Tourism time series forecast

In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The index time features associated to the previous twelve values of the time series proved its relevance in this forecast task. The insolation variable can improved results with some architectures, namely the cascade forward architecture. Finally, the experimented ANN models/architectures produced a mean absolute percentage error between 4 and 6%, proving the ability of the ANN models based to forecast this time series. Besides, the feedforward architecture behaved better considering validation and test sets, with 4.2% percentage error in test set.

Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study

This study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.

Vazão em soleiras controladas ou não por comportas

Os controladores de caudal, normalmente implementados em sistemas Supervisory control and data acquisition (SCADA), apresentam uma grande relevância no controlo automático de canais de adução. Para garantir que os controladores de caudal sejam fiáveis em todo o seu domínio de funcionamento (em situações de escoamento com ressalto livre ou submerso e de transição entre escoamentos com ressalto livre e ressalto submerso) foram comparados os resultados dos ensaios experimentais com diferentes métodos de cálculo da vazão em comportas e/ou sobre soleiras. O programa de ensaios foi realizado nos canais laboratorial e experimental da Universidade de Évora. Foram realizados ensaios em comportas planas verticais e em soleiras do tipo Waterways Experiment Station (WES) controladas ou não por comportas planas verticais. Em ambos os casos, foram contempladas as situações de escoamento com ressalto livre e submerso. Os resultados obtidos mostram que: a) para as comportas, o método Rajaratnam e Subramanya (1967a) conduz a bons resultados com um erro percentual médio absoluto MAPE < 1% para o escoamento com ressalto livre e MAPE < 4% para o submerso; a transição entre escoamentos foi identificada corretamente por este método; b) para as soleiras, obtiveram-se bons resultados para o escoamento com ressalto livre para o método USACE (1987), com MAPE < 2%, e para o submerso através do método Alves e Martins (2011), com MAPE < 5%; a transição entre escoamentos pode ser considerada adequada de acordo com a curva experimental de Grace (1963); c) para soleiras controladas por comporta, conseguiram-se bons resultados para o escoamento com ressalto livre recorrendo à equação dos orifícios de pequenas dimensões, com MAPE < 1, 5%, e para o submerso com a equação dos orifícios totalmente submersos com MAPE < 1, 6%; em ambos os casos foi necessária calibração do coeficiente de vazão; a transição entre escoamentos foi adequada pelo método de Grace (1963). Com base nos resultados obtidos, foi possível definir um algoritmo de vazão generalizado para comportas e/ou soleiras que permite a determinação da vazão para as situações de escoamento com ressalto livre e submerso incluindo a transição entre escoamentos; ABSTRACT: Flow controllers, usually implemented in Supervisory Control and Data Acquisition (SCADA) systems, are very important in the automatic control of irrigation canal systems. To ensure that flow controllers are reliable for the entire operating range (free or submerged flow and flow transitions) the experimental results were compared with different methods of flow measurement for gates and/or weirs. The test program was conducted in the laboratory flume and in the automatic canal of the University of ´Evora. Tests were carried in sluice gates and in broad-crested weirs controlled or not by sluice gate. In both cases free and submerged flow conditions were analyzed. The results show that: a) for the sluice gates, the method of Rajaratnam e Subramanya (1967a) leads to good results with a mean absolute percentage error (MAPE) < 1% for free flow and MAPE < 4% for submerged flow. The transition between flows is correctly identified by this method; b) for the uncontrolled weir, good results were obtained for free flow with the method USACE (1987) with MAPE < 2%, and for submerged flow by the method Alves e Martins (2011) with MAPE < 5%. The transition between flows can be accurately defined by the experimental curve of Grace (1963); c) for the controlled weir, good results were achieved for the free flow with the small orifice equation with MAPE < 1.5% and for submerged flow with the submerged orifice equation with MAPE < 1.6%; in both cases the calibration of the discharge coefficient is needed. The transition between flows can be accomplished through Grace (1963) method. Based on the obtained results, it was possible to define a generalized flow algorithm for gates and/or weirs that allows flow determination for free and submerged flow conditions including the transition between flows.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

Validation of the SenseWear Mini activity monitor in 5-12-year-old children

OBJECTIVES: This study aimed to validate SenseWear Mini software algorithm versions 2.2 (SW2.2) and 5.2 (SW5.2) for estimating energy expenditure (EE) in children. DESIGN: Laboratory-based validation study. METHODS: 57 children aged 5-12 y completed a protocol involving 15 semi-structured sedentary (SED), light-intensity (LPA), and moderate- to vigorous-intensity (MVPA) physical activities. EE was estimated using portable indirect calorimetry (IC). The accuracy of EE estimates (kcal·min(-1)) from SW2.2 and SW5.2 were examined at the group level and individual level using the mean absolute percentage error (MAPE), Bland-Altman plots and equivalence testing. RESULTS: MAPE values were lower for SW5.2 (30.1±10.7%) than for SW2.2 (44.0±6.2%). Although mean differences for SW5.2 were smaller than for SW2.2 during SED (-0.23±0.22 vs. -0.61±0.20kcal·min(-1)), LPA (-0.69±0.76 vs. -1.07±0.46kcal·min(-1)) and MVPA (-2.22±1.15 vs. -2.57±1.15kcal·min(-1)), limits of agreement did not decrease for the updated algorithms. For all activities, SW2.2 and SW5.2 were not equivalent to IC (p>0.05). Errors increased with increasing intensity. CONCLUSION: The current SenseWear Mini algorithms SW5.2 underestimated EE. The overall improved accuracy for SW5.2 was not accompanied with improved accuracy at the individual level and EE estimates were not equivalent to IC.

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.

RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.

CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.

MODELING OF TRANSFER PATH FOR DETERMINATION OF COMBUSTION AND NOISE METRICS ON DIESEL ENGINES

Determination of combustion metrics for a diesel engine has the potential of providing feedback for closed-loop combustion phasing control to meet current and upcoming emission and fuel consumption regulations. This thesis focused on the estimation of combustion metrics including start of combustion (SOC), crank angle location of 50% cumulative heat release (CA50), peak pressure crank angle location (PPCL), and peak pressure amplitude (PPA), peak apparent heat release rate crank angle location (PACL), mean absolute pressure error (MAPE), and peak apparent heat release rate amplitude (PAA). In-cylinder pressure has been used in the laboratory as the primary mechanism for characterization of combustion rates and more recently in-cylinder pressure has been used in series production vehicles for feedback control. However, the intrusive measurement with the in-cylinder pressure sensor is expensive and requires special mounting process and engine structure modification. As an alternative method, this work investigated block mounted accelerometers to estimate combustion metrics in a 9L I6 diesel engine. So the transfer path between the accelerometer signal and the in-cylinder pressure signal needs to be modeled. Depending on the transfer path, the in-cylinder pressure signal and the combustion metrics can be accurately estimated - recovered from accelerometer signals. The method and applicability for determining the transfer path is critical in utilizing an accelerometer(s) for feedback. Single-input single-output (SISO) frequency response function (FRF) is the most common transfer path model; however, it is shown here to have low robustness for varying engine operating conditions. This thesis examines mechanisms to improve the robustness of FRF for combustion metrics estimation. First, an adaptation process based on the particle swarm optimization algorithm was developed and added to the single-input single-output model. Second, a multiple-input single-output (MISO) FRF model coupled with principal component analysis and an offset compensation process was investigated and applied. Improvement of the FRF robustness was achieved based on these two approaches. Furthermore a neural network as a nonlinear model of the transfer path between the accelerometer signal and the apparent heat release rate was also investigated. Transfer path between the acoustical emissions and the in-cylinder pressure signal was also investigated in this dissertation on a high pressure common rail (HPCR) 1.9L TDI diesel engine. The acoustical emissions are an important factor in the powertrain development process. In this part of the research a transfer path was developed between the two and then used to predict the engine noise level with the measured in-cylinder pressure as the input. Three methods for transfer path modeling were applied and the method based on the cepstral smoothing technique led to the most accurate results with averaged estimation errors of 2 dBA and a root mean square error of 1.5dBA. Finally, a linear model for engine noise level estimation was proposed with the in-cylinder pressure signal and the engine speed as components.

