Version Of Model Research Articles

#2739 Determinants of well-being of spousal caregivers of CKD patients: a directed qualitative content analysis

Abstract Background and Aims Informal caregivers are essential to patients with chronic kidney disease (CKD) in all stages of the disease. They provide, most often unpaid, practical and emotional support to someone with whom they have a personal relationship, mostly spouses, family members and friends. Support from caregivers helps CKD patients adjust to their disease and increase treatment adherence. However, CKD puts a large burden on both patients themselves and their caregivers. Overburdening is especially seen in spousal caregivers, as they are more often older, co-residents, perform longer hours of care, and suffer more from relational stress compared to other types of informal caregivers. Although previous studies show high levels of caregiver burden, a comprehensive understanding of caregiver role, caregiver burden, and factors that may relieve this burden is lacking. Therefore, our aim was to explore both the contributing and alleviating determinants of well-being of spousal caregivers of patients with CKD in a qualitative way. Method A qualitative interview study was performed among spousal caregivers of patients with CKD. Informal caregivers were recruited by the Dutch Kidney Patients Association to assist in the development of an informal caregiver module with interviews for the Kidney Guide (Nierwijzer.nl). Fifteen face-to-face interviews were held between December 2021 and April 2022 at participants’ homes. Interviews lasted approximately 1 to 1.5 hours. All participants were adults and able to speak and understand Dutch. A directed qualitative content analysis was performed, using the stress-appraisal model as a framework. The model proposes that the care demand (primary stressors) leads to the actual provision of care by the informal caregiver (primary appraisal). Consequently, the provision of care leads to consequences for the caregiver's own life (secondary stressors), leading to burden (secondary appraisal). Finally, the experienced burden leads to the outcome, namely caregivers’ well-being. Moderators change the effect of stressors (e.g., social support may alleviate burden by sharing care tasks). Results Of the fifteen participating spousal caregivers, nine were female. Caregivers’ age ranged from 34 to 82 years. Duration of informal caregiving ranged from approximately 4 to 37 years. Most patients were on a type of kidney replacement therapy (KRT). In this study, we extended the stress-appraisal model to the context of CKD, providing a disease-specific model for practice (Fig. 1). Through this model, we describe the impact of care provision on all aspects of spousal caregivers’ lives and the burden associated with it, and identify possible modifiable factors to prevent overburdening of spousal caregivers. CKD-specific determinants included, amongst others, the impact of kidney disease and KRT on patients themselves as well as the associated caregiver tasks, for example adjusting to dietary restrictions and attending dialysis appointments. KRT options and flexibility (e.g., the choice for home or in-center dialysis) were CKD-specific moderators that alleviated burden. The care for someone on dialysis led to additional tasks (e.g., transportation to the dialysis center or assisting with home dialysis), negative impact on caregivers’ personal and social lives, and additional burden, emphasizing a distinction with caring for a CKD patient not on dialysis. Conclusion To our knowledge, this is the first study that gives a comprehensive overview of the contributing and alleviating determinants of the well-being of spousal caregivers of CKD patients. A disease-specific version of the stress-appraisal model for use in practice was provided, with evidence for all paths in the model. Moreover, we underline the need for acknowledgement of spousal caregivers in four areas: (1) personal and relational, (2) social environment, (3) healthcare, and (4) work and legislation (Fig. 2). This acknowledgement is of great importance to support and facilitate informal care within nephrology.

#2924 Comparison of large language models and traditional natural language processing techniques in predicting arteriovenous fistula failure

