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Fatigue crack is one of the main failure modes of pressure vessels. Online monitoring and predicting methods of crack growth play an important role in the operation of important pressure vessel. The SH0 wave is non-dispersive, and it is not disturbed by internal media of pressure vessel and very sensitive to cracks, therefore it is suitable for fatigue crack growth monitoring. Moreover, fatigue crack growth in industry is affected by material properties, loads, which usually shows some uncertainty. And the particle filter (PF) is well suited to deal with prediction problems affected by uncertainty. Hence, the prediction method of crack growth based on SH0 wave monitoring and PF is proposed (short for SH0-PF). The basic theory of crack monitoring method using SH0 wave is introduced, and the signal feature extraction using the damage index is studied. The state equation characterizing the fatigue crack growth is established by Paris model, and the observation equation is established based on the normalized correlation moment damage index according to monitoring signal using SH0 wave. The prediction reliability of the fatigue crack growth applying SH0-PF is verified by experiment with the single edge notched specimen. The experimental results indicate that the prediction accuracy of SH0-PF is better than that of the traditional Paris model.

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Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam.

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In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of phenotypes, which serve as external representations of a crop's growth, development, and genetic characteristics, are crucial for the realization of precision agriculture. Applications surrounding phenotypic indices also provide significant technical support for optimizing crop cultivation management and advancing smart agriculture, contributing to the efficient and high-quality development of precision agriculture.This paper focuses on lettuce and employs common nutritional stress conditions during growth as experimental settings. By collecting RGB images throughout the lettuce's complete growth cycle, we developed a deep learning-based computational model to tackle key issues in the lettuce's growth and precisely identify and assess phenotypic indices. We discovered that some phenotypic indices, including custom ones defined in this study, are representative of the lettuce's growth status. By dynamically monitoring the changes in phenotypic traits during growth, we quantitatively analyzed the accumulation and evolution of phenotypic indices across different growth stages. On this basis, a predictive model for lettuce growth and development was trained.The model incorporates MSE, SSIM, and perceptual loss, significantly enhancing the predictive accuracy of the lettuce growth images and phenotypic indices. The model trained with the reconstructed loss function outperforms the original model, with the SSIM and PSNR improving by 1.33% and 10.32%, respectively. The model also demonstrates high accuracy in predicting lettuce phenotypic indices, with an average error less than 0.55% for geometric indices and less than 1.7% for color and texture indices. Ultimately, it achieves intelligent monitoring and management throughout the lettuce's life cycle, providing technical support for high-quality and efficient lettuce production.

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OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs). MATERIALS AND METHODS: A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm. RESULTS: All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs. CONCLUSION: Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.

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Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 °C. Repeated E. coli O157 enumeration was performed at 0-96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination (R2) and mean squared error (MSE) are two essential criteria used to evaluate the model performance regarding the comparison between the observed value and the prediction made by the model. RF model showed superior performance with 0.98 R2 and 0.08 MSE values for predicting the growth performance of E. coli O157 at different temperatures. MLR model predictions were obtained further from the observed values with 0.66 R2 and 2.7 MSE values. Our results indicate that ML methods can predict of E. coli O157:H7 growth in ground beef at different temperatures to strengthen food safety professionals and legal authorities to assess contamination risks and determine legal limits and criteria proactively.

