Living in a Simulation? An Empirical Investigation of a Smart Driving-Simulation Testing System

The internet of things (IoT) generally refers to the embedding of computing and communication devices in various types of physical objects (e.g., automobiles) used in people’s daily lives. This paper draws on feedback intervention theory to investigate the impact of IoT-enabled immediate feedback interventions on individual task performance. Our research context is a smart test-simulation service based on internet-of-vehicles (IoV) technology that was implemented by a large driver-training service provider in China. This system captures and analyzes data streams from onboard sensors and cameras installed in vehicles in real time and immediately provides individual students with information about errors made during simulation tests. We postulate that the focal smart service functions as a feedback intervention (FI) that can improve task performance. We also hypothesize that student training schedules moderate this effect and propose an interaction effect on student performance based on feedback timing and the number of FI cues. We collected data about students’ demographics, their training session records, and information about their simulation test(s) and/or their official driving skills field tests and used a quasi-experimental method along with propensity score matching to empirically validate our research model. Difference-in-difference analysis and multiple regression results support the significant impact of the simulation test as an FI on student performance on the official driving skills field test. Our results also supported the interaction effect between feedback timing and the number of corrective FI cues on official test performance. This paper concludes with a discussion of the theoretical contributions and practical significance of our research

2-(4-Chloro­phen­yl)-5-(3,4-dimethoxy­pheneth­yl)-6,7-dihydro­pyrazolo[1,5-a]pyrazin-4(5H)-one

In the title compound, C22H22ClN3O3, the dihedral angles between the planes of the benzene rings and the pyrazole ring are 16.05 (10) and 84.84 (10)°. The conformation of the six-membered heterocyclic ring is close to a screw-boat. The crystal packing is stabilized by weak inter­molecular C—H⋯O inter­actions and is also consolidated by C—H⋯π inter­actions

Online Preconcentration and Determination of Trace Amounts of Zinc in Nature Waters

A simple, sensitive, reliable and flexible flow injection spectrophotometric method is proposed for on-line preconcentration and determination of trace amounts of zinc in water. At the presence of Tween-80 in pH 9.3 buffer solutions, the shade of color of Zn (II)-PAN complex is in a linear relation to the zinc amount at the point of the maximum absorption peak of 560 nm. The optimal experimental conditions, including reaction conditions and preconcentration conditions, had been obtained. The linear range of the proposed method was between 2.0 and 360 μg L−1 and the detection limit was 0.42 μg L−1. The relative standard deviation was 3.55% and 2.14% for 5.0 μg L−1 and 50 μg L−1 of zinc standard solution (n = 8). The method had been successfully applied to zinc determination in water samples and the analytical results were satisfactory

Investigating Graph Structure Information for Entity Alignment with Dangling Cases

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which play an important role in knowledge engineering. Recently, EA with dangling entities has been proposed as a more realistic setting, which assumes that not all entities have corresponding equivalent entities. In this paper, we focus on this setting. Some work has explored this problem by leveraging translation API, pre-trained word embeddings, and other off-the-shelf tools. However, these approaches over-rely on the side information (e.g., entity names), and fail to work when the side information is absent. On the contrary, they still insufficiently exploit the most fundamental graph structure information in KG. To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model. We propose a novel Gated Graph Attention Network to capture local and global graph structure similarity. (ii) Training. Two learning objectives: contrastive learning and optimal transport learning are designed to obtain distinguishable entity representations via the optimal transport plan. (iii) Inference. In the inference phase, a PageRank-based method is proposed to calculate higher-order structural similarity. Extensive experiments on two dangling benchmarks demonstrate that our WOGCL outperforms the current state-of-the-art methods with pure structural information in both traditional (relaxed) and dangling (consolidated) settings. The code will be public soon

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but their performance deviates greatly. We realize that the lack of actual IM settings most probably hinders the development and usage of these methods in real-world applications. As far as we know, IAD methods are not evaluated systematically. As a result, this makes it difficult for researchers to analyze them because they are designed for different or special cases. To solve this problem, we first propose a uniform IM setting to assess how well these algorithms perform, which includes several aspects, i.e., various levels of supervision (unsupervised vs. semi-supervised), few-shot learning, continual learning, noisy labels, memory usage, and inference speed. Moreover, we skillfully build a comprehensive image anomaly detection benchmark (IM-IAD) that includes 16 algorithms on 7 mainstream datasets with uniform settings. Our extensive experiments (17,017 in total) provide in-depth insights for IAD algorithm redesign or selection under the IM setting. Next, the proposed benchmark IM-IAD gives challenges as well as directions for the future. To foster reproducibility and accessibility, the source code of IM-IAD is uploaded on the website, https://github.com/M-3LAB/IM-IAD

Flexible Smart Acoustic Wave Patches for Effective Detection and Elimination of Surface Condensation

In this paper, flexible ZnO/ Al Lamb wave device was used to detect and eliminate surface condensations, commonly occurring in chemical process, agriculture, automobile and pipelines. The flexible and smart patch based on acoustic wave devices can identify dew formation through the change of frequency spectrum and then actuate to remove the condensation through acousto-thermal effect. To distinguish the differences between environmental interference (temperature and humidity) and the real condensation, a specified 3D space was built to quantify the degree of distortion of the transmission spectra. For droplets uniformly distributed on the surface of the device with an input power of ∼0.6 W, the evaporation time was significantly reduced to ∼ 1/13 of the natural evaporation time

Flexible Smart Acoustic Wave Patches for Effective Detection and Elimination of Surface Condensation

In this paper, flexible ZnO/ Al Lamb wave device was used to detect and eliminate surface condensations, commonly occurring in chemical process, agriculture, automobile and pipelines. The flexible and smart patch based on acoustic wave devices can identify dew formation through the change of frequency spectrum and then actuate to remove the condensation through acousto-thermal effect. To distinguish the differences between environmental interference (temperature and humidity) and the real condensation, a specified 3D space was built to quantify the degree of distortion of the transmission spectra. For droplets uniformly distributed on the surface of the device with an input power of ∼0.6 W, the evaporation time was significantly reduced to ∼ 1/13 of the natural evaporation time