Analysis related to combustion noise research

Ph.D.Warren C. Strahl

Master of Science

thesisThis work studies the removal of arsenic (V), arsenate, and arsenic (III), arsenite, from aqueous solution using calcined Quintinite-3T (Al/Mg mixed oxides) and calcined La-LDHs (La/Mg mixed oxides). The two adsorbents Quintinite-3T and La-LDHs were prepared, calcined at different temperatures, and characterized by XRD, BET, and SEM. The surface area of the calcined Quintinite-3T was 193.4 m2/g, while the surface area of the calcined La-LDHs was 112.4 m2/g. The particles size ranged from 12 to 56 nm for the uncalcined Quintinite-3T and from 24 to 42 nm for the calcined Quintinite-3T. The particle size ranged from 27 to 56 nm for as-synthesized La-LDHs and from 18 to 35 nm for the calcined La-LDHs. Also, new hybrid adsorbent was synthesized and characterized as well. Kinetic analysis, adsorption isotherm, and factors affecting the adsorption were investigated. Calcined Quintinite-3T retained As(v) and did not release it back to water even after an entire month. The adsorption of As(V) and As(III) by calcined La-LDHs increased with time up to 2 weeks and 1 month, respectively

Cost information for corporate social responsibility performance

The main objective of this study is to develop an accounting and reporting system for the costs of corporate social responsibility performance. Secondary objectives of the study are: (1) to review the current issues of corporate social responsibility to be measured and reported; (2) to provide some guide-lines to help a company's management in selecting the socially relevant activities; (3) to investigate the rationale for corporate social involvement; (4) to indicate the legal minimum requirements in the major areas of corporate social performance; (5) to determine the concept of accounting for corporate social performance and the appropriate method for measuring and reporting the costs of such performance; and (6) to ascertain the current state of corporate social reporting practice in the United Kingdom. To achieve these objectives, the study begins by discussing the social activities to be covered in corporate social reporting. The rationale for initiating corporate social activities is also discussed and some guide-lines are offered. Moreover, the social actions which should be taken by all British companies, as they are legally required, are examined. Thereafter, the current development of accounting for corporate social responsibility is reviewed and it is concluded that this sort of accounting is still in its infancy. The reasons for which accountants should be concerned with corporate social responsibility accounting are then presented. The concept of accounting for corporate social performance is discussed and the scope of the concept determined. Several approaches for measuring and reporting corporate social performance are examined and it is indicated that the cost approach is the proper method to be employed in this study. Then, the cost concepts of accounting for corporate social performance are discussed from both accounting and economic points of view, and a method for measuring the social costs of a company's economic activities is presented. After reviewing the literature, an attempt is made to develop a framework for accounting and reporting of corporate social performance costs. The identification and classification of such costs are presented and the accounting treatment of these costs is discussed. Separate disclosures of such costs in the traditional financial statements are suggested and illustrated in this study. Finally, a survey of the current state of corporate' social reporting practice in the United Kingdom is given. This survey involved the analysis of corporate social responsibility disclosures in the annual reports and accounts of 207 companies extracted from the top 265 of the 1000 largest U. K. industrial companies. The results of the survey and examples of corporate social responsibility disclosures are presented. The detailed analysis is provided in an Appendix

Exploring Influence of Spreading Sport’s Culture on Employees’ Happiness and Tolerance as a Part of Sustainable Development at American College of Dubai

Sustainable development is a cultural and global demand regardless of the geographical region of this planet. In this context, the goals of sustainable development are broad and diverse, and the human dimension is one of the most important elements among sustainable development issues. The research purpose focuses on employees in both public and private sectors to shift from the sum of daily or weekly hours to the quality of daily work in their organizations. In this context, the current research team chooses the initiative of the American College of Dubai to designate a weekly sports day for its employees. The main justification behind this research is likely to achieve happiness and tolerance among human relatedness. The research unit at the college initiated the monitoring of the phenomenon and constructed a scale that would explore the achievement of this goal at the American College of Dubai. Sports play an important role in improving employees’ mood and willingness toward healthy performance in a healthy organizational environment. It is considered as a source of energy to improve the organizations working environment and improve cultural homogeneity. Our research observations are based on the case of the American College of Dubai (ACD) as a unique example to identify the impact of weekly sports days on employee happiness. It is designated for this type of social activity among the diversified feature of the collegial working environment. It is worth mentioning that the initiative taken up by the college in (2019) has helped us to identify the impact of such non-academic initiatives on generating an environment of happiness and tolerance among the ACD community

Survey on encode biometric data for transmission in wireless communication networks

The aim of this research survey is to review an enhanced model supported by artificial intelligence to encode biometric data for transmission in wireless communication networks can be tricky as performance decreases with increasing size due to interference, especially if channels and network topology are not selected carefully beforehand. Additionally, network dissociations may occur easily if crucial links fail as redundancy is neglected for signal transmission. Therefore, we present several algorithms and its implementation which addresses this problem by finding a network topology and channel assignment that minimizes interference and thus allows a deployment to increase its throughput performance by utilizing more bandwidth in the local spectrum by reducing coverage as well as connectivity issues in multiple AI-based techniques. Our evaluation survey shows an increase in throughput performance of up to multiple times or more compared to a baseline scenario where an optimization has not taken place and only one channel for the whole network is used with AI-based techniques. Furthermore, our solution also provides a robust signal transmission which tackles the issue of network partition for coverage and for single link failures by using airborne wireless network. The highest end-to-end connectivity stands at 10 Mbps data rate with a maximum propagation distance of several kilometers. The transmission in wireless network coverage depicted with several signal transmission data rate with 10 Mbps as it has lowest coverage issue with moderate range of propagation distance using enhanced model to encode biometric data for transmission in wireless communication

A deep learning framework for noninvasive fetal ECG signal extraction

Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions.Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks.Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework.Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications

Deep learning identifies cardiac coupling between mother and fetus during gestation

In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20–40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices

NSCLC molecular testing in Central and Eastern European countries

Background: The introduction of targeted treatments for subsets of non-small cell lung cancer (NSCLC) has highlighted the importance of accurate molecular diagnosis to determine if an actionable genetic alteration is present. Few data are available for Central and Eastern Europe (CEE) on mutation rates, testing rates, and compliance with testing guidelines. Methods: A questionnaire about molecular testing and NSCLC management was distributed to relevant specialists in nine CEE countries, and pathologists were asked to provide the results of EGFR and ALK testing over a 1-year period. Results: A very high proportion of lung cancer cases are confirmed histologically/cytologically (75-100%), and molecular testing of NSCLC samples has been established in all evaluated CEE countries in 2014. Most countries follow national or international guidelines on which patients to test for EGFR mutations and ALK rearrangements. In most centers at that time, testing was undertaken on request of the clinician rather than on the preferred reflex basis. Immunohistochemistry, followed by fluorescent in situ hybridization confirmation of positive cases, has been widely adopted for ALK testing in the region. Limited reimbursement is a significant barrier to molecular testing in the region and a disincentive to reflex testing. Multidisciplinary tumor boards are established in most of the countries and centers, with 75-100% of cases being discussed at a multidisciplinary tumor board at specialized centers. Conclusions: Molecular testing is established throughout the CEE region, but improved and unbiased reimbursement remains a major challenge for the future. Increasing the number of patients reviewed by multidisciplinary boards outside of major centers and access to targeted therapy based on the result of molecular testing are other major challenges