Linear Prediction Model Research Articles

A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning

BackgroundTo develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms. MethodsFrom March 2021 to March 2023, 231 consecutive patients underwent mitral valve repair. Clinical and echocardiographic data were included in the analysis. The end points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or greater mitral regurgitation [MR] before discharge). Various machine learning algorithms were used to establish the complexity evaluation system. ResultsA total of 231 patients were included in this study; the median ejection fraction was 66% (63–70%), and 159 (68.8%) patients were men. Mitral repair was successful in 90.9% (210 of 231) of patients. The linear support vector classification model has the best prediction results in training and test cohorts and the variables of age, A2 lesions, leaflet height, MR grades, and so on were risk factors for failure of mitral valve repair. ConclusionThe linear support vector classification prediction model may allow the evaluation of the complexity of mitral valve repair. Age, A2 lesions, leaflet height, MR grades, and so on may be associated with mitral repair failure.

Comparative analysis of the heritability, estimated breeding value, and accuracy of carcass traits in Hanwoo from the Yeongnam Region, considering breeding area and climate effects, utilizing genomic information

Climate change is causing abnormal weather patterns worldwide. In Korea, local governments are characterized by various climatic conditions. Therefore, research integrating climate factors with Hanwoo is necessary. In this study, 562 Hanwoo were analyzed using Pedigree Best Linear Unbiased Prediction (PBLUP) and Genomic BLUP (GBLUP) models while considering climate factors such as temperature and humidity in the Yeongnam region. Carcass Weight (CWT) varied significantly in PBLUP depending on the model, while the heritability of Eye Muscle Area (EMA) and Back-Fat Thickness (BFT) decreased. However, the Marbling Score (MS) maintained high heritability across all models. GBLUP showed a slight decrease in the analysis model that considers breeding areas and climatic factors, yet still led to a high estimate of heritability for all traits. However, MS consistently had a heritability of 0.50 across all models. In PBLUP, the accuracy of the Estimated Breeding Value (EBV) increased by 0.15 for CWT (0.62-0.77) compared to Model 1. However, a decrease of 0.01–0.06 was observed for EMA (0.62–0.68) and BFT (0.53–0.58), while an increase of 0.01 was noted for MS (0.72–0.73). The accuracy of EBV using GBLUP was as follows: CWT (0.75-0.77), EMA (0.72-0.80), BFT (0.71-0.81), and MS (0.75). The accuracy of BFT significantly increased compared to PBLUP. For MS, it did not appear to be influenced by breeding area and climatic conditions. These results suggest the need for ongoing research on the relationship between Hanwoo carcass traits and climate effects, requiring additional test groups from various breeding areas.

Early Bloomer and Future Master: Getting to the Top of the Modern Badminton World

The road to the top is never easy. This study investigated whether the career trajectories of top-level men’s and women’s badminton players could be predicted by their age at first major debut and the time taken to reach the top 150. Data from the BWF from October 2009 to October 2022 were analyzed using a predictive linear regression model with Bayesian inference, adjusting for anthropometric factors. The results suggested that age at debut influences career high rankings among over 120 elite players. Additionally, observations challenged the Matthew effect in early-career success for young players, proposing that inherent talent may be more significant than early nurturing. The study also examined the potential impact of relative age and early specialization in sports.

Detection of medial vascular calcification in chronic kidney disease based on pulse wave analysis in the frequency domain

Medial vascular calcification (mVC), particularly prevalent in patients with chronic kidney disease (CKD), is a pathological deposition of minerals in the medial layer of the vessel wall and is associated with an increased risk of cardiovascular disease. Currently, there is no cost–effective and non-invasive procedure of mVC assessment in routine clinical practice. We explored whether in–depth analysis of non-invasively recorded peripheral pulse waves can serve as an effective mVC biomarker.The study included 97 CKD patients with histological assessment of mVC in the epigastric artery and pulse wave measurements in the brachial artery. Pulse waves were analysed in the frequency domain to obtain features, which, together with traditional mVC risk factors (i.e., age, sex, and body mass index), were used to build generalized linear models for mVC prediction. The classifiers with the best scores in terms of balanced accuracy were combined in an ensemble establishing the final model.The final, ensembled model, assessed using a leave-one-out cross-validation process, achieved a balanced accuracy equal to 0.87, an accuracy of 0.93, an AUC ROC of 0.91, and an F-score of 0.96. Apart from the features associated with pulse waves, the selected variables included age, sex, body mass index, heart rate and diastolic blood pressure.Analysis of non-invasively recorded peripheral pulse waveforms combined with traditional risk factors, can help to detect mVC in CKD patients and thus, potentially introduce risk-lowering therapeutic strategies at earlier disease stages in the future.

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion and forms the basis of strategies like breathing-guided imaging and gated dose delivery. However, due to inherent system latencies, there exists a temporal lag between the acquired respiratory signal and the system response. Respiratory signal prediction models aim to compensate for the time delays and to improve imaging and dosedelivery. The present study explores and compares six state-of-the-art machine and deep learning-based prediction models, focusing on real-time and real-world applicability. All models and data are provided as open source and data to ensure reproducibility of the results and fosterreuse. The study was based on 2502 breathing signals ( h) acquired during clinical routine, split into independent training (50%), validation (20%), and test sets (30%). Input signal values were sampled from noisy signals, and the target signal values were selected from corresponding denoised signals. A standard linear prediction model (Linear), two state-of-the-art models in general univariate signal prediction (Dlinear, Xgboost), and three deep learning models (Lstm, Trans-Enc, Trans-TSF) were chosen. The prediction performance was evaluated for three different prediction horizons (480, 680, and 920ms). Moreover, the robustness of the different models when applied to atypical, that is, out-of-distribution (OOD) signals, was analyzed. The Lstm model achieved the lowest normalized root mean square error for all prediction horizons. The prediction errors only slightly increased for longer horizons. However, a substantial spread of the error values across the test signals was observed. Compared to typical, that is, in-distribution test signals, the prediction accuracy of all models decreased when applied to OOD signals. The more complex deep learning models Lstm and Trans-Enc showed the least performance loss, while the performance of simpler models like Linear dropped the most. Except for Trans-Enc, inference times for the different models allowed for real-time application. The application of the Lstm model achieved the lowest prediction errors. Simpler prediction filters suffer from limited signal history access, resulting in a drop in performance for OODsignals.

A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data

BackgroundHigh-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization.ResultsWe proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models.ConclusionsThe proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.

Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion

In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.

Relegated Thrust Ripples for Linear Induction Motors Based-Four Voltage Vectors Finite-Set Predictive Control and Model Reference Adaptive System

—These days, there are extensive signifies to the evolution of newfound control procedures like model predictive control. The finite-state predictive thrust control (FSPTC) for LIMs is still not deftly addressed by scholars and limited research was acted to exploit the FSPTC for LIM drive systems. Therefore, the current study proposes a novel finite-state predictive thrust control (NFSPTC) for linear induction motors (LIMs) with reduced numbers of voltage vectors (VVs) in the prediction phase. The NFSPTC utilizes the conventional direct thrust control (DTC) concept to reduce the required VVs from eight to only four which consist of two active vectors and two zero vectors. Decreasing the number of predicted VVs leads to a significant reduction in computational time. Moreover, the weighting factor is removed to reduce the effort in selecting the best weighting factor. Since the LIM drive system can be succeeded by developing a sensorless speed estimation, the linear speed is estimated depending on the model reference adaptive system (MRAS) before using actual sensors; thus, low cost can be attained. The suggested control approach is validated by simulation proofs based on a 3-kW arc machine. The gained findings clarified the effectiveness of the proposed four VVs control over the eight VVs procedure in dipping the thrust ripple. Besides, using the four VVs control under variable speed and fixed thrust load, the execution time was decreased by 50%. Furthermore, the proposed control procedures ensued in maintaining the primary flux linkage constant alongside succeeding a glowing-tracking speed profile throughout the tackled speed and thrust load variations.”

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Prediction and effectiveness study of fracture distribution based on an artificial neural network technology and production performance data detection method: a case study of Ordovician carbonate formation in the Tahe oilfield, Tarim Basin

To solve the difficulty of predicting the spatial distribution of fracture linear density and clarify the controlling influence of tectonic factors on the effectiveness of fractures. This article combines core, conventional logging, imaging logging, and 3D seismic volume data and uses artificial intelligence neural network technology combined with the sequential Gaussian method to establish a linear density volume prediction model for fractures in the study area. In addition, production dynamic data are applied to verify the effectiveness of the fractures. The results show that the artificial intelligence neural network technique is highly applicable with an accuracy of up to 76.8% in predicting the fracture linear density. The fracture linear density is controlled by tectonic activity, and the fracture linear density increases sharply in the areas adjacent to the main faults, the fracture superposition site, and the intersection site of multiple faults, with the fracture linear density reaching 8.65 fractures per meter in study areas. The effective distance of fracture connectivity is mostly concentrated between 550 and 670 m. When the difference in fracture density along the structural ridge is between 2 and 4 fractures/meter, the effectiveness of the fractures is strong.

Fluorescence spectroscopy detection of carbendazim residue in cucumber juice based on BC

Excessive residues of the carbendazim pesticide in food will cause food safety issues. Particularly, cucumber is one of the common vegetables with carbendazim residues. This study, using fluorescence spectroscopy technology, explored the residues of carbendazim in cucumbers. First, a fluorescence spectrophotometer was used to scan and obtain the three-dimensional fluorescence spectra of carbendazim standard and four carbendazim formulations. Analyzing and comparing their respective fluorescence characteristics, fluorescence peak 1: Ex/Em=245/300–320 nm and fluorescence peak 2: Ex/Em=280/300–320 nm have been identified as the fluorescence characteristic peaks of carbendazim, and the optimal excitation wavelengths Ex=245 nm and Ex=280 nm with carbendazim specificity was screened. Subsequently, two-dimensional fluorescence spectroscopy was performed on the carbendazim and cucumber-carbendazim mixed solutions using the optimal excitation wavelength. In order to obtain more effective two-dimensional spectral data, the original fluorescence spectrum of the cucumber-carbendazim mixed solution was subjected to Blank Correction (BC), significantly reducing the impact of cucumber's own fluorescent characteristics on the carbendazim fluorescent peak in the mixed solution. Polynomial models and linear prediction models were developed based on the concentration of carbendazim and the fluorescence peak intensity values obtained after BC. The detection limit value of carbendazim residues in the cucumber-carbendazim mixed system included the maximum pesticide residue limit specified by national food safety regulations. This research provides valuable data and technical support for efficient detection of pesticide residues in cucumbers.

Symmetric and asymmetric Gaussian weighted linear prediction for voice inverse filtering

Weighted linear prediction (WLP) has demonstrated its significance in voice inverse filtering, contributing to enhanced methods for estimating both the vocal tract filter and the glottal source. WLP provides a mechanism to mitigate the effect on the linear prediction model of voice samples that affects the vocal tract filter estimation, particularly those samples around glottal closure instants (GCIs). This article studies the Gaussian weighted linear prediction (GLP) strategy, which employs a Gaussian attenuation window centered at the GCIs to reduce its contribution in the WLP analysis. In this study, the Gaussian attenuation is revisited and a parameterization of the window that adjusts to the typical variability in voice periodicity is introduced. In addition, an asymmetric Gaussian window is proposed to diminish the relevance of voice samples preceding GCIs on the WLP model, thus providing a quasi closed phase inverse filtering method. Characterization of symmetric and asymmetric GLP methods for glottal source estimation is addressed based on synthetic and natural phonation data, resulting in a set of optimal parameters for the Gaussian attenuation windows. The results show that the proposed asymmetric attenuation improves voice inverse filtering with respect to the symmetric GLP method. Comparisons with other state-of-the-art techniques suggest that the proposed GLP approaches are competitive, falling slightly short in performance only when contrasted with the well-known quasi closed inverse filtering analysis. The simplicity of implementing the attenuation windows, coupled with their robust performance, positions the proposed GLP methods as two attractive and straightforward voice inverse filtering techniques for practical application.

The 90% effective concentration of alfentanil combined with 0.075% ropivacaine for epidural labor analgesia: a single-center, prospective, double-blind sequential allocation biased-coin design.

More literature studies have reported that alfentanil is safe and effective for labor analgesia. However, there is no unified consensus on the optimal dosage of alfentanil used for epidural analgesia. This study explored the concentration at 90% of minimum effective concentration (EC90) of alfentanil combined with 0.075% ropivacaine in patients undergoing epidural labor analgesia to infer reasonable drug compatibility and provide guidance for clinical practice. In this prospective, single-center, double-blind study, a total of 45 singleton term primiparas with vaginal delivery who volunteered for epidural labor analgesia were recruited. The first maternal was administered with 3μg/mL alfentanil combined with 0.075% ropivacaine with the infusion of 10mL of the mixture every 50min at a background dose of 3mL/h. In the absence of PCEA, a total of 15mL of the mixture is injected per hour. The subsequent alfentanil concentration was determined on the block efficacy of the previous case, using an up-down sequential allocation with a bias-coin design. 30min after epidural labor analgesia, the block of patient failed with visual analog score (VAS) > 3, the alfentanil concentration was increased in a 0.5μg/mL gradient for the next patient, while the block was successful with VAS ≤ 3, the alfentanil concentration was remained or decreased in a gradient according to a randomized response list for the next patient. EC90 and 95% confidence interval were calculated by linear interpolation and prediction model with R statistical software. In this study, the estimated EC90 of alfentanil was 3.85μg/mL (95% confidence interval, 3.64-4.28μg/mL). When combined with ropivacaine 0.075%, the EC90 of alfentanil for epidural labor analgesia is 3.85μg/mL in patients undergoing labor analgesia.

