Abstract Proton radioactivity is a rare form of nuclear decay near the proton drip line, where proton-rich nuclei emit a proton to become more stable. The process provides useful information on the nuclear structure and shell effects in exotic regions of the nuclear chart. In this work, we obtain compact and robust empirical models with few parameters for the prediction of logarithmic half-lives of oneproton radioactivity through symbolic regression. A set of 44 experimentally determined one-proton emitters with decay energy (Q), orbital angular momentum (ℓ), and charge number (Z) are used to train an interpretable symbolic regression model (PySR). The model uses evolutionary algorithms to search a vast space of mathematical expressions and identify brief, physically insightful formulas for the logarithmic half-lives of proton decay. Among the expressions generated, the best-performing model achieves a root mean square error (RMSE) of 0.394 and a mean absolute error (MAE) of 0.322, highlighting high predictive accuracy. The model further precisely predicts the logarithmic half-life of recently observed heaviest proton-emitting isotope, 188 At, which was excluded from the training set. The prediction exhibited an absolute error of 0.407 (on the logarithmic scale) compared to the experimental value, demonstrating strong generalization capability. These results confirm that physics-informed symbolic regression is an effective and interpretable tool for modeling proton radioactivity and can be useful to explore the uncharted regions of the nuclear landscape.

Function diversity, the range of tasks individuals perform, and specialization, the distribution of function abundances, are fundamental to complex adaptive systems. In the absence of overarching principles, these properties have appeared domain-specific. Here, we introduce an empirical framework and a mathematical model for the diversification and specialization of functions across disparate systems, including bacteria, federal agencies, universities, corporations, and cities. We find that the number of functions grows sublinearly with system size, with exponents from 0.35 to 0.57, consistent with Heaps' law. In contrast, cities exhibit logarithmic scaling. To explain these empirical findings, we generalize the Yule-Simon model by introducing two key parameters: a diversification parameter that characterizes how existing functions inhibit the creation of new ones and a specialization parameter that describes how a function's attractiveness depends on its abundance. Our model enables cross-system comparisons, from microorganisms to metropolitan areas. The analysis suggests that what drives the creation of new functions depends on the system's goals and structure: federal agencies tend to ensure comprehensive coverage of necessary functions; cities tend to slow the creation of new occupations as existing ones expand; and cells occupy an intermediate position. Once functions are introduced, their growth follows a remarkably universal pattern across all systems.

Abstract Technologies utilising laser dot scanning to measure surface fibre orientation have evolved into advanced, system-ready solutions for machine strength grading. Although these methods significantly enhance predictive models of timber strength, they generally rely on surface-based interpolations that may not fully represent internal fibre architecture. In this study, a gradient structure tensor (GST) approach is investigated to estimate the normal direction of growth layers and infer internal fibre orientation. The method is compared with existing internal fibre orientation determination (IFOD) techniques combining laser dot measurements and destructive serial sectioning (DSS), and is assessed through local bending stiffness profiles derived from digital image correlation (DIC) tests and finite element (FE) simulations. Results indicate that accurate characterisation of internal fibre orientation, particularly when supported by laser dots-based measurements, enables highly reliable predictions of timber mechanical performance. DSS-based implementations yield determination coefficients of $$R^2 \approx 0.8-0.9$$ , while GST applied directly to DSS images—despite the images stack modest quality and without filtering optimisation—remains computationally efficient and shows promising correlation ( $$R^2 \approx 0.5$$ ) for tomographic applications. The proposed methodology provides a basis for generating robust fibre orientation datasets and for developing data-driven models capable of inferring internal architecture from surface or CT information. These outcomes open perspectives for improving mechanical grading procedures, integrating knot modelling and elastic property refinement, and ultimately reconstructing fibre orientation at the scale of entire logs for forestry and industrial use.

The Collatz map is investigated from a nonlinear-dynamics perspective with emphasis on the structure of its iterative orbits. By embedding integers within Sharkovsky’s ordering, odd initial values are shown to be sufficient for a complete characterization of dynamics. A “direction-phase” decomposition is introduced to separate iterative orbits into upward and downward phases, yielding a family of recursive functions parameterized by the number of upward phases. This formulation reveals a logarithmic scaling relation between the total iteration count and the initial value, confirming finite-time convergence to the period-three orbit. The Collatz dynamics is further shown to be dynamically equivalent to a binary shift map, whose ergodicity implies inevitable evolution toward attractors, thereby reinforcing convergence. Numerical analysis indicates that attraction basins follow a power-law distribution and display pronounced self-similarity. Moreover, odd integers grouped by upward-phase counts are found to follow Gamma statistics. Beyond its research implications, the framework provides a concise pedagogical case study illustrating how nonlinear dynamics, symbolic dynamics, and statistical characterization can be integrated to analyze a classical discrete problem.

ABSTRACT This study examines the dynamic interplay between public debt, corruption, and economic growth in 36 Sub‐Saharan African (SSA) countries over the period 2000–2022. Utilizing a two‐step System Generalized Method of Moments (GMM) estimator to address endogeneity concerns, we assess both the individual and interactive effects of public debt and corruption on economic growth. Robustness checks employing fixed‐effects, random‐effects, and leverage‐distance diagnostic techniques confirm the stability of our estimates. The findings reveal that both public debt and corruption independently exert statistically significant negative effects on economic growth. However, their interaction yields a counterintuitive result: in contexts characterized by high levels of corruption, public debt is positively associated with short‐term growth, suggesting the presence of a nonlinear relationship. Specifically, we identify a corruption threshold—4.90 on the logarithmic scale of the Corruption Perceptions Index—above which debt‐financed spending may temporarily boost output by circumventing bureaucratic inefficiencies. These results underscore the complex policy challenges facing SSA economies, where weak institutional environments constrain effective fiscal policy implementation. While the analysis does not endorse corruption, it highlights the relevance of second‐best policy considerations in governance‐fragile settings. Achieving sustainable and inclusive growth in the region requires comprehensive reforms aimed at reducing corruption, enhancing institutional capacity, improving fiscal governance, and ensuring the efficient utilization of public debt. Strategic investment and cross‐sector collaboration will be critical to building resilient and equitable development pathways.

Abstract This paper introduces a quantum-enhanced finite element method (FEM) designed for noisy intermediate-scale quantum (NISQ) devices, leveraging variational quantum algorithms (VQAs) to solve engineering partial differential equations (PDEs). We demonstrate the framework by solving the Euler-Bernoulli beam and the NAFEMS T4 heat transfer problems, which involve Dirichlet, Neumann, and Robin boundary conditions. A key innovation is a ``set-to-zero" strategy that incorporates boundary conditions through a correction matrix, $K_{bc}$, allowing for flexible imposition at any node without domain decomposition. The global stiffness matrix is decomposed into a constant number of Pauli terms, $O(1)$, using the method by Sato et al., while boundary terms are handled with a sublinearly scaling Partial Pauli Measurement (PPM) technique. The algorithm achieves logarithmic qubit scaling ($n = \lceil \log_2 N \rceil $ qubits for N degrees of freedom) and employs shallow, hardware-efficient circuits with empirically trainable depth for small-scale systems. Validation on the Qiskit statevector simulator shows high accuracy. For the Euler-Bernoulli beam problem with 4 to 64 degrees of freedom, the algorithm achieves relative errors of 0.5–1.5\% and fidelities of 0.998–0.999. For the NAFEMS T4 heat transfer benchmark, a 5.4\% relative error is observed. The VQA converges robustly within 77–350 iterations, though barren plateaus are a known challenge for scaling to larger systems. This work establishes a scalable framework for quantum FEM, offering a significant memory advantage over classical methods and advancing the potential for quantum-enhanced engineering simulations.

Ultraslow diffusion revisited: Logarithmic scaling in single-term fractional diffusion models for anomalous transport of complex systems

Enteral autonomy and the role of abdominal surgery in a cohort of infants with gastroschisis.

Identification of genetic variations associated with complicated characteristics, including susceptibility to particular diseases, has become a crucial function of Genome-Wide Association Studies (GWAS). However, the challenge of predicting statistical Single Nucleotide Polymorphisms (SNPs) remains due to high-dimensional data and complex genetic architectures. In this study, seven regression models are assessed: Random Forest, Extra Trees Regressor, XGBoost, CatBoost, LightGBM, Support Vector Regressor, and Elastic Net for their predictive capacity and precision in predicting SNP significance using transformed P-values (−log10 scale) as a continuous regression target, with Bonferroni and False Discovery Rate corrections applied during preprocessing to define significance thresholds for interpretation. By transforming P-values using a logarithmic scale, we examine model performance based on various metrics, including mean squared error, mean absolute error, explained variance and R-squared, to determine which models best predict SNP significance. Research findings show that tree-based ensemble methods, particularly the Random Forest and Extra Trees Regressor, achieve the highest predictive accuracy, with Random Forest emerging as the top performer. Gradient boosting models, such as XGBoost and CatBoost, also demonstrate robust results, indicating their ability to capture complex SNP interactions. The research study provides insight into the selection of the model for GWAS-based predictions and contributes to methodologies for more accurately identifying SNPs with potential implications in disease risk prediction. The results obtained offer practical guidance for researchers in choosing appropriate regression models for high-dimensional genetic data.

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions (×10 and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, C=102) achieves R2=0.9999, MAPE=0.0018, MAE=966.04, and RMSE=1589.64 on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability.

Accurate position estimation is critical for the effectiveness of automatic weapon and navigation systems. Standard Extended Kalman Filter (EKF) models typically adopt flat-Earth assumptions and static noise covariances, which limit their accuracy in operational environments. This study proposes an optimized EKF framework that integrates two complementary approaches. First, ship trajectories are represented in Earth-Centered Earth-Fixed (ECEF) coordinates with a WGS-84 reference to account for Earth’s curvature. Second, process (Q) and measurement (R) covariances are adaptively determined using Joint Likelihood Maximization (JLM) with logarithmic scale exploration, allowing the filter to automatically identify the most accurate configuration. Each Q/R setting is evaluated within the EKF framework using root mean square error (RMSE) derived from radar data logs. The method was tested under short-history scenarios (5 and 10 data points) within an operational range of ±15 km, reflecting conditions commonly encountered in Combat Management Systems (CMS). Results show that while coordinate transformation alone provides only marginal improvements at short ranges, the combination of curvature modelling and adaptive Q/R tuning significantly reduces RMSE, achieving average errors approaching zero with high repeatability as measured by standard deviation. This research demonstrates a novel integration of geometric and statistical optimization in EKF design and highlights its applicability to ship trajectory estimation and defence systems.

The importance of gene-environment interactions (G×E) for complex human traits is heavily debated. Recently, biobank-based GWAS have revealed many statistically significant G×E signals, though most lack clear evidence of biological significance. Here, we partly explain this discrepancy by showing that many G×E signals simplify to additive effects on a different phenotype scale, a classical concern that is currently underappreciated. Our results clearly distinguish G×Sex effects on height, which vanish on the log scale, from G×Sex effects on testosterone, where the log scale uncovers biologically meaningful female-specific effects. Across 32 phenotypes in UK Biobank, we find that scaling by a power transformation can explain 46% of PGS×Sex interactions, and that simple log transformation can explain 23%, with similar results for other environments. We also show that phenotype scale can substantially impact GWAS discovery and the construction and evaluation of polygenic scores. Finally, we provide a set of guidelines to consider and choose phenotype scale in modern genetic studies.

Abstract Magic resources and entanglement are fundamental components for achieving the universal quantum computation, so is the interplay between them. Herein, we uncover an intrinsic scaling law of the magic resource and bond dimension of matrix product states in Haar-random quantum circuits, that is, the magic resource is converged on a bond dimension in logarithmic scale with the system size. From a practical perspective, this finding substantially enhances the classical simulability of nonstabilizerness. It also allows us to utilize the bond dimension as a bridge to link the entanglement and the nonlocal magic resource, which extends the capacity perspective that the entanglement plays the role of container for the nonlocal magic resource. Furthermore, the intrinsic scaling enables an information separation between the nonlocal magic resource and the extra entanglement. This, in turn, leads to the conclusion that, any dynamical relation between magic and entanglement resources is ruled out. In other words, it is inappropriate to regard the entanglement as the driving force of the growth and spreading of nonlocal magic resource.

For biological cells, their viscoelastic properties play critical roles in both physiological and pathological processes, and indentation has emerged as a key technique to extract mechanical properties. If purely elastic behavior is assumed, the achieved elastic moduli become depth-dependent and highly scattered, underscoring the need to account for cellular viscoelasticity. However, the complexity of existing methods poses significant challenges for the practical extraction of viscoelastic parameters from standard indentations. In this work, we formulate explicit expressions describing spherical and conical indentation responses for viscoelastic cells elucidated by power-law rheology (PLR) model. Combining Lee and Radok's approach and traditional Hertzian and Sneddon's contact models, the relations between apparent modulus and loading time are obtained analytically, which are independent of loading velocity. Notably, the linear dependence of the normalized apparent modulus on loading time on a logarithmic scale can be utilized as a signature of the PLR behavior of cells, and its explicit expression can be directly adopted to accurately extract the viscoelastic parameters of cells. Applications of this approach to standard indentations enable robust extraction of viscoelastic parameters, with high consistency demonstrated across both virtual numerical experiments and actual experiments. This work presents a straightforward and reliable approach to accurately determine the viscoelastic properties of biological cells from standard indentations, without the need for complex fitting procedures or velocity-dependent corrections.

Background:CYFRA 21–1, a cytokeratin-19 fragment, is a validated serum biomarker for non-small cell lung cancer (NSCLC). However, most studies rely on single time-point measurements, limiting its specificity in differentiating malignancy from benign pulmonary conditions. Inspired by the clinical utility of serial PSA measurements in prostate cancer, we investigated whether longitudinal trends in CYFRA 21–1 could enhance diagnostic and monitoring capabilities in patients with pulmonary nodulesMethods and Findings:We analyzed 132 patients with pulmonary nodules, including 41 with lung cancer and 91 with benign diagnoses. CYFRA 21–1 levels were measured serially using electrochemiluminescence assays. Longitudinal trends were assessed using linear mixed-effects models to estimate biomarker trajectories. Subgroup analyses examined differences between benign, untreated cancer, and post-treatment cancer groups, as well as within-patient changes in a subset of 16 cancer patients with both pre- and post-surgical measurements. Log-transformed data were used for the analysis. At baseline, CYFRA 21–1 levels were significantly higher in malignant versus benign nodules. Over time, CYFRA trajectories diverged: benign cases showed slight increases, whereas cancer patients exhibited greater biomarker volatility. In treated cancer patients, trend of CYFRA levels on the natural log scale decline from −0.00137 pre-surgery to −0.00263 to post-surgery, and both cancer groups showed significantly higher absolute slopes than the benign group (p < 0.05). While pre- vs post-treatment slope differences did not reach significance (p = 0.211), the general pattern indicated that CYFRA 21–1 is a dynamic marker responsive to tumor presence and removal.Conclusions:CYFRA 21–1 exhibits substantial within-patient variability over time, with trajectories that reflect disease state and treatment. These findings suggest that longitudinal monitoring of CYFRA 21–1—analogous to PSA velocity in prostate cancer—may offer improved diagnostic and prognostic insight in the evaluation of pulmonary nodules. Further studies in larger cohorts are warranted to validate these findings and explore clinical implementation of CYFRA trajectory analysis.

Abstract Background Migrants detained by U.S. Immigration and Customs Enforcement (ICE) are thought to have a high risk of preventable infectious diseases due to crowding and poor healthcare access. Prior studies had limited data access and none report post-pandemic trends. We assessed epidemiologic patterns of three vaccine-preventable diseases across ICE facilities. Three month sliding average of case rate (per 100,000 person-months) over time stratified by reporting detention facility from 2019 through 2023. Seasonal analysis of variations in facility-level case rate by month from 2019 through 2023. Bar plots of mean facility-level incidence per year and grouped by month. Panel A. Case rate of influenza; Panel B. Case rate of mumps; Panel C. Case rate of hepatitis A. Note: Variation in Y-axis scale dependent on disease panel. Panel A. Case rate of influenza on linear scale; Panel B. Case rate of mumps on linear scale; Panel C. Case rate of hepatitis A on linear scale; Panel D. Case rate of influenza on log scale; Panel E. Case rate of mumps on log scale; Panel F. Case rate of hepatitis A on log scale. Black line is mean facility-level case rate with a shadow of the standard deviation. Note: Variation in Y-axis scale dependent on disease panel. Y-axis axis on log scale. Methods We obtained ICE detainee data on influenza, mumps, and hepatitis A from January 2019–October 2023 from the Department of Homeland Security for 20 centers. National influenza data came from CDC FluView. Case counts were aggregated monthly, and case rates estimated using annualized average daily populations. Outbreaks were defined as clusters of three or more monthly cases within a facility. We characterized case demographics, system- and facility-level trends, seasonality, outbreaks, and geospatial patterns. Number of outbreaks and outbreak cases over time across the system from 2019 through 2023. Bar plot of number of facilities with active outbreaks per month and line plot of number of system-level outbreak cases over time. Panel A. Case rate of influenza; Panel B. Case rate of mumps; Panel C. Case rate of hepatitis A. Note: Variation in X-axis and Y-axis scale dependent on disease panel. Spatial distribution of average case rate (per 100,000 person-months) from 2019 through 2023 at facility level. Choropleth map of averaged case rate per disease at facility level with circle size and color corresponding to case rate. Panel A. Case rate of influenza; Panel B. Case rate of mumps; Panel C. Case rate of hepatitis A. Note: Variation in legend. Results From 2019–2023, 2,035 influenza cases were reported, averaging 35.1 cases monthly (range: 0–276). The average facility-level case rate was 19.4 cases per 100,000 person-months [P-M]; range: 0–1009.1). December had the highest average facility-level case rate (25.6 cases) and July the lowest (2.7), paralleling national trends. Across 15 facilities, 79 outbreaks occurred involving 1,739 cases with an average duration of 2.5 months (range: 1–13). For mumps, 252 reported cases averaged 4.3 monthly (range: 0–61). The average facility-level case rate (per 100,000 p-m) was 1.4 (range: 0–160.2). June had the highest average facility-level rate (1.3) and February the lowest (0.8); 16 outbreaks across 8 facilities involved 177 with an average duration of 1.8 months (range: 1–6). For hepatitis A, 486 cases were reported, averaging 8.4 monthly (range: 0–40). The average facility-level rate (per 100,000 p-m) was 5.2 (range: 0–273.4). July had the highest average facility-level case rate (5.5) and November the lowest (0.8); 33 outbreaks across 11 facilities involved 158 cases, averaging 1.2 months (range: 1–2). No geospatial clusters were observed. Conclusion ICE detainees have high rates of vaccine-preventable infectious diseases with wide variation across facilities. ICE vaccination campaigns and improved facility procedures could reduce disease burden, protecting migrant and staff health. Disclosures All Authors: No reported disclosures

Abstract Background HIV Pre-exposure prophylaxis (PrEP) remains a highly effective tool in HIV prevention. While studies on national gains in PrEP access, little is known about how access has over time and geography sub-nationally. We characterize trends in PrEP access across U.S. metropolitan counties from 2016-2022, identifies regional disparities, and evaluate the Ending the HIV Epidemic (EHE) initiative.Figure 1.County-level trends in metropolitan PrEP use rate (per 100,000 people) and PrEP-to-need ratio from 2016 through 2022.Panel A. PrEP use rate (per 100,000 people) on linear scale; Panel B. PrEP-to-need ratio on linear scale. Panel C. PrEP use rate (per 100,000 people) on log scale. Note: Variation in y-axis scale by panel.Figure 2.Geospatial trends in county-level PrEP-to-need ratio and PrEP use rates (per 100,000 people) across the metropolitan United States.Panel A. PrEP use rate (per 100,000 people) in 2016; Panel B. PrEP use rate (per 100,000 people) in 2022. Panel C. PrEP-to-need ratio in 2016; Panel D. PrEP-to-need ratio in 2022. Note: Variation in color scale by panel. Methods Annual, county-level data for 2016-2022 PrEP-to-need ratios (PNRs) and PrEP rates (recipients per 100,000 people) were from AIDSVu. We included metropolitan counties ( &amp;gt; 100,000 people). We calculated descriptive statistics and temporal trends, performed spatial autocorrelation analyses using global and local Moran’s I, and conducted a difference-in-difference analysis between EHE and non-EHE counties.Figure 3.Geospatial analysis of hotspots and coldspots in county-level PrEP-to-need ratio and PrEP rates (per 100,000 people) across the metropolitan United States. A Local Moran’s I analysis was performed to estimate hotspots (counties of significantly greater PrEP-to-need ratio or PrEP rate compared to the surrounding counties) and coldspots (counties of significantly lower PrEP-to-need ratio or PrEP rate compared to the surrounding counties). Panel A. PrEP-to-need ratio hotspots and coldspots in 2016; Panel B. PrEP-to-need ratio hotspots and coldspots in 2022. Panel C. PrEP use rate hotspots and coldspots in 2016; Panel D. PrEP use rate hotspots and coldspots in 2022.Figure 4.Trends in the PrEP-to-need ratio and PrEP use rate (per 100,000 people) across metropolitan United States counties from 2016 to 2022 by inclusion in the Ending the HIV Epidemic program.Panel A. County-level trends of PrEP use rate (per 100,000 people) stratified by inclusion in the Ending the HIV Epidemic program; Panel B. Averaged county-level trends of PrEP use rate (per 100,000 people) by inclusion in the Ending the HIV Epidemic program. Panel C. County-level trends of PrEP-to-need ratio stratified by inclusion in the Ending the HIV Epidemic program; Panel D. Averaged county-level trends of PrEP-to-need ratio by inclusion in the Ending the HIV Epidemic program. Note: Variation in y-axis scale by panel by outcome. Results The average county PNR was 2.5 (range: 0.2-18.0) in 2016 (N=218) and 12.5 (range: 0.4-56.6) in 2022 (N=534); the average PrEP rate was 27.6 (range: 3-599) in 2016 and 121.2 (range: 8-1376) in 2022. Global Moran’s I analyses of PrEP rates and PNRs for counties with geospatial data were significant in 2016 (N=153) and 2022 (N=455), indicating spatial autocorrelation. Local Moran’s I analyses of PNRs identified 11 hotspots in 2016 and 46 in 2022; PrEP rate analyses found 3 hotspot counties in 2016 and 30 in 2022. The average PNR in 2016 was 2.1 in EHE counties (N=46) and 2.6 in non-EHE counties (N=172); in 2022, it was 10.3 in EHE counties (N=124) and 13.1 in non-EHE counties (N=410). The average PrEP rate in 2016 was 36.1 in EHE counties and 25.3 in non-EHE counties; in 2022, it was 158.4 in EHE counties and 109.9 in non-EHE counties. The annual PrEP rate of change was 12.3 recipients greater (p=0.04) in EHE counties compared to the baseline annual change; no significant difference was found in the annual PNR change between EHE and baseline trends. Conclusion Despite overall increases in PrEP access, marked sub-national disparities persist. While EHE counties made measurable progress, they continued to lag behind non-EHE counties in terms of PNRs. Emerging hotspots, including in the Northwest and the Southeast, demonstrate potential models for expanding access. These findings underscore the need for targeted, local strategies to improve PrEP equity and support national HIV prevention goals. Disclosures All Authors: No reported disclosures

This paper presents a hierarchical scaling and normalization method to multi‐area signal measurements that dynamically relates both the angular phase domain and its amplitude in empirical analysis of power system oscillations. The proposed approach combines logarithmic relations and the Hilbert transform to derive an effective multi‐area data scaling and normalization method, improving objectively the numerical performance and reliability of data‐based modal extraction algorithms. This method is developed in order to numerically minimize multiscale angular phase and amplitude effects in decomposition and identification processes of inter‐area oscillation modes. It employs conventional data‐based analysis algorithms on interconnected power systems to achieve this objective. Results show that the presented method guarantees the most effective description of interscale interaction effects and fluctuations among detected modal oscillation patterns, enhancing empirical modal extraction for inter‐area electromechanical modes.

In this work, we challenge the dominant use of logarithmic scales to communicate values spanning multiple orders of magnitude-Orders of Magnitude Values (OMVs)-to the general public. Focusing on bar charts, we incorporate cognitive insights into visualization design to better align with how humans perceive OMVs. Studies in cognitive psychology suggest that, for large numerical ranges such as millions and billions, people do not think logarithmically. Instead, they perceive numbers in a piecewise linear manner, grouping values into scale words (e.g., millions) and applying linear reasoning within each group. We build upon a recently introduced piecewise linear scale, EplusM, and validate its use in bar charts, which we refer to as EplusM bar charts. We also introduce two novel variants of the EplusM bar chart informed by findings in numerical perception: Bricks, which builds on the concepts of round numbers and subitizing, and Multi-Magnitude, which leverages categorical perception of large numbers. In a crowdsourced experiment, we evaluate four bar chart designs: 1) Log, 2) EplusM, 3) Bricks, and 4) Multi-Magnitude, across value retrieval and quantitative comparison tasks. Our results show that EplusM bar charts are significantly preferred over logarithmic designs, increase user confidence, and reduce perceived mental demand, while maintaining task performance. These findings suggest that EplusM bar charts can serve as effective alternatives to logarithmic ones when visualizing OMVs for general audiences.

Purpose: To evaluate visual and refractive outcomes obtained with an intraocular lens (IOL) in a large, unselected cohort at an Argentine cataract surgery center. Methods: This retrospective consecutive-case study included patients implanted with the TECNIS Eyhance IOL (ICB00) between November 2020 and November 2024. Postoperative evaluation was performed at one month. Recorded variables included preoperative corrected distance visual acuity (CDVA) and postoperative uncorrected distance visual acuity (UDVA) in logarithmic scale, uncorrected near visual acuity (UNVA) at 32 cm, manifest spherical equivalent (SE), implanted IOL power, and the presence of complications. Near visual acuity was measured with a Jaeger chart until March 2023 and subsequently with a standardized logarithmic chart (Byromat); Jaeger values were converted to logMAR for analysis. Results: A total of 899 eyes from 518 patients were included. The mean implanted IOL power was 22.1 ±2.5 D. Preoperative SE averaged 1.23 ±2.3 D, decreasing to -0.42 ±0.50 D postoperatively. Preoperative CDVA was 0.16 logMAR, improving to a postoperative UDVA of 0.04 logMAR. No eye lost lines of vision. UNVA demonstrated functional performance, with most measurements between J1 and J3 during the initial phase. Using the logarithmic chart, mean UNVA at 32 cm was 0.26 ±0.11 logMAR, with most eyes between 0.2–0.3 logMAR. Conclusion: The Eyhance IOL provided reliable uncorrected distance and functional near visual outcomes, with a favorable safety profile throughout the four-year study period.