In the modern era of high-stakes accountability, test data have become much more than a simple comparison (Schmoker, 2006; Payne & Miller, 2009). The information provided in modern data reports has become an invaluable tool to drive instruction in classrooms. However, there is a lack of good training for educators to evaluate data and translate findings into solid practices that can improve student learning (Blair, 2006; Dynarski, 2008; Light, Wexler, & Heinze, 2005; Payne & Miller, 2009). Some schools are good at collecting data, but often fall short at what to do next. It is the role of the principal to serve as an instructional leader and guide teachers to the answer the reoccurring question of "now what?" The purpose of this study was to investigate ways in which principals build successful data-driven instructional systems within their schools using a qualitative multi-site case study method. This research utilized a triangulation approach with structured interviews, on-site visits, and document reviews from various middle school supervisors, principals, and teachers. The findings are presented in four common themes and patterns identified as essential components administrators used to implement data-driven instructional systems to improve student achievement. The themes are 1) administrators must clearly define the vision and set the expectation of using data to improve student achievement, 2) administrators must take an active role in the data-driven process, 3) data must be easily accessible to stakeholders, and 4) stakeholders must devote time on a regular basis to the data-driven process. The four themes led to the conclusion of ten common steps administrators can use to acculturate their school or school division with the data-driven instruction process. / Ed. D.

Noise is defined as any undesired sound. Urban noise and its effect on citizens area significant environmental problem, and the increasing level of noise has become a critical problem in some cities. Fortunately, noise pollution can be mitigated by better planning of urban areas or controlled by administrative regulations. However, the execution of such actions requires well-established systems for noise monitoring. In this thesis, we present a solution for noise measurement and classification using a low-power and inexpensive IoT unit. To measure the noise level, we implement an algorithm for calculating the sound pressure level in dB. We achieve a measurement error of less than 1 dB. Our machine learning-based method for noise classification uses Mel-frequency cepstral coefficients for audio feature extraction and four supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregating, and random forest). We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for the classification of sound samples in the dataset under study. We achieve noise classification accuracy in the range of 88% – 94%.

