Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

Horizontal Grid Size Selection and its Influence on Mesoscale Model Simulations

The use of two-dimensional spectral analysis applied to terrain heights in order to determine characteristic terrain spatial scales and its subsequent use for the objective definition of an adequate grid size required to resolve terrain forcing are presented in this paper. In order to illustrate the influence of grid size, atmospheric flow in a complex terrain area of the Spanish east coast is simulated by the Regional Atmospheric Modeling System (RAMS) mesoscale numerical model using different horizontal grid resolutions. In this area, a grid size of 2 km is required to account for 95% of terrain variance. Comparison among results of the different simulations shows that, although the main wind behavior does not change dramatically, some small-scale features appear when using a resolution of 2 km or finer. Horizontal flow pattern differences are significant both in the nighttime, when terrain forcing is more relevant, and in the daytime, when thermal forcing is dominant. Vertical structures also are investigated, and results show that vertical advection is influenced highly by the horizontal grid size during the daytime period. The turbulent kinetic energy and potential temperature vertical cross sections show substantial differences in the structure of the planetary boundary layer for each model configuration

International research partnerships: establishing partner selection criteria in the university-university context

The main objective of the study was to identify and evaluate criteria for international partner selection in university-university context. This study attempted at promoting better understanding of how universities should proceed in selecting partners for producing joint research publications. Thus, the aim of the study was to gain an understanding of how research collaborations can be developed and how partners can be selected. The choice of a right partner has been identified as a precondition for partnership success. In international research collaborations partnering scientists with different skills and backgrounds bring together complementary knowledge into research projects, which in most cases results in a higher quality output. Therefore, prior to selecting a partner, the set of criteria should be established. This research examined twelve Russian universities with the status of national research university as potential partners for Lappeenranta University of Technology, and selected the most appropriate universities based on established set of criteria. Potential partners’ evaluation was done using secondary sources by tracking partners’ academic success during the period 2005 – 2010. Based on established criteria, the study calculated the partnership index for each university. The results of the research reveal that among twelve examined universities there are four potential partners who have been rather active in publishing scientific articles during 2005 – 2010.

The role of selection in functional explanations

In this essay I will argue that natural selection is more important to functional explanations than what has been thought in some of the literature in philosophy of biology. I start by giving a brief overview of the two paradigms cases of functional explanations: etiological functions and causal-role functions. i then consider one particular attempt to conciliate both perspectives given by David Buller (1998). Buller's trial to conciliate both etiological functions and causal-role functions results in what he calls a weak etiological theory. I argue that Buller has not succeeded in his construal of the weak etiological theory: he underestimates the role that selective processes have in functional explanations and so his theory may not be classified as an etiologial theory. As an alternative, I consider the account of etiological functions given by Ruth Millikan (1984) and I argue that Millikan's theory is more comprehensive to assess contentious case in biology like exaptations. Finally, I conclude by analyzing where the adoption of Millikan's theory leave us. I argue, contrary to Millikan and others, that once we assume the importance of natural selection in functional explanations, there is no strong reason to resist a linguistic reform of the word function and hence that the attempts to conciliate both etiological functions and causal-role functions are misplaced.

Using aster multispectral imagery for mapping woody invasive species in pico da vara natural reserve (Azores Islands, Portugal)

This paper aims to assess the effectiveness of ASTER imagery to support the mapping of Pittosporum undulatum, an invasive woody species, in Pico da Vara Natural Reserve (S. Miguel Island, Archipelago of the Azores, Portugal). This assessment was done by applying K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Maximum Likelihood (MLC) pixel-based supervised classifications to 4 different geographic and remote sensing datasets constituted by the Visible, Near-Infrared (VNIR) and Short Wave Infrared (SWIR) of the ASTER sensor and by digital cartography associated to orography (altitude and "distance to water streams") of which the spatial distribution of Pittosporum undulatum directly depends. Overall, most performed classifications showed a strong agreement and high accuracy. At targeted species level, the two higher classification accuracies were obtained when applying MLC and KNN to the VNIR bands coupled with auxiliary geographic information use. Results improved significantly by including ecology and occurrence information of species (altitude and distance to water streams) in the classification scheme. These results show that the use of ASTER sensor VNIR spectral bands, when coupled to relevant ancillary GIS data, can constitute an effective and low cost approach for the evaluation and continuous assessment of Pittosporum undulatum woodland propagation and distribution within Protected Areas of the Azores Islands.

