Number Of Layers Research Articles

Risk assessment of land subsidence based on GIS in the Yongqiao area, Suzhou City, China.

This study focuses on the Yongqiao District in Suzhou City, Anhui Province, China, aiming to analyze the current situation of ground settlement and its influencing factors in the area. The selected risk indices include settlement rate, cumulative settlement amount, groundwater level drop funnel, thickness of loose sediment layer, thickness of soft soil layer, and the number of groundwater extraction layers. Additionally, vulnerability indices such as population density, building density, road traffic, and functional zoning are considered. An evaluation index system for assessing land Subsidence risk was established. The risk evaluation of land Subsidence was conducted using the Hierarchical analysis-composite index method and ArcGIS spatial analysis, The evaluation results show that the area of higher risk area is about 2.82 km2, accounting for 0.96% of the total area, mainly distributed in the area of Jiuli village, Sanba Street. The middle risk area is distributed around the higher area, with an area of about 9.18 km2, accounting for 3.13% of the total area. The lower risk areas were distributed in most of the study area, covering an area of 222.24 km2, accounting for 75.82% of the total area. The low risk assessment area is mainly distributed in Bianhe Street and part of Zhuxianzhuang Town, with an area of about 58.88 km2, accounting for 20.09% of the total area. The findings of this study are not only crucial for informing local policies and practices related to land use planning, infrastructure development, and emergency response but also enhance our understanding of the complexities of land Subsidence processes and their interactions with human activities, informing future research and practice in environmental risk assessment and management.

Concomitant Near-Infrared Photothermy and Photoluminescence of Rod-Shaped Au52(PET)32 and Au66(PET)38 Synthesized Concurrently.

Gold nanoclusters exhibiting concomitant photothermy (PT) and photoluminescence (PL) under near-infrared (NIR) light irradiation are rarely reported, and some fundamental issues remain unresolved for such materials. Herein, we concurrently synthesized two novel rod-shaped Au nanoclusters, Au52(PET)32 and Au66(PET)38 (PET = 2-phenylethanethiol), and precisely revealed that their kernels were 4 × 4 × 6 and 5 × 4 × 6 face-centered cubic (fcc) structures, respectively, based on the numbers of Au layers in the [100], [010], and [001] directions. Following the structural growth mode from Au52(PET)32 to Au66(PET)38, we predicted six more novel nanoclusters. The concurrent synthesis provides rational comparison of the two nanoclusters on the stability, absorption, emission and photothermy, and reveals the aspect ratio-related properties. An interesting finding is that the two nanoclusters exhibit concomitant PT and PL under 785 nm light irradiation, and the PT and PL are in balance, which was explained by the qualitative evaluation of the radiative and non-radiative rates. The ligand effects on PT and PL were also investigated.

Triaxial Test and Discrete Element Numerical Simulation of Geogrid-Reinforced Clay Soil

Indoor triaxial tests on geogrid-reinforced clay elucidate the macroscopic changes in soil strength indices post-reinforcement, yet the underlying mechanisms of strength enhancement require further investigation. By conducting indoor triaxial tests and establishing a corresponding discrete element numerical model, we can delve into the fine-scale mechanisms of geogrid-reinforced soil. This includes analyzing changes in fine-scale parameters such as porosity, the coordination number, and contact stress between soil particles. The findings suggest that an increase in the number of geogrid reinforcement layers leads to a more pronounced improvement in peak strength and cohesion, albeit with minimal impact on the internal friction angle of the specimens. Furthermore, analysis of the triaxial test curves of reinforced soils indicates that the stress–strain relationship adheres to the Duncan–Chang model. Parameters derived from this model have been validated against experimental data, confirming their accuracy. The discrete element model was used to analyze the variations in fine-scale parameters such as porosity and coordination number. It revealed that reinforcement reduces the fluctuation amplitude of porosity and significantly increases the number of particle contacts, resulting in a denser soil structure. Further analysis of the change in contact stress between particles in the discrete element model revealed that the contact force between particles increased significantly after reinforcement and that the reinforcement played a role in restraining the soil particles and dispersing the reinforcement stress, which explains the increase in the strength of the mesh-reinforced clays from another perspective. This further elucidates the strength enhancement mechanism in geogrid-reinforced clay, offering a new perspective on the mechanical behavior and strength development of such materials.

