The Consumed Natural Diet of Chondrostoma regium (Heckel, 1843) from Tigris River, Salah Al-Deen Province

The foreguts of a total of 515 fish of Chondrostoma regium (Heckel, 1843) (locally: Bala’aot Malloky) were studied. These fish were collected from Tigris River at Salah Al-Deen Province (between Al-Hagag & Yathrib) for 20 months between March and October of the next year. Detritus, plant in origin materials (19.6%, 23.0% & 24.9%); green and blue green algae, mostly Cladophora, Cosmarium and Merismpedia sp. (17.1%, 12.9% & 12.2%) and diatoms, mostly Diatoma, Chanathes, Amphora and Cyulbella sp. (16.9%, 8.8% & 8.2%) were the main food categories taken by these fishes according to occurrence (O%), volumetric methods (V%) and ranking index (R%). Debris (not part of the diet) took 45.3% of the studied fish foreguts by volume. Detritus was also the most important food category (25.9%, 18.2%, 22.9% & 19.8%, by ranking index) at all sampling stations respectively, and taken by different fish size groups (168-200, 201-300 & 301-350mm).The diet overlaps between these fish size groups and that between different sampling stations were ranged between 0.86-1.0, i.e. fish were mainly feeding on the same food organisms

Experimental study on a feasibility of using electromagnetic wave cylindrical cavity sensor to monitor the percentage of water fraction in a two phase system

This study proposed a microwave sensor system to monitor single and two phase flow systems. The microwave sensing technology in this study utilises the resonant frequencies that occur in a cylindrical cavity and monitor the changes in the permittivity of the measured phases to differentiate between the volume fractions of air, water and oil. The sensor system used two port configuration S21 (acted as transmitter and receiver) to detect the fluids inside the pipe. In principle, the strong polarity of water molecules results in higher permittivity in comparison to other materials. A tiny change of water fraction will cause a significant frequency shift. Electromagnetic waves in the range of 5 GHz to 5.7 GHz have been used to analyse a two phase air-water and oil-water stratified flow in a pipeline. The results demonstrated the potential of a microwave sensing technique to be used for the two phase systems monitoring. A significant shift in the frequency and change in the amplitude clearly shows the percentage fraction change of water in the pipe. The temperature study of water also demonstrated the independence of microwave analysis technique to the temperature change. This is accounted to overlapping modes negating the affect. Statistical analysis of the amplitude data for two phase systems shows a linear relationship of the change in water percentage to the amplitude. The electromagnetic wave cavity sensor successfully detected the change in the water fraction inside the pipe between 0-100%. The results show that the technique can be developed further to reduce the anomalies in the existing microwave sensor

Real-Time Monitoring of Bodily Fluids Using a Novel Electromagnetic Wave Sensor

The use of a novel low power electromagnetic sensor for real-time detection of lactate in cerebrospinal fluid (CSF) is investigated. CSF holds key indicators relating to a patient’s future health. A multipurpose sensor platform is currently being developed with the capability to detect the concentration of materials in volumes =1 ml. This paper presents results from a microwave cavity resonator designed and created for this purpose, using varying concentrations of lactate in water. The work demonstrates the feasibility of monitoring bodily fluids in real-time. Such advancements are essential for improved and cost-effective delivery of healthcare services to patients

Prediction of Flood Severity Level Via Processing IoT Sensor Data Using Data Science Approach

The ‘riverine flooding’ is deemed a catastrophic phenomenon caused by extreme climate changes and other ecological factors (e.g., amount of sunlight), which are difficult to predict and monitor. However, the use of internet of things (IoT), various types of sensing including social sensing, 5G wireless communication and big data analysis have devised advanced tools for early prediction and management of distrust events. To this end, this paper amalgamates machine learning models and data analytics approaches along-with IoT sensor data to investigate attribute importance for the prediction of risk levels in flood. The paper presents three river levels: normal, medium and high-risk river levels for machine learning models. Performance is evaluated with varying configurations and evaluations setup including training and testing of support vector machine and random forest using principal components analysis-based dimension reduced dataset. In addition, we investigated the use of synthetic minority over-sampling technique to balance the class representations within dataset. As expected, the results indicated that a “balanced” representation of data samples achieved high accuracy (nearly 93%) when benchmarked with “imbalanced” data samples using random forest classifier 10-folds cross-validation

Detection of the gas–liquid two-phase flow regimes using non-intrusive microwave cylindrical cavity sensor

Gas–liquid two-phase flow phenomenon occurs in various engineering applications and the measurement of it is important. A microwave sensor in the form of a cylindrical cavity has been designed to operate between 5 and 5.7 GHz. The aim is to analyse a two phase gas–liquid flow regime in a pipeline. LabVIEW software is utilised to capture the data, process them and display the results in real time. The results have shown that the microwave sensor has successfully detected the two-phase flow regimes in both the static and dynamic flow environments with reasonable accuracy. The study has also shown the independence of the technique and its accuracy to the temperature change (28–83 °C). Several flow regimes of the gas–liquid two-phase flow have been discussed. The system is also able to detect the stratified, wavy, elongated bubbles and homogeneous flow regimes

Rapid Non-Destructive Prediction of Water Activity in Dry-Cured Meat

Water activity (aw) describes the amount of free water available in a matrix for growth of microbiological pathogens and spoilage flora. It is used to predict the safety of food products, and has particular importance for dry-cured meat manufacturers. Results from tests on dry-cured pork (n = 83) demonstrate a high degree of correlation (R2 = 0.909) with current industry standard equipment. System accuracy at the 95% confidence interval (0.0125) is comparable with existing equipment available to industry. However, the added advantage of the microwave sensor to enable rapid and non-destructive measurement means that it could be used for day-to-day monitoring and optimization of products within the dry-cured meat value chain. This would reduce per-product operating costs and waste, in addition to facilitating recipe development (e.g., reduced salt)