Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

Progressive NO2 Sensors with Rapid Alarm and Persistent Memory-Type Responses for Wide-Range Sensing Using Antimony Triselenide Nanoflakes

Antimony triselenide (Sb₂Se₃) nanoflake-based nitrogen dioxide (NO₂) sensors exhibit a progressive bifunctional gas-sensing performance, with a rapid alarm for hazardous highly concentrated gases, and an advanced memory-type function for low-concentration (<1 ppm) monitoring repeated under potentially fatal exposure. Rectangular and cuboid shaped Sb₂Se₃ nanoflakes, comprising van der Waals planes with large surface areas and covalent bond planes with small areas, can rapidly detect a wide range of NO₂ gas concentrations from 0.1 to 100 ppm. These Sb₂Se₃ nanoflakes are found to be suitable for physisorption-based gas sensing owing to their anisotropic quasi-2D crystal structure with extremely enlarged van der Waals planes, where they are humidity-insensitive and consequently exhibit an extremely stable baseline current. The Sb₂Se₃ nanoflake sensor exhibits a room-temperature/low-voltage operation, which is noticeable owing to its low energy consumption and rapid response even under a NO₂ gas flow of only 1 ppm. As a result, the Sb₂Se₃ nanoflake sensor is suitable for the development of a rapid alarm system. Furthermore, the persistent gas-sensing conductivity of the sensor with a slow decaying current can enable the development of a progressive memory-type sensor that retains the previous signal under irregular gas injection at low concentrations.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

Experimental investigation of supercapacitor based regenerative energy storage for a fuel cell vehicle equipped with an alternator

Recently, researchers have devoted more attention to supercapacitors (SCs) to integrate with batteries in energy storage systems (ESSs) for vehicle applications. In this study, we attempted to characterize the use of SCs in the ESS for a PEM fuel cell vehicle equipped with an alternator to maximize the performance of regenerative braking. We applied lithium-ion batteries (LIBs) and SCs as energy storage devices to examine their effect on ESS. Then we used a hysteresis brake to apply controllable braking force on the flywheel to form hybrid braking (HB) and made efforts to study its behavior to suggest a braking control strategy. We also ran the whole system over the rotational speed to cover the range of driving speed. At last, we sized the SCs for the most commonly used fuel cell electric vehicle (FCEV) in Korea, i.e., Hyundai NEXO, based on the results obtained from the above study by alternator efficiencies.     

Performance enhancement of methanol reforming reactor through finned surfaces and diffused entry for on-board hydrogen generation

Hydrogen-rich combustion in engines helps in reducing pollutants significantly. But hydrogen usage on a moving vehicle is not getting large-scale user acceptance mainly due to its poor energy storage density resulting in shorter driving ranges. This storage issue led to the hunt for mediums that can efficiently produce on-board hydrogen. Methanol proves to be an efficient alcohol fuel for producing hydrogen through steam reforming reaction. The heat energy required for such endothermic reaction is obtained through exhaust engine waste energy and this process is collectively known as thermochemical recuperation. However, the conventional reactor used for this process faces a lot of problems in terms of efficiency and methanol conversion. In this study, an attempt has been made to improve the design of the reactor for on-board hydrogen generation using engine exhaust heat for addressing the challenges related to performance and hydrogen yield. For enhancing the heat transfer, a finned surface (straight & wavy) was introduced in the reactor which resulted in an increment in methanol conversion significantly. It was found that wavy fin improved the methanol conversion up to 96.8% at an exhaust inlet temperature of 673 K. Also, a diffusing inlet section was introduced to increase the residence time of reactant gases while passing through the catalyst zone. Under given inlet conditions, the methanol conversion for 6° diffuse inlet reactor goes up to 87.9% as compared to 75.4% for the conventional reactor.     

Statistical optimization of operating parameters of microbial electrolysis cell treating dairy industry wastewater using quadratic model to enhance energy generation

The performance of Microbial electrolysis cell (MEC) is affected by several operating conditions. Therefore, in the present study, an optimization study was done to determine the working efficiency of MEC in terms of COD (chemical oxygen demand) removal, hydrogen and current generation. Optimization was carried out using a quadratic mathematical model of response surface methodology (RSM). Thirteen sets of experimental runs were performed to optimize the applied voltage and hydraulic retention time (HRT) of single chambered batch fed MEC operated with dairy industry wastewater. The operating conditions (i.e) an applied voltage of 0.8 V and HRT of 2 days that showed a maximum COD removal response was chosen for further studies. The MEC operated at optimized condition (HRT- 2 days and applied voltage- 0.8 V) showed a COD removal efficiency of 95 ± 2%, hydrogen generation of 32 ± 5 mL/L/d, Power density of 152 mW/cm² and current generation of 19 mA. The results of the study implied that RSM, with its high degree of accuracy can be a reliable tool for optimizing the process of wastewater treatment. Also, dairy industry wastewater can be considered to be a potential source to generate hydrogen and energy through MEC at short HRT.     

Total Site Hydrogen Integration with fresh hydrogen of multiple quality and waste hydrogen recovery in refineries

This paper proposes a novel method combining Pinch Methodology and waste hydrogen recovery, aiming to minimise fresh hydrogen consumption and waste hydrogen discharge. The method of multiple-level resource Pinch Analysis is extended to the level of Total Site Hydrogen Integration by considering fresh hydrogen sources with various quality. Waste hydrogen after Total Site Integration is further regenerated. The technical feasibility and economy of the various purification approaches are considered, demonstrated with a case study of a refinery hydrogen network in a petrochemical industrial park. The results showed that fresh hydrogen usage and waste hydrogen discharge could be reduced by 21.3% and 67.6%. The hydrogen recovery ratio is 95.2%. It has significant economic benefits and a short payback period for Total Site Hydrogen Integration with waste hydrogen purification. The proposed method facilitates the reuse of waste hydrogen before the purification process that incurs an additional environmental footprint. In line with the Circular Economy principles, hydrogen resource is retained in the system as long as possible before discharge.     

海上热采防砂封隔器研制与室内试验

海上稠油热采开发过程中,通常采用蒸汽吞吐的方式进行原油开采。为满足稠油热采长效密封要求,研制了HPHT-215型热采防砂封隔器。封隔器胶筒采用特制的氟硅基复合材料+玻璃纤维网结构,并开展了材料性能评价测试。通过有限元分析和室内胶筒组合测试,对胶筒材料和组合结构进行了评价优化。室内全尺寸整机测试表明:封隔器能够满足耐温350 ℃、耐压21 MPa密封性能要求,并开展了高低温多轮次密封性能评价测试。该型热采防砂封隔器的研制成功,为海上稠油蒸汽吞吐多轮次开发提供了保障,具有广泛的应用前景。     

FPGA互连测试中的反馈桥接故障覆盖问题

现场可编程门阵列(FPGA)内部资源众多,其中互连资源出现故障的概率远远高于片内其他资源,而在以往许多互连测试研究中,所生成的测试配置存在无法覆盖反馈桥接故障的难题,所以较难有测试配置实现故障列表的100%覆盖。因此通过约束桥接故障只发生在单个查找表(LUT)内的信号线上,并结合单项函数,对反馈桥接故障模型进行优化改进,从根本上解决难题；然后对优化后的反馈桥接故障设置相应的约束条件,再使用布尔可满足性理论(SAT)生成满足约束条件的测试配置。采用优化后的故障模型对ISCAS"89基准电路进行了测试配置生成实验,结果表明生成的测试向量解决了反馈桥接故障的覆盖难题,并且在实现故障列表的100%覆盖下,优化后的故障模型所需要的测试配置数最少。     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.