Purificación Galindo-Villardón

High-Andean wetlands regulate water supply, but their volcanic soils may release arsenic and trace metals. We quantified As, Cd, Cr, Hg, and Pb in roots, stems, and leaves of the six dominant species of the seven wetlands of the Guaranda canton of the Chimborazo Fauna Production Reserve, Ecuador. A stratified inventory identified the dominants: Plantago rigida, Azorella aretioides, Lachemilla orbiculata, Juncus balticus, Agrostis breviculmis, and Eryngium humile. Composite organ samples were analysed by atomic absorption spectrometry. Arsenic was present in every tissue (5.15 ± 1.18 mg kg⁻¹ dry weight), above the range typical of uncontaminated plants; Hg was low and uniform (0.66 ± 0.06 mg kg⁻¹), and Pb and Cr were mostly below reporting limits. Cadmium was translocated to leaves in four species but retained in the roots of L. orbiculata and A. aretioides. Principal component analysis, HJ-Biplot, and PERMANOVA showed that species, not organ, structured tissue composition (52.8% vs. 8.8% of variation). All concentrations were two to three orders of magnitude below hyperaccumulation thresholds, so none of the species is a viable phytoextractor. Lachemilla orbiculata and A. aretioides are candidates for phytostabilisation trials, and all six species can serve as As biomonitors. These data provide a baseline for As monitoring in protected Andean wetlands.

The Hotelling T² chart relies on Phase 1 parameters estimated from in-control data. When the calibration sample contains outlying observations or anomalous batches, the classical covariance is inflated, and this masking effect hides later mean shifts. We propose MFA-MCD, a robust T² chart for batch processes. It estimates the covariance of each batch with the minimum covariance determinant (MCD), weights each batch by the inverse of its first eigenvalue, as in multiple factor analysis (MFA), and obtains the reference center with a second MCD on the batch centers. The control limit is a heuristic F-type approximation, not an exact distributional result, based on the size of the MCD subset. With six of thirty Phase 1 batches contaminated and a one-standard-deviation shift in all variables, it detects 88.5% of the shifted batches, against 27.0% for the classical chart and 73.0% for the MCD alone; its advantage stays above 0.49 across nine configurations. With a clean Phase 1 and shifts that do not move all variables together, it detects between 5.3 and 5.8 points less than the classical chart. On the Tennessee Eastman process it detects all twenty faulty batches without false alarms; the classical chart detects two. The method targets mean shifts and is implemented in the R package robustT2AFM.

This paper introduces FTucker-3, a basis-preserving Tucker framework for three-way functional data in which each tensor cell is a function. Functional observations are represented in a common finite basis, organized as a fourth-order coefficient tensor, and reduced only along the three structural modes while preserving the selected functional coefficient mode. A preliminary theoretical characterization establishes an exact functional–coefficient error decomposition, identifiability of the structural loading subspaces under full-row-rank core unfoldings, and perturbation control for the conditional SVD updates. Numerical evaluation combined a five-scenario Monte Carlo study with 1500 fitted models, a controlled periodic experiment, a real NASA POWER application, a Tucker-4 benchmark, and a scalability analysis. In the controlled experiment, FTucker-3 explained 96.71% of coefficient variability and 97.14% of functional variability. For NASA POWER, leakage-free cross-validation selected ranks (4, 5, 1), yielding 94.67% coefficient-domain and 69.45% functional explained variability across 240 daily trajectories. Under functional and structural misspecification, dominant loading-subspace similarities remained above 0.999 at 5 dB. Tucker-4 compression reduced storage but also reduced functional reconstruction accuracy, whereas the full-rank fourth-mode case was numerically equivalent to FTucker-3. These results support FTucker-3 as an interpretable framework for three-way arrays of functions.

The canonical biplot and the gravity model of trade have developed in isolation, yet both rest on symmetry principles that suggest common analytical ground. This article maps the two literatures (PRISMA 2020; Web of Science and Scopus; 2004–2024; 16 canonical biplot and 2066 gravity model open-access documents) and proposes a symmetry-aware proof of concept for their integration through neutrosophic Z-numbers. Economic mass, geographical distance, and trade policy were represented as neutrosophic Z-numbers elicited against a documented rubric, weighted by an analytic hierarchy process with consistency ratio 0.0079, and aggregated with the weighted arithmetic and weighted geometric operators. The principal components of the biplot are treated as the projection space rather than as a fourth criterion since a methodological artefact and an economic determinant are not commensurable. The arithmetic operator lies marginally closer to the positive ideal (0.1689 against 0.1700), and a Monte Carlo experiment over 400,000 draws preserves that order in every draw, so it is robust but negligible. Against four alternative operators and two reduced frameworks, discarding the reliability layer understates the distance to the ideal by about 9%. The contribution is methodological feasibility; a pre-specified protocol for empirical validation is also provided.

