Search Results (204)

Yucca decipiens is a native species from arid and semi-arid regions with emerging nutritional and biotechnological potential. This study evaluated its proximate composition, elemental profile determined by inductively coupled plasma mass spectrometry (ICP-MS), and growth performance under nursery conditions. Proximate analysis revealed a high dietary fiber content in leaves (58.93%) and higher carbohydrate levels in stems (28.83%). Free amino acid content was significantly higher in stems (2.75 g histidine equivalents kg⁻¹) than in leaves (1.76 g kg⁻¹). Multi-elemental profiling (63 elements) showed organ-specific accumulation patterns, with essential macro- and micronutrients predominantly concentrated in leaves, including potassium (28,334 ppm) and calcium (15,345 ppm), while iron was the most abundant trace element in stems (1253 ppm). Principal component analysis (PCA) revealed clear organ-specific mineral partitioning between leaves and stems, indicating differentiated physiological roles and potential selective biomass utilization. Growth assessment conducted over a two-year period demonstrated steady biomass accumulation and good adaptive performance under nursery conditions. Overall, the results highlight the emerging nutritional and agroindustrial relevance of Yucca decipiens for applications in semi-arid environments. Full article

This work proposes a design-oriented numerical modelling approach for predicting the seismic response of multi-storey Cross-Laminated Timber (CLT) buildings. The model is based on a phenomenological approach and is capable of accurately replicating the seismic behaviour of multi-storey CLT wall systems by means of a properly calibrated equivalent wall stiffness, taking into account both connections and panel deformability. An extensive set of multi-parametric linear analyses is performed to calibrate the wall equivalent stiffness by varying significant design parameters such as: CLT wall geometry, connection pattern, seismic mass and level of seismic intensity. An ad hoc iterative procedure is developed in order to calibrate the wall equivalent stiffness in terms of significant design parameters (e.g., principal elastic period, internal forces in the connection elements and inter-storey drifts). The aim of the procedure was to minimise the error between the results obtained with the proposed phenomenological model and those obtained with refined numerical models. The latter were designed to accurately reproduce the actual response of the CLT systems analysed. The results of the multi-parametric analyses are discussed and summarised in a design abacus that allows a direct implementation of the proposed phenomenological model and, therefore, a simple and efficient seismic analysis for CLT buildings. Full article

Uranium mining generates complex multi-element contamination that affects interconnected ecosystem components, posing long-term ecological and sanitary risks; this review places these impacts in a broad environmental context and aims to synthesize current knowledge on the distribution, migration, and accumulation of uranium and associated heavy metals in water, soil, and plants. A structured analysis of international peer-reviewed literature was conducted, focusing on documented pathways of metal release from tailings and waste dumps, geochemical controls on mobility, and biological uptake by vegetation. The reviewed studies consistently show that tailings and disturbed ore-bearing strata act as persistent sources of uranium and heavy metals (e.g., Cd, Pb, Cr, Ni, Zn, Mn, As), which migrate through infiltration, acid mine drainage, and atmospheric dispersion, leading to elevated concentrations in surface and groundwater and long-term accumulation in soils. Soils function as the principal sink controlling metal bioavailability, while vegetation reflects the bioavailable fraction and exhibits pronounced species-specific accumulation patterns. These processes establish an active “soil–water–plant” transfer chain that facilitates entry of contaminants into food webs. The synthesis indicates that combined uranium and heavy metal contamination represents a sustained ecological and public health concern in uranium-mining regions and underscores the need for integrated monitoring of soils, waters, and vegetation, along with quantitative risk assessment and scientifically grounded remediation strategies. Full article

