Model Input Research Articles

Using exploratory modeling to challenge narratives of risk governance in Mexico City.

Achieving more sustainable adaptation to social-environmental change demands the transformation of the narratives that provide the rationale for risk governance. These narratives often reflect long-standing beliefs about social and political relationships, ascribe actions and responsibilities, and specify solutions to risk. When such solutions are implemented through material investments in landscapes, these narratives become embedded in physical infrastructure with long legacies. Dominant narratives can mask a range of divergent problem framings. By masking alternatives, narratives can contribute to the persistence of unsustainable governance trajectories. Decision-support tools have begun to represent narratives as drivers of system dynamics; making narratives visible can reveal opportunities for more sustainable governance. We present the results of the project "The Dynamics of Multi-Scalar Adaptation in the Megalopolis", a dynamic, exploratory model of socio-hydrological risks in Mexico City that was designed to both endogenize and simultaneously challenge the dominant narratives that characterize water-risk governance in the city. Qualitative data characterize dominant narratives at city and borough scales. An agent-based model, informed by multicriteria decision analysis and coupled with hydrological, urbanization, and climatic model inputs, permitted the development of exploratory governance scenarios designed to challenge dominant narratives. Scenarios revealed how dominant narratives may contribute to the persistence of vulnerability "hotspots" in the city, despite stated goals of equity and vulnerability alleviation. Participatory workshops with representatives of the city government illustrate how making such narratives visible through exploratory modeling can lead to a questioning of prior assumptions and causal relations, recognition of a need for intersectoral collaboration, and insights into potential management strategies.

Electrophysiology lab efficiency using cryoballoon for pulmonary vein isolation in Latin America: A sub-analysis of the Cryo Global Registry Study.

Cryoballoon ablation for pulmonary vein isolation is a time-efficient procedure that can alleviate stress on electrophysiology lab resources. This analysis modeled the impact of cryoballoon ablation on electrophysiology lab operation using data from Latin America. Data from centers in Argentina, Mexico, Colombia, and Chile of the were used as inputs for an electrophysiology lab efficiency simulation model. The model used the assumption that either two (today's electrophysiology lab operations) or three (including electrophysiology lab operational changes) cryoballoon ablation procedures could be performed per day. The endpoints were the percentage of days that resulted in 1) overtime and 2) time left for an extra non-ablation electrophysiology procedure. Data from a total of 232 procedures from six Latin American centers were included in the analysis. The average electrophysiology lab occupancy time for all procedures in Latin America was 132 ± 62 minutes. In the Current Scenario (two procedures per day), 7.4% of simulated days resulted in overtime, and 81.4% had enough time for an extra electrophysiology procedure. In the Enhanced Productivity Scenario (three procedures per day), 16.4% of days used overtime, while 67.4% allowed time for an extra non-ablation electrophysiology procedure. Using real-world, Latin American-specific data, we found that with operational changes, three ablation procedures could feasibly be performed daily, leaving time for an extra electrophysiology procedure on more than half of days. Thus, use of cryoballoon ablation is an effective tool to enhance electrophysiology lab efficiency in resource-constrained regions such as Latin America.

Field evidence and indicators of rockfall fragmentation and implications for mobility

Rockfall fragmentation can play an important role in hazard studies and the design of protective measures. However, the current lack of modeling tools that incorporate rock fragmentation mechanics is a limitation to enhancing studies and design. This research investigates the fragmentation patterns of rockfalls and analyzes the resulting distribution of fragment sizes within corresponding rockfall deposits. We focus on small rock fragments, which provide insights into the dynamics of the rockfall event and can be used as input for numerical modeling. We analyzed multiple rockfall events from locations worldwide, each exhibiting different degrees of fragmentation. Using image analysis techniques, we mapped all visible blocks, determined their volumes, and measured the distances they travelled from the initial point of impact. A key finding is the identification of three indicators of fragmentation. First, in cases where fragmentation was largely absent, we observed a trend of increasing block size with distance from the impact point or source area, which aligns with previously published findings. However, for energetic rockfall events characterized by intense fragmentation, we observed that small fragments exhibited longer travel distances compared to larger fragments. This distinction allowed us to differentiate blocks primarily resulting from the disaggregation process from those primarily resulting from dynamic fragmentation, with implications for rockfall mobility. Second, although the size distribution of rockfall deposits exhibits a power-law scaling for volumes larger than a minimum size threshold corresponding to a rollover of the distribution, in some case studies a deviation from power-law scaling is observed, indicating a process of larger block comminution due to fragmentation. Third, we found that rockfalls with fragmentation experience reduced mobility, indicated by higher reach angles, and higher lateral dispersion showing a wider distribution of trajectories. We interpret these findings as being directly related to the energy-consuming nature of fragmentation, which prevents farther deposition of fragmented rock blocks.

Utilizing TGAN and ConSinGAN for Improved Tool Wear Prediction: A Comparative Study with ED-LSTM, GRU, and CNN Models

Developing precise deep learning (DL) models for predicting tool wear is challenging, particularly due to the scarcity of experimental data. To address this issue, this paper introduces an innovative approach that leverages the capabilities of tabular generative adversarial networks (TGAN) and conditional single image GAN (ConSinGAN). These models are employed to generate synthetic data, thereby enriching the dataset and enhancing the robustness of the predictive models. The efficacy of this methodology was rigorously evaluated using publicly available milling datasets. The pre-processing of acoustic emission data involved the application of the Walsh-Hadamard transform, followed by the generation of spectrograms. These spectrograms were then used to extract statistical attributes, forming a comprehensive feature vector for model input. Three DL models—encoder-decoder long short-term memory (ED-LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN)—were applied to assess their tool wear prediction capabilities. The application of 10-fold cross-validation across these models yielded exceptionally low RMSE and MAE values of 0.02 and 0.16, respectively, underscoring the effectiveness of this approach. The results not only highlight the potential of TGAN and ConSinGAN in mitigating data scarcity but also demonstrate significant improvements in the accuracy of tool wear predictions, paving the way for more reliable and precise predictive maintenance in manufacturing processes.

Sensitivity analysis of a hydrodynamic and harmful algal model in a riverine system

Simulating algae blooms using a hydrodynamic-water quality model is challenging because it requires a thorough understanding of physical and biological processes and involves numerous parameters. This study conducted a sensitivity analysis of the EFDC+ hydrodynamic and water quality model for simulating cyanobacteria growth, an important Harmful Algal Bloom (HAB) species in the Ohio River, USA. The sensitivity analysis assessed 23 model input parameters, divided into nine functional groups according to their characteristics. This assessment analyzes the impact of changing these input parameters on four water quality model outputs including algae (i.e., cyanobacteria), dissolved oxygen, total nitrogen, and total phosphorus. Light extinction parameters, maximum algal growth rate, and algal base metabolism were identified as the most sensitive parameters for simulating algal growth. Solar radiation required for algal growth was moderately sensitive. Currently, there are only a few studies that simulate HAB dynamics in riverine systems. This study deepens our understanding of HAB development in rivers with lock and dam structures that create a series of pools along the river. Future work will involve focusing on the sensitive parameters in model calibration.

Kinetic modelling of UVC and UVC/H2O2 oxidation of an aqueous mixture of antibiotics in a completely mixed batch photoreactor

The removal kinetics of an aqueous mixture of thirteen antibiotics (i.e., ampicillin, cefuroxime, ciprofloxacin, flumequine, metronidazole, ofloxacin, oxytetracycline, sulfadimethoxine, sulfamethoxazole, sulfamethazine, tetracycline, trimethoprim and tylosin) by batch UVC and UVC/H2O2 processes has been modeled in this work. First, molar absorption coefficients (ε), direct quantum yields (Φ) and the rate constants of the reaction of antibiotics with hydroxyl radical (kHO•) (model inputs) were determined for each antibiotic and compared with literature data. The values of these parameters range from 0.3 to 21.8 mM−1 cm−1 for ε, < 0.01 to 67.8 mmol·E−1 for Φ and 3.8 × 109 to 1.7 × 1010 M−1 s−1 for kHO•. Second, a regression model was developed to compute the rate constants of the reactions of the antibiotics with singlet oxygen (k1O₂) from experimental data obtained in batch UVC experiments treating a mixture of the antibiotics. k1O₂ values in the 1–50 × 106 M−1 s−1 range were obtained for the antibiotics studied. Finally, a semi-empirical kinetic model comprising a set of ordinary differential equations was solved to simulate the evolution of the residual concentration of antibiotics and hydrogen peroxide (model outputs) in a completely mixed batch photoreactor. Model predictions were reasonably consistent with the experimental data. The kinetic model developed might be combined with computational fluid dynamics to predict process performance and energy consumption in UVC and UVC/H2O2 applications at full scale.

Towards development of a roadway flood severity index

Flooding is among the costliest and deadliest weather disasters. Moreover, different types of flooding have significant impacts on the transportation network and infrastructure including flash, riverine, urban, coastal, and storm surge. The variety of flooding scenarios makes it challenging to quantify the impacts of flooding on transportation across spatial scales; however, such metrics would be beneficial both prior to and after the event. Pre-flood metrics can promote enhanced impact-based decision-support guidance and hazard communication, while post-flood metrics may include larger regional disruptions located away from the most inundated areas and their associated secondary societal impacts. This study developed a retrospective Roadway Flood-Severity Index (RFSI) from 1982 to 2020 capable of integrating geo-located hydrometeorological data and transportation mobility information across localized and multi-state, sub-national regions to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? The overall mobility impacts of flooding on transportation were highly variable and relatively uncommon throughout the study period. Given this variability in other mobility data (e.g., vehicle speeds), hydrometeorological parameters were used exclusively as model inputs and crowdsourced Waze flood reports were used as the target response variable. A logistic regression based RFSI was found to best align with the dataset providing a “no flood” or “flood” classification. Eventually, this retrospective analysis will be extended to provide predictive capability as well. The RFSI is intended to provide transportation agencies with a quantitative metric to classify, categorize, and communicate the potential impacts of flood events throughout the transportation network.

Cost-effectiveness of adjuvant endocrine treatment with tamoxifen for male breast cancer.

Tamoxifen (TAM) is recommended as the first-line strategy for men with estrogen receptor (ER)-positive early breast cancer who are candidates for adjuvant endocrine therapy in ASCO guideline. Our study aims to analyze the cost-effectiveness of receiving adjuvant endocrine therapy with TAM compared to no TAM, and to assess the cost-effectiveness of using TAM with high adherence over low adherence for ER-positive early male breast cancer in the USA. Two Markov models comprising three mutually exclusive health states were constructed: (1) the first Markov model compared the cost-effectiveness of adding TAM with not using TAM (TAM versus Not-TAM); (2) the second model compared the cost-effectiveness of receiving TAM with high adherence and low adherence (High-adherence-TAM versus Low-adherence-TAM). The simulation time horizon for both models was the lifetime of patients. The efficacy and safety data of two models were elicited from the real-world studies. Model inputs were derived from the US website and published literature. The main outcomes of two models both included the total cost, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). In the first model, TAM yielded an ICER of $5707.29 per QALY compared to Not-TAM, which was substantially below the WTP threshold of $50,000.00 per QALY in the USA. Probabilistic sensitivity analysis results demonstrated a 100.00% probability of cost-effectiveness for this strategy. In the second model, High-adherence-TAM was dominated absolutely compared to Low-adherence-TAM. The High-adherence-TAM was cost-effective with a 99.70% probability over Low-adherence-TAM when WTP was set as $50,000.00/QALY. All of these parameters within their plausible ranges did not reversely change the results of our models. Our study will offer valuable guidance for physicians or patients when making treatment decisions and provide an effective reference for decision-making to consider the appropriate allocation of funds to this special group.

Cost-effectiveness of a pharmacist-led medication therapy management clinic for management of type 2 diabetes

ObjectivesThis study aimed to evaluate the lifetime cost-effectiveness of a pharmacist-led medication therapy management clinic (MTMC) compared to usual care for individuals with type 2 diabetes mellitus, from a U.S. payer perspective. MethodsA cohort simulation Markov model was developed including the effects of diabetes and major complications from diabetes. Transition probabilities, MTMC treatment effects, health state costs, and utilities, were based on data from electronic health records, and published literature. Outcomes evaluated were lifetime incremental costs, quality-adjusted life years (QALYs), and ratios. Sensitivity analyses were conducted on all model inputs; scenario analyses assessed the impact of preventing additional diabetes complications on economic outcomes and of reduced MTMC visit frequency. ResultsOver a lifetime, MTMC resulted in $160,145 total costs and 6.73 QALYs; usual care resulted in $152,806 total costs and 6.65 QALYs. The incremental cost-effectiveness ratio (ICER) of the MTMC compared to usual care was $93,375 per QALY gained, indicating cost-effectiveness at a willingness-to-pay threshold of $100,000 per QALY gained. Scenario analyses showed that modeling additional complications or reduced visit frequency lowered the ICER. The results were most sensitive to MTMC costs, and hazard ratios for occurrence of stroke, myocardial infarction, and renal failure, with an improvement in glycosylated hemoglobin and systolic blood pressure. ConclusionThe study demonstrates the potential cost-effectiveness of integrating clinical pharmacy services into comprehensive care strategies. Findings support the broader coverage and reimbursement of such services to optimize clinical outcomes and reduce long-term health care costs.

Health, economic and social burden of tobacco in Latin America and the expected gains of fully implementing taxes, plain packaging, advertising bans and smoke-free environments control measures: a modelling study

ObjectiveTo investigate the tobacco-attributable burden on disease, medical costs, productivity losses and informal caregiving; and to estimate the health and economic gains that can be achieved if the main tobacco...

A framework for seasonal groundwater banking in agricultural regions: A case study

Groundwater banking has been used for demand-oriented management of groundwater resources in semi-arid and arid regions. This paper aims to propose a new framework for seasonal groundwater banking. In this framework, crop production functions are used to estimate users' bid packages representing the price of each unit volume of water in each auction period. The bid packages, along with users' water rights, are considered inputs of an optimization model, which maximizes the profit of users participating in the groundwater bank. The main outputs of the optimization model are the volumes of exchange and storage of water, as well as the water price in the auction periods. The efficiency of the proposed framework has been evaluated by applying it to the Nough region in the Rafsanjan Plain, central Iran. An existing plan for inter-basin water transfer to this region is also considered in the water banking framework. The results illustrate that the total annual profit of all users after the establishment of the bank compared to the status quo has increased up to 43.8 %. The profit of the water-recipient basin has also increased more than 4 times compared to the existing condition. In addition, water banking with a water transfer system increases the economic productivity of water and crop production by 37.32 % and 22.41 %, respectively.

Assessment on demersal fisheries status in the FMA 573 under data-limited situation: A combination of statistic and CPUE data as inputs for bayesian surplus production model

Abstract Demersal stock potential, total allowable catch (TAC), and exploitation level status in the FMA 573 were assessed and determined by the National Commission of Stock Assessment (KOMNASKAJISKAN) through Ministerial Decree No. 19/2022. However, the fisheries status, i.e., the biomass level concerning the BMSY and the effort level compared to FMSY, was not completely determined. This study demonstrated the alternative approach to determining fisheries status under limited data. Statistical time series production and existing CPUE data were used to perform the Bayesian Surplus Production Model (cMSY). Based on the results, the stock of demersal fisheries in FMA 573 is overexploited, whereas the F/FMSY and B/BMSY are&gt;1 and &lt;1, respectively. The situation needs concern from all stakeholders. Rebuilding the stock formulation scheme through harvest strategy development for demersal fisheries in FMA 573 must be prioritized to ensure the sustainability of the stock and the fisheries business.

