Monitoring of aquatic ecosystems has been historically accomplished by intensive campaigns of direct measurements (by probes and other boat instruments) and indirect extensive methods such as aero-photogrammetry and satellite detection. These measurements characterized the research in the last century, with significant but limited improvements within those technological boundaries. The newest advances in the field of smart devices and increased networking capabilities provided by emerging tools, such as the Internet of Things (IoT), offer increasing opportunities to provide accurate and precise measurements over larger areas. These perspectives also correspond to an increasing need to promptly respond to frequent catastrophic impacts produced by drilling stations and intense transportation activities of dangerous materials over ocean routes. The shape of coastal ecosystems continuously varies due to increasing anthropic activities and climatic changes, aside from touristic activities, industrial impacts, and conservation practices. Smart buoy networks (SBNs), autonomous underwater vehicles (AUVs), and multi-sensor microsystems (MSMs) such as smart cable water (SCW) are able to learn specific patterns of ecological conditions, along with electronic “noses”, permitting them to set innovative low-cost monitoring stations reacting in real time to the signals of marine environments by autonomously adapting their monitoring programs and eventually sending alarm messages to prompt human intervention. These opportunities, according to multimodal scenarios, are dramatically changing both the coastal monitoring operations and the investigations over large oceanic areas by yielding huge amounts of information and partially computing them in order to provide intelligent responses. However, the major effects of these tools on the management of marine environments are still to be realized, and they are likely to become evident in the next decade. In this review, we examined from an ecological perspective the most striking innovations applied by various research groups around the world and analyzed their advantages and limits to depict scenarios of monitoring activities made possible for the next decade.

The transition to sustainable mobility is progressing rapidly, with electric vehicles (EVs) playing a pivotal role in lowering greenhouse gas emissions and reducing the reliance on fossil fuels. At the core of this transformation are lithium-ion batteries (Li-ion), valued for their high energy density and long cycle life. However, the increasing demand for EVs necessitates continuous improvements in battery technology and the integration of advanced systems to ensure safe, efficient, and reliable performance. This review offers a clear and comprehensive summary of the latest innovations in Li-ion battery chemistry, battery pack design, and Battery Management System (BMS) functionalities. Unlike other reviews, this work emphasizes practical considerations, such as voltage, power, size, and weight for commercial vehicles. It also addresses integrated safety solutions, including disconnection systems and pre-charge circuits, which are vital for enhancing battery safety and lifespan. Additionally, it explores key BMS functions, like cell monitoring, balancing, and thermal management, all crucial for maximizing battery performance and ensuring safe operation. By consolidating current research and industry practices, this article provides essential information in a concise yet accessible format. It enables researchers to quickly gain a solid understanding of the field, distinguishing itself from reviews that focus on narrower aspects of battery technology. Its holistic approach delivers valuable insights for improving EV charging systems’ safety and performance, making it a highly useful resource for researchers and industry professionals alike.

A compact electronic circuit capable of performing Electrochemical Impedance Spectroscopy (EIS) on either single Lithium-ion cells or modules formed by the series of two cells is presented. The proposed device, named Double Cell Management Unit (DCMU), constitutes an important improvement to a recently proposed cell management unit, which combined EIS acquisition functions with a multichannel sensor interface compatible with thermistors, strain-gauges and moisture detectors. The proposed circuit maintains the versatility of the previous version and significantly extends the EIS frequency range, allowing vector impedance measurements from 0.1 Hz to about 15 kHz. The capability of handling both single Lithium-ion cells or series of two cells is obtained by adding a few external components to the previous version. This also allowed increasing the stimulation current to a maximum amplitude of 200 mA, resulting in improved resolution. Experiments consisting in EIS acquisition performed on batteries of different capacity at different temperatures and states of charge are described. Estimated impedance resolution (standard deviation) is 20 μΩ obtained at 1 kHz with a stimulation current of 100 mA amplitude.

A voltage reference is strictly required for sensor interfaces that need to perform nonratiometric data acquisition. In this work, a voltage reference capable of working with supply voltages down to 0.5 V is presented. The voltage reference was based on a classic CMOS bandgap core, properly modified to be compatible with low-threshold or zero-threshold MOSFETs. The advantages of the proposed circuit are illustrated with theoretical analysis and supported by numerical simulations. The core was combined with a recently proposed switched capacitor, inverter-like integrator implementing offset cancellation and low-frequency noise reduction techniques. Experimental results performed on a prototype designed and fabricated using a commercial 0.18 $\mathsf{\mu }$m CMOS process are presented. The prototype produces a reference voltage of 220 mV with a temperature sensitivity of 45 ppm/°C across a 10–50 °C temperature range. The proposed voltage reference can be used to source currents up to 100 $\mathsf{\mu }$A with a quiescent current consumption of only 630 nA.

In smart city framework, the water monitoring through an efficient, low-cost, low-power and IoT-oriented sensor technology is a crucial aspect to allow, with limited resources, the analysis of contaminants eventually affecting wastewater. In this sense, common interfering substances, as detergents, cannot be classified as dangerous contaminants and should be neglected in the classification. By adopting classical machine learning approaches having a finite set of possible responses, each alteration of the sensor baseline is always classified as one out of the predetermined substances. Consequently, we developed an anomaly detection system based on one-class classifiers, able to discriminate between a recognized set of substances and an interfering source. In this way, the proposed detection system is able to provide detailed information about the water status and distinguish between harmless detergents and dangerous contaminants.