International Journal of Applied Sciences: Current and Future Research Trends

Seasonal Variations for Petroleum Hydrocarbons in the Tissues of Fishes from Shatt Al-Arab River and Southern Iraqi Marshes (2020)

2023-07-29T13:12:42+00:00

Petroleum hydrocarbons were investigated in the tissues of five fish species: (1) Carrasius auratus, (2) Coptodon zillis, (3) Oreochromis auratus, (4) Oreochromis niloticus, and (5) Tenualosa ilisha, caught at three stations : 1) El-Manthory within Al-Hammar marshland, and 2) Sinbad Island, and 3) Basrah station along Shatt Al-Arab River during the year 2020. Fishes were caught by net , kept in cool box on the board of fishing boat, then transferred to the labs of Marine Science Centre / Basrah University, cleaned and frozen at – 20^oC prior to analysis. In the lab fishes were thawed and cut from their sides. Tissues were freeze dried by Edwards freeze drier type Modulyo, homogenized and 10 grams were weighted from each fish, petroleum hydrocarbons were extracted in hot soxhelet by using 40 ml n- hexane solvent for 4 hours . Extracts were reduced to 10 ml each by rotary evaporator. Finally concentrations of petroleum hydrocarbons in each sample were estimated spectrofluorometricaly by UV spectrofluorometry type Shimadzu RF-530IPC at excitation wavelength 310 nm and emission wavelength 360 nm. Higher levels were recorded during winter and spring in fishes caught at Shatt Al-Arab River, Stations 2 and 3, while lower levels were recorded in Tenualosa ilisha caught at all stations during spring, increased during summer, then autumn and winter . levels recorded in ?g/g dry weight were in the trend ND < 2.15 < 5.33 ? 4.51 respectively at Sinbad Island site No. 2, ND < 3.09 < 9.18 < 11.64 respectively at Basrah site No 3, and ND < 1.14 < 6.73 < 8.46 respectively at Manthory site No. 1. These levels were comparable to levels reported in fishes studies at nearby areas.

The Effects of Age and Some Vital Signs on Prostate-Specific Antigen Concerning Early Diagnosis of Prostate Cancer: A Multinomial Logistic Regression Approach

2023-10-20T21:54:26+00:00

Prostate cancer, regarded as a health anomaly frequently experienced by males over the age of 45, has gained prominence among cancer disorders experienced by the entire human species in recent years. Discussions about the anomaly's management, treatment, and early diagnosis have also gained attention. There is a paucity of literature on the application of multinomial logistic regression (MLR) to model prostate-specific antigen (PSA) for early diagnosis of prostate cancer through the effects of age and some vital signs associated with fluctuations in PSA. In this study, multinomial logistic regression was applied to model changes in PSA under two different classifications of the PSA levels (four categories and five categories) with age, pulse rate, systolic blood pressure, and diastolic blood pressure as the predictor variables. In each classification, the procedure begins by only grouping the age predictor variable and finding the effects of the predictor variables on the categories of the PSA. The procedure is then repeated with age and pulse rate grouped. The MLR for the two classifications were then compared based on prediction accuracy, no information rate, and kappa value. The results show that the model with the first classification of PSA was better than the second classification especially when the pulse rate is also grouped.

Age and pulse rate significantly affect prostate-specific antigen (PSA) categories. The 45-55 age group is the most significant, while no-risk individuals have no significant difference in PSA levels. Increased pulse rates may reduce prostate cancer risk in males with PSA levels greater than 50.

NORM in the Surface Sediments of Iraqi International Marine Waters

2023-07-29T13:13:44+00:00

Most radioactivity studies focus on the activity concentration of 232Th and 238U which are associated with heavy minerals, as well as 40K which associated with clay minerals. Within this study samples of surface sediments were collected from three sites 5, 7, and 11 along Khor Abdullah within Iraqi National Waters in order to document activity concentrations of radionuclides, 226Ra, 232Th, 238U, and 40K. Core sediments were investigated as well for determination of TOC. For studied radionuclides, levels recorded were 3.22 ± 0.59 – 37.6 ± 9.9 Bq/kg for 226Ra, 0.1 ± 0.1 – 6 ± 1.6 Bq/kg for 232Th, 1.02 ± 0.2-- 2.7 ±1.2 Bq/kg for 238U, and 124.09 ± 10 – 548.36 ± 44 Bq/kg for 40K. which are comparable to word wide levels. For TOC values recorded in bottom surface sediments from the three sites were 0.919% in site No. 2 to 1.904 % in site No. 1 Which were higher than levels reported in the same sites earlier averaged as 0.53 % . Radium equivalent activity was calculated for the sediments of Khor Abdullah and recorded as 51 Bq/kg which is quite low compared to the world wide level of 350 Bq/kg.

Review of Artificial Intelligence Applications in the Geomatics Field

2023-10-06T10:36:41+00:00

Artificial intelligence (AI) is rapidly transforming the field of geomatics, which is the science of collecting, managing, and analyzing spatial data. AI is being used to automate tasks, improve accuracy, and enable new applications in geomatics.

One of the most significant impacts of AI in geomatics is in the area of data processing and analysis. AI can be used to automate the processing of large amounts of geospatial data, which can lead to improved accuracy and efficiency in tasks such as land surveying, cartography, and environmental monitoring. For example, AI can be used to identify patterns and trends in data that would be difficult or impossible to detect by human analysts. Another area where AI is having a major impact in geomatics is in 3D modeling and visualization. AI can be used to create 3D models of the real world, which can be used for applications such as urban planning, infrastructure design, and disaster management. For example, AI can be used to create digital twins of cities, which can be used to simulate the impact of different planning decisions. Machine learning is a type of AI that allows computers to learn from data without being explicitly programmed. Machine learning is being used in a variety of geomatics applications, such as image classification, object detection, and natural language processing. For example, machine learning can be used to identify objects in satellite images, such as buildings or roads. Robotics is another area where AI is having a major impact in geomatics. Robots can be used to perform tasks in dangerous or inaccessible environments. They can also be used to collect data in remote areas. For example, robots can be used to survey disaster areas or to collect data in the ocean.

The impact of AI on geomatics is still in its early stages, but it has the potential to be transformative. AI is already being used in a variety of geomatics applications, and its use is only going to increase in the future.