An optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modelling

Vehicular traffic plays a significant role in terms of economic development; however, it is also a major source of noise pollution. Therefore, it is highly imperative to model traffic noise, especially for expressways due to their high traffic volume and speed, which produce very-high level of traffic noise. Previous traffic prediction models are mostly based on the regression approach and the artificial neural network (ANN), which often fail to describe the trends of noise. In this paper, a deep neural network-based optimization approach is implemented in two ways: i) using different algorithms for training and activation, and ii) integrating with feature selection methods such as correlation-based feature selection (CFS) and wrapper for feature-subset selection (WFS) methods. These methods are integrated to produce traffic noise maps for different time of the day on weekdays, including morning, afternoon, evening, and night. The novelty of this study is the integration of the feature selection method with the deep neural network for vehicular traffic noise modelling. New Klang Valley Expressway (NKVE) in Malaysia was used as a case study due to its increasing heavy and light vehicles, and the motorbike during peak hours, which result in high traffic noise. The results from the models indicate that the WFS-DNN model has the least mean-absolute-deviation (MAD) of 2.28, and the least root-mean-square-error (RMSE) of 3.97. Also, this model shows the best result compared to the other models such as DNN without integration with feature selection methods, CFS-DNN and the ANN networks (MLP and RBF). MAD improvement of 27% - 47% and RMSE improvement of 25% - 38% was achieved compared to other methods. The study provides a generic approach to key parameter selection and dimension reduction with novel trend descriptor which could be useful for future such modelling applications

Orthorectification of WorldView-3 Satellite Image Using Airborne Laser Scanning Data

Satellite images have been widely used to produce land use and land cover maps and to generate other thematic layers through image processing. However, images acquired by sensors onboard various satellite platforms are affected by a systematic sensor and platform-induced geometry errors, which introduce terrain distortions, especially when the sensor does not point directly at the nadir location of the sensor. To this extent, an automated processing chain of WorldView-3 image orthorectification is presented using rational polynomial coefficient (RPC) model and laser scanning data. The research is aimed at analyzing the effects of varying resolution of the digital surface model (DSM) derived from high-resolution laser scanning data, with a novel orthorectification model. The proposed method is validated on actual data in an urban environment with complex structures. This research suggests that a DSM of 0.31 m spatial resolution is optimum to achieve practical results (root-mean-square error = 0.69   m ) and decreasing the spatial resolution to 20 m leads to poor results (root-mean-square error = 7.17 ). Moreover, orthorectifying WorldView-3 images with freely available digital elevation models from Shuttle Radar Topography Mission (SRTM) (30 m) can result in an RMSE of 7.94 m without correcting the distortions in the building. This research can improve the understanding of appropriate image processing and improve the classification for feature extraction in urban areas.</jats:p

Controlled Growth of WO3Nanostructures with Three Different Morphologies and Their Structural, Optical, and Photodecomposition Studies

Tungsten trioxide (WO3) nanostructures were synthesized by hydrothermal method using sodium tungstate (Na2WO4·2H2O) alone as starting material, and sodium tungstate in presence of ferrous ammonium sulfate [(NH4)2Fe(SO4)2·6H2O] or cobalt chloride (CoCl2·6H2O) as structure-directing agents. Orthorhombic WO3having a rectangular slab-like morphology was obtained when Na2WO4·2H2O was used alone. When ferrous ammonium sulfate and cobalt chloride were added to sodium tungstate, hexagonal WO3nanowire clusters and hexagonal WO3nanorods were obtained, respectively. The crystal structure and orientation of the synthesized products were studied by X-ray diffraction (XRD), micro-Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM), and their chemical composition was analyzed by X-ray photoelectron spectroscopy (XPS). The optical properties of the synthesized products were verified by UV–Vis and photoluminescence studies. A photodegradation study on Procion Red MX 5B was also carried out, showing that the hexagonal WO3nanowire clusters had the highest photodegradation efficiency

An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein – ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Developing vehicular traffic noise prediction model through ensemble machine learning algorithms with GIS

Vehicular traffic noise is an important aspect that requires environmental impact assessment (EIA) due to the increase in vehicular traffic. This has called for the need for detailed assessment, quantification and modelling of traffic noise pollution among industrial and scientific communities. This study uses an ensemble of machine learning algorithms to enhance the vehicular traffic noise prediction. The noise prediction is performed for the noise measure of the equivalent continuous noise level per 15 min (Leq, 15 min) along the Subang Jaya and Shah Alam New Klang Valley Expressway (NKVE). In this study, three machine learning methods—(i) artificial neural network model (ANN), (ii) correlation-based feature selection with artificial neural network model (CFS-ANN) and (iii) ensemble machine learning algorithms random forest with artificial neural network model (Ensemble RF-ANN) are deployed to estimate the Leq, 15 min during the peak hours of the morning (6:30–8:30 a.m.), evening (6:00–8:00 p.m.) and night (10:00 p.m.–12:00 midnight) for the weekday (Monday). The root mean square error (RMSE) and coefficient of determination (R2) were used to select the best model. The prediction maps for morning, evening and night are prepared using geospatial modelling for the two sites under consideration. The results showed that the Ensemble RF-ANN model is the best model recording the lowest RMSE during the training and testing with values of 1.767 and 2.378, respectively. It also achieved the highest R2 values of 0.923 and 0.835, respectively, for the training and testing. The study provides novel noise models and excellent analysis of results capable of identifying key factors affecting noise in a geographical area. The study outcomes could be utilised by noise modelling consultants, town planners and traffic modelling experts for traffic noise mapping of larger geographical areas

Problem-based learning: medical students&rsquo; perception toward their educational environment at Al-Imam Mohammad Ibn Saud Islamic University

Abdulaziz Abdulrahman Aldayel, Abdulrahman Omar Alali, Ahmed Abdullah Altuwaim, Hamad Abdulaziz Alhussain, Khalid Ahmed Aljasser, Khalid A Bin Abdulrahman, Majed Obaid Alamri, Talal Ayidh Almutairi College of Medicine, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia Background: Problem-based learning (PBL) is a student-centered innovating instructional approach in which students define their learning objectives by using triggers from the problem case or scenario.Objectives: To assess undergraduate medical students&rsquo; perception toward PBL sessions and to compare their perceptions among different sex and grade point average (GPA) in the college of medicine, Al-Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.Materials and methods: We conducted a cross-sectional study based on a self-administered anonymous online questionnaire during the first semester of the 2017&ndash;2018 academic year in IMSIU. The data were collected from male and female students of the second and third year, as well as male students of the fourth year.Results: Out of 259 students, 152 (58.7%) completed the questionnaire. The students&rsquo; perception toward PBL was more positive than negative. Most of the students reported that PBL sessions increased their knowledge of basic sciences (P=0.03). Furthermore, most students agreed that PBL provided a better integration between basic and clinical sciences which differed significantly between the different GPA groups (P=0.02). Nevertheless, only 28.3% of the students agreed that the teaching staff is well prepared to run the sessions with significant statistical difference among different GPA groups (P=0.02). Moreover, only 26.3% of the students reported that there was proper student training before starting the PBL sessions with no significant difference. Additionally, only 34.2% and 28.9% of the students felt that they learn better and gain more knowledge thorough PBL than lectures respectively, with no significant difference.Conclusion: This study showed that tutors should be trained to guide the process of PBL effectively to achieve its goals. Moreover, students should be securely introduced to PBL and experience the development of their clinical reasoning through PBL. Further improvements are needed to provide students with an effective favorable learning environment and to take the students recommendations into consideration. Keywords: medical students, problem-based learning, education, perception, curriculum &nbsp

Effect of smoking on the genetic makeup of toll-like receptors 2 and 6

Muhammad Kohailan,1 Mohammad Alanazi,1 Mahmoud Rouabhia,2 Abdullah Alamri,1 Narasimha Reddy Parine,1 Abdullah Alhadheq,1 Santhosh Basavarajappa,3 Abdul Aziz Abdullah Al-Kheraif,3 Abdelhabib Semlali1 1Genome Research Chair, Department of Biochemistry, College&nbsp;of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia; 2D&eacute;partement de Stomatologie, Facult&eacute; de M&eacute;decine Dentaire, Groupe de Recherche en &Eacute;cologie Buccale, Universit&eacute; Laval, Qu&eacute;bec City, QC, Canada; 3Dental Biomaterial Research Chair, Department of Dental Health, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom&nbsp;of Saudi&nbsp;Arabia Background: Cigarette smoking is a major risk factor for lung cancer, asthma, and oral cancer, and is central to the altered innate immune responsiveness to infection. Many hypotheses have provided evidence that cigarette smoking induces more genetic changes in genes involved in the development of many cigarette-related diseases. This alteration may be from single-nucleotide polymorphisms (SNPs) in innate immunity genes, especially the toll-like receptors (TLRs).Objective: In this study, the genotype frequencies of TLR2 and TLR6 in smoking and nonsmoking population were examined.Methods: Saliva samples were collected from 177 smokers and 126 nonsmokers. The SNPs used were rs3804100 (1350 T/C, Ser450Ser) and rs3804099 (597 T/C, Asn199Asn) for TLR2 and rs3796508 (979 G/A, Val327Met) and rs5743810 (745 T/C, Ser249Pro) for TLR6.Results: Results showed that TLR2 rs3804100 has a significant effect in short-term smokers (OR =2.63; P=0.04), and this effect is not observed in long-term smokers (&gt;5 years of smoking). Therefore, this early mutation may be repaired by the DNA repair system. For TLR2 rs3804099, the variation in genotype frequencies between the smokers and control patients was due to a late mutation, and its protective role appears only in long-term smokers (OR =0.40, P=0.018). In TLR6 rs5743810, the TT genotype is significantly higher in smokers than in nonsmokers (OR =6.90). The effect of this SNP is observed in long-term smokers, regardless of the smoking regime per day.Conclusion: TLR2 (rs3804100 and rs3804099) and TLR6 (rs5743810) can be used as a potential index in the diagnosis and prevention of more diseases caused by smoking. Keywords: polymorphism, toll-like receptor, genotyping, smoking, TLR2, TLR

Chitosan-carboxylic acid grafted multifunctional magnetic nanocomposite as a novel adsorbent for effective removal of methylene blue dye from aqueous environment

In this work, magnetite nanoparticle decorated graphene oxide (MGO) is modified with triethylenetetramine (TETA), which is supported by maleated chitosan (MACS), named MGO@TETA@MACS. The novel magnetic nanohybrid is successfully fabricated via an in situ coprecipitation method, which is for methylene blue (MB) uptake from liquid phase environment. XRD, FTIR, TGA, nitrogen isotherm, SEM, and Zeta potential experiments were utilized to explore the MGO@TETA@MACS nanocomposite. The mean particle size and surface area of MGO@TETA@MACS were measured to be 11.5 nm and 128.04 m2·g−1, respectively. The nonlinear kinetic and isotherm models were utilized to evaluate the adsorption equilibrium. Data analysis revealed that the uptake kinetics fits the PFO model while the adsorption equilibrium follows the Langmuir isotherm. The maximum adsorption capacity of MGO@TETA@MACS nanocomposite, estimated from Langmuir isotherm, was 247.37 mg/g at the ambient temperature. Thermodynamic calculations confirmed the exothermic and spontaneous nature of the MB decontamination.</p