The accurate electrical modeling of photovoltaic (PV) module is vital due to the extensive installation of photovoltaic power plants. Therefore, the scientists suggested a three-diode photovoltaic ...(TDPV) model for precise modeling of PV losses. However, TDPV is a complex and nonlinear model that contains nine unknown parameters. Hence, this paper presents a new method that is combining the computation and Harris Hawk Optimization (HHO) algorithm to extract the unknown parameters of the TDPV model. Also, this paper exhibits a new objective function based on the datasheet values instead of using extensive experiments for PV modeling for time-saving. The industrialists provided the datasheet values of PV modules at standard test conditions (STC) and normal operating cell temperature (NOCT). Therefore, this paper utilized these data to compute four parameters using equations and identify the remaining five parameters using the HHO algorithm. In this paper, the offered method is employed to find the TDPV model of two commercial PV panels, such as multi-crystal KC200GT and monocrystalline CS6K280M. After that, the I–V and P–V curves of these TDPV models plotted and compared with the curves of the measured data under different temperatures and solar irradiations. Moreover, the absolute current error of the proposed method compared with that obtained by using other methods. Accordingly, the results revealed that the proposed method is efficient and can be easily applied to identify the electrical parameters of any commercial PV panel based on the datasheet values only.
•This paper presents a novel method for PV modeling, combined computation and optimization.•A novel application of Harris Hawk Optimization is presented.•Three-diode PV (TDPV) model with nine electrical parameters is used in this paper.•The effectiveness of the proposed method verified using experimental data.•Two commercial PV modules are used in this paper (CS6K-280M and KC200GT).
Ferulic acid (FA) is an abundant dietary antioxidant which may offer beneficial effects against cancer, cardiovascular disease, diabetes and Alzheimer’s disease. The impact of FA on health depends on ...its intake and pharmacokinetic properties. In this article, the literature pertaining to chemistry, natural sources, dietary intake and pharmacokinetic properties of FA is critically reviewed. High levels of FA are found in both free and bound forms in vegetables, fruits, cereals, and coffee. We have estimated that consumption of these foods may result in approximately 150–250mg/day of FA intake. FA can be absorbed along the entire gastrointestinal tract and metabolized mainly by the liver. The absorption and metabolism of FA seem to be dose dependent at least in experimental settings. Further pharmacokinetic and pharmacodynamic studies are required to characterize the impact of FA on human health.
In this paper, the mechanical properties of vapor grown carbon nanofiber (VGCNF)/polymer composites are reviewed. The paper starts with the structural and intrinsic mechanical properties of VGCNFs. ...Then the major factors (filler dispersion and distribution, filler aspect ratio, adhesion and interface between filler and polymer matrix) affecting the mechanical properties of VGCNF/polymer composites are presented. After that, VGCNF/polymer composite mechanical properties are discussed in terms of nanofibers dispersion and alignment, adhesion between the nanofiber and polymer matrix, and other factors. The influence of processing methods and processing conditions on the properties of VGCNF/polymer composite is also considered. At the end, the possible future challenges for VGCNF and VGCNF/polymer composites are highlighted.
Conductive polymer nanocomposites (CPNC) are promising materials for electromagnetic interference shielding (EMI) applications. However, the relatively high cost of high aspect ratio nanofillers ...hinders the wide commercial use of these materials. Promoting the competitiveness of CPNCs requires formulation nanocomposites with the desired EMI capabilities at the lowest possible nanofiller loading. This requires better understanding for relation between the microstructure and EMI attenuation mechanisms, which is the objective of this work. Herein, CNPCs with segregated conductive network were prepared by placing carbon nanotube (CNT) particles at the external surface of ultrahigh molecular weight polyethylene (UHMWPE) powder by wet mixing. The microstructure, electrical and EMI shielding properties of the nanocomposites after compression molding were investigated. The EMI SE was found to increase with CNT content. An EMI SE of 50dB was reported for a 1.0mm thick plate made of 10wt% CNT/UHMWPE nanocomposite. This nanostructured material is suitable for many applications in the computer and electronics industries. Compared to CNT/polymer nanocomposite of fine and well-dispersed CNT microstructure, the unique structure of the CNT/UHMWPE characterized by thick and segregated CNT network was found to enhance the EMI shielding by absorption and reduce the reflection of the EMI.
The objective of this paper is to synthesize the large literature recording changing patterns of precipitation in the observed data, thus indicating that climate change is already a reality. Such a ...synthesis is required not only for environmental researchers but also for policy makers. The key question is the broad picture at major regional and continental levels. Some interesting conclusions for this survey are emerging. For example, the review shows increased variance of precipitation
everywhere. Consistent with this finding, we observe that wet areas become wetter, and dry and arid areas become more so. In addition, the following general changing pattern is emerging: (a) increased precipitation in high latitudes (Northern Hemisphere); (b) reductions in precipitation in China, Australia and the Small Island States in the Pacific; and (c) increased variance in equatorial regions. The changes in the major ocean currents also appear to be affecting precipitation patterns. For example, increased intensity and frequency of El Niño and ENSO seem associated with evidence of an observed “dipole” pattern affecting Africa and Asia, although this time series is too short so far. But the changing pattern calls for renewed efforts at adaptation to climate change, as the changing precipitation pattern will also affect the regional availability of food supply.
