Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

Retracted: Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

[This retracts the article DOI: 10.1155/2021/7149631.].

Growth Optimization and Rearing of Mealworm (Tenebrio molitor L.) as a Sustainable Food Source

As a sustainable food source for humans, mealworms (Tenebrio molitor) have a great deal of potential, due to the fact that they have a very favorable nutritional profile and a low environmental impact. For meal production, feed formulation and optimization are important. The mealworm Tenebrio molitor (Coleoptera: Tenebrionidae) is the most consumed insect in the world. Mealworms were given a variety of diets, including wheat bran as constant diet supplemented with different levels of Ospor (Bacillus clausii) at 0.002 g, 0.004 g, 0.006 g, and 0.008 g; imutec (Lacticaseibacillus rhamnosus) at 0.2 g. 0.4 g, 0.6 g, and 0.8 g; fungi (Calocybe indica) at 250 g, 500 g, and 750 g; yeast (Saccharomyces cerevisiae) at 50 g, 100 g, and 150 g; and wheat bran (standard diet) were examined in complete randomized design (CRD). Different parameters, i.e., the larval, pupal, and adult weight, size, life span, and nutritional profile of mealworm were studied. When compared with other insect growth promoters, only wheat bran was discovered to be the most efficient. It generated the heaviest and longest larvae at 65.03 mg and 18.32 mm, respectively, as well as pupae weighing 107.55 mg and 19.94 mm, respectively, and adults weighing 87.52 mg and 20.26 mm, respectively. It was also determined that fungi (C. indica) and ospor (B. clausii) promoted faster larval development than yeast (S. cerevisiae) and imutec (L. rhamnosus). Larval mortality was also greater in the imutec (L. rhamnosus) and yeast (S. cerevisiae) diets than the others. No pupal mortality was recorded in all diets. Furthermore, the protein content of Tenebrio. molitor raised on a diet including fungi (C. indica) was the highest at (375 g), with a content of 68.31%, followed by a concentration of (250 g) with a content of 67.84%, and wheat bran (1 kg) (normal diet) with the lowest content at 58.91%. T. molitor larvae fed a diet supplemented with bacterial and fungal had lower fat and ash content than bran-fed T. molitor larvae (standard diet). Wheat bran (normal diet) had the highest fat at 16.11%, and ash at 7.71%. Hence, it is concluded that wheat bran alone or diet containing fungi (C. indica) and ospor (B. clausii) performed better in terms of growth, and these diets and protein content are recommended for the mass rearing of mealworms.

Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network

Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning.

Human Resource Planning and Configuration Based on Machine Learning

Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company's human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources.