This paper discuses the mindset contribution of the organization Science in organizational effectiveness (OE), and the most valuable thoughts they introduced in this subject, starting from the pre- classical era, through classical era to present time. This paper has identified the main contribution in OE. The concept of OE didn’t arise until what had been introduced by Chester Barnard, although Max Weber mentioned this concept in his bureaucratic theory, but he didn’t explain it as Barnard does. After that no clear focus was pointed to this subject until Peter Drucker did so, which considered as the main participant in OE after Barnard. After that many researchers be interested in OE and they start studying this subject like

بناء على ما تقدم يتضح ان القطاع العام والقطاع الخاص ليسا موقفا ايدولوجيا انتقائيا، وانما هو انبثاق من العمود الفقري للصيرورة الرأسمالية القائمة على تقديس الملكية الخاصة، وان الادوار التي يمكن ان يلعباها ليسا منعزلين عن الحركة العامة للاقتصاد في كليته، وليس هما ابنا السياسات الاقتصادية، والبناء الفوقي بقدر ماهما نتاج الحركة الموضوعية وصيرورة التطور الاجتماعي معبرا عنها في تطور علاقات الانتاج وقوى الانتا

التعددية السياسية بين الرفلض والقبول دراسة نظرية لأبرز الاتجاهات الاسلامية المعاصرة

his study aims to determine most stable isobar from some isobaric elements with mass number (A= 50-65 & 180-195). This aim achieved by, firstly: plot mass parabolas for these isobaric family, second: calculated the atomic number for most stable isobar (ZA) value. To plot the mass parabola, the binding energy (B.E) calculated from semi empirical formula for these isobars. The mass number (A) plotted as a function to the (ZA) for each range; we get a linear relationship between them. An empirical formula for the most stable isobar has been developed from this linear dependence. From the results, we can see that mass parabolas for isobaric elements with odd mass number (A) are different from the mass parabolas of even mass number (A) isobars,

In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Many oil and gas processes, including oil recovery, oil transportation, and petroleum processing, are negatively impacted by the precipitation and deposition of asphaltene. Screening methods for determining the stability of asphaltenes in crude oil have been developed due to the high cost of remediating asphaltene deposition in crude oil production and processing. The colloidal instability index, the Asphaltene-resin ratio, the De Boer plot, and the modified colloidal instability index were used to predict the stability of asphaltene in crude oil in this study. The screening approaches were investigated in detail, as done for the experimental results obtained from them. The factors regulating the asphaltene precipitation are different fr

      The Matching and Mosaic of the satellite imagery play an essential role in many remote sensing and image processing projects. These techniques must be required in a particular step in the project, such as remotely change detection applications and the study of large regions of interest. The matching and mosaic methods depend on many image parameters such as pixel values in the two or more images, projection system associated with the header files, and spatial resolutions, where many of these methods construct the matching and mosaic manually. In this research, georeference techniques were used to overcome the image matching task in semi automotive method. The decision about the quality of the technique can be considered i