The rising global demand for vegetables has expanded their production through both conventional and non-conventional systems, reinforcing their contribution to food security while simultaneously intensifying environmental pressures. Jalapeño pepper (Capsicum annuum L.), a crop of high economic importance, represents a relevant case study for assessing these trade-offs. This study presents a comparative attributional Life Cycle Assessment (LCA) of conventional open-field cultivation and controlled-environment aeroponic production systems in México. A comparative life cycle assessment (LCA) was carried out in accordance with ISO 14040/44 guidelines, using the ReCiPe 2016 methodology at midpoint and end point level. The functional unit was defined as 1 kg of fresh jalapeño pepper. Inventory data for conventional cultivation were obtained from technical field reports, while aeroponic data were collected directly from measurements in an experimental aeroponic chamber. The system boundaries included upstream processes related to raw material and energy inputs. Emissions to soil, air, and water were estimated using emission factors reported by various authors and methodologies, including IPCC guidelines, EMEP, and GREET. Aeroponics exhibited greater environmental burdens in most indicators, with a carbon footprint more than ten times higher (6.11 vs. 0.56 kg CO2 eq./kg) and a fossil resource use fourteen times higher (1.90 vs. 0.14 kg oil eq./kg). However, the lower aeroponic impact compared to open-field farming in land use (7.23 × 10− 03 vs. 5.41 × 10− 02 m²⋅a/kg) and freshwater eutrophication (1.61 × 10− 04 vs. 2.45 × 10− 04 kg P eq./kg) suggest specific mitigation opportunities. Comparisons with other vegetable production systems show that aeroponics and greenhouses generally display elevated carbon footprints per kg, strongly influenced by energy dependence. In Mexico, this is exacerbated by an electricity matrix dominated by fossil fuels, which amplifies upstream burdens. A scenario-based uncertainty analysis confirmed that although absolute impacts varied with alternative electricity mixes, the structural dominance of electricity in the aeroponic system remained consistent across scenarios. These findings highlight that yield improvements, energy efficiency, and renewable integration are crucial to enhance environmental performance. This study demonstrates that the sustainability of advanced agricultural systems such as aeroponics critically depends on agronomic efficiency and the energy profile of the electricity grid. In contexts where fossil fuels dominate power generation, aeroponics may shift environmental impacts upstream, limiting its overall competitiveness. These findings provide key insights for designing resilient, scalable, and environmentally viable non-conventional agricultural systems on a global scale.
Parkinson’s disease (PD) is a progressive disorder that affects movement and speech, making early diagnosis important to improve patient care and quality of life. In this work, we developed and compared several machine learning models for detecting PD based on voice recordings. The speech samples used in the study came from two different public datasets and included both people with Parkinson’s and healthy individuals. From these recordings, we extracted 159 features capturing temporal, spectral, and cepstral information, such as MFCCs and Bark-scale energies. To reduce the number of features and enhance model interpretability, we applied two different selection methods: Random Forest feature importance and linear SVM with L1 regularization. This resulted in two smaller sets containing 52 and 40 features, respectively. Using these reduced sets, we trained and tested a range of classifiers including Random Forest, SVM (RBF), k-nearest neighbors, logistic regression, and a simple neural network. Our best results were obtained with an SVM using the SVM L1-selected features, reaching an accuracy and F1-score of 86
Trichoderma virens and plant growth-promoting bacteria (PGPB) are well-known agents that promote plant development and control pathogens. This study assessed the compatibility, biocontrol potential, and plant growth promotion of T. virens in combination with four PGPB strains (Pseudomonas fluorescens UM270, Rouxiella badensis SER3, Bacillus velezensis AF12, and Bacillus halotolerans AF23) against Fusarium brachygibbosum and Arabidopsis thaliana. The results showed that single inoculations significantly inhibited the growth of F. brachygibbosum by the 7th day of confrontation. However, co-inoculating T. virens with PGPB exhibited synergistic effects on the inhibition percentages for the consortia Tv + UM270 (48.94%), Tv + AF12 (67.04%), and Tv + SER3 (78.63%). Plant assays demonstrated that most microorganisms enhanced root development and plant height, with UM270 having the strongest beneficial effect. Expression analysis of T. virens effector genes (sm1, tvsep3, and tvhydii1) indicated early induction of tvhydii1 in the condition of Fb + AF12 at day 3, while sm1 was downregulated. No significant changes in the expression of these genes were detected during interaction with A. thaliana and PGPB. These findings demonstrate that T. virens-PGPB can simultaneously promote plant growth and suppress pathogens, with effector genes such as tvhydii1 contributing to these interactions, highlighting their potential for sustainable agriculture.
The experimental determination of Forming Limit Curves, standardized by ISO 12004-2 and ASTM E2218, remains the conventional framework for evaluating sheet metal formability, whereas the Small Punch Test, supported by ASTM E3205, has traditionally evolved as a miniaturized mechanical tensile characterization technique. The incorporation of Digital Image Correlation into both methodologies has significantly expanded the capabilities of full-field deformation analysis and experimental characterization. In this context, the present study examines the extent to which the Small Punch Test, particularly when combined with Digital Image Correlation, may be conceptually integrated into the framework of reduced-scale formability characterization. A structural bibliometric analysis of 129 documents published between 2004 and 2026 was conducted through journal analysis, author analysis, collaboration networks, and conceptual structure analysis. Three search equations were employed to capture the dimensions associated with Forming Limit Curves and Digital Image Correlation integration, geometrical scaling of formability tests, and the convergence between Small Punch Test and Digital Image Correlation methodologies. The results reveal a consolidated Forming Limit Curve research domain strongly centered on the Nakajima test and Digital Image Correlation based strain measurement techniques, whereas the Small Punch Test remains structurally separated from the dominant scientific core. Nevertheless, conceptual analyses indicate thematic convergence between experimental miniaturization, geometrical scaling, and optical full-field characterization approaches. These findings suggest that the integration between Small Punch Test and Digital Image Correlation constitutes a promising but still insufficiently explored research direction within the broader framework of formability assessment.
This article establishes a non-parametric validation framework for photoplethysmography (PPG) signals intended for heart rate monitoring, formulated as a methodological proof-of-concept study. The study applies the two-sample Kolmogorov–Smirnov test as a robust and versatile method for comparing the distributions of PPG signals. By integrating the two-sample Kolmogorov–Smirnov test into the validation process of cardiac pulse measurement devices, the work demonstrates its effectiveness in enhancing the accuracy and reliability of biomedical signal analysis. Results show that, when comparing signals against calibrated reference devices and visualizing cumulative distribution functions, the two-sample Kolmogorov–Smirnov test is capable of detecting subtle differences in signal behavior. This innovative use of the two-sample Kolmogorov–Smirnov test provides valuable insights for the design and validation of biomedical signal processing systems and contributes to the advancement of non-invasive health monitoring technologies. The proposed framework is evaluated under controlled experimental conditions using data acquired from a single subject. Consequently, the results should be interpreted as a methodological validation of the proposed statistical approach rather than as a definitive clinical validation, and are intended to demonstrate feasibility and analytical relevance rather than population-level generalizability.