Abstract Background and Aims Large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), marking a shift from traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF). We developed a traditional NLP model to predict arteriovenous fistula (AVF) failure within next 30 days using clinical notes. The goal of this analysis was to investigate whether LLMs would outperform traditional NLP techniques, specifically in the context of predicting AVF failure within the next 30 days using clinical notes. Method We defined AVF failure as the change in status from active to permanently unusable status or temporarily unusable status. We used data from a large kidney care network from January 2021 to December 2021. Two models were created using LLMs and traditional TF-IDF technique. We used “distilbert-base-uncased”, a distilled version of BERT base model [1], and compared its performance with traditional TF-IDF-based NLP techniques. The dataset was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising of unseen patients’ data was used to evaluate the performance of the model. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Results The incidence of 30 days AVF failure rate was 2.3% in the population. Both LLMs and traditional showed similar overall performance as summarized in Table 1. Notably, LLMs showed marginally better performance in certain evaluation metrics. Both models had same AUROC of 0.64 on test data. The accuracy and balanced accuracy for LLMs were 72.9% and 59.7%, respectively, compared to 70.9% and 59.6% for the traditional TF-IDF approach. In terms of specificity, LLMs scored 73.2%, slightly higher than the 71.2% observed for traditional NLP methods. However, LLMs had a lower sensitivity of 46.1% compared to 48% for traditional NLP. However, it is worth noting that training on LLMs took considerably longer than TF-IDF. Moreover, it also used higher computational resources such as utilization of graphics processing units (GPU) instances in cloud-based services, leading to higher cost. Conclusion In our study, we discovered that advanced LLMs perform comparably to traditional TF-IDF modeling techniques in predicting the failure of AVF. Both models demonstrated identical AUROC. While specificity was higher in LLMs compared to traditional NLP, sensitivity was higher in traditional NLP compared to LLMs. LLM was fine-tuned with a limited dataset, which could have influenced its performance to be similar to that of traditional NLP methods. This finding suggests that while LLMs may excel in certain scenarios, such as performing in-depth sentiment analysis of patient data for complex tasks, their effectiveness is highly dependent on the specific use case. It is crucial to weigh the benefits against the resources required for LLMs, as they can be significantly more resource-intensive and costly compared to traditional TF-IDF methods. This highlights the importance of a use-case-driven approach in selecting the appropriate NLP technique for healthcare applications.

Deep behavioural phenotyping of the Q175 Huntington disease mouse model: effects of age, sex, and weight

BackgroundHuntington disease (HD) is a neurodegenerative disorder with complex motor and behavioural manifestations. The Q175 knock-in mouse model of HD has gained recent popularity as a genetically accurate model of the human disease. However, behavioural phenotypes are often subtle and progress slowly in this model. Here, we have implemented machine-learning algorithms to investigate behaviour in the Q175 model and compare differences between sexes and disease stages. We explore distinct behavioural patterns and motor functions in open field, rotarod, water T-maze, and home cage lever-pulling tasks.ResultsIn the open field, we observed habituation deficits in two versions of the Q175 model (zQ175dn and Q175FDN, on two different background strains), and using B-SOiD, an advanced machine learning approach, we found altered performance of rearing in male manifest zQ175dn mice. Notably, we found that weight had a considerable effect on performance of accelerating rotarod and water T-maze tasks and controlled for this by normalizing for weight. Manifest zQ175dn mice displayed a deficit in accelerating rotarod (after weight normalization), as well as changes to paw kinematics specific to males. Our water T-maze experiments revealed response learning deficits in manifest zQ175dn mice and reversal learning deficits in premanifest male zQ175dn mice; further analysis using PyMouseTracks software allowed us to characterize new behavioural features in this task, including time at decision point and number of accelerations. In a home cage-based lever-pulling assessment, we found significant learning deficits in male manifest zQ175dn mice. A subset of mice also underwent electrophysiology slice experiments, revealing a reduced spontaneous excitatory event frequency in male manifest zQ175dn mice.ConclusionsOur study uncovered several behavioural changes in Q175 mice that differed by sex, age, and strain. Our results highlight the impact of weight and experimental protocol on behavioural results, and the utility of machine learning tools to examine behaviour in more detailed ways than was previously possible. Specifically, this work provides the field with an updated overview of behavioural impairments in this model of HD, as well as novel techniques for dissecting behaviour in the open field, accelerating rotarod, and T-maze tasks.

