Fog computing scheduling algorithm for smart city

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model

HIV-Care Outcome in Saudi Arabia; a Longitudinal Cohort

Background: Clinical characteristics of HIV-1 infection in people inhabiting Western, Sub-Saharan African, and South-East Asian countries are well recognized. However, very little information is available with regard to HIV-1 infection and treatment outcome in MENA countries including the Gulf Cooperation Council (GCC) states. Methods: Clinical, demographic and epidemiologic characteristics of 602 HIV-1 infected patients followed in the adult Infectious Diseases Clinic of King Faisal Specialist Hospital and Research Centre, in Riyadh, Kingdom of Saudi Arabia a tertiary referral center were longitudinally collected from 1989 to 2010. Results: Of the 602 HIV-1 infected patients in this observation period, 70% were male. The major mode of HIV-1 transmission was heterosexual contact (55%). At diagnosis, opportunistic infections were found in 49% of patients, most commonly being pneumocysitis. AIDS associated neoplasia was also noted in 6% of patients. A hundred and forty-seven patients (24%) died from the cohort by the end of the observation period. The mortality rate peaked in 1992 at 90 deaths per 1000 person-year, whereas the mortality rate gradually decreased to <1% from 1993-2010. In 2010, 71% of the patients were receiving highly active retroviral therapy. Conclusions: These data describe the clinical characteristic of HIV-1-infected patients at a major tertiary referral hospital in KSA over a 20-year period. Initiation of antiretroviral therapy resulted in a significant reduction in both morbidity and mortality. Future studies are needed in the design and implementation of targeted treatment and prevention strategies for HIV-1 infection in KSA

Genetic Polymorphisms and Drug Susceptibility in Four Isolates of Leishmania tropica Obtained from Canadian Soldiers Returning from Afghanistan

Cutaneous leishmaniasis (CL) is a vector-borne parasitic disease transmitted by the bite of sandflies, resulting in sores on the skin. No vaccines are available, and treatment relies on chemotherapy. CL has been frequently diagnosed in military personnel deployed to Afghanistan and returning from duty. The parasites isolated from Canadian soldiers were characterized by pulsed field gels and by sequencing conserved genes and were identified as Leishmania tropica. In contrast to other Leishmania species, high allelic polymorphisms were observed at several genetic loci for the L. tropica isolates that were characterized. In vitro susceptibility testing in macrophages showed that all isolates, despite their genetic heterogeneity, were sensitive to most antileishmanial drugs (antimonials, miltefosine, amphotericin B, paromomycin) but were insensitive to fluconazole. This study suggests a number of therapeutic regimens for treating cutaneous leishmaniasis caused by L. tropica among patients and soldiers returning from Afghanistan. Canadian soldiers from this study were successfully treated with miltefosine

Complementing compost with biochar for agriculture, soil remediation and climate mitigation

We are racing to manage a phenomenally increasing volume of organic wastes from urban, industrial and agricultural entities. Composting is one of the preferred ways to convert biodegradable wastes into nutrient-rich soil conditioners. The age-old technique of composting process is being improved with innovative scientific means. Biochar, a widely studied soil amendment, is a carbonaceous material that can hold nutrients from endogenic/exogenic sources. Biochar-compost, a biochar-complemented compost, may provide a wide range of benefits expected from both materials. Compost and biochar can improve physicochemical and microbiological attributes of soils by supplying labile and stable carbons, and nutrients. Compost may also supply beneficial microbes. This means biochar-compost is a synergic soil amendment that can improve soil quality, increase crop production, and remediate contaminated soils. Having stable carbon, large reactive surface with nutrient loads, biochar can interact widely with organic biomass and modify physicochemical and-microbial states during a composting process while making biochar-compost. Production and application methods of biochar, compost and biochar-compost are covered for agricultural and contaminated soils. Metal and organic contaminations are also discussed. A case study on making and field-testing a mineral-enhanced biochar and a biochar-compost to improve rice yield, is presented at the end

Chemotactic and Inflammatory Responses in the Liver and Brain Are Associated with Pathogenesis of Rift Valley Fever Virus Infection in the Mouse

Rift Valley fever virus (RVFV) is a major human and animal pathogen associated with severe disease including hemorrhagic fever or encephalitis. RVFV is endemic to parts of Africa and the Arabian Peninsula, but there is significant concern regarding its introduction into non-endemic regions and the potentially devastating effect to livestock populations with concurrent infections of humans. To date, there is little detailed data directly comparing the host response to infection with wild-type or vaccine strains of RVFV and correlation with viral pathogenesis. Here we characterized clinical and systemic immune responses to infection with wild-type strain ZH501 or IND vaccine strain MP-12 in the C57BL/6 mouse. Animals infected with live-attenuated MP-12 survived productive viral infection with little evidence of clinical disease and minimal cytokine response in evaluated tissues. In contrast, ZH501 infection was lethal, caused depletion of lymphocytes and platelets and elicited a strong, systemic cytokine response which correlated with high virus titers and significant tissue pathology. Lymphopenia and platelet depletion were indicators of disease onset with indications of lymphocyte recovery correlating with increases in G-CSF production. RVFV is hepatotropic and in these studies significant clinical and histological data supported these findings; however, significant evidence of a pro-inflammatory response in the liver was not apparent. Rather, viral infection resulted in a chemokine response indicating infiltration of immunoreactive cells, such as neutrophils, which was supported by histological data. In brains of ZH501 infected mice, a significant chemokine and pro-inflammatory cytokine response was evident, but with little pathology indicating meningoencephalitis. These data suggest that RVFV pathogenesis in mice is associated with a loss of liver function due to liver necrosis and hepatitis yet the long-term course of disease for those that might survive the initial hepatitis is neurologic in nature which is supported by observations of human disease and the BALB/c mouse model

A procedure for semi‐Automatic Orthophoto Generation from High Resolution Satellite Imagery

The General Directorate of Surveying and Mapping (GDSM), under the Ministry of Municipal and Rural Affairs (MOMRA) is responsible for the production and dissemination of accurate geospatial data for all the metropolitan cities, towns and rural settlements in the Kingdom of Saudi Arabia. GDSM maintains digital geospatial databases that support the production of conventional line and orthophoto maps at scales ranging from 1:1,000 to 1:20,000. The current procedures for the acquisition of new aerial imagery cover a long time cycle of three or more years. Consequently, the availability of recently acquired High Resolution Satellite Imagery (HRSI) presents an attractive alternative image data source for rapid response to updated geospatial data needs. The direct sensor orientation of HRSI is not accurate enough requiring ground control points (GCP). A field survey of GCP is time consuming and costly. Seeking an alternative approach, a research study has recently been completed to use existing image and data base information instead of traditional ground control for the orthoprojection of HRSI in order to automate and speed up as much as possible the whole process. Based on a series of practical experiments, the ability for automated matching of aerial and satellite images by using the Speeded-Up Robust Features (SURF) algorithm is demonstrated to be useful for this task. Practical results from matching with SURF validate the ability for multi-scale, multi-sensor and multi-season matching of aerial and satellite images. The matched tie points are then used to transform the satellite orthophoto to the aerial orthophoto through a 2D affine coordinate transformation. GeoEye-1 and IKONOS imagery, when geo-referenced through SURF-based matching and transformed meet the MOMRA Map Accuracy Standards for 1:10,000 and 1:20,000 scale. However, a similarly processed SPOT-5 image does not meet these standards. This research has led to the development of a simple and efficient tool for the geo-referencing of HRSI of 0,5 m to 1 m ground sampling distance (GSD) that can be used for updating map information. The process completely eliminates the need for any ground control as well as image measurements by human operators

Detection of Road Condition Defects Using Multiple Sensors and IoT Technology: A Review

The transportation efficiency and driving safety of road networks, which play an essential role in economic prosperity, are impacted significantly by damage and defects on the road surface. In current practice, it can take weeks or even months before related government departments repair such road conditions, mainly due to lack of awareness of any damage. This paper reviews the current status and limitation of a framework for sensors devices and assessment of road surface conditions. The review also incorporates the most relevant machine learning-based methods, challenges, and future trends to underpin large-scale deployment of road defects automation identification. It is expected that the technology can provide both qualitative and quantitative information about the road surface condition and thus enable timely maintenance to improve transportation efficiency and driving safety