The detection of glucose is crucial in the management of diabetes and other medical conditions but also crucial in a wide range of industries such as food and beverages. The development of glucose sensors in the past century has allowed diabetic patients to effectively manage their disease and has saved lives. First-generation glucose sensors have considerable limitations in sensitivity and selectivity which has spurred the development of more advanced approaches for both the medical and industrial sectors. The wide range of application areas has resulted in a range of materials and fabrication techniques to produce novel glucose sensors that have higher sensitivity and selectivity, lower cost, and are simpler to use. A major focus has been on the development of enzymatic electrochemical sensors, typically using glucose oxidase. However, non-enzymatic approaches using direct electrochemistry of glucose on noble metals are now a viable approach in glucose biosensor design. This review discusses the mechanisms of electrochemical glucose sensing with a focus on the different generations of enzymatic-based sensors, their recent advances, and provides an overview of the next generation of non-enzymatic sensors. Advancements in manufacturing techniques and materials are key in propelling the field of glucose sensing, however, significant limitations remain which are highlighted in this review and requires addressing to obtain a more stable, sensitive, selective, cost efficient, and real-time glucose sensor.

This paper reports on a low-cost turbidity sensor design for continuous on-line water quality monitoring applications. The measurement of turbidity by agricultural and environmental scientists is restricted by the current cost and functionality of available commercial instruments. Although there are a number of low-cost turbidity sensors exploited within domestic `white-goods', such as dishwashers, the lack of sensitivity, and power-usage of these devices make them unsuitable for fresh-water quality monitoring purposes. The recent introduction of wireless protocols and hardware, associated with the `Internet-of-Things' concept for machine-to-machine autonomous sensing and control, has enabled the large-scale networked intelligent water turbidity monitoring system that implements relatively low-cost sensors to be developed. The proposed sensor uses both transmitted light and orthogonal (90 degrees) scattered light detection principles, and is 2-3 orders of magnitude lower in cost compared to the existing commercial turbidity sensors. With an 850-nm infrared LED, and dual orthogonal photodetectors, the proposed design is capable of measuring turbidity within the range of 0-1000 Nephelometric Turbidity Unit (NTU) with improved accuracy and robustness as compared with the existing low cost turbidity sensors. The combination of orthogonal and transmitted light detection unit provides both 0-200 NTU high resolution and accuracy sensing and 0-1000 NTU lower resolution and accuracy sensing capability. Results from calibration experiment are presented, which proved that the proposed sensor design produced comparable turbidity readings as that of a commercial turbidity sensor.

This survey explores recent advances in addressing class imbalance issues for developing machine learning models in precision agriculture, with a focus on techniques used for plant disease detection, soil management, and crop classification. We examine the impact of class imbalance on agricultural data and evaluate various resampling methods, such as oversampling and undersampling, as well as algorithm-level approaches, to mitigate this challenge. The paper also highlights the importance of evaluation metrics, including F1-score, G-mean, and MCC, in assessing the performance of machine learning models under imbalanced conditions. Additionally, the review provides an in-depth analysis of emerging trends in the use of generative models, like GANs and VAEs, for data augmentation in agricultural applications. Despite the significant progress, challenges such as noisy data, incomplete datasets, and lack of publicly available datasets remain. This survey concludes with recommendations for future research directions, including the need for robust methods that can handle high-dimensional agricultural data effectively.

The search for materials with improved mechanical and biological properties is a major challenge in tissue engineering. This paper investigates, for the first time, the use of Polyethylene Terephthalate Glycol (PETG), a glycol-modified class of Polyethylene Terephthalate (PET), as a potential material for the fabrication of bone scaffolds. PETG scaffolds with a 0/90 lay-dawn pattern and different pore sizes (300, 350 and 450 µm) were produced using a filament-based extrusion additive manufacturing system and mechanically and biologically characterized. The performance of PETG scaffolds with 300 µm of pore size was compared with polycaprolactone (PCL). Results show that PETG scaffolds present significantly higher mechanical properties than PCL scaffolds, providing a biomechanical environment that promotes high cell attachment and proliferation.