Accurately tracing the geographical origin of Zanthoxylum schinifolium Sieb. et Zucc. is important for brand authentication, quality control, and food safety assurance. In this study, the stable isotope ratios (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) and the contents of 20 elements were analyzed in samples from three major production regions. Significant differences (p < 0.05) were observed in δ¹³C, δ²H, δ¹⁸O and most elemental profiles across origins. Chemometric methods—including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA)—were applied to classify samples by geographical origin. OPLS-DA identified key discriminators (VIP > 1) such as Ca, δ¹³C, Mg, δ²H, B, δ¹⁸O, Cr, Ni, Na, Pb, As, Co, Se, and Zn, achieving a classification accuracy of 96.8%. LDA based on the combined isotope and element datasets showed even higher performance, with an original discrimination rate of 98.4% and a cross-validated rate of 92.8%. The results demonstrate that integrating stable isotope and multi-element fingerprints with supervised classification models provides a reliable and effective approach for verifying the geographical origin of Zanthoxylum schinifolium, supporting its use in traceability systems and fair trade practices. Full article

A Cu-containing FeCrMnNiAl multi-principal element alloy was processed by laser-based and electron beam-based powder bed fusion (PBF-LB/M and PBF-EB/M) to investigate processing–microstructure–property relationships. In focus were alloy variants with a relatively high Cu content. Two PBF-LB/M scan strategies, employing a Gaussian beam with and without a re-scan with a laser featuring a flat-top profile, were compared to PBF-EB/M processing, followed by heat-treatments between 300 °C and 1000 °C. The phase constitution, elemental partitioning and grain boundary characteristics were analyzed by X-ray diffraction, electron backscatter diffraction and energy-dispersive X-ray spectroscopy. Mechanical behavior was assessed by hardness and tensile testing. Both manufacturing routes promoted the evolution of stable multi-phase microstructures composed of face-centered-cubic (FCC)- and body-centered-cubic (BCC)-type phases across all heat-treatment conditions. PBF-LB/M processing resulted in finer, dendritic microstructures and suppressed formation of a Cu-rich FCC phase due to higher cooling rates, whereas PBF-EB/M promoted the evolution of Cu-rich FCC segregates and equiaxed grain morphologies. Heat-treatment above 700 °C led to recrystallization, accompanied by an increase of the FCC phase fraction, grain coarsening, and recovery. At lower heat-treatment temperatures, the changes in microstructure are different. Here, it is assumed that small, non-clustered Cu-rich precipitates formed at the grain and sub-grain boundaries, although this assumption is only based on the assessment of the mechanical properties. The size of these precipitates is below the resolution limit of the techniques applied for analysis in the present work. Additional structures seen within the Cu-rich areas of PBF-EB/M-manufactured samples treated at lower temperatures also seem to have an influence on the hardness and yield strength. All of the conditions investigated exhibited pronounced brittleness, limiting reliable tensile property evaluation and indicating the need for further optimization of processing strategies and microstructural control for high-Cu-fraction-containing multi-principal element alloys. Full article

Soil provides essential ecosystem services and is pivotal for achieving multiple United Nations (UN) Sustainable Development Goals amid growing population pressures and resource demands. In arid to semi-arid regions such as Maio Island (Cape Verde), nutrient-poor soils and unsustainable land-use practices increase agricultural vulnerability, while volcanic geochemistry introduces elements that are not human friendly, further challenging environmental quality and long-term sustainability. Assessing soil (physical–chemical–biological) condition is therefore crucial for informed environmental and land-use planning. Here, Maio’s topsoil was evaluated using protocols adapted from Santiago, the largest Cape Verdean island. Estimated Background Values (EBVs) indicated naturally elevated V, Cr, Ni, Co, and Cu concentrations, consistent with mafic volcanic terrains. Robust Principal Component Analysis (rPCA) revealed geochemical groupings linked to volcanic–sedimentary units, with the dominant component (PC1) defined by Co–V–Cu–Mn–Ni versus As–Cd. Environmental Risk Indices (ERIs) and Multi-Element ERIs (ME–ERIs) quantified elemental enrichment relative to international land-use standards (residential and agricultural) and subsequently to Maio’s EBVs. The highest exceedances were observed for Cr, Co, Ni, V, and Cu, whereas As, Cd, Hg, Pb, and Zn fell within thresholds. The EBV-based assessment identified fewer exceedances than stricter international guidelines, though a few multi-element “hotspots” persist, highlighting potential land-use constraints and the need for preventive management. Overall, the integrated EBV/ERI/ME–ERI framework establishes an environmental geochemical baseline for Maio and offers a screening tool applicable across the entire archipelago. Full article

