10 research outputs found
Understanding factors underlying actual consumption of organic food: The moderating effect of future orientation
No full textThe majority of past studies focused on investigating the motivational factors to purchase organic food as a proxy to foster organic food consumption. However, the preceding studies’ foci do not embrace the consumption itself where purchasing may come secondary to consumption decisions. Consumption reflects high involvement with the product; and the barriers and motivations are as real as the product itself, which makes it an ideal moment to examine the motivation. The research model was analyzed using the Partial Least Square Structural Equation Modeling technique. Results show that product-specific attitude (PSA), willingness to pay (WTP) and perceived availability (PA) had a significant positive influence on individuals’ organic food consumption (OFC), while environmental attitude (EA) and subjective norms (SN) were not significantly related. The moderating role of future orientation (FO) between PSA, EA, WTP and OFC were examined and found to be significant except for EA. The result suggests that PSA and WTP are stronger and higher respectively when future orientation is high. The research provides a significant insight and better understanding of organic food actual consumption behavior and adds a new momentum to the growing literature. Discussions and implications of these findings are further discussed
Utilising a Real-Time Continuous Glucose Monitor as Part of a Low Glycaemic Index and Load Diet and Determining Its Effect on Improving Dietary Intake, Body Composition and Metabolic Parameters of Overweight and Obese Young Adults: A Randomised Controlled Trial
No full textA randomised controlled trial to measure the effects of integrating real-time continuous glucose monitor (rtCGM) into a low glycaemic index (GI) and glycaemic load (GL) dietary intervention on dietary intake, body composition and specific metabolic parameters was carried out. A total of 40 overweight young adults [(means ± SD) age: 26.4 ± 5.3 years, BMI: 29.4 ± 4.7 kg/m(2)] were randomised into an intervention and control groups for a period of eight weeks. Both groups received nutrition education on low GI and GL foods. The intervention group also received an rtCGM system to monitor their glucose levels 24 h a day. While controlling for physical activities and GI and GL nutrition knowledge, the results indicated that the rtCGM system further improved body weight, BMI, fat mass, fasting plasma glucose, HbA1c, total cholesterol, HDL cholesterol and LDL cholesterol in the intervention group (p < 0.05). This trial unveils the robustness of the rtCGM where non-diabetic overweight and obese young adults can benefit from this device and utilise it as a management tool for overweight and obesity and a primary prevention tool for type 2 diabetes, as it provides real-time and personalised information on physiological changes
Determinants of Organic Food Consumption in Narrowing the Green Gap
No full textUnderstanding and recognizing environmentally friendly behaviour are vital in achieving the Sustainability Development Goals and driving the economy for countries and producers of environmentally-friendly goods. Nevertheless, various stakeholders have expressed concern about the existing green gap, which greatly hinders their marketing efforts. This situation persists as mainstream research investigates people’s purchasing intentions, under the notion that the intention to perform a specific behaviour would generally predict the actual behaviour. The key argument of this study is that examining the actual consumption behaviour of organic foods is the ideal approach towards investigating purchase intention drivers as a proxy for consumption. In response to the green gap, the theory of planned behaviour is expanded by including the dimension of temporal orientation, i.e., a future orientation that has an influential but unrecognised effect on many human behaviours. In contrast to the prevalent operationalization of attitude, the term is defined as a product-specific attitude which is markedly dissimilar to the environmental attitude in its orientations. The Partial Least Squares Structural Equation Modeling technique was used to analyse the research model. The findings indicate that while product-specific attitudes and perceived availability positively affect organic food consumption, subjective norms do not. Additionally, the data implies that product specific attitudes are stronger when future orientation is high
Use of a Continuous Glucose Monitor to Determine the Glycaemic Index of Rice-Based Mixed Meals, Their Effect on a 24 h Glucose Profile and Its Influence on Overweight and Obese Young Adults’ Meal Preferences
