Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications.

Air-coupled ultrasound (ACU) is emerging as a fully non-contact sensing modality in biomedical applications. ACU applications can be broadly classified into two main domains: (i) contactless monitoring of physiological parameters and (ii) assistive aids, robotic perception in unstructured real-world environments, enabling tracking and geometric reconstruction. Advances in electronic materials and sensor design have enhanced ultrasonic sensor characteristics (e.g., bandwidth, directivity, and intensity). In parallel, progress in front-end electronics and signal processing, including artificial intelligence (AI)-assisted analysis, has enhanced ACU performance under low signal-to-noise (SNR) conditions. This review focuses on low-frequency ACU systems, with emphasis on sensor technologies, electronic interfaces, and system architectures that enable non-contact biomedical and robotic applications.

A rapid method using FTIR-ATR spectroscopy combined with PCA and a classification model was applied to distinguish between natural, synthetic, and adulterated Bergamot essential oil (BEO). Synthetic BEOs are often composed of specific alcohols such as ethanol and dipropylene glycol (DPG), which are used to dilute synthetic metabolites like limonene, linalyl acetate, and linalool. Synthetic BEOs exhibited a distinct peak at 1340 cm-1, linked to CH bending of alcohols or methyl group deformation in artificial esters like linalyl acetate, a peak that is absent in natural BEOs. Additionally, an absorption band between 3600 and 3100 cm-1 indicated the presence of DPG and synthetic ethanol, a byproduct of synthetic linalyl acetate. These findings were validated by comparison with NMR spectroscopy for metabolite recognition. A logistic regression (LR) model using PCA was applied to 369 samples, achieving an overall accuracy of 0.976 ± 0.016 through five-fold cross-validation (CV).

Agricultural activities are particularly influenced by environmental conditions. The chemical variability of rainwater is often an overlooked factor in agricultural systems, despite its decisive role in irrigation processes and in the preservation of soil balance. Atmospheric deposition and environmental contaminants can rapidly change the characteristics of rainwater with potential repercussions on plant growth and production quality. To address this issue, the present work proposes an autonomous monitoring platform designed for the continuous and in-situ acquisition of the main parameters indicative of rainwater quality. The system is based on a compact, self-powered architecture that combines sensors for measuring pH (0÷14±0.2), electrical conductivity (< 20,000 μS/cm), temperature (–40÷135±0.2 °C) and ammonium ion concentration (1–18,000 mg/L), with measurements every 30 s and a configurable acquisition interval as needed. The system is powered by an integrated solar energy harvesting unit for energy storage, ensuring operation even in the absence of irradiation for up to 48 h. Thanks to the simplicity of construction, energy autonomy and modularity of the system, the solution lends itself to being used in any type of agricultural context to detect unsuitability in rainwater, contributing to a sustainable management of water resources.

In the evaluation of high-value edible oils, increasing attention is devoted to non-destructive techniques capable of supporting authenticity assessment and quality control. Variations in the thermal behaviour of oils under controlled excitation can provide indirect but informative indicators of compositional changes and adulteration. In this study, an infrared (IR) thermography-based approach combined with deep learning is proposed for the analysis of tomato seed oil (TSO) and sunflower oil (SO). Thermographic sequences acquired during repeatable heating–cooling cycles were processed as spatiotemporal thermal signals reflecting heat absorption and dissipation dynamics. A convolutional neural network–long short-term memory (CNN–LSTM) model was employed to classify TSO, SO, and adulterated oil samples. The proposed system achieved an overall classification accuracy of 95.1% and a macro F1score of 94.8% in three-class classification. The results confirm that thermographic fingerprints capture stability-related differences induced by oil composition and blending, highlighting the potential of IR thermography coupled with CNN-based modelling as a rapid, non-contact decision support tool for edible oil authenticity screening.

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions.

Accurate visualization of the plasma membrane is essential for assessing morphological changes induced by experimental perturbations. Scanning electron microscopy (SEM) enables high-resolution imaging of cell surface architecture, but the fixation, dehydration, and drying steps critically affect membrane preservation. Here, we present an improved SEM preparation workflow for two cultured cell models with different growth phenotypes, HepG2 and IM-9, and evaluate the effects of fixation time and drying strategy on plasma membrane preservation. Among the tested conditions, a shorter glutaraldehyde fixation time (15 min) combined with stepwise ethanol-to-HMDS substitution provided the best overall preservation of membrane continuity, cell shape, and surface regularity. Morphometric analysis supported the qualitative SEM observations, and HMDS-processed samples also showed better structural stability during storage under the tested conditions. This workflow provides a simple, reproducible, and cost-effective strategy for SEM-based analysis of cell surface morphology in cultured cell models.

