Due to a party's violation of his obligations or responsibilities indicated in the contract, many engineering projects confront extensive contractual disputes, which in turn need arbitration or other forms of dispute resolution, which negatively impact the project's outcome. Each contract has its terms for dispute resolution. Therefore, this paper aims to study the provisions for dispute resolution according to Iraqi (SBDW) and the JCT (SBC/Q2016) and also to show the extent of the difference between the two contracts in the application of these provisions. The methodology includes a detailed study of the dispute settlement provisions for both contracts with a comparative analysis to identify the differences in the appli

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

Abstract

The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative,  the first one is  (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable

This  investigation was conducted to recognize the structure for (RHETI version 2.5  1999) by using exploratory and confirmatory factor analysis. Sample of (620) student of Al-Mustansrya University were administered the (RHETI).

The data of their responses was analyzed by using (PAF) and oblique rotating .

The findings explored (9) factors as one factor for each type and (184) items were loaded by the factors: (60) item for feeling center, (61) items for instinctive center and (63) items for thinking center.

Results of confirmatory factorial analysis supported a model designed by the researcher depended upon a theoretical views of Riso and Hudson

The PbSe alloy was prepared in evacuated quarts tubs by the method of melt quenching from element, the PbSe thin films prepared by thermal evaporation method and deposited at different substrate temperature (Ts) =R.T ,373 and 473K . The thin films that deposited at room temperature (R.T=303)K was annealed at temperature, Ta= R.T, 373 and 473K . By depended on D.C conductivity measurements calculated the density of state (DOS), The density of extended state N(Eext) increases with increasing the Ts and Ta, while the density of localized state N(Eloc) is decreased . We investigated the absorption coefficient (?) that measurement from reflection and transmission spectrum result, and the effect of Ts and Ta on it , also we calculated the tai

DEMs, thus, simply regular grids of elevation measurements over the land surface.The aim of the present work is to produce high resolution DEM for certain investigated region (i.e. Baghdad University Campus\ college of science). The easting and northing of 90 locations, including the ground-base and buildings of the studied area, have been obtained by field survey using global positioning system (GPS). The image of the investigated area has been extracted from Quick-Bird satellite sensor (with spatial resolution of 0.6 m). It has been geo-referenced and rectified  using 1^st order polynomial transformation. many interpolation methods have been used to estimate the elevation such as ordinary Kriging, inverse distance weight

In many applications such as production, planning, the decision maker is important in optimizing an objective function that has fuzzy ratio two functions which can be handed using fuzzy fractional programming problem technique. A special class of optimization technique named fuzzy fractional programming problem is considered in this work when the coefficients of objective function are fuzzy. New ranking function is proposed and used to convert the data of the fuzzy fractional programming problem from fuzzy number to crisp number so that the shortcoming when treating the original fuzzy problem can be avoided. Here a novel ranking function approach of ordinary fuzzy numbers is adopted for ranking of triangular fuzzy numbers with simpler an

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob