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Sex determination from skeletons is a significant step in the analysis of forensic anthropology. Previous skeletal sex assessments were analyzed by anthropologists' subjective vision and sexually dimorphic features. In this paper, we proposed an improved backpropagation neural network (BPNN) to determine gender from skull. It adds the momentum term to improve the convergence speed and avoids falling into local minimum. The regularization operator is used to ensure the stability of the algorithm, and the Adaboost integration algorithm is used to improve the generalization ability of the model. 267 skulls were used in the experiment, of which 153 were females and 114 were males. Six characteristics of the skull measured by computer-aided measurement are used as the network inputs. There are two structures of BPNN for experiment, namely, [6; 6; 2] and [6; 12; 2], of which the [6; 12; 2] model has better average accuracy. While η = 0.5 and α = 0.9, the classification accuracy is the best. The accuracy rate of the training stage is 97.232%, and the mean squared error (MSE) is 0.01; the accuracy rate of the testing stage is 96.764%, and the MSE is 1.016. Compared with traditional methods, it has stronger learning ability, faster convergence speed, and higher classification accuracy.

Escalating global demand for wildlife products and consequential illegal wildlife trade has become one of the major threats to biodiversity conservation. In the recent COVID-19 pandemic, growing public health risks of wildlife trade and consumption have triggered widespread public concern. In this review, we adopt a multidisciplinary perspective, including sociology, psychology, behavioral science and other disciplines, to understand the motivations for wildlife consumption in China, and to propose scientifically guided behavioral change countermeasures. The current state of wildlife consumption in China reveals certain functional, social, experiential and other non-essential needs of wildlife as major drivers of consumption, which are affected by a host of complex factors. Based on our understanding of the drivers of综述 demand, we suggest using behavioral change frameworks, and a variety of behavioral change methods, including education, social influence, regulation and nudging, to effectively influence and change wildlife consumption behavior. For effective implementation of behavioral change strategies, collaboration needs to be strengthened, both among and across diverse disciplines, actors and scales of interest.

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The actual multimodal process data usually exhibit non-linear time correlation and non-Gaussian distribution accompanied by new modes. Existing fault diagnosis methods have difficulty adapting to the complex nature of new modalities and are unable to train models based on small samples. Therefore, this paper proposes a new modal fault diagnosis method based on meta-learning (ML) and neural architecture search (NAS), MetaNAS. Specifically, the best performing network model of the existing modal is first automatically obtained using NAS, and then, the fault diagnosis model design is learned from the NAS of the existing model using ML. Finally, when generating new modalities, the gradient is updated based on the learned design experience, i.e., new modal fault diagnosis models are quickly generated under small sample conditions. The effectiveness and feasibility of the proposed method are fully verified by the numerical system and simulation experiments of the Tennessee Eastman (TE) chemical process. As a primary goal, the abstract should render the general significance and conceptual advance of the work clearly accessible to a broad readership. References should not be cited in the abstract. Leave the Abstract empty if your article does not require one–please see the “Article types” on every Frontiers journal page for full details.