Large language models (LLMs) are increasingly used across diverse cultural contexts, making accurate cultural understanding essential. Prior evaluations have mostly focused on output-level performance, obscuring the factors that drive differences in responses, while studies using circuit analysis have covered few languages and rarely focused on culture. In this work, we trace LLMs' internal cultural understanding mechanisms by measuring activation path overlaps when answering semantically equivalent questions under two conditions: varying the target country while fixing the question language, and varying the question language while fixing the country. We also use same-language country pairs to disentangle language from cultural aspects. Results show that internal paths overlap more for same-language, cross-country questions than for cross-language, same-country questions, indicating strong language-specific patterns. Notably, the South Korea-North Korea pair exhibits low overlap and high variability, showing that linguistic similarity does not guarantee aligned internal representation.
Mohammadreza Narimani, Ali Hajiahmad, Ali Moghimi
et al.
Controlling environmental conditions and monitoring plant status in greenhouses is critical to promptly making appropriate management decisions aimed at promoting crop production. The primary objective of this research study was to develop and test a smart aeroponic greenhouse on an experimental scale where the status of Geranium plant and environmental conditions are continuously monitored through the integration of the internet of things (IoT) and artificial intelligence (AI). An IoT-based platform was developed to control the environmental conditions of plants more efficiently and provide insights to users to make informed management decisions. In addition, we developed an AI-based disease detection framework using VGG-19, InceptionResNetV2, and InceptionV3 algorithms to analyze the images captured periodically after an intentional inoculation. The performance of the AI framework was compared with an expert's evaluation of disease status. Preliminary results showed that the IoT system implemented in the greenhouse environment is able to publish data such as temperature, humidity, water flow, and volume of charge tanks online continuously to users and adjust the controlled parameters to provide an optimal growth environment for the plants. Furthermore, the results of the AI framework demonstrate that the VGG-19 algorithm was able to identify drought stress and rust leaves from healthy leaves with the highest accuracy, 92% among the other algorithms.
Aditya Tomar, Nihar Ranjan Sahoo, Ashish Mittal
et al.
Although mathematics is often considered culturally neutral, the way mathematical problems are presented can carry implicit cultural context. Existing benchmarks like GSM8K are predominantly rooted in Western norms, including names, currencies, and everyday scenarios. In this work, we create culturally adapted variants of the GSM8K test set for five regions Africa, India, China, Korea, and Japan using prompt-based transformations followed by manual verification. We evaluate six large language models (LLMs), ranging from 8B to 72B parameters, across five prompting strategies to assess their robustness to cultural variation in math problem presentation. Our findings reveal a consistent performance gap: models perform best on the original US-centric dataset and comparatively worse on culturally adapted versions. However, models with reasoning capabilities are more resilient to these shifts, suggesting that deeper reasoning helps bridge cultural presentation gaps in mathematical tasks
Chris M. Harrison, James W. Trayford, Arron George
et al.
Sonification can be part of educational resources that can be accessible to those who prefer, or require, non-visual learning methods. Furthermore, sonification can contribute to an engaging multi-sensory learning experience, which are known to benefit general learners. Whilst some sonification can be relatively agnostic to musical culture, many sonifications are subject to culturally influenced choices, such as the chosen harmonies, rhythmic structures, and instrumentation. This is important when considering how universally inclusive and relatable sonification-based educational resources will be. Here we present a case study of translating a sonification-based educational show about the Solar System, that was originally designed with influences from Euro-American (Western-classical) music, to be more culturally relevant to the Caribbean region. We describe the motivation, approach, some of the challenges, and the initial feedback of the resulting output of the project. Finally, we provide reflections on the importance of further work exploring how educational sonifications can transcend international borders and musical cultures.
<i>Populus koreana</i> emits a wide array of volatile organic compounds (VOCs) with potential ecological functions; however, the tissue-specific distribution and underlying regulatory mechanisms of these compounds remain poorly understood. This study employed an integrated approach combining gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq to systematically profile VOC composition and gene expression in terminal buds, stems and leaves of <i>P. koreana</i>. A total of 207 VOCs were identified, predominantly terpenes and aromatic compounds, exhibiting distinct tissue-specific accumulation patterns. Terminal buds were enriched in limonene and caryophyllene, while leaves showed higher concentrations of alcohols and phenolic aldehydes. Transcriptomic analysis revealed 12,733 differentially expressed genes (DEGs) among the three organs, with substantial enrichment in terpenoid and phenylpropanoid biosynthetic pathways. Notably, key upregulated genes in buds, including TPS21 and PAL1, correlated with observed VOC profiles. Weighted gene co-expression network analysis (WGCNA) further identified 6365 genes strongly associated with bud-specific VOC biosynthesis. Integrated omics analyses indicated coordinated regulation of phenylalanine metabolism and transcription factors in VOC production. These findings illuminate the molecular mechanisms underlying tissue-specific VOC accumulation in <i>P. koreana</i>, enhancing our understanding of metabolic specialization and ecological adaptation in woody plants.
IntroductionPhytoremediation is a promising strategy for cleaning up polycyclic aromatic hydrocarbon (PAH)-contaminated soils. This study investigated the effectiveness of four plant species—cotton, ryegrass, tall fescue, and wheat—in enhancing PAH removal from soils contaminated with diesel oil, PAHs, and aged oily sludge.MethodsAged oily sludge-contaminated soil was artificially prepared, and the selected plants were cultivated in different hydrocarbon-contaminated soils (diesel oil, PAHs, and oily sludge). The fate of PAHs was analyzed by measuring their distribution in rhizospheric soil and plant tissues. Root concentration factors (RCFs) and transpiration stream concentration factors (TSCFs) were used to evaluate PAH translocation and accumulation in plant tissues and their interactions with the rhizosphere.ResultsThe study demonstrated that plants enhanced PAH removal by 20%–80%, with wheat showing the highest efficiency. PAH removal was generally more effective in oily sludge-contaminated soil than in diesel oil or PAH-contaminated soil. Plant uptake of PAHs accounted for 2%–10% of total removal and exhibited a strong linear correlation with root weight. RCFs were linearly correlated with LogKow (3–6), indicating that the four plant species did not significantly concentrate PAHs in their roots.DiscussionThe findings confirm the potential of phytoremediation for PAH-contaminated soils, particularly using wheat as an effective species. The low RCFs and TSCFs suggest that PAH uptake was limited, implying that rhizodegradation and microbial interactions may play a more critical role than direct plant accumulation. This study supports phytoremediation as a cost-effective and eco-friendly alternative to conventional soil remediation methods, reducing economic and environmental burdens.
In this study, we tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution, meticulously curated through several field expeditions across a mountainous region in France covering various landscapes. The task of plant species identification is framed as a semantic segmentation problem for its practical and efficient implementation across vast geographical areas. However, when dealing with segmentation masks, we confront instances where distinguishing boundaries between plant species and their background is challenging. We tackle this issue by introducing a fuzzy loss within the segmentation model. Instead of utilizing one-hot encoded ground truth (GT), our model incorporates Gaussian filter refined GT, introducing stochasticity during training. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology, as well as the need for future improvement.
Masayuki Shiba, Nagisa Kobayashi, Shiori Harada
et al.
We conducted comparative analyses using an open-top chamber (OTC) to reduce wind stress to clarify the impact of decreased wind stress on the invasive species Bidens pilosa L. (Asteraceae), which ranks among the worst 100 species on the Invasive Alien Species List in Japan. Morphological analyses revealed that the number and size of leaves in the OTC group were significantly higher than those in the control group (wind). There was also a significantly higher investment in stems in the former than in the latter. No significant differences were observed in root dry mass; however, the resource allocation ratio to the roots was significantly higher in the wind group than in the OTC group. Although the total seed mass was greater in the OTC group, there were no significant differences in the ratio of resource allocation to seeds between the groups, and no significant differences were observed in the mass of each seed. However, the number of seeds was significantly higher in the OTC group. Adaptive changes in the leaves, stems, and roots to avoid and/or resist wind were reflected in differences in the number of seeds. In addition, a decrease in wind stress contributed to an increase in the number of seeds in B. pilosa. Such mechanisms are likely widespread because B. pilosa is often highly abundant in urban systems.
Michiel Stock, Olivier Pieters, Olivier Pieters
et al.
Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as “simulation intelligence”, has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.
Lukasz Stelinski, Erik Roldan, Kirsten Pelz-Stelinski
This project will combine threshold-based management of ACP to reduce unnecessary insecticide sprays, which may facilitate investment in other therapeutic strategies while not compromising vector suppression. Preliminary data suggest that trunk-injected OTC should have greater positive impact on tree health and productivity than previous attempts with foliar application of antibiotics. However, it is unknown whether such treatments, with and without simultaneously application of phytohormones, can bring trees currently on the brink of death back into production. Therefore, we will simultaneously validate the effects of trunk-injected OTC on pathogen load and vector transmission and determine if the savings gained by reducing insecticide input with treatment thresholds could ‘pay for’ additional input of antibiotics.
Japan K. Patel, Athi Varuttamaseni, Robert W. Youngblood
et al.
A Python interface is developed for the GPWR Simulator to automatically simulate cyber-spoofing of different steam generator parameters and plant operation. Specifically, steam generator water level, feedwater flowrate, steam flowrate, valve position, and steam generator controller parameters, including controller gain and time constant, can be directly attacked using command inject, denial of service, and man-in-the-middle type attacks. Plant operation can be initialized to any of the initial conditions provided by the GPWR simulator. Several different diagnostics algorithms have been implemented for anomaly detection, including physics-based diagnostics with Kalman filtering, data-driven diagnostics, noise profiling, and online sensor validation. Industry-standard safety analysis code RELAP5 is also available as a part of the toolkit. Diagnostics algorithms are analyzed based on accuracy and efficiency. Our observations indicate that physics-based diagnostics with Kalman filtering are the most robust. An experimental quantum kernel has been added to the framework for preliminary testing. Our first impressions suggest that while quantum kernels can be accurate, just like any other kernels, their applicability is problem/data dependent, and can be prone to overfitting.
Adalberto Di Benedetto, Claudio Galmarini, Jorge Tognetti
Green and variegated Benjamin fig (Ficus benjamina) often suffer from root restriction when grown in pots. While exogenous cytokinin applications have proven effective in reversing this stress, the possibility that exogenous auxins, either alone or in combination with cytokinin, may also be helpful has received little attention. In this work, we analyse the response of green and variegated Ficus benjamina rooted cuttings growing in small pots to exogenous supply of auxin and cytokinin at different concentrations, either in single or combined applications. Our results show that both benzyl aminopurine (BAP) and indole acetic acid (IAA), at the highest concentration tested (100 mg L-1) increased leaf development and plant biomass accumulation in green and variegated Ficus genotypes. However, exogenous IAA and BAP appeared to elicit differential plant morpho-physiological responses. While BAP tended to enhance leaf appearance more than IAA did, the latter promoted leaf expansion in a steadier manner than BAP, thus resulting in plants with less, but larger, leaves than those treated with cytokinin. Despite these differences in plant architecture, regression analysis suggests that hormonal-induced growth promotion was solely attributable to enhanced carbon assimilation. Rather unexpectedly, IAA promoted net assimilation and photosynthesis rates at least as effectively as cytokinin. Possible mechanisms involved in growth and development promotion by exogenous application of both hormones are discussed. Auxin treatment may help overcome root restriction in Ficus as effectively as cytokinin in terms of growth promotion, although differences in plant architecture may arise as compared with plants sprayed with the latter hormone.
Garrett Eickelberg, Yuan Luo, L. Nelson Sanchez-Pinto
Microbiology culture reports contain critical information for important clinical and public health applications. However, microbiology reports often have complex, semi-structured, free-text data that present a barrier for secondary use. Here we present the development and validation of an open-source package designed to ingest free-text microbiology reports, determine whether the culture is positive, and return a list of SNOMED-CT mapped bacteria. Our rule-based natural language processing algorithm was developed using microbiology reports from two different electronic health record systems in a large healthcare organization, and then externally validated on the reports of two other institutions with manually-extracted results as a benchmark. Our algorithm achieved F-1 scores >0.95 on all classification tasks across both validation sets. Our concept extraction Python package, MicrobEx, is designed to be reused and adapted to individual institutions as an upstream process for other clinical applications, such as machine learning studies, clinical decision support, and disease surveillance systems.