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We proposed, for the first time, that the angle between the horizontal projection of the magnetic field line and the sunrise line (AMFS) is a crucial factor that controls predawn heating. Through quantitative analysis, we determined that both the AMFS and the length of the magnetic field line (LMF) significantly affect predawn heating. We found that an increased AMFS intensifies predawn heating, while an increased LMF counteracts it. Our research indicates that the optimal conditions for peak predawn heating occur at an AMFS of approximately 30 degrees and an LMF of about 4000 km, where the effect surpasses 400 K. Additionally, we observed that the effects of both the AMFS and the LMF on predawn heating exhibit a saturation effect. This study provides a clearer understanding of the factors driving predawn ionospheric heating, with implications for topside ionosphere research. [ABSTRACT FROM AUTHOR]

To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003–2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003–2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003–2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEE in 2003–2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO2 were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO2 storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive model for ultrasonic energy transfer (UET) using a 33-mode piezoelectric transducer to advance wireless sensor powering in challenging environments. One of the advantages of UET is that it is not stoppable by electromagnetic shielding and can penetrate metal. Existing models focus on feasibility and numerical analysis but lack an effective link between input and output power in different media applications. The proposed model fills this gap by incorporating key factors of link loss, including resonant frequency, impedance matching, acoustic coupling, and boundary conditions, to predict energy transfer efficiency more accurately. The model is validated through numerical simulations and experimental tests in air, metal, and underwater environments. An error analysis has shown that the maximum error between theoretical and experimental responses is 3.11% (air), 27.37% (water), and 1.76% (aluminum). This research provides valuable insights into UET dynamics and offers practical guidelines for developing efficient wireless powering solutions for sensors in difficult-to-access or electromagnetically shielded conditions. [ABSTRACT FROM AUTHOR]

Today's growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models using machine learning techniques and compares these models with two existing empirical models, including a commercial one. To build the models, the databases produced by EVERLASTING and Bills et al. were utilized. The aim is to create universally applicable models that can address any battery-aging scenario. In this study, three types of models were developed: a vanilla neural network, a neural network inspired by extreme learning machines, and an encoder coupled with a neural network. The inputs for these models are derived from established knowledge in battery science, allowing the models to capture aging effects across different use cases. The models were trained on cells subjected to specific aging conditions and they were tested on other cells from the same database that experienced different aging conditions. The results obtained during the test for the vanilla neural network showed an RMSE of 1.3% on the Bills et al. test data and an RMSE of 2.7% on the EVERLASTING data, demonstrating similar or superior performance compared to the empirical models and proving the ability of the models to capture battery aging. [ABSTRACT FROM AUTHOR]

In recent years, an increase in the frequency of low-sunlight conditions due to climate change has resulted in a decline in the yield and quality of crops for greenhouse farmers, leading to significant challenges in maintaining optimal plant growth. The crop growth model can be used to predict changes in cucumber yield in response to variations in sunlight, which can help efficiently address sunlight shortages. The objective of this study was to improve and validate the model for predicting cucumber yield under different light environment conditions, including shading and supplemental lighting. The model comprises three steps: LAI prediction, daily assimilate yield prediction, and fruit yield prediction, each of which involves modifying the coefficients applied to suit the cucumber cultivar and environment condition. The improved model demonstrated a high degree of accuracy in predicting cucumber yields in the control and low-sunlight treatments (10, 20, and 30% shading), with a coefficient of determination (R2) > 0.98. When supplemental lighting was incorporated into the control and shading treatments, the accuracy of the improved model in predicting cucumber yield was also high, with a coefficient of determination (R2) > 0.99. The model also accurately predicted the decrease in cucumber fruit yield under low-sunlight conditions (shading treatments) and the increase in yield due to supplemental lighting. The findings of this study indicate that the improved cucumber yield prediction model can be applied to assess the efficacy of yield reduction in low-sunlight conditions and the potential for yield enhancement through supplemental lighting. [ABSTRACT FROM AUTHOR]

This study aims to investigate ionospheric responses to the February 27, 2023 intense geomagnetic storm over Kharkiv and the Akademik Vernadsky (hereinafter – Vernadsky) station using ionosondes. The behavior of the key ionospheric parameters (hmF2 and NmF2) before, during, and after the geomagnetic storm was investigated. The observational F2-layer peak electron density and height were compared with those derived by the International Reference Ionosphere (IRI-2016) model. Significant negative ionospheric storms were identified over Kharkiv during all the nights after the geomagnetic storm beginning (up to ~70% decrease of the NmF2). At the same time, during the daytime hours of February 27, a moderate positive ionospheric storm (up to ~40% increase of the NmF2) was registered. Over Vernadsky Station, during the main phase of the geomagnetic storm, a very strong negative ionospheric storm (up to a factor of ~4 reduction of the NmF2) was observed both during daytime and night-time. We offer hypotheses of the possible physical mechanisms (electrodynamics, changes in the neutral composition, partial depletion of the plasmasphere, and a shift of the ionospheric trough) responsible for the observed ionospheric effects. Further investigations with physical models of the coupled atmosphere, ionosphere, and plasmasphere are necessary to identify the dominant drivers in each case. Comparison of the observed ionospheric parameters with the predictions of IRI F2-layer peak sub-models shows that neither hmF2 (AMTB-2013 and SHU-2015), and NmF2 (URSI and CCIR) sub-models can qualitatively reproduce strong storm-induced ionospheric variations over Kharkiv. Over Vernadsky Station, both hmF2 sub-models underestimate the observed hmF2 during the storm period, whereas the NmF2 sub-models are more sensitive to the changes in geomagnetic activity. Under geomagnetically quiet conditions, the qualitative agreement between observations and the model is satisfactory, but further improvements of the empirical models are required to reach acceptable accuracy of quantitative predictions.

The magnetopause is the boundary between the Earth’s magnetic field and the interplanetary magnetic field (IMF), located where the supersonic solar wind and magnetospheric pressure are in balance. Although empirical models and global magnetohydrodynamic simulations have been used to define the magnetopause, each of these has limitations. In this work, we use 15 years of magnetopause crossing data from the THEMIS (Time History of Events and Macroscale Interactions during Substorms) spacecraft and their corresponding solar wind parameters to investigate under which solar wind conditions these models predict more accurately. We analyze the pattern of large errors in the extensively used magnetopause model and show the specific solar wind parameters, such as components of the IMF, density, velocity, temperature, and others that produce these errors. It is shown that (1) the model error increases notably with increasing solar wind velocity, decreasing proton density, and increasing temperature; (2) when the cone angle becomes smaller or |Bx| is larger, the Shue98 model errors increase, which might be caused by the magnetic reconnection on the dayside magnetopause; (3) when |By| is large, the error of the model is large, which may be caused by the east–west asymmetry of the magnetopause due to magnetic reconnection; (4) when Bz is southward, the error of the model is larger; and (5) the error is larger for positive dipole tilt than for negative dipole tilt and increases with an increasing dipole tilt angle. However, the global simulation model by Liu ZQ et al. (2015) shows a substantial improvement in prediction accuracy when IMF Bx, By, or the dipole tilt cannot be ignored. This result can help us choose a more accurate model for forecasting the magnetopause under different solar wind conditions.

Abstract Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R2). In this study, the evaluations were conducted using a substantial collection of axial compression test data involving 765 circular specimens of FRP-confined concrete assembled from published sources. The results indicate that the proposed GBRT algorithm considerably enhances the performance of machine learning models and empirical approaches for predicting strength ratio of confinement (f′ cc /f′ co ) by an average improvement in RMSE as 17.3%, 0.65%, 66.81%, 46.12%, 46.31%, 46.87% and 69.94% compared to RF, ANNMLP, ANNRBF, and four applied empirical models, respectively. It is also found that the proposed ANNMLP algorithm exhibits notable superiority compared to other models in terms of reducing RMSE values as 9.67%, 11.29%, 75.11%, 68.83%, 73.64%, 69.49% and 83.74% compared to GBRT, RF, ANNRBF and four applied empirical models for predicting strain ratio of confinement (ε cc /ε co ), respectively. The superior performance of the GBRT and ANNMLP compared to other methods in predicting the strength and strain ratio confinements is important in evaluating structural integrity, guaranteeing secure functionality, and streamlining engineering plans for effective utilization of FRP confinement in building projects.

The development of the economy and technology has attracted greater attention to regional seismic resistance, a key technology for which is the simulation of regional seismic ground motions. This study proposes a simple and highly operable method for the generation of regional seismic ground motions, which includes five steps. First, a seismic ground motion recorded at a seismic station is selected as the original ground motion. The original ground motion is then decomposed into eight sub-signals by wavelet packet transform (WPT). Next, the eight sub-signals are adjusted by the frequency attenuation model. The generated ground motion is then amplitude modulated by the peak ground acceleration (PGA) attenuation model. The frequency attenuation model and the PGA attenuation model are obtained by fitting seismic data from the 1999 Chi-Chi earthquake. Finally, the propagation of the ground motion is considered. The proposed method can provide a time history of seismic ground motions for any point in a region. To investigate the rationality and applicability of the proposed method, the characteristics of the generated seismic ground motions are compared with those of true seismic ground motions. The average error of the PGA of the generated ground motions and the true ground motions is 0.072g; thus, the proposed method is suitable for use in small-scale regions. Furthermore, 4-story and 15-story structures are used for structural response analysis. The story drift ratio error between the generated ground and true ground motions is found to be within an acceptable error range. The proposed method provides a new way to generate ground motions for regional seismic investigation. [ABSTRACT FROM AUTHOR]

Topside total electron content (TEC) data measured by COSMIC/FORMAT-3 during 2008 and 2016 were used to analyze and model the global plasmaspheric electron content (PEC) above 800 km with the help of the empirical orthogonal function (EOF) analysis method, and the potential role of the proposed PEC model in helping Global Navigation Satellite System (GNSS) users derive accurate slant TEC (STEC) from existing high-precision vertical TEC (VTEC) products was validated. A uniform gridded PEC dataset was first obtained using the spherical harmonic regression method, and then, it was decomposed into EOF basis modes. The first four major EOF modes contributed more than 99% of the total variance. They captured the pronounced latitudinal gradient, longitudinal differences, hemispherical differences, diurnal and seasonal variations, and the solar activity dependency of global PEC. A second-layer EOF decomposition was conducted for the spatial pattern and amplitude coefficients of the first-layer EOF modes, and an empirical PEC model was constructed by fitting the second-layer basis functions related to latitude, longitude, local time, season, and solar flux. The PEC model was designed to be driven by whether solar proxy or parameters derived from the Klobuchar model meet the real-time requirements. The validation of the results demonstrated that the proposed PEC model could accurately simulate the major spatiotemporal patterns of global PEC, with a root-mean-square (RMS) error of 1.53 and 2.24 TECU, improvements of 40.70% and 51.74% compared with NeQuick2 model in 2009 and 2014, respectively. Finally, the proposed PEC model was applied to conduct a vertical-slant TEC conversion experiment with high-precision Global Ionospheric Maps (GIMs) and dual-frequency carrier phase observables of more than 400 globally distributed GNSS sites. The results of the differential STEC (dSTEC) analysis demonstrated the effectiveness of the proposed PEC model in aiding precise vertical-slant TEC conversion. It improved by 18.52% in dSTEC RMS on a global scale and performed better in 90.20% of the testing days compared with the commonly used single-layer mapping function. [ABSTRACT FROM AUTHOR]

