Treating Students' Errors in Oral Production

[Sin resumen]

Mercury contamination in fanned fish setup on former garimpo mining areas in the Northern Mato Grosso State, Amazonian region, Brazil

The garimpo gold mining activity has released about 2.500 tons of mercury in the Brazilian Amazonian environment in the 1980-1995 period. The northern region of Mato Grosso State, an important gold mining and trading area during the Arnazonian gold rush is now at a turning point regarding its economic future. Nowadays, the activities related to gold mining have only a low relevance on its economy. Thus, the local communities are looking for economic alternatives for the development of the region. Cooperative fish farming is one of such alternatives. However, some projects are directly implemented on areas degraded by the former garimpo activity and the mercury left behind still poses risks, especially by its potential accumulation in fish. The objective of the present study was to evaluate the levels of mercury contamination in two fish farming areas, Paranaita and Alta Floresta, with and without records of past gold-washing activity, respectively. Data such as mercury concentration in fish of different trophic level, size, and weight as well as the water physical and chemical parameters were measured and considered. These preliminary data have shown no significant difference between these two fish fanning areas, relatively to mercury levels in fish. (c) 2004 Elsevier B.V. All rights reserved.

MODELING AND EXPERIMENTAL SETUP OF AN HCCI ENGINE

For the past three decades the automotive industry is facing two main conflicting challenges to improve fuel economy and meet emissions standards. This has driven the engineers and researchers around the world to develop engines and powertrain which can meet these two daunting challenges. Focusing on the internal combustion engines there are very few options to enhance their performance beyond the current standards without increasing the price considerably. The Homogeneous Charge Compression Ignition (HCCI) engine technology is one of the combustion techniques which has the potential to partially meet the current critical challenges including CAFE standards and stringent EPA emissions standards. HCCI works on very lean mixtures compared to current SI engines, resulting in very low combustion temperatures and ultra-low NOx emissions. These engines when controlled accurately result in ultra-low soot formation. On the other hand HCCI engines face a problem of high unburnt hydrocarbon and carbon monoxide emissions. This technology also faces acute combustion controls problem, which if not dealt properly with yields highly unfavorable operating conditions and exhaust emissions. This thesis contains two main parts. One part deals in developing an HCCI experimental setup and the other focusses on developing a grey box modelling technique to control HCCI exhaust gas emissions. The experimental part gives the complete details on modification made on the stock engine to run in HCCI mode. This part also comprises details and specifications of all the sensors, actuators and other auxiliary parts attached to the conventional SI engine in order to run and monitor the engine in SI mode and future SI-HCCI mode switching studies. In the latter part around 600 data points from two different HCCI setups for two different engines are studied. A grey-box model for emission prediction is developed. The grey box model is trained with the use of 75% data and the remaining data is used for validation purpose. An average of 70% increase in accuracy for predicting engine performance is found while using the grey-box over an empirical (black box) model during this study. The grey-box model provides a solution for the difficulty faced for real time control of an HCCI engine. The grey-box model in this thesis is the first study in literature to develop a control oriented model for predicting HCCI engine emissions for control.

Optical Access Engine Setup and Validation

The optical access engine integrated with the diagnostic and optical measurement techniques is a great platform for engine research because it provides clear visual access to the combustion chamber inside the engines. An optical access engine customized based on a 4-cylinder spark ignited direct injection (SIDI) production engine is located in the Advanced Power Systems Laboratories (APS LABS) at Michigan Technological University. This optical access engine inside the test cell has been set up for different engine research. In this report, two SAE papers in engine research utilizing the optical access engine are reviewed to gain basic understanding of the methodology. Though the optical engine in APS LABS is a little bit different from the engines used in the literature, the methodology in the papers provides guidelines for engine research through optical access engines. In addition, the optical access engine instrumentation including the test cell setup and the optical engine setup is described in detail in the report providing a solid record for later troubleshooting and reference. Finally, the motoring tests, firing tests and optical imaging experiment on the optical engine have been performed to validate the instrumentation. This report only describes so far the instrumentation of the optical engine in the APS LABS by April 2015.

Test of a Smock System on CPR Primary Emergency Measures and Medical Errors During Simulated Emergencies

