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Exploring barriers to adoption of climate-smart agriculture among smallholder farmers in Odisha, India

Decoding living systems: Reassessing crop model frontiers via biological dynamics and optimized phenotype

Modeling and optimizing phenotypic performance of biological systems demands understanding how physiological processes mediate genotype-by-environment interactions. While AI-driven approaches achieve predictive accuracy, they often function as black boxes that obscure biological causality. Process-based models address this limitation through explicit mechanistic representation, enabling both quantitative optimization and biological interpretation. This study contributes an inverse engineering framework with three integrated layers: sensitivity analysis validating biological coherence, genetic algorithm exploring virtual phenotypes to identify adaptive strategies, and similarity analysis quantifying routes from computational optima to field-validated cultivars. Sensitivity analysis identified eight genetic-based coefficients governing yield with robust rankings (95% CI width = 0.04). The genetic algorithm explored 5,364 virtual cultivars across 40 generations, revealing two strategies: extended growth (116 days) achieving 4,837 kg/ha under higher water availability (815 mm, field capacity 0.30), and shortened cycles (100–103 days) maintaining high efficiency (HI: 0.55–0.58) under water deficit (540 mm, field capacity 0.23)—covering 89% of the cultivation area. Similarity analysis against 21 field-validated cultivars identified WAB56−50 (70.7%) and DKAP2 (67.2%) as breeding candidates, quantifying a 22–30% genetic gap between current germplasm and computational optima. The framework, built upon 3 years of field characterization, compressed the evaluation and selection cycle, enabling adaptation across regional precipitation gradients identified through GMM-based classification. The principles demonstrated here extend across biological scales—from organismal phenotyping to cellular systems where biological dynamics can be modeled and traits measured.

From Lipoic Acid to 1,2-Dithianes: Expanding Radical Ring-Opening to Less-Activated Monomers Such as Vinyl Acetate

A lightweight transformer-based hybrid encoder-decoder model for chest X-ray medical report generation

Steric Engineering of Phenanthrenequinone for Ultrafast and Tunable Visible Light-Induced Photoclick Reaction

Comparative performance evaluation of chemical coagulants in dairy wastewater treatment: a multi-criteria decision-making approach

Solid-State Side-Chain Functionalization of Conjugated Polymers for Expanded Chemical and Functional Versatility

Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection

Abstract As digital imaging in healthcare grows quickly, dealing with vast medical image data is getting trickier. Content-Based Medical Image Retrieval (CBMIR) systems help with this, but they struggle because of the gap between simple image details and what these images mean in a clinical setting. This paper presents a new approach using deep learning for CBMIR that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Explainable AI (XAI). Using the Breast Ultrasound Image (BUSI) dataset for training, this hybrid model classifies images and finds the relevant results based on predictions. It reaches a classification accuracy of 99.24% and performs well in retrieval tasks.

Latent profile analysis of kinesiophobia during the out-of-hospital early rehabilitation phase after PCI in patients with CHD

Coverage and associated factors of inactivated polio vaccine uptake among children aged 12–23 months in Sub-Saharan Africa

Abstract The Inactivated Polio Vaccine (IPV) protects against all strains of the polio virus and poses no risk of vaccine-associated paralytic poliomyelitis. However, its coverage remains below an optimal level in Sub-Saharan Africa (SSA), and instances of vaccine-derived poliovirus have been reported. While several studies explored vaccine coverage, research specifically focused on IPV in SSA remains limited. Hence, this study aimed to assess the coverage and determinants of IPV uptake among children 12–23 months of age. A secondary data analysis was conducted using data from the recent demographic and health survey in 20 SSA countries between 2016 and 2023. The study included a total weighted sample of 43,564 children aged 12–23 months. Due to the hierarchical nature of the data, multilevel logistic regression was employed to identify associated factors. Model fitness and comparison were assessed using the median odds ratio, intra-class correlation coefficient, proportional change in variance, and deviance. Adjusted Odds Ratios (AORs) with their 95% CI were computed. Variables with a P-value < 0.05 in the multivariable multilevel analysis were considered statistically significant. The pooled inactivated polio vaccine coverage was 65.01% with a 95% CI (55.44, 74.76). Maternal age 20–35 years (AOR = 1,08, 95% CI: 1.00, 1.18) and above 35 years (AOR = 1.22, 95%CI: 1.11, 1.34), maternal education at primary level (AOR = 1.30, 95% CI: 1.23,1.37), and secondary level or above (AOR = 1.87, 95% CI: 1.76, 1.99), marital status (married (AOR = 0.83, 95% CI: 0.75,0.90), and widowed/divorced (AOR = 0.85, 95% CI: 0.75, 0.95))), media exposure (AOR = 1.20, 95% CI: 1.14,1.25), antenatal visit 1–3 (AOR = 1.86, 95%CI: 1.72, 2.01) and ≥  4 visit (AOR = 2.51, 95% CI: 2.33,2.70), postnatal care (AOR = 1.73, 95%CI: 1.65,1.82), delivery at a health facility(AOR = 1.86, 95%CI: 1.77,1.97), birth interval more than 48 months (AOR = 1.34, 95%CI: 1.24, 1.44), urban residence (AOR = 1.24, 95%CI: 1.17,1.31), high community female literacy (AOR = 1.68, 95%CI: 1.54,1.83) were statistically significant positive determinants of IPV uptake. Conversely, rich household wealth (AOR = 0.84, 95% CI: 0.79,0.89) showed an inverse association. In most SSA countries, the inactivated polio vaccine coverage among children aged 12–23 months is substantially below the WHO-recommended herd immunity threshold of 90%, as well as beneath the 2024 global coverage of 85%. To improve this, stakeholders should focus on public health interventions like investing in maternal education, promoting antenatal and postnatal care, strengthening health service delivery, and raising community awareness through social media. Additionally, vaccination programs should target underserved areas and include mobile vaccination services.

Ligand-Driven Tuning of Adsorption Energy in Nanocrystals for High-Performance H ₂ O ₂ Electrosynthesis

An advanced rapid-visual CRISPR assay for detecting porcine reproductive and respiratory syndrome virus

Stabilizing Radicals in Aggregation-Induced Emission Rotor-Polyoxometalate Crystals for Efficient Photothermal Conversion

CD28 co-stimulatory domain enhances efficacy of CER T cell therapy compared to 4-1BB in an ovarian cancer mouse model

Aromatic Interactions Select for Homodimeric Assembly in a Quadruply Hydrogen-Bonded DADA 1,2-Azaphosphinine Dimer

Sensitivity-informed framework for enrichment distribution in MNR for thermal performance enhancement

Abstract As interest in space exploration and remote power systems grows, Micro Nuclear Reactors (MNRs) are being explored for their compactness, long operational life, and inherent safety. However, challenges such as Radial Power Peaking (RPP), structural integrity, and fuel utilization under high-temperature conditions remain inadequately addressed. In this study, the issues of RPP, uneven power distribution, and related safety concerns have been analyzed. RPP occurs because neutron flux and subsequent fission rates decrease while moving from the core center toward the periphery. In the annular-fueled MNR core presented, a maximum power peaking factor of 1.28 is observed. RPP poses a significant design challenge as it induces geometric distortion, reduces the fuel clad gap, and may lead to Fuel Clad Mechanical Interaction (FCMI), which is treated as a design-basis operational constraint for long-term autonomous operation of the reactor presented in this study. Consequently, the designed 1 MWth core power must be reduced in proportion to the peaking factor, limiting the allowable operating power to 738 kWth to ensure local thermal-mechanical limits are not exceeded. This study proposes a novel, zone-based, sensitivity-informed non-uniform fuel enrichment framework that mitigates RPP without any physical design changes. For the annular-fueled MNR core, the strategy reduces RPP by 75%, enabling a 28.7% increase in safe operating power from 738 kWth to approximately 950 kWth.

Total Synthesis of the Glycoside Antibiotic Paulomycin A

Correction: Biplot analysis and correlations between changes in terpenes in calf milk and immune variables, performance and rumen variables

A Shared Theta-Rhythmic Process for Selective Sampling of Environmental Information and Internally Stored Information

Selective attention is the collection of mechanisms through which the brain preferentially processes behaviorally important information. Many everyday tasks, such as shopping for groceries, require selective sampling of both external information (i.e., information from the environment) and internally stored information (i.e., information being maintained in working memory). While there is clear evidence that selective sampling of external information is influenced by internally stored information (and vice versa), the extent to which selective sampling of external and internal information compete for the same neural resources and attention-related processes remains a focus of debate. Previous research has linked theta-rhythmic (3–8 Hz) neural activity in higher-order (e.g., frontal cortices) and sensory regions to theta-rhythmic changes in behavioral performance during selective sampling. Here, we used electroencephalography and a dual-task design (i.e., a task that required both external and internal information), in male and female humans, to directly compare theta-dependent fluctuations in behavioral performance during external sampling with those during internal sampling. Our findings are consistent with a shared theta-rhythmic process for selectively sampling external information or internal information. This theta-rhythmic sampling is associated with both phase-dependent changes in sensory responses (i.e., as measured with the N1 component) and phase-dependent changes in interactions between external and internal information. The theta phase associated with weaker sensory responses and relatively worse behavioral performance (i.e., the “bad” phase) is also associated with a slowed perceptual decision-making process (as measured with the centroparietal positivity component), specifically during dual-task trials when to-be-detected external information matches to-be-remembered internal information.

Solution-Phase Synthesis of Nanostructured AgSbTe ₂ : Glass-like Thermal Conductivity and High Thermoelectric Performance