Synergistic interaction of D1 and D2 dopamine receptors in the modulation of the reinforcing effect of brain stimulation.

Rats were trained to press a bar for hypothalamic stimulation, and a frequency-response function was plotted. Quinpirole (a selective D2 agonist) facilitated self-stimulation when injected alone but failed to show the facilitatory effect when injected either 1 hr before or 1 hr after injection of SCH 23390 (a D1 antagonist). Injection of reserpine followed by α-methyl-p-tyrosine virtually eliminated self-stimulation. Subsequent injection of either SKF 38393 (a D1 agonist) alone or quinpirole alone did not restore self-stimulation, but a combination of quinpirole and SKF 38393 did. Results suggest that a D2 dopamine agonist facilitates the reinforcing effect of brain stimulation only if D1 receptors are activated by endogenous dopamine or by an exogenous agonist. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Brillouin-Scattering Study of the Elastic Constants of ErVO4

The room-temperature elastic constants of ErVO₄ were considerably smaller than those of isostructural silicate and phosphate analogs. The generally "less-rigid" crystalline lattice and weaker metal-oxygen bond-strength in the RVO₄ (R = rare earth elements) phases indicates that these materials are of interest for potential applications as an interphase component in toughened oxide ceramic composites.     

Delignification kinetics of empty fruit bunch (EFB): a sustainable and green pretreatment approach using malic acid-based solvents

Recently, the development of efficient and environmentally benign solvents has received great attention to replace current harsh organic solvents. In this context, low-transition-temperature mixtures (LTTMs) have emerged as favorable green solvents for biomass delignification. Palm oil biomass, empty fruit bunch (EFB) was pretreated with commercial l-malic acid and microwave hydrothermally extracted cactus malic acid-derived LTTMs at 60, 80, and 100 °C. The LTTMs applied in this study were derived from malic acid–choline chloride–water and malic acid–monosodium glutamate–water with a molar ratio of 2:4:2 and 3:1:5, respectively. Three first-order reactions were used to express the delignification kinetic model of EFB. The first term was based on the initial stage and assigned as infinite due to the fast rate of delignification which could not be detected. The second and third terms were proportional to bulk and residual delignification stages. A good agreement was obtained between the kinetic model and the experimental data obtained in this study with R²?≥?0.91. The activation energies for the delignification reactions using l-malic acid and cactus malic acid-based LTTMs in the bulk and residual stages were approximated as 36–56 and 19–26 kJ/mol and 34–90 and 47–87 kJ/mol, respectively.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

CO2 methanation over Ni and Rh based catalysts: Process optimization at moderate temperature

H₂ was produced from aluminum/water reaction and reacted with CO₂ over Ni and Rh based catalysts to optimize the process conditions for CO₂ methanation at moderate temperature. Monometallic catalysts were prepared by incorporating Ni and Rh using nickel nitrate hexahydrate (Ni(NO₃)₂·6H₂O) and rhodium(III) chloride trihydrate (RhCl₃·3H₂O)as a precursor chemical. The preliminary study of the catalysts revealed higher activity and CH₄ selectivity for Rh based catalyst compared to that of Ni based catalyst. Further, Rh based catalyst was investigated using response surface methodology (RSM) involving central composite design. The quadratic model was employed to correlate the effects of variable parameters including methanation temperature, %humidity, and catalyst weight with the %CO₂ conversion, %CH₄ selectivity, and CH₄ production capacity. Analysis of variance revealed that methanation temperature and humidity play an important role in CO₂ methanation. Higher response values of CO₂ conversion (54.4%), CH₄ selectivity (73.5%) and CH₄ production capacity (8.4 μmol g^?1 min^?1) were noted at optimum conditions of 206.7°C of methanation temperature, 12.5% humidity and 100 mg of the catalyst. The results demonstrated the ability of Rh catalyst supported on palm shell activated carbon (PSAC) for CO₂ methanation at low temperature and atmospheric pressure.     

Hydrogen from renewable palm kernel shell via enhanced gasification with low carbon dioxide emission

Hydrogen economy has become more attractive with the energy crises and environmental issues associated with fossil fuel utilization more so with the discovery that hydrogen can be produced from renewable biomass. This provides good prospects to Malaysia that generates abundant palm wastes. Nevertheless, there is still limited knowledge on kinetics parameters for hydrogen production from palm kernel shell (PKS) gasification. Hence, this work aims to develop a mathematical model that is able to describe the kinetics of steam gasification of PKS with in situ CO₂ capture while considering tar formation. A mean-squared error minimization approach has been used to estimate the kinetics parameters of the gasification process. Using the calculated kinetics parameters the process efficiencies are profiled with respect to the effect of gasification temperature, steam/biomass ratio and sorbent/biomass ratio. The parametric study indicates that the three variables promote hydrogen production at different degree of influence. This developed model can be further extended to incorporate optimization study on the potential clean production of hydrogen from PKS.     

Synthesis,structure, and magnetic property of hexagonal perovskite Ba5Sn1.1Mn3.9O15

A new compound Ba₅Sn_1.1Mn_3.9O₁₅ has been synthesized at 1300 °C by solid-state reaction. The structure was characterized by X-ray, electron and neutron diffraction methods. Ba₅Sn_1.1Mn_3.9O₁₅ crystallizes in hexagonal space group P6₃/mmc with a = 5.717 Å and c = 23.534 Å. The magnetic measurement reveals that Ba₅Sn_1.1Mn_3.9O₁₅ has a spin glass transition at 7 K.     

Synthesis of regional networks for the supply of energy and bioproducts

This article presents a method for the synthesis of regional renewable energy supply chains, based on Mixed-Integer Linear Programming (MILP). This method addresses the challenges presented by biomass resources. The main challenges are the distributive and varied availabilities regarding both location and time. This work also aims to maximise the economically viable utilisation of resources, accounting for the competition between energy and food production. A four-layer supply chain superstructure has been developed, which includes the harvesting, preparation, core processing and distribution of products. This considered system’s boundaries involve a region, which is then divided into zones for optimising conversion operations and transportation flows. An MILP model has been formulated with profit maximisation as the optimisation criterion. The environmental impact is evaluated by the carbon footprint. The sensitivity of the optimal solutions is analysed for different regions’ sizes, transportation costs, pre-processing alternatives and the co-production of food and energy.     

Docosahexaenoic acid production from crude glycerol by <Emphasis Type="Italic">Schizochytrium limacinum</Emphasis> SR21

In our search for a substitute energy source, microalgal biodiesel has presented itself as a potential candidate. However, the development of biodiesel results in the overproduction of crude glycerol, which can cause undesirable environmental issues. The environmental harm can be minimized by converting crude glycerol into value-added products. One solution involves using a microalga Schizochytrium limacinum SR21 to convert crude glycerol into docosahexaenoic acid (DHA). DHA is an essential fatty acid, necessary for developing brain functions in infants and maintaining healthy brain activity in adults. In our study, the highest DHA productivity of 233.73 mg/g biomass was obtained using 3 % crude glycerol in Medium 2 at 20 °C under mixo/heterotrophic cultivation.     

Investigation of phase behavior during melt processing of novel inorganic‐organic polymer hybrid material

The phase behavior of novel, binary organic‐inorganic hybrids consisting of an ultra‐low T_g tin‐based phosphate glass (Pglass) and polystyrene (PS) was investigated. Dynamic mechanical analysis (DMA) revealed that the glass transition peaks of the PS changed slightly with Pglass volume fraction, leading to a broad peak at the phase inversion point. The phase inversion and degree of phase continuity of the hybrid were studied through solvent extraction, optical/scanning electron microscopy, and dynamic rheology. The Jordhamo and Utracki viscosity ratio models provided reliable estimates of the inversion point. Torque rheometry revealed a trend toward linear additivity within the temperature range 200°C–230°C. Small‐angle neutron scattering experiments gave further evidence of the hybrid phase incompatibility. The results of this study point to a promising new class of blend materials with the potential to present a unique combination of properties impossible to achieve with classical polymer blends. Polym. Eng. Sci. 44:1692–1701, 2004. © 2004 Society of Plastics Engineers.