Abstract: The efficacy of postbiotics vary significantly comparing different strains and preparation processes. We aimed at evaluating the effect of an innovative postbiotic (iPB) generated through the sequential fermentation of Lacticaseibacillus rhamnosus GG and Lacticaseibacillus paracasei NPB-01, compared to single-strain postbiotics, on epithelial barrier integrity and innate immunity in human enterocytes. Comparative evaluation of the effects elicited by iPB or by single-strain postbiotics on gut epithelial barrier using a Caco-2 cells-based experimental model by measuring cell growth proliferation, tight junction proteins (occludin and zonula occludens 1, ZO-1), mucus protein Mucin-2 (Muc-2), and lactase expression. The modulatory action on the production of innate immunity peptide Human Beta-Defensin 2 (HBD-2) via RT-qPCR and ELISA was also comparatively assessed. iPB exposure resulted in a higher up-regulation of occludin, ZO-1, MUC2 and lactase expression, if compared with the single-strain postbiotics, suggesting a beneficial synergistic action in modulating epithelial gut barrier. Furthermore, iPB induced a significantly higher production of HBD-2, suggesting a synergistic enhancement of innate immune response. Our findings suggested that the sequential fermentation process acts as a biotechnological catalyst, optimizing the immunomodulatory action and gut barrier-protective properties of Lacticaseibacillus strains. This study introduces iPB as a high-performance postbiotic candidate for the prevention and management of conditions characterized by alterations of epithelial gut barrier and innate immunity.

Abstract: Soil physicochemical and biochemical properties are fundamental to soil processes and ecosystem functioning in forested landscapes. However, their responses to dominant tree species in humid montane regions remain unclear. This study examined how three widespread broadleaf species—Quercus pontica, Quercus petraea, and Fagus orientalis—influence the physical, chemical, and biochemical properties of the soil in natural forests in the Eastern Black Sea region. Fifteen soil samples (five from each forest type) were collected under comparable climatic and geological conditions and analyzed for texture, pH, electrical conductivity, organic carbon content, and key biochemical indicators of microbial activity. Significant differences in soil properties were observed among forest types. Soils beneath Q. pontica exhibited a lower pH level (3.26), a higher organic carbon content (3.82), microbial abundance, and enhanced biochemical activity. enhanced biochemical activity. In contrast, Pontic oak-dominated stands were characterized by distinct textural and chemical signatures. Multivariate analyses revealed that soil texture fractions, pH, and microbial carbon acted as the primary edaphic filters driving differentiation of soils among forest types. These patterns suggest that species-specific litter inputs and belowground processes regulate soil biochemical functioning by modifying resource availability and physical habitat conditions. Our results demonstrate that, even under similar environmental conditions, dominant tree species exert a strong influence over soil physicochemical and biochemical properties. Understanding these species-specific soil responses is essential for predicting ecosystem functioning, carbon cycling, and sustainable forest management in a changing environment.

Abstract: Companies frequently utilized multiple sales channels to ensure sustainability. However, attempts to fulfill all incoming orders without sufficient consideration of operational constraints resulted in significant challenges in planning and production processes. Therefore, effective order management was considered essential for optimizing overall business performance. The strategic evaluation of sales orders reduced post-acceptance difficulties, thereby improving both customer satisfaction and operational efficiency. In this context, a machine-learning-based model for sales order classification was proposed for a hybrid production environment accommodating both Make-to-Stock (MTS) and Make-to-Order (MTO) strategies. Initially, the attributes employed in sales order evaluation were identified through an extensive literature review and subsequently refined using a heuristic approach to determine the most relevant classification features. Based on these attributes, sales orders were first clustered into three groups using the k-means algorithm to generate meaningful class labels. The labeled datasets were then utilized to train three supervised machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The performance of these models was evaluated and compared, resulting in accuracy rates of 99.67%, 99.55%, and 99.49%, respectively. The findings demonstrated that the Random Forest algorithm achieved the highest classification performance.

Abstract: This paper presents a parametric modeling and aerodynamic optimization methodology for the second-stage stator of a multi-stage centrifugal compressor. Based on the geometric configuration of the two-stage components, a flexible parametric template is established for the second-stage stator. Numerical simulations are conducted to analyze the internal flow field and evaluate the performance of the initial design of this compressor, revealing performance deficits such as significant vortex-induced losses and a large outlet circumferential flow angle (-12.138°). To this end, an aerodynamic optimization framework integrating a Kriging surrogate model and a Genetic Algorithm (GA) is applied to the second-stage stator, targeting at the aerodynamic matching optimization under multiple operating conditions. The optimization objectives include maximizing the overall polytropic efficiency of compressor and static pressure ratio of second-stage stator, as well as minimizing the total pressure loss coefficient and the outlet circumferential flow angle of second-stage stator. The results demonstrate that the optimized design achieves a 2.17% improvement in the overall polytropic efficiency and a 12.01% improvement in the static pressure recovery coefficient at the design condition, along with a notable reduction in the outlet circumferential flow angle to 0.663°. Under multi-condition operation, the optimized stator exhibits enhanced the performance stability. The overall polytropic efficiency is improved by 2.06% and the static pressure recovery coefficient is improved by 23.31% at the low-flow condition, confirming the effectiveness of the proposed parametric modeling and sequential optimization approach.

Abstract: Graph Neural Networks (GNNs) have demonstrated exceptional performance in modeling structural dependencies within networked data. However, in complex decision-making environments, structural information alone often fails to capture the latent semantic logic and domain-specific heuristics. While Large Language Models (LLMs) excel in semantic reasoning, their integration with graph-structured data remains loosely coupled in existing literature. This paper proposes CSSA, a novel Cross-modal Semantic-Structural Alignment framework that synergizes the zero-shot reasoning of LLMs with the topological aggregation of GNNs through a contrastive learning objective. Specifically, we treat node attributes as semantic prompts for LLMs to distill high-level "risk indicators," while a GNN branch encodes the local neighborhood topology. A cross-modal alignment layer is then introduced to minimize the representational gap between semantic intent and structural behavior. We evaluate CSSA on a massive dataset of 2.84 million online transaction records. Experimental results demonstrate that CSSA achieves a superior F1-score and AUC compared to state-of-the-art GNNs, particularly in scenarios characterized by extreme class imbalance and covert adversarial patterns.

Abstract: Sexual objectification is the treatment of a person as a body or a collection of body parts that are valued primarily for their sexual appeal. The main purpose of this study was to determine the relevance of sexual objectification to women's health and well-being across the life cycle, from middle adolescence to old age. Additionally, the relevance of age and education to sexual objectification and its association with traditional gender role attitudes was examined. The study was cross-sectional and the sample consisted of 6,112 Spanish women between the ages of 16 and 85, who were assessed using seven questionnaires and scales. The results showed that lower age and lower number of children were associated with greater importance of sexual and physical attractiveness and with a more sexualized image, although there were no differences between adolescent and emerging adult women. Greater importance placed on sexual and physical attractiveness, as well as total sexual objectification, was associated with greater mental distress, lower psychological well-being, lower life satisfaction, and lower self-esteem at every life stage. Greater importance placed on sexual and physical attractiveness was associated with more traditional gender role attitudes among all age groups, except for older women. We conclude that sexual objectification is a threat to women's mental health and well-being.

