Highly Interfacial Active Gemini Surfactants as Simple and Versatile Emulsifiers for Stabilizing, Lubricating and Structuring Liquids.

To date, locking the shape of liquids into non-equilibrium states usually relies on jamming nanoparticle surfactants at an oil/water interface. Here we show that a synthetic water-soluble zwitterionic Gemini surfactant can serve as an alternative to nanoparticle surfactants for stabilizing, structuring and additionally lubricating liquids. By having a high binding energy comparable to amphiphilic nanoparticles at the paraffin oil/water interface, the surfactant can attain near-zero interfacial tensions and ultrahigh surface coverages after spontaneous adsorption. Owing to the strong association between neighboring surfactant molecules, closely packed monolayers with high mechanical elasticity can be generated at the oil/water interface, thus allowing the surfactant to produce not only ultra-stable emulsions but also structured liquids with various geometries by using extrusion printing and 3D printing techniques. By undergoing tribochemical reactions at its sulfonic terminus, the surfactant can endow the resultant emulsions with favorable lubricity even under high load-bearing conditions. Our study may provide new insights into creating complex liquid devices and new-generation lubricants capable of combining the characteristics of both liquid and solid lubricants.

Aberrant brain functional hubs convergence in the acute severe traumatic brain injury patients with rapidly recovering.

PURPOSE: We aimed to identify the aberrant functional hubs in patients with acute severe traumatic brain injury (sTBI) and investigate whether they could help inform prognosis. METHODS: Twenty-eight sTBI patients and health controls underwent imaging scanning. The graph-theoretical measure of degree centrality (DC) was applied to identify the abnormal brain functional hubs and conjoined with regions of interest-based analysis to investigate their interaction and impact on whole-brain. We further split sTBI patients into two subgroups according to their recovery to explore whether the fractional amplitude of low-frequency fluctuation (fALFF) roles in functional connectivity (FC) differential areas to help inform the patients' long-term prognosis. RESULTS: We identified the part of prefrontal cortex (PFC), precentral and postcentral gyrus (Pre-/Post-CG), cingulate gyrus (CgG), posterior medial cortex (PMC), and brainstem that could be core hubs whose DC was significantly increased in patients with acute sTBI. The interaction strength of the paired hubs could be enhanced (CG-PFC, CgG-PFC, CG-brainstem, CgG-brainstem, PMC-brainstem, and PFC-brainstem) and weakened (CG-CgG, CG-PMC, CgG-PMC, and PMC-PFC), compared with healthy controls. We also found abnormal FC in 5 hubs to whole-brain. The spontaneous brain activities in the FC differential regions [e.g., the fALFF and mean fALFF value] were valid to predict outcome at 6-month in patients with sTBI. CONCLUSION: We demonstrated a compensatory mechanism that part of brain regions will converge into abnormal functional hubs in patients with acute sTBI, which provides a potential approach to objectively predicting patients' long-term outcome.

Investigating the mechanism and prognosis of patients with disorders of consciousness on the basis of brain networks between the thalamus and whole-brain.

Purpose: This study aimed to investigate the changes in the functional connectivity between the bilateral thalamus and the whole-brain in patients with severe traumatic brain injury (sTBI) patients suffering from disorders of consciousness (DOC) and to explore their potential prognostic representation capacity. Methods: The sTBI patients suffering from DOC and healthy controls underwent functional magnetic resonance imaging. We defined patients with the Extended Glasgow Outcome Score (GOS-E) ≥ 3 as the wake group and GOS-E = 2 as the coma group. The differences in functional connectivity between sTBI and healthy controls and between wake and coma groups were compared. Based on the brain regions with altered functional connectivity between wake and coma groups, they were divided into 26 regions of interest. Based on the Z-values of regions of interest, the receiver operating characteristic analysis was conducted to classify the prognosis of patients. Results: A total of 28 patients and 15 healthy controls were finally included. Patients who had DOC indicated a significant disruption of functional connectivity between the bilateral thalamus and the whole-brain (FDR corrected, P < 0.0007). The functional connectivity strength (bilateral thalamus to whole-brain) was significantly different between coma patients who went on to wake and those who were eventually non-awake at 6 months after sTBI (Alphasim corrected, P < 0.05). Furthermore, the 26 regions of interest had a similar or even better prognostic distinction ability than the admission Glasgow coma score. Conclusion: The thalamus-based system of consciousness of sTBI patients suffering from DOC is disrupted. There are differences in the thalamus-to-whole-brain network between wake and coma groups and these differences have potential prognostic characterization capability.

Spatiotemporal Dynamics of Surface Ozone and Its Relationship with Meteorological Factors over the Beijing-Tianjin-Tangshan Region, China, from 2016 to 2019.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing-Tianjin-Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution. The results revealed that the surface ozone concentration throughout the BJ-TJ-TS region from 2016 to 2019 exhibited an overall pattern of high values in the northwest and low values in the southeast, as well as an obvious difference between built-up and non-built-up areas (especially in Beijing). Meanwhile, a notable increasing trend of ozone levels was discovered in the BJ and TJ areas from 2016 to 2019, whereas this upward trend was not evident in the TS area. In all three areas, the highest monthly average ozone values occurred in the summer month of June, while the lowest monthly average levels occurred in the winter month of December. Their diurnal variation values reached a maximum value at approximately 3:00-4:00 p.m. and a minimum value at approximately 7:00 a.m. It is clear that high temperature, long sunshine duration, low atmospheric pressure, and weak wind velocity conditions, as well as certain relative humidity levels, readily led to high-concentration ozone pollution. Meanwhile, the daily average values of the five meteorological factors on days with Grade I and Grade II ozone pollution displayed different characteristics.

Abnormal default mode network could be a potential prognostic marker in patients with disorders of consciousness.

OBJECTIVES: The study aimed to investigate disorders of consciousness (DOC) mechanisms of patients with severe traumatic brain injury (sTBI) related to default mode network (DMN) and to introduce a machine learning model that predicts the prognosis of these patients for 6 months. METHODS: The sTBI patients suffering from DOC and healthy controls underwent functional magnetic resonance imaging. We defined patients with Extended Glasgow Outcome Score ≥ 5 as good outcome group, otherwise they were poor outcome group. The differences of DMN between sTBI and healthy controls and between good and poor outcome groups were compared. Based on the brain regions with altered functional connectivity between good and poor outcome groups, they were divided into 8 regions of interests according to side. The Z values of the regions of interests were extracted by Rest 1.8. Based on Z values, the Subspace K-Nearest Neighbor (Subspace KNN) was conducted to classify prognosis of sTBI patients suffering from DOC. RESULTS: A total of 84 DMNs derived from patients and 45 DMNs from healthy controls were finally analyzed. The connectivity of the DMN was significantly decreased in sTBI patients suffering from DOC (Alphasim corrected, P < 0.05). In addition, compared with the poor outcome group (DMN samples = 60), the brain regions of DMN with decreased functional connectivity in the good outcome group (DMN samples = 24) the following bilateral areas: brodman Area 11, anterior cingulate and paracingulate gyri, brodman Area 25, olfactory cortex (Alphasim corrected, P < 0.05). The ability of Subspace KNN machine learning to distinguish the prognosis of patients (area under curve) was 0.97. CONCLUSIONS: The interruption of DMN may be one of the reasons for DOC in patients with sTBI. Furthermore, based on early DMN (1-4 weeks), Subspace KNN machine learning has the potential value to distinguish the prognosis (6 months after brain trauma) of sTBI patients suffering from DOC.

Synthesis and Characterization of Surfactant for Retarding Acid-Rock Reaction Rate in Acid Fracturing.

Acid fracturing is an effective method to develop ultra-low permeability reservoirs. However, the fast reaction rate reduces the effect of the acid fracturing and increases the near-well collapse risk. Therefore, it is necessary to retard the acid-rock reaction rate. In this work, we synthesized an acid-resistant Gemini zwitterionic viscoelastic surfactant (named VES-c), which has good performances such as temperature resistance, salt resistance, and shear resistance. Besides, a low concentration of VES-c increases the viscosity of the acid solution. The CO2 drainage method was used to measure the reaction rate between the dibasic acid and dolomite/broken core. We find that the dibasic acid containing 0.3% VES-c retards the dolomite reaction rate of 3.22 times compared with only dibasic acid. Furthermore, the dibasic acid containing 0.3% VES-c exhibits uniform distribution and is not easy to adhere to the solid surface. The VES-c also is favorable to reduce the formation of amorphous calcium carbonate. Retarding the rate of acid-rock reaction and enhancing the acidification are mainly attributed to VES-c's salt-tolerance, anti-adsorption, and the property of increasing the viscosity of the solution. Hopefully, this kind of surfactant retarding reaction rate is applied to other acid-rock reactions.

Chimney Detection Based on Faster R-CNN and Spatial Analysis Methods in High Resolution Remote Sensing Images.

Spatially location and working status of pollution sources are very important pieces of information for environment protection. Waste gas produced by fossil fuel consumption in the industry is mainly discharged to the atmosphere through a chimney. Therefore, detecting the distribution of chimneys and their working status is of great significance to urban environment monitoring and environmental governance. In this paper, we use an open access dataset BUAA-FFPP60 and the faster regions with convolutional neural network (Faster R-CNN) algorithm to train the preliminarily detection model. Then, the trained model is used to detect the chimneys in three high-resolution remote sensing images of Google Maps, which is located in Tangshan city. The results show that a large number of false positive targets are detected. For working chimney detection, the recall rate is 77.27%, but the precision is only 40.47%. Therefore, two spatial analysis methods, the digital terrain model (DTM) filtering, and main direction test are introduced to remove the false chimneys. The DTM is generated by ZiYuan-3 satellite images and then registered to the high-resolution image. We set an elevation threshold to filter the false positive targets. After DTM filtering, we use principle component analysis (PCA) to calculate the main direction of each target image slice, and then use the main direction to remove false positive targets further. The results show that by using the combination of DTM filtering and main direction test, more than 95% false chimneys can be removed and, therefore, the detection precision is significantly increased.

[High Resolution Remote Sensing Monitoring and Assessment of Secondary Geological Disasters Triggered by the Lushan Earthquake].

The secondary geological disasters triggered by the Lushan earthquake on April 20, 2013, such as landslides, collapses, debris flows, etc., had caused great casualties and losses. We monitored the number and spatial distribution of the secondary geological disasters in the earthquake-hit area from airborne remote sensing images, which covered areas about 3 100 km2. The results showed that Lushan County, Baoxing County and Tianquan County were most severely affected; there were 164, 126 and 71 secondary geological disasters in these regions. Moreover, we analyzed the relationship between the distribution of the secondary geological disasters, geological structure and intensity. The results indicate that there were 4 high-hazard zones in the monitored area, one focused within six kilometers from the epicenter, and others are distributed along the two main fault zones of the Longmen Mountain. More than 97% secondary geological disasters occurred in zones with a seismic intensity of VII to IX degrees, a slope between 25 A degrees and 50 A degrees, and an altitude of between 800 and 2 000 m. At last, preliminary suggestions were proposed for the rehabilitation and reconstruction planning of Lushan earthquake. According to the analysis result, airborne and space borne remote sensing can be used accurately and effectively in almost real-time to monitor and assess secondary geological disasters, providing a scientific basis and decision making support for government emergency command and post-disaster reconstruction.

Snow cover monitoring method by using HJ-1 satellite data.

Environment and Disasters Monitoring Microsatellite Constellation with high spatial resolution, high temporal resolution and high spectral resolution characteristics was put forward by China. HJ-1B satellite, one of the first two small optical satellites, had a CCD camera and an infrared camera, which would provide an important new data source for snow monitoring. In the present paper, through analyzing the sensor and data characteristics of HJ-1B, we proposed a new infrared normalized difference snow index (INDSI) referring to the traditional normalized difference snow index (NDSI). The accuracy of these two automatic snow recognition methods was estimated based on a supervised classification method. The accuracy of the traditional NDSI method was 97.761 9% while that of the new INDSI method was 98.617 1%.

[Application and prospect of multi-spectral remote sensing in major natural disaster assessment].

After the occurrence of major natural disasters, it is of great significance that disaster states are assessed timely and accurately for decision-making departments to draw up effective response programs. Multi-spectral remote sensing has a great advantage and potential in disaster assessment, with the characteristics of a wide range of data acquisition, high speed, etc. In several major natural disaster assessments in China, multi-spectral remote sensing technology has played an important role. Firstly, the present paper takes earthquake disasters, floods disasters and drought disasters as examples to summarize the specific applications of major natural disaster assessment based on the multi-spectral remote sensing. Secondly, in these specific applications they suffer from both relative shortage of data sources and limited breadth and depth of application; both of these problems are analyzed. Finally, the future development direction of major natural disaster assessment based on the multi-spectral remote sensing, such as the expansion of multi-spectral remote sensing data acquisition means, the establishment of major natural disasters assessment index system based on remote sensing, and the improvement of the assessment technology system based on multi-spectral remote sensing are also discussed.

[Algorithm of inland water retrieval based on spectral matching].

This present paper presents a new inversion method of inland water based on spectral matching. First step of this method is using the known water surface measured hyperspectral and the absorption coefficient of each component, and obtaining backscattering coefficient of suspended matter with bio-optical model. The second step is calculation of the spectral reflectance of water bodies based on bio-optical model, through the cross-combination of inherent optical property of water components (chlorophyll, suspended matter, yellow substance), in order to create a look-up table of Rrs that corresponds to all combinations of water component. The third step is changing the look-up table data into MODIS spectral data using MODIS channel response function. The final study examined the applicability of the look-up table using the Hyperspectral and MODIS bands spectra, based on the minimum distance principle, to find the best matched spectra, thus it has found corresponding concentrations of three components. The average relative error of chlorophyll and suspended matter is 38.6% and 28% respectively. Optical properties of water components interfere with each other because of the complexity of inland water bodies, resulting in difficulty to extract the feature band for statistical model, and while it is hard for bio-optical model inversion method to solve the unstable problem of special inherent optical property with the seasons and regional issues, the method of this paper would have the advantages of bio-optical model, while eliminating instability of special inherent optical property, so it is a good approach to inland water retrieval.

[Remote chlorophyll a retrieval in Taihu Lake by three-band model using hyperion hyperspectral data].

To retrieve chlorophyll a (Chla) concentration in Taihu Lake by three-band model, a field study was conducted on August 19, 2004 to collect water samples (N=38), which contained widely variable Chla (7.8-154.3 microg x L(-1)) and total suspended solids (65.0-190.2 mg x L(-1) dry wt), and the synchronous Hyperion images was also acquired as remote sensing data. After obtaining the approximate range of wavelengths for the three bands by analyzing the inherent optical properties of Taihu Lake, the three-band models were spectrally tuned to select the bands for most accurate Chla estimation. Finally Hyperion B34 (691.37 nm), B37 (721.90 nm) and B50 (854.18 nm) were selected to establish a three-band model. The results show that strong linear relationship is found between analytically measured Chla and the three-band model (r = 0.934), which accounts for 87.2% of variation in Chla and allows estimation of Chla with a root mean square error (RMSE) of 13.93 microg x L(-1), whereas the traditional two-band models accounts for lower accuracies of Chla estimation (spectral ratio, R2 = 0.844, RMSE = 15.41 microg x L(-1), and reflectance first-derivative, R2 = 0.831, RMSE = 16.00 microg x L(-1)). The findings prove that the three-band model is applicable for Chla retrieval in turbid, productive inland waters and by using Hyperion hyperspectral data.