title: A quick, reliable solution for modelling cheese chilling process

authors: Hoang, Duy

subjects: Modelling

abstract: Optimisation of the refrigerating system for cheese processing requires an accurate prediction of chilling time, product temperature distributions and heat flow. Many existing CFD models can provide the best predictions by directly solving the three-dimensional heat transfer problem within the product and for air flow around the product; however, they are time-consuming and not suitable for routine use. A one-dimensional numerical solution proposed by Ghraizi, Chumak, Onistchenko, and Terziev (1996) has been used to provide the quick answer to food processing engineers with a good accuracy. In this method, the partial differential equation describing one-dimensional non-linear unsteady heat conduction inside the product has been solved by a finite difference technique. The method can take into account the temperature-dependence of thermal properties of foods and a general shape factor was used to reflect the product geometry. The model was applied to a single block of cheese, and agar. Predicted results are compared to experimentally-measured temperature profiles as well as to results generated by the Food Product Modeller, FPM, software.

text: A quick, reliable solution for modelling cheese chilling process Optimisation of the refrigerating system for cheese processing requires an accurate prediction of chilling time, product temperature distributions and heat flow. Many existing CFD models can provide the best predictions by directly solving the three-dimensional heat transfer problem within the product and for air flow around the product; however, they are time-consuming and not suitable for routine use. A one-dimensional numerical solution proposed by Ghraizi, Chumak, Onistchenko, and Terziev (1996) has been used to provide the quick answer to food processing engineers with a good accuracy. In this method, the partial differential equation describing one-dimensional non-linear unsteady heat conduction inside the product has been solved by a finite difference technique. The method can take into account the temperature-dependence of thermal properties of foods and a general shape factor was used to reflect the product geometry. The model was applied to a single block of cheese, and agar. Predicted results are compared to experimentally-measured temperature profiles as well as to results generated by the Food Product Modeller, FPM, software.

year: 2018

title: Enhancing the precision of the Self-Compassion Scale Short Form (SCS-SF) with Rasch methodology

authors: Adu, Peter

abstract: Objectives: Precise measurement of self-compassion is essential for informing well-being–related policies. Traditional assessment methods have led to inconsistencies in the factor structure of self-compassion scales. We used Rasch methodology to enhance measurement precision and assess the psychometric properties of the Self-Compassion Scale Short Form (SCS-SF), including its invariance across Ghana, Germany, India, and New Zealand. Method: We employed the Partial Credit Rasch model to analyse responses obtained from 1000 individuals randomly selected (i.e. 250 from each country) from a total convenience sample of 1822 recruited from the general populations of Germany, Ghana, India, and New Zealand. Results: The initial identification of local dependency among certain items led to a significant misfitting of the SCS-SF to the Rasch model (χ2 (108) = 260.26, p < 0.001). We addressed this issue by merging locally dependent items, using testlets. The solution with three testlets resulted in optimal fit of the SCS-SF to the Rasch model (χ2 (27) = 23.84, p = 0.64), showing evidence of unidimensionality, strong sample targeting (M = 0.20; SD = 0.72), and good reliability (Person Separation Index = 0.71), including invariance across sociodemographic factors. We then developed ordinal-to-interval conversion tables based on the Rasch model’s person estimates. The SCS-SF showed positive correlations with measures of compassion towards others, optimism, and positive affect, alongside negative associations with psychological distress and negative affect. Conclusions: The current study supports the reliability, as well as the structural, convergent, and external validity of the SCS-SF. By employing the ordinal-to-interval conversion tables published here, the precision of the measure is significantly enhanced, offering a robust tool for investigating self-compassion across different cultures.

text: Enhancing the precision of the Self-Compassion Scale Short Form (SCS-SF) with Rasch methodology Objectives: Precise measurement of self-compassion is essential for informing well-being–related policies. Traditional assessment methods have led to inconsistencies in the factor structure of self-compassion scales. We used Rasch methodology to enhance measurement precision and assess the psychometric properties of the Self-Compassion Scale Short Form (SCS-SF), including its invariance across Ghana, Germany, India, and New Zealand. Method: We employed the Partial Credit Rasch model to analyse responses obtained from 1000 individuals randomly selected (i.e. 250 from each country) from a total convenience sample of 1822 recruited from the general populations of Germany, Ghana, India, and New Zealand. Results: The initial identification of local dependency among certain items led to a significant misfitting of the SCS-SF to the Rasch model (χ2 (108) = 260.26, p < 0.001). We addressed this issue by merging locally dependent items, using testlets. The solution with three testlets resulted in optimal fit of the SCS-SF to the Rasch model (χ2 (27) = 23.84, p = 0.64), showing evidence of unidimensionality, strong sample targeting (M = 0.20; SD = 0.72), and good reliability (Person Separation Index = 0.71), including invariance across sociodemographic factors. We then developed ordinal-to-interval conversion tables based on the Rasch model’s person estimates. The SCS-SF showed positive correlations with measures of compassion towards others, optimism, and positive affect, alongside negative associations with psychological distress and negative affect. Conclusions: The current study supports the reliability, as well as the structural, convergent, and external validity of the SCS-SF. By employing the ordinal-to-interval conversion tables published here, the precision of the measure is significantly enhanced, offering a robust tool for investigating self-compassion across different cultures.

