Prediction Of Timing Research Articles

A cost-consequence analysis comparing three distinct cardiac ablation strategies for the treatment of paroxysmal atrial fibrillation

Abstract Background Cardiac ablation is a well-established treatment option for preventing atrial fibrillation (AF). While radiofrequency, cryo-energy, and laser energies are commonly employed in catheter ablation, they present the risk of potential damage to adjacent organs such as the oesophagus and phrenic nerve. To address these concerns, new technologies are being explored including pulsed field ablation (PFA), a non-thermal energy approach that employs high-voltage, short-duration pulses to selectively destroy target cells. Purpose The present analysis aims at: i) quantifying the short-term costs related to three ablation strategies for paroxysmal AF (PFA, radiofrequency ablation -RFA-, and cryo-balloon ablation -CRYO-), identify the cost drivers, and assess potential variations in resource consumption for each treatment alternative; ii) comparing the medium-term (1-year) economic implications of adopting the three alternatives, in terms of redo procedures (effectiveness) and complications (safety). Methods A cost-consequence model was developed to compare economic outcomes of the three ablation strategies for paroxysmal AF in the short- (during procedure) and medium-term (1-year). Resource consumption data during the procedure were collected by two European medical centres specialized in cardiac ablation (N=61 patients). For the medium-term analysis, costs were correlated to the effectiveness and safety of the procedures. The clinical data (i.e., redo and complication rates) considered in the model were retrieved from published literature. Costs and tariffs were collected from economic formularies of the respective hospitals. A scenario analysis was conducted to evaluate economic outcomes under various hypothetical difference in the prices of treatment kits. Results A significant difference was registered in terms of pre-procedural (mean±SD: 13.9±4.4 min for PFA, 20.1±7.6 min for CRYO and 20.5±7.7 min for RFA; ANOVA test p=0.003) and procedural time (skin-to-skin), favouring PFA over RFA and CRYO (53.6±26.8 min for PFA, 72.4±28.1 min for CRYO and 149.9±99.9 min for RFA; p<0.0001; Table 1). PFA was associated with lower procedural costs vs CRYO (-€400) and RFA (-€827). A similar economic advantage was observed in the medium-term (Table 2). The scenario analysis showed that PFA would be cost-saving vs CRYO (Δtotal costs ≤ 0) up to a price difference of +€881 (i.e., PFA kit is more expensive than CRYO kit), and similarly, up to a price difference of +€1,306 vs RFA. Above these thresholds, PFA would be considered cost-effective vs CRYO or RFA, due to its efficacy despite being more expensive. Conclusion In summary, PFA emerges as cost-effective or even cost-saving over CRYO and RFA, thanks to its faster execution, lower redo, and complications rate. Moreover, PFA time predictability would facilitate hospital planning. For a complete analysis, these benefits should be evaluated against the difference in acquisition costs among treatments.Procedural time by phase and techniqueCumulative savings per technique

Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

ABSTRACTThe research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

Anticipatory and evoked visual cortical dynamics of voluntary temporal attention.

We can often anticipate the precise moment when a stimulus will be relevant for our behavioral goals. Voluntary temporal attention, the prioritization of sensory information at task-relevant time points, enhances visual perception. However, the neural mechanisms of voluntary temporal attention have not been isolated from those of temporal expectation, which reflects timing predictability rather than relevance. Here we use time-resolved steady-state visual evoked responses (SSVER) to investigate how temporal attention dynamically modulates visual activity when temporal expectation is controlled. We recorded magnetoencephalography while participants directed temporal attention to one of two sequential grating targets with predictable timing. Meanwhile, a co-localized SSVER probe continuously tracked visual cortical modulations both before and after the target stimuli. We find that in the pre-target period, the SSVER gradually ramps up as the targets approach, reflecting temporal expectation. Furthermore, we find a low-frequency modulation of the SSVER, which shifts approximately half a cycle in phase according to which target is attended. In the post-target period, temporal attention to the first target transiently modulates the SSVER shortly after target onset. Thus, temporal attention dynamically modulates visual cortical responses via both periodic pre-target and transient post-target mechanisms to prioritize sensory information at precise moments.

