Layer Dimensions Research Articles

Epicardial adipose tissue thickness - a new predictive factor for coronary artery disease

Abstract Introduction The role of pericardial adipose tissue has been investigated in numerous studies in relation to the development of coronary artery disease (CAD) and other cardiac pathologies. Total pericardial fat (TPF) consists of epicardial adipose tissue (EAT), which is located between the myocardium and the visceral pericardium, and pericardial fat (PCF), which adheres to the parietal pericardium. The suspected mechanisms are complex and include the acceleration of inflammation and oxidative stress as well as proximity to the coronary arteries. Purpose The aim of the study was to investigate the existence of a correlation between the thickness of epicardial and pericardial adipose tissue and the presence of CAD, with the hope of using this marker in the future for risk stratification of cardiovascular disease. Methods This study enrolled 264 patients with a mean age of 59,9 +/- 10.6 years, 60,6% (160 pts) male, with cardiovascular risk factors who were investigated for suspected CAD. In all patients, the thickness of the TPF, EAT, and the PCF were measured echocardiographically during end-diastole, at the anterior atrioventricular groove. Intra- and interobserver variability was excellent (ICC 0.995-0.998 and ICC 0.992-0.995 for all three layers). Results Out of the total patients included, 54,2% (142 pts) had lesions on conventional angiography, of which 46% had hemodynamically significant lesions requiring revascularization. There were no statistically significant differences in the thickness of the three layers according to the presence of cardiovascular risk factors (diabetes mellitus, smoking, hypertension) and there was no correlation with body mass index. There were statistically significant differences in the dimensions of the pericardial adipose tissue layers between the group of patients with CAD and the group of patients without CAD (EAT 7,1 +/-2,34 vs 5,07+/-2,36 mm, PCF 9.3+/-3,01 vs 7,2+/-2,9 mm and TPF 16,7+/-3,8 vs 12.59+/-4,3 mm, p=0.001 for all cases). There were no statistically significant differences between patients who had one, two, or all three coronary arteries affected (p=0.5). There is a significant correlation between EAT and TPF (r 0.81), PCF and TPF (r 0.83), but no significant correlation between the two substrates (r 0.44). Women without CAD had a lower epicardial layer thickness than men without CAD (4,8+/-0,26 vs 5,4+/-0,29,p <0,001), but this difference did not exist in those with CAD (6.9+/-0.47 vs 7.2+/-0.24). Conclusions The study shows that echocardiographic measurement of the thickness of pericardial adipose tissue layers is a feasible method, easy to perform with good intra- and interobserver reproducibility. The data presented suggest that the thickness of the pericardial adipose tissue layers (EAT and TPF) may represent an independent risk factor for CAD and could be included in the pre-test risk assessment of patients with suspected CAD.

Optimization of CuOx/Ga2O3 Heterojunction Diodes for High-Voltage Power Electronics.

This study optimizes the CuOx/Ga2O3 heterojunction diodes (HJDs) by tailoring the structural parameters of CuOx layers. The hole concentration in the sputtered CuOx was precisely controlled by adjusting the Ar/O2 gas ratio. Experimental investigations and TCAD simulations were employed to systematically evaluate the impact of the CuOx layer dimension and hole concentration on the electrical performance of HJDs. The results indicate that increasing the diameter dimension of the CuOx layer or tuning the hole concentration to optimal values significantly enhances the breakdown voltage (VB) of single-layer HJDs by mitigating the electric field crowing effects. Additionally, a double-layer CuOx structure (p+ CuOx/p- CuOx) was designed and optimized to achieve an ideal balance between the VB and specific on-resistance (Ron,sp). This double-layer HJD demonstrated a high VB of 2780 V and a low Ron,sp of 6.46 mΩ·cm2, further yielding a power figure of merit of 1.2 GW/cm2. These findings present a promising strategy for advancing the performance of Ga2O3 devices in power electronics applications.

Integration of cobalt coordination polymer-based triboelectric nanogenerators as sustainable power sources for self-driven selective photocatalytic reactions

The design and development of dual-function materials combining photocatalytic activity and electrical output from a triboelectric nanogenerator (TENG) is a promising approach for harnessing the energy of human movement via flooring for self-powered systems but only a preliminary. Herein, we addressed this challenge by combining a cobalt coordination polymer (Co-CP) as a tribomaterial and catalyst to produce a dual-functional TENG/photocatalytic system. Different sizes of Co-CP-based triboelectrodes were used to construct TENGs, revealing that the power output was dependent on the dimensions of the triboelectric layers. To facilitate large-scale application, different amounts of Co-CP were added to cellulose acetate (CA) to fabricate flexible Co-CP@CA composite films, which effectively enhanced the output of the TENG devices, and the output of the 12 % Co-CP@CA-TENG was the highest. Subsequently, 12 % Co-CP@CA-TENGs were integrated to construct multiple TENG devices (M−TENG) as daily-life floors with an extended dimension to availably harvest humans walking energy for continuously driving numbers blue LEDs, affording the blue-light irradiation to efficiently implement selective self-powered photocatalytic oxidation reactions with Co-CP as photocatalyst. This work indicates that bifunctional CPs can be used for large-scale integration through flooring systems to harvest and monitor human mechanical energy and as photocatalysts for regulating self-powered photocatalytic systems.

Optimizing CZTS Solar Cell Performance with Advanced Layer Configurations Using SCAPS Simulation

This research analyzes the modeling of CZTS (Copper Zinc Tin Sulfide) solar cells, with a focus on advanced layer configuration and thermal management to improve operational efficiency. Using SCAPS-1D software, this investigation seeks to augment cell efficiency by fine-tuning the dimensions of absorptive layers, modifying buffer compositions, and adjusting other critical components. The study evaluates the role of transition metal dichalcogenides (TMDs), specifically MoS₂ as the hole transport layer and ZnO as the window layer, in influencing open-circuit voltage (Voc) and short-circuit current density (Jsc). Furthermore, the research delves into temperature-related effects, demonstrating that elevated temperatures lead to a decrease in Voc and Jsc attributable to bandgap narrowing and heightened recombination processes. Through the optimization of the thicknesses of the CZTS, MoS₂, and WSe₂ layers, this study elucidates the manner in which material adjustments influence Voc, Jsc, fill factor (FF), and overall efficiency (η). In addition, effective thermal management emerges as a critical factor, given that increased temperatures elevate recombination rates, thereby adversely affecting FF and efficiency. The results of this study provide essential information for increasing the performance, durability and stability of CZTS solar cells under various environmental conditions.

Reducing the Dimensionality of Data Arrays Using Multi-Layer Autoencoders in the Task of Classifying Mobile Applications

The problem of reducing the dimension of the initial data arrays to improve the efficiency of mobile application traffic processing is considered. The relevance of the study is due to the need to optimize the volume of transmitted and stored data when working in conditions of limited computing resources, as well as to increase the speed and quality of analytical operations. To solve this problem, multi-layer autoencoders are used, capable of forming compact representations of the source data with minimal losses in their informativeness. The approach is based on the idea of training neural network models that extract the most significant features from the source arrays and are able to restore them with a given level of accuracy. Methods used. During the experiments, various architectures of multilayer autocoders were used, differing in the number of layers and dimensions of hidden representations. The research was conducted on real data sets collected from mobile applications with a wide range of functionality. The analysis was carried out by varying the internal parameters of the networks and evaluating the results through an integral statistical indicator reflecting the degree of compression. This indicator allows you to identify how much the spread of attributes changes when passing data through the autoencoder. Results. To evaluate the filtering properties of multilayer autoencoders, an integral compression indicator is proposed that characterizes the change in the spread of attributes of mobile applications when passing them through an autoencoder of a given structure. The indicator is calculated as the ratio of the standard deviation of the attributes at the input and at the output, which allows you to assess the degree of data compression and the degree of information preservation after processing. It is shown that an increase in the integral compression index indicates a more significant compression of the initial data. It was found that filtering is practically independent of the type of application and lies within 10-20 % for three-layer autoencoders, whereas for five-layer auto-encoders, preference is given to encoders with a minimum dimension of the inner layer. The main novelty of the work lies in the development of an integral statistical indicator that not only reflects the degree of compression of mobile application data, but also takes into account the preservation of the original information structure. Unlike existing approaches, this indicator allows for a systematic comparison of various architectures of autoencoders, taking into account not only the reduction in dimension, but also the quality of recovery of the original information. This creates the basis for a more objective assessment of the effectiveness of multilayer autoencoders in specific application conditions. Practical significance. The proposed methodology may be useful for developers and researchers working on optimizing systems for collecting, storing and processing mobile application data. In conditions of limited computing resources, which are typical for mobile devices and embedded systems, the use of multilayer autoencoders aimed at achieving a given balance between compression and preservation of information provides a significant reduction in the volume of transmitted data. The results of the study can be implemented into existing analytical platforms, monitoring systems and classification of mobile applications.

