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ABSTRACT Whole‐genome sequence (WGS) data was used to estimate genomic breeding values for growth and carcass traits in Australian Angus cattle. The study aimed to compare the accuracy and bias of genomic predictions with three marker densities, including 50K, high‐density (HD) and WGS. The dataset used in this study consisted of animals born between 2013 and 2022. Body weight traits included birthweight, weight at 400 days and weight at 600 days of age. The carcass traits were carcass weight, carcass intramuscular fat and carcass marbling score. The accuracy and bias of prediction were assessed using the cross‐validation. Further, for the growth traits, animals in the validation group were subdivided into two subgroups, which were moderately or highly related to the reference. Genomic best linear unbiased prediction (GBLUP) was used to compare genomic predictions with the three marker densities. The prediction accuracies were generally similar across the marker densities, ranging between 0.61 and 0.68 for the body weight traits and between 0.40 and 0.52 for the carcass traits. However, the accuracies marginally decreased as the marker density increased for all the traits studied. A similar lack of difference was found when considering the accuracy by the relatedness subgroups. The results indicated that no meaningful difference in prediction accuracy was estimated when comparing the three marker densities due to the population structure. In conclusion, there was no substantial improvement in genomic prediction when using the WGS in this study. [ABSTRACT FROM AUTHOR]

ABSTRACT Prediction error variance (PEV) and prediction accuracy (PA) of breeding values (BVs) are essential for formulating breeding plans and predicting response to selection. However, restricted best linear unbiased prediction method (RBLUP method) carries many unknowns: in particular, the formulas for calculating PEV and PA are not clear. New findings were obtained using the RBLUP method. The uniqueness of RBLUP of BVs was proven. The formulas of PEV and PA for the RBLUP of BVs were derived from restricted mixed model equations. A method was also devised for easily calculating the PEV and PA for the RBLUP of BVs. Finally, the relationship between the RBLUP and ordinary BLUP of BVs was derived. It has become easier to calculate the PEV and PA for the RBLUP of BVs. This method is particularly effective for calculating the PEV and PA when applying the RBLUP method to achieve relative desired changes in all traits. This has also made it possible to predict the response to selection using the RBLUP method. [ABSTRACT FROM AUTHOR]

Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study's results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis. [ABSTRACT FROM AUTHOR]

Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts. [ABSTRACT FROM AUTHOR]

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. [ABSTRACT FROM AUTHOR]

Incorporating prediction models developed based on machine learning algorithms into the traditional prediction market creates hybrid intelligence. We design and conduct an online controlled experiment to investigate the impacts of two dimensions of human–machine interaction, whether to introduce machines as traders and whether to disclose their presence, on the prediction performance. The results of the experiment reveal that the introduction of machines creates two competing effects on prediction accuracy. The positive influence comes from the intensified competition brought by machines, which fosters a strong desire to win among human participants and motivates them to engage in more deliberate decision-making efforts. Conversely, in the context of intensive competition, humans are inclined to trade at a large magnitude, consequently leading to a decrease in prediction performance. Furthermore, the results indicate that simply disclosing the presence of machines can have a detrimental impact on prediction performance, as it may lead to a reduction in human deliberation efforts. Furthermore, this article delves into the potential mechanisms involved. This study contributes to the understanding of human behaviors in hybrid prediction markets and highlights the need for careful human–machine interaction design to optimize prediction market performance [ABSTRACT FROM AUTHOR]

Essential soil nutrients are dynamic in nature and require timely management in farmers' fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km2 located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R2 of model fit. Lin's concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R2 of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents. [ABSTRACT FROM AUTHOR]

