Search Results (1,840)

Background: Same-day discharge after laparoscopic appendectomy for uncomplicated appendicitis has been proposed as a strategy to reduce hospital costs without compromising safety, but evidence in pediatric patients remains limited. Methods: We conducted a retrospective study of pediatric patients undergoing laparoscopic appendectomy for uncomplicated appendicitis, comparing same-day and next-day discharge. Baseline differences were addressed using 1:2 propensity score matching. Costs were analyzed using linear regression with cluster-robust standard errors, and inverse probability of treatment weighting was used as a sensitivity analysis. Results: Among 102 patients (37 same-day discharge and 65 delayed discharge), 28 same-day discharge patients were matched to 36 delayed-discharge controls. Same-day discharge was associated with a mean reduction in total cost of €319.9 (95% CI: −€413.6 to −€226.2; p < 0.0001), mainly explained by lower admission-related costs, with no significant difference in outpatient consultation costs. Sensitivity analysis using inverse probability weighting in the full cohort showed a similar estimate (−€311.7; 95% CI: −€421.1 to −€202.3; p < 0.0001). Complications were rare and similar in both groups (1/28 vs. 1/36), which limited formal comparison of complication risk. Conclusions: Same-day discharge after laparoscopic appendectomy for uncomplicated appendicitis was associated with lower healthcare costs, largely due to reduced admission-related costs, with no apparent signal of increased complications. Full article

Wool is often regarded as a low-value by-product of Alpine sheep farming, despite its potential to convey the ecosystem services (ESs) generated by these systems. This study examines how awareness and perception of these services are associated with consumer attitudes towards locally produced wool in the Lanzo Valleys in Northwestern Italy. A convenience sample of 400 adults familiar with the study area completed an online questionnaire. Principal Component Analysis identified two orientations towards wool products—Sustainability Orientation and Product-Value Orientation—which together explained 69.33% of the variance. A TwoStep Cluster Analysis based on the component scores identified four profiles within the study sample: Sustainability-sensitive (22%), Disinterested (14.25%), Eco-aesthetic (36%), and Aesthetically aware (27.75%). Sustainability-oriented respondents attributed greater importance to ES related to biodiversity, native-breed conservation and animal welfare, whereas aesthetic and experiential attributes represented an additional, distinct dimension of product evaluation. Residence status was not significantly associated with cluster membership. Given the non-probabilistic sampling design, these profiles should be interpreted as exploratory attitudinal patterns. The findings may inform differentiated communication and wool-valorisation strategies linking environmental, cultural, aesthetic and functional values. Full article

Akerlof’s classical theory of the “Market for Lemons,” which conceptualizes adverse selection driven by information asymmetry, established the foundation of information economics. While the traditional model assumes static equilibria among a limited number of agents, analyzing its behavioral dynamics within large-scale, complex network environments remains a highly relevant task in computational social science. This study extends the classical lemon market model into a nonlinear dynamical system on adaptive networks. We mathematically elucidate macro-level social phase transitions—specifically structural exclusion such as scapegoating and collective ostracism—induced by computational cognitive limits, and evaluate optimal intervention strategies to mitigate these systemic failures. Multi-agent simulations utilizing large-scale tensor operations demonstrate that autonomous edge rewiring under incomplete information does not merely result in the uniform displacement of high-quality goods as predicted by static theory. Instead, the network self-organizes into an irreversible structural division: a core group of influential agents monopolizes high-quality information, while marginalized agents are isolated into a peripheral “lemon echo chamber” where only low-quality information circulates. To address this structural pathology under a resource constraint limiting intervention to 10% of the total agents, we evaluated two distinct approaches. The results indicate that providing informational support to influential hubs functions as a trap that exacerbates systemic inequality, superficially elevating the overall market evaluation but permanently fixing the exclusion gap. Conversely, the forced maintenance and protection of “weak ties” bridging disconnected clusters constitutes the mathematically optimal solution to dissolve fragmentation, effectively eliminating the price gap and facilitating social inclusion. Furthermore, this study demonstrates that the mechanism of social exclusion exhibits strong hysteresis effects. A distinct tipping point governs the progression toward a fragmented lemon echo chamber. Interventions implemented after crossing this critical threshold fail to restore the system to its baseline state despite identical resource expenditure, confirming the presence of an irreversible phase transition. These findings establish that the collapse dynamics outlined in the classical lemon market serve as a generalized model for explaining contemporary collective ostracism driven by information cascades. Consequently, the analysis highlights the necessity of early intervention prior to critical thresholds and the systemic preservation of structural bypasses rather than post-hoc remediation. Full article

Landfill leachate circuits that operate without discharge, by recirculating aerated leachate onto the waste mass, are widespread in South-East Europe, yet their long-term behaviour is rarely documented with sample-level data. This study reports a six-year (2020–2025) seasonal monitoring campaign at a sanitary landfill in northern Serbia (alluvial aquifer of the Sava River, transboundary Danube basin) and re-examines it with a transparent multivariate protocol. Seventy-two leachate samples (collection well, aeration lagoon, sedimentation lagoon; n = 24 each, 30 parameters), 28 realised surface-water campaigns, and six years of groundwater summaries were evaluated by principal component analysis/factor analysis (PCA/FA, Varimax normalized), hierarchical cluster analysis, PERMANOVA, non-parametric paired tests and, for benchmarking, supervised machine learning. The pooled leachate model (n = 72; 21 variables; KMO = 0.700; Bartlett χ² = 956, p < 0.001) retained four factors by parallel analysis, explaining 61.6% of total variance; after rotation the factors accounted for 27.7%, 14.3%, 10.4%, and 9.3%. Factor 1 grouped organic load with particle-reactive metals (COD, BOD₅, Fe, Ni, Cr, As, Zn), Factor 2 a reduced sulfur–fluoride–BTEX signature, Factor 3 temperature-driven nitritation, and Factor 4 a nitrate–manganese redox contrast. Crucially, paired campaign-by-campaign comparison showed no removal of the dominant pollutants along the circuit. Median COD, BOD₅ and NH₄-N were not lower in the sedimentation lagoon than in the collection well, while pH rose from 8.08 to 8.75 (p < 0.001); only Cu, Pb, NO₃-N, and NO₂-N decreased significantly. The circuit therefore homogenises and concentrates dissolved load rather than removing it. Downstream surface water was significantly enriched in electrical conductivity (+110 µS/cm), total dissolved solids, NH₄-N, and NO₂-N relative to upstream (Wilcoxon, p < 0.05), and groundwater showed episodic conductivity up to 12,760 µS/cm and NH₄-N up to 102 mg/L. Cytotoxicity (MTT) confirmed biological relevance, with MRC-5 viability falling to 37% after 24 h exposure to 50 vol.% groundwater (Pw3) versus 60% in A549 cells. A random-forest classifier separated circuit units far better than PCA-based discrimination (76.4% versus 54.2% cross-validated accuracy) and distinguished the 2020–2021 pandemic period from 2022–2025 with 94.2% accuracy, a period effect also confirmed by PERMANOVA (R² = 7.2%, p < 0.001). The results indicate that closed-loop recirculation without an engineered discharge barrier transfers, rather than eliminates, contaminant load, and that after-care of such systems requires mass-balance monitoring and polishing treatment. Full article

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

The rule of law can be considered as a multidimensional institutional phenomenon, which emerges through interplay between legal, governance and administrative institutions. The paper offers an exploratory statistical modeling approach to find empirical patterns in national rule-of-law profiles according to the 2024 World Justice Project (WJP) Rule of Law Index. Eight dimensions of the index are considered to identify differences between countries and similarities of their multidimensional institutional performance. Principal Component Analysis reveals strong associations between eight dimensions, which are structured along the same performance institutional scale; the first principal component explains 85.7% of the overall variation and two principal components explain 92.5% of it. K-Means, hierarchical and DBSCAN clustering methods are then used to examine the empirical similarities between countries. While the six-cluster solution of K-Means offers distinct group descriptions, low bootstrap stability of this solution suggests that these groups cannot be regarded as fixed rule-of-law regimes. In addition, the Random Forest analysis reveals Regulatory Enforcement, Absence of Corruption, and Criminal Justice as the three dimensions, which contribute to the empirical differentiation of the described profiles the most. In general, the results imply that international variations in rule-of-law performance are viewed as heterogeneous locations in a multidimensional institution space, rather than as stable and distinct legal systems. The above-presented methodology allows for an exploratory approach to analyze international variations in rule-of-law performance that considers the limitations of cross-section data and instability of clusters. Full article

Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, K-Nearest Neighbors), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches. We summarize recent progress in the application of metabolomics-driven ML to personalized medication, with a focus on drug dosage optimization, therapeutic efficacy prediction, and adverse drug reaction assessment. Despite these advances, significant challenges remain, including limited explainability, insufficient prospective clinical validation, lack of standardization and reproducibility, and data dimensionality and quality issues. Addressing these issues will be essential for the clinical translation of ML-metabolomics integration. Looking ahead, continued methodological innovation, large-scale multi-center prospective validation, and integration with other omics platforms will be key to unlocking the full potential of metabolomics combined with ML in precision healthcare. Full article

High-dimensional engineering datasets often combine class imbalance, noisy structures, and limited ground truth, making unsupervised analysis difficult to evaluate reliably. This study quantifies how three properties—partition stability, minority class recovery, and topological interpretability—are traded off across clustering methods, using a capacity-matched 25-seed comparison on a TCGA-derived RNA expression dataset (10,095 samples, 19 cancer types, 13,634 genes). We compare K-means across cluster counts $k\in \{19,\dots ,400\}$, self-organizing maps (SOMs) across lattice sizes from 25 to 625 nodes, consensus K-means, a granularity-matched SOM-Super20 control, and four modern baselines (HDBSCAN, spectral clustering, Gaussian mixtures, and Leiden). At matched prototype budgets, K-means is both more reproducible and substantially better at recovering minority classes than SOMs: at 400 prototypes, K-means achieves pairwise NMI $0.819$ versus $0.621$ for the $20\times 20$ SOM and recovers the smallest cancers 6–$14\times$ more effectively (pancreas effective coverage $0.760$ vs. $0.054$).Crucially, the SOM does not close this gap even when given more prototypes ($0.07$ at 625 nodes), so, under matched capacity, minority recovery is better explained by representational capacity and centroid allocation freedom than by topology preservation. The recovery is not free: increasing k overfragments the partition and lowers the pairwise ARI stability ($0.643\to 0.419$ from $k=20$ to $k=400$), while the NMI remains robust ($\approx 0.82$). The hardest minority, pancreas, is recovered only by high-capacity K-means and by no other method evaluated, including SOMs at any size, consensus K-means, SOM-Super20, HDBSCAN, Gaussian mixtures, spectral clustering, and Leiden. The SOM’s distinct value is therefore not stability or recovery but the interpretable two-dimensional topological visualization that it uniquely provides, including a gradient-organized structure that is reproducible across seeds for kidney (weaker for uterus). No single method optimizes all three properties; the appropriate choice depends on whether a task prioritizes reproducibility, minority recovery, or visual interpretability. Because these conclusions follow from the shape of the data and the allocation behavior of the algorithms rather than from biological semantics, we expect them to transfer to high-dimensional imbalanced engineering data, such as those from fault clustering, condition monitoring, and anomaly detection. Full article

