Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The application of deep learning in computer vision has recently gain popularity. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. The application areas of the hybrid of natured inspired algorithms and deep learning architecture includes: machine vision and learning, image processing, data science, autonomous vehicles, medical image analysis, biometrics, etc. In this paper, we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future

Copper Catalyzed C−H Activation

Activation of C−H bonds and their application in cross coupling chemistry has received a wider interest in recent years. The conventional strategy in cross coupling reaction involves the pre-functionalization step of coupling reactants such as organic halides, pseudo-halides and organometallic reagents. The C−H activation facilitates a simple and straight forward approach devoid of pre-functionalization step. This approach also addresses the environmental and economical issues involved in several chemical reactions. In this account, we have reported C−H bond activation of small organic molecules, for example, formamide C−H bond can be activated and coupled with β-dicarbonyl or 2-carbonyl substituted phenols under oxidative conditions to yield carbamates using inexpensive copper catalysts. Phenyl carbamates were successfully synthesized in moderate to good yields by cross dehydrogenative coupling (CDC) of phenols with formamides using copper catalysts in presence of a ligand. We have also prepared unsymmetrical urea derivatives by oxidative cross coupling of formamides with amines using copper catalysts. Synthesis of N,N-dimethyl substituted amides, 5-substituted-γ-lactams and α-acyloxy ethers was carried out from carboxylic acids using recyclable CuO nanoparticles. Copper nanoparticles afforded N-aryl-γ-amino-γ-lactams by oxidative coupling of aromatic amines with 2-pyrrolidinone. Reusable transition metal HT-derived oxide catalyst was used for the synthesis of N,N-dimethyl substituted amides by the oxidative cross-coupling of carboxylic acids and substituted benzaldehydes. Overview of our work in this area is summarized

Hydrogen processing by Fe<SUP>III</SUP>-exchanged montmorillonite: a unique geochemical protocol

The production of hydrogen by the relay of electrons from I− to H+ in an acidic, aqueous medium and the consumption of hydrogen by reductive N acylation open up enormous opportunities in hydrogen chemistry

Fokusstyrd bildkodning baserad på vinkel och djup perception

In normal image coding the image quality is the same in all parts of the image. When it is known where in the image a single viewer is focusing it is possible to lower the image quality in other parts of the image without lowering the perceived image quality. This master's thesis introduces a coding scheme based on depth perception where the quality of the parts of the image that correspond to out-of-focus scene objects is lowered to obtain data reduction. To obtain further data reduction the method is combined with angular perception coding where the quality is lowered in parts of the image corresponding to the peripheral visual field. It is concluded that depth perception coding can be done without lowering the perceived image quality and that the coding gain increases as the two methods are combined

Allocation of optimal energy from storage systems using solar energy

In order to reduce carbon emissions, a growing reliance on renewable energy sources such as solar energy is required. As a result of their ability to store excess solar electricity that may be used at a later time to reduce waste and increase utility profits, battery energy storage systems (BESSs) have emerged as a factor for power systems that integrates solar power system. BESSs are traditionally put on buses in solar farms, allowing extra electricity via solar to be stored instantaneously and transmission line losses to be kept to an absolute minimum. According to this placement strategy, BESS is exclusively built in the proximity of solar power plants. In this way, deployment of BESS without network topology consideration, and collaboration among BESSs is limited with capacity pooling to store excess electricity from photo voltaic (PV) panels. In this paper, we develop an optimal deployment of BESSs and it is associated with the estimation of the capacity using a multi-objective constraint modelling. The soft margin classifier minimize the curtailment associated with solar energy that considers both the power flow constraint and network topology. The results of entire model shows that the proposed soft margin classifier is efficient in storing the surplus power in the batter devices than other methods

Oxidative coupling of carboxylic acids or benzaldehydes with DMF using hydrotalicite-derived oxide catalysts

Hydrotalcite-derived (HT-derived) oxide catalysts were synthesised from hydrotalcite-like (HT-like) materials prepared by co-precipitation method (Cu-Al, Cu-Fe, Mg-Al, Mg-Fe, Ni-Fe and Ni-Al) followed by calcination and their catalytic activity was studied for oxidative amidation of carboxylic acids and substituted benzaldehydes with N,N-dimethylformamide (DMF). Catalyst screening was done using benzoic acid and DMF as a model reaction. Subsequently, we have optimised the reaction with different reaction parameters, catalyst loading, temperatures and oxidants. Cu-Fe HT-derived oxide catalyst showed excellent activity towards oxidative amidation using TBHP as an oxidant at 80 °C in 10 h. The fresh and spent catalysts were thoroughly characterised by different analytical techniques; XRD, SEM, HRTEM, TPD, N 2 adsorption-desorption isotherm and XPS. Moreover, the catalyst was easily separated by simple filtration and reused up to four cycles without significant loss in catalytic activity

High Efficiency Conversion of Glycerol to 1,3-Propanediol Using a Novel Platinum–Tungsten Catalyst Supported on SBA-15

The hydrogenolysis of glycerol to 1,3-propanediol was conducted over a series of Pt-WO₃/SBA-15 catalysts with Pt content ranging from 0.5 to 3 wt % and W content of 10 wt % in vapor phase under atmospheric pressure for the first time. The catalysts prepared via sequential impregnation method were systematically characterized using XRD, NH₃-TPD, Py-IR, CO chemisorption, TPR, TEM, and surface area measurements. The catalysts exhibited unprecedented activity for selective formation of 1,3-propanediol via hydrogenolysis of glycerol. The effect of various reaction parameters such as catalyst loading, reaction temperature, hydrogen flow rate, glycerol concentration and reaction time were studied. The optimized reaction conditions showed that a high glycerol conversion (86%) and 1,3-propanediol selectivity (42%) was obtained over 2Pt-10WO₃/SBA-15 catalyst illustrating the potential of SBA-15 supported platinum–tungsten catalyst to be highly active and efficient. The Brønsted acid sites of the catalyst formed due to addition of WO₃ enhanced selective formation of 1,3-propanediol

Platinum Supported on H‑Mordenite: A Highly Efficient Catalyst for Selective Hydrogenolysis of Glycerol to 1,3-Propanediol

The selective production of 1,3-propanediol from glycerol under mild reaction conditions is of high interest. The current work describes the use of a highly selective catalyst consisting of platinum supported on mordenite zeolite employed for the first time for vapor phase hydrogenolysis of glycerol to 1,3-propanediol under atmospheric pressure. The catalysts with varying Pt content (0.5–3 wt %) were prepared and thoroughly characterized by X-ray diffraction, temperature-programmed desorption of ammonia, FT-IR of adsorbed pyridine, CO chemisorptions, transmission electron microscopy, X-ray photoelectron spectroscopy, and BET surface area. The influence of reaction parameters has been studied to unveil the optimized reaction conditions. A high 1,3-propanediol selectivity (48.6%) was obtained over a 2 wt % Pt/H–mordenite catalyst at 94.9% glycerol conversion. According to the results obtained, the selectivity to 1,3-propanediol is better influenced by Pt dispersion and Brønsted acidity of the support. A plausible reaction mechanism has been presented. The spent catalyst exhibited consistent activity and selectivity toward the desired product during the glycerol hydrogenolysis reaction