No full textPostprandial hyperglycaemia is associated with an increased risk of type-2 diabetes. This study aims to determine the glycaemic index (GI) of three varieties of rice-based mixed meals and their effects on glycaemic variability (GV), 24 h mean glucose levels and target ranges, and rice variety preferences among overweight and obese young adults using real-time continuous glucose monitoring (rtCGM). In a randomised controlled crossover design, 14 participants (22.8 ± 4.6 years, 32.9 ± 5.8 kg/m2) were randomly assigned to receive 3 rice-based mixed meals containing 50 g of available carbohydrates (white rice meal = WRM; brown rice meal = BRM; and parboiled basmati rice meal = PBRM) and 50 g of a glucose reference drink on alternate days. GI, GV, 24 h mean glucose levels and target ranges were measured. Rice variety preferences were compared with those of baseline data and determined at the end of the study period. Results: The analysis found that PBRM was low in GI (45.35 ± 2.06), BRM medium in GI (56.44 ± 2.34), and WRM high in GI (83.03 ± 2.19). PBRM had a significantly (p 0.05) lower GV compared to WRM. Prior to observing their postprandial glucose levels generated by rtCGM, the participants preferred WRM (64.3%) over other meals, whereas this preference changed significantly (p < 0.05) at the endpoint (PBRM, 71.4%). PBRM reduced 24 h glucose level and GV of overweight and obese young adults. The rtCGM is proven to be reliable in measuring GI, while providing robust continuous glycaemic information. This may serve as an educational tool that motivates eating behaviour changes among overweight and obese young adults
Islamic FinTech: A bibliometric analysis using R
No full textUsing a literature review and bibliometric analysis, this research aims to systematically document the intellectual structure, volume, tendencies of knowledge development, the author, and source impact on the area of Islamic Fintech. Using Microsoft Excel and R Studio, information is compiled from Scopus databases and analysed. We gathered a searchable database of the 136 most relevant papers from 2017 to 2022. Preliminary data suggests that between 2018 and 2022, there has been an increase in the number of works written on Islamic Fintech. The bibliometric study using R identifies the subject's most influential journals, authors, and papers. Based on our result, future research needs to explore how technology and innovation are driving the growth of Islamic fintech
Assessing the Authors and Source Impact of Financial Literacy: A Bibliometric Analysis Using R
No full textUsing a bibliometric analysis, the goal of this review is to assess and systematically document the author and source's impact on the area of financial literacy. Using R Studio, information is compiled from Scopus databases and analysed. We gathered a searchable database of the 1737 most relevant papers from 2018 to 2022. Assuming the trend continues, the number of publications dedicated to financial literacy is expected to rise between 2018 and 2022. During that period, it was seen that the authors and journals contributed significant impact with a high reputation. The most influential authors and sources on the topic are identified by the bibliometric analysis done using R. It demonstrates the writers’ comprehensive knowledge and grasp of their speciality topic. Furthermore, it aids readers in determining the most relevant source for future study of the scientific and empirical article
Islamic Economy and Sustainability: A Bibliometric Analysis Using R
No full textUsing a literature review and bibliometric analysis, this research aims to analyse the relationship between the Islamic economy and sustainability. The study aimed to systematically document the intellectual structure, volume, tendencies of knowledge development, the author, and source impact. Using Microsoft Excel and R Studio, information is compiled from Scopus databases and analysed. We gathered a searchable database of the 76 most relevant papers from the last twenty-two years based on a vast amount of literature. Preliminary data suggests that between 2000 and 2022, there has been an increase in the number of works written on the Islamic economy. The bibliometric study using R identifies the subject’s most influential journals, authors, and papers. This study demonstrates that a new research topic can be derived by condensing the essential aspects of the Islamic economy and sustainability into a single concept, thereby opening up new research avenues in both the expansive field of the Islamic economy and the relatively new and hotly debated field of sustainability
The Malaysian Food Barometer 2 dataset: bridging socio-anthropology and nutrition through extended 24-h recall
No full textDesarrollo de una Interfaz Cerebro Computador con señales electroencefalográficas (EEG) que utilice el pensamiento del lenguaje para el control de una prótesis de miembro superior con aplicación a personas discapacitadas con amputaciones debidas al conflicto armado colombiano
No full textA Brain-Computer Interface (BCI) system is a powerful tool that decodes signals from the brain and translates it into codes which are understood by software to perform a specific task. Through a BCI, a disabled person can communicate with the world using a speller or move a hook prosthesis just thinking in a movement or a word. Three main stages compose the BCI systems, the capturing data stage, the processing and decoding of the signals, and the translation of the features into a pattern for a control system. Among the possibilities offered by the market for a prosthesis, e.g., of the upper limb, the most popular is the electromyographic, which uses the muscles to control it. Also, there are the neuro-prosthesis that capture the brain activity by implanted sensors in the cortex through a chirurgical procedure. Finally, in a new growing research line, this research considering the ones which use the electroencephalography (EEG) technique to capture data from the mental tasks of people.
