Queensland University of Technology

About: Queensland University of Technology is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Context (language use). The organization has 14188 authors who have published 55022 publications receiving 1496237 citations. The organization is also known as: QUT.

Process Mining Manifesto

Abstract: Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

‘Small Changes’ to Diet and Physical Activity Behaviors for Weight Management

Abstract: Obesity is associated with numerous short- and long-term health consequences. Low levels of physical activity and poor dietary habits are consistent with an increased risk of obesity in an obesogenic environment. Relatively little research has investigated associations between eating and activity behaviors by using a systems biology approach and by considering the dynamics of the energy balance concept. A significant body of research indicates that a small positive energy balance over time is sufficient to cause weight gain in many individuals. In contrast, small changes in nutrition and physical activity behaviors can prevent weight gain. In the context of weight management, it may be more feasible for most people to make small compared to large short-term changes in diet and activity. This paper presents a case for the use of small and incremental changes in diet and physical activity for improved weight management in the context of a toxic obesogenic environment.

Porous P-doped graphitic carbon nitride nanosheets for synergistically enhanced visible-light photocatalytic H2 production

Abstract: Novel porous P-doped graphitic carbon nitride (g-C3N4) nanosheets were for the first time fabricated by combining P doping and thermal exfoliation strategies. The as-prepared P-doped g-C3N4 nanosheets show a high visible-light photocatalytic H2-production activity of 1596 μmol h−1 g−1 and an apparent quantum efficiency of 3.56% at 420 nm, representing one of the most highly active metal-free g-C3N4 nanosheet photocatalysts. This outstanding photocatalytic performance originates from the P-doped conjugated system and novel macroporous nanosheet morphology. Particularly, the empty midgap states (−0.16 V vs. standard hydrogen electrode) created by P doping are for the first time found to greatly extend the light-responsive region up to 557 nm by density functional theory and experimental studies, whilst the novel macroporous structure promotes the mass-transfer process and enhances light harvesting. Our study not only demonstrates a facile, eco-friendly and scalable strategy to synthesize highly efficient porous g-C3N4 nanosheet photocatalysts, but also paves a new avenue for the rational design and synthesis of advanced photocatalysts by harnessing the strong synergistic effects through simultaneously tuning and optimizing the electronic, crystallographic, surface and textural structures.