Two-stage robust optimal capacity configuration method for wind solar hydrogen storage system considering source-side uncertainty

Improving breeding efficiency for superior soybean (Glycine max (L.) Merr.) germplasm used in natto and sprouts requires understanding how seed coat properties relate to quality and functional traits. We measured seed weight, water uptake, and sprout length and thickness across various genotypes tested at different locations and years. For the 2023-2024 set, we also analyzed fresh sprout weight and the percentage of good sprouts. Seed coat percentage was determined as the seed coat weight relative to the total seed weight. Additionally, we established a robust and reproducible method using scanning electron microscopy (SEM) to directly measure seed coat thickness. Genotype was the dominant factor influencing nearly all traits, while location, year, and genotype × environment interactions were negligible - except for sprout length in one dataset. Seed coat percentage was unrelated to water uptake but inversely correlated with seed size, and did not consistently predict sprout thickness or length. Importantly, this study is the first to directly measure seed coat thickness and demonstrate its association with water uptake, offering a practical selection criterion for breeding programs targeting natto and sprout quality. SEM imaging further enables detailed analysis of seed coat layers and structural features, opening new opportunities for trait characterization. By introducing a direct, scalable approach to quantify seed coat traits, this work provides a foundation for more precise breeding strategies and highlights the role of structural seed attributes in improving specialty soybean products. Future research could integrate seed coat thickness into genomic selection models to accelerate breeding progress. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Applying nephelometry for analyzing liquid yeast cultures.

Economic dispatch of distribution network considering EV integration: A three-stage robust optimization method for multiple uncertainties

High-throughput screening for the discovery of antidepressants targeting adenosine A2A receptors.

Time spectrum repaint domain-wise diffusion model for bearing fault diagnosis under time-varying conditions in high-speed trains.

This article investigates fixed-time synchronized convergence for disturbed second-order multiagent systems (MASs) in distributed optimization under the zero-gradient-sum (ZGS) scheme. A fixed-time ZGS distributed optimization method via sliding mode is first proposed for the second-order MASs, which avoids local minimization and rejects disturbances. To further achieve time-synchronized convergence, a hierarchical robust optimization method is then introduced. It employs a time-varying function-based local-minimization-free ZGS scheme within a virtual MAS to generate a reference signal that reaches the global cost function's minimizer and a fixed-time synchronized sliding mode tracking controller to drive the original second-order MAS to track this signal. Beyond the capabilities of the first protocol, this method also ensures the time-synchronized convergence of each agent's state components, low conservatism in terms of convergence time bounds, and privacy preservation. Numerical simulations demonstrate the effectiveness of the proposed methods.

Optimizing a new robust location-pricing problem in agricultural economy by customized bi-level algorithm

A robust safety assessment method based on belief rule base with dynamic rule regulation

In computational pathology, the gigapixel scale of Whole-Slide Images (WSIs) requires their decomposition into thousands of patches, resulting in high-dimensional embeddings that are computationally costly to process and often dominated by uninformative regions. Existing patch selection methods typically rely on heuristic sampling and do not explicitly address the trade-off between representation compactness and diagnostic accuracy. To address this gap, we propose EvoPS (Evolutionary Patch Selection), a novel framework that formulates patch selection within the training embedding space as a multi-objective optimization problem and leverages an evolutionary search to simultaneously minimize the number of selected patch embeddings and maximize the performance of a downstream similarity search task, generating a Pareto front of optimal trade-off solutions. By identifying a compact and diagnostically informative subset of training patches, EvoPS produces higher-quality training representations that reduce memory requirements and improve the signal-to-noise ratio of the training set. We validated our framework across four major cancer cohorts from The Cancer Genome Atlas (TCGA) using five histopathology foundation models. The results demonstrate that EvoPS can reduce the required number of training patches by over 90% while consistently maintaining or even improving the final classification F1-score compared to a state-of-the-art patch selection method. The EvoPS framework provides a robust and principled method for creating efficient, accurate, and interpretable WSI representations, empowering users to select an optimal balance between computational cost and diagnostic performance.

Lenacapavir is a new antiretroviral agent that targets the HIV-1 capsid, and is currently indicated for the treatment of patients with multidrug-resistant infection. It was recently approved by the U.S. Food Drug and Administration for use in pre-exposure prophylaxis. A robust sequencing method to determine HIV-1 gag sequences and resistance-associated mutations (RAMs) in people living with HIV-1 (PLWH), both prior and during lenacapavir treatment, is essential. In this study, we compared short-read (DeepChek® gag HIV-1) and long-read (PacBio gag SMRT) next-generation sequencing approaches for detecting gag mutations from blood samples of 47 PLWH (24 plasma HIV-1 RNA and 23 cell HIV-1 DNA). We found that both methods exhibited robust performance for sequencing gag from plasma HIV-1 RNA. For cell HIV-1 DNA, low viral load appeared to be a limiting factor for sequencing success for both approaches. Focusing on key positions associated with lenacapavir resistance, we observed concordant interpretation in all but one sample, where the 107S RAM was detected by DeepChek® only. Overall nucleotide concordance between methods remained high (99.2% to 100%). Both NGS methods proved effective for gag genotyping in clinical samples, supporting their use in monitoring resistance to capsid inhibitors, although minor differences in consensus sequences were observed.

ADCSim: Software for attitude determination and control system design and simulation

Science co-curricular experiences (SciXP)-such as research experiences and internships-are a common, yet understudied, strategy to enhance undergraduates' science career pursuit. Using a doubly robust method to account for initial differences in science motivation, academic readiness, and career intentions, we investigated whether SciXP participation across four years of college predicted undergraduates' (N = 1984) science achievement, career persistence, and motivation at the end of college and after graduation. Participation in science co-curricular experiences was positively associated with science GPA, earning a science degree, being in a more science-intensive career one totwo years post-graduation, and viewing science as important and useful at the end of college. Follow-up analyses suggested these relations were generally stronger when SciXPs occurred earlier in college and across multiple years. SciXPs were especially associated with higher science achievement, degree attainment, and motivation of underrepresented racial/ethnic minority students and the science motivation of women, but not for first-generation students, particularly when SciXP participation occurred early in college. These findings suggest undergraduate science co-curricular experiences are an effective means for increasing the scientific workforce and that these benefits may be especially promising for women and for students with racial/ethnic identities that are underrepresented in STEM fields.

Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop’s growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics. • Skeleton-guided 3D segmentation and tracking: Developed a robust method for organ-level point cloud segmentation and time-series matching, enabling accurate monitoring of crop development. • Leaf-scale growth and photosynthesis quantification: Established a pipeline to track leaf-level growth and compute key photosynthetic traits, including photosynthetic rate, absorbed light, and light use efficiency. • Phenotypic Variation Rate (PVR): Proposed a new metric to quantify dynamic changes in 3D plant structure, enhancing the analysis of growth patterns across time.

The use of a startling acoustic stimulus during a simple reaction time task results in the rapid initiation of a prepared response at extremely short latencies (< 80 ms). This so-called "StartReact effect" has been increasingly employed to probe subcortical contributions to response preparation, as it is thought to occur due to increased activation in reticulospinal pathways associated with engagement of the startle reflex. However, the lack of an agreed-upon definition of what exactly constitutes a StartReact effect, combined with differences in methodological protocols, has resulted in inconsistent interpretation of experimental results. Based on a comprehensive review of the literature, including evidence for the physiological mechanism underlying the effect, we propose that the clearest definition of the StartReact effect is "the early and involuntary triggering of a prepared movement in the presence of a startle reflex". Reflexive startle activity has been shown to be strongly associated with involuntary response initiation and avoids other potential confounding variables that have been shown to speed reaction time. Here we argue that classification of trials based on startle-related activation in sternocleidomastoid is the most robust method to confirm a StartReact effect. Special situations, such as pre-pulse inhibition, movements involving musculature that require additional considerations, and lowered response preparation levels, are also considered with regards to how to confirm the presence of a StartReact effect. Future directions, including the use of a StartReact protocol as a potential adjuvant therapy for movement disorders, are discussed.

• We revisit the concept of widely used attribution maps and share the observation that many gradient signals correspond to irrelevant features, obscuring the true drivers behind model decisions. • We introduce CriGrad , a new framework that improves the clarity of feature attribution maps by filtering out noise through critical parameter analysis, while maintaining the faithfulness of attribution maps. • CriGrad can be integrated as an add-on module in various attribution methods, and extensive experiments show that it significantly improves the reliability and explainability in 93% of the cases. Many post-hoc explainable feature attribution techniques analyze gradient propagation and understand decisions of deep learning models. However, conventional gradient analysis used to generate such maps can be noisy, potentially compromising the reliability of explanations. In this work, we introduce a robust method for computing feature attributions by identifying critical parameters and refining gradient propagation through these parameters. This method reduces the impact of non-critical parameters, mitigating the effect of feature leakage and randomized initialization, which introduce noise in attribution maps. We implemented this concept as an add-on module, called CriGrad , and evaluated its efficacy using three benchmarks and seven explainable models. Our results show that focusing on critical parameters improves explanability in 93% of the cases, demonstrating its effectiveness and improved reliability.

Cariprazine hydrochloride is an antipsychotic drug used to treat diseases such as schizophrenia and acute mania in adults. Quality determination of the cariprazine drug molecule is a critical aspect to ensure maximum drug efficacy and to minimize side effects caused by unwanted substances commonly referred to as impurities, formed during manufacturing. Hence, this study aims to develop a chromatographic method for the determination of organic impurities present in cariprazine hydrochloride. A stability-indicating and robust method was developed using reverse-phase high-performance liquid chromatography (HPLC). The analytical method involves the simple preparation of a mobile phase consisting of 0.2% orthophosphoric acid in HPLC grade water as mobile phase A, and acetonitrile as mobile phase B. Separation was achieved using a YMC Pack Pro C18 column (150mm × 4.6mm, 3μm), with a step gradient at a flow rate of 1.0 mL/min and detection at λmax 215nm for the quantitation of organic impurities. The method is capable of detecting impurities at the 0.01% level and quantifying them at the 0.03% level with respect to the test concentration. It was found to be linear, accurate, and precise over the range from the LOQ to 200% of the specification limit of impurities. The method was specific to all degradation products formed during forced degradation studies, thereby demonstrating its stability-indicating nature. The mass balance was found to be within the tolerance limit of 95-105%. All impurities, including process-related and degradation products, were characterized using mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy. The spectroscopic data confirmed the structures of the impurities. Overall, the method is useful for determining the quality of cariprazine hydrochloride in quality control.

This study aims to calculate and compare aerobic and anaerobic energy expenditure in women participating in recreational mini-trampoline activities. Thirteen women volunteered to participate in this study. Cardiorespiratory and metabolic variables-including [Formula: see text] (L/min), [Formula: see text] (ml/min/kg), [Formula: see text] (ml/beat), HR (bpm), [Formula: see text], [Formula: see text], RER, [Formula: see text] (L/min), FAT (g/h), METs, and [Formula: see text] (L/min) were measured using a portable gas analyzer (Cortex Metamax 3B). The mini-trampoline protocol was structured to progressively increase exercise intensity over a 5-minute period (2 and a half min aerobic rhytym; 2 and a half min anaerobic rhytym intensity), with load progression achieved through gradual increases in jump amplitude and movement frequency. The exercise protocol was performed to an instrumental music track characterized by a constant tempo of 128bpm, a regular 4/4 meter, distinct metrical accents, and no lyrics. Although the tempo was kept constant, rhythmic variations were utilized to distinguish between the aerobic and anaerobic phases. Data were analyzed using a paired-samples t-test to compare the phases, with statistical significance set at p < .05. As a result of the research, there were significant differencies found between Phase 1 (aerobic) and Phase 2 (anaerobic) according to [Formula: see text] (L/min), [Formula: see text] (ml/min/kg), [Formula: see text] (ml/beat), HR (bpm), [Formula: see text], [Formula: see text], RER, [Formula: see text] (L/min), FAT (g/h), METS, [Formula: see text] (L/min) variables (p < .001). Mini-trampoline exercise emerges as a robust and time-efficient training method capable of eliciting significant aerobic and anaerobic responses, alongside high energy expenditure. Not applicable.

ConspectusTwo-dimensional (2D) organic-inorganic hybrid perovskites provide a stable alternative to three-dimensional (3D) absorbers, which often suffer from sensitivity to moisture and light. However, the traditional 2D perovskite architecture functions as a "quantum-well" structure, where insulating organic cations form dielectric barriers that restrict both light absorption and charge transport. The research described in this account focuses on transforming these passive organic spacers into active electronic components. Specifically, this transformation is achieved by incorporating diynes (molecules with two adjacent triple bonds) directly into the perovskite lattice and inducing topochemical polymerization through thermal treatment, which results in the formation of a 2D perovskite that intercalates a conductive polymer between its inorganic layers.The incorporation of such a polymer brings drastic changes in the properties of these materials. For example, it can significantly reduce their bandgap by up to 1.5 eV, thereby moving absorption well into the near-IR (NIR) range. Similarly, it can also improve the conductivity of the resulting material by up to 3 orders of magnitude while also enhancing their hydrophobicity and overall stability.In this Account, we describe the synthesis and characterization of these hybrid materials, highlighting how the inorganic lattice preorganizes diacetylene ligands to facilitate solid-state reactivity. Further, we discuss the impact of oxidative doping, showing that the incorporation of stable organic radicals in the polymers enhances electrical conductivity and the material's absorption. We further establish the versatility of this strategy by expanding the library of diynes and halides, confirming that this approach is a robust and reproducible method for modifying the optoelectronic properties of various 2D perovskite scaffolds.Beyond fundamental material design, we discuss the application of these systems in high-performance optoelectronic devices, specifically air-processed NIR photodetectors. For instance, devices utilizing one of these polymerized 2D-perovskites exhibit remarkable responsivities on par with state-of-the-art devices. Ultimately, this account argues that the integration of conjugated polymers represents a paradigm shift for 2D perovskites, successfully transforming the organic spacer from a passive dielectric barrier into an electronically active component, thereby opening the door to new and exciting properties and applications.

Seasonal influenza epidemics exhibit complex transmission dynamics influenced by time-varying extrinsic factors such as social behavior and seasonal effects. Estimating changes in transmission rates is critical to enable accurate forecasting of the epidemic curve. This study presents a framework for detecting changepoints in the transmission parameter ([Formula: see text]), applied as a piecewise constant function within a deterministic compartmental model. Using hospitalized case data from four recent influenza A seasons in Ireland (2019/2020 and 2022-2025), we applied iterated filtering and kernel density estimation to identify season-specific and cross-seasonal changepoint structures. The algorithm integrates stochastic search, local perturbation, and resampling to infer the most plausible changepoint configurations. Results reveal consistent changepoint patterns across seasons, particularly during periods of increased social mixing, such as the December holiday period. A universal changepoint model was also developed, enabling medium-term forecasting and scenario planning. This approach offers a robust method for capturing abrupt shifts in transmission and may be applicable to other dynamical systems.