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time-frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications.

Rainwater quality is a critical factor in agriculture, as it can affect both soil health and crop quality. Monitoring key physico-chemical parameters—such as pH, electrical conductivity (EC), temperature, and metal ions—is essential to identify potential contaminants that may have negative impacts. These changes can alter the plant's metabolism affecting aroma and influencing plant physiology and inter-plant communication. This work presents a self-powered, stand-alone rainwater monitoring system. The system integrates sensors for $\text{pH}(0-14, \pm 0.2)$, conductivity (up to $20,000 \mu \mathrm{S} / \text{cm}$), temperature (−40 to $135^{\circ} \mathrm{C}, \pm 0.2^{\circ} \mathrm{C}$), and ammonium $(1-18,000 \text{mg} / \mathrm{L})$, supporting accurate, autonomous monitoring of rainwater quality. By default, measurements are recorded every 30 s, with a fully configurable acquisition interval to accommodate diverse monitoring requirements. The system is built with lightweight materials and includes a solar-powered battery with an autonomy of up to $\text{4 8}$ hours. It is also equipped with a ventilation unit. Data are stored locally and can be extracted for off-line analysis. The proposed system enables early detection of unsuitable irrigation water, supporting better crop management and yield optimization.

The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces a hybrid framework for upper limb rehabilitation that combines finite element modelling (FEM), AI-based trend classification, and a custom-designed electronic system for real-time signal acquisition and wireless data transmission. A mechanical model, developed in COMSOL 6.2 Multiphysics, simulates the interaction between a robotic glove and a deformable latex sphere. The latex material is described using a two-parameter Mooney–Rivlin hyperelastic formulation to capture large nonlinear deformations under realistic contact conditions. The high-fidelity simulation data are used to validate the signal acquisition chain and to train a supervised AI algorithm capable of classifying rehabilitation progress—whether improving or worsening—based on biomechanical features. An integrated electronic prototype enables seamless data flow to a cloud-based monitoring platform, supporting real-time feedback and adaptability. The classification algorithm demonstrates robust performance across different test conditions, while the electronic system confirms its applicability in rehabilitation settings. The novelty of this paper lies in the closed-loop integration of FEM-based simulation, AI-driven analysis, and embedded electronics into a unified monitoring architecture. This intelligent and non-invasive approach provides a scalable tool for tracking motor recovery and enhancing therapy effectiveness through adaptive, feedback-driven interventions.

Bergamot essential oil (BEO) is a high-value product, often involving synthetic metabolites. Hereafter is presented a preliminary study for the development of a device which can detect and identify natural and synthetic BEO. Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy was used to identify the chemical fingerprint of the BEO which combined with principal component analysis (PCA), and Random Forest (RF) model can easily classify among BEO samples. Synthetic BEO often contains ethanol and 2-(2-hydroxypropoxy)-1- propanol (DPG) as diluents for artificial metabolites, absent in natural BEO. An adsorption peak at 1340 cm(-1), absent in natural samples, corresponds to C-H bending of alcohols (e.g. ethanol, DPG) or symmetric deformation of methyl (CH3) groups associated with artificial esters (e.g. linalyl acetate). Also, an absorption band between 3600-3100 cm(-1) confirmed the presence of DPG and synthetic ethanol. The classification model, trained on 260 samples using PCA, was validated through five -fold cross-validation (CV) and external testing. RF yielded an accuracy of 0.73 +/- 0.16, with a sensitivity and specificity of 0.96 +/- 0.02. Based on these results a concept based of non-dispersive infrared sensor (NDIR) has been developed for the development of a compact device including the classification model for the rapid detection of adulterated BEO.

The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring device that captures the incidence of specific absorption rate (SAR) and temperature variation (∆T) on the human body. The system transmits data to a cloud platform, where a feedforward neural network (FFNN) processes the received information. SAR and surface temperature values are detected in an indoor environment, monitoring stationary and moving subjects. The results effectively assess temperature distribution due to electromagnetic fields. The prototype detected temperature peaks and high SAR values when the subjects remained motionless. Predictive analysis confirms the need for workplaces with materials shielding external electromagnetic signals and attenuating internal sources. Moderate mobile phone use could lower SAR and temperature values.