The blast-induced vibration during excavation by drilling and blasting method has an important impact on the surrounding structures. In particular, with the development of tunnel engineering, the impact of blasting vibration on tunnel construction has attracted extensive attention. In this paper, the propagation attenuation characteristics of blast-induced vibration (PPV, peak particle velocity) on different tunnel structures were systematically studied based on the field monitoring data. Initially, the attenuation characteristics of blasting vibration PPV on the lower bench surface, the side wall of the excavated tunnel and the closely spaced adjacent tunnel were investigated. Subsequently, the capacity of several widely utilized empirical prediction equations to estimate the PPV on tunnel structures was examined, along with a comparative analysis of their prediction accuracy. The research findings indicate that it is feasible to predict the PPV on the tunnel structures using empirical equations. The attenuation characteristics of blasting vibration PPV are different in different structures and directions. The prediction accuracy of the empirical equations varies, while the discrepancies are minimal. The principal variation among these equations lies in the site-specific coefficients k, β, λ, highlighting the differential impact of structural and directional considerations on the predictive efficacy. Based on the empirical equation and safe PPV provided by the blasting vibration safe standards on tunnels of China (GB6722-2014), and considering the influence of all structures and directions, it is determined that the safe distance of blasting vibration in the tested tunnel project should be larger than 20.28-18.31 m, 18.31-16.16 m, and 16.16-13.75 m for blasting vibration frequency located in ≤10 Hz, 10-50 Hz, and >50 Hz. [ABSTRACT FROM AUTHOR]

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive approach to predict sand production in the Karazhanbas oilfield using machine learning (ML) techniques. By analyzing data from 2000 wells, the research uncovered the complex dynamics of sand production and emphasized the critical need for accurately predicting the peak sand mass and its occurrence time. ML techniques can have a significant impact on prediction of sand production and on the optimization of oilfield operation, which can be improved with the combined use of enriched training data and domain-specific knowledge. The research underscored the influence of geological factors, especially fault proximity, on prediction accuracy. Domain and field knowledge is needed to formulate different production scenarios for prediction purposes such that the relevant data can be selected for the training of ML models. Moreover, new metrics are needed to evaluate model performance as the applied method is tailored for different operational strategies. As the peak sand mass is considered a pivotal event in field operation, new metrics in terms of peak prediction accuracy and peak time prediction accuracy were introduced to evaluate the performance of ML models. A suite of ML algorithms was employed in the study, which demonstrated notable accuracy in the classification of sand-producing wells. [ABSTRACT FROM AUTHOR]

Abstract Background Forest geneticists typically use provenances to account for population differences in their improvement schemes; however, the historical records of the imported materials might not be very precise or well-aligned with the genetic clusters derived from advanced molecular techniques. The main objective of this study was to assess the impact of marker-based population structure on genetic parameter estimates related to growth and wood properties and their trade-offs in Norway spruce, by either incorporating it as a fixed effect (model-B) or excluding it entirely from the analysis (model-A). Results Our results indicate that models incorporating population structure significantly reduce estimates of additive genetic variance, resulting in substantial reduction of narrow-sense heritability. However, these models considerably improve prediction accuracies. This was particularly significant for growth and solid-wood properties, which showed to have the highest population genetic differentiation (QST) among the studied traits. Additionally, although the pattern of correlations remained similar across the models, their magnitude was slightly lower for models that included population structure as a fixed effect. This suggests that selection, consistently performed within populations, might be less affected by unfavourable genetic correlations compared to mass selection conducted without pedigree restrictions. Conclusion We conclude that the results of models properly accounting for population structure are more accurate and less biased compared to those neglecting this effect. This might have practical implications for breeders and forest managers where, decisions based on imprecise selections can pose a high risk to economic efficiency.

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications. [ABSTRACT FROM AUTHOR]

The increasing global reliance on renewable energy sources, particularly solar energy, underscores the critical importance of accurate solar irradiance forecasting. As solar capacity continues to grow, precise predictions of solar irradiance become essential for optimizing the performance and reliability of photovoltaic (PV) systems. This study introduces a novel hybrid forecasting model that integrates Nonlinear Autoregressive with Exogenous Inputs (NARX) with Long Short-Term Memory (LSTM) networks. The purpose is to enhance the precision of predicting daily solar irradiance in fluctuating meteorological scenarios, particularly in southwestern France. The hybrid model employs the NARX model's capacity to handle complex non-linear relationships and the LSTM's aptitude to manage long-term dependencies in time-series data. The performance metrics of the hybrid NARX-LSTM model were thoroughly assessed, revealing a mean absolute error (MAE) of 9.58 W/m2, a root mean square error (RMSE) of 16.30 W/m2, and a Coefficient of Determination (R2) of 0.997. Consequently, the proposed hybrid model outperforms the benchmark model in all metrics, showing a significant improvement in prediction accuracy and better alignment with the observed data. These results highlight the model's effectiveness in enhancing forecasting accuracy under unpredictable conditions, improving solar energy integration into power systems, and ensuring more reliable energy 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]

