Classical Model Research Articles

Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine.

Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.

Generalization to differential–algebraic equations of Lyapunov–Schmidt type reduction at Hopf bifurcations

The Lyapunov–Schmidt procedure, a well-known and powerful tool for the local reduction of nonlinear systems at bifurcation points or for ordinary differential equations (ODEs) at Hopf bifurcations, is extended to the context of strangeness-free differential–algebraic equations (DAEs), by generalizing the comprehensive presentation of the method for ODEs provided in the classical textbook by Golubitsky and Schaeffer (1985). The appropriate setting in the context of DAEs at Hopf bifurcations is first detailed, introducing suitable operators and addressing the question of appropriate numerical algorithms for their construction as well. The different steps of the reduction procedure are carefully reinterpreted in the light of the DAE context and detailed formulas are provided for systematic and rational construction of the bifurcating local periodic solution, whose stability is shown, likely to the ODE context, to be predicted by the reduced equations. As an illustrative example, a classical DAE model for an electric power system is considered, exhibiting both supercritical and subcritical Hopf bifurcations, demonstrating the prediction capability of the reduced system with regard to the global dynamics.

Phytoconstituents of Kenyan stinging nettle (Urtica species) and their molecular docking interactions revealed anti-inflammatory potential as cyclooxygenase-2 inhibitors

Inflammation is a complex, natural protective response towards different stimuli characterized by the dilation and permeation of the blood vessels with a surge in leukocytes in the tissues. The current treatment involves the use of anti-inflammatory drugs, corticosteroids and non-steroidal anti-inflammatory drugs (NSAIDs), which have been associated with adverse side effects especially gastrointestinal ulcers. Therefore, there is a growing need to explore alternative sources from medicinal plants. In the present study we investigated anti-inflammatory activities of the leaves of the Kenyan stinging nettle using in vivo and in silico molecular docking. Molecular identification of plant sample was carried out based on DNA barcoding. Crude extracts were prepared using water and methanol: dichloromethane (1:1) and preliminary identification of total phenolic and flavonoids was carried out using Folin-Ciocalteu and aluminum chloride colorimetric methods, respectively. The classical model of Carrageenan- induced paw edema was used to test the in vivo anti-inflammatory activity of the extracts. The extract was screened using Laser Raman Spectroscopy and Liquid Chromatography Mass Spectroscopy (LC-MS) and the molecular interaction between the identified compounds within the binding site of Cyclooxygenase-2 (COX-2) performed through molecular docking as the confirmatory tool of in vivo experiments. Based on DNA barcoding analysis, the plant sample was identified as Urtica species. The total phenolic content of the aqueous and methanol: dichloromethane extracts were 3.75 mg gallic acid equivalents (GAE)/g dry sample and 6.26 mg GAE/g dry sample while total flavonoid content were 0.3872 mg quercetin/g dry sample and 1.76 mg quercetin/g dry sample, respectively. The aqueous extract significantly (p < 0.05) reduced the paw edema in the Carrageenan model of inflammation. LC-MS confirmed the presence of 19 phytochemicals, of which 10 and 9 were phenolic and flavonoid compounds, respectively. From these identified compounds quercetin attained the lowest binding energy when complexed with COX-2, followed by rhamnetin, quercetin rhamnoside, epigallocatechin gallate and chlorogenic acid. Molecular docking studies supported the in vivo findings and confirmed the anti-inflammatory potential of Urtica sp. These findings suggest Urtica sp. is a potential source of bioactive compounds that could be employed in the future development of anti-inflammation drugs. Further studies can attempt to assess the individual isolated active compound(s) for their anti-inflammatory activity.

Nonlinear properties and structural rearrangements in thermally poled niobium germanate glasses

Second order nonlinear optical properties and structural rearrangements in GeO2Na2ONb2O5 glasses were achieved by thermal poling. The effects of applied voltage as well as sodium and niobium contents on nonlinear optical (NLO)-active layer were investigated. Structural rearrangements in the anodic microlayer were investigated and occur due to sodium depletion promoting variation in bridging/non-bridging oxygen ratio and formation of a more polymerized network. Quantitative analysis of second harmonic generation signals confirm the electrooptical origin of the nonlinear optical response described by the electric-field-induced second harmonic model. χ(2) susceptibility values range from 0.42 to 0.76 pm/V depending on the niobium content. Lastly, the charge compensation mechanism with increasing applied voltage was described in detail. A progressive decrease in χ(2) for higher voltages was observed due to a greater poled thickness than expected by classical electrostatic models. In this case, the compensation mechanism occurs due to structural rearrangement, redox reactions, and motion of negative charges.

Compromise or choose: shared movement decisions in wild vulturine guineafowl

Shared-decision making is beneficial for the maintenance of group-living. However, little is known about whether consensus decision-making follows similar processes across different species. Addressing this question requires robust quantification of how individuals move relative to each other. Here we use high-resolution GPS-tracking of two vulturine guineafowl (Acryllium vulturinum) groups to test the predictions from a classic theoretical model of collective motion. We show that, in both groups, all individuals can successfully initiate directional movements, although males are more likely to be followed than females. When multiple group members initiate simultaneously, follower decisions depend on directional agreement, with followers compromising directions if the difference between them is small or choosing the majority direction if the difference is large. By aligning with model predictions and replicating the findings of a previous field study on olive baboons (Papio anubis), our results suggest that a common process governs collective decision-making in moving animal groups.

A modelling framework for equity portfolio projections under different carbon price scenarios

It is recognised that climate risks (including carbon pricing policy) are a new source of risk for the financial system. We propose a modelling framework for medium-term projections of stock returns under different carbon price scenarios. First, we construct a green factor to augment the classic capital asset pricing model (CAPM) and capture a firm’s exposure to climate policy risks. Then, we project to 2040 the factors of the CAPM, conditional on different carbon price pathways, using the estimated medium-scale Bayesian vector autoregressive model (MBVAR). Finally, we project the stock returns of each firm in the portfolio. Our scenarios suggest that the impacts of a carbon price policy are not confined to the most polluting firms (mining and quarrying, transportation and storage firms) for the effect of systematic risk. Moreover, in the short term, the negative impacts of a carbon price policy are more accentuated under a disorderly than an orderly transition. In the medium term, the projections suggest that the stock market progressively adapts to new conditions and believes in the benefits of rigorous carbon policies to incentivise the transition to a low-carbon economy. The proposed toolbox may represent effective support for investors in preparing portfolios for climate transition risks in the context of uncertainty regarding the timing of the introduction of a carbon policy. For policymakers, it can help shed light on how policies to prevent or mitigate climate change effects can affect financial stability.

Suppression method of ultralow-frequency oscillation under ambient excitation in hydro-dominant power systems

Ultralow-frequency oscillations (ULFOs) have frequently occurred in hydro-dominant power systems in recent years. These phenomena seriously threaten the safe and stable operation of power systems. Therefore, measures that can effectively suppress ULFO need to be taken. Currently, the hydropower unit governor can enhance the dynamic stability and dampening oscillations of a system through adjusting the proportional–integral–derivative parameters. However, modifying these parameters can affect the primary frequency regulation performance, which compromises the assurance of grid frequency quality. This study proposes a switching type of hydro generator governor based on the local damping contribution rate to address the aforementioned problems. The approach incorporates the energy structure into the classical governor model, calculates the local damping contribution rate, and enables automatic switching of the hydro generator governor parameters based on positive and negative local damping contributions. This way allows for segmented control with different parameter settings, which enables the system to effectively consider the damping characteristics of ULFO while maintaining primary frequency regulation performance. It can also accurately and efficiently suppress the ULFO. The proposed method is verified through arithmetic analysis conducted on a 16-machine 5-zone system and an actual grid system. Simulation results demonstrate the effectiveness of the method.

Grain boundary segregation predicted by quantum-accurate segregation spectra but not by classical models

In alloys, solute segregation at grain boundaries is classically attributed to three driving forces: a high solution enthalpy, a high size mismatch, and a high difference in interfacial energy. These effects are generally cast into a single scalar segregation energy and used to predict grain boundary solute enrichment or depletion. This approach neglects the physics of segregation at many competing grain boundary sites, and can also miss electronic effects that are energetically significant to the problem. In this paper, we demonstrate that such driving forces cannot explain, nor thus predict, segregation in some alloys. Using quantum-accurate segregation spectra that have recently become available for some polycrystalline alloys, we predict strong segregation for gold in aluminum, a solvent-solute combination that does not conform to classical driving forces. Our experiments confirm these predictions and reveal gold enrichment at grain boundaries that is two orders of magnitude over the bulk lattice solute concentration.

Kinetic pathway and micromechanics of fusion/fission for polyelectrolyte vesicles.

Despite the wide existence of vesicles in living cells as well as their important applications like drug delivery, the underlying mechanism of vesicle fusion/fission remains under debate. Classical models cannot fully explain recent observations in experiments and simulations. Here, we develop a constrained self-consistent field theory that allows tracking the shape evolution and free energy as a function of center-of-mass separation distance. Fusion and fission are described in a unified framework. Both the kinetic pathway and the mechanical response can be simultaneously captured. By taking vesicles formed by polyelectrolytes as a model system, we predict discontinuous transitions between the three morphologies: parent vesicle with a single cavity, hemifission/hemifusion, and two separated child vesicles, as a result of breaking topological isomorphism. With the increase in inter-vesicle repulsion, we observe a great reduction in the cleavage energy, indicating that vesicle fission can be achieved without hemifission, in good agreement with simulation results. The force-extension relationship elucidates typical plasticity for separating two vesicles. The super extensibility in the mechanical response of vesicle is in stark contrast to soft particles with other morphologies, such as cylinder and sphere. Our work elucidates the fundamental physical chemistry based on intrinsic topological features of vesicle fusion/fission, which provides insights into various phenomena observed in experiments and simulations.

Study of radioactive decay process of uranium atoms via fractals-fractional analysis

This manuscript is devoted to derive a mathematical formulation for radioactivity process of uranium to radium which decays to produce radon. Radon which further decays to produce polonium atoms. The said interesting phenomenon is investigated using the concept of fractals-fractional calculus. The concerned differential operator plays significant role to describe the complex and irregular geometry of real world problems. Also, we deduce the existence theory of such process through mathematical analysis using fractal type Laplace transform. Finally, a computational algorithm is established to simulate the corresponding results for various fractals and fractional orders. In this regard, computational software Matlab-16 is used to simulate our theoretical results. Various graphical presentations are displayed for the computed theoretical results. A comparison with classical traditional order model has also done.

Effect of Acetamiprid, a Neonicotinoid Insecticide, on Locomotor Activity of the American Cockroach.

Toxicological studies have shown that the American cockroach Periplaneta americana (Linnaeus) is a classical model for studying the mode of action of commonly used insecticides. In a previous study, we demonstrated that thiamethoxam and clothianidin decreased locomotor activity in an open-field-like apparatus. Here, we tested the effect of the neonicotinoid acetamiprid when applied orally, topically, or injected into the haemolymph. We found that acetamiprid was also able to impair locomotor activity in the open-field-like apparatus. When treated with acetamiprid, a strong alteration in locomotor activity was observed 1 h, 24 h, and 48 h after haemolymph and topical applications. Oral application induced an impairment of locomotor activity at 24 h and 48 h. A comparison of the present data with our previously published results showed that neonicotinoids were more active when injected into the haemolymph compared to oral and topical applications. These findings increased our understanding of the effect of neonicotinoid insecticides on insect locomotor activity, and demonstrated that the cyano-substituted neonicotinoid, acetamiprid, was able to alter cockroach locomotor activity.

Limited ecological opportunity influences the tempo of morphological evolution in birds

According to classic models of lineage diversification and adaptive radiation, phenotypic evolution should accelerate in the context of ecological opportunity and slow down when niches become saturated.1,2 However, only weak support for these ideas has been found in nature, perhaps because most analyses make the biologically unrealistic assumption that clade members contribute equally to reducing ecological opportunity, even when they occur in different continents or specialize on different habitats and diets. To view this problem through a different lens, we adapted a new phylogenetic modeling approach that accounts for the fact that competition for ecological opportunity only occurs between species that coexist and share similar habitats and diets. Applying this method to trait data for nearly all extant species of landbirds,3 we find a widespread signature of decelerating trait evolution in lineages adapted to similar habitats or diets. The strength of this pattern was consistent across latitudes when comparing tropical and temperate assemblages. Our results provide little support for the idea that increased diversity and tighter packing of niches accentuates evolutionary slowdowns in the tropics and instead suggest that limited ecological opportunity can be an important factor determining the rate of morphological diversification at a global scale.