#2742 Artificial intelligence accurately approximates MGPE dose response relationship in a North American Dialysis Population

Abstract Background and Aims The Anemia Control Model (ACM) is a certified medical device aimed at optimizing the management of CKD-related anemia among patients on dialysis. ACM is based on an artificial neural network (ANN) trained on over 900’000 patients treated in the European Nephrocare Network. Over the years, ACM has offered physicians hundreds of thousands of erythropoietin stimulating agent (ESA) and intravenous iron dosing suggestions and ensured high hemoglobin target achievement rate while minimizing drug use. However, evidence concerning its validity in non-European samples is still limited. Apollo Dial DB is the largest, fully anonymized, collaborative, international database of dialysis patients. The database includes detailed clinical information of more than 500,000 patients over millions of patient-months of observation with an extensive representation over geographies as well as ethnic groups. Given its detailed medical information and size, the database may offer unprecedented opportunities to develop and test equitable and fair decision support systems based on artificial intelligence. In this study we sought to re-train and validate and assess the accuracy of the ACM in an ethnically diverse sample of North American dialysis patients in Apollo Dial DB. Method We constructed the cohort for training and validation of this new ACM version by including a random sample of patients treated in the North American Fresenius Medical Care Dialysis Network. The sample accounted for 20% of the entire North American Section of the Apollo Dial DB dataset. Each patient history has been segmented in a collection of patient-months, which was the unit of analysis. The initial cohort was split in 3 datasets for training (70%), validation (20%) and test (10%). The input variables included laboratory test results, body weight, treatment data, age, sex, and previous ESA and iron prescription. The outcome of interest was the variation in serum hemoglobin concentration over 1 month of follow up. We compared the accuracy of the original ACM based on ANN (baseline model) with a newly trained algorithm based on high-performance light GBM. Results We included 44,220 patients followed by 2,277,276 patient-months overall. The mean age=60.9 ± 13.4, 59.2% were male, 56.4% were Caucasians, 32.2% African American, 3.2% Asians and 8.2% were others. At baseline, mean Hb= 10.9 ± 1.2. The original EMEA version of the Anemia Control Model (ACM) was still accurate in North American patients (mean absolute error, MAE = 0.65 g/dL). The fine-tuned light GBM model achieved a 16% reduction in error rate (MAE = 0.55 g/dL; Fig. 1). No difference in MAE was observed across sex and ethnic groups. Conclusion This study demonstrates the excellent predictive performance of the original ANN-based ACM in the North American dialysis population. Fine tuning by re-training ACM on the dataset further improved the accuracy of the model with substantial equivalence in performance across different demographics, thus ensuring algorithmic fairness. ACM is a promising tool for anemia management in ethnically diverse dialysis population and different healthcare settings.

Application of VIC-WUR model for assessing the spatiotemporal distribution of water availability in anthropogenically-impacted basins

Climate change, rapid increase in population, and urbanization have given rise to over-exploitation and shortage of freshwater resources. Acquiring accurate representations of fresh water availability is therefore crucial to sustainable development. Based on the underlying hydrological processes and storage types, fresh water can be divided into blue and green water components. Hence, quantification of the spatio-temporal distribution of blue and green water requires estimating the components of terrestrial water cycle. In this study, a framework is proposed to evaluate the water scarcity using the blue and green water concept. In this framework, the Wageningen Institute and Research (WUR) version of the Variable Infiltration Capacity Model (VIC) is employed to simulate terrestrial water cycle. Compared to VIC model, VIC-WUR model employs additional modules to account for sectoral water demands, return flows, and groundwater withdrawals, however, very limited number of its applications have been reported so far. The Divandareh-Bijar (DBJ) basin in west of Iran is selected because it has experienced severe anthropogenic alterations in the past three decades. To address precipitation and parameter uncertainty, the VIC-WUR model is separately calibrated for three precipitation datasets. As a part of this framework, the calibrated model is used to simulate blue and green water components and their corresponding scarcity metrics for the first time. According to the results, blue and green water resources in the DBJ are relatively scarce. The results also highlight the importance of considering anthropogenic impacts on the spatiotemporal dynamics of green and blue water resources. Additionally, quantification of the uncertainty in blue and green water components can help produce more reliable estimations for intelligent management of water resources. The ANalysis Of VAriance (ANOVA) method is therefore used to quantify the contribution of three major factors, including precipitation inputs, model parameters, and the interaction of these two, on the uncertainty of calculated water scarcity metrics. Structural uncertainty is implicitly addressed through the use of the modified version of the VIC model. The results indicate seasonality of these contributions for the two metrics under investigation and the differences in relative contribution of different sources to the overall uncertainty. The proposed framework could be employed to identify water stress in various spatiotemporal scales across the globe.

