66 research outputs found
TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models
With the promotion of chatgpt to the public, Large language models indeed
showcase remarkable common sense, reasoning, and planning skills, frequently
providing insightful guidance. These capabilities hold significant promise for
their application in urban traffic management and control. However, LLMs
struggle with addressing traffic issues, especially processing numerical data
and interacting with simulations, limiting their potential in solving
traffic-related challenges. In parallel, specialized traffic foundation models
exist but are typically designed for specific tasks with limited input-output
interactions. Combining these models with LLMs presents an opportunity to
enhance their capacity for tackling complex traffic-related problems and
providing insightful suggestions. To bridge this gap, we present TrafficGPT, a
fusion of ChatGPT and traffic foundation models. This integration yields the
following key enhancements: 1) empowering ChatGPT with the capacity to view,
analyze, process traffic data, and provide insightful decision support for
urban transportation system management; 2) facilitating the intelligent
deconstruction of broad and complex tasks and sequential utilization of traffic
foundation models for their gradual completion; 3) aiding human decision-making
in traffic control through natural language dialogues; and 4) enabling
interactive feedback and solicitation of revised outcomes. By seamlessly
intertwining large language model and traffic expertise, TrafficGPT not only
advances traffic management but also offers a novel approach to leveraging AI
capabilities in this domain. The TrafficGPT demo can be found in
https://github.com/lijlansg/TrafficGPT.git
Adpositional Supersenses for Mandarin Chinese
This study adapts Semantic Network of Adposition and Case Supersenses (SNACS) annotation to Mandarin Chinese and demonstrates that the same supersense categories are appropriate for Chinese adposition semantics. We annotated 20 chapters of The Little Prince, with high interannotator agreement. The parallel corpus substantiates the applicability of construal analysis in Chinese and gives insight into the differences in construals between adpositions in two languages. The corpus can further support automatic disambiguation of adpositions in Chinese, and the common inventory of supersenses between the two languages can potentially serve cross-linguistic tasks such as machine translation
Source Apportionment of Gaseous and Particulate PAHs from Traffic Emission Using Tunnel Measurements in Shanghai, China
Understanding sources and contributions of gaseous and particulate PAHs from traffic-related pollution can provide valuable information for alleviating air contamination from traffic in urban areas. On-road sampling campaigns were comprehensively conducted during 2011–2012 in an urban tunnel of Shanghai, China. 2–3 rings PAHs were abundant in the tunnel\u27s gas and particle phases. Diagnostic ratios of PAHs were statistically described; several were significantly different between the gas and particle phases. Principal component analysis (PCA), positive matrix factorization (PMF), bivariate correlation analysis and multiple linear regression analysis (MLRA) were applied to apportion sources of gaseous and particulate PAHs in the tunnel. Main sources of the gaseous PAHs included evaporative emission of fuel, high-temperature and low-temperature combustion of fuel, accounting for 50–51%, 30–36% and 13–20%, respectively. Unburned fuel particles (56.4–78.3%), high-temperature combustion of fuel (9.5–26.1%) and gas-to-particle condensation (12.2–17.5%) were major contributors to the particulate PAHs. The result reflected, to a large extent, PAH emissions from the urban traffic of Shanghai. Improving fuel efficiency of local vehicles will greatly reduce contribution of traffic emission to atmospheric PAHs in urban areas. Source apportionment of PM10 mass was also performed based on the organic component data. The results showed that high-temperature combustion of fuel and gas-to-particle condensation contributed to 15–18% and 7–8% of PM10 mass, respectively, but 55–57% of the particle mass was left unexplained. Although the results from the PCA and PMF models were comparable, the PMF method is recommended for source apportionment of PAHs in real traffic conditions. In addition, the combination of multivariate statistical method and bivariate correlation analysis is a useful tool to comprehensively assess sources of PAHs
Carbonation of Water Repellent-Treated Concrete
Water repellent treatment has been considered an effective preventive method against water and aggressive ions penetration into concrete and consequently can improve the durability of concrete structures. In reality, many concrete structures are exposed to conditions with high risk of carbonation. In this contribution, one type of ordinary concrete had been prepared and surface impregnated by 400 g/m2 silane cream and 100 g/m2 and 400 g/m2 silane gel. In addition, integral water repellent concrete was produced by adding 2% silane emulsion. Then, the specimens were exposed to accelerated carbonation for 7, 28, and 72 days. The effect of water repellent treatment on carbonation of concrete has been investigated. The results indicate that surface impregnation reduced carbonation depth of concrete under RH 70%, but integral water repellent concrete increased carbonation. Carbonation reaction started behind the hydrophobic layer in the surface-impregnated concrete. The coefficient of carbonation can be described better by a hyperbolic function of time. Treatment by 400 g/m2 silane gel and silane cream showed better efficiency on reducing carbonation than usage of 100 g/m2. Coefficient of water capillary suction was decreased significantly by both surface impregnation and integral water repellent treatment. It is an effective method to protect concrete from water penetration into the material
Comprehensive genomic profiling reveals prognostic signatures and insights into the molecular landscape of colorectal cancer
BackgroundColorectal cancer (CRC) is a prevalent malignancy with diverse molecular characteristics. The NGS-based approach enhances our comprehension of genomic landscape of CRC and may guide future advancements in precision oncology for CRC patients.MethodIn this research, we conducted an analysis using Next-Generation Sequencing (NGS) on samples collected from 111 individuals who had been diagnosed with CRC. We identified somatic and germline mutations and structural variants across the tumor genomes through comprehensive genomic profiling. Furthermore, we investigated the landscape of driver mutations and their potential clinical implications.ResultsOur findings underscore the intricate heterogeneity of genetic alterations within CRC. Notably, BRAF, ARID2, KMT2C, and GNAQ were associated with CRC prognosis. Patients harboring BRAF, ARID2, or KMT2C mutations exhibited shorter progression-free survival (PFS), whereas those with BRAF, ARID2, or GNAQ mutations experienced worse overall survival (OS). We unveiled 80 co-occurring and three mutually exclusive significant gene pairs, enriched primarily in pathways such as TP53, HIPPO, RTK/RAS, NOTCH, WNT, TGF-Beta, MYC, and PI3K. Notably, co-mutations of BRAF/ALK, BRAF/NOTCH2, BRAF/CREBBP, and BRAF/FAT1 correlated with worse PFS. Furthermore, germline AR mutations were identified in 37 (33.33%) CRC patients, and carriers of these variants displayed diminished PFS and OS. Decreased AR protein expression was observed in cases with AR germline mutations. A four-gene mutation signature was established, incorporating the aforementioned prognostic genes, which emerged as an independent prognostic determinant in CRC via univariate and multivariate Cox regression analyses. Noteworthy BRAF and ARID2 protein expression decreases detected in patients with their respective mutations.ConclusionThe integration of our analyses furnishes crucial insights into CRC’s molecular characteristics, drug responsiveness, and the construction of a four-gene mutation signature for predicting CRC prognosis
Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics
The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino
detector currently under construction in China. Thanks to the tight
requirements on its optical and radio-purity properties, it will be able to
perform leading measurements detecting terrestrial and astrophysical neutrinos
in a wide energy range from tens of keV to hundreds of MeV. A key requirement
for the success of the experiment is an unprecedented 3% energy resolution,
guaranteed by its large active mass (20 kton) and the use of more than 20,000
20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution
sampling electronics located very close to the PMTs. As the Front-End and
Read-Out electronics is expected to continuously run underwater for 30 years, a
reliable readout acquisition system capable of handling the timestamped data
stream coming from the Large-PMTs and permitting to simultaneously monitor and
operate remotely the inaccessible electronics had to be developed. In this
contribution, the firmware and hardware implementation of the IPbus based
readout protocol will be presented, together with the performances measured on
final modules during the mass production of the electronics
Mass testing of the JUNO experiment 20-inch PMTs readout electronics
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose,
large size, liquid scintillator experiment under construction in China. JUNO
will perform leading measurements detecting neutrinos from different sources
(reactor, terrestrial and astrophysical neutrinos) covering a wide energy range
(from 200 keV to several GeV). This paper focuses on the design and development
of a test protocol for the 20-inch PMT underwater readout electronics,
performed in parallel to the mass production line. In a time period of about
ten months, a total number of 6950 electronic boards were tested with an
acceptance yield of 99.1%
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