Quantifying Food Loss and Waste in Saudi Arabia

Using the FAO model calculations proposed by Gustavsson et al. (2013) and FAO (2014), food loss and waste (FLW) is measured in Saudi Arabia with a special focus on wheat, rice, dates, poultry, vegetables, fruits, fish, and meat. Results show that the overall FLW rate is 33.1%, where the food loss rate is 14.2%, and the food waste rate is 18.9%. Acceding to the disaggregated results, we find that FLW rates are distributed as follows: 29.7% for wheat, 33.6% for rice, 21.4%, for dates 29.1% for poultry, 39.5% for vegetables, 39.6% for fruits, 33% for fish, and 31.3% for meat. The Sustainable Development Goal (SDG 12.3) target is to reduce the rates of food loss and waste by 50% in 2030, and to help achieve that goal, we employed a nonlinear optimisation simulation model with the objective function of reducing FLW by 50% over the period 2020–2030. Based on the findings achieved, recommendations are made to cover the various aspects of the whole food supply chain (FSC) and to aim at more efficiency and higher levels of productivity. Our findings have significant implications by estimating the FLW baseline indicator and providing the different stakeholders of FSC with the optimal actions to do to reduce FLW rates.

A phenomenological constitutive model for one-dimensional shape memory alloys based on artificial neural network

Shape Memory Alloys (SMA) have become a material with great application prospects because of their unique characteristics and superior properties. A phenomenological constitutive model for SMA is constructed in the current paper. The proposed constitutive model is based on the phenomena observed in the experiment, and Artificial Neural Networks (ANN) are used to simulate part of the characteristics of SMA. The parameter identification method is also proposed, where Back-Propagation (BP) algorithm and the nonlinear optimisation algorithm are used at the same time. The numerical experiment has been carried out, which can well capture the constitutive relationship curve obtained from uniaxial tension and compression experiments of SMA, thus the model can be verified. The model can also describe the phase transformation characteristics of SMA well.

Measuring battery discharge characteristics for accurate UAV endurance estimation

ABSTRACTThe increasing use of unmanned aerial vehicles in areas such as rescue, mapping, and transportation have made it necessary to study more accurate techniques for calculating flight time estimates. Such calculations require knowing the battery discharge profile. Simplified flight time calculation methods provide data with uncertainties as they are based solely on manufacturer datasheet information. This study presents a setup to measure the battery discharge curve using a LabVIEW interface with a low-cost acquisition system. The acquired data passes through a nonlinear optimisation algorithm to find the battery coefficients, which enables the more precise estimation of its range and endurance. The great advantage of this model is that it makes it possible to predict how the battery will discharge at different rates using just one experimental curve. The methodology was applied to three different batteries and the model was validated with different discharge rates in a controlled environment, which resulted in endurance lower than 3.0% for most conditions and voltage estimation error lower than 3.0% in operational voltage. The work also presented a methodology for estimating cruise time based on the current used during each flight stage.

Configuration Optimisation of Laser Tracker Location on Verification Process

Machine tools are verified and compensated periodically to improve accuracy. The main aim of machine tool verification is to reduce the influence of quasi-static errors, especially geometric errors. As these errors show systematic behavior, their influence can be compensated. However, verification itself is influenced by random uncertainty sources that are usually not considered but affect the results. Within these uncertainty sources, laser tracker measurement noise is a random error that should not be ignored and can be reduced through adequate location of the equipment. This paper presents an algorithm able to analyse the influence of laser tracker location based on nonlinear optimisation, taking into consideration its specifications and machine tool characteristics. The developed algorithm uses the Monte Carlo method to provide a zone around the machine tool where the measurement system should be located in order to improve verification results. To achieve this aim, different parameters were defined, such as the number of tests carried out, and the number and distribution of points, and their influence on the error due to the laser tracker location analysed.