Spectroscopic infrared extinction mapping as a probe of grain growth in IRDCs

We present spectroscopic tests of MIR to FIR extinction laws in IRDC G028.36+00.07, a potential site of massive star and star cluster formation. Lim & Tan (2014) developed methods of FIR extinction mapping of this source using ${\it Spitzer}$-MIPS ${\rm 24\mu m}$ and ${\it Herschel}$-PACS ${\rm 70\mu m}$ images, and by comparing to MIR ${\it Spitzer}$-IRAC $3$--${\rm 8\mu m}$ extinction maps, found tentative evidence for grain growth in the highest mass surface density regions. Here we present results of spectroscopic infrared extinction (SIREX) mapping using ${\it Spitzer}$-IRS (14 to ${\rm 38\mu m}$) data of the same IRDC. These methods allow us to first measure the SED of the diffuse Galactic ISM that is in the foreground of the IRDC. We then carry out our primary investigation of measuring the MIR to FIR opacity law and searching for potential variations as a function of mass surface density within the IRDC. We find relatively flat, featureless MIR-FIR opacity laws that lack the $\sim{\rm 12\mu m}$ and $\sim{\rm 35\mu m}$ features associated with the thick water ice mantle models of Ossenkopf & Henning (1994). Their thin ice mantle models and the coagulating aggregate dust models of Ormel et al. (2011) are a generally better match to the observed opacity laws. We also find evidence for generally flatter MIR to FIR extinction laws as mass surface density increases, strengthening the evidence for grain and ice mantle growth in higher density regions.Comment: 12 pages, 12 Figures, 1 Table, Accepted to be published to Ap

To buy or not to buy: Factors influencing life insurance purchase intention

The purpose of this thesis paper is to investigate the influence of five factors namely Word-of-mouth, Trust, Reputation, Loyalty, and Customer Satisfaction on life insurance Purchase Intention. Existing customers in Great Eastern Life Assurance (M) Berhad, Alor Setar were chosen as samples of this study. A survey using 400 questionnaires was distributed to the respondents and 327 of them were returned and usable. Correlation and regression analysis were adopted to analyse all data. The findings indicated that all the independent variables (Word-of-mouth, Trust, Reputation, Loyalty, and Customer Satisfaction) had a certain degree of relationship with Purchase Intention. The results showed that customer satisfaction had the strongest significant positive relationship with purchase intention with correlation value of 0.796, followed by reputation with correlation value of 0.774. Only two variables which are reputation and customer satisfaction influenced purchase intention. The findings suggest that reputation of the company can be an important factor that influences customers’ purchase intentions. In other words, good reputation of an insurance company brings good impact in terms of image to customers. Customers will feel confident towards the insurance company and increase their intention to purchas

Chameleon: a Blind Double Trapdoor Hash Function for Securing AMI Data Aggregation

Data aggregation is an integral part of Advanced Metering Infrastructure (AMI) deployment that is implemented by the concentrator. Data aggregation reduces the number of transmissions, thereby reducing communication costs and increasing the bandwidth utilization of AMI. However, the concentrator poses a great risk of being tampered with, leading to erroneous bills and possible consumer disputes. In this paper, we propose an end-to-end integrity protocol using elliptic curve based chameleon hashing to provide data integrity and authenticity. The concentrator generates and sends a chameleon hash value of the aggregated readings to the Meter Data Management System (MDMS) for verification, while the smart meter with the trapdoor key computes and sends a commitment value to the MDMS so that the resulting chameleon hash value calculated by the MDMS is equivalent to the previous hash value sent by the concentrator. By comparing the two hash values, the MDMS can validate the integrity and authenticity of the data sent by the concentrator. Compared with the discrete logarithm implementation, the ECC implementation reduces the computational cost of MDMS, concentrator and smart meter by approximately 36.8%, 80%, and 99% respectively. We also demonstrate the security soundness of our protocol through informal security analysis

Understanding the high activity of mildly reduced graphene oxide electrocatalysts in oxygen reduction to hydrogen peroxide

The direct electrochemical synthesis of hydrogen peroxide (H2O2) would provide an attractive alternative to the traditional anthraquinone oxidation process for continuous on-site applications. Its industrial viability depends greatly on developing cost-effective catalysts with high activity and selectivity. Recent experiments have demonstrated that mildly reduced graphene oxide (mrGO) electrocatalysts exhibit highly selective and stable H2O2 formation activity [e.g., H. W. Kim, M. B. Ross, N. Kornienko, L. Zhang, J. Guo, P. Yang and B. D. McCloskey, Nat. Catal., 2018, 1, 282-290]. However, the identification of active site structures for this catalytic process on mrGO is doubtful. Herein, by means of first-principles calculations, we examine the H2O2 formation activities of the active site structures proposed in experiments and find that their activities are actually very low. Then, we systematically investigate the H2O2 formation activities of different oxygen functional group structures on mrGO based on experimental observations, and discover two types of oxygen functional group structures (2EP and 1ET + 1EP) that have comparable or even lower overpotentials (<0.10 V) for H2O2 formation compared with the state-of-the-art PtHg4 electrocatalyst. Our theoretical results reveal that the graphene edge and the synergetic effects between different oxygen functional groups are essential for the superior performance of mrGO for H2O2 production. This work not only provides a feasible explanation of the cause of high H2O2 formation activity of mrGO but also offers a guide for the design, synthesis, and mechanistic investigation of advanced carbon-based electrocatalysts for effective H2O2 production.This research was undertaken with the assistance of resources provided by the National Computational Infrastructure (NCI) facility at the Australian National University; allocated through both the National Computational Merit Allocation Scheme supported by the Australian Government and the Australian Research Council grant LE160100051 (Maintaining and enhancing merit-based access to the NCI National Facility, 2016–2018). The study was financed by an ARC Discovery Grant (DP170104853)

