Influence of Environmental Knowledge and Green Trust on Green Purchase Behaviour

This study investigates the influence of environmental knowledge and green trust on green purchase intentions and behavior, and the mediating role of purchase intentions.  Through questionnaire surveys and statistical analysis, the research findings reveal that environmental knowledge and green trust significantly influence green purchase intentions and actual green purchasing behaviour. The results of this study hold significant meaning for the formulation of environmental education and environmental advocacy strategies, contributing to enhancing consumers' environmental awareness and promoting green purchasing behaviour

An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture

The simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic systems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of processing recourses in embedded systems. Especially when landmarks are not identifiable, the amount of computer processing will dramatically increase due to unknown data association. In this work, we propose an intelligible SLAM solution for an embedded processing platform to reduce computer processing time using a low-variance resampling technique. Our prototype includes a low-cost pixy camera, a Robot kit with L298N motor board and Raspberry Pi V2.0. Our prototype is able to recognise artificial landmarks in a real environment with an average 75% of identified landmarks in corner detection and corridor detection with only average 1.14 W

Photooxidation of a twisted isoquinolinone

Understanding the oxidation mechanism and positions of twistacenes and twistheteroacenes under ambient conditions is very important because such knowledge can guide us to design and synthesize novel, larger stable analogues. Herein, we demonstrated for the first time that a twisted isoquinolinone can decompose under oxygen and light at room temperature. The as‐decomposed product 1 was fully characterized through conventional methods as well as single‐crystal structure analysis. Moreover, the physical properties of the as‐obtained product were carefully investigated and the possible formation mechanism was proposed

Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

In recent years, the development of deep learning is noticeably influencing the progress of computational fluid dynamics. Numerous researchers have undertaken flow field predictions on a variety of grids, such as MAC grids, structured grids, unstructured meshes, and pixel-based grids which have been many works focused on. However, predicting unsteady flow fields on unstructured meshes remains challenging. When employing graph neural networks (GNNs) for these predictions, the message-passing mechanism can become inefficient, especially with denser unstructured meshes. Furthermore, unsteady flow field predictions often rely on autoregressive neural networks, which are susceptible to error accumulation during extended predictions. In this study, we integrate the traditional finite volume method to devise a spatial integration strategy that enables the formulation of a physically constrained loss function. This aims to counter the error accumulation that emerged in autoregressive neural networks during long-term predictions. Concurrently, we merge vertex-center and cell-center grids from the finite volume method, introducing a dual message-passing mechanism within a single GNN layer to enhance the message-passing efficiency. We benchmark our approach against MeshGraphnets for unsteady flow field predictions on unstructured meshes. Our findings indicate that the methodologies combined in this study significantly enhance the precision of flow field predictions while substantially minimizing the training time cost. We offer a comparative analysis of flow field predictions, focusing on cylindrical, airfoil, and square column obstacles in two-dimensional incompressible fluid dynamics scenarios. This analysis encompasses lift coefficient, drag coefficient, and pressure coefficient distribution comparison on the boundary layers

Recovery from Non-Decomposable Distance Oracles

A line of work has looked at the problem of recovering an input from distance queries. In this setting, there is an unknown sequence $s \in \{0,1\}^{\leq n}$, and one chooses a set of queries $y \in \{0,1\}^{\mathcal{O}(n)}$ and receives $d(s,y)$ for a distance function $d$. The goal is to make as few queries as possible to recover $s$. Although this problem is well-studied for decomposable distances, i.e., distances of the form $d(s,y) = \sum_{i=1}^n f(s_i, y_i)$ for some function $f$, which includes the important cases of Hamming distance, $\ell_p$-norms, and $M$-estimators, to the best of our knowledge this problem has not been studied for non-decomposable distances, for which there are important special cases such as edit distance, dynamic time warping (DTW), Frechet distance, earth mover's distance, and so on. We initiate the study and develop a general framework for such distances. Interestingly, for some distances such as DTW or Frechet, exact recovery of the sequence $s$ is provably impossible, and so we show by allowing the characters in $y$ to be drawn from a slightly larger alphabet this then becomes possible. In a number of cases we obtain optimal or near-optimal query complexity. We also study the role of adaptivity for a number of different distance functions. One motivation for understanding non-adaptivity is that the query sequence can be fixed and the distances of the input to the queries provide a non-linear embedding of the input, which can be used in downstream applications involving, e.g., neural networks for natural language processing.Comment: This work has been presented at conference The 14th Innovations in Theoretical Computer Science (ITCS 2023) and accepted for publishing in the journal IEEE Transactions on Information Theor

In-Orbit Demonstration of a MEMS-based Micropropulsion system for Cubesats

Cubesats is rapidly maturing beyond educational projects and low cost technology demonstrators. Today we want cubesats to provide useful data with a scientific or commercial value. One of the unique advantages of cubesats is that the size and cost enables large numbers of satellites to be built and launched at the same time. Both commercial examples such as Planet Labs or SPIRE as well as scientific mission like QB50 are utilizing fleets of cubesats to achieve valuable or unique data that was not thinkable a decade ago. However, there are still a few more steps to be taken in order to make fleets or constellations of cubesats even more viable or efficient in providing useful data. One field that is still immature is propulsion. Propulsion capability on every cubesat in a large constellation can provide faster deployment, better dispersion, longer life time and possibly also controlled de-orbit of the satellites at end of life. NanoSpace has for more than a decade worked on a various types of miniaturized propulsion using MEMS (Micro Electro Mechanical Systems) technology. In 2015 the first flight of a MEMS-based propulsion system for cubesats was peformed onboard a 3U cubesat STU-2A that was part of a three satellite constellation built and launched by Shanghai Engineering Centre for Microsatellite (SECM). The CubeSat MEMS Propulsion Module comprises of four individually controlled thrusters, each containing a proportional valve and sensors for closed-loop thrust control. The nominal thrust is 1 mN per thruster. Total mass is 330g including 50g propellant. The size of the module is less than ½ U even including a control and interface electronics board. Apart from the proportional valve, there are two additional valves to create independent barriers between each thruster and the propellant tank. It has the capability of 40Ns total impulse (specific impulse is rated as 81s). The average power consumption during operation is about 3W. Since the launch and initial tests of the system, the propulsion system has been activated tested multiple times. The propulsion system has demonstrated both the ability to raise the orbit of STU-2A and has also been used to de-spin the spacecraft after and accidental spin up. In this case the spacecraft was despun from about 65 to 13 deg/s using thrusters, after which magnetic tourqers could be used to stabilize and fine tune the spin rate. Further details and results from the tests already done and the upcoming rendezvous with STU-2C will be given in the presentation