Use of Oil-Based Mud Cutting Waste in Cement Clinker Manufacturing

Oil-based Mud (OBM) cutting waste is generated during the process of oil well drilling. The drilled rocks are removed from deep within the drilled well and pumped to the surface. The portion removed , known at "cutting", is a mixture of rocks, mud, water and oil. Most drilling companies store this waste in open yards with no specific treatment solution. The environmental regulations in Oman specify that storage should involve isolation, to prevent penetration of the contamination to the surface and underground water. This has made OBM waste an environmental problem, with an associated cost for oil companies. OBM chemical analysis shows an interesting compositionthat may be used in cement manufacture. It has high calcium, silicon and aluminium contents, which are the major oxides in cement manufacture. Also the oil contents are useful for reducing the fuel used during the calcining and clinkerization process. In this research, the OBM waste has been analysed and used as a constituent of the raw meal for cement clinker production. The impact of OBM addition on the resultant clinker has also been investigated

Persistent Pneumothorax: Alveolar pleural fistula due to a hole in a bulla

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Rheumatoid Arthritis Diagnosis Based on Intelligent System

التهاب المفاصل الروماتويدي  يؤثر على كثير من الناس مستهدفا المفاصل وخاصة المفاصل الصغيرة، ويستهدف جميع الأعمار حيث هو أكثر شيوعا في النساء. هذا المرض له العديد من الأعراض مشابهة لأمراض أخرى. لذلك، فمن الصعب جدا كشفه. كما أن أدوات التشخيص معقدة وغير اقتصادية. في هذا البحث، شبكة الذكاء الاصطناعي استخدمت لتشخيص والكشف المبكر عن التهاب المفاصل الروماتويدي وفقا للمعايير التي وضعتها الكلية الأمريكية للروماتيزم. أفضل أداء يحدث مع الحد الأدنى لعدد الخلايا العصبية المطلوبة عندما يكون عدد الخلايا العصبية هو 6. بحيث، فإن الأداء يساوي 10-10×3.8968. عند تقليل عدد الخلايا العصبية إلى 5 أو زيادة إلى 8، والنتيجة هي 0.0041 و  10-10×1.0611 ,على التوالي. مع ذلك، يمكن اعتبار جميع النتائج مقبولة و أن أفضل خيار لهذه التصاميم سيكون 6 خلايا عصبية من جانب التعقيد والدقة.The Rheumatoid Arthritis (RA) affects many people targeting their joints, especially small joints, and it targets all ages which it is more common in women. This disease has many symptoms similar to other diseases. Therefore, it is very hard to detect. Also, the diagnostic tools are complex and uneconomical. In this paper, artificial intelligence network used for diagnosis and early detection of RA in accordance with criteria developed by the American College of Rheumatology. The best performance occurs with the minimum number of neurons required when the number of neurons is 6. So that, the performance is equal to 3.8968x1010-.  When reducing the number of neurons to 5 or increasing to 8, the result is  0.0041 and 1.0611×10-10, respectively. However, all results can be consider acceptable and indicate that the best choice from this structure will be 6 neurons in the form of complexity and accuracy

Performance assessment of antenna array for an unmanned air vehicle

In this paper, the performance of Linear Antenna Array Element (LAAE) has been evaluated at the Base Station (BS) with a different number of elements for Unmanned Air Vehicle UAV application. The Switched Beam (SB) and Phase Array (PA) have been used as a steering beam mechanism. The beam steering tracker is based on the GPS points of the UAV and the BS. In addition, the Misalignment angle has been analyzed for SB and PA corresponding to the maximum speed of the UAV. The compression between SB and PA in term of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) and BER vs. Misalignment angle have been examined by using Matlab. The results show that the PA has better performance than SB in both terms under Additive White Gaussian Noise (AWGN) channel with an interference signal. When the number of the elements is eight provides longer distance than four by the factor (1.5 in SB case and 2 in PA case) and wider Misalignment angle range than twelve by factor (2 in SW case and 3 in PA case). Therefore, it is becoming a useful option for many applications

Adapted LZW Protocol for ‎ ECG Data Compression

خوارزمية الـ(LZW) هي واحدة من طرق ضغط البيانات المستخدمة في عدة تطبيقات كضغط بيانات تخطيط القلب الكهربائي( ECG) لتقليل حجمها مما يسهل عملية نقلها عبر الشبكة. بما ان بيانات الـ(ECG) الخاصة بالمرضى تنقل عبر الشبكة طول الوقت لذلك ظهرت الحاجة الى تقليل حجمها من اجل ضمان وصولها بالسرعة الممكنة  لقاعدة البيانات. في هذه البحث نحن نهتم بطريقة الـ (LZW) التي هي واحدة من اهم واشهر طرق ضغط البيانات وقد اقترحنا بروتوكول لتحسين الطريقة التي تعتمدها خوارزمية الـ(LZW) في خزن المؤشرات الخاصة بالبيانات المضغوطة. البروتوكول المقترح يمكن ان يقلل حجم المؤشر لخوارزمية الـ(LZW). تم اعتماد خمس عينات اخذت من بنك المعلومات الخاص بـ(Physionet) لغرض اختبار البروتوكول المقترح. وقد اظهرت نتائج الاختبارت العملية ان البروتوكول المقترح يعطي نسبة ضغط افضل لبيانات الـ(ECG) مقارنة بطريقة الـ(LZW) الاصلية.Lempel–Ziv–Welch (LZW) is a data compression method, which is adopted by many applications likes Electrocardiography (ECG) data to reduce the size of transferred data. Because of the ECG data moves over the network all the time, which means there is a need to reduce its size to improve the network performance. In this paper, we concerned with the LZW method, which is one of the important and famous data compression method. We propose a protocol to improve the way in which the LZW saving an index for the compressed data. The proposed protocol could reduce the size of the index in LZW method. Five samples data groups provided by Physionet are used for evaluation. The experimental result shows that the proposed protocol can give best compression ratio compared with the original method

Real-Time classification of various types of falls and activities of daily livings based on CNN LSTM network

In this research, two multiclass models have been developed and implemented, namely, a standard long-short-term memory (LSTM) model and a Convolutional neural network (CNN) combined with LSTM (CNN-LSTM) model. Both models operate on raw acceleration data stored in the Sisfall public dataset. These models have been trained using the TensorFlow framework to classify and recognize among ten different events: five separate falls and five activities of daily livings (ADLs). An accuracy of more than 96% has been reached in the first 200 epochs of the training process. Furthermore, a real-time prototype for recognizing falls and ADLs has been implemented and developed using the TensorFlow lite framework and Raspberry PI, which resulted in an acceptable performance

Intake of caffeine and its association with physical and mental health status among university students

In Western populations, the caffeine intake of young adults has received significant attention in the research literature; our knowledge in other societies remained limited. The objective of this research is to quantify the amount of ingested caffeine and how this is related to measures of physical and mental health in a Bahraini population. A semi-quantitative food frequency questionnaire was used to estimate caffeine intake from coffee, tea, cocoa, soft drinks, energy drinks, chocolates, and over-the-counter medications. Associations between caffeine intake, demographic variables and 25 symptoms measured using the Hopkins Symptoms Checklist-25 were examined. A convenience sample of university students in Bahrain (n = 727) was surveyed. Caffeine, in any form, was consumed by 98% of students. Mean daily caffeine consumption was 268 mg/day, with males consuming more than females. Coffee was the main source of caffeine intake, followed by black tea and energy drinks. Participants consuming 400 mg/day or more showed a statistically and significantly twice as high risk for five symptoms, these were: headaches, spells of terror or panic, feeling trapped or caught, worrying too much about things, and having feelings of worthlessness. The prevalence of caffeine intake among university students in Bahrain is high. The overall mean intake of caffeine from all sources by university students was within levels considered to be acceptable by many dietary recommendations. High caffeine intake was associated with an anxiogenic effect in the surveyed students. View Full-Tex

Eye Disease Classification Using Deep Learning Techniques

Eye is the essential sense organ for vision function. Due to the fact that certain eye disorders might result in vision loss, it is essential to diagnose and treat eye diseases early on. By identifying common eye illnesses and performing an eye check, eye care providers can safeguard patients against vision loss or blindness. Convolutional neural networks (CNN) and transfer learning were employed in this study to discriminate between a normal eye and one with diabetic retinopathy, cataract, or glaucoma disease. Using transfer learning for multi-class classification, high accuracy was achieved at 94% while the traditional CNN achieved 84% rate