Research on the Sustainability of Property Insurance under the Background of Extreme Weather Events

In recent years, the frequency of extreme weather events has significantly increased, resulting in global losses exceeding $1 trillion. The profit crisis of insurance companies and the issue of homeowners' payment ability have become more apparent. This article focuses on the sustainability of property insurance under extreme weather events, establishes a comprehensive insurance and protection model, and provides recommendations for relevant departments.Firstly, a set of seven indicators including the proportion of household insurance expenditure was selected to establish an indicator system. After conducting Spearman correlation coefficient testing on the data and combining it with LSTM neural network to predict the frequency of extreme weather in the next decade, this paper uses the SVM binary classification model to classify high-risk areas as uninsurable areas and other areas as insurable areas. This decision-making process resulted in insurance companies covering areas such as Alabama and refusing to underwrite insurance in Arizona, while also making relevant recommendations.Secondly, by calculating indicators for communities and real estate developers, and considering the impact of population growth on housing demand, this article uses the ARIMA model to predict population growth in different regions over the next five years. After standardizing the data, the entropy weighting method is applied to consider the subjective importance of different real estate developers for different indicators and assign weights to the indicators. Weighted indicator data is used as input for the SVM model, dividing regions into two categories, with regions with better overall conditions being more favorable for real estate development. This has led people to prioritize the development of regions such as North Carolina and Indiana. In addition, this article introduces the TOPSIS regional ranking comprehensive evaluation index, which is beneficial for the development of higher ranked regions. Similar to our SVM classification results, Indiana and North Carolina rank second and third.

Defect Recognition and Classification Using Ultrasonic Scattered Signals Based on Convolutional Neural Network

Abstract Carbon Fiber Reinforced Polymer (CFRP) is widely used in the aerospace, aircraft, and railway industries because of its remarkable advantages, such as its lightweight and high strength. Detecting and identifying defects in CFRP is crucial for ensuring its secure and efficient use. Ultrasonic non-destructive testing is one of the most practical and effective methods for defect detection and identification. The ultrasonic scattered signals of defects, which contain abundant defect feature information, are advantageous for defect recognition and classification. However, the anisotropy and inhomogeneity of CFRP, which lead to a low signal-to-noise ratio and poor image quality, pose challenges in flaw recognition. To address the issue, this paper proposes using Convolutional Neural Networks (CNN) methods for defect classification, which can adequately extract information from ultrasonic scattered signals directly. The scattering signals from various types of defects in CFRP, including the different sizes of holes and folds, were selected as inputs for CNN models. Different network models combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with 1D-CNN were employed to classify and identify defect signals. To generate sufficient input sample signals, the data sets were further enlarged by incorporating noise into the original signals. The experimental results showed that using the 1D-LSTM-CNN and 1D-GRU-CNN network models can reduce computational resources while ensuring the accuracy of defect recognition.

A spatiotemporal data mining method for advanced prediction and assessment of large combined excavation-induced wall deformations and risks

A holistic and precise assessment of retaining wall deformations is critical for on-site risk management of large combined deep excavation projects, where the risk-related points are highly dispersed, evolving, and interacting. Despite extensive exploration of this topic in previous studies, the omission of intricate spatiotemporal characteristics of wall deformations has resulted in diminished prediction accuracy and stability. To mitigate this deficiency, a spatiotemporal characteristics matrix for all data points and time series was first generated for a deep excavation scenario and used as input for a new hybrid model that combines convolutional neural network (CNN) and long short-term memory (LSTM) with incorporated attention mechanism (CNN-LSTM-Att), which enables the cross-learning mechanism and improves interpretability. In addition, by leveraging the attention weight, a new risk assessment index for retaining wall deformations across various scenarios was formulated. Then the proposed method was applied in a large combined deep excavation in Shanghai, China. The results show that: (1) The incorporation of fed-in characteristic data and the attention mechanism enables the proposed method to produce satisfactory prediction results for the holistic spatiotemporal distribution of a large combined excavation; (2) Compared with other published models, the proposed model shows much better prediction accuracy, interpretability, and stability, especially in medium- and long-term predictions; and (3) The new risk assessment index serves as a reliable decision-making tool for assessing the risk evolution of retaining wall deformations and provides valuable guidance for effective risk management in multi-scenario excavation projects.

