Advancing Open Science

This study investigates how pulsatile injection influences two-phase displacement efficiency in a pore-scale micromodel, with emphasis on the roles of porosity, amplitude, and frequency. Simulations were performed using a conservative level-set formulation in COMSOL Multiphysics across three porosity levels (φ ≈ 0.75, 0.50, 0.30) and a range of amplitudes (25–75%) and frequencies (0–200 Hz), with fixed fluid properties and wettability. In the baseline (non-pulsed) regime, residual saturation decreased with increasing inlet velocity but reached a plateau, indicating a velocity-limited mobilization. Superimposing sinusoidal pulsations led to improved displacement only within finite frequency bands. For each porosity, a “working window” emerged, where residual saturation reached a minimum: this optimum shifted toward lower frequencies and higher amplitudes with decreasing φ. These trends were quantified using the normalized residual saturation $S_{\mathrm{res}}/S_{\mathrm{ref}}$ and linked to the dimensionless Strouhal number $\mathrm{St}$, defined via the capillary time scale. Phase maps and velocity fields confirmed that at optimal conditions, pulsations activated transverse throats and suppressed capillary bridges, while excessive frequencies led to inefficient re-entrapment. A fixed observation time was used to enable consistent comparison across regimes. The findings delineate the parameter ranges under which pulsations yield tangible benefit and suggest practical guidance for tuning flow modulation based on pore structure.

Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an $R^2$ score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an $R^2$ of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach.

Remote northern regions face unique energy challenges due to geographic isolation, harsh climates, and limited access to centralized power grids. In response to growing environmental and economic pressures, there is a rising interest in hybrid energy systems that integrate renewable and conventional sources. This study investigates sustainable and cost-effective energy supply strategies for off-grid northern communities through the modeling and simulation of multi-energy microgrids. Focusing on case studies from Yakutia (Russia), Hordaland (Norway), and Alaska (United States), the research employs a comprehensive methodology that combines a critical literature review, system design using HOMER Pro software (version 3.16.2), and a comparative analysis of simulation outcomes. Three distinct microgrid configurations are proposed, incorporating various combinations of solar photovoltaic (PV), wind energy, diesel generators, and battery storage systems. The findings reveal that integrating solar PV significantly enhances economic efficiency, particularly in regions with high solar irradiance, underscoring its pivotal role in shaping resilient, sustainable energy systems for remote northern areas. This study is innovative in its cross-regional comparative approach, linking techno-economic simulation with climatic variability analysis to identify context-specific energy strategies. The key findings highlight how hybrid microgrids combining PV, wind, and storage systems can reduce both costs and emissions by up to 35% compared to diesel-only systems, offering practical pathways toward sustainable electrification in high-latitude regions.

Enterococci from raw goat milk were taxonomically allotted in the species Enterococcus faecalis using sequencing (16S rRNA and BLASTn analysis) with a percentage identity up to 99.91%. The virulence factor gene gelE was found in the strains EE/K3, EE/G3, and EE/G6. The agg gene was detected in the strain EE/G6, and the esp gene was detected in the strains EE/K5 and EE/G7. Each strain possessed at least one virulence factor gene. In the strain EE/G6, the gelE and esp genes were found. The strains EE/G6 and EE/G3 showed resistance to tetracycline and vancomycin. EE/G7 was resistant to vancomycin and gentamicin. All strains possessed low-grade biofilm-forming ability (0.1 < A570 ≤ 1.0). They possessed genes for biofilm formation (bopD, srt, and/or ace). They also produced esterase (20–40 nmo/L), esterase lipase, and α-chymotrypsin (10–40 nmoL). The values of acid phosphatase reached 20–40 nmoL. The strains EE/G3, EE/G6, and EE/G7 were observed to possess the most pathogenicity. However, all strains were susceptible to postbiotic active substances produced by two autochthonous lactococci, MK2/8 and MK1/3 (inhibitory activity up to 400 AU/mL). These postbiotic substances provide a new potential alternative to reducing contaminants in milk.

Objectives: This study assessed socioeconomic inequalities in cancer screening at the district level in Peru, focusing on bilateral mammography, fecal occult blood test (FOBT), and prostate-specific antigen (PSA) test. Methods: An ecological study was conducted using 2021–2023 data from the Health Information System (HIS) of MINSA. Screening rates were calculated per 1000 eligible individuals. Socioeconomic disparities were assessed using concentration indices (CIs) and gap analysis, with the Human Development Index (HDI) as the stratification variable. Results: Screening rates were higher in districts with greater HDI. The mean district-level rates were 15.41 (SD: 72.66) for mammography, 97.27 (SD: 107.34) for FOBT, and 104.87 (SD: 101.92) for PSA per 1000 eligible individuals. Positive concentration indices indicated a pro-rich inequality: CI for mammography (0.1745, p = 0.045), FOBT (0.0633, p < 0.001), and PSA (0.0290, p = 0.028). The largest gaps were observed in Amazonian and Andean regions, where screening coverage remained markedly low. Spatial distribution revealed that certain districts, particularly in Loreto, Ucayali, and Amazonas, had screening gaps exceeding 97%. Conclusions: Significant disparities in cancer screening exist across Peruvian districts, disproportionately affecting lower-HDI districts areas. Targeted interventions, including education, telemedicine, and improved infrastructure, are necessary to enhance equitable access to early detection services and reduce the burden of disease.

Passion for studying represents a crucial motivational resource in students’ academic functioning, yet its role remains complex. Based on Vallerand’s dualistic model of passion, this paper examines how harmonious and obsessive study passion relate to students’ well-being, academic burnout, and dropout intentions. Across three complementary studies, we employed a multi-method approach combining cross-sectional correlational analyses (N = 142), longitudinal structural equation modeling (N = 100), and Bayesian psychological network analysis (N = 132). The results consistently indicated that harmonious passion was positively associated with well-being and negatively with burnout, psychological distress, and dropout intentions. Longitudinal findings confirmed its predictive role, showing that harmonious passion at the beginning of the semester protected against exhaustion and disengagement later on. In contrast, obsessive passion demonstrated weaker and less consistent associations, functioning mainly through links with anxiety in the network structure. Together, these findings suggest that harmonious passion acts as a central protective factor in students’ academic and emotional adjustment, whereas obsessive passion may represent a potential risk under certain conditions. By identifying the motivational and emotional mechanisms that sustain students’ well-being and engagement, this study contributes to the goals of sustainable education, emphasizing the creation of learning environments that support development of harmonious passion for studying, with its beneficial effects for long-term mental health.

We propose a geometry topological framework to analyze storm dynamics by coupling persistent homology with Anti-de Sitter (AdS)-inspired metrics. On radar images of a bow echo event, we compare Euclidean distance with three compressive AdS metrics ($\alpha$ = 0.01, 0.1, 0.3) via time-resolved $H_1$ persistence diagrams for the arc and its internal cells. The moderate curvature setting ($\alpha =0.1$) offers the best trade-off: it suppresses spurious cycles, preserves salient features, and stabilizes lifetime distributions. Consistently, the arc exhibits longer, more dispersed cycles (large-scale organizer), while cells show shorter, localized patterns (confined convection). Cross-correlations of $H_1$ lifetimes reveal a temporal asymmetry: arc activation precedes cell activation. A differential indicator $\Delta \left(t\right)$ based on Wasserstein distances quantifies this divergence and aligns with the visual onset in radar, suggesting early warning potential. Results are demonstrated on a rapid Corsica bow echo; broader validation remains future work.

Objectives: This paper reports the preclinical evaluation of stable tumor-specific gold nanoparticles (AuNPs) activated by neutron irradiation as a therapeutic option for the treatment of cancers characterized by high tumor angiogenesis. Methods: A selection of promising AuNPs with high avidity to α_vβ₃-expressing glioma (U-87 MG) cells (IC₅₀ = 82–104 nM) were chosen with different surface loading of Arg-Gly-Asp (RGD) peptides as tumor targeting vectors for integrin α_vβ₃, a target which is overexpressed in tissues displaying high tumor angiogenesis. Three different [¹⁹⁸Au]AuNPs were evaluated applying three injection methods, intravenous (i.v.), intraperitoneal (i.p.), and intratumoral (i.t.), each in a group of six U-87 MG xenograft–bearing mice (54 female athymic nude mice in total). Their biodistribution and tumor accumulation was assessed by in vivo imaging within 1–7 days after injection and 7 days after injection by ex vivo measurement. Results: The developed [¹⁹⁸Au]AuNPs exhibited suboptimal biodistribution by i.v. application (accumulation pattern tail > liver > spleen, no significant tumor accumulation) and by i.p. application (accumulation pattern spleen >> liver > pancreas, slight tumor accumulation of <0.3 %ID/g). However, an acceptable biodistribution by i.t. application was observed (5.5 %ID/g in liver, 4.9 %ID/g in spleen, and 3.0 %ID/g in tumor). Conclusions: Despite the very promising in vitro results, the in vivo evaluation suggests that the [¹⁹⁸Au]AuNPs represent a platform for the development of restricted therapeutic strategies.