Modeling Approaches Research Articles

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

A new look at genetic variability in pomegranate: Landscape genetics and structural equation modeling (SEM)

Pomegranate (Punica granatum L.) is an important horticultural plant in Iran and has been a food source since ancient times. Despite several genetic diversity studies conducted on these taxa, there are no reports on the landscape genetics of these important plants in Iran, nor is there any information on the effects of environmental, spatial, and climatic variables on the genetic structure of pomegranate. Therefore, this study used genetics, structural equation modeling (SEM), and species distribution modeling (SDM) approaches to explore the above-mentioned objectives. We used both cytogenetic and molecular analyses (SSR and SCoT markers) of several pomegranate samples along with environmental, spatial, and climatic variables. The redundancy analysis (RDA) analysis showed a significant positive association between terminal chiasmata with the longitudinal distribution of the pomegranate accession, while total chiasma number and interstitial chiasmata have an association with the altitude. The spatial PCA analysis indicated the role played by both global and local spatial variables in the cytogenetic variability like the univalent and terminal chiasmata formation. The Mantel test indicates that the cytogenetic difference in pomegranate plants is not related to their geographical distance, while the Moran I test result shows that geographical regions with similar spatial conditions affect the cytogenetic structuring of pomegranate plants in a similar way. SEM methods showed a strong negative association between bivalent formation and the environmental variables, while they showed both positive and negative associations between some of the environmental variables and different kinds of chiasmata. The sPCA analysis of molecular data showed a significant global, and local, spatial structuring of the molecular data and the genetic cline formation within the pomegranate accessions studied. Similarly, the spatial PCA analysis of climate variables, precipitation, and temperature revealed a significant structuring of genetic diversity and the formation of genetic clines by these factors. RDA analysis identified some of the loci that are significantly associated with temperature and precipitation. Therefore, both geographical/ or spatial variables and climate factors contribute to the genetic structure of the pomegranate accessions studied. SEM models based on genetic data revealed a positive association between environmental and climate variables with genetic diversity parameters and SSR-SCoT molecular loci studied. Climate change is unlikely to hurt pomegranate growing areas due to the high genetic diversity of pomegranate plants and the diverse genetic lineages present in the country.

APPLICATION OF MATHEMATICAL MODELS IN THE DIAGNOSIS OF DISEASES OF INTERNAL ORGANS

The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems.

Application of ternary nanofluid and rotating cylinders in the cooling system of photovoltaic/thermoelectric generator coupled module and computational cost reduction

Innovative cooling systems and solar panel modeling approaches have become critical for effective photovoltaic module performance and higher energy efficiency. In the present study, a novel cooling system for thermoelectric generator integrated photovoltaic module is proposed. Three dimensional coupled photovoltaic module/thermoelectric generator/cooling channel system is numerically analyzed by using Galerkin weighted residual finite element method. The cooling channels include rotating circular cylinders, with ternary nanofluid serving as the cooling medium. Three dimensional coupled numerical simulations are carried out for various values of cylinder’s rotational speed (rotational Reynolds number, Rew between 0 and 5000), number of channels with rotating cylinders (N between 2 and 8), size of the cylinders in the cooling channel (RC between 0.006H and 0.433H) and nanoparticle amount in water (ϕ between 0 and 3%). Higher rotational speed of the cylinders, higher nanoparticle loading and higher channel number with cylinder contribute positively to the performance enhancement of the photovoltaic-thermoelectric generator coupled system. In the case of motionless cylinder in cooling channels, larger cylinder sizes have negative impact on the performance. By using cylinders that rotate at the fastest possible rate together with the usage of ternary nanofluid and only water, photovoltaic-cell temperature drops of 61.6 °C and 61 °C in comparison to un-cooled photovoltaic are achieved. When compared to motionless cylinder arrangements, cell temperature reductions of 4.3 °C and 3 °C are achieved by using the fastest rotation speeds for the base fluid and nanofluid. The temperature of the photovoltaic cells decreases and the thermoelectric power increases as the number of rotating cylinders in the cooling channels increases while temperature of the cell drops by 2 °C from N=2 to N=8 and becomes 4.3 °C as compared to motionless cylinder configuration. An efficient computational method by using proper orthogonal decomposition is proposed. While the computational time is reduced by a factor of 1/50, the approach is effective in properly predicting the variation in photovoltaic-cell temperature. The proposed cooling system and practical computational method are useful for further development and optimization of efficient thermal management of photovoltaic panels and integrated systems.

Influence of modelling approach for reinforced concrete underground structures, with application to the CMS cavern at CERN

Representative modelling of reinforced concrete (RC) components in underground structures is essential for accurate assessment of structural performance (deformations and internal forces) within numerical simulations. This paper examines the implications of selecting different structural modelling approaches within the seismic (dynamic) finite element analysis of a buried structure of complex shape, using the CMS (Compact Muon Solenoid) Detector Cavern of the Large Hadron Collider in Geneva, Switzerland, as a case study. Two alternate modelling approaches were employed to model the cavern lining: (i) a composite continuum approach, with the concrete and embedded reinforcement being explicitly modelled; and (ii) the use of a nonlinear elasto-plastic plate element. The pre-earthquake ground initial conditions were determined through simulation of the construction and detector installation operations consistent with field measurements from extensometers and internal survey of floor deformations. The results demonstrate the importance of adopting a non-linear continuum modelling approach in representing the RC lining under strong shaking events to avoid under-prediction of seismic actions at locations of potential seismically induced damage. Such an approach will be essential in 3D problems where multi-axial dynamically varying stresses are applied on the RC section. Finally, it offers a realistic approach in representing structures of complex shape and that contains volume and thick elements.

Molecular modeling and simulation approaches to characterize potential molecular targets for burdock inulin to instigate protection against autoimmune diseases.

In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1β. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1β, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1β during DSS-induced colitis to reduce the role of these inflammatory mechanisms. Furthermore, residual flexibility, hydrogen bonding, and structural packing were reported with uniform trajectories, showing no significant perturbation throughout the simulation. The protein motions within the simulation trajectories were clustered using principal component analysis (PCA). The IL-1β-Inulin complex, approximately 70% of the total motion was attributed to the first three eigenvectors, while the remaining motion was contributed by the remaining eigenvectors. In contrast, for the COX2-Inulin complex, 75% of the total motion was attributed to the eigenvectors. Furthermore, in the iNOS-Inulin complex, the first three eigenvectors contributed to 60% of the total motion. Furthermore, the iNOS-Inulin complex contributed 60% to the total motion through the first three eigenvectors. To explore thermodynamically favorable changes upon mutation, motion mode analysis was carried out. The Free Energy Landscape (FEL) results demonstrated that the IL-1β-Inulin achieved a single conformation with the lowest energy, while COX2-Inulin and iNOS-Inulin exhibited two lowest-energy conformations each. IL-1β-Inulin and COX2-Inulin displayed total binding free energies of - 27.76kcal/mol and - 37.78kcal/mol, respectively, while iNOS-Inulin demonstrated the best binding free energy results at - 45.89kcal/mol. This indicates a stronger pharmacological potential of iNOS than the other two complexes. Thus, further experiments are needed to use inulin to target iNOS and reduce DSS-induced colitis and other autoimmune diseases.

The coupling of carbon and energy fluxes reveals anaerobiosis in an aerobic soil incubation with a Bacillota-dominated community

Soil microorganisms rely on coupled fluxes of carbon and energy to fuel their maintenance, to switch from dormancy to activity and to grow under dynamically changing conditions in microhabitats. To identify the principles underlying this coupling, we measured heat and CO2 release from soil after glucose addition along with estimates of microbial biomass and community composition. The results revealed bi-directional deviations of the ratio of heat to CO2 release (Calorespirometric Ratio, CR) that are inconsistent with theoretical predictions for aerobic respiration, which is commonly assumed to be the major metabolic pathway in incubation experiments. Moreover, the microbial community was dominated by members of the Bacillota, whose relative abundance increased from 4 percent to 65 percent in 18 h. To interpret these findings, we developed a dynamic model of carbon and energy fluxes during the microbial growth on glucose. The model simulates aerobic respiration as well as anaerobic fermentation pathways to lactate, acetate and propionate depending on the time-varying availability of O2. Simulations captured the observed temporal CR pattern and suggested a gradual depletion of O2 and a shift to anaerobic pathways as the main driver. This interpretation is consistent with the dominance of Bacillota, many members of which are well adapted to anaerobic conditions. Our results highlight the potential of the joint analysis of matter and energy fluxes in combined experimental and modeling approaches and indicate the presence of facultative anaerobiosis under common experimental conditions, which could confer a competitive advantage to certain microbial taxa during growth on labile substrate.

