Communicating under extreme noise conditions remains challenging in spite of higher-order noise-canceling microphones, throat microphones, and signal processing. Both natural and man-made background ambient noise can disturb the conveyance of information because of high volume and/or extreme frequencies.  Noise cancellation, which is used frequently in audio technology, has limits in noise reduction and does not guarantee clear vocal pickup in these severe situations.  A contact microphone that is attached directly to the medium of interest has the potential to pick up vocal signals without reduced noise.  In this study, an electrostatic transducer with an elastomer layer that is impedance-matched to the human body is used to pick up speech sounds through constant contact on the chin and cheek.  By attaching the wearable device directly to the skin, the medium of air is bypassed, and airborne noise is passively canceled.  Because of the acoustic impedance-matched layer, the sensor is more sensitive to low frequencies under 500 Hz, so frequency equalization was implemented to flatten the frequency response throughout the vocal range.  The perceptual evaluation of speech quality (PESQ) scores of the wearable device with equalization average around 2.5 on a scale from –0.5 to 4.5 in comparison to a dynamic mic without noise. Speech recordings were also collected in a noise field of 85 dB, and the performance was compared to a cardioid lapel mic, a cardioid dynamic mic, and an omnidirectional condenser mic.  The recordings revealed a significantly reduced presence of white noise. This study provides preliminary results that show potential in vocal applications for a wearable impedance-matched sensor.

Marine environmental monitoring has gained increasing attention due to growing concerns over climate change and the development of the Blue Economy, which recognizes the significance of oceans and seas as drivers of the economy. Over the past two decades, advanced Information and Communication Technologies (ICT) have been applied to the development of monitoring systems for the marine environment and its anthropogenic activities. In this context, the Internet of Things (IoT) is a technology that is increasingly demonstrating its role in this area. IoT offers data processing capabilities that enable intelligent control of objects and the agile development of new applications and businesses, which can be aligned with biodiversity conservation and economic development.

One key technology for implementing IoT is Wireless Sensor Networks (WSN), which consist of autonomous devices distributed throughout an area of interest to monitor physical or environmental parameters. However, the application of IoT in the marine environment is still far from being a reality, and the utilization of WSN in the marine environment remains limited. Challenges such as modeling, energy supply, range, bandwidth, among others, persist. In fact, the number of deployments of these technologies in the marine environment lags significantly behind their land-based counterparts. Moreover, the extensive and contextualized study of wireless communication technologies in the marine environment is still lacking.

Therefore, this paper presents a comprehensive study on different communication technologies (Bluetooth, ZigBee, WiFi, WiMax, LoRa, LoRaWAN, SigmaFox, GSM, 3G, 4G, etc.), considering not only their spatial coverage but also deployment and maintenance costs, energy consumption, stability, data throughput, and more. Special attention is given to the opportunities that wireless technologies offer for marine conservation and sustainable development of activities such as port operations, aquaculture, fishing, offshore renewable energy, autonomous vehicles for risk mitigation, and more.

Finally, this paper presents the results of implementing and testing some of the aforementioned technologies in real coastal locations in the Region of Murcia, evaluating their performance at various distances and data rates. The findings provide valuable insights for the future deployment of wireless communication technologies in the marine environment, fostering both environmental preservation and the sustainable advancement of marine-related activities.

The growth of commercial activities and the need for competitiveness in the global market have prompted ports worldwide to pursue optimization and cost reduction while mitigating adverse environmental effects. Among these effects, air pollution from emissions generated by maritime traffic and the dispersion of particles, as well as water pollution from spills, pose significant challenges. In response, the concept of Green Ports has emerged, placing sustainability at the core of port development and operations, encompassing social, economic, and environmental aspects. The aim is to achieve a balanced approach that fosters competitiveness, integration with the host city, and environmental stewardship. This paper focuses on addressing a real pollution problem faced by Castelló Port (APC), specifically atmospheric pollution resulting from the dispersion of suspended particles during the loading and unloading of solid bulk cargo on ships at the port's docks (CS05, CS09, CS06 and CS26).

The primary objective of this study is to anticipate episodes of atmospheric pollution related to cargo handling activities and assess the quantitative causality between these variables. We employ causality inference and predictive methods based on time series analysis to investigate the applicability and validity of these techniques in a real-world problem setting. Concretely, causality models such as CCM, CMI and PCMCI along with forecasting models such as ARIMA, SARIMA and LSTM are used. Data provided by the APC for the years 2019-2021 encompass both port operations (cargo handling) and air quality parameters collected through the air quality monitoring network (PM2.5, PM10, wind direction, wind speed, maximum wind speed, temperature, relative humidity, precipitation).