Modeling of multi-temperature Type I and II benzene/ammonia adsorption isotherms: Dual LF model and linearized DR model

Adsorption isotherm is basic data for evaluating the porous adsorbent adsorption capacity and explaining gas–solid adsorption behavior. Adsorption equilibrium modeling is a simple method to describe isotherm data in application. In this paper, taking C6H6 and NH3 as an example, multi-temperature Type I or Type II adsorption isotherms on AC, ASZMT, and 13X were investigated and fitted. Results show that C6H6 and NH3 adsorption behaviors on 13X both existed in chemisorption. At certain adsorbed capacity, the isosteric heats are higher than 50 kJ·mol−1. Type I and Type II C6H6 or NH3 adsorption isotherms could be successfully described using the extended Dual LF model·NH3 temperature-independent characteristic curves on AC and ASZMT were obtained using the Linearized DR model and successfully used to predict NH3 adsorption data at any pressure within the whole room temperature. Further, the Dual LF model was extended to describe other adsorption isotherms including IUPAC Type III, IV, and V. The R2 between the model and data were higher than 0.98, proving that it has universal applicability than other classical models such as Langmuir, Freundlich, Toth, and so on. It has the potential to be used as a general equilibrium model for adsorption process simulation in the future.

Extensional Flow-Induced Concentration Gradient in Entangled Polymer Solutions with Chemically Identical Species.

Entangled polymer solutions show different rheological behavior from melts in fast extensional flow. This discrepancy is not expected according to the classic tube model and is an urgent issue to be solved in polymer physics. While in the tube model the polymer concentration is considered to be homogeneous, we show that extensional flow-induced concentration gradient may happen in polymer solutions even with chemically identical solutes and solvents. Through labeling an aggregation-induced emission (AIE) probe on oligomeric solvents, the flow-induced concentration gradient is visualized by combining extensional rheology and ex situ fluorescence microscopy. Microdomains of oligomeric solvents with a length scale of tens of micrometers are observed, and their influence on rheological behavior cannot be ignored.

Scale-Fractal Detrended Fluctuation Analysis for Fault Diagnosis of a Centrifugal Pump and a Reciprocating Compressor.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

Fully-coupled modeling and analysis of the influence of anti-icing bleed air system of engine inlet duct on compressor performance

A fully-coupled method that integrates the compressor, inlet duct and anti-icing bleed air system (AI-BAS) was established to offer a quick prediction of the effects of anti-icing bleeding on compressor performance, facilitating the development of a fully-coupled AI-BAS model and a thermodynamic cycle model for gas turbine engines. While the flow within the inlet duct was solved by 3-D numerical simulation, the AI-BAS was simulated by a reduced-order model and the thermodynamic performance of the compressor was estimated by a parallel-compressor method. Comparison with the results by the full-annular CFD simulation suggested that the proposed fully coupled method can adeptly predict the effects of inflow distortion on compressor performance. In this study, as the increase of anti-icing bleed air ratio, the distortion on AIP and the resulted performance degradation of compressor becomes obvious, especially the surge margin. In the fully coupled modeling with AI-BAS, both the bleed air ratio and temperature of discharged bleed air increase with the compressor pressure ratio. The variation of bleed air ratio reaches nearly 1 %. The compressor's operating point is observed to traverse among lines representing constant bleed air flow ratio, which cannot be captured by the classical bleed air model.

Deformation Behavior and Texture Evolution of Ferrite and Austenite Phases in Lean Duplex Stainless Steel

To avoid cracks during hot rolling, a short process of direct cold rolling following solution treatment of the casting billet for preparing lean duplex stainless steel plates is proposed. The effect of cold rolling reduction on the microstructure, texture, and mechanical properties of UNS S32101 is investigated. The results show that dislocation slip is the main character in ferrite, which leads to dislocation tangles, dislocation cells, high‐density dislocation walls, and deformed microbands. However, twinning and strain‐induced martensite transformation (SIMT) occur in austenite, and the SIMT mechanism of austenite follows the classical model γ → ε → α′ and γ → α′, with the orientation relationship of Kurdjumov–Sachs ( and ) and Nishyama–Wassermann (N–W) ( and ). Meanwhile, at 50%, there is a transition from Cu texture to Brass texture in the austenite phase. At 12.5%, the yield strength is 344 MPa higher than that of the traditional hot rolling process, and the elongation remains about 35%. With the increasing cold rolling reduction, the elongation decreases while the strength rises significantly. Strengthening dislocations, fine‐tuning grains, and SIMT are the primary contributors to the improvement in strength.

Study on intentional control strategy of COVID-19

With the ongoing evolution of the novel coronavirus pathogen and continuous improvements in our social environment, the mortality rate of COVID-19 is on a decline. In response to this, we introduce an adaptive control strategy known as intentional control, which offers cost-efficiency and superior control effectiveness. The classical SEIR model faces limitations in accurately representing close contacts and sub-close contacts and fails to distinguish their varying levels of infectivity. To address this, our study modifies the classical model by incorporating close contact (E) and a sub-close contact (E2) while reworking the infectious mechanism. Once the model is formulated, we employ various statistical methods to identify crucial parameters, including R2, adjusted R2, and standard deviation. For disease control, we implement an intentional control program with four distinct grades. We develop and apply a scheme in MATLAB for our proposed model, generating diverse simulation results based on realistic parameter values for discussion. Additionally, we explore a range of strategy combinations to differentiate their effectiveness under various social conditions, aiming to identify an optimal approach. Comparing the intentional control strategy to random control, our findings consistently demonstrate the superiority of intentional control across all scenarios. Furthermore, the results indicate that our approach better aligns with the characteristics of the novel coronavirus, characterized by an “extremely low fatality rate and strong infectivity,” while offering detailed insights into the transmission dynamics among different compartments.

Contact mechanics modeling of fractal surface with complex multi-stage actual loading deformation

The contact mechanical behavior of rough surfaces is related to the friction and wear of mechanical components, and also has an important impact on the dynamic performance and force transfer characteristics of assembly surfaces. This paper aims to solve the complex multi-stage mechanical deformation mode of the loading surface, and establishes all multi-stage contact mechanical models involved in the actual loading deformation process from the perspective of the actual deformation and critical deformation of the asperity. Firstly, based on the three-dimensional fractal contact theory, the force deformation relationship of a single asperity during the full deformation stage of loading is constructed. Then, six critical contact areas and four critical deformation quantities of a multi scale asperity are derived. Finally, the complex multi-stage deformation contact mechanical models of three-dimensional fractal surfaces under actual loading are established. The area power spectral density (APSD) function spectrum of rough surface under different fractal parameters, as well as the variation of contact load, contact stiffness, and contact area are analyzed numerically. Compared with several classical models, the rationality of the proposed model is verified. This study can be used as the basis for constructing the overall dynamic model of an assembly system

Generalized latent space model for one-mode networks with awareness of two-mode networks

Latent space models have been widely studied for one-mode networks, in which the same type of nodes connect with each other. In many applications, one-mode networks are often observed along with two-mode networks, which reflect connections between different types of nodes and provide important information for understanding the one-mode network structure. However, the classical one-mode latent space models have several limitations in incorporating two-mode networks. To address this gap, a generalized latent space model is proposed to capture common structures and heterogeneous connecting patterns across one-mode and two-mode networks. Specifically, each node is embedded with a latent vector and network-specific degree parameters that determine the connection probabilities between nodes. A projected gradient descent algorithm is developed to estimate the latent vectors and degree parameters. Moreover, the theoretical properties of the estimators are established and it has been proven that the estimation accuracy of the shared latent vectors can be improved through incorporating two-mode networks. Finally, simulation studies and applications on two real-world datasets demonstrate the usefulness of the proposed model.