In this research, an improvement of the technical efficiency of the capture, processing, and identification process of the silent speech EEG signals of vowels, syllables, and words are presented. First, for the signal acquisition stage, novel locations for the electrodes are proposed to maximize the capturing of the brain signals due to the language process in contrast with the 10-20 system. For the second and third stages, four novel methodologies were implemented, each one with its pros and cons. However, considering the propose of scaling in future the method of an online application, only the third algorithm excels in skills like high discriminability, reliability in the prediction of labels, robustness facing noisy data and variability inter-subject, and low computational resources consumption to reduce the processing time.
For the preprocessing stage, a novel solution for the cleaning of artifacts is proposed, which is based on an algorithm called “Singular Vector Decomposition Multivariate Empirical Mode Decomposition” (SVD-MEMD), that uses the singular vector decomposition to project data into a new dimensional space and separate useful data from noise. The output of this stage is a cleaned signal matrix with the same dimensions as input which its significant power remains in the range of [18 Hz to 50 Hz]. The algorithm exhibits outstanding yields in front of noisy, non-linear, and non-stationary data. Also, in compassion with the MEMD, the computational costs are low, and the processing time is quick.
The main changes between proposed the first three algorithms fall into the feature extraction stage. The fourth methodology changes a little the conception of signals' analysis creating images from the captured data that then be classified. The first proposal methodology uses the singular vector decomposition technique to extracts the discriminative features which after are discriminated by an Extremely randomizes tree (ET) achieving an overall accuracy for five classes classifier of 0.79 ±0.07 using the Neurophysiology database - (NDB). The second algorithm uses a combination of non-parametric modeling called Multivariate Adaptive Regression Splines (MARS) with Maximum Relevance Minimum Common Redundancy (mRMR) dimensional reduction technique to obtain the features vectors which after are labeled by an Adaboost classifier getting an average accuracy score of 0.84±0.03 and 0.77±0.04 for the ET using the KARA ONE database in a five-class classifier.
The third proposal combines the Phase Locking Value (PLV) for feature extraction with the Linear Discriminant Analysis (LDA) for dimensional reduction technique to increase the discriminability. The algorithm uses the ET to classify data. The implementation of the third proposal delivers a light, adaptative, and flexible methodology, which accomplishes an average accuracy of 0.86±0.04 in a five classes classifier with low processing time using the December Database (DDB). The fourth methodology aims to combine in a pseudo-image spatial, frequency, and time information which after are discriminated using a convolutional neural network. The best person yields an average accuracy of 0.51 ±0.045 in a five-class classifier using as input the DDB database.
Considering the outstanding results of the third proposal, it was decided to codec it in a portable device. The FPGA board PYNQ-Z2 hosts the third algorithm which after several tests the methodology delivers a prediction in only 380 ms ± 9.69 ms per loop using the DDB database that has a sampling rate of 128 Hz and fourteen electrodes. Also, several testing trials were sent to the FPGA simulating the capture process, achieving high accuracy results. The before allows us to conclude that it is possible to implement an algorithm which discriminates EEG silent speech signals in portable hardware that allows us to achieve high processing speeds (around milliseconds in the first processing tests) without losing accuracy.Una interfaz cerebro-computadora (BCI) es una herramienta poderosa que decodifica las señales del cerebro y las traduce en códigos que el software entiende para realizar una tarea específica. A través de un BCI, una persona discapacitada puede comunicarse con el mundo usando un deletreador o mover una prótesis de gancho simplemente pensando en un movimiento o una palabra. Tres etapas principales componen los sistemas BCI, la etapa de captura de datos, el procesamiento y decodificación de las señales y la traducción de las características en un patrón para un sistema de control. Entre las posibilidades que ofrece el mercado para una prótesis, por ejemplo, de la extremidad superior, la más popular es la electromiográfica, que utiliza los músculos para controlarla. Además, existen las neuroprótesis que capturan la actividad cerebral mediante sensores implantados en la corteza a través de un procedimiento quirúrgico. Finalmente, en una nueva línea de investigación en crecimiento, esta investigación considera las que utilizan la técnica de electroencefalografía (EEG) para capturar datos de las tareas mentales de las personas.
En esta investigación, se presenta una mejora de la eficiencia técnica del proceso de captura, procesamiento e identificación de las señales EEG de voz silenciosa de vocales, sílabas y palabras. Primero, para la etapa de adquisición de señal, se proponen nuevas ubicaciones para los electrodos para maximizar la captura de las señales cerebrales debido al proceso del lenguaje en contraste con el sistema 10-20. Para la segunda y tercera etapa, se implementaron cuatro nuevas metodologías, cada una con sus pros y sus contras. Sin embargo, considerando la propuesta de escalar en el futuro el método de una aplicación en línea, solo el tercer algoritmo sobresale en habilidades como alta discriminabilidad, confiabilidad en la predicción de etiquetas, robustez frente a datos ruidosos y variabilidad entre sujetos, y bajo consumo de recursos computacionales para Reducir el tiempo de procesamiento.
Para la etapa de preprocesamiento, se propone una solución novedosa para la limpieza de artefactos, que se basa en un algoritmo llamado "Descomposición de vectores singulares Descomposición de modo empírico multivariante" (SVD-MEMD), que utiliza la descomposición de vectores singulares para proyectar datos en un nuevo espacio dimensional y separar datos útiles del ruido. La salida de esta etapa es una matriz de señal limpia con las mismas dimensiones que la entrada, cuya potencia significativa permanece en el rango de [18 Hz a 50 Hz]. El algoritmo exhibe rendimientos sobresalientes frente a datos ruidosos, no lineales y no estacionarios. Además, en compasión con el MEMD, los costos computacionales son bajos y el tiempo de procesamiento es rápido.
Los principales cambios entre los tres primeros algoritmos propuestos caen en la etapa de extracción de características. La cuarta metodología cambia un poco la concepción del análisis de señales creando imágenes a partir de los datos capturados que luego se clasifican. La metodología de la primera propuesta utiliza la técnica de descomposición vectorial singular para extraer las características discriminatorias que luego son discriminadas por un árbol extremadamente aleatorio (ET) logrando una precisión general para el clasificador de cinco clases de 0.79 ± 0.07 utilizando la base de datos de Neurofisiología - (NDB). El segundo algoritmo utiliza una combinación de modelado no paramétrico llamado Splines de regresión adaptativa multivariante (MARS) con la técnica de reducción dimensional de Máxima relevancia Mínima redundancia común (mRMR) para obtener los vectores de características que luego son etiquetados por un clasificador Adaboost obteniendo un puntaje de precisión promedio de 0.84 ± 0.03 y 0.77 ± 0.04 para el ET usando la base de datos KARA ONE en un clasificador de cinco clases.
La tercera propuesta combina el valor de bloqueo de fase (PLV) para la extracción de características con el análisis discriminante lineal (LDA) para la técnica de reducción dimensional para aumentar la discriminabilidad. El algoritmo usa el ET para clasificar los datos. La implementación de la tercera propuesta ofrece una metodología ligera, adaptativa y flexible, que logra una precisión promedio de 0.86 ± 0.04 en un clasificador de cinco clases con bajo tiempo de procesamiento utilizando la base de datos de diciembre (DDB). La cuarta metodología tiene como objetivo combinar en una pseudoimagen información espacial, de frecuencia y de tiempo que luego se discrimina utilizando una red neuronal convolucional. La mejor persona produce una precisión promedio de 0.51 ± 0.045 en un clasificador de cinco clases utilizando como entrada la base de datos DDB.
Teniendo en cuenta los excelentes resultados de la tercera propuesta, se decidió codificarlo en un dispositivo portátil. La placa FPGA PYNQ-Z2 aloja el tercer algoritmo que, después de varias pruebas, la metodología entrega una predicción en solo 380 ms ± 9.69 ms por bucle utilizando la base de datos DDB que tiene una frecuencia de muestreo de 128 Hz y catorce electrodos. Además, se enviaron varias pruebas de prueba al FPGA simulando el proceso de captura, logrando resultados de alta precisión. Lo anterior nos permite concluir que es posible implementar un algoritmo que discrimina las señales de voz silenciosa de EEG en hardware portátil que nos permite alcanzar altas velocidades de procesamiento (alrededor de milisegundos en las primeras pruebas de procesamiento) sin perder precisiónColcienciasDoctorad
