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PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images
No full textWomen of reproductive age are susceptible to
polycystic ovarian syndrome (PCOS), a hormonal condition.
Multiple small follicles or cysts on the ovaries are one of the
symptoms of PCOS and can be found using ultrasound imaging.
Wavelet ConvNets have been applied in various applications,
including image classification, object detection, and biomedical
signal analysis. A Wavelet ConvNet is a type of deep learning
model that applies wavelet transformation to input data before
feeding it into a convolutional neural network. The wavelet
transform is a mathematical technique that breaks down a
signal or image into a series of sub-bands, each representing
different frequency components of the original data. In this
work, A 2D Discrete Wavelet Transform (2D-DWT) with the
Haar wavelet is applied to each image. The resulting sub-bands
namely Low-Low (LL), Low-High (LH), High-Low (HL), and
High-High (HH) are concatenated to create a 4-channel feature
map. Further, this concatenated feature map is fed into the
ConvNet for classification. The PCOS-WaveConvNet classifier
has attained 99.7% accuracy which is better than a usual
ConvNet model
Non-Linear Time History Analysis of Moment Frame Structure with Performance-Based Approach on Different Concrete Compressive Strengths
No full textThe non-linear time history analysis and Pushover analysis are performed on the two models of different compressive strengths but of the same element and component sizes by keeping the same reinforcement arrangement. The concrete strengths were 30Mpa and 40Mpa. The structure was first analysed based on the linear model approach with the two compressive strengths 30Mpa and 40Mpa in SAP2000 by following the code ACI318. The two nonlinear model were then analysed in the Perform3D. For the modelling of the beams, the plastic hinge modelling approach was used. The modelling of the column was done on the fibre hinge modeling approach. The structure is 7 stories high, and it has a footprint of 50 x 50 m. Each bay is 5 meters. The nonlinear concrete model was constructed based on the Razvi (1992) confined mode. All of the seven earthquakes were scaled for the design response spectrum. The results show a trend in the better performance of 40Mpa structure over 30Mpa. The 30Mpa structure reaches the Immediate Occupancy level earlier than the 40 Mpa structure. The energy consumption patterns do not show any trend or similarity. Usage ratios were found significantly lower in the 40Mpa structure, especially at the Immediate Occupancy level in all of the seven earthquakes. The 40Mpa structure has predominantly less drifts the 30Mpa structure.
Keywords: Performance Based Design, PBD, Perform3D, time history analysis, pushover analysis, nonlinear concrete, concrete compressive strengt
The impact of global renewable energy demand on economic growth – evidence from GCC countries
No full textPurpose – This study aims to examine the relationship between global renewable energy consumption and
economic growth in Gulf Cooperation Council (GCC) countries from 2001 to 2019.
Design/methodology/approach – This paper used a panel regression model to study the six GCC countries
over the period from 2001 to 2019.
Findings – As expected, the findings indicated a significant and negative relationship between global
renewable energy consumption and GCC economic growth. Additionally, there was a positive and significant
relationship between GCC economic growth and the control variables, specifically labor, capital, CO2 emissions
and non-renewable energy production.
Practical implications – The results are of great importance to policymakers in GCC oil-exporting countries,
as expected growth in renewable energy consumption will lower their economic growth in the future. Hence,
they should first diversify their economy and lower their dependence on oil. Second, these countries can invest
in solar energy through international joint ventures, especially with North African countries in close proximity
to Europe, to become leaders in solar energy production.
Originality/value – How global energy consumption is related to GCC countries’ economic growth remains
unclear, not only in GCC countries but also in many oil-exporting countries around the world, so future studies
are needed. Furthermore, GCC governments will be able to create appropriate policies for the green economy
and achieve their objectives if they have a comprehensive understanding of how global growth in renewable
energy demand affects GCC economies.
Keywords Gulf Cooperation Council (GCC), Global renewable energy, Non-renewable energy,
Economic growth
Paper type Research pap
Does technological progress make OECDcountries greener? New evidence from panel CS-ARDL
No full textPurpose– This paper aims to examine the impact of information and telecommunication technologies
(ICT-proxied by mobile phone subscription and Internet usage) on carbon dioxide (CO2) emissions in the
Organization for Economic Cooperation and Development (OECD) countries from 1990 to 2018.