As the largest known ancient jade mining site, the Mazongshan Site is crucial for understanding the “West–to–East Jade Transportation” system in ancient China. However, its vast nephrite materials remain poorly characterized mineralogically and geochemically. This study employs a multi-technique approach, including polarized light microscopy, SEM-EDS, XRD, CRS, EPMA, and ICP-MS to analyze Mazongshan nephrite. The results identify tremolite as the principal mineral, with accessory minerals including diopside, apatite, serpentine, calcite, dolomite, graphite, hornblende, epidote, forsterite, and albite, as well as limonite occurring as a secondary mineral formed by oxidation. Its rare earth element patterns show significant negative Eu anomalies, low total REE concentrations, and low levels of Cr, Ni, and Co. These results confirm a metamorphic origin for the deposit. Most significantly, the high compositional affinity it exhibits with Hetian nephrite from Xinjiang, together with evidence of ancient mining, has led us to reconsider the prevalence of nephrite materials used during the Warring States to Han periods. Full article

Substitutional elements are introduced to face-centered cubic (FCC) multi-principal element alloys (MPEAs) to effectively enhance the mechanical performance by solid solution strengthening and second-phase strengthening. Commonly, relatively large atomic radius elements introduced into the alloy matrix result in lattice distortion and hinder grain boundary migration, thus achieving matrix strengthening. However, owing to the complex compositions of MPEAs, different substitutional elements introduced affect the microstructure evolution behavior and corresponding strengthening effects. In this work, an abnormal grain growth behavior of Ni-Co-Cr-based MPEAs based on Al alloying was observed. Systematic annealing experiments combined with quantitative grain growth analysis were conducted to clarify the effects of Al, W, and Mo on grain boundary migration. The results show that substitutional Al reduces the apparent activation energy for grain growth, resulting in both a lower grain growth component (n = 2) and a lower activation energy for grain growth of 219 kJ/mol, thereby enhancing grain boundary mobility. On the contrary, minor additions of high-melting-point W and Mo effectively inhibited the Al-induced rapid grain growth by increasing the activation energy and resulting in a higher grain growth component and a lower activation energy for grain growth of 251 kJ/mol. These findings provide new insights into the role of substitutional solutes in controlling grain growth kinetics in multi-principal element alloys. Full article

Machine learning has been widely applied to phase prediction and property evaluation in multi-principal element alloys. In this work, a data-driven machine learning framework is proposed to predict the ultimate tensile strength (UTS) and total elongation (TE) of Fe-Co-Cr-Ni-Mn-Al-Ti multi-principal element alloys (MPEAs), offering a cost-effective route for the design of new MPEAs. A dataset was compiled through an extensive literature survey, and six different machine learning models were benchmarked, from which XGBoost was ultimately selected as the optimal model. The feature set was constructed on the basis of theoretical considerations and experimental data reported in the literature, and SHAP analysis was employed to further elucidate the relative importance of individual features. By imposing constraints on the screened features, two alloys predicted to exhibit superior performance under different heat-treatment conditions were identified and fabricated for experimental validation. The experimental results confirmed the reliability of the model in predicting fracture strength, and the errors observed in ductility prediction were critically examined and discussed. Moreover, the strengthening mechanisms of the designed MPEAs were further explored in terms of microstructural characteristics and lattice distortion effects. The alloy design methodology developed in this study not only provides a theoretical basis for exploring unexplored compositional spaces and processing conditions in multi-principal element alloys, but also offers an effective tool for developing novel alloys that simultaneously achieve high strength and good ductility. Full article

A novel stochastic finite element model updating method based on multi-point sensitivities is proposed to improve the reproduction and prediction ability of finite element models for experimental data. Drawing upon the theory of small perturbations, this approach employs the sensitivity matrix in conjunction with the probability distribution of responses evaluated at multiple parameter points to determine the probability density associated with each parameter point and to estimate the statistical properties of the parameters. To achieve this objective, principal component analysis is employed to unify the dimensionality of the parameters and the responses; the least squares method was used to estimate the characteristics of the parameters. The reliability and validity of this method were confirmed through experimentation with a 3-degree-of-freedom spring-mass system and an aerospace thermal insulation structure. A comparison of this method with classical methods reveals significant advantages in terms of robustness across varying computational scales. Notably, it attains superior accuracy with smaller sample sizes while maintaining precision comparable to conventional methods with large samples. Consequently, this characteristic confers upon the method a distinct advantage in scenarios where the costs of finite element computation are prohibitively high. Full article

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets. Full article

This study aimed to analyze the biomechanics of three abutment systems with distinct retention mechanisms and their impact on the implant–abutment interface (IAI). The finite element analysis method was used to model maxillary three-unit restorations with conventional cement-retained abutment (CRA), multi-unit abutment (MUA), and innovative cementless link-retained abutment (LRA) systems. Dental implants were positioned at 0°/0°, 15°/15°, and 25°/25° angulation combinations. Analyses were performed under 400 N vertical and 200 N oblique loading applied at a 45° angulation. The LRA system exhibited lower stress on the implants and abutments under both loading conditions, whereas the CRA system demonstrated the highest stress. In contrast, the maximum principal stresses within the peri-implant bone were the highest in the LRA system under both loading conditions. Despite greater IAI displacement in the molar region, no specific abutment system exhibited distinct superiority under different scenarios. Overall, an increase in implant angulation led to higher stress values across all parameters. The MUA and LRA systems demonstrated reduced stress concentration and more uniform load distribution compared with the CRA system under tilted implant configurations. The findings suggest that the innovative cementless LRA system may serve as a feasible alternative to conventional CRA and MUA systems, exhibiting superior biomechanical performance, particularly compared with the CRA system. Full article

We introduce Causal Incentive Design (CID), a framework that applies causal inference to canonical single-stage principal–agent problems (PAPs) characterized by bilateral private information. Within CID, the operating rules of PAPs are formalized using an additive-noise causal graphical model (CGM). Incentives are modeled as interventions on a function space variable, $\Gamma$, which correspond to policy interventions in the principal–follower causal relation. The causal inference target estimand $V(\Gamma )$ is defined as the expected value of the principal’s utility variable under a specified policy intervention in the post-intervention distribution. In the context of additive-Gaussian independent noise, the estimand $V(\Gamma )$ decomposes into a two-layer expectation: (i) an inner Gaussian smoothing of the principal’s utility regression; and (ii) an outer averaging over the conditional probability of the follower’s action given the incentive policy. A Gauss–Hermite quadrature method is employed to efficiently estimate the first layer, while a policy-local kernel reweighting approach is used for the second. For offline selection of a single incentive policy, a Functional Causal Bayesian Optimization (FCBO) algorithm is introduced. This algorithm models the objective functional $\gamma \mapsto V\left(\gamma \right)$ using a functional Gaussian process surrogate defined on a Reproducing Kernel Hilbert Space (RKHS) domain and utilizes an Upper Confidence Bound (UCB) acquisition functional. Consequently, the policy value $V\left(\gamma \right)$ becomes an interventional query that can be answered using offline observational data under standard identifiability assumptions. High-probability cumulative-regret bounds are established in terms of differential information gain for the proposed FBO algorithm. Collectively, these elements constitute the central contributions of the CID framework, which integrates causal inference through identification and estimation with policy search in principal–agent problems under private information. This approach establishes a causal decision-making pipeline that enables commitment to a high-performing incentive in a single-shot game, supported by regret guarantees. Provided that the data used for estimation is sufficient, the resulting offline pipeline is appropriate for scenarios where adaptive deployment is impractical or costly. Beyond the methodological contribution, this work introduces a novel application of causal graphical models and causal reasoning to incentive design and principal–agent problems, which are central to economics and multi-agent systems. Full article

