Assessment of the Genetic Diversity of Echinococcus multilocularis from Copro-Isolated Eggs

The genetic diversity of the parasite Echinococcus multilocularis, the infectious agent of alveolar echinococcosis, is generally assessed on adult worms after fox necropsy. We aimed to investigate E. multilocularis polymorphism through the microsatellite EmsB marker using a noninvasive approach. We tested batches of isolated eggs (1, 5, and 10) from 19 carnivore fecal samples collected in a rural town located in a highly endemic area in France to determine the best strategy to adopt using a minimal quantity of parasite DNA while avoiding genetic profile overlapping in the analysis. Several molecular controls were performed to formally identify the Taeniidae eggs. In total, 112 egg batches were isolated and 102 EmsB electrophoregrams were obtained in duplicate. Quality sorting was performed through the Pearson correlation coefficient (r) between each EmsB duplicate. Forty-nine batches with r > 0.9 remained in the analysis, mainly 5- or 10-egg batches. Three EmsB profiles were emphasized by hierarchical clustering and matched those from human lesions and adult worms previously genotyped and collected in the same area. We show that the genetic diversity of the parasite can be assessed from isolated E. multilocularis eggs in a spatiotemporal context using a noninvasive approach.

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

A Novel Multi-Criteria Sorting Model Based on AHP-Entropy Grey Clustering for Dealing with Uncertain Incoming Core Quality in Remanufacturing Systems

Remanufacturing is a key pillar of a circular economy and helps in recovering used products by extending their life cycle via remanufacturing them into new products. A vital aspect in a remanufacturing system is the quality assessment of incoming worn-out products (cores) prior to remanufacturing to ensure that non-conforming cores are discarded at an early stage in order to avoid unnecessary processing. Therefore, quality sorting plays an important role in core acquisition for remanufacturing systems when attempting to mitigate uncertain incoming core quality as an immediate solution. The main problem is that it is difficult to acquire the important information required to decide on the sorting of incoming cores, such as the core quality. The data are also commonly limited, not always available, or inaccurate. Grey systems are powerful methods in decision making when handling uncertainty with small data. In this paper, we consider the usefulness of grey systems for handling uncertain quality information for sorting incoming cores in a remanufacturing system. For this reason, we propose a multi-criteria quality sorting model based on an analytical hierarchy process (AHP)-entropy model that is coupled with grey clustering using possibility functions. The quality criteria for sorting the incoming cores are considered according to the technological, physical, and usage conditions. To demonstrate the practical contribution of this research, a case study of the quality sorting problem with a heavy-duty equipment remanufacturer is presented. The proposed model consistently classifies the quality of used hydraulic cylinders into two grey classes.

Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3

This research used deep learning methods to develop a set of algorithms to detect die particle defects. Generative adversarial network (GAN) generated natural and realistic images, which improved the ability of you only look once version 3 (YOLOv3) to detect die defects. Then defects were measured based on the bounding boxes predicted by YOLOv3, which potentially provided the criteria for die quality sorting. The pseudo defective images generated by GAN from the real defective images were used as the training image set. The results obtained after training with the combination of the real and pseudo defective images were 7.33% higher in testing average precision (AP) and more accurate by one decimal place in testing coordinate error than after training with the real images alone. The GAN can enhance the diversity of defects, which improves the versatility of YOLOv3 somewhat. In summary, the method of combining GAN and YOLOv3 employed in this study creates a feature-free algorithm that does not require a massive collection of defective samples and does not require additional annotation of pseudo defects. The proposed method is feasible and advantageous for cases that deal with various kinds of die patterns.

GALLERIA MELLONELLA L. GARBAGE SORTING MECHANIZATION TECHNOLOGY

In the modern world, insects are widely used for scientific and industrial purposes. For their cultivation in the laboratory, various plants have been developed, the main disadvantage of which is poor-quality sorting from garbage. Therefore, the question of cultivating and sorting the larvae of bee moth (Galleria mellonella L.) in laboratory conditions for the purpose of further use as a model object in various fields of biological sciences and medical purposes is relevant. The effect of the temperature gradient and exposure on the movement of G. mellonella larvae from the heated compartment of the plant was determined by their reproduction into the cold by the number of individuals that moved for 10, 15, and 20 minutes at 35, 40, 45, 50, 55 0С, visual counting. At 35 0С, regardless of the exposure time, the larvae remained on a honeycomb frame; at 40 ° C and 45 0С, on average, 11.5% and 31.8% of individuals, respectively, moved. The higher the temperature gradient, the faster the larvae moved into the cold compartment. More larvae passed from the lower frame to it than from the upper one. The difference at a temperature of 45 ° C averaged 2%, 50 0С - 18.7%, 55 0С - 0.4%. The optimum temperature gradient for sorting larvae is 50 ... 55 0С during an exposure of 15 ... 20 minutes, in this case more than 98% of the larvae were transferred to the cold compartment. The use of an infrared electric heating system will optimize the breeding process of G. mellonella larvae and ensure their high-quality sorting

The Impact of Exchange Rates on French Wine Exports

In this article, we analyze the impact of varying exchange rates on French wine exports using a dynamic Armington panel model for the time period from 2000 to 2011. Our results suggest that French wines have become less competitive during the 2000s. This is due to two factors: rising domestic wine prices relative to foreign competitors and the appreciation of the euro against the USD and the GBP. Chinese demand appears to be a key driver of French wine exports. In addition, we find some compositional effects in Bordeaux wine exports. In response to the appreciation of the euro, the share of high-priced wines has increased, suggesting some degree of quality sorting in response to exchange-rate changes. (JEL Classification: F14, F31, Q17)