Modeling Tools Research Articles

Phytochemical inhibitors from Leucas aspera against the target proteins induced by Trichophyton mentagrophytes using computational techniques.

Dermatophytosis is a cutaneous infection that is able to degrade the keratinized tissues of the animal/human body, like skin, nails, and hair, causing chronic or subacute infection with the contact of some specific fungal strains. Trichophyton mentagrophytes are the most potential fungal pathogen causing dermatophytoses. The present study focuses on computationally based in silico antifungal activity of selected phytocompounds of Leucas aspera (Willd.) Link. against dermatophytic fungus, T. mentagrophytes. Validation and screening of derived phytocompounds is performed using Lipinski rule of five and toxicity test through Protox-II. Five target genes involved in dermatophytosis, induced by T. mentagrophytes are retrieved from the UniProt Database, and the corresponding proteins such as glucan 1,3-beta-glucosidase ARB_02797, Probable class II chitinase ARB_00204, squalene monooxygenase, actin, and ubiquitin are selected for in silico study. Three-dimensional structures of the target protein were computationally determined and validated through modeling tools and techniques due to the lack of validated protein structures in the database. Then, these proteins are used for in silico molecular docking through the AutoDock Vina tool to find out the promising phytocompounds. This study could be utilized in designing more effective drugs against T. mentagrophytes. Based on this work, a plant-based natural alternative can be added to the treatment of dermatophytosis rather than synthetic supplements.

Comprehensive analysis of the bZIP gene family in ‘Duli’ pear (Pyrus betulifolia Bunge): Insights for research on abiotic stress, especially drought, in pear

Plants are unavoidably subjected to various abiotic stresses, such as drought, salt, and cold, during their growth and development. Drought, accompanied by water shortages in North China, has become an environmental factor seriously affecting agriculture production. Basic leucine zipper (bZIP) is one of the most diverse transcription factor families in plants. It participates in the regulation of many physiological processes and plays an important role in plants’ response to abiotic stress. In this study, for the first time, 88 bZIP family members (PbbZIPs) in ‘Duli’ pear (Pyrus betulifolia Bunge) were identified using both hidden Markov model (HMM) and basic local alignment search tool (BLAST) methods. A phylogenetic tree of all the PbbZIPs members was constructed. The physicochemical properties, subcellular localisation, chromosomal location, gene structure, conserved motifs and domains, secondary and tertiary structures, and cis-acting promoter elements of the 88 family members were comprehensively analysed. The results indicated that PbbZIPs may play relevant roles in stress response in ‘Duli’. Therefore, we determined the expressions of all PbbZIPs in ‘Duli’ seedlings under drought, salt, and cold conditions, and in seedlings treated with stress-related hormones such as abscisic acid (ABA), salicylic acid (SA), methyl jasmonate (MeJA), and melatonin (MT). PbbZIPs responsive to various abiotic stresses and hormones were screened. In addition, we determined the expression patterns of 12 PbbZIP genes that strongly responded to both drought and ABA in ‘Duli’ tissue culture plants treated with different concentrations of PEG4000 at different time points. Finally, we selected three key genes (PbbZIP65, PbbZIP77 and PbbZIP81) that may play a major role in drought resistance in ‘Duli’, and preliminarily identified their function through the VIGS method, finding that PbbZIP77 could positively regulate the drought resistance of ‘Duli’ seedlings, while PbbZIP65/81 could negatively regulate the drought resistance of ‘Duli’ seedlings. This study not only broadens our knowledge about the bZIP gene family in ‘Duli’ pear, but also provides notable guidance for further functional research on PbbZIPs in abiotic stress, especially drought resistance, in the pear plant.