Soliton Solutions of Some Ocean Waves Supported by Physics Informed Neural Network Method

In this study, we aim to obtain numerical results of the modified Benjamin-Bona-Mahony equation, Ostrovsky-Benjamin-Bona-Mahony equation and Mikhailov-Novikov-Wang equation via the physics-informed neural networks (PINN) method. The equations are modeled for shallow and long water waves, as well as fundamental and phenomenonal models in ocean engineering. According to the implementation, we obtained the PINN solutions of kink, bright, multisoliton (two-soliton) and mixed dark-bright soliton solutions. According to the inference from the obtained results, we achieved good results in some cases compared to other approximate solution methods in the literature. However, it was also observed that the best possible results could not be obtained in cases where the soliton type was intricate and layered. While the results were obtained, the number of hidden layers and the number of neural networks in the layers also varied. These results are shown in tables. Since it is known that the aforementioned models are not solved by the PINN method, we anticipate that the study will lead to other studies in the field of ocean engineering.

Diabetes Insipidus After Endoscopic Endonasal Pituitary Macroadenoma Resection, Correlation of Patient and Surgery-Related Risk Factors

Purpose: To identify patient and surgery related factors that could predict the development of postoperative central diabetes insipidus (CDI). Methods: This is a retrospective case-control study conducted at a single-institution tertiary referral center. Patients undergoing endoscopic endonasal skull base surgery for pituitary adenoma between November 2018 and April 2023 were recruited. The main outcome measures collected include age, gender, comorbidities, tumor type, postoperative diabetes insipidus, intraoperative and postoperative cerebrospinal fluid (CSF) leak, flow of CSF leak, number of layers required for repair, the use of nasal packing, and hospital length of stay. Results: A total of 20 patients developed DI postoperatively. Patients demographic and comorbidity profile did not correlate with DI development. The encounter of an intraoperative CSF leak was correlated with post-op DI (X2(1) =18.35, p<0.001) with a RR of 2.7 (CI=1.37-5.28). The use of nasal packs was also correlated with post-op DI (X2(1) =10.17, p=0.001) with a RR of 1.8 (CI=1.15-2.87). Defects requiring a two or more layers for reconstruction also correlated with post-op DI compared to single layer repairs (X2(1) =12.15, p<0.001) irrelevant of the materials used. Development of DI post-op correlated with an increased hospital length of stay (t(64) = -3.35, p =0.001). Conclusion: The physician should be careful when evaluating patients with pituitary adenomas in the postoperative period, particularly those with intraoperative CSF leak, nasal packing, and those who underwent multilayer reconstruction of the surgical defect.

Understanding mechanism conversion in hydrogen evolution reaction on PtSe2: Role of layer number explored by density functional theory

The emerging two-dimensional (2D) transition metal dichalcogenide PtSe2 is a promising catalyst for hydrogen evolution reactions (HER). In this study, we performed density functional theory (DFT) calculations to investigate the mechanism conversion of the HER over PtSe2 with respect to the changes in the number of layers of the catalyst. We found that the adsorption energy of hydrogen (ΔGH) changed with an increase in the number of 2D material layers, and the most significant change occurred when the number of layers was increased from one to two. Furthermore, we conducted dynamic calculations to reveal the mechanisms of the HER on the basal and Vse surfaces. Owing to the changes in ΔGH, the HER mechanism undergoes a conversion between Volmer–Heyrovsky and Volmer–Volmer'–Tafel mechanisms. Through frontier orbitals analysis, layer-stacking altered the bonding capability between hydrogen and PtSe2 surfaces by altering the electronic structure of the PtSe2 surface. This study demonstrates the synergistic effect of interlayer interactions on the HER over PtSe2 catalysts and can be helpful in stimulating innovative ideas for 2D material design.

Minimal Conditioned Stiffness Matrices with Frequency-Dependent Path Following for Arbitrary Elastic Layers over Half-Spaces

This paper introduces an efficient computational procedure for analyzing the propagation of harmonic waves in layered elastic media. This offers several advantages, including the ability to handle arbitrary frequencies, depths, and the number of layers above an elastic half-space, and efforts to follow dispersion curves and flag up possible singularities are investigated. While there are inherent limitations in terms of computational accuracy and capacity, this methodology is straightforward to implement for studying free or forced vibrations and obtaining relevant response data. We present computations of wavenumber dispersion diagrams, phase velocity plots, and response data in both the frequency and time domains. These computational results are provided for two example cases: plane strain and axisymmetry. Our methodology is grounded in a well-conditioned dynamic stiffness approach specifically tailored for deep-layered strata analysis. We introduce an innovative method for efficiently computing wavenumber dispersion curves. By tracking the slope of these curves, users can effectively manage continuation parameters. We illustrate this technique through numerical evidence of a layer resonance in a real-life case study characterized by a fold in the dispersion curves. Furthermore, this framework is particularly advantageous for engineers addressing problems related to ground-borne vibrations. It enables the analysis of phenomena such as zero group velocity (ZGV), where a singularity occurs, both in the frequency and time domains, shedding light on the unique characteristics of such cases. Given the reduced dimension of the problem, this formulation can considerably aid geophysicists and engineers in areas such as MASW or SASW techniques.

Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems.

Recent connections in the adaptive control literature to continuous-time analogs of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume that the system's uncertainties are linear-in-the-parameters (LIP). To compensate for non-LIP uncertainties, our preliminary results developed a neural network (NN)-based accelerated gradient adaptive controller to achieve trajectory tracking for nonlinear systems; however, the development and analysis only considered single-hidden-layer NNs. In this article, a generalized deep NN (DNN) architecture with an arbitrary number of hidden layers is considered, and a new DNN-based accelerated gradient adaptation scheme is developed to generate estimates of all the DNN weights in real-time. A nonsmooth Lyapunov-based analysis is used to guarantee the developed accelerated gradient-based DNN adaptation design achieves global asymptotic tracking error convergence for general nonlinear control affine systems subject to unknown (non-LIP) drift dynamics and exogenous disturbances. A comprehensive set of simulation studies are conducted on a two-state nonlinear system, a robotic manipulator, and a complex 20-D nonlinear system to demonstrate the improved performance of the developed method. Our simulation studies demonstrate enhanced tracking and function approximation performance from both DNN architectures and accelerated gradient adaptation.

Effect of anchorages on C-FRCM/concrete interfacial shear performance

In this study, single-lap shear tests were performed on 42 concrete specimens that were reinforced with Carbon Fabric Reinforced Cementitious Matrix (C-FRCM). The interfacial properties were enhanced through the use of fabric end-bending anchorage, a U-shaped hoop, and multiple U-shaped hoops. The bond performance of the C-FRCM/concrete interface was analyzed by examining the failure mode, ultimate stress, and interface slip of the specimens, considering four parameters: bond length of C-FRCM, number of fabric layers, fabric end-bending anchorage, and U-shaped hoop anchorage, as well as their respective mechanisms. The bond-slip constitutive parameters were calculated based on the trilinear cohesive material law and load-displacement simulation curves were obtained for each specimen through numerical simulation, which were in agreement with the test results, verifying the law's validity. Finally, the positive effect of anchorages on the constitutive parameters was analyzed.

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Study on the fracture propagation of ground fissures with syn-depositional structure in Fenwei Basin, China

In Fenwei Basin, most of the tectonic ground fissures show characteristics of growth faults on the section. They continue to destroy the engineering properties of soil at different depths. This has introduced significant security risks to the construction processes of deep underground spaces. However, there are few studies have been conducted on syn-depositional ground fissures. Therefore, in this study, a physical simulation test was used to study the fracture propagation of syn-depositional ground fissures. The characteristics of sections and surface fractures were analyzed. The engineering properties of model soil were divided into bad and poor areas. The syn-depositional ground fissure fracture propagation process was divided into five phases. The results show that soil profile exhibited a composite Y-shaped fracture morphology. Syn-deposition affects the fracture angle and healing state of fractures. The soil strain and surface displacement were positively correlated with the number of deposition layers. The conclusions of this study provide a theoretical geological basis and practical engineering significance for design of deep underground space structures.

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

Layered construction process with the material point method

This paper presents a novel procedure to simulate layered construction processes with the material point method (MPM), a method suitable for modelling large deformations. For the first time, the paper provides the MPM with the capability of modelling the behaviour of earth structures from their construction until the eventual post-failure behaviour in an integrated manner without requiring, as currently done, the use of other numerical tools for the construction simulation. In addition, the proposed methodology includes the option of simulating the engineering practice of compensating settlements that occurred during construction, to match the designed geometry. This option, typically neglected in alternative numerical methods, is implemented by filling the computational elements of the layers that become empty by adding new material points.The construction process proposed and its numerical implementation into an open-source code are described in detail. The validation is carried out by modelling an elastic oedometric column with varying number of layers and by comparing it with analytical solutions and finite element-based results. The construction of a soil embankment in dry, saturated, and partially saturated conditions is also simulated. It shows the capability of the method to capture the excess pore water pressures that may develop.

Residual Stress Evaluation in L-PBF-Produced SS 316L Specimens

The identification of residual stresses (RS) in components made by selective laser melting (SLM) is necessary for subsequent technological optimization. The presented research is devoted to evaluating the influence of the combination of laser power (P), scanning velocity (v) and the rarely considered number of layers (nL) on surface residual stresses in SLM stainless steel SS 316L. Experimental parameters were set based on the Design of Experiment (DoE) method, with follow-up X-ray diffraction (XRD) measurements and data processing using analysis of variance (ANOVA) and regression analysis. The obtained data are a valuable stepping-stone for the subsequent design of research focused on the application of sustainable eco-friendly Abrasive Water Jet (AWJ) peening for RS modification in the evaluated material.

SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction.

Options to improve the extrapolation power of the neural network designed using the SchNetPack package with respect to top docking scores prediction are presented. It is shown that hyperparameter tuning of the atomistic model representation (in the schnetpack.representation) improves the prediction of the top scoring compounds, which have characteristically a low incidence in randomized data sets for training of machine learning models. The prediction robustness is evaluated according to the mean square error (MSE) and the entropy of the average loss landscape decrease. Admittedly, the improvement of the top scoring compounds' prediction accuracy comes with the penalty of worsening the overall prediction power. It is revealed that the most impactful hyperparameter is the cutoff (5 Å is reported as the optimal choice). Other parameters (e.g., number of radial basis functions, number of interaction layers of the neural network, feature vector size or its batch size) are found to not affect the prediction robustness of the top scoring compounds in any comparable way relative to the cutoff. The MSE of the best docking score prediction (below -13 kcal/mol) improves from ca. 3.5 to 0.9 kcal/mol, while the prediction of less potent compounds (-13 to -11 kcal/mol) shows a lesser improvement, i.e., a decrease of MSE from 1.6 to 1.3 kcal/mol. Additionally, oversampling and undersampling of the training set with respect to the top scoring compounds' abundance is presented. The results indicate that the cutoff choice performs better than over- or undersampling of the training set, with undersampling performing better than oversampling.

Tunable Magnetism in Atomically Thin Itinerant Antiferromagnet with Room-Temperature Ferromagnetic Order.

Addressing the need for modulated spin configurations is crucial, as they serve as the foundational building blocks for next-generation spintronics, particularly in atomically thin structures and at room temperature. In this work, we realize intrinsic ferromagnetism in monolayer flakes and tunable ferro-/antiferromagnetism in (Fe0.56Co0.44)5GeTe2 antiferromagnets. Remarkably, the ferromagnetic ordering (≥1 L) and antiferromagnetic ordering (≥4 L) remain discernible up to room temperature. The TC (∼310 K) of the monolayer flakes sets a record high for known exfoliated monolayer van der Waals magnets. Within the framework of A-type antiferromagnetism, a notable odd-even layer-number effect at elevated temperatures (T = 150 K) is observed. Of particular interest is the strong ferromagnetic order in even-layer flakes at low temperatures. The intricate interplay among magnetic field strength, layer number, and temperature gives rise to a diverse array of phenomena, holding promise not only for new physics but also for practical applications.

Preparation and properties of liquid-core yogurt balls by layer-by-layer assembly of edible packaging materials

Abstract To address the issue of yogurt adhering to the inner walls of packaging, resulting in resource wastage, a controllable particle size of alginate-calcium yogurt liquid core ball (LC-Yoba) was prepared using a mold-reverse spherification method. The effectiveness of layer-by-layer (LBL) assembly techniques in enhancing the performance of LC-Yoba was investigated. The results showed that a multilayer structure composed of beeswax-chitosan (BW-CS) was successfully assembled on the surface of LC-Yoba, wherein the number of assembly layers significantly impacts its performance. Compared with the unassembled LC-Yoba, the bursting force of the assembled three layers of LC-Yoba increased by 194.67 %, the elasticity increased by 19.76 %, and the weight loss rate decreased by 86.58 %. In addition, the appearance of the three layers of LC-Yoba and the pH, acidity, and water holding capacity of the yogurt were maintained in a stable state within 72 h at room temperature.

Photonic quasi-crystal fiber electro-optical modulator

The integration of graphene with optical fiber is considered to be a new interdisciplinary research hotspot for functional fiber. In this paper, an electro-optical modulator based on a six-fold Stampfli-type photonic quasi-crystal fiber (PQF) is theoretically proposed with a sandwiched graphene/hexagonal boron nitride/graphene (Gr/hBN/Gr) film covering all the hole walls. This design exhibits a strong light-graphene interaction with an excellent modulation depth of ∼64 dB mm−1 at 1550 nm by applying an external bias voltage (below 30 V) on both graphene layers. As the Fermi level of the graphene changes with voltage, the fiber shows ‘On’ and ‘Off’ states, serving well as a light-switch. For the modulator performance, the dependence of modulation depth on multiple factors is studied in terms of the layer numbers of graphene and hBN films, the incident wavelength, and the structure parameters. Interestingly, an attenuation peak occurs due to the epsilon-near-zero effect in graphene and shows a linear relationship between the wavelength and the Fermi level. This design provides a guidance for the integration of PQF and graphene, and holds great promise for future all-fiber systems.

Solution of nonlinear vibration problem of shear deformable multilayer nonhomogeneous orthotropic plates using Poincare-Lindstedt method

In this study, one of the first attempts is made to solve the nonlinear (NL) vibration problem of shear-deformable multilayer plates consisting of nonhomogeneous orthotropic layers (NHOLs) using the Poincaré-Lindstedt method. First, the shear deformation theory (SDT) for homogeneous plates is extended to multilayer plates composed of NHOLs. In the framework of von-Karman type nonlinear theory, the basic relations of the plates in question are established and then NL equations of motion based on four functions such as rotation angles, Airy stress and deflection functions are derived. Then, NL-partial differential equations (NL-PDEs) are reduced to NL-ordinary differential equations (NL-ODE) and the solution of NL-ODE is performed for the first time by the modified Poincaré −Lindstedt method, yielding new amplitude dependent expressions for NL frequency, and for the ratio of NL frequency to linear (NL/L) frequency for multilayer plates consisting of NHOLs. Finally, detailed parametric studies are carried out to gain insight into the effects of various factors such as shear strains, non-homogeneity, number and array of layers on the NL frequencies under different rectangular plate characteristics.

Layer-controllable synthesis, lattice structural Determination, and fluorescence lifetime imaging of (HA)2(MA)n-1PbnI3n+1 Quasi-2D organic-inorganic perovskite microsheets

Hybrid quasi-two-dimensional (quasi-2D) perovskites have attracted great interest due to their excellent photovoltaic and light-emitting properties, which enable the rapid development of high-performance perovskite-based solar cells, light-emitting diodes, and lasers. Although many efforts have been made to improve the quality and stability of organic-inorganic perovskites, the controlled synthesis of high-quality layered perovskites remains challenging. Here, single-crystalline quasi-2D (HA)2(MA)n-1PbnI3n+1 perovskites with layer numbers of n from 1 to 3 have been synthesized in a controllable manner, exhibiting stable lattice structures and tunable optical emission. Structurally, hexylamine (HA) with longer alkyl chains was used as an organic spacer in Ruddlesden-Popper (RP) phase perovskites to improve the chemical stability of perovskites. Various ratios of precursor materials were adopted for separating pure and large-scale quasi-2D (HA)2(MA)n-1PbnI3n+1 perovskites, in which different layer-number perovskites with tunable band gaps were obtained with the modulated fluorescence lifetimes. Our lattice structural and fluorescence-lifetime characterizations of RP-phase perovskites provide critical information on the emerging quasi-2D perovskite-crystal systems and their fluorescence relaxation dynamics. The employed strategy to synthesize high-quality perovskites (HA)2(MA)n-1PbnI3n+1 is technically essential to develop the rising perovskite-based photovoltaic and light-emitting applications.