Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.

This study was conducted to investigate the profound impact of human activities on the environment, based on scientific data, recognizing the potential of environmental problems to turn into devastating crises if appropriate measures are not taken. It emphasizes the important role of education in developing environmental awareness, knowledge and sensitivity to counter adverse environmental consequences. For this purpose, a dataset was created for the emotional tendencies of university students, who represent a demographic that has the potential to influence the sustainable future of the world. A survey data including 34 different variables was collected from 388 university students in Turkey. Environmental Sensory Tendencies Dataset is intended to provide valuable guidance for the development of effective environmental education programs and policies aimed at increasing university students\' awareness and participation in environmental issues. Our research underlines the vital importance of developing responsible attitudes and behaviors to effectively address environmental challenges and thereby contribute to a healthier and more sustainable global ecosystem. This study will make a significant contribution to the literature and highlight the interconnection between human actions and environmental well-being.

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation-identifying crops from the background-crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.

OBJECTIVE: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, but such methods often have some limitations due to the complexity of the data. In this regard, ensemble learning has shown promising results. There is an urgent need to evaluate different algorithms and then suggest a robust ensemble algorithm in liver disease prediction.
METHODS: Three ensemble approaches with nine algorithms are evaluated on a large dataset of liver patients comprising 30,691 samples with 11 features. Various preprocessing procedures are utilized to feed the proposed model with better quality data, in addition to the appropriate tuning of hyperparameters and selection of features.
RESULTS: The models\' performances with each algorithm are extensively evaluated with several positive and negative performance metrics along with runtime. Gradient boosting is found to have the overall best performance with 98.80% accuracy and 98.50% precision, recall and F1-score for each.
CONCLUSIONS: The proposed model with gradient boosting bettered in most metrics compared with several recent similar works, suggesting its efficacy in predicting liver disease. It can be further applied to predict other diseases with the commonality of predicate indicators.

The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation. The bagging technique is an elegant way to form WNN estimators with weights automatically generated to the nearest neighbors (Steele, 2009; Biau et al., 2010); we name the resulting estimator as the distributional nearest neighbors (DNN) for easy reference. Yet, there is a lack of distributional results for such estimator, limiting its application to statistical inference. Moreover, when the mean regression function has higher-order smoothness, DNN does not achieve the optimal nonparametric convergence rate, mainly because of the bias issue. In this work, we provide an in-depth technical analysis of the DNN, based on which we suggest a bias reduction approach for the DNN estimator by linearly combining two DNN estimators with different subsampling scales, resulting in the novel two-scale DNN (TDNN) estimator. The two-scale DNN estimator has an equivalent representation of WNN with weights admitting explicit forms and some being negative. We prove that, thanks to the use of negative weights, the two-scale DNN estimator enjoys the optimal nonparametric rate of convergence in estimating the regression function under the fourth-order smoothness condition. We further go beyond estimation and establish that the DNN and two-scale DNN are both asymptotically normal as the subsampling scales and sample size diverge to infinity. For the practical implementation, we also provide variance estimators and a distribution estimator using the jackknife and bootstrap techniques for the two-scale DNN. These estimators can be exploited for constructing valid confidence intervals for nonparametric inference of the regression function. The theoretical results and appealing finite-sample performance of the suggested two-scale DNN method are illustrated with several simulation examples and a real data application.

UNASSIGNED: Fresh Aareca nut fruit for fresh fruit chewing commonly found in green or dark green hues. Despite its economic significance, there is currently insufficient research on the study of color and luster of areca. And the areca nut fruits after bagging showed obvious color change from green to tender yellow. In the study, we tried to explain this interesting variation in exocarp color.
UNASSIGNED: Fruits were bagged (with a double-layered black interior and yellow exterior) 45 days after pollination and subsequently harvested 120 days after pollination. In this study, we examined the the chlorophyll and carotenoid content of pericarp exocarp, integrated transcriptomics and metabolomics to study the effects of bagging on the carotenoid pathway at the molecular level.
UNASSIGNED: It was found that the chlorophyll and carotenoid content of bagged areca nut (YP) exocarp was significantly reduced. A total of 21 differentially expressed metabolites (DEMs) and 1784 differentially expressed genes (DEGs) were screened by transcriptomics and metabolomics. Three key genes in the carotenoid biosynthesis pathway as candidate genes for qPCR validation by co-analysis, which suggested their role in the regulation of pathways related to crtB, crtZ and CYP707A.
UNASSIGNED: We described that light intensity may appear as a main factor influencing the noted shift from green to yellow and the ensuing reduction in carotenoid content after bagging.

