process-based modeling
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Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA's process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.
Process-based models and empirical modelling techniques are frequently used to (i) explore the sensitivity of tree growth to environmental variables, and (ii) predict the future growth of trees and forest stands under climate change scenarios. However, modelling approaches substantially influence predictions of the sensitivity of trees to environmental factors. Here, we used tree-ring width (TRW) data from 1630 beech trees from a network of 70 plots established across European mountains to build empirical predictive growth models using various modelling approaches. In addition, we used 3-PG and Biome-BGCMuSo process-based models to compare growth predictions with derived empirical models. Results revealed similar prediction errors (RMSE) across models ranging between 3.71 and 7.54 cm2 of basal area increment (BAI). The models explained most of the variability in BAI ranging from 54 % to 87 %. Selected explanatory variables (despite being statistically highly significant) and the pattern of the growth sensitivity differed between models substantially. We identified only five factors with the same effect and the same sensitivity pattern in all empirical models: tree DBH, competition index, elevation, Gini index of DBH, and soil silt content. However, the sensitivity to most of the climate variables was low and inconsistent among the empirical models. Both empirical and process-based models suggest that beech in European mountains will, on average, likely experience better growth conditions under both 4.5 and 8.5 RCP scenarios. The process-based models indicated that beech may grow better across European mountains by 1.05 to 1.4 times in warmer conditions. The empirical models identified several drivers of tree growth that are not included in the current process-based models (e.g., different nutrients) but may have a substantial effect on final results, particularly if they are limiting factors. Hence, future development of process-based models may build upon our findings to increase their ability to correctly capture ecosystem dynamics.
- Klíčová slova
- Dendrochronology, Ecosystem dynamics, European beech, Global climate change, Process-based growth model, Tree growth,
- MeSH
- buk (rod) * MeSH
- ekosystém * MeSH
- klimatické změny MeSH
- lesy MeSH
- stromy MeSH
- Publikační typ
- časopisecké články MeSH
Significant alterations of cambial activity might be expected due to climate warming, leading to growing season extension and higher growth rates especially in cold-limited forests. However, assessment of climate-change-driven trends in intra-annual wood formation suffers from the lack of direct observations with a timespan exceeding a few years. We used the Vaganov-Shashkin process-based model to: (i) simulate daily resolved numbers of cambial and differentiating cells; and (ii) develop chronologies of the onset and termination of specific phases of cambial phenology during 1961-2017. We also determined the dominant climatic factor limiting cambial activity for each day. To asses intra-annual model validity, we used 8 years of direct xylogenesis monitoring from the treeline region of the Krkonoše Mts. (Czechia). The model exhibits high validity in case of spring phenological phases and a seasonal dynamics of tracheid production, but its precision declines for estimates of autumn phenological phases and growing season duration. The simulations reveal an increasing trend in the number of tracheids produced by cambium each year by 0.42 cells/year. Spring phenological phases (onset of cambial cell growth and tracheid enlargement) show significant shifts toward earlier occurrence in the year (for 0.28-0.34 days/year). In addition, there is a significant increase in simulated growth rates during entire growing season associated with the intra-annual redistribution of the dominant climatic controls over cambial activity. Results suggest that higher growth rates at treeline are driven by (i) temperature-stimulated intensification of spring cambial kinetics, and (ii) decoupling of summer growth rates from the limiting effect of low summer temperature due to higher frequency of climatically optimal days. Our results highlight that the cambial kinetics stimulation by increasing spring and summer temperatures and shifting spring phenology determine the recent growth trends of treeline ecosystems. Redistribution of individual climatic factors controlling cambial activity during the growing season questions the temporal stability of climatic signal of cold forest chronologies under ongoing climate change.
- Klíčová slova
- VS-model, cambial phenology, dendrochronology, growing season, process-based modeling, treeline, xylogenesis,
- Publikační typ
- časopisecké články MeSH
E-photosynthesis framework is a web-based platform for modeling and analysis of photosynthetic processes. Compared to its earlier version, the present platform employs advanced software methods and technologies to support an effective implementation of vastly diverse kinetic models of photosynthesis. We report on the first phase implementation of the tool new version and demonstrate the functionalities of model visualization, presentation of model components, rate constants, initial conditions and of model annotation. The demonstration also includes export of a model to the Systems Biology Markup Language format and remote numerical simulation of the model.
Natural selection is considered to be the main process that drives biological evolution. It requires selected entities to originate dependent upon one another by the means of reproduction or copying, and for the progeny to inherit the qualities of their ancestors. However, natural selection is a manifestation of a more general persistence principle, whose temporal consequences we propose to name "stability-based sorting" (SBS). Sorting based on static stability, i.e., SBS in its strict sense and usual conception, favours characters that increase the persistence of their holders and act on all material and immaterial entities. Sorted entities could originate independently from each other, are not required to propagate and need not exhibit heredity. Natural selection is a specific form of SBS-sorting based on dynamic stability. It requires some form of heredity and is based on competition for the largest difference between the speed of generating its own copies and their expiration. SBS in its strict sense and selection thus have markedly different evolutionary consequences that are stressed in this paper. In contrast to selection, which is opportunistic, SBS is able to accumulate even momentarily detrimental characters that are advantageous for the long-term persistence of sorted entities. However, it lacks the amplification effect based on the preferential propagation of holders of advantageous characters. Thus, it works slower than selection and normally is unable to create complex adaptations. From a long-term perspective, SBS is a decisive force in evolution-especially macroevolution. SBS offers a new explanation for numerous evolutionary phenomena, including broad distribution and persistence of sexuality, altruistic behaviour, horizontal gene transfer, patterns of evolutionary stasis, planetary homeostasis, increasing ecosystem resistance to disturbances, and the universal decline of disparity in the evolution of metazoan lineages. SBS acts on all levels in all biotic and abiotic systems. It could be the only truly universal evolutionary process, and an explanatory framework based on SBS could provide new insight into the evolution of complex abiotic and biotic systems.
