A Comparative Study of Segmentation Algorithms in the Classification of Human Skin Burn Depth

A correct first assessment of a skin burn depth is essential as it determines a correct first burn treatment provided to the patients. The objective of this paper is to conduct a comparative study of the different segmentation algorithms for the classification of different burn depths. Eight different hybrid segmentation algorithms were studied on a skin burn dataset comprising skin burn images categorized into three burn classes by medical experts; superficial partial thickness burn (SPTB), deep partial thickness burn (DPTB) and full thickness burn (FTB). Different sequences of the algorithm were experimented as each algorithm was able to segment differently, leading to different segmentation in the final output. The performance of the segmentation algorithms was evaluated by calculating the number of correctly segmented images for each burn depth. The empirical results showed that the segmentation algorithm that was able to segment most of the burn depths had achieved 40.24%, 60.42% and 6.25% of correctly segmented image for SPTB, DPTB and FTB respectively. Most of the segmentation algorithms could not segment well for FTB images because of the different nature of the burn wounds as some of the FTB images contained dark brown and black colors. It can be concluded that a good segmentation algorithm is required to ensure that the representative features of each burn depth can be extracted to contribute to higher accuracy of classification of skin burn depth

Wind energy assessment considering wind speed correlation in Malaysia

Renewable energy is the current trend of energy sourcing. Numerous scientists, inventors, and engineers are working hard to harness renewable energy. The application of renewable energy is very wide; it can be as small as lighting an LED bulb or as large as generating the electricity of a town or even a country. Wind energy plays an important role in the context of electricity generation. Wind energy is highly dependent on the wind speed at a wind site. Wind prediction is necessary for a wind energy assessment of a potential wind farm. In this study, the wind energy assessment is based on wind prediction using the Mycielski algorithm and K-means clustering in Kudat, Malaysia. The predicted results are analysed using Weibull analysis to obtain the most probable wind speed. From the results of this study, K-means clustering is more accurate in prediction when compared with the Mycielski algorithm. The most probable wind in Kudat is sufficient to operate the wind turbines

Analysis between Perturb & Observe Controller and Fuzzy Logic Controller for a Photovoltaic System with CUK and SEPIC Converter

The power generation is using Photovoltaic (PV) cell is the best alternative developing for fossil fuel since it renewable green power, energy conservation and demand-side management. Solar energy most useful for sustainable development but due to it has a nonlinear current-voltage characteristic. It is difficult to track the maximum power produce by the PV module. This paper presents a comparison between the Single-Ended Primary-Inductor Converter (SEPIC) and CUK converter by using both Fuzzy Logic Controller (FLC) and Perturb & Observe (P&O) methods in maximum power point tracking (MPPT). In this paper, the performance, advantage and disadvantage for both converters and MPPT algorithm are described. A general model of a Photovoltaic system with proposed MPPT controller and converters is implemented in MATLAB/Simulink software. The input parameter of temperature and irradiation level will be under constant and variable level as to prove the system efficiency towards changing conditions. The simulation result will be analyzed in different case studies in order to prove the effectiveness timely response performances, efficiencies of our power of converting over input power of the PV module and the comparison of transient response of voltage ripple of the systems

Essential role of PKC delta in histone deacetylase inhibitor-induced Epstein-Barr virus reactivation in nasopharyngeal carcinoma cells

Histone deactylase inhibitors (HDACi) are common chemotherapeutic agents that stimulate Epstein-Barr virus (EBV) reactivation; the detailed mechanism remains obscure. In this study, it is demonstrated that PKC delta is required for induction of the EBV lytic cycle by HDACi. Inhibition of PKC delta abrogates HDACi-mediated transcriptional activation of the Zta promoter and downstream lytic gene expression. Nuclear translocation of PKC delta is observed following HDACi stimulation and its overexpression leads to progression of the EBV lytic cycle. Our study suggests that PKC delta is a crucial mediator of EBV reactivation and provides a novel insight to study the regulation of the EBV lytic cycle

A Sarawak experience on the use of IPC ID PEP regimen in patients bitten by laboratory confirmed rabies animals

On 1st July 2017, rabies outbreak was declared in the state of Sarawak, which is located in the Borneo Island following the deaths of 3 children due to human rabies infection. Rabies has the highest case fatality rate but is highly preventable through prompt and effective post-exposure prophylaxis (PEP)

Sparsity without the Complexity: Loss Localisation using Tree Measurements

We study network loss tomography based on observing average loss rates over a set of paths forming a tree -- a severely underdetermined linear problem for the unknown link loss probabilities. We examine in detail the role of sparsity as a regularising principle, pointing out that the problem is technically distinct from others in the compressed sensing literature. While sparsity has been applied in the context of tomography, key questions regarding uniqueness and recovery remain unanswered. Our work exploits the tree structure of path measurements to derive sufficient conditions for sparse solutions to be unique and the condition that $\ell_1$ minimization recovers the true underlying solution. We present a fast single-pass linear algorithm for $\ell_1$ minimization and prove that a minimum $\ell_1$ solution is both unique and sparsest for tree topologies. By considering the placement of lossy links within trees, we show that sparse solutions remain unique more often than is commonly supposed. We prove similar results for a noisy version of the problem

Nonlinear Time Series Analysis of Sunspot Data

This paper deals with the analysis of sunspot number time series using the Hurst exponent. We use the rescaled range (R/S) analysis to estimate the Hurst exponent for 259-year and 11360-year sunspot data. The results show a varying degree of persistence over shorter and longer time scales corresponding to distinct values of the Hurst exponent. We explain the presence of these multiple Hurst exponents by their resemblance to the deterministic chaotic attractors having multiple centers of rotation.Comment: 10 pages, 6 figures, accepted for publication in Solar Physics, journal style corrections done in this versio

WHO global research priorities for antimicrobial resistance in human health

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR