The conversion of the energy supply to renewable sources (wind, photovoltaics) will increase the volatility in electricity generation in the future. In order to ensure a balanced power balance in the power grid, storage is required - not only for a short time, but also seasonally. The bidirectional coupling of existing energy infrastructure with the power grid can help here by using the electricity in electrolysis systems to produce hydrogen. The hydrogen can be mixed with natural gas in the existing infrastructure (gas storage, pipelines) to a limited extent or converted directly to methane in a gas-catalytic reaction, methanation, with carbon dioxide and/or carbon monoxide. By using the natural gas infrastructure, the electricity grids are relieved and renewable energies can also be stored over long periods of time. Another advantage of this technology, known as “Power-to-Gas”, is that the methane produced in this way represents a sink for CO₂ emissions, as it replaces fossil sources and CO₂ is thus fed into a closed cycle.

Research in the field of Power-to-Gas technology is currently addressing technological advances both in the field of electrolysis and for the subsequent methanation, in particular to reduce investment costs. In the field of methanation, load-flexible processes are to be developed that are adapted to the fluctuating supply of hydrogen. The profitability of the Power-to-Gas process chain can be increased through synergistic integration into existing industrial processes. For example, an integrated smelting works offers a promising infrastructural environment, since, on the one hand, process gases containing carbon are produced in large quantities and, on the other hand, the oxygen as a by-product from the water electrolysis can be used directly. Such concepts suggest an economic application of Power-to-Gas technology in the near future.

The US Infrastructure Investment and Job Act (IIJA), also commonly referred to as the Bipartisan Infrastructure Bill, passed in 2021, has drawn international attention. It aims to help to rebuild US infrastructure, including transportation networks, broadband, water, power and energy, environmental protection and public works projects. An estimated $1.2 trillion in total funding over ten years will be allocated. The Bipartisan Infrastructure Bill is the largest funding bill for US infrastructure in the recent history of the United States. This review article will specifically discuss funding allocations for roads and bridges, power and grids, broadband, water infrastructure, airports, environmental protection, ports, Western water infrastructure, electric vehicle charging stations and electric school buses in the new spending of the Infrastructure Investment and Job Act and why these investments are urgently necessary. This article will also briefly discuss the views of think tank experts, the public policy perspectives, the impact on domestic and global arenas of the new spending in the IIJA, and the public policy implications.

The resistance of platinum filament on heating to different temperatures have been measured. Measurements showed platinum wire resistivity matching to tabulated values, and therefore can be used to obtain the temperature dependence of conductors used in bolometric measurers of radiation.The results obtained make it possible to createabsolute bolometricmeasurer of continuous power and pulse energy of laser radiation.

Map is the basic language of geography and an indispensable tool for spatial analysis. But for a long time, maps have been regarded as an objective and neutral scientific achievement. Inspired by critical geography, critical cartography/GIS came into being with the goal of clarifying the discourse embedded in cartographic practice. Power relationship challenges the untested assumption in map representation that is taken for granted. After more than 40 years of debate and running in, this research field has initially shown an outline, and critical cartography/GIS has roughly formed two research directions: the deconstruction path mainly starts from the identity of cartography subject and the process of map knowledge production, and analyzes the inseparable relationship between cartography and national governance and its internal power mechanism respectively; the construction path mainly relies on cooperative mapping and anti-mapping to realize the reproduction of map data. Domestic critical cartography/GIS research has just started, and it is necessary to continue to absorb the achievements of critical geography and carry out research in different historical periods. The deconstruction research of different types of maps also needs to strengthen the in-depth bridging between the construction path and the deconstruction path, and to be more open to the public. Impartial map application research, and actively apply the research results to social practice.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.