Due to its high market demand and its exceptional nutritional value, pomegranate juice is an expensive product particularly vulnerable to adulteration. This fraud consists of diluting with cheaper juices, such as apple or grape juice, or masking poor-quality pomegranate juice with dark colored juices such as blueberry or black grape juice. To address this issue, a simple, rapid, and green methodology based on headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometrics was proposed, to detect adulteration using volatile organic compounds (VOCs) as markers. Spectra from 295 samples of pure and adulterated pomegranate juice were analyzed using a non-targeted approach and, in addition, 28 VOCs were identified. Orthogonal partial least squares discriminant analysis (OPLS-DA) models enabled the classification of samples as pure or adulterated pomegranate juice (100 % validation success rate) and discriminated among the three studied adulterants (blueberry, apple, and grape juices) with misclassifications occurring only at low levels of adulteration. Partial least squares (PLS) regression models were also developed to estimate the percentage of adulterants. High greenness (AGREE score of 0.73/1) and applicability (BAGI score of 72.5/100), together with encouraging analytical performance, highlight the proposed approach as a promising tool for pomegranate juice authentication.
Coastal lagoons are heterogeneous and highly dynamic ecosystems that are particularly sensitive to anthropogenic pressures. This study analyses how polychaete assemblages in the Mar Menor lagoon (Spain) reorganized their taxonomic and trophic structure across main habitats between 1980s and 2017–2018 periods. We tested whether species composition, assemblage structure and trophic-group structure differed among habitats, and habitat-specific temporal changes. Polychaeta data from soft and hard bottom habitats and their biocenoses were analysed using PERMANOVA, PCO and SIMPER analyses. Species richness decreased from 1980s (77 species) to 2017 (54 species) and 2018 (67 species), whereas mean density increased significantly. Dissimilarities among periods were very high, indicating strong species composition turnover. For its part, trophic diversity declined up to the most recent periods, with predator richness decreasing and detritivore richness increase. Spatially, soft bottom habitats were mainly dominated by detritivore species of Capitellidae and Spionidae families, whereas hard bottoms assemblages were characterized by the non-indigenous species Timarete caribous and Hydroides elegans. Temporal changes were strongly habitat-dependent: whereas Caulerpa prolifera dominated biocenosis, unvegetated muddy bottoms and infralittoral rocky bottoms showed marked shifts towards detritivore and filter-feeder dominance; shallower soft bottoms dominated by Cymodocea nodosa, sandy bottoms and docks exhibited comparatively limited temporal variation in taxonomic and trophic structure. These findings demonstrate that polychaete assemblages experienced great reorganization either in taxonomic and trophic structure, and highlight the need to integrate taxonomic composition, diversity and trophic structure when monitoring ecological changes in coastal lagoons.
Irrigated agriculture is increasingly challenged by climate change, water scarcity, and the need to improve irrigation efficiency, particularly in arid and semi-arid regions. In this context, remote sensing technologies have become widely adopted within precision agriculture frameworks to support crop water status assessment and irrigation management through optical, thermal, and hyperspectral observations acquired from satellite, unmanned aerial vehicle/remotely piloted aircraft system (UAV/RPAS), and proximal sensing platforms. In this review, precision agriculture is understood as the use of spatially and temporally explicit information to support site-specific crop and resource management. Within this framework, precision irrigation refers more specifically to the use of such information to optimise irrigation timing, amount, spatial distribution, or deficit-irrigation strategies according to crop water requirements and within-field variability. The rapid expansion of sensing technologies and analytical approaches, including machine learning and data-driven modelling, has generated a diverse and methodologically heterogeneous body of research focused on operational precision irrigation applications. This systematic review aims to synthesise technological and methodological patterns in the use of remote sensing for precision irrigation and crop water status assessment. This review integrates evidence across platforms, sensor types, analytical approaches, vegetation-index functional groups, irrigation systems, crop types, and climatic contexts within a precision irrigation-oriented framework. Studies published between 2015 and 2024 were retrieved from the Scopus and Web of Science databases using a structured search strategy focused on operational and technology-oriented agricultural applications. A total of 108 articles were analysed and categorised according to climate region, crop type, irrigation system, sensing platform, sensor type, vegetation index groups, and analytical methods. Multiple Correspondence Analysis (MCA) was used to explore category proximities and co-occurrence patterns within the reviewed corpus. The results identify recurrent combinations of sensing platforms, sensor types, analytical methods, and agronomic contexts. UAV/RPAS platforms (63% of studies) and multispectral sensors (69%) predominated, while regression-based approaches (51%) were more frequently reported than machine learning methods (34%). Exploratory analyses identified co-occurrence patterns involving UAV/RPAS platforms, multispectral sensors, and Structure/Cover indices, as well as satellite platforms and Water/Moisture indices. Thermal and hyperspectral sensors were less frequently represented than multispectral sensors, with thermal observations providing complementary information relevant to crop water status assessment. Overall, the findings highlight the methodological diversity of remote sensing applications within precision irrigated agriculture, while also identifying important limitations related to uncertainty assessment, methodological transferability, and operational integration into irrigation decision-support systems. The review provides a structured framework to support future research and the development of more scalable and operational precision irrigation strategies under increasing climate and water constraints.
We consider semi-coherent binary systems that are subject to simultaneous failures of its components. These are systems whose components can be either working or failed; the system can also be working or failed depending on the state of the components; and repairing a component cannot cause the system to fail. We consider that one or more components can fail simultaneously, allowing us to model external shocks and disasters. For this, we use the L & eacute;vy-frailty Marshall-Olkin (LFMO) multivariate distribution to model the failure times of the components. We aim to answer in which states of the system we should repair the components. This is a challenging question, as the number of repair policies grows super-exponentially in the number of components. To tackle this, we propose a simple family of repair policies, which we call r-out-of-n repair policies, where one repairs all failed components when the system fails or when there are r or more failed components. Our main contribution is that we derive exact and simple expressions for key performance-evaluation quantities of the system operating under our proposed repair policies. That is, we give explicit expressions for the mean time-to-failure of the system, mean time-to-repair, probability of system-failure before repair, and component-and system-repair rate. We also give expressions for the expected cost and long-term average cost, when there are components' and system repair cost. The only relevant parameters involved in the derived expressions are the structural signature of the system, and the Laplace exponent associated to the LFMO distribution.
Background: Muscle mass and strength influence bone and respiratory health in children, yet measuring muscle mass is costly and time-consuming. The body mass-to-waist ratio (BM/W) is a simple anthropometric parameter that may predict muscle mass. Objective: This study examined the relationships between BM/W, muscle and bone mass, handgrip strength (HGS), and pulmonary function in children. Methods and Procedures: In this cross-sectional study, 189 children aged 7 to 8 years were analyzed according to sex-specific BM/W quartiles. Differences across quartiles were tested by one-way ANOVA with Bonferroni post-hoc. Multiple regression analyses, stratified by sex and adjusted for height, were performed. Pulmonary function was assessed in a subsample of 133 children. Results: Children in the lowest BM/W quartile showed the lowest muscle (-17%) and bone mass (-25%), HGS (-29%), height-indexed HGS (-21%), FVC (-22%) and FEV1 (-20%) (all P < 0.001), accounting for 73.7% of dynapenia cases. In multivariate models, BM/W was independently associated with muscle mass in both sexes; the association with strength was most robust for height-indexed HGS (P < 0.001), while that with absolute HGS was attenuated after adjusting for height. Pulmonary function was explained by body size, height being the dominant correlate. Conclusion: BM/W is positively associated with muscle mass and strength in school-aged children; lowest-quartile children also show lower bone mass and pulmonary function. Low BM/W may indicate the need for early screening and targeted exercise interventions, especially with low handgrip strength, to prevent functional decline.