A comprehensive study on predicting the elastic modulus and Poisson ratio of hardened cement pastes via micro-scale cement hydration simulations

A comprehensive study was conducted to predict the elastic modulus and Poisson ratio of hardened cement pastes (HCPs), including numerical work, comparison of two modelling approaches, and experimental validation. Modelling approach 1 (M1) applied a continuum micromechanics model (CMM) whereas modelling approach 2 (M2) used finite element analysis (FEA). This study presents a reliable method for predicting the mechanical properties of HCPs by firstly employing the microstructures of HCPs, obtained by CEMHYD3D analysis with random distribution of cement particles as input parameters for both M1 and M2 approaches. The elastic modulus and Poisson ratio predicted by M1 and M2 approaches were verified through the experimental data obtained from compressive tests combined with digital image correlation analysis. The elastic moduli of HCPs were significantly affected by the input C-S-H modulus, with 90% of reported C-S-H elastic moduli falling within the range of 20.25 GPa and 30.4 GPa. The predicted elastic moduli of HCPs were well-matched with experimental data when assigning the elastic modulus, bulk modulus, and shear modulus, of C-S-H, as 20.25, 12.98, and 8.17 GPa, respectively. A sensitivity analysis demonstrated a linear increase in the elastic modulus of HCPs with increase of C-S-H modulus, with correlation coefficients R² of 0.9985 and 0.9996 for HCPs with water-to-cement ratios (w/c) of 0.3 and 0.5, respectively. Additionally, the predicted results showed greater consistency at later age (after 7 d) compared to earlier stages. The integral absolute error values of the elastic moduli obtained via M1 and M2 for HCPs with w/c of 0.3 were 6.32% and 6.23%, respectively while those for HCPs with w/c of 0.5 were 4.8% and 5.67%, respectively. M1 approach is more recommended since it is straightforward with time and cost efficiency compared to M2 approach in addition to the suitable prediction accuracy.

Enhancing Surface Integrity and Quality through Roller Burnishing: A Comprehensive Review of Parameters Optimization, and Applications.

Surface integrity is a critical aspect in ensuring the quality of components or products, particularly in industries such as automotive, aerospace, and machinery where superior surface finish and prolonged functionality of cylindrical parts are essential. Burnishing, a chip-less cold working finishing process, enhances the surface finish of machined workpieces through plastic deformation. This review paper delves into various aspects of the burnishing process and its impact on surface properties, drawing insights from multiple research projects. It explores topics including residual stresses, surface roughness, microhardness, cooling strategies, and modeling techniques. Key findings underscore the influence of burnishing parameters such as speed, feed rate, penetration depth, and number of passes on surface roughness and microhardness. Moreover, the process induces compressive residual strains on the workpiece surface, with their magnitude varying based on operational parameters. The choice of cooling methods, including kerosene, minimum quantity lubrication (MQL), cryogenic cooling, and hybrid cooling, significantly affects surface integrity. Various modeling approaches, such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Response Surface Methodology (RSM), aid in predicting and optimizing surface characteristics. Despite the extensive discussion on surface roughness, microhardness, residual stresses, cooling methods, and modeling, out-of-roundness in the burnishing process remains understudied and warrants further investigation. This review contributes valuable insights for enhancing surface qualities in manufacturing applications and deepening our understanding of the burnishing process.

Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Evaluation of a Simplified Modeling Approach for SEE Cross-Section Prediction: A Case Study of SEU on 6T SRAM Cells

Electrical models play a crucial role in assessing the radiation sensitivity of devices. However, since they are usually not provided for end users, it is essential to have alternative modeling approaches to optimize circuit design before irradiation tests, and to support the understanding of post-irradiation data. This work proposes a novel simplified methodology to evaluate the single-event effects (SEEs) cross-section. To validate the proposed approach, we consider the 6T SRAM cell a case study in four technological nodes. The modeling considers layout features and the doping profile, presenting ways to estimate unknown parameters. The accuracy and limitations are determined by comparing our simulations with actual experimental data. The results demonstrated a strong correlation with irradiation data, without requiring any fitting of the simulation results or access to process design kit (PDK) data. This proves that our approach is a reliable method for calculating the single-event upset (SEU) cross-section for heavy-ion irradiation.

Association between weight-adjusted-waist index and periodontitis risk: A cross-sectional study.

There may be an association between obesity and periodontitis, yet no studies have investigated the correlation between the new obesity indicator, the weight-adjusted-waist Index (WWI), and periodontitis. This study aims to investigate the association between the novel obesity index, weight-adjusted-waist index, and periodontitis. WWI was utilized to assess obesity, through measuring waist circumference (WC) and body weight. We analyzed cross-sectional NHANES data from 2009 to 2014 (1) using multivariate logistic regression to explore WWI's association with moderate/severe periodontitis; (2) conducting subgroup analyses and interaction tests; and (3) fitting smoothed curves to the age-stratified logistic regression model. The study involved 11,256 individuals, with 48.55% having moderate/severe periodontitis. Upon adjusting for all relevant variables, a significant correlation between WWI and moderate/severe periodontitis was observed (OR = 1.08, 95% CI: 1.01-1.17). Compared to the lowest quartile of WWI, there was a significant increase in the likelihood of moderate/severe periodontitis in Quartile 2 (OR = 1.21, 95% CI: 1.06-1.39) and Quartile 3 (OR = 1.23, 95% CI: 1.07-1.42). Subgroup analyses for gender, age, education, smoking, and diabetes highlighted a positive association between WWI and moderate/severe periodontitis in all subgroups, except for the diabetic population and individuals aged 65 years and older. The analysis revealed a positive correlation between WWI, a novel obesity index, and moderate/severe periodontitis prevalence through diverse modeling approaches.

Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient

The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.

Defining the geomechanical operating limits for subsurface CO2 storage

Carbon capture and storage (CCS) is a critical component of proposed pathways to limit global warming, though considerable upscaling is required to meet emissions reduction targets. Quantifying and managing the risks of fault reactivation is a leading barrier to scaling global CCS projects from current levels of ~40 million tonnes of carbon dioxide(CO2) per year (to target levels of several gigatonnes of CO2 per year), because CO2 injection into reservoirs can result in increased pore-fluid pressure and temperature changes, which can reduce the strength of rocks and faults and induce brittle failure. This can result in induced seismicity, whilst hydraulic fracturing of seals could provide pathways for CO2 leakage. Consequently, identifying favourable geomechanical conditions (typically determined through data on pre-injection rock stress, mechanical and elastic properties, and pore-fluid pressures) to minimise deformation of reservoirs and seals represents a key challenge in the selection of safe and effective sites for CCS projects. Critically, however, such geomechanical data are typically spatially limited (i.e. restricted to wells) and mainly consist of pre-injection crustal stress orientation measurements, rather than a full 3D description of the stress tensor and related geomechanical properties. This paper reviews some key geomechanical issues and knowledge gaps (particularly those associated with data availability and limitations) that need to be understood to enable successful reservoir and seal management for CCS projects. We also highlight recent advances in multi-scale and dimensional geomechanical modelling approaches that can be used to assess sites for the secure storage of CO2 as well as other gases, including hydrogen.

Intercontinental invasion dynamics of Cercopagis pengoi, an IUCN-listed planktonic invasive species

Predicting the spread of invasive species and understanding the role of niche dynamics in niche transferability are critical challenges in the management of biological invasions, both theoretically and practically. We used complementary species distribution modelling approaches, such as multivariate niche analysis and reciprocal distribution models, to test the niche conservatism hypothesis and to predict the potential distribution of the fishhook waterflea, Cercopagis pengoi. Our analysis indicated a significant similarity between its native and invasive ranges, suggesting that a subset of the Ponto-Caspian propagules may have been the founders of European populations. However, our results contradict the niche conservatism hypothesis, showing that C. pengoi has not fully occupied the available niche within its current invasive ranges. Moreover, we observed a notable niche expansion, reflecting a significant shift in niche following its intercontinental introduction in North America. Given the suitability of new environments for the expansion of C. pengoi and its tendency to evade detection prior to population surges, we recommend a focus on early detection through monitoring of both water columns and bottom sediments. This should be complemented by strict enforcement of ballast water regulations to curtail its spread in North America, Europe, and other suitable non-native regions globally.

Finite Element Analysis of Anterior Implant-Supported Restorations with Different CAD-CAM Restorative Materials.

Due to the lack of literature concerning the selection of crown materials for the restoration of anterior teeth, this study aimed to investigate the effects of six distinct computer-aided design and computer-aided manufacturing (CAD-CAM) crown materials on stress and strain distribution within implant-supported maxillary central incisor restorations, employing finite element analysis (FEA). Furthermore, a comparative analysis was conducted between models that incorporated adjacent natural teeth and those that did not, intending to guide the selection of the most suitable modeling approach. Crown materials, including Lava Ultimate, Enamic, Emax CAD, Suprinity, Celtra Duo, and Cercon xt ML, were the subjects of the investigation. FEA models incorporating Coulomb friction were developed. These models were subjected to an oblique load, simulating the average maximum bite force experienced by anterior teeth. The potential for failure in titanium implant components and the prosthesis crown was evaluated through von Mises and principal stress, respectively. Furthermore, the failure of crestal bone was assessed through principal strain values. Stress values for each implant component and strain values of the bone were extracted from the models. To assess the impact of the six groups of crown materials, Kruskal-Wallis analysis of variance and post-hoc comparisons were conducted. Additionally, a statistical comparison between the two groups with Lava Ultimate and Cercon xt ML was performed using the Mann-Whitney U test to determine the difference in the two modeling approaches. Higher crown material stiffness led to decreased stress in the abutment, fixture, and retaining screw, along with reduced strain in the surrounding bone. However, the decrease in stress and strain values became less significant with increasing crown stiffness. Additionally, the model with adjacent teeth showed significantly lower stress and strain concentrations compared to the model without adjacent teeth. Crowns with a high elastic modulus were the optimal choice for anterior teeth restoration. Constructing FEA models with adjacent teeth was highly recommended to gain a deeper understanding of the mechanical behavior of dental implant restorations.

A Survey on Automatic Generation of Figurative Language: From Rule-based Systems to Large Language Models

Figurative language generation (FLG) is the task of reformulating a given text to include a desired figure of speech, such as a hyperbole, a simile, and several others, while still being faithful to the original context. This is a fundamental, yet challenging task in Natural Language Processing (NLP), which has recently received increased attention due to the promising performance brought by pre-trained language models. Our survey provides a systematic overview of the development of FLG, mostly in English, starting with the description of some common figures of speech, their corresponding generation tasks, and datasets. We then focus on various modelling approaches and assessment strategies, leading us to discussing some challenges in this field, and suggesting some potential directions for future research. To the best of our knowledge, this is the first survey that summarizes the progress of FLG including the most recent development in NLP. We also organize corresponding resources, e.g., article lists and datasets, and make them accessible in an open repository. We hope this survey can help researchers in NLP and related fields to easily track the academic frontier, providing them with a landscape and a roadmap of this area.

Simulating multi-hazard event sets for life cycle consequence analysis

Abstract. In the context of natural hazard risk quantification and modeling of hazard interactions, some literature separates “Level I” (or occurrence) interactions from “Level II” (or consequence) interactions. The Level I interactions occur inherently due to the nature of the hazards, independently of the presence of physical assets. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., flooding due to heavy rain, liquefaction and landslides triggered by an earthquake), thus creating a dependency between the features characterizing such hazard events. They differ from Level II interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impacts due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard life cycle consequence (LCCon) analysis aims to quantify the consequences (e.g., repair costs, downtime, casualty rates) throughout a system’s service life and should account for both Level I and II interactions. The available literature generally considers Level I interactions – the focus of this study – mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events incorporating the identified interrelations among them. This paper addresses this gap, proposing modeling approaches associated with different types of Level I interactions. It describes a simulation-based method for generating multi-hazard event sets (i.e., a sequence of hazard events and associated features throughout the system’s life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs multi-hazard event sets that can be integrated into LCCon frameworks to quantify interacting hazard consequences. An application incorporating several hazard interactions is presented to illustrate the potential of the proposed method.

Energy optimization of the smart residential electrical grid considering demand management approaches

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Dealing With Variable Drug Exposure DuetoVariable Hepatic Metabolism: A Proof-of-Concept Application of Liquid Biopsy in Renal Impairment.

Precision dosing strategies require accounting for between-patient variability in pharmacokinetics (PK), affecting drug exposure, and in pharmacodynamics (PD), affecting response achieved at the same drug concentration at the site of action. Although liquid biopsy for assessing different levels of molecular drug targets has yet to be established, individual characterization of drug elimination pathways using liquid biopsy has recently been demonstrated. The feasibility of applying this approach in conjunction with modeling tools to guide individual dosing remains unexplored. In this study, we aimed to individualize physiologically-based pharmacokinetic (PBPK) models based on liquid biopsy measurements in plasma from 25 donors with different grades of renal function who were previously administered oral midazolam as part of a microdose cocktail. Virtual twin models were constructed based on demographics, renal function, and hepatic expression of relevant pharmacokinetic pathways projected from liquid biopsy output. Simulated exposure (AUC) to midazolam was in agreement with observed data (AFE = 1.38, AAFE = 1.78). Simulated AUC variability with three dosing approaches indicated higher variability with uniform dosing (14-fold) and stratified dosing (13-fold) compared with individualized dosing informed by liquid biopsy (fivefold). Further, exosome screening revealed mRNA expression of 532 targets relevant to drug metabolism and disposition (169 enzymes and 361 transporters). Data related to these targets can be used to further individualize PBPK models for pathways relevant to PK of other drugs. This study provides additional verification of liquid biopsy-informed PBPK modeling approaches, necessary to advance strategies that seek to achieve precise dosing from the start of treatment.