Hazardous Chemical Source Localisation in Indoor Environments Using Plume-tracing Methods

Bio-inspired chemical plume-tracing methods have been applied to mobile robots to detect chemical emissions in the form of plumes and localise the plume sources in various indoor environments. Nevertheless, it has been found from the literature that most of the research has focused on plume tracing in free-stream plumes, such as indoor plumes where the chemical sources are located away from walls. Moreover, most of the experimental and numerical studies regarding the assessment of indoor plume-tracing algorithms have been undertaken in laboratory-scale environments. Since fluid fields and chemical concentration distributions of plumes near walls can be different from those of free-stream plumes, understanding of the performance of existing plume-tracing algorithms in near-wall regions is needed. In addition, the performance of different plume-tracing algorithms in detecting and tracing wall plumes in large-scale indoor environments is still unclear. In this research, a simulation framework combining ANSYS/FLUENT, which is used for simulating fluid fields and chemical concentration distributions of the environment, and MATLAB, with which plume-tracing algorithms are coded, is applied. In general, a plume-tracing algorithm can be divided into three stages: plume sensing, plume tracking and source localisation for analysis and discussion. In the first part of this research, an assessment of the performance of sixteen widely-used plume-tracing algorithms equipped with a concentration-distance obstacle avoidance method, was undertaken in two different scenarios. In one scenario, a single chemical source is located away from the walls in a wind-tunnel-like channel and in the other scenario, the chemical source is located near a wall. It is found that normal casting, surge anemotaxis and constant stepsize together performed the best, when compared with all the other algorithms. Also, the performance of the concentration-distance obstacle avoidance method is unsatisfactory. By applying an along-wall obstacle avoidance method, an algorithm called vallumtaxis, has been proposed and proved to contribute to higher efficiencies for plume tracing especially when searching in wall plumes. The results and discussion of the first part are presented in Chapter 4 of this thesis. In the second part, ten plume-tracing algorithms were tested and compared in four scenarios in a large-scale indoor environment: an underground warehouse. In these four scenarios, the sources are all on walls while their locations are different. The preliminary testing results of five algorithms show that for most failure cases, the robot failed at source localisation stage. Consequently, with different searching strategies at source localisation stage, this research investigated five further algorithms. The results demonstrated that the algorithm with a specially-designed pseudo casting source localisation method is the best approach to localising hazardous plume sources in the underground warehouse given in this research or other similar environments, among all the tested algorithms. The second part of the study is reported in Chapter 5 of this thesis.Thesis (MPhil) -- University of Adelaide, School of Mechanical Engineering, 202

Microbial immobilization technology for remediation of petroleum hydrocarbon contaminated soil

Petroleum hydrocarbon is a kind of global pollutant that is difficult to degrade. The remediation of petroleum hydrocarbon contaminated soil has always been a challenging subject for environmentalists. Microbial immobilization technology (MIT) has the advantages of high efficiency, stability, low cost and environmental friendliness. It shows great application potential in soil remediation. In recent years, the study of microbial immobilization technology for remediation of petroleum hydrocarbon contaminated soil is in the ascendant. Microbial immobilization technology has become an effective way to improve microbial degradation of petroleum hydrocarbons in soil. This paper discusses the research progress of microbial immobilization technology, summarizes the different characteristics of carrier materials, microorganisms, immobilization methods and influencing factors in the immobilization process and their effects on the immobilization effect, and expounds the research status and development trend of immobilization technology for the remediation of petroleum hydrocarbon contaminated soil

ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives

Movement Primitives (MPs) are a well-known concept to represent and generate modular trajectories. MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that capture higher-order statistics of the motion, e. g., Probabilistic Movement Primitives (ProMPs). To date, however, there is no method that unifies both, i. e. that can generate smooth trajectories from an arbitrary initial state while capturing higher-order statistics. In this paper, we introduce a unified perspective of both approaches by solving the ODE underlying the DMPs. We convert expensive online numerical integration of DMPs into basis functions that can be computed offline. These basis functions can be used to represent trajectories or trajectory distributions similar to ProMPs while maintaining all the properties of dynamical systems. Since we inherit the properties of both methodologies, we call our proposed model Probabilistic Dynamic Movement Primitives (ProDMPs). Additionally, we embed ProDMPs in deep neural network architecture and propose a new cost function for efficient end-to-end learning of higher-order trajectory statistics. To this end, we leverage Bayesian Aggregation for non-linear iterative conditioning on sensory inputs. Our proposed model achieves smooth trajectory generation, goal-attractor convergence, correlation analysis, non-linear conditioning, and online re-planing in one framework.Comment: 12 pages, 13 figure

