Personalized Quantification of Facial Normality using Artificial Intelligence

While congenital facial deformities are not rare, and surgeons typically perform operations to improve these deformities, currently the success of the surgical reconstruction operations can only be “measured” subjectively by surgeons and specialists. No efficient objective mechanisms of comparing the outcomes of plastic reconstruction surgeries or the progress of different surgery techniques exist presently. The aim of this research project is to develop an efficient software application that can be used by plastic surgeons as an objective measurement tool for the success of an operation. The long-term vision is to develop a software application that is user-friendly and can be downloaded on a regular laptop and used by doctors and patients to assess the progress of their surgical reconstruction procedures. The application would work by first scanning a face before and after an operation and providing the surgeon with a normality score of the face from 0 to 3 where 3 represents normal and 0 represents extreme abnormality. A score will be given when the face is scanned before and after surgery. The difference between those scores is what we will call the delta. A high delta value would point to a high improvement in the normality of a face post-surgery, and a low delta value would indicate a small improvement. The first chapter of the thesis represents the introduction which describes the general aspects of the project. The second chapter presents the methodology employed for building the application and the existing solutions and proposed functional model structure. The results chapter presents the process behind collecting and labeling the image database and analyzes the scores produced by the program when fed with new images from the database. Finally, the last chapter of this thesis presents the conclusions. The list of references completes this work

Test Procedures for Advanced Characterization of Bituminous Binders Employed for Pavement Construction in Public Works Authority Road Projects - State of Qatar

This paper illustrates the approach adopted by the Public Works Authority (Ashghal) of the State of Qatar for the advanced chemical and rheological characterization of bituminous binders (both neat and modified). The ultimate objective of testing is to create a database of employed binders, which may be meaningful for the optimization of the selection of materials and for the consequent enhancement of pavement performance. This paper provides an illustration of employed testing procedure and briefly discusses typical experimental results

Use of Crumb Rubber Modified Binders and Asphalt Mixtures in Public Works Authority Road Projects - State of Qatar

This paper illustrates the approach adopted by the Public Works Authority (Ashghal) of the State of Qatar for the widespread implementation in road projects of paving technologies related to the use of crumb rubber modified binders (CRMBs). Such an approach has entailed the monitoring of a full-scale preliminary trial, the definition of a prequalification system for crumb rubber and CRMB producers, and the development of mix design and quality control guidelines applicable to CRMB asphalt mixtures. Experimental results obtained in the preparatory phases of work and during the approval process of materials and mixtures are critically presented

S. Cheema et al .

Objectives Oral health is a crucial determinant of quality of life. We aimed to determine oral health condition and factors associated with poor oral status in the adult national population of Qatar. Methods We used data from the World Health Organization supported STEPS (STEPwise approach to Surveillance) Survey conducted by the Supreme Council of Health, Qatar in 2012. A total of 2,496 Qataris (1,053 men, 1,443 women) answered the national survey. The Rao-Scott Chi-Square test was used to analyze oral health characteristics and multinomial logistic regression to assess risk factors. Results The self-perceived oral status of approximately 40 percent of respondents was either "average" or "poor" rather than "good." Poor oral status was more often reported by women (OR = 1.93; 95%CI = 1.30-2.80), by older (OR = 3.38; 95%CI = 1.59-7.19) and less educated respondents (OR = 3.58; 95%CI = 2.15-5.96). Other risk groups included people with diabetes (OR = 1.87; 95%CI = 1.24-2.81), smokeless tobacco users (OR = 3.90; 95%CI = 1.75-8.68), or ever tobacco users (OR = 1.66; 95%CI = 1.03-2.67). Oral health status appeared to be independent of diet, BMI status, and history of hypertension. Difficulties and behaviors related to oral health were more frequently reported by women than by men. These included pain (P < 0.001), difficulty chewing (P < 0.001), and discomfort over appearance of teeth (P < 0.001). Participants used toothbrushes, toothpicks, dental floss, and miswak to maintain oral hygiene. Conclusion Our results provide evidence that oral health remains a public health concern in Qatar

Explainable deep learning model for automatic mulberry leaf disease classification

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world’s raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for three-class classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models’ findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDS-CNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves

Process–Structure–Property Relationships of Copper Parts Manufactured by Laser Powder Bed Fusion

The process–structure–property relationships of copper laser powder bed fusion (L-PBF)-produced parts made of high purity copper powder (99.9 wt %) are examined in this work. A nominal laser beam diameter of 100 μm with a continuous wavelength of 1080 nm was employed. A wide range of process parameters was considered in this study, including five levels of laser power in the range of 200 to 370 W, nine levels of scanning speed from 200 to 700 mm/s, six levels of hatch spacing from 50 to 150 μm, and two layer thickness values of 30 μm and 40 μm. The influence of preheating was also investigated. A maximum relative density of 96% was obtained at a laser power of 370 W, scanning speed of 500 mm/s, and hatch spacing of 100 μm. The results illustrated the significant influence of some parameters such as laser power and hatch spacing on the part quality. In addition, surface integrity was evaluated by surface roughness measurements, where the optimum Ra was measured at 8 μm ± 0.5 μm. X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDX) were performed on the as-built samples to assess the impact of impurities on the L-PBF part characteristics. The highest electrical conductivity recorded for the optimum density-low contaminated coils was 81% IACS

The Perceived Impacts of Staging the 2022 FIFA World Cup in Qatar

This paper aimed to examine residents’ attitudes and perceptions of the social and cultural impacts of the 2022 FIFA World Cup and to understand the legacy dimensions of this sport event in Qatar. Drawing on social exchange theory, this paper reports results from survey data comprising 1018 nationals and 1014 expatriates who participated in the Survey of Perceptions and Attitudes towards the 2022 FIFA World Cup conducted in 2019 in Qatar. An impact scale comprising 21 social and cultural impact items was used to elicit participants’ responses. Results from ANOVA indicated that nationality matters with respect to perceived significant predictors of the World Cup. Results revealed that residents, national and expatriates, were supportive of hosting the event in Qatar but were concerned over traffic, pollution, price increases, and the potential rise in the overall cost of living. The paper concludes with implications for mega-sports events and future research are proposed

Review of optimization techniques applied for the integration of distributed generation from renewable energy sources

Several potential benefits to the quality and reliability of delivered power can be attained with the installation of distributed generation units. To take full advantage of these benefits, it is essential to place optimally sized distributed generation units at appropriate locations. Otherwise, their installation could provoke negative effects to power quality and system operation. Over the years, various powerful optimization tools were developed for optimal integration of distributed generation. Therefore, optimization techniques are continuously evolving and have been recently the focus of many new studies. This paper reviews recent optimization methods applied to solve the problem of placement and sizing of distributed generation units from renewable energy sources based on a classification of the most recent and highly cited papers. In addition, this paper analyses the environmental, economic, technological, technical, and regulatory drivers that have led to the growing interest on distributed generation integration in combination with an overview about the challenges to overcome. Finally, it examines all significant methods applying optimization techniques of the integration of distributed generation from renewable energy sources. A summary of common heuristic optimization algorithms with Pro-Con lists are discussed in order to raise new potential tracks of hybrid methods that haven't been explored yet. 2017This publication was made possible by the National Priorities Research Program (NPRP) award [NPRP6-244-2-103] from the Qatar National Research Fund (QNRF); a member of the Qatar Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of QNRF.Scopus2-s2.0-8502003130

Triglyceride profiling in adipose tissues from obese insulin sensitive, insulin resistant and type 2 diabetes mellitus individuals

Abstract Background Lipid intermediates produced during triacylglycerols (TAGs) synthesis and lipolysis in adipocytes interfere with the intracellular insulin signaling pathway and development of insulin resistance. This study aims to compare TAG species and their fatty acid composition in adipose tissues from insulin sensitive (IS), insulin resistant (IR) and type 2 diabetes mellitus (T2DM) obese individuals. Methods Human subcutaneous and omental adipose tissue biopsies were obtained from 64 clinically characterized obese individuals during weight reduction surgery. TAGs were extracted from the adipose tissues using the Bligh and Dyer method, then were subjected to non-aqueous reverse phase ultra-high performance liquid chromatography and full scan mass spectrometry acquisition and data dependent MS/MS on LTQ dual cell linear ion trap. TAGs and their fatty acid contents were identified and compared between IS, IR and T2DM individuals and their levels were correlated with metabolic traits of participants and the adipogenic potential of preadipocyte cultures established from their adipose tissues. Results Data revealed 76 unique TAG species in adipose tissues identified based on their exact mass. Analysis of TAG levels revealed a number of TAGs that were significantly altered with disease progression including C46:4, C48:5, C48:4, C38:1, C50:3, C40:2, C56:3, C56:4, C56:7 and C58:7. Enrichment analysis revealed C12:0 fatty acid to be associated with TAGs least abundant in T2DM whereas C18:3 was found in both depleted and enriched TAGs in T2DM. Significant correlations of various adipose tissue-derived TAG species and metabolic traits were observed, including age and body mass index, systemic total cholesterol, TAGs, and interleukin-6 in addition to adipogenic potential of preadipocytes derived from the same adipose tissues. Conclusion Pilot data suggest that adipose tissues from obese IR and T2DM individuals exhibit TAG-specific signatures that may contribute to their increased risk compared to their IS counterparts. Future experiments are warranted to investigate the functional relevance of these specific lipidomic profiles

Oxidation–reduction potential and sperm DNA fragmentation, and their associations with sperm morphological anomalies amongst fertile and infertile men

Objective: To assess seminal oxidation–reduction potential (ORP) and sperm DNA fragmentation (SDF) in male infertility and their relationships with sperm morphology in fertile and infertile men. Patients and methods: Prospective case-control study comparing the findings of infertile men (n = 1168) to those of men with confirmed fertility (n = 100) regarding demographics and semen characteristics (conventional and advanced semen tests). Spearman rank correlation assessed the correlation between ORP, SDF, and different morphological indices. Means of ORP and SDF were assessed in variable levels of normal sperm morphology amongst all participants. Results: Infertile patients had a significantly lower mean sperm count (32.7 vs 58.7 × 106 sperm/mL), total motility (50.1% vs 60.4%), and normal morphology (5.7% vs 9.9%). Conversely, infertile patients had significantly higher mean head defects (54% vs 48%), and higher ORP and SDF values than fertile controls. ORP and SDF showed significant positive correlations and significant negative correlations with sperm head defects and normal morphology in infertile patients, respectively. ORP and SDF were significantly inversely associated with the level of normal sperm morphology. Using receiver operating characteristic curve analysis, ORP and SDF threshold values of 1.73 mV/106 sperm/mL and 25.5%, respectively, were associated with 76% and 56% sensitivity and 72% and 72.2% specificity, respectively, in differentiating <4% from ≥4% normal morphology. Conclusion: A direct inverse relationship exists between seminal ORP and SDF with various levels of normal sperm morphology. Using ORP and SDF measures in conjunction with standard semen morphology analysis could validate the result of the fertility status of patients. Keywords: Male infertility, Oxidation-reduction potential, Sperm, Sperm DNA fragmentation, Sperm morpholog