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Microalgae cultivation could contribute to the achievement of several sustainable development goals (SDGs). However, cultivating Chlorella vulgaris, like any other microalgae, is challenging due to various biotic, abiotic and process related factors that can affect its growth and biomass productivity. Nutrient availability, particularly N and P, and their ratio play a crucial role in building cellular structures and maintaining metabolic processes, determining basically the maximum achievable biomass productivity under given circumstances. The present article aims to improve the N and P ratio to enhance the biomass productivity of Chlorella vulgaris microalgae as well as to characterize the biomass growth kinetics that can be used for prediction purposes. The results showed that the nutrient solutions prepared with increased nitrate concentration (T1 - N:P = 55:1 and T3 - N:P = 28:1) promoted chlorophyll formation and significantly outperformed the control sample (BG-11 - N:P = 35:1) with 192% and 183%, leading to higher biomass productivity with 1160 µg L-1 and 1103 µg L-1, respectively. Moreover, a strong positive correlation was revealed (0.81) between phosphate concentration and microalgae activity rate, indicating the role of phosphorous in energy transfer, resulted in stimulated microalgae activity rates with 71.2% and 70.66% in the phosphate-increased nutrient solutions (T2 - N:P = 14:1 and T3 - N:P = 28:1). In addition, an exponential equation was introduced to characterize the biomass growth kinetics, of which the theoretically achievable maximum chlorophyll concentration (CTAM) and the theoretical cultivation time (tcultivation) were determined for the tested nutrient solutions with variable N:P ratio. It was concluded, that the higher the N:P ratio, the higher the CTAM is, nevertheless the absolute concentration of these nutrients need to be considered as well. The introduced two key parameters could provide valuable information for decision makers regarding the optimization of growth conditions, nutrient supplementation, and harvesting, additionally decreasing the production costs and making the cultivation cycles more effective and sustainable.

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OBJECTIVES: To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI). MATERIALS AND METHODS: Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared. RESULTS: On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively. CONCLUSIONS: PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.

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This study identified the most accurate model for predicting longitudinal craniofacial growth in a Japanese population using statistical methods and machine learning. Longitudinal lateral cephalometric radiographs were collected from 59 children (27 boys and 32 girls) with no history of orthodontic treatment. Multiple regression analysis, least absolute shrinkage and selection operator, radial basis function network, multilayer perceptron, and gradient-boosted decision tree were used. The independent variables included 26 coordinated values of skeletal landmarks, 13 linear skeletal parameters, and 17 angular skeletal parameters in children ages 6 to 12 years. The dependent variables were the values of the 26 coordinated skeletal landmarks, 13 skeletal linear parameters, and 17 skeletal angular parameters at 13 years of age. The difference between the predicted and actual measured values was calculated using the root-mean-square error. The prediction model for craniofacial growth using the least absolute shrinkage and selection operator had the smallest average error for all values of skeletal landmarks, linear parameters, and angular parameters. The highest prediction accuracies when predicting skeletal linear and angular parameters for 13-year-olds were 97.87% and 94.45%, respectively. This model incorporates several independent variables and is useful for future orthodontic treatment because it can predict individual growth.

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Biopreservation using lactic acid bacteria (LAB) is a promising technology to prevent the growth of pathogenic microorganisms in fresh and mildly processed food. The main aim of this study was to select LAB, originally isolated from ready-to-eat (RTE) seafood, for biopreservation of fresh salmon and processed salmon products. Ten LAB strains (five Carnobacterium and five Leuconostoc) were selected based on previously demonstrated bioprotective properties to investigate their antimicrobial mechanisms and temperature-dependent growth kinetics in a sterile salmon juice model system. Furthermore, five strains (three Carnobacterium and two Leuconostoc) were selected to test process-dependent growth kinetic parameters relevant to the secondary processing of salmon. Two strains (Carnobacterium maltaromaticum 35 and C. divergens 468) showed bacteriocin-like activity against Listeria innocua, while inhibitory effect of cell-free supernatants (CFS) was not observed against Escherichia coli. All selected strains were able to grow in sterile salmon juice at tested temperatures (4, 8, 12 and 16 °C), with specific growth rates (µ) ranging from 0.01 to 0.04/h at 4 °C and reaching a maximum population density of 8.4-9 log CFU/ml. All five strains tested for process-dependent growth kinetic parameters were able to grow in the range of 0.5-5% NaCl and 0.13-0.26% purified condensed smoke (VTABB and JJT01), with inter- and intraspecies variation in growth kinetics. According to the temperature-dependent growth kinetics and antimicrobial assay results, two strains, Leuconostoc mesenteroides 68 (Le.m.68) and C. divergens 468 (C d.468), were selected for in situ test to validate their ability to grow in vacuum-packed fresh salmon at 4 °C. Both strains were able to grow at maximum growth rates of 0.29 ± 0.04/d for Le. m.68 and 0.39 ± 0.06/d for C.d.468, and their final concentrations were 7.91 ± 0.31 and 8.02 ± 0.25 log CFU/g, respectively. This study shows that LAB, originally isolated from RTE seafood, have promising potential as bioprotective strains in fresh and processed salmon products.

