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Given a hypergraph ${\mathscr{H}}$, we introduce a new class of evaluation toric codes called edge codes derived from ${\mathscr{H}}$. We analyze these codes, focusing on determining their basic parameters. We provide estimations for the minimum distance, particularly in scenarios involving $d$-uniform clutters. Additionally, we demonstrate that these codes exhibit self-orthogonality. Furthermore, we compute the minimum distances of edge codes for all graphs with five vertices.

Veolia Water Solutions & Technologies receives two prizes at AquaTech China for AnoxKaldnes™ MBBR technology and its Carbon Footprint Reduction Program.

International Consortium Led by Chinese Scientists Announce the First Complete Sequencing of Pear Genome.

Eli Lilly Opens Diabetes Research Center in China.

China 'Soaring Ahead' in Nanotechnology Research.

Latest Genomic Studies Shed New Light on Maize Diversity and Evolution.

Insight Genetics and Kindstar Global Partner to Enhance Cancer Care in China.

Two studies from the Institute of Plant and Microbial Biology published in PNAS show how plants respond to changing environments.

China's Pharmaceuticals Sales Force Levels Surpass US for First Time.

A hyperbolic code is an evaluation code that improves a Reed–Muller code because the dimension increases while the minimum distance is not penalized. We give necessary and sufficient conditions, based on the basic parameters of the Reed–Muller code, to determine whether a Reed–Muller code coincides with a hyperbolic code. Given a hyperbolic code ${𝒞}$, we find the largest Reed–Muller code contained in ${𝒞}$ and the smallest Reed–Muller code containing ${𝒞}$. We then prove that similar to Reed–Muller and affine Cartesian codes, the $r$th generalized Hamming weight and the $r$th footprint of the hyperbolic code coincide. Unlike for Reed–Muller and affine Cartesian codes, determining the $r$th footprint of a hyperbolic code is still an open problem. We give upper and lower bounds for the $r$th footprint of a hyperbolic code that, sometimes, are sharp.

Within the Antarctic Treaty Consultative Meetings, momentum has been building to define and manage the human footprint of research stations in Antarctica. This has been reflected by national operators and researchers offering varied approaches to measuring "footprint". By not having a standard method, comparative measurements have shown great disparity. By formulating a standard approach, this study delivered a method that enables comparison. To achieve this, recognition was needed of the vastly different environments in which Antarctic stations are situated. To aid this, defining what to measure, resources consumed, and location descriptors were developed to represent the actual impact of the footprint. The model was then tested on Australia's Davis Station. Inspection of aerial photography and mapping with geographical information systems was supported by field measurements. The model was found to be applicable, with on-the-ground measurements detecting additional footprint area not obvious from the desktop methods. While open to refinement, this study offers a standardised and comparable approach to measuring the footprint of Antarctic research stations.

The competitive process of creating blocks in blockchains — the technological backbone of cryptocurrencies — is computation intensive and consumes a large amount of energy. However, its environmental impact may not be significant, or at least not significant enough to undermine the application of digital currency as both a medium of exchange and a store of value. This chapter uses a unique dataset from the United States Environmental Protection Agency (EPA) to compare the carbon footprints of various industry sectors in the US with that of Bitcoin. Overall, the equivalent amount of greenhouse gas emissions from the Bitcoin network in 2020 is about one-third of the amount emitted by the entire mining sector in the US. In cross-sectional analysis, Bitcoin has a significantly larger carbon footprint than many companies and even industries after controlling for the economic value created. What is alarming is the rate of increase in Bitcoin’s energy consumption since 2017 and the possibility that its carbon footprint may eventually surpass all industries in the near future.