HYBRID ARCHITECTURE WITH IMPROVED SCORE LEVEL FUSION FOR PATIENT WAITING TIME PREDICTION

Hospitals are overcrowded in proportion to the massive increase in population, making it difficult for hospitals to manage or arrange or shorten patient wait time during medical care. However, it is difficult to overstate the significance of patient waiting time management and predictability. In most of the hospitals, the clinical workflow is defined and driven by the patient queues. Predicting the incoming patients and waiting times has thus emerged as one of the most crucial clinical management techniques. Our proposed work aims to address the limitations of current waiting time prediction models by providing a more sophisticated, data-driven approach that can adapt to the complexities of healthcare environments. This work focuses on developing a new model for predicting the patient’s waiting time. The primary aim of this research is to create a reliable predictive model capable of accurately anticipating patient waiting times. This innovation aims to enhance both patient satisfaction and operational efficiency within healthcare settings. By offering healthcare administrators a tool to better manage patient flow, allocate resources efficiently, and minimize waiting times, this model seeks to optimize overall healthcare service delivery. The initial process carried out is pre-processing which includes data acquisition and improved [Formula: see text]-score normalization. Subsequently, entropy features, correlation features, and improved mutual information (MI) features are extracted. Then, prediction is carried out using hybrid classifier (HC) that involves a convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU). Finally, improved score level fusion (ISLF) is introduced to get absolute outcomes on WTP. The analysis outcomes on varied error metrics show the efficacy of the proposed WTP model.

Spatio-temporal Prediction of Air Quality Using Spatio-temporal Clustering and Hierarchical Bayesian Model

Air pollution can cause many negative impacts, so it is meaningful to establish an air quality prediction model with high accuracy for air pollution prevention and control. In previous studies, most models did not consider the spatio-temporal distribution characteristics of air quality. So this study proposes a novel spatio-temporal prediction approach STC-HBM, which combines spatio-temporal clustering (STC) and hierarchical Bayesian model (HBM) to build a spatio-temporal prediction model. In this study, we first perform spatio-temporal clustering on the Beijing-Tianjin-Hebei region in China and then apply hierarchical Bayesian models to different clusters separately. We consider three hierarchical Bayesian models: the separable spatio-temporal (SST) model, the Gaussian process (GP) model, and the autoregressive (AR) model. Since the prior distribution affects the prediction accuracy of the HBM, the resultant output of the Bayesian linear regression (BLR) model is used as the prior input of the HBM, which improves the flexibility of the model. The experimental results show that 13 cities in the BTH region are clustered into two clusters according to their spatio-temporal characteristics. Based on MAE, RMSE, MAPE, and coverage (CVG), cluster 1, and cluster 2 are better in both temporal and spatial prediction compared to the overall prediction model. In addition, for cluster 1, the models with the best prediction in time and space are AR and GP, respectively; for cluster 2, the models with the best prediction in time and space are SST and GP, respectively.

Hybrid Deep-Learning Approach with Geoffrey E. Havers-Based Loss Function and Evaluation Metric for Multilocation Traffic-Flow Forecasting

Traffic forecasting can enhance the efficiency of traffic control strategies such as routing decisions, variable speed limits, and ramp metering, resulting in a decrease in congestion, pollutants, and expenses, and an improvement in journey time predictability. Traffic forecasting, however, remains challenging because of the complex, heterogeneous, and cyclic nature of traffic data. To address this complexity, this research employs a multi-input hybrid deep self-attention network (MIHDSAN) for multilocation forecasting. The model inputs are selected using correlation analysis. New tunable loss and evaluation metrics formulations are proposed based on the traffic-modeling Geoffrey E. Havers (GEH) statistic. The proposed method was validated on two independent real-world traffic datasets from Stockton and Oakland, California. The weekly periodicity was the more relevant periodic input feature compared with daily variations; however, the daily variation was also significant for the Stockton dataset. The inclusion of weekly traffic periodicity (>95% correlated) improved the performance of the model by 3%. Adding daily periodicity was only beneficial for the Stockton dataset (91% correlated). The proposed GEH metric and its standard acceptance criterion offer both quantitative and qualitative means of evaluating the forecasts produced. The GEH loss function was consistent and outperformed current industry-standard methodologies of mean absolute error (MAE) in 80% and mean squared error (MSE) in 94% of cases. Therefore, this research presents evidence to suggest that the proposed GEH loss and evaluation functions validated in this paper become a standard criterion for traffic forecasting.

Crosstalk between salicylic acid signalling and the circadian clock promotes an effective immune response in plants

The rotation of Earth creates a cycle of day and night, leading to predictable changes in environmental conditions. The circadian clock synchronizes an organism with these environmental changes and alters their physiology in anticipation. Prediction of the probable timing of pathogen infection enables plants to prime their immune system without wasting resources or sacrificing growth. Here, we explore the relationship between the immune hormone salicylic acid (SA), and the circadian clock in Arabidopsis. We found that SA altered circadian rhythmicity through the SA receptor and master transcriptional coactivator, NPR1. Reciprocally, the circadian clock gates SA-induced transcript levels of NPR1-dependent immune genes. Furthermore, the clock gene CCA1 is essential for SA-induced immunity to the major bacterial plant pathogen Pseudomonas syringae. These results build upon existing studies of the relationship between the circadian clock and SA signalling and how interactions between these systems produce an effective immune response. Understanding how and why the immune response in plants is linked to the circadian clock is crucial in working towards improved crop productivity.

Multi-View Graph Learning for Path-Level Aging-Aware Timing Prediction

As CMOS technology continues to scale down, the aging effect—known as negative bias temperature instability (NBTI)—has become increasingly prominent, gradually emerging as a key factor affecting device reliability. Accurate aging-aware static timing analysis (STA) at the early design phase is critical for establishing appropriate timing margins to ensure circuit reliability throughout the chip lifecycle. However, traditional aging-aware timing analysis methods, typically based on Simulation Program with Integrated Circuit Emphasis (SPICE) simulations or aging-aware timing libraries, struggle to balance prediction accuracy with computational cost. In this paper, we propose a multi-view graph learning framework for path-level aging-aware timing prediction, which combines the strengths of the spatial–temporal Transformer network (STTN) and graph attention network (GAT) models to extract the aging timing features of paths from both timing-sensitive and workload-sensitive perspectives. Experimental results demonstrate that our proposed framework achieves an average MAPE score of 3.96% and reduces the average MAPE by 5.8 times compared to FFNN and 2.2 times compared to PNA, while maintaining acceptable increases in processing time.

Enhancing heart disease diagnosis through ECG image vectorization-based classification

Heart disease is a major issue, and the severity of its effects can be reduced through early detection and prevention. ECG is an effective diagnostic tool. Automating ECG analysis increases the possibility of timely analysis and prediction of heart conditions, improving patient outcomes. The extraction of heart-related features further enhances the accuracy of ECG-based classification models, paving the way for more effective and efficient online detection and prevention of heart diseases.The image-vectorization technique suggested in this study produces a vector representation that precisely captures the distinctive features of the heart signal. It involves image cropping, erasing the ECG grid lines, and assigning pixels to distinguish the heart signal from the background. Compared to the feature vector produced by VGG16, the extracted feature vector is 589 times shorter than the feature vector produced by VGG16, which significantly decreased the amount of memory required, increased algorithm convergence, and required less computing power. The feature vector extracted using image-vectorization is used to create the training dataset, which is used to train artificial neural networks (ANNs). The results demonstrate that using image-vectorization techniques improved the performance of machine learning algorithms compared to using conventional feature extraction algorithms like CNNs and VGG16.

Runoff prediction based on the IGWOLSTM model: Achieving accurate flood forecasting and emergency management

With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people’s lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes.

Enhancing severe weather predictions with the I-ConvGRU model: An iterative approach for radar echo time series through ConvGRU and RainNet integration

ABSTRACT Precipitation nowcasting plays a crucial role in disaster prevention and mitigation. Existing forecasting models often underutilize output data, leading to suboptimal forecasting performance. To tackle this issue, we introduce the I-ConvGRU model, a novel radar echo timing prediction model that synergizes the temporal dynamics optimization of ConvGRU with the spatial feature enhancement capabilities of RainNet. The model forecasts future scenarios by processing 10 sequential time-series images as input while employing skip connections to boost its spatial feature representation further. Evaluation of the radar echo data set from the Hong Kong Hydrological and Meteorological Bureau spanning from 2009 to 2015 demonstrates the I-ConvGRU model's superiority, with reductions of 17(3.8%) and 49(3.2%) in MSE and MAE metrics, respectively, compared with the TrajGRU model; meanwhile, the I-ConvGRU model had 52(5.8%) and 144(3.8%) lower values on the B-MSE and B-MAE metrics, respectively, than the slightly better performing TrajGRU model. Notably, it significantly improves the prediction of severe precipitation events, with the CSI and HSS metrics increasing by 0.0251(9.6%) and 0.0277(6.8%). These results affirm the model's enhanced effectiveness in radar echo forecasting, particularly in predicting heavy rainfall events.

Geographic Gradients in Species Interactions: From Latitudinal Patterns to Ecological Mechanisms

The idea that species interactions are more ecologically and evolutionarily important toward lower latitudes underpins seminal theories in ecology and evolution. Recent global studies have found the predicted latitudinal gradients in interactions, particularly predation. However, latitudinal patterns alone do not reveal why interactions vary geographically and so do not provide strong predictions in space (e.g., for specific ecosystems) or time (e.g., forecasting responses to global change). Here, I review theory to identify a clearer, mechanistic, and testable framework for predicting geographic variation in the importance of species interactions. I review competing metrics of importance, proximate mechanisms that can increase interaction importance, and environmental gradients that could generate predictable geographic patterns (climate extremes and stability, warmth, productivity, and biodiversity). Strong empirical tests are accumulating thanks to the rise of global experiments and datasets; renewed focus on testing why interactions vary spatially will help move the field from identifying latitudinal patterns to understanding broader mechanisms.

Pre-route timing prediction and optimization with graph neural network models

In recent years, the application of deep learning (DL) models has sparked considerable interest in timing prediction within the place-and-route (P&R) flow of IC chip design. Specifically, at the pre-route stage, an accurate prediction of post-route timing is challenging due to the lack of sufficient physical information. However, achieving precise timing prediction significantly accelerates the design closure process, saving considerable time and effort. In this work, we propose pre-route timing prediction and optimization framework with graph neural network (GNN) models combined with convolution neural network (CNN). Our framework is divided into two main stages, each of which is further subdivided into smaller steps. Precisely, our GNN-driven arc delay/slew prediction model is divided into two levels: in level-1, it predicts net resistance (net R) and net capacitance (net C) using GNN while the arc length is predicted using CNN. These predictions are hierarchically passed on to level-2 where delay/slew is estimated with our GNN based prediction model. The timing optimization model utilizes the precise delay/slew predictions obtained from the GNN-driven prediction model to accurately set the path margin during the timing optimization stage. This approach effectively reduces unnecessary turn-around iterations in the commercial EDA tools. Experimental results show that by using our proposed framework in P&R, we are able to improve the pre-route prediction accuracy by 42%/36% on average on arc delay/slew, and improve timing metrics in terms of WNS, TNS, and the number of timing violation paths by 77%, 77%, and 64%, which are an increase of 32%/35% on arc delay/slew and 30%, 20% and 31% on timing optimization compared with the existing DL prediction model.

Neural Delays in Processing Speech in Background Noise Minimized after Short-Term Auditory Training.

Background noise disrupts the neural processing of sound, resulting in delayed and diminished far-field auditory-evoked responses. In young adults, we previously provided evidence that cognitively based short-term auditory training can ameliorate the impact of background noise on the frequency-following response (FFR), leading to greater neural synchrony to the speech fundamental frequency(F0) in noisy listening conditions. In this same dataset (55 healthy young adults), we now examine whether training-related changes extend to the latency of the FFR, with the prediction of faster neural timing after training. FFRs were measured on two days separated by ~8 weeks. FFRs were elicited by the syllable "da" presented at a signal-to-noise ratio (SNR) of +10 dB SPL relative to a background of multi-talker noise. Half of the participants participated in 20 sessions of computerized training (Listening and Communication Enhancement Program, LACE) between test sessions, while the other half served as Controls. In both groups, half of the participants were non-native speakers of English. In the Control Group, response latencies were unchanged at retest, but for the training group, response latencies were earlier. Findings suggest that auditory training can improve how the adult nervous system responds in noisy listening conditions, as demonstrated by decreased response latencies.

Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data

Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. Most approaches to predicting slope failures, such as the inverse velocity method, focus on predicting the timing of a potential failure. However, such approaches are deterministic and require some subjective analysis of displacement monitoring data to generate reliable timing predictions. In this study, a more objective, probabilistic-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time-series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time-series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018-26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018-24 December 2018) are detected by the algorithm as the empirical points of inflection and maximum on a risk trajectory, respectively. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time-series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early-warning that could help mitigate catastrophic slope failures.

Analysis and Prediction of Indonesia Stock Exchange (IDX) Stock Prices Using Long Short Term Memory (LSTM) Algorithm

Stock investment is now a popular choice for many individuals and business entities. To optimize profits in investing, a deep understanding of price movements, timing, and accurate predictions in trading is required. The Long Short-Term Memory (LSTM) algorithm, a type of neural network suitable for time series data such as stock prices, can recognize complex temporal patterns in financial data. This algorithm has the potential to help investors and financial analysts predict BBRI stock prices more accurately. The purpose of this research is to predict the closing price of BBRI stock using the LSTM algorithm. This system can also conduct technical analysis with various indicators to understand the characteristics of the financial market. The research data includes BBRI stock prices from January 2006 to the present, with closing prices as the main variable. The research results show good model performance with a Mean Squared Error (MSE) of 0.000279, Mean Absolute Error (MAE) of 0.0133, and Root Mean Squared Error (RMSE) of 0.0167 on the training data. This reflects the model's level of accuracy against the training data. Although there is a slight increase in the validation data, these values remain within an acceptable level, indicating the model's ability to recognize data patterns that have not been seen before.

Numerical Research on Leakage Characteristics of Pure Hydrogen/Hydrogen-Blended Natural Gas in Medium- and Low-Pressure Buried Pipelines

To investigate the leakage characteristics of pure hydrogen and hydrogen-blended natural gas in medium- and low-pressure buried pipelines, this study establishes a three-dimensional leakage model based on Computational Fluid Dynamics (CFD). The leakage characteristics in terms of pressure, velocity, and concentration distribution are obtained, and the effects of operational parameters, ground hardening degree, and leakage parameters on hydrogen diffusion characteristics are analyzed. The results show that the first dangerous time (FDT) for hydrogen leakage is substantially shorter than for natural gas, emphasizing the need for timely leak detection and response. Increasing the hydrogen blending ratio accelerates the diffusion process and decreases the FDT, posing greater risks for pipeline safety. The influence of soil hardening on gas diffusion is also examined, revealing that harder soils can restrict gas dispersion, thereby increasing localized concentrations. Additionally, the relationship between gas leakage time and distance is determined, aiding in the optimal placement of gas sensors and prediction of leakage timing. To ensure the safe operation of hydrogen-blended natural gas pipelines, practical recommendations include optimizing pipeline operating conditions, improving leak detection systems, increasing pipeline burial depth, and selecting materials with higher resistance to hydrogen embrittlement. These measures can mitigate risks associated with hydrogen leakage and enhance the overall safety of the pipeline infrastructure.

Petrophysical evaluation based on three-stream convolutional neural networks in the Berkine Basin, Algeria

Petrophysical evaluation is a key stage before formation test in exploration field, it allows the identification of the best plays in term of effective porosity, shale volume and hydrocarbon saturation. This paper introduces an innovative approach to petrophysical evaluation by leveraging deep learning techniques, our scheme is based on three streams, in each stream we predict a petrophysical parameter (Volume of shale, Effective Porosity and water saturation), each stream represents a convolutional neural network model that has been trained using traditional wireline logging data from the Silurien argilo-greseux reservoir units in the Berkine Basin, Algeria. experimental results show that our proposed method achieves stat-of-the-art predictions by giving correlated results in comparison against conventional analysis methods (calculated Shale Volume, Porosity and water saturation), furthermore assessment of test data shows also that our method gives good prediction in thin beds reservoirs and offers the ability to detect low resistivity pays. Because our method is based on Convolutional neural network models, it is fast and allows the petrophysical parameters prediction in real time.

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.

How Stress Barriers and Fracture Toughness Heterogeneities Arrest Buoyant Hydraulic Fractures

AbstractIn our study, we investigated the impact of changes in Mode I fracture toughness and stress barriers on fully developed planar, buoyant hydraulic fractures assuming linear elastic hydraulic fracture mechanics. We present scaling-based arguments to predict the interaction type and use numerical simulations to validate our findings. Through a two-dimensional simplification, we estimate the lower limit for the fracture to feel a change in fracture toughness (so-called immediate breakthrough). Our simulations show that this approach only captures the order of magnitude of the toughness jump necessary for immediate breakthrough compared to the actual value due to three-dimensional solid effects, emphasizing their importance in such systems. We show that we can estimate the occurrence of indefinite containment at depth by considering that lateral spreading occurs at an approximately constant height. However, timing predictions in the case of a transient containment suffer from our simplified approach, which cannot model the injection history of the spreading constant height fracture. The same observations regarding immediate breakthrough and indefinite containment hold when considering stress barriers using pressure-scale-based arguments. Our study shows that the required toughness changes for fracture arrest are more significant than the observed values in the field. In contrast, stress barriers with a magnitude of around 1 MPa are generally sufficient to contain buoyant hydraulic fractures indefinitely. Stress barriers, in combination with other arrest mechanisms, are thus the most prominent mitigation factor of buoyant growth in industrially created hydraulic fractures.