<p>  </p> <p>Hazard identification is one of the most significant components in safety management at construction jobsites to prevent undesired fatalities and injuries of construction workers. The current practice, which relies on a limited number of safety managers’ manual and subjective inspections, and existing research efforts analyzing workers’ physical and physiological signals have achieved limited success, leaving many hazards unidentified at the jobsites. Motivated by this critical need, this research aims to develop a human sensing network that allows for ubiquitous hazard identification in the construction workplace.</p> <p>To attain this overarching goal, this research analyzes construction workers’ collective EEG signals collected from wearable EEG sensors based on machine learning, virtual reality (VR), and advanced signal processing techniques. Three specific research objectives are: (1) establishing a relationship between EEG signals and the existence of construction hazards, (2) identifying correlations between EEG signals/physiological states (e.g., emotion) and different hazard types, and (3) developing an integrated platform for real-time construction hazard mapping and comparing the results developed based on VR and real-world experimental settings.</p> <p>Specifically, the first objective establishes the relationship by investigating the feasibility of identifying construction hazards using a binary EEG classifier developed in VR, which can capture EEG signals associated with perceived hazards. In the second objective, correlations are discovered by testing the feasibility of differentiating construction hazard types based on a multi-class classifier constructed in VR. In the first and second objectives, the complex relationships are also analyzed in terms of brain dynamics and EEG signal components. In the third objective, the platform is developed by fusing EEG signals with heterogeneous data (e.g., location), and the discrepancies in VR and real-world environments are quantitatively assessed in terms of hazard identification performance and human behavioral responses.</p> <p>The primary outcome of this research is that the proposed approach can be applied to actual construction jobsites and used to detect all potential hazards, which was challenging to be achieved based on the current practice and existing research efforts. Also, the human cognitive mechanisms revealed in this research discover new neurocognitive knowledge in construction workers’ hazard perception. As a result, this research contributes to enhancing current hazard identification capability and improving construction workers’ safety and health.</p>

<p>Every 12 minutes, 12 women are diagnosed with breast cancer in the US, and 1 dies out of  it. Globally, every 46 seconds, a woman loses her life due to breast cancer, meaning more than  1,800 deaths every day. The condition makes the prediction of breast cancer very important. To  achieve the goal, supervised machine learning (ML) methods are used for breast cancer  likelihood predictions. However, due to imbalance in the real-world data with very low portion  of positive cases, the prediction accuracy of ML models for positive cancer cases was limited. Two procedures were done to address the issues in the study. Firstly, four supervised ML  models, including Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), using WEKA, the industry-standard software, were  applied to the Breast Cancer Surveillance Consortium (BCSC) dataset to assess the impact of the  data imbalance on breast cancer prediction. Secondly, the data was manually built as balanced  (24,558 cases, 12,279 for each class-positive and negative) and unbalanced (99,000 cases for  negative) training datasets and a non-overlapping testing dataset (11,000 cases) based on the  same dataset and a decision support system was developed for two ML models, NB and LR to  tackle the class imbalance issue for breast cancer prediction. Overall, the results indicate that  MLP had the best performance on positive breast cancer prediction with 0.959 sensitivity and  0.907 PPV and balanced dataset predicted better results for all ML models than unbalanced  dataset. Furthermore, the proposed method improved the sensitivity of positive cancer case  prediction from 0.687 to 0.936 using the NB model and from 0.358 to 0.8306 using the LR  model. The improvement demonstrated that the approach provided higher confidence ML-based  predictions and filtered weaker ones, and the technique could efficiently address the class  imbalance issue in breast cancer likelihood prediction and be used in clinical practice.</p>

