The Intelligent Crude Oil Anti-theft System Based on IoT Under Different Scenarios

AbstractOil theft always results in huge economic loss, human casualties, and extremely environmental pollution especially when the leaks from crude oil pipeline are not detected and repaired timely. In this paper, we focus on how to detect and monitor abnormal noise and vibration beforehand or in real time by the Internet of Things (IoT). Firstly, the diversities of crude oil theft and the difficulties of oil anti-theft are analyzed in China, and the requirement analysis of the IoT application is stated. Secondly, the intelligent anti-theft system based on the IoT is planned and designed for crude oil transportation by tank trucks and by oil pipelines according to the current situation in China. Thirdly, the problems of anti-theft system implementation are discussed, and the suggestions and advice are put forward to ensure that the system can be implemented successfully. The intelligent anti-theft system application can not only stop oil theft timely, but also prevent oil mice from stealing crude oil beforehand

Reverse Microemulsion-Mediated Synthesis of SiO2-Coated ZnO Composite Nanoparticles: Multiple Cores with Tuneable Shell Thickness

Monodispersed SiO2-shell/ZnO-core composite nanospheres have been prepared in an oil-in-water microemulsion system. By using cyclohexane as the oil phase and Triton X-100 as the surfactant, composite nanospheres with high core loading levels and tunable shell thickness were obtained. Utilization of PVP capping agent on ZnO allowed the synthesis of composite nanospheres without forming any coreless SiO2 spheres or shell-less ZnO particles. The photoactivity of ZnO nanoparticles was greatly reduced by SiO2-coating, which enables their applications as durable, safe, and nonreactive UV blockers in plastics, coating, and other products. � 2010 American Chemical Society

Dissimilar thermal transport properties in κ\kappa-Ga2_2O3_3 and β\beta-Ga2_2O3_3 revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations

The lattice thermal conductivity (LTC) of Ga$_2$O$_3$ is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline $\beta$-Ga$_2$O$_3$ and $\kappa$-Ga$_2$O$_3$, and apply them to perform homogeneous nonequilibrium molecular dynamics simulations to predict their LTCs. The LTC of $\beta$-Ga$_2$O$_3$ was determined to be 10.3 $\pm$ 0.2 W/(m K), 19.9 $\pm$ 0.2 W/(m K), and 12.6 $\pm$ 0.2 W/(m K) along [100], [010], and [001], respectively, aligning with previous experimental measurements. For the first time, we predict the LTC of $\kappa$-Ga$_2$O$_3$ along [100], [010], and [001] to be 4.5 $\pm$ 0.0 W/(m K), 3.9 $\pm$ 0.0 W/(m K), and 4.0 $\pm$ 0.1 W/(m K), respectively, showing a nearly isotropic thermal transport property. The reduced LTC of $\kappa$-Ga$_2$O$_3$ versus $\beta$-Ga$_2$O$_3$ stems from its restricted low-frequency phonons up to 5 THz. Furthermore, we find that the $\beta$ phase exhibits a typical temperature dependence slightly stronger than $\sim T^{-1}$, whereas the $\kappa$ phase shows a weaker temperature dependence, ranging from $\sim T^{-0.5}$ to $\sim T^{-0.7}$.Comment: 8 pages, 7 figure

Unveiling the Implicit Toxicity in Large Language Models

The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show that LLMs can generate diverse implicit toxic outputs that are exceptionally difficult to detect via simply zero-shot prompting. Moreover, we propose a reinforcement learning (RL) based attacking method to further induce the implicit toxicity in LLMs. Specifically, we optimize the language model with a reward that prefers implicit toxic outputs to explicit toxic and non-toxic ones. Experiments on five widely-adopted toxicity classifiers demonstrate that the attack success rate can be significantly improved through RL fine-tuning. For instance, the RL-finetuned LLaMA-13B model achieves an attack success rate of 90.04% on BAD and 62.85% on Davinci003. Our findings suggest that LLMs pose a significant threat in generating undetectable implicit toxic outputs. We further show that fine-tuning toxicity classifiers on the annotated examples from our attacking method can effectively enhance their ability to detect LLM-generated implicit toxic language. The code is publicly available at https://github.com/thu-coai/Implicit-Toxicity.Comment: EMNLP 2023 Main Conferenc