In vitro EVALUATION OF EUCALYPTUS ECTOMYCORRHIZAE ON SUBSTRATE WITH PHOSPHORUS DOSES FOR FUNGAL PRE-SELECTION

The benefit promoted by ectomycorrhizal depends on the interaction between symbionts and phosphorus (P) contents. Phosphorus effect on ectomycorrhizal formation and the effectiveness of these in promoting plant growth for fungal pre-selection were assessed under in vitro conditions. For P effect evaluation, Eucalyptus urophylla seedlings inoculated with four Pisolithus sp. isolates and others non-inoculated were grown on substrate containing 0.87, 1.16 and 1.72 mg P per plant. For evaluation of effectiveness and fungal pre-selection, other 30 isolates of Pisolithus sp., Pisolithus microcarpus ITA06 isolate, Amanita muscaria AM16 isolate, Scleroderma areolatum SC129 isolate were studied. D26 isolate promoted the highest plant heights for the three P doses, D51 at the lower dose and D72 at the intermediate dose. P doses did not influenced shoot fresh weight and fungal colonization. In the pre-selection of fungi, 14 isolates of Pisolithus sp., P. microcarpus ITA06 isolate and S. areolatum SC129isolate increased plant height and fresh weight. D82 isolate of Pisolithus sp. had effect singly on plant height while D17 and D58 on fresh weight. Of these, only D15, D17, D58 and ITA06 had typical ectomycorrhizae. The cultivation in vitro has shown adequate for pre-selection of ectomycorrhizal fungi. Colonization and benefits depend on species and isolate. D15, D17 and D58 of Pisolithus sp. and P. microcarpus isolate ITA06 are the most promising for nursery studies.

REPEATABILITY OF FRUITS AND SEEDS PRODUCTION AND SELECTION OF BRAZIL NUT GENOTYPES IN NATIVE POPULATIONS IN RORAIMA1

ABSTRACT This study estimates the repeatability coefficients of two production traits in two native populations of Brazil nut trees. It determines the number of years of suitable evaluations for an efficient selection process, determines the permanent phenotypic correlation between production traits and also the selection of promising trees in these populations. Populations, located in the Itã region (ITA) and in the in the Cujubim region (CUJ), are both belonging to the municipality of Caracaraí, state of Roraima - Brazil, and consist of 85 and 51 adult trees, respectively. Each tree was evaluated regarding the number of fruits per plant (NFP) and fresh seed weight per plant (SWP), for eight (ITA) and five consecutive years (CUJ). Statistical analyses were performed according to the mixed model methodology, using Software Selegen-REML/BLUP (RESENDE, 2007). The repeatability coefficients were low for NFP (0.3145 and 0.3269 for ITA and CUJ, respectively) and also for SWP (0.2957 and 0.3436 for ITA and CUJ, respectively). It on average takes nine evaluation years to reach coefficients of determination higher than 80%. Permanent phenotypic correlation values higher than 0.95 were obtained for NFP and SWP in both populations. Although trees with a high number of fruits and seed weight were identified, more evaluation years are needed to perform the selection process more efficiently.

Selection of microorganisms producer of lipase for fat removal from biodiesel purification water

The objective this study has been the selection of lipase productor microorganism, for removal of oils and grease, in the pre-treatment of biodiesel wastewater washing. For this, analyses of the physicist-chemistries characteristics had been made with the wastewater of the biodiesel washing, and then it had been isolated and chosen, by means of determinations of the lipase activity. Following, it was made a test of fat biodegradation, in the conditions: pH (5.95), temperature (35 ºC), rotation (180 rpm) and ammonium sulfate as nitrogen source (3 g L-1) and establishing as variable the two microorganism preselected and the time (24; 48; 72; 96 and 120 h). The biodiesel purification wastewater had presented high potential of environmental impact, presenting a concentration of O of 6.76 g L-1. From the six isolated microbiological cultures, two microorganisms (A and B) had been selected, with enzymatic index of 0.56 and 0.57, respectively. The treatment of the wastewater using the isolated microorganism (Klebsiella oxytoca) had 80% of the fatty removal in 48 h.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

Use of preclinical and early clinical data for dose selection of a selective estrogen receptor modulator toremifene in treatment of breast cancer

