Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

© Copyright © 2020 Elmahdy, Ali, Mohamed, Howari, Abouleish and Simonet. Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

Redundancy Effect of Vehicular Cloud’s Environment using Location Estimate (ReViSE)

The rapid growth of modern wireless technology increases the invention of new applications using the concept of Internet of Things (IoT). Vehicular Cloud, which is a concept introduced recently to use the vehicles sitting idle for hours as a processing devices, has been a hot topic. This subject became more popular after the Department of Transportation’s (DOT) announcement of mandatory wireless capabilities of each vehicle since 2015. We investigate the idea of benefiting from the unused processing power of vehicles sitting in parking lots for a long period of time (e.g. an airport, or train station). Moreover, researchers propose the concept of running the same job on several vehicles, to prevent the loss of jobs when a vehicle departures suddenly (quitting the assigned job). In this research, we study to reduce the redundancy effect by estimating how long a vehicle will stay in a specific parking lot, using information provided from the driver’s smartphone (e.g. flight ticket schedule, or train schedule). This allows the vehicle to save the assigned job and prepare for job switching. A smartphone app would be installed on the driver’s phone, without breaching the driver’s privacy, to inform the system of the estimated amount of time each car can be used. This information would be encrypted to prevent security attacks. Our results shows an enhancement in the performance compared to redundancy mechanism

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques

Efficacy of oral celecoxib and hyoscine butyl-bromide versus placebo during copper intrauterine device placement in women delivered only by elective cesarean section: a randomized controlled study

Objective: To compare the efficacy and tolerability of celecoxib to hyoscine butyl bromide (HBB) and placebo in reducing pain scores during placement of copper intrauterine devices (IUD) in parous women who have undergone elective cesarean section and who have had no previous vaginal deliveries. Methods: We conducted a randomized, double-blind, placebo-controlled trial at a tertiary University hospital from April 2018 to September 2018. The study included women who had never delivered vaginally and who desired copper IUD insertion. We randomized the study participants in a 1:1:1 ratio to celecoxib, HBB or placebo groups. They took the tablets orally two hours before IUD insertion. The study outcomes were the self-reported pain measurements, using a 10-cm Visual Analogue Scale (VAS), taken during tenaculum placement, sound insertion, IUD insertion and five minutes post-insertion, as well as an ease of insertion score. Results: The study included 105 women (n=35 in each group). The baseline characteristics were similar among all groups. The mean pain score in the celecoxib group was lower during IUD insertion than placebo (1.97 vs 4.34, p<0.001). Moreover, the ease of insertion score was significantly better with celecoxib [1.56 vs. 3.03, p< 0.001] than with placebo. Similarly, Women in the HBB group were more likely to report lower pain scores during IUD insertion (2.91 vs 4.34, p<0.001) and lower ease of insertion score [1.43 vs. 3.03, p< 0.001]. Conclusions: The use of celecoxib and HBB may both reduce the pain associated with copper IUD insertion among women with no previous vaginal delivery. However, celecoxib is better tolerated with fewer side effect

Morphological Variation between Life and Death Gastropod Populations in the Nile Delta: A Pollution-Induced Evolution

Wetland ecosystems of the Nile Delta face severe threats due to natural climatic changes and anthropogenic activities. Life and death assemblage comparisons can be implemented as a historical record to detect anthropogenic-induced environmental changes in the past few decades. A geometric morphometric approach was applied to quantify the pollution-induced morphological variation between life and death populations of the gastropod Melanoides tuberculata. The results indicated that life populations differ significantly from the death ones, where the first tend to be much smaller, more globular, and with a depressed aperture and whorl section. In addition, the phenetic diversity of the life populations was also decreased, and the allometric growth was shifted. These morphological changes in the life populations are well-known adaptations for reducing the cost of shell maintenance in polluted water. No distinct morphospace was found between life populations from different habitats, suggesting that habitats have no significant role in the current pollution-induced evolution.This work is funded by the Researchers Supporting Project number (RSP2023R455), King Saud University, Riyadh, Saudi Arabia

Phytochemical composition and antimicrobial properties of Markhamia platycalyx (Baker) Sprague leaf

Purpose: To isolate new antimicrobial agents from the leaves of Markhamia platycalyx (Baker) Sprague and assess their phytochemical characteristics and antimicrobial activity. Methods: Different chromatographic and spectroscopic techniques (NMR and ESI-MS) were applied for the identification of antimicrobial compounds. Agar-well diffusion technique was used for determination of antimicrobial activity. Anti-HCV effects were investigated using VITROS Anti-HCV assay. Results: Eighteen compounds were isolated for the first time from this genus. These were phytol, noctacosanoic acid (OCTA), tormentic acid and β-sitosterol-3-O-(6'-O-heptadecanoyl)-β-Dglucopyranoside. The other compounds were β-sitosterol, ursolic acid (URSA), oleanolic acids, pomolic acid (POMA), 2-epi-tormentic and β-sitosterol-3-O-β-D-glucopyranoside. However, stigmasterol and acteoside, which were seen in previous studies, were also present. Total ethanol extract (TEE) was the most effective against Escherichia coli, with the lowest minimum inhibitory concentration (MIC) of 1.0 µg/mL. Acteoside, URSA and 2-epi-tormentic acid showed the highest antibacterial effect on Pseudomonas aeruginosa while 2-epi-tormentic acid and acteoside produced the least MIC on Candida glabrata. These effects were superior to those produced by standard antibiotics. However, 2-epitormentic acid and β-sitosterol-3-O-β-D-glucopyranoside had no anti-HCV effects. Conclusion: Due to the good antimicrobial properties of Markhamia platycalyx, it is a potential source of new antimicrobial drugs

Deep Convolutional Neural Networks for Accurate Diagnosis of COVID-19 Patients Using Chest X-Ray Image Databases from Italy, Canada, and the USA

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls. Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the Radiology Society of North America (RSNA), and the Kaggle Chest X-ray Database for COVID-19 (n = 240), Viral Pneumonia (n = 274), and Normal Controls (n = 267). Images were resized, normalized, without any augmentation, and arranged in m batches of 16 images before supervised training, testing, and cross-validation of the DCNN classifier. Results: The ResNet-34 had a diagnostic accuracy as of the receiver operating characteristic (ROC) curves of the true-positive rate versus the false-positive rate with the area under the curve (AUC) of 1.00, 0.99, and 0.99, for COVID-19 and Viral Pneumonia patient and Normal control CXR images; respectively. This accuracy implied identical high sensitivity and specificity values of 100, 99, and 99% for the three groups, respectively. ResNet-34 achieved a success rate of 100%, 99.6%, and 98.9% for classifying CXR images of the three groups, with an overall accuracy of 99.5% for the testing subset for diagnosis/prognosis. Conclusions: Based on this high classification precision, we believe the output activation map of the final layer of the ResNet-34 is a powerful tool for the accurate diagnosis of COVID-19 infection from CXR images

Hormonal Management for the Induction of Luteolysis and Ovulation in Andalusian Jennies: Effect on Reproductive Performance, Embryo Quality and Recovery Rate

Two prostanglandins (luprostiol, LUP, and dinoprost, DIN) and two ovulation-inducing agents (human Chorionic Gonadotropin, hCG, and deslorelin, DES) were evaluated for luteolysis and estrus induction, and for ovulation induction, respectively, in embryo donor jennies. Twenty-six fertile Andalusian jennies were used. In Experiment 1, jennies (n = 112 cycles) were randomly treated with either LUP or DIN after embryo flushing. In Experiment 2, donors (n = 84 cycles) were randomly treated with either hCG or DES to induce ovulation. No differences were found between prostaglandins for all variables studied (prostaglandin–ovulation interval (POI), interovulatory interval (IOI), embryo recovery rate (ERR), positive flushing rate (PFR) and embryo grade (EG)). The ovulation rate was similar for hCG and DES (60.9% vs. 78.7%). However, the interval to ovulation (ITO) was affected (62.61 ± 7.20 vs. 48.79 ± 2.69 h). None of the other variables studied (ERR, PFR and EG) were affected (p > 0.05), except for embryo quality (p = 0.009). In short, both prostaglandins evaluated are adequate to induce luteolysis and estrus. Both ovulation-inducing agents hastened ovulation, but DES seems to be more effective than hCG. Follicular diameter affected the interval from treatment to ovulation, and high uterine edema was related to low embryo quality

Effect of dexamethasone on reducing pain and gastrointestinal symptoms associated with cesarean section: a systematic review and meta-analysis

Background: Dexamethasone has analgesic and antiemetic actions that have been documented in the literature. Therefore, we performed a systematic review and meta-analysis to investigate its overall effectiveness in reducing a variety of negative outcomes after cesarean section. Objectives: To investigate the efficacy and safety of dexamethasone for reducing pain associated with cesarean section, nausea, vomiting, pruritus, postoperative need for analgesia, postoperative antiemetic requests and headache. Methods: We searched PubMed, Cochrane CENTRAL, SCOPUS, and Web of Science for relevant clinical trials. We then performed a systematic review and meta-analysis, including only randomized, placebo-controlled clinical trials. Our main population target was women undergoing elective cesarean delivery. The intervention under consideration was dexamethasone administered both by intravenous (IV) or subcutaneous (SC) over a variety of doses. The comparator was a placebo. Our main outcomes included: (1) perceptions as indicated by pain scores, (2) occurrence of nausea and (3) occurrence of vomiting. Secondary outcomes included: (4) occurrence of pruritus, (5) need for postoperative analgesia, (6) need for postoperative antiemetic drugs and (7) occurrence of headache. We assessed the quality of included studies using the risk of bias tool described in Cochrane\u27s handbook for systematic reviews of interventions. Results: We found that dexamethasone seemed to significantly reduce scores for pain at rest (p<0.001), as well as occurrence of nausea (p<0.001) and vomiting (p<0.001). The drug also showed significant reduction of negative symptoms in other secondary outcomes, including need for postoperative analgesia (p<0.001) and postoperative antiemetic drugs (p<0.001). However, the drug showed no significant effect in reducing headache and pruritus or in improving pain at movement scores. Conclusion: Dexamethasone appears to decrease perception of pain at rest and protects against nausea and vomiting. However, it does not seem effective against headaches or pruritus