Persistently exploring ever more sustainable and economically promising alternative protein sources is a grand challenge that humanity must tackle today. Edible insects, as one long-standing food source for mankind, are attracting considerable attention in both academia and capital markets due to the exacerbated global food crisis and new technological advancements. To massively and safely rear edible insects, all stages in their lifecycle must be carefully engineered, monitored, and controlled. This work aims to first investigate the natural mating and ovipositing behaviors of black soldier flies and then engineer an artificial environment that well suits their needs. In particular, a modularized and scalable facility equipped with dedicated light recipe and climate control has been designed and built. The results of our comparative experiments revealed that black soldier flies in the artificial facility tended to have better mating and oviposition quantitatively and qualitatively as compared to the control group in the nature environment. As a result, this work demonstrated that within a well-controlled and comfortable artificial environment, it is possible and feasible to stably breed black soldier flies in large scale. This paves the foundation for industrialized insect-based bioconversion.

Brittleness is an important parameter characterizing the fracturing properties of shale reservoir, which can be predicted by the pre-stack seismic inversion. In order to overcome the low efficiency and ill-posed problems of the traditional pre-stack brittleness inversion, we propose a new unsupervised deep learning (DL) inversion method for seismic brittleness parameters based on the physical equation. This method integrates DL framework and the physical equation, and provides a DL inversion strategy without actual labels. We first input the original seismic data into the Fastformer network, and use the low-frequency model as the physical constraint to predict the brittle parameters. Then, the prediction results of brittleness parameters are sent to the forward modeling module (a linear approximation equation) to calculate the synthetic seismic data. Next, the error between the calculated seismic data and original seismic data is used to update the network prediction results. The network parameters are iteratively optimized to minimize the error, and the brittle prediction parameters are finally output. In the whole training process, it is not necessary to use the real brittle parameters as the labels. Through this method, the effect of approximate unsupervised learning is obtained. Finally, we apply the proposed method to the synthetic data and field data, and compared with the results inverted by the traditional L1 method. The experimental results show that the proposed method has higher inversion accuracy and efficiency than the traditional L1 method, which has a great potential in the practical application.

Currently, lots of real social relations in social networks force users to face the potential risk of privacy leakage. Consequently, data holders would like to disturbor anonymize their individual data before publishing them, for the purpose of privacy protection. Due to the characteristics of high sensitivity and large volume data of social network graph structure, it is difficult for privacy protection schemes to enable a reasonable allocation of noises while keeping desirable data availability and execution efficiency. On the basis of differential privacy model, combining with clustering and randomization algorithms, a privacy protection approach PBCN (Privacy Preserving Approach Based on Clustering and Noise) is proposed. This proposal is composed of five algorithms including random disturbance based on clustering, graph reconstruction after disturbing degree sequence and noise nodes generation, etc. Furthermore, a privacy measure algorithm based on adjacency degree is put forward in order to objectively evaluate the privacy-preserving strength of various schemes against graph structure and degree attacks. Simulation experiments are conducted to achieve performance comparisons between PBCN, Spctr Add/Del, Spctr Switch, DER and HPDP. The experimental results show that PBCN realizes more satisfactory data availability and execution efficiency. Finally, parameters utility analysis demonstrates PBCN can achieve a “trade-off” between data availability and privacy protection level.

To avoid deteriorating the mechanism's performance, joint clearance can be eliminated by preloading the pairing elements of the joint. However, this paper proves rigorously that in the real world, the unavoidable assembly and manufacturing errors will cause overconstrained parallel manipulators to lose degree of freedoms, or even unable to be assembled if they are composed of purely clearance-free pairs (e.g., preloaded pairs). Introducing joint clearance is an essential and efficient way for the correct functioning and easy assembly of overconstrained parallel manipulators.

Due to the joint clearance, parallel manipulators always exhibit some position and orientation errors at the mobile platform. This paper aims to provide a systematic framework for the error analysis problem of general parallel mechanisms influenced by the joint clearance. A novel and efficient method is proposed to evaluate the maximal pose errors of general spatial parallel manipulators with joint clearance.

In this paper we concentrate on contouring control for surface machining. The object of the motion control system is tracking the spatial curve lying on the surface. Observing that the contour error can be approximated by the tracking error (projected to the normal subspace of the surface), we propose a new design procedure based on the geometrical properties of the curves and surfaces. Essentially the controllers look ahead using the information provided by the curvature of the curves and surfaces. The simulation results show the efficiency of the design method

This paper proposes to optimally design a parallel machine based on multiple criteria. Many criteria, workspace, condition number, accuracy, stiffness, maximum velocity, and maximum force, are considered. The optimal design problem is proposed as to find a set of design parameters such that (a) the Cartesian workspace generated by the resulting manipulator contains a prescribed workspace; (b) the resulting manipulator possesses a good condition number at each points in the prescribed workspace; (c) the resulting manipulator possesses good performance on accuracy, stiffness, velocity/force transmission factor. By some manipulations, the requirements on the latter four criteria are reduced to constraints on singular values of the kinematic Jacobian. A trade-off must be made since there're opposite requirements among those four criteria. The singular values of kinematic Jacobian are limited in a given interval to guarantee good properties. All the requirement are finally reduced to polynomial inequalities with respect to design parameters. The optimal design problem is transformed into a Max-Det optimization problem that can be ef ficiently solved. The Orthoglide is used as an example to demonstrate the procedure.

In the contouring control, the trajectory and the tolerance information are specified in the task space. Based on this observation we propose to design the controller in the task space directly. First, by defining the projection map on the cotangent space of the mechanical system the equation of motion is derived in the task space. Then a novel contouring controller is got based on the geometric control theory. The controller is transferred into the joint space for implementation. The simulation results show the performance of the controller.