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Wildfires have a strong influence on various geotechnical and hydraulic properties of soils and sediments, which may become more vulnerable to landslides or debris flows. In the present study, a case investigation of the 2018 post-wildfire debris flows in Montecito, California, USA, was conducted, with a focus on the wildfire-affected areas and debris volume estimation. Significant debris were deposited around four major creeks, i.e., Montecito Creek, San Ysidro Creek, Buena Vista Creek, and Romero Creek in January, 2018, one month after the Thomas fire. Satellite images utilizing remote sensing techniques and geographic information system (GIS) data were analyzed to identify areas affected by the wildfire. Relevant data, including the slope, catchment area, and rainfall were used in two empirical models to estimate the debris volumes around the four creeks. As compared with field observation, each debris volume estimated with these empirical models was within the same order of magnitude. The debris volumes were generally underestimated when using the rainfall recorded at the Montecito Weather Station; the estimates considerably improved with the rainfall record from the Doulton Tunnel Station. The results showed that, overall, such empirical approaches are still of benefit for engineering practice, as they are capable of offering first-order approximations. The accuracy and availability of rainfall data are critical factors; the rainfall data in mountainous areas are generally higher than in the low lands, and consequently were more suitable for debris volume estimation in the present study, where the debris flows typically occurred in areas with steep slopes and at higher elevations.

Resumo Pela complexidade anatômica e variabilidade intrínseca das propriedades encontradas dentro de uma mesma espécie, a madeira tem sido tratada, pelos diversos documentos normativos, como material isotrópico, com valores de resistência e de rigidez determinados por meio de formulações advindas da resistência dos materiais. A falta de informações experimentais, aliada ao desconhecimento da utilização de critérios de resistência para materiais anisotrópicos, aumenta as incertezas na análise de falha das estruturas quanto às estimativas das propriedades de resistência e rigidez em função da orientação das fibras. Com o objetivo de solucionar este problema, modelos empíricos têm sido desenvolvidos e incorporados em diversos documentos normativos nacionais e internacionais. Tais modelos são ajustados em função de um expoente (n) que fornece o melhor ajuste, entretanto, mesmo em se tratando do mesmo modelo, ainda há divergências com relação ao uso desse expoente. Nesse contexto, esta pesquisa objetivou propor um modelo para a estimativa do valor característico de resistência à tração inclinada (ftθ,k) e compará-lo com modelos empíricos provenientes da literatura correlata (com os ajustes ótimos do expoente n), para evidenciar o melhor ajuste. Os resultados obtidos revelaram que o modelo desenvolvido na pesquisa foi o mais preciso. Abstract Due to the anatomical complexity and intrinsic variability of properties found in the same species, wood is treated as an isotropic material by various normative codes, where the strength and stiffness values are determined by the strength of materials. The lack of experimental information combined with the lack of knowledge on using strength criteria for anisotropic materials increases the uncertainties in the analysis of structures failures with regards to estimating strength and stiffness properties depending on fiber orientation. To address this issue, empirical models have been developed and incorporated in several Brazilian and international normative documents. Those models are adjusted according to an exponent (n) that provides the best fit, however, even in the case of the same model, there are still differences regarding the use of this exponent. Hence, the aim of this study is to propose a model to estimate the characteristic off-axis tensile strength value (f tθ,k ), as well as to compare it with empirical models from the literature (with the optimal adjustments of exponent n), making it possible to highlight the best fit. The results showed that the model developed in the research study was the most accurate.

An accurate estimation of ionospheric variables such as the total electron content (TEC) is important for many space weather, communication, and satellite geodetic applications. Empirical and physics-based models are often used to determine TEC in these applications. However, it is known that these models cannot reproduce all ionospheric variability due to various reasons such as their simplified model structure, coarse sampling of their inputs, and dependencies to the calibration period. Bayesian-based data assimilation (DA) techniques are often used for improving these model’s performance, but their computational cost is considerably large. In this study, first, we review the available DA techniques for upper atmosphere data assimilation. Then, we will present an empirical decomposition-based data assimilation (DDA), based on the principal component analysis and the ensemble Kalman filter. DDA considerably reduces the computational complexity of previous DA implementations. Its performance is demonstrated by updating the empirical orthogonal functions of the empirical NeQuick and the physics-based TIEGCM models using the rapid global ionosphere map (GIM) TEC products as observation. The new models, respectively, called ‘DDA-NeQuick’ and ‘DDA-TIEGCM,’ are then used to predict TEC values for the next day. Comparisons of the TEC forecasts with the final GIM TEC products (that are available after 11 days) represent an average 42.46% and 31.89% root mean squared error (RMSE) reduction during our test period, September 2017.

Microplastic (mP) pollution has been indicated as an area of concern in the marine environment. However, there is no consensus on their potential to cause significant ecological harm and a comprehensive risk assessment of mP pollution is unattainable due to gaps in our understanding of their transport, uptake, and exchange processes. This research considers drag models that have been proposed to calculate the terminal settling velocity of regularly and irregularly shaped particles to assess their applicability in a mP modelling context. The evaluation indicates three models that predict the settling velocity of mPs to a high precision and suggests that an explicit model is the most appropriate for implementation in a mP transport model. The research demonstrates that mP settling velocity does not vary significantly over time and depth relevant to the scale of an ocean model and that the terminal settling velocity is independent of the initial particle velocity. These findings contribute towards efforts to simulate the vertical transport of mPs in the ocean, which will improve our understanding of the residence time of mPs in the water column and subsequently their availability for uptake into the marine ecosystem.

This study aimed to evaluate the predictive ability of NIRS for the estimation of the chemical composition and organic matter digestibility (OMD) of total mixed rations (TMR) for dairy cows. Moreover, herein, empirical equations based on chemical parameters were developed for calculating the OMD. Samples were collected from Galician dairy farms and were scanned in duplicate a Foss NIRSystem 6500 monochromator (1100-2500 nm). The predictive ability of NIRS models was evaluated according to the coefficient of determination in external validation ($r$2). High $r$2 (equal to or higher than 0.90) was shown in predicting chemical composition, however, the estimation of OMD was acceptable ($r$2 = 0.83). The ability of the NIRS models for estimating the chemical composition was considered excellent, with the ratio performance deviation of external validation ($RPD$) higher than 3.0, allowing for quantitative predictions. The estimation of the OMD value by NIRS was considered acceptable ($RPD$ = 2.5), and led to reduction in the standard error of external validation, in comparison of the best empirical model based on the chemical composition of samples (from ± 2.06% to ± 1.89%). Therefore, this study demonstrated that the NIRS is an effective technology for the rapid and precise nutritional evaluation of TMR in Galicia.