Understanding tourist behavior requires analytical frameworks capable of capturing both symmetric relationships among motivational constructs and asymmetric causal effects on behavioral intentions. Conventional segmentation approaches, particularly those based on Structural Equation Modeling, primarily estimate directional relationships and provide limited insight into the multivariate interaction structures underlying tourist decision making. To address this limitation, this study proposes an integrated analytical framework that combines GH-Biplot, Co-Inertia Analysis (COIA), STATICO, and Neutrosophic Psychology to analyze tourist motivations under uncertainty. The proposed framework was applied to a sample of 400 tourists participating in poverty-reducing tourism research. Measurement models were validated using confirmatory factor analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM), while the proposed multivariate approach was employed to identify latent symmetric and asymmetric structures linking behavioral intentions, motivational constructs, and personal values. The results show that biospheric values exhibit the strongest association with intentions to participate in poverty-reducing tourism. More importantly, the proposed framework reveals multivariate relationships and behavioral patterns that remain hidden when conventional asymmetric causal models are applied independently. The incorporation of Neutrosophic Psychology further extends the analysis by explicitly representing indeterminacy in tourist motivations through truth, falsity, and indeterminacy components. This study contributes by introducing a novel analytical framework that integrates complementary multivariate techniques to improve tourism segmentation, enhance the interpretation of complex behavioral relationships, and support evidence-based decision making for sustainable tourism management under uncertainty. The proposed framework provides a transferable methodological approach that can be applied to other behavioral research contexts involving complex multivariate structures and uncertainty.

Sparse Tucker decomposition (STD) is a framework for tensor decomposition that extends the classical Tucker model with sparsity constraints, with the aim of achieving better representation and analysis of multidimensional data. STD has attracted growing interest from various scientific fields, such as neuroimaging, machine learning, telecommunications, statistical computing, and brain connectivity analysis, due to its flexibility and computational efficiency. The purpose of this study is to present a systematic and structured review of the scientific literature on STD, describing its main trends, applications, and the evolution of research over time. Methods: A systematic review was conducted in accordance with the PRISMA 2020 guidelines using the Scopus and Web of Science databases. The search retrieved 21 records, of which 13 were selected for detailed analysis according to the established inclusion criteria. To improve the evaluation process, the selected studies were assessed using the plithogenic neutrosophic hypersoft set framework, which allowed for the evaluation of scientific contributions in terms of degrees of truth, indeterminacy, and falsity. The evaluation covered aspects such as methodological rigor, citation impact, thematic relevance, journal quality, and international collaboration networks. Results: Research on STD spans 2013 to 2025—the year of the first indexed record through the search date—and shows a marked increase after 2021. Five main application domains were identified: computational optimization, neuroimaging and neuropsychology, telecommunications, statistical analysis, and studies on brain connectivity. The literature on STD is still relatively limited and geographically concentrated, especially in the United States and China, despite the strong interdisciplinary potential and the growing scientific relevance of STD. Conclusions: STD is symmetric by construction, treating every tensor mode and component equally and applying the same sparsity penalty throughout, yet the literature surrounding it is markedly uneven, being concentrated in a few countries and a small number of technical fields. The PNHS framework allowed us to characterize this mismatch while accounting for the uncertainty of each judgement, scoring productivity, impact, collaboration, and thematic relevance through separate degrees of truth, indeterminacy, and falsity. This study identifies STD as a promising interdisciplinary analytical framework and demonstrates the value of plithogenic neutrosophic hypersoft methodologies for evaluating the scientific literature under uncertainty and multidimensionality. The review emphasizes the need for greater methodological standardization, increased international collaboration, and more robust interdisciplinary dissemination to establish STD as a robust framework for the analysis of multidimensional data.

This study extends the existing Neutrosophic STATIS framework toward the dual analysis of relationships among financial variables observed over multiple periods. Financial uncertainty was operationalized as modeled indeterminacy derived from local temporal volatility and deviation from annual sectoral behavior. Each observation was represented through truth, indeterminacy, and falsity components and subsequently aggregated into a scalar neutrosophic score. Dual STATIS was then applied to annual matrices containing seven financial indicators for 25 Segment 1 Ecuadorian savings and credit cooperatives observed from 2016 to 2023. The resulting structure was compared with classical Dual STATIS applied to the original standardized indicators, and sensitivity analyses were conducted under alternative weighting specifications. The neutrosophic-score analysis revealed generally high temporal similarity among annual financial structures. ROA and ROE primarily defined the profitability dimension, LIQCOR and INTFIN characterized the second dimension, and DEP and SCC were principally represented by the third dimension. The first three dimensions explained 64.94% of the total inertia, compared with 64.23% in the classical analysis. The comparison showed that the neutrosophic transformation preserved the main profitability and institutional-activity relationships but modified the temporal similarity ordering, annual weight ranking, factorial configuration, and position of MORAMP. Sensitivity results indicated that the principal structural conclusions remained stable under moderate changes in the weighting parameters. The proposed approach provides a complementary exploratory tool for incorporating modeled indeterminacy into longitudinal financial-structure analysis and cooperative-sector supervision.

The Andes serve as natural laboratories where biomes shape faunal communities into mosaics of habitats prone to fragmentation, making protected areas such as the Chimborazo Reserve (RC) critical refuges in the face of climate change. Based on this premise, patterns of biotic specialization and inter-biome faunal connectivity were evaluated through the ordering and co-scaling of family-level taxa in factorial space. A geospatial design was employed, stratifying the area into 400 ha cells, which were distributed across the high-Andean tundra (T), paramo grassland (G), and Andean forest (F) biomes and selected via simple proportional random sampling. A validated database of 2511 occurrences was compiled for the classes reptilia, aves, mammalia, amphibia, and insecta, comprising a total of 48 families, integrating records from global platforms and field surveys. The resulting matrix of 102 sampling cells (across three biomes) with 48 taxonomic families was analyzed using principal component analysis (HJ-biplot), k-means clustering, and bidirectional biclustering. It was found that Cluster 3 reflected a marked ecotone effect and faunal connectivity between F and G, dominated by generalist families with high evolutionary success such as Thraupidae, Tyrannidae, and waterbirds. In contrast, Clusters 1 and 2 revealed structural discontinuities and high biogeographic specialization in response to abiotic stress in the tundra, isolating taxa adapted to extreme conditions (Tropiduridae, Cricetidae, and Thinocoridae). Biclustering confirmed that the heterogeneity of the terrain mitigates climatic severity at the forest edge, acting as a microclimatic refuge for specialized nectar-eaters (Trochilidae) associated with Chuquiraga jussieui. The zoological structure of the RC reflects a distinct and predictable faunal partition that is strongly influenced by the interaction between the altitudinal gradient and microtopography. The coexistence of zones of diffuse transitions that promote biotic connectivity alongside nuclei of maximum specialization and ecological isolation was confirmed, which is essential for planning conservation measures.

Climate change and glacial retreat in the tropical Andes—as evidenced by environmental changes in the Carihuairazo volcano area and human-induced alterations to the páramo—reveal complex environmental dynamics that require an integrated understanding. This study evaluates edaphic variation along a periglacial–agricultural spatial gradient, relating soil properties to sustainable management strategies. Using a methodological approach that includes multi-criteria spatial delineation, altitude-stratified sampling, and multivariate modeling via HJ-Biplot, the physical, chemical, and biological properties were analyzed, with a focus on basal microbial respiration. The data show that periglacial soils exhibit geochemical–mineral control and high basal respiration, potentially influenced by moisture peaks, despite their low organic matter content. In contrast, lowland andisols exhibit biological–structural control conditioned by organic matter accumulation, reflecting distinct conditions associated with agricultural management and land use. It is concluded that understanding these spatial edaphic patterns and their vulnerability to human intervention is essential for designing sustainable management and conservation frameworks that mitigate the impact of climate change on high-mountain ecosystems.

Multivariate analysis of multi-table databases presents significant challenges when underlying structures exhibit nonlinear and time-varying behavior, limiting the applicability of classical linear methods such as STATIS. This study introduces Neural STATIS, a novel methodological approach that integrates the representational capacity of neural networks with the structural logic of classical STATIS to capture nonlinear dependencies across multiple data tables. Longitudinal OECD data encompassing socioeconomic and sustainable development variables across multiple time intervals were used to evaluate the method’s feasibility and enable direct comparison with traditional STATIS. Neural STATIS successfully identified complex trajectories and subtle temporal patterns that classical linear analysis failed to detect, providing deeper visualization of dynamic relationships across tables. These findings demonstrate that Neural STATIS offers a more adaptive and comprehensive analytical framework than conventional linear methods, particularly in contexts characterized by ambiguity and nonlinearity. Grounded in Neutrosophic philosophy, which explicitly acknowledges uncertainty as an inherent component of complex phenomena, this approach broadens the scope of multi-table analysis and facilitates more informed decision-making in real-world settings where linearity assumptions are not sustainable.