Apatite is a key indicator mineral whose chemical signature can reveal the genesis and evolution of ore-forming systems. However, correctly interpreting these signatures requires a robust discrimination between apatite types formed by different geological processes, such as metamorphism and hydrothermal activity. This study aims to chemically characterize and genetically classify apatite samples from the Slyudyanka deposit (Siberia, Russia) to establish discriminative geochemical fingerprints for metamorphic and hydrothermal apatite types. We analyzed 80 samples of apatite using total reflection X-ray fluorescence (TXRF) and inductively coupled plasma mass spectrometry (ICP-MS). The geochemical data were processed using principal component analysis (PCA) and k-means cluster analysis to objectively discriminate the apatite types. Our analysis reveals three distinct geochemical groups. Metamorphic veinlet apatite is defined by high U and Pb, low REE, Sr, and Th, and suprachondritic Y/Ho ratios. Massive metamorphic apatite from silicate–carbonate rocks shows extreme REE enrichment and chondritic Y/Ho ratios. Hydrothermal–metasomatic apatite features high Sr, Th, and As, with intermediate REE concentrations and chondritic Y/Ho ratios. Furthermore, we validated the critical and anomalous Y concentrations in the metamorphic veinlet apatite by cross-referencing TXRF and ICP-MS data, confirming the reliability of our measurements for this monoisotopic element. We successfully established diagnostic geochemical fingerprints that distinguish apatite formed in different geological environments at Slyudyanka. The anomalous Y/Ho ratio in metamorphic veinlet apatite serves as a key discriminant and provides insight into specific fractionation processes that occurred during the formation of phosphorites in oceanic environments, which later transformed to apatites during high-grade metamorphism without a change in the Y/Ho ratio. This work underscores the importance of multi-method analytical validation for accurate geochemical classification. Full article

The Hadamengou gold deposit, located on the northern margin of the North China Craton, represents one of the region‘s most significant gold mineralization clusters. However, exploration in its deeper and peripheral sectors is constrained by ecological protection policies and the structural complexity of the ore-forming systems. Multivariate analysis combined with multi-model integration provides an effective mathematical approach for interpretating geochemical datasets and guiding mineral exploration, yet, its application in the Hadamengou region has not been systematically investigated. To address this research gap, this study developed a pilot framework in the key Buerhantu area, on the periphery of the Hadamengou metallogenic cluster, applying and adapting a multivariate-multimodel methodology for mineral prediction. The goal is to improve exploration targeting, particularly for concealed and deep-seated mineralization, while addressing the methodological challenges of mathematical modeling in complex geological conditions. Using 1:10,000-scale lithogeochemical data, we implemented a three-step workflow. First, isometric log-ratio (ILR) and centered log-ratio (CLR) transformations were compared to optimize data preprocessing, with a reference column (YD) added to overcome ILR constraints. Second, principal component analysis (PCA) identified a metallogenic element association (Sb-As-Sn-Au-Ag-Cu-Pb-Mo-W-Bi) consistent with district-scale mineralization patterns. Third, S-A multifractal modeling of factor scores (F1–F4) effectively separated noise, background, and anomalies, producing refined geochemical maps. Compared with conventional inverse distance weighting (IDW), the S-A model enhanced anomaly delineation and exploration targeting. Five anomalous zones (AP01–AP05) were identified. Drilling at AP01 confirmed the presence of deep gold mineralization, and the remaining anomalies are recommended for surface verification. This study demonstrates the utility of S-A multifractal modeling for geochemical anomaly detection and its effectiveness in defining exploration targets and improving exploration efficiency in underexplored areas of the Hadamengou district. Full article