This study evaluates the unique ecological and floristic identity of the coastal dune vegetation in the Enez delta and adjacent dune coast of Saros Bay (southern Thrace) by comparing it with diverse dune systems across the Anatolian peninsula. A comprehensive data matrix of 97 phytosociological relevés across nine representative coastal dunes spanning the East Mediterranean, Aegean, Marmara, and Black Sea coasts was analyzed. Methodologically, univariate non-parametric approaches (Friedman variance analysis and Durbin–Conover tests) were integrated with multivariate techniques, including Hierarchical Cluster Analysis and Principal Coordinates Analysis (PCoA) based on a Bray–Curtis dissimilarity matrix. Ephedra distachya ssp. monostachya was found to be the most characteristic and differentiating taxa from the clustering. Friedman test results demonstrated highly heterogeneous species abundance across localities (χ² = 27.2, p < 0.001). The multivariate synthesis revealed a profound ecological decoupling for Saros Bay dunes: while macroclimatic filtering forces a powerful functional convergence with arid Mediterranean models dominated by therophyte, strict composition-based metrics isolate Saros Bay coastal dunes into an entirely independent taxonomic clade. PCoA ordination confirmed this distinctiveness (p < 0.05 against six of the eight national localities), with the first two axes explaining 33.58% of the total variation (Axis 1: 19.66%, Axis 2: 13.92%). This isolation is driven by a high density of Irano-Turanian elements and specialized local lineages like Silene kotschyi. Conversely, a sharp latitudinal bio-climatic macro-gradient was mapped, showing a transition toward humid Black Sea systems strictly dictated by macroclimatic filtering rather than biotic competition (p > 0.05). To preserve these specialized niches, designating coastal dunes of Saros Bay as a Priority Conservation Unit and establishing international, transboundary catchment to coast monitoring frameworks are essential. Full article

Background/Objectives: Acne vulgaris can impair quality of life (QoL) beyond lesion burden, and distress, insomnia, and stress may co-occur. We compared patient-reported burden across treatment-intensity groups used as pragmatic proxies of disease burden and management complexity, quantified associations between clinical severity and psychosocial measures, and modeled predictors of high QoL impairment. Methods: Cross-sectional outpatient study (N = 97) in Timișoara, Romania. Severity was assessed using the Global Acne Grading System (GAGS) and scarring grade (0–3). Participants completed Romanian-language versions of the Dermatology Life Quality Index (DLQI), Cardiff Acne Disability Index (CADI), Patient Health Questionnaire-9 (PHQ-9), Insomnia Severity Index (ISI), and Perceived Stress Scale-10 (PSS-10). Group comparisons used analysis of variance (ANOVA)/Kruskal–Wallis and chi-square tests; associations used Spearman correlations. Multivariable ordinary least squares (OLS) regression modeled DLQI; penalized logistic regression modeled DLQI ≥ 11. Treatment intensity was analyzed as a descriptive stratification variable rather than as an independent exposure, and a prespecified sensitivity analysis re-examined the DLQI gradient after adjustment for GAGS and scarring grade. The DLQI ≥ 11 cut-off was prespecified from validated DLQI banding. Clustering, mediation, and network analyses were exploratory and hypothesis-generating in view of the modest sample size. Results: DLQI differed across treatment intensity strata (topical only: 11.1 ± 5.7, oral antibiotic + topical: 16.9 ± 7.6, isotretinoin: 20.4 ± 7.1; p < 0.001). High impairment (DLQI ≥ 11) occurred in topical only: 40.6%, oral antibiotic + topical: 78.8%, isotretinoin: 87.5% (overall 69.1%; p < 0.001). DLQI correlated with PHQ-9 (ρ = 0.59), ISI (ρ = 0.49), and PSS-10 (ρ = 0.60) (all p < 0.001). In multivariable analysis, GAGS (B 0.35, p < 0.001), PHQ-9 (B 0.27, p = 0.017), and PSS-10 (B 0.24, p = 0.007) independently predicted DLQI. The combined predictive model achieved an area under the curve (AUC) of 0.841 (5-fold cross-validation, CV) for identifying DLQI ≥ 11. Conclusions: In this Romanian outpatient cohort, psychosocial measures explained substantial variability in acne-related disability alongside clinical severity. These cross-sectional findings support brief integrated screening and suggest that higher-burden treatment strata merit particular psychosocial attention, but they should not be interpreted as evidence that treatment intensity itself independently causes worse mental health outcomes. The contribution of this work lies less in confirming known associations than in quantifying, in an under-represented eastern European outpatient setting, how much of the identification of high-impact patients depends on patient-reported rather than lesion-based information. Full article

Background/Objectives. Annual flaxseed (Linum usitatissimum) is the most cultivated Linum species. Perennial species (Linum austriacum, Linum lewisii, Linum perenne) show potential as alternative oilseed and fiber sources. It is critical to determine genetic variation in any collection to understand relationships and utilization within a breeding program. The research objectives were to analyze the genetic structure of the trifecta perennial flax using single-nucleotide polymorphic (SNP) markers for species differentiation and to discover potential Centers of Origin and/or diversity. Methods. We tested 70 USDA-GRIN-global wild populations (19 L. austriacum, 32 L. lewisii, 19 L. perenne; N = 850 seedling genotypes) to generate 9804 DArTseqLD SNPs (Group 1). Results. After filtering, within L. austriacum, 1199 SNPs (261 genotypes; Group 2) remained; in L. lewisii, there were 90 unique SNPs (273 genotypes; Group 3); in L. perenne, there were 2716 SNPs (309 genotypes; Group 4). Four genetic clusters were detected using STRUCTURE, consistent with principal coordinate analysis and SplitsTrees. Clear distinctions within and among each perennial species’ populations were found, separating into two distinct pure taxon groupings, along with peripheral populations or outliers. Both L. austriacum and L. lewisii potentially had two putative Centers of Origin, although L. perenne had one. Conclusions. The occurrence of sympatric Linum species in the wild could explain the occurrence of peripheral population groups within each taxon, although other explanations are also possible. This may be consistent with the potential for genetic exchange between the perennial species and the greater genetic variability available. Future research will evaluate additional Linum to further delineate the genetic structure and variation within the genus Linum. Full article

Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics and urban infrastructure systems. The authors synthesize a corpus of 50 DOI-indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimization, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimizing protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments. Full article

Introduction: The Kumamoto sub-breed of Japanese Brown cattle is a small population facing increasing inbreeding and declining effective population size driven by the intensive use of a limited number of elite sires. Methods: We analyzed 811 Japanese Brown cows genotyped using a 30K SNP array, retaining 19,745 SNPs after quality control. We calculated and compared ten genomic inbreeding estimators, including SNP-by-SNP and segment-based measures. Population structure was first assessed by rearing region using principal component analysis (PCA) and was then evaluated using ADMIXTURE-based clustering and distance-based hierarchical clustering, from which representative subsets were retained under predefined filtering criteria and further examined using PCA and neighbor-joining (NJ) tree analyses. Finally, we calculated the contribution of each ADMIXTURE group to total gene and allelic diversity, and integrated these two measures into a final conservation index after Z-score standardization. Results: ROH- and HBD-based estimators showed high concordance, whereas allele-frequency-dependent SNP-by-SNP estimators exhibited distinct distributions. Rearing region did not explain the main genetic structure. Instead, four representative ADMIXTURE-based groups, supported by independently identified family groups, captured the major genetic structure associated with paternal backgrounds. ADMIXTURE group 2 made the largest contribution to both gene and allelic diversity, and showed lowest genomic inbreeding. Conclusions: These findings suggest that ROH- and HBD-based estimators may serve as valuable indicators of genomic inbreeding in the Kumamoto sub-breed of Japanese Brown cattle; paternal background has played an important role in shaping the current genomic structure; and ADMIXTURE group 2, mainly associated with the Haru-yama-to/-sakae sire background, may provide a valuable breeding resource for limiting future inbreeding accumulation and maintaining genetic diversity. Full article

Thermomagnetic-fraction geochemistry has shown promise beneath transported cover, but area-scale applications rarely combine explicit element-association analysis, statistical robustness tests and transparent target prioritization. We evaluate this workflow in the largely covered Jing’erquan Ni-Cu metallogenic area using 985 soil samples collected over approximately 140 km² at an average spacing of about 300 m (7.0 samples km⁻²). After removal of pre-existing strongly magnetic grains, 100 g aliquots were heated under oxygen-limited conditions at 650 °C for 45 min, and the newly generated magnetic fraction was separated at an instrument-current setting of 1 A. Twelve indicators were analyzed using descriptive statistics, correlation analysis, hierarchical clustering, principal component analysis (PCA), spatial clustering and a three-term composite score. Four rotated factors explain 75.1% of the variance; Cr and Ni load at 0.95 on F2, while TFe₂O₃, Zn, Co and Cu covary on F1. Pearson–Spearman matrix agreement (r = 0.990), winsorized-versus-original Pearson agreement (r = 0.999), and PCA factor-congruence coefficients (>0.998) show that the principal associations are not controlled by the largest Cu, Ni or Cr values. Alternative composite weights retain all eight named follow-up targets, although internal ranking changes when Cu is emphasized. Among them, JQ-1–JQ-4 are assigned high follow-up priority by the expert-informed score. The coincidence of JQ-1 with the known Jing’erquan Ni-Cu mining area provides an internal plausibility check for the workflow, whereas JQ-2–JQ-8 remain unverified follow-up targets. These results show that the thermomagnetic-fraction dataset delineates multielement surface geochemical anomalies in covered terrain and can serve as a complementary screening tool for subsequent Ni-Cu exploration. Full article

Magnetic-field effects have been reported in controlled biological systems, but the relevance of weak geomagnetic variability to vegetation under natural conditions remains uncertain. We examined temporal associations between satellite-derived solar-induced chlorophyll fluorescence (SIF) and geomagnetic disturbance derived from the Disturbance Storm Time (Dst) index. We defined the sign-inverted Dst index, SII, as the sign-inverted daily mean Dst, so that stronger negative Dst excursions corresponded to larger positive SII values. The primary exposure was the trailing 28-day mean SII, excluding the day of the SIF observation. The primary analysis used Orbiting Carbon Observatory-2 (OCO-2) SIF at 771 nm with leave-one-year-out harmonic adjustment for the annual cycle and linear calendar-time trend. Sensitivity and robustness analyses examined a 21-day exposure, a more flexible cyclic-spline seasonal adjustment, spatial cluster bootstrap, and three temporal-surrogate null models. We also examined temperature and vegetation strata, land-cover and geographic controls, adjustment for an ECMWF Reanalysis version 5 (ERA5) surface solar radiation downwards (SSRD)-derived photosynthetically active radiation (PAR) energy proxy and vapour-pressure deficit, and comparisons with planetary Kp index, 10.7 cm solar radio flux index (F10.7), and supplementary SIF wavelengths. In the primary analysis, the 28-day trailing mean of SII was weakly negatively associated with SIF anomalies (Spearman ρ = −0.051; 95% cluster-bootstrap CI, −0.053 to −0.050), with an estimated linear change of −0.0381 residual-SIF units per 100 nT. Empirical p-values were 0.084 for year permutation, 0.011 for circular shift, and 0.001 for 30-day block permutation. The association remained negative at 21 days and in persistently vegetated cells, but its magnitude was substantially reduced with cyclic-spline adjustment (ρ = −0.013 in the pairwise-matched sample). Negative estimates were found in several vegetated land-cover classes, whereas estimates for the Barren land-cover class and Sahara geographic control were close to zero. The contribution of SII to explained variance remained below one percentage point across the examined temperature regimes. Overall, the results show a weak temporal association whose magnitude depends on analytical choices. Independent observational replication and controlled experiments are needed to determine whether it reflects a biological response to natural geomagnetic variability. Full article