Research on road surface temperature characteristics and road ice warning model of ordinary highways in winter in Hunan province, central China

The study of road surface temperature (Ts) characteristics in winter and the early warning method of road icing is of great significance to reduce traffic accidents and improve transportation efficiency. Using the hourly observation data of Hunan traffic meteorological stations from December 2020 to February 2022, this study analyzes the winter Ts characteristics of ordinary roads in Hunan Province, and uses the Logistic regression model to establish the temperature threshold for icing of ordinary roads in the province. So as to build a road icing early warning model hierarchically. The results show that the Ts in southern Hunan is relatively high, the Ts at most stations is above 10 °C, and the low Ts area is in western Hunan, and the stations below 8 °C are mostly distributed in this area. This may be due to the higher altitude in western Hunan. In terms of diurnal variation, the lowest value of average Ts and air temperature (Ta) in Hunan Province in winter both appeared at 7:00 Beijing Time (BT), while the highest value appeared at 15:00 BT, and the average Ta is always lower than the Ts. The temperature variation on the bridge surface is more pronounced. When the Ta is lower than −2.5 °C, more than 70% of the sites have a rapid increase in the risk of icing; and when the Ta is lower than −5°C, nearly 87% of the sites have a risk level of 4, which means icing risk is extremely high. Furthermore, combining the warning model with thermal spectrum mapping can improve the spatial resolution of the warning model and also solve the problem of lack of observations in some areas

Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions

Ignition temperature and explosion pressure of suspended coal dust cloud under different conditions and suppression characteristics

Abstract The ignition and explosion processes of suspended coal dust clouds and their suppression characteristics are important aspects of dust prevention and control. To understand the ignition temperature and explosion pressure of coal dust clouds, as well as the inhibitory effect of explosion suppressants, experimental tests are conducted. The study found that during the ignition process of coal dust clouds, the optimal dust spray pressure is 20 kPa, because coal dust clouds are more likely to ignite under this condition. When the mass concentration of coal dust cloud is 500 g m−3, the maximum pressure and maximum pressure rise rate are both the highest. When Al(OH)3 is mixed with coal dust and the mass percentage is 60%, the coal dust cloud can still be ignited. When KH2PO4 is mixed with coal dust, the upper limit of the test temperature is reached when the percentage of mixture is 55%. When NH4H2PO4 is mixed with coal dust and the mass percentage is greater than 40%, the coal dust cloud can’t be ignited anymore. The suppression effect of mixing Al(OH)3 and NH4H2PO4 is not as good as that of mixing KH2PO4 and NH4H2PO4

Image2_Research on road surface temperature characteristics and road ice warning model of ordinary highways in winter in Hunan province, central China.png

The study of road surface temperature (Ts) characteristics in winter and the early warning method of road icing is of great significance to reduce traffic accidents and improve transportation efficiency. Using the hourly observation data of Hunan traffic meteorological stations from December 2020 to February 2022, this study analyzes the winter Ts characteristics of ordinary roads in Hunan Province, and uses the Logistic regression model to establish the temperature threshold for icing of ordinary roads in the province. So as to build a road icing early warning model hierarchically. The results show that the Ts in southern Hunan is relatively high, the Ts at most stations is above 10 °C, and the low Ts area is in western Hunan, and the stations below 8 °C are mostly distributed in this area. This may be due to the higher altitude in western Hunan. In terms of diurnal variation, the lowest value of average Ts and air temperature (Ta) in Hunan Province in winter both appeared at 7:00 Beijing Time (BT), while the highest value appeared at 15:00 BT, and the average Ta is always lower than the Ts. The temperature variation on the bridge surface is more pronounced. When the Ta is lower than −2.5 °C, more than 70% of the sites have a rapid increase in the risk of icing; and when the Ta is lower than −5°C, nearly 87% of the sites have a risk level of 4, which means icing risk is extremely high. Furthermore, combining the warning model with thermal spectrum mapping can improve the spatial resolution of the warning model and also solve the problem of lack of observations in some areas.</p

Image1_Research on road surface temperature characteristics and road ice warning model of ordinary highways in winter in Hunan province, central China.png

The study of road surface temperature (Ts) characteristics in winter and the early warning method of road icing is of great significance to reduce traffic accidents and improve transportation efficiency. Using the hourly observation data of Hunan traffic meteorological stations from December 2020 to February 2022, this study analyzes the winter Ts characteristics of ordinary roads in Hunan Province, and uses the Logistic regression model to establish the temperature threshold for icing of ordinary roads in the province. So as to build a road icing early warning model hierarchically. The results show that the Ts in southern Hunan is relatively high, the Ts at most stations is above 10 °C, and the low Ts area is in western Hunan, and the stations below 8 °C are mostly distributed in this area. This may be due to the higher altitude in western Hunan. In terms of diurnal variation, the lowest value of average Ts and air temperature (Ta) in Hunan Province in winter both appeared at 7:00 Beijing Time (BT), while the highest value appeared at 15:00 BT, and the average Ta is always lower than the Ts. The temperature variation on the bridge surface is more pronounced. When the Ta is lower than −2.5 °C, more than 70% of the sites have a rapid increase in the risk of icing; and when the Ta is lower than −5°C, nearly 87% of the sites have a risk level of 4, which means icing risk is extremely high. Furthermore, combining the warning model with thermal spectrum mapping can improve the spatial resolution of the warning model and also solve the problem of lack of observations in some areas.</p

Direct exfoliation of graphite into graphene in aqueous solutions of amphiphilic peptides

Peptide-mediated solution phase graphene exfoliation.</p

Prognostic Significance of CD56 Antigen Expression in Patients with De Novo Non-M3 Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a heterogeneous group of disorders with distinct characteristics and prognoses. Although cytogenetic changes and gene mutations are associated with AML prognosis, there is a need to identify further factors. CD56 is considered a prognostic factor for AML, which is abnormally expressed in leukemia cells. However, a clear consensus for this surface molecule is lacking, which has prompted us to investigate its prognostic significance. Bone marrow samples of de novo non-M3 AML were collected to detect CD56 expression using multiparameter flow cytometry (FCM). As a result, the CD56 expression in de novo non-M3 AML was found to be significantly higher than that in acute lymphoma leukemia (ALL, P=0.017) and healthy controls (P=0.02). The X-Tile program produced a CD56 cutoff point at a relative expression level of 24.62%. Based on this cutoff point, high CD56 expression was observed in 29.21% of de novo non-M3 AML patients. CD56-high patients had a poor overall survival (OS, P=0.015) compared to CD56-low patients. Bone marrow transplantation (BMT) improved OS (P=0.004), but a poor genetic risk was associated with an inferior OS (P=0.002). Compared with CD56-low patients, CD56-high patients had lower peripheral blood platelet (PLT) counts (P=0.010). Our research confirmed that high CD56 expression is associated with adverse clinical outcomes in de novo non-M3 AML patients, indicating that CD56 could be used as a prognostic marker for a more precise stratification of de novo non-M3 AML patients

An engineered Cas12i nuclease that is an efficient genome editing tool in animals and plants

Summary: The type V-I CRISPR-Cas system is becoming increasingly more attractive for genome editing. However, natural nucleases of this system often exhibit low efficiency, limiting their application. Here, we used structure-guided rational design and protein engineering to optimize an uncharacterized Cas12i nuclease, Cas12i3. As a result, we developed Cas-SF01, a Cas12i3 variant that exhibits significantly improved gene editing activity in mammalian cells. Cas-SF01 shows comparable or superior editing performance compared to SpCas9 and other Cas12 nucleases. Compared to natural Cas12i3, Cas-SF01 has an expanded PAM range and effectively recognizes NTTN and noncanonical NATN and TTVN PAMs. In addition, we identified an amino acid substitution, D876R, that markedly reduced the off-target effect while maintaining high on-target activity, leading to the development of Cas-SF01HiFi (high-fidelity Cas-SF01). Finally, we show that Cas-SF01 has high gene editing activities in mice and plants. Our results suggest that Cas-SF01 can serve as a robust gene editing platform with high efficiency and specificity for genome editing applications in various organisms

Artificial intelligence for screening of multiple retinal and optic nerve diseases

IMPORTANCE: The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. OBJECTIVE: To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies. EXPOSURES: Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study. MAIN OUTCOMES AND MEASURES: The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score. RESULTS: In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment. CONCLUSIONS AND RELEVANCE: In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas