Our work examines the Mexican capital market with the objective of identifying the key assets within each sector. This is accomplished through a principal component analysis (PCA) applied to time series data. The findings elucidate the assets that predominantly contribute to each sector concerning overall variability and illustrate the associations among them according to their correlation vectors. Ultimately, the linear combinations of each sector, referred to as the principal components, are provided, which serve as indicators of their cyclical behavior. These combinations can be utilized as measures of sector dynamism within the market and, in conjunction with other technical analysis tools, may prove valuable as trading signals and for constructing investment portfolios. These insights will be extended for future research applications.

An integrated biomarker panel for early computational prediction of dengue severity during the acute phase.

Distinct cytokine signature in juvenile dermatomyositis: linking myositis-specific antibodies and clinical manifestations.

This study provides a multivariate seasonal assessment of surface water quality for irrigation in western Iraq. Water samples were collected during winter and summer of 2024 from three primary sources—Euphrates River, Lake Habbaniyah, and Lake Tharthar. Key physicochemical parameters were analyzed, including EC, TDS, TH, Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and pH. Multivariate statistical analyses, including Principal Component Analysis (PCA), were conducted to investigate seasonal patterns, assess irrigation suitability, and identify dominant hydrochemical parameters. One-way ANOVA revealed no significant seasonal differences (p > 0.05); however, EC and TDS values showed an increasing trend during summer, particularly in stagnant lakes. Subsequent multivariate analyses, including Pearson correlation and Principal Component Analysis (PCA), identified EC, Na⁺, and TH as the principal hydrochemical drivers, collectively accounting for over 95% of the total variance. The Sodium Adsorption Ratio (SAR) was calculated to quantify the risk of sodicity in irrigation practices. SAR values remained acceptable for Euphrates water (~4.5), but exceeded 5.0 in lake sources, indicating moderate sodicity hazards. A comparative analysis revealed that water from the Euphrates River is the most suitable for irrigation. In contrast, water from the lakes requires dilution or treatment due to elevated salinity and sodium levels. The findings underscore the need for source-specific water management, seasonal monitoring, and SAR-based risk evaluation to maintain soil health and irrigation sustainability in semi-arid regions.

Rapid elemental imaging of copper-bearing critical ores using laser-induced breakdown spectroscopy coupled with PCA and PLS-DA.

A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis.

Analysis of different cultivars and origins of Chinese cigar tobacco leaves based on headspace solid phase microextraction-gas chromatography-high resolution mass spectrometry combined with multivariate statistical methods.

This study aimed to design and evaluate an advanced Long Short-Term Memory (LSTM) deep learning model to accurately detect fraudulent claims in supplementary health insurance by leveraging sequential data patterns and domain-specific engineered features. An applied research design was used to build a robust fraud detection framework. A dataset of 20,000 health insurance claims was obtained from a supplementary insurance provider, containing both legitimate and fraudulent cases. Raw relational tables — including policy, insured individuals, claims, disease registry, and branch information — were merged into a single structured dataset using SQL. Rigorous data preprocessing was performed: irrelevant variables were removed, highly correlated features were eliminated through correlation analysis, dimensionality reduction was applied via Principal Component Analysis (PCA), and extreme outliers were excluded using the interquartile range (IQR) method. All numerical features were standardized, and class imbalance was addressed by weighting fraudulent cases during training. The processed data were reshaped into sequences suitable for LSTM input and divided into training and testing sets. An LSTM model with 32 hidden units and a Sigmoid output layer was trained using the Adam optimizer and binary cross-entropy loss, with performance validated through k-fold cross-validation. The LSTM model achieved outstanding predictive performance, with an overall accuracy of 100% on the test dataset. Both fraudulent and non-fraudulent claims reached perfect precision, recall, and F1-scores. Macro and weighted averages also recorded 1.00 across all metrics, indicating the model’s ability to detect rare fraudulent events without sacrificing specificity. By combining advanced deep learning with systematic data preparation and domain-informed feature engineering, the proposed LSTM framework effectively identified complex fraud patterns in supplementary health insurance. This approach offers a scalable and reliable solution to strengthen fraud risk management and reduce financial losses in the insurance sector.

Fourier-transform infrared spectroscopy reliably differentiates Klebsiella pneumoniae outbreak isolates.

Integrated risk assessment of heavy metals in marine sediments from Salwa Bay, Qatar using pollution indices and multivariate analysis.

GhNTF2.1 facilitates cotton fiber elongation by regulating sucrose metabolism through interacting with GhABI5.

Although Anacardium humile and Anacardium occidentale fruits are widely consumed in Brazil, quantitative data on their bioactive compound content remain scarce. This study aimed to develop and validate a high-performance liquid chromatography mass spectrometry (HPLC‑MS/MS) method for the targeted quantification of 12 metabolites and to assess their antioxidant potential. Twenty-four mature fruit samples were collected from individual trees and purchased from open-air markets in Goiás and the Federal District over three different weeks (during September and October) and were analyzed using a validated method (r > 0.99; relative standard deviation (RSD) < 12.84%; recovery 88-111%). Ascorbic acid was the predominant compound in both species, although notable interspecific differences were observed for catechin, isoquercetin, and gallic acid. Principal component analysis (PCA) confirmed distinct chemical profiles between species. Antioxidant activity varied significantly, with the effective concentration (EC50) values ranging from 65.67 to 381.94 g fruit per g 2,2-diphenyl-1-picrylhydrazyl (DPPH) for A. occidentale and from 85.37 to 227.62 for A. humile. These findings underscore the importance of robust analytical techniques for characterizing the complex phytochemistry of native Brazilian fruits.

QSAR study and molecular modeling of pleuromutilin derivatives to predict antibacterial activity: Chemometrics and docking studies.

Comprehensive evaluation and physiological responses of 20 table grape varieties to sole and combined salt and drought stress.

Spatial distribution and diversity of mcyA gene-carrying cyanobacteria in planktic and benthic communities of Swiss alpine lakes.

Tracing source footprints of heavy metal(oid)s in coastal soils using traditional statistical techniques and machine learning data-driven models.

Disparities in bioaccumulation of chlorinated organic pollutants in O. bartramii and seawater from the Northwest Pacific Ocean: Compound - specific mechanisms and health risks.

Detection of antibiotic-resistant Escherichia coli using surface-enhanced Raman spectroscopy and infrared spectroscopy.

Comparison of toxic effects of phenanthrene and perfluorooctane sulfonate on Chinese tongue sole Cynoglossus semilaevis larva in a muti-biomarker approach.

Quantitative proteomics analysis to identify potential biomarkers associated with promoted quality profiles of frankfurters by ultrasound treatment combined with κ-carrageenan.