Sensitivity Analysis and Modeling for DEM Errors

The Digital Elevation Model (DEM) can be created using airborne Light Detection And Ranging (LIDAR), Image or Synthetic-Aperture Radar (SAR) mapping techniques. The direct georeferencing of the DEM model is conducted using a GPS/inertial navigation system. The airborne mapping system datasets are processed to create a DEM model. To develop an accurate DEM model, all errors should be considered in the processing step. In this research, the errors associated with DEM models are investigated and modeled using Principal Component Analysis (PCA) and the least squares method. The sensitivity analysis of the DEM errors is investigated using PCA to define the significant GPS/inertial navigation data components that are strongly correlated with DEM errors. Then, the least squares method is employed to create a functional relationship between the DEM errors and the significant GPS/inertial navigation data components. The DEM model errors associated with airborne mapping system datasets are investigated in this research. The results show that the combined PCA analysis and least squares method can be used as a powerful tool to compensate the DEM error due to the GPS/inertial navigation data with about 27% in average for DEM errors produced by the direct georeferenced airborne mapping system

Prevalence and some risk factors with therapeutic trial of sheep dermatophytosis in Egypt

Dermatophytosis is a fungal disease that affects the superficial skin layers and hair of farm animals all over the world including Egypt. Despite being a self-limiting disease, it has serious effects on public health and devastating economic losses due to its serious skin damage, a long course of treatment, and loss of weight. This study determines the most prevalent species of dermatophyte in sheep and identifies the incriminated species by both microscopic and culture methods with an assessment of animal and environmental risk factors. Moreover, it evaluates the effectiveness of three antifungal compounds (tioconazole cream and clotrimazole spray, and fluconazole capsule), on twenty-four naturally infected sheep. One hundred and three sheep from Sharkia and Dakahalia governorates were examined with clinically suggestive lesions from 2018 to 2019. 47.6% of the cases were positive for the dermatophyte infection either by clinical signs, microscopic or culture, or both. The highest registered infection rate is in males, at the age of ˂ 6 months, and in the winter season. Three antifungal medications are used for the first time in the treatment of ovine dermatophytosis. They are proved to have been effective in subsiding skin lesions with hair growth to return to its normal clinical state with a 100% curative rate. The treatment with preferable and easily applicable topical cures, especially tioconazole cream, is highly effective in the short run. This cream treatment is easily applicable and provides a good alternative to the traditional antifungal medication for sheep. Consequently, such treatment can reduce the possibility of spreading the infection by other animals, and may allow the adaption of efficient control measures

Optimal Lowest Astronomical Tide Estimation Using Maximum Likelihood Estimator with Multiple Ocean Models Hybridization

Developing an accurate Lowest Astronomical Tide (LAT) in a continuous form is essential for many maritime applications as it can be employed to develop an accurate continuous vertical control datum for hydrographic surveys applications and to produce accurate dynamic electronic navigation charts for safe maritime navigation by mariners. The LAT can be developed in a continuous (surface) using an estimated LAT surface model from the hydrodynamic ocean model along with coastal discrete LAT point values derived from tide gauges data sets to provide the corrected LAT surface model. In this paper, an accurate LAT surface model was developed for the Red Sea case study using a Maximum Likelihood Estimator (MLE) with multiple hydrodynamic ocean models hybridization, namely, WebTide, FES2014, DTU10, and EOT11a models. It was found that the developed optimal hybrid LAT model using MLE with multiple hydrodynamic ocean models hybridization ranges from 0.1 m to 1.63 m, associated with about 2.4 cm of uncertainty at a 95% confidence level in the Red Sea case study area. To validate the accuracy of the developed model, the comparison was made between the optimal hybrid LAT model developed from multiple hydrodynamic ocean models hybridization using the MLE method with the individual LAT models estimated from individual WebTide, FES2014, DTU10, or EOT11a ocean models based on the associated uncertainties estimated at a 95% confidence level. It was found that the optimal hybrid LAT model accuracy is superior to the individual LAT models estimated from individual ocean models with an improvement of about 50% in average, based on the estimated uncertainties. The importance of developing optimal LAT surface model using the MLE method with multiple hydrodynamic ocean models hybridization in this paper with few centimeters level of uncertainty can lead to accurate continuous vertical datum estimation that is essential for many maritime applications

Chart Datum-to-Ellipsoid Separation Model Development for Obhur Creek Using Multibeam Hydrographic Surveying

A traditional shore-based discrete point chart datum (CD) that represents the lowest astronomical tide (LAT) in Saudi Arabia using tide gauge data is utilized to reduce the observed depth collected from hydrographic surveying test to CD-referenced depth for producing navigation charts for maritime navigation applications. A need for developing CD in a continuous form is essential to replace the traditional discrete CD using tide gauge data. The importance of the development of CD-to-ellipsoid (WGS84) separation model is that it can be utilized by the hydrographers to develop an accurate vertical control for hydrographic surveys applications and can be utilized by the mariners to produce accurate dynamic electronic navigation charts (ENCs). In this paper, a continuous CD to WGS84 ellipsoid separation model for the Sharm Obhur area is developed using a multibeam hydrographic surveying test. It is shown that the continuous chart datum ranges from −4.920 m to −4.766 m and can be achieved with standard deviation ranges from 0.1 cm to 2.3 cm. To validate the separation model, a comparison was made with the gravimetric/oceanographic method based on the separation height developed from geoid height, the sea surface topography and LAT value (chart datum to mean sea level) at the tide gauge located in the study area. The comparison showed that the average value of the developed continuous CD to WGS84 separation model heights using multibeam hydrographic surveying agrees with the separation height estimated from gravimetric/oceanographic method

A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors

In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at −40 °C, −20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration

Article A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors

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Effect of Spirulina on Somatic Cell Count and Milk Quality

Subclinical mastitis is a major problem threating the cows' industry in Egypt. This study aimed to investigate the impact of SCC on the milk composition and evaluate the effect of spirulina supplementation on SCC and milk quality. Total of 270 milk samples were examined using CMT, BacSomatic and MilkoscanTMFT1 system. For evaluation of spirulina effect, ten cows were supplemented with spirulina powder (20 g per head for one month).The prevalence of SCM according to CMT and SCC was 32.2% and 54.4%, respectively.  SCC negatively correlated with Fat, protein, lactose, and casein% which had Means of 2.94±0.75, 3.48±0.37, 4.82±0.23, and 2.58±0.39, respectively at SCC (<200 ×103cell/ml) while, at SCC above 400 ×103cell/ml were 2.43±0.95, 3.24 ±0.33, 4.61±0.22, and 2.4±0.38, respectively. There was a significant decrease in the average of SCC from 6638.9±4675.9×103 to 361.1±321.4×103cell/ml after 3 weeks of spirulina supplementation. The Mean of Fat, protein, lactose, and casein% were increased from 2.84±0.29, 3.02±0.4, 4.49±0.4 and 2.34±0.28, respectively to reach 3.62±0.16, 3.65±0.43, 4.86±0.41 and 2.59±0.3, respectively and Milk yield increased from average of 21.7±3.23 kg/day to 24.2±2.39kg after 21 days of spirulina treatment. Therefore, milk quality and quantity can be improved by using Spirulina supplementation which reduces SCC.

Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Accurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the least-squares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average

Virulence Genes of Multi-drug Resistance Pseudomonas species Isolated from Milk and Some Dairy Products

Pseudomonas species is one of the psychotropic bacteria that can survive in low-tempered milk and dairy products besides producing heat-resistant spoilage enzymes. In this study, one hundred and fifty samples of milk and some dairy products were analyzed. The overall prevalence of Pseudomonas spp. was 44.66% (0% pasteurized milk, 16% butter, 20% pasteurized cream, 48.5% Talaga cheese, 50% bulk milk tank, 66.6% raw market milk, and 70% in raw cream). From 67 positive samples, eighty-three isolates were confirmed biochemically as Pseudomonas spp. The most prominent species were P. aeruginosa, then P. fluorescence, P. Fragi, P. psychrophile, P. proteolytica, P. alcaligens, P. lundensis, and P. brenneri by a percent of 38.5%, 37.5%, 10.8%, 6%, 2.4%, 2.4%, 1.2%, and 1.2%, respectively. Fourteen antibiotic discs were selected to measure the antimicrobial susceptibility of 59 isolates of Pseudomonas spp. The higher antimicrobial resistance was against Ampicillin (100%) followed by Colistin (98%), while the antibiotic sensitivity was higher against Imipenem (96.6%) then Meropenem (91.5%). The average MAR index of isolated Pseudomonas spp. was 0.462. Ten isolates of antimicrobial resistance serotypes of P. aeruginosa were O11: E, O8: C, O5: B, O4: F, and O2: B. Molecular identification of P. aeruginosa, P. fluorescence, and P. fragi was carried out using polymerase chain reaction (PCR) to determine their virulence genes (LasB, ExoS, pilB for P. aeruginosa, aprX for P. fluorescence and carA gene for P. fragi). High levels of antimicrobial-resistant (AMR) Pseudomonas spp.  threaten public health and cause global concern. The economic and public health impacts were discussed