The importance of physical and nonphysical architectural design values made architectural designers need good experience to be experts of architectural values reasonably without neglecting any value in the design process. The importance of such values made that ignoring any values and mistakes occurs in the design process. Simultaneously, architectural designers' different nature and the difference in their experiences are causing different understandings of the design values, thus causing architectural mistakes. The research problem appears from the randomly propagating of mistakes in contemporary architecture, which is about to become a phenomenon in Al Sulaymaniyah city. The research aims to find the main reason
... Show MoreThe data preprocessing step is an important step in web usage mining because of the nature of log data, which are heterogeneous, unstructured, and noisy. Given the scalability and efficiency of algorithms in pattern discovery, a preprocessing step must be applied. In this study, the sequential methodologies utilized in the preprocessing of data from web server logs, with an emphasis on sub-phases, such as session identification, user identification, and data cleansing, are comprehensively evaluated and meticulously examined.
This paper delves into some significant performance measures (PMs) of a bulk arrival queueing system with constant batch size b, according to arrival rates and service rates being fuzzy parameters. The bulk arrival queuing system deals with observation arrival into the queuing system as a constant group size before allowing individual customers entering to the service. This leads to obtaining a new tool with the aid of generating function methods. The corresponding traditional bulk queueing system model is more convenient under an uncertain environment. The α-cut approach is applied with the conventional Zadeh's extension principle (ZEP) to transform the triangular membership functions (Mem. Fs) fuzzy queues into a family of conventional b
... Show MoreData scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Introduction: Nitrofurantoin (NFT) is abroad spectrum bactericidal antibiotic. The bioavailability of NFT is affected by many factors. Samafurantin® tablets containing 50 mg NFT were manufactured by Samarra drug industry. Urinary excretion studies were employed since; the urinary tract is the main site of NFT action and excretion. Objective: The objective of the study was to investigate the effect of Uricol® and food on secondary pharmacokinetic parameters of Samafurantin® tablets with different doses by applying urinary data. Methods: Twelve healthy male volunteers participated in this study. Urine samples were collected from each volunteer after overnight fasting at a specified time intervals which considered as a blank sample for meas
... Show MoreThe Sonic Scanner is a multifunctional instrument designed to log wells, assess elastic characteristics, and support reservoir characterisation. Furthermore, it facilitates comprehension of rock mechanics, gas detection, and well positioning, while also furnishing data for geomechanical computations and sand management. The present work involved the application of the Sonic Scanner for both basic and advanced processing of oil-well-penetrating carbonate media. The study aimed to characterize the compressional, shear, Stoneley slowness, rock mechanical properties, and Shear anisotropy analysis of the formation. Except for intervals where significant washouts are encountered, the data quality of the Monopole, Dipole, and Stoneley modes is gen
... Show MoreThe proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.
In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete
... Show More