Increased occurrence of two intrinsic atmospheric circulations in Asia during the boreal summer

AbstractThe k‐means clustering of reanalysis datasets is used to classify the intrinsic atmospheric circulation in Asia based on the daily geopotential height of 500 hPa during the boreal summer (June, July and August) for 1958–2020. Among the total clusters of atmospheric circulation patterns in Asia, two distinct clusters of atmospheric circulation are characterized by a significant increasing trend in occurrence. They correspond to different zonal wave numbers (1–2 vs. 3–4) of atmospheric circulation in the mid‐to‐high latitudes of the Northern Hemisphere, and their associated sea surface temperature structures are not the same in the tropical Pacific and the North Atlantic. Further analysis indicates that two clusters of atmospheric circulation alter the amount of summer monsoon precipitation in East Asia, and an increasing amount of precipitation in southern China in recent decades could be explained by the combined effect of an increasing trend in the frequency of occurrence of two clusters of atmospheric circulation. Finally, a long‐term simulation of the Community Earth System Model version 2 suggests that anthropogenic forcing may be responsible for the increasing trend of occurrence of the two atmospheric circulations in Asia.

The Correlational Study of the Quality of Students Enrolled in the University and Employability of Graduates from Selected Universities in South-Western Uganda

The employability of university graduates is a concern for higher education stakeholders globally and Uganda is also concerned due to high unemployment rates post-graduation. Not much has been written about the relationship between quality of students enrolled in the university and their employability of graduates from selected Universities in South Western Uganda. This study aims to establish this relationship in selected universities in south western Uganda. A correlational study collected data from 512 participants, including graduates, employers, university lecturers, administrators, and officers from quality assurance directorate of national council for Higher Education. Data on students discipline, grades, performance and employability were collected. SPSS and Structural equation modelling (SEM) version 21 was used to analyse associations between predictor and outcome variables. The findings derived from conducting this research clearly demonstrate the presence of a significant statistical association between the quality of students enrolled in the university and employability of graduates at R= 0.589, R squared change 0.347, P =0.000 This suggests that by ensuring the quality of students enrolled in the University through discipline, students’ grades and performance increases the likelihood of producing graduates who are employable. It is very important that stakeholders in higher education make it a priority to keep a high standard of students and their moral fibre and this may facilitate their integration into the workforce.

A real-time surface defects detection model via dual-branch feature extraction and dynamic multi-scale fusion attention

Surface defect detection is a challenging task in industrial manufacturing, and the detection method based on deep learning has become the mainstream trend in the industry. However, the task of detecting defects on transparent plastic bottles poses unique challenges due to their clear, smooth surfaces and minimal texture, which contribute to specular reflections and make small, low-contrast defects difficult to discern. To tackle these challenges, we introduce the BiD-YOLO framework, an advanced version of the YOLOv5 model, tailored for real-time surface defect detection on such substrates. This framework notably enhances the detection accuracy for small, low-contrast defects and minimizes miss rates. At its core, BiD-YOLO incorporates a dual-branch feature extractor (DBFE) that utilizes both standard, dilated convolutions and a specialized small-object feature extraction branch to capture a broad spectrum of contextual information, optimizing the detection of fine details. Additionally, the dynamic multi-scale pyramid fusion attention (DyMFA) block with coordinate attention (CA) effectively addresses the loss of information in small-scale defects—a typical shortfall in conventional attention mechanisms—thereby improving detection precision across both channel and spatial dimensions. To achieve an optimal balance between accuracy and computational efficiency, we introduce two versions of the dynamic multi-scale pyramid attention block: multi-scale pyramid channel attention and dynamic channel attention. We also curated and annotated the TPSD dataset, a specialized collection of images showcasing surface defects on transparent plastic bottles under diverse real-world production conditions. Together with the NEU-DET dataset, these resources were employed to validate the BiD-YOLO model's effectiveness. Experimental results demonstrate a substantial improvement in defect detection capabilities, achieving mean average precision (mAP) values of 63.1% and 85.6%, respectively. Moreover, the two working models exhibit FPS values of 47.23FPS and 50.21FPS respectively. These findings underscore the BiD-YOLO model's pivotal role in reducing oversight in defect detection, thereby enhancing quality control within industrial manufacturing environments. The BiD-YOLO framework represents a significant advancement in the automated detection of surface defects on transparent plastic bottles, merging innovative technical features with robust practical applicability for industrial use.

Embodying Human-Like Modes of Balance Control Through Human-In-the-Loop Dyadic Learning

In this paper, we explore how humans and AIs trained to perform a virtual inverted pendulum (VIP) balancing task converge and differ in their learning and performance strategies. We create a visual analogue of disoriented IP balancing, as may be experienced by pilots suffering from spatial disorientation, and train AI models on data from human subjects performing a real-world disoriented balancing task. We then place the trained AI models in a dyadic human-in-the-loop (HITL) training setting. Episodes in which human subjects disagreed with AI actions were logged and used to fine-tune the AI model. Human subjects then performed the task while being given guidance from pretrained and dyadically fine-tuned versions of an AI model. We examine the effects of HITL training on AI performance, AI guidance on human performance, and the behavior patterns of human subjects and AI models during task performance. We find that in many cases, HITL training improves AI performance, AI guidance improves human performance, and after dyadic training the two converge on similar behavior patterns.

Rising risks of hydroclimatic swings: A large ensemble study of dry and wet spell transitions in North America

Hydroclimatic swings between dry and wet spells are increasing globally, raising concerns due to their severe impacts on society, agriculture, infrastructure systems, and water resource management. These extremes and their projected changes are traditionally assessed in isolation, which can underestimate associated risks and future resilience and adaptation plans. This study investigates climate change projections of such lagged compound dry and wet spells across North America using a single model initial-condition large ensemble (SMILE). Three dry-wet spell indices are merged into an integrated indicator to provide a comprehensive perspective on changing risks. We apply an ensemble pooling approach using the Canadian Regional Climate Model version 4 Large Ensemble (CanRCM4-LE) to enhance sample size for index estimation. Results suggest that hydroclimatic swings across North America are expected to become more frequent and intensified in a warmer climate. Trends of spatial fraction and aggregation during transitions between dry and wet spells indicate future water resource management challenges. Hot spots for intensified transitions with higher frequency, such as Northern Central America, and the southern part of Eastern and Western North America, including Mexico and the state of California, overlap with larger spatial extent and higher aggregation during transitions. The seasonal analysis of spatial characteristics indicates winter may become wetter and summer drier at higher warming levels, potentially intensifying transitions between dry and wet extremes. Clustered hydroclimatic swings may lead to severe environmental, hydrological, and socio-economic consequences, necessitating appropriate mitigation and adaptation measures.

Unsupervised feature based algorithms for time series extrinsic regression

AbstractTime Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, DrCIF is the only one that significantly outperforms a standard rotation forest regressor.

Evaluating the significance of wetland representation in isotope-enabled distributed hydrologic modeling in mesoscale Precambrian shield watersheds

