Disabilities are a global issue due to the decrease in life quality and mobility of patients, especially people suffering from hand disabilities. This paper presents a review of active hand exoskeleton technologies, over the past decade, for rehabilitation, assistance, augmentation, and haptic devices. Hand exoskeletons are still an active research field due to challenges that engineers face and are trying to solve. Each hand exoskeleton has certain requirements to fulfil to achieve their aims. These requirements have been extracted and categorized into two sections: general and specific, to give a common platform for developing future devices. Since this is still a developing area, the requirements are also shaped according to the advances in the field. Technical challenges, such as size requirements, weight, ergonomics, rehabilitation, actuators, and sensors are all due to the complex anatomy and biomechanics of the hand. The hand is one of the most complex structures in the human body; therefore, to understand certain design approaches, the anatomy and biomechanics of the hand are addressed in this paper. The control of these devices is also an arising challenge due to the implementation of intelligent systems and new rehabilitation techniques. This includes intention detection techniques (electroencephalography (EEG), electromyography (EMG), admittance) and estimating applied assistance. Therefore, this paper summarizes the technology in a systematic approach and reviews the state of the art of active hand exoskeletons with a focus on rehabilitation and assistive devices.

This research combines a set of optimization algorithms together with methods of finding and using the expected values of forecasted weather variables to create an optimal velocity profile for a solar electric vehicle to minimize energy usage while traveling the furthest distance during the Sasol Solar Challenge 2018 as such work has not been formalized yet. The probabilistic characteristics of the solar irradiance are described by various Probability Mass Functions and applied to the forecasted weather data to increase the probability of better accuracy. These algorithms and Probability Mass Functions are implemented and tested in an international event on South African national roads across a vast distance of 2396 km under varying weather conditions and challenging route topography. Two algorithms are discussed: the first for high-resolution single day optimization of stored energy (by controlling the vehicle velocity) and the second, for multiple-day lower resolution distance optimization (by controlling the amount of additional road to be traveled each day). The results are meaningful in predicting the energy required by a solar electric vehicle traveling along a route in South Africa and the importance of the optimal velocity requirements to attain the best results is shown to be essential. The authors of this paper believe that implementing this work on the Sun Chaser III solar car, which competed in the Sasol Solar Challenge 2018, owe its position of first place among local teams, and fourth place internationally, to the accuracy and robustness of the work.

This research investigated an alternate method for establishing the complex coefficients used in an electric vehicle’s mathematical energy consumption model. While other methods for creating electric vehicle energy models exist, it would be beneficial to have a rapid and inexpensive technique that remains accurate. Producing a mathematical energy model for such a vehicle has the challenge of determining its aerodynamic drag and rolling resistance coefficients. Currently and most often, expensive and tedious (time-consuming) methods are used to find these coefficients. Computational fluid dynamics (CFD), wind tunnel testing, and extensive mathematics make this objective challenging. For this work, a solar-powered electric vehicle provided the source data to derive its coefficients cost-effectively and efficiently. Data were collected during a road test of the solar electric vehicle from South Africa to Namibia stretching over 2000 km, in which all required energy variables were recorded. The collected data were used in an optimisation routine to establish the two coefficients by minimising the actual and modelled energy consumption error and controlling the driving speed. The outcome of the optimisation routine produced accurate coefficients with a final error value of less than 5% when applied to a validation data set not used during optimisation. With minor modifications, this method may be integrated into any electric vehicle computer system to autonomously identify its two hard-to-find coefficients while driving, which can be used to provide an accurate and realistic driving range estimation to the driver.

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

An intelligent, computer vision–assisted cassava peeling system was designed, developed, and evaluated to address the limitations of manual and conventional mechanical peeling, particularly inefficiency, flesh wastage, and inability to handle irregular tuber geometry. The system integrates a microcontroller-based control unit with a vision module for contour recognition and an automated peeling mechanism. Image acquisition and processing were performed using the OpenCV library on a Raspberry Pi platform, which guided the knife’s trajectory for adaptive peeling. Performance evaluation was conducted at rotational speeds ranging from 30 to 45 rpm, feeding one tuber at a time into the peeling chamber. Results revealed that peeling efficiency increased from 82.4% to a maximum of 94.7% at 37.5 rpm, after which it slightly declined. Flesh loss decreased from 6.2% to 3.0%, and tuber breakage was minimized to 2.1% at the same optimum speed. Throughput capacity increased linearly from 15 to 22.7 tubers per hour, while power consumption rose from 65 W to 90 W across the test range. The study established 37.5 rpm as the optimal operational speed, offering a balance between efficiency, energy use, and product quality. The developed vision-guided peeler demonstrates a viable, low-energy, and high-precision approach to cassava processing, capable of enhancing productivity, reducing waste, and supporting sustainable postharvest mechanization in small- and medium-scale agro-industries.