The cellular carbon content and chlorophyll a (Chl a) concentration are two of the most significant indices for assessing phytoplankton biomass. Recording and monitoring these biomasses are essential tasks in phytoplankton research, and the carbon-to-chlorophyll a (C:Chl a) ratio serves as a crucial conversion tool between them. Although the C:Chl a ratio varies widely, it is influenced by external environmental factors, making modeling studies of C:Chl a particularly important. This paper provides an overview of the historical development of the C:Chl a model, beginning with early empirical models and progressing to the development of mechanistic models. This discussion is followed by an examination of existing gaps and future challenges in current C:Chl a modeling, particularly the potential underestimation of carbon biomass in existing C:Chl a models for dinoflagellates exhibiting multiple growth strategies. Finally, it is suggested that future C:Chl a models should strive to achieve a balance between reliability and applicability.

To properly formulate diets, the ability to accurately estimate feed intake is critical as the amount of feed consumed will influence the amount of nutrients delivered to the animal. Inaccurate intake estimates may lead to under- or over-feeding of nutrients to the animal. Individual differences in equine forage intake are well-known, but predictive equations based on animal and nutritional factors are not comprehensive. The objective of the present study was to consolidate the current body of knowledge in the published literature on voluntary forage DM intake (VFDMI) in equines and conduct a meta-analysis to identify driving factors, sources of heterogeneity, and develop predictive equations. Therefore, a systematic literature search was applied and identified 61 publications which met the inclusion criteria. From each study, the outcomes of interest (e.g., forage intake), diet composition (e.g., forage information, nutrient composition), and animal factors (e.g., sex, age, breed, BW, exercise level) were extracted. Forage intake was analyzed as two different outcome variables: (1) VFDMI in kg/d and (2) VFDMI in g/kg BW. Linear mixed model analysis treating study as a random effect was applied, using a backward−stepping approach to identifying potential driving variables for VFDMI (both units) where all terms have P

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue. In this context, we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e. 0°, 30°, 45°, 60°, and 90°), under multiple levels of direct shearing for the first time. The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior. Under a low shear stress, the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations, and the increase depends on the bedding orientation and creep time. Under high shear stress conditions, the creep compliance of shale is minimal when the bedding orientation is 0°, and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°, 45°, 60°, and 90°. The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation. A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed. The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate. The applicability of the five classical empirical creep models is quantitatively evaluated. It shows that the logarithmic model can well explain the experimental creep strain and creep rate, and it can accurately predict long-term shear creep deformation. Based on an improved logarithmic model, the variations in creep parameters with shear stress and bedding orientations are discussed. With abovementioned findings, a mathematical method for constructing an anisotropic shear creep model of shale is proposed, which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

Since the beginning of the 20th century, many studies have focused on the possibility of considering the sky as a body characterized by an apparent temperature, and several correlations to quantify the apparent sky temperature have been proposed. However, the different models were obtained for specific meteorological conditions and through measurements at specific sites. The available models do not cover all locations in the world, although the evaluation of the sky temperature is fundamental for estimating the net radiative heat transfer between surfaces and the sky. Here, experimental data logged from a regional micrometeorological network (in Italy, within the Lazio region) were processed and used to identify an empirical model for the estimation of the sky temperature in the Mediterranean area. Data relating to atmospheric infrared radiation were used to compute the sky temperature, aiming at identifying a direct correlation with the ambient temperature. Climatic data acquired during 2022 were processed. The proposed correlations were compared with other models available in the literature, including the standard ISO 13790. This study proposes an annual-based direct correlation in its initial phase, demonstrating a superior fit to the measured data compared to well-known direct empirical models from the literature. Subsequently, quarterly-based correlations are introduced further in a secondary phase of the work to improve the model's adaptation to experimental observations. The results reveal that quarterly-based correlations improve goodness-of-fit indexes compared to annual-based and well-known direct empirical correlations. Finally, a detached building was modeled via a dynamic code to highlight the influence of different correlations on annual energy needs. [ABSTRACT FROM AUTHOR]

Commercial scale tidal stream turbines (TST) are expected to be deployed in shallow water where the depth varies from 1.5 to 3 turbine diameters. In this study, numerical simulation is conducted at realistic hydrodynamic conditions of potential tidal sites using the stationary actuator disc method at ambient turbulence varying from 5% to 20%, a range of rotor realistic rotor thrust coefficient from 0.64 to 0.98 and a rotor-diameter-to-depth ratio of 20% to 60%. The result shows that the TST wake is affected by the rotor-diameter-to-depth ratio, ambient turbulence, and thrust coefficient. The new empirical model is in accordance with the numerical simulation of a full-scale turbine and is validated with the TST experiment at different rotor-diameter-to-depth ratios with reasonable results in the far wake. This low computational model can benefit the investigation of tidal turbine parks at different configurations where the far wake is pertinent. [ABSTRACT FROM AUTHOR]

With the increasing use of oceans for engineering purposes, such as the installation of suction anchors and pipelines, the stability of seabed structures has become a pivotal concern and is intricately linked to the characteristics of seabed soils. This study focuses specifically on deep-sea soft clay, a predominant seabed soil type distinguished by its high water content, thixotropy, and low permeability. These clays are vulnerable to destabilization and damage when disturbed, thereby posing threats to seabed installations. While the existing literature extensively examines the cyclic behavior of clay, considering factors such as the pore pressure response and strain and deformation characteristics, there is a notable gap in research addressing the behavior of deep-sea soft clay under comprehensive stress levels and prolonged cyclic loading. In this study, cyclic shear tests of the natural marine clay of the South China Sea were conducted, and the cyclic stress ratio (CSR), overpressure consolidation ratio (OCR), consolidation ratio (Kc), and loading frequency were varied. It was found that the CSR, OCR, and Kc significantly impact the cumulative dynamic strain in deep-sea soft clay during undrained cyclic dynamic tests. Higher CSR values lead to increased dynamic strain and structural failure risk. Subsequently, a dynamic strain-dynamic pore pressure development model was proposed. This model effectively captures the cumulative plastic deformation and dynamic pore pressure development, showing correlations with the CSR, OCR, and Kc, thus providing insights into the deformation and pore pressure trends in deep-sea clay under high cyclic dynamic loading conditions. This research not only furnishes essential background information but also addresses a critical gap in understanding the behavior of deep-sea soft clay under cyclic loading, thereby enhancing the safety and stability of seabed structures. [ABSTRACT FROM AUTHOR]

The models compared in this study were applied to a catchment located in the western part of Slovakia in order to analyze and compare the amount of soil loss in different periods. Using of the empirical model universal soil loss equation, the average annual soil loss in the pilot basin was modeled for 2015–2020. The universal soil loss equation includes a topographic factor modeled using the USLE2D external model. In order to compare the empirical model and the physically-based model a continual sequence of individual rainfall events was created and used as an input to the physically-based EROSION-3D model. The results reflect different behaviors of erosion processes in a catchment together with a comparison between empirical and physically-based approaches. [ABSTRACT FROM AUTHOR]

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope's yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed. [ABSTRACT FROM AUTHOR]

Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers. [ABSTRACT FROM AUTHOR]

This paper implements and compares the performance of three controllers based on Sliding Mode Control (SMC) applied to the motion control of mobile robots. The controllers include a standard SMC controller and two variations, namely Dynamic Sliding Mode Control (D-SMC) and Dual Sliding Mode Control (DM-SMC). The three controllers differ from conventional SMC approaches since they are designed based on a generic reduced-order empirical model that can represent any system that exhibits behavior akin to a First-Order Plus Delay Time (FOPDT) system. In this study, the comparison of controllers focuses on the Trajectory Tracking Problem (TTP) of a Nonholonomic Mobile Robot (NMR). Performance indices are employed to quantify the controllers' results across three trajectory types. Simulation results evidence that if a smooth curvature trajectory is tracked, the best performance (in terms of minimizing trajectory tracking error and controller effort) can be obtained by using a DM-SMC. On the other hand, if there are discontinuities in the curvature, the results suggest that a better alternative is the D-SMC since it compensates for abrupt changes as in the case of tracking a square trajectory. Overall, the findings from this work demonstrated that the use of simplified empirical models to design SMC-based motion controllers can be successfully applied to solve the TTP of NMR.

Soil conservation requires a suitable model and framework to assess soil erosion and sediment yield based on land use scenarios. Therefore, the present study was conducted with the aim of investigating the sensitivity of the G2 model to the soil erodibility factor in the Kasilian representative watershed in the period of 2001-2021. Points such as the use of global soilgrid database and comparison with soil sampling data, removal of soil erodibility in rocky areas, and removal of outlier data created by systematic error were included in the current research. The results showed that qualitatively, the rate of soil erosion estimated based on each of the mentioned points was completely in the minimum category. The initial results of the soil erosion rate obtained from the global soilgrid database and the IDW method were 4.31 and 4.33 t ha-1 y-1, respectively, which were not significantly different based on the independent t test. But the effect of other changes, including removal of soil erodibility in rocky areas and removal of outlier data on the change of soil erosion estimates using one-way ANOVA and Duncan's tests were significant (P

In this work, we present the design and modeling of a new type of choke horn antenna. It incorporates a rectangular waveguide and a rectangular choke acting as a parasitic element. The four-sided geometry of the antenna is applicable to systems that utilize rectangular waveguides. Also, it can overcome the need for rectangular-to-circular transition of transmission line or mode conversion. The main objective of this paper is to develop a model that calculates the far field radiation characteristics of the proposed antenna (analytical part) and to incorporate a finite element method (FEM) solver that adds to the theoretical solution (empirical part), which finally leads to obtaining a hybrid model. The omnidirectional radiation property of the choke is demonstrated, which gives an insight into the influence of this parasitic element on the total radiated power. The same observations made on the rectangular choke can be translated to the circular choke as well. At an operating frequency of 2.45 GHz, the design is numerically and experimentally validated. Also, the demonstrated hybrid model can leverage the integration of supervised machine learning (ML) models by exporting radiation variables such as gain and half-power beamwidth (HPBW) and performing predictions based on the training data from the model. Therefore, we incorporate gradient boosting and neural network ML algorithms, which are tailored to the desired radiation pattern parameters.

Electron-impact ionization (EII) processes are essential for modelling high-temperature plasma in quite different research areas, from astrophysics to material science to plasma and fusion research and in several places elsewhere. In most, if not all, of these fields, partial and total EII cross sections are required, and often for a good range of electron energies, in order to determine, for instance, the level population of ions and spectral line intensities in plasma under both local and non-local thermodynamic equilibrium conditions. To obey these needs, various kinds of semi-empirical EII cross sections have been applied in practice, often simply because of the large computational demands in dealing explicitly with two free electrons within the continuum. Here, we expand Jac, the Jena Atomic Calculator, to provide such empirical EII cross sections for (most) atoms and ions across the periodic table. Five empirical models from the recent literature have been implemented to support a simple and rapid access to the partial EII cross sections for electrons from a (partly filled) shell (nℓ)q as well as the total ionization cross sections. We here restrict ourselves to the direct part of the EII cross section, whereas the impact excitation of electrons with subsequent autoionization and the resonant electron capture with double autoionization have been left aside in this first implementation. Rapid access to the (direct) EII cross sections will help already to better understand the role of electron-impact processes in the diagnostics of fusion plasma or the interpretation of astrophysical spectra.

Artificial neural network models were developed to estimate forming limit diagrams from tensile test results based on our own experiments and data from the literature for steel and aluminium sheet metals. Experimental data were obtained from tensile tests and Nakazima tests. The input parameters used in the models were yield strength, ultimate tensile strength, uniform elongation, elongation at fracture, anisotropy coefficient and hardening exponent or combinations of these. The forming limit curves were defined by the measured minor and major strains using seven standard test specimens. After training the artificial neural network, the difference between measured and predicted results was evaluated by linear regression parameters and by the absolute errors. For steel sheet data taken from the literature, the estimated outputs of ANN models were compared with the results of empirical formulae developed by different authors. It was found that there was a high correlation coefficient between predicted and measured values for models using neural networks, which gave better approximations than other linear and non-linear models.

Underground space creation and energy extraction, which induce unloading on rock fractures, commonly occur in various rock engineering projects, and rock engineering projects are subjected to high temperatures with increasing depth. Fluid flow behavior of rock fractures is a critical issue in many subsurface rock engineering projects. Previous studies have extensively considered permeability evolution in rock fractures under loading phase, whereas changes in fracture permeability under unloading phase have not been fully understood. To examine the unloading-induced changes in fracture permeability under different temperatures, we performed water flow-through tests on fractured rock samples subjected to decreasing confining pressures and different temperatures. The experimental results show that the permeability of fracture increases with unloading of confining pressure but decreases with loading-unloading cycles. Temperature may affect fracture permeability when it is higher than a certain threshold. An empirical model of fracture hydraulic aperture including two material parameters of initial normal stiffness and maximum normal closure can well describe the permeability changes in rough rock fracture subjected to loading-unloading cycles and heating. A coupled thermo-mechanical model considering asperity damage is finally used to understand the influences of stress paths and temperatures on fracture permeability.

We proposed, for the first time, that the angle between the horizontal projection of the magnetic field line and the sunrise line (AMFS) is a crucial factor that controls predawn heating. Through quantitative analysis, we determined that both the AMFS and the length of the magnetic field line (LMF) significantly affect predawn heating. We found that an increased AMFS intensifies predawn heating, while an increased LMF counteracts it. Our research indicates that the optimal conditions for peak predawn heating occur at an AMFS of approximately 30 degrees and an LMF of about 4000 km, where the effect surpasses 400 K. Additionally, we observed that the effects of both the AMFS and the LMF on predawn heating exhibit a saturation effect. This study provides a clearer understanding of the factors driving predawn ionospheric heating, with implications for topside ionosphere research.

To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003–2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003–2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003–2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEE in 2003–2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO2 were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO2 storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming.

Today’s growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models using machine learning techniques and compares these models with two existing empirical models, including a commercial one. To build the models, the databases produced by EVERLASTING and Bills et al. were utilized. The aim is to create universally applicable models that can address any battery-aging scenario. In this study, three types of models were developed: a vanilla neural network, a neural network inspired by extreme learning machines, and an encoder coupled with a neural network. The inputs for these models are derived from established knowledge in battery science, allowing the models to capture aging effects across different use cases. The models were trained on cells subjected to specific aging conditions and they were tested on other cells from the same database that experienced different aging conditions. The results obtained during the test for the vanilla neural network showed an RMSE of 1.3% on the Bills et al. test data and an RMSE of 2.7% on the EVERLASTING data, demonstrating similar or superior performance compared to the empirical models and proving the ability of the models to capture battery aging.

In recent years, an increase in the frequency of low-sunlight conditions due to climate change has resulted in a decline in the yield and quality of crops for greenhouse farmers, leading to significant challenges in maintaining optimal plant growth. The crop growth model can be used to predict changes in cucumber yield in response to variations in sunlight, which can help efficiently address sunlight shortages. The objective of this study was to improve and validate the model for predicting cucumber yield under different light environment conditions, including shading and supplemental lighting. The model comprises three steps: LAI prediction, daily assimilate yield prediction, and fruit yield prediction, each of which involves modifying the coefficients applied to suit the cucumber cultivar and environment condition. The improved model demonstrated a high degree of accuracy in predicting cucumber yields in the control and low-sunlight treatments (10, 20, and 30% shading), with a coefficient of determination (R2) > 0.98. When supplemental lighting was incorporated into the control and shading treatments, the accuracy of the improved model in predicting cucumber yield was also high, with a coefficient of determination (R2) > 0.99. The model also accurately predicted the decrease in cucumber fruit yield under low-sunlight conditions (shading treatments) and the increase in yield due to supplemental lighting. The findings of this study indicate that the improved cucumber yield prediction model can be applied to assess the efficacy of yield reduction in low-sunlight conditions and the potential for yield enhancement through supplemental lighting.

This study presents a comprehensive model for ultrasonic energy transfer (UET) using a 33-mode piezoelectric transducer to advance wireless sensor powering in challenging environments. One of the advantages of UET is that it is not stoppable by electromagnetic shielding and can penetrate metal. Existing models focus on feasibility and numerical analysis but lack an effective link between input and output power in different media applications. The proposed model fills this gap by incorporating key factors of link loss, including resonant frequency, impedance matching, acoustic coupling, and boundary conditions, to predict energy transfer efficiency more accurately. The model is validated through numerical simulations and experimental tests in air, metal, and underwater environments. An error analysis has shown that the maximum error between theoretical and experimental responses is 3.11% (air), 27.37% (water), and 1.76% (aluminum). This research provides valuable insights into UET dynamics and offers practical guidelines for developing efficient wireless powering solutions for sensors in difficult-to-access or electromagnetically shielded conditions.

Soil conservation requires a suitable model and framework to assess soil erosion and sediment yield based on land use scenarios. Therefore, the present study was conducted with the aim of investigating the sensitivity of the G2 model to the soil erodibility factor in the Kasilian representative watershed in the period of 2001-2021. Points such as the use of global soilgrid database and comparison with soil sampling data, removal of soil erodibility in rocky areas, and removal of outlier data created by systematic error were included in the current research. The results showed that qualitatively, the rate of soil erosion estimated based on each of the mentioned points was completely in the minimum category. The initial results of the soil erosion rate obtained from the global soilgrid database and the IDW method were 4.31 and 4.33 t ha-1 y-1, respectively, which were not significantly different based on the independent t test. But the effect of other changes, including removal of soil erodibility in rocky areas and removal of outlier data on the change of soil erosion estimates using one-way ANOVA and Duncan's tests were significant (P<0.01). The estimated average rate of soil erosion in the watershed after following the mentioned points was reduced by more than 65% compared to the initial results and became closer to the data of erosion plot. [ABSTRACT FROM AUTHOR]

This research explores the ability of 60mm thick reinforced concrete (RC) plates to resist impacts from ogive-nosed hard steel projectiles. The projectiles used in the present study have a diameter of 19mm, a length of 200mm, and a mass of 0.4kg impacted on the RC plates with incident velocities ranging from 92m/s to 161m/s. Numerical simulations were conducted using ABAQUS/Explicit finite element software to validate the experimental results. The Holmquist–Johnson–Cook (HJC) material model was employed to simulate the constitutive behavior of concrete, while the Johnson–Cook (JC) material model was used to simulate the material response of reinforcing steel bars. The residual velocities obtained from the simulations closely matched the actual experimental results, showing a polynomial correlation with the incidence velocities of the projectiles. Moreover, the experimentally and numerically determined ballistic limit for the 60mm thick RC plate was found to be 108m/s and 109.5m/s, respectively. In contrast, the ballistic limit calculated using empirical mathematical expressions was 107.4m/s. This alignment between predicted, calculated, and actual ballistic limits underscores the reliability and accuracy of both numerical and empirical approaches. [ABSTRACT FROM AUTHOR]

Landslides represent geological hazards wherein a part of a slope loses its static equilibrium and initiates movement. Once this movement begins, it becomes crucial to evaluate the land-slide runout distance (LRD). Currently, there exist numerous tools for estimating LRD, among which geometrical approximations stand as one of the most popular. These empirical models are particularly useful for wide-scale studies, aiding in the scale-down of the problem by identifying the critical areas. This study examines the application of geometrical approximations in the Colombian north–east Andean region. Within this area, a sampling of 49 was conducted using photogrammetric techniques, enabling the morphometrical characterization of each study unit. The results showcase the relationship between geometrical characteristics and LRD in the studied area, considering both land use and geomorphological settings. By exploiting these relationships, the study compares the estimation of LRD using various empirical models, many of which are already employed by practitioners within the studied region. For instance, the relationships in literature display a relative error in the estimation ranging around −50% and 100%. Furthermore, this research proposes new relationships for estimating LRD, enhancing the error estimations in a range between 0% and 50%, highlighting both the advantages and limitations of such empirical estimations. Consequently, it contributes new data to enrich the field of LRD studies. [ABSTRACT FROM AUTHOR]

In the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers' practices. Mechanistic models and machine learning are two modelling options to consider from this perspective. In this study, multiple regression (MR) and random forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers' wheat fields. Additionally, MR and RF models were calibrated both with or without remotely sensed leaf area index (LAI), while considering all farmers' fields, or specifically to agroecological zoning in Morocco. The same farmers' fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (normalized root mean square errors (NRMSEs) below 35 %), but were always outperformed by the APSIM model. Both RF and MR selected remotely sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely sensed LAI in the calibration process reduced NRMSE by 4.5 % and 1.8 % on average for MR and RF models, respectively. Calibration of region-specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short-term tactical adjustments to farmers' practices, while machine learning models are easier to use in the perspective of mid-term regional prediction. [ABSTRACT FROM AUTHOR]

Artificial neural network models were developed to estimate forming limit diagrams from tensile test results based on our own experiments and data from the literature for steel and aluminium sheet metals. Experimental data were obtained from tensile tests and Nakazima tests. The input parameters used in the models were yield strength, ultimate tensile strength, uniform elongation, elongation at fracture, anisotropy coefficient and hardening exponent or combinations of these. The forming limit curves were defined by the measured minor and major strains using seven standard test specimens. After training the artificial neural network, the difference between measured and predicted results was evaluated by linear regression parameters and by the absolute errors. For steel sheet data taken from the literature, the estimated outputs of ANN models were compared with the results of empirical formulae developed by different authors. It was found that there was a high correlation coefficient between predicted and measured values for models using neural networks, which gave better approximations than other linear and non-linear models. [ABSTRACT FROM AUTHOR]

Rain attenuation based on the drop size distribution (DSD) with different rainfall rates (R) at low THz frequencies is investigated in this paper. The rain attenuation is calculated using the DSD measured for one year and the extinction cross-section (ECS) by the Mie scattering theory. Moreover, the obtained specific rain attenuation is verified by the empirical model using the measurement system consisting of a transmitter, a receiver, and weather measurement units. We measured the received power against the uniform transmitted power at 240, 270, and 300 GHz on the rooftop of the National Radio Research Agency (RRA) in Korea during the same period as the DSD measurement period. After curve fitting by regression analysis, we compared both rain attenuations obtained in two methods with the recommendation International Telecommunication Union Radiocommunication Sector (ITU-R) P.838-3. The root mean square errors (RMSEs) of the DSD model are 2.8977, 2.8646, and 2.8331 at 240, 270, and 300 GHz, respectively. The calculated result using the Mie scattering and the measured DSD methods shows the best fit to the data of the ITU-R recommendation for a rainfall rate of up to 5 mm/h. On the other hand, the empirical results using the T/Rx antenna system are slightly higher compared to the data of the ITU-R recommendation. As the rainfall rate increases, the difference between our results and ITU-R recommendation increases. This study will be useful for predicting rain attenuation for terrestrial wireless links operating at low THz frequencies. [ABSTRACT FROM AUTHOR]

This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model's performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models. [ABSTRACT FROM AUTHOR]

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, crop productivity, and irrigation systems. As the standard ET0 estimation method, the Penman-Monteith equation has been widely recommended worldwide. However, its application is still restricted to comprehensive meteorological data deficiency, making the exploration of alternative simpler models for acceptable ET0 estimation highly meaningful. Concerning the aforementioned requirement, this study developed the novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, and regression component, to estimate ET0 based on radiation-based (Rn-based), humidity-based (RH-based), and temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal and external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), and empirical models, the result indicated that MA-CNN-BiLSTM achieved superior precision, with values of Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 mm d−1 for internal strategy and 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 mm d−1, 0.294–0.699 mm d−1 for external strategy. Specifically, Rn-based MA-CNN-BiLSTM excelled in the temperate continental zone (TCZ) and mountain plateau zone (MPZ), while RH-based MA-CNN-BiLSTM yielded best precision in others. Furthermore, the internal strategy was superior to external strategy by 2.74–106.04% for R2, 1.11–120.49% for NSE, 1.41–40.27% for RRMSE, 1.68–45.53% for RMSE, and 1.21–38.87% for MAE, respectively. In summary, the main contribution of the present study is the proposal of a novel LSTM-type ET0 model (MA-CNN-BiLSTM) to cope with various data-missing scenarios throughout China, which can provide effective support for decision-making in regional agriculture water management.

Food waste has become a source of concern as it is generated abundantly worldwide and needs to be valorised into new products. In this study, cucumber, tomato, and carrot wastes were investigated as pyrolysis feedstocks as a single component (cucumber), a binary component mixture (cucumber and tomato), and a ternary component blend (cucumber, tomato, and carrot). Fourteen scenarios were simulated and evaluated based on varying the feedstock blend (single, binary, and tertiary), temperature (300 and 500 °C), and feedstock moisture content (5, 20, and 40%). Using an established empirical model, the effect of these parameters on product yields, techno-economic implications, energy requirements, and life cycle analysis (LCA) outcomes were investigated. The best performers of each scenario were determined, and their strengths and weaknesses were identified and compared with other scenarios. In terms of product yields, all three systems (single, binary, and tertiary) followed a similar pattern: bio-oil yields increased as temperature and feedstock moisture content increased, while biochar yields decreased as temperature and feedstock moisture content increased. The production of syngas, on the other hand, was only observed at elevated temperatures. The total energy requirement exhibited an increase with increasing temperature and feedstock moisture content. The economic evaluation revealed that the return on investment (ROI) value for the single component at 5% moisture content at 300 °C is 29%, with a payback period (PB) of only 3.4 years, which is potentially very appealing. The water footprint increased with increasing pyrolysis temperature but decreased with increasing moisture content in all scenarios. The land footprint is observed to remain constant despite changes in process conditions. The study's findings contribute to the pyrolysis process's scalability, technological advancement, and commercialisation.

Squat-reinforced concrete (RC) shear walls with an aspect ratio of less than two are considered effective structural members, where shear is the dominant failure mechanism. Squat shear walls are widely used in nuclear power plants and building construction and feature optimal cost and outstanding performance, due to their lateral strength and high rigidity to resist lateral loads. However, since the accurate evaluation of the shear strength of squat shear walls must meet the design specifications, its calculation may be very complex, challenging, and inaccurate using experimental and theoretical equations due to many influential and overlapping design factors, so it takes more time and higher cost to determine it. This study uses machine learning (ML) methods to build a shear strength prediction efficient model for squat RC walls to address these issues. First, a huge dataset of 1424 RC squat wall test specimens gathered from the literature is utilized for developing an ML model, by employing XGBoost, to predict the shear strength. Results verified that the XGBoost model had the best accuracy and least error while assessing the squat walls' strength at shear. Moreover, an XGBoost optimum algorithm fared better than the empirical models based on mechanics, with a 99.2% accuracy. Finally, to prove that the model can identify the most important variables that significantly affect the shear strength, parameter and sensitivity analyses were performed and the results showed that the wall length is the factor that contributes most to the ultimate shear strength of the squat shear wall as a percentage (7.62%), followed by the yield strength. For the web as a ratio. (6.88%), concrete strength (6.75%), reinforcement ratio information (6.56%), and geometric properties (6.01%), while the axial load represents the smallest contribution, reaching (4.16%).

The development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and densely populated regions in China, has posed an increasing threat to the health of the water environment in adjacent coastal oceans. However, the spatiotemporal variations of water clarity in the coastal oceans of the GBA (COGBA) have not been well-documented. Therefore, this study aims to develop a remote sensing retrieval algorithm for assessing water clarity, represented by Secchi disk depth (ZSD), based on Landsat-8 OLI imagery for the COGBA using a machine learning method. Cross-validation demonstrated that the algorithm performed exceptionally well, with an R2 value greater than 0.8. By applying the algorithm to 263 Landsat-8 OLI images, we obtained a time series of ZSD for the period of 2013–2021 over the COGBA. Results indicate that the Pearl River Estuary (PRE) exhibited the highest turbidity, followed by the Daya Bay and the Mirs Bay. Generally, ZSD increased from the northwest to the southeast of the COGBA. Seasonal, interannual, and long-term variations in ZSD were also observed with long-term increases in the PRE and the Daya Bay. The interannual variations in ZSD during fall were primarily regulated by different factors in each region. In the PRE, the negative effects of sediment discharge and wind speed played a significant role. In Mirs Bay, wind speed and sediment discharge had a negative impact on ZSD. In Daya Bay, precipitation and wind speed were the key factors influencing ZSD. The development and findings of our algorithm contribute to the protection and management of the water environment in the COGBA, facilitating effective governance measures.

Abstract The main objective of this study is to develop an empirical model for predicting the chloride penetration into concrete exposed to the sea water along shorelines. Concrete mixed with 0–50% of palm oil fuel ash, using W/B ratio of 0.40–0.50 were examined. Available database of concrete samples under the sea water submersion at 1, 3, 5, 7 and 10 years was collected. The study found that the developed empirical model could predict total chloride content in palm oil fuel ash concrete at any position below the concrete surface, which had been submerged under sea water for 1 year and over. The W/B ratio is also limited between 0.40 and 0.50. Application of this model is valid in the situation where penetration of chloride is in one direction. It was also shown that the margin of errors in this study is within ± 35% range when compared to the results presented by other researchers.

Soil permeability is the ability of the soil to pass water or air. Soil permeability is affected by texture, structure, and soil porosity. This study aims to develop a mathematical model to predict the value of soil permeability as a function of the percentage of the constituent fraction of the soil and soil permeability as a function of porosity. The study used soil taken from 7 different locations, with 6 samples for each location, 4 samples for model building and 2 samples for model validation. Parameters observed consisted of the percentage of sand (x1), the percentage of silt (x2), the percentage of clay, (x3), soil porosity (x4) and soil permeability (y). From the analysis, the empirical model obtained is soil permeability as a function of the percentage of constituent fractions of the soil which is expressed by the equation y1=36.796-16.022x2-23.938x3 and soil permeability as a function of porosity is expressed by the equation y2=12+0.65(x4-0.06)-2.92 . The permeability equation as a function of soil constituent fraction (y1) can predict soil permeability with a value of R2 = 0.925 and an RRMSE value of 5.461%, better than the permeability equation as a function of porosity.

Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021–2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R2 = 0.60 and RMSE = 2.80 cm2/cm2 for LAI and R2 = 0.45 and RMSE = 32.71 μg/cm2 for LCC, whereas the separate inversion models result in R2 = 0.59 and RMSE = 2.82 cm2/cm2 for LAI and R2 = 0.35 and RMSE = 35.86 μg/cm2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.

The Earth's cusp is a critical "window" for the solar wind plasma to enterthe low-altitude magnetosphere and ionosphere. However, the overall configuration of cusp has not been established. Based on the simulation of two successive substorms on 8 March 2008, we propose a 2D model of low-altitude (1.1 RE) cusp modulated by the interplanetary magnetic field (IMF) BY and BZ. This model is constructed from curve fitting of low-altitude cusp with an elliptic function controlled by the cusp center and width, which is dependent on IMF BY and BZ. The plasma thermal pressure PT derived from the simulation data is used to extract the cusp center on the sphere surface with a radius of 6 RE. The cusp center is defined as where PT reaches the maximum, and the cusp boundary is identified as where PT decreases to 68% of that at the cusp center. After the high-altitude cusp has been determined, the low-altitude (0.1 RE altitude in the ionosphere) cusp can be obtained from mapping the high-altitude cusp along the magnetic field lines. The coordinate system of the low-altitude cusp used throughout this paper is the magnetic coordinate. The low-altitude cusp extracted from simulations is fitted with the elliptic function controlled by cusp location and width. The cusp geomagnetic latitude (MLAT) increases gradually with northward IMF BZ but decreases significantly with southward IMF BZ. The local magnetic time (MLT) is nearly 12 when BY = 0, corresponding to the observation results. When IMF BY is duskward (dawnward), the cusp center will locate at the post-noon (pre-noon) sector in the northern hemisphere. The MLAT width decreases as IMF BZ swings from north to south, and the MLT width is the opposite. This model is validated by comparing with observations from DMSP satellites during this concerning time interval. Based on this 2D model of low-altitude cusp, the 3D cusp could be obtained further, which would help to space weather prediction.

Information on the diameter increment of tree species is important for developing a sustainable forest harvesting plan and for reforestation. Fitting a forest growth model for diameter increment can be utilized to get the information. The objective of this study was to develop a predictive model for the annual diameter increment of a multispecies stand in the Sinharaja Rain Forest in Sri Lanka. To fulfill this objective, four linear mixedeffect models were fitted to predict the annual diameter at breast height (dbh) increment of trees by using dbh, the natural logarithm of dbh, and species as a random effect. The model that contained random coefficients for the intercept term, dbh and log (dbh) were selected as the best-fitted model based on the Akaike Information Criterion (AIC) value and the likelihood ratio test. Then, the bestfitted model was expanded by adding other subsequent variables that describe the effects of competition from surrounding trees, and a size structure component, which is the maximum dbh. The final model comprised of this information dbh, log(dbh), stocking density, maximum dbh, and competition from trees smaller than the subject tree. Using the best-fitted final model, the annual dbh increment of the 10 selected abundant species was calculated. It was noted that all the selected species have growth rates that increase rapidly at the initial stage, and then reach its unique maximum growth rate. These increment patterns reflected that different species followed different annual dbh increment patterns. Therefore, this final model can be consolidated into an effective empirical model to project the future growth of a tropical rainforest.