Molecular biology of amitraz resistance in cattle ticks of the genus Rhipicephalus

Amitraz is an important product for the control of cattle ticks around the world. In comparison with other products for the control of ticks, it is quite affordable and it has a rapid knock-down effect. It binds with and activates adrenergic neuro-receptors of animals and it inhibits the action of monoamine oxidases (MAO). Resistance to amitraz has been documented in Rhipicephalus microplus, R. decoloratus and R. appendiculatus. Four mechanisms of resistance have been proposed, each of which is supported by evidence but none of which has been definitively confirmed as the cause of resistance in the field. The proposed mechanisms include genetic target site insensitivity in two G protein-coupled receptors, the beta-adrenergic octopamine receptor (BAOR) and the octopamine/tyramine receptor (OCT/Tyr), increased expression or activity of monoamine oxidases and increased expression or activity of the ATP binding cassette transporter

Information Security Governance: A Case Study of the Strategic Context of Information Security

Security governance influences the quality of strategic decision-making towards ensuring that investments in security are not wasted. Security governance involves a range of activities including adjusting organisational structures, designating roles and responsibilities, allocating resources, managing risks, measuring results, and gauging the adequacy of security audits and reviews. We draw on a case study to identify three security issues in an organisation around strategic context. These are (1) limited diversity in decision-making; (2) lack of guidance in corporate-level mission statements to security decision-makers; (3) a bottom-up approach to security strategic context development. We further argue that instead of an approach that is based on risk and controls, organisations should address objectives and strategies through developing depth in their security strategic context

Does caffeine consumption affect work performance across different job types?

We propose executing a panel series case study for a large organization in Singapore over a year to examine how caffeine impacts work performance across different job types. Our research question is how does caffeine affect work performance under different conditions due to work type differences. Our dependent variable would be work performance as measured by employees’ Key Performance Indicators (KPIs). We propose to gather data for two of our important independent variables: caffeine use through a recorded pantry system and average sleep hours from a survey. We will analyze the data by using two-factor ANOVA with replication to find the interaction between Caffeine and Work types, as well as regression analysis to determine the impact of key-independent variables. The findings of our study has the potential to influence safety regulations surrounding jobs relating to caffeine, similar to the regulations for alcohol

Electrocatalytic Reduction of Carbon Dioxide to Methane on Single Transition Metal Atoms Supported on a Defective Boron Nitride Monolayer: First Principle Study

The electrochemical conversion of carbon dioxide (CO2) and water into useful multi‐electron transfer products, such as methanol (CH3OH) and methane (CH4), is a major challenge in facilitating a closed carbon cycle. Here, a systematic first principle study of the potential of single transition metal atoms (Sc to Zn, Mo, Rh, Ru, Pd, Ag, Pt, and Au) supported on experimentally available defective boron nitride monolayers with a boron monovacancy (TM/defective BN) to achieve highly efficient electrocatalytic CO2 reduction (ECR) to CH4 is carried out. Our computations reveal that Fe/defective BN, Co/defective BN, and Pt/defective BN nanosheets possess outstanding ECR activities with quite low (less negative) onset potentials of −0.52, −0.68, and −0.60 V, respectively. Given that Fe and Co are nonprecious metals, Fe/defective BN and Co/defective BN may provide cost‐effective electrocatalysts. The high ECR activities of these TM/defective BN catalyst systems stem from the moderate electrocatalysts’ affinities for C and O, which modulate the free energies of ECR intermediates in the reaction pathways. Moreover, it is found that Fe/defective BN and Pt/defective BN show high selectivity of ECR to CH4. This finding highlights a strategy to design highly active and selective single‐atom electrocatalysts for ECR to CH4.S.S. and H.A. acknowledge the financial support by the Australian Research Council under Discovery Project (DP170104853). This research was undertaken with the assistance of resources provided by the National Computing Infrastructure facility at the Australian National University, allocated through both the National Computational Merit Allocation Scheme supported by the Australian Government and the Australian Research Council grant LE120100181 (Enhanced merit-based access and support at the new NCI petascale supercomputing facility, 2012–2015)

Efficient 3D Implicit Head Avatar with Mesh-anchored Hash Table Blendshapes

3D head avatars built with neural implicit volumetric representations have achieved unprecedented levels of photorealism. However, the computational cost of these methods remains a significant barrier to their widespread adoption, particularly in real-time applications such as virtual reality and teleconferencing. While attempts have been made to develop fast neural rendering approaches for static scenes, these methods cannot be simply employed to support realistic facial expressions, such as in the case of a dynamic facial performance. To address these challenges, we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality. Our key idea lies in the introduction of local hash table blendshapes, which are learned and attached to the vertices of an underlying face parametric model. These per-vertex hash-tables are linearly merged with weights predicted via a CNN, resulting in expression dependent embeddings. Our novel representation enables efficient density and color predictions using a lightweight MLP, which is further accelerated by a hierarchical nearest neighbor search method. Extensive experiments show that our approach runs in real-time while achieving comparable rendering quality to state-of-the-arts and decent results on challenging expressions.Comment: In CVPR2024. Project page: https://augmentedperception.github.io/monoavatar-plu