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Artificial intelligence tools are rapidly growing in education, highlighting the imperative need for a thorough and critical evaluation of their performance. To this aim, this study tests the effectiveness of ChatGPT and Google Bard in answering a range of questions within the engineering and health sectors. True/false, multiple choice questions (MCQs), matching, short answer, essay, and calculation questions are among the question types investigated. Findings showed that ChatGPT 4 surpasses both ChatGPT 3.5 and Google Bard in terms of creative problem-solving and accuracy across various question types. The highest accuracy achieved by ChatGPT 4 was in true/false questions, reaching 97.5%, while its least accurate performance was noted in calculation questions with an accuracy of 82.5%. Prompting both ChatGPT and Google Bard to provide short responses apparently prevented them from hallucinating with unrealistic or nonsensical responses. The majority of the problems for which ChatGPT and Google Bard provided incorrect answers demonstrated a correct problem-solving approach; however, both AI models struggled to accurately perform simple calculations. In MCQs related to health sciences, ChatGPT seemed to have a challenge in discerning the correct answer among several plausible options. While all three tools managed the essay questions competently, avoiding any blatantly incorrect responses (unlike with other question types), some nuanced differences were noticed. ChatGPT 3.5 consistently adhered more closely to the essay prompts, providing straightforward and essential responses, while ChatGPT 4 demonstrated superiority over both models in terms of adaptability. ChatGPT4 fabricated references, creating nonexistent authors and research titles in response to prompts for sources. While utilizing AI in education holds a promise, even the latest and most advanced versions of ChatGPT and Google Bard were not able to accurately answer all questions. There remains a significant need for human cognitive skills and further advancements in AI capabilities.

Medical technology companies have focused on gait analysis and monitoring for several years due to their importance in the diagnosis of various movement abnormalities. Studying pressure distribution on the foot is very important for the detection of abnormalities, unwanted symptoms, and consequences. This paper aims to design a wearable, low-cost, and real-time gait cycle monitoring system, based on a Flexiforce sensor. In the proposed design, eight force sensors were attached to the insole to estimate the pressure distribution on the foot. Pressure distribution monitoring helps in the estimation of foot disorders and assists in the design of medical shoes for manipulating pressure into the right positions. Sensors were connected to an appropriate microcontroller for real-time monitoring. MATLAB was used to visualize and simulate the real-time plantar pressure variation through static and dynamic states. The obtained experimental results show that the system was stable in both static and dynamic measurements, which could be used to estimate the pressure distribution on the foot.

Nitrogen stress plays a critical role in corn yield reduction. Thermal remote sensing has many applications: as an assessment tool for urban heat island, as an ecological indicator of ecosystem development, and as a water-stress-detection tool. In this study, it was hypothesized that corn crops supplied with optimum or high rates of nitrogen would have lower surface temperatures compared to corn grown under nitrogen-stressed conditions. Two experiments were conducted in the greenhouse at the University of Guelph, Canada, from the period between 2015 and 2016, involving three rates of nitrogen (high, medium, and low rates) supplied to corn plants after seed emergence. Leaf and whorl temperatures were collected by using a high-resolution thermal camera, an infrared handheld point measurements gun, and a type T thermocouple, respectively. An approximate difference of 2 °C was observed in temperatures between plants receiving high and low rates of nitrogen. These results supported the hypothesis that nitrogen-stressed plants have higher temperatures compared to less stressed plants, at a 0.05 significance level. This study investigated the application of the exergy destruction principle through thermal remote sensing, to detect crop stress at early growth stages under greenhouse conditions, to increase the production and reduce the harmful environmental impact.