Design/methodology/approach– The Cross-section Autoregressive Distributed Lag (CS-ARDL) model is
employedtoaddressthepotential cross-section dependence problem. CommonCorrelated Effects Mean Group
(CCEMG) and Augmented Mean Group (AMG) estimators are used to test for robustness of results.
Findings– Results reveal contrasting effects of mobile phone subscription and Internet usage on CO2
emissions. While mobile phone penetration helps mitigate CO2 emissions, Internet usage tends to increase the
emissions. Findings show that renewable energy is beneficial to the environment while economic growth is
harmful to the environment. The effects of financial development and trade openness seem negligible.
Practical implications– This study offers practical implications for policymakers. As different proxies of
ICTcouldhavecontradictoryimpactonCO2,governmentsshouldbecautiousagainstutilizingICTtomitigate
CO2. Findings point to the benefits of renewable energy in alleviating CO2 emissions. Therefore, governments
are strongly advised to implement policies facilitating renewable energy consumption.
Originality/value– Previous studies ignored the problem of cross-section dependence which could lead to
biased results and cause misleading inferences. This study aims to fill this void in the literature
Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning
No full textUsing Machine Learning (ML) in industry has vast applications, however using it in medical domain alerts a priority to help doctors determine unseen or hidden indicators of any probable illness or medical condition, which if not treated urgently may affect patient health. In this paper, the author aims to review and enhance Image recognition and classification using ML methodologies. The data input of X-ray images taken for medical proposes, used to gain better outcomes through advanced analysis of the training data, this includes specifying the average amount of data needed for training to make a good enough predictions using deep learning (DL) in order to save costs. In addition, exploring training data by applying data cleaning techniques to gain a well-balanced model for classification purposes. Author shown that setting 1600 x-ray images or more, as a training data input, tend to enforce a steady percentage of accuracy greater than 90%. Moreover, author described the results of using dirty (unclean) or unbalanced data to the ML model, which showed a clearly drop in precision, recall and F1 score percentages. Overall, our proposed experiments showed the importance of having a quality training data in achieving higher performance results
Analyzing the Importance of Cost Overrun Planning and Control for Construction Projects Success in the UAE Region
No full textCost overruns are considered a major hindrance to project completion as they lead to a decline in the contractor's income, resulting in significant financial setbacks and endangering the entire project. The cost of construction stands as a critical element in determining the project's overall success. Throughout the entire lifespan of the construction industry, a prominent concern arises, namely, the pervasive impact of uncertainty and deadlines on all projects, regardless of their size or complexity. These elements continuously pose challenges and demand careful attention from stakeholders involved in construction endeavors. Cost overruns are a prevailing issue in construction projects within developing countries. Such overruns manifest across various construction undertakings, exhibiting different degrees of severity. Hence, it is imperative to recognize and address the critical concern of construction cost overruns. Several factors contribute to the occurrence of cost overruns in construction projects, with the most prominent ones being design and contract-related aspects, estimation uncertainties, planning and scheduling challenges, project management complexities, labor constraints, financial factors, material and machinery considerations, construction phase issues, communication hurdles, and external influences. The current study holds significant value as it consolidates the most frequent causes of construction cost overruns, enabling project participants to identify, tackle, and mitigate the adverse effects of these factors
Exploring Nursing Education Stakeholders’ Perceptions of Students with Disabilities Inclusion in Nursing Education Programs in the United Arab Emirates: Issues and Challenges
No full textThe purpose of the study was to explore nursing education stakeholders’ perceptions about inclusion of students with disabilities in nursing education programs in the UAE and the barriers and facilitators to their inclusion. A sequential exploratory mixed-methods design was used to conduct this study. Data was collected using unstructured interviews, -semi-structured interviews, and questionnaires. Thematic analysis of the interviews with 7 nursing education stakeholders revealed the following barriers to the inclusion of nursing students with disabilities: 1) Nature of Disability 2) Knowledge of Nursing Faculty 3) Attitudes 4) Communication 5) Resources 6) Nursing Program Requirements 7) Admission and Support Policies 8) Disability Outreach Activities. Semi-structured interviews with 14 nursing education stakeholders showed eight themes related to the facilitators of the inclusion of nursing students with disabilities in nursing education programs: 1) Disability Laws and Policies 2) Disability Awareness 3) Establish Early Detection of Cases 4) Education and Training Programs 5) Creative Access 6) Attitudes 7) Communication to Meet Needs 8) Collaboration with Potential Employers. 284 nursing education students and 29 nursing education faculty members from health science academic institutions in Abu Dhabi, Ajman, Al-Ain, and Al Dhafra in the UAE completed the questionnaires. The descriptive and inferential statistical analysis of the responses of nursing education faculty members and nursing education students was performed using SPSS software. The findings showed that the educators and students had concerns regarding inclusion of students with disabilities. However, implementing facilitators and overcoming barriers can enhance the accessibility of nursing students with disabilities to nursing education programs. The findings also showed significant differences in nursing students' and faculty's perceptions toward inclusion concerning their interactions with students with disabilities and having completed a course about individuals with disabilities. This research fills a knowledge gap related to disability inclusivity in nursing education in the UAE. Understanding the perceptions of nursing education stakeholders towards the inclusion of students with disabilities in nursing education may help in reducing negative attitudes and discriminatory practices and may assist in improving the general understanding of how to increase the participation of students with disabilities in nursing education and the access to care for underrepresented groups. Moreover, the findings of the will support nursing educators in meeting the needs of students with disabilities from legal, ethical and individualistic perspectives. Furthermore, findings will aid nursing education faculty members, nursing education administrators, disability services, and clinical practice partners to determine and provide reasonable accommodations that promote success of nursing students with disabilities in theory and clinical settings, Finally, findings will aid in the revision and subsequent development of policies and guidelines related to educating and supporting students with disabilities in nursing education programs in the UAE
Optimizing the construction team’s performance The influence of an agile project manager on a construction project team’s Success
No full textThe United Arab Emirates, particularly the Emirates of Dubai, is one of the world's leading cities in mega projects, characterized by their modernity and speed compared to the rest of developed countries. The aggressive competition between the leading developers among the horizontal spread and high-rise buildings in this city is one of the most obvious things for construction experts and even residents.
Those main developers and even main contractors always aim to complete projects within Budget, on time and with high-quality meeting customer expectations, these goals can be delivered by a successful project team. And continuously searching to have a successful construction team translates into following many methods and ways to improve the performance of the individual team members and the performance of the whole team, which can only be achieved through our focus on the basic rules or pillars that play a significant role and influence the efficiency of the construction tea
Barriers of Adopting Artificial Intelligence Tools in Engineering Construction Projects
No full textEngineering construction projects using AI technologies create obstacles. Barriers prevent AI integration in construction. Insufficient knowledge and understanding of AI, data management and integration issues, legal and regulatory hurdles, financial considerations, reluctance to change, and interoperability issues all hinder AI system adoption and implementation.
First, many engineering and construction specialists may not understand or use artificial intelligence. The situation may cause concern and trepidation about integrating AI technologies and lack understanding of their optimal deployment and operation. Construction data management and integration are difficult. AI algorithms depend on data for training and analysis. AI technologies often struggle with construction data's fragmentation, inconsistency, and confidentiality.
AI technologies may also face regulatory and legal hurdles related to data confidentiality, protection, and property rights. These issues may limit AI tool use or increase legal liability, hindering adoption. Integrating AI technology may be costly, especially for smaller companies with limited funds. Hardware, software, and training costs may prevent certain construction businesses from using AI solutions.
Industry culture and change aversion may slow AI tool adoption. Conventional attitudes, fear of termination, and reluctance to change processes may hinder AI adoption in construction workflows. Finally, interoperability and integration issues may hinder AI adoption. Incorporating AI into pre-existing software, systems, and processes may require significant effort and tailoring, and compatibility issues may hinder the easy integration of AI utilities into building projects
