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The ladybug, Cryptolaemus montrouzieri (Mulsant) (Coleoptera: Cocccinellidae) (Mulsant)(Coleoptera: Cocccinellidae) , is a highly efficient predator in controlling mealybug populations and is considered an effective agent for controlling the papaya mealybugs (Paracoccus marginatus) (Williams & Granara de Willink) (Hemiptera: Pseudococcidae). Various criteria have been proposed for evaluating predator effectiveness, with the consumption rate of prey by individual predators, specifically the functional response, emerging as a common and crucial metric. This study evaluated the functional responses of third- and fourth-instar larvae, as well as male and female adults (<48 h old) of C. montrouzieri to adult females of P. marginatus at 3 different temperatures (22 °C, 28 °C, and 35 °C) with 70% ± 5% RH and a 12L:12D h photoperiod. Prey densities were 2, 4, 8, 16, 32, 45, or 60 papaya mealybugs per predator for all tests. The response to prey density by third- and fourth-instar larvae or both sexes of adult C. montrouzieri was a type II at all temperatures. The highest attack rate and lowest handling time were estimated at 28 °C in males and 35 °C in females, respectively. The highest daily prey consumption rate occurred at 35 °C in both the immature and adult stages of C. montrouzieri. These findings support the potential of C. montrouzieri in controlling the papaya mealybug, especially in tropical and subtropical regions, given its search efficiency at high temperatures tested in this study. However, additional field investigations are needed to ascertain the control efficacy of C. montrouzieri for this mealybug in biocontrol programs. [ABSTRACT FROM AUTHOR]

Objective To prepare 3D-printed porous materials of titanium alloy by electron beaming melting (EBM) and to confirm its biosafety to osteoblasts (OB). Methods Three topological structures of titanium alloy porous materials (2.0 mm, 2.25 mm and 2.5 mm Dode-Medium structure) were prepared by 3D printing technology. The extracts of titanium alloy porous materials were collected at multiple time points (24,48,72,96 h) and used for in vitro culture of osteoblasts cell line MC3T3-E1. The relative growth rate (RGR) of osteoblasts was cal-culated by MTT test. The morphological observation of osteoblasts was performed by HE staining. The activity of ALP was determined for analyzing the differentiate capacity of osteoblasts. The expressional level of Col Ⅰ in osteoblasts was assessed by immunofluorescence staining. Results Following incubation with extracts of titanium alloy porous materials, the osteoblasts were found to adhere and proliferate actively. There was no significant difference in the RGR of osteoblasts between extract group and the negative control group of DMEM. However, the RGR of osteoblasts increased significantly in extract groups compared with that in the positive control group of DMSO (P＜0.01). The cytotoxic degree of osteoblasts in extract groups was all less than or equal to 1. There was no significant difference in the level of ALP and Col Ⅰ of osteoblasts between extract groups and the negative control group of DMEM. However, the level of ALP and Col Ⅰ of osteoblasts enhanced significantly in extract groups compared with that in the positive control group of DMSO (P＜0.01). Conclusions The extracts of titanium alloy materials do not affect the proliferation and differentiation of osteoblasts in vitro that confirmed its biosafety.

Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation (SAFE‐PSO) approach. Firstly, the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE‐PSO algorithm is tested on two widely used datasets: The 10‐category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE‐PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost. [ABSTRACT FROM AUTHOR]