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Introduction: Skeletal maturity assessment involves radiographic analysis and visual inspection of developing bone and their initial appearance or sequential ossification and related changes in size and shape along with the expression of various biomarkers in body fluids. Aim: To investigate the correlation of biomarkers such as salivary alkaline phosphatase (S-ALP) and salivary total protein (STP) with skeletal maturity assessment and growth prediction in growing children. Materials and methods: A total of 8-15-year-old 150 healthy children were divided into five groups depending upon radiographic stage maturity of the middle phalanx of the left hand's third finger according to the Hagg and Taranger method. Radiographs were taken using intraoral periapical (IOPA) radiographic films. Results: Salivary alkaline phosphatase (S-ALP) activity in the MP3 G group was significantly higher than MP3 F group and MP3 I group. Total protein levels in MP3 F were significantly lower than in MP3 G. The mean value of S-ALP (33541.45 IU/L) and that of STP (2.77 mg/mL) was observed to be highest in the MP3 G group (G3) group. Conclusion: Salivary total protein (STP) and S-ALP may be used as an additional diagnostic tool to assess skeletal maturation and optimize growth prediction during myofunctional orthodontic treatment. Clinical significance: Skeletal maturity assessment plays a significant role in orthodontic diagnosis, treatment planning, and stability of orthodontic treatment. Radiographic parameters involve radiographic exposure; hence in this study noninvasive biomarkers such as S-ALP and STP have been evaluated for skeletal maturity assessment and growth prediction. How to cite this article: Abhangi KK, Choudhari SR, Butala PB, et al. Salivary Total Protein and Alkaline Phosphatase Activity as Biomarkers for Skeletal Maturity and Growth Prediction in Healthy Children: An In Vivo Study. Int J Clin Pediatr Dent 2023;16(4):603-607.

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The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11-6.07 mm to 0.85-2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32-5.28 mm to 1.25-1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.

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The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs.

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Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.

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OBJECTIVE: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth. MATERIAL AND METHODS: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. RESULTS: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. CONCLUSION: global shape features might provide an important contribution for predicting the aneurysm growth.

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BACKGROUND: Choroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment. PURPOSE: Accurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment. METHODS: In this paper, we develop a CNN-based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD-OCT) B-scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co-segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow-up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder-recurrent-decoder framework is developed for CNV growth prediction, in which an attention-based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations. RESULTS: The registration network rigidly registers the follow-up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state-of-the-art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow-up visit. CONCLUSION: The proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

