Retrospective Radiographic Survey of Unconventional Ectopic Impacted Teeth in Al-Madinah Al-Munawwarah, Saudi Arabia

OBJECTIVES: Ectopic unconventional impacted teeth are rare. These teeth erupt in an unusual direction with limited unconventional access and have increased surgical risks. AIM: This study aimed to investigate and assess the prevalence and distribution of rare ectopic impacted teeth at the Taibah University Dental College and Hospital (TUDCH), Al-Madinah Al-Munawwarah, Saudi Arabia. METHODS: The study designed through a retrospective radiographic cross-sectional survey involving the review and examination of 9000 archived digital orthopantomograms of patients who visited the (TUDCH) in the period from January 2014 to December 2019 and to analyze any associated factors. RESULTS: There were 63 ectopically impacted teeth, with an incidence of 0.7%. The age of the patients ranged from 18 to 68 years, with a mean of 32.4 ± 13 years. Regarding patient nationality, 68.3% were Saudis. The most common ectopically impacted teeth were the extra impacted premolars, with an incidence of 0.2%, followed by the inverted molars, impacted first or second molars, and buccoversion or lingoversion third molars, with incidences of 0.16%, 0.13%, and 0.12%, respectively. The mandible was affected with ectopic impaction more than the maxilla, with an incidence of 55.6%. There was no difference between the right and left sides. Impacted teeth in the sinus were the least common. CONCLUSION: The prevalence of ectopic impacted teeth was 0.7% among the surveyed patients at TUDCH, Al-Madinah Al-Munawwarah, Saudi Arabia. Hence, the oral surgeon must have readiness for such a challenging, increasing situation

Moisture computing-based internet of vehicles (IoV) architecture for smart cities

Recently, the concept of combining 'things' on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches

Global genomic epidemiology of chromosomally mediated non-enzymatic carbapenem resistance in Acinetobacter baumannii: on the way to predict and modify resistance

IntroductionAlthough carbapenemases are frequently reported in resistant A. baumannii clinical isolates, other chromosomally mediated elements of resistance that are considered essential are frequently underestimated. Having a wide substrate range, multidrug efflux pumps frequently underlie antibiotic treatment failure. Recognizing and exploiting variations in multidrug efflux pumps and penicillin-binding proteins (PBPs) is an essential approach in new antibiotic drug discovery and engineering to meet the growing challenge of multidrug-resistant Gram-negative bacteria.MethodsA total of 980 whole genome sequences of A. baumannii were analyzed. Nucleotide sequences for the genes studied were queried against a custom database of FASTA sequences using the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) system. The correlation between different variants and carbapenem Minimum Inhibitory Concentrations (MICs) was studied. PROVEAN and I-Mutant predictor suites were used to predict the effect of the studied amino acid substitutions on protein function and protein stability. Both PsiPred and FUpred were used for domain and secondary structure prediction. Phylogenetic reconstruction was performed using SANS serif and then visualized using iTOL and Phandango.ResultsExhibiting the highest detection rate, AdeB codes for an important efflux-pump structural protein. T48V, T584I, and P660Q were important variants identified in the AdeB-predicted multidrug efflux transporter pore domains. These can act as probable targets for designing new efflux-pump inhibitors. Each of AdeC Q239L and AdeS D167N can also act as probable targets for restoring carbapenem susceptibility. Membrane proteins appear to have lower predictive potential than efflux pump-related changes. OprB and OprD changes show a greater effect than OmpA, OmpW, Omp33, and CarO changes on carbapenem susceptibility. Functional and statistical evidence make the variants T636A and S382N at PBP1a good markers for imipenem susceptibility and potential important drug targets that can modify imipenem resistance. In addition, PBP3_370, PBP1a_T636A, and PBP1a_S382N may act as potential drug targets that can be exploited to counteract imipenem resistance.ConclusionThe study presents a comprehensive epidemiologic and statistical analysis of potential membrane proteins and efflux-pump variants related to carbapenem susceptibility in A. baumannii, shedding light on their clinical utility as diagnostic markers and treatment modification targets for more focused studies of candidate elements

Role of Stem Cells in Orthopaedic Surgery: Theoretical Survey

This study aims at analyzing the Stem cell application is a burgeoning field of medicine that is likely to influence the future of orthopaedic surgery. Stem cells are associated with great promise and great controversy. For the orthopaedic surgeon, stem cells may change the way that orthopaedic surgery is practiced and the overall approach of the treatment of musculoskeletal disease. Stem cells may change the field of orthopaedics from a field dominated by surgical replacements and reconstructions to a field of regeneration and prevention. This review will introduce the basic concepts of stem cells pertinent to the orthopaedic surgeon and proceed with a more in depth discussion of current developments in the study of stem cells in orthopaedic surgery. Keywords: Stem cell, orthopaedic, surgery

BCAP-SA—Towards a blockchain based cryptocurrency adoption model as a payment method in Saudi Arabia

Online transactions have grown to gain significant attention from various stake-holders in governments, business, and research practitioners all over the world. A decade ago, the first cryptocurrency transaction was executed to pave the way for a new era of online transactions. However, there is a relatively low adoption of blockchain-based cryptocurrency transactions as a payment method by service providers, organizations, and customers. Further, there is no study investigating the adoption of cryptocurrency technology as a payment method in Saudi Arabia. Therefore, this research proposes a new adoption model to investigate the ac-ceptance of a blockchain based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA). A thorough research of the literature has yielded only a few studies covering external factors over a range of technical, economic, personal, and environmental aspects in different parts of the world. Hypotheses have been developed for each factor which will be examined by both qualitative (interviews) and quantitative (surveys) methods. The proposed study aims to offer service providers with insights about which factors customers would concentrate on if they were to accept blockchain based cryptocurrency as a payment method

Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts

Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts

An ensemble learning based classification approach for the prediction of household solid waste generation

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed