PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15824576

Deterministic vs. probabilistic analyses to identify sensitive parameters in dose assessment using RESRAD.

Sunita Kamboj·Jing-Jy Cheng·Charley Yu
View original source ↗Export metadata ↓

Abstract

The dose assessments for sites containing residual radioactivity usually involve the use of computer models that employ input parameters describing the physical conditions of the contaminated and surrounding media and the living and consumption patterns of the receptors in analyzing potential doses to the receptors. The precision of the dose results depends on the precision of the input parameter values. The identification of sensitive parameters that have great influence on the dose results would help set priorities in research and information gathering for parameter values so that a more precise dose assessment can be conducted. Two methods of identifying site-specific sensitive parameters, deterministic and probabilistic, were compared by applying them to the RESRAD computer code for analyzing radiation exposure for a residential farmer scenario. The deterministic method has difficulty in evaluating the effect of simultaneous changes in a large number of input parameters on the model output results. The probabilistic method easily identified the most sensitive parameters, but the sensitivity measure of other parameters was obscured. The choice of sensitivity analysis method would depend on the availability of site-specific data. Generally speaking, the deterministic method would identify the same set of sensitive parameters as the probabilistic method when 1) the baseline values used in the deterministic method were selected near the mean or median value of each parameter and 2) the selected range of parameter values used in the deterministic method was wide enough to cover the 5th to 95th percentile values from the distribution of that parameter.

Explore related subjects

AgricultureBody BurdenComputer SimulationDecontaminationEnvironmental PollutionHumansModels, BiologicalModels, StatisticalRadiation DosageRadiation MonitoringRadiation ProtectionRadioisotopesRelative Biological EffectivenessRisk AssessmentRisk FactorsSafety ManagementUnited StatesWaste ManagementJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sunita Kamboj, Jing-Jy Cheng, Charley Yu. 2005. Deterministic vs. probabilistic analyses to identify sensitive parameters in dose assessment using RESRAD.. https://doi.org/10.1097/01.hp.0000156058.46817.2c

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Plant-derived and microbial biostimulants in sustainable agriculture: mechanisms, applications, and challenges.

Plant biostimulants have emerged as transformative and sustainable tools for improving crop productivity, resource-use efficiency, and resilience under rapidly intensifying environmental stresses. Unlike conventional agrochemicals, biostimulants function by activating physiological, biochemical, and molecular processes that optimize plant performance without directly supplying nutrients or exerting pesticidal effects. This review comprehensively examines the integrated roles of plant-derived and microbial biostimulants in sustainable agriculture, with particular emphasis on microbial-mediated mechanisms underlying plant stress adaptation and rhizosphere functioning. Plant-derived biostimulants, including seaweed extracts, humic substances, protein hydrolysates, amino acids, and chitosan, enhance nutrient acquisition, root architecture, hormonal regulation, and antioxidant defense systems. More importantly, microbial biostimulants, such as plant growth-promoting rhizobacteria (PGPR), endophytic microorganisms, mycorrhizal fungi, actinomycetes, yeasts, and cyanobacteria, exert multifunctional effects through biological nitrogen fixation, mineral solubilization, phytohormone biosynthesis, volatile signaling, osmolyte accumulation, pathogen suppression, and modulation of stress-responsive genes. These beneficial microorganisms reshape rhizosphere microbial communities, improve nutrient cycling, and enhance plant tolerance to drought, salinity, heat, and heavy metal toxicity. Emerging evidence from genomics, transcriptomics, metabolomics, and microbiome-based investigations has further revealed the molecular networks and signaling pathways governing biostimulant-induced resilience and plant-microbe interactions. Despite their substantial promise, inconsistent field performance, formulation instability, regulatory limitations, and inadequate mechanistic understanding continue to restrict their large-scale adoption. This review highlights recent advances in microbial and plant-derived biostimulants while identifying critical knowledge gaps and future opportunities for precision biostimulant engineering, microbiome manipulation, and climate-resilient crop management. The integration of next generation biostimulant technologies into sustainable agricultural systems may significantly reduce dependence on agrochemicals while improving crop productivity, environmental sustainability, and global food security.

Agriculture↗
PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗
PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