year: 2024

title: Mana Motuhake Ringa: The non-invasive neural interface based artificial hand

authors: Owen, Mahonri W.

subjects: Prosthetics

abstract: Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the same again. Independence can be lost and never regained. The loss of a limb can be an overpowering event that effects the mental, social, and physical well-being of a person. Although there has been many attempts to restore hand function through neural prosthetics there is little to no credible solutions to regain it. This thesis presents methods to restore hand function based on the human grasping system and addresses key challenges in the practical application of those methods. The contribution of this work lies in improving artificial hand function by mimicking the human grasping system. The human grasping system consists of the brain, the nervous system and the hand. The methods used investigate: biomechanical design principles for artificial hands, the limitations of machine learning in non-invasive neural interfaces (NI’s) and the development of an autonomous hand control framework for artificial hand control. 

The application of the Support Vector Machine (SVM) to a non-invasive neural interface is principally limited to binary applications in the real world. In order to overcome this limitation an autonomous approach is developed to achieve better response and accuracy from NI control frameworks. In binary applications the SVM performed well with a classification accuracy of around 80% accompanied by a response time of just under 5 seconds. When applied to multiple classifications the SVM decreased in accuracy and response dramatically. The accuracy of the autonomous hand control framework had a classification accuracy of 95% with a response time comparable to that of the SVM. This outcome has the potential to move non-invasive NI research into the real world and to restore hand function in an intuitive and meaningful way. A key learning of this research is that machine learning methods for non-invasive neural interfaces are not the best way to restore hand function for amputees. Hand function is best restored through the combining of machine learning and machine vision into a single NI based control framework. Additionally, this work is based in Mātauranga Māori with a western science approach. By so doing, amputees will be empowered, have increased independence and enjoy a better quality of life.

The scope of this work is limited to the consideration of hand function restoration for upper extremity amputees. In particular trans-radial amputees. Focus is given to non-invasive methods of control, therefore, investigations are limited to the electroencephalography (EEG) signal. Mimicking the human grasping system is a complex and difficult process, therefore, the attempts in this work to restore hand function only consider mechanisms and processes related directly to the motor control of the human hand.

text: Mana Motuhake Ringa: The non-invasive neural interface based artificial hand Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the same again. Independence can be lost and never regained. The loss of a limb can be an overpowering event that effects the mental, social, and physical well-being of a person. Although there has been many attempts to restore hand function through neural prosthetics there is little to no credible solutions to regain it. This thesis presents methods to restore hand function based on the human grasping system and addresses key challenges in the practical application of those methods. The contribution of this work lies in improving artificial hand function by mimicking the human grasping system. The human grasping system consists of the brain, the nervous system and the hand. The methods used investigate: biomechanical design principles for artificial hands, the limitations of machine learning in non-invasive neural interfaces (NI’s) and the development of an autonomous hand control framework for artificial hand control. 

The application of the Support Vector Machine (SVM) to a non-invasive neural interface is principally limited to binary applications in the real world. In order to overcome this limitation an autonomous approach is developed to achieve better response and accuracy from NI control frameworks. In binary applications the SVM performed well with a classification accuracy of around 80% accompanied by a response time of just under 5 seconds. When applied to multiple classifications the SVM decreased in accuracy and response dramatically. The accuracy of the autonomous hand control framework had a classification accuracy of 95% with a response time comparable to that of the SVM. This outcome has the potential to move non-invasive NI research into the real world and to restore hand function in an intuitive and meaningful way. A key learning of this research is that machine learning methods for non-invasive neural interfaces are not the best way to restore hand function for amputees. Hand function is best restored through the combining of machine learning and machine vision into a single NI based control framework. Additionally, this work is based in Mātauranga Māori with a western science approach. By so doing, amputees will be empowered, have increased independence and enjoy a better quality of life.

The scope of this work is limited to the consideration of hand function restoration for upper extremity amputees. In particular trans-radial amputees. Focus is given to non-invasive methods of control, therefore, investigations are limited to the electroencephalography (EEG) signal. Mimicking the human grasping system is a complex and difficult process, therefore, the attempts in this work to restore hand function only consider mechanisms and processes related directly to the motor control of the human hand.

year: 2019