Two-layer Model Research Articles

Towards a profound understanding of methyl orange removal from industrial wastewater using a raw walnut shell: Kinetics, equilibrium, thermodynamics, and statistical physics calculations

The present study investigates the adsorption of the anionic dye methyl orange (MO) using raw walnut shells (RWS). Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy techniques were used to evaluate RWS collected in the Moroccan High Atlas (Tounfite) highlands. The batch mode adsorption experiments showed a remarkable removal during the first ten minutes, suggesting that the adsorption capacity is controlled by several parameters related to both the medium and the adsorbent. Based on the experimental results, 98 % of the MO dye was adsorbed on the walnut shell at pH >5 and room temperature (25±2 °C). The general order best explained the adsorption of MO on the walnut shells, suggesting that the number of active sites required changes with temperature. The two-layer model was the most appropriate physical-statistical model to explain the L-2 isotherms regarding the adsorption equilibrium. According to the models, methyl orange tends to be adsorbed on the surface of the monolayer, with individual molecules then adsorbing on top of each other. At all temperatures studied, the adsorption is endothermic and tends to be spontaneous, according to simulations of thermodynamic functions of RWS.

Transfer-Learning Prediction Model for Low-Cycle Fatigue Life of Bimetallic Steel Bars

The prediction of the low-cycle fatigue life of bimetallic steel bars (BSBs) is essential to promote the engineering application of BSBs. However, research on the low-cycle fatigue properties of BSB is limited, and fatigue experiments are time-consuming. Moreover, considering that sufficient data are needed for model training, the lack of data hinders the leverage of typical data-driven machine learning, which is widely used in fatigue life prediction. To address this issue, a transfer learning framework was suggested to accurately predict the low-cycle fatigue life of BSBs with limited data. To achieve this goal, 54 data points obtained from low-cycle fatigue tests on BSBs and 264 data points of other metallic bars were collected. Source models based on artificial neural networks (ANNs) were first constructed using the collected source dataset. Then, the learned knowledge stored in the source models was transferred to the transfer models. After that, transfer models were further fine-tuned and then tested using the target dataset of BSBs. The ANN models, which were of the same structure as the transfer models but only trained with the target dataset without transferring deep features from the source models, were set as baseline models. Compared with baseline models, the constructed transfer models could be used to accurately predict the fatigue life of BSBs. Moreover, the influence of hidden layers of ANNs on accuracy was examined by comparing one-layer and two-layer transfer models. Furthermore, the influence of key parameters on fatigue life of metallic bars was evaluated by feature analysis.

Hyperspectral imaging of 4T1 mammary carcinomas grown in dorsal skinfold window chambers: spectral renormalization and fluorescence modeling.

Hyperspectral imaging (HSI) of murine tumor models grown in dorsal skinfold window chambers (DSWCs) offers invaluable insight into the tumor microenvironment. However, light loss in a glass coverslip is often overlooked, and particular tissue characteristics are improperly modeled, leading to errors in tissue properties extracted from hyperspectral images. We highlight the significance of spectral renormalization in HSI of DSWC models and demonstrate the benefit of incorporating enhanced green fluorescent protein (EGFP) excitation and emission in the skin tissue model for tumors expressing genes to produce EGFP. We employed an HSI system for intravital imaging of mice with 4T1 mammary carcinoma in a DSWC over 14 days. We performed spectral renormalization of hyperspectral images based on the measured reflectance spectra of glass coverslips and utilized an inverse adding-doubling (IAD) algorithm with a two-layer murine skin model, to extract tissue parameters, such as total hemoglobin concentration and tissue oxygenation ( ). The model was upgraded to consider EGFP fluorescence excitation and emission. Moreover, we conducted additional experiments involving tissue phantoms, human forearm skin imaging, and numerical simulations. Hyperspectral image renormalization and the addition of EGFP fluorescence in the murine skin model reduced the mean absolute percentage errors (MAPEs) of fitted and measured spectra by up to 10% in tissue phantoms, 0.55% to 1.5% in the human forearm experiment and numerical simulations, and up to 0.7% in 4T1 tumors. Similarly, the MAPEs for tissue parameters extracted by IAD were reduced by up to 3% in human forearms and numerical simulations. For some parameters, statistically significant differences ( ) were observed in 4T1 tumors. Ultimately, we have shown that fluorescence emission could be helpful for 4T1 tumor segmentation. The results contribute to improving intravital monitoring of DWSC models using HSI and pave the way for more accurate and precise quantitative imaging.

Predicting Pump Inspection Cycles for Oil Wells Based on Stacking Ensemble Models

Beam pumping is currently the broadly used method for oil extraction worldwide. A pumpjack shutdown can be incurred by failures from the load, corrosion, work intensity, and downhole working environment. In this study, the duration of uninterrupted pumpjack operation is defined as the pump inspection cycle. Accurate prediction of the pump inspection cycle can extend the lifespan, reduce unexpected pump accidents, and significantly enhance the production efficiency of the pumpjack. To enhance the prediction performance, this study proposes an improved two-layer stacking ensemble model, which combines the power of the random forests, light gradient boosting machine, support vector regression, and Adaptive Boosting approaches, for predicting the pump inspection cycle. A big pump-related oilfield data set is used to demonstrate the proposed two-layer stacking ensemble model can significantly enhance the prediction quality of the pump inspection cycle.

Study on the influencing factors of combined processing of active and passive surface-wave data on dispersion imaging

Active and passive surface-wave methods have garnered significant attention in the near-surface geophysical community for their non-destructive, non-invasive, low-cost, and accurate advantages in delineating subsurface shear (S)-wave velocity structures. They are increasingly being utilized to address numerous engineering and environmental problems. Surface waves obtained from actively excited sources such as a hammer and a harmonic shaker, however, lack low-frequency components, resulting in limited investigation depth. Conversely, passive surface waves such as microseisms (< 0.4 Hz, associated with natural ocean wave activity) and microtremor (>1 Hz, generated by cultural and wind sources) retrieved from ambient seismic noise typically lack high-frequency components, which is not conductive to characterizing fine near-surface structures. To overcome these frequency limitations, we employ a “mixed-source data” strategy, imposing active shot gathers into ambient noise data, to widen the frequency range of dispersion images and depth of investigative capabilities. We simulate both active and passive surface-wave data based on a two-layer model, noting that their dispersion images suffer from a mode kissing phenomenon at lower frequencies. By analyzing influencing factors such as the amplitude intensity, the signal-to-noise ratio and the excitation locations of active shot gathers, as well as the length of passive surface-wave data, we better understand their impacts on dispersion images from mixed-source surface-wave data. Simulation tests demonstrate that processing mixed-source data can effectively distinguish the mode kissing phenomenon. Moreover, the effectiveness of this strategy in enhancing the quality of dispersion image is verified, especially when surface-wave dispersion images perform poorly in either the low- or high-frequency bands. Additionally, a real-world example further demonstrates that processing mixed-source data offers significant advantages in improving the quality of dispersion images. This way provides a convenient and efficient measurement strategy for delineating shear-wave velocity profiles in finer shallow layers and deeper penetration depths.

Flow analysis of a two-layered micropolar fluid in a catheterized oesophageal tube under the influence of a dilating amplitude: Application to pre-diagnosis of oesophageal motility disorder

This study employs the Homotopy perturbation method to analyze the behavior of immiscible, incompressible fluids within a cylindrical coaxial tube, focusing on scenarios relevant to physiological fluid dynamics, particularly in the catheterized oesophagus and similar biological systems. Adopting long-wavelength and low Reynolds number approximations, a two-layered model is proposed with a micropolar fluid in the core and a Newtonian fluid in periphery regions. Parameters such as velocity, flux, friction, pressure, and impedance variations are formulated, particularly under the influence of dilating wave amplitude. Generally, when a catheter is introduced, pressure rises. It is further found that while pressure falls with increasing micropolar parameter, it rises with coupling number upon catheter insertion. Thus feeding patients with micropolar fluids during catheter-assisted pre-diagnosis is impractical due to associated pressure rise. Observations suggest a complex pressure profile during bolus passage through the oesophagus due to the broadening of the catheter size. Additionally, impedance exponentially increases with catheter size, influenced by the micropolar parameters and the coupling numbers, with micropolar fluids exhibiting higher impedance than that with Newtonian fluids. However, this study underscores the significant impact of catheterization on physiological fluid dynamics, notably increasing oesophageal impedance by two to threefold. This highlights the critical role of catheters in altering flow characteristics, emphasizing the need for a careful medical intervention during pre-diagnostic assessments.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Temperature and field dependencies of the magnetization of ferrimagnetic Gd-Co films: Chemical inhomogeneity or spin-flop transition

Magnetic properties of amorphous Gd-Co ferrimagnetic films prepared by magnetron sputtering have been studied for various thicknesses. In a certain temperature range close to the magnetic compensation temperature, triple hysteresis loops and the characteristic features of the tempera-ture dependencies of magnetization were observed for all samples under consideration. Two-layer film model with an inhomogeneous chemical composition and a spin-flop transition scenario in two sub-lattice ferrimagnet was employed for understanding the origins of these phenomena. Within the framework of the spin-flop transition, the inter sub-lattice exchange coupling constant was estimated using various methods.

Interval Assessment Method for Distribution Network Hosting Capacity of Renewable Distributed Generation

The traditional fixed value assessment of the renewable distributed energy hosting capacity of a distribution network cannot accurately and comprehensively reflect the change in hosting capacity; therefore, we propose the interval assessment method for the renewable distributed energy hosting capacity of a distribution network. The renewable distributed energy hosting capacity interval consists of an optimistic upper boundary and a pessimistic lower boundary. First, the optimistic upper bound is described by a deterministic model that takes into account the constraints of safe system operation. Second, the pessimistic lower bound is portrayed by a two-layer robust assessment model that accounts for the DG temporal uncertainty, DG spatial uncertainty, and active distribution network flexible resource dispatch uncertainty. Each pessimistic sub-model was constructed in turn, and then the model was solved by linear simplification using pairwise transformation, as well as McCormick relaxation. Finally, simulations were carried out in the IEEE 135 system, and the results validated the effectiveness and practicality of the proposed method.

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies induced by secondary and tertiary structure, as well as mechanisms of structural flexibility, resulting in a model that can be successfully used for the design of allosteric RNA such as riboswitches. They have recently been experimentally validated as generative models for the SAM-I riboswitch aptamer domain sequence family. We introduce RBM mathematically and practically, providing self-contained code examples to download the necessary training sequence data, train the RBM, and sample novel sequences. We present in detail the implementation of algorithms necessary to use RBMs, focusing on applications in biological sequence modeling.

Highly Sensitive Terahertz Metasurface Based on Electromagnetically Induced Transparency-Like Resonance in Detection of Skin Cancer Cells.

Terahertz (THz) metasurfaces based on high Q-factor electromagnetically induced transparency-like (EIT-like) resonances are promising for biological sensing. Despite this potential, they have not often been investigated for practical differentiation between cancerous and healthy cells. The present methodology relies mainly on refractive index sensing, while factors of transmission magnitude and Q-factor offer significant information about the tumors. To address this limitation and improve sensitivity, we fabricated a THz EIT-like metasurface based on asymmetric resonators on an ultra-thin and flexible dielectric substrate. Bright-dark modes coupling at 1.96 THz was experimentally verified, and numerical results and theoretical analysis were presented. An enhanced theoretical sensitivity of 550 GHz/RIU was achieved for a sample with a thickness of 13 µm due to the ultra-thin substrate and novel design. A two-layer skin model was generated whereby keratinocyte cell lines were cultured on a base of collagen. When NEB1-shPTCH (basal cell carcinoma (BCC)) were switched out for NEB1-shCON cell lines (healthy) and when BCC's density was raised from 1 × 105 to 2.5 × 105, a frequency shift of 40 and 20 GHz were observed, respectively. A combined sensing analysis characterizes different cell lines. The findings may open new opportunities for early cancer detection with a fast, less-complicated, and inexpensive method.

A novel two-layer fuzzy neural network for solving inequality-constrained [formula omitted]-minimization problem with applications

In this paper, we propose a novel two-layer fuzzy neural network model (TLFNN) for solving the inequality-constrained ℓ1-minimization problem. The stability and global convergence of the proposed TLFNN model are detailedly analyzed using the Lyapunov theory. Compared with the existing three-layer neural network model (TLNN) recently designed by Yang et al., the proposed TLFNN model possesses less storage, stronger robustness, faster convergence rate and higher convergence accuracy. These advantages are illustrated by some numerical experiments, where it is shown that the TLFNN model can achieve a convergence accuracy of 10−13 within 5s while the TLNN model can only acquire 10−6 in 105s when some random coefficient matrices are applied. Since the linear equality-constrained conditions can be equivalently transformed into double inequality-constrained ones, some simulation experiments for sparse signal reconstruction show that the proposed TLFNN model also has less convergence time and stronger robustness than the existing state-of-the-art neural network models for the equality-constrained ℓ1-minimization problem.

Baroclinic vortex pulsars in unstable westward flows

We present a computational modeling study of geophysical coherent vortices embedded in horizontally homogeneous, baroclinically unstable, westward background flows with vertical shear. Within an idealized two-layer quasigeostrophic beta-plane model, we discovered two types of robust vortex-wave structures with distinct properties, which remain asymmetric and nonstationary in statistically-equilibrated turbulent flow regimes. The corresponding vortices, referred to as baroclinic vortex pulsars, are characterized by intense vorticity core coupled to the Rossby wave wake. The main conclusion — on the top of various analyses discussed in the paper — are that the vortex pulsars are fundamentally non-isolated coherent vortices, because they extract energy from the background circulation and expel excess potential vorticity, accumulating due to down-gradient material propagation, back into the environment. Both types may coexist as multiple statistically equilibrated states in some range of physical parameters, complicating any parameterization of eddy effects in climate-type models.

Hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade

The district integrated energy system has the characteristics of multi-layer and multi-subjects. Aiming at the problems of data privacy, conflict of interest, and mismatch of interaction power among these multi-subjects, a hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade is proposed. Firstly, the operational communication architecture of the district integrated energy system based on the cloud-pipe-edge-end is constructed to solve the problems of data transmission, data computation, and data privacy. Secondly, a two-layer distributed scheduling model of district integrated energy system considering waste heat recovery is established based on a laddered carbon trading mechanism. At the upper level, an optimal scheduling model targeting low-carbon and economic performance is developed by considering the participation of district integrated energy system in demand response and the purchase and sale of electricity. At the lower level, an optimal scheduling model considering waste heat recovery for subsystem energy-coupled equipment is developed. Then, the analytical target cascade is used to realize the decoupling of the two-layer model, and the efficient solution is achieved in the process of multiple iterations. Finally, by analyzing the arithmetic examples, it is verified that the proposed method can improve the economy of system operation by 4.38% and the absorption capacity of wind power by 9.89%, while protecting the privacy and interest claims of multiple parties.

Temperature dependent conductivity, dielectric relaxation, electrical modulus and impedance spectroscopy of Ni substituted Na[formula omitted]Zr[formula omitted]Ni[formula omitted]Si[formula omitted]PO[formula omitted]

We investigate the structural, dielectric relaxation, electric modulus and impedance behavior of Ni-doped NASICON ceramic Na3+2xZr2−xNixSi2PO12 (x=0.05–0.2) prepared using the solid-state reaction method. The increase in dielectric constant with temperature and decrease with frequency is explained on the basis of space charge polarization using the two-layer model of Maxwell–Wagner relaxation. The dielectric loss peak at lower temperatures follows the Arrhenius-type behavior with frequency having activation energy of 0.27 ± 0.01 eV of dipolar relaxation, suggests similar type of defects are responsible for all the doped samples. The real (ϵ′) and imaginary (ϵ′′) permittivity variation with frequency shows the broad relaxation behavior indicates the non-Debye type of relaxation in the measured temperature range. The permittivity values decrease with the amount of doping due to the increased number of charge carriers upon Ni doping at the Zr site. The impedance variation with frequency at various temperatures shows two types of relaxation for all the samples correspond to grain and grain boundary contribution in total impedance. The grain contributions are observed at higher frequencies, while grain-boundary contributions occur at the lower side of frequencies. The imaginary part of the electric modulus also shows two types of relaxation peaks for all the samples indicating similar activation energy at low temperatures and variable activation energy at higher temperatures. The fitting of the imaginary modulus using KWW function shows the non-Debye type of relaxation. We find that all modulus curves merge with each other at low temperatures showing a similar type of relaxation, while curves at high temperatures show the dispersed behavior above the peak frequency. The a.c. conductivity data are fitted using the double power law confirming the grain and grain boundary contributions in total conductivity. The double power law exponent variation with temperature shows the small polaron hopping conduction over the measured temperature range for all the doped samples.

Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy

In the future, the active load of the distribution network side will be dominated by electric vehicles (EVs), showing that the charging power demand of electric vehicles will change with the change in charging electricity price. With the popularity of electric vehicles in the distribution network, their aggregation operators will play a more prominent role in pricing management and charging behavior, and setting an appropriate charging price can achieve a win–win situation for operators and electric vehicle users. At the same time, the proportion of scenery in the distribution network is relatively high, and the uncertainty of self-output has a certain impact on the pricing strategy of operators and the charging behavior of electric vehicle users, which has become an important research topic. Based on the above background, an EV operator pricing strategy considering the landscape uncertainty is proposed, a Stackelberg game model is established to maximize the respective benefits of operators and EV users, and the two-layer model is further transformed into a single-layer model through the Karush–Kuhn–Tucker (KKT) condition and duality theorem. Finally, the IEEE 33 system is simulated with the CPLEX solver, and the global optimal pricing strategy is obtained. Simulation results prove that electric vehicle operators experience a maximum profit increase of 2.6% due to the impact of maximum capacity of energy storage equipment and the uncertainty of renewable energy output can result in electric vehicle operators losing approximately 20% of their profits at most.

Prediction of two-layers twisted phosphorene carbide modified by various nonmetal atoms as enhanced gas sensing materials: A first-principles investigation

Phosphorene carbide (PC) has achieved much attention because of its distinctive structural and electronic properties. Specifically, the two-layer twisted PC, which has been regarded as a new structure of PC-based material, its electronic performance can be readjusted, thereby providing great potential as the gas sensing material. The two-layer twisted phosphorene carbide (TPCT), which is a new structure of PC-based material, its electronic performance can be readjusted, thereby providing great potential as the gas sensing material. Additionally, doping of nonmetal atoms has been used as an efficient way to modify the structural and electrical performance of two-dimensional materials. Herein, we have studied the TPCT doped with different nonmetal atoms by carrying out the density functional theory (DFT) calculations. We have analyzed the distribution of the density of states (DOS), the Bader charges distribution, the projected band curves, and the gas adsorption energies (ΔEgas*). The results show that in the B-doped two-layers twisted PC model (TPCT-B), the doped B atom will capture electron from the TPCT substrate, and it presents the shortest adsorption distance between NH3 and TPCT-B through the B–N bonding. It means that the TPCT-B materials have great potential in the NH3 gas sensing area. This work provides insightful understanding in the design and development of PC-based materials used as gas sensing materials.

Retrieval of sea ice thickness using FY-3E/GNOS-II data

Sea ice, a significant component in polar regions, plays a crucial role in climate change through its varying conditions. In Global Navigation Satellite System-Reflectometry (GNSS-R) studies, the observed surface reflectivity Γ serves as a tool to examine the physical characteristics of sea ice covers. This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model. However, it is important to note that when Sea Ice Thickness (SIT) becomes thicker, the accuracy of SIT retrieval via this two-layer model begins to decline. In this paper, we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E (FY-3E). Soil Moisture Ocean Salinity (SMOS) data are treated as the reference. The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval. The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m. By comparing the retrieved SITs against reference values, the three-layer model achieved a Root Mean Square Error (RMSE) of 0.149 m and Correlation Coefficient (r) of 0.830, while the two-layer model reported the RMSE of 0.162 m and r value of 0.789. A scheme incorporating both models yielded superior results than either individual model, with the RMSE of 0.137 m and r reaching up to 0.852. This study is the first application of FY-3E for GNSS-R SIT retrieval, combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m. This provides a good reference for the future studies on GNSS-R SIT retrieval.

Investigation of in-plane visco-deformation in PMMA indentation via TLV model calibrated with tensile specimens

Time-dependent in-plane deformation in PMMA indentation is investigated via two-layer visco-plasticity model (TLV). The parameters of TLV model for PMMA were calibrated for tensile specimens using finite element analysis (FEA) and an optimization approach. The TLV model with the calibrated parameters was then employed to simulate nano- and micro-indentation behaviors including creep, relaxation, and other responses under various test conditions. Comparisons of experimental and FEA indentation load-depth curves showed agreement except the last unloading segment. This discrepancy was attributed to the inability of TLV model in replicating the delayed elastic recovery. In addition, deformation recovery with in-plane displacement (ui) was observed over a few days after indentation load removal. The characteristics of ui recovery and the factors influencing its magnitude are discussed. The proportionality of relative ui recovery rate prominently featured in the viscoelastic properties of PMMA, regardless of indentation history.

Mathematical modelling of three-layer amperometric biosensor and analytical expressions using homotopy perturbation method

In this study, a biosensor based on an electrode that has been chemically altered is represented mathematically. The homotopy perturbation method analytically solves the model, and simulation results are verified against analytical solutions for various physical parameter values. The biosensor's mathematical model comprises three layers: an exterior diffusion layer, a dialysis membrane, and an enzyme layer. An effective diffusion coefficient is added to the mathematical model to integrate the diffusion layer with the dialysis membrane. Then, the problem is reduced to the two-layer model, which can effectively replace the three-layer model. The homotopy perturbation method (HPM) is used to solve the non-linear system of chemically modified electrode equations into an approximate analytical formulation. These formulations will be compared with MATLAB-based numerical simulations. Furthermore, an alternative to the current density in steady-state and the biosensor's sensitivity, resistance are explored.