Purpose: To determine whether correcting the axial length (AL) measurement error of the IOLMaster 700 could improve the refractive prediction accuracy in silicone oil‐filled eyes. Methods: This study included 265 cataract patients (265 eyes) with silicone oil tamponade who were scheduled for phacoemulsification with intraocular lens (IOL) implantation. The performances of various formulas, including Barrett Universal II, Emmetropia Verifying Optical, Hoffer‐QST, Kane, Ladas Super Formula, Pearl‐DGS, Radial Basis Function and traditional formulas (Haigis, Hoffer Q, Holladay 1 and SRK/T), were evaluated. The refractive prediction errors (PE) calculated with measured AL (ALmeas) and corrected AL with silicone oil adjustment (SOAL) were compared. Subgroup analysis was performed based on the ALmeas (<23 mm; 23–26 mm; ≥26 mm). Results: Using SOAL significantly reduced the hyperopic PE of formulas when compared to ALmeas (−0.05 to 0.17 D vs 0.15 to 0.38 D, p < 0.001). After applying AL correction, all formulas showed a lower mean absolute PE (0.47–0.57 D vs 0.50–0.69 D). The percentage of eyes within ±1.0 D of PE increased from 84.91%–88.68% to 89.81%–91.32% for new formulas and from 78.11%–83.40% to 85.66%–88.68% for traditional formulas, with the use of SOAL. Subgroup analysis showed that the majority of formulas with SOAL in prediction accuracy for eyes with an AL ≥26 mm (p < 0.05). Conclusions: The refractive prediction accuracy in silicone oil‐filled eyes was improved by correcting the AL measurement error of the IOLMaster 700, especially for long eyes. [ABSTRACT FROM AUTHOR]

• I measure actual and predicted discrimination along multiple identity dimensions. • Responding to expected discrimination, people strategically reveal social identity. • Discrimination allows large potential gains from strategic identity revelation. • Actual gains are lesser, partly due to certain misperceptions about discrimination. • Individuals sacrifice expected payoffs to reveal their preferred characteristics. Individuals often have the power to manipulate which of their multiple dimensions of social identity are salient when entering economic interactions. This paper represents the first empirical exploration of this phenomenon. Experimental results show identity dimension choice is of substantial importance, providing potential earnings gains up to around 56%. Actual gains are smaller: people choose identity dimensions strategically and benefit from broadly accurate beliefs about counterparts' discriminatory tendencies, but are also hampered by some misperceptions (e.g. overestimating levels of in-group favouritism). Furthermore, many individuals sacrifice expected payoffs to make their preferred dimensions salient, suggesting intrinsic utility derives from social identity. [ABSTRACT FROM AUTHOR]

This study investigates the application of artificial neural networks (ANNs) in predicting the flowability of mining tailings based on operational variables. As the mining industry seeks to enhance operations with complex ores, the constant improvement and optimization of mineral waste management are crucial. The flowability of tailings was investigated with data driven by properties such as particle-size distribution, water content, compaction capacity, and viscoelastic characteristics that can directly affect stacking, water recovery capabilities, and stability at disposal, influencing storage capacity, operational continuity, and work safety. There was a strong correlation between water content and tailings flowability, emphasising its importance in operational transport and deposition. Three ANN models were evaluated to predict tailings flowability across three and five categories, where a model based on thickening operational variables, including yield stress and turbidity, demonstrated the highest accuracy, achieving up to 94.4% in three categories and 88.9% in five categories. Key variables such as flocculant dosage, water content, yield stress, and solid concentration were identified as crucial for prediction accuracy The findings suggest that ANN models, even with limited datasets, can provide reliable flowability predictions, supporting tailings management and operational decision-making. [ABSTRACT FROM AUTHOR]

