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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]

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]

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]

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]

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]

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]

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]

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]

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.

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.

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]

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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]

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]

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]

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]

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]

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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]

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]

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

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.

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 research aimed to investigate the effect of temperature on air drying kinetics of coconut residue in a fluidized bed dryer (FBD), to fit the drying curves with six empirical models, and to calculate the diffusivity coefficients and the activation energy. The Midilli model is the best for explaining the drying behavior. It is possible to predict the moisture content of the product with a generalized model showing the effect of a drying air temperature of 70–100°C and a velocity of 1.07 m/s. The Midilli model has shown an excellent fit to predict the drying behavior and best fit for the experimental data since it yielded the highest R2 and the lowest Chi square (χ2) and RSME values for all runs. The experimental data were also modeled using Fick's diffusion equation..The activation energy value for the drying temperature of dried coconut residue in an FBD is 26.09 kJ/mol. [ABSTRACT FROM AUTHOR]

The article focuses on the limitations of Cunningham et al.'s (2023) analysis, which examined the impact of historic forestry on the thermal regimes of salmon-bearing streams in the North Thompson River watershed, British Columbia. Topics include challenges with spatial comparison approaches, the potential inadequacy of selected predictor variables, and the need for further research to understand the effects of forestry on stream temperature in medium to large catchments.