In the evaluation processes of vegetable oils, special attention is paid to the distribution of fatty acids, given its relevance to organoleptic and functional properties. Changes in the amount of some specific molecules prove decisive both in verifying the authenticity of the product and in assessing its potential health benefits. In particular, volatile organic compounds (VOCs), which originate from the degradation of fatty acids, have proven to be effective tools for detecting possible fraud or alterations. These substances not only contribute to the aroma and taste profile of oil, but also act as a kind of chemical signature, capable of returning detailed information about its composition. The present study proposes an innovative method for the analysis of tomato seed oil (TSO), based on the use of a photoionization detector (PID) coupled with a thin layer of 5A zeolite, the latter used to promote the selective adsorption of specific compounds. The analysis is done by thermal desorption at $100^{\circ} \mathrm{C}$. The PID-zeolite system was successfully applied to selectively identify palmitic acid in a mixture of pentane and TSO, providing a reliable indication of the authenticity of the oil and the possible presence of adulteration.

Tomato seed oil (TSO) is an edible product characterized by a wide range of molecules, with beneficial effects on human health. Volatile organic compounds (VOC) formed from the degradation of fatty acids, are promising candidates for the characterization of vegetable oils. Hereafter, a sensor based on photoionization detector for palmitic acid is presented. The sensor exploits thermal emission profile analysis from a thin layer of zeolite 5 A. Emissive profiles were acquired through a Photoionization Detector (PID) at 100 degrees C. Specifically, the combination of use of zeolite with pore size of 5.1 & Aring; and an ultraviolet lamp of 10.9 eV allows selective adsorption and detection of palmitic acid in a solution of pentane and TSO. The PID-zeolite sensor was investigated using oils at different dilution and at different storage conditions (-20 degrees C, 4 degrees C and 25 degrees C). Results evidenced that pentane dilution plays a significant role in palmitic acid adsorption, with a maximum emissive profile at similar to 885 mu mol/L. Low temperature storage (-20 degrees C) of samples before analysis results in 1.5 times higher emission peak due to the formation of triple chain molecular arrangement of palmitic acid. Calibration evidenced a linear range from 0.45 mmol/L up to 1.8 mmol/L with a sensitivity of 34.65 ppm center dot mmol(-1)center dot L and an R-2 = 0.92. Real scenario analysis was performed a mixture of TSO with soybean oil (SO) at different storage stability. A significant emissive reduction in palmitic acid was observed in mixed oil, depending on its stability which allows for the evaluation of adulterated samples.

Hemodialysis is the primary treatment for patients with total loss of renal function and is performed through a vascular access called an arteriovenous fistula (AVF). AVF can be associated with several complications, such as stenosis, thrombosis, and aneurysms. While digital subtraction angiography (DSA) is the gold standard for AVF monitoring, its limited availability, invasiveness, and harmfulness has enabled Doppler ultrasound (DUS) as alternative technique. Herein, the development of a portable device based on a triboelectric sensor for AVF monitoring that exploits the concept of impedance cardiography (ICG) is presented. The triboelectric sensor consists of a thin polyvinylidene fluoride (PVDF) layer placed close to the AVF to evaluate the triboelectric ICG signal (T-ICG), thus avoiding the need for standard multiple-electrode equipment. Electrocardiographic (ECG) dry electrodes based on a composite of polydimethylsiloxane (PDMS) and multi-walled carbon nanotubes (MWCNTs) were fabricated. Concept validation was performed in a cohort of hemodialysis patients with and without AVF stenosis. The results demonstrated a linear dependence between blood flow and the maximum deviation (dZ/dtmax) of the T-ICG pattern. Patients with stenosis showed a statistically significant change in morphology at the B-point. The extent of stenosis was evaluated by analyzing the temporal slope of the ICG pattern in the B- and C-point regions, evidencing a linear trend for stenosis from 20 % up to 60 %. The proposed device represents an innovative approach compared with the gold clinical standards for the continuous monitoring of AVF in hemodialysis patients.

Volatile organic compounds (VOCs) include a wide range of molecules (e.g., carboxylic acids, terpenes, alcohols, etc.) characterized by a low boiling point. Most of them are released by plants in response to biotic and abiotic stresses as a defense system from pathogens and in promoting growth and crops (e.g., photosynthesis, nutrient metabolism). VOC analysis can be a useful tool for retrieving health status information, promoting sustainable agriculture. Hereafter, the development of a VOC sensor, for the monitoring of VOCs emitted by plants is presented. To improve the selectivity of commercial detector, a nanoporous adsorbing layer of zeolite layer was combined with commercial photoionization device (PID). VOC emission analysis was conducted on samples of propionic and succinic acids at different concentration using by a temperature-controlled desorption process. VOCs are detected and quantified by evaluating emission profiles considering an evaporation/diffusion model. Results showed that the proposed approach can improve the selective of a commercial PID taking advantage of short time analysis and low volumes liquid samples.

