The Margin Abatement Costs of CO2 in Chinese industrial sectors

AbstractUsing the directional distance function estimating by a non-parametric method, this paper measured shadow prices indicating the margin abatement costs (MACs) of CO2 emissions of China's industrial sectors. The results show that the MACs are within 0.2 thousand Yuan per ton to 120.3 thousand Yuan per ton, differentiating among sectors. In average, the MACs of heavy and chemical industries are lower than that of light and high-tech industries

PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios

Perception algorithms in autonomous driving systems confront great challenges in long-tail traffic scenarios, where the problems of Safety of the Intended Functionality (SOTIF) could be triggered by the algorithm performance insufficiencies and dynamic operational environment. However, such scenarios are not systematically included in current open-source datasets, and this paper fills the gap accordingly. Based on the analysis and enumeration of trigger conditions, a high-quality diverse dataset is released, including various long-tail traffic scenarios collected from multiple resources. Considering the development of probabilistic object detection (POD), this dataset marks trigger sources that may cause perception SOTIF problems in the scenarios as key objects. In addition, an evaluation protocol is suggested to verify the effectiveness of POD algorithms in identifying the key objects via uncertainty. The dataset never stops expanding, and the first batch of open-source data includes 1126 frames with an average of 2.27 key objects and 2.47 normal objects in each frame. To demonstrate how to use this dataset for SOTIF research, this paper further quantifies the perception SOTIF entropy to confirm whether a scenario is unknown and unsafe for a perception system. The experimental results show that the quantified entropy can effectively and efficiently reflect the failure of the perception algorithm.Comment: 7 pages, 5 figures, 4 tables, submitted to 2023 ICR

Numerical Investigation on the Impact of Exergy Analysis and Structural Improvement in Power Plant Boiler through Co-Simulation

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)In current power station boilers, fuel burns at a low temperature, which results in low exergy efficiency. This research combined the second law of t with the boiler structure to maximize the efficiency of a 350 MW power plant boiler. A three-dimensional simulation of the combustion process at the power plant boiler is performed. A one-dimensional simulation model of the boiler is then constructed to calculate the combustion exergy loss, heat transfer exergy loss, and boiler exergy efficiency. Under the principle of high-temperature air combustion technologies, this paper also proposes a new structure and improved operating parameters to improve the exergy efficiency of boilers by reducing the heat exchange area of the economizer and increasing the heat exchange area of the air preheater. Simulation results show that the exergy efficiency of the boiler increased from 47.29% to 48.35% through the modified model. The simulation outcomes can instruct future optimal boiler design and controls.Peer reviewe

Research on the Architecture Model of Volatile Data Forensics

AbstractThis paper proposed a new architecture model of volatile data forensic. The model applied to all the volatile data sources is a general model. It can rebuild the evidence data fragment to chains of evidence which contains the behavior characteristics, so as to assist investigators to do case analysis. With the accumulated experience, the model can help judicial officers to intelligently analyze the same type of computer crimes, and based on currently available information to predict the impending crimes

SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inherently in the performance limitation risk of the implemented algorithms themselves. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. The core of this system is the risk monitoring of the implemented artificial intelligence algorithms within the AV. As a demonstration of the Self-Surveillance and Self-Adaption System, the risk monitoring of the perception algorithm, i.e., YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, several challenging scenarios are demonstrated, and the Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the Self-Surveillance and Self-Adaption System enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT

Metamemory judgments have dissociable reactivity effects on item and interitem relational memory

Making metamemory judgments reactively changes item memory itself. Here we report the first investigation of reactive influences of making judgments of learning (JOLs) on interitem relational memory-specifically, temporal (serial) order memory. Experiment 1 found that making JOLs impaired order reconstruction. Experiment 2 observed minimal reactivity on free recall and negative reactivity on temporal clustering. Experiment 3 demonstrated a positive reactivity effect on recognition memory, and Experiment 4 detected dissociable effects of making JOLs on order reconstruction (negative) and forced-choice recognition (positive) by using the same participants and stimuli. Finally, a meta-analysis was conducted to explore reactivity effects on word list learning and to investigate whether test format moderates these effects. The results show a negative reactivity effect on interitem relational memory (order reconstruction), a modest positive effect on free recall, and a medium-to-large positive effect on recognition. Overall, these findings imply that even though making metacognitive judgments facilitates item-specific processing, it disrupts relational processing, supporting the item-order account of the reactivity effect on word list learning. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Simulated Annealing Algorithm Combined with Chaos for Task Allocation in Real-Time Distributed Systems

This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality

Prediction of fully metallic {\sigma}-bonded boron framework induced high superconductivity above 100 K in thermodynamically stable Sr2B5 at 40 GPa

Metal borides have been considered as potential high-temperature superconductors since the discovery of record-holding 39 K superconductivity in bulk MgB2. In this work, we identified a superconducting yet thermodynamically stable F43m Sr2B5 at 40 GPa with a unique covalent sp3-hybridized boron framework through extensive first-principles structure searches. Remarkably, solving the anisotropic Migdal-Eliashberg equations resulted in a high superconducting critical temperature (Tc) around 100 K, exceeding the boiling point (77 K) of liquid nitrogen. Our in-depth analysis revealed that the high-temperature superconductivity mainly originates from the strong coupling between the metalized {\sigma}-bonded electronic bands and E phonon modes of boron atoms. Moreover, anharmonic phonon simulations suggest that F43m Sr2B5 might be recovered to ambient pressure. Our current findings provide a prototype structure with a full {\sigma}-bonded boron framework for the design of high-Tc superconducting borides that may expand to a broader variety of lightweight compounds.Comment: 5 page