SILVA, Guelson Batista da. Dinâmica populacional e pesca de atuns em cardumes associados no Atlântico oeste equatorial. 2013. 155 f. : Tese (doutorado) - Universidade Federal do Ceará, Centro de Ciências Agrárias, Departamento de Engenharia de Pesca, Fortaleza-CE, 2013 / Submitted by Nádja Goes (nmoraissoares@gmail.com) on 2016-07-26T14:55:57Z No. of bitstreams: 1 2013_tese_gbsilva.pdf: 25046778 bytes, checksum: fc5e4863285fe78962906db438bfe4a9 (MD5) / Approved for entry into archive by Nádja Goes (nmoraissoares@gmail.com) on 2016-07-26T14:56:11Z (GMT) No. of bitstreams: 1 2013_tese_gbsilva.pdf: 25046778 bytes, checksum: fc5e4863285fe78962906db438bfe4a9 (MD5) / Made available in DSpace on 2016-07-26T14:56:11Z (GMT). No. of bitstreams: 1 2013_tese_gbsilva.pdf: 25046778 bytes, checksum: fc5e4863285fe78962906db438bfe4a9 (MD5) Previous issue date: 2013 / The present study aims to characterize the technolo gical features and the catch composition, complemented with studies on the age a nd growth and feeding of the target species in the fishery for tunas in associated schools in the western equatorial Atlantic. For this purpose, a total of 109 landings by the fleet based on Areia Branca, RN, were observed in the period from June 2010 to M ay 2013, as well as on board in nine fishing cruises in the period from February 2011 to December 2012. The age and growth study was performed based on readings of age rings of the dorsal spines and daily microincrements present in the otolith of Thunnus albacares, adjusted to von Bertalanffy , Gompertz and Logistic growth models, which were selected based on their AIC (Akaike 's Information Criterion) valu es. The feeding study was developed through the identification and assessment of food items present in the stomach contents from T. obesus and T. albacares . The fleet consisted of 9 boats, with an average length overall of 13.4 m, which use various fishing techniques such as handline, rod and line and trolling, combined with natural baits or lures. Landings amounted 691,533 kg, from which, the target species were bigeye and yellowfin tunas, as well as bycatch, species such as dolphinfish, blue marlin, skipjack, wahoo, rainbow runner and, even occasionally, species such as swordfish and triggerfish. The values of L were estimated at 175.13 and 171.6 cm for the obser ved and backcalculated data from the dorsal spines and 166.6, 171.14 and 155.38 cm, respectively for the pooled data, males and females based on otolith daily microstructure. The main food items observed in the diet of both predators were the following fish families: Myctophidae, Exocoetidae, Bramidae and Nomeidae. Cephalopods and crustaceans were occasionally observed. We can conclude that results provide essential information that will ass ist in the understanding of fishing activities as well as expand the knowledge on the biology of the target species. / O presente trabalho tem por objetivo a caracterização dos aspectos tecnológicos e da composição das capturas, complementados com estudos sobre o crescimento e a alimentação das principais espécies na pesca de atuns e afins em cardumes associados no Atlântico Oeste Equatorial. Para tanto, foram acompanhados 109 desembarques em Areia Branca, RN, no período entre jun/2010 e mai/2013, assim como o embarque em nove cruzeiros de pesca no período entre fev/2011 e dez/2012. O estudo de idade e crescimento, foi realizado com base nas leituras de anéis etários nos espinhos dorsais e microincrementos diários de otólitos de Thunnus albacares ajustados aos modelos de crescimento de von Bertalanffy, Gompertz e Logístico, os quais foram selecionados a partir dos valores de AIC (Akaike’s Criterion Information). O estudo de alimentação foi desenvolvido por meio da identificação e avaliação dos itens alimentares presentes no conteúdo estomacal de T. obesus e T. albacares. Foram observadas 9 embarcações, com comprimento total médio de 13,4 m, as quais utilizam diversas técnicas de captura como a linha de mão, vara e linha, corrico e corso, com o uso de iscas artificiais e naturais. Os desembarques totalizaram 691.533 kg de pescado, dentre os quais, as espécies alvo foram a albacora bandolim e a albacora laje, sendo retidas tambem espécies como o dourado, agulhão negro, bonito-listrado, cavala empinge, peixe rei e ainda, ocasionalmente, espécies como espadarte e cangulo. Os valores de L ̅_∞ foram estimados em 175,13 e 171,6 cm para os dados observados e retrocalculados a partir dos espinhos dorsais e em 166,6; 171,14; e 155,38 cm respectivamente para os dados agrupados, para machos e fêmeas a partir dos microincrementos diários de otólitos. Os principais itens alimentares observados na dieta de ambos os predadores foram os peixes das famílias Myctophidae, Exocoetidae, Bramidae e Nomeidae, sendo observadas com menor frequência os cefalópodes e os crustáceos. Podemos concluir que os resultados obtidos fornecem informações essênciais que ajudarão a compreender as atividades de pesca, bem como ampliarão os conhecimentos sobre a biologia das principais espécies capturadas.

Depuis le milieu des années 1990, l’utilisation de Dispositifs de Concentration de Poissons (DCP), des objets artificiels spécifiquement mis à l’eau pour agréger des bancs de poissons, est devenue de plus en plus importante pour la pêche au thon tropical à la senne. Cette utilisation massive des DCP, qui s’accompagne d’une utilisation massive de dispositifs de suivi comme les balises GPS et les balises échosondeurs, est aujourd’hui source d’inquiétude pour les stocks de thons, les prises accessoires mais aussi pour le fonctionnement des écosystèmes pélagiques. Cependant, les modalités d’utilisation des DCP et des balises GPS qui servent à les suivre restent mal connues, ce qui complique considérablement l’évaluation et la gestion des impacts de ces pratiques de pêche. Afin d’améliorer les connaissances actuelles de la pêcherie, les positions des balises GPS utilisées par les 3 armements français dans les océans Atlantique et Indien, constituant une part significative des DCP utilisés dans ces deux océans, ont été analysées. Ces données ont été combinées avec des multiples sources d’information : les livres de bord, les trajectoires VMS des senneurs français ainsi que des entretiens avec les patrons français. Elles nous permettent de mieux comprendre les stratégies de mise à l’eau des DCP et des balises, d’estimer le nombre d’objets flottants utilisés par les flottes de senneurs dans les océans Atlantique et Indien, de mesurer la contribution des DCP et des navires auxiliaires à l’efficacité de pêche des senneurs, d’identifier des destructions potentielles d’habitats par les DCP échoués and pour finir de proposer des solutions de gestion pour la pêcherie. Les résultats montrent une grande saisonnalité dans les mises à l’eau des deux océans, une croissance rapide du nombre de balises GPS au cours des 7 dernières années puisqu’elle est multipliée par 4.2 dans l’Océan Indien et 7 dans l’Océan Atlantique, des dommages possibles causés à des écosystèmes côtiers fragiles avec une probabilité d’échouage de l’ordre de 10% et finalement une augmentation de l’efficacité de pêche entre 2003 et 2014 de l’ordre de 3.8-18.8% dans l’Océan Atlantique et 10.7%-26.3% dans l’Océan Indien. Les entretiens avec les capitaines des senneurs soulignent la nécessité d’une gestion plus efficace de la pêcherie, avec entre autres l’instauration de quotas, une régulation de la capacité de la flotte de senneurs et un meilleur suivi des navires auxiliaires. Les résultats obtenus constituent les premières étapes nécessaires à une meilleure gestion de la pêche sous objet flottant / Since the mid 1990s, the use of drifting Fish Aggregating Devices (dFADs) by purse seiners, artificial objects specifically designed to aggregate fish, has become an important mean of catching tropical tunas. In recent years, the massive deployments of dFADs, as well as the massive use of tracking devices on dFADs and natural floating objects, such as GPS buoys, have raised serious concerns for tropical tuna stocks, bycatch species and pelagic ecosystem functioning. Despite these concerns, relatively little is known about the modalities of GPS buoy tracked objects use, making it difficult to assess and manage of the impacts of this fishing practice. To fill these knowledge gaps, we have analyzed GPS buoy tracks provided by the three French fishing companies operating in the Atlantic and the Indian Oceans, representing a large proportion of the floating objects monitored by the French fleet. These data were combined with multiple sources of information: logbook data, Vessel Monitoring System (VMS) tracks of French purse seiners, information on support vessels and Local Ecological Knowledge (LEK) of purse seine skippers to describe GPS buoy deployment strategies, estimate the total number of GPS buoy equipped dFADs used in the Atlantic and Indian Oceans, measure the contribution of strategies with FOBs and support vessels to the fishing efficiency of tropical tuna purse seiners, identify potential damages caused by lost dFADs and finally to propose management options for tropical tuna purse seine FOB fisheries. Results indicate clear seasonal patterns of GPS buoy deployment in the two oceans, a rapid expansion in the use of dFADs over the last 7 years with an increase of 4.2 times in the Indian Ocean and 7.0 times in the Atlantic Ocean, possible damages to fragile coastal ecosystems with 10% of GPS buoy tracks ending with a beaching event and an increased efficiency of tropical tuna purse seine fleets from 3.9% to 18.8% in the Atlantic Ocean over 2003-2014 and from 10.7% to 26.3% in the Indian Ocean. Interviews with purse seine skippers underlined the need for a more efficient management of the fishery, including the implementation of catch quotas, a limitation of the capacity of purse seine fleets and a regulation of the use of support vessels. These results represent a first step towards better assessment and management of purse seine FOB fisheries.

No description available.

Abstracts in English, Afrikaans and Northern Sotho / More time series forecasting methods were researched and made available in recent years. This is mainly due to the emergence of machine learning methods which also found applicability in time series forecasting. The emergence of a variety of methods and their variants presents a challenge when choosing appropriate forecasting methods. This study explored the performance of four advanced forecasting methods: autoregressive integrated moving averages (ARIMA); artificial neural networks (ANN); support vector machines (SVM) and regression models with ARIMA errors. To improve their performance, bagging was also applied. The performance of the different methods was illustrated using South African air passenger data collected for planning purposes by the Airports Company South Africa (ACSA). The dissertation discussed the different forecasting methods at length. Characteristics such as strengths and weaknesses and the applicability of the methods were explored. Some of the most popular forecast accuracy measures were discussed in order to understand how they could be used in the performance evaluation of the methods. It was found that the regression model with ARIMA errors outperformed all the other methods, followed by the ARIMA model. These findings are in line with the general findings in the literature. The ANN method is prone to overfitting and this was evident from the results of the training and the test data sets. The bagged models showed mixed results with marginal improvement on some of the methods for some performance measures. It could be concluded that the traditional statistical forecasting methods (ARIMA and the regression model with ARIMA errors) performed better than the machine learning methods (ANN and SVM) on this data set, based on the measures of accuracy used. This calls for more research regarding the applicability of the machine learning methods to time series forecasting which will assist in understanding and improving their performance against the traditional statistical methods / Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse. Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes. Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes: outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute. Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is. Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te evalueer. Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig. Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige prestasiemaatstawwe vir party metodes marginaal verbeter. Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes (ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om hul prestasie vergeleke met dié van die tradisionele metodes te verbeter. / Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya maleba ya go akanya. Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo (ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo (SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe. Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye. Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga mešomo. Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya dipalopalo. / Decision Sciences / M. Sc. (Operations Research)