Search Results (31)

Background: Accurate assessment of CYP2C induction-mediated drug–drug interactions (DDIs) remains a challenge, despite the importance of CYP2C enzymes in drug metabolism. Limitations in available models and scarce clinical induction data have hampered quantitative preclinical DDI risk evaluation. Methods: In this study, the authors utilized an all-human hepatocyte triculture system to capture CYP2C induction using the perpetrators rifampicin, efavirenz, carbamazepine, and apalutamide. In vitro induction parameters were quantified by measuring changes in both mRNA and enzyme activities for CYP2C8, CYP2C9, and CYP2C19. These induction parameters, along with CYP-specific intrinsic clearance (CL_int) for the victim compounds, were incorporated into a physiologically based pharmacokinetic (PBPK) model, and pharmacokinetics (PK) of known CYP2C substrates were predicted with and without co-administration of perpetrator compounds using clinical dosing regimens. The results were quantitatively compared with the currently utilized mechanistic static modeling (MSM) approach and the reported clinical DDI outcomes. Results: By incorporating the measured f_m of CYP2C substrates into PBPK modeling, we observed a lower propensity to over- or underpredict the exposure of these substrates as victims of CYP2C induction-based DDIs when co-administered with known perpetrators, which resulted in an excellent correlation to observed clinical outcomes. The MSM approach predicted the CYP3A4 induction-based DDI risk accurately but could not capture CYP2C induction with similar precision. Conclusions: Overall, this is the first study that demonstrates the utility of PBPK modeling as a complementary approach to MSM for CYP2C induction-based DDI risk assessment. Full article

Background: Viral–bacterial co-infections can amplify disease severity through complex biological mechanisms. Mathematical models are critical tools for understanding these threats, but it is unclear how well they capture the underlying biology. This systematic review addresses a central question: to what extent does the current generation of models mechanistically represent co-infections, or do the mathematical assumptions underlying these models adequately represent the known biological mechanisms? Methods: Following PRISMA guidelines, we systematically reviewed the literature on mechanistic models of human virus–bacteria co-infections. A systematic search of articles on the scientific literature repositories PubMed, Scopus, and Dimensions was conducted and data on study objectives, model structure, assumptions about biological interactions (e.g., susceptibility, mortality), control measures (if evaluated), and the empirical sources used for key parameters were extracted. Results: We identified 72 studies for inclusion in this analysis. The reviewed models are consistently built on the established premise that co-infection alters disease severity and host susceptibility. However, we found they incorporate these dynamics primarily through high-level mathematical shortcuts, such as applying static “multiplicative factors” to transmission or progression rates. Our quantitative analysis also revealed questionable approaches; for example, 79% (57) of these studies relied on non-empirical sources (assumed or borrowed values) for parameter values including interaction parameters (e.g., increased susceptibility to a secondary pathogen following primary infection, or elevated mortality rates in co-infected individuals). Conclusions: An apparently unjustified practice exists in co-infection modeling, where complex biological processes are simplified to fixed numerical assumptions, often without empirical support. This practice limits the predictive reliability of current models. We identify an urgent need for data-driven parameterization and interdisciplinary collaboration to bridge the gap between biological complexity and modeling practice, thereby enhancing the public health relevance of co-infection modeling. Full article

During the transition of culture-featured district planning from static conservation to innovation-driven models, existing research remains constrained by mechanistic paradigms, reducing districts to functional containers and neglecting human perceptual interactions and meaning-production mechanisms. This study explores and quantifies the generative mechanisms of spatial communicability and cultural dissemination efficacy within human-centered frameworks. Grounded in humanistic urbanism, we analyze Harbin Central Street as a case study integrating historical heritage with contemporary vitality, developing a tripartite communicability assessment framework comprising perceptual experience, infrastructure utility, and behavioral dynamics. Machine learning-based threshold analysis reveals that spatial encoding elements govern communicability through significant nonlinear mechanisms. The conclusion shows synergies between street view-quantified greenery visibility and pedestrian accessibility establish critical human-centered design thresholds. Spatial data analysis integrating physiologically sensed emotional experiences and topologically analyzed spatial morphology resolves metric fragmentation while examining spatial encoding’s impact on interaction efficacy. This research provides data-driven decision support for sustainable urban renewal and enhanced cultural dissemination, advancing heritage sustainability. Full article

The dynamic non-equilibrium effect (DNE) describes the non-unique character of saturation–capillary pressure relationships observed under static, steady-state, or monotonic hydrodynamic conditions. Macroscopically, the DNE manifests as variations in soil hydraulic characteristic curves arising from varying hydrodynamic testing conditions and is fundamentally governed by soil matrix particle size distribution. Changes in the DNE across porous media with discrete particle size fractions are investigated via stepwise drying experiments. Through quantification of saturation–capillary pressure hysteresis and DNE metrics, three critical signatures are identified: (1) the temporal lag between peak capillary pressure and minimum water saturation; (2) the pressure gap between transient and equilibrium states; and (3) residual water saturation. In the four experimental sets, with the finest material (Test 1), the peak capillary pressure consistently precedes the minimum water saturation by up to 60 s. Conversely, with the coarsest material (Test 4), peak capillary pressure does not consistently precede minimum saturation, with a maximum lag of only 30 s. The pressure gap between transient and equilibrium states reached 14.04 cm H₂O in the finest sand, compared to only 2.65 cm H₂O in the coarsest sand. Simultaneously, residual water saturation was significantly higher in the finest sand (0.364) than in the coarsest sand (0.086). The results further reveal that the intensity of the DNE scales inversely with particle size and linearly with wetting phase saturation (Sw), exhibiting systematic decay as Sw decreases. Coarse media exhibit negligible hysteresis due to suppressed capillary retention; this is in stark contrast with fine sands, in which the DNE is observed to persist in advanced drying stages. These results establish pore geometry and capillary dominance as fundamental factors controlling non-equilibrium fluid dynamics, providing a mechanistic framework for the refinement of multi-phase flow models in heterogeneous porous systems. Full article

Building upon the geological cycle theory, this study proposes fault cycles as a critical component of tectonic cyclicity in petroliferous basins. Focusing on reservoir-controlling faults in the southwestern Qaidam Basin, we systematically analyze fault architectures and identify three distinct fault activation episodes: the Lulehe Formation (LLH Fm.), the upper part of the Xiaganchaigou Formation (UXG Fm.), and the Shizigou Formation (SZG Fm.). Three types of fault cycle models are established. These fault cycles correlate with the evolution of regional tectonic stress fields, corresponding to the Cenozoic transition from extensional to compressional stress regimes in the basin. Mechanistic analysis reveals the hierarchical control of fault cycles in hydrocarbon systems: the early cycle governs the proto-basin geometry and low-amplitude structural trap development; the middle cycle affects the source rock distribution; and the late cycle controls trap finalization and hydrocarbon migration. This study proposes a fault cycle-controlled accumulation model, providing a dynamic perspective that shifts from conventional static fault concepts to reveal fault activity periodicity and its critical multi-phase control over hydrocarbon migration and accumulation, essential for exploration in multi-episodic fault provinces. Full article

Accurate prediction of the dynamic compressive strength of brittle engineering materials is of significant theoretical and engineering importance for underground engineering design, safety assessment, and dynamic hazard prevention. To enhance prediction accuracy and model interpretability, this study proposes a novel framework integrating stacking ensemble learning with SHapley Additive exPlanations (SHAP) for dynamic strength prediction. Leveraging multidimensional input variables, including static strength, strain rate, P-wave velocity, bulk density, and specimen geometry parameters, we constructed six machine learning regression models: K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), LightGBM, XGBoost, and Multilayer Perceptron Neural Network (MLPNN). Through comparative performance evaluation, optimal base models were selected for stacking ensemble training. Results demonstrate that the proposed stacking model outperforms individual models in prediction accuracy, stability, and generalization capability. Further SHAP-based interpretability analysis reveals that strain rate dominates the prediction outcomes, with its SHAP values exhibiting a characteristic nonlinear response trend. Additionally, structural and mechanical variables such as static strength, P-wave velocity, and bulk density demonstrate significant positive contributions to model outputs. This framework provides a robust tool for intelligent prediction and mechanistic interpretation of the dynamic strength of brittle materials. Full article

Cadmium (Cd) pollution poses a severe threat to rice safety and human health, while traditional linear models exhibit significant limitations in predicting rice Cd accumulation due to environmental complexities. This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. The results showed that the RF model performed best on the test set (R² = 0.62), with the dominant features being soil’s available Cd (contributing 9.74%) and precipitation during the rice-filling stage (joint contribution of 15.96%). However, the model’s predictive performance experienced a sharp decline on the independent 2023 validation set comprising 393 samples from Yizhang County and Lengshuitan District, with R² values ranging from −0.12 to −0.31. This highlighted the fundamental limitations of static data-driven paradigms. Agronomic management measures, simplified by heterogeneous data and binary encoding, failed to effectively represent the actual intervention intensity. The study demonstrated that while machine learning models captured nonlinear relationships in laboratory environments, they struggled to adapt to the dynamic interactions and spatiotemporal heterogeneity of farmland systems. Future efforts should focus on developing hybrid models guided by mechanistic insights, integrating dynamic environmental processes and real-time data, and promoting localized “one model per region” strategies to enhance predictive robustness. This study provides methodological insights for the technological transformation of agricultural artificial intelligence, emphasizing that the deep integration of data-driven approaches and mechanistic understanding is crucial for overcoming the “last mile” challenge. Full article

A phase-locked loop (PLL), as a synchronization unit commonly employed in grid-connected converters (GCCs), jeopardizes system stability under a weak grid. Therefore, synchronization units without a PLL, such as active power synchronization (APS), have received much attention. However, GCCs adopting APS suffer from two typical issues: static angular error and the risk of destabilization. In this paper, the mechanism for static angular error is analyzed, which is attributed to the presence of active power current, resulting in a non-zero q-axis voltage at the point of common coupling. To eliminate the static error in angular tracking, conventionally, a proportional–integral (PI) controller is integrated into APS, which arouses system instability. Thus, a closed-loop single-input single-output (SISO) model of a GCC is derived from angular perturbation to analyze the mechanism of this destabilization. It is revealed that the gain overshoot in the closed-loop SISO model resulting from the PI controller would lead to system instability under a weak grid. Consequently, according to the above mechanistic analysis, a composite direct damping controller is proposed, which achieves the elimination of static angular error while enhancing system stability. Finally, the effectiveness of the mathematical analysis and derived models are verified by experimental results. Full article

To address the technical limitations of traditional pavement performance prediction models in capturing temporal features and analyzing multi-factor coupling, this study proposes a Bayesian Optimization Dual-Layer Feature Fusion Model (BO-DLFF). The framework integrates heterogeneous data streams from embedded strain sensors, temperature/humidity monitoring nodes, and weigh-in-motion (WIM) systems, combined with pavement distress detection and historical maintenance records. A dual-stage feature selection mechanism (BP-MIV/RF-RFECV) is developed to identify 12 critical predictors from multi-modal sensor measurements, effectively resolving dimensional conflicts between static structural parameters and dynamic operational data. The model architecture adopts a dual-layer fusion design: the lower layer captures statistical patterns and temporal–spatial dependencies from asynchronous sensor time-series through Local Cascade Ensemble (LCE) ensemble learning and improved TCN-Transformer networks; the upper layer implements feature fusion using a Stacking framework with logistic regression as the meta-learner. BO is introduced to simultaneously optimize network hyperparameters and feature fusion coefficients. The experimental results demonstrate that the model achieves a prediction accuracy of R² = 0.9292 on an 8-year observation dataset, effectively revealing the non-linear mapping relationship between the Pavement Condition Index (PCI) and multi-source heterogeneous features. The framework demonstrates particular efficacy in correlating high-frequency strain gauge responses with long-term performance degradation, providing mechanistic insights into pavement deterioration processes. This methodology advances infrastructure monitoring through the intelligent synthesis of IoT-enabled sensing systems and empirical inspection data. Full article

The air separation process is an important industrial process for the production of high-purity nitrogen and oxygen, representing the level of technological development in a country’s chemical industry. It has high energy consumption but very low energy utilization efficiency. In the overall environment of increasingly scarce global energy, the application of internal heat coupling technology in the air separation process can effectively reduce energy consumption. However, due to the low-temperature characteristics, ultra-high purity characteristics, and the nature of multi-component systems of the heat-integrated air separation column (HIASC), its modeling process and dynamic characteristic analysis are complex. To solve the disadvantages of overly complex mechanistic models and insufficient accuracy of traditional simplified models, a concentration distribution curve description method based on the mass transfer mechanism is proposed, and combined with the traditional wave theory, a nonlinear wave model of the HIASC is established. Based on this model, static and dynamic analyses were carried out, and the research results prove that the newly established nonlinear wave model maintains high accuracy while simplifying the model complexity. It can not only accurately track the concentration changes of key products but also fully reflect various typical nonlinear characteristics of the system. Compared to the mechanism model, the wave model can reduce the running time by approximately 20%, thereby improving operational efficiency. This method explains various characteristics of the system from a perspective different from that of the mechanistic model. Full article

The integration of geodiversity elements and contexts into fire management frameworks remains limited due to a lack of actionable tools for assessing geosite sensitivity. This study addresses this gap by developing and testing a mechanistic model to evaluate soil and lithological fire sensitivity, using a geodiversity database of Tasmanian geosites at various temperature thresholds. Initial results indicate the utility of the approach to distinguish between sensitive and robust geosites, providing a simple delineation between the relative sensitivities of in situ elements. A subsequent iterative approach applied modelled outputs to an existing geosite database, giving coarse indicators of sites with a propensity to be modified by fire. With static inventory, this approach allows decision-makers to develop new risk parameters for the management of burns and wildfires. Geographically complex environments have led to misalignments between geosite boundaries and broader processes, data inaccessibility for remote or offshore sites, and fire as both a destructive and formative agent; these must all be resolved. Future work should consider the necessity of incorporating values, recovery trajectories, and hydrological processes into fire sensitivity assessments. The study concludes with recommendations for refining the model to enhance its utility for fire managers, ultimately contributing to the integration of geodiversity into fire management strategies and geoconservation planning. Full article

Background: Pharmacokinetic drug–drug interactions (DDIs) can be caused by the effect of a pharmaceutical compound on the activity of one or more subtypes of the Cytochrome P450 (CYP) family, UDP-glucuronosyltransferases (UGTs), and/or transporters. As the number of therapeutic areas with polypharmacy has increased, interest has grown in assessing the risk of DDIs during the early phases of drug development. Various lines of research have led to improved mathematical models to predict DDIs, culminating in the Food and Drug Administration’s (FDA) guidelines on evaluating pharmacokinetic DDI risks. However, the recommended static models are highly conservative and often result in false positive predictions. The current research aims to improve the workflow for assessing CYP-mediated DDI risk using Boehringer Ingelheim (BI) proprietary compounds. Methods: The Drug–drug Interaction Risk Calculator (PharmaPendium) was used to evaluate the mechanistic static model, and predictions were correlated with human pharmacokinetic studies from Phase I clinical trials. Results: The results demonstrated that the FDA formula performed well in predicting DDIs for BI proprietary compounds. Furthermore, the integration of either human renal excretion or preclinical species total excretion data into the mechanistic static model enhanced the predictive performance for candidate drugs as victims in DDIs. Conclusions: The basic static models (BSMs) for drug interactions should be used in early drug discovery to “rule out” DDI risks because of the minimal inputs required and the low rate of false negative predictions. Mechanistic static models (MSMs) can then be implemented for compounds that require additional evaluation. Full article

The strength and energy of processed biological materials depend, among others, on their properties. Despite the numerous studies available, the relationship between the internal structure of corn grains and their mechanical properties has not yet been explained. Hence, the aim of the work is to explore the relationship between the internal composition of maize kernels and its mechanical properties by studying the impact of the maize seed coat thickness on its breakage susceptibility. To achieve the assumed goal, selected physical properties (length, width, and thickness) of corn grains were distinguished, and a static compression test was carried out on the Insight 50 kN testing machine (MTS Systems Corporation, Eden Prairie, MN, USA) with a test system for experimental verification of the compression behavior of biological materials. Furthermore, after the compression test, the thickness of the seed coat was measured using a laboratory microscope. It was found that there is a correlation between the thickness of the maize seed coat and force, deformation, and mass-specific energy at the bioyield point. The presented data constitute a foundation for the development of a mechanistic breakage model considering the variable strength properties of the seed coat and endosperm as the structural elements of kernels. Further research should be focused on the determination of the strength properties under dynamic conditions and revealing the relationship between the loading rate, strength properties, and internal structure for several maize varieties, which better reflect the ranges of variability in the real nature of mechanical processing. Full article

The transition from batch to continuous production in the catalytic hydrogenation of nitrile butadiene rubber (NBR) into hydrogenated NBR (HNBR) marks a significant advance for applications under demanding conditions. This study introduces a continuous process utilizing a static mixer (SM) reactor, which notably achieves a hydrogenation conversion rate exceeding 97%. We thoroughly review a mechanistic model of the SM reactor to elucidate the internal dynamics governing the hydrogenation process and address the inherent uncertainties in key parameters such as the Peclet number (Pe), dimensionless time (θτ), reaction coefficient (R), and flow rate coefficient (q). A comprehensive dataset generated from varied parameter values serves as the basis for training an artificial neural network (ANN), which is then compared against traditional models including linear regression, decision tree, and random forest in terms of efficacy. Our results clearly demonstrate the ANN’s superiority in predicting the degree of hydrogenation, achieving the lowest root mean squared error (RMSE) of 3.69 compared to 21.90 for linear regression, 4.94 for decision tree, and 7.51 for random forest. The ANN’s robust capability for modeling complex nonlinear relationships and dynamics significantly enhances decision-making, planning, and optimization of the reactor, reducing computational demands and operational costs. In other words, this approach allows users to rely on a single ML-based model instead of multiple mechanistic models for reflecting the effects of possible uncertainties. Additionally, a feature importance study validates the critical impact of time and element number on the hydrogenation process, further supporting the ANN’s predictive accuracy. These findings underscore the potential of ML-based models in streamlining and enhancing the efficiency of chemical production processes. Full article

Opioids are commonly prescribed for the treatment of chronic pain. Approximately 50% of adults who are prescribed opioids for pain co-use cannabis with their opioid treatment. Morphine is primarily metabolized by UDP-glucuronosyltransferase (UGT) 2B7 to an inactive metabolite, morphine-3-glucuronide (M3G), and an active metabolite, morphine-6-glucuronide (M6G). Previous studies have shown that major cannabis constituents including Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) inhibit major UGT enzymes. To examine whether cannabinoids or their major metabolites inhibit morphine glucuronidation by UGT2B7, in vitro assays and mechanistic static modeling were performed with these cannabinoids and their major metabolites including 11-hydroxy-Δ⁹-tetrahydrocannabinol (11-OH-THC), 11-nor-9-carboxy-Δ⁹-tetrahydrocannabinol (11-COOH-THC), 7-hydroxy-cannabidiol (7-OH-CBD), and 7-carboxy-cannabidiol (7-COOH-CBD). In vitro assays with rUGT-overexpressing microsomes and human liver microsomes showed that THC and CBD and their metabolites inhibited UGT2B7-mediated morphine metabolism, with CBD and THC exhibiting the most potent K_i,u values (0.16 µM and 0.37 µM, respectively). Only 7-COOH-CBD exhibited no inhibitory activity against UGT2B7-mediated morphine metabolism. Static mechanistic modeling predicted an in vivo drug–drug interaction between morphine and THC after inhaled cannabis, and between THC, CBD, and 7-OH-CBD after oral consumption of cannabis. These data suggest that the co-use of these agents may lead to adverse drug events in humans. Full article