<p>This dissertation investigates the influence of spatially explicit animal behavior active subsidy distribution patterns. Active subsidies are animal-transported consumption and resources transfers from donor to recipient ecosystems. Active subsidies influence ecosystem structure, function and services in recipient ecosystems. Even though active subsidies affect ecosystem dynamics, most ecosystem models consider the influence of spatially-explicit animal behavior on active subsidy distributions, limiting the ability to predict corresponding spatial impacts across ecosystems. Spatial subsidy research documents the need for systematic models and analyses frameworks to provide generally insights into the relationship between animal space use behavior and active subsidy patterns, and advance knowledge of corresponding ecosystem impacts for a variety of taxa and ecological scenarios.</p> <p> </p> <p>To advance spatial subsidy research, this dissertation employs a combined individual-based and movement ecology approach in abstract modeling frameworks to systematically investigate the influence of 1) animal movement behavior given mortality (chapter 2), 2) animal sociality (chapter 3) and 3) landscape heterogeneity (chapter 4) on active subsidy distribution. This dissertation shows that animal movement behavior, sociality and landscape heterogeneity influence the extent and intensity of active distribution and impacts in recipient ecosystems. Insights from this dissertation demonstrate that accounting for these factors in the development of ecosystem models will consequentially enhance their utility for predicting active subsidy spatial patterns and impacts. This dissertation advances spatial subsidy research by providing a road map for developing a comprehensive, unifying framework of the relationship between animal behavior and active subsidy distributions.</p>

<div><div>Designing trajectories in dynamically complex environments is challenging and can easily become intractable via solely manual design efforts. Thus, the problem is recast to blend traditional astrodynamics approaches with machine learning to develop a rapid and flexible trajectory design framework. This framework incorporates knowledge of the spacecraft performance specifications via the computation of Accessible Regions (ARs) that accommodate specific spacecraft acceleration levels for varied mission scenarios in a complex multi-body dynamical regime. Specifically, pathfinding agents, via Heuristically Accelerated Reinforcement Learning (HARL) and Dijkstra's algorithms, engage in a multi-dimensional combinatorial search to sequence advantageous natural states emerging from the ARs to construct initial guesses for end-to-end transfers. These alternative techniques incorporate various design considerations, for example, prioritizing computational time versus the pursuit of globally optimal solutions to meet multi-objective mission goals. The initial guesses constructed by pathfinding agents then leverage traditional numerical corrections processes to deliver continuous transport of a spacecraft from departure to destination. Solutions computed in the medium-fidelity Circular Restricted Three Body (CR3BP) model are then transitioned to a higher-fidelity ephemeris regime where the impact of time-dependent gravitational influences from multiple bodies is also explored.</div><div><br></div><div>A broad trade-space arises in this investigation in large part due to the rich and diverse dynamical flows available in the CR3BP. These dynamical pathways included in the search space via: (i) a pre-discretized database of known periodic orbit families; (ii) flow-models of these families of orbits/arcs `trained' via the supervised learning algorithms Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs); and, finally (iii) a free-form search that permits selection of both chaotic and ordered motion. All three approaches deliver variety in the constructed transfer paths. The first two options offer increased control over the nature of the transfer geometry while the free-form approach eliminates the need for a priori knowledge about available flows in the dynamical environment. The design framework enables varied transfer scenarios including orbit-orbit transport, s/c recovery during contingency events, and rendezvous with a pre-positioned object at an arrival orbit. Realistic mission considerations such as altitude constraints with respect to a primary are also incorporated.</div></div>

Introdução: Visando a prevenção da transmissão de microrganismos, o Centers for Disease Control and Prevention (CDC), recomenda a aplicação de conjuntos de medidas denominadas Precauções Padrão (PP) e Precauções Específicas (PE). Assim, um dos grandes desafios dos serviços de saúde em especial na Atenção Primária à Saúde (APS) é identificar as lacunas dos conhecimentos e comportamentos concernentes a PP e PE. Objetivo: Avaliar os conhecimentos e o comportamento autoreferidos dos profissionais de saúde da APS sobre PP e PE e propor uma intervenção educativa baseada em casos. Métodos: Trata-se de um estudo longitudinal, prospectivo e de intervenção, desenvolvido por meio de uma abordagem quantitativa, cujo percurso metodológico ocorreu em seis momentos. A coleta de dados foi realizada por meio da aplicação de um questionário previamente validado de avaliação de conhecimento dos profissionais com relação à PP e PE e de avaliação de comportamento auto-referido sobre boas práticas de precauções. O questionário abordou as seguintes dimensões relativas ao conhecimento e comportamento sobre PP e PE: Identificação de risco, Higienização das mãos, Uso de luvas comum, Uso de máscaras e a etiqueta de tosse e Medicação segura e descarte de material perfurocortante. Realizou-se uma intervenção educativa, por meio do método de Aprendizagem Baseada em Casos (ABC) no qual foram entregues estudos de casos extraídos da vivência prática do pesquisador. O questionário foi aplicado pré e pós esta intervenção. A coleta de dados foi realizada em uma Unidade Básica de Saúde do município de São Paulo, tendo como população alvo profissionais da Estratégia Saúde da Família (ESF) e do Núcleo de Apoio à Saúde da Família (NASF) que atuavam diretamente na assistência. Os dados foram analisados de acordo com cada momento, por meio de estatística descritiva e apresentada em forma de gráficos e tabelas. Resultado: A análise dos dados nos permitiu identificar que os profissionais de saúde da APS apresentaram fragilidade em relação ao conhecimento e ao comportamento auto referido sobre o uso da PP e PE. Os valores de acertos individuais variaram, respectivamente: de 36,3% a 100% no momento I e de 50% a 100% no momento IV. A intervenção educativa obteve resultados positivos, embora não tenha sido plenamente eficaz por não ter conseguido atingir mudanças relevantes em todas as dimensões avaliadas. O número de questões que obtiveram menos de 70% de profissionais que acertaram foi respectivamente 15 no momento I e 10 no momento IV. Contudo, este modelo de intervenção educativa pode ser considerada uma importante ferramenta para promover reflexão e oportunidade de aprendizagem a todos os trabalhadores da área de saúde, tornando-os críticos de suas próprias atitudes e fornecendo instrumento para combater situações de risco para aquisição de patógenos nas unidades de saúde. Conclusão: O presente estudo trouxe contribuições importantes para o conhecimento sobre o tema dentro da APS, destacando as deficiências de conhecimento e comportamento autoreferido dos profissionais na APS e propondo uma intervenção educativa que contribui potencialmente para mudança neste cenário. / Introduction: In order to prevent the transmission of microorganisms, the Centers for Disease Control and Prevention (CDC) recommends the implementation of joint measures known Standard Precautions (PP) and Specific Precautions (PE). Thus one of the challenges of non-hospital health services, especially in primary health care (PHC) is to identify gaps in knowledge and behavior concerning PP and PE. Objective: To evaluate the knowledge and self-reported behavior of APS professionals on PP and PE and propose an educational intervention. Methods: This is a longitudinal study, prospective and intervention, developed through a quantitative approach, whose methodological approach occurred in six moments.Data collection was performed by applying a previously validated questionnaire assessment of professional knowledge with regard to PP and PE and assessment of self-reported behavior on good practices precautions. The questionnaire included the following dimensions for the knowledge and behavior of PP and PE: \"Risk Identification\", \"Handwashing\", \"Use of common gloves,\" \"Using masks and cough etiquette\" and \"Safe Medication and disposal of sharps. An educational intervention was carried out, using Case- Based Learning (ABC) method in which case studies were extracted from the practice of research experience. The questionnaire was applied before and after this intervention. Data collection was performed at a Basic Health Unit in the city of São Paulo, whose target population was the Health Strategy professionals Family (ESF) and the Support Center for Family Health (NASF) working directly in assistance. Data were analyzed according to each moment through descriptive statistics and presented in graphics and tables Results: The data analysis allowed us to identify that health professionals at APS showed weakness in relation to knowledge and the selfreported conduct on the use of PP and PE. The individual values ranged, respectively: 36.3% to 100% in the moment I and 50% to 100% at moment IV. The educational intervention model positive results, although it was not fully effective for failing to achieve significant changes in all dimensions evaluated. The number of questions that have obtained less than 70% of professionals who agreed was respectively 15 in the moment I and 10 at moment IV. The educational intervention can be considered an important appliance to promote reflection and learning opportunity to all workers in the health area, making them critical of their own attitudes and providing a tool to combat risk situations for the acquisition of pathogens in units health. Conclusion: This study has brought important contributions to the knowledge on the subject within the APS, highlighting the deficiencies of knowledge and self-reported behavior of professionals in APS and proposing an educational intervention that potentially contribute to change this scenario.

