The objective of this article is to investigate the application of the Isoplot method to evaluate a continuous measurement system. What is investigated here is the impact that the number of pieces used in the method can have, and to review the application of the Analysis of Variance (ANOVA) method to estimate the variances of the pieces and of the measurement instead of to use range estimates. The probability of accepting a measurement system is estimated to compare an original characteristic that says if it is acceptable or not. Empirical rules reported in the available literature are compared. The situation in which the variation of parts is not normal is valued and it refers to other sample plans of analysis of measurement systems.
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.
Las Opuntia spp. son un recurso fitogenético Mexicano de gran valor nutritivo y alto contenido de betalaínas, compuestos conocidos por sus propiedades antioxidantes. Este estudio evaluó las características fisicoquímicas, el contenido de betalaínas y su capacidad antioxidante (CA), así como el perfil sensorial de frutos de O. robusta y O. ficusindica. Esta última presentó mayor acidez y contenido de sólidos solubles (F= 769,2; P= 0,0001), (F= 360,4; P ≤0,0001), que O. robusta. En humedad y contenido de cenizas no hubo diferencias significativas entre ambas especies. La concentración de betalaínas fue superior en Opuntia robusta (F=529,1; P= ≤0,0001) betacianinas (0,114 mg/mL pulpa) y betaxantinas (0,073 mg/ mL de pulpa), en O. ficus-indica (0,023 mg/ mL de pulpa y 0,0198 mg/ mL de pulpa). Se encontraron diferencias significativas en la CA (F=545,9; P ≤0,0001), en O. ficus-indica hasta 195,38 µmol equivalente Trolox/ mL por el método Ácido2, 2´-azino-bis-(3-etilbenzotiazolina)6-sulfónico (ABTS) y 22% de inhibición de radicales libres por el método 2,2 difenil-1-pricrilhidrazilo (DPPH), para O. robusta 165,6 µmol equivalente Trolox/ mL y más del 36% de inhibición de radicales libres. Los resultados mostraron que la CA está directamente relacionada con la concentración de betacianinas y betaxantinas. Ambas variedades de Opuntia exhiben una tendencia a lo dulce y ácido, con aromas, sabores y resabios con notas frutales y vegetales. Estos resultados sugieren que estas especies pueden ser empleadas para la extracción de betalaínas debido a su gran potencial para utilizarse en la industria como fuente de pigmentos naturales con propiedades antioxidantes y agradables características sensoriales.
A controlled drilling method for specimen hole preparation is proposed to characterize the edge stretchability of high-strength low-alloy steel sheets by means of the hole expansion ratio (HER). This low-cost alternative employs a standard vertical drilling machine equipped with an end-mill and dedicated tooling for precise end-mill positioning under controlled drilling parameters. The controlled drilling method was statistically validated through comparison with specimens prepared by conventional drilling (with a drill-bit), waterjet cutting, and wire electrical discharge machining (WEDM). Hole expansion tests were performed on a universal deep-draw machine according to ISO 16630, and the results were evaluated through statistical analysis. Controlled drilling achieved a mean HER value of 80.3
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