PHREEQ C Modelling Tool Application to Determine the Effect of Anions on Speciation of Selected Metals in Water Systems within Kajiado North Constituency in Kenya

Heavy metals and anions in water pose a significant risk to living organisms, including humans, and must therefore be carefully monitored. There are numerous approaches and methods of analysis to choose from. Using the PHREEQ C chemical modeling technique, this study looked at the effect of certain anions on the speciation of selected metals in the water environment of Kajiado North. Twenty-five sampling points were used in the study, which covered five Kajiado North election wards. Samples were taken twice during the dry and rainy seasons, in November 2019 and March 2020. Using standard methods, physicochemical characteristics were analyzed, concentrations of selected heavy metals using the (ICP- OES) 7000 series, and selected anions using a multi parameter photometer, their concentration values compared to the specified maximum contaminant level and speciation analysis done using the Computer-based technique PHREEQ C. The range observed for pH, temperature, electrical conductivity, sulphate, chloride, fluoride, nitrate and carbonates was 7.43- 9.30, 23.0 -28.0oC, 335-1265μS/cm, 4.54-361.41mgL-1, 5.19-184.6 mgL-1, 0.01-2.90 mgL-1, 0.1-8.6 mgL-1, and 3.18-55.19 mgL-1 respectively. The range observed for heavy metals was 0.06-1.08 mgL-1 , 0.00 -0.05082 mgL-1, 0.01-0.02597 mgL-1, 0.01-0.01610 mgL-1, 0.00 -0.0327 mgL-1, 0.00-42.7842 mgL-1, and below detection limit, for Fe, Mn, Cu, Zn, Pb, Mg, and Cd respectively. The findings revealed that Cd, Zn, Cu, Cl-, NO2-, and eElectrical cconductivity concentrations were below the WHO levels. At certain sampling points, however, SO42-, F-, Pb, Fe, and Mn were above WHO limits. In some water systems, traces of SO42-, F-, Cu, Fe, Cd, and Mn were found to pose significant health risks, according to PHREEQ C speciation analysis. Anion concentrations, pH, temperature, and metal concentrations are all important variables in modeling. Metals formed anion complexes and the total abundance of metal species was classified at the highest levels as Fe(OH)3, PbCO3, Zn(CO3)2, Cu(CO3)2, Mn+2, MgF-, and Mg+2. At a water system temperature of 25oC without anions, it was found to have the highest chemical species. CuCO3, CuNO3, and CuOH+ complexes are also formed at low pH. Based on the data for different metal species at different pH values, the bioavailability of metal species definitely depends on pH.

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area.In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8mg/g and 100.7mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data.

Can the Establishment of National Parks Promote the Coordinated Development of Land, the Environment, and Residents’ Livelihoods?

National parks play a crucial role in the worldwide system of natural conservation, significantly influencing land utilization, the sustainability of local livelihoods, and the environmental integrity of nearby regions. In this study, the coupled coordination model and ArcGIS tools are applied to examine the temporal and spatial progression of the coordination of the system of land, the environment, and residents’ livelihoods (the LEL system). The construction of the LEL system and the use of natural geographic and socio-economic data are the main innovations of this research. The findings indicate that over the last 15 years, Wuyishan National Park has witnessed growth in its ecological and agricultural areas alongside a decline in its water-based ecological zones. The creation of the national park has obviously promoted the coordination of the LEL systems in nearby areas and has a significant spatial spillover effect. Consequently, for the upcoming development of Wuyishan National Park, it is crucial to modify the framework of the agricultural sector, enhance local public infrastructure, and bolster the safeguarding of water ecological areas.

Regulatory framework for the assessment of the impacts of plant protection products on biodiversity: review of strengths and limits.

The placing of plant protection products (PPPs) on the market in the European Union is governed by numerous regulations. These regulations are among the most stringent in the world, however they have been the subject of criticisms especially because of the decline in biodiversity. The objectives of this work were to review (1) the functioning and actors involved in the PPP framework processes, (2) the construction of the environmental risk assessment focused on biodiversity, and (3) the suggested ways to respond to the identified limits. Both literature from social sciences and ecotoxicology were examined. Despite the protective nature of the European regulation on PPPs, the very imperfect consideration of biodiversity in the evaluation process was underlined. The main limits are the multiplicity of applicable rules, the routinization of the evaluation procedures, the lack of consideration of social data, and the lack of independence of the evaluation. Strengths of the regulation are the decision to integrate a systemic approach in the evaluation of PPPs, the development of modeling tools, and the phytopharmacovigilance systems. The avenues for improvement concern the realism of the risk assessment (species used, cocktail effects…), a greater transparency and independence in the conduct of evaluations, and the opening of the evaluation and decision-making processes to actors such as beekeepers or NGOs. Truly interdisciplinary reflections crossing the functioning of the living world, its alteration by PPPs, and how these elements question the users of PPPs would allow to specify social actions, public policies, and their regulation to better protect biodiversity.

The Birth of a New BIM Standard: From PED 2018 to 2023, New Parameters and Workflows "Going Live" for Everyone

Building Information Modelling (BIM) has become the de facto standard for the digital representation of buildings. However, from the pedestrian dynamics perspective, BIM Industry Foundation Classes (IFC) schema specification do not fully support data properties required for two-way data sharing with pedestrian modelling tools. An international team of academic and industry researchers, supported by buildingSMART International (bSI), is developing an Occupant Movement Analysis (OMA) standard. The project is focused on expanding the IFC schema specification to support workflows for pedestrian simulation tools and is close to completion. So far, multiple process maps and a list of data properties synchronised with several representative pedestrian modelling tools have been produced. This list of data properties was then converted into bSI's recommended flexible and machine interpretable Information Delivery Specification (IDS) format for specifying data exchange requirements and to add clarity. Currently, this is undergoing testing and review by the project team. Once completed, it will be submitted to bSI’s committees for review. Also, to support this work, a prototype open-source Add-in has been developed to demonstrate a two-way integrated data sharing between BIM authoring tools and pedestrian simulation tools. This standard will enhance data sharing between BIM authoring and pedestrian modelling tools by facilitating the capturing of the required data and addressing friction in multiple design iterations and reassessment.

A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles

Abstract The biocompatibility property has made silver nanoparticles powerful candidates for various nanomedical applications. Research interest in silver nanoparticles as a viable alternative to antibiotics is gaining more attention due to their enhanced antimicrobial activity, better antibacterial activity and low cytotoxicity. Machine Learning (ML) has become a state-of-the-art analytic and modelling tool in recent times, due to its prediction capabilities and increased accuracy of the results. In this work, we present machine-learning techniques to predict the antibacterial capacity of silver nanoparticles and extended the work on antifungal studies. In the first phase, we reviewed 50 articles and collected data points for training the model, which consists of features such as core size, shape of the nanoparticle, dosage, bacteria/fungi species and zone of inhibition (ZOI). Then, we trained the data using eight different machine-learning regression algorithms and validated the models' performance using four metrics such as RMSE, MSE, MAE and R2. Furthermore, the importance of features used in the prediction models has been evaluated. The feature importance revealed that the core size of silver nanoparticles is the prominent feature in the prediction of the antibacterial capacity. The optimum model for the prediction of antibacterial and antifungal activity has been identified. Finally, the model’s validation has also been demonstrated. This work enables researchers to utilize Machine Learning which in turn can address the challenges of time consumption, and cost in laboratory experiments while minimising the reliance on trial and error.

Retrofitting Battery Electric Machinery with Unchanged Hydraulic System and Enhanced Control Strategies

The push for environmental sustainability has accelerated the acceptance of electric vehicles, as well as the exploration of electrified Non-Road Mobile Machinery. This study emphasizes the challenges of electrifying off-highway machinery, which include the many machinery layouts and the presence of Small- and Medium-sized Enterprises in the market. Recognizing the barriers faced by these companies, this paper shows how modeling and simulation can be effective tools for system integration and control optimization, even when lacking extensive expertise in the topic. However, it emphasizes the need for user-friendly modeling tools and methods adaptable to the operational needs of Small- and Medium-sized Enterprises. This study presents a case study of a retrofitted battery-electric hydraulic material handler. The machinery is simulated using Simscape, and the accuracy of the model is confirmed through experimental validation. By simulating a rational duty cycle, this study proposes two solutions for performance enhancement while maintaining the integrity of the hydraulic system. These solutions offer a balanced compromise between energy consumption and productivity and a novel control algorithm to minimize energy consumption. Most importantly, the two proposed solutions can be easily switched by the operator, which can decide to favor productivity over energy saving based on driving needs.