By analyzing the available data and applying predictive models, we aim to gain insights into the occurrence of atmospheric pollution episodes resulting from cargo handling activities. Additionally, we seek to quantify the causal relationships between these variables, providing valuable information for decision-making processes. Thereby, this study contributes to the understanding of the feasibility and effectiveness of predictive techniques in addressing real pollution challenges in port environments.

Results show that the studied cargo handling in the studied docks are particularly influential on the PM measurements. Concretely, bulk discharges from CS05 and CS09 stand out as the most responsible variables for the PM dynamics (PM2.5, particularly). In addition, regarding causality times, significant causal relationships appear for short (1h), intermediate (5h) and long (10h) times. Regarding forecasting results, whilst classic predictive models such as ARIMA and SARIMA have shown proper results, LSTM networks provide more accurate results, achieving an accuracy above 85% on the environmental parameters.

Surface-enhanced Raman spectroscopy (SERS) is a variant of Raman scattering and a very active field of research, used for the chemical analysis of compounds and the detection of chemical and biological molecules of environmental interest. This technique consists of an intensification of several orders about the magnitude of the Raman signal of a molecule obtained by using metal nanostructures (for instance, metal nanoparticles) or nanopatterned substrates. This intensification is due to various effects, the most important is thought to be the interaction between the electromagnetic wave associated with the laser used and the metal substrate (i.e. silver/copper/gold surfaces) on which the molecule is placed. When substrates are used, their characteristics are crucial for the reliability and sensitivity of experiments, as well as the ease of reproducibility of measurements. In the present work, we report on preliminary measurements to investigate the characteristics of two commercial SERS substrates, which have different nanostructures and patterns, properly designed to operate at an excitation wavelength of 785 nm. Aspirin C was used as a representative molecule to evaluate their SERS capability, thanks to its characteristic fingerprint. Aspirin C is commercially available in the form of effervescent tablets, with acetylsalicylic acid and ascorbic acid as active principles with mainly analgesic and anti-inflammatory properties. The results are discussed also considering future applications for the detection of analytes of environmental interest.

Carbon dioxide produced by human activities (use of fossil fuels, deforestation and livestock farming) is the main greenhouse gas causing global warming. In 2020 the concentration in the atmosphere exceeded the pre-industrial level by 48% (before 1750). Carbon capture and storage (CCS), in underground geological formations, represents a key technology to counter CO2 emissions. Monitoring techniques, essential to ensure that there are no leaks from the storage site, can be refined through the study of natural CO2 emissions due to volcanic activity: this is the main goal of the IPANEMA project which provide for the monitoring, through underwater acoustic techniques, of the volcanic activity in the Mediterranean Sea. Through the installation of two underwater acoustic stations, one in Panarea and one in the gulf of Catania, we want to investigate techniques for estimating the flux of CO2 emitted by natural sources, for locating the emission sources and, in general, for the monitoring of volcanic activity.

The purpose of the study was to evaluate physiological stress and fatigue based on cardiac activity in healthy subjects with help of textile sensors embedded in smart clothes. 18 practically healthy subjects aged 19-55 years participated in the study. The cardio-respiratory activity was collected with help of Hexoskin smart garments (Hexoskin Shirt, Carré Technologies Inc., Canada) in daily life (rest condition, physical and mental professional and leisure activity). Heart rate, respiratory rate and motion were monitored. From the heart rate, variety linear and nonlinear parameters of heart rate variability (HRV) were computed. In addition, subjects evaluated their level of stress with help of analogous visual scale. The data were processed with help of Machine Learning Algorithms (Random Forest, CatBoost, XGB, LGBM). All algorithms allowed predicting the level of both strain and fatigue (ranged from 1 to 10) with probability 83%. The Random Forest Classifier proved the best in assessing the level of both stress and fatigue.