Accurate estimation of evapotranspiration (ET) is vital for water resources development, planning and management, particularly in the present global warming context. A large number of empirical ET models have been developed for estimating ET. The main limitations of this method are that it requires several meteorological variables and an extensive data span to comprehend the ET pattern accurately, which is not available in most developing countries. The efficiency of 30 empirical ET models has been evaluated in this study to rank them for Pakistan to facilitate the selection of suitable models according to the availability of data. Princeton Global Meteorological Forcing daily climate data with a 0.25°×0.25° resolution for 1948–2016 was utilized. The ET estimated using Penman-Monteith (PM) was considered as the reference. Multi-criteria group decision-making (MCGDM) was used for the ranking of the models for Pakistan. The results showed the temperature-based Hamon as the best model for most Pakistan, followed by Hargreaves-Samani and Penman models. Hamon also showed the best performance in terms of different statistical metrics used in the study with a mean bias (PBias) of -50.2%, mean error (ME) of -1.62 mm and correlation coefficient (R2) of 0.65. Ivan showed the best performance among the humidity-based models, Irmak-RS and Ritch among the radiation-based models and Penman among the mass transfer-based models. Northern Pakistan was the most heterogeneous region in the relative performance of different ET models.

Pan evaporation is an important climatic variable for developing efficient water resource management strategies. In the past, many machine learning models are reported in the literature for pan evaporation modeling using the different combinationof available climatic variables. In order to develop a novel model with improved accuracy and reduced computational complexity, the functional link artificial neural network (FLANN) is chosen as an architecture to estimate daily pan evaporation in three agro-climatic zones (ACZs) of Chhattisgarh state in east-central India. Single neuron and single layer in its structure make it less complex as compared to other multilayer neural networks and neuro-fuzzy based hybrid models. Estimation results obtained with the FLANN model are compared with those obtained by multi-layer artificial neural networks (MLANN) and two empirical methods using the same raw data and corresponding features. Statistical indices like root mean square error (RMSE), mean absolute error (MAE) and efficiency factor (EF) is also computed to evaluate the model performance. It is demonstrated that pan evaporation estimates obtained with the proposed FLANN models provide an improved estimation of pan evaporation (RMSE = 0.85 to 1.27 m m d - 1 , MAE = 0.63 to 0.95 m m d - 1 and EF = 0.70 to 0.89) as compared to MLANN (RMSE = 0.94 to 1.58 m m d - 1 , MAE = 0.73 to 1.14 m m d - 1 and EF = 0.62 to 0.88) and empirical (RMSE = 1.19 to 2.19 m m d - 1 , MAE = 0.91 to 1.62 m m d - 1 and EF = 0.49 to 0.88) models in different ACZs.

This study investigated the effects of different temperatures (15, 20, 25, 27, 30, 35, and 40 °C) on the development rate of Spodoptera exigua (Hübner) eggs, larvae, pupae, and total immatures on plant hosts (soybean, maize, potato, and green pea). The eggs of S. exigua developed successfully at all the tested temperatures, except at 40 °C. The total developmental time (egg-adult) decreased with an increasing temperature from 15 to 35 °C on plant hosts. Stage-specific parameters such as the lower threshold temperature (TH) were determined using linear and nonlinear models (Sharpe-Schoolfield-Ikemoto [SSI]). The lower developmental threshold (LDT) and thermal constant (K) were determined using a linear model. The LDT and K for the total immature stage had respective values of 11.9 °C and 397.27° -day (DD) on soybean, 11.6 °C and 458.34° -day (DD) on maize, 11.2 °C and 446.23° -day (DD) on potato, 10.7 °C and 439.75° -day (DD) on green pea, and 12.2 °C and 355.82° -day (DD) on the artificial diet. The emergence frequency of adult S. exigua over the full range of constant temperatures was simulated using nonlinear developmental rate functions and the Weibull function. This study predicted the spring emergence date in the first to second weeks of June, with approximately five generations for plant hosts. The interaction of temperature and plant host also influenced the development and longevity of the adults. Overall, the findings of this study may be useful for predicting the number of generations, occurrence, population dynamics in crop fields, and management of S. exigua.

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

In this study, four empirical models on the basis of the linear Angstrom- Prescotte model for predicting monthly mean daily global solar radiation on the horizontal surface were developed, correlating solar radiation to climatic parameters of sunshine hours, temperature and relative humidity. The models were used to predict Global solar radiation, an average solar radiation of 16.9690 Mjm-2 day-1 and maximum and a minimum of 19.2647 and 13.5222 C 0 were respectively predicted. For validation and the accuracy of the models, statistical coefficient of determination, R2, mean percentage error, (MPE), mean bias error, (MBE), and root mean square error (RMSE) were used. The result showed that, model correlating solar radiation with Sunshine hours, temperature and relative humidity gives the best fit for the study location with R2= 83.89%.

Global solar irradiance (GSI) is a fundamental source of energy on Earth. Despite its importance, sunshine or solar irradiance data are rarely available from weather stations. In the absence of available data, there are empirical methods that can be used to estimate solar irradiance. The objective of this study is to calibrate the parameters and to evaluate the performance of four empirical models of solar irradiance estimation (those of Chen, Hargreaves, Hunt, and Richardson) from air temperature data for eight localities in the state of Paraná, Brazil. Data were obtained from the Meteorological Database for Teaching and Research (BDMEP). For the comparison of means among the models, the Kruskal-Wallis non-parametric test was used. Dunn’s multiple comparison tests were used to analyze which models presented different means from the others. The performance of each model was assessed using the indices Pearson correlation coefficient (r), mean bias error (MBE), root mean square error (RMSE), Wilmott concordance index (d), performance index (c) and the Nash-Sutcliffe efficiency (NSE) coefficient. It was observed that the models proposed by Chen and Hunt presented the best performances in the estimation of GSI for the studied Paraná state localities, given that they yielded results which are closer to the observed historical data.

Satellite images have been widely used in the production of geospatial information such as land use and land cover mapping, as well as the generation of several thematic layers via image processing. Images acquired by sensors onboard various satellite platforms are influenced by systematic sensor and platform-induced geometry errors. Thus, geometric correction of satellite images is an important step of image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used within this scope. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values can be achieved with the model that uses RPC from data providers for PHR and SPOT that is followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracies are observed with PHR image except for single test.

Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations.

The soil nonlinear hysteretic behaviour is usually described, in the moderate strain range, through the shear modulus reduction and material damping ratio (MRD) curves. In common practice, in absence of specific laboratory tests, the curves are estimated by employing empirical regression models. Such predictive models, typically calibrated on large experimental datasets, correlate the soil response to its physical properties. This research fits within this context, presenting a comprehensive database of cyclic and dynamic laboratory tests conducted on natural Italian soils. The database, publicly available as supplementary data of the paper, contains the results of the tests conducted by the geotechnical laboratories of the Politecnico di Torino (Turin, Italy) and the Sapienza Università di Roma (Rome, Italy) over the past 30 years. The experimental data are employed to assess the performance of some widely used empirical models in predicting the MRD curves of natural uncemented fine-grained soils, emphasizing the importance of using an independent dataset for conducting a reliable statistical analysis. The results show that the use of many soil parameters as proxies for predicting the soil response does not necessarily lead to an improvement in the performance of the model. Therefore, according to Occam’s razor principle, simple models are to be preferred.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Due to the anisotropic characteristics, wood has distinct mechanical performance when requested in longitudinal, radial and tangential directions. In structural design, the longitudinal direction is considered to be parallel to the grains (0°) and the radial and tangential directions are considered normal to the grains (90°). In Brazil, the Brazilian Standard NBR 7190 (ABNT, 1997) establishes the test methods for obtaining strength properties in the parallel (f0) and normal (f90) directions to the wood grains. For this, formulations from the theory of mechanics of materials, developed for isotropic materials, are used. The estimation of strength properties in intermediate directions (off-axis strengths, 0° < θ < 90°) can be performed using empirical models or failure criteria (mathematical models) developed for anisotropic materials. These criteria, such as the Tsai and Wu criterion (1971) and the Tsai-Hill criterion (Azzi and Tsai, 1965), because they are little known by the technical community, are rarely used to estimate wood failure. On the other hand, the empirical models, adjusted according to the exponent n of the trigonometric terms, have been developed and incorporated in several standards documents. Among such, NBR 7190 (ABNT, 1997) recommends the use of Hankinson's empirical model (1921) to estimate the normal off-axis stresses (θ > 6°). Because it was developed through compression tests on softwoods, Hankinson's (1921) model, in its original form (exponent n = 2), may not be a good estimator of the characteristic off-axis tensile strength (ftθ,k) of hardwoods. In this context, this research has the main objective of determining, for hardwoods, models that lead to good estimates of the ftθ,k. Aiming at comprehensive results, 5 species, distributed in the hardwood resistance classes and 6 grain inclination angles (0°, 10°, 20°, 45°, 60° and 90°) were adopted. The statistical analysis showed that the Hankinson model (1921), with an exponent n equal to 2, does not lead to accurate estimates of ftθ,k. With the exception of linear, all regression models tested were significant and with good fits (coefficient of determination - R2 > 96%). Compared to empirical models and failure criteria present in the literature, the model obtained in this research proved to be, for the grouping of species, the most accurate in estimating ftθ,k. Furthermore, the use of the proposed model requires only the knowledge of the characteristic tensile strength parallel to the grains; therefore, this model is easy use in the development of wooden structures projects. Devido as características anisotrópicas, a madeira apresenta desempenho mecânico distinto quando solicitada nas direções longitudinal, radial e tangencial. Em projetos estruturais, considera-se a direção longitudinal como paralela (0°) e as demais direções como normais às fibras (90°). No Brasil, o documento normativo NBR 7190 (ABNT, 1997) estabelece os métodos de ensaio para a obtenção das propriedades de resistência nas direções paralela (f0) e normal (f90) às fibras da madeira. Para tanto, adota-se formulações advindas da teoria da resistência dos materiais, desenvolvida para materiais isotrópicos. A estimativa das propriedades de resistência em direções intermediárias (solicitações inclinadas, 0° < θ < 90°) pode ser realizada por meio de modelos empíricos ou critérios de falha (modelos matemáticos) desenvolvidos para materiais anisotrópicos. Tais critérios, como o de Tsai e Wu (1971) e o de Tsai-Hill (Azzi e Tsai, 1965), por não serem conhecidos pelo meio técnico, são pouco utilizados para prever a falha da madeira. Por outro lado, os modelos empíricos, ajustados em função do expoente n dos termos trigonométricos, têm sido desenvolvidos e incorporados em diversos documentos normativos. Dentre tais, a NBR 7190 (ABNT, 1997) prescreve a adoção do modelo empírico de Hankinson (1921) para a estimativa de solicitações normais inclinadas (θ > 6°). Por ter sido desenvolvido a partir de ensaios de compressão em madeiras coníferas, o modelo de Hankinson (1921), na sua forma original (expoente n = 2), pode não ser um bom estimador da resistência característica à tração inclinada (ftθ,k) de madeiras folhosas. Nesse contexto, este trabalho teve como objetivo principal determinar, para madeiras folhosas, modelos que conduzissem a boas estimativas da ftθ,k. Visando resultados abrangentes, foram adotadas 5 espécies distribuídas nas classes de resistência das folhosas e 6 ângulos de inclinação das fibras (0°, 10°, 20°, 45°, 60° e 90°). A análise estatística mostrou que o modelo de Hankinson (1921), com expoente n igual a 2, não conduz a estimativas precisas de ftθ,k. Com exceção do linear, todos os modelos de regressão testados foram significativos e com bons ajustes (coeficiente de determinação - R2 > 96%). Comparado aos modelos empíricos e aos critérios de falha presentes na literatura, o modelo obtido neste estudo mostrou ser, para o conjunto das espécies, o mais preciso nas estimativas de ftθ,k. Além disso, a utilização do modelo proposto demanda, apenas, o conhecimento da resistência característica à tração paralela às fibras; sendo, portanto, de fácil aplicação no desenvolvimento de projetos de estruturas de madeira. 88887.484245/2020-00

This paper presents an experimental and analytical study about the mechanical response at a different temperature on glass fiber-reinforced polymer laminates. The effect of different environmental conditions on compressive, tensile, stiffness, and viscoelastic behavior (storage modulus, loss modulus and damping ratio) of laminates were investigated. Before testing, laminates were preserved in a deep freezer at −80 °C, −20 °C, 0 °C, and room temperature (25 °C) for up to 60 days. Results confirmed that temperatures ranging from −80 to 50 °C, which were below the glass transition temperature of the epoxy resin, did not significantly affect the compressive, tensile, and stiffness performance of all laminates. When the testing temperature increased to 100 °C, the properties were decreased significantly due to the damaging of the fiber/matrix interface. Additionally, results obtained from dynamic mechanical analyses tests showed a drop-in storage modulus, high peaks in loss modulus and high damping factor at the glass transition region of the epoxy resin. The highest storage modulus, two phases of glassy states and highest damping ratio on the −80/G group of laminates were obtained. The accuracy of experimental results was assessed with empirical models on the storage modulus behavior of laminates. The empirical model developed by Gibson et al. provided accurate estimates of the storage modulus as a function of temperature and frequency. The remaining empirical models were less accurate and non-conservative estimations of laminates stiffness.

Models for carbon (C) stock assessment are widely applied in forest science, and mainly differ according to the scale of application, the required data, and the objectives for their implementation. This work presents the methodology implemented into the second version of an empirical model, WOody biomass and Carbon ASsessment (WOCAS v2), that uses the data of forest management plans (FMP) to calculate the mass of wood (t∙year−1 of dry matter, DM) and C (t∙year−1 C) at the stand level and from the year in which the FMPs came into force until a predefined reference year, for an Italian Case Study Area of Central Alps. The mass of wood and C are computed for (i) aboveground wood biomass (AWB), (ii) belowground wood biomass (BWB), and (iii) dead organic matter (DOM; i.e., dead wood and litter) according to the 2006 IPCC Guidelines. WOCAS v2 was tested for the first time for 2019 public forest stands (3.67 × 104 ha) of Valle Camonica for the period 1984–2018. Results showed that, in 2018 and at the landscape level, the total living wood biomass (TLB; AWB + BWB) reached 5.35∙106 t DM. TLB yield (t·ha−1·year−1 DM) ranged from 44.72 ± 44.42 t·ha−1·year−1 DM (1984) to 145.49 ± 70.76 t·ha−1·year−1 DM (2018). In the same year, DOM amounted to 6.12∙105 t DM, ranging from 8.28 ± 7.79 t·ha−1·year−1 DM (1989) to 17.11 ± 12.03 t·ha−1·year−1 DM (2015). The total weighted C yield, computed as the sum of C yield in AWB, BWB, and DOM of each stand, ranged from 26.63 ± 26.80 t∙ha−1∙year−1 C (1984) to 80.28 ± 41.32 t∙ha−1∙year−1 C (2018). The results demonstrated that FMPs data can be useful in estimating wood and C mass at the stand level and their variation over space and time for AWB as well as for BWB and DOM, which are not considered in the FMPs. This can represent a starting point for defining sustainable forest management policies and practices to improve forest vitality and conservation in compatibility with ecosystem services provision. Moreover, as the model is based on a standardized methodology it can be applied in any other forest area where the same input data are made available; this may constitute the basis for further applications on a broader scale.

This study investigates an experimental and analytical study on the flexural, failure, and viscoelastic properties of hybrid and non-hybrid composite laminates at increasing temperatures and frequencies. Carbon, glass, and hybrids of the two fibre materials with stacking sequences of [0/90]s were considered, and specimens were prepared via the resin transfer moulding method. Three-point bending and dynamic mechanical analysis tools were used. The failure surfaces of the laminates were examined using a scanning electron microscope. The results indicated that the flexural strength, modulus, and strain at failure of all groups of laminates decreased as the temperature increased. In particular, the storage modulus, damping factor, flexural strength, and flexural modulus properties of all groups of laminates increased as the hybrid ratio decreased on each targeted temperature and frequency test. However, the strain at failure increased as the hybrid ratio increased. Additionally, results obtained from the scanning electron microscope images confirmed that combinations of delamination and debonding failure modes were observed on the stacking sequences of [0]s and [90]s layers of bidirectional laminates. Finally, a comparison between the storage modulus results of all groups of laminates was conducted with three empirical models. The empirical model developed by Gibson et al. provided the most accurate prediction for all groups of laminates in the targeted temperature and frequency range. The predictions using the remaining empirical models were broadly similar. Further work is needed to optimise the empirical parameters and minimise the errors.

The ability to accurately predict daily solar radiation reaching the earth’s surface is essential in applications such as solar power generation. Given their ease of use, many empirical models have been proposed based on different dependent variables such as cloud cover, daily temperature range, etc. In this study we evaluate 23 of these models for the prediction of daily solar radiation in the northern coastal zone of the Iberian Peninsula. Daily measurements during the period 2000–2018 from 16 meteorological stations spread over this area are used to adjust the parameters of each model, whose predictive capacity is then evaluated using measurements made between 2019 and 2022. Using different statistical metrics to assess their predictive performance, it was found that models based on hours of sunshine and level of cloudiness are significantly more accurate than those based on maximum and minimum daily temperature and day of the year. Specifically, the sunshine-based model by SBM3 obtained the highest Global Performance Indicator at 5.05. The results offer insight on the ability of each type of empirical model to accurately predict daily solar radiation in the Mediterranean region.

Made available in DSpace on 2022-04-29T08:39:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-02-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) This work presents reliable empirical correlations of slump and spread measurements with yield stress of different materials based on the automated slump test methodology (capable of monitoring the transient regime of the slump test and minimizing operational errors — better control and reproducibility of tests). Measures of the final samples, such as slump and spread, are then correlated to yield stress, assessed through a rotational rheometer. The reliability of this methodology is corroborated by numerical simulations of the slump test using Ansys Fluent 14.5. A series of sensitivity tests was made and numerical results showed the importance of including the mold and its lifting velocity in the simulations. Such experimental control and good agreement of numerical-experimental results allowed the definition of well-validated empirical models for yield stress estimation. Correlations with an elevated coefficient of determination (R2> 0.90) were obtained using a new dimensionless parameter relating slump and spread measurements. Graphical abstract: [Figure not available: see fulltext.]. Department of Mechanical Engineering Faculty of Engineering of Ilha Solteira São Paulo State University, Avenida Brasil, 56, SP Department of Civil Engineering Faculty of Engineering of Ilha Solteira São Paulo State University, Alameda Bahia, 550, SP Department of Mechanical Engineering Faculty of Engineering of Ilha Solteira São Paulo State University, Avenida Brasil, 56, SP Department of Civil Engineering Faculty of Engineering of Ilha Solteira São Paulo State University, Alameda Bahia, 550, SP CNPq: 133576/2016-7 FAPESP: 2015/25518-8 CNPq: 449550/2014-1

Renewable energy must be prioritized by humankind, mainly if there is an expected increase of 50% in energy consumption by 2030 and climate change scenarios are also confirmed. Urban areas consume 70% of the available energy on the planet. Brazil, the largest country in South America, concentrates more than 85% of its population in urban areas, facing a challenge to increase the renewable power plants in its energy matrix. This work presents the solar radiation components behavior for the city of Salvador to contribute with initiatives for the use of solar energy resource. Firstly, a radiometric platform was implemented to obtain direct measurements of global (EG) and diffuse (EDF) components of incoming solar radiation at the surface. The knowledge of EDF is an important requirement to support photovoltaic system projects, and there is no information on direct measurements of this component in the State of Bahia. The diffuse radiation measurement device (DRMD) was designed and built for this purpose. The measurements of solar radiation components performed in this research were submitted to a specific quality control, statistically analyzed and used to evaluate the performance of different empirical models to represent the behavior of EDF in the tropical coastal city. The results demonstrate the potential to explore solar energy in the city of Salvador, with annual values of sunshine higher than 2200 h year−1 and average daily intensities of EG and EDF equal to 18.7 MJm−2day−1 and 7.35 MJm−2day−1, respectively. The analysis of the diurnal cycle shows that EG in summer reaches a maximum of 4.2 MJm−2h−1 and in the rainy season it reaches a minimum of 3.7 MJm−2h−1, both at noon, and in summer the EDFh is 35% of the EGh and it is 46% in the rainy season.

Las cuencas hidrológicas son consideradas una delimitación del territorio drenada por un único sistema de drenaje natural, por ende, esta es considerada la unidad mínima de gestión desde la cual es posible manejar de manera adecuada las interacciones entre los factores naturales y los factores sociales. En un contexto más físico, estas representan una fuente natural de captación y concentración de agua superficial por lo que su importancia radica a nivel hidrológico y volumétrico. Las cuencas, en específico el agua captada por estos sistemas son una fuente de vida para los seres humanos, más, sin embargo, en presencia de eventos naturales extremos, como es el caso de las sequias e inundaciones, esta se convierte en una fuente de riesgo para los mismos, esto teniendo en cuenta que en ocasiones estos son ocasionados de manera antrópica como en el caso de la contaminación hídrica Recientemente los modelos hidrodinámicos se han convertido en elementos de ayuda para predicción de situaciones que probablemente generen un efecto al medio ambiente o a las personas, razón por la cual mediante estos modelos se ha logrado tomar decisiones para la organización y manejo de las cuencas hidrográficas. En este orden de ideas los modelos Hidrodinámicos se han convertido en una herramienta útil para la resolución de eventos que requieren de un alto nivel de ingeniería. Por otro lado, el manejo y ordenación de cuencas tiene como objetivo planificar el uso y manejo sostenible del medio ambiente y los recursos naturales y así crear planes con la finalidad de generar consensos en torno al manejo, aprovechamiento y conservación de la cuenca Este documento detalla la identificación de los requerimientos y procedimientos necesarios para desarrollar un modelo hidrodinámico. Adicionalmente, abarca una breve descripción de los diferentes tipos de modelos, los cuales son requeridos para la simulación del comportamiento hidrodinámico de una cuenca rural . Hydrological basins are considered a delimitation of the territory drained by a single natural drainage system, therefore, this is considered the minimum management unit from which it is possible to adequately manage the interactions between natural factors and social factors. In a more physical context, they represent a natural source of uptake and concentration of surface water, so its importance lies at the hydrological and volumetric levels. The basins, specifically the water captured by these systems, are a source of life for human beings, more, however, in the presence of extreme natural events, such as droughts and floods, this becomes a source of risk for them, this taking into account that sometimes these are caused in an anthropic way, as in the case of water pollution. Recently, hydrodynamic models have become helpful elements for predicting situations that are likely to generate an effect on the environment or on people, which is why through these models it has been possible to make decisions for the organization and management of hydrographic basins. In this vein, hydrodynamic models have become a useful tool for resolving events that require a high level of engineering. On the other hand, the management and planning of basins aims to plan the use and sustainable management of the environment and natural resources and thus create plans in order to generate consensus around the management, use and conservation of the basin. This document details the identification of the necessary requirements and procedures to develop a hydrodynamic model. Additionally, it covers a brief description of the different types of models, which are required for the simulation of the hydrodynamic behavior of a rural basin. Magíster en Ingeniería Civil con Énfasis en Hidroambiental Maestría

The rising unpredictability in the food supply chain in many parts of the world is related to soil loss and poor agricultural output. The Revised Universal Soil Loss Equation (RUSLE), widely used for estimating soil loss, was applied in the western mid-hills in Nepal, with steep slopes and fragile geology. This region is at high risk for rapid soil erosion and mass wasting. To estimate soil loss, this study utilized the RUSLE model with experimental erosion plots in the Aadhikhola and Tinahukhola watersheds, capturing real-time erosion in the field. The annual soil loss for the Aadhikhola watershed is estimated at ∼41.4 tons ha−1 yr−1. In contrast, in the Tinahukhola watershed, soil loss is low (∼24.1 tons ha−1 yr−1). Although annual rainfall showed an increasing trend in both watersheds, the change in soil loss was statistically insignificant. The high erosion rates from the experimental plots in both watersheds support the model outputs. Results from the experimental plots recorded the rate of soil erosion for different land use as: irrigated agricultural land > rainfed agricultural land > forests. The trends highlight the role of human activities in enhancing soil erosion in these mountainous terrains in terms of medium to long-term perspectives. Therefore, sustainable agriculture practices in these terrains must investigate alternate ways to decrease soil erosion to support people's livelihoods.

Salinity is a major environmental factor that influences the population dynamics of fish and shellfish along coasts and estuaries, yet methods for predicting the salinity history at specific sampling stations are not widely available. The specific aim of this research was to predict the history of salinity experienced by juvenile and adult oysters (Crassostrea virginica) collected at sampling stations in Delaware Bay as part of the Selection along Estuarine Gradients in Oysters (SEGO) project. To do so, empirical relationships were created to predict salinity at five oyster bed stations using observing systems data and then applied to construct indices of salinity exposure over an oyster’s lifetime. The desired accuracy was +/- 2 psu. Three independent sources of salinity data were used in conjunction with observing systems data to construct and validate the predictive relationships. Observing systems data from the USGS station at Reedy Island Jetty and continuous near-bottom measurements taken by the U.S. Army Corps of Engineers (ACOE) from 2012-2015 and 2018 were employed to fit nonlinear empirical models at each station. Haskin Shellfish Research Laboratory (Haskin) data were used to evaluate model fit, then ACOE data from 2018 (withheld from model fitting in the validation analysis) and SEGO data from 2021 were used to validate models. The best-fitting models for predicting salinity at the oyster bed stations given the salinity at Reedy Island Jetty were logarithmic in form. The root mean square error (RMSE) of the models ranged from 1.3 to 1.6 psu when model predictions were compared with Haskin data, 0.5 to 1.5 when compared with ACOE data, and 0.6 to 0.8 when compared with SEGO data. All of these models were within the desired accuracy range. Results demonstrate that observing systems data can be used for predicting salinity within +/- 2 psu at oyster bed stations within 39 km in upper Delaware Bay. When these models were applied to estimate low salinity exposure of 2-year-old oysters via the metric of consecutive days below 5 psu, the indices suggested that there could be as much as a 42-day difference in low salinity exposure for oysters at stations 31 km apart. This study helps further our understanding of the salt distribution in Delaware Bay as well as the effect of low-salinity stress on the life cycle and genetic differentiation of oysters. In addition, the approach of using observing systems data to predict salinity could be applied to advance understanding of salt distribution and the effect of low salinity exposure on living resources in other estuaries.

Lakes cover only ~2 % of the global land area, but their connections to the surrounding catchment make them important for the global carbon cycle. A considerable amount of the carbon input to lakes is emitted to the atmosphere as carbon dioxide (CO2) through diffusive flux. This CO2 flux varies with surface water CO2 concentrations (CwCO2) and the transfer velocity of CO2 across the thin boundary layer between surface water and atmosphere (k), which both in turn depend on physical, biological, and chemical factors that interplay with lake and catchment characteristics over various time scales. Comprehensive studies of these interlinkages across lake types are rare, and current assessments of lake CO2 emissions are therefore uncertain. In this thesis, the variability and regulation of lake CO2 fluxes across a latitudinal gradient in Sweden is investigated. The thesis explores how CwCO2 and k regulate lake CO2 fluxes and how spatiotemporal patterns of CO2 fluxes vary within and across lakes. Regulation of CO2 flux at shorter temporal scales (

Evapotranspiration is a key variable for hydrologic, climatic, agricultural, and environmental studies. Given the non-availability of economically and technically easy to implement direct measurement methods, evapotranspiration is estimated primarily through the application of empirical and regression models, and machine learning algorithms that incorporate conventional meteorological variables. While the FAO-56 Penman-Monteith equation worldwide has been recognized as the most accurate equation to estimate the reference evapotranspiration (ETo), the number of required climatic variables makes its application questionable for regions with limited ground-based climate data. This note provides a summary of empirical and semi-empirical equations linked to its data requirement and the problems associated with these models (transferability and data quality), an overview of regression models, the potential of machine learning algorithms in regression tasks, trends of reference evapotran-spiration studies, and some recommendations of the topics future research should address that would lead to a further improvement of the performance and generalization of the available models. The terminology used in this note is consistent in both the theoretical and practical field of evapotranspiration, which is often dispersed in the academic literature. The goal of this note is to provide some perspective to stimulate discussion.