Multi-Scale Thermo-Mechanical Model Simulation of Residual Stress in Atmospheric Plasma Spray Process

This work presents a multi-scale one-way-coupled thermo-mechanical method to determine the residual stress in an Atmospheric Plasma Spray (APS) process. The model uses three submodelling scale levels that range from the entire component (macroscopic) to a splat coating layer (microscopic) dimension. The three-level scale temperature and stress evolutions of an Al2O3 coating material on a flat aluminium substrate were analysed. The quenching stress for different substrate preheating temperatures up to 600 K at the end of the APS coating process was discussed and validated through an experimental in situ curvature method and Stoney’s quenching stress equation.

Designing Leidenfrost Puddles.

Leidenfrost puddles exhibit erratic bubble bursts that release vapor trapped beneath the liquid, becoming amorphous and unstable. We report a method to stabilize and design a Leidenfrost puddle. When a thin hydrophilic layer with a suitable design is placed over the liquid, the puddle adopts the layer shape due to adhesive forces and becomes stable. We show a variety of puddle designs with the required layer dimensions to avoid vapor accumulation, as well as wetting and buoyancy conditions. With the layer, the puddle evaporation rate increases significantly and can be modified by varying the layer dimensions. Finally, an illustrative use of this method in a cooling process is presented.

Characterization of three-dimensional printed hydroxyapatite/collagen composite slurry

Nowadays, biomaterial composites for bone tissue engineering are developing rapidly. Many research studies have been done on hydroxyapatite (HA) and collagen because these materials are biomimetic and can be used in human bones. This study aimed to characterize a three-dimensional (3D) printed hydroxyapatite/collagen composite slurry material with a ratio of 99.84 % (w/v) and 0.16 % (w/v). The composite material was printed using 3D printing with a print speed of 10 mm/min and a layer height of 0.5 mm. Characterization layers were investigated using a scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), energy dispersive X-ray (EDX), and thermogravimetric analysis (TGA). SEM showed the occurrence of overlapping between layers, which was investigated by the reduction in layer dimensions after printing (layer size 432 μm). Moreover, there were no boundaries between layers; the connection between layers occurred, and porosity and the rough surface were presented. FTIR analyses showed spectrum peaks at 559.36, 628.79, 1022.27, 1562.34, and 1639.49 cm−1 which was confirmed as hydroxyapatite and amide (indicates the presence of spectrum collagen). The XRD pattern peaks show the crystallinity of HA/collagen composite (41 %) and HA (42 %). The Ca/P ratio of the material composite was 1.77. The ratio was osteoconductive, and this characteristic was the main requirement for bone grafts. From TGA, the weight loss occurred between temperatures of 25 °C and 1000 °C with three stages: water absorption (1.844 %), removal of organic content (2.854 %), and decomposition of inorganic compounds (3.517 %).

Investigation of Oxide Layer Development of X6CrNiNb18-10 Stainless Steel Exposed to High-Temperature Water.

The oxide layer development of X6CrNiNb18-10 (AISI 347) during exposure to high-temperature water has been investigated. Stainless steels are known to form a dual oxide layer in corrosive environments. The secondary Fe-rich oxide layer has no significant protective effect. In contrast, the primary Cr-rich oxide layer is known to reach a stabilized state, protecting the base metal from further oxidation. This study's purpose was to determine the development of oxide layer dimensions over exposure time using SEM, TEM and EDX line scans. While a parabolic development of Cr in the protective primary layer and Fe in the secondary layer was observed, the dimensions of the Ni layer remained constant. Ni required the presence of a pronounced Fe-rich secondary layer before being able to reside on the outer secondary layer. With increasing immersion time, the Ni element fraction surpassed the Cr element fraction in the secondary layer. Oxide growth on the secondary layer could be observed. After 480 h, nearly the entire surface was covered by the outer oxide layer. In the metal matrix, no depletion of Cr or Ni could be observed over time; however, an increased presence of Cr and Ni in the primary layer was found at the expense of Fe content. The Nb-stabilized stainless steel was subject to the formation of Niobium pentoxide (Nb2O5), with the quantity and magnitude of element fraction increasing over exposure time.

Theory of Electron Transport in Two-Barrier Five-Layer Semiconductor Structures

The dependence of the transparency coefficient of a five-layer two-barrier structure on the electron energy and the ratio of the widths of two neighboring potential barriers is calculated. It is shown that the extremum of the transparency coefficient significantly depends on the geometric dimensions of the structure layers. In a symmetric five-layer two-barrier semiconductor structure, the condition for the occurrence of "resonant" electron transitions is defined. It is demonstrated that the mechanism of such (resonant) transitions is explained by the interference of de Broglie waves of electrons in the potential well, where the phases of de Broglie waves are determined by the geometric dimensions of the structure, and their amplitudes - by the ratio of the carrier energy to the height of the potential barrier. It has been established that with an increase in the effective mass of charge carriers, the number of intersections of the quantities fR (ξ) and ((1-2ξ))/(√(ξ-ξ2) increases. These intersections determine the dimensionally-quantized levels where electrons are localized.

Mechanical reconfigurable infrared filter for stress sensing applications

In this study, we introduce a novel tunable infrared filter design aimed at stress sensor applications such as health monitoring, advanced robotics and industrial automation. The design utilizes a double-layer plasmonic frequency-selective surface (FSS) of indium tin oxide (ITO) with an aerogel inter-space region. The unique feature of this design is its ability to control the transmission pattern based on the stress exerted, which alters the distance between the two filter layers. Our approach employs both numerical simulations and a simplified model grounded in equivalent circuit theory and the transfer matrix method for accurate transmission pattern estimation where the sensor achieved a sensitivity of 20nm/MPa for average stress of 14MPa and spectrum range (λ>800nm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda >800 nm$$\\end{document}). The work provides an in-depth analysis of how layer dimensions and inter-layer distance influence both transmission and force, taking into account the elastic properties of the inter-space material.

Performance analysis of perovskite solar cell in presence and absence of defects

Studies of the effect of defects in any solar cell are important in achieving a satisfactory level of its performance. A comparative study with a defect-free against the defect-induced cell carries instant and ready information on laboratory/industry-based fabricated cell performance, which is prone to unavoidably induced defects. In spite of good deal of works on defects of cells such a study in an organized and comparative way remains absent to the knowledge of the authors. Ready and classified presentation of such a study, therefore, is considered to be significant. Present work is a result of motivation to fulfil this gap. This article presents a study of the effects of bulk and interface defects in perovskite solar cells. On examining the effects of deep and shallow defect levels on key performance metrics such as open-circuit voltage, short-circuit current density, and fill factor, the resulting study discusses an analysis of the impact of the defects on cell efficiency. A defect-free cell with optimal perovskite, hole-transport layer (HTL), and electron-transport layer (ETL) dimensions are analysed simultaneously to assess the level at which the defects can degrade the performance of a defect-free cell. It is observed that the defects, particularly in the deep levels, significantly impair the performance of a cell, including the open circuit voltage, short circuit current density, fill factor, and efficiency, compared to those in shallow levels.

Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU

Abstract Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine.

Size-Dependent Thresholds in CuO Nanowires: Investigation of Growth from Microstructured Thin Films for Gas Sensing.

An experimental characterization of cupric oxide nanowire (CuO NW) growth from thermally oxidized, microstructured Cu thin films is performed. We have systematically studied the influence of the thickness and dimension of Cu layers on the synthesis of CuO NW. The objective was to determine the optimum Cu geometries for increased CuO NWs growth to bridge the gap between adjacent Cu structures directly on the chip for gas sensing applications. Thresholds for CuO-NW growth regarding film thickness and lateral dimensions are identified based on SEM images. For a film thickness of 560 nm, NWs with lengths > 500 nm start to grow from the edges of Cu structures with an area ≥ 4 µm2. NWs growing from the upper surface were observed for an area ≥ 16 µm2. NW growth between adjacent thermally oxidized thin films was analyzed. The study provides information on the most relevant parameters of CuO NWs growth, which is mandatory for integrating CuO NWs as gas sensor components directly on microchips. Based on this result, the gap size of the structure was varied to find the optimum value of 3 µm.

Modeling of multi-phase flow in plasma transferred arc cladding of NiCrBSi/WC metal matrix composite

In this paper, a 3D multiphase flow model has been established for the plasma transferred arc (PTA) cladding of composite powder (NiCrBSi/50 wt% WC) on a 40CrNi2MoA substrate. The multiphase flow model simulates both the discrete phase (WC particles) and continuous phase in the melt pool, including the behaviors of heat transfer and particle distribution. The buoyancy, arc pressure, surface tension, and electromagnetic force are calculated. The model is then used to predict the influences of welding currents and preheating temperatures on the depth of heat-affected zone (HAZ), the temperature field, and the dimensions of the cladding layers. To validate this model, the dimensions and the HAZ depth are analyzed and compared to experimental data. The results show that the dimensions and HAZ depth obtained by the model simulation are in good agreement with the experiment. The HAZ depth, the melt pool length, and the dimensions of cladding layer would increase with the increasing welding current or preheating temperature. Driven by the inward convection in the melt pool, WC particles tend to distribute around the center line of cladding layers. This is the first work of modeling 3D multiphase flow in PTA cladding process of particle-reinforced metal matrix composite (MMCs). The work can provide theoretical guidance for adjusting process parameters to control HAZ depth and cladding quality in actual production.

Fault Location and Detection in Power Distribution Systems with Presence of Distributed Generations using Improved CNNs

This paper introduces a deep learning approach for addressing fault detection and location issues in power distribution grids. The proposed method utilizes a 20-layer deep neural network (CNN) that employs the ReLU activation function in the hidden layers and the Softmax function in the pre-classification layer. The network’s input layer dimensions are 224 x 224 pixels, with each input image being a composite of seven images, each sized 32 x 224 pixels. Data is gathered using continuous wavelet transform and Hilbert transform on each signal, followed by feature extraction and conversion into RGB images. The signals include three-phase voltages, currents, and zero-sequence voltages, collected across different locations, times, and fault resistances for use in the training and testing of the deep neural network. The output layer categorizes faults into ten classes, covering types such as single-phase to ground, two-phase, and three-phase faults. Additionally, the location identification layer categorizes faults into five classes corresponding to five zones on a specific feeder. The proposed method is validated using a test power system simulated in MATLAB 2020b, which includes six main feeders and two sub-feeders. The validation is conducted under two scenarios: one without distributed generation sources and with data noise at SNR = 10dB, and another with distributed generation sources and SNR = 40dB. The method demonstrated strong performance in all cases, and the trained network successfully detected and located faults in previously untested areas.

Prediction of Geometric Dimensions of Deposited Layer Produced Using Laser-Arc Hybrid Additive Manufacturing.

Laser-arc hybrid additive manufacturing (LAHAM) holds substantial potential in industrial applications, yet ensuring dimensional accuracy remains a major challenge. Accurate prediction and effective control of the geometrical dimensions of the deposited layers are crucial for achieving this accuracy. The width and height of the deposited layers, key indicators of geometric dimensions, directly affect the forming precision. This study conducted experiments and in-depth analysis to investigate the influence of various process parameters on these dimensions and proposed a predictive model for accurate forecasting. It was found that the width of the deposited layers was positively correlated with laser power and arc current and negatively correlated with scanning speed, while the height was negatively correlated with laser power and scanning speed and positively with arc current. Quantitative analysis using the Taguchi method revealed that the arc current had the most significant impact on the dimensions of the deposited layers, followed by scanning speed, with laser power having the least effect. A predictive model based on extreme gradient boosting (XGBoost) was developed and optimized using particle swarm optimization (PSO) for tuning the number of leaf nodes, learning rate, and regularization coefficients, resulting in the PSO-XGBoost model. Compared to models enhanced with PSO-optimized support vector regression (SVR) and XGBoost, the PSO-XGBoost model exhibited higher accuracy, the smallest relative error, and performed better in terms of Mean Relative Error (MRE), Mean Square Error (MSE), and Coefficient of Determination R2 metrics. The high predictive accuracy and minimal error variability of the PSO-XGBoost model demonstrate its effectiveness in capturing the complex nonlinear relationships between process parameters and layer dimensions. This study provides valuable insights for controlling the geometric dimensions of the deposited layers in LAHAM.

Analytical model of magnetic Barkhausen noise testing on surface-modified ferromagnetic plates with stress distributions

Surface treatment is a critical technology in engineering that enhances the mechanical properties of materials, in which the stress state within the material and the thickness of the surface modification layer are essential for optimizing performance. Magnetic Barkhausen Noise (MBN) serves as an effective nondestructive evaluation technique for ferromagnetic structures. In this work, we develop an analytical model that incorporates the attenuation mechanism of the MBN signal strength, and the strength of the stress-induced magnetic Barkhausen excitation (MBE) as determined by the magneto-mechanical constitutive law of the material. The proposed theoretical model can accurately characterize the MBN signal influenced by internal stresses and surface modification layers, as confirmed by comparison with existing experimental results. The theoretical analysis shows the influence of internal stress fields, layer dimensions, and electromagnetic properties on the MBN signal, revealing a positive relationship between signal intensity and the thickness of surface modification layer and the magnitude of internal stresses. This research provides a valuable reference for interpreting MBN signals and evaluating internal stress distributions for surface-modified ferromagnetic plates.

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.

The discrimination of tidal- and fluvial-dominated sedimentary systems based on quantifying the degree of serration in gamma-ray logging curves: East China Sea Shelf Basin

Using quantitative methods to distinguish tidal- and fluvial-dominated sedimentary systems is currently a challenge in sedimentological research. This paper establishes and verifies a new quantitative method through the degree of serration of gamma-ray logging curves. This method depends on the fundamentals of core-based and gamma-ray logging pattern interpretation of the depositional environment, using one-dimensional continuous wavelet transform to denoise the initial curve to enhance the effects of tides and rivers processes, and then expands to an analysis of the degree of serration in noise-reduced gamma-ray curves. The results may then be used to constrain a single best-boundary value to distinguish a tidal- or fluvial-dominated area, avoiding subjectivity and uncertainty. A case study of the Pinghu Formation showed that the degree of serration (defined here as DeltaGr) of the fluvial-dominated sediments was less than 10 in the depth dimension and that the average degree of serration (defined here as the average square of DeltaGr) was less than 7 in the layer dimension. Values that deviate from these imply a close relation to tide-dominated sedimentary systems, which provides a quantitative basis for the interpretation of sedimentary environments in both the depth and layer dimensions in the Pingbei Slope. Seismic attribute-based sedimentary analysis, relative sea-level changes, and the basin architecture of the Pingbei Slope clarify the reason for these changes and verify that this method is reliable. However, owing to the limitations of logging and core samples, the criteria remain an approximation of the actual situation. This study may, however, enable the discrimination of other similar sedimentary systems dominated by tidal and fluvial processes and therefore provide a further constraint on the reconstruction of the filling of basins.