency Anemia on Blood Glucose and Insulin Resistance in Women with Type II Diabetes.

Iron deficiency anemia (IDA) is one of the disorders recently associated with an increase in insulin resistance (IR) and, consequently, diabetes mellitus (DM) affection by causing oxidative stress. In this study, we look at how IDA may contribute to developing type II diabetes mellitus (T2DM), controlling diabetes, and reducing IR in women with T2DM. In this single group, clinical interventional study, we enrolled 40 women with T2DM and IDA. Before and after intervention with ferrous sulfate tablets, their blood glucose (BG) levels and IR levels were evaluated. This study was approved by the Ethics Committee of Qom University of Medical Sciences (ethics code: IR.MUQ.REC.1397.031) and registered at the Iranian Center for Clinical Trials (No. IRCT20170215032587N3). A significant level was considered p <0.05. The mean age of patients was 48.18 ± 4.6 years, with 5.3-5.8 years duration of T2DM. After the intervention, the mean fasting blood glucose (FBG) level reached 198.53 ± 48.11 to 170.93 ± 37.41, which was significant (p <0.0001). Also, hemoglobin A1C level reached from 8.49 ± 0.9 to 7.96 ± 0.58, which was significant (p <0.0001). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) demonstrating a significant reduction of IR levels after intervention with ferrous sulfate tablets (p <0.018). IDA treatment in patients with T2DM can significantly reduce the BG and IR levels. To better control BG, checking iron status and its correction may provide better clinical outcomes in these patients. IRCT20170215032587N3.

Deep learning-based GLOF modelling for hazard assessment and risk management

ABSTRACT Glacial Lake Outburst Flood (GLOF) has become a crucial aspect as the increase in the meltdown of glaciers results in the breach of unstable debris dams. Hence, it is essential to understand the nature of the glacial lakes for proper planning and development of the region in the long term. In this paper, a deep learning network is developed for GLOF hazard and risk assessment. The Shepard Convolutional Neural Network Fused Deep Maxout Network (ShCNNFDMN) is developed by fusing the Shepard Convolutional Neural Networks (ShCNN) and the Deep Maxout Network (DMN) based on regression analysis. Here, various data and feature attributes, like geometric properties, location properties, lake-based properties, and global properties are determined from the glacial lake data. Afterthat, hazard assessment is carried out based on these parameters by the ShCNNFDMN. Then, risk assessment is performed based on the hazard levels and the feature attributes. The ShCNNFDMN is analyzed based on metrics, such as Hazard modelling error, Risk prediction error, Mean Average Error (MAE), and R-Squared are found to produce values of 0.462, 0.423, 0.358, and 0.288, respectively. The proposed method is useful in applications, like infrastructure planning, taking preventive and mitigative actions in downstream areas of glacier lakes.

A wavelet packet deep learning model for Energy-Based structural collapse assessment under Earthquake-Fire Scenarios: Framework and hybrid simulation

An energy-based framework is proposed for the dynamic stability assessment of structures subjected to earthquake-fire scenarios and verified through earthquake-fire hybrid simulation. In this framework, the wavelet packet Long Short-Term Memory (LSTM) model is used to separate the noise and residue caused by earthquake and fire within the structural response signals, guided by wavelet packet energy and power spectral density of signals. Moreover, a comparative analysis is performed with 4 previous signal processing models (empirical mode decomposition, variational mode decomposition, wavelet packet transform, and LSTM). Additionally, Latin hypercube sampling is employed to account for uncertainties in structural characteristics and hazards for dataset establishment and fragility analysis. The experimental findings suggest a decline in the structural dominant frequency with the emergence of high-frequency noise and residuals in structural responses due to the multi-hazard effect. The wavelet packet transform eliminates the high-frequency noise in the signal and avoids the oscillation of the LSTM prediction results. Post-earthquake fire increases the structural collapse possibility even under a moderate earthquake excitation. The proposed framework proves to be rational and has the potential to be further applied to other multi-hazard scenarios.

Reliability of toxicokinetic modelling for PFAS exposure assessment in contaminated water in northern Italy

IntroductionLong-term contamination of tap water and groundwater by perfluoroalkyl and polyfluoroalkyl substances (PFASs) has been documented in the Veneto region of northern Italy. This study aimed to assess the exposure of individuals residing in the contaminated area and to test several toxicokinetic (TK) models of varying complexities to identify an efficient method for predicting perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) concentrations in human serum using observed data.The ultimate goal is to provide public health officials with guidance on selecting the appropriate TK model for specific contexts, a reliable and rapid tool to support human bio-monitoring (HBM) studies. MethodsTwo simpler empirical TK models and a more complex multi-compartment physiologically based toxicokinetic (PBTK) model were compared with individual and aggregate data from an HBM study. In addition, the PBPK model was modified by adjusting input parameters and introducing new terms into the equations within the original model code. These modifications aimed to optimize the results compared to the original model, with some versions incorporating adjustments to account for the influence of menstruation in women. All models were evaluated to understand their strengths and weaknesses, providing guidance on the appropriate model to use according to specific scenarios. ResultsThe results obtained from the tested models were quite similar, with significant improvements observed only in the modified models. Simpler models also provided satisfactory results in scenarios involving low PFOS serum concentrations and recent exposure cessation. In many cases, predictions demonstrated high accuracy, particularly at the aggregate level and for women. ConclusionsThese findings suggest that environmental protection agencies and health authorities may benefit from employing the tested models at the aggregate level as an initial step in HBM studies, rather than conducting more invasive and expensive screening campaigns.

Plasticization-assisted slow crack growth modeling of high-density polyethylene

The existing crack layer model can theoretically predict the slow crack growth behavior of high-density polyethylene. However, it can only be applied to oxidative environments causing chemical degradation. Most oil and gas field components, such as pressurized pipes, are subjected to hydrocarbon exposures, leading to a plasticized material response, i.e., shear yielding instead of crazing. Therefore, the effect of such sorptive media diffusion on the slow crack growth behavior and lifespans tf should be understood. In this work and for the first time, a novel crack layer model is developed that can simulate the diffusion-assisted slow crack growth behavior of plasticized high-density polyethylene. The proposed model was validated by comparing its prediction with experimental results and by conducting a sensitivity study on several input parameters. Using the proposed model, the reported plasticization results were reconstructed successfully including the SCG rate with R2 = 0.95. This study expands the applicability of the crack layer model for the reliability assessment of polyethylene pipes under various environmental conditions, including plasticizers.

Predicting borrower default risk using support vector machine AI models

Precise prediction on the likelihood of borrower default is pivotal for credit institution and decision makers to mitigate the loss of capital and rationalize decision process. This article reviewed the Effects of Support Vector Machine (SVM) models with radial basis function (RBF) kernel in predicting the mortality rate of borrowers. By integrating with a dataset of approximately 100,000 borrowers profile harvested through historical loan performance, we set up the SVM model, and employed a feature-distribution method utilizing grid search and cross-validation technique to fine-tune the predictive model of SVM. Results indicated that the model accomplished an excellent performance with accuracy of 92%, precision of 89%, the recall and F1-score of 85% and 87%, respectively, alongside an Area Under the Curve -Receiver Operating Characteristic (AUC-ROC value of 0.95). It was evinced that the model performed substantially better than traditional logistic regression and decision trees in discriminating defaulter from non-defaulter. The outcome informs that an in-depth process should be implemented on data preprocessing, featureselection, and parameter tuning to achieve a robust predictive model for credit risk assessment. The article concludes the potentials of AI based on the resort to artificial technology in revolutionising the risk assessment scheme within the financial industry.