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup.

Effet de la douleur expérimentale tonique sur l’acquisition et la rétention d’une adaptation locomotrice

Introduction : Une proportion importante des individus ayant recours à des services de réadaptation physique vit avec de la douleur et des incapacités locomotrices. Plusieurs interventions proposées par les professionnels de la réadaptation afin de cibler leurs difficultés locomotrices nécessitent des apprentissages moteurs. Toutefois, très peu d’études ont évalué l’influence de la douleur sur l’apprentissage moteur et aucune n’a ciblé l’apprentissage d’une nouvelle tâche locomotrice. L’objectif de la thèse était d’évaluer l’influence de stimulations nociceptives cutanée et musculaire sur l’acquisition et la rétention d’une adaptation locomotrice. Méthodologie : Des individus en santé ont participé à des séances de laboratoire lors de deux journées consécutives. Lors de chaque séance, les participants devaient apprendre à marcher le plus normalement possible en présence d’un champ de force perturbant les mouvements de leur cheville, produit par une orthèse robotisée. La première journée permettait d’évaluer le comportement des participants lors de la phase d’acquisition de l’apprentissage. La seconde journée permettait d’évaluer leur rétention. Selon le groupe expérimental, l’apprentissage se faisait en présence d’une stimulation nociceptive cutanée, musculaire ou d’aucune stimulation (groupe contrôle). Initialement, l’application du champ de force provoquait d’importantes déviations des mouvements de la cheville (i.e. erreurs de mouvement), que les participants apprenaient graduellement à réduire en compensant activement la perturbation. L’erreur de mouvement moyenne durant la phase d’oscillation (en valeur absolue) a été quantifiée comme indicateur de performance. Une analyse plus approfondie des erreurs de mouvement et de l’activité musculaire a permis d’évaluer les stratégies motrices employées par les participants. Résultats : Les stimulations nociceptives n’ont pas affecté la performance lors de la phase d’acquisition de l’apprentissage moteur. Cependant, en présence de douleur, les erreurs de mouvement résiduelles se trouvaient plus tard dans la phase d’oscillation, suggérant l’utilisation d’une stratégie motrice moins anticipatoire que pour le groupe contrôle. Pour le groupe douleur musculaire, cette stratégie était associée à une activation précoce du muscle tibial antérieur réduite. La présence de douleur cutanée au Jour 1 interférait avec la performance des participants au Jour 2, lorsque le test de rétention était effectué en absence de douleur. Cet effet n’était pas observé lorsque la stimulation nociceptive cutanée était appliquée les deux jours, ou lorsque la douleur au Jour 1 était d’origine musculaire. Conclusion : Les résultats de cette thèse démontrent que dans certaines circonstances la douleur peut influencer de façon importante la performance lors d’un test de rétention d’une adaptation locomotrice, malgré une performance normale lors de la phase d’acquisition. Cet effet, observé uniquement avec la douleur cutanée, semble cependant plus lié au changement de contexte entre l’acquisition des habiletés motrices et le test de rétention (avec vs. sans douleur) qu’à une interférence directe avec la consolidation des habiletés motrices. Par ailleurs, malgré l’absence d’influence de la douleur sur la performance des participants lors de la phase d’acquisition de l’apprentissage, les stratégies motrices utilisées par ceux-ci étaient différentes de celles employées par le groupe contrôle.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

A new error measure for forecasts of household-level, high resolution electrical energy consumption

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction.

