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Recent studies with the new program RoseBoom© claim it can predict reliable detonation parameters only based on the structural formula, without the need of a heat of formation or density obtained using a different method. In this study, it was investigated how big the impact on the calculated detonation parameters is, when one uses the density and heat of formation predicted by RoseBoom2.2© vs. densities and the heat of formations published with the corresponding molecules. A range of traditionally used models in terms of the sensitivity to the accuracy to the input values is tested. Furthermore, it proofs the need to agree on one software for predicting the performance of energetic materials, starting with the input of values of energetic materials. Additionally, it puts further trust into the predictions by RoseBoom© and raises awareness of the uncertainty of published performance values. [ABSTRACT FROM AUTHOR]

This study investigates underwater depth estimation in the coastal region of Kish Island (KI) in the northern Persian Gulf (PG), Iran, using data from three satellite sensors: WorldView-2 (WV2), Sentinel-2B MSI (S2), and Landsat-8 OLI (L8). Field measurements were conducted with a portable echosounder and a handheld GPS device, resulting in a dataset of 689 points, with 460 used for model training and 229 reserved for validation and accuracy assessment. Atmospheric correction was then performed using the ACOLITE module, due to its adaptability across various sensors. Conventional (linear and ratio transform) and machine learning (ML)-based (random forest [RF], fuzzy inference system [FIS], and adaptive neuro-fuzzy inference system [ANFIS]) techniques were applied to extract depth values from the satellite data. The results showed that L8 provided higher accuracy and precision for both conventional and ML-based methods compared to WV2 and S2. ML-based techniques, especially ANFIS, further improved accuracy, particularly when using WV2. Although depth estimation for areas with less than 2 m of depth was less accurate, both approaches showed high accuracy for depths of 2–4, 4–8, and more than 8 m, respectively, with a mean absolute percentage error (MAPE) ≤ 40%. Final results demonstrated that the ANFIS-L8 model performed best, achieving R² values of 0.951 for training and 0.915 for testing, RMSE values of 0.822 m for training and 1.06 m for testing, and a MAPE of 10.95%. Overall, ML-based methods, particularly ANFIS, demonstrated superior performance, offering valuable insights for coastal and marine applications. [ABSTRACT FROM AUTHOR]

BACKGROUND: The common cocklebur (Xanthium strumarium L.) is an invasive weed species in the Asteraceae family that probably originated in Central or South America but has now spread worldwide, where it infests numerous crop fields, including sorghum. It is also a significant invasive weed in various parts of Ethiopia, including the eastern region. In this study field experiments were conducted to investigate the effect of various densities of sorghum and X. strumarium on their growth and reproductive output at Haramaya and Babile Research Stations of Haramaya University in Eastern Ethiopia during the 2022/2023 growing seasons. RESULTS: Sorghum yield loss was greatly affected by X. strumarium density, reaching maximum yield losses of 79.2% and 93.1% at the maximum weed density at Haramaya and Babile, respectively. The presence of X. strumarium in sorghum resulted in reduced aboveground dry matter and leaf area index (LAI). The extent of this reduction depended on the density of X. strumarium. As crop density increased, X. strumarium dry matter, LAI, and bur production m−2 decreased. The highest bur production per unit area for X. strumarium was observed at its highest density (16 plants m−2) with 1097 and 869 burs per unit area at Haramaya and Babile, respectively. CONCLUSION: These results indicated that higher densities of sorghum were effective in suppressing the bur production of this weed, leading to reduced yield loss. Therefore, sorghum competitiveness against X. strumarium can be improved using higher crop densities. This could play a key role in weed management by reducing the use of herbicides and mechanical controls, thereby forming an important part of integrated weed management. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to determine the most suitable model for predicting global solar radiation in the Lakes Region (Isparta, Burdur, Antalya). Through ATATEK-Solar software, a total of 15 models were tested, including 14 empirical models from the literature and a new artificial intelligence-supported model. Each model was analyzed with three different optimization algorithms (Nelder-Mead Simplex, Pattern Search, Simulated Annealing). In province-based evaluations, the Model 9 (RMSE: 0.1507, R²: 0.9990) for Isparta, and the Model 14 for Burdur and Antalya (RMSE: 0.1940, R²: 0.9992 and RMSE: 0.2218, R²: 0.9987, respectively) provided the most successful results. In regional analysis results, while the Model 5 (RMSE: 0.2626, R²: 0.9980) gave the lowest average error, the Model 13 (RMSE: 0.2649, R²: 0.9979, standard deviation: 0.0122) showed the highest consistency. These models were followed by the Model 6 (RMSE: 0.2646, R²: 0.9979, standard deviation: 0.0444). Although the Model 15 gave the best results in Burdur and Antalya, it had a high standard deviation value (0.2201) due to its low performance in Isparta. The characteristic features of the Lakes Region, including the presence of lake ecosystems, elevation differences, and the resulting microclimatic diversity, necessitate a regional approach in predicting global solar radiation. In this context, the Model 13 has been determined as the most suitable model that can be used throughout the region with its low error rate and high consistency. The obtained results can provide reliable predictions in evaluating the solar energy potential of the region and designing solar energy systems. [ABSTRACT FROM AUTHOR]

DNI assessment is essential for developing any CSP projects. In this manuscript, an approach is made for the analysis and estimation of DNI by developing the empirical models for India. The historical data of around 15 years (1986-2000) of DNI and cloud cover (CC) from six representative locations of major climatic zones namely Bhopal, Jaipur, Srinagar, Patna, New Delhi & Thiruvanantpuram have been employed. From these locations, first four have been used to develop the models correlating DNI and cloud cover; however, rest two were used to evaluate their validity. The standard statistical predictor's viz. CoD, MPE, MBE, RMSE, skewness, kurtosis & chi-square were applied to evaluate these empirical models. The CoD was observed greater than 0.8 while performance criteria have low and acceptable values (MPE-11.4% to -13.77%; MBE 0.49 to 0.96; RMSE 1.22 to 1.44; Skewness 0.90 to -0.15; Kurtosis 0.90 to -1.36 and Chi-square 6.87 to 8.50). The adequecy of empirical models was evaluated through comparing estimated and measured DNI for New Delhi and Thiruvanantpuram which coincides very closely. The study established an approach to estimate DNI using cloud cover data which may facilities CSP projects developers for site selection and decision making in the absence of ground data. [ABSTRACT FROM AUTHOR]

The evolution of the mechanical properties of Zr-2.5Nb pressure tubing during irradiation is dependent on dislocation loop densities that are represented by the broadening of X-ray diffraction lines. Empirical models for the integral breadth of the diffraction peaks as a function of operating conditions have been developed to predict the mechanical properties of CANDU reactor pressure tubes as a function of fast neutron flux, time and temperature. Apart from predicting mechanical property changes based on integral breadth measurements, a new model has been developed to retrospectively deduce abnormal operating temperatures of ex-service pressure from the measured line broadening. The application of integral breadth measurements to assess mechanical properties and temperature variations in pressure tubes is described and discussed in terms of the implications for pressure tube integrity. [ABSTRACT FROM AUTHOR]

Confined masonry (CM) is becoming a widely adopted construction building method even in earthquake-prone regions due to its economic viability, construction simplicity, and material availability. However, existing empirical models for predicting lateral and cracking loads often fall short due to varied material properties, detailing of confining elements and construction practices. In this study, machine learning (ML) algorithms, such as Extreme Gradient Boosting (XGB), Random Forest (RF), and Extremely Randomized Tree (ERT), were employed to predict the seismic performance of CM walls, focusing on maximum lateral load capacity and cracking load based on an experimental dataset from 84 published studies, with 59 samples for training and 25 for testing. Different material, load, geometrical, and reinforcement detailing, related to the lateral load capacity of CM, were considered. This study also compares the performance of the existing empirical equations against the proposed ML models. The ML models demonstrated strong predictive capabilities, outperforming empirical equations in both maximum lateral load and cracking load predictions, with XGBoost yielding the highest accuracy, reflected by R2 values of 0.903 for lateral load and 0.876 for cracking load predictions, and lowest the RMSE (28.742 for lateral and 23.982 for cracking load). Additionally, a comparative analysis shows that while some empirical equations produce reasonably accurate predictions, most exhibit significant deviations from experimental results. This study finally employs Partial Dependence Plot (PDP) analysis to explain the importance and contribution of the factors that influence the lateral strength, and concludes that ML models, especially XGBoost, are highly effective in capturing the complex behavior of CM walls under vertical and lateral loads, making them valuable tools for enhancing the accuracy of seismic performance evaluations. [ABSTRACT FROM AUTHOR]

Tractor is the most prominent off-road agricultural machinery that is significant to the global food security. The tractive modelling of tyre–soil interaction and agricultural implement dynamics is a complex phenomenon that require holistic approach. Terramechanics techniques such as empirical, semi-empirical, analytical, and numerical methods such as finite element models and discrete element models have gained traction in tractive performance studies. Some of these approaches are premised on large arrays of variables for modelling tractive performance based on the soil–tyre and tools interactions. In this study, soft computing in R software domain was used to model the tractor tractive performance during ploughing operations on a tropical Alfisol. The research farm at the National Centre for Agricultural Mechanization was used for the field experiment. The experimental design was a nested-factorial under a Randomized Complete Block Design having three replications. The input factors were tractor power size, T, (60, 65, and 70 hp); tyre inflation pressure, P, (83, 124, and 165 kPa); implement configuration, I, (2 and 3 bottoms disc plough); and operational speed, S, (6.31, 7.90, 9.47, 11.05, and 12.63 km/h). Standard procedures were followed to obtain the measured parameters in the field, which were statistically analysed. Correlation analysis and analysis of variance of the measured parameters at 5% significance level were established. Multiple linear regression was used to develop the model, validated using the 10-fold cross-validation method. The results revealed that the evaluated variables have a range of 1.56–7.79 kN, 5.15–27.20%, 9.10–32.00 cm, 4.50–13.94%, 1.31–1.67 g/cm3, 95.89–207.78 kPa, and 98.67–295.56 for draught, wheel slip, depth of cut, moisture content, bulk density, cone index (CI), and shear stress, respectively. A positive correlation exists between the towing force (TF) and the measured variables except for the shear stress and CI. The final developed model has seven variables for predicting TF with a 6.5% error and an average of 0.4735 cross validation root mean square error. The model quality of fit achieved an RAdj2=0.8754{R}_{\text{Adj}}^{2}=0.8754 which satisfactorily described the response variable. The study provides insights into tractive dynamic systems modelling of machine, tractive medium (soil), and agricultural tools anchored on soft computing approach. Its adoption will assist in quality ploughing operation integrating the variables established in the model.

This paper proposes an improved high-precision surface solar radiation estimation model using the integration of the local meteorological data and air quality index based linear regression analysis. The proposed model was evaluated and compared to 8 conventional models and one generated by the commonly used PVsyst simulation software. The actual solar radiation, meteorological data and air quality index collected over 10 years (during 2011-2021) from standard measuring stations located at the northern zone of Thailand were used for developing the models while the collected data year 2022 were used for validating the developed models compared to the conventional models. The statistical error estimations in terms of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used for the precision evaluation. The study found that the proposed models achieved better prediction results and the highest precision for monthly estimating of solar radiation than the other models by having the highest estimation precision of 94.70-97.19% compared to 87.53-96.74% of the conventional models and 90.38-95.96% of the PVsyst program. [ABSTRACT FROM AUTHOR]

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In this work, we present a study about the application of bi-temporal, large interval aerial laser scans for constructing of tree growth models and estimating site index quality based on the measured increments from the laser scans. We compared two LiDAR scans with 14 years of difference in the national park area, where most areas are unmanaged. We derived the increment curve based on the Chapman-Richard growth formula. We used site index estimates from forest management plans from the national scale as the ground truth (both absolute and relative). We constructed three predictive models for site index estimates from bi-temporal scans, in modalities with and without stand age. Including the stand age improved all models, but even without the age, the models performed relatively well for differentiation between better and worse sites. At this moment, it is not directly possible to estimate age from remotely sensed data, but consistent monitoring, with laser scanning or photogrammetry, undoubtedly detects the harvest or dieback, so in the future, age can be considered as a variable easily estimated from remotely sensed data and so remote sensed material are viable source for understanding of forest growth and production. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models' prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes. [ABSTRACT FROM AUTHOR]

The dissolution of Ca and Mg in soil and their translocation in plants from different families when using different doses of liming materials of industrial waste origin have not yet been sufficiently studied. In this study, the influence of increasing doses of ameliorants of carbonate (dolomite flour—DF) and silicate (blast furnace slag—BFS) natures on the change in acid–base properties of soddy-podzolic light loamy soil, yield, and chemical composition of plants of the families Gramíneae (spring wheat), Brassicáceae (spring rapeseed), and Leguminósae (vetch and beans) was studied in five-year pot experiments. In the five-year experiments, the ameliorant of a carbonate nature showed greater effect on soil acid–base properties than that of a silicate nature. A return to the initial state of soil pH was not established in any of the treatments. Both ameliorants showed similar effects on wheat straw biomass, but DF had a greater positive effect on wheat grain yield than BFS. Regardless of the dose of DF applied, the accumulation of Ca and Mg by the plants throughout the study period was higher than when BFS was applied. Among the studied plants, those of the family Brassicáceae were the most responsive to liming and, at the same time, showed high ecological adaptability. Differences in the effects of the two ameliorants on the soil chemical properties were more significant than differences in their effects on plant productivity. [ABSTRACT FROM AUTHOR]

Polymer and ceramic-based composites offer a unique blend of desirable traits for improving dielectric permittivity. This study employs an empirical approach to estimate the dielectric permittivity of composite materials and uses a finite element model to understand the effects of permittivity and filler concentration on mechanical and electrical properties. The empirical model combines the Maxwell-Wagner-Sillars (MWS) and Bruggeman models to estimate the effective permittivity using Barium Titanate (BT) and Calcium Copper Titanate Oxide (CCTO) as ceramic fillers dispersed in a Polydimethylsiloxane (PDMS) polymer matrix. Results indicate that the permittivity of the composite improves with increased filler content, with CCTO/PDMS emerging as the superior combination for capacitive applications. Capacitance and energy storage in the CCTO/PDMS composite material reached 900 nF and 450 nJ, respectively, with increased filler content. Additionally, increased pressure on the capacitive model with varied filler content showed promising effects on mechanical properties. The interaction between BT filler and the polymer matrix significantly altered the electrical properties of the model, primarily depending on the composite's permittivity. This study provides comprehensive insights into the effects of varied filler concentrations on estimating mechanical and electrical properties, aiding in the development of real-world pressure-based capacitive models. [ABSTRACT FROM AUTHOR]

The article provides a comprehensive review of the latest developments in the field of laser surface hardening (LSH) and its modeling techniques. LSH is a crucial process for enhancing the surface properties of metals, particularly their hardness and wear resistance, without compromising their bulk properties. This review highlights the fundamental principles of LSH, the types of lasers used, and the key parameters influencing the hardening process. It delves into various modeling approaches, including finite element method (FEM) simulations, analytical models, and empirical models (using statistical methods), emphasizing the integration of advanced computational techniques such as machine learning and artificial intelligence to improve the accuracy and efficiency of LSH simulations. The review also explores practical applications across different industries, showcasing how LSH models have been used to solve real-world challenges in the automotive, aerospace, and tool manufacturing sectors. Finally, it addresses current limitations and outlines future research directions, suggesting potential areas for further advancements in the modeling and application of LSH processes. [ABSTRACT FROM AUTHOR]

The present experimental study assesses the mechanical properties of glass/carbon/glass hybrid composite laminates after being exposed to moisture in a deep freezer and elevated temperatures for extended periods. The top and bottom layers of the hybrid laminates are reinforced with glass fibre, and the middle layer is reinforced with carbon fibre using the epoxy matrix as a binder polymer material. The hybrid laminates were manufactured using the resin transfer moulding method, and their compressive and tensile properties were determined using a tensile testing machine. The storage modulus, loss modulus, and damping factors of all groups of laminates were identified using a dynamic mechanical analysis as a function of temperature and vibration frequency. The experimental results on compressive and tensile properties revealed slight variations when the hybrid laminates were kept at low temperatures in a deep freezer for extended periods. This might occur due to the increasing molecular crosslinking of the polymer network. As the testing temperature increased, compressive, tensile, storage modules, loss modulus, and damping factors decreased. This might occur due to the increasing mobility of the binder material. Particularly, the highest stiffness parameters were obtained at −80 °C/GCG (glass/carbon/glass) laminates due to the presence of a beta transition in the glassy region. The relationships between the glass transitions and the targeted frequencies were characterized. The values of the glass transition shift towards higher temperatures as the frequency increases. This might occur due to a reduction in the gaps between the crosslinking of the epoxy network when the frequency increases. The accuracy of the storage modulus results was compared with the empirical models. The model based on the Arrhenius law provided the closest correlation. Meanwhile, another model was observed that was not accurate enough to predict when gamma and beta relaxations occur in a glassy state. [ABSTRACT FROM AUTHOR]

This paper presents empirical models for the frequency content parameters of offshore ground motions in the Japan Trench area based on the S-net dataset, comprising 6,436 recordings from 496 events. Significant variations in frequency content among shallow crustal, upper-mantle, subduction interface and subducting slab are revealed. Empirical models for To, Tavg, and Tm are proposed, considering moment magnitude, source distance, focal depth, and tectonic types. A comparative analysis with four existing land models highlights differences. Total residual results indicate that land models, based on shallow crustal datasets, tend to overestimate observed frequency content parameters in the Japan Trench area. [ABSTRACT FROM AUTHOR]

The aim of this work was to conduct a rheological assessment of mortars based on the automated slump test. Using this test to monitor the transient and permanent regime of slump and its spread, the parameters were correlated with the rheological properties of the mortars. Based on dimensionless slump/spread, yield stress and viscosity, empirical models were developed for obtaining rheological parameters, which provided correlations with good coefficients of determination (R2 > 0.85). Through analysis of the slump behaviour of the mortars, a correlation between the viscosity and the maximum slump velocity was obtained (R2 = 0.90), showing that viscosity could be determined based on the slump velocity. Furthermore, based on a physical description of the slump test related to the Reynolds number, three distinct stages in the slump process were defined: the viscous phase (characterised by high viscosities), the intermediate phase (the coexistence of viscous and inertial effects) and the inertial phase (characterised by low viscosities and a higher sensitivity to lifting of the mould). The dynamic similarity of the analysed mortars (vertical Reynolds number/radial Reynolds approximately equal to one) indicated that the shear rate could be measured based on the vertical direction of the flow (slump). [ABSTRACT FROM AUTHOR]

Abstract Evapotranspiration (ETo) is an important component of the hydrological cycle and reliable estimates of ETo are essential for assessing crop water requirements and irrigation management. Direct measurement of evapotranspiration is both costly and involves complex and intricate procedures. Hence, empirical models are commonly utilized to estimate ETo using accessible meteorological data. Given that empirical methods operate on various assumptions, it is essential to assess their performance to pinpoint the most suitable methods for ETo calculation based on the availability of input data and the specific climatic conditions of a region. This study aims to evaluate different empirical methods of ETo in the tropical highland Udhagamandalam region of Tamil Nadu, India, utilizing sixty years of meteorological data from 1960–2020. In this study, 8 temperature-based and 10 radiation-based empirical models are evaluated against ETo estimates derived from pan evaporation observation and the FAO Penman–Monteith method (FAO-PM), respectively. Statistical error metrics indicate that both temperature and radiation-based models perform better for the Udhagamandalam region. However, radiation-based models performed better than the temperature based models. This is possibly due to the high humidity of the study region throughout the year. The results suggest that simple temperature and radiation-based models using minimum meteorological information are adequate to estimate ETo and thus find potential application in agricultural water practices, hydrological processes, and irrigation management.

The development of equipment to mechanise the handling and processing of neem seeds requires an understanding of their physical characteristics. Thus, the physical properties of neem seeds were assessed at moisture content levels ranging from 7.78% to 20.20% on a dry basis. Standard procedures were used to evaluate the seeds' axial dimensions, weight, sphericity, aspect ratio, true and bulk densities, porosity, surface area, and coefficients of dynamic friction (μ) on surfaces made of galvanised steel, wood, and glass. Except for the true and bulk densities, all of the evaluated properties showed an increase in the results for the moisture range mentioned above. The μ on galvanized steel, wood and glass surfaces ranged from 129.00 – 145.90, 127.80 – 141.00, and 130.00 – 144.00, respectively. All the measured parameters had empirical models established for them. The high correlation coefficients of each parameter's model suggest that it can be simulated within the moisture domain under investigation.

The evolution of the mechanical properties of Zr-2.5Nb pressure tubing during irradiation is dependent on dislocation loop densities that are represented by the broadening of X-ray diffraction lines. Empirical models for the integral breadth of the diffraction peaks as a function of operating conditions have been developed to predict the mechanical properties of CANDU reactor pressure tubes as a function of fast neutron flux, time and temperature. Apart from predicting mechanical property changes based on integral breadth measurements, a new model has been developed to retrospectively deduce abnormal operating temperatures of ex-service pressure from the measured line broadening. The application of integral breadth measurements to assess mechanical properties and temperature variations in pressure tubes is described and discussed in terms of the implications for pressure tube integrity.

Evapotranspiration (ETo) is an important component of the hydrological cycle and reliable estimates of ETo are essential for assessing crop water requirements and irrigation management. Direct measurement of evapotranspiration is both costly and involves complex and intricate procedures. Hence, empirical models are commonly utilized to estimate ETo using accessible meteorological data. Given that empirical methods operate on various assumptions, it is essential to assess their performance to pinpoint the most suitable methods for ETo calculation based on the availability of input data and the specific climatic conditions of a region. This study aims to evaluate different empirical methods of ETo in the tropical highland Udhagamandalam region of Tamil Nadu, India, utilizing sixty years of meteorological data from 1960–2020. In this study, 8 temperature-based and 10 radiation-based empirical models are evaluated against ETo estimates derived from pan evaporation observation and the FAO Penman–Monteith method (FAO-PM), respectively. Statistical error metrics indicate that both temperature and radiation-based models perform better for the Udhagamandalam region. However, radiation-based models performed better than the temperature based models. This is possibly due to the high humidity of the study region throughout the year. The results suggest that simple temperature and radiation-based models using minimum meteorological information are adequate to estimate ETo and thus find potential application in agricultural water practices, hydrological processes, and irrigation management. [ABSTRACT FROM AUTHOR]

The growing popularity of GIS technology in Ethiopia has encouraged multiple scholars to investigate landslide hazards using quantitative approaches, despite its limitations. The present review examined the approach used in the evaluation of landslide hazards by five prior studies that shared catchments. The review results reveal that the controlling factors assumed by the five researchers were inconsistent and resulted in highly divergent frequency ratio (FR) values, even for the same factors. This implies that the contribution of a single instability factor can be inferred sufficiently for landslide hazard assessment and mapping; otherwise, the results are highly subjective and disputable. Since the soil type in the region was alluvial-colluvial in the five studies, and a majority of the failures occurred shortly after rainfall, rainfall data and basic soil properties (classification and shear strength) should not be overlooked. In addition to the nonstandard use of morphometric parameters, the inherent limits of GIS methodologies, the omission of hydrogeotechnical properties, and the observed subjective outcomes make the GIS-based approach imprecise, error-prone, and doubtful. The total effect will result in ineffective early warning systems and unworthy mitigation measures, resulting in significant life costs and damage. As a result, it is recommended that GIS technology should be coupled with software (TRIGRS, Scoops3D, SINMAP, OpenLISEM, GLM, and SLIP) that considers hydrogeotechnical properties to provide more reliable conclusions. In addition to using instability factors consistently, regional statistical correlations of all morphometric parameters can be developed, allowing for less complex and realistic empirical models to be used. [ABSTRACT FROM AUTHOR]

Soil erosion has attracted the attention of researchers and managers as an environmental crisis. One of the effective factors in soil erosion is land use and land cover (LULC) change. Accordingly, the purpose of the present study is to explore the effect of LULC change on soil erosion in a semi-arid region in the southwest of Iran. LULC change map was generated over a period of 30 years (1989–2019) using a new approach in which the Normalized Difference Vegetation Index (NDVI) time series of each year was classified in the google earth engine (GEE). For classifying the NDVI time series, a nonparametric Support Vector Machine (SVM) classification method was employed. The LULC maps were also used as an input factor in the soil erosion estimation model. The amount of soil erosion in the region was estimated using the Revised Universal Soil Loss Equation (RUSLE) empirical model in the Geographical Information System (GIS) environment. Validation of LULC maps generated in GEE indicated overall accuracy higher than 86% and the kappa coefficient higher than 0.82. The study of LULC change trends revealed that the area of forests, pastures, and rock outcrop in the region has diminished, while the area of agricultural and residential LUs has been expanded. Also, the highest rate of LULC conversion was related to the conversion of forests to agricultural lands. Estimating the amount of soil erosion in the region using the RUSLE model indicated that the average annual erosion in 1989 and 2019 was 15.48 and 20.41 tons per hectare, respectively, which indicates an increase of 4.93 tons in hectares, while the hot spots of erosion in the area have increased at the confidence levels of 90, 95, and 99%. Finally, matching the LULC change map with the soil erosion map revealed that the degradation of forests and pastures as well as their conversion to agricultural lands has had the greatest impact on the increase in soil erosion. [ABSTRACT FROM AUTHOR]

This study presents new neural‐network (NN)‐based models to predict the axial load‐carrying capacities of fiber‐reinforced polymer (FRP) bar reinforced‐concrete (RC) circular columns. A database of FRP‐reinforced concrete (RC) circular columns having outside diameter and height ranged between 160–305 and 640–2500 mm, respectively was established from the literature. The axial load‐carrying capacities of FRP‐RC columns were first predicted using the empirical models developed in the literature and then predicted using deep neural‐network (DNN) and convolutional neural‐network (CNN)‐based models. The developed DNN and CNN models were calibrated using various neurons integrated in the hidden layers for the accurate predictions. Based on the results, the proposed DNN and CNN models accurately predicted the axial load‐carrying capacities of FRP‐RC circular columns with R2 = 0.943 and R2 = 0.936, respectively. Further, a comparative analysis showed that the proposed DNN and CNN models are more accurate than the empirical models with 52% and 42% reduction in mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively involved in the empirical models. Moreover, within NN‐based prediction models, the prediction accuracy of DNN model is comparatively higher than the CNN model due to the integration of neurons in each layer (9‐64‐64‐64‐64‐1) and embedded rectified linear unit (ReLu) activation function. Overall, the proposed DNN and CNN models can be utilized as paramount in the future studies. [ABSTRACT FROM AUTHOR]

Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Polyester Glass Fiber Reinforced Plastic (Polyester GFRP), a thermosetting plastic comprised of glass fiber and polyester polymer compounds, is extensively utilized in high-speed trains. Unraveling its pyrolysis mechanism is crucial as it significantly influences the combustion characteristics and fire safety aspects. Currently, kinetic research on polyester GFRP primarily focuses on employing the Coats-Redfern method to derive a theoretical kinetic model. However, the pyrolysis process of polyester GFRP is complex and the aforementioned theoretical model fails to accurately describe the pyrolysis mechanism. Therefore, this study seeks to utilize the Sestak and Berggren (SB) model as a methodological approach to reveal the complex reaction mechanism during the pyrolysis process. Based on thermogravimetric analysis, the entire pyrolysis process of polyester GFRP is divided into two primary stages. Furthermore, model-free methods are employed to ascertain the activation energy and pre-exponential factor. The results show that the fitted empirical models of the two main pyrolysis stages are f(α)=(1−α)1.47[−ln(1−α)]1.50 and f(α)=(1−α)1.77[−ln(1−α)]1.72, respectively. The predicted results are in good agreement with experimental data under different heating rates, which indicates that the empirical model can sufficiently describe the pyrolysis process of polyester GFRP. [ABSTRACT FROM AUTHOR]

In an attempt to reduce the consumption of natural resources, much research has focused on the development of mix designs with waste aggregates. While this research has demonstrated feasibility, investigations into the broader mechanical properties of concrete with various types of waste aggregates have been limited. To this end, this paper studies a wide variety of concrete properties with plastic, rubber, slag, glass and recycled concrete aggregates by analysing a large database of 5321 concrete mixes. The effects of waste aggregate type and replacement ratio on mechanical properties, workability and durability are investigated and relationships between the mechanical properties of concrete with various types of waste aggregates are quantified and compared to the existing design codes. The findings from this study can be used to assist in identifying optimum waste aggregate type and replacement ratio based on intended use and the databases compiled will assist in future data-driven modelling approaches.

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

The article is devoted to the assessment of attenuation in wireless links in various environmentalconditions. A simplified damping estimation methodology based on the Floating-intercept (FI) modelis presented [1]. The methodology used makes it possible easily estimate the attenuation of wirelesslinks in various environmental and propagation conditions. A review of ground propagation modelswas made, showing their complexity and the structure of their analytical description.Keywords: propagation propagation models, multipath effects, environmental conditions, wirelesslinks, empirical models, range, inter-channel interference, internal compatibility, external compatibility

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate both the temperature and the initial moisture content of the material in mathematical models of drying. For this, empirical lumped parameter models were fitted based on experimental data of moisture over time. Furthermore, a new semi‐empirical drying kinetics model was applied. This model was developed using the generalization of arbitrary order of the Lewis equation obtained through the Laplace transform. After performing the fit, the fractional order model for drying wheat seeds as a temperature function was generalized. Distributed parameter models were also fitted to evaluate the influence of initial moisture content on drying kinetics and to estimate the moisture profile along the position inside the seed. It was verified that the fractional order model presented statistical results similar to models with a higher number of constants, being used to generalize the kinetic drying model for the three wheat cultivars. Generalized models showed better fits for the 3 cultivars with first‐degree function, and the maximum global deviation was 10%, 15%, and 20% for the cultivars BRS–Atobá, BRS–Jacana, and BRS–Sanhaço, respectively. In addition, the distribution of moisture content inside the seed was verified by the distributed parameter model, which predicted the experimental data with an overall deviation of around 10%. [ABSTRACT FROM AUTHOR]

The development of equipment to mechanise the handling and processing of neem seeds requires an understanding of their physical characteristics. Thus, the physical properties of neem seeds were assessed at moisture content levels ranging from 7.78% to 20.20% on a dry basis. Standard procedures were used to evaluate the seeds' axial dimensions, weight, sphericity, aspect ratio, true and bulk densities, porosity, surface area, and coefficients of dynamic friction (µ) on surfaces made of galvanised steel, wood, and glass. Except for the true and bulk densities, all of the evaluated properties showed an increase in the results for the moisture range mentioned above. The µ on galvanized steel, wood and glass surfaces ranged from 129.00 - 145.90, 127.80 - 141.00, and 130.00 - 144.00, respectively. All the measured parameters had empirical models established for them. The high correlation coefficients of each parameter's model suggest that it can be simulated within the moisture domain under investigation. [ABSTRACT FROM AUTHOR]

In the present paper, a Cu(BDC) metal–organic framework (MOF) is chemically attached to wool via an in-situ synthesizing method. The synthesized novel biocomposite (Cu(BDC)@Wool) was applied as an easily applicable biocomposite to the effective removal of Rifampicin (RIF) from wastewater. Batch adsorption experiments were performed, and the optimum conditions of RIF adsorption (99.3%) were found to be approximately at 25 ppm initial concentration, 1 mg adsorbent, pH 2, time without ultrasonic = 30 min, and time with ultrasonic = 10 which were determined by a response surface methodology. The results of fixed bed experiments showed that better RIF removal was achieved with a low inlet RIF concentration, high adsorbent bed height, and low influent flow rate. The performance of FBAC was maximum (tb = 769, te = 1221 min; %A = 60.6%) at a flow rate of 0.5 mL/min, adsorbent bed height of 7.5 mm, and RIF of 30 ppm. The study of mathematical models shows that the Clark model has the highest correlation with the experimental data. Therefore, the novelty of this work is that ultrasound-assisted removal reduces the equilibration time and can be economically viable for removing RIF from wastewater. [ABSTRACT FROM AUTHOR]

In a fire, the high-temperature creep effect of steel cables can cause structural failure. Due to differences in fabrication and composition, the high-temperature creep behaviors of full-locked/Galfan-coated steel cables significantly differ from those of general structural steel wires. To accurately predict the creep effects of full-locked and Galfan-coated steel cables, it is necessary to obtain their creep strain at high temperatures. This study conducted high-temperature creep tests on full-locked and Galfan-coated steel cables in the range of 350°C to 500°C under different stress ratios and performed tensile strength tests after the creep test. The results showed that under high-temperature and high-stress conditions, the creep strain rate increases rapidly, and different temperatures and stress levels have an impact on the tensile strength of full-locked and Galfan-coated steel cables after exposure to high temperatures. An improved high-temperature creep time strengthening model is proposed to describe the creep behavior of full-locked and Galfan-coated steel cables at high temperatures. The creep behavior obtained in this experiment and the updated model parameters can provide essential data for accurately predicting the creep effects of full-locked and Galfan-coated steel cables. Tensile strength testing can also serve as a reference for the safety assessment and repair of prestressed structures after a fire. [ABSTRACT FROM AUTHOR]

Forecasting evaporation, an important variable in the hydrological cycle, is crucial for managing water resources and taking precautions against severe phenomena, such as droughts and floods. In this study, the prediction of daily pan evaporation was carried out in the Euphrates sub-basin, Turkey, which has different climate characteristics and is a critical region for Turkey or neighbouring countries. In this regard, two empirical models, namely the Griffith model and calibrated Hargreaves-Samani, and four ensemble empirical mode decomposition (EEMD) based data-driven models, namely EEMD-Random Forests (EEMD-RF), EEMD-Artificial Neural Network (EEMD-ANN), EEMD-Gradient Boosting Machines (EEMD-GBM), and EEMD-Regression Tree (EEMD-RT) were used for evaporation forecasting. The EEMD and Recursive Feature Elimination (RFE) were implemented as a signal decomposition technique and determination of the importance of the EEMD components, respectively. Although the empirical models yielded satisfactory performance, they predicted low and high evaporation values poorly, in general. The EEMD-RF, EEMD-ANN, and EEMD-GBM models performed better than the EEMD-RT model. The data-driven models, except EEMD-RT, outperformed the empirical models, especially regarding predicting extreme evaporation values. The sensitivity analysis indicated that wind speed, humidity, and maximum temperature could influence evaporation forecasting. This study shows that using data-driven models benefitting from EEMD and RFE can be a good alternative to empirical models for predicting evaporation. [ABSTRACT FROM AUTHOR]