Search

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. [ABSTRACT FROM AUTHOR]

• Physicochemical soil properties nonlinearly explain the history of LUC in China. • An explainable machine-learning method provides both local and global explanations. • A new perspective on LUC change with reliance on soil properties as drivers. • Toward knowledge gap-filling regarding soil properties in explaining LUC history. Enhancing our comprehension of soil processes and their impact on Earth requires precise quantification of human-induced soil alterations, particularly those related to land use/cover (LUC) histories. Thoroughly validated LUC and soil maps specific to China, and an explainable machine-learning approach were applied to reveal how soil physicochemical properties, independently or in combination, explain LUC histories in the last sixty years. Validation using historical data showed that the proposed machine-learning models can adequately simulate the pattern of LUC histories. Nonlinear mappings of various soil properties in explaining LUC histories were demonstrated and critical thresholds for changes in the explanatory capacities of each soil property to specific LUC histories were identified. Specifically, physical soil properties, except for soil pH, particularly soil thickness, clay content, bulk density, and coarse fragments, play significant roles in explaining the historical trajectories of forests, croplands, and pasture/rangelands than chemical soil properties. Predicted LUC changes suggest more intensive dynamics that are characterized by expansions in forest and pasture/rangeland areas, coupled with a reduction in cropland areas. Overall, improving understanding of the bidirectional links between soil and LUC changes is a crucial and imperative step towards refining the representation of soils in Earth system models. [ABSTRACT FROM AUTHOR]

• Investigation of ZnO/water nanofluid effect on the performance of a wet cooling tower. • Improving the thermal performance of the wet cooling tower using ZnO/water nanofluid. • Increasing the thermal performance of the wet cooling tower by enhancing nanofluid concentration. • Employing three PVC packing with different densities. • Enhancing the thermal performance of the wet cooling tower using denser packing. This article deals with an experimental investigation on the thermal performance of a mechanical draft wet cooling tower when using Zinc Oxide (ZnO) nanoparticles and three packing types with different densities. Stable nanofluids with different weight concentrations of ZnO/water were prepared through a two-step procedure using distilled water. First of all, the influence of three packing types including 7,9 and 18 layers are investigated on the thermal performance of the wet cooling tower using ZnO/water nanofluid with a weight concentration of 0.1%. Secondly, five ZnO/water nanofluids with weight concentrations of 0.02, 0.04, 0.06, 0.08, and 0.1% are utilized to examine the effects of nanoparticle concentration on the performance. The results indicate that water temperature difference, cooling efficiency, and tower characteristic could enhance by increasing layers of packing and weight concentrations of nanofluid. Moreover, the nanofluid effects are more notable when the denser packing is used. [ABSTRACT FROM AUTHOR]

As the field of quantum computing rapidly advances, there is a growing demand for skilled professionals adept in quantum computing and programming. Recognizing this need, in this paper, we share our experiences teaching an introductory-level quantum computing course to students at Cleveland State University (CSU). The course integrates dedicated hands-on programming labs, allowing students to verify their experimental results with corresponding examples from the textbook. These labs cover a diverse range of topics, including fundamental elements such as quantum gates and circuits, quantum key distribution protocols, and quantum algorithms. As educators, our goal is to share teaching insights and resources with fellow instructors in the field. This article elucidates the rationale behind the design of each experiment, providing a deeper understanding of quantum computing. [ABSTRACT FROM AUTHOR]

Pseudomonas aeruginosa (P. aeruginosa) is a common bacteria associated with burn infections and resistance to a wide range of disinfectants and antimicrobial agents which is able to produce different virulence factors. In this study, the susceptibility of P. aeruginosa isolates from the burn (burn=57) and hospital environment (HE=19) to antimicrobial agents and chemical disinfectants was determined by disc and well diffusion agar method, respectively. The results showed 100% sensitivity to polymyxin B, while sensitivity to other agents was low and ranged from 40.8% for imipenem and amikacin to 6.6% for ceftizoxime. Among the disinfectant used, the mean diameter of inhibition zones (DIZ) was higher for Deconex, while nitrofurazone had the lowest DIZ. In most cases, the HE isolates were significantly more susceptible to disinfectants and antimicrobial agents compared to burn isolates (P=0.01). The genes for the exoenzyme T, Y, U, and S were detected in 100%, 89.8%, 43.4%, and 48.7% of the isolates, respectively. The prevalence of exo U and exoY was significantly higher in the burn isolates compared to HE isolates (P=0.001). The results of this study indicate a significantly higher level of resistance against the majority of the antimicrobial agents in the burn isolates compared to HE isolates, which was significantly higher than the environmental isolates. The prevalence of T3SS effectors proteins and their pattern were also different in the burn, and the HE isolates, indicating a divergence in pathogenicity of the burn isolates from those of the environmental isolates. [ABSTRACT FROM AUTHOR]

Highlights • This work approximates the quasi-exact implicit formulation of Haverkamp model up to three-term expansion. • The accuracy of the proposed approximation is evaluated based on the quasi-exact implicit model. • A sensitivity analysis is conducted to define the infiltration time for which this expansion remains valid. • The proposed approximation is compared with Philip (1957) one- and two-term approximate expansions. Abstract The quasi-exact implicit (QEI) analytical formulation of Haverkamp equation, which might be also known as Parlange model, and its one- and two-term approximate expansions are among the mostly used equations for in situ determination of soil sorptivity, S , and saturated hydraulic conductivity, K s , from unsaturated one-dimensional (1D) cumulative infiltration into soils. However, from practical point of view, the approximate expansions are only valid from short to intermediate times and the QEI analytical formulation has a complex resolution, which makes its use complicated in inverting procedures. Therefore, alternative functions are needed to compute cumulative infiltration for longer times and improve inverting procedures for easier, more robust and accurate predictions of S and K s. In this regard, current work presents and evaluates a new three-term approximation of the QEI analytical formulation. As a first step, we checked the accuracy of the proposed three-term approximate expansion with respect to the QEI formulation for three different soils (sand, loam and silt) in order to define its time domain validity. Next, the accuracy of the three-term approximate expansion to estimate S , K s and the β parameter was compared to those obtained with one- and two-term approximate expansions. Lastly, a large dataset of field experimental data was inverted using the proposed three approximate expansions and the QEI analytical formulation, and goodness of fits predicted and measured cumulative infiltration curves and accuracy of the parameters (S , K s and the β) estimates were compared. The results revealed that the three-term approximate has a larger validity time interval compared to one- and two-term approximate expansions, which allows its use for larger infiltration times durations resulting in more accurate estimates of K s. Its capability of estimating S is also improved. The accurate prediction of β parameter is not still guaranteed for none of the approximate expansions. Compared to the one- and two-term approximate expansions, the experimental infiltration data usage revealed that the three-term approximate expansion resulted in higher performance and better prediction of hydraulic parameters. [ABSTRACT FROM AUTHOR]

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (˜76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type (˜40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it. [ABSTRACT FROM AUTHOR]

This article deals with an experimental investigation of thermal performance of mechanical draft wet cooling towers by changing various parameters such as hot water temperature, water flow rate, air mass flow rate and stage numbers of packing. In fact, this research attempts to focus on packing density influences with an emphasis on the role of stage numbers of packing. The results imply that coefficient of efficiency is in direct relation with air mass flow rate, hot water temperature and stage numbers of packing, while it diminishes by increasing the water flow rate. On the other hand, thermal images are provided for visualizing temperature distribution within the studied cooling tower. Afterward, these images are utilized so as to obtain pertinent temperature histograms and also temperature distribution along the vertical and horizontal directions for three packings. Finally, regression analysis is employed in order to derive design equations from the measured data. [ABSTRACT FROM AUTHOR]

This article deals with investigation of probabilistic stability of pipes conveying fluid with stochastic flow velocity in time domain. As a matter of fact, this study has focused on the randomness effects of flow velocity on stability of pipes conveying fluid while most of research efforts have only focused on the influences of deterministic parameters on the system stability. The Euler–Bernoulli beam and plug flow theory are employed to model pipe structure and internal flow, respectively. In addition, flow velocity is considered as a stationary random process with Gaussian distribution. Afterwards, the stochastic averaging method and Routh’s stability criterion are used so as to investigate the stability conditions of system. Consequently, the effects of boundary conditions, viscoelastic damping, mass ratio, and elastic foundation on the stability regions are discussed. Results delineate that the critical mean flow velocity decreases by increasing power spectral density (PSD) of the random velocity. Moreover, by increasing PSD from zero, the type effects of boundary condition and presence of elastic foundation are diminished, while the influences of viscoelastic damping and mass ratio could increase. Finally, to have a more applicable study, regression analysis is utilized to develop design equations and facilitate further analyses for design purposes. [ABSTRACT FROM AUTHOR]

Hard disk drives (HDDs) are sensitive devices responsible for storing and retrieving digital information in computer systems and electronic products. In particular, HDDs of laptop personal computers (LPCs) are very sensitive especially in harsh mechanical environments. Accordingly, it is necessary to protect them against damages attributable to shock and vibration so as to reach better magnetic read/write performance. In this paper, the nonlinear multi-objective optimization method is utilized to enhance the passive control performance of rubbers through minimizing destructive effects of shock and random excitations. As a matter of fact, this investigation will focus on improving the shock and vibration isolation system with special emphasis on the role of rubber feet. A zero mean, stationary process with Gaussian distribution is applied to the base in order to consider effects of random excitation. Moreover, a half-sine pulse of acceleration is assumed as the base shock excitation. According to limitations of geometrical parameters and lumped modal parameters, twelve inequality constraints are presented. Modal parameters of rubber mounts and rubber feet are selected as design variables of optimization, and the multi-objective optimization problem is solved for three commercial cases of HDD by using non-dominated sorting genetic algorithm II (NSGA-II), which was proposed based on Pareto-optimality concept. Therefore, new characteristics of rubber mounts and rubber feet are recommended to be manufactured for the purpose of protecting HDDs against shock and random excitations. [ABSTRACT FROM AUTHOR]

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs’ development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5–45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity ( K s ), bulk ( D b ) and particle ( D p ) densities, organic carbon ( OC ), wet-aggregate stability ( WAS ), electrical conductivity ( EC ), and soil antecedent ( θ i ) and field saturated ( θ fs ) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding K s . According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including K s resulted in more accurate (with E values of 0.673–0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356–0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed that K s exclusion from input variables list caused around 30 percent decrease in PTFs accuracy for all applied procedures. However, it seems that K s exclusion resulted in more practical PTFs especially in the case of GMDH network applying input variables which are less time consuming than K s . In general, it is concluded that GMDH provides more accurate and reliable estimates of cumulative infiltration (a non-readily available characteristic of soil) with a minimum set of input variables (2–4 input variables) and can be promising strategy to model soil infiltration combining the advantages of ANN and MLR methodologies. [ABSTRACT FROM AUTHOR]

One of the most fundamental properties of nanofluids is their viscosity. To determine the viscosity of these fluids, many models based on experimental or theoretical studies have been developed. However, the bulk of these models either offer outcomes that are inaccurate or are restricted to a certain operating condition. As a result, determining the viscosity of nanofluids is a difficult undertaking. In this work, a novel model based on Least Squares Support Vector Machines (LSSVM) was provided to predict the viscosity of hybrid nano-lubricants of MWCNT-metal oxide nanoparticles/engine oil. Metal oxide nanoparticles including Al 2 O 3 , ZnO, SiO 2 , TiO 2 , CuO, and MgO, and engine oils including 5W50, 10W40, 20W50, SAE40, and SAE50 were considered in the studied hybrid nano-lubricants. The reliability of the proposed model was determined by comparing its outputs to experimental data obtained at various temperatures, nanoparticles' concentrations, shear rates, and solid volume fractions. Additionally, the LSSVM model's coefficient of determination for all data was found to be 0.99649, demonstrating its high accuracy. Moreover, using the Monte-Carlo technique, the sensitivity analysis of the input variables on the output was computed which showed that the most sensitive variables for this model are shear rate, lubricant density, and nanoparticles volume fraction. This work highlights the requirement for a trustworthy approach to determine the viscosity of hybrid nano-lubricants and suggests one. [ABSTRACT FROM AUTHOR]

Hard disk drives (HDDs) of laptop personal computers (LPCs) are devices vulnerable in harsh mechanical environments. Hence, they need to be protected against damages due to vibration in order to have better read/write performance. In the present study, a LPC and its HDD are modeled as a system with two degrees of freedom and the nonlinear optimization method is employed to perform a passive control through minimizing the root mean square of HDD absolute acceleration due to a base random excitation. The presented random excitation is considered as a stationary, zero mean process with Gaussian distribution. In addition, eleven inequality constraints are defined based on geometrical limitations and allowable intervals of lumped modal parameters. The target of the optimization is to obtain optimum modal parameters of rubber mounts and rubber feet as design variables and subsequently propose new characteristics of rubber mounts and rubber feet to be manufactured for HDD protection against random excitation. In this paper, a nonlinear optimization problem is separately solved for three widely-used cases of HDD by using modified constrained steepest descent algorithm (PLBA) which was extended based on sequential quadratic programming. Finally, the genetic algorithm is used to verify results of the PLBA algorithm. [ABSTRACT FROM AUTHOR]

In this article, the possibility of predicting soil electrical conductivity (EC) from the enhanced thematic mapper plus (ETM+) data is studied using the multiple regression technique. In this regard, soil EC was measured in 188 points in an area of 5000 ha, Western Urmia Lake, northwest of Iran. ETM+ data for sampling dates were also acquired. Then, the measured EC (as the dependent variable) and reflectance from the original bands of ETM+ data and their ratios besides the different extracted indices (as independent variables) were used to construct different regression relations to predict soil salinity. Different data reduction algorithms, including principal component (PC) analysis, minimum noise fraction (MNF) transformation, pixel purity index (PPI), and n-dimensional visualizer (nDV) algorithms, were also applied to extract the independent factors of original bands. During the construction of regression relations, different criteria including normalized difference vegetation index (NDVI) and (R is reflectance and NIR and SWIR1 are near-infrared and shortwave infrared 1 bands of ETM+ data, respectively) were applied to group data sets in order to increase the prediction accuracies. Results revealed that the constructed regression relations were robust enough to predict the soil salinity showing adjusted determination coefficient (R2) up to 0.875. The best equation was obtained for the data set with NDVI values higher than 0.35. In general, the results show that the multiple regression technique, along with remotely sensed data, has enough accuracy to predict soil salinity. [ABSTRACT FROM AUTHOR]

Since the direct measurement of soil hydraulic conductivity [Kw(θ)] is time consuming and difficult, several methods have been suggested for its prediction. However, their application still reθuires some kind of direct or experimental measurement, even after calibration. Considering much easier measurement of the air permeability [Ka(θ)] and the water and air flows in the soil medium, several attempts have been made to link Kw(θ) and Ka(θ), most of which have used Ka(θ) to predict the saturated hydraulic conductivity (Ks). The current research was aimed to model the air permeability and evaluate its application for Kw(θ) prediction. In this regard, laboratory measurements were conducted for Kw(θ) and Ka(θ) at different soil moisture contents (θ). Then, we introduced a semitheoretical model and evaluated it using measured data. Results showed that the proposed model can predict Kw(θ), with an average evaluation error of 3.69% and average R2 of 0.88. Regarding the fast and nondestructive measurement of Ka(θ) compared with Kw(θ), the proposed model seems to be more practical to characterize unsaturated soil hydraulic conductivity and can improve the accuracy and efficiency of Kw(θ) estimation. [ABSTRACT FROM AUTHOR]

Wet cooling towers play a prominent role in the transfer of waste heat from industrial thermodynamic cycles. However it is a complicated task to dictate and optimize their operational conditions on account of different mass and heat transfer mechanisms between water and air flows. In this research, air mass flow rate, hot water temperature, water flow rate and stage numbers of packing are changed experimentally in order to investigate their influences on the cooling tower performance. As a matter of fact, this research attempts to focus on packing density effects with an emphasis on the role of stage numbers of packing. Consequently, the obtained results indicate that coefficient of efficiency is in direct relation with the hot water temperature, stage numbers of packing and air mass flow rate, while it diminishes by increasing the water flow rate. On the other hand, the captured thermal images and pertinent temperature histograms are investigated so as to visualize and calculate the temperature distribution within the studied cooling tower. Eventually, mathematical equations are derived from regression analysis of the measured data to recommend some practical procedures for the sake of reaching optimum operating conditions of forced draft wet cooling towers. [ABSTRACT FROM AUTHOR]

Natural draft wet cooling towers are one of the most widely-used types of wet cooling towers in the large-scale power plants. In the present research, the heat transfer performance of natural draft counter-flow wet cooling towers is investigated under cross-wind and windless conditions by virtue of an innovative wind-creator setup provided so as to implement intrinsic features of the natural wind speed profile precisely. As a matter of fact, this investigation will focus on the influences of cross-wind velocity, inlet water temperature and water flow rate on the water temperature difference and cooling efficiency of the studied cooling tower, with an emphasis on the role of wind speed features. Interestingly, a twofold trend is exhibited by the water temperature difference and cooling efficiency with respect to variation of the cross-wind velocity. Furthermore, to have a more applicable study, regression analysis of the experimental results is utilized for the sake of developing general mathematical equations and thus facilitating further analyses. Finally, these mathematical equations are employed in order to recommend a guideline for the purpose of reaching optimum operating conditions of natural draft wet cooling towers. [ABSTRACT FROM AUTHOR]

There are different mass and heat transfer mechanisms between water and air flows inside wet cooling towers (WCTs) so that it is a very difficult task to manage or optimize their operations. In this experimental work, the WCT performance is parametrically investigated in order to understand effects of water flow rate, inlet water temperature, type and arrangement of packing, and mass flow rate of air. Three different types of PVC packings (7, 9 and 18 ribs) are studied in this article separately to investigate influences of rib numbers. Furthermore, operation of the WCT is investigated by changing the inlet water temperature, rib numbers of packing, and flow rate of air and water. Ultimately, a guideline is proposed to reach optimum operating conditions of WCTs by virtue of mathematical equations derived from regression analysis of the measured data. [ABSTRACT FROM AUTHOR]

A close correlation between water conductivity (K(θ)) and air permeability (Ka), measured at various water contents, is expected due to tight dependence of water filled porosity to air filled porosity of soils. Finding such a relation will greatly facilitate the prediction of unsaturated water conductivity (K(θ)). So, the purpose of the current investigation was to find out if a reliable relation or function between the two permeabilities can be established. In this regard, K(θ) and Ka were measured by pressure plate outflow and variable head methods, respectively, at the range of 0 to -100 kPa matric potential (ψm). A linear regression function between relative water conductivity (Kr(θ)) and Ka(LogK, (θ)=a+bLogKa) with the correlation coefficient (R) from 0.884 to 0.999 were established for the 22 examined soils. The overall R for 128 data pairs (Kr(θ) and Ka) became 0.821 (being significant at P<0.01) with the slope (b) of -2.54 and intercept (a) of -10.93. For the comparison propose Kr(θ) were also predicted from RETC using experimental SMC data and van Genuchten and Brooks-Corey models. The reliability of the Kr(θ) prediction from Ka based on root mean square error (RMSE), geometric mean error ratio (GMER), and geometric standard deviation of error ratio (GSDER) criteria became considerable greater than those predicted from the two mentioned models. [ABSTRACT FROM AUTHOR]

Soil bulk density (ρb) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρb is often omitted from global datasets due to the costs of making many direct ρb measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρb. However, PTF performance often declines when applied to soils outside those in the training dataset. Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρb estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρb dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data. Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρb estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from −22.07 to 0.30 (uncalibrated) to 0.20–0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m−3 (uncalibrated) to 0.10–0.13 Mg m−3 (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data‐scarce regions. Highlights: Existing global PTFs were calibrated and tested using a small dataset for local utilisation.Several new local PTFs were also developed using the same datasets.Recalibration of existing global PTFs is comparable to or more accurate than developing new PTFs. [ABSTRACT FROM AUTHOR]

• Conservation tillage practices (CTP's) increased TOC compared to conventional tillage (CT). • CTP's increased OC stabilized by sands and aggregates + particulate OC compared to CT. • Reduced and minimum tillage reduced dissolved OC compared to CT. • Stubble cultivator increased the OC stabilized by clay and silt compared to CT. Tillage not only affects the quantity of soil organic carbon, SOC, but also its quality. Therefore, an investigation was conducted to see how a five-year (2011–2016) implementation of conventional tillage, CT, and conservation tillage practices, CTP's, under dryland vetch – wheat farming system can affect the total organic carbon, TOC, of soil and its fractions as well as its biological indicators including the half-life of organic materials, HL, and the mean residence time, MRT. The applied tillage practices included three CTP's of reduced tillage, RT, with chisel plow, minimum tillage, MT, with stubble cultivator, and no-tillage, NT, with direct seeding as well as one conventional tillage, CT, with moldboard plow. The results revealed that all applied CTP's (RT, MT, and NT) significantly increased the TOC of examined surface soils (0−25 cm) compared to CT. The labile fraction of SOC (dissolve organic carbon, DOC) under CTP's, excluding NT, showed a significant decrease compared to CT. While MT significantly increased the fraction of organic carbon stabilized by clay and silt particles, OC{c + s} compared to CT, NT and RT made no considerable change in OC{c + s} compared to CT. There was also a considerable (but insignificant) increase in the amount of organic carbon stabilized by sand and aggregates plus particulate organic matter, OC{S + A+POM}, under CTP's compared to CT. We recorded the greatest (228 ± 21 μg N g−1 2 h−1) and least (98 ± 27 μg N g−1 2 h−1) activity of urease under RT and CT systems, respectively, and the differences were significant. Based on the incubation data, the NT and RT systems significantly resulted in highest HL, (170 ± 9 days) for soil organic materials while CT had the lowest HL, (127 ± 5 days). While RT showed significantly the highest aggregate stability (55 ± 7 %), the CT and MT had the lowest aggregate stability (29 ± 4 %). In general, our results demonstrated the benefits of CTP's, and more specifically the benefits of RT and NT systems, in improving SOC, as primary indicator of soil quality, in surface soils under dryland agriculture in terms of quantity and quality of organic carbon. [ABSTRACT FROM AUTHOR]

Kisspeptin is a multifunctional neurohormone, primarily involved in the regulation of reproduction. We tested whether peripheral administration of kisspeptin10 (KP-10) via intraperitoneal injection or slow release affects reproductive hormones and metabolites in Sterlet sturgeon (Acipenser ruthenus). Plasma and mucus 17β-estradiol (E 2), and testosterone (T), plasma and follicular vitellogenin (VTG) and calcium (Ca) as well as glucose and lipids were determined. Mature Sterlet sturgeon were grouped into six groups: saline i.p injection (control), human kisspeptin (hKP-10) i.p injection; acipenser kisspeptin (aKP-10) i.p injection; hKP-10 (slow release); aKP-10 (slow-release) and no treatment control. No effect for KP-10 on sturgeon body weight was found after 4 weeks of treatment. Multivariate analysis revealed a significant disparity in plasma E 2 levels. It was significantly different between groups (time, P = 0.0022). E 2 in epithelia mucosa showed significant difference between and within groups in the acute group (time, P = 0.0252; treatment, P = 0.0423; time × treatment, P = 0.0429). T levels were unaffected by treatments (P > 0.05). The presence of synthetic aKP-10 led to an elevation in oocyte and plasma VTG levels (P < 0.05). Prolonged exposure to this peptide resulted in an increase in plasma calcium levels. Simultaneously, there was an augmentation in the number of mature follicles. Regardless of the duration of exposure, aKP-10 significantly elevated plasma glucose levels in Sterlet (P < 0.0). Additionally, KP-10 led to an increase in plasma lipids and cholesterol in Sterlet. Overall, our data support an involvement for KP-10 in the regulation of gonadal steroid hormones, oocyte maturation and metabolite levels in sturgeon, suggesting a positive role for this peptide in the reproductive physiology of this species. [Display omitted] • KP-10 administration increased plasma 17β-estradiol levels in Sterlet sturgeon. • Surface epithelial mucosa served as an immediate hormonal indicator post aKP-10 administration. • Peripheral KP-10 modulated vitellogenic development and calcium levels in Sterlet sturgeon. • Metabolite factors such as glucose and lipids were increased by KP-10 in Sterlet sturgeon. [ABSTRACT FROM AUTHOR]

Well test analysis is a method for evaluating the average properties of the reservoir by characterizing the ability of the fluid to flow through the reservoir and to the well. Well test output parameters that descript reservoir are permeability, reservoir heterogeneities, boundaries and pressure; also, parameters that descript well are skin factor, productivity index and well geometries. The suggested models for prediction of pressure drop in vertical well have obtained from the solution of diffusivity equation for radial and elliptical regime. The purpose of this project is to generate a model for the horizontal well in fracture reservoir and estimate a parameter for this type of well by ANN method and compare the results with well test software. In this study, pressure data versus time was obtained for horizontal wells in naturally fractured reservoirs by solving diffusivity Equation using Stehfest algorithm, and for each set of data, a polynomial was developed for pressure derivative data by applying Chebyshev polynomials method. The polynomial coefficients along with reservoir and horizontal well data were fed into Artificial Neural Network (ANN) as input data and as a result, a model was presented for the horizontal well in naturally-fractured reservoir. In addition, output of the model was compared to the well log and well test software’s data and it was found that the presented model has better and higher performance than well test software’s. [ABSTRACT FROM AUTHOR]

Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyze the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS‐1D were simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in‐depth analysis of the soil SHPs and derived soil characteristics was performed to analyze why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time‐integrated behavior of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter‐comparison studies to avoid artifacts originating from the choice of PTF rather from different model structures. Plain Language Summary: Hydrological models need information about the soil physical characteristics (soil hydraulic parameters), which are in general not available if the models are applied at larger scales (region to global scale). Therefore, pedotransfer functions (PTFs) are classically used, which relate easily available soil properties such as sand‐, silt‐, clay‐content, soil organic carbon content, and soil bulk density, which are available from soil maps, to the soil hydraulic parameters. Unfortunately, there are many different PTFs available in literature. In the study presented, we analyzed the impact of different PTFs on the simulation results of water fluxes and found, that the choice of PTF impacts the simulation results. Further, some PTFs were identified as being less robust compared to others. In general, the study shows that harmonizing PTFs in model‐inter‐comparisons is needed to avoid artifacts originating from the choice of PTF rather from different model structures. Key Points: Using different PTFs in hydrological models causes substantial variability in predicted fluxesWe strongly recommend to harmonize the PTFs used in model inter‐comparison studies [ABSTRACT FROM AUTHOR]

This study reviews the history of seismometry in Iran and specifically the evolution of Iranian Seismological Center's (IRSC) seismic network and investigates the artifacts and systematic errors of this center's earthquake catalog. Our analysis indicates instability of magnitude estimation in the IRSC catalog to the extent that in the western and eastern parts of Iran, events with the same USGS reported mb magnitude could result in MN values 0.3 units apart. This catalog is contaminated with quarry and mine blasts, in which about 19% of the reported events are most likely not tectonic earthquakes. In addition, heterogeneity of magnitude of completeness as a function of space and time is observed. While Mc has gradually improved since 2006, its reduction in different parts of the country varies between 0 to 0.5 units of magnitude. In addition to artifacts, more than 54% of the reported events were located using a simplified velocity model and need to be relocated using more accurate velocity models. Moreover, in the hypocenter determination of about 7% of earthquakes, at least data from one station with wrong coordinates was employed. For these two groups of events, a clear dependence exists between the azimuthal gap and the value of the observed shift in the location parameters; with increasing the azimuthal gap, the shift increases. Investigations on the manually picked phases in the IRSC catalog show a large number of phase misidentification cases which are the other source of systematic errors. Examining the depth reported for events associated with mine blasts in a region in southern Iran implies systematic errors and high overestimation of depth for these events. The reported depth for most of these blasts is more than 10 km. [ABSTRACT FROM AUTHOR]

• Saturated soil hydraulic conductivity was predicted reasonably using easily accessible environmental data and limited soil observations. • Random Forest outperformed best, resulting in high-resolution hydraulic conductivity which can be used in the hydrological model at a large scale. • Silt content, Bands 7, 6, 4, 2, NDPI, and NDVI covariates explained most of the soil hydraulic conductivity variations. Saturated soil hydraulic conductivity (K sat) is a key component in hydrogeology and water management. This study aimed at evaluating popular tree-based machine learning algorithms (Random forest (RF), Quantile random forest (QRF), Cubist (Cu), and Decision tree regression (DTr)) to assess the spatial distribution of K sat in a sandy agricultural field. Soil surface reflectance derived from Landsat-8 (OLI/TIRS) (several remotely sensed data including original surface reflectances of spectral bands and 22 remote sensing indices) as well as limited ground measured soil data were used as predictor covariates. Using collected disturbed and undisturbed soil samples, physicochemical properties (K sat , porosity, organic matter, and texture contents) were determined. Based on novel supervised feature selection, covariates including Landsat-8 spectral Band2, Band4, Band6, and Band7, normalized difference pond index (NDPI), normalized difference vegetation index (NDVI), and silt content were identified as the most important factors in K sat prediction. Variable importance analysis showed Band7 and Band6 were explaining 54% of the total K sat variation. The model evaluation, using five statistics cretria, Taylor diagram and the Kruskal–Wallis (KW) test, demonstrates the outperformance of RF being followed by QRF, Cu, and DTr models. Accordingly, the RF method combined with an optimized sampling approach provides the most accurate digital soil K sat map. The uncertainty analysis showed no significant different trend in K sat predictions by applying more dense datasets confirming original-limited data (n = 28) was efficient and sufficient to derive a reliable K sat -map. The developed approach, which can be scaled up at a large scale, seems to be useful in modelling water flow and solute transport for water and nutrient management purposes toward precision agriculture. [ABSTRACT FROM AUTHOR]

Background: One of the most challenging isolates in nosocomial infections is Acinetobacter baumannii which is rapidly changing towards multi-drug resistant. Resistance to the last resort antibiotics, carbapenems, is reported around the world. In particular, metallo-β- lactamases (MBL) are responsible and detection of MBLs is of interest. Objectives: In this study we have evaluated the prevalence of MBLs in A. baumannii isolated during 2012 by two feasible described methods. Materials and Methods: In this cross sectional study, during 2012, 40 A. baumannii non-replicate isolates gathered from clinical and environmental specimens from Afzalipur hospital of Kerman. Isolates were characterized by conventional biochemical tests as A. baumannii-calcoaceticus complex (ABC). For antimicrobial susceptibility testing, 17 different antibiotics were sought by agar diffusion method. Phenotypic MBL detection was assessed by double disk test and Modified Hodge test for carbapenem resistant isolates. Results: During the study 40 isolates of ABC were collected. Of which, 23 (57%) isolates were resistant to carbapenems. Phenotypic MBLs detection was negative by two methods. Conclusions: The rates of resistance of the isolated ABCs in our clinical setting were at high level. Though, carbapenems were the most efficient antibiotic, but, there was a pronounced rate of resistant. MBL genes were not responsible for carbapenem resistance in under study clinical setting. [ABSTRACT FROM AUTHOR]

Background and Objectives: The main cause of serious nosocomial infections is a Gram-negative pathogen known as Pseudomonas aeruginosa (P. aeruginosa). Carbapenems are widely used as an appropriate treatment for these infections, however resistance to these agents has been observed and is increasing. Metallo beta-lactamase (MBLs) enzyme is one of the main causes of resistance to carbapenem. In the current study the frequency and production of VIM1 and VIM2 by imipenem-resistant P. aeruginosa isolates of patients hospitalized in Imam Reza hospital were evaluated. Materials and Methods: In this study, 131 clinical samples were collected from patients hospitalized in Imam Reza hospital in Mashhad during a 15-month period from May 2011 to November 2012. After verification of P. aeruginosa isolates, antibiotic resistance patterns of isolates were determined for 14 antibiotics by Kirby-Bauer standard disk diffusion according to the CLSI guidelines. Combined-disk test was used for phenotypic determination of MBLs-producing isolates and after DNA extraction, genotypic determination of VIM1 and VIM2 metallo beta-lactamase genes was carried out using Multiplex- PCR. Results: Of 63 imipenem-resistant isolates (48.5%), 56 (88.8%) were MBL-producing in phenotypic assessments. Also amongst imipenem-resistant isolates, the frequency of VIM1 and VIM2 genes were 58.7 and 3.17%, respectively. Conclusion: The results of the current study along with the results of the other conducted studies in Iran in recent years demonstrate that the average resistance to imipenem in P. aeruginosa isolates was 51.3% which has increased in comparison with the results in 2006 (32.9%). It was also determined that the frequency of VIM1 gene was more than VIM2 gene. In phenotypic assessment by using CD method, 49.6% of isolates were determined as MBLs-producing. The sensitivity and specificity of this method were verified in comparison with the results of PCR test. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the effectiveness of different hormones on spawning success and sex steroid changes in Caspian Kutum, Rutilus frisii kutum. Groups of five fish were injected intramuscularly as follows: 2 mg kg-1 common carp pituitary extract (CPE) as the positive control, 100 IU kg-1 human chorionic gonadotropin (hCG), 2 μg kg-1 luteinizing hormone releasing hormone analog (LHRHa2), 10 μg kg-1 synthetic analogue gonadoliberins (sGnRHa), 0.2 mL kg-1 Ovaprim which is a complex of salmon GnRH analogue with a dopamine receptor antagonist (domperidone), a combination of 2 μg kg-1 LHRHa2 and 100 IU kg-1 hCG, a complex of 10 μg kg-1 sGnRHa and 100 hCG IU kg-1, and saline solution as the negative control. Blood samples were taken simultaneously with injections and after ovulation. The results showed that no fish ovulated in the negative control group. In the CPE treatment group, 4 of 5 fish ovulated. In the other experimental groups; no response was observed in the hCG group, while the females injected with sGnRHa-alone, LHRHa2-alone, and sGnRH+hCG had low spawning percentages. Of the hormones tested, the combination of sGnRHa+domperidone was the most effective spawning inducer and the greatest spawning success was obtained. Significant decreases after injection were observed in plasma concentrations of 17α estradiol, 17β-hydroxy progesterone, and progesterone in all treatments, while plasma testosterone concentrations increased significantly. This study revealed that a combined sGnRHa and dopamine antagonist can improve the effectiveness of artificial reproduction of Kutum. Since the combination of sGnRHa and domperidone is purer than CPE and has the most effective in inducing ovulation, it is preferred for inducing spawning in this species. [ABSTRACT FROM AUTHOR]

• Several playa surfaces have developed on geomorphic surfaces of Urmia Playa Lake. • Erodible fraction varied between 40% and 90% among playa surfaces. • Playa surface type was a determining factor in its resilience to wind erosion. • Abandoned agricultural lands had the highest erodible fraction. • Salt crusts and clay flats were more resilience against wind erosion. Despite the great extent of Urmia Playa Lake (UPL), researchers have rarely ever studied the development of playa surfaces and their vulnerability to dust emission. Therefore, we aimed the current research to study the formation of UPL surfaces and how their physicochemical, mineralogical and geomorphological characteristics affect their susceptibility to wind erosion. An area of 41 km2 was chosen including lands from UPL and adjacent Agricultural Lands (AL). Different playa surfaces were identified on different geomorphic surfaces and mapped using Landsat images. Later, 75 soil samples from depth of 0–5 cm of different map units were collected and subjected to physicochemical analysis. Soil Erodible Fraction (EF) was also measured as an index for the wind erodibility of the examined soil surfaces. X-Ray Diffraction (XRD) and X-Ray Fluorescence (XRF) analysis of soil samples from three playa surfaces with significantly different EF values were also conducted. Major reasons for the development of different playa surfaces were found to be basically related to geomorphic surfaces on which they have developed, size and mineralogical composition of the sediments as well as the landscape position and depth to the groundwater. Statistical analysis of EF of different playa surfaces and AL showed that Salt Crusts (SC) had the lowest mean EF value due to the formation of a thin layer of salt on its surface as it has developed on low stand geomorphic surface being fine-textured by nature and rich in Na, Mg and Cl ions. Abandoned Agricultural lands (AA) and AL had the highest mean EF values. High sand content of soils with anthropogenic origin along with conventional tillage practices resulting in soil organic matter loss and over grazing were the major reasons of their high EF. Therefore, these surfaces need to be monitored carefully and to be of high priority of conservation and dust control programs in the region. [ABSTRACT FROM AUTHOR]

Under the fifth strategic development plan of Iran, the government is necessitated to trigger the agro-ecological initiatives like conservation agriculture (CA) by stimulating farmers to become involved in the schemes as efficiently as possible, along the lines of global policies to enhance environmentally-friendly development. This study, as being the case, calls for the knowledge gap of motivation-intention-behavior, investigating the association of diverse motivations of 382 farmers in Tabriz, East Azerbaijan, with the soil conservation behavior (SCB), also inclusive of the contribution of CA features, including relative advantage, compatibility, complexity, observability, and testability, as the farmers were stratified to low and high SCB. The theoretical framework consists of the theory of planned behavior (TPB) and motivational model (MM), the former is commonly used and the latter has the potential to be given much attention, conceptualized by well-being, identity, hedonistic, and idealistic concepts. The study provides evidence that 70.42% of farmers adhere to and carry out SCB at a low rate; whereas, 29.58% of the rest has a high adoption rate. The results also illustrate that attitude towards CA contributes to influencing SCB, as well as the significant and positive impact of PBC on behavioral intention and SCB. While well-being motivation has a negative impact on attitude towards CA, behavioral intention, and SCB, identity and hedonistic motivations have a significant and positive impact on attitude and SCB that are mediated by behavioral intention. The paper concludes that (1) the agricultural extension needs to change farmers' attitudes by making farmers aware of the relative advantage, compatibility, and testability of CA, (2) farmers should receive technical, financial, and educational support to increase their volitional control on CA, (3) it is strongly suggested addressing the primary obstacles to SCB, which are facilitated by delivering the subsidized agricultural inputs (e.g., fertilizer, fuel, and livelihoods) to farmers with well-being motivations, (4) the agricultural extension is also recommended recognizing farmers with identity and hedonistic incentives, they are as a reference group and are imitated by non-adopter or slowly adopter farmers. • Study addresses the intake of conservation agriculture. • Attitude towards conservation agriculture affects soil conservation behavior. • Behavioral control affects behavioral intention and soil conservation behavior. • Well-being motivations affect attitude towards conservation agriculture negatively. • Identity and hedonistic motivations affect intention and soil conservation behavior. [ABSTRACT FROM AUTHOR]