The impacts of climate change on agriculture are still shadowed with uncertainty. However, climate change is expected to adversely affect Iran＇s agricultural practices through changes in ...precipitation, temperature and carbon dioxide fertilization. Therefore, adaptation of this sector to the increasing weather events is imperative. This study is aimed to document the likely impacts of climate change on Iran＇s agriculture and the current adaptation efforts made by government and farmers. The review of literature shows that changes in rainfall and water endowments will have significant impacts on crop yield, crops＇ water requirements and income and welfare of farm families. The extent of the changes in yield depends on the crop type, assumptions related to the CO2 fertilization effect, climate scenarios and adaptation abilities. On adaptation, the government＇s efforts have been distinguished in the improving agricultural productivity and irrigation development based on current technology, developing new technologies and policy reforms. Farmers＇ adaptive responses have also been identified. Some conclusions and recommendations are offered to increase the adaptive capacity of farmers and reduce negative impacts of climate change.
Sediment yield is important for maintaining soil health, reservoir sustainability, environmental pollution, and conservation of natural resources. The main aim of the present work is to develop four ...machine learning models, artificial neural networks (ANNs), radial basis function (RBF), support vector machine (SVM) and multiple model (MM)-ANNs for forecasting daily sediment yield. These models were applied to the Shakkar and Manot watersheds covering 25 years (1990–2015) and 10 years (2000–2010) of rainfall and discharge data, respectively. Results showed that the MM-ANNs model satisfactorily predicted sediment yield and outperformed the other models providing the highest correlation coefficient (0.921, 0.883) and Nash-Sutcliffe efficiency (0.744, 0.763) and the lowest relative absolute error (0.360, 0.344) and root mean square error (23,609.5, 269,671.5) for the Shakkar and Manot during the test period, respectively. Hence, the MM-ANNs model can be successfully used for sediment prediction.
Agriculture stands as the basis of human life, and it is important to sustain affordable food provision of this support system through powerful adaptation to climate change as the worldwide effects ...of climatic hazards are becoming more evident. Specifically, some semiarid and arid areas of developing countries are more vulnerable to the impacts of climate change. To reduce vulnerability of these regions, realizing context-specific impediments of robust adaptation to climate change is necessary. Also, it is imperative to consider anticipated transformations of agricultural systems that are required to enhance resilience of agriculture in coping with climate change. Therefore, this study aims to investigate major adaptation barriers in agriculture sector of Fars province, Iran. It also attempts to identify if any transformation from productivist agricultural systems is needed. To achieve these objectives, the group analytic hierarchy process was conducted with representatives from local government, academic institutes and farmers. The results revealed that local stakeholders prioritized barriers to adaptation, differently, and they showed various levels of concern about the importance of some barriers. However, they identified the governance and policy-related issues as the most important barriers. The results also indicated that transformational adaptation of agriculture sector from productivist to multifunctional farming system is required in order to enhance its resilience under uncertain climatic conditions. Some recommendations are offered to eliminate barriers of agricultural adaptation to climate change and also facilitate transformational adaptation of agriculture, in the developing world.
The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene ...genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP
m
) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.
Doing experiments in order to determine mechanical properties of nanocomposites costs a lot. Therefore, finding the ways by which the properties of the nanocomposites can be predicted without any ...empirical or few number of empirical experiments seems to be useful. In the present study, efforts have been made to create a relation between mathematical model based on dimensionless number (Ch) and mechanical properties of low-density polyethylene/linear low-density polyethylene/styrene ethylene butadiene styrene (LDPE/LLDPE/SEBS) nanocomposite reinforced by nanoparticles of calcium carbonate. The proposed model is able to predict mechanical properties of nanocomposites based on their composition. This model is capable to predict elongation at break in terms of Young’s modulus and fracture toughness, and to predict fracture toughness in terms of Young’s modulus and elongation at break. The results showed prediction of the dimensionless number Ch model, and the experimental results are consistent.
In this paper, a novel adaptive algorithm to detect and track targets with low grazing angle is addressed. For this purpose, an orthogonal frequency division multiplexing (OFDM) radar signal is ...employed through an edge computing framework over the radar platform. However, detecting the targets in the low grazing angle area is a great challenge due to severe multipath reflection effects. The Earth’s curvature geometry model is presented as the multipath propagation model. Based on the fact that the different scattering centers of a target resonate at different frequencies, we use the optimized OFDM waveform and propose a novel target tracking procedure for low grazing angle target tracking scenarios. The obtained results show that using an OFDM radar waveform provides a more uniform detection coverage in the presence of multipath propagation such that this will fill in the nulls. Finally, simulations are used to compare the performance of the proposed OFDM waveform with the conventional equal-power, the generalized likelihood ratio (GLR)-based and single-carrier waveforms.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study ...the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensional phase space using delay embedding (i.e. phase-space reconstruction), where the appropriate delay time and optimal embedding dimension of the time series are determined by using average mutual information and false nearest neighbors methods, respectively. Then, this reconstructed phase space is treated as a ‘network,’ with the reconstructed vectors serving as ‘nodes’ and the connections between them serving as ‘links’. Finally, the strength of the nodes are calculated to identify some key properties of the temporal rainfall network. The approach is employed independently to monthly rainfall data observed over a period of 38 years (1979–2016) from 14 rain gauge stations in the Vu Gia Thu Bon River basin in central Vietnam. Moreover, entropy values of the original rainfall time series are calculated for obtaining additional information on the properties of the rainfall dynamics. The average node strengths are also examined in terms of the mean annual rainfall, entropy of the time series, and elevation of the rain gauge station. The results indicate that: (1) while some adjacent stations (i.e. networks) have somewhat similar strength (average node strength) values, several others that are geographically close show significantly different network strengths; (2) similar entropies for adjacent stations are found more frequently than similar average node strengths; (3) there is generally a positive and proportional relationship between average strengths of nodes and entropies; and (4) the average node strengths of different months have some distinct temporal patterns (3-month, 4-month, and 6-month patterns) in rainfall dynamics, depending upon the specific region of the study area. These results have important implications for prediction, interpolation, and extrapolation of rainfall data.
Our research focuses on studying the multipole Mie resonances that are excited within the antennas of transition-metal carbides and nitrides (MXenes), particularly focusing on Ti3C2T x MXene as an ...array component. Through analytical models, numerical simulations, and experimental characterizations, we explore how collective resonances of the lossy nanostructures, such as MXene or lossy metals, lead to absorption enhancement, reflection suppression, and 2π variations of phase for the transmitted signal. Analytical models provide insights into the nature of the optical resonances excited in the antenna array, allowing for the identification of the strongest multipole and designing the antenna array accordingly. This study presents experimental measurements of the near-infrared reflection from a titanium antenna array, highlighting the emergence of a generalized Kerker effect due to multipole scattering compensation. The well-pronounced and optically responsive collective multipole resonances in nanostructured MXene enable metasurfaces with broad bandwidth absorption, using its large permittivity and scattering enhancement to improve light conversion efficiency in large-scale energy-harvesting systems.
The quantitative analysis of rainfall provides an in-depth understanding of the spatio-temporal variation of rainfall patterns. The present study aims to implement complex networks for studying the ...temporal connections of monthly rainfall in different rainfall regimes of Turkey between 1977 and 2016. The rainfall data of 151 rain gauges were reconstructed in the phase space, and the optimal embedding dimensions (OED) for the optimal delays are selected using the false nearest neighbors (FNN) approach. Subsequently, the reconstructed phase space (RPS) is served as a network, and the strength for each node of the network is calculated by applying a distance criterion. The results showed the utility of the RPS-based network for studying the temporal correlations in rainfall data. Moreover, the regional characteristics and rainfall properties reflect the strength values. The insights gained from the study provide baseline information for climate change adaptation and pave the way to similar applications on a global scale.
Avian malaria (Plasmodium) and related genera (Haemoproteus and Leucocytozoon) are diverse and widespread parasites. Despite the extent of knowledge on avian haemosporidian parasites, information ...about domestic and wild bird's blood parasites is overall insufficient in Iran. Prevalence of the haemosporidian parasites’ and phylogenetic relationship of lineages are studied by using molecular and morphological results of 152 examined hosts belonging to 17 species. Molecular analysis for haemosporidian detections demonstrated overall prevalence 22.36%. Inspected hosts mostly belonging to Common Pigeons (Columba livia) parasitized by Haemoproteus spp., and Hooded Crows (Corvus cornix) and Carrion Crow (C. corone) were identified as hosting Plasmodium spp. Detected lineages COLIV03, COQUI05, LINN01, ROFI04 and SGS01 are identified as new reports from Iran. We detected no evidence of Leucocytozoon lineages, while the high prevalence of H. columbae was found in Common Pigeons. Such investigation on avian blood parasites contributes to providing new information on the prevalence, epidemiology and geographical distribution of haemosporidian parasites circulating in domestic, pets and wild birds.
Despite the extent of knowledge on avian haemosporidian parasite, information about domestic and wild bird's blood parasites is overall insufficient in Iran. Prevalence of the haemosporidian parasite' and phylogenetic relationship of lineages was estimated by molecular and morphological results of 152 examined hosts belonging to 17 species.
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