43 research outputs found
TESTING THE EFFECT OF A NOVEL HYDROGEN SULFIDE RELEASING PEPTIDE ON INFECTED BURN WOUNDS
No full textBurn wounds are a devastating form of injury that leads to substantial morbidity, mortality, and cost. One of the critical complications of burn wounds is infections, especially with Staphylococcus aureus. Rising antimicrobial resistance is contributing to the complexity of wound management. According to the CDC, each year in the U.S. at least 2 million people are diagnosed with antibiotic-resistant bacteria, and more than 20,000 people die as a result
AUTHOR VERIFICATION OF ELECTRONIC MESSAGING SYSTEMS
No full textMessaging systems have become a hugely popular new paradigm for sending and delivering text messages; however, online messaging platforms have also become an ideal place for criminals due to their anonymity, ease of use and low cost. Therefore, the ability to verify the identity of individuals involved in criminal activity is becoming increasingly important. The majority of research in this area has focused on traditional authorship problems that deal with single-domain datasets and large bodies of text. Few research studies have sought to explore multi-platform author verification as a possible solution to problems around forensics and security. Therefore, this research has investigated the ability to identify individuals on messaging systems, and has applied this to the modern messaging platforms of Email, Twitter, Facebook and Text messages, using different single-domain datasets for population-based and user-based verification approaches. Through a novel technique of cross-domain research using real scenarios, the domain incompatibilities of profiles from different distributions has been assessed, based on real-life corpora using data from 50 authors who use each of the aforementioned domains. The results show that the use of linguistics is likely be similar between platforms, on average, for a population-based approach. The best corpus experimental result achieved a low EER of 7.97% for Text messages, showing the usefulness of single-domain platforms where the use of linguistics is likely be similar, such as Text messages and Emails. For the user-based approach, there is very little evidence of a strong correlation of stylometry between platforms. It has been shown that linguistic features on some individual platforms have features in common with other platforms, and lexical features play a crucial role in the similarities between users’ modern platforms. Therefore, this research shows that the ability to identify individuals on messaging platforms may provide a viable solution to problems around forensics and security, and help against a range of criminal activities, such as sending spam texts, grooming children, and encouraging violence and terrorism
Predictors of morbidity and mortality post emergency abdominal surgery: A national study
No full textBackground/Aim: Emergency surgeries have increased in Saudi Arabia. This study examines these surgeries and associated complications. Patients and Methods: This was a prospective multicenter cohort study of patients undergoing emergency intraperitoneal surgery from the eight health sectors of Saudi Arabia. Patients' data were collected over 14 days. Results: In total, 283 patients were included (163 men [54.06%]). The majority of cases were open surgery (204 vs. 79). The 24 h and 30-day mortality rates for the cohort were 0.7 and 2.47%, respectively. Twenty-nine patients (10.24%) required re-intervention, while 19 (8.12%) needed critical care admission. The median length of hospital stay was 3 days. Multivariate analysis showed American Society of Anesthesiologist (ASA) classification score (P = 0.0003), diagnosis (P < 0.0001), stoma formation (P = 0.0123), and anastomotic leak (P = 0.0015) to correlate significantly with 30-day mortality. Conclusion: American Society of Anesthesiologist score, diagnosis, stoma formation and anastomotic leak are associated with 30-day mortality after emergency surgery in Saudi Arabia
Consumer Buying Behavior and ML: How Machine Learning and Analytics can Utilize Consumer Behavior Data for Better Customer Service?-Retracted
No full textThis article is retracted:Dear IJGASR Readers,
It is with regret that we announce the retraction of the article titled "Consumer Buying Behavior and ML: How Machine Learning and Analytics can Utilize Consumer Behavior Data for Better Customer Service," which was published in Volume 2, Issue 4, 2023.This decision follows discussions with the authors, copy editor, and the internal editorial board regarding improper use of other work and unjustified reasons. The editorial board has unanimously decided to retract the article in accordance with our Article Correction, Retraction & Misconduct Policy.
We sincerely apologize for any inconvenience or confusion this may have caused and want to reiterate our commitment to maintaining the integrity of our publication. We appreciate the author\u27s cooperation in bringing this matter to our attention and taking the necessary steps to retract the article.
Sincerely,Editorial Team, IJGASR
Announcement: https://journals.icapsr.com/index.php/ijgasr/announcement/view/2
Treatment Options for Iron Deficiency Anemia in Children in Saudi Arabia
No full textAbstract: Methods: To assess the diagnostic criteria and therapeutic modalities for pediatric IDA employed by physicians in a major public healthcare facility in Riyadh, a validated questionnaire including demographic data and patient case scenarios related to diagnosis and treatment of IDA was employed. Robust regression analysis was used to identify factors associated with overall score of participants. Wide variability was observed in IDA diagnosis and therapy practices. For nutritional IDA, only 15.6% recommended no other laboratory tests in addition to CBC. The majority preferred treatment with ferrous sulfate (77.6%) divided into two doses (57.1%), but the total daily elemental iron doses varied widely from 2 to 6 mg/kg. Of all assessed factors, median score was significantly highest in pediatric hematologists compared with pediatricians, family medicine specialists and GPs; p = 0.007, and those work in tertiary care compared with those in primary care; p = 0.043. However, in multivariate robust regression analysis, overall score was only significantly associated with professional qualification [pediatric hematologist β = 13.71,95%CI 2.48–24.95, p = 0.017; pediatrician β = 1.77,95%C (− 6.05–9.59, p = 0.66; family medicine β = 2.66,95%CI-4.30-9.58, p = 0.45 compared with general practitioner]. Conclusion: Wide variations exist among physicians in diagnosis and treatment of pediatric IDA.
