Brazil nut sorting for aflatoxin prevention: a comparison between automatic and manual shelling methods

The impact of automatic and manual shelling methods during manual/visual sorting of different batches of Brazil nuts from the 2010 and 2011 harvests was evaluated in order to investigate aflatoxin prevention.The samples were tested as follows: in-shell, shell, shelled, and pieces in order to evaluate the moisture content (mc), water activity (Aw), and total aflatoxin (LOD = 0.3 µg/kg and LOQ 0.85 µg/kg) at the Brazil nut processing plant. The results of aflatoxins obtained for the manually shelled nut samples ranged from 3.0 to 60.3 µg/g and from 2.0 to 31.0 µg/g for the automatically shelled samples. All samples showed levels of mc below the limit of 15%; on the other hand, shelled samples from both harvests showed levels of Aw above the limit. There were no significant differences concerning the manual or automatic shelling results during the sorting stages. On the other hand, the visual sorting was effective in decreasing the aflatoxin contamination in both methods.

Dynamic modeling and simulation of the airflow cleaning device

The objective of the work is to study the flow behavior and to support the design of air cleaner by dynamic simulation.In a paper printing industry, it is necessary to monitor the quality of paper when the paper is being produced. During the production, the quality of the paper can be monitored by camera. Therefore, it is necessary to keep the camera lens clean as wood particles may fall from the paper and lie on the camera lens. In this work, the behavior of the air flow and effect of the airflow on the particles at different inlet angles are simulated. Geometries of a different inlet angles of single-channel and double-channel case were constructed using ANSYS CFD Software. All the simulations were performed in ANSYS Fluent. The simulation results of single-channel and double-channel case revealed significant differences in the behavior of the flow and the particle velocity. The main conclusion from this work are in following. 1) For the single channel case the best angle was 0 degree because in that case, the air flow can keep 60% of the particles away from the lens which would otherwise stay on lens. 2) For the double channel case, the best solution was found when the angle of the first inlet was 0 degree and the angle of second inlet was 45 degree . In that case, the airflow can keep 91% of particles away from the lens which would otherwise stay on lens.

Applying software performance engineering methods to development of IT device management systems

Ohjelmiston suorituskyky on kokonaisvaltainen asia, johon kaikki ohjelmiston elinkaaren vaiheet vaikuttavat. Suorituskykyongelmat johtavat usein projektien viivästymisiin, kustannusten ylittymisiin sekä joissain tapauksissa projektin täydelliseen epäonnistumiseen. Software performance engineering (SPE) on ohjelmistolähtöinen lähestysmistapa, joka tarjoaa tekniikoita suorituskykyisen ohjelmiston kehittämiseen. Tämä diplomityö tutkii näitä tekniikoita ja valitsee niiden joukosta ne, jotka soveltuvat suorituskykyongelmien ratkaisemiseen kahden IT-laitehallintatuotteen kehityksessä. Työn lopputuloksena on päivitetty versio nykyisestä tuotekehitysprosessista, mikä huomioi sovellusten suorituskykyyn liittyvät haasteet tuotteiden elinkaaren eri vaiheissa.

Benefits of a low-cost one-phase current distortion detection device in appliance supply in low-voltage residential networks

In this bachelor’s thesis are examined the benefits of current distortion detection device application in customer premises low voltage networks. The purpose of this study was to find out if there are benefits for measuring current distortion in low-voltage residential networks. Concluding into who can benefit from measuring the power quality. The research focuses on benefits based on the standardization in Europe and United States of America. In this research, were also given examples of appliances in which current distortion detection device could be used. Along with possible illustration of user interface for the device. The research was conducted as an analysis of the benefits of current distortion detection device in residential low voltage networks. The research was based on literature review. The study was divided to three sections. The first explain the reasons for benefitting from usage of the device and the second portrays the low-cost device, which could detect one-phase current distortion, in theory. The last section discuss of the benefits of usage of current distortion detection device while focusing on the beneficiaries. Based on the result of this research, there are benefits from usage to the current distortion detection device. The main benefitting party of the current distortion detection device was found to be manufactures, as they are held responsible of limiting the current distortion on behalf of consumers. Manufactures could adjust equipment to respond better to the distortion by having access to on-going current distortion in network. The other benefitting party are system operators, who would better locate distortion issues in low-voltage residential network to start prevention of long-term problems caused by current distortion early on.

Earning logics of a portal solution for a medical device manufacturer

The need behind this thesis was in the development of a more modern earning logic for a pelvic floor muscle home training device to which a portal solution is going to be introduced as a part of new upgraded version of the device. The goal is to offer useful guidelines and recommendations for the medical device manufacturer to use in the process of creating the new business model around the new product version. In the theoretical part of this thesis, the used theoretical frameworks for business model generation and pricing models are presented. The special characteristics of healthcare technology industry are also introduced as initial data for the empirical part. The empirical data is collected via interviews and meetings from both inside and outside of the company to gain a comprehensive picture of the issue at hand. The needed changes in the business model as well as possible pricing options are gone through in the empirical chapters with the main focus being on the incoming revenue streams and pricing. As results of the thesis recommendations are presented for the changes that are needed in the business model after the introduction of the portal solution. The results of this thesis can be used for finishing the development process of the new version of the device and especially the earning logic of it.

Decision-making theory applied to India's explosion of a nuclear device in May, 1974

This essay reviews the decision-making process that led to India exploding a nuclear device in May, 1974. An examination of the Analytic, Cybernetic and Cognitive Theories of decision, will enable a greater understanding of the events that led up to the 1974 test. While each theory is seen to be only partially useful, it is only by synthesising the three theories that a comprehensive account of the 1974 test can be given. To achieve this analysis, literature on decision-making in national security issues is reviewed, as well as the domestic and international environment in which involved decisionmakers operated. Finally, the rationale for the test in 1974 is examined. The conclusion revealed is that the explosion of a nuclear device by India in 1974 was primarily related to improving Indian international prestige among Third World countries and uniting a rapidly disintegrating Indian societal consensus. In themselves, individual decision-making theories were found to be of little use, but a combination of the various elements allowed a greater comprehension of the events leading up to the test than might otherwise have been the case.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Automatic evolution of conceptual building architectures

This thesis describes research in which genetic programming is used to automatically evolve shape grammars that construct three dimensional models of possible external building architectures. A completely automated fitness function is used, which evaluates the three dimensional building models according to different geometric properties such as surface normals, height, building footprint, and more. In order to evaluate the buildings on the different criteria, a multi-objective fitness function is used. The results obtained from the automated system were successful in satisfying the multiple objective criteria as well as creating interesting and unique designs that a human-aided system might not discover. In this study of evolutionary design, the architectures created are not meant to be fully functional and structurally sound blueprints for constructing a building, but are meant to be inspirational ideas for possible architectural designs. The evolved models are applicable for today's architectural industries as well as in the video game and movie industries. Many new avenues for future work have also been discovered and highlighted.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.