The \'Huangguan\' pear is one of the high-quality pear cultivars produced in China. However, the bagged fruit of the \'Huangguan\' pear often suffers from peel browning spots after rain during their mature period. In this study, in an effort to discover the impact of bagging treatments on the occurrence of peel browning spots and fruit quality, fruits were covered by single-layer, two-layer, or triple-layer paper bags six weeks after reaching full bloom. The results showed that the bagged fruits were characterized by smooth surfaces and reduced lenticels compared with the unbagged ones. The unbagged and the two-layer bagged fruits had yellow/green peels, while the single- and triple-layer bagged ones had yellow/white peels. Compared with the unbagged fruits, the bagged fruits had higher vitamin C (Vc) contents and values of peel color indexes L and a and lower soluble solid contents (SSCs), titratable acid (TA) contents, absorbance index differences (IAD), and b values. Additionally, the triple-layer bagged group was superior to other groups in terms of fruit quality, but it also had the maximum incidence of peel browning spots. Before and after the appearance of peel browning spots, the bagged fruits had smoother and thinner cuticles compared with the unbagged ones. Furthermore, the triple-layer bagged fruits had minimum lignin contents and maximum phenolic contents in their peels, with minimum activity of lignin synthesis-related enzymes such as phenylalanine ammonia lyase (PAL), peroxidase (POD), and polyphenol oxidase (PPO), as well as minimum expressions of relevant genes such as cinnamyl alcohol dehydrogenase (CAD), cinnamoyl CoA reductase (CCR), 4-coumarate: coenzyme A ligase (4CL6), and cinnamate 4-hydroxylase (C4H1). It was deduced that POD activity and the relative expressions of CAD9, CCR3, CCR4, and CCR5 may play key roles in the occurrence of peel browning spots. In summary, lignin synthesis affected the incidence of peel browning spots in bagged \'Huangguan\' pears. This study provides a theoretical basis for understanding the incidence of peel browning spots in \'Huangguan\' pears.

Flow obstructed by bridge piers can increase sediment transport leading to local scour. This local scour poses a risk to the stability of bridge structures, which could lead to structural failures. There are two main approaches for evaluating the scour depth (ds) of bridge piers. The first is based on understanding hydraulic phenomena and developing relationships with properties affecting scour. The second uses data-driven soft computing models that lack physical interpretations but rely on algorithms to predict outcomes. Methods are chosen by researchers based on their goals and resources. This study aims to create innovative ensemble frameworks comprising support vector machine for regression (SVMR), random forest regression (RFR), and reduced error pruning tree (REPTree) as base learners, alongside bagging regression tree (BRT) and stochastic gradient boosting (SGB) as meta learners. These ensembles were developed to analyse maximum scour depths (dsm) in clear water conditions, utilizing 35 literature\'s experimental data published in last 63 years. The performance of each machine learning (ML) approach was assessed using statistical performance indicators. The proposed model was also compared with top six empirical equations with strong predictive ability. Results show that among these empirical equations, the equation from Nandi and Das (2023) performs best. Performance evaluation considering training, testing, and the entire dataset, SGB (REPTree), BRT(SVMR-PUK), and SGB (REPTree) exhibited the highest performance, securing the top rank among all ML models and empirical equations. Sensitivity analysis identified sediment gradation and flow intensity as the most influential variables for predicting dsm during both training and testing phases, respectively.

Cork spot-like physiological disorder (CSPD) is a newly identified issue in \'Kurenainoyume\' apples, yet its mechanism remains unclear. To investigate CSPD, we conducted morphological observations on \'Kurenainoyume\' apples with and without pre-harvest fruit-bagging treatment using light-impermeable paper bags. Non-bagged fruit developed CSPD in mid-August, while no CSPD symptoms were observed in bagged fruit. The bagging treatment significantly reduced the proportion of opened lenticels, with only 17.9% in bagged fruit compared to 52.0% in non-bagged fruits. In non-bagged fruit, CSPD spots tended to increase from the lenticels, growing in size during fruit development. The cuticular thickness and cross-sectional area of fresh cells in CSPD spots were approximately 16 µm and 1600 µm², respectively. Healthy non-bagged fruit reached these values around 100 to 115 days after full bloom from mid- to late August. Microscopic and computerized tomography scanning observations revealed that many CSPD spots developed at the tips of vascular bundles. Therefore, CSPD initiation between opened lenticels and vascular bundle tips may be influenced by water stress, which is potentially caused by water loss, leading to cell death and the formation of CSPD spots.

BACKGROUND: TCP proteins are plant specific transcription factors that play important roles in plant growth and development. Despite the known significance of these transcription factors in general plant development, their specific role in fruit growth remains largely uncharted. Therefore, this study explores the potential role of TCP transcription factors in the growth and development of sweet cherry fruits.
RESULTS: Thirteen members of the PavTCP family were identified within the sweet cherry plant, with two, PavTCP1 and PavTCP4, found to contain potential target sites for Pav-miR159, Pav-miR139a, and Pav-miR139b-3p. Analyses of cis-acting elements and Arabidopsis homology prediction analyses that the PavTCP family comprises many light-responsive elements. Homologs of PavTCP1 and PavTCP3 in Arabidopsis TCP proteins were found to be crucial to light responses. Shading experiments showed distinct correlation patterns between PavTCP1, 2, and 3 and total anthocyanins, soluble sugars, and soluble solids in sweet cherry fruits. These observations suggest that these genes may contribute significantly to sweet cherry light responses. In particular, PavTCP1 could play a key role, potentially mediated through Pav-miR159, Pav-miR139a, and Pav-miR139b-3p.
CONCLUSIONS: This study is the first to unveil the potential function of TCP transcription factors in the light responses of sweet cherry fruits, paving the way for future investigations into the role of this transcription factor family in plant fruit development.