The Impact of the Weather Forecast Model on Improving AI-Based Power Generation Predictions through BiLSTM Networks

This study aims to comprehensively analyze five weather forecasting models obtained from the Open-Meteo historical data repository, with a specific emphasis on evaluating their impact in predicting wind power generation. Given the increasing focus on renewable energy, namely, wind power, accurate weather forecasting plays a crucial role in optimizing energy generation and ensuring the stability of the power system. The analysis conducted in this study incorporates a range of models, namely, ICOsahedral Nonhydrostatic (ICON), the Global Environmental Multiscale Model (GEM Global), Meteo France, the Global Forecast System (GSF Global), and the Best Match technique. The Best Match approach is a distinctive solution available from the weather forecast provider that combines the data from all available models to generate the most precise forecast for a particular area. The performance of these models was evaluated using various important metrics, including the mean squared error, the root mean squared error, the mean absolute error, the mean absolute percentage error, the coefficient of determination, and the normalized mean absolute error. The weather forecast model output was used as an essential input for the power generation prediction models during the evaluation process. This method was confirmed by comparing the predictions of these models with actual data on wind power generation. The ICON model, for example, outscored others with a root mean squared error of 1.7565, which is a tiny but essential improvement over Best Match, which had a root mean squared error of 1.7604. GEM Global and Gsf Global showed more dramatic changes, with root mean squared errors (RMSEs) of 2.0086 and 2.0242, respectively, indicating a loss in prediction accuracy of around 24% to 31% compared to ICON. Our findings reveal significant disparities in the precision of the various models used, and certain models exhibited significantly higher predictive precision.

Evaluating the performance of herd-specific Long Short-Term Memory models to identify automated health alerts associated with a ketosis diagnosis in early lactation cows

The growing use of automated systems in the dairy industry generates a vast amount of cow-level data daily, creating opportunities for using these data to support real-time decision-making. Currently, various commercial systems offer built-in alert algorithms to identify cows requiring attention. To our knowledge, no work has been done to compare the use of models accounting for herd-level variability on their predictive ability against automated systems. Long Short-Term Memory (LSTM) models are machine learning models capable of learning temporal patterns and making predictions based on time series data. The objective of our study was to evaluate the ability of LSTM models to identify a health alert associated with a ketosis diagnosis (HAK) using deviations of daily milk yield, milk FPR, number of successful milkings, rumination time, and activity index from the herd median by parity and DIM, considering various time series lengths and numbers of d before HAK. Additionally, we aimed to use Explainable Artificial Intelligence method to understand the relationships between input variables and model outputs. Data on daily milk yield, milk fat-to-protein ratio (FPR), number of successful milkings, rumination time, activity, and health events during 0 to 21 d in milk (DIM) were retrospectively obtained from a commercial Holstein dairy farm in northern Indiana from February 2020 to January 2023. A total of 1,743 cows were included in the analysis (non-HAK = 1,550; HAK = 193). Variables were transformed based on deviations from the herd median by parity and DIM. Six LSTM models were developed to identify HAK 1, 2, and 3 d before farm diagnosis using historic cow-level data with varying time series lengths. Model performance was assessed using repeated stratified 10-fold cross-validation for 20 repeats. The Shapley additive explanations framework (SHAP) was used for model explanation. Model accuracy was 83, 74, and 70%, balanced error rate was 17 to 18, 26 to 28, and 34%, sensitivity was 81 to 83, 71 to 74, and 62%, specificity was 83, 74, and 71%, positive predictive value was 38, 25 to 27, and 21%, negative predictive value was 97 to 98, 95 to 96, and 94%, and area under the curve was 0.89 to 0.90, 0.80 to 0.81, and 0.72 for models identifying HAK 1, 2, and 3 d before diagnosis, respectively. Performance declined as the time interval between identification and farm diagnosis increased, and extending the time series length did not improve model performance. Model explanation revealed that cows with lower milk yield, number of successful milkings, rumination time, and activity, and higher milk FPR compared with herdmates of the same parity and DIM were more likely to be classified as HAK. Our results demonstrate the potential of LSTM models in identifying HAK using deviations of daily milk production variables, rumination time, and activity index from the herd median by parity and DIM. Future studies are needed to evaluate the performance of health alerts using LSTM models controlling for herd-specific metrics against commercial built-in algorithms in multiple farms and for other disorders.