One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training

Deep neural networks (DNNs) are widely deployed on real-world devices. Concerns regarding their security have gained great attention from researchers. Recently, a new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques such as row hammer to attack quantized models in the deployment stage. With only a few bit flips, the target model can be rendered useless as a random guesser or even be implanted with malicious functionalities. In this work, we seek to further reduce the number of bit flips. We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release. This high-risk model, obtained coupled with a corresponding malicious model, behaves normally and can escape various detection methods. The results on benchmark datasets show that an adversary can easily convert this high-risk but normal model to a malicious one on victim's side by \textbf{flipping only one critical bit} on average in the deployment stage. Moreover, our attack still poses a significant threat even when defenses are employed. The codes for reproducing main experiments are available at \url{https://github.com/jianshuod/TBA}.Comment: This work is accepted by the ICCV 2023. 14 page

Therapeutic effects of neuregulin-1 in diabetic cardiomyopathy rats

BACKGROUND: Diabetic cardiomyopathy (DCM) is a disorder of the heart muscle in people with diabetes, which is characterized by both systolic and diastolic dysfunction. The effective treatment strategy for DCM has not been developed. METHODS: Rats were divided into 3 groups with different treatment. The control group was only injected with citrate buffer (n = 8). The diabetes group and diabetes treated group were injected with streptozotocin to induce diabetes. After success of diabetes induction, the rats with diabetes were treated with (diabetes treated group, n = 8) or without (diabetes group, n = 8) recombinant human Neuregulin-1 (rhNRG-1). All studies were carried out 16 weeks after induction of diabetes. Cardiac catheterization was performed to evaluate the cardiac function. Apoptotic cells were determined by TUNEL staining. Left ventricular (LV) sections were stained with Masson to investigate myocardial collagen contents. Related gene expressions were analyzed by quantitative real-time PCR (qRT-PCR). RESULTS: Diabetes impaired cardiac function manifested by reduced LV systolic pressure (LVSP), maximum rate of LV pressure rise and fall (+dp/dt max and -dp/dt max) and increased LV end-diastolic pressure (LVEDP). The rhNRG-1 treatment could significantly alleviate these symptoms and improve heart function. More TUNEL staining positive cells were observed in the diabetic group than that in the control group, and the rhNRG-1 treatment decreased apoptotic cells number. Furthermore, qRT-PCR assay demonstrated that rhNRG-1 treatment could decrease the expression of bax and caspase-3 and increase that of bcl-2. Collagen volume fraction was higher in the diabetic group than in the control group. Fibrotic and fibrotic related mRNA (type I and type III collagen) levels in the myocardium were significantly reduced by administration of rhNRG-1. CONCLUSION: rhNRG-1 could significantly improve the heart function and reverse the cardiac remodeling of DCM rats with chronic heart failure. These results support the clinical possibility of applying rhNRG-1 as an optional therapeutic strategy for DCM treatment in the future

There Is Always a Way Out! Destruction-Resistant Key Management: Formal Definition and Practical Instantiation