Abstract: Background/Objectives: Metabolic alterations, including dyslipidemia, are increasingly recognized in patients with neuroendocrine tumors and may influence tumor biology and treatment outcomes. However, the clinical relevance of dyslipidemia and the potential impact of lipid-lowering therapies in bronchopulmonary neuroendocrine tumors (BP-NETs) treated with somatostatin analogues (SSAs) remain poorly defined. This study aimed to evaluate the progression-free survival (PFS) in patients with advanced BP-NETs receiving SSAs according to dyslipidemia as well as statin therapy. In addition, an exploratory in vitro analysis was performed to assess the combined biological effect of statins and SSAs. Methods: This study investigated the combined effects of atorvastatin and lanreotide therapy both in vitro and in a clinical setting. In NCI-H727 cells, we assessed cell viability, proliferation, apoptosis, DNA damage, and metabolic activity following single and combined treatments. Concurrently, we retrospectively evaluated the impact of dyslipidemia and statin therapy on progression-free survival (PFS) in patients with advanced BP-NETs receiving SSAs. Results: Combined treatment resulted in reduced cell viability, proliferation, and ATP production, alongside increased apoptosis and DNA damage, and was associated with impaired cellular energy metabolism compared with lanreotide alone and control conditions. In the clinical analysis, dyslipidemia was associated with shorter progression-free survival (PFS), whereas atorvastatin therapy in dyslipidemic patients showed a positive trend toward improved PFS. Conclusions: These findings support the potential relevance of lipid metabolism modulation as an adjunct strategy in advanced BP-NETs, warranting further validation in larger prospective studies and encouraging additional biochemical investigation of the underlying pathways.

Abstract: Public participation in planning, though a foundational democratic principle, faces implementation challenges across diverse planning systems worldwide. This study examines participatory planning practice in Ireland and Serbia – two contexts shaped by distinct planning traditions yet confronting similar tensions between democratic ideals and practice realities. Through comparative analysis of four local land-use planning instruments (Development Plans and Local Area Plans in Ireland; Spatial Plans and General Regulation Plans in Serbia), the research investigates how institutional design, power relations, and democratic commitments embedded within planning systems fundamentally shape participatory outcomes. Beyond external pressures such as neoliberalisation and democratic decline, the study demonstrates that the internal dynamics of participation, seen in the quality of dialogue, distribution of knowledge, strength of civic networks, and negotiation of power among stakeholders, ultimately determine whether participatory processes enable genuine democratic engagement or reproduce existing hierarchies. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across multiple planning scales. Employing a four-dimensional analytical framework, including informing, consultation, collaboration, and monitoring, the analysis traces information dissemination strategies, consultation mechanisms, collaborative practices, and transparency structures. Findings reveal that, while both systems remain largely at the informing and consulting levels, critical differences emerge: Ireland demonstrates multi-channel, immersive approaches, feedback-oriented consultation, and structured collaborative experimentation, whereas Serbia exhibits statutory-minimal information provision, objection-based adversarial procedures, and exceptional rather than systematic collaboration. The study advances comparative European planning scholarship by identifying how planning cultures, legislative frameworks, and institutional responsiveness generate divergent participatory outcomes even under similar global pressures, offering practical insights for strengthening inclusive urban governance across varied institutional contexts.

Abstract: Virtual Reality (VR) is increasingly recognized as a valuable tool for sports training, providing immersive environments that support skill acquisition and performance improvement. Comparative studies across hand-intensive sports such as basketball, volleyball, and table tennis show substantial research on VR’s effectiveness in basketball and table tennis, yet volleyball remains relatively underexplored, particularly in terms of skill transfer to real-world play. Research in basketball and table tennis indicates that VR can improve motor coordination, tactical awareness, and user motivation. However, volleyball-specific literature is limited. Existing studies generally focus on areas such as eye–hand coordination and tactical decision-making but provide little evidence on whether VR-acquired skills translate effectively to the court. This paper addresses the gap in volleyball-focused VR research and emphasises the need for further investigation to maximise VR’s potential for volleyball training. Ten beginner-level volleyball players (mean age = 20.4 years) participated in this study, which examined the effectiveness of VR-based serving training. Participants completed an initial physical pre-test to determine their baseline serving performance, followed by a three-week VR training program consisting of structured serving drills. After the program, a post-test assessment was conducted to measure improvement. A paired t-test comparing pre- and post-training results showed a statistically significant improvement in serving performance (p = 0.0147), meeting the 0.05 significance threshold. This indicates that the observed performance gains were unlikely due to chance and demonstrates the positive impact of VR training on serving skills in beginner volleyball players.