The uniaxial compressive strength (UCS) and Youngs’ modulus (E) of rock are important parameters required during design and stability analysis of mining and geotechnical structures. There is correlation between UCS and E of rock, and the proper estimation of such correlation is important for reliable mining engineering analysis. However, limited quantity of UCS and E data pairs often available for most mining project sites makes it difficult to estimate reliable correlation between UCS and E. This study addresses the difficulty by developing Bayesian approach for characterizing the site-specific joint probability distribution of UCS and E that is data-driven, without the use of an empirical model. A major novelty of the proposed approach over previous studies is that it does not require selection or integration of a regression model as input to characterize the correlation between UCS and E. The likelihood function in the proposed approach is directly constructed from only limited UCS and E data pairs and their prior information as inputs. The Bayesian approach is incorporated into Markov Chain Monte Carlo (MCMC) simulation to generate samples pairs of UCS and E, which are then analysed for marginal statistics, marginal probability distribution, joint probability distribution and correlation. Real data of UCS and E obtained from uniaxial compression tests on migmatites at the Sanandaj-Sirjan zone in Iran is used to demonstrate the approach. The marginal statistics, distributions and correlation coefficient from the proposed approach is consistent with those of the measured data from the adopted site. This indicates that the approach is effective in characterizing the correlation between UCS and E, and can be used when there is need for such characterization at a site with limited data. Simulated data are also used in the approach and the results show that the quality and quantity of information available as inputs play an important role in the efficiency of the characterization by the approach. The hallmark of the proposed approach is that it is data-driven, and practitioners do not need to determine and select an appropriate site-specific regression model to evaluate the correlation between UCS and E of rock.

Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations. Validerad;2022;Nivå 2;2022-05-11 (sofila)

A scoping review of the relevant literature was carried out to identify the existing N recommendation systems, their temporal and geographical diffusion, and knowledge gaps. In total, 151 studies were identified and categorised. Seventy-six percent of N recommendation systems are empirical and based on spatialised vegetation indices (73% of them); 21% are based on mechanistic crop simulation models with limited use of spatialized data (26% of them); 3% are based on machine learning techniques with the integration of spatialised and non-spatialised data. Recommendation systems appeared worldwide in 2000; they were often applied in the exact location where calibration had been carried out. Thirty percent of the studies use advanced recommendation techniques, such as sensor/approach fusion (44%), algorithm add-ons (30%), estimation of environmental benefits (13%), and multi-objective decisions (13%). However, some limitations have been identified. For example, empirical systems need specific calibrations for each site, species, and sensor, rarely using soil, vegetation, and weather data together, while mechanistic systems need large input data sets, often non-spatialised. We conclude that N recommendation systems can be improved by better data and the integration of algorithms. Highlights- A scoping review of the main side-dress nitrogen recommendations systems.- Empirical models are the most common but difficult to generalize.- Mechanistic models and machine learning rarely consider spatial variability.- Advanced solutions propose data/algorithm fusion and study environmental outcomes.- Future research must maximize the integration of high-resolution monitoring data.

Solar radiation is considered the main renewable energy source which reshapes the global sustainability plan for future development. Due to the lack of solar radiation measurements, this work investigates the performance of several temperature-based hybrid solar radiation models combining the parametric, statistical and satellite data approaches to estimate the global solar radiation on a horizontal surface. Over 35 years of meteorological data in the new location, Arar City, KSA (Latitude 30°96′ N and longitude 41°05′ E) are employed to establish and validate the models. These models are validated using two datasets with different averaging time spans to investigate the accuracy and reliability of different models as forecasting tools for the solar radiation. The mostly common statistical indicators are calculated to identify the most accurate model. The results show that Model (1) has the best performance among all models with high reliability as a solar radiation forecasting tool in this new location. This model is also validated against the widely-used datasets, namely NASA, On-Site measurements and PVGIS-SARAH data. The model shows excellent values for statistical indicators with high values of coefficient of determination, R2 > 0.955, presenting the best performance regardless of the time span of the validation datasets.

Specific leaf area (SLA) is a good predictor of aboveground net primary productivity. However, the SLA of bamboo species is generally estimated on the basis of destructive measurements rather than the cost-effective and recyclable nondestructive measurements using easily accessible leaf traits such as leaf length (L) and width (W). Considering the strong empirical relationships between leaf area (LA) and leaf structural parameters of bamboo species that were developed by previous studies, this study explores the feasibility of estimating the leaf dry mass (LDM) and SLA of 50 bamboo species using L and W. The results show that the Montgomery equation and its similar forms precisely estimated LA of the 50 bamboo species at both leaf scale (R2 &gt, 0.96 and MAE% &lt, 4.67%) and the canopy scale (R2 &gt, 0.99 and RMSE &lt, 0.09), the LDM of the 50 bamboo species could also be estimated using L and W at both leaf scale (R2 &gt, 0.52 and MAE% &lt, 26.35%) and the canopy scale (R2 &gt, 0.003), and the estimated mean SLA of each of the 50 bamboo species had good agreement with the measured values (R2 &gt, 1.88) because of the precisely estimated mean LA and mean LDM at the canopy scale, indicating the feasibility of estimating SLA of the 50 bamboo species at the canopy scale based on nondestructive measurements. However, the empirical relationships used for mean SLA estimations are not suitable for SLA estimations at the leaf scale because of the uncertainties in the estimated LDM at the leaf scale.

In response to the global climate crisis, the Nova Scotia Department of Lands and Forestry is using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) and associated methodologies to assess the carbon dynamics of the provincial forestry sector. The CBM-CFS3 bases simulations on a range of studies and national forest inventory plots to predict carbon dynamics using merchantable volume yield curves. Nova Scotia has also maintained thousands of permanent forest sample plots (PSPs) for decades, offering the opportunity to develop empirical, province-specific carbon models. This study used PSP tree measurements and allometric equations to compute plot-level forest carbon models from the PSP dataset and compared their output to that of the CBM-CFS3 model. The PSP-based models were stratified into five forest types and predict the carbon for seven carbon pools as a function of the plot age. Predictions with the PSP- and CBM-CFS3 models were compared to observed PSP data at the plot level and compared against each other at the stand and landscape level. At the plot level, the PSP-derived models predicted carbon closer to the observed data than the CBM-CFS3 model, the extent of over- or under-estimation depending on the carbon pool and forest type. At the stand scale, the CBM-CFS3 model predicted forest carbon to within 3.1–17.6% of the PSP method on average. Differences in predictions between the CBM-CFS3 and PSP models decreased to within 2.4% of the PSP-based models at the landscape level. Thus, the implications of using one method over the other decrease as the prediction scale increases from stand to landscape level, and the implications fluctuate as a function of the forest type and age.

The Turrell Arkansas Testing Site (TATS) is located in Northeastern Arkansas, United States. The site consists of approximately six meters of overconsolidated clay underlain by loose liquefiable sand deposits. Numerous traditional (rotary-wash boring with sample collection) and advanced (seismic piezocone soundings) geotechnical investigations have been performed at this site. A controlled-blasting testing program was also performed at the TATS to determine the blasting layout (the appropriate amount of explosive charges, the detonation delays, and the charge spacing), and to verify induced liquefaction of the soil deposit at a testing site located within the New Madrid Seismic Zone (NMSZ). The results obtained from the installed transducers and the pre- and postblast cone penetration tests (CPT) are discussed. Although, the CPT were performed when the excess porewater pressures were dissipated, a review of CPT profiles after blasting showed no evidence of increase of cone tip resistance and sleeve friction. Due to the small amount of explosive charge weight that was used, the excess porewater pressure ratio values only increased above the unity at the depth of 11.30 m. A review of the existing empirical models used to predict blast-induced porewater pressure responses and liquefaction is presented. A new empirical model that accounts for the in-situ soil properties, to estimate the excess pore pressure ratio, was developed and is presented herein.

Background and Objective: Although Boilover occurs with a low frequency; however, in case of occurrence, it can cause severe damage to people and equipment around the tank. The prediction of the consequences followed by a fireball of the Boilover phenomenon has an important role in adopting appropriate strategies for fire suppression of the atmospheric storage tank. This study aimed to predict the consequences after fireball caused by the Boilover phenomenon using empirical models. Materials and Methods: This study was conducted based on a descriptive-analytical design. Initially, empirical models presented for the prediction of the Boilover consequences were identified in this study. Subsequently, the models were utilized to predict the fireball geometry and its radiation heat flux around the tank. Eventually, the results obtained from the predictions were compared with the data of a smallscale experimental study and a large-scale real Boilover accident. Results: According to the results, the errors obtained from INERIS and Buang model to predict the radiation heat flux around the experimental Boilover were 23% and 31%, respectively. Moreover, the errors of these two prediction models were 52% and 71% for a real accident, respectively. Conclusion: The INERIS and Buang models showed lower rates of errors for the prediction of the Boilover fireball consequences, compared to other models. The results of this study can be used to assess the risk of Boilover in atmospheric storage tanks. Moreover, they help accident decision-makers decide on strategies and tactics for the fire suppression of atmospheric storage tanks.

Object and purpose of research. This paper discusses the empirical model of irregular ship motions. The purpose is to develop a correct, bidifferentiable and experimentally validated model that would adequately describe this kind of motions (so called “modified motions”). Materials and methods. Correlational and spectral theories of random processes. Main results. The authors have developed the analytical model of ship motions and justified the possibility to identify its parameters as per experimental data. Conclusion. The model of “modified motions” suggested by the authors gives more accurate description of ship motions as random process for the given moment of time.

The problem of efficient forecasting for the agriculture is connected with the imperfect methodological and instrumental substantiation of its development parameters. Systematisation of the western models of partial equilibrium has proved that projecting the international experience of forecasts’ substantiating on the Russian agrarian economy reduces the forecasts’ quality and distorts the predicted situation for the food market. That happens because such models are based on different criteria and objectives for the farmers’ development. We developed modern models for substantiating the parameters of agricultural development and scenario forecasts. The research objectives are connected with generalisation, systematisation and critical assessment of the instruments used for agricultural forecasting in Russian and foreign practice. We focused on shaping the substantially different instruments of scenario forecasting and model calculations. We applied the method of econometric diagnostics, the partial equilibrium model, and Cobb-Douglas production function. We used the data of financial reporting of 250 agricultural manufacturers from Penza oblast for the period from 2006 to 2016 as the study’s information basis. Further, we developed a set of scenario forecasting models, which are significantly different. These models have the functional capabilities of assessing the prospects of the agricultural development taking into account the attained production level, agrarian market’s environment, state measures, key factors of the agricultural products’ output. Based on the assessment of these models and diagnosed results, we concluded that the existing trends of forward movement in terms of the agricultural production’s supply are insufficient, as they do not fully satisfy the demand. We revealed the elasticity and disproportionality of the relationships between the growth rates of supply and demand in the agro-food market. Moreover, we collected the data on the qualitative dependence of the activities’ results in total on the factors of agricultural production and the state support measures. These factors show the threshold of expediency for additional investments of land, labour, capital and subsidies. This information will allow improving the economic mechanism of forecasting the agricultural strategic development. Additionally, it will contribute to strengthening the focus on the outcome of the manufacturers’ activities and increasing the level of achievement of the anticipated indicators.

The rate at which timber chars varies between species and predominately dependent on density and moisture content. This study involved determination of charring performance of Nigerian wood species used for constructional purposes and comparing their charring rate with selected existing models. Six out of ten identified wood species commonly used for constructional purposes: Terminalia superba (Black Afara), Milicia excels (Iroko), Nauclea diderrichii (Opepe), Khaya ivorensis (Mahogany), Mansonia altissima (Mansonia), Tectona grandis (Teak) were selected for the study. The densities of the woods were determined at Moisture Contents (MC) of 9.0, 12.0, and 15.0%. Selected samples from each species, were exposed to fire at temperature ranges of 20° to 230°C for 30 min; 230° to 300°C for 30 min; 20° to 300°C for 60 min. The charring rate results based on 15% moisture content for all 0 - 30 min, 30 - 60 min and 0 - 60 min, being the ones having the lowest correlation values at each of the fire exposure time were analysed using ANOVA at and compared with selected models; Australian standard AS 1720.4 relation, Eurocode EN recommendation and White's model. At 0-30 min fire exposure (20 to 230°C), and 15% MC, the experimental charring rate results showed significant regression relations at R2 = 0.9961, 0.8586, and 0.9523 for Eurocode EN recommendation, Australian standard AS 1720.4 relation, and White's model respectively. At 0-60 min fire exposure (20 to 300°C), and 15% MC, the results of experimental charring rate also showed significant regression relations at R2= 0.9925, 0.8926, and 0.9701 for Eurocode EN recommendation, Australian standard AS 1720.4 relation, and White's model respectively. Afara at 15.0% MC, and 20° to 300°C temperature, had the highest mean charring rates of 0.68 ± 0.02mm/min, while Opepe had the lowest charring rates of 0.47 ± 0.02 mm/min at the same MC level and temperature. The experimental test results indicated that density was a major predictor of the charring rate of constructional timber. Opepe specie, having the highest density exhibited the lowest charring rate and is recommended to ensure the safety and comfort of occupants in case of fire outbreak.

The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation.

Inter-criteria analysis was employed in VGO samples having a saturate content between 0.8 and 93.1 wt.% to define the statistically significant relations between physicochemical properties, empirical structural models and vacuum gas oil compositional information. The use of a logistic function and employment of a non-linear least squares method along with the aromatic ring index allowed for our newly developed correlation to accurately predict the saturate content of VGOs. The empirical models developed in this study can be used not only for obtaining the valuable structural information necessary to predict the behavior of VGOs in the conversion processes but can also be utilized to detect incorrectly performed SARA analyses. This work confirms the possibility of predicting the contents of VGO compounds from physicochemical properties and empirical models.

Numerous models have been proposed in the past to predict the maximum scour depth around bridge piers. These studies have all focused on the different parameters that could affect the maximum scour depth and the model accuracy. One of the main parameters individuated is the critical velocity of the approaching flow. The present study aimed at investigating the effect of different equations to determine the critical flow velocity on the accuracy of models for estimating the maximum scour depth around bridge piers. Here, 10 scour depth estimation equations, which include the critical flow velocity as one of the influencing parameters, and 8 critical velocity estimation equations were examined, for a total combination of 80 hybrid models. In addition, a sensitivity analysis of the selected scour depth equations to the critical velocity was investigated. The results of the selected models were compared with experimental data, and the best hybrid models were identified using statistical indicators. The accuracy of the best models, including YJAF-VRAD, YJAF-VARN, and YJAI-VRAD models, was also evaluated using field data available in the literature. Finally, correction factors were implied to the selected models to increase their accuracy in predicting the maximum scour depth.

The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (r) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (r = −0.79) and CHL-a (r = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (r = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (r = −0.76). Considering the r values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir.

The review of the literature and design guidelines indicates a lack of design codes governing the shear strength of reinforced concrete (RC) beams strengthened with ultrahigh-performance fiber-reinforced concrete (UHPFRC). This study uses the results of a 3D finite element model constructed previously by the authors and verified against an experimental programme to gain a clear understanding of the shear strength of RC beams strengthened with UHPFRC by using different schemes. Experimental results found in the literature along with the numerical results for shear capacities of normal-strength RC and UHPFRC beams without stirrups are compared with available code design guidelines and empirical models found in the literature. The results show variance between the empirical models and the experimental results. Accordingly, proposed equations derived based on empirical models found in the literature were set to estimate the shear capacity of normal-strength RC beams without stirrups. In addition, the term 'shear span-to-depth ratio' is not considered in the equations for design guidelines found in the literature regarding the shear capacity of UHPFRC beams without stirrups. Consequently, a formula estimating the shear strength of UHPFRC and RC beams strengthened with UHPFRC plates and considering the effect of shear span-to-depth ratio is proposed and validated against an experimental programme previously conducted by the authors. The review of the literature and design guidelines indicates a lack of design codes governing the shear strength of reinforced concrete (RC) beams strengthened with ultrahigh-performance fiber-reinforced concrete (UHPFRC). This study uses the results of a 3D finite element model constructed previously by the authors and verified against an experimental programme to gain a clear understanding of the shear strength of RC beams strengthened with UHPFRC by using different schemes. Experimental results found in the literature along with the numerical results for shear capacities of normal-strength RC and UHPFRC beams without stirrups are compared with available code design guidelines and empirical models found in the literature. The results show variance between the empirical models and the experimental results. Accordingly, proposed equations derived based on empirical models found in the literature were set to estimate the shear capacity of normal-strength RC beams without stirrups. In addition, the term 'shear span-to-depth ratio' is not considered in the equations for design guidelines found in the literature regarding the shear capacity of UHPFRC beams without stirrups. Consequently, a formula estimating the shear strength of UHPFRC and RC beams strengthened with UHPFRC plates and considering the effect of shear span-to-depth ratio is proposed and validated against an experimental programme previously conducted by the authors.

Freshwater quality maintenance is essential for human use and ecological functions. To ensure this objective, governments establish programs for a continuous monitoring of the inland waters state. This could be possible with Sentinel-2 (S2) and Sentinel-3 (S3), two remote sensing satellites of the European Space Agency, equipped with spectral optical sensors. To determine optimal water quality algorithms applicable to their spectral bands, 36 algorithms were tested for different key variables (chlorophyll a (Chl_a), colored dissolved organic matter (CDOM), colored dissolved organic matter (TSS), phycocyanin (PC) and Secchi disk depth (SDD)). A database of 296 water-leaving reflectance spectra were used, as well as concomitant water quality measurements of Mediterranean reservoirs and lakes of Spain. Two equal data sets were used for calibration and validation. The best algorithms were recalculated using all database and used the following band relations: SDD, R560/R700, CDOM, R665/R490, PC, R705/R665 for S2 and R620, R665, R709 and R779 for S3, using a semi-analytical algorithm, R700 for TSS &lt, 20 mg/L and R783/R492 (S2) or R779/R510 (S3) for TSS &gt, 20 mg/L, and for Chl_a, the maximum (R443, R492)/R560 for Chl_a &lt, 5 mg/m3 and R700/R665 for Chl_a &gt, 5 mg/m3. A preliminary test with a satellite image in a well-known reservoir showed results consistent with the expected ranges and spatial patterns of the variables.

Soil respiration (SR) is one of the largest fluxes in the global carbon cycle. The temperature sensitivity of SR (often termed as Q10) is a principal parameter for evaluating the feedback intensity between soil carbon efflux and global warming. The present study aimed to estimate the seasonal and interannual dynamics of the temperature sensitivity of SR based on a long-term 24-year series of measurements in two temperate forest ecosystems in European Russia. The study was conducted in a mature mixed forest with sandy Entic Podzol and in a secondary deciduous forest with loamy Haplic Luvisol. The SR rate was measured continuously from December 1997 to November 2021 at 7–10-day intervals using the closed chamber method. Sandy Entic Podzol demonstrated a higher temperature sensitivity of SR in comparison with loamy Entic Luvisol. The Q10 values for both soils in dry years were 1.3–1.4 times lower than they were in the years with normal levels of humidity. For both types of soil, we observed a significant positive correlation between the Q10 values and wetness indexes. The interannual variability of Q10 values for SR in forest soils was 18%–40% depending on the calculation approach and levels of aridity/humidity over the growing season. The heterogeneous Q10 values should be integrated into SR and C balance models for better predictions.

The objective of this research was to evaluate the effect of drying temperature and innovative pre-treatments (i.e., microwave and ultrasound) on “Rocha” pear drying behavior and quality characteristics, such as color, total phenolic content and antioxidant activity. Experiments were carried out with pear slabs subjected to microwaves (2450 MHz, 539 W, 4 min, microwave oven) and ultrasounds (35 kHz, 10 min, in an ultrasonic bath) as well as control samples. The drying process was conducted in a tray dryer at three different temperatures (50, 55 and 60 °C) and a fixed air velocity of 0.75 m/s. Microwave technology resulted in a higher quality deterioration in dried pear samples compared to those of controls and ultrasound pre-treated samples. The combined application of ultrasound pre-treatment and the higher drying temperature of 60 °C was characterized by the lowest color changes (ΔE = 3.86 ± 0.23) and higher preservation of nutritional parameters (total phenolic content, TPC = 345.60 ± 8.99, and antioxidant activity, EC50 = 8.80 ± 0.34). The drying characteristics of pear fruits were also analyzed by taking into account empirical models, with the Page model presenting the best prediction of the drying behavior. In conclusion, ultrasound application is a promising technology to obtain healthy/nutritious dried “Rocha” pear snacks as dietary sources for consumers.

The paper deals with the study of empirical models for the analysis of blasts-induced fragmentation. To verify the reliability of the method, the results obtained a priori through the application of the models are compared with the analyses of muck piles photos taken from a series of blasts in an open pit quarry, thanks to the Split-desktop software (Split Engineering, LLC, Tucson, USA). The study of the particle size distribution is very important when sizing an excavation site, as the average size of the pile must be compatible with the equipment used for clearing and conveyance, and with the first phase of the processing plant, i.e. primary crushing. After a description of the excavation technique in use at the quarry site, its main characteristics are outlined. The parameters used to create the empirical models refer to the geomechanical characteristics of the rock, the geometry of the blasts, the characteristics of the explosive and its interaction with the rock mass, paying attention to the correlation between initiation timing and induced fragmentation. A detailed description of some empirical models together with the Split-desktop software were therefore provided. Thanks to both data and photos of the muck-piles from the exploitation at the quarry, the particle size curves resulting from each model were compared with those obtained from the software. This allowed to validate the empirical method in a preliminary design phase.