High-order finite-element forward-modeling algorithm for the 3D borehole-to-surface electromagnetic method using octree meshes

The borehole-to-surface electromagnetic (BSEM) method complements surface and airborne electromagnetic (EM) methods. The borehole is typically filled with conductive media, which significantly impacts the responses. To accurately simulate the responses, the borehole needs to be modeled. However, the size of the borehole is very small compared with the entire computational domain, making it challenging to discretize the borehole accurately. We develop a high-accuracy 3D finite-element forward-modeling algorithm for the BSEM method using octree meshes. The basic idea of octree meshes is to locally refine the mesh by dividing the hexahedral element into eight smaller elements. This approach has the advantage of being able to accurately discretize small geometric features while using coarse meshes in other areas, thus reducing the computational cost. In addition, high-order basis functions are used to improve the accuracy of numerical solutions. We develop a 3D forward-modeling code for BSEM using the C++ programming language. We verify the correctness and accuracy of our implementation by comparing the numerical responses with analytical solutions. Furthermore, we determine the influence of the borehole on the EM responses, showing that the borehole will cause a decrease of several orders of magnitude in the responses when the borehole is filled with highly conductive media. We also use two 3D models to demonstrate the efficacy of octree meshes in the discretization of complex structures and to discuss the effectiveness and sensitivity of BSEM data in the exploration of mineral and oil-gas resources. The code is open source under the Massachusetts Institute of Technology license and can be used as an accurate modeling tool for BSEM responses, providing useful guidance for field exploration. The code is designed with a modular architecture, ensuring ease of maintenance and the ability to extend to other EM methods and serve as the foundation for 3D inversion.

Vesicle and reaction-diffusion hybrid modeling with STEPS

Vesicles carry out many essential functions within cells through the processes of endocytosis, exocytosis, and passive and active transport. This includes transporting and delivering molecules between different parts of the cell, and storing and releasing neurotransmitters in neurons. To date, computational simulation of these key biological players has been rather limited and has not advanced at the same pace as other aspects of cell modeling, restricting the realism of computational models. We describe a general vesicle modeling tool that has been designed for wide application to a variety of cell models, implemented within our software STochastic Engine for Pathway Simulation (STEPS), a stochastic reaction-diffusion simulator that supports realistic reconstructions of cell tissue in tetrahedral meshes. The implementation is validated in an extensive test suite, parallel performance is demonstrated in a realistic synaptic bouton model, and example models are visualized in a Blender extension module.

PRODIGE - Planet-forming disks in Taurus with NOEMA. II. Modeling the CO (2-1) isotopologue emission of the Class II T Tauri disks in Taurus

To understand how planets form in protoplanetary disks, it is necessary to characterize their gas and dust distribution and masses. This requires a combination of high-resolution dust continuum and molecular line interferometric observations, coupled with advanced theoretical models of protoplanetary disk physics, chemical composition, and radiative transfer. We aim to constrain the gas density and temperature distributions as well as gas masses in several T Tauri protoplanetary disks located in Taurus. We use the 12CO, 13CO, and C18O (2-1) isotopologue emission observed at $0.9 with the IRAM NOrthern Extended Millimeter Array (NOEMA) as part of the MPG-IRAM Observatory Program PRODIGE (PROtostars and DIsks: Global Evolution \, PIs: P. Caselli Th. Henning). Our sample consists of Class II disks with no evidence of strong radial substructures. We use these data to constrain the thermal and chemical structure of these disks through theoretical models for gas emission. To fit the combined optically thick and thin CO line data in Fourier space, we developed the DiskCheF code, which includes the parameterized disk physical structure, machine-learning (ML) accelerated chemistry, and the RADMC-3D line radiative transfer module. A key novelty of DiskCheF is the fast and feasible ML-based chemistry trained on the extended grid of the disk physical-chemical models precomputed with the ANDES2 code. This ML approach allows complex chemical kinetics models to be included in a time-consuming disk fitting without the need to run a chemical code. We present a novel approach to incorporate chemistry into disk modeling without the need to explicitly calculate a chemical network every time. Using this new disk modeling tool, we successfully fit the 12CO, 13CO, and C$ $O (2-1) data from the CI, CY, DL, DM, DN, and IQ Tau disks. The combination of optically thin and optically thick CO lines allows us to simultaneously constrain the disk temperature and mass distribution, and derive the CO-based gas masses. The best-fit disk gas masses range between 0.005 and $0.04 M_ sun $. These values are in reasonable agreement with the disk dust masses rescaled by a factor of 100 as well as with other indirect gas measurements via, for example, modeling of the wavelength dependence of the dust continuum emission radii, and HD and CO isotopologue emission.

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PurposeStandard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. MethodsWe describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. ResultsUsing two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. ConclusionsMDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction

The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections for electric utilities and policymakers, leading to the adoption of machine learning-based modelling tools due to the limitations of traditional power usage projection approaches. However, successful modeling of power usage in neuro-fuzzy models depends on the optimal selection of hyper-parameters. Consequently, this research looked at the major impact clustering methods and hyper-parameter modifications on a particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS) model. The study examined two distinct clustering methods and other key hyperparameters such as the number of clusters and cluster radius, resulting in a total of 10 sub-models. The performance of the developed models was assessed using four widely recognized performance indicators: root mean square error, mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of variation of the root mean square error (CVRMSE). Additionally, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models based on three different PSO variants. The results revealed that the combination of the ANFIS approach and PSO, specifically with two clusters, yielded the most accurate forecasting scheme with the optimal values for MAPE (7.7778%), MAE (712.6094), CVRMSE (9.5464), and RMSE (909.4998).

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.

Construction and validation of clinical prediction model of acupuncture and moxibustion for Bell's palsy

To establish and validate a clinical prediction model of acupuncture and moxibustion for Bell's palsy so as to provide a tool for predicting the effect of acupuncture and moxibustion on Bell's palsy. A total of 269 patients with Bell's palsy were collected from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital, neurology department, Shenxian County Central Hospital and department of rehabilitation medicine, Dongying Municipal Hospital of TCM from June 2018 to June 2023. All of these cases were treated with acupuncture and moxibustion. Of them, 182 cases, from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital and neurology department, Shenxian County Central Hospital, were randomized into a training group (128 cases) and an internal validation group (54 cases); 87 cases from department of rehabilitation medicine, Dongying Municipal Hospital of TCM were assigned to an external validation group. The clinical data of all of the cases were extracted from the electronic medical record information platform. Using SPSS25.0 and R4.2.3, through univariate and multivariate Logistic regression analysis, the independent factors influencing the effects of acupuncture and moxibustion on Bell's palsy were identified. By means of internal and external validations, the receiver operating characteristic curve (ROC), the goodness-of-fit curve (GFC) and the decision curve analysis (DCA) were plotted. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the model were calculated; and its comprehensive performance was evaluated. The results of the multivariate Logistic regression analysis showed that the independent factors for the unsatisfactory effect on Bell's palsy were advanced age, severe symptoms before treatment, no use of steroids within 72 h of onset, and lack of acupuncture-moxibustion therapy during the acute phase or single acupuncture-moxibustion protocol (P<0.05, P<0.01). Based on these factors, nomogram model and online columnar plot prediction tool (https://bmuchen.shinyapps.io/dynnomapp/) were established. The area under the ROC curve of the model was 0.921 (95% CI: 0.877, 0.966), 0.876 (95% CI: 0.787, 0.966), and 0.846 (95% CI: 0.766, 0.926) in the training group, the internal validation group, and the external validation group, respectively, indicating good predictive value. The model showed a satisfactory calibration curve alignment. The decision threshold in the range of 0 to 0.8 provided clinical benefits for participants. The model exhibited the sensitivity from 65.9% to 88.0%, the specificity ranging from 77.3% to 90.7%, the accuracy from 77.8% to 85.9%, the positive predictive value from 83.3% to 90.1%, and the negative predictive value from 70.8% to 78.7%. The comprehensive evaluation indicated a satisfactory clinical application value of the model. The clinical prediction model of acupuncture and moxibustion for Bell's palsy is valuable in its practice and promotion to a certain extent. The predicted results are conductive to clinicians' judgement of the effect of acupuncture and moxibustion for this disease and making effective and high-quality clinical decisions, as well as formulating the optimal therapeutic regimen.

Preclinical side effect prediction through pathway engineering of protein interaction network models.

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Predicting the hydraulic response of critical transport infrastructures during extreme flood events

Understanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in the face of climate change and extreme events. This study develops robust computational and predictive modeling tools for assessing the impacts of extreme floods on the hydraulic response and structural resilience of bridges. A computational fluid dynamic (CFD) model utilizing RANS equations and k-ω Shear Stress Transport (SST) for simulating supercritical flows is adopted to compute hydrodynamic pressures and the water levels on bridge piers of cylindrical and rectangular shapes during a flood event. The CFD model is validated based on the case study data obtained from the Haj Omran Bridge, built on the Khorramabad River in Iran. The numerical simulations consider hydrological conditions and exclude geotechnical parameters and abutment damages. The numerical results are evaluated based on well-established design guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), are adopted to enhance the accuracy of hydrodynamic pressure forecasting at bridge piers. The XGBoost model exhibits superior performance (R2 = 0.908, RMSE = 0.0279, and E = 3.41%) compared to the RF and SVR models. All the estimated pressure data by XGBoost falls within ±6 percent error lines, highlighting the model's robustness for out-of-range hydrodynamic pressure prediction. Additionally, an optimized Long Short-Term Memory (LSTM) model is adopted to effectively predict free surface flow profiles (i.e. flood depth) over the bridge (R2 = 0.937 and RMSE = 0.083), demonstrating its potential for practical applications of flood depth predictions over bridge infrastructures. The proposed methodological framework outlined in this study can facilitate modeling the impacts of extreme floods on bridges, enabling robust climate resilience assessment of critical infrastructures.

Analysis of Heat Transfer of Molten Salts Startup Flow in Cold Pipes Avoiding Freezing in Solar and Nuclear Energy Systems

Abstract Molten salts are employed as the heat transfer fluid to carry the thermal energy from a solar receiver or a nuclear reactor for delivering to thermal storage systems or thermal power plants for power generation. For the startup operation, molten salts need to be pumped to flow into the pipes which may have lower temperature than the freezing point of molten salt due to the cold ambient temperature overnight or over the suspension of operation. Preventing the freezing of molten salt in cold pipes becomes a critical issue to the safe operation of a concentrating solar thermal power plant or a molten salt nuclear power plant. This study conducted a basic heat transfer analysis of the transient heat transfer from flowing molten salt to a cold pipe to determine the length from entrance to the onset of freezing of the fluid. From our modeling and analytical solution using method of characteristics, the correlation of the location of onset of freezing of molten salt with respect to the flow velocity, heat capacities of molten salt and pipes, dimension of the pipes, and the initial temperatures of salts and pipes, have been understood clearly. The modeling and computational tool can fundamentally help engineers to design a system to avoid freezing and clogging at cold startup when molten salt is applied as a heat transfer fluid.

A framework to characterize, model, and optimize water-energy systems: case study of a novel HDH system

The rapid development in water-energy systems calls for advanced modeling and optimization tools to improve the technologies throughputs and minimize their energy consumption. While having a wide footprint in multiple research areas, Artificial Intelligence applications in water-energy systems are still in early stages. There is a need for generalized models to fully characterize parametric relationships, optimize technologies' performance, and have the flexibility to apply to different complex and non-linear systems. This article describes a digital twin framework that can be used for that purpose. This framework provides fundamental understanding of the process as in process-driven models alongside with the accuracy and usability of data-driven methods. The framework's structure focused on modeling and optimization aspects of digital twin models. As a part of developing a novel humidification-dehumidification desalination technology, this framework was used to model energy consumption based on 16 different operational parameters and was accurate to within 1 % of the data. Pairwise parameters sensitivities were identified by the framework for two case studies involving the whole cycle operation and a critical subsystem of the technology. Moreover, the framework was used to optimize the technology performance through data-efficient Bayesian search methods. Different search realms were used in this process. The framework was able to identify minimum and maximum energy consumption regions by sampling only 25 data points out of possible 5157 cases.