During the last few years, scientists have become increasingly concerned about air quality. Particularly in large cities and industrialised areas, air quality is affected by pollution from natural and anthropogenic sources and this has a significant impact on human health. Continuous monitoring of air quality is an important step in investigating the causes and reducing pollution. In this paper, we propose a new autonomous multi-rotor aerial platform that can be used to perform real-time monitoring of air quality in large cities. The air quality monitoring system is able to cover large areas, with high spatial resolution, even above average buildings, while being relatively low cost. We evaluate the proposed system in several locations throughout a metropolitan city, during different seasons and generate fine-grained heat-maps that display the level of pollution of specific areas based on different altitudes.

Air pollution represents a significant threat to human health and the environment, especially in densely populated metropolitan areas. Determining air pollution levels in urban areas is crucial for raising public awareness about air quality and potential health risks, empowering citizens to make informed decisions about their well-being, potentially leading to improved air quality and healthier communities in the long run. The project proposes CityAirQ, a reliable pollution tracking system, that uses air pollution parameters and environmental data to generate dynamic maps for metropolitan regions. CityAirQ includes the following components: energy-efficient and portable pollution tracking devices equipped with pollution and environmental sensors, a mobile application that displays real-time collected data, together with dynamic environmental maps and, lastly, a cloud-based data pipeline that ingests, processes and stores sensor data. Our system integrates an ultra-compact custom PCB that enables real-time tracking of a broader range of pollutants than any other mobile solution of comparable size, making it a uniquely efficient tool for urban air quality assessment. The system’s performance was assessed in the final phase through testing and data collection in order to validate functionality and reliability. CityAirQ promotes environmental sustainability by providing the tools and information needed to understand, monitor, and mitigate air pollution in urban areas, ultimately contributing to a healthier and more sustainable future.

Background and Objectives: Saliva is of utmost importance for maintaining oral health. Management of saliva flow rate deficiency recently includes salivary neuro-electrostimulation. The aim of this paper is to present a new model of salivary pacemaker—the MICROSAL device (MD), an intelligent, miniaturized, and implant-supported oral device used for salivary stimulation. Materials and Methods: This report presents the development, calibration, and first clinical tests which involved the MD. The novel features of this device are the pH sensor and the fact that it communicates with the patient’s smartphone, where oral wetness and pH are graphically exposed. Saliva samples were taken before and after the MD was used on a 68-year-old patient suffering from post-irradiation xerostomia, and albumin and total protein were analyzed. Results: The device uses up to 3 V and time intervals of 2 s seconds for stimulation. The total volume of all saliva samples collected during the clinical trial was almost seven times higher after the device was used. Albumin decreased from a maximum of 0.15 g/dL to 0.04 g/dL, and total proteins from 0.65 g/dL to 0.21 g/dL, after salivary stimulation. Conclusions: The MD increased saliva secretion of the patient, and we are confident it will be a good solution for future management of salivary gland hypofunction.

Cuffless blood pressure estimation using the finger-pressing method remains sensitive to improper finger centering and inconsistent contact force, which degrade the accuracy of the oscillometric envelope and PPG signal morphology. This paper details the development of a research prototype that integrates three force sensors and a photoplethysmograph (PPG) sensor to quantify the finger–device interaction. This system is intended as a pre-clinical research tool rather than a clinically validated medical device. We implement a weighted centroid algorithm for center of pressure (CoP) feedback to guide geometric centering, alongside a Hybrid Ridge Regression model to estimate the total contact force. The system was evaluated on a pre-clinical pilot cohort of 48 healthy participants (1274 recordings), comparing inflationary (ramp-up) and deflationary (ramp-down) interaction modalities. Force calibration achieved a mean absolute error (MAE) of 1.2 g, with hardware analysis confirming a limited zero-load baseline drift of −0.29% over 50 days. The best single-recording calibrated model achieved a mean absolute error (MAE) of 5.55 mmHg (systolic) and 5.20 mmHg (diastolic), with a mean error (ME) ± standard deviation (SD) of $+0.50\pm 7.25$ and $+1.19\pm 6.47$ mmHg, respectively, in this pilot cohort, demonstrating the feasibility of the three-point force-sensing design with CoP tracking.

Urban air-quality monitoring networks are often sparse, leaving coverage gaps where particulate matter (PM) concentrations cannot be directly observed. This paper extends the CityAirQ pollution tracking platform and its mobile air-quality device prototype by introducing an AI-based benchmark for two Bucharest station networks across three deployment-oriented tasks: multi-station temporal forecasting (Task A), leave-one-station-out same-day spatial estimation (Task B), and a preliminary mobile-site prediction pilot at an uncalibrated location (Task C). The benchmark compares machine-learning models, including ensemble tree methods, recurrent neural networks, and lightweight graph-inspired architectures, evaluated under a unified time-aware rolling protocol. In Task A, the proposed Advanced Stage 0–3 pipeline achieves the best overall MAE (7.12 $\mathsf{\mu }$g/m³), a 4.7% reduction relative to Random Forest (7.47 $\mathsf{\mu }$g/m³), while the Seasonal naïve (10.41 $\mathsf{\mu }$g/m³), Persistence (11.51 $\mathsf{\mu }$g/m³), neural, and graph-inspired references perform worse under recursive forecasting. In Task B, the neighbour-only Random Forest reaches a mean $R^2$ of 0.873 on the classic four-station network and a median $R^2$ of 0.734 on the ten-station city-scale extension. Task C is reported as an exploratory six-day prediction pilot, not as deployment-grade validation: no co-located EPA FRM/FEM or equivalent reference monitor was available at the mobile location ℓ. The historical-transfer Random Forest retained a sample-limited positive PM_2.5 association with the raw mobile readings ($r=0.432$, $n=6$), while a strict one-day-ahead online persistence predictor reduced PM_2.5 MAE from 40.58 to 20.00 $\mathsf{\mu }$g/m³ on the five forecastable mobile days. Ultimately, accurate PM monitoring empowers sustainable urban planning, helping to mitigate exposure risks and supporting long-term public health and environmental sustainability initiatives.