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The stability, reliability, and lifespan of proton exchange membrane fuel cells (PEMFCs) are closely related to power dynamic response. Therefore, it is necessary to study in depth the relationship between the power operation state and typical characteristics. In this paper, the fuel cell system (FCS) power dynamic response based on constant power change amplitude and various power change amplitude is analyzed. Then, three typical power characteristics of the FCS, namely, low single-cell voltage, drastic voltage uniformity change, voltage undershoot and voltage overshoot, are investigated. Furthermore, to solve the high-dimensional datasets problem, a quick evaluation method based on data dimensionality reduction (DDR) is proposed. The innovation of this method is based on principal component analysis to extract typical characteristic relationships of power operation levels. The results demonstrate that the extracted new one-dimensional (1-D) eigenvectors index exhibits a positive correlation with power levels. Meanwhile, the power operation state empirical model is established through data regression. The Pearson coefficients of the empirical model are all greater than 0.9981. Thus, the new index and proposed method offer novel insights for improving the power adaptive control of fuel cells and quickly assessing their health state. • The effects of different loading amplitudes on power output are investigated. • Typical characteristics of power operation state are analyzed. • A quick evaluation method for power operation state is proposed. [ABSTRACT FROM AUTHOR]

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]

The mechanics of methane hydrate‐bearing sediments (MHBS) have been broadly investigated over recent years in the context of methane‐gas production or climate‐change effects. Their mechanical investigation has mainly been carried out using models constructed from experimental data obtained for laboratory‐formed MHBS. Along with the dominant effects of hydrate saturation and morphology within the host soil pores, this study recognizes the effective pressure at which the hydrate is formed as a key factor in the MHBS mechanics. A state‐of‐the‐art experimental study has been conducted in order to isolate the effect of the hydrate formation pressure, for use as a model parameter. Two generalized mechanical prediction models that incorporate the effect of the hydrate formation pressure are developed in this work: (a) an analytical shear strength prediction, and (b) an empiric graphical model for predicting volumetric changes along a given stress path. The models are related to a novel data representation which enables the analysis of a few individual test outcomes as a whole, through a volume‐change mapping that describes the complex influence of the volumetric effect of hydrate in MHBS, under combined hydrostatic and deviatoric loading scenarios. In this study, we delve into a specific configuration of hydrate morphology, hydrate saturation, and host soil type, enabling a distinctive fundamental geotechnical investigation and the development of a conceptual modeling approach. The paper describes the approaches by which the MHBS properties can be extracted for other MHBS samples (than those examined in this work) having different host soils and hydrate pore‐space morphologies. Plain Language Summary: The mechanics of methane hydrate‐bearing sediments (MHBS) have been extensively studied in recent years, particularly in relation to methane gas production and climate change. This paper focuses on understanding and modeling the mechanical effects of the conditions under which hydrates are formed in the laboratory. Using experimental data, two predictive models are developed: (a) an analytical model for predicting shear strength, and (b) an empirical model for predicting volumetric changes in MHBS. These models are based on a novel analytical approach that integrates individual test results into a cohesive framework. The paper also outlines methods for applying these findings to other MHBS samples, including those with different soil types and hydrate configurations. Key Points: The effective hydrate formation pressure is found to be a critical parameter influencing the methane hydrate‐bearing sediments (MHBS) mechanical behaviorAn analytical shear strength prediction model is presented, accounting for the effective formation pressure of laboratory MHBSA novel Δe − σ_ $\overset{\_}{\sigma }$ map is developed, to be used as a general empirical model for predicting MHBS volumetric changes during loading [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]

The degree of gelatinization (DG) of rice starch during paddy parboiling is associated with its physicochemical, water absorption, and cooking properties. The selection of rice with a proper DG that fulfills consumer preferences and matches the quality requirements of rice-based product industries can be guided by empirical models. This study aims to establish the empirical correlation explaining the impact of varying DG on rice properties. Paddy was open-parboiled to varying DG (5–65%), studied for its physicochemical, water absorption, and cooking properties, and empirically modeled for its changes with varying DG. In addition, the water absorption properties of rice with varying DG and the respective changes in hardness and cooking time were also studied and modeled. The empirical relationships developed from the experimental findings relate to rice's physicochemical, water absorption, and cooking properties for varying DGs. The changes in hardness, apparent amylose content, cooking properties, and whiteness index with increasing DG followed polynomial and power law equations. The moisture gained during the soaking of milled rice with varying DG followed the Weibull and Peleg model. With the increase in soaking time, the rice's hardness and cooking time decreased, following the first-order kinetics. The results suggest that the established empirical models can be used to forecast the physicochemical, soaking, and cooking attributes of rice with varying DGs that significantly impact rice and rice-based product development. [ABSTRACT FROM AUTHOR]

