Background: Acute cholecystitis is common surgical
problem, which was treated previously by conservative
treatment .Later early open has been introduced as an
alternative to interval for treatment of acute cholecystitis.
Early open was found to be a safe, successful with
comparable postoperative complication rate. With the
advent of laparoscopy laparoscopic have been used for
chronic cholecystitis and became the first line of
treatment. New reports have shown that laparoscopic can
be used as an alternative to open for surgical treatment of
acute cholecystitis.
Objectives: to compare the success, safety of early
laparoscopic versus early open as a primary treatment of
acute cholecystitis.
Methods:

In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.

Abstract:

           The great importance that distinguish these factorial experiments made ​​them subject a desirable for use and application in many fields, particularly in the field of agriculture, which is considered the broad area for experimental designs applications.

            And the second case for the factorial  experiment, which faces researchers have great difficulty in dealing with the case unbalance we mean that frequencies treatments factorial  are not equal meaning (that is allocated a number unequal of blocks or units experimental per tre

In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might participate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

The establishment of a high quality service in oral health care achieved by a member of dental professional is an important feature of any system of peer review in dentistry. This protocol attempts to discuss two crucial elements (I) suitability and feasibility of treatment modality and (II) quality of treatment performed to Iraqi patients. The Iraq endodontic society is designing a standardized protocol for endodontic treatment following the quality guidelines of European society of Endodontology (2006) to meet the highest standard of care generally given by competent practitioners. The Iraqi endodontic society has the expertise and professional responsibility clinically relevant to empower the dental profession through creating significan

Huge yearly investments were made by organizations for the development and maintenance. However, it has been reported that most of the IT projects fails as it is delayed, over budget and discontinued quality. A systematic literature review (SLR) was conducted to identify the critical success factors (CSFs) for the IT projects. Nine (9) CSFs was identified from the SLR. An online survey was conducted among 103 respondents from developers and IT managers. The data was analyzed using the Statistical Package for Social Science (SPSS 22). The findings showed that the highest CSFs of IT projects is commitment and motivation. Project monitoring was found the lowest score ranked by respondents.

Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa

Spatial and frequency domain techniques have been adopted in this search. mean
value filter, median filter, gaussian filter. And adaptive technique consists of
duplicated two filters (median and gaussian) to enhance the noisy image. Different
block size of the filter as well as the sholding value have been tried to perform the
enhancement process.

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi