A lack of adequate building maintenance is a significant obstacle faced by governmental hospitals. This paper evaluates factors that negatively impact building-maintenance practices in Iraq. A literature review was conducted to identify factors affecting maintenance. A list of 42 factors affecting hospital-buildings was collected from previous studies and tested using a structured questionnaire distributed to hospital-maintenance experts. During the data analysis, 76 valid questionnaires were used. Based on the respondents’ ratings, the relative-importance index (RII) was used to determine the level of importance of each factor. From the results, it was concluded that twelve factors affect maintenance practices in hospital buildin

The melting duration in the photovoltaic/phase-change material (PV/PCM) system is a crucial parameter for thermal energy management such that its improvement can realize better energy management in respect to thermal storage capabilities, thermal conditions, and the lifespan of PV modules. An innovative and efficient technique for improving the melting duration is the inclusion of an exterior metal foam layer in the PV/PCM system. For detailed investigations of utilizing different metal foam configurations in terms of their convective heat transfer coefficients, the present paper proposes a newly developed mathematical model for the PV/PCM–metal foam assembly that can readily be implemented with a wide range of operating condition

The importance of operational risks increases with the increase in technological development, the development of banking operations, the extent of banking compliance, and the attempt of many banks to achieve quality in banking services. And the extent of the position occupied by Iraqi banks for banking compliance and reducing operational risks. The Basel Committee (2) paid its attention to operational risks and the interest of international banks to follow policies that work to ensure banking compliance and cover operational risks, because of its role in reducing losses due to increased costs and achieving an increase in profits. Realizing and working to confront the best possible and traditional methods, that some risks Operational problem

  Tax state institution regards as one of the largest state institutions implementing the tax rules issuing be legislative body and achieving the goals of tax (financial, economic, social and political). So, the tax management should pay attention to the procedures enabling it to achieve those rules starting from the procedures of tax restrict and ending by tax allocation where the process of assessment the taxation must relaxing on modern methods. The problem of the study raising from that in spite of there is a low  obliging the taxable person (registered or not) to submit a declaration about his income and the achieved profit to be the base of taxation˒ where the other ways  are secondary ways helping in rejection of t

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra