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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]

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]

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]

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 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]

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]

In this study, the existing empirical models for sediment management for ungauged watersheds in South Korea developed in 2019 were updated. The existing models were developed based on the Universal Soil Loss Equation structure with domestic river sediment data obtained from 2005 to 2014 and watershed characteristic values. To update the models, the specific degradation (SD) value was calculated first in the same way as the existing method by adding sediment measurement and daily discharge data obtained from 2015 to 2020. The calculated SD values with recent data decreased compared with the past and differed from the results predicted using the existing models and the watershed characteristics investigated in this study. For this reason, the existing models were updated using the latest watershed characteristics and SD values. The root-mean-square error values of the existing models, which ranged from 131 to 90 tons/km2·year, decreased to 102 to 62 tons/km2·year in the updated models with the same structures. Multicollinearity was tested to evaluate the reliability of the updated models, and a new 95% prediction interval for usability was estimated. In addition, quantitative changes in the future sediment load of domestic rivers were analyzed using the future rainfall data obtained from climatic change scenarios and the watershed area. The results of this study reconfirmed that periodic updates are required for the existing empirical models. It is expected that the empirical models will be used in various ways, such as for the prediction of future sediment loads for river sediment management. [ABSTRACT FROM AUTHOR]

Solar drying is a method employed to expedite moisture reduction and enhance preservation capacity, characterized by intricate heat and mass transfer processes, challenging the micro-level description of drying kinetics. This study aims to optimize solar drying conditions for kola fish using a double slope solar dryer. An empirical investigation was conducted in three modes viz: open sun drying, natural convection solar dryer and forced convection solar drying. The research underscores the advantages of forced convection drying, showcasing a notable reduction of 4 h in drying time in comparison to natural convection. Furthermore, natural convection surpassed open sun drying, yielding an impressive 18-hour time-saving. An empirical model was formulated to establish the relationship between surface temperature and influential parameters, including insolation, air temperature, and ambient temperature. This model exhibited a high degree of reliability, featuring a correlation coefficient of 0.982 and a narrow standard deviation of 0.028, enabling precise surface temperature predictions under various conditions. The study delved into the effective moisture diffusivity range of kola fish, pinpointing it within the range of 5.16 × 10–9 to 5.29 ×10–8 m2/s. This understanding of intrinsic moisture migration during drying contributes to process optimization. Furthermore, the determination of the activation energy for kola fish drying, which ranged from 28.34 to 38.83 kJ/mol, elucidates the temperature-dependent nature of drying kinetics and underlying energy-driven mechanisms. These revelations significantly enhance the comprehension and advancement of controlled solar drying techniques for kola fish. [ABSTRACT FROM AUTHOR]

To investigate the relationship between service temperature ranging from –40~250℃, and size effect on tensile properties of thin ASS-304 sheets with nine different thicknesses (40~500 μm), uniaxial tensile tests were performed on thin ASS-304 sheets of the same average grain size in the present study. Within the thickness range of 40 to 300 μm, corresponding to η = t/d values from 1.1 to 8.1. The ultimate tensile strength (UTS), yield strength (YS), and elongation (EL) of ASS-304 exhibits a dimensional effect of "the thinner, the stronger". For example, as the η increases from 1.1 to 8.1, the UTS rapidly decreased from 1798.8 to 839.0 MPa at 20℃, from 1703.1 to 526.9 MPa at 150℃, and from 1661.2 to 478.9 MPa at 250℃, attenuation of 53.36, 69.06, and 71.17%, respectively. Meanwhile, the YS at 20℃ are separately 1768.9 to 418.7 MPa with 1695.2 to 343.3 MPa at 150℃ as well as 1645.7 to 330.1 MPa at 250°C, decrease the proportion of 76.33%, 79.75% and 79.94% respectively. Notably, the UTS, YS, and EL at 150 and 250℃ are lower than those at 20℃. The true stress value of ASS-304 was enhanced at –20 and –40℃, and the true strain increases first and then weakens as the thickness increases, the reason is the transformation- induced-plasticity (TRIP) effect of ASS-304 in stretching. The asymptotic function describes the relationship between strength and the values of η, while the Chapman function represents the relationship between elongation and the η. A linear variation exists between service temperature and tensile properties. And relevant empirical equations including T-η- and T-η-tensile properties were established, which can predict the UTS, YS, and EL of thin ASS-304 sheets under different service temperatures. [ABSTRACT FROM AUTHOR]

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

The hole penetrated in thin metallic plates due to hypervelocity impacts of cylindrical projectiles was analyzed by experimental method. The projectile caused a hole-expanding effect when penetrating the target plate because of dynamic shear failure and extrusion. A new empirical model was presented to predict the perforation diameter in thin plates impacted by high-velocity cylindrical projectiles. The fitting coefficients resulted in a root-mean-square of 0.0641 and a correlation coefficient of 0.991. The errors between the predicted and the experimental values were less than 7.251%, and less than 4.705% for 93.333% cases of the dataset. The accuracy of the proposed model is much higher than that of Hill's model. Compared with historical equations, the new model is more accurate and can well describe the variations of different parameters with the normalized penetrated hole. The model takes into account the strength of materials, which contributes to the excellent results. This paper could provide important theoretical support for the analysis of the perforation process and its mechanism. [ABSTRACT FROM AUTHOR]

Zenith Tropospheric Delay (ZTD) plays a vital role in Global Navigation Satellite System (GNSS) navigation, positioning, and meteorology. The generally accepted empirical models that are now in use can only reflect the periodic changes in ZTD. Nevertheless, capturing its subtle nonlinear changes, like rapid ZTD variations, is challenging as its accuracy needs further improvement. To overcome these drawbacks, the relationship between the residuals of GPT3 ZTD minus GNSS ZTD and the spatiotemporal information was fitted by using random forests (RF). Consequently, a refined model of GPT3 ZTD was established in Mainland China (named RGPT3), and the performance of the proposed model was compared to two accepted empirical models and another model based on a popular algorithm of machine learning (backpropagation neural network algorithm). Based on the results of the study, the RMSE of RGPT3 is 1.83 cm, which has an improvement of 28.0, 16.8, and 34.4% over the three compared models. The RGPT3 performs better in capturing the instantaneous ZTD changes than the empirical models. The result of RGPT3 ZTD constraint GNSS precise point positioning (PPP) is also superior to that of GPT3 ZTD, with the U direction convergence time reduction of 12.3% and accuracy improvement of 7.9%. The new model can offer higher-precision ZTD predictions in the study area. [ABSTRACT FROM AUTHOR]

Post-combustion CO2 capture by aqueous amine solvent is one of the most promising methods for mitigating the presence of CO2 in the environment. In this work, a novel amine blend of Diethylenetriamine and 3-Dimethyl amino-1-propanol was selected. Experiments were performed in the temperature range of 293.15–323.25 K, mole fraction of diethylenetriamine in the range of 0.05–0.2, partial pressure of CO2 in the range of 10.13–25.33 kPa and solution concentration in the range of 1–3 mol·L−1. Effects of these parameters on equilibrium CO2 loading were judged at various operating conditions. An empirical model was developed for the calculation of equilibrium CO2 loading in the aqueous amine blend. The heat of absorption of CO2 for this amine blend was found to be −65.22 kJ·mol−1. Response surface methodology (RSM) was used for optimization and a quadratic model was selected. The analysis of variance was used to prove the significance of the selected model. Three-dimensional diagrams and contour plots of independent variables were also shown. Optimum CO2 loading by RSM was found to be 1.068 mol CO2·mol amine−1 at temperature 294.15 K, mole fraction of diethylenetriamine 0.20, solution concentration 1.3 mol/l, and partial pressure of CO2 24.22 kPa. [ABSTRACT FROM AUTHOR]

In Brazil, research related to the occurrence and prevention of debris flows is incipient when compared to the extent of the impacts caused by the phenomena. There is a need for further studies that consider susceptibility and hazard, especially in areas that are environmentally and socioeconomically vulnerable. This study aimed at assessing debris-flow hazard in the Rio das Pedras watershed, in Cubatão (State of São Paulo, Brazil), based on a set of different physiographic parameters (geomorphological, morphometric, geological) and in the application of empirical models. The hazard assessment was based on: (1) the evaluation of the history of events in the region; (2) the identification of the geomorphic controlling factors; (3) the estimation of the magnitude of a potential event; and (4) the identification of the elements at hazard. The results show that a debris-flow event in Rio das Pedras would more severely impact the Anchieta Highway (SP-150), the gas pipeline GASAN, the oil pipeline OSSP and the districts Pinhal do Miranda and Cota 95. These results highlight the relevance of geomorphological and geological parameters when estimating the extent of debris runoff, which is essential when defining the hazard in a debris-flow-prone watershed. [ABSTRACT FROM AUTHOR]

In this research work, a hybrid model involving nonlinear autoregressive neural networks and empirical models are used to predict global solar radiations on the tilted surface. In the first stage, the predictions of daily global and diffused solar radiations on horizontal surfaces are made using a nonlinear autoregressive model. The predicted data are then fed to the empirical model which predicted/estimated the hourly global solar radiations on the inclined surface. The whole model is built in MATLAB. The percentage increase in average global solar radiation reception on the tilted surface as compared to the horizontal surface is found around 9% using the proposed model. Besides, various empirical models found in the literature are also implemented to estimate hourly global solar radiation throughout the year on the tilted surface at the chosen site. A comparison is also drawn among various empirical models with the proposed hybrid model, depicting less error when global solar radiations are estimated using our method. The mean absolute percentage error for our model came 5.61. It is also found that for the chosen site, i.e., (Rajouri) out of all empirical models Sore's model estimated better followed by Muneer's and Oliverie's, respectively, apart from the proposed hybrid model. The proposed model is universal, because it involves measured input data for global and diffuse solar radiations, collected over time in the empirical model. The utilities can also collect its own data and change input parameters such as latitude accordingly depending upon their locations. [ABSTRACT FROM AUTHOR]

It is typical to observe a decline in production rate and a decrease in reservoir pressure after oil reservoirs have been allowed to produce for a long time. Miscible flooding is a tertiary recovery method for enhancing the reservoirs' sweep efficiency. During miscible injection, gasses such as carbon dioxide, natural gas, and nitrogen are employed to increase production levels. Carbon dioxide is commonly used as a miscible gas, but less abundant and more expensive than nitrogen. Flue gas, a mixture of carbon dioxide and nitrogen gas, is often used to replace pure carbon dioxide. For this study, flue gasses with the compositions, 15% of carbon dioxide, 85% of nitrogen gas and 30% of carbon dioxide,70% of nitrogen gas, are used as the injection gas to develop an empirical correlation for minimum miscibility pressure (MMP) for candidate light oil reservoirs that have been previously waterflooded. In data analysis and data analytics, the dataset was separated into two groups at random: the training set, which consists of 80% of the entire dataset, and the testing set, which made up 20% of the total dataset. The independent variables employed for model development include temperature, oil sample oil gravity, molecular percentage of carbon dioxide in the injection gas, the molecular weight of hexane plus in the oil, and the molecular percentage of intermediates. The findings reveal that the newly built model is more accurate and delivers better predictions than the existing correlations. For the testing dataset, the new model predicts flue gas MMP with an average absolute percentage error of 5.5519% and a correlation coefficient of 0.92. [ABSTRACT FROM AUTHOR]

Alternate precipitation and evaporation often cause desiccation cracks in clayey soils, which necessarily change the soil water retention capacity. This work aimed to examine the influence of wet-dry cycles on the water retention behavior of compacted lateritic soil and further model the soil water characteristic curve (SWCC) of the soil containing cracks. Different wet-dry cycles were imposed on compacted lateritic soil specimens to produce desiccation cracks. Microscopic tests were performed to reveal the microstructural change of the soil. The SWCCs of the specimens after various wet-dry cycles were then measured and analyzed. The early six wet-dry cycles contribute most to the development of desiccation cracks. At microscale, some soil aggregates are broken into individual clay particles and further lost during wet-dry cycles, producing numerous microcracks and mesopores, which accounts for desiccation crack development. The SWCC of compacted lateritic soil changes from a unimodal form to a bimodal form under wet-dry cycles because of crack occurrences. With increasing cycle number, the first air entry value decreases and the reduction in volumetric water content becomes more evident, leading to a weaker water retention capacity of the soil. An empirical equation of the bimodal SWCC was proposed by incorporating a sigmoid function into the van Genuchten model and then verified with the experimental data. This equation can provide continuous and smooth bimodal SWCCs with all parameters having clear physical meanings. [ABSTRACT FROM AUTHOR]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

Crown width is one of the most important crown dimensions that influence tree growth and survival. Accurate crown width prediction is vital for forest management. However, measuring crown width is time-consuming and labor-intensive, making it necessary to construct a convenient and accurate crown width prediction model. Nowadays, machine learning technologies have already been increasingly used to accurately predict tree growth, but there is still a lack of systematic and comprehensive comparison. This paper provided a comparative analysis of various machine learning methods (i.e., the Linear Regression, the Least Absolute Shrinkage and Selection Operator, the k-NearestNeighbors, the Random Forest, the Gradient Boosting Decision Tree, the Support Vector Regression, the Voting Regressor and the Multi-Layer Perceptron), simple crown width-diameter of breast height model, generalized crown width-diameter of breast height model and nonlinear mixed-effect crown width model for estimating the crown width of Larix olgensis in terms of the coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error using hold-out validation and 10-fold cross-validation. The study showed that machine learning algorithms performed better than common nonlinear regression and nonlinear mixed-effect models. Specifically, the voting regressor and random forest algorithm yielded the highest prediction quality of crown width among the different models. In addition, from a practical point of view, the advantage of machine learning is that its implementation does not require crown width measurements. On the contrary, the calibration of the mixed-effect model requires prior information, which limits its use. [ABSTRACT FROM AUTHOR]

In recent years, rapid economic development and urbanization have led to severe environmental problems such as noise pollution. The use of fibrous porous materials is considered to be an effective method to control noise pollution. This study investigates the morphological and acoustical characteristics of fibrous materials made of aluminosilicate fibers (ASFs). To this end, morphological and structural properties of samples were investigated using field emission scanning electron microscope (FE-SEM), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) method. The effect of sample thickness and air gap was experimentally studied using the impedance tube method in the frequency range of 63–6300 Hz. The frequency-dependent acoustic behavior of the samples was also predicted using the empirical models of Delany-Bazley (D-B), Garai-Pompoli (G-P), and the proposed revised models of D-B and G-P. The XRD patterns showed that the main phases of the ASF are quartz, tridymite, and mullite. The BET analysis confirmed that the samples are classified as macro-porous. It was found that with the increase of the sample thickness sound absorption coefficient (SAC) increases at low frequencies. The averages of SACs for samples with thicknesses of 10 and 25 mm at low frequencies were 0.17 and 0.32, respectively. These values for the middle frequencies were 0.78 and 0.80, and for high frequencies were 0.83 and 0.84, respectively. Additionally, it was observed that with the increase of air gap depth, SAC enhances at low-frequency bands, and the SAC peak shifts toward the lower frequencies. Additionally, excellent agreement was observed between the experimentally measured SACs and those predicted by the revised D-B and G-P models. [ABSTRACT FROM AUTHOR]

The hydrographic and bio-optical properties of the Bering Sea shelf were analyzed based on in-situ measurements obtained during four cruises from 2007 to 2009. According to the temperature and salinity of the seawater, the spring water masses on the Bering Sea shelf were classified as the Alaskan Coast Water, Bering Sea Shelf Water, Anadyr Water, Spring Mixed Layer Water, Remnant Winter Water, and Winter Water, each of which had varying chlorophyll a concentrations. Among them, the highest chlorophyll a concentration occurred in the nutrient-rich Anadyr Water ((7.57±6.16) mg/m3 in spring). The spectrum-dependent diffuse attenuation coefficient (Kd(λ)) of the water column for downwelling irradiance was also calculated, exhibiting a decrease at 412–555 nm and then an increase within the range of 0.17–0.48 m−1 in spring. Furthermore, a strong correlation between the chlorophyll a concentration and the attenuation coefficient was found at visible wavelengths on the Bering Sea shelf. Spatially, the chlorophyll a concentration was higher on the northern shelf ((5.18±3.78) mg/m3) than on the southern shelf ((3.64±2.51) mg/m3), which was consistent with the distribution of the attenuation coefficient. Seasonally, the consumption of nutrients by blooms resulted in minimum chlorophyll a concentration ((0.78±0.51) mg/m3) and attenuation coefficient values in summer. In terms of the vertical structure, both the attenuation coefficient and the chlorophyll a concentration tended to reach maximum values at the same depth, and the depth of the maximum values increased as the surface temperature increased in summer. Moreover, an empirical model was fitted with a power function based on the correlation between the chlorophyll a concentration and the attenuation coefficient at 412–555 nm. In addition, a spectral model was constructed according to the relationship between the attenuation coefficients at 490 nm and at other wavelengths, which provides a method for estimating the bio-optical properties of the Bering Sea shelf. [ABSTRACT FROM AUTHOR]

Accurately measuring the dielectric constant of soil and modeling it during freezing and thawing processes are important foundations of estimating the physical and chemical properties and moisture transfer characteristics of soil. In this study, a new test method was developed by combining five temperature probes and a 5TM sensor. Based on this method, a series of experiments for measuring the dielectric constant were conducted using silty clay as an example. The effects of freezing and thawing processes, water content, and salt on the dielectric constant of soil were comprehensively compared and analyzed. The results showed that the trends of soil dielectric constant as a function of temperature can be divided into a linear stage and a nonlinear stage. The soil dielectric constant measured during the freezing process was larger than that measured during the thawing process at the same negative temperature. However, the differences between them were related to the water content of the soil sample in the positive temperature range. In addition, the soil dielectric constant increased with the increase in water, NaCl, and K2SO4 contents, which was particularly major in the positive temperature range. Finally, a new empirical model was proposed to calculate the dielectric constant of soil. The verification results demonstrated that the calculated dielectric constants agreed well with the measured results. This work can provide references for the measurement and prediction of soil dielectric constant. [ABSTRACT FROM AUTHOR]

Compacted clay-aggregate composites are extensively utilized or found in various geotechnical engineering projects such as the core of earth embankment dams, the pile foundation of offshore structures, or the impervious blankets in waste disposal landfills. In this study, data from resonant column tests at small shear strains are exploited to examine the dynamic shear modulus and energy dissipation characteristics of compacted clay-sand mixtures and develop empirical expressions for their small-strain dynamic properties. The models incorporate the influences of confining pressure (p'), void ratio (e), aggregate content (AC) and clay plasticity index (PI). Moreover, the application of the equivalent plasticity index (PI*) in the estimation of small-strain dynamic properties is rigorously examined. The level of accuracy of the developed models is inspected with simple comparisons against the experimental data. The proposed empirical correlations can be utilized in geotechnical earthquake engineering problems for the seismic stability analysis of geo-structures containing compacted clay-sand mixtures. These empirical models also comprise a basis to discuss the mechanical behavior of composite granular systems at multi-scales. In this regard, the involved micromechanisms, particularly the contact response of sand-clay systems, are discussed integrating the macroscopic results from the present work with micromechanical-based data previously published in the literature. This analysis highlighted the important role of the formed coating of microparticles on the constitutive behavior of sand grains at their contacts, which in turn, provided some additional insights to explain the macroscopic behavior of clay-sand mixtures as obtained from the element-size experiments. [ABSTRACT FROM AUTHOR]

The existing empirical models do not consider the influence of material composition of landslide deposits on the peak breach flow due to the uncertainty in the material composition and the randomness of its distribution. In this study, based on the statistical analyses and case comparison, the factors influencing the peak breach flow were comprehensively investigated. The highlight is the material composition-based classification of landslide deposits of 86 landslide cases with detailed grain-size distribution information. In order to consider the geometric morphology of landslide dams and the potential energy of dammed lakes, as well as the material composition of landslide deposits in an empirical model, a multiple regression method was applied on a database, which comprises of 44 documented landslide dam breach cases. A new empirical model for predicting the peak breach flow of landslide dams was developed. Furthermore, for the same 44 documented landslide dam failures, the predicted peak breach flow obtained by using the existing empirical models for embankment and landslide dams and that obtained by using the newly developed model were compared. The comparison of the root mean square error (Erms) and the multiple coefficient of determination (R2) for each empirical model verifies the accuracy and rationality of the new empirical model. Furthermore, for fair validation, several landslide dam breach cases that occurred in recent years in China and have reliable measured data were also used in another comparison. The results show that the new empirical model can reasonably predict the peak breach flow, and exhibits the best performance among all the existing empirical models for embankment and landslide dam breaching. [ABSTRACT FROM AUTHOR]

Deformation modulus of rock mass (Em) is an important parameter for the analysis and design of mining engineering projects. However, field tests for measuring deformation modulus of rock mass are difficult, time-consuming, and capital intensive. This has led to the development of numerous empirical models for estimating rock mass deformation modulus, which are in different forms and scattered in the literature. The numerous models available in the literature use different types of inputs. Therefore, this study provides a comprehensive compilation of different empirical models for estimating the deformation modulus of rock masses. The compiled models are grouped based on their type of input parameter(s) into three categories such as those using intact rock properties, rock mass classification indices, and combination of intact rock properties and rock mass classification indices. Then, a comparative analysis was performed using absolute average relative error percentage (AAREP) and variance accounted for (VAF) to assess the reliability of using different types of inputs for estimation of deformation modulus of rock masses using data from two sites. The results of the analyses show that rock mass classification indices are the most reliable indices for estimating the deformation modulus of rock masses among the categories considered for analyses. For AAREP analyses in the two illustrative examples considered in this study, models (7 out of 10) using rock mass classification indices in the estimation of Em have the best performances with AAREP values ranging from 24.07 to 55.15%. For VAF analyses in the two examples, models (8 out of 10) using rock mass classification indices in the estimation of Em have the best performances with values ranging from 59.81 to 88.11%. The lowest errors and highest deviation similarities from models using rock mass classification indices indicate that they produce the most reliable estimations of Em. It is important to note that the reliability of deformation modulus estimated from empirical models depends on the quality of input data as the models performed differently across the sites used in this study. This study therefore provides a compilation of available models for estimating deformation modulus, performance evaluation of available models for estimating deformation modulus, and guidelines for selecting appropriate model for estimating deformation modulus of rock mass. [ABSTRACT FROM AUTHOR]

Inland lake of Vembanad has benefited from continuous monitoring to evaluate water quality which has declined due to increased anthropogenic activities and climate change. Remote sensing techniques can be used to estimate and monitor inland water quality both spatially and temporally. An empirical model is presented in Vemaband lake that retrieves the specific water quality parameters through correlations between various spectral wavelengths of Sentinel-2MSI (S2MSI) with field-measured water quality parameters. This approach includes the combinations of various bands, band ratios, and band arithmetic computation of satellite sensors of spectral datasets. The specific inland water quality parameters such as chlorophyll-a (chl-a), total suspended solids (TSS), turbidity, and secchi disc depth (SDD) were retrieved from the developed water quality model through Sentinel-2A remote sensing reflectance. The result illustrates that Specific Inland Water Quality Parameters (SIWQP) strongly correlated with S2MSI reflection spectral wavelengths. The SIWQP models are constructed for TSS (R2 = 0.8008), Chl-a (R2 = 0.8055), Turbidity (R2 = 0.6329) and SDD (R2 = 0.7174).The spatial distribution of SIWQPs in Vembanad lake for March 2018 is mapped and shows the lake's water quality distribution. The research from Sentinel-2, MSI has potential and is appropriate in high spectral and spatial characteristics for retrieving and continuous monitoring of water quality parameters in the regional scale of inland water bodies. [ABSTRACT FROM AUTHOR]

Assessing exposure to ambient fine particulate matter (PM2.5) is important for improving human health. With rapidly expanding low-cost sensor networks globally, it is possible for monitoring networks to be located by a variety of users (i.e., crowd sourcing) to increase measurement density and coverage for use in exposure assessment, e.g., national land use regression (LUR) models. Few studies have integrated low-cost sensors into LUR models across multiple cities, limiting the ability of modelers to fully utilize growing low-cost sensor networks worldwide. We developed five LUR models to predict annual average PM2.5 concentrations using combinations of regulatory (six cities: n = 68; national: n = 757) and low-cost monitors (n = 149) from six US cities. We found that developing Hybrid LURs that include the low-cost (i.e., PurpleAir) network may better capture within-city variation. LURs with the PurpleAir data only (tenfold CV R2 = 0.66, MAE = 2.01 µg/m3) performed slightly worse than a conventional LUR based on regulatory data only (tenfold CV R2 = 0.67, MAE = 0.99 µg/m3). Hybrid models that included both low-cost and regulatory data performed similarly to existing national models that rely on regulatory data (hybrid models: tenfold CV R2 = 0.85, MAE = 1.02 µg/m3; regulatory monitor models: R2 = 0.83, MAE = 0.72 µg/m3). Integrating crowd-sourced low-cost sensor networks in LUR models has promising applications to help identify intra-city exposure patterns especially for regions with limited regulatory networks internationally. [ABSTRACT FROM AUTHOR]

Wind erosion is one of the most severe environmental problems in arid, semiarid, and dry sub-humid regions of the planet. This paper aimed to identify areas sensitive to wind erosion in Northeastern Algeria (Wilaya of Tebessa) based on empirical model using analytic hierarchy process, fuzzy analytic hierarchy process approaches, and geomatics-based techniques. Sixteen causative factors were used incorporating meteorological, soil erodibility, physical environment, and anthropogenic impacts as main available inputs in this approach. Weighted linear combination algorithm was adopted to combine all standardized raster layers. Area under curve value equal to 0.96 indicates an excellent accuracy for the proposed approach. Globally, wind erosion risk increases gradually from the North to South of the whole area. Besides, it was found that areas with slight, moderate, high, and very high risk covered 9.65%, 25.83%, 24.30%, and 40.22% of the total area, respectively. Our results highlighted the potential of additive linear model and free available medium resolution multi-source remote sensing data in studying natural hazards and disasters mainly under data-scarce or areas of difficult access in developing countries. In addition, restoration and re-vegetation activities of sensitive areas at high risk of wind erosion represent a challenge for researchers and decision-makers. [ABSTRACT FROM AUTHOR]

In this study, the influence of solid particle erosion on the fracture strength of low density polyethylene (LDPE) film under controlled conditions is investigated through impact experiments. The variations in the residual fracture stress as well as the residual fracture strain of the LDPE film after solid particle impact against the impact angle (α), impact velocity (vp) and impact duration (t) are analysed. The study revealed that the fracture stress and the fracture strain of the LDPE film decrease with an increase in the impact duration, and the degradation rate increases with the impact velocity and impact angle. Furthermore, the fracture stress and the fracture strain of LDPE film decrease exponentially against the impact energy under the same particle impact angle condition, and the reductions of fracture stress and fracture strain increase quasi-linearly with the sine-squared impact angle under the same impact energy. The study proposes empirical models to predict the attenuation of the fracture stress and the fracture strain of LDPE films due to the finite particle impact energy. [ABSTRACT FROM AUTHOR]

In the recent past, partial replacement of cement by rice husk ash (RHA) in concrete is a prime focus of global researchers for sustainable development in energy and environmental aspects. The present investigation aims at testing the water absorption capacity of the different types and sizes of the RHA-incorporated cement concrete. A design of experiments (DOE) was conducted using the Taguchi method to develop an L27 matrix to assess the individual effects of each variable. From the experimental study, decreasing the RHA size and increasing the RHA loading, higher bulk density, and surface area led to decreasing the water absorption capacity of the RHA-blended cement concrete during curing. Furthermore, 20 wt% replacement of cement by RHA in concrete furnishes the 3-fold decrease of water absorption capacity compared to normal concrete (without RHA). An empirical model was developed to predict the water absorption capacity of the RHA-incorporated cement concrete. The model indicates that RHA loading, silica content, and specific surface area are the key factors influencing the water absorption capacity of the concrete. And the model appears to be able to predict the water absorption capacity of concrete quite accurately with > 95% confidence level. [ABSTRACT FROM AUTHOR]

Water that evaporates from reservoirs can be considered water lost by the system. To estimate the volume of water evaporated from a reservoir, the climate and reservoir morphometric factors should be considered. The model developed by this research estimated the annual evaporation volume rate (EVR) of reservoirs through morphometric factors, based on reservoir maximum volume, maximum depth, and maximum area. In the study area, located in a semi-arid region of Brazil, 5–88% of reservoir maximum volumes were determined to be lost annually during the evaporation process. The results of the model adjustment were considered good since the model did not take into account climatic factors and uses only easily obtained morphometric data. The model developed is a new, easy-to-use tool for predicting the evaporation efficiency of reservoirs and can be used for planning new dam projects and assessing the operation of those already installed. The model was developed for reservoirs in the Brazilian semi-arid region, and we recommend building on this methodology for estimating the annual EVR for regions with other specific climates. [ABSTRACT FROM AUTHOR]

Natural materials are being employed as an option for controlling noise pollution, mainly via sound absorption mechanism. Specifically, natural fibers have been selected for use since they are environmentally friendly as well as easily and abundantly available. This paper discusses the mathematical and experimental examination of the sound absorption behavior of sustainable kenaf fiber at low-frequency range using the Delany–Bazley model (D–B model) as well as Nelder–Mead method and comparing the results with experimental findings. For this reason, we prepared S1-S16 samples of natural kenaf fibers at different thicknesses and bulk densities to measure the values of airflow resistivity and sound absorption coefficients. The predicted values obtained from both the D–B model and best-fit inverse approach presented by the Nelder–Mead method compared with experimental data measured using impedance tube. Accordingly, by applying a least-square fit procedure, the values that have best predicted both the impedance test and the propagation constant laws were evaluated. The inverse laws approach applied to determine the different physical parameters such as porosity, thickness, airflow resistivity as well as predicting the absorption performance of the kenaf fiber at low frequency ranges. [ABSTRACT FROM AUTHOR]

Proper measurement and correlation/prediction of solubility data of dye compounds (azobenzene and anthraquinone derivatives) in supercritical carbon dioxide (ScCO2) are essential in the development supercritical dyeing technology. Ample data are available for several dye compounds in the literature, but models that are satisfactorily correlating/predicting solubility data are rare. Therefore, in this study, a simple model is developed for the solubility ( y 2 ) in terms of reduced temperature ( T r ) and reduced density ( ρ r ) to correlate industrially important dye compounds solubility in ScCO2. The proposed model is y 2 = A + B + C ρ r T r + D + E ρ r ρ r T r 2 + F ρ r 2 T r 3 . The performance ability of the new model is compared with number of existing literature solubility models in terms of various statistical parameters of (AARD, R2, Adj. R2, SSE, and RMSE) as well as Akaike's Information Criterion (AIC). Moreover, thermodynamic parameters such as sublimation pressure ( P sub ), sublimation enthalpy ( Δ H sub ) and enthalpy of solvation ( Δ H sol ) were estimated for anthraquinone and azobenzene derivatives. It was found the proposed model is correlating better than existing models. [ABSTRACT FROM AUTHOR]

The thrombin generation (TG) assay is a well-established tool to capture the clotting potential of any healthy or haemophiliac subject. It measures ex vivo the kinetics of thrombin activation throughout the coagulation. Clinical studies allowed to create two databases gathering the coagulation factor levels and the thrombin generation profile of 40 healthy and 40 haemophiliac A (HA) subjects. Besides, portions of all HA samples were spiked with increasing levels of a TFPI antibody (considered as a possible therapeutic target) and corresponding TG profiles were determined. The non-linear mixed-effect (NLME) modelling aims at describing and explaining the experimentally observed important variability of the TG curves between subjects and the individual effects of spiking with a TFPI antibody. The models consist of an empirical description of the TG kinetics, accounting for an additive residual error and between-subject variability on its parameters. Factor VIII and TFPI were found to significantly explain and reduce the variability of the TG of haemophilia A samples. Besides, the model is shown to correctly reproduce the variability in the response to the ex vivo spiking with the TFPI antibody, by combining the empirical description of TG to a simple Hill equation that accounts for the binding between TFPI and different doses of its antibody. Such models can be useful for clinical practice, with the analysis and comparison of the distributions of TG profiles in healthy and haemophilia populations; and also for research, with the analysis of the effect of TFPI and its neutralization on individual TG profiles. [ABSTRACT FROM AUTHOR]

We present a new development stage of a generalized empirical model of optical characteristics of tropospheric aerosol in Western Siberia. An algorithm is suggested for taking into account the distribution function of absorbing matter and the condensation activity of aerosols as functions of aerosol particle sizes. Optical characteristics have been calculated with the complex refractive index of particles of different sizes variable with the relative air humidity. With aerosol weather types, referred to as "atmospheric haze" type (background and suburban haze), used as an example, the results of retrieving the angular scattering coefficients at small angles and the spectral behavior of the extinction coefficients are compared with experimental data. [ABSTRACT FROM AUTHOR]

The previous research is rich in plain concrete columns reinforced with glass fiber-reinforced polymer (GFRP) bars, but it is deficient in GFRP-reinforced concrete columns incorporating hybrid fibers. The present study aims to explore and compare the structural behavior of steel and GFRP-reinforced concrete columns having hybrid fibers (SHFC and GHFC columns). Two types of fibers, i.e. steel fibers and polypropylene fibers, were used to develop the hybrid fiber-reinforced concrete. A total of 18 circular concrete columns (9 SHFC columns and 9 GHFC columns) having a height of 1150 mm and a diameter of 250 mm were constructed and tested under axial loading at various eccentricities. The test measurements portrayed that the SHFC columns presented a better performance in terms of axial strength (about 20% higher) and ultimate deflections. The GHFC columns presented higher ductility indices compared with SHFC columns. Both steel and GHFC columns showed similar failure modes and similar reductions in the axial strength when subjected to eccentricities. The newly developed empirical model for the prediction of the axial strength of GHFC columns showed a close agreement with the experimentation. Thus, the comparative study solidly substantiates the application of the proposed empirical model. [ABSTRACT FROM AUTHOR]

Frictional heat-induced severe adhesive wear has been limiting the lifetime of metal-to-metal wear systems for many years. Five graphitic white irons were previously designed and produced to introduce flake graphite into white iron, with the goal to expedite heat dissipation and reduce the frictional heat effect. This paper focused on the graphite addition effect on adhesive wear resistance for graphitic white irons and its comparison with abrasive wear. Adhesive wear resistance was assessed with a block on ring apparatus in accordance with ASTM G77. An empirical model was formulated to quantitatively evaluate graphite additions' effect on the adhesive wear resistance. The model indicated that 1 vol.% graphite addition had the same effect as a hardness increase of 2.66 HRC, which was a 14% higher contribution than previously observed for three-body abrasive wear of the same alloys studied following ASTM G65. The improved wear resistance was related to an increased thermal diffusivity of the composite microstructure, and the surface lubricating effect of graphite. [ABSTRACT FROM AUTHOR]

In the present work, we evaluate the accuracy of the Solar Quiet Reference Field (SQRF) model for estimating and predicting the geomagnetic solar quiet (Sq) daily field variation in the South America Magnetic Anomaly (SAMA) region. This model is based on the data set of fluxgate magnetometers from 12 magnetic stations of the Embrace Magnetometer Network (Embrace MagNet) from 2010 to 2018. The model predicts the monthly average horizontal field of the geomagnetic quiet (Sq-H) daily variation solving a set of equations for the specified geographic coordinates in terms of the solar cycle activity, the day of the year, and the universal time. We carried out two comparisons between the prediction and observational data of the Sq-H field. The first part attempts to evaluate the accuracy for estimating the Sq-H field over Medianeira (MED, 25.30° S, 54.11° W, dip angle: − 33.45°) by using linear interpolation on the SQRF coefficients and comparing it with the data collected from April to December in 2018. None of the datasets collected at MED is part of the dataset used to build the SQRF model. The second part of the analysis attempts to evaluate the accuracy for predicting the quiet daily field variation over Cachoeira Paulista (CXP, 22.70° S, 45.01° W, dip angle: − 38.48°). The dataset collected at CXP before the period analyzed in the present work is part of the dataset used to build the SQRF model. Thus, the prediction accuracy is tested using magnetic data outside the time interval considered in the model. The prediction results for both locations show that this empirical model's outputs present a good agreement with the Sq-H field obtained from the ground-based magnetometer measurements. The accuracy of the SQRF model (high correlation, r > 0.9) indicates a high potential for estimating and predicting geomagnetic quiet daily field variation. Concerning space weather applications, the model improves the scientific insight and capability of space weather prediction centers to predict the variability of the regular solar quiet field variation as reference conditions, which may include areas with no measurements. [ABSTRACT FROM AUTHOR]

The present paper has developed a new multivariate linear regression and adaptive neuro-fuzzy inference processing to predict UCS for weathered granite grade III based on simple input data from point load index, Schmidt rebound hardness, and P wave velocity. Data from 85 rock core samples of this granite type have been selected. By using multivariate regression analysis, three models with two independent variables and one model with three independent variables have been developed. Furthermore, another model has been obtained by using a neuro-fuzzy logic analysis. The root means square error, RMSE, coefficient of determination, R2, and the mean absolute percentage error, MAPE, were used as the evaluation criteria of the accuracy of the models. The results have indicated that the regression-based and neuro-fuzzy models are effective, but the accuracy of the neuro-fuzzy model is in good agreement with the realistic data from the direct test. [ABSTRACT FROM AUTHOR]

Caving process is a complex dynamic phenomenon influences safety and productivity of coal longwall mining. It improves safety due to reduction of load on support, face convergence and abutment stresses. Proper caving with respect to the quality and time of occurrence ensures continuity of operation and subsequently, the productivity of coal extraction. Therefore, a reliable prediction of strata behaviour and its caving potential are imperative in design of longwall projects. This paper presents a hybrid probabilistically qualitative–quantitative model to evaluate cavability of immediate roof and to estimate main caving span in longwall mining by combining empirical model and numerical solution. For this purpose, numerical simulation was incorporated to Roof Strata Cavability index (RSCi) as summation of ratings for nine significant parameters. Distinct element code was used to simulate numerically main caving span corresponding to various RSCi classes probabilistically. The newly proposed model was verified against actual field data collected from different longwall panels around the world. The results of proposed model agreed well with those of collected data. [ABSTRACT FROM AUTHOR]

Aluminum alloy is the main structural material of aircraft, launch vehicle, spaceship, and space station and is processed by milling. However, tool wear and vibration are the bottlenecks in the milling process of aviation aluminum alloy. The machining accuracy and surface quality of aluminum alloy milling depend on the cutting parameters, material mechanical properties, machine tools, and other parameters. In particular, milling force is the crucial factor to determine material removal and workpiece surface integrity. However, establishing the prediction model of milling force is important and difficult because milling force is the result of multiparameter coupling of process system. The research progress of cutting force model is reviewed from three modeling methods: empirical model, finite element simulation, and instantaneous milling force model. The problems of cutting force modeling are also determined. In view of these problems, the future work direction is proposed in the following four aspects: (1) high-speed milling is adopted for the thin-walled structure of large aviation with large cutting depth, which easily produces high residual stress. The residual stress should be analyzed under this particular condition. (2) Multiple factors (e.g., eccentric swing milling parameters, lubrication conditions, tools, tool and workpiece deformation, and size effect) should be considered comprehensively when modeling instantaneous milling forces, especially for micro milling and complex surface machining. (3) The database of milling force model, including the corresponding workpiece materials, working condition, cutting tools (geometric figures and coatings), and other parameters, should be established. (4) The effect of chatter on the prediction accuracy of milling force cannot be ignored in thin-walled workpiece milling. (5) The cutting force of aviation aluminum alloy milling under the condition of minimum quantity lubrication (mql) and nanofluid mql should be predicted. [ABSTRACT FROM AUTHOR]

Aims: Quantification of variations in plant available water holding capacity (PAWC) of soils helps to improve yield forecast and inform spatially variable management practices in dryland agriculture systems. We developed and tested a general inverse approach to estimate PAWC from crop yield. Methods: The APSIM model was used to simulate wheat yield on synthetic soils with contrasting PAWC and climates. The simulated results were used to develop an empirical model to relate simulated yield to PAWC. The empirical model was inversely used to predict PAWC from observed crop yield. Potential prediction ability was quantified using independently simulated wheat yield on actual soils. The actual ability was assessed with measured wheat yields and PAWC. Results: The approach had higher accuracy for sites with high rainfall or dominant summer rainfall. It could potentially provide acceptable PAWC predictions across contrasting climate regions (prediction error < 37 mm, 33.5%). The prediction error using crop yield against measured PAWC was <25 mm (26.5%). Conclusions: Our results demonstrate that soil PAWC can be reliably predicted from crop yield. This approach provides an alternative way to predict PAWC rather than directly measuring it via soil sampling, with profound implications for reducing labour and costs. [ABSTRACT FROM AUTHOR]

The modern production of quality products is based on advanced technologies, modern equipment, qualified personnel, and careful control of processes and products. These are necessary but not sufficient conditions. The competitiveness of an enterprise is largely determined by an effective quality management system (QMS) and the organization of production and management. The problem of combining the capabilities of all control systems of the enterprise is challenging. The existing integrated management systems require optimization, which is impossible without modern information technologies. The specific features of the integration of QMS with the related standards using machine learning methods are considered. [ABSTRACT FROM AUTHOR]

This work studied the transesterification reaction of palm kernel oil to produce Biodiesel FAME, using as catalyst KOH incorporated as a potassium methoxide intermediate. The catalytic tests were performed modifying representative variables such as reaction temperature (°C), methanol/oil molar ratio, and catalyst content (%KOH). The experimental data were adjusted to a linear empirical model, finding that the best experimental condition was observed at 50 °C with a methanol/oil ratio of 5.5 and a% KOH of 0.8. Finally, the FAME was characterized by FTIR spectroscopy, gas chromatography, and ASTM quality control techniques for analysis of cold properties, transport properties, and combustion properties. The reaction rate was determined and a reaction mechanism was proposed based on the experimental results. [ABSTRACT FROM AUTHOR]

Gypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Several parameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying all these parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the paste strength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neural network (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) and blast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constituted the input variables of the models while the paste strength was the target output. The trained and tested GRNN model was found to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing duration and temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed for the strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset used for the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm (GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were also tested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed the outperformance of the GRNN and the GA_based empirical models over the others. [ABSTRACT FROM AUTHOR]

Ground-motion models (GMMs) are a key driver for the results of probabilistic seismic hazard analyses and their uncertainty. GMMs that bridge seismological and empirical approaches are an effective tool to represent the distribution of ground motion and its uncertainty in seismic hazard assessment. A methodology is presented that uses ground-motion data recorded at seismograph sites in eastern North America and shows how they can be used to calibrate simple scalable seismological models of ground-motion generation and propagation. Such GMMs can directly account for the gross features of source scaling (magnitude and stress parameter), attenuation, site response, and kappa effects. It is shown that, by application of appropriate GMM strategies, sigma (aleatory uncertainty) could be greatly reduced, resulting in lower calculated hazard for nuclear plants founded on rock. This reduction in sigma requires that high-quality seismic monitoring (e.g., broadband seismograph stations) be installed and operated over a period of years (in addition to strong-motion stations), and that an ongoing investment be made in data analysis and targeted GMM development using the data. [ABSTRACT FROM AUTHOR]

Saltwater intrusion in the estuary area threatens the use of freshwater resources. If river discharge increases to a critical value, then saltwater intrusion frequency and salinity level decreases. In this study, long-term river discharge and tidal range data in the Yangtze River Estuary (YRE) and salinity data obtained in the upper South Branch of the YRE were used to analyze the characteristics of different variables and the basic law of their interactions. Two methods, namely, the material analysis method and empirical models, were applied to determine the critical river discharge for saltwater intrusion control. Results are as follows: (1) the salinity might exceed the drinking water standard of China when the river discharge was less than 30,000 m3/s. Approximately 69% of salinity excessive days occurred when the river discharge was less than 15,000 m3/s; (2) the tidal range in the YRE roughly varied in sinusoidal pattern with a 15-day cycle length. Exponential relationship existed between daily salinity (chlorinity) and daily mean tidal range. Combining these two features with the cumulative frequency statistics of tidal ranges, it was showed that notable saltwater intrusion occurred when the tidal range was more than 2.7 m at Qinglonggang station. Moreover, the critical discharge was found to be slightly higher than 11,000 m3/s; (3) various of empirical models for salinity prediction could be chosen to calculate the critical discharge. The values obtained by different models were in the range of 11,000-12,000 m3/s; (4) the proposed critical discharge to reduce notable saltwater intrusion was 11,500 m3/s. After the Three Gorges Reservoir operation, the minimum river discharge into the YRE in 2008–2017 was below the critical discharge, thereby suggesting an increase in the minimum river discharge by reservoir regulation in drought periods. [ABSTRACT FROM AUTHOR]