Upper limb rehabilitation is critical for neurological recovery. This paper introduces a sensor-based monitoring framework enhanced by finite element method (FEM) simulation and artificial intelligence (AI) integration for personalised rehabilitation. A COMSOL-based FEM model simulates hand-object interaction to guide the design and placement of sensors within a custom wearable glove. The embedded system acquires biomechanical data at $\mathbf{1 0 0 ~ H z}$, filters the signals via Kalman estimation, and transmits them wirelessly to an AI-based classification engine. A Random Forest model achieved an AUC of 0.94 and a correlation of $\mathbf{r}$ = 0.84 with clinical Fugl-Meyer scores from 12 subjects, showing that the system is clinically relevant. This integration of physics-informed modelling and wearable sensing provides a promising pathway for remote, adaptive neurorehabilitation.

Fatty acid composition represents one of the key parameters in assessing the organoleptic properties of vegetable oils. Variations in the concentration of specific molecules play a crucial role in identifying the authenticity of the oil and determining its potential health benefits. Among these compounds, volatile organic compounds (VOCs), resulting from the degradation of fatty acids have proven to be valuable indicators for detecting adulteration. VOCs not only influence the distinctive aroma and taste of the product, but also represent a real fingerprint, providing detailed information about its characteristics. This work focuses on an innovative approach to analyze tomato seed oil (TSO) using a photoionization detector (PID). The system exploits a thin layer of 5A zeolite for selective adsorption and subsequently thermal desorption analysis at 100°C. PID-zeolite sensor was validated on TSO for the selective detection of palmitic acid in a solution of pentane and TSO. Palmitic acid levels in TSO allow the assessment of oil authenticity and adulteration.

Monitoring machinery in wastewater treatment plants is crucial due to its impact on sensitive issues such as environmental health and quality of life. A designed and fabricated monitoring system ensures compliance with all operating and safety conditions. In the initial phase, the assessment of the electrical panel using non-destructive methodology was conducted. A thermographic survey, combined with an artificial intelligence algorithm, verified thermal dissipation and led to the creation of an current consumption database. Information derived from the measurement campaign assists in establishing a control system for machinery operation. Upon identifying the causes of malfunctions, data were sent in real-time to a monitoring platform consisting of current sensors and a control unit based on a Raspberry Pi and an Arduino board. Real-time transmission of data to the monitoring platform occurred via a 4G L TE internet stick. The collected data and results optimize consumption by managing the on/off switching of monitored machinery. Activation times can be set to control electrical overload values. The proposed system provides more accurate consumption and operating forecasts than existing systems. Finally, the system offers end-users suggestions on classifying thermal energy dissipated on the switchboard, distinguishing machinery or devices presenting overload issues. This classification translates into information on lower current consumption and enhanced process quality.

Single-photon avalanche diodes (SPADs) belong to a family of avalanche photodiodes (APDs) with single-photon detection capability that operate above the breakdown voltage (i.e., Geiger mode). Design and technology constraints, such as dark current, photon detection probability, and power dissipation, impose inherent device limitations on avalanche photodiodes. Moreover, after the detection of a photon, SPADs require dead time for avalanche quenching and recharge before they can detect another photon. The reduction in dead time results in higher efficiency for photon detection in high-frequency applications. In this work, an electronic interface, based on the pole-zero compensation technique for reducing dead time, was investigated. A nanosecond pulse generator was designed and fabricated to generate pulses of comparable voltage to an avalanche transistor. The quenching time constant (τq) is not affected by the compensation capacitance variation, while an increase of about 30% in the τq is related to the properties of the specific op-amp used in the design. Conversely, the recovery time was observed to be strongly influenced by the compensation capacitance. Reductions in the recovery time, from 927.3 ns down to 57.6 ns and 9.8 ns, were observed when varying the compensation capacitance in the range of 5–0.1 pF. The experimental results from an SPAD combined with an electronic interface based on an avalanche transistor are in strong accordance, providing similar output pulses to those of an illuminated SPAD.