Abstract
Removal of heavy metal ions from wastewater is of prime importance for a clean environment and human health. Different reported methods were devoted to heavy metal ions removal from various ...wastewater sources. These methods could be classified into adsorption-, membrane-, chemical-, electric-, and photocatalytic-based treatments. This paper comprehensively and critically reviews and discusses these methods in terms of used agents/adsorbents, removal efficiency, operating conditions, and the pros and cons of each method. Besides, the key findings of the previous studies reported in the literature are summarized. Generally, it is noticed that most of the recent studies have focused on adsorption techniques. The major obstacles of the adsorption methods are the ability to remove different ion types concurrently, high retention time, and cycling stability of adsorbents. Even though the chemical and membrane methods are practical, the large-volume sludge formation and post-treatment requirements are vital issues that need to be solved for chemical techniques. Fouling and scaling inhibition could lead to further improvement in membrane separation. However, pre-treatment and periodic cleaning of membranes incur additional costs. Electrical-based methods were also reported to be efficient; however, industrial-scale separation is needed in addition to tackling the issue of large-volume sludge formation. Electric- and photocatalytic-based methods are still less mature. More attention should be drawn to using real wastewaters rather than synthetic ones when investigating heavy metals removal. Future research studies should focus on eco-friendly, cost-effective, and sustainable materials and methods.
Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, ...commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.
•A hybrid weight bat algorithm (WBA) proposed to training deep neural network (DNN).•The proposed WBA-DNN is used for classification of poisonous wild plants.•The proposed WBA-DNN outperformed the ...most well-known classification algorithms.•The poisonous and harmful wild plants in fields could be successfully recognized.
In this study, a deep neural network structure is proposed for the classification of poisonous and harmful wild plants in fields. Furthermore, a novel metaheuristic weight bat-inspired algorithm is developed for training the devised deep neural network. The harmful wild plant dataset is obtained from the agricultural field contains the purslane plants and harmful plants. The feature of the plants extract with mean absolute deviation, the dataset is consists of four features, two classes information, and contains 3452 samples, one-third of these samples are classified as purslane plants. Firstly, the performance of the proposed weight bat-inspired algorithm based deep neural network is evaluated by using ten UCI data repository datasets and the obtained results are compared with state-of-the-art classification algorithms. Then, classification of the harmful wild plant dataset is performed, results of the proposed weight bat-inspired algorithm based deep neural network are compared to two categories of classification algorithms, including (i) the most well-known classification algorithms, including decision tree, k-nearest neighbors, backpropagation based deep neural network, naïve bayes, random forest, AdaBoost, and support vector machine; (ii) optimization-based deep neural network, including bat algorithm, genetic algorithm, particle swarm optimization, equilibrium optimizer, A bio-inspired based optimization algorithm, and salp swarm algorithm. The proposed weight bat-inspired algorithm based deep neural network has outperformed the most well-known classification algorithms and optimization-based deep neural network in terms of CA, FPR, REC, PRE, TNR, AUC, F1-M, and F-M by 0.980, 0.020, 0.980, 0.980, 0.980, 0.980, 0.980, and 0.980, respectively. The highest performance has indicated that the training of deep neural networks by the weight bat-inspired algorithm is proven to be a very effective and useful tool for the classification of poisonous and harmful wild plants.
Elemental ratios (δ13C, δ15N and C/N) and carbon and nitrogen concentrations in macrophytes, sediments and sponges of the hypersaline Al-Kharrar Lagoon (KL), central eastern Red Sea coast, were ...measured to distinguish their sources, pathways and see how they have been influenced by biogeochemical processes and terrestrial inputs. The mangroves and halophytes showed the most depleted δ13C values of -27.07±0.2 ‰ and -28.34±0.4 ‰, respectively, indicating their preferential 12C uptake, similar to C3-photosynthetic plants, except for the halophytes Atriplex sp. and Suaeda vermiculata which showed δ13C of -14.31±0.6 ‰, similar to C4-plants. Macroalgae were divided into A and B groups based on their δ13C values. The δ13C of macroalgae A averaged -15.41±0.4 ‰, whereas macroalgae B and seagrasses showed values of -7.41±0.8 ‰ and -7.98 ‰, suggesting uptake of HCO3- as a source for CO2 during photosynthesis. The δ13C of sponges was -10.7±0.3 ‰, suggesting that macroalgae and seagrasses are their main favoured diets. Substrates of all these taxa showed δ13C of -15.52±0.8 ‰, suggesting the KL is at present a macroalgae-dominated lagoon. The δ15N in taxa/sediments averaged 1.68 ‰, suggesting that atmospheric N2-fixation is the main source of nitrogen in/around the lagoon. The heaviest δ15N (10.58 ‰) in halophytes growing in algal mats and sabkha is possibly due to denitrification and ammonia evaporation. The macrophytes in the KL showed high C %, N %, and C/N ratios, but this is not indicated in their substrates due possibly to a rapid turnover of dense, hypersaline waters carrying most of the detached organic materials out into the Red Sea. The δ13C allowed separation of subaerial from aquatic macrophytes, a proxy that could be used when interpreting paleo-sea level or paleoclimatic changes from the coastal marine sediments.
Bones as an alive organ consist of about 70% mineral and 30% organic component. About 200 million people are suffering from osteopenia and osteoporosis around the world. There are multiple ways of ...protecting bone from endogenous and exogenous risk factors. Planned physical activity is another useful way for protecting bone health. It has been investigated that arranged exercise would effectively regulate bone metabolism. Until now, a number of systems have discovered how exercise could help bone health. Previous studies reported different mechanisms of the effect of exercise on bone health by modulation of bone remodeling. However, the regulation of RANKL/RANK/OPG pathway in exercise and physical performance as one of the most important remodeling systems is not considered comprehensive in previous evidence. Therefore, the aim of this review is to clarify exercise influence on bone modeling and remodeling, with a concentration on its role in regulating RANKL/RANK/OPG pathway.
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