Visual melodies : design and evaluation of an interactive art installation for clinical environments

University of Technology, Sydney. Faculty of Arts and Social Sciences.Over the past decades there has been a growing recognition of the value of art activities, such as visual arts, music, drawing, dance, poetry and writing, as therapeutic approaches to enhance healthcare settings. Numerous studies have identified the value of art and design in decreasing depression and anxiety and creating a holistic healing environment for hospital visitors and patients, as well as offering a positive working environment for staff. Therefore, I propose that interactive multimedia art offers an important new therapeutic avenue as a service for engaging visitors, patients and staff in hospitals. Visual Melodies is an interactive art installation that engenders feelings of calm and relaxation in users. In this exegesis, I describe the theoretical background, development and evaluation of Visual Melodies. This creative trajectory draws on practice-based research, with the aims to create an interactive art installation, to evaluate its therapeutic potential, and to identify the semiotic dimensions of multimedia art that are most generally effective for producing therapeutic effects. Based on the literature of the different techniques used in art therapy, colour therapy and music therapy, I propose a bridge between these three therapies through a platform of an interactive multimedia installation – harnessing images, colours and sounds. Eight design principles that form the foundation of the practice were developed along with the discussion of the design elements that have been shown to be effective for enhancing relaxation. In line with the design principles, the design practice was then developed as a series of original landscape artworks and interactive animations accompanied by music specifically composed for the researcher. Audience feedback to the installation in a hospital waiting room was studied as a way of assessing its therapeutic potential. The evaluation feedback has been very positive and welcoming from visitors, patients and staff of all ages. Overall, Visual Melodies provides a relaxing and playful experience for the participants. The feelings most often reported were that of being relaxed, followed by calm, diverted, evoking memories and happy. This project demonstrates that it is beneficial to create a relaxing and supportive therapeutic interactive multimedia artwork for promoting holistic healing environments. The practice-based research and findings in this exegesis extend our understanding of how we can fuse artwork and technology, to transform our healthcare settings from sterile treatment spaces, into healing places where ‘care’ is built into the environment itself

Variability of mitochondrial energy balance across brain regions

Brain is not homogenous and neurons from various brain regions are known to have different vulnerabilities to mitochondrial mutations and mitochondrial toxins. However, it is not clear if this vulnerability is connected to different energy metabolism in specific brain regions. Here, using live‐cell imaging, we compared mitochondrial membrane potential and nicotinamide adenine dinucleotide (NADH) redox balance in acute rat brain slices in different brain regions and further detailed the mitochondrial metabolism in primary neurons and astrocytes from rat cortex, midbrain and cerebellum. We have found that mitochondrial membrane potential is higher in brain slices from the hippocampus and brain stem. In primary co‐cultures, mitochondrial membrane potential in astrocytes was lower than in neurons, whereas in midbrain cells it was higher than in cortex and cerebellum. The rate of NADH production and mitochondrial NADH pool were highest in acute slices from midbrain and midbrain primary neurons and astrocytes. Although the level of adenosine tri phosphate (ATP) was similar among primary neurons and astrocytes from cortex, midbrain and cerebellum, the rate of ATP consumption was highest in midbrain cells that lead to faster neuronal and astrocytic collapse in response to inhibitors of ATP production. Thus, midbrain neurons and astrocytes have a higher metabolic rate and ATP consumption that makes them more vulnerable to energy deprivation

DeltaPhish: Detecting Phishing Webpages in Compromised Websites

The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.Comment: Preprint version of the work accepted at ESORICS 201

Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordResource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning longterm memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using realworld workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads

Mental Health during the COVID-19 Crisis in Africa: A Systematic Review and Meta-Analysis.

We aim to provide a systematic review and meta-analysis of the prevalence rates of mental health symptoms among major African populations during the COVID-19 pandemic. We include articles from PubMed, Embase, Web of Science, PsycINFO, and medRxiv between 1 February 2020 and 6 February 2021, and pooled data using random-effects meta-analyses. We identify 28 studies and 32 independent samples from 12 African countries with a total of 15,071 participants. The pooled prevalence of anxiety was 37% in 27 studies, of depression was 45% in 24 studies, and of insomnia was 28% in 9 studies. The pooled prevalence rates of anxiety, depression, and insomnia in North Africa (44%, 55%, and 31%, respectively) are higher than those in Sub-Saharan Africa (31%, 30%, and 24%, respectively). We find (a) a scarcity of studies in several African countries with a high number of COVID-19 cases; (b) high heterogeneity among the studies; (c) the extent and pattern of prevalence of mental health symptoms in Africa is high and differs from elsewhere-more African adults suffer from depression rather than anxiety and insomnia during COVID 19 compared to adult populations in other countries/regions. Hence, our findings carry crucial implications and impact future research to enable evidence-based medicine in Africa

Mental disorder symptoms during the COVID-19 pandemic in Latin America - a systematic review and meta-analysis.