In development of human medicines, it is important to predict early and accurately enough the disease and patient population to be treated as well as the effective and safe dose range of the studied medicine. This is pursued by using preclinical research models, clinical pharmacology and early clinical studies with small sample sizes. When successful, this enables effective development of medicines and reduces unnecessary exposure of healthy subjects and patients to ineffectice or harmfull doses of experimental compounds. Toremifene is a selective estrogen receptor modulator (SERM) used for treatment of breast cancer. Its development was initiated in 1980s when selection of treatment indications and doses were based on research in cell and animal models and on noncomparative clinical studies including small number of patients. Since the early development phase, the treatment indication, the patient population and the dose range were confirmed in large comparative clinical studies in patients. Based on the currently available large and long term clinical study data the aim of this study was to investigate how the early phase studies were able to predict the treatment indication, patient population and the dose range of the SERM. As a conclusion and based on the estrogen receptor mediated mechanism of action early studies were able to predict the treatment indication, target patient population and a dose range to be studied in confirmatory clinical studies. However, comparative clinical studies are needed to optimize dose selection of the SERM in treatment of breast cancer.

Acquisition target selection of a serial acquirer : case KONE Oyj

Acquisitions are a way for a company to grow, enter new geographical areas, buy out competition or diversify. Acquisitions have recently grown in both size and value. Despite of this, only approximately 25 percent of acquisitions reach their targets and goals. Companies making serial acquisitions seem to be exceptionally successful and succeed in the majority of their acquisitions. The main research question this study aims to answer is: “What issues impact the selection of acquired companies from the point of view of a serial acquirer? The main research question is answered through three sub questions: “What is a buying process for a serial acquirer like?”, “What are the motives for a serial acquirer to buy companies?” and “What is the connection between company strategy and serial acquisitions?”. The case company KONE is a globally operating company which mainly produces and maintains elevators and escalators. Its headquarter is located in Helsinki, Finland. The company has a long history of making acquisitions and does 20- 30 acquisitions a year. By a key person interview, the acquisition process of the case company is compared with the literature about successful serial acquirers. The acquisition motives in this case are reflected upon three of the acquisition motive theories by Trautwein: efficiency theory, monopoly theory and valuation theory. The linkage between serial acquisitions and company strategy is studied through the key person interview. The main research findings are that the acquisition process of KONE is compatible with a successful acquisition process recognized in literature (RAID). This study confirms the efficiency theory as an acquisition motive and more closely the operational synergies. The monopoly theory can only vaguely be supported by this study, but cannot be totally rejected because of the structure of the industry. The valuation theory does not get any support in this study and can therefore be rejected. The linkage between company strategy and serial acquisitions is obvious and making acquisitions can be seen as growth strategy and a part of other company strategies.

A quality and cost approach for welding process selection

The aim of this work was to propose, apply and evaluate a methodical approach to select welding processes in a productive environment based on market requirements of Quality and Costs. A case study was used. The welds were carried out in laboratory, simulating the joint conditions of a manufacturer and using several welding processes: SMAW, GTAW, pulsed GTAW, GMAW with CO2 and Ar based shielding gases and pulsed GMAW. For Quality analysis geometrical aspects of the beads were considered and for Cost analysis, welding parameters and consumable prices. Quantitative indices were proposed and evaluated. After that, evaluation of both Quality and Costs was done, showing to be possible to select the most suitable welding process to a specific application, taking into account the market conditions of a company.

The Use of Data Envelopment Analysis as Equity Portfolio Selection Criterion in the Finnish Stock Market

This thesis examines the application of data envelopment analysis as an equity portfolio selection criterion in the Finnish stock market during period 2001-2011. A sample of publicly traded firms in the Helsinki Stock Exchange is examined in this thesis. The sample covers the majority of the publicly traded firms in the Helsinki Stock Exchange. Data envelopment analysis is used to determine the efficiency of firms using a set of input and output financial parameters. The set of financial parameters consist of asset utilization, liquidity, capital structure, growth, valuation and profitability measures. The firms are divided into artificial industry categories, because of the industry-specific nature of the input and output parameters. Comparable portfolios are formed inside the industry category according to the efficiency scores given by the DEA and the performance of the portfolios is evaluated with several measures. The empirical evidence of this thesis suggests that with certain limitations, data envelopment analysis can successfully be used as portfolio selection criterion in the Finnish stock market when the portfolios are rebalanced at annual frequency according to the efficiency scores given by the data envelopment analysis. However, when the portfolios were rebalanced every two or three years, the results are mixed and inconclusive.