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To help China achieve the "dual carbon" goal at an early date and mitigate global climate change brought about by the greenhouse effect, people are increasingly interested in topics such as carbon neutrality, sustainability, and energy, which has led to apparent changes in many cross-disciplinary disciplines. While upgrading the organization industry, people often neglect to implement measures from the energy side. The study is based on 492 articles retrieved from the Web of Science (WoS) database between 1900 and 2023, and conducted comprehensive data and visual analysis of keywords such as "carbon neutrality", "carbon emissions", and "carbon footprint". Bibliometrics show that publications have proliferated since 2008 and also involve multiple disciplines, such as management, sustainable development studies, public health, energy, and other related major fields. At the same time, research institutions in China, the United Kingdom, and Singapore have excelled in such disciplines. The authors use big data analysis technology and visualization software to comprehensively analyze the research results in the form of charts from the aspects of literature publication and citation trends, top research institutions, and keyword clustering, which provides research ideas for research organizations to use carbon emission data to achieve carbon neutrality.

Influenza A virus is an RNA virus that encodes up to 11 proteins and this small coding capacity demands that the virus use the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only informs us of the molecular pathways exploited by the virus but also provides further targets that could be pursued for antiviral drug development. Here we use an integrative systems approach, based on genome-wide RNA interference screening, to identify 295 cellular cofactors required for early-stage influenza virus replication. Within this group, those involved in kinase-regulated signalling, ubiquitination and phosphatase activity are the most highly enriched, and 181 factors assemble into a highly significant host-pathogen interaction network. Moreover, 219 of the 295 factors were confirmed to be required for efficient wild-type influenza virus growth, and further analysis of a subset of genes showed 23 factors necessary for viral entry, including members of the vacuolar ATPase (vATPase) and COPI-protein families, fibroblast growth factor receptor (FGFR) proteins, and glycogen synthase kinase 3 (GSK3)-beta. Furthermore, 10 proteins were confirmed to be involved in post-entry steps of influenza virus replication. These include nuclear import components, proteases, and the calcium/calmodulin-dependent protein kinase (CaM kinase) IIbeta (CAMK2B). Notably, growth of swine-origin H1N1 influenza virus is also dependent on the identified host factors, and we show that small molecule inhibitors of several factors, including vATPase and CAMK2B, antagonize influenza virus replication.

LSM-tree based Key-value (KV) stores greatly fit the needs of write-intensive applications with its efficient data store and retrieval operations to datasets. To accommodate ever-growing datasets, shingled magnetic recording (SMR) drives have become a popular option to provide large storage capacity for KV stores at low cost. SMR drives achieve high storage density via overlapping tracks on the disk surface. However, the overlapped track layout induces the sequential-write constraint and prevents KV stores from storing and rearranging KV pairs efficiently. In this paper, we present KVSTL, a KV store aware Shingled Translation Layer (STL), to preserve the merits of existing KV stores, while exploiting the high storage density of SMR drives. KVSTL is proposed as an application support to hide the management complexity of SMR drives and facilitates the management of SMR drives via passing only the “level” and “invalidation” information of LSM-tree based KV stores onto SMR drives. The proposed KVSTL achieves its performance enhancement via managing key-value pairs with level awareness and enabling efficient storage space management with the invalidation information. The results show that KVSTL can reduce the written data amount for 69.45 percent on average and the latency for up to 62.72 percent when compared with SMR-based LevelDB. [ABSTRACT FROM AUTHOR]

This article presents the results of a comparative study on the effects of social welfare programs in selected developed countries. Using a fully comparable longitudinal data file from nationally representative household surveys, we estimate the poverty rate and poverty gap before and after social welfare transfers and taxes in order to identify the direct effects of social welfare on poverty. In general, the findings reveal that countries with conservative welfare regimes demonstrate higher poverty performance than do countries with liberal welfare regimes. In particular, the United States lagged behind through all periods under investigation, whereas Germany exhibited the highest performance in poverty reduction. [ABSTRACT FROM PUBLISHER]