AIMS: There is a lack of evidence related to the prevalence of mental health symptoms as well as their heterogeneities during the coronavirus disease 2019 (COVID-19) pandemic in Latin America, a large area spanning the equator. The current study aims to provide meta-analytical evidence on mental health symptoms during COVID-19 among frontline healthcare workers, general healthcare workers, the general population and university students in Latin America. METHODS: Bibliographical databases, such as PubMed, Embase, Web of Science, PsycINFO and medRxiv, were systematically searched to identify pertinent studies up to August 13, 2021. Two coders performed the screening using predefined eligibility criteria. Studies were assigned quality scores using the Mixed Methods Appraisal Tool. The double data extraction method was used to minimise data entry errors. RESULTS: A total of 62 studies with 196 950 participants in Latin America were identified. The pooled prevalence of anxiety, depression, distress and insomnia was 35%, 35%, 32% and 35%, respectively. There was a higher prevalence of mental health symptoms in South America compared to Central America (36% v. 28%, p < 0.001), in countries speaking Portuguese (40%) v. Spanish (30%). The pooled prevalence of mental health symptoms in the general population, general healthcare workers, frontline healthcare workers and students in Latin America was 37%, 34%, 33% and 45%, respectively. CONCLUSIONS: The high yet heterogenous level of prevalence of mental health symptoms emphasises the need for appropriate identification of psychological interventions in Latin America

A Systematic Review and Meta-Analysis of Symptoms of Anxiety, Depression, and Insomnia in Spain in the COVID-19 Crisis.

BACKGROUND: General population, frontline healthcare workers (HCWs), and adult students in Spain are at risk of anxiety, depression, and insomnia symptoms during the COVID-19 crisis. A meta-analysis of the individual studies on these symptoms would provide systematic evidence to aid policymakers and researchers in focusing on prevalence, risk, and best interventions. OBJECTIVE: This paper aims to be the first meta-analysis and systematic review to calculate the prevalence of anxiety, depression, and insomnia symptoms in Spain's adult population (general population, frontline healthcare workers (HCWs), and adult students) during the Covid-19 epidemic. METHOD: Random-effect meta-analysis was used to estimate the prevalence of anxiety, depression, and insomnia. RESULTS: The meta-analysis includes 28 studies with 38 individual samples in Spain. The pooled prevalence of anxiety symptoms in 22 studies comprising a sample population of 82,024 was 20% (95% CI: 15-25%), that of depression symptoms in 22 articles with a total sample comprising 82,890 individuals was 22% (95% CI: 18-28%), and that of insomnia symptoms in three articles with a sample population of 745 was 57% (95% CI: 48-66%. CONCLUSIONS: The accumulative evidence reveals that adults in Spain suffered higher prevalence rates of mental symptoms during the COVID-19 crisis, with a significantly higher rate relative to other countries such as China. Our synthesis also reveals a relative lack of studies on frontline and general HCWs in Spain

In Situ Measurement of the Junction Temperature of Light Emitting Diodes Using a Flexible Micro Temperature Sensor

This investigation aimed to fabricate a flexible micro resistive temperature sensor to measure the junction temperature of a light emitting diode (LED). The junction temperature is typically measured using a thermal resistance measurement approach. This approach is limited in that no standard regulates the timing of data capture. This work presents a micro temperature sensor that can measure temperature stably and continuously, and has the advantages of being lightweight and able to monitor junction temperatures in real time. Micro-electro-mechanical-systems (MEMS) technologies are employed to minimize the size of a temperature sensor that is constructed on a stainless steel foil substrate (SS-304 with 30 μm thickness). A flexible micro resistive temperature sensor can be fixed between the LED chip and the frame. The junction temperature of the LED can be measured from the linear relationship between the temperature and the resistance. The sensitivity of the micro temperature sensor is 0.059 ± 0.004 Ω/°C. The temperature of the commercial CREE® EZ1000 chip is 119.97 °C when it is thermally stable, as measured using the micro temperature sensor; however, it was 126.9 °C, when measured by thermal resistance measurement. The micro temperature sensor can be used to replace thermal resistance measurement and performs reliably

Current progress on removal of recalcitrance coloured particles from anaerobically treated effluent using coagulation–flocculation

The palm oil industry is the most important agro industries in Malaysia and most of the mills adopt anaerobic digestion as their primary treatment for palm oil mill effluent (POME). Due to the public concern, decolourisation of anaerobically treated POME (AnPOME) is becoming a great concern. Presence of recalcitrant-coloured particles hinders biological processes and coagulation–flocculation may able to remove these coloured particles. Several types of inorganic and polymers-based coagulant/flocculant aids for coagulation–flocculation of AnPOME have been reviewed. Researchers are currently interested in using natural coagulant and flocculant aids. Modification of the properties of natural coagulant and flocculant aids enhanced coagulation–flocculation performance. Modelling and optimization of the coagulation–flocculation process have also been reviewed. Chemical sludge has the potential for plant growth that can be evaluated through pot trials and phytotoxicity test