Abstract: This report describes a 33-year-old male initially suspected of Inflammatory Bowel Disease (IBD) due to radiological findings, but finally diagnosed with Irritable Bowel Syndrome (IBS) based on the Rome IV criteria, normal colonoscopy findings and inflammatory biomarkers. When symptomatic pharmacotherapy for pain, constipation and heartburn alone did not bring lasting relief, he was referred for lifestyle therapy. Dietary, lifestyle advice, and physical activity are mainstay in the current guidelines on the management of IBS. However, beyond these generalities, patients receive little guidance on day-to-day decision making and essential elements of healthy living. We addressed the limitations in current guidelines on IBS by following the 21-point Health Building Guideline, along with Yoga and delivered through a seven-point protocol that uses the traffic light approach to promote and sustain behavioural changes in chronic diseases. After seven months of consistent adherence to intervention; following the traffic light approach, the patient achieved remission.

Abstract: Sustainable human resource management is critical in infrastructure sectors, yet firm-level resilience may conceal uneven health burdens within the workforce. This study examines a »resilience paradox« in a large Slovenian energy company using a two-level design. At the macro level (2012–2022), we explore associations between absenteeism categories and three efficiency ratios. At the micro level, we estimate a Poisson quasi-maximum-likelihood model with log planned hours as an exposure offset and cluster-robust inference on a balanced group-month panel (960 observations) built from 82,033 payroll records (2018–2022). Macro indicators remain stable, and we do not detect negative correlations between absenteeism and efficiency ratios, suggesting that operational continuity can be maintained despite absence shocks. However, micro-level estimates reveal pronounced inequalities: compared with employees aged ≤30, absenteeism rates are higher for ages 31–45 (incidence rate ratio—IRR 1.335), 46–55 (IRR 1.538), and >55 (IRR 1.829). Field/operational groups have higher rates than office/administrative groups (IRR 1.829), and female groups show higher rates than male groups (IRR 1.252). During COVID-19, absenteeism declined for office groups (IRR 0.840), while the additional effect for field groups was small and statistically uncertain (interaction IRR 1.179). The results call for targeted sustainable HRM interventions addressing aging, occupational risk, and equitable health protection across job types.

Abstract: Feeding and Eating Disorders (FEDs) require integrated and recovery-oriented care models that extend beyond clinical treatment and incorporate supportive environments capable of enhancing psychosocial wellbeing. In this perspective, nature-based and socio-agricultural practices represent promising yet underexplored therapeutic resources, particularly when embedded within a spatial planning framework. This study develops and tests a Geographic Information Systems (GIS)-based Decision Support System (DSS) that matches the specific needs of individuals undergoing treatment for FEDs with the territorial distribution and characteristics of green and agricultural environments. The research is based on a case study of the FED care center “Il Pellicano A.P.S.” in Perugia (Italy). Demand data were collected through questionnaires administered to patients, while supply data were gathered from 65 agricultural and social farms and gardens. The spatial matching process was implemented in a GIS environment using a multi-criteria approach integrating thematic activities, accessibility, organizational models, attendance levels, spatial capacity, and distance. Results reveal a significant mismatch between demand and supply, with the current system able to satisfy only 37% of expressed needs. The main gaps concern the lack of medium-sized, low-attendance, and freely accessible environments. Beyond the local case study, the proposed DSS serves as a transferable planning support tool for designing personalized therapeutic pathways and integrating green infrastructure, social farming, and healthcare services. The study highlights the strategic role of spatial planning in promoting health equity, social inclusion, and community wellbeing.