Closed-loop control of mean arterial blood pressure during surgery with alfentanil: clinical evaluation of a novel model-based predictive controller

BACKGROUND: In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. METHODS: The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). RESULTS: Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. CONCLUSION: The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Ocular characteristics in myopic anisometropia

PURPOSE: To investigate the interocular symmetry of ocular optical, biometric and biomechanical characteristics between the more and less ametropic eyes of myopic anisometropes. METHODS: Thirty-four young, healthy myopic anisometropic adults (≥ 1 D spherical equivalent difference between eyes) without amblyopia or strabismus were recruited. A range of biometric and optical parameters were measured in the more and less ametropic eye of each subject including; axial length, ocular aberrations, intraocular pressure and corneal topography, thickness and biomechanics. Morphology of the anterior eye in primary and downward gaze was examined using custom software analysis of high resolution digital images. Ocular sighting dominance was assessed using the hole-in-the-card test. RESULTS: Mean absolute spherical equivalent anisometropia was 1.74 ± 0.74 D. There was a strong correlation between the degree of anisometropia and the interocular difference in axial length (r = 0.81, p < 0.001). The more and less ametropic fellow eyes displayed a high degree of interocular symmetry for the majority of biometric, biomechanical and optical parameters measured. When the level of anisometropia exceeded 1.75 D (n = 10), the more myopic eye was the dominant sighting eye in nine of these ten subjects. Subjects with greater levels of anisometropia (> 1.75 D) also showed high levels of correlation between the dominant and non-dominant eyes in their biometric, biomechanical and optical characteristics. CONCLUSIONS: Although significantly different in axial length, anisometropic eyes display a high degree of interocular symmetry for a range of anterior eye biometrics and optical parameters. For higher levels of anisometropia, the more myopic eye tends to be the dominant sighting eye.

Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter

In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular Visual Odometry and GPS measurements in a similar manner to a classic loosely-coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.

An innovative approach to the assessment of nutrient intake in adults with type 2 diabetes : the development, trial and evaluation of a mobile phone photo/voice dietary record

