Staff member

Marco, Santiago

JTD

Fugitive odour emissions from wastewater treatment plants (WWTPs) present ongoing analytical and environmental challenges. Drone-mounted Instrumental Odour Monitoring Systems (IOMS) enable real-time, spatially resolved chemical sensing; however, large sensor arrays increase calibration complexity and cost. To address this, IOMS optimization is formulated as a machine-learning feature-selection problem. A two-stage selection strategy is introduced, combining Sequential Forward Selection (SFS) and Interval Partial Least Squares (iPLS) regression to identify minimal, information-rich sensor subsets and optimal temporal measurement windows. The approach is evaluated using data from a hexacopter-borne IOMS equipped with 21 sensors operating over an active WWTP. Sensors are ranked according to their incremental contribution to odour-concentration prediction error reduction, followed by refinement of measurement intervals to capture relevant temporal dynamics. Validation on independent flight data demonstrates that a configuration comprising only three sensors with optimized time windows retains or improves predictive performance relative to the full array. For quantification, the Bland-Altman limits of agreement improve from +/- 7 to +/- 5.3 dBod, and the Pearson correlation increases from 0.80 to 0.89. For odour-detection task, a single sensor achieves an AUC of 0.95, slightly outperforming the full sensor set (AUC = 0.93). Bootstrap analysis reveals variability in feature selection, though consistent trends are observed: ammonia sensors dominate quantitative models, whereas low-temperature MOX sensors are preferred in detection. The findings demonstrate the effectiveness of feature-selection strategies in simplifying IOMS hardware while preserving chemometric performance.

JTD

© BME-PT. Ultrasound micromolding (USM) preparation of hybrid scaffolds based on polylactide (PLA) and hydroxyapatite (HAp) particles has been evaluated. PLA was stable under the applied ultrasound source since a minimum degradation was detected. Porous materials were achieved using polyethylene glycol (PEG) and NaCl salts to the initial PLA and the subsequent leaching of the micromolded specimens. To avoid cavitation and decomposition problems during micromolding, it was necessary to use HAp free of typical synthesis impurities like carbonate and nitrate compounds. Compact PLA/HAp pieces allowed a maximum HAp load of 60 wt%, while porous specimens could be obtained with a maximum load of 38 wt%. Physical characterization of new scaffolds was performed by X-ray diffraction, spectroscopic and calorimetric techniques, stress-strain tests and contact angle measurements. Results indicated that a degree of porosity of 35% and relatively good mechanical properties could be achieved (i.e., 580 MPa, 4%, and 15.6 MPa for the Young modulus, elongation at break, and tensile strength, respectively). Scaffolds showed the positive effect of HAp and porosity on cell proliferation; this latter was 40% higher than that detected for non-porous PLA specimens.

JTD Keywords: apatite, conformation, fabrication, hydroxyapatite, micropieces, polymers, porous scaffolds, proliferation, tissue, ultrasound micromolding, vibration, Composite scaffolds, Hydroxyapatite, Micropieces, Porous scaffolds, Processing technologies, Ultrasound micromolding