Abstract: Yam bean is a leguminous crop that produces toxic seeds with potential for biopesticide development. This study evaluated seed yield of nine yam bean progenies and their activity against Ralstonia solanacearum (RS) phylotype II isolate FIO104B, which was collected from tomato in Iranduba, Amazonas, Brazil. A field experiment was conducted using a trellis system following a randomized complete block design with four replications and four plants per plot. Antibacterial activity was evaluated by exposing 30 µL of RS (1 × 10⁶ cells·mL⁻¹) to 20 mL of yam bean seed extracts at 0.5% and 5% (w/v) in sterile yeast-peptone-glucose medium for 6, 12, and 24 h. Seed yield ranged from 0.56 to 0.98 t·ha⁻¹. Antibacterial assays revealed biphasic, concentration-dependent activity: 0.5% (w/v) extracts stimulated bacterial growth at 6 and 12 h but suppressed multiplication at 24 h, whereas 5% extracts at 6, 12, and 24 h promoted bacterial growth. Progenies P14, P20, and P23 demonstrated both relatively high seed yields and strongest antibacterial efficacy (88-90% growth reduction at 24 h and 0.5% concentration). These findings establish baseline seed productivity for Amazonian yam bean and demonstrate that optimal biopesticide formulations require low concentrations (0.5%) with extended exposure periods (≥ 24 h).

Abstract: We propose a causal-diamond formulation of semiclassical gravity where a finite-resolution boundary regulator supplies the edge structure for a local Wheeler–DeWitt description. Because the diffeomorphism-invariant Hilbert space does not factorize, each diamond is equipped with a boundary-completed algebra ?_?, ensuring the reduced state ?_? and reference family ?_? [Λ] are defined on identical degrees of freedom. Dynamics are defined by an informational principle: the relative-entropy functional quantifies the mismatch ?rel (?_? ∥?_? [Λ]) between data and reference. By discretizing gravitational stiffness (implemented as a finite response capacity of the boundary completion at the diamond waist) rather than the metric degrees of freedom, the variational principle is rendered well-defined. In the modular/KMS regime, the vacuum is at entanglement equilibrium, and the leading dynamics reduce to linear response governed by the Hessian of relative entropy (Kubo–Mori metric). This Hessian decouples tensor, vector and scalar deformations, recovering Einstein stiffness, Yang–Mills susceptibilities and mass gaps. Finite-resolution open updating induces a canonical reduction of non-abelian symmetry data to commuting Cartan phases, yielding a natural three-stage generation hierarchy. A quasi-local heat-kernel expansion maps the near-equilibrium response to a matching-scale EFT, identifying the leading ?² saturation operator and accommodating a spinorial transport structure. Topological edge-mode counting fixes the effective internal degeneracy ?; combined with Newton’s constant ?, this calibrates the matching scale ?_? ∼ 3 × 10¹³ GeV. At ?_?, the framework yields analytic relations for couplings and mass ratios as functions of discrete group-theoretic data of the boundary completion. Identifying ?_? with stiffness saturation places the high-curvature regime in a plateau universality class with ? at the 10⁻³ level. The architecture yields correlated, falsifiable targets governed by a single scale.

Abstract: This study evaluated the seasonal variability, origin, and ecological risk of heavy metals in the Pom-Atasta lagoon system, a tropical estuary in southeastern Mexico subject to increasing anthropogenic pressure. The main objective was to determine how seasonal changes influence the distribution, bioavailability, and risk of metals in sediments and benthic organisms. Thirty sampling stations were monitored during dry, rainy, and north wind seasons. Sediment concentrations of As, Cd, Cr, Ni, Pb, and V were measured, and bioaccumulation was assessed in the bivalve Rangia cuneata. Ecotoxicological risk was evaluated using the Adverse Effects Index (AEI), Toxic Risk Index (TRI), and Potential Ecological Risk Index (ERI). Results showed higher metal concentrations during the rainy and north wind seasons, likely due to increased runoff and sediment resuspension. Cr and Ni exhibited the highest enrichment, with values ​​from 115.0 to 130.4 µg g-1 and from 60.5 to 75.9 µg g-1, respectively. The Ni showed the highest bioaccumulation factor (BSAF > 1.51) in R. cuneata, indicating high mobility and environmental availability. Weak correlations among some metals (As, Cr, Pb) suggest mixed natural and anthropogenic sources. TRI values indicated low to moderate toxic risk, and ERI classified most sites as low risk (ERI <60) at several stations. Organic carbon levels remained within tolerable limits (<10%) for benthic fauna. These findings highlight the role of seasonal dynamics in metal distribution and confirm R. cuneata as a suitable bioindicator for monitoring ecological health in tropical estuarine systems.

Abstract: Water pollution is one of the most critical societal, environmental challenges and remains a persisting problem worldwide. The origin of this pollution is diverse while organic matter occupies a significant portion originating from different sources. This creates major environmental and health risks, requiring reliable and sensitive analytical tools for effective monitoring. The permanganate index stands as a conventional assessment method for organic pollution, but it demonstrates compound non-specificity toward compounds and limited sensitivity to various contaminant structures. This research introduces cyclic voltammetry as a standalone electrochemical method which provides sensitive detection and characterization of organic oxidizing compounds. Six organic compounds including gallic acid, phenol, oxalic acid, ascorbic acid, salicylic acid and p-benzoquinone were used as model compounds and studied in aqueous media. These compounds were analyzed individually, in single-compound mode, to characterize its redox behavior and to identify the voltammetric peaks. Subsequently, a multi-compound analysis was studied to check for the validity of the concept in a more complex matrix. Notably, a strong linear correlation was observed between the measured charge and the theoretical permanganate index, highlighting the quantitative reliability of the electrochemical method. Comparing the obtained results with the permanganate index method confirmed the superiority of cyclic voltammetry in terms of response time and detection capability. The outcomes demonstrate that cyclic voltammetry functions as a robust alternative to the classical chemical oxidation method for environmental water assessment.

Abstract: In this paper we study the performance and feasibility of integrating a novel key encapsulation protocol into Quantum Key Distribution (QKD). The key encapsulation protocol includes a challenge-response pair (CRP). In our design, Alice and Bob derive identical cryptographic tables from shared challenges, allowing the ephemeral key to be encoded and recovered without disclosing helper data. Software simulations show error-free key recovery for quantum channel bit error rates up to 40% when using longer response lengths. Additionally, we designed the protocol to detect eavesdropping solely from the statistics of the received quantum stream, without sacrificing key bits for public comparison. We formalize the encoding and decoding model, analyze trade-offs between response length and latency, and report key recovery and error detection performance across different noise levels. The results indicate that this CRP-based multi-wavelength QKD protocol can reduce the reliance on classical reconciliation while preserving security in noisy settings.

Abstract: AI-enabled fitness services rely on continuous collection of activity, physiological, and location data to support monitoring and personalized feedback, which raises persistent privacy and security concerns and ethical tensions regarding data use and user autonomy. Nevertheless, sustained engagement with these services remains common, indicating a divergence between privacy concern and continued use. Using online survey data from 596 adults aged 18 years and above, this study examines AI fitness use from an AI ethics perspective grounded in bounded rationality. A Deviation index is constructed as the standardized difference between privacy concern and risk acceptance. High willingness to use AI fitness services is analyzed using a parsimonious probability-based approach. Logistic regression models examine how the likelihood of high use varies across the Deviation range, while accounting for perceived transparency and safety, measured as Information Control Level, and stated privacy trade-off attitudes. The results show that continued use varies systematically across the Deviation spectrum. Higher Deviation values are not associated with a collapse in use probability. Instead, predicted probabilities change gradually across the observed range. Privacy concern and continued AI fitness use therefore coexist within this adult user sample. This pattern supports a descriptive AI ethics interpretation of privacy satisficing under bounded rationality rather than a binary privacy paradox.

Abstract: Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and able to represent co-evolving behavioral and network processes under compound conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. The framework prioritizes planning-relevant, spatially resolved outputs, including neighborhood clearance time, isolation probability, and shelter demand imbalance. By prioritizing modularity, configurability, and policy-aligned metrics, this review bridges the gap between methodological advances in evacuation modeling and the operational needs of local multi-hazard planning.

Abstract: The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall anomalies vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in a region frequently affected by rainfall extremes.