Deep learning methods improve genomic prediction of wheat breeding.

In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.

Drought influences habitat associations and abundances of birds in California's Central Valley

AbstractAimAs climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways.LocationCentral Valley, California, USA.MethodsWe used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance.ResultsWe found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased.Main ConclusionsOur findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought.

An attempt using equatorial waves to predict tropical sea surface temperature anomalies associated with the Atlantic zonal mode

IntroductionThe forecast for anomalous sea surface temperature (SST) events associated with Atlantic zonal mode, also known as Atlantic Niño/Niña, is full of challenge for both statistical and dynamical prediction models. MethodsThis study combines SST, wind and equatorial wave signal to construct a linear model, aiming to evaluate the potential of equatorial waves in extending the lead time of a skilful prediction for Atlantic Niño/Niña events. Wave-induced geopotential simulated by linear ocean models and potential energy flux calculated using a group-velocity-based wave energy flux scheme are involved to capture the signal of equatorial waves in the model establishment. ResultsThe constructed linear prediction model has demonstrated comparable prediction skill for the SST anomaly to the dynamical models of the North American Multimodel Ensemble (NMME) during the test period (1992-2016). Compared with the statistical forecast using SST persistence, the model notably improves the six-month-lead prediction (Anomaly correlation coefficient increases from 0.07 to 0.28), which owes to the conservation of wave energy in the narrow Atlantic basin that the Rossby waves reflected in the eastern boundary will transfer the energy back to the central equatorial basin and again affect the SST there. ConclusionThis study offers a streamlined model and a straightforward demonstration of leveraging wave energy transfer route for the prediction of Atlantic Niño/Niñas.

An initial investigation into the use of machine learning methods for prediction of carcass component yields in F2 broiler chickens

Context As evaluation of carcass components is costly and time consuming, models for prediction of broiler carcass components are useful. Aims The aim was to investigate the feasibility of machine learning methods in the prediction of carcass components from measurements on live birds during the rearing period. Methods Three machine learning methods, including regression tree, random forest and gradient-boosting trees, were applied to predict carcass yields, and benchmarked against classical linear regression. Two scenarios were defined for prediction. In the first scenario, carcass yields were predicted by live bodyweight, shank length and shank diameter features, recorded at 2, 3 and 4 weeks of age. In the second scenario, predictor features recorded at 5, 6 and 7 weeks of age were used. The two scenarios were reanalysed by including effective single-nucleotide polymorphisms associated with bodyweight, shank length and shank diameter as new predictor features. Key results The correlation coefficient between predicted and observed values for predicting weight of carcass traits ranged from 0.50 for wing to 0.59 for thigh in the first scenario, and from 0.63 for wing to 0.74 for carcass in the second scenario. These predictions for the percentage of carcass components ranged from 0.30 for wing to 0.39 for carcass and breast in the first scenario, and from 0.34 for thigh to 0.43 for carcass in the second scenario when random forest was used. Conclusions Predictive accuracy in the first scenario was lower than in the second scenario for all prediction methods. Including single-nucleotide polymorphisms as predictor features in either scenario did not increase the accuracy of the prediction. Implications In general, random forest had the best performance among machine learning methods, and classical linear regression in two scenarios, suggesting that it may be considered as an alternative to conventional linear models for prediction of carcass traits in broiler chickens.

Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models.

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Forecasting GDP growth: The economic impact of COVID‐19 pandemic

AbstractThe primary goal of this study is to effectively measure the impact of a severe random shock, such as the COVID‐19 pandemic on aggregate economic activity in Greece, seven other euro area economies, three Scandinavian countries, and the United States. The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (GDP) growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. Our analysis confirms that the outbreak of the pandemic had a profound effect on the economies under study, and reveals that different predictor variables are able to explain different quantiles of the underlying real GDP growth distribution for analyzed countries, suggesting that the quantile modeling approach improves the ability to adequately explain real GDP series compared with the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables.