Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

Fish biomass estimation is one of the most common and important practices in aquaculture. The regular acquisition of fish biomass information has been identified as an urgent need for managers to optimize daily feeding, control stocking densities and ultimately determine the optimal time for harvesting. However, it is difficult to estimate fish biomass without human intervention because fishes are sensitive and move freely in an environment where visibility, lighting and stability are uncontrollable. Until now, fish biomass estimation has been mostly based on manual sampling, which is usually invasive, time‐consuming and laborious. Therefore, it is imperative and highly desirable to develop a noninvasive, rapid and cost‐effective means. Machine vision, acoustics, environmental DNA and resistivity counter provide the possibility of developing nonintrusive, faster and cheaper methods for in situ estimation of fish biomass. This article summarizes the development of these nonintrusive methods for fish biomass estimation over the past three decades and presents their basic concepts and principles. The strengths and weaknesses of each method are analysed and future research directions are also presented. Studies show that the applications of information technology such as advanced sensors and communication technologies have great significance to accelerate the development of new means and techniques for more effective biomass estimation. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Through close cooperation between fisheries experts and engineers, the precision and the level of intelligence for fish biomass estimation will be further improved based on the above methods

Fracture of 2D crystalline nanomaterials: effect of hydrogen functionalization and complex loading

We performed molecular dynamics simulation for comprehensive analysis of fracture of different 2D crystalline nanomaterials: Boron Nitride, Graphene and its various allotropes. We considered the effect of hydrogen functionalization and complex loading. For graphene allotropes, different H-coverage spanning the entire range (0–100%) is considered. The effect of degree of functionalization and molecular structure on the Young’s modulus and strength are investigated, and the failure processes of some new allotropes are reported for the first time. The effect of hydrogen arrangement and different defect geometry are investigated. For complex loading, we have considered Graphene and Boron Nitride nanoribbon. Cracks with different length and orientation are subjected to normal loading. In another model, we kept the crack shape unchanged and varied the loading angle. For both cases, we have investigated variation of Lagrangian, failure mechanism, fracture strength, etc. This comprehensive study will help understand deeply the fracture mechanics of 2D crystalline nanomaterials

Dual Contrastive Network for Sequential Recommendation with User and Item-Centric Perspectives

With the outbreak of today's streaming data, sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted item of given user based on history item sequence. Some recent works tend to improve the sequential recommendation via randomly masking on the history item so as to generate self-supervised signals. But such approach will indeed result in sparser item sequence and unreliable signals. Besides, the existing sequential recommendation is only user-centric, i.e., based on the historical items by chronological order to predict the probability of candidate items, which ignores whether the items from a provider can be successfully recommended. The such user-centric recommendation will make it impossible for the provider to expose their new items and result in popular bias. In this paper, we propose a novel Dual Contrastive Network (DCN) to generate ground-truth self-supervised signals for sequential recommendation by auxiliary user-sequence from item-centric perspective. Specifically, we propose dual representation contrastive learning to refine the representation learning by minimizing the euclidean distance between the representations of given user/item and history items/users of them. Before the second contrastive learning module, we perform next user prediction to to capture the trends of items preferred by certain types of users and provide personalized exploration opportunities for item providers. Finally, we further propose dual interest contrastive learning to self-supervise the dynamic interest from next item/user prediction and static interest of matching probability. Experiments on four benchmark datasets verify the effectiveness of our proposed method. Further ablation study also illustrates the boosting effect of the proposed components upon different sequential models.Comment: 23 page

Friction between bilayer of 2D crystalline nanomaterials: graphene–graphene, graphene–boron nitride, and boron nitride–boron nitride.

We performed molecular dynamics simulation for comprehensive analysis of friction between bilayer of 2D crystalline nanomaterials: Graphene–Graphene, Graphene–Boron Nitride (BN) and BN–BN. For Graphene–Graphene and BN–BN friction, we investigated the effect of defect (vacancy, stone-wale) and surface functionalization by hydrogen. Our results shows that presence of defect influences the frictional properties. Moreover, hydrogen functionalization will increase the friction because of the increase of surface roughness. We investigated the variation of frictional force with surface functionalization. The last model considered the friction between Graphene–BN. The interface between Graphene–BN substrate plays a crucial role in application of nanodevices. We analyzed the energy landscape, evolution of Moire pattern and frictional force between sheets for various conditions. This comprehensive study will help understand deeply the mechanics of friction of 2D crystalline nanomaterials