Recent warming of Tonle Sap Lake,Cambodia: Implications for one of the world’s most productive inland fisheries

Tonle Sap Lake in Cambodia is arguably the world's most productive freshwater ecosystems, as well as the dominant source of animal protein for the country. The rapid rise of hydropower schemes, deforestation, land development and climate change impacts in the Mekong River Basin, however, now represent serious concerns in regard to Tonle Sap Lake's ecological health and its role in future food security. To this end, the present study identifies significant recent warming of lake temperature and discusses how each of these anthropogenic perturbations in Tonle Sap's floodplain and the Mekong River Basin may be influencing this trend. The lake's dry season monthly average temperature increased by 0.03°C/year between 1988 and 2018, being largely in synchrony with warming trends of the local air temperature and upstream rivers. The impacts of deforestation and agriculture development in the lake's floodplain also exhibited a high correlation with an increased number of warm days observed in the lake, particularly in its southeast region (agriculture R² = .61; deforestation R² = .39). A total of 79 dams, resulting in 72 km³ of volumetric water capacity, were constructed between 2003 and 2018 in the Mekong River Basin. This dam development coincided with a decreasing trend in the number of dry season warm days per year in the lower Mekong River, while Tonle Sap Lake's number of dry season warm days continued to increase during this same period. The present study revealed that Tonle Sap Lake's temperature trends are highly influenced by temperature trends in the local climate, agriculture development and deforestation of the lake's watershed. Although there were no noticeable impacts observed from upstream dam development in the Mekong River Basin, local‐to‐regional agricultural and land management of the lake's watershed appear to be effective strategies for maintaining a stable thermal regime in the lake in order to facilitate maximum ecosystem health.     

Gaussian bare-bones artificial bee colony algorithm

As a relatively new global optimization technique, artificial bee colony (ABC) algorithm becomes popular in recent years for its simplicity and effectiveness. However, there is still an inefficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this drawback, a Gaussian bare-bones ABC is proposed, where a new search equation is designed based on utilizing the global best solution. Furthermore, we employ the generalized opposition-based learning strategy to generate new food sources for scout bees, which is beneficial to discover more useful information for guiding search. A comprehensive set of experiments is conducted on 23 benchmark functions and a real-world optimization problem to verify the effectiveness of the proposed approach. Some well-known ABC variants and state-of-the-art evolutionary algorithms are used for comparison. The experimental results show that the proposed approach offers higher solution quality and faster convergence speed.     

Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

This paper presents a novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional optimization problems efficiently. The proposed approach, namely GODE, employs similar schemes of opposition-based DE (ODE) for opposition-based population initialization and generation jumping with GOBL. Experiments are conducted to verify the performance of GODE on 19 high-dimensional problems with D = 50, 100, 200, 500, 1,000. The results confirm that GODE outperforms classical DE, real-coded CHC (crossgenerational elitist selection, heterogeneous recombination, and cataclysmic mutation) and G-CMA-ES (restart covariant matrix evolutionary strategy) on the majority of test problems.     

Global optimization of laminated cylindrical panels based on fundamental natural frequency

This investigation presents an optimization of laminated cylindrical panels based on fundamental natural frequency. Also, trends of change in optimum stacking sequence while the proportions of structures vary, are studied which can be insightful for design purposes. A displacement based finite element model is used, in order to extract fundamental natural frequencies of T300/5208 Carbon/Epoxy cylindrical panels. To obtain optimum designs, the Globalized Bounded Nelder–Mead (GBNM) algorithm is employed. Predictions are compared with the results of Genetic Algorithm (GA) method and show faster and more accurate convergence to the global optimum, while variables are continuous in GBNM and discrete in GA. Moreover, verification of novel convergence criteria to ameliorate local searcher in GBNM is examined.     

Elasticity solution for static analysis of laminated cylindrical panel using differential quadrature method

The three-dimensional (3D) solution for static analysis of cross-ply cylindrical panel is presented using differential quadrature method (DQM) and Fourier series approach. The panel is assumed to be simply-supported at one pair of opposite edges and arbitrary conditions at the other pair. Applying the DQM to the governing differential equations and to the boundary conditions along the axial or circumferential directions, new state equations about state variables at discrete points are derived. Displacement and stress fields according to the various kinds of edges conditions are obtained by solving these state equations. The method is validated by comparing numerical results for the simply-supported edges with that available in the literature.     

An intuitive distance-based explanation of opposition-based sampling

The impact of the opposition concept can be observed in many areas around us. This concept has sometimes been called by different names, such as, opposite particles in physics, complement of an event in probability, absolute or relative complement in set theory, and theses and antitheses in dialectic. Recently, opposition-based learning (OBL) was proposed and has been utilized in different soft computing areas. The main idea behind OBL is the simultaneous consideration of a candidate and its corresponding opposite candidate in order to achieve a better approximation for the current solution. OBL has been employed to introduce opposition-based optimization, opposition-based reinforcement learning, and opposition-based neural networks, as some examples among others. This work proposes an Euclidean distance-to-optimal solution proof that shows intuitively why considering the opposite of a candidate solution is more beneficial than another random solution. The proposed intuitive view is generalized to N-dimensional search spaces for black-box problems.     

Reliability Evaluation of an Open‐Cathode PEMFC at Operating State and Longtime Vibration by Mechanical Loads

Reliability and durability of proton exchange membrane fuel cells (PEMFCs) are one of the most complex issues in real applications' environment. Especially, when it subjects to the various mechanical loads and vibrations. This topic requires more attention for research and experimental works. In this study, the effect of mechanical loads was investigated on an open‐cathode PEMFC in operating state. Several long time vibration tests have been applied on non‐operating PEMFC, and the performance of the fuel cell was evaluated during the test. Hydrogen leakage as a key parameter was investigated in performance monitoring instead of measuring torque on compression bolts. The vibration tests were consisted of sine, shock and random for X, Y and Z axes in operating state and random vibration in non‐operating state of PEMFC. The experimental results in operating state were indicated that the fuel cell performance has not been affected by the proposed vibrations. Furthermore, the test results of non‐operating state have been shown that the performance of PEMFC reduces about 0.6% in each four‐hour step of the vibration test. In addition, the experiments reveal that if the mechanical loads and vibrations cause physical damage on the fuel cell components, they can change the performance and reliability of the fuel cell.     

Vibration Modeling of PEM Fuel Cell for Prediction of Cell Number Effects by Experimental Data

One of the most important challenges in increasing the performance, reliability and lifetime of fuel cells is the mechanical load effects that occur on real applications. Therefore, the vibration model of fuel cell that predicts the behavior of various fuel cell layouts is very useful. The fuel cell is made up of different adjacent layers that may have semi opposite mechanical properties. This special structure leads to occurrence of non‐linear behavior of fuel cell under dynamic mechanical vibrations and so, a black box method is selected for modeling of its vibration behavior. In this study, the mechanical load experiments in various shape and axes were applied on five layouts of proposed fuel cell and the vibration of its body measure by some accelerometers. The NNARXM neural network is created and trained with the experimental data of three layouts of the fuel cell. Then, the prediction error of this neural network, validated with the two other experimental data of fuel cell layouts, by correlation coefficients and histogram of prediction errors. Neural network validation shows the well prediction of both untrained layout and suitable estimation for any desired layout.     

Stable repeated-batch production of recombinant dye-decolorizing peroxidase (rDyP) from Aspergillus oryzae

Recombinant Aspergillus oryzae expressing a dye-decolorizing peroxidase gene (dyp) was cultivated for repeated-batch production of recombinant dye-decolorizing peroxidase (rDyP) using maltose as a carbon source. High-level rDyP activity in limitation of carbon and nitrogen sources was maintained stably for 26 cycles of repeated 1-d batches of A. oryzae pellets without any additional pH control. Cultures maintained at 4 degrees C for 20 d resumed rDyP production following a single day of incubation. One liter filtrated crude rDyP containing 4600 U rDyP decolorized 5.07 g RBBR at the apparent decolorization rate of 17.7 mg l(-1) min(-1).     

Self-healing semi-IPN materials from epoxy resin by solvent-free furan–maleimide Diels–Alder polymerization

Novel self-healing Diels–Alder (DA) polymer and the corresponding semi-interpenetrated polymer networks (semi-IPNs) were synthesized and characterized. Initially, a furan-functionalized resin (FFR) was synthesized through the ring-opening reaction of a conventional epoxy resin [diglycidyl ether bisphenol A (DGEBA)] with furfuryl alcohol as a bio-based compound. Subsequently, semi-IPNs with different compositions were obtained through the blending of DGEBA, FFR, 4,4′-diaminodiphenylmethane, and 1,1′-(methylenedi-1,4-phenylene) bismaleimide in the molten state by following a predetermined time–temperature program. Fourier transform infrared and nuclear magnetic resonance analyses confirmed the successful synthesis of the materials. Thermoreversibility via retro-DA (rDA) reaction was evidenced by differential scanning calorimetry (DSC) and sol–gel transition tests. Repeated DSC cycle was successfully performed thrice on the DA polyadduct which corroborated repeatability of the DA/rDA association/dissociation. Self-healing and mechanical properties were preliminarily evaluated by scanning electronic microscopy and flexural testing analyses, respectively. The self-healing efficiencies were around 80 and 95% for semi-IPN and DA polyadduct, respectively, based on flexural strength. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48015.