The demand of glucose monitoring devices and even of updated guidelines for the management of diabetic patients is dramatically increasing due to the progressive rise in the prevalence of diabetes mellitus and the need to prevent its complications. Even though the introduction of the first glucose sensor occurred decades ago, important advances both from the technological and clinical point of view have contributed to a substantial improvement in quality healthcare. This review aims to bring together purely technological and clinical aspects of interest in the field of glucose devices by proposing a roadmap in glucose monitoring and management of patients with diabetes. Also, it prospects other biological fluids to be examined as further options in diabetes care, and suggests, throughout the technology innovation process, future directions to improve the follow-up, treatment, and clinical outcomes of patients.

Since the 1970s, a great deal of attention has been paid to the development of semiconductor-based biosensors because of the numerous advantages they offer, including high sensitivity, faster response time, miniaturization, and low-cost manufacturing for quick biospecific analysis with reusable features. Commercial biosensors have become highly desirable in the fields of medicine, food, and environmental monitoring as well as military applications, whereas increasing concerns about food safety and health issues have resulted in the introduction of novel legislative standards for these sensors. Numerous devices have been developed for monitoring biological processes such as nucleic acid hybridization, protein–protein interaction, antigen–antibody bonds, and substrate–enzyme reactions, just to name a few. Since the 1980s, scientific interest moved to the development of semiconductor-based devices, which also include integrated front-end electronics, such as the extended-gate field-effect transistor (EGFET) biosensor, one of the first miniaturized chemical sensors. This work is intended to be a review of the state of the art focused on the development of biosensors and chemosensors based on extended-gate field-effect transistor within the field of bioanalytical applications, which will highlight the most recent research reported in the literature. Moreover, a comparison among the diverse EGFET devices will be presented, giving particular attention to the materials and technologies.

An ultrasonic sensor for in-air applications has been fabricated using ferroelectric polymer technology. The proximity sensor, designed to be incorporated into a multiple sensing robotic gripper, consists of two unimodal transducers, a transmitter, and a receiver and is obtained by curving a strip of 40- mu m-thick polyvinylidene fluoride. The transducers have a resonance frequency of 63.5 kHz, a quality factor Q approximately equal to 12 and a transverse piezoelectric coupling coefficient k/sub 31/ of 0.29. The maximum detectable distance is 300 mm, with an axial resolution of about 3 mm. Furthermore transducers resonating at 380 kHz, which operate at a maximum distance of 100 mm with a resolution of 2 mm can be fabricated by using the same technique. Features and performance of both transmitter and receiver are discussed, together with the echo acquisition and the preprocessing electronic unit.<>

This paper deals with the problem of stabilizing a bilinear system via linear state-feedback control. The proposed procedures enable us to compute a static state-feedback controller such that the zero-equilibrium point of the closed-loop system is asymptotically stable; moreover, it ensure that an assigned polytopic region is enclosed into the domain of attraction of the equilibrium point. The controller design requires the solution of a convex optimization problem involving linear matrix inequalities. The applicability of the technique is illustrated through an example, dealing with the design of a controller for a Cuk dc-dc converter.

The evolution of electronic systems has significantly improved the monitoring of electrical consumption, ensuring precise measurement of energy assets. This paper proposes a three-step approach to improving the energy system management of an industrial plant. The first phase involves the identification of hot surfaces in electrical panels using non-destructive instrumentation for thermographic measurements. The second phase develops an electronic system consisting of current sensors and a temperature control system, a control unit made with Raspberry Pi integrated with Arduino, and a data transmission system using a 4G LTE Internet device capable of measuring the electrical current used by a machine in the plant. The electronic system measures current absorption (CA) by the lines of the electrical switchboards. The final phase focuses on the implementation of deep learning method based on the integration of an LSTM model for the forecasting of hourly CA and a U-Net model for supporting the classification and detection of anomalies identified during thermographic assessment. The results demonstrated substantial improvements in the accuracy of power consumption classification and the potential to optimise power distribution and reduce consumption accordingly. This classification results in reduced energy consumption and improved process quality.