The number of pore volumes to breakthrough is one of the most critical indicators of wormhole characterization in the matrix acidizing process. This number is often needed for experimental work that is time-consuming and costly. In this article, an empirical model was used to evaluate the acidizing procedure in the carbonate cores, including limestone and dolomite. This empirical model measures the number of wormholes formed in the carbonate cores after acid injection by using the conservation of mass law. This approach maintained the transport relative reaction rates of core and acid within the wormhole structure during the wormhole formation procedure. In addition, this empirical model accounted for changes in acid concentration as an injected fluid flow. Also, the changes in carbonate porosity, the effect of the Damköhler number, and injection rate were included in the model. Compared to other experimental and semi-empirical works, the established model gave an excellent assessment of 95.45% for the average accuracy and 0.9933 for the average coefficient of determination. Therefore, an empirical technique to approximate this number in the carbonate formations with high accuracy using physical core and acid properties is herein proposed. [ABSTRACT FROM AUTHOR]

This work reports the development of the first version of the E‐region Prompt Radio Occultation Based Electron Density (E‐PROBED) Model. This is an empirical model of E‐region electron density (Ne) between 90 and 120 km developed using radio occultation measurements from the COSMIC‐1 mission. This first version captures more than 80% of the observed variability in monthly‐mean latitude‐local time‐altitude E‐region Ne profiles but it does not account for longitudinal variability at constant local‐time. This work also reports a validation of E‐PROBED simulations through comparisons with ionosondes and incoherent scatter radar (ISR) E‐region Ne profiles. E‐PROBED generally agrees with these ground‐based observations during day‐time. During night‐time, there is a large disparity between E‐PROBED and ISR values. Finally, this work compares E‐PROBED with E‐region Ne simulated by the International Reference Ionosphere (IRI) and the Specified Dynamics—Whole Atmosphere Community Climate Model with Ionosphere/Thermosphere eXtension (SD‐WACCM‐X). One of the main differences amongst these models is on the simulation of variabilities that cannot be attributed to photoionization. IRI barely simulates any variability not driven by photoionization. Both E‐PROBED and SD‐WACCM‐X simulates variability not driven by photoionization. Another main difference is in the absolute magnitude of night‐time E‐region Ne values. Both IRI and SD‐WACCM‐X are substantially lower than E‐PROBED. This work first concludes that E‐PROBED can conveniently provide E‐region Ne latitude—local time variabilities and structures that COSMIC‐1 observes. This work also concludes that E‐region Ne have significant non‐photoionization driven variabilities. Plain Language Summary: Navigation and communication systems reliant on sending signals into space require accurate predictions from empirical models of ionospheric electron density (Ne) values because the frequency of the appropriate signals to use are a function of Ne. Empirical models currently struggle the most with the ionospheric E‐region (between 90 and 120 km) because most of these models were constructed using sparsely sampled E‐region Ne data from ground‐based observations. Recently, E‐region Ne profiles were finally retrieved from Global Navigation Satellite Systems Radio Occultation (GNSS RO) measurements. This work reports on a new empirical model of monthly‐mean E‐region Ne that uses global observations of E‐region Ne from GNSS RO. The model is called the E‐region Prompt Radio Occultation Based Electron Density (E‐PROBED) Model, and it captures more than 80% of the observed variability in monthly‐mean latitude‐local time‐altitude E‐region Ne profiles. It agrees well with both ground‐based observations and first principles Physics‐based model simulations. These findings suggest that E‐PROBED makes a viable alternative E‐region Ne empirical model for those interested in an E‐region Ne model constrained by satellite observations. Key Points: The first version of E‐region Prompt Radio Occultation Based Electron Density Model (E‐PROBED) is developedE‐PROBED agrees with ground‐based observations during day‐time but not during night‐timeE‐PROBED agrees with SD‐WACCM‐X that E‐region Ne have significant non‐photoionization‐driven variabilities [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]

Concrete structures undergo integral fragmentation under explosion loads. The fragmentation degree and particle-size distribution of concrete blocks under explosion loads must be considered during mining to ensure safety. In this study, the impulse is calculated based on the relationship between overpressure and time, and the impact energy of the explosion wave is obtained based on blast theory. Subsequently, the Mohr-Coulomb shear strength fracture criterion is introduced to determine the ultimate shear stress of the concrete materials, and an empirical model that can effectively calculate the energy consumption of concrete blocks under explosion loads is established. Furthermore, concrete fragments with different particle sizes under explosion scenarios are quantitatively predicted with the principle of energy conservation. Finally, explosion tests with different top standoff distances are conducted, and the concrete fragments after the explosion tests are recovered, sieved, weighed, and counted to obtain experimental data. The effectiveness of the fragment empirical model is verified by comparing the model calculation results with the experimental data. The proposed model can be used as a reference for civil blasting, protective engineering design, and explosion-damage assessment. [ABSTRACT FROM AUTHOR]

We use the full NASA Van Allen Probes mission (2012–2019) to extract the electron plasma density from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) and Electric Field and Waves (EFW) instruments and discuss the evolution of the plasmasphere. We generate new statistics including mean and standard deviations of the plasma density with respect to L‐shell, magnetic local time (MLT), and various geomagnetic indices. These statistics are generated to be applied in radiation belt physics and space weather codes (with fits provided). The mean plasmasphere is circular around Earth with respect to MLT for Kp < 1. The mean 100 cm−3 level line is above L = 5 and mean 10 cm−3 level expands above the Van Allen Probes apogee for Kp < 1. The outer electron belt lies within the plasmasphere for 60% of all times. As activity increases (Kp > 2), a gradual MLT asymmetry forms with higher mean density in the afternoon sector due to plumes expanding outward. Conversely, the mean density decreases on the dawn and night sectors. The mean density is between ∼500 and ∼50 cm−3 between L ∼ 4 and L ∼ 6 during quiet and moderately active times (Kp < 3), representing ∼80% of all times. Statistics in regions of high density below L = 2 are underdefined for intense activity. The highest standard deviation of density represents a factor 2.5 to 3 times the mean above L = 5 and for active times. We find the percent difference between the EFW and EMFISIS densities is bounded by ±20% for quiet and moderate activity (Kp < 5) and goes up to ±100% for extreme activity. Plain Language Summary: The Earth's plasmasphere, discovered in the 1950s, is a region of cold plasma made of ions and electrons of a few electronvolts in energy, originating from upwelling ionized gas from the ionosphere and forming a rotating torus around the Earth. The radial profile of the electron cold plasma density within the plasmasphere decays from 10,000 electrons per cubic centimeter at ∼1,000 km altitude to 10s electrons per cubic centimeter at its outer edge, sometimes exceeding ∼36,000 km in altitude at the equator. The state of the plasmasphere is highly dependent on geomagnetic conditions, with geomagnetic storms and substorms eroding parts of this plasma. Here, we analyze 7 years of NASA Van Allen Probes measurements of the electron plasma density and generate statistics with respect to L‐shell, magnetic local time, and geomagnetic indices. In this way, we show statistical variations of the plasmasphere, a strong magnetic local time dependence, and erosion with increasing geomagnetic activity. New mean electron densities and their standard deviation are generated and fitted with model functions that can be incorporated into space weather codes. This is important because the electron density is a key parameter influencing the strength of wave‐particle interactions that accelerate and scatter energetic particles in the inner magnetosphere. Key Points: Generation of new statistics of mean and standard deviation of the plasma density from Electric Field and Waves and Electric and Magnetic Field Instrument Suite and Integrated Science of RBSP for the whole missionEmpirical density models made of fits are provided versus L‐shell, magnetic local time (MLT), and geomagnetic indices to be used in radiation belt codesAn extensive analysis of the cold plasma statistics is provided with respect to L‐shell, MLT, and geomagnetic activity [ABSTRACT FROM AUTHOR]

The review and comparative analysis of known literature approaches and methods for predicting the packing density of binary powder systems are carried out. A theoretical model is proposed for a binary mixture to provide the spline approximation of calculated and research data with an appropriate accuracy. The model parameters are physical values, whose comparison for different binary systems makes it possible to estimate both the effect of a method of their formation on the packing density and the degree of adequacy to real powder materials for the available theoretical models. [ABSTRACT FROM AUTHOR]

Pulsating heat pipes (PHPs) offer a promising solution for thermal management in diverse industries owing to their simplicity, cost-effectiveness, and impressive cooling capabilities. The angle of inclination of the heat pipe with reference to the horizontal plane significantly influences PHP performance. Unsatisfactory outcomes were observed at 90° and 180° inclination angles. A comprehensive investigation explored the impact of tilt angle and fill ratio on PHP performance indicators, specifically heat transfer rate (HTR), overall heat transfer coefficient (OHTC), and thermal resistance (TR). Tilt angles ranged from 30° to 60°, while fill ratios varied from 40 to 80%. To optimize PHP working parameters that maximize HTR, minimize TR, and enhance OHTC, a modified Taguchi approach was integrated with a robust multi-objective optimization technique. Empirical models for HTR, TR, and OHTC were developed and validated against experimental data. The recommended PHP working parameters are a 60% fill ratio and a 30° tilt angle. For the optimal parameters, HTR estimates ranged from 33.96 to 34.29 W, with the experimental value of 34.124 W falling within this range. TR estimates ranged from 0.0207 to 0.1097 °C W−1, encompassing the experimental value of 0.078 °C W−1. OHTC estimates varied between 835.8 and 958.42 W m−2 K−1, including the experimental value of 835.79 W m−2 K−1. The developed empirical relationships provide valuable insights into PHP performance for any fill ratio and tilt angle within the applicable range. PHPs find diverse applications in refrigeration, aerospace, waste heat recovery, and the utilization of low-grade energy. [ABSTRACT FROM AUTHOR]

A new method for assessing volumes of tephra deposits based on only two thickness data is presented. It is based on the assumptions of elliptical shape for isopachs, a statistical characterization of their eccentricity, and an empirical relationship between their deposit thinning length scale and volumes. The method can be applied if the pair of thickness data are sufficiently distant from the volcano source, with a minimum distance ratio larger than 2. The method was tested against about 40 published volumes, from both equatorial belt and mid-latitude volcanoes. The results are statistically consistent with the published results, demonstrating the usefulness of the method. When applied in forward, the model allowed us to calculate the volume for some important tephra layers in the Mediterranean tephrostratigraphy, providing, for the first time, an assessment of the size of these eruptions or layers. [ABSTRACT FROM AUTHOR]

Stream temperature governs many aquatic ecosystem processes and plays a key role in determining the distribution of cold‐water amphibians and cool‐ and cold‐water fish, including salmonids. Decades of research have focused on the effects of forestry and forest disturbance on stream temperature, and new projects are underway or being planned in jurisdictions including the Provinces of Alberta and British Columbia, Canada, and Washington State, USA. The objective of this paper is to provide a critical review of methodologies employed in previous studies. The review initially focuses on the range of metrics used to quantify stream thermal regimes and the factors that control stream temperature variability in time and space, then focuses on sampling and analytical methodologies used to quantify stream temperature response to forestry activity and forest disturbance. Empirical methods include sampling in time, space, or both, and may or may not include pre‐ and post‐harvest data. Process‐based mechanistic and hybrid empirical‐mechanistic models have also been applied. The advantages and disadvantages of these approaches are discussed, and recommendations provided to support the design and execution of future studies. [ABSTRACT FROM AUTHOR]

Ionospheric delay, as one of the largest error sources in radio propagation, can only be corrected for this error using the ionospheric delay correction model for Global Navigation Satellite System (GNSS) single‐frequency users. In this paper, the 2021 geomagnetic storm event is selected, and based on the measured ionospheric data from the GNSS observatory, the perturbation of the ionosphere by the geomagnetic storm event is analyzed, and it is found that the response of the ionosphere to the geomagnetic storm has obvious differences in the response characteristics and response time in different latitude regions. The performance of the global ionospheric map (GIM), the empirical model, and the broadcast ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is analyzed and the change in the accuracy of each ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is investigated, using the measured electron content of the GNSS as a benchmark. The results show that there is good agreement between the GIM products and the measured electron content during the period of ionospheric calm and the period of ionospheric perturbation. It is worth noting that geomagnetic storms do not necessarily lead to a decrease in the accuracy of ionospheric delay‐correction models, and in some cases, the models that were originally under‐accurate show a tendency to improve their accuracy during the period of perturbation instead. Neither the broadcast ionospheric model nor the electron content of the empirical model output responds to geomagnetic storm‐induced ionospheric perturbations. Plain Language Summary: The change characteristics of the ionosphere during geomagnetic storms are very complex. In this paper, we study the characteristics of ionospheric changes during geomagnetic storms and the performance of models during ionospheric perturbations. The results show that the ionospheric characteristics at different latitudes during geomagnetic storms are very different; and there is a phenomenon that the accuracy of the model correction is improved during the perturbation of geomagnetic storms. This paper provides a reference for improving the performance enhancement and application of ionospheric delay correction models. Key Points: Ionospheric disturbances caused by geomagnetic storms have different spatial and temporal characteristicsGlobal ionospheric map has the optimal performance in the calm period and the perturbation periodThe broadcast ionospheric model and the empirical model do not respond to ionospheric perturbations [ABSTRACT FROM AUTHOR]

The Quality of Service (QoS) rendered by mobile communication network is pivotal to the economic development of any country. Therefore, considering the quality of service in Nigeria mobile communication network is not out of place. This study focuses on the evaluation and empirical modeling of the Call Setup Success Rate (CSSR). The research method is divided into three unique sections: evaluation of Call Setup Success Rate (CSSR), measurement of Call Setup Success Rate (CSSR) using the old Engineering Faculty and medical college area in Ekpoma town. Additionally, modeling of Call Setup Success Rate (CSSR) is based on the obtained data from the area under investigation. Measurement was carried out from two basic points, considering the attitudes and longitudes of two points respectively. Three mobile network operators were considered for this study, MTN NG, GLO NG and AIRTEL NG, six different mobile phones were deployed, along with ten stopwatches. Calls are initiated from MTN to GLO and Airtel, to determine the respective CSSR using a stopwatch. Additionally, the process was repeated from GLO to MTN and Airtel, and lastly from Airtel to MTN and GLO. This procedure is of subjective nature, where ten (10) evaluators with their stopwatches are made to record CSSR time independently. The corresponding results are averaged, and the outputs are presented in graphical format. It was observed that the CSSR obtained is not deterministic in nature due to poor signal strength level, and the CSSR flow pattern can best be described as a logarithmic distribution pattern based on the developed empirical model. Furthermore, it was observed that the call setup success rate is at a minimum when calls are setup within the same mobile network operator compared to inter-mobile network operator calls. [ABSTRACT FROM AUTHOR]

AbstractPneumatic tires are unsuitable for agricultural machinery due to their susceptibility to punctures from stumps in plantations. To address this, non-pneumatic tires (NPTs) were developed. This study experimented with and simulated the Michelin TWEEL NPT on sandy clay loam soil. An integrated method combining the finite element method and an empirical model was used to simulate NPT-soil compaction. A custom tire-soil compaction machine measured soil sinkage, aligning well with the experimental data. A novel arrow-shaped spoke for agricultural NPTs, made from polyurethane, significantly reduced soil compaction. Future work will involve prototyping and manufacturing this innovative agricultural NPT. [ABSTRACT FROM AUTHOR]

A Pt(1.5%)Sn(0.25%)/Al2O3 catalyst synthesized by the sequential impregnation of γ-alumina with aqueous solutions of hydroplatinic acid and tin chloride has been characterized by X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The kinetic laws governing methylcyclohexane dehydrogenation to toluene and hydrogen in a tubular flow reactor in the isothermal mode at atmospheric pressure have been studied, while varying the reactant concentrations, contact time, and temperature. An empirical mathematical model of kinetics, which includes three adsorption forms of a bifunctional active site, has been developed. The model adequately describes the test data for the forward and reverse reaction routes. [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]

Traffic noise is emerging as major challenge for cities in India. It affects human health in terms of annoyance and other major health problems. This study aims to develop a traffic noise model for mid-sized cities. Vehicle noise and spot speed of individual vehicles were studied to develop a reference energy mean emission level (REMEL) by using the sound level meter from a reference distance of 7.5 m from the centerline of a nearby lane under free flow condition. This urban traffic noise model is specific to different geographical regions and depends on vehicle categories and characteristics of in use vehicles. Subsequently, mid-block studies were conducted for measuring traffic noise Leq (in dB(A)), average speed, and traffic volumes of different vehicle categories. To develop the traffic noise prediction model, physical condition of the road, carriageway width, width of the footpath, median size, number of lanes, and ground cover (hard or soft) need to be accounted for. A modified traffic noise prediction model has been developed using REMEL models and different adjustment factors. Subsequently, the correlation analysis was performed between predicted and observed Leq (in dB(A)) to check the model fitness by correlation coefficient R2 = 0.76 and mean absolute percentage error is ranged from 0.8 to 1.2% for the predicted and observed Leq at all the selected sites of measurement periods for the Kanpur city. With this calibrated model, a noise map has been developed to identify noise hotspots during different time intervals within the city. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

The current study explores the linkage and possible mechanisms of the Southern Annular Mode (SAM) with rainfall variability over southeastern South America (SESA). The findings indicate that the preceding May SAM can modulate the subsequent rainfall over the SESA region in September‐October‐November (SON), independent of the El Niño‐Southern Oscillation (ENSO) signal. Further analysis suggests that the SAM‐rainfall relationship is mainly due to the response of sea surface temperature (SST) in the subtropical and extratropical Southern Hemisphere to SAM variability via air‐sea interactions. Such SST anomalies persist to the subsequent SON and stimulate an upstream wave train extending into the study region from the South Pacific. Such wave patterns form a cyclone anomaly over SESA, indicating strong subsidence and reduced convection over the region. Meanwhile, the low‐level anomalous cyclone and anticyclone located over the subtropical South Atlantic (on the coast of the study region) are associated with easterly winds to the South (North) of the anomalous cyclone (anticyclone), which advect colder and drier air into the study region due to the cold SST over the subtropical South Atlantic and leading to increased moisture scarcity and decreased rainfall over SESA. Besides, model results based on the joint climate indices (i.e., SAM, ENSO and Indian Ocean Dipole (IOD)) revealed a satisfactory estimate of the SON rainfall compared with either single index, indicating that the SAM can be one of the precursors of the SESA rainfall, besides ENSO and IOD. Plain Language Summary: Drivers of rainfall variability over the southeastern South America (SESA) region in September–October–November (SON) include the tropical sea surface temperature (SST) and climate modes of variability, among which El Niño‐Southern Oscillation (ENSO) exerts the greatest influence. Though the Southern Annular Mode (SAM) has a concurrent influence on rainfall during this season, whether or not the SAM has a lead impact on the variability of SON rainfall in SESA has yet to be uncovered. Using reanalysis and satellite data, we found that the preceding May SAM can modulate the subsequent rainfall over the SESA region in SON without the ENSO influence. The SAM‐related SST anomalies in the extratropical and subtropical Southern Hemisphere are found to be important in establishing the link between SAM and rainfall through stimulating an upstream wave train pattern associated with a cyclone anomaly over the SESA region, contributing to suppressed moisture convection and reduced rainfall. In addition, model results constituting the SAM, ENSO and Indian Ocean Dipole lead combined indices can replicate well the temporal variations of SON rainfall compared to a single index, suggesting that the May SAM can improve the predictability of SON rainfall over SESA. Key Points: The preceding May Southern Annular Mode modulates the subsequent September–October–November rainfall conditions over southeastern South AmericaThe Southern Annular Mode‐induced sea surface temperature over the subtropical and extratropical Southern Hemisphere plays a key mediating role in linking the Southern Annular Mode to rainfallConsidering May Southern Annular Mode as a precursory signal can advance September‐October‐November rainfall predictability over southeastern South America [ABSTRACT FROM AUTHOR]

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]

The present study investigates the lead relationship of the April–May (AM) Southern Annular Mode (SAM) with the northern Australia rainfall (NAR) variability in the subsequent December–February (DJF). The AM SAM was revealed to exhibit a considerable negative relationship with the NAR in the following DJF, which was still significant after removing the possible influence of El Niño–Southern Oscillation (ENSO). Further analysis revealed that the AM SAM can trigger the dipole sea surface temperature anomalies (SSTA) in the southern Indian Ocean through air-sea interactions. The dipole SSTA persists across the following months from June to September, and then it evolves to form the dipole SSTA at locations to the east of previous definitions of the Subtropical Indian Ocean Dipole from October to February of the following year. Based on the circulation anomalies, the AM SAM-related dipole SSTA in DJF is associated with an eastward shift of the Walker circulation between the tropical eastern Indian Ocean and the western Pacific Ocean, resulting in upward (downward) motion over the Indian (Pacific) Ocean and easterly surface wind anomalies across northern Australia. The anomalous easterlies are part of a localized anticyclonic circulation centred over the study region, which indicates diminished moisture influx into the northern parts of the continent, resulting in dry conditions over northern Australia. Besides, model results based on the preceding AM SAM and September ENSO indicate enhanced predictive skill of the NAR associated with combined indices compared to either single index. [ABSTRACT FROM AUTHOR]

The physicochemical combination method (PCCM), improving upon the conventional pure cement solidification method (PCSM), has been developed as an integrated and sustainable approach to treat and recycle slurry-like mud (MS). To facilitate the practical design of the PCCM, it is essential to establish the relationship between the strength of PCCM-treated MS and the governing factors. In this study, a comparison is made between PCCM-treated MS and PCSM-treated MS to clarify the differences in the microstructures and strength behaviors, and the influence of some key parameters (i.e., preloading pressure p, equivalent initial water content wei, and cementitious binder content wc) on the strength behavior of PCCM-treated MS is investigated via unconfined compression tests. The results indicate that flocculants play a crucial role in increasing the ductility of PCCM-treated MS, and the preloading process further enhances the toughness of PCCM-treated MS. Moreover, empirical equations of deformation modulus (E50) and failure strain (ɛf) are formulated based on the results of unconfined compression tests. Additionally, an attempt is made to predict the unconfined compressive strength (UCS) using a simple empirical equation based on the modified water/cement ratio. The main findings are of practical significance for the optimal design and application of PCCM in treatment of MS. [ABSTRACT FROM AUTHOR]

Modelling surface runoff and infiltration in an agricultural watershed is essential for understanding the nutrient cycle and water quality management. This research evaluates the critical rainfall events causing maximum surface runoff and infiltration. Empirical models such as Soil Conservation Service (SCS) curve number and Explicit Green-Ampt (EGA) were analysed for different soil hydraulic conditions and storm events with specific return periods. The present study forecasted the maximum daily rainfall and intensity as 113.6 mm and 19.9 cm h–1, respectively, considering a 100-year return period storm. The deterministic part of the model calculates the daily maximum surface runoff as 5.51 cm (from the SCS model). The EGA model revealed that the rainfall intensity of 0.143, 0.163, 0.175, 0.179, 0.181, 0.182, and 0.182 cm h−1 (duration of 24 h) was found critical for 1, 2, 5, 10, 25, 50, 100 year return period of the storm, respectively in different soil texture classes (sand, loamy sand, sandy loam, loam, silt loam, sandy clay loam, clay loam, silty clay soils). Exceptions imply for three occasions where the intensity of 0.407 cm h−1 and duration of 18 h produced maximum infiltration for silty clay loam, and intensity of 0.352 and 0.354 cm h−1 with 9 h rainfall (each) produced maximum infiltration for clay. The probabilistic model revealed that a daily storm event probability of 2.74 × 10–5 is associated with a simulated mean runoff depth of 0.69 cm and infiltration of 20.7 cm. Critical rainfall probabilities calculated in this study can be multiplied with the overall microbial/pollutant human health risk assessment framework to calculate conditional probability and better estimate microbial risk through the drinking water pathway. This study is very relevant for the wet climatic condition of Ireland, where most land use is pastures. [ABSTRACT FROM AUTHOR]

Microbially induced carbonate precipitation (MICP) stands as a potent technique for remediating soils contaminated with heavy metals. However, the lack of efficient methods to detect the efficacy of MICP necessitates the use of electrical resistivity as an indicator. Consequently, an empirical model was devised to assess the strength and lead curing rate of the remediated soil. Through resistivity tests and microscopic experiments, it became evident that water content and lead contamination concentration exerted an influence on the electrical resistivity of the soil. Remarkably, the MICP technology led to a significant increase in the electrical resistivity of the remediated soil. This phenomenon can be attributed to the immobilization of lead ions within the contaminated soil, which consequently alters the soil's pore structure, thereby resulting in noticeable modifications in electrical resistivity. The empirical model further revealed a linear correlation between the strength of the remediated soil and its electrical resistivity. As the electrical resistivity increased from 1.09 to 8.71 Ω m, the strength of the soil improved from 175 to 1070 kPa. Additionally, a multifactor linear framework elucidated the interrelation between the lead curing rate and water content, primary lead contamination concentration, and electrical resistivity. The rate of lead solidification showed a positive correlation with water content but exhibited a negative correlation with both the initial concentration of heavy metal pollutants and the electrical resistivity. Notably, the highest rate of lead curing rate, reaching 90.89%, was observed at a water content of 16.1%, a pollutant concentration of 100 mg/kg, and an electrical resistivity of 1.54 Ω m. These findings firmly establish electrical resistivity as an effective means of evaluating the remediation effect of MICP, thereby providing a theoretical foundation for assessing the impact of MICP technology in the field. [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]

It is commonly known that mechanical stimulation, for example, wall shear stress (WSS), can affect cellular behaviours. In vitro experiments have been performed by applying fluid-induced WSS to investigate the cell physiology and pathology. Porous scaffolds are used in these experiments for housing and facilitating the micro-physical/chemical environment on cells during 3-dimensional (3D) cell culturing. It is known that scaffold porous geometries influence scaffold permeability and internal WSS. Computational simulations are commonly employed to determine the WSS; however, these simulations can be computationally expensive and may not be readily accessible to everyone due to a knowledge gap. To address this limitation, this study proposes an empirical equation for calculating the scaffold permeability based on the Kozeny-Carman equation. The new equation considers the porous geometric features, providing an accurate estimation of the scaffold permeability. Furthermore, the study introduces a new correlation between WSS and permeability, aiming to establish an efficient and precise estimation of internal WSS. This correlation enables efficient estimation of the WSS within porous scaffolds without relying on computationally demanding simulations. Therefore, the output from this study can negate the issues of using computational simulation for determining scaffold permeability and internal WSS under perfusion flow by providing empirical equations. [ABSTRACT FROM AUTHOR]

Accurate prediction of water quality changes in the water treatment process is an important factor for optimal decision-making process such as the design, operation, and diagnosis of water treatment facilities. This study developed an Artificial Intelligence (AI) algorithm model predicting dissolved organic carbon (DOC) removal and disinfection byproducts formation, and comparatively analyzed existing empirical models and prediction results to examine the applicability of AI algorithm techniques to water quality prediction in the water treatment process. We enhanced empirical models for predicting DOC removal and disinfection byproduct formation in Korea water purification plant. Six AI algorithm techniques were applied and tested using real-world data. All AI algorithm models outperformed the original empirical models in predicting DOC removal and byproduct formation. In terms of the DOC prediction model, multi-layer perceptron (MLP) showed the optimal performance (R² = 0.9795; root mean square error [RMSE] = 0.0365 mg/L). MLP also showed the optimal performance in disinfection byproduct formation prediction (R² = 0.9781; RMSE = 0.0008 mg/L. As a result, the prediction performance of AI algorithm models improved with larger sample sizes. By securing data samples for approximately 1 year, these models were confirmed to outperform empirical models. [ABSTRACT FROM AUTHOR]

Copyright of Earthquake (1000-3274) is the property of Editorial Office of Earthquake Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)