Development of severe secondary hypertension in a patient with systemic entomophthoromycosis

The sudden onset of hypertension mandates investigation for secondary causes. We report the case of a young man with a very rare systemic fungal disease that included massive unilateral perirenal adenopathy. Treatment was associated with the abrupt onset of severe hypertension. Imaging studies suggested progressive constriction of one kidney with treatment, presumably as a result of healing adenopathy. We suggest that this case is a new example of hypertension secondary to constrictive perinephritis (Page kidney).     

Prony-Based Optimal Bayes Fault Classification of Overcurrent Protection

The development of deregulation and demand for high-quality electrical energy has lead to a new requirement in different fields of power systems. In the protection field, this means that high sensitivity and fast operation during the fault are required while maltripping of relay protection is not acceptable. One case that may lead to a maltrip of the high-sensitive overcurrent relay is the starting current of the induction motor or inrush current of the transformer. This transient current has the potential to affect the correct operation of protection relays close to the component being switched. In the case of switching events, such transients must not lead to overcurrent relay operation; therefore, a reliable and secure relay response becomes a critical matter. Meanwhile, proper techniques must be used to prevent maltripping of such relays, due to transient currents in the network. In this paper, the optimal Bayes classifier is utilized to develop a method for discriminating the fault from nonfault events. The proposed method has been designed based on extracting the modal parameters of the current waveform using the Prony method. By feeding the fundamental frequency damping and ratio of the 2nd harmonic amplitude over the fundamental harmonic amplitude to the classifier, the fault case is discriminated from the switching case. The suitable performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power system using PSCAD/EMTDC software.     

CHID1 Is a Novel Prognostic Marker of Non-Small Cell Lung Cancer

There is an urgent need for identification of new prognostic markers and therapeutic targets for non-small cell lung cancer (NSCLC). In this study, we evaluated immune cells markers in 100 NSCLC specimens. Immunohistochemical analysis revealed no prognostic value for the markers studied, except CD163 and CD206. At the same time, macrophage markers iNOS and CHID1 were found to be expressed in tumor cells and associated with prognosis. We showed that high iNOS expression is a marker of favorable prognosis for squamous cell lung carcinoma (SCC), and NSCLC in general. Similarly, high CHID1 expression is a marker of good prognosis in adenocarcinoma and in NSCLC in general. Analysis of prognostic significance of a high CHID1/iNOS expression combination showed favorable prognosis with 20 months overall survival of patients from the low CHID1/iNOS expression group. For the first time, we demonstrated that CHID1 can be expressed by NSCLC cells and its high expression is a marker of good prognosis for adenocarcinoma and NSCLC in general. At the same time, high expression of iNOS in tumor cells is a marker of good prognosis in SCC. When used in combination, CHID1 and iNOS show a very good prognostic capacity for NSCLC. We suggest that in the case of lung cancer, tumor-associated macrophages are likely ineffective as a therapeutic target. At the same time, macrophage markers expressed by tumor cells may be considered as targets for anti-tumor therapy or, as in the case of CHID1, as potential anti-tumor agents.     

Color image DNA encryption using mRNA properties and non-adjacent coupled map lattices

This paper proposes a novel algorithm for encrypting color images. The innovation in this study is the use of messenger ribonucleic acid (mRNA) encoding to import into Deoxyribonucleic acid (DNA) encoding. For permutation of the plain image bits, we use Arnold’s Cat Map at the bit-level. Then, using Non-Adjacent Coupled Map Lattices (NCML), we apply diffusion operations to the permuted color channels. We also provide the upgrade of the diffusion phase with DNA encoding. In the proposed algorithm, the choices are random depending on the secret key, which is implemented using a simple logistic map. Hashing the string entered by the user, the secret key, parameters, and initial values are generated by the Double MD5 method. The results of tests and security analysis showed that the results of encryption with this scheme are effective, and the key space is large enough to withstand common attacks.

     

Coordinated design of TCSC and PSS controllers using VURPSO and Genetic algorithms for multi-machine power system stability

Thyristor controlled series capacitor (TCSC) can regulate line impedance and therefore increase transferred power of the system. On the other hand power system stabilizer (PSS) increases dynamic stability of generator. To enhance the stability, combination of TCSC and PSS can be applied, and in such a case coordination of TCSC and PSS is essential. This paper applies this combined controller in order to enhance the stability of multi-machine system. Parameters of these controllers are optimized by velocity update relaxation particle swarm optimization (VURPSO) algorithm and Genetic algorithm (GA). The simulation results show that the combination of VURPSO algorithm and GA leads to a better design and stability.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

High responsivity silicon MOS phototransistors

We describe a metal-oxide silicon (MOS) phototransistor that relies on a novel lateral doping scheme that creates a p-i-n junction configuration for light detection. This is essentially a hybrid device with the horizontal structure of a p-i-n diode and the vertical structure of a MOS field-effect transistor. The lateral p-i-n diode detects light whereas the gate can be used to change the current flowing through the device; making it appear as a MOSFET. This feature makes it easy to integrate it with other conventional MOSFETs on a CMOS process flow. The device shows high optical responsivities that persist to wavelengths in the near-ultraviolet region. The fabrication of the device as well as its electrical and optical characteristics is described.     

Applying a normalized ratio scale technique to assess influences of urban expansion on land surface temperature of the semi-arid city of Erbil

The difference between surface and air temperature within a city and its surrounding area is a result of variations in surface cover, thermal capacity, and 3-dimensional geometry. This research has examined and quantified the decreasing daytime land surface temperature (LST) in Erbil, Kurdistan region of Iraq, and the influence of rapid urban expansion on urban heat/cool island effect over a 20 year period. Land-use/land-cover change across this time period is also established using pixel samples. The current study proposes the application of the normalized ratio scale (NRS) to adjust the temperature of images acquired at different dates to the same range. Eleven satellite images acquired by Landsat 4, 5, 7, and 8 during the period 1992–2013 are used to retrieve LST. The results indicate that 55.3 km² of city land cover changed from bare soil to urban; consequently, the mean LST of the new urbanized area decreased by 2.28°C. The normalized difference vegetation index (NDVI) of Sami Abdul-Rahman (S.A.) Park increased from 0.09 ± 0.01 to 0.32 ± 0.11, resulting in a decrease of the mean LST by 7.29°C. This study shows that the NRS method is appropriate for detecting temperature trends from urbanization using remote-sensing data. It also highlights that urban expansion may lead to a decrease in daytime LST in drylands.     

Analysis of solar desiccant cooling system for an institutional building in subtropical Queensland,Australia

Institutional buildings contain different types of functional spaces which require different types of heating, ventilating and air conditioning (HVAC) systems. In addition, institutional buildings should be designed to maintain an optimal indoor comfort condition with minimal energy consumption and minimal negative environmental impact. Recently there has been a significant interest in implementing desiccant cooling technologies within institutional buildings. Solar desiccant cooling systems are reliable in performance, environmentally friendly and capable of improving indoor air quality at a lower cost. In this study, a solar desiccant cooling system for an institutional building in subtropical Queensland (Australia) is assessed using TRNSYS 16 software. This system has been designed and installed at the Rockhampton campus of Central Queensland University. The system's technical performance, economic analysis, energy savings, and avoided gas emission are quantified in reference to a conventional HVAC system under the influence of Rockhampton's typical meteorological year. The technical and economic parameters that are used to assess the system's viability are: coefficient of performance (COP), solar fraction, life cycle analysis, payback period, present worth factor and the avoided gas emission. Results showed that, the installed cooling system at Central Queensland University which consists of 10 m² of solar collectors and a 0.400 m³ of hot water storage tank, achieved a 0.7 COP and 22% of solar fraction during the cooling season. These values can be boosted to 1.2 COP and 69% respectively if 20 m² of evacuated tube collector's area and 1.5 m³ of solar hot water storage volume are installed.