Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing

The latest developments in mobile computing technology have increased the computing capabilities of smartphones in terms of storage capacity, features support such as multimodal connectivity, and support for customized user applications. Mobile devices are, however, still intrinsically limited by low bandwidth, computing power, and battery lifetime. Therefore, the computing power of computational clouds is tapped on demand basis for mitigating resources limitations in mobile devices. Mobile cloud computing (MCC) is believed to be able to leverage cloud application processing services for alleviating the computing limitations of smartphones. In MCC, application offloading is implemented as a significant software level solution for sharing the application processing load of smartphones. The challenging aspect of application offloading frameworks is the resources intensive mechanism of runtime profiling and partitioning of elastic mobile applications, which involves additional computing resources utilization on Smart Mobile Devices (SMDs). This paper investigates the overhead of runtime application partitioning on SMD by analyzing additional resources utilization on SMD in the mechanism of runtime application profiling and partitioning. We evaluate the mechanism of runtime application partitioning on SMDs in the SmartSim simulation environment and validate the overhead of runtime application profiling by running prototype application in the real mobile computing environment. Empirical results indicate that additional computing resources are utilized in runtime application profiling and partitioning. Hence, lightweight alternatives with optimal distributed deployment and management mechanism are mandatory for accessing application processing services of computational clouds.     

Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes

For an effective and efficient application of machining processes it is often necessary to consider more than one machining performance characteristics for the selection of optimal machining parameters. This implies the need to formulate and solve multi-objective optimization problems. In recent years, there has been an increasing trend of using meta-heuristic algorithms for solving multi-objective machining optimization problems. Although having the ability to efficiently handle highly non-linear, multi-dimensional and multi-modal optimization problems, meta-heuristic algorithms are plagued by numerous limitations as a consequence of their stochastic nature. To overcome some of these limitations in the machining optimization domain, a software prototype for solving multi-objective machining optimization problems was developed. The core of the developed software prototype is an algorithm based on exhaustive iterative search which guarantees the optimality of a determined solution in a given discrete search space. This approach is justified by a continual increase in computing power and memory size in recent years. To analyze the developed software prototype applicability and performance, four case studies dealing with multi-objective optimization problems of non-conventional machining processes were considered. Case studies are selected to cover different formulations of multi-objective optimization problems: optimization of one objective function while all the other are converted into constraints, optimization of a utility function which combines all objective functions and determination of a set of Pareto optimal solutions. In each case study optimization solutions that had been determined by past researchers using meta-heuristic algorithms were improved by using the developed software prototype.     

A hybrid approach for data clustering based on modified cohort intelligence and K-means

Clustering is an important and popular technique in data mining. It partitions a set of objects in such a manner that objects in the same clusters are more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms too suffer several short-comings. In this paper, we present an efficient hybrid evolutionary data clustering algorithm referred to as K-MCI, whereby, we combine K-means with modified cohort intelligence. Our proposed algorithm is tested on several standard data sets from UCI Machine Learning Repository and its performance is compared with other well-known algorithms such as K-means, K-means++, cohort intelligence (CI), modified cohort intelligence (MCI), genetic algorithm (GA), simulated annealing (SA), tabu search (TS), ant colony optimization (ACO), honey bee mating optimization (HBMO) and particle swarm optimization (PSO). The simulation results are very promising in the terms of quality of solution and convergence speed of algorithm.     

Application of a modified GA,ACO and a random search procedure to solve the production scheduling of a case study bakery

Based on the constraints and frame conditions given by the real processes the production in bakeries can be modelled as a no-wait permutation flow-shop, following the definitions in scheduling theory. A modified genetic algorithm, ant colony optimization and a random search procedure were used to analyse and optimize the production planning of a bakery production line that processes 40 products on 26 production stages. This setup leads to 8.2 × 10⁴⁷ different possible schedules in a permutation flow-shop model and is thus not solvable in reasonable time with exact methods. Two objective functions of economical interest were analysed, the makespan and the total idle time of machines. In combination with the created model, the applied algorithms proved capable to provide optimized results for the scheduling operation within a predefined runtime of 15 min, reducing the makespan by up to 8.6% and the total idle time of machines by up to 23%.     

Tocotrienols from palm oil: Electron spin resonance spectra of tocotrienoxyl radicals

The major vitamin E components present in palm oil, viz. α-tocopherol, α⁻, ψ-and δ-tocotrienols, have been isolated and their structures verified by the NMR spectra of their acetate and succinate derivatives. Oxidation of γ-and δ-tocotrienols with alkaline K₃Fe(CN)₆ gave isolable dimeric species, which were studied by¹³C NMR. Free radicals generated from the monomeric and dimeric tocotrienols were investigated using ESR spectroscopy. The distinction between antioxidant activity and antioxidant capacity of vitamin E isomers is discussed.     

A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes

Global economic competition has spurred the manufacturing sector to improve and invest in modern equipment to satisfy the needs of the market. In particular, machine tool selection is the most important problem; it plays a primary role in the improvement of productivity and flexibility in the manufacturing environment and involves the imprecise, vague and uncertain information. This paper presents the hybrid approach of the fuzzy ANP (Analytic Network Process) and COPRAS-G (COmplex PRoportional ASsessment of alternatives with Grey relations) for fuzzy multi-attribute decision-making in evaluating machine tools with consideration of the interactions of the attributes. The fuzzy ANP is used to handle the imprecise, vague and uncertain information from expert judgments and model the interaction, feedback relationships and interdependence among the attributes to determine the weights of the attributes. COPRAS-G is employed to present the preference ratio of the alternatives in interval values with respect to each attribute and calculate the weighted priorities of the machine alternatives. Alternatives are ranked in ascending order by priority. As a demonstration of the proposed model, a numerical example is implemented based on the collected data and the literature. The result is then compared with the rankings provided by other methods such as TOPSIS-G, SAW-G and GRA. Moreover, a sensitivity analysis is conducted to verify the robustness of the ranking. The result highlights that the hybrid approach of the fuzzy ANP and COPRAS-G is a highly flexible tool and reaches an effective decision in machine tool selection.     

Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.     

Dynamometer evaluation and engine wear characteristics of palm oil diesel emulsions

Dynamometer engine tests at steady-state conditions and a wear characteristics study were carried out on an indirect-injection diesel engine with palm oil diesel (POD) and its emulsions. The POD fuel was obtained in commercial form, and its emulsions were created by mixing POD fuel to contain 5 and 10% of water by volume. Variations in the engine’s performance characteristics were determined from the results of steady-state tests carried out at fifteen selected torque-speed matrix points of the engine’s performance map. The wear characteristics tests were performed by running the engine at half throttle setting for twenty hours for each fuel system. Then a desk-top comparison study was performed between the base-line fuel system of ordinary diesel (OD), POD, and its emulsions. Promising results have been obtained. Neither the lower cetane number of POD fuel nor its emulsification with water presented obstacles to the operation of the diesel engine during a series of steady-state engine tests and the twenty-hour endurance tests. Engine performance and fuel consumption for POD and its emulsions are comparable with those of OD fuel. Accumulations of wear metal debris in crank-case oil samples were lower with POD and its emulsions than with baseline OD fuel.     

Production of carbon molecular sieves from palm shell based activated carbon by pore sizes modification with benzene for methane selective separation

Palm shell based activated carbon prepared by K₂CO₃ activation is used as precursor in the production of carbon molecular sieve by chemical vapor deposition (CVD) method using benzene as depositing agent. The influences of deposition temperature, time, and flow rate of benzene on pore development of carbon molecular sieve (CMS) and methane (CH₄) adsorption capacity were investigated. The parameters that varied are the deposition temperature range of 600 to 1000 °C, time from 5.0 to 60 min, and benzene flow rate from 3.0 to 15 mL/min. The results show that in all cases, increasing the deposition temperature, time, and flow rate of benzene result in a decrease in adsorption capacity of N₂, pore volume and pore diameter of CMS. The BET surface area of CMS (approximately 1065 m²/g) and the adsorption capacity of CH₄ were at a maximum value at a deposition temperature of 800 °C, time of 20 min and benzene flow rate of 6 mL/min. The product has a good selectivity for separating CH₄ from carbon dioxide (CO₂), nitrogen (N₂), and oxygen (O₂).     

An investigation on Ribbon interface design guidelines for people with less computer literacy

In the recent years, there is an intense competition between software development companies to design better interfaces. In this marketing rat race, Ribbon interface came to make software user interface easier. After the introduction of Ribbon by Microsoft, it was widely used by various software development companies. Ribbon is a replacement for menus and toolbars and it tends to organize tools in tabs based on their similarities. Although Ribbon interface has many advantages, previous researches have shown that there are serious usability issues that hinder usage of Ribbon interfaces for users with less computer literacy. In order to solve Ribbon interfaces usability issues, this study tried to introduce Ribbon interface design guidelines by focusing on the issues related to users with less computer literacy. In this study two separate sets of moderated (in-person) usability testing were used. The first set evaluated the usability issues of an experimental Ribbon interface software in terms of both visual and cognitive issues. The second set was used to evaluate the Ribbon interface prototype that was designed based on the discovered usability issues in the first usability test. In order to ensure the validity of the data, the researchers tried to triangulate the data collection process by collecting data from different sources, namely, quantitative measurement of participants' performance, direct observation, and interview. Based on the comparison of the usability tests results which points out the factors that have led to participants' performance improvement in the prototype version, a number of guidelines are extracted for Ribbon interfaces. These guidelines are applicable to Microsoft Office, Microsoft SharePoint and most of the software that can be developed with Ribbon interface. Putting these guidelines into action, self-learning would be promoted and learning issues of users with less computer literacy would be decreased.