Engineering biphasic hybrid phosphide nanowires as efficient electrocatalyst for hydrogen evolution reaction: Experimental and theoretical insights

Development of highly efficient and cheap electrocatalysts towards the hydrogen evolution reaction (HER) is of great importance for electrochemical water splitting. Herein, hybrid Cu/NiMo-P nanowires on the copper foam were successfully fabricated via a simple two-step method. The hierarchically structured Cu/NiMo-P exhibits large surface areas and rapid electron transfer ability, leading to enhanced catalytic activity. The as-prepared Cu/NiMo-P electrodes need overpotentials of 34 mV and 130 mV to obtain 10 mA cm^?2 for HER in acidic and alkaline solutions, respectively. Density functional theory (DFT) calculations reveal that the Cu/NiMo-P hybrid has a more thermo-neutral hydrogen adsorption free energy and enhanced charge transfer ability as well.     

Display illumination with modulated directional backlight

Illumination is essential for modern life as colorful world is perceived by human visionary system. Display technology has been developing rapidly in recent decades, and the basic principle is related to the way that the image is illuminated and light is emanated. Traditional illumination is provided by different types of light sources, and the display image is visible in large viewing space until the emanating light decays to zero. This work proposes and demonstrates a novel illumination scheme for a display in which the displaying images are visible only in specific spatial regions. The directional backlight ensures the image propagating to specific direction while imaging visibility can be controlled to terminate abruptly at certain distance from the display screen while exerting no influence to nearby regions. The working principle for such an illumination scheme is the use of the modulated coherent directional backlight through an axicon lens. It is shown that the illumination scheme can robustly deliver carried image information to the designated viewing region. This new illumination scheme has many advantages over conventional illumination, including its usage for personal display, very lower energy consumption, as well as minimizing light hazard pollution.     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.     

Strategic planning optimization for natural gas to liquid transportation fuel (GTL) systems

A strategic planning optimization model is proposed for a network of natural gas to liquids (GTL) systems, and it is solved using a rolling horizon strategy. The model formulation determines the strategic and tactical decisions of the GTL supply chain over a long time horizon. The decisions to build new GTL refineries may be made over the span of 30 years and their operations cover the span of 60 years. Multiple capacities of GTL refineries (i.e., 1, 5, 10, 50, and 200 thousand barrels per day) that produce gasoline, diesel, and kerosene commensurate to the United States demand ratio may exist in the network. The parameter inputs include the locations, availabilities, and prices of natural gas in the United States discretized by county, the delivery locations of fuel products, and the transportation costs of every input and output of the refinery, defined for each time period. Formulated as a large-scale mixed-integer linear optimization (MILP) model, the problem is solved using a rolling horizon strategy for tractability. Case studies on the state of Pennsylvania are presented for different planning schemes and their impact on the economic performance of the GTL network is discussed.     

澳门融入粤港澳大湾区世界机场群路向研究

粤港澳大湾区世界城市群与世界机场群相伴而生、联动发展.建设世界级机场群，需要5大机场错位发展和良性互动.澳门国际机场是澳门融入粤港澳大湾区重要一极，但机场发展存在与澳门定位、香港珠海空域空运矛盾、澳门土地和环境现状等相关的问题.因此，澳门国际机场今后发展需在葡语国家航线、珠澳密切合作、公务机产业方面努力实现跨越式发展.然而，澳门国际机场改扩建问题争议不断，本文就原址扩建、填海区新建、飞地黄茅岛新建3种模式进行比较分析，探索符合澳门长远利益之方案.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Genetic diversity by AFLP analysis within Jatropha curcas L. populations in the State of São Paulo,Brazil

The genetic diversity of 5 populations of Jatropha curcas L. (Euphorbiaceae), a species cultivated in tropical countries and used in biodiesel production, was analyzed by amplified fragment length polymorphism (AFLP). Plants from distinct populations found in the State of São Paulo, Brazil, had their genetic diversity characterized by using three primer combinations. The number of polymorphic bands obtained reached 184 and the base pair length of bands ranged from 75 to 350, with average PIC values of 0.418. Accessions from the Aguas de Santa Barbara population presented the highest percentage of polymorphic loci (89.76%), followed by the populations of Catanduva (84.24%), Jales (80.98%), Jurucê (78.80%) and Taquaritinga (70.65%). Plants collected from the populations of Taquaritinga and Jales presented the smallest and highest genetic diversities, respectively, measured by using both Nei's genetic variability index (h = 0.2242 and 0.2973) and Shannon's index (I = 0.3359 and 0.4319). The results obtained indicated that 73.1% of genetic variability corresponds to intrapopulational variation and 26.8% to variation among populations. The clustering dendrogram using Jacquard showed four clusters. Three clusters with low genetic diversity grouped most of individuals collected in distinct regions (63.3% JU, 47.0% JA and 82.5% TA) and the fourth with the higher genetic diversity was composed with basically individuals collected in CA and AS, but it also had samples collected in JU, JA and TA, where it is possible to select individuals to be included in breeding programs.     

A simple approach for fabrication of interconnected graphitized macroporous carbon foam with uniform mesopore walls by using hydrothermal method

Three-dimensional (3D) highly interconnected graphitized macroporous carbon foam with uniform mesopore walls has been successfully fabricated by a simple and efficient hydrothermal approach using resorcinol and formaldehyde as carbon precursors. The commercially available cheap polyurethane (PU) foam and Pluronic F127 were used as a sacrificial polymer and mesoporous structure-directing templates, respectively. The graphitic structure of carbon foam was obtained by catalytic graphitization method using iron as catalyst. Three different carbon foams such as graphitized macro-mesoporous carbon (GMMC) foam, amorphous macro-mesoporous carbon (AMMC) foam and graphitized macroporous carbon (GMC) foam were fabricated and their physicochemical and mechanical properties were systematically measured and compared. It was found that GMMC possess well interconnected macroporous structure with uniform mesopores located in the macroporous skeletal walls of continuous framework. Besides, GMMC foam possesses a well-defined graphitic framework with high surface area (445 m²/g), high pore volume (0.35 cm³/g), uniform mesopores (3.87 nm), high open porosity (90%), low density (0.30 g/cm³) with good mechanical strength (1.25 MPa) and high electrical conductivity (11 S/cm) which makes it a promising material for many potential applications.