Evaluation of regional-scale models against both streamflow and stable isotopes of water is a relatively new and evolving area of distributed hydrological modeling. While applications of isoWATFLOOD, an isotope-enabled distributed hydrological model are increasing, evaluation of the impacts of model structural features, such as wetland connectivity, on simulation of runoff components is still limited. Wetlands are important water bodies that impact streamflow generation and its spatial variation in Precambrian shield watersheds in parts of Canada and elsewhere. This study investigates the efficiency of isoWATFLOOD in simulating streamflow and instream δ18O and the effects of model percent connected wetlands (%CW) in a mesoscale (∼12,000 km2) Precambrian Shield watershed in Northeastern Ontario, Canada. Model calibration and validation are performed using daily streamflow for five hydrometric gauging stations and biweekly to monthly instream δ18O from 11 river sampling locations from across the watershed. Five model versions with different %CW are generated, calibrated, and validated separately. Results show all models can simulate the timing and pattern of streamflow and δ18O. %CW impacts simulated streamflow during both calibration and validation periods, calibrated parameter sets and simulated internal hydrological processes. Model performance varies spatially with no single value of %CW providing best performance across sub-catchments with 40 % CW providing best mean streamflow simulations in calibration. Increasing %CW (10 % to 40 %) results in higher contribution of lower soil zone water to streamflow that markedly improves baseflow simulation. This study builds on recent isotope-enabled hydrologic simulation capacity of isoWATFLOOD, contributing new examination of model representation of wetland connectivity, a critical component of Precambrian Shield watersheds and evidence of how stable isotopes can contribute to addressing the challenge of equifinality. Results suggest the need to develop model capability for representation of spatial variation in wetland connectivity to recognize this important aspect of heterogeneity in watersheds influencing hydrologic function.

Enhanced Skorohod–Olevsky viscous model incorporating microstructure evolution for finite element analysis of ceramic sintering

Skorohod and Olevsky’s constitutive relation for viscous sintering (SOVS) is employed to simulate the shrinkage and relative density evolution of ceramics during the sintering process. While this model effectively simulates shrinkage and density evolution, the original formulation of the SOVS model does not explicitly incorporate grain growth or microstructural evolution. This paper introduces an enhanced version of the SOVS model, incorporating a novel calibration method to obtain the temperature dependence of the viscosity parameter. It considers grain growth as well as the evolution of the microstructure by adding new parameters. Simulations were conducted using Code_Aster, an open-source finite element software, in conjunction with MFront, a tool for formulating material behaviour equations. Dilatometer tests were performed with probes of porcelain stoneware composition to calibrate the model. The predictions of dimensional shrinkage and relative density evolution showed a high accuracy of the model (>95%), enabling its use in the manufacturing process.

Balancing homophily and prejudices in opinion dynamics: An extended Friedkin–Johnsen model

In this paper we propose an extended version of the Friedkin–Johnsen (FJ) model where we analyze the effects that a homophily-based influence matrix has on the opinion formation process. In particular, we assume that the influence matrix varies over time, and its entries (that represent the appraisals that individuals have of the other individuals) update based on the comparison between their opinions and can take both positive and negative values. This leads to a system of two difference equations, where the first one corresponds to the standard FJ model, except that the influence matrix is no longer constant, and the second one models the dynamics of this matrix. We show that a necessary and sufficient condition for the convergence of this modified version of the classical FJ model is that the influence matrix becomes constant in a finite number of steps. Moreover, we provide the explicit expression for the agents’ asymptotic opinions in some special cases: the purely cooperative setting, the case of a structurally balanced network and the case of a single discussion topic. Finally, the case when the topics are correlated and the influence matrix depends on the average opinion vectors is investigated.

Frontiers in Operations: Employees vs. Contractors: An Operational Perspective

Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .

A New Modification of the Weibull Distribution: Model, Theory, and Analyzing Engineering Data Sets

Symmetrical as well as asymmetrical statistical models play a prominent role in describing and predicting the real-world phenomena of nature. Among other fields, these models are very useful for modeling data in the sector of civil engineering. Due to the applicability of the statistical models in civil engineering and other related sectors, this paper offers a statistical methodology to improve the distributional flexibility of traditional models. The suggested method/approach is called the extended-X family of distributions. The proposed method has the ability to generate symmetrical and asymmetrical probability distributions. Based on the extended-X family approach, an updated version of the Weibull model, namely, the extended Weibull model, is studied. The proposed model is very flexible and has the ability to capture the symmetrical and asymmetrical shapes of its density function. For the extended-X method, the estimation of parameters, a simulation study, and some mathematical properties are derived. Finally, the practical illustration/usefulness of the suggested model is shown by analyzing two data sets taken from the field of engineering. Both data sets represent the fracture toughness of alumina (Al2O3).

Aerosol impacts on the East Asian winter monsoon: Insights from TaiESM1 and CMIP6 simulations

AbstractThe East Asian winter monsoon (EAWM) is an important climate pattern in Southeast Asia. This study delved into the aerosol effects on the EAWM by analysing simulations conducted with the Taiwan Earth System Model version 1 (TaiESM1) model, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our assessment, validated against observational and reanalysis data, revealed that TaiESM1 effectively replicates the robust circulation of the EAWM and aerosol concentrations, placing its performance above average level among CMIP6 models. Notably, the simulated global mean aerosol optical thickness exhibited an approximately 20% increase from 1850 to 2014. The increased aerosols corresponded to a weakened Aleutian Low and an intensified Siberian High, leading to a weakened EAWM in the lower atmosphere over extratropical areas. In addition, the cooling across the Northern Hemisphere might affect enhanced northerly winds at approximately 10°N, which are situated in the southern part of the EAWM. An analysis of the near‐surface temperature budget indicated that the aerosol‐induced cooling in East China and India was primarily driven by changes in shortwave and longwave radiation fluxes, albeit slightly offset by sensible heat and latent heat fluxes. Furthermore, our study also reveals that while the mechanisms through which anthropogenic aerosols affect the northern portion of the EAWM vary across most CMIP6 models, their impacts on the tropical region remain consistent.

The role of the basic state in the climate response to future Arctic sea ice loss

There is great uncertainty in the atmospheric circulation response to future Arctic sea ice loss, with some models predicting a shift towards the negative phase of the North Atlantic Oscillation (NAO), while others predicting a more neutral NAO response. We investigate the potential role of systematic model biases in the spread of these responses by modifying the unperturbed (or ‘control’) climate (hereafter referred to as the ‘basic state’) of the Canadian Earth system model version 5 (CanESM5) in sea ice loss experiments based on the protocol of the Polar Amplification Model Intercomparison Project. We show that the presence or absence of the stratospheric pathway in response to sea ice loss depends on the basic state, and that only the CanESM5 version that shows a weakening of the stratospheric polar vortex features a strong negative NAO response. We propose a mechanism that explains this dependency, with a key role played by the vertical structure of the winds in the region between the subtropical jet and the stratospheric polar vortex (‘the neck region winds’), which determines the extent to which anomalous planetary wave activity in response to sea ice loss propagates away from the polar vortex. Our results suggest that differences in the models’ basic states could significantly contribute to model spread in the simulated atmospheric circulation response to sea ice loss, which may inform efforts to narrow the uncertainties regarding the impact of diminishing sea ice on mid-latitude climate.

Optimal taxation of nonrenewable resources during clean energy transition: A general equilibrium approach

In this paper we study clean energy transition in a modified version of the Ramsey growth model by including nonrenewable and renewable resources as well as pollution externalities. The main difference from previous works is that we consider imperfect substitution between nonrenewable and renewable resources. We characterize the social optimum and show that the economy converges to a clean state in the long run. We then study the decentralized equilibrium and show that the economy converges to the same state even without regulation, but with higher environmental damage. Further, we investigate the properties of the taxation trajectory that drives the laissez-faire economy to follow the optimal path and show that it can be either increasing or decreasing over time. We identify different channels that influence the path of optimal taxation and show that it depends, among other things, on the level of capital, the cost of renewable energy and the degree of substitution between renewable and nonrenewable resources.

Sensitivity of Arctic clouds to ice microphysical processes in the NorESM2 climate model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study we examine the impact of PIP, SIP and ice aggregation on Arctic clouds, using the Norwegian Earth System model version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional break-up, drop-shattering and sublimation break-up) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional break-up. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly-constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through its impact on collisional break-up efficiency. Overall, enhancement in ice production though the addition of SIP mechanisms and the reduction of ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months.