To realize efficient parallel processing, which is one of effective methods that deal with computing intensive applications, the technology of solving the problems of task matching and scheduling becomes extremely important. In this thesis, an Ant Colony Optimization (ACO) approach is employed for allocating task graphs onto a heterogeneous computing system. The approach uses a new state transition rule to reduce the time needed for finding a satisfactory solution. And a local search procedure is designed to improve the obtained solution. Furthermore, by applying the Taguchi Method in the technology of Quality Engineering, and further utilizing the Orthogonal Array (OA) to reduce the number of experiments and find the optimal combination of parameters, which allows the Ant Colony Algorithm to find solutions more efficient. The proposed algorithm is compared with the genetic-algorithm-based approach and the dynamic priority scheduling (DPS) heuristic. Experimental results show that the ACO approach outperforms two computing approaches in solving the task matching and scheduling problem.

Functional expression of a protein in life form is decided by its tertiary structure. In the past few decades, a significant number of studies have been made on this subject. However, the folding rules of a protein still stay unsolved. The challenge is to predict the three-dimensional tertiary structure of a protein from its primary amino acid sequence. We propose a hybrid method combining homology model and the folding approach to predict protein three-dimensional structure from amino acid sequence. The previous researches on folding problem mostly take the HP (Hydrophobic-Polar) model, which is not able to simulate the native structure of proteins. We use a more exquisite model, the sliced lattice model, to approximate the native forms. Another essential factor influencing protein structures is disulfide bonds, which are ignored in the HP model. We use the ant colony optimization algorithm to approximate the folding problem with the constrained disulfide bond on the sliced lattice HP model. We show that the prediction results are better than previous methods by the measurement of RMSD(Root Mean Square Deviation).

The Human Genome Project completed in 2003 and the draft of human genome sequences were also yielded. It has been known that any two human gnomes are almost identical, and only very little difference makes human diversities. Single nucleotide polymorphism (SNP) means that a single-base nucleotide changes in DNA. A SNP sequence from one of a pair of chromosomes is called a haplotype. In this thesis, we study how to reconstruct a pair of chromosomes from a given set of fragments obtained by DNA sequencing in an individual. We define a new problem, the chromosome pair assembly problem, for the chromosome reconstruction. The goal of the problem is to find a pair of sequences such that the pair of output sequences have the minimum mismatch with the input fragments and their lengths are minimum. We first transform the problem instance into a directed multigraph. And then we propose an efficient algorithm to solve the problem. We apply the ACO algorithm to optimize the ordering of input fragments and use dynamic programming to determine SNP sites. After the chromosome pair is reconstructed, the two haplotypes can also be determined. We perform our algorithm on some artificial test data. The experiments show that our results are near the optimal solutions of the test data.

Alpha diversity, population dynamics and interactions of ant assemblages were studied in a pecan orchard located in Mumford, Texas. The assemblages included the invasive species Solenopsis invicta Buren, known as the red imported fire ant (RIFA). The study addressed the major question of what is the response of the ant assemblage to the reduction of RIFA following insecticide applications (bait and contact insecticide) and the impact of these on individual species. To address this question three treatments were established in a 16 hectare area in the orchard. The treatments were randomly assigned in 1.33 hectare blocks with four replications and periodically monitored. Treatments were: 1) insect growth regulator (IGR) bait treatment (ExtinguishTM, active compound is 0.5% s-methoprene) applied twice in 2000 and once in 2001; 2) the contact insecticide chlorpyrifos (LorsbanTM) applied on tree trunks four times in 2000 and once in 2001; and 3) untreated Control. Blocks were sampled using pitfall traps, baited vials, direct sampling, and colony counts. Data were analyzed by using ANOVA-GLM with the LSD multiple comparison test to compare the effect of treatment on the ant assemblage (using the Shannon index) and the effect on individual species. Additionally, data obtained from Control plots were used to compare sampling techniques and to determine what method is most efficient for collecting ants in this agroecosystem. Shannon indices were estimated for each method and compared. The ant assemblage consisted of 16 ant species. S. invicta was the most abundant followed by Paratrechina sp. and Monomorium minimum. The IGR treatment consistently reduced RIFA (77%). Native ants were found to coexist with RIFA in the Control and chlorpyrifos plots at lower densities and maintained higher densities in IGR plots. Chlorpyrifos trunk treatment did not have a significant impact on RIFA or native ant densities. The native ant, Dorymyrmex flavus, was greater in IGR plots following RIFA reduction and higher densities were found to persist for more than two years after the last IGR treatment. During this period, D. flavus was observed carrying large numbers of dead RIFA, some taken inside the nest, and some disarticulated RIFA taken out of the nest. RIFA remains were accumulated in D. flavus middens, further indication of an important interaction between these two species. These results indicate D. flavus resisted reinvasion by RIFA.

Colony fitness, size, and reproductive potential are determined by their ability to locate and consume the optimal amounts of various macronutrients. Understanding the nutritional regulation of an ant colony furthers our understanding of their life history and can be used to produce a better baiting system. The "Geometric Framework" was used to conduct experiments determining how Paratrechina sp.nr. pubens regulated their protein and carbohydrate intake when given two sub-optimal, but complementary food sources, as well as when confined to a single food source. By analyzing how much food they consumed, we can determine how P. sp.nr. pubens regulates their food intake. Examination of the consumption results when given two choices, showed a preference for carbohydrate rich foods as well as a trend in regulation along a set nutritional trajectory. Further examination of the amount eaten when confined to a single food source, showed a higher consumption rate of the carbohydrate rich foods (p7:c35 and p14:c28). Analysis also showed a narrower range of protein intake when compared to carbohydrate. Accordingly, behavioral data indicate a pattern of consumption following seasonal shifts.

The k-LCS problem is to find the longest common subsequence (LCS) of k input sequences. It is difficult while the number of input sequences is large. In the past, researchers focused on finding the LCS of two sequences (2-LCS). However, there is no good algorithm for finding the optimal solution of k-LCS up to now. For solving the k-LCS problem, in this thesis, we first propose a mixed algorithm, which is a combination of a heuristic algorithm, genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Then, we propose an enhanced ACO (EACO) algorithm, composed of the heuristic algorithm and matching pair algorithm (MPA). In our experiments, we compare our algorithms with expansion algorithm, best next for maximal available symbol algorithm, GA and ACO algorithm. The experimental results on several sets of DNA and protein sequences show that our EACO algorithm outperforms other algorithms in the lengths of solutions.

<p>Titel FRÄMJA & FÖREBYGGA</p><p>– hälsofrämjande och drogförebyggande strategier inom skolan</p><p>Engelsk titel PROMOTE & PREVENT</p><p>– strategies in school to promote health and prevent drugs.</p><p>Författare Lotta Berg</p><p>Handledare Inger Holm</p><p>Datum Mars 2007</p><p>Antal sidor 32</p><p>Nyckelord hälsofrämjande, drogförebyggande, ANT, drogundervisning, hälsoundervisning, narkotika, droginformation, KASAM</p><p>SAMMANFATTNING</p><p>Syftet med min undersökning var att belysa rektorers erfarenheter av drogpreventivt arbete inom skolan. På vilket sätt man kan nå ut till alla elever för att upptäcka och motverka uppkomst av narkotika. Likaså avsåg jag att ta del av de tankar rektorerna har kring de kriterier som Statens skolverk utformat för en god drogundervisning, liksom hur deras egna strategier förhåller sig mot dessa. Jag har använt mig av en kvalitativ metod, baserad på intervjuer. Resultatet visade att det upptäckande arbetet tycktes vara svårt, då mycket bygger på obekräftad misstanke. För att motverka uppkomst av missbruk framhölls av rektorerna en drogundervisning med inslag av droginformation och värderingsövningar. Därtill betonades värdet av goda relationer, elevdelaktighet och friskvård, samt att se dessa i en större helhetsbild, vilket kan anses ha starka kopplingar till Antonovskys salutogena perspektiv och känsla av sammanhang, KASAM, vilken ligger till grund för min studie.</p>

Denna undersökning behandlar spel i kontext till lärande och matematik. Genom att gestalta ett spel undersökte vi, författare hur olika designval kunde påverka lärande. Målet var att spelare skulle lära sig genom situationsbaserat lärande och där genom få möjlighet att överföra sin kunskap till sitt vardagsliv. Undersökningen hade sin utgångspunkt i ANT (actor-network theory) som användes som metodologi för att analysera och reflektera över de val som gjordes. MDA (Mechanics Dynamics Aesthetics) användes som ett ramverk för att utveckla själva gestaltningen. Gestaltningen som skapades blev ett tvåpersonspussel och plattforms spel. Där spelarna fick interagera med spelmiljön och objekten i den för att lösa matematiska problem. Genom undersökningen kom vi fram till att inlevelse och flow kan bidra till situationsbaserat lärande och öka engagemang hos elever. Vi fann även att lärospel kan presentera abstrakta problem så som matematiska uppgifter i konkreta representationer, vilket kunde åstadkommas genom att tillåta interaktion med miljö och objekt. Slutligen fann vi att låta spelare spela tillsammans med varandra kan vara gynnsamt för spelares motivation och lärande. / This study examined digital games in context to learning and mathematics. By creating a game we, the authors researched different design choices could effect learning. The goal was that players could learn through situated based learning and b e able to transfer their knowledge into their everyday life. The study had used ANT(actor-network theory) as methodology. Through it choices were analyzed. MDA(mechanics dynamics aesthetics) was used as a framework to develop the game itself. In the end the game became a two player, puzzle platform game. Within which players got to interact with the game environment and objects in it to solve mathematical problems. Through this study we found immersion and flow to be beneficial for a situated based learning game. We also found that games can translate abstract problems such as mathematical tasks into more concrete representations. This can be done by letting players sole the tasks through interaction with the digital environment and the objects within it. Lastly we saw that there can be positive effects of students playing multiplayer when it comes to motivation and learning.

In recent years, lots of research effort was spent on the integration of process planning and job-shop scheduling. Various integrated process planning and scheduling (IPPS) models and solution approaches have been proposed. The previous and existing research approaches are able to demonstrate the feasibility of implementing IPPS. However, most of them assumed that setup time is negligible or only part of the processing time. For machined parts, the setup for each operation includes workpiece loading and unloading, tool change, etc. For setup that depends only on the operation to be processed (sequence-independent), it is applicable to adopt the assumption of not considering setup in IPPS. For setup that depends on both the operation to be processed and the immediately preceding operation (sequence-dependent), it is an oversimplification to adopt such assumption. In such cases, the setup time varies with the sequence of the operations. The process plans and schedules constructed under such assumption are not realistic or not even feasible. In actual practice, therefore, the setup time should be separated from the process time in performing the IPPS functions. In this thesis, a new approach is proposed for IPPS problems with setup time consideration for machined parts. Inseparable and sequence-dependent setup requirements are added into the IPPS problems. The setup times are separated from the process times and they vary with the sequence of the operations. IPPS is regarded as NP-hard problem. With the separated consideration of setup times, it becomes even more complicated. An Ant Colony Optimization (ACO) approach is proposed to handle this complicated problem. The system is constructed under a multi-agent system (MAS). AND/OR graph is used to record the set of feasible production procedures and sequences. The ACO algorithm computes results by an autocatalytic process with the objective to minimize the makespan. Software agents called “artificial ants” traverse through the feasible routes in the graph and finally construct a schedule. A setup time parameter is added into the algorithm to influence the ants to select the process with less setup time. The approach is able to construct a feasible solution with less setup time. Experimental studies have been performed to evaluate the performance of MAS-ACO approach in solving IPPS problems with separated consideration of setup times. The experimental results show that the MAS-ACO approach can effectively handle the problem. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

D. Tech. Electrical Engineering. / Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.