Experimental Comparison of Simulation Tools for Efficient Cloud and Mobile Cloud Computing Applications

Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities

Double Median Ranked Set Sample: Comparing To Other Double Ranked Samples For Mean And Ratio Estimators

Double median ranked set sample (DMRSS) and its properties for estimating the population mean, when the underlying distribution is assumed to be symmetric about its mean, are introduced. Also, the performance of DMRSS with respect to other ranked set samples and double ranked set samples, for estimating the population mean and ratio, is considered. Real data that consist of heights and diameters of 399 trees are used to illustrate the procedure. The analysis and simulation indicate that using DMRSS for estimating the population mean is more efficient than using the other ranked samples and double ranked samples schemes except in case of uniform distribution. Also, using double sampling schemes substantially increase the relative efficiency of ratio estimators relative to their counterpart schemes of one stage samples. Moreover, DMRSS is superior to other double sampling schemes for ratio estimation

Assessment of Calotropis natural dye extracts on the efficiency of dye-sensitized solar cells

ArticleThis work presents the construction and testing of solar cells sensitized with natural dyes extracted from plants indigenous to the desert. Calotropis plants are self - sufficient as they grow in very harsh environments, and yet are not consumed by humans or livestock due to their irritating agents to the skin and eyes. The energy generators of these plants are the leaves, which are crushed and processed to produce the dye solution. Also, the Calotropis leaves are covered in a white powder that is thought to aid in mitigating the heat by scattering incident radiation. This powder material is examined and added to the dye as it proved advantageous for the o verall cell efficiency, which reached 0.214% compared with 0.108% for cells with no powder. The produced cells are also compared with ones sensitized by spinach, another common natural sensitizer for dye - sensitized solar cells, and the performance proved t o be significantly better. The fact that Calotropis is a non - food plant is an added advantage to utilizing it as a dye source, along with its intrinsic heat resistance that allows it to survive the harsh desert conditions all year round

The Role of the Educational Advisor in Solving School Problems from an Islamic Perspective دور المستشار التربوي في حل المشكلات المدرسية من المنظور الإسلامي

Abstract: This study aimed to identify the role of the educational advisor in solving school problems from an Islamic perspective. The descriptive survey methodolgy was used. The study sample consisted of (415) administrators, teachers and consultants, to achieve the study goals. The questionnaire was used, the study results showed that the educational advisor’s role in solving school problems from the Islamic perspective, was very high degree, and the results showed that there were no statistically significant differences for the level of responses of the sample subjects in the role of the school advisor from the Islamic perspective in solving educational problems due to the different variables: (gender, educational qualification, and experience). And the presence of statistically significant difference attributable to the effect of the job title variable in favor of a principal, advisor, and in light of the results of the study, the study recommended enhancing the role of the educational advisor in supporting the principal and teachers, by ensuring that instructions, brochures, and circulars related to the problems that students suffer from; reach to all teachers, also providing educational means and using them. ملخص: هدفت هذه الدراسة إلى تعرف دور المستشار التربوي في حل المشكلات المدرسية من المنظور الإسلامي، تم استخدام المنهج الوصفي المسحي، تكونت عينة الدراسة من(415) مديراً ومعلماً ومستشاراً، ولتحقيق أهداف الدراسة تم استخدام الاستبانة، وقد أظهرت نتائج الدراسة أن دور المستشار التربوي في حل المشكلات المدرسية من المنظور الإسلامي، جاء بدرجة كبيرة جداً ، وبينت النتائج عدم وجود فروق ذات دلالة إحصائية لمستوى استجابات أفراد العينة في دور المستشار التربوي من المنظور الإسلامي في حل المشكلات المدرسية تعزى لاختلاف المتغيرات: (الجنس، والمؤهل العلمي، والخبرة). ووجود فروق ذات دلالة إحصائية تعزى لأثر متغير المسمى الوظيفي لصالح فئة مدير، مستشار، وفي ضوء النتائج التي توصلت إليها الدراسة، أوصت الدراسة بتعزيز دور المستشار التربوي في مساندةُ المدير والمعلمين والمعلمات ومؤازرتهم من خلال ضمانِ وصول النشرات والمطويات والتعاميم المتعلقة بالمشكلات التي يعاني منها الطلبة إلى كلّ المعلمين، وتوفيرِ الوسائل التعليميّة واستخدامها

Impact of CO2 concentration and ambient conditions on microalgal growth and nutrient removal from wastewater by a photobioreactor

The increase in atmospheric CO2 concentration and the release of nutrients from wastewater treatment plants (WWTPs) are environmental issues linked to several impacts on ecosystems. Numerous technologies have been employed to resolves these issues, nonetheless, the cost and sustainability are still a concern. Recently, the use of microalgae appears as a cost-effective and sustainable solution because they can effectively uptake CO2 and nutrients resulting in biomass production that can be processed into valuable products. In this study single (Spirulina platensis (SP.PL) and mixed indigenous microalgae (MIMA) strains were employed, over a 20-month period, for simultaneous removal of CO2 from flue gases and nutrient from wastewater under ambient conditions of solar irradiation and temperature. The study was performed at a pilot scale photo-bioreactor and the effect of feed CO2 gas concentration in the range (2.5–20%) on microalgae growth and biomass production, carbon dioxide bio-fixation rate, and the removal of nutrients and organic matters from wastewater was assessed. The MIMA culture performed significantly better than the monoculture, especially with respect to growth and CO2 bio-fixation, during the mild season; against this, the performance was comparable during the hot season. Optimum performance was observed at 10% CO2 feed gas concentration, though MIMA was more temperature and CO2 concentration sensitive. MIMA also provided greater removal of COD and nutrients (~83% and >99%) than SP.PL under all conditions studied. The high biomass productivities and carbon bio-fixation rates (0.796–0.950 gdw·L−1·d−1 and 0.542–1.075 gC·L−1·d−1 contribute to the economic sustainability of microalgae as CO2 removal process. Consideration of operational energy revealed that there is a significant energy benefit from cooling to sustain the highest productivities on the basis of operating energy alone, particularly if the indigenous culture is used

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities

Laser Time-of-Flight Mass Spectrometry for Future In Situ Planetary Missions

Laser desorption/ionization time-of-flight mass spectrometry (LD-TOF-MS) is a versatile, low-complexity instrument class that holds significant promise for future landed in situ planetary missions that emphasize compositional analysis of surface materials. Here we describe a 5kg-class instrument that is capable of detecting and analyzing a variety of analytes directly from rock or ice samples. Through laboratory studies of a suite of representative samples, we show that detection and analysis of key mineral composition, small organics, and particularly, higher molecular weight organics are well suited to this instrument design. A mass range exceeding 100,000 Da has recently been demonstrated. We describe recent efforts in instrument prototype development and future directions that will enhance our analytical capabilities targeting organic mixtures on primitive and icy bodies. We present results on a series of standards, simulated mixtures, and meteoritic samples

What is a smart device? - a conceptualisation within the paradigm of the internet of things

The Internet of Things (IoT) is an interconnected network of objects which range from simple sensors to smartphones and tablets; it is a relatively novel paradigm that has been rapidly gaining ground in the scenario of modern wireless telecommunications with an expected growth of 25 to 50 billion of connected devices for 2020 Due to the recent rise of this paradigm, authors across the literature use inconsistent terms to address the devices present in the IoT, such as mobile device, smart device, mobile technologies or mobile smart device. Based on the existing literature, this paper chooses the term smart device as a starting point towards the development of an appropriate definition for the devices present in the IoT. This investigation aims at exploring the concept and main features of smart devices as well as their role in the IoT. This paper follows a systematic approach for reviewing compendium of literature to explore the current research in this field. It has been identified smart devices as the primary objects interconnected in the network of IoT, having an essential role in this paradigm. The developed concept for defining smart device is based on three main features, namely context-awareness, autonomy and device connectivity. Other features such as mobility and userinteraction were highly mentioned in the literature, but were not considered because of the nature of the IoT as a network mainly oriented to device-to-device connectivity whether they are mobile or not and whether they interact with people or not. What emerges from this paper is a concept which can be used to homogenise the terminology used on further research in the Field of digitalisation and smart technologies

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised