Search

The enormous demand for computational resources due to rapid Cloud growth has led to the creation of large-scale data centers (DC). Consequently, a tremendous amount of energy is consumed with high carbon dioxide emissions, which necessitates that cloud service providers (CSP) develop high quality of service (QoS) strategies to address such challenges. Proper management and utilization of resources ensure a well-balanced load distribution that makes energy consumption sufficient. Developing new algorithms and exploring efficient methods and techniques is highly desired for the management of virtualized DCs. This research proposes Finite Automata using Ant Colony Optimization with Spanning Tree (FAACOST), a machine-learning concept for managing cloud resources in a multi-tier environment. The overall objective is to minimize energy consumption through data placement leverage and virtual machines (VM) consolidation. The proposed technique's efficiency was benchmarked on four performance metrics (machine learning, dynamic nature, scalability, and QoS). Based on the extensive experiments conducted, FAACOST recorded an energy consumption of 132 w with 20 tasks compared to the benchmarked techniques that consumed 363 w. The experimental results show that FAACOST achieves the optimal number of physical machines (PMs) and is more energy-efficient.

Background and study aim: Despite the global priority given to malaria control and prevention, antimalarial resistance is a major factor that encourages persistence of malaria in developing countries. This prospective study sought to determine the antimalarial resistance pattern of P. falciparum isolated from infected pregnant women attending antenatal clinics at Kaduna state, Northern Nigeria. Materials and Methods: Between 16th February and 28th April, 2015, EDTA anticoagulated blood samples were collected from seventy nine pregnant women with plasmodiasis. Antimalarial susceptibility of chloroquine, artesunate, artemether and sulfadoxin-pyrimethamine (SP) against P falciparum was done using schizont maturation assay. Multidrug resistant plasmodiasis was defined by resistance to ≥ 3 antimalarial drugs. Results: Malaria parasites from the pregnant women exhibited the highest resistance against chloroquine, 85 (Session [UserIDID].1%) followed by Artemether, 30 (8.5%) then sulfadoxin-pyrimethamine, 29 (8.2%) and least resistant to artesunate, 28 (7.9%). The occurrence rate of multidrug resistance was 40.5%. There was no significant association between occurrence of multidrug resistance and malaria parasitaemia (p=0.092). Seventy five, 94.8% of the P. falciparum infected subjects exhibit resistant to at least one antimalarial. Antimalarial resistance was highest in women with severe malaria 20 (80.0%), followed by those with moderate malaria, 15 (62.5%) and least in those with mild malaria, 4 (13.3%). There was significant association between occurrence of antimalarial resistance and densities of malaria parasitaemia (p=0.0125). Conclusion: Considering the high degree of antimalarial resistance reported from this study, there will be challenges in eradicating malaria in this environment. These findings necessitate the need for regular surveillance for resistant P. falciparum and evaluation of more effective alternative drug (s).

Objective: Pregnant women infected with malaria represent a significant obstetric problem, especially in the face of antimalarial resistance. This cross-sectional study investigated the prevalence of malaria parasitemia, associated risk factors as well as the antimalarial resistance pattern of Plasmodium isolates from pregnant women attending four selected secondary health facilities in Kaduna State, Nigeria. Materials and Methods: Blood samples were collected from 353 pregnant women attending selected hospitals. Malaria microscopy and parasite density count were conducted based on standard protocols. Antimalarial susceptibility test (using chloroquine, artesunate, artether, and sulfadoxine-pyrimethamine), and hemoglobin concentrations were determined using schizont maturation assay and methemoglobin method, respectively. Multiple-drug resistance (MDR) was defined by resistance against ≥3 antimalarial drugs. Results: The overall prevalence of plasmodiasis was 22.4%. Out of those infected, 5.2% was found to be anemic. Malaria parasitemia was significantly associated with parity, residential area, age of women, and use of preventive measures against malaria (P < 0.05) but not with hemoglobin concentration, occupation, and trimester of pregnancy (P > 0.05). Malaria parasites from the pregnant women exhibited the highest resistance against chloroquine, 75 (94.9%) followed Artemether, 30 (37.9%) then sulfadoxine-pyrimethamine, 29 (36.7%) and least resistant to artesunate, 28 (35.4%). The prevalence of MDR was 40.5% (32/79). Conclusion: The prevalence of malaria was relatively high due to inadequate and/or ineffective preventive measures adopted by pregnant women. More so, significant isolates of Plasmodium falciparum exhibited MDR against antimalarial agents tested.

