Research collaboration between China and Denmark for development of systemic approaches to agro-ecological pest management without pesticides with focus on vegetable, fruit and berry crops. Proceedings and recommendations from two network workshops

This report is the result of a network project which was established to discuss the potential for collaboration on development of systemic approaches to pest management without pesticides between Chinese and Danish researchers. The focus is on systemic approaches rather than input substitution of synthetic chemicals with agents of natural origin, however, the latter is considered as an integrated tool for the development and design of systemic approaches. The discussions were, furthermore, limited to management of invertebrate pests as well as diseases, while other pests such as weeds have not been included in the discussions. The discussions took place at two workshops and were based on presentations of research from the two countries and field visits in China and Denmark. After the first workshop that took place in China, it was agreed that Chinese and Danish researchers in this particular field had mutual interests and priorities and that there was a potential for creating collaboration that could yield results beneficial for the agricultural/horticultural sectors in both countries. It was also agreed that in spite of the many differences between variation in climate and ecosystems, as well as in farming systems and their organization in China and Denmark, there were many similarities in the production of high-value crops in the two countries, such as vegetables, fruit and berries and, therefore, an obvious focus for joint research efforts. It was also agreed that joint research efforts could aim at specific crops as well as aiming at the development of specific research approaches. Based on the observations and the agreements of the first workshop, the second workshop, which took place in Denmark, focused more specifically on the development of a research framework with specified research questions/topics. Two groups were formed – one working with vegetables and one with fruit and berries working in parallel – both looking into what kind of research is needed for development of systemic approaches to pesticide-free pest management should include both well-known practices and new practices. Although the discussions in the two groups took separate routes and unfolded and described the research topics in each their way, there was a clear consistency between the outputs of the work of the two groups. Each had identified three main research themes that more or less followed the same line and has been merged into three specific recommendations on themes for collaboration, namely: 1) ‘Research to provide the biological foundation and understanding of mechanisms and interactions for development of non-chemical solutions and to improve efficiency of new and existing control methods for severe pest problems’. 2) Research in ‘How best to integrate multifunctional plants (and crops) and use diversification to create a more healthy and productive farming system which is resilient to pests?’ 3) Research in ‘How to design and integrate pest management in eco-functional cropping systems at field and farm/landscape level?

NFCSense: Data-Defined Rich-ID Motion Sensing for Fluent Tangible Interaction Using a Commodity NFC Reader

This paper presents NFCSense, a data-defined rich-ID motion sensing technique for fluent tangible interaction design by using commodity near-field communication (NFC) tags and a single NFC tag reader. An NFC reader can reliably recognize the presence of an NFC tag at a high read rate (∼ 300 reads/s) with low latency, but such high-speed reading has rarely been exploited because the reader may not effectively resolve collisions of multiple tags. Therefore, its human–computer interface applications have been typically limited to a discrete, hands-on interaction style using one tag at a time. In this work, we realized fluent, hands-off, and multi-tag tangible interactions by leveraging gravity and anti-collision physical constraints, which support effortless user input and maximize throughput. Furthermore, our system provides hot-swappable interactivity that enables smooth transitions throughout extended use. Based on the design parameters explored through a series of studies, we present a design space with proof-of-concept implementations in various applications

Manifold learning based on straight-like geodesics and local coordinates

In this article, a manifold learning algorithm based on straight-like geodesics and local coordinates is proposed, called SGLC-ML for short. The contribution and innovation of SGLC-ML lie in that; first, SGLC-ML divides the manifold data into a number of straight-like geodesics, instead of a number of local areas like many manifold learning algorithms do. Figuratively speaking, SGLC-ML covers manifold data set with a sparse net woven with threads (straight-like geodesics), while other manifold learning algorithms with a tight roof made of titles (local areas). Second, SGLC-ML maps all straight-like geodesics into straight lines of a low-dimensional Euclidean space. All these straight lines start from the same point and extend along the same coordinate axis. These straight lines are exactly the local coordinates of straight-like geodesics as described in the mathematical definition of the manifold. With the help of local coordinates, dimensionality reduction can be divided into two relatively simple processes: calculation and alignment of local coordinates. However, many manifold learning algorithms seem to ignore the advantages of local coordinates. The experimental results between SGLC-ML and other state-of-the-art algorithms are presented to verify the good performance of SGLC-ML.This work was supported in part by the National Natural Science Foundation of China under Grant 61773022, in part by the Character and Innovation Project of Education of Guangdong Province under Grant 2018GKTSCX081, and in part by the Project of Education Scientific Planning of Guangzhou under Grant 201811675

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm.

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA

On-demand tunable metamaterials design for noise attenuation with machine learning

Metamaterials with structure-dominated properties provide a new way to design structures to obtain desired performance. To achieve a wide range of applications, on-demand tunable metamaterials would fulfill various and changing needs. The design of on-demand tunable metamaterials requires a higher-level understanding of the relationship between the properties of the metamaterials and the geometrical parameters, which in many cases are complicated and implicit. With the advancement of machine learning and evolutionary methods, it becomes possible to design on-demand tunable metamaterials. This paper designs on-demand tunable acoustic metamaterials for noise attenuation at varying frequencies by employing a genetic algorithm based neural network method. The C-shaped acoustic metamaterials with slidable shells are combined with the specifically designed tri-stable origami-inspired metamaterials to realize the on-demand tunable structure. Experiments were conducted and showed that the designed tunable metamaterials exhibited desired characteristics in different targeting frequency ranges. The present general methodology is expected to provide a route for on-demand tunable design while exploring more possibilities for the application of metamaterials