AIM: To determine the value of the combination of thin-section 3 mm coronal and standard axial DWI and their impact in facilitating the diagnosis of acute brainstem infarction. METHODS: A cross-sectional study conducted from the 1st of April 2017 to the end of February 2018 on 100 consecutive patients (66% were male, and 34% were female) with isolated acute ischemic infarction in the brainstem. The abnormal MRI findings concerning the ischemic lesions were interpreted on standard axial 5 mm and thin-section coronal 3mm DWI. RESULTS: The mean age of the studied group was 69.2 ± 4.3 for male and 72.3 ± 2.5 years. The standard axial DWI can diagnose 20%, 6.7% and 6.7% of the infarctions in midbrain, pons and medulla oblongata respectively, while both axial and thin coronal sections together can diagnose 80% of midbrain infarctions, 93.3% of pons infarctions and 93.3% of medulla oblongata infarctions. Furthermore, the thin section coronal 3 mm section can diagnose very smaller ischemic lesion volume in comparison to the standard axial 5mm section (3.4 ± 0.45 / cm3 versus 4.6 ± 0.23 / cm3, P < 0.001) CONCLUSION: The addition of thin-section coronal DWI can facilitate the detection of brainstem ischemic lesions. We suggest its inclusion in the stroke MRI protocol.
In this study, the response and behavior of machine foundations resting on dry and saturated sand was investigated experimentally. In order to investigate the response of soil and footing to steady state dynamic loading, a physical model was manufactured. The manufactured physical model could be used to simulate steady state harmonic load at different operating frequencies. Total of (84) physical models were performed. The parameters that were taken into considerations include loading frequency, size of footing and different soil conditions. The footing parameters were related to the size of the rectangular footing and depth of embedment. Two sizes of rectangular steel model footing were used (100 200 12.5 mm) and (200 400 5.0 mm).
... Show MoreThe rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
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