Reliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of a

It is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major in

Artificial roughness on the absorber plate of a Solar Air Heater (SAH) is a popular technique for increasing its effective efficiency. The study investigated the effect of geometrical parameters of discrete multi-arc ribs (DMAR) installed below the SAH absorber plate on the effective efficiency. The effects of major roughness factors, such as number of gaps (Ng = 1-4), rib pitch (p/e = 4-16), rib height (e/D = 0.018-0.045), gab width (wg/e = 0.5-2), angle of attack ( = 30-75), and Reynolds number (Re= 2000-20000) on the performance of a SAH are studied. The performance of the SAH is evaluated using a top-down iterative technique. The results show that as Re rises, SAH-effective DMAR's efficiency first ascends to a specified value o

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Despite not being digested, trace elements and/or heavy metals are important for the activity of enzymes, physiological processes, and homeostasis. If certain trace elements are present in excess, they can have harmful effects and pose major health hazards. Objective: The aims to examine the connection between serum zinc, copper levels, and the Cu/Zn ratio, and several anthropometric parameters, including an index of body mass and the waist-hip ratio. In our study, we used atomic absorption spectrometry (AAS) to measure serum levels of copper (Cu) and zinc (Zn) in 60 individuals, 30 patients with kidney cancer and 30 healthy controls. We assessed serum uric acid, creatinine, and urea using the semi-auto analyzer BA-88A (Korea). The results

The engagement of pharmacists in research activities is pivotal in the advancement of the pharmacy practice. The study aims to evaluate the confidence and competence of Malaysian hospital pharmacists in conducting clinical and practice-based research.

A cross-sectional study was carried out between September 2019 and April 2020 using an online survey. Pharmacists from eight different hospitals in Malaysia were involved in the study. The survey link was sent to all pharmacists of the included hospitals via email. Data were analysed using SPSS version 25.

A total of 226 pharmacists participated in this study, and their average age was 28 years old. About 82 % of the participants reported that they did not have any previous re

Abstract. Fibrewise micro-topological spaces be a useful tool in various branches of mathematics. These mathematical objects are constructed by assigning a micro-topology to each fibre from a fibre bundle. The fibrewise micro-topological space is then formed by taking the direct limit of these individual micro-topological spaces. It can be adapted to analyze various mathematical structures, from algebraic geometry to differential equations. In this study, we delve into the generalizations of fibrewise micro-topological spaces and explore the applications of these abstract structures in different branches of mathematics. This study aims to define the fibrewise micro topological space through the generalizations that we use in this paper, whi

Surface electromyography (sEMG) and accelerometer (Acc) signals play crucial roles in controlling prosthetic and upper limb orthotic devices, as well as in assessing electrical muscle activity for various biomedical engineering and rehabilitation applications. In this study, an advanced discrimination system is proposed for the identification of seven distinct shoulder girdle motions, aimed at improving prosthesis control. Feature extraction from Time-Dependent Power Spectrum Descriptors (TDPSD) is employed to enhance motion recognition. Subsequently, the Spectral Regression (SR) method is utilized to reduce the dimensionality of the extracted features. A comparative analysis is conducted between the Linear Discriminant Analysis (LDA) class