Rates of survival of victims of sudden cardiac arrest (SCA) using cardio pulmonary resuscitation (CPR) have shown little improvement over the past three decades. Since registered nurses (RNs) comprise the largest group of healthcare providers in U.S. hospitals, it is essential that they are competent in performing the four primary measures (compression, ventilation, medication administration, and defibrillation) of CPR in order to improve survival rates of SCA patients. The purpose of this experimental study was to test a color-coded SMOCK system on:1) time to implement emergency patient care measures 2) technical skills performance 3) number of medical errors, and 4) team performance during simulated CPR exercises. The study sample was 260 RNs (M 40 years, SD=11.6) with work experience as an RN (M 7.25 years, SD=9.42).Nurses were allocated to a control or intervention arm consisting of 20 groups of 5-8 RNs per arm for a total of 130 RNs in each arm. Nurses in each study arm were given clinical scenarios requiring emergency CPR. Nurses in the intervention group wore different color labeled aprons (smocks) indicating their role assignment (medications, ventilation, compression, defibrillation, etc) on the code team during CPR. Findings indicated that the intervention using color-labeled smocks for pre-assigned roles had a significant effect on the time nurses started compressions (t=3.03, p=0.005), ventilations (t=2.86, p=0.004) and defibrillations (t=2.00, p=.05) when compared to the controls using the standard of care. In performing technical skills, nurses in the intervention groups performed compressions and ventilations significantly better than those in the control groups. The control groups made significantly (t=-2.61, p=0.013) more total errors (7.55 SD 1.54) than the intervention group (5.60, SD 1.90). There were no significant differences in team performance measures between the groups. Study findings indicate use of colored labeled smocks during CPR emergencies resulted in: shorter times to start emergency CPR; reduced errors; more technical skills completed successfully; and no differences in team performance.

A retrospective study examining the nature, contributory factors and predictors of dispensing errors in community pharmacy

Introduction: Since 2005, the workload of community pharmacists in England has increased with a concomitant increase in stress and work pressure. However, it is unclear how these factors are impacting on the ability of community pharmacists to ensure accuracy during the dispensing process. This research seeks to extend our understanding of the nature, outcome, and predictors of dispensing errors. Methodology: A retrospective analysis of a purposive sample of incident report forms (IRFs) from the database of a pharmacist indemnity insurance provider was conducted. Data collected included; type of error, degree of harm caused, pharmacy and pharmacist demographics, and possible contributory factors. Results: In total, 339 files from UK community pharmacies were retrieved from the database. The files dated from June 2006 to November 2011. Incorrect item (45.1%, n = 153/339) followed by incorrect strength (24.5%, n = 83/339) were the most common forms of error. Almost half (41.6%, n = 147/339) of the patients suffered some form of harm ranging from minor harm (26.7%, n = 87/339) to death (0.3%, n = 1/339). Insufficient staff (51.6%, n = 175/339), similar packaging (40.7%, n = 138/339) and the pharmacy being busier than normal (39.5%, n = 134/339) were identified as key contributory factors. Cross-tabular analysis against the final accuracy check variable revealed significant association between the pharmacy location (P < 0.024), dispensary layout (P < 0.025), insufficient staff (P < 0.019), and busier than normal (P < 0.005) variables. Conclusion: The results provide an overview of some of the individual, organisational and technical factors at play at the time of a dispensing error and highlight the need to examine further the relationships between these factors and dispensing error occurrence.

Some problems and errors in cytogenetic biodosimetry

Human radiosensitivity is a quantitative trait that is generally subject to binomial distribution. Individual radiosensitivity, however, may deviate significantly from the mean (by 2-3 standard deviations). Thus, the same dose of radiation may result in different levels of genotoxic damage (commonly measured as chromosome aberration rates) in different individuals. There is significant genetic component in individual radiosensitivity. It is related to carriership of variant alleles of various single-nucleotide polymorphisms (most of these in genes coding for proteins functioning in DNA damage identification and repair); carriership of different number of alleles producing cumulative effects; amplification of gene copies coding for proteins responsible for radioresistance, mobile genetic elements, and others. Among the other factors influencing individual radioresistance are: radioadaptive response; bystander effect; levels of endogenous substances with radioprotective and antimutagenic properties and environmental factors such as lifestyle and diet, physical activity, psychoemotional state, hormonal state, certain drugs, infections and others. These factors may have radioprotective or sensibilising effects. Apparently, there are too many factors that may significantly modulate the biological effects of ionising radiation. Thus, conventional methodologies for biodosimetry (specifically, cytogenetic methods) may produce significant errors if personal traits that may affect radioresistance are not accounted for.

Hardware-In-The Loop setup and test of an air spring control system

In this work, a Hardware-in-the-loop test bench is designed. The bench is used to test the behaviour of an electronic control unit used in Maserati to control the dynamics of an air spring system. First the mathematical model of the plant has been defined, then the simulation enviroment and the test environment have been set up. The performed tests succesfully highlighted some bugs in the device under test.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Learning from errors: Psychological, relational, and cultural aspects

Although errors might foster learning, they can also be perceived as something to avoid if they are associated with negative consequences (e.g., receiving a bad grade or being mocked by classmates). Such adverse perceptions may trigger negative emotions and error-avoidance attitudes, limiting the possibility to use errors for learning. These students’ reactions may be influenced by relational and cultural aspects of errors that characterise the learning environment. Accordingly, the main aim of this research was to investigate whether relational and cultural characteristics associated with errors affect psychological mechanisms triggered by making mistakes. In the theoretical part, we described the role of errors in learning using an integrated multilevel (i.e., psychological, relational, and cultural levels of analysis) approach. Then, we presented three studies that analysed how cultural and relational error-related variables affect psychological aspects. The studies adopted a specific empirical methodology (i.e., qualitative, experimental, and correlational) and investigated different samples (i.e., teachers, primary school pupils and middle school students). Findings of study one (cultural level) highlighted errors acquire different meanings that are associated with different teachers’ error-handling strategies (e.g., supporting or penalising errors). Study two (relational level) demonstrated that teachers’ supportive error-handling strategies promote students’ perceptions of being in a positive error climate. Findings of study three (relational and psychological level) showed that positive error climate foster students’ adaptive reactions towards errors and learning outcomes. Overall, our findings indicated that different variables influence students’ learning from errors process and teachers play an important role in conveying specific meanings of errors during learning activities, dealing with students’ mistakes supportively, and establishing an error-friendly classroom environment.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Numerical integration errors in deep space orbit determination

Il progetto di tesi è stato sviluppato durante il periodo di tirocinio svolto all’interno del “Laboratorio di Radio Scienza ed Esplorazione Planetaria” da un'esperienza da cui prende il nome lo stesso elaborato: ”Numerical integration errors in deep space orbit determination”. Lo scopo del sopraccitato laboratorio è stato quello di studiare in modo approfondito il problema kepleriano dei due corpi, per poi passare ad un’analisi del problema dei tre corpi e successivamente a n corpi (con particolare attenzione alle orbite dei satelliti medicei di Giove). Lo studio è stato affiancato ad un costante utilizzo della piattaforma di programmazione Matlab per l’elaborazione e la stesura di codici per il calcolo di traiettorie orbitali ed errori numerici. Infatti, il fulcro del lavoro è stato proprio il confronto di vari integratori e degli errori numerici derivanti dall’integrazione. Nella tesi, dapprima, viene introdotto il sistema Gioviano, vengono presentati i satelliti medicei, delineate le caratteristiche fisiche fondamentali e i principali motivi che portano ad avere particolare interesse nel conoscere lo sviluppo orbitale di tale sistema. In seguito, l'elaborato, dopo una dettagliata descrizione teorica del problema dei due corpi, presenta un codice per la rappresentazione di orbite kepleriane e il calcolo dei relativi errori commessi dal metodo numerico rispetto a quello analitico. Nell'ultimo capitolo, invece, il problema è esteso a più corpi dotati di massa e a tal proposito viene proposto un codice per la rappresentazione delle orbite descritte nel tempo da n corpi, date le condizioni iniziali, e il calcolo dei rispettivi errori nel sistema di riferimento (r,t,n). In merito a ciò, vengono infine testati diversi integratori per cercare quello con le migliori performance e sono poi analizzati alcuni parametri in input al problema per verificare sotto quali condizioni l’integratore lavora meglio.

Cytogenetic And Morphologic Approaches Of Hybrids From Experimental Crosses Between Triatoma Lenti Sherlock & Serafim, 1967 And T. Sherlocki Papa Et Al., 2002 (hemiptera: Reduviidae).

The reproductive capacity between Triatoma lenti and Triatoma sherlocki was observed in order to verify the fertility and viability of the offspring. Cytogenetic, morphological and morphometric approaches were used to analyze the differences that were inherited. Experimental crosses were performed in both directions. The fertility rate of the eggs in crosses involving T. sherlocki females was 65% and 90% in F1 and F2 offspring, respectively. In reciprocal crosses, it was 7% and 25% in F1 and F2 offspring, respectively. The cytogenetic analyses of the male meiotic process of the hybrids were performed using lacto-acetic orcein, C-banding and Feulgen techniques. The male F1 offspring presented normal chromosome behavior, a finding that was similar to those reported in parental species. However, cytogenetic analysis of F2 offspring showed errors in chromosome pairing. This post-zygotic isolation, which prevents hybrids in nature, may represent the collapse of the hybrid. This phenomenon is due to a genetic dysregulation that occurs in the chromosomes of F1. The results were similar in the hybrids from both crosses. Morphological features, such as color and size of connexive and the presence of red-orange rings on the femora, were similar to T. sherlocki, while wins size was similar to T. lenti in F1 offspring. The eggshells showed characteristics that were similar to species of origin, whereas the median process of the pygophore resulted in intermediate characteristics in the F1 and a segregating pattern in F2 offspring. Geometric morphometric techniques used on the wings showed that both F1 and F2 offspring were similar to T. lenti. These studies on the reproductive capacity between T. lenti and T. sherlocki confirm that both species are evolutionarily closed; hence, they are included in the brasiliensis subcomplex. The extremely reduced fertility observed in the F2 hybrids confirmed the specific status of the species that were analyzed.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.