Performance of Different Surface Incoming Solar Radiation Models and Their Impacts on Reference Evapotranspiration

Reference evapotranspiration (ET₀) from FAO-Penman-Monteith equation is highly sensitive to the surface incoming solar radiation (SISR) and therefore accurate estimate of this parameter would result in more accurate estimation of ET₀. In this study, the accuracy of three main approaches for SISR estimation including empirical models (Angstrom and Hargreaves-Samani), physically-based data assimilation models (Global Land Data Assimilation System-Noah, GLDAS/Noah, and National Centers of Environmental Predictions/National Center for Atmospheric Research, NCEP/NCAR), and a satellite observation model (Satellite Application Facility on Climate Monitoring, CM-SAF) were evaluated using ground-based measurements from 2012 to 2015. Then SISR outputs from introduced approaches were implemented in FAO-Penman-Monteith equation for ET₀ estimation on daily and monthly basis. The Angstrom calibrated model was the most accurate model with a coefficient of determination (R²) of 0.9 and standard error of estimate (SEE) of 2.58 MJ. m^?2. d^?1, and GLDAS/Noah, Hargreaves-Samani, NCEP/NCAR, and CM-SAF, had lower accuracy, respectively. However, the lack of the meteorological data and required empirical coefficients are the main limitations of applying the empirical models, however, satellite-based approaches are more practical for operational purposes. The results indicated that, in spite of slight overestimation in warm months, GLDAS/Noah model had better performance with R²=0.87 and SEE?=?3.5 MJ. m^?2. d^?1 in case of lack of meteorological data. The accuracy of ET₀ derived from FAO-Penman-Monteith equation was directly depended on the accuracy of SISR estimation. The ET₀ estimation error was related to SISR estimation error with a fourth-degree function and had a linear relationship with SISR error at daily and monthly scales, respectively.     

Organic-inorganic Au/PVP/ZnO/Si/Al semiconductor heterojunction characteristics

The paper reports the fabrication and characterization of a novel Au/PVP/ZnO/Si/Al semiconductor heterojunction (HJ) diode. Both inorganic n type ZnO and organic polyvinyl pyrrolidone (PVP) layers have grown by sol-gel spin-coating route at 2000 rpm. The front and back metallic contacts are thermally evaporated in a vacuum at pressure of 10^-6 Torr having a diameter of 1.5 mm and a thickness of 250 nm. The detailed analysis of the forward and reverse bias current-voltage characteristics has been provided. Consequently, many electronic parameters, such as ideality factor, rectification coefficient, carrier concentration, series resistance, are then extracted. Based upon our results a non-ideal diode behavior is revealed and ideality factor exceeds the unity (n > 4). A high rectifying (~4.6×10⁴) device is demonstrated. According to Cheung-Cheung and Norde calculation models, the barrier height and series resitance are respectively of 0.57 eV and 30 kΩ. Ohmic and space charge limited current (SCLC) conduction mechanisms are demonstrated. Such devices will find applications as solar cell, photodiode and photoconductor.     

Kuznetsov–Ma waves train generation in a left-handed material

We analyze the behavior of an electromagnetic wave which propagates in a left-handed material. Second-order dispersion and cubic–quintic nonlinearities are considered. This behavior of an electromagnetic wave is modeled by a nonlinear Schrödinger equation which is solved by collective coordinates theory in order to characterize the light pulse intensity profile. More so, a specific frequency range has been outlined where electromagnetic wave behavior will be investigated. The perfect combination of second-order dispersion and cubic nonlinearity leads to a robust soliton. When the quintic nonlinearity comes into play, it provokes strong and long internal perturbations which lead to Benjamin–Feir instability. This phenomenon, also called modulational instability, induces appearance of a Kuznetsov–Ma waves train. We numerically verify the validity of Kuznetsov–Ma theory by presenting physical conditions which lead to Kuznetsov–Ma waves train generation. Thereafter, some properties of such waves train are also verified.     

Size dependent buckling analysis of nano sandwich beams by two schemes

Abstract

Within the framework of Timoshenko beam theory, the buckling of nano sandwich beams is developed. The material properties are assumed to vary arbitrarily in both axial and thickness directions. These types of beams are referred to as bi-directional functionally graded (BDFG) beams. Two types of nano sandwich beams with different material distribution patterns and immovable supports are considered. Since the size effects play a significant role in mechanical behavior of nanostructures, the small-scale effects are captured by Eringen’s nonlocal theory of elasticity. The governing equations are derived using the variational formulation. Symmetric smoothed particle hydrodynamics (SSPH) and the Galerkin method are adopted as numerical solution approaches. As a truly meshless method, the convergence of the SSPH technique mainly depends on the smoothing length value and distribution of particles in the compact support domain of the kernel function. The Revised Super Gauss Function is used as the kernel function and an optimum value for the smoothing length that bears the fastest convergence rate is obtained. The solution methods are verified through benchmark problems found in the literature. Numerical and illustrative results show that various parameters, including the aspect ratio, nonlocal parameter, gradient indexes, and cross-sectional types have significant effects on the buckling responses of BDFG nano sandwich beams.     

A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makespan and cost of resources

In traditional flow shop scheduling problems (FSSPs), the processing times are assumed to be pre-known and fixed parameters while in many practical environments, the processing times can be controlled by consumption of extra resources. In this paper, we propose resource-dependent processing times (RDPT) for permutation FSSP in which the processing time of a job depends on the amount of additional resources assigned to that job. To make a trade-off between makespan and required amount of resources, two conflict objective functions are considered: the minimisation of maximum completion time and the minimisation of total cost of resources. In order to solve the problem considered in this paper, a decomposition approach is suggested that strives to deal with the original model via two sub-problems: (i) sequencing problem; and (ii) resource allocation problem. A hybrid discrete differential evolution (HDDE) algorithm with an effective coordinate directions search and a variable neighbourhood search (VNS) are combined to solve two sub-problems. Furthermore, a statistical procedure is employed to adjust the significant parameters of the proposed HDDE and VNS algorithms. This procedure is based on the stepwise regression (SR) technique. The effectiveness of the suggested hybrid algorithm is investigated through a computational study and obtained results show the good performance of our approach with regard to the other algorithms.     

Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the T_s–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, T_s, F_vegetation, F_soil, temperature (T), precipitation at time t (P_t, P_{t – 1}, P_{t – 2}), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R²) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between ?2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.     

乳状液膜中1,3-桥连杯[4]芳烃/冠醚对碱金属离子的选择性萃取(英文)

Nano-assisted inclusion separation of alkali metals from basic solutions was reported by inclu sion-facilitated emulsion liquid membrane process. The novelty of this study is application of nano-baskets of calixcrown in the selective and efficient separation of alkali metals as both the carrier and the surfactant. For this aim, four derivatives of diacid calix[4]-1,3-crowns-4,5 were synthesized, and their inclusion-extraction parameters were optimized including the calixcrown scaffold （4.4%, by mass） as the carrier/demulsifier, the commercial kero sene as diluent in membrane, sulphonic acid （0.2 mol.L-1） and ammonium carbonate （0.4 mol.L-1） as the strip and the feed phases, the phase and the treat ratios of 0.8 and 0.3, mixing speed （300 r.min-1）, and initial solute concentration （100 mg.L-1）. The selectivity of membrane over more than ten interfering cations was examined and the re sults reveled that under the optimized operating condition, the degree of inclusion-extraction of alkali metals was as high as 98%-99%.     

Emulsion liquid membrane for selective extraction of bismuth from nitrate medium

The novelty of this work is the selective extraction of bismuth ions from nitrate medium by emulsion liquid membrane. Di(2-ethylhexyl)phosphoric acid was used as extractant of bismuth ions from nitrate medium by emulsion liquid membrane, and Triton X-100 was used as the biodegradable surfactant in n-pantanol n-pentanol bulk membrane. The extraction of bismuth ions was evaluated by the yield of extraction. The experimental parameters were evaluated and were optimized. They included the ratio of di(2-ethylhexyl)phosphoric acid concentration to the concentration of /Triton X-100 concentration (1.0: 0.5% w/w), nature of diluents (n-pentanol), nature and concentration of the stripping solution (sulfuric acid, 0.5M), stirring speed (1,800 rpm) and equilibrium time of extraction (20min), initial feed solution of bismuth (350 ppm) and the volume ratio of the internal stripping phase to the membrane phase (14 times). The experimental parameters of kinetic extraction revealed that the bismuth ions were extracted at 100% 97%.