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity. [ABSTRACT FROM AUTHOR]

Brain disease is considered a major cause of increased mortality worldwide. Clinical decision support system (CDSS) is utilized for predicting individuals with brain disease in its earlier state. This work proposes a novel disease prediction approach for earlier prediction by handling the dataset issues, where an improved SMOTE sampling approach is used for balancing the target data distribution. Then, Extreme Convolutional Network Model (XCNM) is used for predicting the disease with better accuracy. For the validation purpose, two publicly available ADNI-1 and ADNI-2 online datasets are used for the model construction, and the outcomes are compared with other techniques like Support Vector Machine (SVM), Artificial Neural Network (ANN), Voxel-based SVM (VW-SVM), standard Convolutional Neural Network (CNN), Deep Neural Network (DNN) and Weighted-Score Multimodal DNN (WS-MTDNN). The outcomes show that the proposed XCNM model outperforms various existing approaches with 94% and 95% accuracies on the input ADNI-1 and ADNI-2 datasets. Also, the CDSS-based framework is designed to assist the doctors in critical cases and help reduce the mortality rate. [ABSTRACT FROM AUTHOR]

The Machine Learning domain has made a major process with the progression of state-of-the-art technologies. Since current algorithms often don’t provide palatable learning performance, it is necessary to continually upgrade them. This paper has illustrated the comparison of the Long Short-Term Memory (LSTM) model and the Artificial Neural Networks (ANN) model in the prediction of the Internet Movie Database (IMDb) website. These evaluations were then related to sentiment assessment approaches to evaluate their predicted accuracy and performances. The results demonstrate that the ANN model outperforms the LSTM model with a high accuracy rate in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task. The accuracy of prediction on the test dataset of the ANN model is 83.5 % and the LSTM model is 83.5%. Therefore, it can be concluded that the standard artificial neural network model that was utilized is an appropriate technique for sentiment assessment tasks in IMDb rating text data. [ABSTRACT FROM AUTHOR]

Forest fires in central China pose significant threats to ecosystem health, public safety, and economic stability. This study employs advanced Geographic Information System (GIS) technology and Convolutional Neural Network (CNN) models to comprehensively analyze the factors driving the occurrence of these fire events. A predictive model for forest fire occurrences has been developed, complemented by targeted zoning management strategies. The key findings are as follows: (i) Spatial analysis reveals substantial clustering and spatial autocorrelation of fire points, indicating high-density areas of forest fire occurrence, primarily in Hunan and Jiangxi provinces, as well as the northeastern region. This underscores the need for tailored fire prevention and management approaches. (ii) The forest fire prediction model for the central region demonstrates exceptional accuracy, reliability, and predictive power. It achieves outstanding performance metrics in both training and validation sets, with an accuracy of 86.00%, precision of 88.00%, recall of 87.00%, F1 score of 87.50%, and an AUC value of 90.50%. (iii) Throughout the year, the occurrence of forest fires in central China varies by location and season. Low-occurrence periods are observed in summer and winter, particularly in Hunan and Hubei provinces, due to moderate weather conditions, agricultural practices, and reduced outdoor activities. However, spring and autumn also present localized risks due to uneven rainfall and dry climates. This study provides valuable insights into the dynamics of forest fire occurrences in central China, offering a solid framework for proactive fire management and policy formulation to effectively mitigate the impacts of these events. [ABSTRACT FROM AUTHOR]

Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits. [ABSTRACT FROM AUTHOR]

Within the academic context, students often expect to perform significantly better on an upcoming exam when compared to their actual performance. Unfortunately, students who are least accurate in their performance predictions are also the most at risk of being underprepared compared to their peers. Thus, efforts to enhance accuracy in performance predictions would benefit the student population that is most at risk of academic shortcomings and failure. Two studies examined whether incentives improve accuracy of academic performance predictions. In Study 1, 126 students in a 200-level undergraduate course provided estimates of how well they would perform on an upcoming exam. Two weeks later, the same students were randomly assigned to receive a reward incentive, punishment incentive, or no incentive for accuracy in performance predictions and their exam performance estimates were reassessed then compared to their actual scores on an exam completed one week later. In Study 2, 144 students from multiple 300-level courses provided performance predictions for an upcoming exam that were then compared to their actual exam scores for accuracy. For a subsequent exam, the same students were randomly assigned to varying levels of reward incentives (no incentive, modest incentive, large incentive) for prediction accuracy and their performance estimates were examined and compared to actual performance on a second exam. Findings from both studies indicated performance estimates are malleable and with the appropriate incentives, accuracy in predictions may be improved and contribute to better actual performance. [ABSTRACT FROM AUTHOR]

Diverse methods to evaluate the prediction model of a time to event have been proposed in the context of right censored data where all subjects are subject to be susceptible. A time-dependent AUC (area under curve) measures the predictive ability of a marker based on case group and control one which are varying over time. When a substantial portion of subjects are event-free, a population consists of a susceptible group and a cured one. An uncertain curability of censored subjects makes it difficult to define both case group and control one. In this paper, our goal is to propose a time-dependent AUC for a cure rate model when a censoring distribution is related with covariates. A class of inverse probability of censoring weighted (IPCW) AUC estimators is proposed to adjust the possible sampling bias. We evaluate the finite sample performance of the suggested methods with diverse simulation schemes and the application to the melanoma dataset is presented to compare with other methods. [ABSTRACT FROM AUTHOR]

In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model's output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather conditions, the prediction error metrics showcased superior performance: the mean absolute percentage error (MAPE) consistently remained below 13%, meeting the stringent requirement of the power grid's tolerance level below 20%. Moreover, the normalized root mean square error (NRMSE) ranged between 6% and 9%, while the coefficient of determination (R2) exceeded 0.9. These underscored the remarkable prediction accuracy achieved by the model. Under rainy and snowy weather conditions, although MAPE slightly increased to the range of 14% to 20% compared to that of scenarios without rain and snow, it still adhered to the stringent requirement. NRMSE varied between 4.5% and 8%, and R2 remained consistently above 0.9, indicative of satisfactory model performance even in adverse weather conditions. The successful application of the proposed model in predicting hourly photovoltaic power generation output during winter in Henan Province bears significant practical implications for the advancement and integration of renewable energy technologies. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systematically studied. The research is carried out using a co-simulation test platform integrating TRNSYS and Python. Results show that the XGBoost model achieves the highest prediction accuracy. LightGBM model's accuracy is marginally lower but requires significantly less time for both prediction and training. In this research, the proposed control strategy decreases the economic cost by 21.61% compared to the baseline case under traditional control, with the weighted indoor temperature rising by only 0.10 K. The result also suggests that it is worth exploring advanced prediction models to increase prediction accuracy, even within the high prediction accuracy range. Furthermore, implementing MPC control for demand response remains beneficial even when the model prediction accuracy is relatively low. [ABSTRACT FROM AUTHOR]

In this study, the performance of a pre-designed and low-cost Arduino electronic nose for determining the quality of stuffed mussels was analyzed. In addition, 1000 images were taken on each storage day in order to determine the quality levels of stuffed mussel groups with open and closed shells by machine learning. Freshness limit values of stuffed mussels were determined as 200 for MQ3 and MQ135 sensors and 100 for MQ9 on the 3rd storage day when the total viable count (TVC) value exceeded 3 log CFU/g. In the study, faster neural networks with lower prediction times, such as SqueezeNet and GoogLeNet, were compared with ResNet-50, ResNet-101 and DenseNet-201 neural networks, which have larger prediction times but better accuracy. Study data showed that residual network (ResNet) 50 and Teachable Machine (TM) had high success in determining the quality levels of stuffed mussels. [ABSTRACT FROM AUTHOR]

Patients with cardiovascular diseases who experience disease‐related short‐term events, such as hospitalizations, often exhibit diverse long‐term survival outcomes compared to others. In this study, we aim to improve the prediction of long‐term survival probability by incorporating two short‐term events using a flexible varying coefficient landmark model. Our objective is to predict the long‐term survival among patients who survived up to a pre‐specified landmark time since the initial admission. Inverse probability weighting estimation equations are formed based on the information of the short‐term outcomes before the landmark time. The kernel smoothing method with the use of cross‐validation for bandwidth selection is employed to estimate the time‐varying coefficients. The predictive performance of the proposed model is evaluated and compared using predictive measures: area under the receiver operating characteristic curve and Brier score. Simulation studies confirm that parameters under the landmark models can be estimated accurately and the predictive performance of the proposed method consistently outperforms existing methods that either do not incorporate or only partially incorporate information from two short‐term events. We demonstrate the practical application of our model using a community‐based cohort from the Atherosclerosis Risk in Communities (ARIC) study. [ABSTRACT FROM AUTHOR]

Background: Forest geneticists typically use provenances to account for population differences in their improvement schemes; however, the historical records of the imported materials might not be very precise or well-aligned with the genetic clusters derived from advanced molecular techniques. The main objective of this study was to assess the impact of marker-based population structure on genetic parameter estimates related to growth and wood properties and their trade-offs in Norway spruce, by either incorporating it as a fixed effect (model-B) or excluding it entirely from the analysis (model-A). Results: Our results indicate that models incorporating population structure significantly reduce estimates of additive genetic variance, resulting in substantial reduction of narrow-sense heritability. However, these models considerably improve prediction accuracies. This was particularly significant for growth and solid-wood properties, which showed to have the highest population genetic differentiation (QST) among the studied traits. Additionally, although the pattern of correlations remained similar across the models, their magnitude was slightly lower for models that included population structure as a fixed effect. This suggests that selection, consistently performed within populations, might be less affected by unfavourable genetic correlations compared to mass selection conducted without pedigree restrictions. Conclusion: We conclude that the results of models properly accounting for population structure are more accurate and less biased compared to those neglecting this effect. This might have practical implications for breeders and forest managers where, decisions based on imprecise selections can pose a high risk to economic efficiency. [ABSTRACT FROM AUTHOR]

The importance of enterprise credit risk management is increasingly prominent. Under the background of financial technology and big data, this paper studies a model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) by comprehensively analyzing multi-dimensional data such as corporate financial statements, credit scores and transaction records, so as to capture and learn the complex characteristics of credit risk of Internet financial enterprises. Firstly, this paper collects the relevant data of Internet finance enterprises from multiple data sources, and carries out standardization processing and missing value filling. Then, CNN-LSTM model is constructed based on these data, and model training and hyperparametric optimization are carried out by adjusting convolution layer and LSTM layer. In addition, this study also designed a comparative experiment to evaluate the performance of CNN-LSTM model and CNN and LSTM models alone in prediction time, prediction accuracy and interpretability. The results show that CNN-LSTM model has significant advantages in predicting the credit risk of Internet finance enterprises. The model also shows a faster response speed, and the maximum warning time is only 701ms, which is much lower than the LSTM and CNN models. The highest accuracy of the model is 94.1%, which is significantly higher than that of LSTM and CNN models. In addition, the model also has high confidence in interpretability, which provides a solid basis for financial decision-making. [ABSTRACT FROM AUTHOR]

An appropriate flowering period is an important selection criterion in maize breeding. It plays a crucial role in the ecological adaptability of maize varieties. To explore the genetic basis of flowering time, GWAS and GS analyses were conducted using an associating panel consisting of 379 multi-parent DH lines. The DH population was phenotyped for days to tasseling (DTT), days to pollen-shedding (DTP), and days to silking (DTS) in different environments. The heritability was 82.75%, 86.09%, and 85.26% for DTT, DTP, and DTS, respectively. The GWAS analysis with the FarmCPU model identified 10 single-nucleotide polymorphisms (SNPs) distributed on chromosomes 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. The GWAS analysis with the BLINK model identified seven SNPs distributed on chromosomes 1, 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. Three SNPs 3_198946071, 9_146646966, and 9_152140631 showed a pleiotropic effect, indicating a significant genetic correlation between DTT, DTP, and DTS. A total of 24 candidate genes were detected. A relatively high prediction accuracy was achieved with 100 significantly associated SNPs detected from GWAS, and the optimal training population size was 70%. This study provides a better understanding of the genetic architecture of flowering time-related traits and provides an optimal strategy for GS. [ABSTRACT FROM AUTHOR]

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In this study, six feedforward neural network models were developed to estimate the net heat of combustion of light kerosene distillates. These networks use different sets of physicochemical properties of the distillates as input variables and are all composed of 8 sigmoid hidden neurons and one linear output neuron. The networks were designed in MATLAB software with 205 data points using the nftool command. Determining the relative importance of input variables in the networks revealed the significant effect of density on the estimates. The developed models as well as two correlative methods taken from the literature were used to predict the net heat of combustion of 40 other samples. The statistical analysis of the results was carried out by calculating for each estimation method the absolute errors, the mean absolute error, the standard deviation of the absolute errors and the coefficient of determination. It was found that the most accurate method is the neural network model based on the density, viscosity, aromatics content and sulfur content of the distillates. The least efficient method is the neural network that does not include density in its inputs, which once again indicates the importance of this property. Consequently, density should be taken into account to ensure high prediction ability of estimation methods. [ABSTRACT FROM AUTHOR]

This study explores the relation between financial uncertainty and volatility in China. The time variation in financial uncertainty shocks is theoretically closely related to stock return dynamics. Empirically, the financial uncertainty measure is based on a large set of economic and financial variables and captures its unpredictable component. Over the sample period from 2000 to 2021, we find that financial uncertainty positively impacts the trend component of market volatility and that it improves volatility predictions in both statistical and economic terms. Our study sheds new light on the sources driving volatility and the dynamic relation between uncertainty and volatility components. [ABSTRACT FROM AUTHOR]

One of the most demanding applications of accurate Artificial Neural Networks (ANN) can be found in medical fields, mainly to make critical decisions. To achieve this goal, an efficient optimization and training method is required to tune the parameters of ANN and to reach the global solutions of these parameters. Equilibrium Optimizer (EO) has recently been introduced to solve optimization problems more reliably than other optimization methods which have the ability to escape from the local optima solutions and to reach the global optimum solution. In this paper, to achieve a higher performance, some modifications are applied to the EO algorithm and the Improved Equilibrium Optimizer (IEO) method is presented which have enough accuracy and reliability to be used in crucial and accurate medical applications. Then, this IEO approach is utilized to learn ANN, and IEO-ANN algorithm will be introduced. The proposed IEO-ANN will be implemented to solve real-world medical problems such as breast cancer detection and heart failure prediction. The obtained results of IEO are compared with those of three other well-known approaches: EO, Particle Swarm Optimizer (PSO), Salp Swarm Optimizer (SSO), and Back Propagation (BP). The recorded results have shown that the proposed IEO algorithm has much higher prediction accuracy than others. Therefore, the presented IEO can improve the accuracy and convergence rate of tuning neural networks, so that the proposed IEO-ANN is a suitable classifying and predicting approach for crucial medical decisions where high accuracy is needed. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) model of a multilayer perceptron-type was developed to predict flash points of petroleum middle distillates. The ANN model was designed using 252 experimental data points taken from the literature. The properties of the distillates, namely, specific gravity and distillation temperatures, were the input parameters of the model. The training of the network was carried out using the Levenberg– Marquardt backpropagation algorithm and the early stopping technique. A comparison of the statistical parameters of different networks made it possible to determine the optimal number of neurons in the hidden layer with the best weight and bias values. The network containing nine hidden neurons was selected as the best predictive model. The ANN model as well as the Alqaheem–Riazi's model was evaluated for the prediction of flash points by a statistical analysis based on the calculation of the mean square error, Pearson correlation coefficient, coefficient of determination, absolute percentage errors, and the mean absolute percentage error. The ANN model provided higher prediction accuracy over a wide distillation range than the Alqaheem–Riazi's model. The developed ANN model is a reliable and fast tool for the low-cost estimation of flash points of petroleum middle distillates. [ABSTRACT FROM AUTHOR]

With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.

Abstract The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P