SRank: Guiding schema selection in NoSQL document stores

The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores.

Experimental Study and Neural Network Predictions of Early-Age Behavior of Microexpansion Concrete in Large-Diameter Steel Tube Columns

The improved mechanical performance of concrete-filled steel tube (CFST) components has led to their widespread application in megastructures. Therein, CFST hybrid arch bridge has become an optimal selection of large span bridges. Nonetheless, for massive concrete poured in CFST members with large diameter, the thermal cracking and sustained rising temperature caused by early hydration heat of core concrete are urgently required to be studied. The two large-diameter CFST columns in this work, each having a diameter of 2.1 m, had their temperature and strain fields recorded in-situ. An existing CFST arch bridge served as the model for the two CFST columns’ design. Additionally, early-age characteristics of several scaled CFST columns with varying diameters were documented. A multi-field finite element (FE) model that combines linked chemical (hydration), heat, and mechanical fields is suggested in order to properly characterize the evolutions of temperature and strain fields. The model is validated by comparing the in-situ measurements with the numerical results. Finally, to investigate the affect factors on the hydration temperature of core concrete in CFST columns, early-age hydration behaviors of CFST columns was simulated using the validated FE models input parameters as water to cement ratio (w/c), cement dosage, heat release of cement and diameter of CFST columns. Based on the numerical results under the input parameters mentioned, the LSTM neural network was constructed, and the hydration temperature variances computed by FE models were selected as the input dataset. Afterwards, the temperature variance of core concrete of CFST columns was predicted using the established LSTM network. It is discovered that the LSTM neural network that was previously constructed was able to predict the peak temperature of CFST columns as well as the hydration temperature of CFST specimens with respect to time.

O27 DEVELOPMENT OF A CONCEPTUAL EVALUATION MODEL AND TESTING ITS PERFORMANCE IN HYPOTHETICAL PAY-FOR-PERFORMANCE INTERVENTIONS TO IMPROVE HYPERTENSION CONTROL

Background and Objective: Hypertension is a growing concern, particularly in low- and middle-income countries. Financial incentives, such as pay-for-performance, have shown promise in improving non-hypertension outcomes. However, their efficacy and cost-effectiveness in hypertension management, especially in low-resource contexts, remain uncertain. Our objectives were to 1) document studies exploring health financial interventions for hypertension management 2) develop a conceptual model which can facilitate future research on health financing approaches for hypertension management, and 3) assess the model's performance in hypothetical pay-for-performance interventions. Methods: Our literature review identified few studies evaluating finance interventions on hypertension management with somewhat more evidence for pay-for-performance strategies. Thus, we developed a conceptual model and assessed its performance in hypothetical scenarios of pay-for-performance schemes to enhance public hypertension care in Bangladesh. Using a state-transition modeling framework, we estimated the incentive costs and impact on cardiovascular disease burden in 18 scenarios with varying intervention coverage, blood pressure effects, and incentive rates (Figure 1). Key model inputs were derived from the 2019 Global Burden of Disease Study, the 2018 WHO STEPS survey, and WHO-CHOICE. Our primary outcome was incentive costs per health-adjusted life year (HALY) gained. Results: Based on our model, the incentive costs per HALY gained depended on the intervention coverage, intervention effect and incentive rate (Figure). If only currently treated patients were included, the most optimistic scenario (i.e., the largest blood pressure reduction with the lowest incentive rate) was estimated to cost 11,043 USD in incentives per HALY gained (Figure). If the intervention also included new, previously untreated patients while maintaining the same total incentives, the estimated incentive costs per HALY gained was 5,515 USD. Conclusions: Our model suggests that pay-for-performance interventions for hypertension care may demand substantial financial resources, but that the intervention could become more favorable with increased treatment coverage. While more studies on the impact of financial interventions in hypertension care are necessary, our model can guide planning and designing such a study.