Three distinct homogeneous multilayer self-standing thin films, composed of stacked reduced graphene oxide (rGO) planes, were produced by improved Hummer’s method. In order to investigate their structural, electrical, and optical properties, the samples were characterized by Raman spectroscopy, field emission scanning electron microscopy (FESEM), four-point probe measurements, and Fourier-transform infrared spectroscopy (FTIR). The Raman spectra of the samples indicate the presence of minor surface defects and a relatively low oxygen content of rGOs. The FESEM images obtained from the samples reveals a smooth sheet-like surface with few wrinkles. Additionally, the cross-sectional images provide confirmation of the presence of multi-stacked layer structures. Based on the resistance decreasing by about 0.35 to 0.65 percent per kelvin within the region of ambient temperature, the electrical resistance vs. temperature curves imply semiconductive behavior in the rGOs. The FTIR analysis of the samples conducted within the wavelength range of 2.5 to 25 um, demonstrates a significant absorption value exceeding 90%. This observation shows that the developed materials possess favorable characteristics making them an excellent absorber candidate as for sensing detectors in the infrared range. We systematically analyzed and confirmed that the structural as well as optical and electrical properties of our obtained rGOs, may be fine-tuned by adjusting the initial reactants concentration and annealing temperature.

The AQUASENSE project is a multi-site Innovative Training Network (ITN) that focuses on water and food quality monitoring by using Internet of Things (IoT) technologies. In this paper, we present the communication system that is suitable for supporting the pollution scenarios examined in the AQUASENSE project. The proposed system has been designed and developed in the SimuLTE/OMNeT++ simulation for simulating an LTE network infrastructure connecting the Wireless Sensors Network (WSN) with a remote server, where data are collected.

In this frame two network topologies are studied: Scenario A, a single-hop (one-tier) network, which represents a multi-cell network where multiple sensors are associated with different base stations, sending water measurements to the remote server through them, and Scenario B, a two-tier network, which is again a multi-cell network, but this time multiple sensors are associated to local aggregators, which first collect and aggregate the measurements and then send them to the remote server through the LTE base stations.

For these topologies, from the network perspective, delay and goodput parameters are studied as representative performance indices in two conditions: (i) periodic monitoring, where the data are transmitted to the server at larger intervals (every 1 or 2 seconds), and (ii) alarm monitoring, where the data are transmitted more often (every 0.5 or 1 second); and by varying the number of sensors to demonstrate the scalability of the different approaches. Besides these overall network performances, from the application perspective, to evaluate the efficiency of the system we implemented a monitoring of river pollution by simulating the collection of pH measurements from sensors and evaluated the performance in terms of reconstructing the pollution pattern at the server side in the two different network topologies (Scenario A and Scenario B).

This research presents a comparative analysis of the two important design methodologies involved in developing Microelectromechanical Systems (MEMS) vibrating ring gyroscopes, namely internal and external ring frame structures. Internal ring frames are constructed with the outside placement of support pillars connected with the elastic beams that are attached to the vibrating ring structure. The design importance of this particular setup effectively isolates the vibrating ring structure from any external mechanical vibrations, significantly improving the gyroscope's performance. The Internal ring structure provides exceptional precession and reliability, making this design an ideal candidate for harsh conditions, as they can sustain substantial amounts of unwanted and external vibrations without degrading the performance of the gyroscope. On the other hand, external ring frame designs include the placement of the support pillars within the vibrating ring structure. This particular design setup is quite convenient in terms of fabrication and provides higher gyroscopic sensitivity. However, this design may lead to coupling the vibrational modes and potentially compromise the performance of the gyroscope. This research discusses and compares the finding of static structural, modal, and harmonic analyses of the two distinguished design approaches for the MEMS vibrating ring gyroscopes.

A wide variety of materials have historically been considered when developing electrochemical sensors. Initial studies relied on mercury electrodes, which were soon replaced by different inert metals (mainly Au and Pt) and carbon-based materials (e.g., glassy carbon electrodes). However, the performance of such bare electrodes is usually limited, lacking the sensitivity and/or selectivity required for the analysis of complex real samples. In this direction, modification of electrode surfaces with different electrocatalysts was explored as a solution given their more efficient electron exchange and/or faster reaction rate. This lead to the popularization of chemically modified electrodes (CMEs), with carbon paste electrodes first, and screen printed electrodes later, as the most common choices.

Nowadays, 3D-printing is emerging as an alternative approach for the fabrication of customized electrochemical sensors, owing to their many unique advantages such as its low-cost (both of the material and equipment), tunability and easy prototyping. Concretely, electrodes are fabricated by fused deposition modelling from thermoplastics such as polylactic acid (PLA) or acrylonitrile-butadiene-styrene (ABS), commonly doped with different carbon-based materials to overcome the insulating nature of PLA and ABS. In this regard, herein we explore the preparation of bulk-modified conductive filaments through the incorporation of redox mediators/electrocatalysts for the manufacturing of 3D-printed voltammetric sensors. Developed electrodes were characterized electrochemically by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), and morphologically by scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX). Finally, their performance was benchmarked against commercial electrodes and applied for the voltammetric detection of drugs.