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The thyroid gland is the largest endocrine organ in the body, and when it behaves abnormally, patient health may suffer. The development of thyroid diagnostic techniques is reviewed considering direct and indirect techniques. Combining spectroscopic approaches with machine deep learning represents a promising diagnostic tool due to its low cost, speed, and good precision. The machine deep learning models for spectral profiling and the evaluation methods are used to test the performance on the bases of precision, sensitivity, specificity, subject operating characteristic (ROC) curve, and F1-score. The analysis of the spectra with machine deep learning models has significant potential for disease diagnosis. [ABSTRACT FROM AUTHOR]

Using microsimulations this paper analyzes the poverty and tax implications of replacing current transfers and subsidies by a budget-neutral (no change in the fiscal deficit) universal basic income program (UBI) in Brazil, Chile, India, Russia, and South Africa. We consider three UBI transfers with increasing levels of generosity and identify scenarios in which the poor are no worse off than in the baseline scenario of existing social transfers. We find that for poverty levels not to increase under a UBI reform, the level of spending must increase substantially with respect to the baseline. Accordingly, the required increase in tax burdens is high throughout. We find that the increase in the average tax rate that would be consistent with not hurting the poor is almost universally above 30%, limiting the feasibility of a UBI reform due to political resistance and efficiency costs. [ABSTRACT FROM AUTHOR]

The coal industry is dominated by the largest mines, with 1% of coal mines in the U.S. being responsible for 65% of the coal cumulatively produced. We show that this "heavy tail" can be well approximated by a power law, where mine-level cumulative production is inversely proportionate to distributional rank. Maximum likelihood and regression-based procedures estimate the counter-cumulative power-law parameter to be less than one, indicating there is no well-defined mean or variance for cumulative production. Goodness of fit tests indicate the power-law is a better fit to the data than other competing distributions, including the lognormal. • Mine-level cumulative coal production is power-law distributed. • Distribution indicates there is no well-defined mean or variance. • Distribution shows how heavily concentrated coal production is in the US. [ABSTRACT FROM AUTHOR]

The rapid development of image processing technology and the improvement of computing power in recent years have made deep learning one of the main methods for plant disease identification. Currently, many neural network models have shown better performance in plant disease identification. Typically, the performance improvement of the model needs to be achieved by increasing the depth of the network. However, this also increases the computational complexity, memory requirements, and training time, which will be detrimental to the deployment of the model on mobile devices. To address this problem, a novel lightweight convolutional neural network has been proposed for plant disease detection. Skip connections are introduced into the conventional MobileNetV3 network to enrich the input features of the deep network, and the feature fusion weight parameters in the skip connections are optimized using an improved whale optimization algorithm to achieve higher classification accuracy. In addition, the bias loss substitutes the conventional cross-entropy loss to reduce the interference caused by redundant data during the learning process. The proposed model is pre-trained on the plant classification task dataset instead of using the classical ImageNet for pre-training, which further enhances the performance and robustness of the model. The constructed network achieved high performance with fewer parameters, reaching an accuracy of 99.8% on the PlantVillage dataset. Encouragingly, it also achieved a prediction accuracy of 97.8% on an apple leaf disease dataset with a complex outdoor background. The experimental results show that compared with existing advanced plant disease diagnosis models, the proposed model has fewer parameters, higher recognition accuracy, and lower complexity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Quan, Wang, Jia, Yang and Xu.)

When many people say the same thing, the individual is more likely to endorse this information than when just a single person says the same. Yet, the influence of consensus information may be modulated by many personal, contextual and cultural variables. Here, we study the sensitivity of Chinese ( N = 68) and Spanish ( N = 82) preschoolers to consensus in social decision making contexts. Children faced two different types of peer-interaction events, which involved (1) uncertain or ambiguous scenarios open to interpretation (social interpretation context), and (2) explicit scenarios depicting the exclusion of a peer (moral judgment context). Children first observed a video in which a group of teachers offered their opinion about the events, and then they were asked to evaluate the information provided. Participants were assigned to two conditions that differed in the type of consensus: Unanimous majority ( non-dissenter condition) and non-unanimous majority ( dissenter condition). In the dissenter condition, we presented the conflicting opinions of three teachers vs. one teacher. In the non-dissenter condition, we presented the unanimous opinion of three teachers. The general results indicated that children's sensitivity to consensus varies depending both on the degree of ambiguity of the social events and the presence or not of a dissenter: (1) Children were much more likely to endorse the majority view when they were uncertain (social interpretation context), than when they already had a clear interpretation of the situation (moral judgment context); (2) The presence of a dissenter resulted in a significant decrease in children's confidence in majority. Interestingly, in the moral judgment context, Chinese and Spanish children differed in their willingness to defy a majority whose opinion run against their own. While Spanish children maintained their own criteria regardless of the type of condition, Chinese children did so when an "allied" dissenter was present (dissenter condition) but not when confronting a unanimous majority (non-dissenter condition). Tentatively, we suggest that this difference might be related to culture-specific patterns regarding children's deference toward adults.