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Preparing polycaprolactone scaffolds using electrospinning technique for construction of artificial periodontal ligament tissue
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Objectives The strategies of tissue-engineering led to the development of living cell-based therapies to repair lost or damaged tissues, including periodontal ligament and to construct biohybrid implant. This work aimed to isolate human periodontal ligament stem cells (hPDLSCs) and implant them on fabricated polycaprolactone (PCL) for the regeneration of natural periodontal ligament (PDL) tissues. Methods hPDLSCs were harvested from extracted human premolars, cultured, and expanded to obtain PDL cells. A PDL-specific marker (periostin) was detected using an immunofluorescent assay. Electrospinning was applied to fabricate PCL at three concentrations (13%, 16%, and 20% weight/volume) in two forms, which were examined through field emission scanning electron microscopy (FESEM). The isolated hPDLSCs were implanted on the fabricated PCL. After 21 days, FESEM was conducted to evaluate the implanted scaffolds, and an MTT assay was performed to characterize the biological response of the PCL scaffold at different cell exposure durations (24, 48, and 72 h). Results Periostin was expressed in the expanded PDL cells, and this result revealed that 20% weight/volume PCL scaffold with a pore size of more than 10 μm was the best. The growth rates of PDLSCs were high. Cytotoxicity test of fabricated PCL scaffold demonstrated no significant change in the cell viability when compared with the negative control and no deteriorating or inhibitory effect on growth after different durations. Conclusions A cell sheet was successfully formed by using PCL as a scaffold to cover dental implants and promote PDL cell attachment, proliferation, and growth for biohybrid implant construction.

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Publication Date
Sat Jan 01 2022
Journal Name
International Journal Of Management And Enterprise Development
Studying the decision-making state and impact in Iraqi construction projects
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Publication Date
Tue Sep 01 2015
Journal Name
Journal Of Engineering
Internal Convective Heat Transfer Effect on Iraqi Building Construction Cooling Load
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This work involves the calculation of the cooling load in Iraqi building constructions taking in account the effect of the convective heat transfer inside the buildings. ASHRAE assumptions are compared with the Fisher and Pedersen model of estimation of internal convective heat transfer coefficient when the high rate of ventilation from ceiling inlet configuration is used. Theoretical calculation of cooling load using the Radiant Time Series Method (RTSM) is implemented on the actual tested spaces. Also the theoretical calculated cooling loads are experimentally compared by measuring the cooling load in these tested spaces. The comparison appears that using the modified Fisher and Pedersen model when large ventilation ra

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Sat Jan 01 2022
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Archives Of Civil Engineeringthis Link Is Disabled
Factors affecting time and cost trade-off in multiple construction projects
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Thu Oct 01 2020
Journal Name
Green Engineering
Sustainability and recovery project management implementation on construction projects in Iraq
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Sat Aug 01 2020
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Practice Periodical On Structural Design And Construction
Case Study to Evaluate Work-Zone Safety Technologies in Highway Construction
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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Significant Safety Factors Affecting the Safety Performance in Iraqi Construction Projects
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Despite recent attempts to improve safety in the construction sector, this sector is considered dangerous and unsafe. Iraq is one of the emerging nations that suffers from a lack of construction safety management. In 2018, the construction sector in Iraq was responsible for 38% of all industrial accidents. Creating a safety program minimizes this problem by making safety an intrinsic part of construction projects. As a result, this article aims to identify the crucial safety factors that affect the safety performance in Iraqi construction projects. After conducting a critical literature review of the related literature, a list of 35 sub-factors classified into nine categories of main factors was chosen to rank each facto

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Publication Date
Thu Jan 01 2026
Journal Name
Malaysian Journal Of Nursing
Pediatric Nursing Students and Artificial Intelligence: A Cross-Sectional Study
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Background: The rapid integration of Artificial Intelligence (AI) into healthcare necessitates that nursing education evolves to equip students with essential technological competencies. Objectives: To explore pediatric nursing students' perceptions of AI in nursing and analyze associations with sociodemographic factors and prior AI knowledge. Methods: A descriptive cross-sectional study was conducted from December 2024 to March 2025 across five universities in Baghdad. A non-probability sample of 500 pediatric nursing students completed the Shinners Artificial Intelligence Perception (SAIP) tool. Data were analyzed using descriptive statistics and inferential comparisons (t-tests/ANOVA) via SPSS. Results: Participants had a mean ag

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Publication Date
Mon Jan 01 2024
Journal Name
International Journal Of Mathematics And Computer Science
Artificial Intelligence Techniques to Identify Individuals through Palm Image Recognition
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Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

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Publication Date
Wed Nov 01 2023
Journal Name
Al-rafidain Journal Of Medical Sciences ( Issn 2789-3219 )
After Introducing Artificial Intelligence, can Pharmacists Still Find a Job?
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Publication Date
Sun Nov 26 2017
Journal Name
Journal Of Engineering
Compression Index and Compression Ratio Prediction by Artificial Neural Networks
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Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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