- Klíčová slova
- Dynamic stability, Evolutionary theory, Frozen evolution, Persistence, Selection, Static stability,
- MeSH
- biologická evoluce * MeSH
- modely genetické MeSH
- rozmnožování MeSH
- selekce (genetika) * MeSH
- společenstvo MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications.
- MeSH
- algoritmy * MeSH
- lidé MeSH
- počítačová simulace MeSH
- řešení problému MeSH
- řízení motorových vozidel * MeSH
- učení MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Although there are several articles that have carried out a systematic literature review of the analytical hierarchy process (AHP), many of them work with a limited number of analyzed documents. This article presents a computer-aided systematic literature review of articles related to AHP. The objectives are: (i) to identify AHP usage and research impact in different subject areas; (ii) to identify trends in the popularity of the AHP from the first introduction of the method in 1980 to the present; (iii) to identify the most common topics related to AHP and topic development over time. We process 35,430 documents related to AHP, published between 1980 and 2021, retrieved from the Scopus database. We provide detailed statistics about research interest, research impact in particular subject areas over the analyzed time period. We use Latent Dirichlet Allocation (LDA) using Gibbs sampling to perform topic modeling based on the corpus of abstracts. We identify nine topics related to AHP: Ecology & Ecosystems; Multi-criteria decision-making; Production and performance management; Sustainable development; Computer network, optimization and algorithms; Service quality; Fuzzy logic; Systematic evaluation; Risk assessment. We also present the individual topics trends over time and point out the possible future direction of AHP.
In direct compression of tablets, it is crucial to maintain content uniformity within acceptable margins, especially in formulations with low drug loading. To assure it, complex and multistep mixing processes are utilized in the industry. In this study, we suggest the use of a simple segregation test to evaluate mixing process performance and mixture segregation to produce tablets having satisfying content uniformity while keeping the process as simple and low cost as possible. Eventually, the formulation propensity to segregation can be evaluated using process analytical technology (PAT) to adjust the mixing process parameters to changing source drug properties. In this study, that approach was examined on a model drug with a broad batch-to-batch variability in particle size and shape. Excipients were chosen so that the resulting blend composition mimicked some marketed formulations. For each drug batch, two formulation blends were prepared through different preparation processes (one simple and one complex) and subsequently subjected to segregation tests. From those, segregation coefficients were obtained to compare segregation tendencies and homogeneity robustness between the drug batches and the blend preparation methods. The inter-particulate interactions were substantially influenced by the drug particle morphology and size and resulted in different segregation behavior. Based on these findings, a simple segregation test proved to be a useful tool for determining the suitability of different batches of the model drug to be used in a certain formulation. Moreover, for a particular batch A, the test revealed a potential for mixing process simplification and therefore process intensification and cost reduction.
- Klíčová slova
- batch-to-batch variability, direct tablet compression, mixing process, segregation, segregation test,
- MeSH
- farmaceutická technologie metody MeSH
- pomocné látky chemická syntéza MeSH
- prášky, zásypy, pudry MeSH
- příprava léků metody MeSH
- tablety MeSH
- tlak MeSH
- velikost částic * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- pomocné látky MeSH
- prášky, zásypy, pudry MeSH
- tablety MeSH
BACKGROUND: Physically based tier-II models may serve as possible alternatives to expensive field and laboratory leaching experiments required for pesticide approval and registration. The objective of this study was to predict pesticide fate and transport at five different sites in Hawaii using data from an earlier field leaching experiment and a one-dimensional tier-II model. As the predicted concentration profiles of pesticides did not provide close agreement with data, inverse modeling was used to obtain adequate reactive transport parameters. The estimated transport parameters of pesticides were also utilized in a tier-I model, which is currently used by the state authorities to evaluate the relative leaching potential. RESULTS: Water flow in soil profiles was simulated by the tier-II model with acceptable accuracy at all experimental sites. The observed concentration profiles and center of mass depths predicted by the tier-II simulations based on optimized transport parameters provided better agreements than did the non-optimized parameters. With optimized parameters, the tier-I model also delivered results consistent with observed pesticide center of mass depths. CONCLUSION: Tier-II numerical modeling helped to identify relevant transport processes in field leaching of pesticides. The process-based modeling of water flow and pesticide transport, coupled with the inverse procedure, can contribute significantly to the evaluation of chemical leaching in Hawaii soils.
- MeSH
- chemické látky znečišťující vodu analýza chemie MeSH
- chemické modely MeSH
- látky znečišťující půdu analýza chemie MeSH
- monitorování životního prostředí metody MeSH
- pesticidy analýza chemie MeSH
- počítačová simulace MeSH
- pohyb vody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Havajské ostrovy MeSH
- Názvy látek
- chemické látky znečišťující vodu MeSH
- látky znečišťující půdu MeSH
- pesticidy MeSH
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
- Klíčová slova
- Association Rule Mining, Data mining, Drug Response Prediction, Machine Learning, Precision Medicine,
- MeSH
- data mining * MeSH
- lidé MeSH
- nádory farmakoterapie MeSH
- počítačová simulace MeSH
- strojové učení * MeSH
- výsledek terapie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
- Research Support, N.I.H., Extramural MeSH
