Implicitly estimating the cost of mental illness in Australia: a standard-of-living approach

Background Estimating the costs of mental illness provides useful policy and managerial information to improve the quality of life of people living with a mental illness and their families. Objective This paper estimates the costs of mental health in Australia using the standard-of-living approach. Methods The cost of mental illness was estimated implicitly using a standard of living approach. We analyse data from 16 waves of the Household, Income and Labour Dynamics in Australia Survey (HILDA) using 209,871 observations. Unobserved heterogeneity was mitigated using an extended random-effects estimator. Results The equivalised disposable income of people with mental illness, measured by a self-reported mental health condition, needs to be 50% higher to achieve a similar living standard as those without a mental illness. The cost estimates vary considerably with measures of mental illness and standard of living. An alternative measure of mental illness using the first quintile of the SF-36 mental health score distribution resulted in an increase of estimated costs to 80% equivalised disposable income. Conclusion People with mental illness need to increase equivalised disposable income, which includes existing financial supports, by 50%-80% to achieve a similar level of financial satisfaction as those without a mental illness. The cost estimate can be substantially higher if the overall life satisfaction is used to proxy for standard of living

GROUND WATER POLLUTION IN HOCHIMINH CITY AND IT'S PREVENTION-CASE STUDY

Joint Research on Environmental Science and Technology for the Eart

Negative positional externality of conspicuous and positional goods on society: An empirical analysis on income and clothing consumption for 9 EU countries.

Objectives The main objectives of this study were first, through secondary sources, to analyze the way positional externality exist and its negative impact of on society, focusing on conspicuous and positional goods. Second, it tries to find the empirical evidence for the effect of positional externality in income and clothing consumption. Third, the thesis discuss various ways to reduce positional externality. Summary The thesis first analyzes the literatures which shows the negative impact of conspicuous and positional goods on society. Then, using data from Life in Transition survey III, the thesis tests four hypotheses on the effect of positional externality on life satisfaction. Two hypotheses are related to income comparisons, while the others are related to clothing consumption. Finally, the author discusses some of the measures to reduce the effect of positional externality. Conclusions The main findings shows controlled for income of each individual, GDP per capita and average clothing consumption has negative correlation with life satisfaction of each individual. This result shows the existence of positional externality and support the argument that positional externality has a negative impact on society

An Algorithmic Weakening of the Erd?s-Hajnal Conjecture

We study the approximability of the Maximum Independent Set (MIS) problem in H-free graphs (that is, graphs which do not admit H as an induced subgraph). As one motivation we investigate the following conjecture: for every fixed graph H, there exists a constant ? > 0 such that MIS can be n^{1-?}-approximated in H-free graphs, where n denotes the number of vertices of the input graph. We first prove that a constructive version of the celebrated Erd?s-Hajnal conjecture implies ours. We then prove that the set of graphs H satisfying our conjecture is closed under the so-called graph substitution. This, together with the known polynomial-time algorithms for MIS in H-free graphs (e.g. P?-free and fork-free graphs), implies that our conjecture holds for many graphs H for which the Erd?s-Hajnal conjecture is still open. We then focus on improving the constant ? for some graph classes: we prove that the classical Local Search algorithm provides an OPT^{1-1/t}-approximation in K_{t, t}-free graphs (hence a ?{OPT}-approximation in C?-free graphs), and, while there is a simple ?n-approximation in triangle-free graphs, it cannot be improved to n^{1/4-?} for any ? > 0 unless NP ? BPP. More generally, we show that there is a constant c such that MIS in graphs of girth ? cannot be n^{c/(?)}-approximated. Up to a constant factor in the exponent, this matches the ratio of a known approximation algorithm by Monien and Speckenmeyer, and by Murphy. To the best of our knowledge, this is the first strong (i.e., ?(n^?) for some ? > 0) inapproximability result for Maximum Independent Set in a proper hereditary class

A Proposed Scheduling Algorithm for IoT Applications in a Merged Environment of Edge, Fog, and Cloud

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes

Power Analysis Attacks on Keccak

Side Channel Attacks (SCA) exploit weaknesses in implementations of cryptographic functions resulting from unintended inputs and outputs such as operation timing, electromagnetic radiation, thermal/acoustic emanations, and power consumption to break cryptographic systems with no known weaknesses in the algorithm’s mathematical structure. Power Analysis Attack (PAA) is a type of SCA that exploits the relationship between the power consumption and secret key (secret part of input to some cryptographic process) information during the cryptographic device normal operation. PAA can be further divided into three categories: Simple Power Analysis (SPA), Differential Power Analysis (DPA) and Correlation Power Analysis (CPA). PAA was first introduced in 1998 and mostly focused on symmetric-key block cipher Data Encryption Standard (DES). Most recently this technique has been applied to cryptographic hash functions. Keccak is built on sponge construction, and it provides a new Message Authentication Code (MAC) function called MAC-Keccak. The focus of this thesis is to apply the power analysis attacks that use CPA technique to extract the key from the MAC-Keccak. So far there are attacks of physical hardware implementations of MAC-Keccak using FPGA development board, but there has been no side channel vulnerability assessment of the hardware implementations using simulated power consumption waveforms. Compared to physical power extraction, circuit simulation significantly reduces the complexity of mounting a power attack, provides quicker feedback during the implementation/study of a cryptographic device, and that ultimately reduces the cost of testing and experimentation. An attack framework was developed and applied to the Keccak high speed core hardware design from the SHA-3 competition, using gate-level circuit simulation. The framework is written in a modular fashion to be flexible to attack both simulated and physical power traces of AES, MAC-Keccak, and future crypto systems. The Keccak hardware design is synthesized with the Synopsys 130-nm CMOS standard cell library. Simulated instantaneous power consumption waveforms are generated with Synopsys PrimeTime PX. 1-bit, 2-bit, 4-bit, 8-bit, and 16-bit CPA selection function key guess size attacks are performed on the waveforms to compare/analyze the optimization and computation effort/performance of successful key extraction on MAC-Keccak using 40 byte key size that fits the whole bottom plane of the 3D Keccak state. The research shows the larger the selection function key guess size used, the better the signal-noise-ratio (SNR), therefore requiring fewer numbers of traces needed to be applied to retrieve the key but suffer from higher computation effort time. Compared to larger selection function key guess size, smaller key guess size has lower SNR that requires higher number of applied traces for successful key extraction and utilizes less computational effort time. The research also explores and analyzes the attempted method of attacking the second plane of the 3D Keccak state where the key expands beyond 40 bytes using the successful approach against the bottom plane

Exploring land use land cover change to understand urban warming effect in Hanoi inner city, Vietnam

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesRecently, urbanization is occurring rapidly in Hanoi, the second largest city in Vietnam. The process is profoundly reflected in Hanoi inner city where the socio-economic development is faster than in other areas. This has led to the acquisition of agricultural land that in turn, has resulted in land use changes, and subsequently increasing the residential, commercial and industrial land. The transformation between different land use types especially the urban expansion will crucially influence the land surface temperature pattern (LST). This will severely affect to the community in relation to people’s health and energy consumption. Exploring land use land cover (LULC) change to understand urban warming effect is a necessary work for community and local government. The research can be used as a scientific basis for urban planners in urban planning and management as well as to increase the community awareness in urban warming effect. The purpose of this research is to determine and analyze the relationship between LULC change and LST pattern. To achieve the research goal, we need to accomplish a series of specific objectives. First, we perform supervised maximum likelihood classification method and change detection to determine the patterns and rate of change, and land cover and land use transformation within and around Hanoi inner city. Then we explore the relationship between land surface temperature and a) vegetation, b) man-made features, and c) crops land using normalized vegetation, and built-up indices within each LULC type. After that, we employ a Markov chains model to simulate future LULC change using different environmental and planning scenarios. Finally, we apply linear and non- linear regression to predict future urban climate patterns in Hanoi inner city using the predicted land cover and land use change

Applying computer vision for detection of diseases in plants

Early detection and quantification of diseases in food plants are critical to agriculture industry and national food security. However, limitation in technology and cost has limited the success of applying Computer Vision in Plant Science. This research builds on the recent advance of Machine Learning, GPU and smartphones to tackle the problem of fast and low cost diagnosis of plant disease. In particular, we choose soybean as the subject for applying automatic disease detection. The reason is because soybean is an important crop for the state of Iowa and an important source of food for America. The plant is however, highly vulnerable to several type of diseases. This thesis consists of two sub-analyses of soybean diseases, which are: First, detection of a single disease in soybean, particularly Sudden Death Syndrome (SDS) with high detail (including location and severity). Second, detection of multiple diseases in soybean, using mobile phones which are resource- constraine