• Economic and Environmental Geology
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• v.56 no.1
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• pp.75-85
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• 2023

The potential sources and spatial distribution of heavy metals and polycyclic aromatic hydrocarbons (PAHs) were investigated in the leaf plants of Al-Zubair city. A total of 14 samples of conocarpus lancifolius plant leaf were collected and analyzed for their heavy metals and PAHs content using inductively coupled plasma mass spectrometry (ICP-MS) and a 7890 Agilent capillary gas chromatograph (GC) respectively. Bioaccumulation factor calculation revealed the highest pollution of heavy metals , due to the activity of a petrochemical in the area. The diagnostic ratio of Ant/(Phe+Ant), BaA/BaA+Chr), In/(In+BghiP), Flu/Pyr, FlA/FlA+Pyr), FlA/FlA+Pyr), ∑LMW/∑HMW are commonly used for determining the origin and source of PAHs in various environmental media. The diagnostic ratio indicated the anthropogenic origin. PAHs with five-to-six membered rings were dominant in the plant leaf, which likely results from anthropogenic activities. The leaves of C. lancifolius have a preponderance of high molecular weight PAHs compared to low molecular weight PAHs, indicating a combustion origin (car exhaust, petroleum emissions, and fossil fuel). C. lancifolius leaves are a reliable indication of atmospheric PAHs absorption. The background level of heavy metals in the city (or the near environment) is in the order of Fe > Cu > Ni > Cr. On the other hand, the bioaccumulation in plant leaves showed greater tendencies as follows: Co>Cd>Zn=As>Cu>Mn>Ni>Pb>Cr>Fe. Cobalt showed high bioaccumulation, indicating strong uptake of Co by plant leaves. These findings point to human activity and car emissions as the primary sources of roadside vegetation pollution in Al-Zubair city.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.4873-4880
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• 2015

Purpose: To describe the clinicopathological features of gastrointestinal stromal tumors (GIST) diagnosed in our section and to perform risk stratification of our cases by assigning them to specific risk categories and groups for disease progression based on proposals by Fletcher et al and Miettinen and Lasota. Materials and Results: We retrieved 255 cases of GIST diagnosed between 2003 and 2014. Over 59% were male. The age range was 16 to 83 years with a mean of 51 years. Over 70% occurred between 40 and 70 years of age. Average diameter of tumors was 10 cms. The stomach was the most common site accounting for about 40%. EGISTs constituted about 16%. On histologic examination, spindle cell morphology was seen in almost of 85% cases. CD117 was the most useful immunohistochemical antibody, positive in 98%. Risk stratification was possible for 220 cases. Based on Fletcher's consensus proposal, 62.3 gastric, 81.8% duodenal, 68% small intestinal, 72% colorectal and 89% EGISTs were assigned to the high risk category; while based on Miettinen and Lasota's algorithm, about 48% gastric, 100% duodenal, 76% small intestinal, 100% colorectal and 100% EGISTs in our study were associated with high risk for disease progression, tumor metastasis and tumor related death. Follow up was available in 95 patients; 26 were dead and 69 alive at follow up. Most of the patients who died had high risk disease and on average death occurred just a few months to a maximum of one to two years after initial surgical resection. Conclusions: Epidemiological and morphologic findings in our study were similar to international published data. The majority of cases in our study belonged to the high risk category.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.