At present, the combination of high drug development costs and external pressure to lower consumer prices is forcing the pharmaceutical industry to innovate in ways unlike ever before. One of the main drivers of increased productivity in research and development recently has been the application of computational methods to the drug discovery process. While this investment has generated promising insights in many cases, there is still much progress to be made.<div><br></div><div>There currently exists a dichotomy in the types of algorithms employed which are roughly defined by the extent to which they compromise predictive accuracy for computational efficiency, and vice versa. Many computational drug discovery algorithms exist which yield commendable predictive power but are typically associated with overwhelming resource costs. High-throughput methods are also available, but often suffer from disappointing and inconsistent performance. <br></div><div><br></div><div>In the world of kinase inhibitor design, which often takes advantage of such computational tools, small molecules tend to have myriad side effects. These are usually caused by off-target binding, especially with other kinases (given the large size of the enzyme family and overall structural conservation), and so inhibitors with tunable selectivity are generally desirable. This issue is compounded when considering therapeutically relevant targets like Abelson Protein Tyrosine Kinase (Abl) and Lymphocyte Specific Protein Tyrosine Kinase (Lck) which have opposing effects in many cancers. <br></div><div><br></div><div>This work attempts to solve both of these problems by creating a methodology which incorporates high-throughput computational drug discovery methods, modern machine learning techniques, and novel protein-ligand binding descriptors based on backbone hydrogen bond (dehydron) wrapping, chosen because of their potential in differentiating between kinases. Using this approach, a procedure was developed to quickly screen focused chemical libraries (in order to narrow the domain of applicability and keep medicinal chemistry at the forefront of development) for detection of selective kinase inhibitors. In particular, five pharmacologically relevant kinases were investigated to provide a proof of concept, including those listed above.</div><div><br></div><div>Ultimately, this work shows that dehydron wrapping indeed has predictive value, though it's likely hindered by common and current issues derived from noisy training data, imperfect feature selection algorithms, and simplifying assumptions made by high-throughput algorithms used for structural determination. It also shows that the procedure's predictive value varies depending on the target, leading to the conclusion that the utility of dehydron wrapping for drug design is not necessarily universal, as originally thought. However, for those targets which are amenable to the concept, there are two major benefits: relatively few features are required to produce modest results; and those structural features chosen are easily interpretable and can thereby improve the overall design process by pointing out regions to optimize within any given lead. Of the five kinases explored, Src and Lck are shown in this work to fit particularly well with the general hypothesis; given their importance in treating cancer and evading off-target related side effects, the developed methodology now has the potential to play a major role in the development of drug candidates which specifically inhibit and avoid these kinases.<br></div>

Made available in DSpace on 2016-07-27T13:54:48Z (GMT). No. of bitstreams: 1 CLAUDIA REGINA MAJOR.pdf: 2123265 bytes, checksum: a970f3e8da85426769a62d39df8bc7fb (MD5) Previous issue date: 2011-03-29 / The present study had as investigative focus to analysis the pedagogic projects of Medicine Courses, that into them should be have the theoretical beddings that characterize methodologies that work intencionally with problems, which are: Learning Based in Problems (PBL) and Problematization Methodologies (MP). It was analyzed the pedagogic projects in two courses of Medicines in the state of Goiás and one in Federal District. The research was directed forward the following asks: Which do teaching learning conception work with problem that fundaments the pedagogic projects in Medicine courses in the state of Goiás and Federal District? Which do the characteristics presents the curricula of Medicine Courses organized based in problematization of methodology and the learning based in problems? Answering these questions were researched authors that argued the teaching and learning problems, such as Mamede and Pena Forte et al (2001), Ribeiro (2008); Cunha (2001), Cyrino and Toralles Pereira (2004); Carlini (2006),; Barbel (1998) and Freire (1987). The main reason that take to realization of these research were the author s envolvement at the coordination in the Medicine Course in the State of Goiás. The research was developed by the bibliographic and documental study. In interpretative terms had proceeded the documental and content analysis. The pedagogic projects analyzed presented gaps in the respect of the systematization principles that can serve the north to its elaboration. PBL and MP methodologies have presented summons relation to the educational proposals in National Curricula Directions (Diretrizes Curriculares Nacionais) to the Medicine Course glimpsed in all courses that take part of the research, presenting some similarities and differentiations in their applicabilities. Although the evidences and relevances to such methodologies in current days, the expression learning based in problems presents epistemological elements based in pedagogic thinking of John Dewey and Paulo Freire