Science Mapping for Recent Research Regarding Urban Underground Infrastructure

The presented research conducted a bibliometric analysis regarding academic publications, especially journal publications, in the area of urban underground infrastructure (UI) systems (which include sewer pipes, drinking water pipes, cables, tunnels, etc.). In total, 547 journal papers published from 2002 to July 2022 (around 20 years period) were retrieved from Scopus using the proposed data collection method. Bibliometric analysis was conducted to extract and map the hidden information from retrieved papers. As a result, networks regarding co-citation, co-authorship, and keywords co-occurrence were generated to visualise and analyse the knowledge domain, patterns, and relationships. The eight most investigated topics in the UI research are identified and discussed, which provides an overview of the research history and focuses. Further, five potential research directions are suggested for researchers in the UI research area. The main contribution of this research is on revealing the knowledge domain of UI research in a quantitative manner as well as identifying the possible research directions

Screening and characterization of petroleum-degrading bacterium

Petroleum-degrading bacterium JY6 was isolated from petroleum-contaminated soils in DaQing oil field. It was identified as Bacillus cereus based on its morphological, physiological and biochemical characteristics, and analysis of its 16SrRNA gene. Biodegradation function of petroleum and oil degradation rates were studied, including different pH value, culture temperature, oxygen etc. Results show the optimum pH value and inoculation was 7.0 and 0.1% (v/v), respectively. 40 ml of liquid medium in 250 ml flask, shaking at 150 r/min and 32°C, petroleum-degrading rate was 75 to 77%. The petroleum-degrading bacterium might be a useful resource for bioremediation of oil-contaminated soils and biotreatment of oil wastewater.Key words: Petroleum-degrading bacterium, screening, Bacillus cereus, bioremediation

Witness of topological phase transition and Weyl points in an open topological system

Recently, the tunable Weyl-semimetal bands and the associate topological phase transition have been successfully simulated in superconducting quantum circuits [X. Tan, \textit{et al.} Phys. Rev. Lett. {\bf 122}, 010501 (2019)]. Since the superconducting quantum circuits inevitably couple to the environment, we here focus on the steady state and decoherence process by taking the reservoir into consideration via quantum master equation. Our results show that the purity of the steady state can be used to indicate the topological phase transition and Weyl points. Furthermore, the coherence will exponentially decay to zero at the Weyl points, and decay to a nonzero value with oscillation at other points in the momentum space. Our work may have significant impact on the study of quantum open topological system.Comment: 5 pages, 4 figure

Earnings Expectations and the Quality of Financial Services

Using complaint data filed by consumers with the Consumer Financial Protection Bureau against financial institutions, we show that banks receive, on average, 13.3% more customer complaints in the quarter immediately after they narrowly beat analysts’ earnings forecasts. The effect is mainly driven by banks’ attempts to reduce their non-interest expenses to beat earnings benchmarks. The relationship is stronger when bank CEOs receive a greater proportion of incentive-based compensation. Overall, our

Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey

Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area