A central advantage of deploying cryptosystems is that the security of large high-sensitive data sets can be reduced to the security of a very small key. The most popular way to manage keys is to use a $(t,n)-$threshold secret sharing scheme: a user splits her/his key into $n$ shares, distributes them among $n$ key servers, and can recover the key with the aid of any $t$ of them. However, it is vulnerable to device destruction: if all key servers and user\u27s devices break down, the key will be permanently lost. We propose a $\mathrm{\underline{D}}$estruction-$\mathrm{\underline{R}}$esistant $\mathrm{\underline{K}}$ey $\mathrm{\underline{M}}$anagement scheme, dubbed DRKM, which ensures the key availability even if destruction occurs. In DRKM, a user utilizes her/his $n^{*}$ personal identification factors (PIFs) to derive a cryptographic key but can retrieve the key using any $t^{*}$ of the $n^{*}$ PIFs. As most PIFs can be retrieved by the user $\textit{per se}$ without requiring $\textit{stateful}$ devices, destruction resistance is achieved. With the integration of a $(t,n)-$threshold secret sharing scheme, DRKM also provides $\textit{portable}$ key access for the user (with the aid of any $t$ of $n$ key servers) before destruction occurs. DRKM can be utilized to construct a destruction-resistant cryptosystem (DRC) in tandem with any backup system. We formally prove the security of DRKM, implement a DRKM prototype, and conduct a comprehensive performance evaluation to demonstrate its high efficiency. We further utilize Cramer\u27s Rule to reduce the required buffer to retrieve a key from 25 MB to 40 KB (for 256-bit security)

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research

Comparative Extracellular Proteomics of Aeromonas hydrophila Reveals Iron-Regulated Secreted Proteins as Potential Vaccine Candidates

In our previous study, several iron-related outer membrane proteins in Aeromonas hydrophila, a serious pathogen of farmed fish, conferred high immunoprotectivity to fish, and were proposed as potential vaccine candidates. However, the protective efficacy of these extracellular proteins against A. hydrophila remains largely unknown. Here, we identified secreted proteins that were differentially expressed in A. hydrophila LP-2 in response to iron starvation using an iTRAQ-based quantitative proteomics method. We identified 341 proteins, of which 9 were upregulated in response to iron starvation and 24 were downregulated. Many of the differently expressed proteins were associated with protease activity. We confirmed our proteomics results with Western blotting and qPCR. We constructed three mutants by knocking out three genes encoding differentially expressed proteins (Δorf01830, Δorf01609, and Δorf03641). The physiological characteristics of these mutants were investigated. In all these mutant strains, protease activity decreased, and Δorf01609, and Δorf01830 were less virulent in zebrafish. This indicated that the proteins encoded by these genes may play important roles in bacterial infection. We next evaluated the immune response provoked by the six iron-related recombinant proteins (ORF01609, ORF01830, ORF01839, ORF02943, ORF03355, and ORF03641) in zebrafish as well as the immunization efficacy of these proteins. Immunization with these proteins significantly increased the zebrafish immune response. In addition, the relative percent survival (RPS) of the immunized zebrafish was 50–80% when challenged with three virulent A. hydrophila strains, respectively. Thus, these extracellular secreted proteins might be effective vaccine candidates against A. hydrophila infection in fish

Futuristic 6G Pervasive On-Demand Services:Integrating Space Edge Computing With Terrestrial Networks

Futuristic 6G technologies will integrate emerging low-Earth orbit (LEO) megaconstellations into terrestrial networks, promising to provide ubiquitous, low-latency and high-throughput network services on-demand. However, several unique characteristics of satellites (e.g., high dynamics and error-prone operational environments) make it very challenging to unleash the potential of megacons-tellations and accomplish these aforementioned promises

Exosomal miRNAs in autoimmune skin diseases

Exosomes, bilaterally phospholipid-coated small vesicles, are produced and released by nearly all cells, which comprise diverse biological macromolecules, including proteins, DNA, RNA, and others, that participate in the regulation of their biological functions. An increasing number of studies have revealed that the contents of exosomes, particularly microRNA(miRNA), play a significant role in the pathogenesis of various diseases, including autoimmune skin diseases. MiRNA is a class of single-stranded non-coding RNA molecules that possess approximately 22 nucleotides in length with the capability of binding to the untranslated as well as coding regions of target mRNA to regulate gene expression precisely at the post-transcriptional level. Various exosomal miRNAs have been found to be significantly expressed in some autoimmune skin diseases and involved in the pathogenesis of conditions via regulating the secretion of crucial pathogenic cytokines and the direction of immune cell differentiation. Thus, exosomal miRNAs might be promising biomarkers for monitoring disease progression, relapse and reflection to treatment based on their functions and changes. This review summarized the current studies on exosomal miRNAs in several common autoimmune skin diseases, aiming to dissect the underlying mechanism from a new perspective, seek novel biomarkers for disease monitoring and lay the foundation for developing innovative target therapy in the future