Keywords: Iron deficiency anemia, Treatment, Diagnosis, Assessment, Pediatric.
Title: Treatment Options for Iron Deficiency Anemia in Children in Saudi Arabia
Author: Moneerah Mohammed Alzoman, Kassem Jawad Alobaid, Norah alnashmi Alshalwi, Bedah Doujain alsuhali, Qamra Saud Alshlwai, Sally faisal alharbi, Talal ali alenazi, hessah Falah AlTamimi, Talal Marui Adiri
International Journal of Healthcare Sciences
ISSN 2348-5728 (Online)
Vol. 11, Issue 1, April 2023 - September 2023
Page No: 262-269
Research Publish Journals
Website: www.researchpublish.com
Published Date: 12-September-2023
DOI: https://doi.org/10.5281/zenodo.8337379
Paper Download Link (Source)
https://www.researchpublish.com/papers/treatment-options-for-iron-deficiency-anemia-in-children-in-saudi-arabiaInternational Journal of Healthcare Sciences, ISSN 2348-5728 (Online), Research Publish Journals, Website: www.researchpublish.co
Predicting Critical Courses Affecting Students Performance: A Case Study
No full textAbstractPredicting student academic performance is one of the important applications of educational data mining. It allows academic institutions to provide appropriate support for students facing difficulties. Classification is a data mining technique that can be used to build prediction models. In this paper, we use the ID3 decision tree induction algorithm to build prediction models for academic performance. Our models are built based on records for female students in the Bachelors program at the Information Technology (IT) department, King Saud University, Riyadh, Saudi Arabia. The results indicate that reliable predictions can be achieved based on the performance of students in second year courses. We also identify key courses that can be used as performance predictors. We believe our findings are useful for decision makers at the IT department
Abstract 110: Testing The Effect Of A Novel Hydrogen Sulfide Releasing Peptide On Infected Burn Wounds
No full textA Critical Review of Fiqh (Islamic Jurisprudence) of Oil and Gas
No full textThis paper reviews the book “Fiqh (Islamic Jurisprudence) of Oil and Gas”, and provides a critical view on the content of the book. The emphasis of the author for writing the book is to make the MA and PhD students of “Oil and Gas Law”, “Energy Law” and “Oil and Gas Contracts” and other interested people be familiar with basic concepts and foundations of “Fiqh (Islamic Jurisprudence) of Oil and Gas” and give them access to short and simple texts in this regard. Although the book has tried to fill the gap mentioned in the goal for writing the script, it has failed so far as chosen texts and their arrangement do not have a suitable relation with oil and gas law and contracts, and it seems that review and reorganization of the script in such a way that the reader can understand the relationship between the chosen texts and oil and gas law and contracts help to increase the coherence of the book
Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning
No full textForest management can be seen as a sequential decision-making problem to determine an optimal scheduling policy, e.g., harvest, thinning, or do-nothing, that can mitigate the risks of wildfire. Markov Decision Processes (MDPs) offer an efficient mathematical framework for optimizing forest management policies. However, computing optimal MDP solutions is computationally challenging for large-scale forests due to the curse of dimensionality, as the total number of forest states grows exponentially with the numbers of stands into which it is discretized. In this work, we propose a Deep Reinforcement Learning (DRL) approach to improve forest management plans that track the forest dynamics in a large area. The approach emphasizes on prevention and mitigation of wildfire risks by determining highly efficient management policies. A large-scale forest model is designed using a spatial MDP that divides the square-matrix forest into equal stands. The model considers the probability of wildfire dependent on the forest timber volume, the flammability, and the directional distribution of the wind using data that reflects the inventory of a typical eucalypt (Eucalyptus globulus Labill) plantation in Portugal. In this spatial MDP, the agent (decision-maker) takes an action at one stand at each step. We use an off-policy actor-critic with experience replay reinforcement learning approach to approximate the MDP optimal policy. In three different case studies, the approach shows good scalability for providing large-scale forest management plans. The results of the expected return value and the computed DRL policy are found identical to the exact optimum MDP solution, when this exact solution is available, i.e., for low dimensional models. DRL is also found to outperform a genetic algorithm (GA) solutions which were used as benchmarks for large-scale model policy.Structural Design & Mechanic
Improved constraints on models of glacial isostatic adjustment: a review of the contribution of ground-based geodetic observations
No full textThe provision of accurate models of Glacial Isostatic Adjustment (GIA) is presently a priority need in climate studies, largely due to the potential of the Gravity Recovery and Climate Experiment (GRACE) data to be used to determine accurate and continent-wide assessments of ice mass change and hydrology. However, modelled GIA is uncertain due to insufficient constraints on our knowledge of past glacial changes and to large simplifications in the underlying Earth models. Consequently, we show differences between models that exceed several mm/year in terms of surface displacement for the two major ice sheets: Greenland and Antarctica. Geodetic measurements of surface displacement offer the potential for new constraints to be made on GIA models, especially when they are used to improve structural features of the Earth's interior as to allow for a more realistic reconstruction of the glaciation history. We present the distribution of presently available campaign and continuous geodetic measurements in Greenland and Antarctica and summarise surface velocities published to date, showing substantial disagreement between techniques and GIA models alike. We review the current state-of-the-art in ground-based geodesy (GPS, VLBI, DORIS, SLR) in determining accurate and precise surface velocities. In particular, we focus on known areas of need in GPS observation level models and the terrestrial reference frame in order to advance geodetic observation precision/accuracy toward 0.1 mm/year and therefore further constrain models of GIA and subsequent present-day ice mass change estimate