Mutual Information (MI) is an information theory concept often used in the recent time as a criterion for feature selection methods. This is due to its ability to capture both linear and non-linear dependency relationships between two variables. In theory, mutual information is formulated based on probability density functions (pdfs) or entropies of the two variables. In most machine learning applications, mutual information estimation is formulated for classification problems (that is data with labeled output). This study investigates the use of mutual information estimation as a feature selection criterion for regression tasks and introduces enhancement in selecting optimal feature subset based on previous works. Specifically, while focusing on regression tasks, it builds on the previous work in which a scientifically sound stopping criteria for feature selection greedy algorithms was proposed. Four real-world regression datasets were used in this study, three of the datasets are public obtained from UCI machine learning repository and the remaining one is a private well log dataset. Two Machine learning models namely multiple regression and artificial neural networks (ANN) were used to test the performance of IFSMIR. The results obtained has proved the effectiveness of the proposed method.

Dynamic branch prediction is a hardware technique used to speculate the direction of control branches. Inaccurate prediction will make all speculative works useless while accurate prediction will significantly improve microprocessors performance. In this work, we have shown that Genetic Algorithm (GA) can be used to select (near) optimal parameters for branch predictors in most cases. The GA-enhanced predictors take time to find suitable parameters, but once the values of these parameters are determined, the GA-enhanced predictors take the same time to execute as the basic predictors with increased accuracy.

Flavonoid and phenolics are secondary metabolites produced by plants in response to harsh environmental conditions. Light is one of the most important factor that affects their production. Mikania micrantha and Tridax procumbens are plants belonging to Asteraceae family, and they have bioactivity due to their flavonoid and phenolic contents. The objective of the research is to determine the effect of varying light intensities on the total flavonoid and total phenolic contents of M. micrantha and T. procumbens using three solvents (ethanol, methanol and water). Total flavonoid contents was determined based on the aluminium chloride colorimetry method while total phenolic contents was determined based on the folin-chiocalteau reagent. The results obtained in the study shows that ethanol recovers more flavonoid and phenolic than the other solvents (P < 0.05). Besides, T. procumbens had more flavonoid and phenolic content compared to M. micrantha (P < 0.05). Nevertheless, the flavonoid and phenolic contents recovered from sun exposed plants was more than that recovered from shaded plants (P < 0.05). This leads to a conclusion that high light intensity can increase the concentration of flavonoid and phenolic of M. micrantha and T. procumbens.

Understanding the impact of nonthermal plasma (NTP) technology on key nutritional and functional food components is of paramount importance for the successful adoption of the technology by industry. NTP technology (NTPT) has demonstrated marked antimicrobial efficacies with good retention of important physical, chemical, sensory, and nutritional parameters for an array of food products. This paper presents the influence of NTPT on selected functional food components with a focus on low-molecular-weight bioactive compounds and vitamins. We discuss the mechanisms of bioactive compound alteration by plasma-reactive species and classify their influence on vitamins and their antioxidant capacities. The impact of NTP on specific bioactive compounds depends both on plasma properties and the food matrix. Induced changes are mainly associated with oxidative degradation and cleavage of double bonds in organic compounds. The effects reported to date are mainly time-dependent increases in the concentrations of polyphenols, vitamin C, or increases in antioxidant activity. Also, improvement in the extraction efficiency of polyphenols is observed. The review highlights future research needs regarding the complex mechanisms of interaction with plasma species. NTP is a novel technology that can both negatively and positively affect the functional components in food.

The recent urbanization process is increasingly rapid and unplanned. With the current projection that seven out of ten people worldwide will be living in the cities by the year 2050, with the majority of the growth occurring in cities of the developing countries, information technology (IT) can be used to positively drive the urbanization process. This chapter provides an integrative framework for which various IT trends can be harnessed to crowdfunding urbanization process by focusing on the use of mobile and other handheld devices. Existing use of mobile phone in developing countries focus on e-education, e-commerce, and social media technologies. However, there is little evidence that researchers have analyzed the role of IT in crowdfunding. Consequently, this chapter develops a framework on how the capabilities of mobile devices can be harnessed for crowdfunding urbanization process in the developing countries.

This chapter seeks to fill a gap in the regional analysis of online crowdfunding platforms and projects by examining selected platforms in Africa and in the Middle East. The authors considered the quantity and quality of the actors: fundraisers, funders, and other stakeholders; analyzed characteristics, quality, fees, and general value for money; and evaluated the capacity of the platforms in terms of system interface, projects, and customer crowds aiming to enhance their overall capacity to become nodes of local and regional crowdfunding activities. These are connected to other district and urban centers that would serve as a springboard to organize and network with overseas centers for the growth and development of the regional centers.