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Background: The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings., Method: Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation., Results: 84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %)., Conclusion: The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD., Methods: Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation., Results: We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery., Conclusions: Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery., (© 2021 Crohn’s & Colitis Foundation. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation.)

We have entered the era of "big data" and with that the explosion of interest in prediction modeling. With this explosion comes the challenge of evaluating statistical prediction models, both from the standpoint of an author as well as a reviewer. This article provides guidance for the evaluation and critique of a statistical prediction model. Hopefully, this will improve the quality of statistical prediction modeling studies and facilitate their review., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games. Methods: The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships. Results: The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r =.99 for both approaches). Conclusions: These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games. [ABSTRACT FROM AUTHOR]

Recently, additive manufacturing (AM) has received increased attention due to its high energy consumption. By extracting hidden information or highly representative features from energy-relevant data, knowledge distillation (KD) reduces predictive model complexity and computational load. By using almost predetermined and fixed models, the distillation process restricts students and teachers from learning from one model to another. To reduce computational costs while maintaining acceptable performance, a teacher assistant (TA) was added to the teacher-student architecture. Firstly, a teacher ensemble was combined with three baseline models to enhance accuracy. In the second step, a teacher ensemble (TA) was formed to bridge the capacity gap between the ensemble and the simplified model. As a result, the complexity of the student model was reduced. Using geometry-based features derived from layer-wise image data, a KD-based predictive model was developed to evaluate the feasibility and effectiveness of two independently trained student models. In comparison with independently trained student models, the performance of the proposed method has the lowest RMSE, MAE, and training time. [ABSTRACT FROM AUTHOR]

In recent years, the vigorous development of e-commerce has also greatly promoted the development of the fashion multimedia field. Fashion compatibility modeling plays a very important role in a series of applications, such as fashion retrieval and personalized recommendation. Because fashion compatibility prediction usually contains multiple modal information, in this paper, we present a type-aware fashion compatibility prediction model based on a hybrid attention mechanism that is centered on using a priori category information for the fusion of multimodal information. Specifically, the model first designs a hybrid attention mechanism to fully explore the inner dependency of the visual modality while enriching with textual modalities to obtain a joint feature representation of multimodal information. Furthermore, the model matches fashion items as a distance measurement problem, and the joint feature representation of multimodal information realizes type-aware characteristics by the design of a distance measurement function with category-aware embedding. The experimental results obtained on the publicly available Polyvore and Polyvore-D datasets demonstrate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi‐evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long‐term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting. [ABSTRACT FROM AUTHOR]

Objective: Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies. Methods: We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China. Results: The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran's I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05). Conclusions: As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different. [ABSTRACT FROM AUTHOR]

Time series, a type of data that measures how things change over time, remains challenging to predict. In order to improve the accuracy of time series prediction, a deep learning model CL-Informer is proposed. In the Informer model, an embedding layer based on continuous wavelet transform is added so that the model can capture the characteristics of multi-scale data, and the LSTM layer is used to capture the data dependency further and process the redundant information in continuous wavelet transform. To demonstrate the reliability of the proposed CL-Informer model, it is compared with mainstream forecasting models such as Informer, Informer+, and Reformer on five datasets. Experimental results demonstrate that the CL-Informer model achieves an average reduction of 30.64% in MSE across various univariate prediction horizons and a reduction of 10.70% in MSE across different multivariate prediction horizons, thereby improving the accuracy of Informer in long sequence prediction and enhancing the model's precision. [ABSTRACT FROM AUTHOR]

An unsupervised change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced which is able to detect change points online under the presence of non-trivial trend patterns which must be prevented from triggering false positives. Different predictive models for the required time series forecasting (Predict) step together with different statistical tests for deciding about the proximity of predicted and actual data (Compare step) are allowed. Its performance is shown for the Predict step being carried out by either an LSTM recursive neural network or an ARIMA linear time series model together with the CUSUM rule as Compare step method. It shows to perform best in comparison to several other online CPD methods for detect times in the regime of low numbers of false positive detections. The method's good performance is based on its ability to detect structural changes in the presence complex underlying trend patterns. The use case concerns tribological wear for which change points separating the run-in, steady-state, and divergent wear phases are detected. [ABSTRACT FROM AUTHOR]

A classic way to design a nonlinear model predictive control (NMPC) scheme with guaranteed stability is to incorporate a terminal cost and a terminal constraint into the problem formulation. While a long prediction horizon is often desirable to obtain a large domain of attraction and good closed‐loop performance, the related computational burden can hinder its real‐time deployment. In this article, we propose an NMPC scheme with prediction horizon N=1$$ N=1 $$ and no terminal constraint to drastically decrease the numerical complexity without significantly impacting closed‐loop stability and performance. This is attained by constructing a suitable terminal cost from data that estimates the cost‐to‐go of a given NMPC scheme with long prediction horizon. We demonstrate the advantages of the proposed control scheme in two benchmark control problems. [ABSTRACT FROM AUTHOR]

Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. To obtain a more robust and precise prediction of WCFZ in undersea mining, a WCFZ prediction dataset with 122 cases of fractured zones was constructed. Five machine learning algorithms (linear regression, XGBRegressor, RandomForestRegressor, LineareSVR, and KNeighborsRegressor) were employed to develop five corresponding predictive models, taking multiple factors into account.The optimal parameters for each model are obtained through ten-fold cross-validation (10CV). The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination (R2) and mean squared error (MSE). A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria, showing a high R2 value of up to 0.925 and a low MSE value of 3.61. The proposed model was validated in a recently established mining area on Sanshan Island, China. It shows low absolute and relative errors of 0.71 m and 2.01%, respectively, between the predicted value from the model and observation result from the field, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ. [ABSTRACT FROM AUTHOR]

CRISPR is a precise and effective genome editing technology; but despite several advancements during the last decade, our ability to computationally design gRNAs remains limited. Most predictive models have relatively low predictive power and utilize only the sequence of the target site as input. Here we suggest a new category of features, which incorporate the target site genomic position and the presence of genes close to it. We calculate four features based on gene expression and codon usage bias indices. We show, on CRISPR datasets taken from 3 different cell types, that such features perform comparably with 425 state-of-the-art predictive features, ranking in the top 2–12% of features. We trained new predictive models, showing that adding expression features to them significantly improves their r2 by up to 0.04 (relative increase of 39%), achieving average correlations of up to 0.38 on their validation sets; and that these features are deemed important by different feature importance metrics. We believe that incorporating the target site's position, in addition to its sequence, in features such as we have generated here will improve our ability to predict, design and understand CRISPR experiments going forward. [ABSTRACT FROM AUTHOR]

Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon—nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms. [ABSTRACT FROM AUTHOR]

How to effectively predict outcomes when initial time series data are limited remains unclear. This study investigated the efficiency of Bayesian model selection to address the lack of initial data for time series analysis, particularly in cold-start scenarios—a common challenge in predictive modeling. We utilized a comprehensive approach that juxtaposed observational data against various candidate models through strategic partitioning. This method contrasted traditional reliance on distance measures like the L2 norm. Instead, it applied statistical tests to validate model efficacy. Notably, the introduction of an interactive visualization tool featuring a slide bar for setting significance levels marked a significant advancement over conventional p-value displays. Our results affirm that when observational data align with a candidate model, effective predictions are possible, albeit with necessary considerations of stationarity and potential structural breaks. These findings underscore the potential of Bayesian methods in predictive analytics, especially when initial data are scarce or incomplete. This research not only enhances our understanding of model selection dynamics but also sets the stage for future investigations into more refined predictive frameworks. [ABSTRACT FROM AUTHOR]

The existing branch prediction models can't predict the behaviors of various instructions in the processor accurately, which leads to the limitation of processing efficiency. Therefore, this paper proposed two hybrid prediction solutions, which aimed to combine multiple branch prediction models to improve prediction accuracy and processor execution efficiency. This paper transferred the prediction results of TAGE branch prediction model and BATAGE branch prediction model to the Hybrid model. In the prediction phase, the Hybrid model selected the prediction results of the best-performing branch prediction model based on the historical performance of TAGE and BATAGE. In the update phase, the Hybrid model updated the saturation counter of the entries that needed to be updated according to the designed hybrid prediction strategy. Experiments on 440 test programs provided by the CBP software simulation platform show that the prediction error rates of both hybrid prediction solutions are lower than those of many of the latest mainstream branch prediction models. This study provides an effective solution to the problem of predicting all command mode behaviors. In the branch instruction prediction of real CPU, this research provides some practical value. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of hydrodynamic forces predictions for underwater vehicle within certain resource constraints, this study integrates high and low-fidelity samples using the Co-Kriging method, combined with an expected improvement (EI) sequential infill criterion to construct a hydrodynamic forces prediction model. Different grid densities of computational fluid dynamics (CFD) calculations are used to distinguish high and low-fidelity samples. Taking the Joubert BB2 underwater vehicle as the research object, hydrodynamic forces predictions are conducted for various angles of attack and speeds. The effectiveness of the EI infill criterion in improving model prediction accuracy is validated. Furthermore, compared to the traditional Kriging model under the same computational resources, the Co-Kriging method, which integrates high and low-fidelity samples, significantly outperforms the Kriging model constructed solely from high or low-fidelity samples in overall prediction accuracy for underwater vehicle hydrodynamic forces. [ABSTRACT FROM AUTHOR]

The chiller energy consumption prediction plays an important role in reducing the building energy consumption. Continuously improving the performance of the prediction model is the key to ensuring the accuracy of the chiller energy consumption prediction. As a result, a new improved ensemble model of the chiller energy consumption prediction is proposed, based on the idea of algorithmic ensemble aided by an attention mechanism (AM). To guarantee that the proposed model can see information from many spatial and structural viewpoints, the improved ensemble model incorporates the benefits of several base prediction methods. The suggested approach chooses crucial features to simplify the model input by utilizing AM, thereby reducing the complexity of the samples. The improved ensemble model was experimentally verified in an actual multi-chiller system, and the results showed that the established model could obtain good prediction results. Finally, the proposed model was compared with existing, well-known prediction models. To evaluate the accuracy of these models, the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), variance accounted for (VAF), and coefficient of determination (R 2) were used as the evaluation indices. With an MAE of 2.858, an MAPE of 1.2755 %, an MSE of 20.1878, a VAF of 93.6058, and an R 2 of 0.9923, the findings demonstrate that the improved ensemble model has a greater prediction accuracy than the other well-known prediction methods. The suggested model enhances the variety of empirical model algorithm libraries. [ABSTRACT FROM AUTHOR]

Most of the earlier proposed action prediction methods, such as PoseGAN, C2GAN, etc. are limited to unidirectional prediction and not intended to perform the desired class of action. In this paper, we propose an action-guided Cycle Generative Adversarial Network (AGCGAN), which is a bi-directional video prediction model to anticipate future frames from current visual frames and vice versa. The proposed CycleGAN architecture consists of two generators and two discriminators along with two additional keypoint detectors to make keypoint loss measurements. The adversarial and cycle losses perform the appearance modelling whereas the keypoint loss provides the necessary motion correction. We evaluated the effectiveness of our proposed method on the UT-Interaction dataset. Furthermore, we also tested our model on the standard Market-1501 dataset against the state-of-the-art schemes. Results show that our proposed method provides comparable visual quality with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective: The main aim of this study was to develop an equation for predicting performance in 42.2 km (MRT) using pacing and packing behavior, age group and previous 21.1 km time as possible explanatory variables. Methods: 1571 men and 251 female runners who took part in the Valencia Marathon and Half-Marathon were selected to display the regression models. Stepwise regression analysis showed as explanatory variables for MRT: pacing behavior, age group, and time in 21.1 km. Results: The analysis showed four regression models to estimate accurately MRT based principally on athletes previous performance in half-marathon and pacing behavior for men (R2= 0.72–0.88; RMSE= 4:03–8:31 [min:s]). For women, it was suggested a multiple linear regression for estimating MRT (R2 0.95; RSE= 8:06 [min:s]) based on previous performance in half-marathon and pacing behavior. The subsequent concordance analysis showed no significant differences between four of the total regressions with real time in the marathon (p>0.05). Conclusion: The present results suggest that even and negative pacing behavior and a better time in 21.1 km, in the previous weeks of the marathon, might accurately predict the MRT. At the same time, nomadic packing behavior was the one that reported the best performance. On the other hand, although the age group variable might partially explain the final performance, it should be included with caution in the final model because of differences in sample distribution, causing an overestimation or underestimation of the final time. [ABSTRACT FROM AUTHOR]

The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalyzed Nieuwland and colleagues' (2018) [Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018] replication of DeLong and colleagues (2015) [DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117–1121, 2005]. Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their EEG was recorded. We measured ERPs preceding the critical words (namely, the semantic prediction potential), in conjunction with postword N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, semantic prediction potential amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues' findings. Strikingly, however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency also modulated the relationship between article surprisal and the N400, emphasizing the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing. [ABSTRACT FROM AUTHOR]

Uncertainty estimates are fundamental to assess the reliability of predictive models in hydrology. We use the framework of conformal prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological predictions. Integrating recent information significantly enhances overall uncertainty predictions, even with substantial gaps between updates. While local information yields good results on average, it proves to be insufficient for peak-flow predictions. Incorporating global information improves the accuracy of peak-flow bounds, corroborating findings from related studies. Overall, the study underscores the importance of continuous data updates and the integration of global information for robust and efficient uncertainty estimation. [ABSTRACT FROM AUTHOR]

The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning, and virtual sample generation was proposed to establish a prediction model of the mass ablation rate based on a small dataset. Briefly, a small number of sample points were collected using uniform design experimentation, which were marked to construct the initial dataset and primitive model. Then, data points were acquired from the sample pool and iterated using various integrated algorithms through active learning to update the above dataset and model. Finally, a large number of virtual samples were generated based on the optimal model, and a further optimized prediction model was achieved. The results showed that after introducing 300 virtual samples, the average percentage error of the gradient boosting decision tree (GBDT) prediction model on the test set decreased to 3.1%, which demonstrates the effectiveness of the proposed method in building prediction models based on a small dataset. [ABSTRACT FROM AUTHOR]

The continuous improvement of bridge construction technology has resulted in an ongoing expansion of bridge spans, which has concomitantly increased the difficulty of controlling the alignment of long-span bridges during construction. In order to address the issue of the grey prediction model exhibiting a significant discrepancy in its alignment predictions for long-span continuous girder bridges, a pre-camber prediction method for bridges based on a combination of the grey model (GM) and BP neural network (GM-BP) is proposed. Firstly, the parameters are identified according to their influence on the pre-camber, and the appropriate parameters are selected as the original data to improve the efficiency of prediction. Subsequently, the original data are preliminarily fitted by the GM(1,1) model, and the predicted values are used as inputs for training the neural network. Finally, the new predicted values are output using the nonlinear fitting ability of the BP neural network. To assess the efficacy of the model, it is applied to the prediction of the pre-camber of the girder segments of a bridge under cantilever construction. The pre-camber prediction for 11#–13# girder sections was based on 10 sets of monitoring data from constructed girder sections. The results demonstrated that the average relative error of the GM-BP combined prediction model was 3.01%, which was 5.68% less than that of the GM(1,1) model, and the overall prediction exhibited a closer alignment with the original data. The GM-BP combined prediction model is an effective method for ensuring the alignment control of bridge construction and is able to achieve high accuracy and stability in its predictions in the case of limited and irregular data. [ABSTRACT FROM AUTHOR]

High-Entropy Alloys (HEAs) represent a transformative class of materials characterized by multiple principal elements and high configurational entropy. This review article provides an in-depth examination of their structural particularities, prediction methodologies, and synthesis techniques. HEAs exhibit unique structural stability due to high-entropy effects, severe lattice distortions, and slow diffusion processes. Predictive models, including thermodynamic and kinetic approaches, are essential for understanding phase stability. Various synthesis methods impact HEA properties, and advanced characterization techniques are crucial for their study. The article highlights current applications and future research directions, emphasizing the potential of HEAs in diverse technological fields. [ABSTRACT FROM AUTHOR]

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available predictive data sources are limited in real-world scenarios. On the other hand, since trip purpose labeling is costly and the development levels of cities are unbalanced, it is infeasible to access large-scale labeled data in less developed cities to train advanced prediction models. To narrow the gaps, this article presents A new Graph Embedding Network and active Domain Adaptation based framework (AGENDA) that only requires open data sources and is capable of predicting in both label-rich cities and label-scarce cities. Specifically, in label-rich source cities, we first use the vehicle's GPS trajectory and open POI check-ins to augment trip contexts. Then we establish a supervised graph embedding network with two attention mechanisms to extract the passenger's latent activity semantics and a classifier to predict trip purpose. To enable the prediction in label-scarce target cities, we further devise an active domain adaptation framework, in which adversarial domain adaptation is used to transfer the source-learned knowledge, and active learning is used to integrate human intelligence in the model training. A group of experiments are conducted with real-world datasets in Beijing and Shanghai. Evaluation results demonstrate that the proposed framework significantly outperforms existing trip purpose prediction algorithms, and could make accurate trip purpose prediction in label-scarce cities with much fewer labeling efforts. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to explain the predictions made by data-driven methods. Since such predictions rely heavily on the data used for training, explanations that convey information about how the training data affects the predictions are useful. The paper proposes a novel approach to quantify how different data-clusters of the training data affect a prediction. The quantification is based on Shapley values, a concept which originates from coalitional game theory, developed to fairly distribute the payout among a set of cooperating players. A player's Shapley value is a measure of that player's contribution. Shapley values are often used to quantify feature importance, ie. how features affect a prediction. This paper extends this to cluster importance, letting clusters of the training data act as players in a game where the predictions are the payouts. The novel methodology proposed in this paper lets us explore and investigate how different clusters of the training data affect the predictions made by any black-box model, allowing new aspects of the reasoning and inner workings of a prediction model to be conveyed to the users. The methodology is fundamentally different from existing explanation methods, providing insight which would not be available otherwise, and should complement existing explanation methods, including explanations based on feature importance. [ABSTRACT FROM AUTHOR]

We assess the relationship between model size and complexity in the time‐varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time. [ABSTRACT FROM AUTHOR]

Predicting the rate of penetration (ROP) is important for optimizing drilling parameters, improving drilling efficiency, and optimizing economic benefits throughout the drilling process. The current prediction model of ROP based on machine learning algorithms does not consider the interference of outliers. Therefore, in this study, we propose a method to predict ROP based on fuzzy support vector regression (FSVR). First, appropriate input parameters were selected from the controllable parameters. Second, based on the local outlier factor, a fuzzy membership degree was assigned to each sample. Finally, the sample with the fuzzy membership value was input into the model for ROP prediction. The results demonstrated that the goodness of fit (R2) of the improved FSVR model is 0.9634, and the mean absolute error is 0.1974. Compared with standard SVR and other models, the improved FSVR model has a stronger anti-interference ability, smaller prediction error for normal samples, and higher accuracy. [ABSTRACT FROM AUTHOR]

The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range. [ABSTRACT FROM AUTHOR]

Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud's cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is significantly impacted by environmental factors that exhibit substantial uncertainty and volatility, posing a critical challenge for accurate PV power prediction in power system management. To address this, a parallel model is proposed for PV short-term prediction utilizing a multi-level attention mechanism. Firstly, gray relation analysis (GRA) and an improved ISODATA algorithm are used to select a dataset of similar days with comparable meteorological characteristics to the forecast day. A transformer encoder layer with multi-head attention is then used to extract long-term dependency features. Concurrently, BiGRU, optimized with a Global Attention network, is used to capture global temporal features. Feature fusion is performed using Cross Attention, calculating attention weights to emphasize significant features and enhancing feature integration. Finally, high-precision predictions are achieved through a fully connected layer. Utilizing historical PV power generation data to predict power output under various weather conditions, the proposed model demonstrates superior performance across all three climate types compared to other models, achieving more reliable predictions. [ABSTRACT FROM AUTHOR]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predictions for crucial project parameters, including completion time, required personnel, and estimated costs. Our analysis utilises a comprehensive dataset that encapsulates the complexities inherent in real-world projects, providing a robust basis for evaluating model performance. The findings, presented through detailed tables and comparative charts, underscore the collective success of the models. The LSTM model stands out for its exceptional performance in consistently predicting completion time, personnel requirements, and estimated costs. Quantitative evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), corroborate the efficacy of the models. This study offers insights into the success observed, reflecting the potential for further refinement and continuous exploration to enhance the accuracy of predictive models in the ever-evolving landscape of project management. [ABSTRACT FROM AUTHOR]

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach. [ABSTRACT FROM AUTHOR]

Background Virtually all cases of hepatitis C virus (HCV) infection in children in the United States occur through vertical transmission, but it is unknown how many children are infected. Cases of maternal HCV infection have increased in the United States, which may increase the number of children vertically infected with HCV. Infection has long-term consequences for a child's health, but treatment options are now available for children ≥3 years old. Reducing HCV infections in adults could decrease HCV infections in children. Methods Using a stochastic compartmental model, we forecasted incidence of HCV infections in children in the United States from 2022 through 2027. The model considered vertical transmission to children <13 years old and horizontal transmission among individuals 13–49 years old. We obtained model parameters and initial conditions from the literature and the Centers for Disease Control and Prevention's 2021 Viral Hepatitis Surveillance Report. Results Model simulations assuming direct-acting antiviral treatment for children forecasted that the number of acutely infected children would decrease slightly and the number of chronically infected children would decrease even more. Alone, treatment and early screening in individuals 13–49 years old reduced the number of forecasted cases in children and, together, these policy interventions were even more effective. Conclusions Based on our simulations, acute and chronic cases of HCV infection are remaining constant or slightly decreasing in the United States. Improving early screening and increasing access to treatment in adults may be an effective strategy for reducing the number of HCV infected children in the United States. [ABSTRACT FROM AUTHOR]

To ensure the timely execution of hard real-time applications, scheduling analysis techniques must consider safe upper bounds on the possible execution durations of tasks or runnables, which are referred to as Worst-Case Execution Times (WCET). Bounding WCET requires not only program path analysis but also modeling the impact of micro-architectural features present in modern processors. In this paper, we model the ARMv8 ISA and micro-architecture including instruction cache, branch predictor, instruction prefetching strategies, out-of-order pipeline. We also consider the complex interactions between these features (e.g., cache misses caused by branch predictions and branch misses caused by instruction pipelines) and estimate the WCET of the program using the Implicit Path Enumeration Technique (IPET) static WCET analysis method. We compare the estimated WCET of benchmarks with the observed WCET on two ARMv8 boards. The ratio of estimated to observed WCET values for all benchmarks is greater than 1, demonstrating the security of the analysis. [ABSTRACT FROM AUTHOR]

The probing depth of the transient electromagnetic method (TEM) refers to the depth range at which the underground conductivity changes can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate probing depth is useful for the selection of appropriate inversion parameters and improving survey accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy are validated through synthetic models and field measurements. We compared the performance of classic deep learning models, including CNN, RESNET, and RNN, and found that RNN performed the best overall on both synthetic and field data. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity model depths in the training set, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy. [ABSTRACT FROM AUTHOR]

In order to address the issue of large errors in predicting SST along the coast using numerical models, this study adopts LSTM, a deep learning method, to develop optimal SST prediction models. The Xiaomaidao Station is selected as an example, and then the method is then extended to 14 coastal stations along the Bohai Sea and the Yellow Sea. The results show that the SST prediction model based on LSTM effectively improves forecast accuracy. The mean absolute errors for 1–3-day SST forecasts of the optimal model at Xiaomaidao Station are 0.20 °C, 0.27 °C, and 0.31 °C, and the root mean square errors are 0.28 °C, 0.36 °C, and 0.41 °C, respectively, representing an average reduction of 78% compared to those of the numerical model. Extending this approach to other forecasting sites along the Bohai Sea and the Yellow Sea results in an average 61% reduction in forecast error when compared with the numerical model. Furthermore, it is found that utilizing an LSTM model can significantly save computational resources and improve the forecasting efficiency. [ABSTRACT FROM AUTHOR]

I consider the problem of model diagnostics, that is, the problem of criticizing a model prior to history matching by comparing data to an ensemble of simulated data based on the prior model (prior predictions). If the data are not deemed as a credible prior prediction by the model diagnostics, some settings of the model should be changed before history matching is attempted. I particularly target methodologies that are computationally feasible for large models with large amounts of data. A multiscale methodology, that can be applied to analyze differences between data and prior predictions in a scale-by-scale fashion, is proposed for this purpose. The methodology is computationally inexpensive, straightforward to apply, and can handle correlated observation errors without making approximations. The multiscale methodology is tested on a set of toy models, on two simplistic reservoir models with synthetic data, and on real data and prior predictions from the Norne field. The tests include comparisons with a previously published method (termed the Mahalanobis methodology in this paper). For the Norne case, both methodologies led to the same decisions regarding whether to accept or discard the data as a credible prior prediction. The multiscale methodology led to correct decisions for the toy models and the simplistic reservoir models. For these models, the Mahalanobis methodology either led to incorrect decisions, and/or was unstable with respect to selection of the ensemble of prior predictions. [ABSTRACT FROM AUTHOR]

This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. Worked examples are included for both cases. This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. [ABSTRACT FROM AUTHOR]

The performance of long-term prediction models is currently impeded due to the mismatch between the nonstationary representations of statistical learning models and the underlying dynamics from real-world systems, which results in low long-term prediction accuracies for many real-world applications. We present a Recurrent Gated Unit-based Mixture Kriging Machine Bayesian Filtering (ReGU-MKMBF) approach for characterizing nonstationary and nonlinear behaviors of one ubiquitous real-world process—dynamic intermittency. It models the transient dynamics in the state space as recurrent transitions between localized stationary segments/attractors. Then, a case study on predicting the onset of pathological symptoms associated with Electrocardiogram signals is presented. The results suggest that ReGU-MKMBF improves the forecasting performance by extending the prediction time horizon with an order of magnitudes while maintaining high accuracies on the foreseen estimates. Implementing the presented approach can subsequently change the current scheme of online monitoring and aftermath mitigation into a prediction and timely prevention for telecardiology. [ABSTRACT FROM AUTHOR]

In this research, we put forward a type of model averaging methods for estimating the conditional density function, based on a non-parametric estimation method and two different loss functions. Such methods provide accurate and stable estimation of the conditional density function. In addition, we develop prediction bands for the conditional density function in the case of finite samples by combining conformal prediction and model averaging. Conclusions from computational simulations and real data assessments based on photometric redshift estimation indicate the superiority of our proposed methods in comparison to other alternative methods. [ABSTRACT FROM AUTHOR]

The decision to perform amputation of a limb in a patient with diabetic foot ulcer (DFU) is not an easy task. Prediction models aim to help the surgeon in decision making scenarios. Currently there are no prediction model to determine lower limb amputation during the first 30 days of hospitalization for patients with DFU. Classification And Regression Tree analysis was applied on data from a retrospective cohort of patients hospitalized for the management of diabetic foot ulcer, using an existing database from two Orthopaedics and Traumatology departments. The secondary analysis identified independent variables that can predict lower limb amputation (mayor or minor) during the first 30 days of hospitalization. Of the 573 patients in the database, 290 feet underwent a lower limb amputation during the first 30 days of hospitalization. Six different models were developed using a loss matrix to evaluate the error of not detecting false negatives. The selected tree produced 13 terminal nodes and after the pruning process, only one division remained in the optimal tree (Sensitivity: 69%, Specificity: 75%, Area Under the Curve: 0.76, Complexity Parameter: 0.01, Error: 0.85). Among the studied variables, the Wagner classification with a cut-off grade of 3 exceeded others in its predicting capacity. Wagner classification was the variable with the best capacity for predicting amputation within 30 days. Infectious state and vascular occlusion described indirectly by this classification reflects the importance of taking quick decisions in those patients with a higher compromise of these two conditions. Finally, an external validation of the model is still required. III [ABSTRACT FROM AUTHOR]

The primary objectives of this research study are to provide a detailed description of machine learning (ML) technology when applied to assessing innovation and to design a model that allows predicting an institution’s degree of innovation. Machine learning technology lacks assumptions or preconceptions and is capable of processing a large amount of data and variables. After data processing, the ML model is built using variables associated with educational context, training is performed, and a web is built to predict the degree of innovation of an educational institution. This model provides an innovation accuracy prediction of 66% and allows assessing the influence of the variables analyzed when predicting the use of active methodologies at a given institution. In conclusion, this approach can open new data analysis techniques supported by ML that complement traditional statistically based approaches. [ABSTRACT FROM AUTHOR]

The fast growth in renewable power generation, crucial for reducing carbon emissions in the traditional energy system, is constrained by negative environmental and economic repercussions, demanding a smarter integration with conventional energy sources. However, the seamless integration of renewable energy into grid imposes major challenges due to the inherent environment dependence of energy production, necessitating proactive forecasts to anticipate fluctuations in efficiency and dependability that might have an influence on the overall grid status and living style of the users. The objective of the work is to add a consistent dataset in the machine learning community and a new ensemble to deal with the problems pertaining to the projection and integration of renewable energy using predictive modeling. The model includes a stacked ensemble of Random Forest and XGBoost algorithms. The proposed ensemble has been successfully applied to the dataset of Agartala City. In this model, the Random Forest model is used to forecast the target variable based on input parameters, followed by the use of the XGBoost model to improve predictions through a combination of Random Forest predictions, with the goal of leveraging the strengths of multiple models while mitigating their weaknesses. The meta-model, a basic logistic regression, then learns the best combination of these predictions, allowing for the maximum potential accuracy. The model has been evaluated on R2 and RMSE. The accuracy of 99% reveals its feasibility and superiority both in short-term and long-term predictions. [ABSTRACT FROM AUTHOR]

Solar irradiation is a crucial parameter in the design and operation of solar energy systems. However, its long-term measurement everywhere is hindered by the maintenance and cost of measurement devices. Therefore, numerous research studies have been conducted to determine solar irradiation, leading to the development of various prediction models. Recently, artificial neural network (ANN) models have been shown to enable researchers to make more accurate predictions. This study aims to identify the most effective algorithms and functions for accurately predicting instantaneous solar irradiation using ANN models with different network structures. Five commonly used training algorithms and two different ANN architectures are examined in this study. These models are tested with various transfer functions, and the impact of the number of neurons in the hidden layer on prediction results is also investigated. Meteorological data collected at 5-s intervals from a meteorology station in Hakkâri Province between 2019 and 2021, totaling one million data points, are used for model training. The ANN model with a network structure consisting of 100 neurons, trained with the Levenberg–Marquardt algorithm and "tansig" transfer function, achieved the best prediction performance with a correlation coefficient (R) of 0.9783 and a mean absolute percentage error of 6.79%. For an 80-10-10 data split, the mean-squared error, normalized root-mean-squared error, and mean bias error were found to be 0.024, 7.206, and 0.800, respectively. The solar irradiation prediction performance varied based on the training algorithm and particularly the transfer functions used. Similar approaches can be employed in regions where measurement devices cannot be installed, enabling successful prediction results even without direct irradiation measurements. [ABSTRACT FROM AUTHOR]