Nutrition interventions in the form of both self-management education and individualised diet therapy are considered essential for the long-term management of type 2 diabetes mellitus (T2DM). The measurement of diet is essential to inform, support and evaluate nutrition interventions in the management of T2DM. Barriers inherent within health care settings and systems limit ongoing access to personnel and resources, while traditional prospective methods of assessing diet are burdensome for the individual and often result in changes in typical intake to facilitate recording. This thesis investigated the inclusion of information and communication technologies (ICT) to overcome limitations to current approaches in the nutritional management of T2DM, in particular the development, trial and evaluation of the Nutricam dietary assessment method (NuDAM) consisting of a mobile phone photo/voice application to assess nutrient intake in a free-living environment with older adults with T2DM. Study 1: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM The effectiveness of an automated telephone system, Telephone-Linked Care (TLC) Diabetes, designed to deliver self-management education was evaluated in terms of promoting dietary change in adults with T2DM and sub-optimal glycaemic control. In this secondary data analysis independent of the larger randomised controlled trial, complete data was available for 95 adults (59 male; mean age(±SD)=56.8±8.1 years; mean(±SD)BMI=34.2±7.0kg/m2). The treatment effect showed a reduction in total fat of 1.4% and saturated fat of 0.9% energy intake, body weight of 0.7 kg and waist circumference of 2.0 cm. In addition, a significant increase in the nutrition self-efficacy score of 1.3 (p<0.05) was observed in the TLC group compared to the control group. The modest trends observed in this study indicate that the TLC Diabetes system does support the adoption of positive nutrition behaviours as a result of diabetes self-management education, however caution must be applied in the interpretation of results due to the inherent limitations of the dietary assessment method used. The decision to use a close-list FFQ with known bias may have influenced the accuracy of reporting dietary intake in this instance. This study provided an example of the methodological challenges experienced with measuring changes in absolute diet using a FFQ, and reaffirmed the need for novel prospective assessment methods capable of capturing natural variance in usual intakes. Study 2: The development and trial of NuDAM recording protocol The feasibility of the Nutricam mobile phone photo/voice dietary record was evaluated in 10 adults with T2DM (6 Male; age=64.7±3.8 years; BMI=33.9±7.0 kg/m2). Intake was recorded over a 3-day period using both Nutricam and a written estimated food record (EFR). Compared to the EFR, the Nutricam device was found to be acceptable among subjects, however, energy intake was under-recorded using Nutricam (-0.6±0.8 MJ/day; p<0.05). Beverages and snacks were the items most frequently not recorded using Nutricam; however forgotten meals contributed to the greatest difference in energy intake between records. In addition, the quality of dietary data recorded using Nutricam was unacceptable for just under one-third of entries. It was concluded that an additional mechanism was necessary to complement dietary information collected via Nutricam. Modifications to the method were made to allow for clarification of Nutricam entries and probing forgotten foods during a brief phone call to the subject the following morning. The revised recording protocol was evaluated in Study 4. Study 3: The development and trial of the NuDAM analysis protocol Part A explored the effect of the type of portion size estimation aid (PSEA) on the error associated with quantifying four portions of 15 single foods items contained in photographs. Seventeen dietetic students (1 male; age=24.7±9.1 years; BMI=21.1±1.9 kg/m2) estimated all food portions on two occasions: without aids and with aids (food models or reference food photographs). Overall, the use of a PSEA significantly reduced mean (±SD) group error between estimates compared to no aid (-2.5±11.5% vs. 19.0±28.8%; p<0.05). The type of PSEA (i.e. food models vs. reference food photograph) did not have a notable effect on the group estimation error (-6.7±14.9% vs. 1.4±5.9%, respectively; p=0.321). This exploratory study provided evidence that the use of aids in general, rather than the type, was more effective in reducing estimation error. Findings guided the development of the Dietary Estimation and Assessment Tool (DEAT) for use in the analysis of the Nutricam dietary record. Part B evaluated the effect of the DEAT on the error associated with the quantification of two 3-day Nutricam dietary records in a sample of 29 dietetic students (2 males; age=23.3±5.1 years; BMI=20.6±1.9 kg/m2). Subjects were randomised into two groups: Group A and Group B. For Record 1, the use of the DEAT (Group A) resulted in a smaller error compared to estimations made without the tool (Group B) (17.7±15.8%/day vs. 34.0±22.6%/day, p=0.331; respectively). In comparison, all subjects used the DEAT to estimate Record 2, with resultant error similar between Group A and B (21.2±19.2%/day vs. 25.8±13.6%/day; p=0.377 respectively). In general, the moderate estimation error associated with quantifying food items did not translate into clinically significant differences in the nutrient profile of the Nutricam dietary records, only amorphous foods were notably over-estimated in energy content without the use of the DEAT (57kJ/day vs. 274kJ/day; p<0.001). A large proportion (89.6%) of the group found the DEAT helpful when quantifying food items contained in the Nutricam dietary records. The use of the DEAT reduced quantification error, minimising any potential effect on the estimation of energy and macronutrient intake. Study 4: Evaluation of the NuDAM The accuracy and inter-rater reliability of the NuDAM to assess energy and macronutrient intake was evaluated in a sample of 10 adults (6 males; age=61.2±6.9 years; BMI=31.0±4.5 kg/m2). Intake recorded using both the NuDAM and a weighed food record (WFR) was coded by three dietitians and compared with an objective measure of total energy expenditure (TEE) obtained using the doubly labelled water technique. At the group level, energy intake (EI) was under-reported to a similar extent using both methods, with the ratio of EI:TEE was 0.76±0.20 for the NuDAM and 0.76±0.17 for the WFR. At the individual level, four subjects reported implausible levels of energy intake using the WFR method, compared to three using the NuDAM. Overall, moderate to high correlation coefficients (r=0.57-0.85) were found across energy and macronutrients except fat (r=0.24) between the two dietary measures. High agreement was observed between dietitians for estimates of energy and macronutrient derived for both the NuDAM (ICC=0.77-0.99; p<0.001) and WFR (ICC=0.82-0.99; p<0.001). All subjects preferred using the NuDAM over the WFR to record intake and were willing to use the novel method again over longer recording periods. This research program explored two novel approaches which utilised distinct technologies to aid in the nutritional management of adults with T2DM. In particular, this thesis makes a significant contribution to the evidence base surrounding the use of PhRs through the development, trial and evaluation of a novel mobile phone photo/voice dietary record. The NuDAM is an extremely promising advancement in the nutritional management of individuals with diabetes and other chronic conditions. Future applications lie in integrating the NuDAM with other technologies to facilitate practice across the remaining stages of the nutrition care process.