not being able to be considered newness. Not yet it has evidence of these methodologies proposals really will represent alternatives to the present and future education. We are all trying new forms of teaching, with epistemological roots already defended at other times. / O presente estudo teve como foco investigativo a análise dos projetos pedagógicos de cursos de Medicina, posto que neles devam estar contidos os fundamentos teóricos que caracterizam metodologias que trabalham intencionalmente com problemas, quais sejam: Aprendizagem baseada em problemas (ABP) e Metodologia da Problematização (MP). Foram analisados os projetos pedagógicos de dois cursos de Medicina do estado de Goiás e um do Distrito Federal. A pesquisa foi direcionada para as seguintes indagações: Que concepção de ensino e aprendizagem que trabalha com problema fundamenta os Projetos Pedagógicos dos cursos de Medicina do estado de Goiás e do Distrito Federal? Que características apresentam os currículos dos cursos de Medicina organizados com base na metodologia da problematização e da aprendizagem baseada em problemas (PBL)? Para responder a essas indagações foram pesquisados autores que discutem o ensino e a aprendizagem por problema, tais como: Mamede e Penaforte et al. (2001); Ribeiro (2008); Cunha (2001); Cyrino e Toralles-Pereira (2004); Carlini (2006); Berbel (1998) e Freire (1987). O motivo principal que levou à realização desta pesquisa foi o envolvimento da autora na coordenação de um curso de Medicina do estado de Goiás. A pesquisa foi desenvolvida por meio de estudo bibliográfico e documental. Em termos interpretativos procederam-se à análise documental e de conteúdo. Os projetos pedagógicos analisados apresentaram lacunas no que diz respeito aos princípios de sistematização que podem servir de norte para a sua elaboração. As metodologias ABP e MP apresentaram íntima relação com as propostas educacionais contidas nas Diretrizes Curriculares Nacionais para os cursos de Medicina, vislumbradas em todos os cursos que fizeram parte desta pesquisa, apresentando algumas similaridades e diferenciações nas suas aplicabilidades. Em que pesem as evidencias da relevância destas metodologias nos dias atuais, a expressão aprendizagem baseada por problema apresenta elementos epistemológicos baseados no pensamento pedagógico de John Dewey e Paulo Freire, não podendo ser considerada uma novidade. Ainda não há comprovação se estas propostas metodológicas realmente representarão alternativas para a educação médica atual e do futuro. Estamos todos experienciando novas formas de ensinar, com raízes epistemológicas já defendidas em outras épocas.

<div>Magnetic resonance imaging (MRI) has become an invaluable tool in health care. Despite its popularity, there is still an ever-increasing need for faster scans and better image quality. Multi-coil MRI, which uses multiple transmit and/or receive coils, holds the potential to address many of these MRI challenges. Multi-coil MRI systems can utilize parallel transmission (pTx) technology using multi-dimensional radio-frequency (RF) pulses for parallel excitation. The pTx platform is shown to be superior in high-field MRI. Therefore, this dissertation is focused on the RF pulse design and optimization on an MRI system with multiple transceiver coils.</div><div> </div><div>This dissertation addresses three major research topics. First, we investigate the optimization of pTx RF pulses when considering both transmitters and receivers of the MRI system. We term this framework multiple-input multiple-output (MIMO) MRI. The RF pulse design method is modeled by minimizing the excitation error while simultaneously maximizing the signal-to-noise ratio (SNR) of the reconstructed MR image. It further allows a key trade-off between the SNR and the excitation accuracy. Additionally, multiple acceleration factors, different numbers of used receive coils, maximum excitation error tolerance, and different excitation patterns are simulated and analyzed within this model. For a given excitation pattern, our method is shown to improve the SNR by 18-130% under certain acceleration schemes, as compared to conventional parallel transmission methods, while simultaneously controlling the excitation error in a desired scope.</div><div> </div><div>Second, we propose a pTx RF pulse design method that controls the peak local specific absorption rates (SARs) using a compressed set of SAR matrices. RF power, peak local SARs, excitation accuracy, and SNR are simultaneously controlled in the designed pTx RF pulses. An alternative compression method using k-means clustering algorithm is proposed for an upper-bounded estimation of peak local SARs. The performance of the pTx design method is simulated using a human head model and an eight-channel transceiver coil array. The proposed method reduces the 10-g peak local SAR by 44.6-54.2%, as compared to the unconstrained pTx approach, when it has a pre-defined lower bound of SNR and an upper bound of excitation error tolerance. The k-means clustering-based SAR compression model shows its efficiency as it generates a narrower and more accurate overestimation bound than the conventional SAR compression model.</div><div> </div><div>Finally, we propose two machine learning based pTx RF pulse design methods and test them for the ultra-fast pTx RF pulse prediction. The two methods proposed are the kernelized ridge regression (KRR) based pTx RF pulse design and the feedforward neural network (FNN) based pTx RF pulse design. These two methods learn the training pTx RF pulses from the extracted key features of their corresponding B1+ fields. These methods are compared with other supervised learning methods (nearest-neighbor methods, etc.). All learned pTx RF pulses should be reasonably SAR-efficient because training pTx RF pulses are SAR-efficient. Longer computation time and pre-scan time are the drawbacks of the current pTx approach, and we address this issue by instantaneously predicting pTx RF pulses using well-trained machine learning models.</div>

Currently available methods for managing glaucoma, e.g. the planimetry on stereo disc photographs, involve a subjective component either by the patient or examiner. In addition, a few structures may overlap together on the essential 2-D images, which can decrease reproducibility. Spectral domain optical coherence tomography (SD-OCT) provides a 3-D, cross-sectional, microscale depiction of biological tissues. Given the wealth of volumetric information at microscale resolution available with SD-OCT, it is likely that better parameters can be obtained for measuring glaucoma changes that move beyond what is possible using fundus photography etc. The neural canal opening (NCO) is a 3-D single anatomic structure in SD-OCT volumes. It is proposed as a basis for a stable reference plane from which various optic nerve morphometric parameters can be derived. The overall aim of this Ph.D. project is to develop a framework to segment the 3-D NCO and its related structure retinal vessels using information from SD-OCT volumes and/or fundus photographs to aid the management of glaucoma changes. Based on the mutual positional relationship of the NCO and vessels, a multimodal 3-D scale-learning-based framework is developed to iteratively identify them in SD-OCT volumes by incorporating each other's pre-identified positional information. The algorithm first applies a 3-D wavelet-transform-learning-based layer segmentation and pre-segments the NCO using graph search. To aid a better NCO detection, the vessels are identified either using a SD-OCT segmentation approach incorporating the presegmented NCO positional information to the vessel classification or a multimodal approach combining the complementary features from SD-OCT volumes and fundus photographs (or a registered-fundus approach based on the original fundus vessel segmentation). The obtained vessel positional information is then used to help enhance the NCO segmentation by incorporating that to the cost function of graph search. Note that the 3-D wavelet transform via lifting scheme has been used to remove high frequency noises and extract texture properties in SD-OCT volumes etc. The graph search has been used for finding the optimal solution of 3-D multiple surfaces using edge and additionally regional information. In this work, the use of the 3-D wavelet-transform-learning-based cost function for the graph search is a further extension of the 3-D wavelet transform and graph search. The major contributions of this work include: 1) extending the 3-D graph theoretic segmentation to the use of 3-D scale-learning-based cost function, 2) developing a graph theoretic approach for segmenting the NCO in SD-OCT volumes, 3) developing a 3-D wavelet-transform-learning-based graph theoretic approach for segmenting the NCO in SD-OCT volumes by iteratively utilizing the pre-identified NCO and vessel positional information (from 4 or 5), 4) developing a vessel classification approach in SD-OCT volumes by incorporating the pre-segmented NCO positional information to the vessel classification to suppress the NCO false positives, and 5) developing a multimodal concurrent classification and a registered-fundus approach for better identifying vessels in SD-OCT volumes using additional fundus information.

The main purpose of this study was to compare the effectiveness of the cooperative learning based on conceptual change conditions and traditionally designed science instruction on 7th grade students&rsquo / understanding of chemical and physical changes and classification of matter concepts and attitudes toward science as a school subject. In this study 102 seventh grade students from four classes of a Science Course instructed by the two teachers from ODT&Uuml / G.V. &Ouml / zel ilk&ouml / gretim Okulu took part. One of the classes of each teacher was randomly assigned as experimental group, which were instructed with cooperative learning based on conceptual change conditions and the other classes were assigned as control group, which were instructed traditionally. This study was conducted during the 2004-2005 fall semester over a period of four weeks. In this study, to examine the effect of the treatment on dependent variables / science achievement related to chemical and physical changes and classification of matter concepts measured with Classification and Changes of Matter Concepts Test, and science attitude scores measured with Attitude Scale Toward Science as a school subject. Science Process Skills Test was used at the beginning of the study to determine students&rsquo / science process skills. ANCOVA and ANOVA were used testing the hypotheses of the study. The results showed that the cooperative learning based on conceptual change conditions group had a significantly higher scores with respect to achievement related to chemical and physical changes and classification of matter concepts than the traditionally designed science instruction group. However, there is no significant difference between the mean scores of cooperative learning based on conceptual change conditions group and traditionally designed science instruction group with respect to attitudes toward science as a school subject. Science process skills were a strong predictor for the achievement related to chemical and physical changes and classification of matter concepts. It may be useful to use the results of this study and instruments and strategies developed for this study for classroom teachers in order to help students to reduce or eliminate their misconceptions.