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Aeromonas are ubiquitous aquatic bacteria and frequently isolated from seafood. There is growing awareness of Aeromonas as foodborne pathogens, particularly in connection with consumption of ready-to-eat (RTE) seafood. The aim of this study was to investigate the effect of food processing factors on the growth kinetics of eight Aeromonas strains (representing seven species) isolated from RTE seafood. The effect of low temperature (4 and 8 °C) in combination with different NaCl concentrations (0.5-6.5 %) or with two purified condensate smokes (PCSs; Red Arrow SmokEz VTABB and JJT01) at different concentrations (0-0.26 %) was studied in Trypton Soy Broth (TSB). In food processing, application of PCS is considered healthier than traditional smoking. Growth kinetics parameters of each strain were estimated by using a primary predictive model. Our result showed that the addition of 3.5 % NaCl at refrigeration temperature (4 °C) was not sufficient to inhibit the growth of A. media, A. bestiarum, A. piscicola, and A. salmonicida, while higher NaCl concentration (≥5.0 %) at 8 °C suppressed their growth. On the other hand, our result demonstrated the antimicrobial potential of using PCS at maximal allowed concentration (0.26 %) against Aeromonas. PCS concentration and phenol content were important factors influencing the growth kinetics parameters of Aeromonas. Moreover, the growth kinetics of three Aeromonas strains were further studied in commercially produced vacuum-packed fresh and cold-smoked salmon stored at 4 °C for 14 and 21 days, respectively. Our results demonstrate that vacuum packing combined with cold storage at 4 °C was insufficient to inhibit the growth of Aeromonas in fresh salmon, while the growth was inhibited in a minimally salted cold-smoked salmon (salt content of 1.8 %). Our study implies that mild food processing factors applied in the production of RTE seafood might not guarantee the total inhibition of Aeromonas. Even though further studies on evaluating the antimicrobial potential of PCSs in actual seafood matrixes are necessary, the present study suggests that PCS technology might be a promising approach to prevent the potential growth of Aeromonas.

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This study aims to develop multipliers for the spine and sitting height to predict sitting height at maturity. With the aid of longitudinal and cross-sectional clinical databases, we divided the total sitting height, cervical, thoracic, and lumbar lengths at skeletal maturity by these same four factors at each age for each percentile given. A series of comparisons were then carried out between the multipliers as well as the percentiles and the varied racial and ethnic groups within them. Regarding sitting height, there was little variability and correlated with the multipliers calculated for the thoracic and lumbar spine. The multiplier method has demonstrated accuracy that is not influenced by generation, percentile, race, and ethnicity. This multiplier can be used to anticipate mature sitting height, the heights of the thoracic, cervical, and lumbar spine, as well as the lack of spinal growth after spinal fusion surgery in skeletally immature individuals.

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Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent years, however, there are still two issues. First, image-based plant growth prediction is currently taken either from time series or image generation viewpoints, resulting in a flexible learning framework and clear predictions, respectively. Second, deep learning-based algorithms are notorious to require a large-scale dataset to obtain a competing performance but collecting enough data is time-consuming and expensive. To address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms. To be more specific, we raise a new framework with a length-changeable time-series processing unit to generate images flexibly. A generative adversarial loss is utilized to optimize our model to obtain high-quality images. Furthermore, we first recognize three key points to perform time-series data augmentation and then put forward T-Mixup and T-Copy-Paste. T-Mixup fuses images from a different time pixel-wise while T-Copy-Paste makes new time-series images with a different background by reusing individual leaves extracted from the existing dataset. We perform our method in a public dataset and achieve superior results, such as the generated RGB images and instance masks securing an average PSNR of 27.53 and 27.62, respectively, compared to the previously best 26.55 and 26.92.

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Understanding the growth status of fruits can enable precise growth management and improve the product quality. Previous studies have rarely used deep learning to observe changes over time, and manual annotation is required to detect hidden regions of fruit. Thus, additional research is required for automatic annotation and tracking fruit changes over time. We propose a system to record the growth characteristics of individual apples in real time using Mask R-CNN. To accurately detect fruit regions hidden behind leaves and other fruits, we developed a region detection model by automatically generating 3000 composite orchard images using cropped images of leaves and fruits. The effectiveness of the proposed method was verified on a total of 1417 orchard images obtained from the monitoring system, tracking the size of fruits in the images. The mean absolute percentage error between the true value manually annotated from the images and detection value provided by the proposed method was less than 0.079, suggesting that the proposed method could extract fruit sizes in real time with high accuracy. Moreover, each prediction could capture a relative growth curve that closely matched the actual curve after approximately 150 elapsed days, even if a target fruit was partially hidden.

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Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris' law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization-neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution.