Nowadays, with the wide installation of distributed energy resources and independent energy storage systems, prosumers as a new type of electricity market entity have emerged. Since numerous prosumers can significantly impact the carbon emission of the power grid, this paper proposes an improved carbon emission flow method for the power grid with prosumers. This method can accurately clarify the detailed distribution of electrical carbon emission flow in power grids. First, based on the power flow, prosumers’ impacts on the electrical carbon emission are quantified from three aspects that include the carbon emission sources, the network flow, and the indirect carbon emission individuals. Then, an improved power carbon emission flow model is proposed, in which the complex carbon emission intensity of prosumers is derived emphatically. Finally, case studies based on the IEEE 30-bus system verify the feasibility of the proposed method. This method provides a measurement basis for further research considering electrical carbon emissions.
Seismic facies characterization plays a key role in hydrocarbon exploration and development. The existing unsupervised methods are mostly waveform-based and involve multiple steps. We propose to leverage unsupervised contrastive learning to automatically analyze seismic facies. To obtain a stable result, we use 3D seismic cubes instead of seismic traces or their variants as inputs of networks to improve lateral consistency. Besides, we treat seismic attributes as geologic constraints and feed them into the network along with the seismic cubes. These different seismic and multiattribute cubes from the same position are regarded as positive pairs and the cubes from a different position are treated as negative pairs. A contrastive learning framework is used to maximize the similarities of positive pairs and minimize the similarities of negative pairs. In this way, we are able to enforce the samples with similar features to get close while push the samples with different features to be separated in the space where we make the seismic facies clustering. This contrastive learning framework is a one-stage, end-to-end and unsupervised fashion without any manual labels. We have demonstrated the effectiveness of this method by employing it to a turbidite channel system in the Canterbury Basin, offshore New Zealand. The obtained facies map is continuous, resulting in a stable and reliable classification.
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Caenorhabditis elegans is widely used as a model organism and its oocyte and embryos' reproductive development are under extensive study due to their ease of observation and cultivation. As one of the main embryo constituents, yolk granules and their motion serve important functions such as storage of energy and materials. However, current observation and analysis methods have issues such as being toxic, problematic identification of densely distributed particles, or being too slow to track fast movement. Here we use a home-built, label-free imaging system that faithfully images the particles at a high frame rate. Then, a trained U-Net with an upsampling block is used to recognize the granules with high accuracy compared to traditional methods (83% vs 50%). Through motion analysis, we found that most granules can only be tracked in a short period of time (thus presenting an additional challenge for slower imaging methods). While typical diffusion model is not appropriate for short tracks, we use the track-averaged speed and its distribution parameters to characterize the intra embryonic motions, classifying embryos from normal and starved mothers, thus demonstrating that our method can be used to quantitatively evaluate the embryo's quality without any extraneous labels.
Over the past fifteen years, tremendous efforts have been devoted to realizing topological superconductivity in realistic materials and systems, predominately propelled by their promising application potentials in fault-tolerant quantum information processing. In this article, we attempt to give an overview on some of the main developments in this field, focusing in particular on two-dimensional crystalline superconductors that possess either intrinsic p-wave pairing or nontrivial band topology. We first classify the three different conceptual schemes to achieve topological superconductor (TSC), enabled by real-space superconducting proximity effect, reciprocal-space superconducting proximity effect, and intrinsic TSC. Whereas the first scheme has so far been most extensively explored, the subtle difference between the other two remains to be fully substantiated. We then move on to candidate intrinsic or p-wave superconductors, including Sr2RuO4,UTe2,Pb3Bi, and graphene-based systems. For TSC systems that rely on proximity effects, the emphases are mainly on the coexistence of superconductivity and nontrivial band topology, as exemplified by transition metal dichalcogenides, cobalt pnictides, and stanene, all in monolayer or few-layer regime. The review completes with discussions on the three dominant tuning schemes of strain, gating, and ferroelectricity in acquiring one or both essential ingredients of the TSC, and optimizations of such tuning capabilities may prove to be decisive in our drive towards braiding of Majorana zero modes and demonstration of topological qubits.
Vein grafts are widely used for coronary artery bypass grafting and hemodialysis access, but restenosis remains the "Achilles' heel" of these treatments. An extravascular stent is one wrapped around the vein graft and provides mechanical strength; it can buffer high arterial pressure and secondary vascular dilation of the vein to prevent restenosis. In this study, we developed a novel Nanocellulose-gelatin hydrogel, loaded with the drug Astragaloside IV (AS-IV) as an extravascular scaffold to investigate its ability to reduce restenosis. We found that the excellent physical and chemical properties of the drug AS-IV loaded Nanocellulose-gelatin hydrogel external stent limit graft vein expansion and make the stent biocompatible. We also found it can prevent restenosis by resisting endothelial-to-mesenchymal transition (EndMT) in vitro. It does so by activating autophagy, and AS-IV can enhance this effect both in vivo and in vitro. This study has added to existing research on the mechanism of extravascular stents in preventing restenosis of grafted veins. Furthermore, we have developed a novel extravascular stent for the prevention and treatment of restenosis. This will help optimize the clinical treatment plan of external stents and improve the prognosis in patients with vein grafts.
The hygroscopicities of calcium and magnesium salts strongly affect the environment and climate, but the aging products of these salts at high relative humidities (RHs) are still poorly understood. In this study, surface plasmon resonance microscopy (SPRM) was used to determine the hygroscopic growth factors (GFs) of Ca(NO3)2 and Mg(NO3)2 separately or mixed with galactose at different mass ratios at different RHs before and after aging. For all particles, the measured GFs showed no indication of deliquescence across the range of RHs tested, and overall hygroscopicity was clearly lower after than before aging. The Ca(NO3)2 and Mg(NO3)2 GFs at 90 % RH were 1.80 and 1.66, respectively, before aging and 1.33 and 1.42, respectively, after 4 h aging, meaning aging decreased the GFs by 26.11 % and 14.46 %, respectively. Aging decreased the hygroscopicity because insoluble or sparingly soluble substances (CaSO3, CaSO4, MgSO3) formed and strongly changed the overall hygroscopicity. For bicomponent aerosols with different mass ratios, the GFs (calculated using the Zdanovskii-Stokes-Robinson method) of the other components except galactose at 90 % RH after 1 h aging were all lower, respectively, than the measured GFs of pure Ca(NO3)2 and Mg(NO3)2 after aging for 1 h, especially with the mass ratio of 1:2, their GFs have decreased by 14.63 % and 7.50 %, respectively. Subsequently, Ion chromatograms indicated that the peak area ratio of SO42- to NO3- ratios were higher for the aged bicomponent particles than aged single-component particles, possibly because adding galactose improved the gas-liquid state stability during drying after the aging process and therefore promoted nitrate consumption and sulfate formation. The results indicated that organic components may play important roles in heterogeneous reactions between trace gases and multicomponent aerosols and should be considered in evaluating the impacts on submicron aerosol composition of high atmospheric SO2 concentrations at high humidities.
Optical flow estimation in human facial video, which provides 2D correspondences between adjacent frames, is a fundamental pre-processing step for many applications, like facial expression capture and recognition. However, it is quite challenging as human facial images contain large areas of similar textures, rich expressions, and large rotations. These characteristics also result in the scarcity of large, annotated real-world datasets. We propose a robust and accurate method to learn facial optical flow in a self-supervised manner. Specifically, we utilize various shape priors, including face depth, landmarks, and parsing, to guide the self-supervised learning task via a differentiable nonrigid registration framework. Extensive experiments demonstrate that our method achieves remarkable improvements for facial optical flow estimation in the presence of significant expressions and large rotations.
In this study, a novel processing route of intercritical Mn partitioning is proposed to stabilize the reverted austenite in a medium Mn steel. The Fe-5Mn-0.2C-1.5Al (wt.%) steel is annealed unceasingly at two intercritical temperatures to produce a compositional core-shell structured reverted austenite which consists of a core in low Mn content and a shell in relatively high Mn content. This distinctive microstructure can not only allow much more retained austenite to be obtained compared with the conventional processing route, but also produce active transformation induced plasticity effect over a broad strain range, leading to excellent mechanical properties of high strength and enhanced ductility.
Large amounts of heat are required in areas such as food processing and textile industries to maintain equipment in the medium-temperature zone 393–573 K. However, thermal storage materials that work at this temperature are rare. Hexagonal iron sulfide FeS exhibits a solid-state phase transition at 420 K, accompanied by a high entropy change. The quenched FeS is unstable when subjected to a thermal cycling test, and the related entropy change declines. A secondary hexagonal phase with Fe deficiency was confirmed by neutron powder diffractions. Annealing FeS at temperatures slightly higher than Tt can accelerate the formation of the secondary phase. Once the sample is stabilized, the entropy change is no longer changeable. The thermally stabilized FeS sample exhibits a relatively high thermal storage density up to 136 J/cm³. This work suggests a new solid candidate for the application of solid-state phase transition latent heat energy storage in the medium-temperature region.
The ceiling gas temperature is an important evaluation indicator for determining tunnel fire hazards. The maximum and longitudinal distributions of the ceiling gas temperature are studied numerically with consideration of the effect of longitudinal fire locations, fire heat release rate, tunnel aspect ratio, and total length of the tunnel. Results show that as the fire moves from the middle of the tunnel to the upstream (left opening), the flame tilts to the downstream (right opening) due to the induced longitudinal flow inside the tunnel. The tilting angle increases as the fire approaching the tunnel opening. Correspondingly, the position of the resulting maximum ceiling gas temperature is shifted from right above the fire source to the downstream side. An offset distance is used to quantify this phenomenon and an empirical equation is proposed to calculate this distance. Meanwhile, the resulting maximum ceiling gas temperature decreases. An empirical equation is also proposed to predict the evolutionary trend of maximum ceiling gas temperature under the effect of the longitudinal fire locations. In addition, asymmetric longitudinal distributions of ceiling gas temperature were observed in the upstream and downstream directions. The ceiling gas temperature decays exponentially in both directions with a lower decay rate on the shorter side (the side with a shorter distance between the fire and the nearest opening). Corresponding correlations for related parameters are proposed for the shorter and longer side, respectively.
Space reactors start-up with on external neutron sources increase both the structural complexity and the potential risk of core criticality in a launch failure. Fortunately, cosmic rays offer the possibility of in-orbit passive start-up. To evaluate the secondary neutrons produced by cosmic rays interacting with reactor materials, the neutron yields and distributions were studied by FLUKA code for a 200 kWe space nuclear reactor with an integrated honeycomb core design. The results show that the neutron yields in the reactor core can reach 10⁶n/s for GCRs, and the total neutron yields can even reach 10⁸n/s for some ERB and SPE. These secondary neutrons are more uniformly distributed than those from ²⁵²Cf as an external neutron source. The results shows that the secondary neutrons produced by cosmic rays can meet the basic source requirement for space reactor passive start-up.
Flood is one of the most destructive disasters in the world with high frequency, which normally results in considerable casualties and economic losses. Understanding the basic laws of crowd movement in water is helpful for establishing effective evacuation guidelines under floods. However, there is a lack of empirical data and research on crowd movement in floods. In this study, a series of experiments were carried out in an 8 m long corridor in a swimming pool with the depth of 0.60 m. It is found that the free speed of pedestrians in water is 51.61% lower than that on land, and the male is +25% faster than the female. The speed-density relation shows two phases under the observed density range [0.30,2.20]ped/m2. The speed firstly decreases with the increasing density and then stays at 0.81 m/s when the density exceeds 1.50 ped/m², whereas the flow increases monotonically with the increase of density. The influence of water on speed and flow is emphasized by comparison with the fundamental diagrams on land. In water, the lateral swaying amplitude of crowds decreases with increasing density, but the swaying frequency remains around 1 Hz. The swaying amplitude (the mean amplitude is 0.10 m) is 100% larger than walking on land (the mean amplitude is 0.05 m) under the speed range [0.50,1.10]m/s, and the crowd step frequency in water was 33.07% less than that on land. Our findings can be useful for the improvement of flood evacuation modeling and establishing evacuation guidelines.
Objective The electrocardiogram (ECG) characteristic waveforms include P wave, QRS wave, and T wave. The detection of ECG characteristic waveforms is particularly important in automatic heart disease diagnosis. Although researchers have proposed many detection methods, the detection under the inter-patient pattern is still a challenge. Method This paper proposed an end-to-end ECG waveform detection method (ECT-net) based on convolutional neural network (CNN) and transformer. We constructed the feature extractor by a group of convolutional layers to extract the local information of ECG signals and generate the input embedding of the transformer encoder. Meanwhile, we adopted the transformer encoder to extract the long-term time-dependent representation between heartbeats, which can make up for the limitation of the CNN in obtaining global information. This structure ensures that both spatial and temporal features from ECG signals are achieved. For precise detection, we employed a corresponding decoder and skip structure to integrate low-level features and high-level features. The model was trained and verified on the China Physiological Signal Challenge 2018 database (CPSC-DB). Result Our method performed best among comparison methods and achieved F1 scores of 94.27% on the P wave, 97.32% on the QRS wave, and 93.92% on the T wave. The ablation result shows that the transformer encoder is beneficial for detecting each characteristic waveform. To assess the generalization ability of the method, it was also evaluated on inter-patient datasets with different types of cardiac diseases, and experimental results demonstrated that the proposed method is effective for all of them. Significance The proposed method applies the transformer encoder to the detection of ECG characteristic waveforms for the first time and achieves competitive performance.
The double-wall bayonet tube type heat exchanger design can monitor and mitigate the occurrence of Steam Generator Tube Rupture (SGTR) accidents, which is one of the most important accidents of GEN-Ⅳ lead-cooled Fast Reactor (LFR). The lead-based reactor engineering validation facility CLEAR-S will be used for the integrated testing and verification of the megawatt-level double-wall bayonet tube heat exchanger. In this study, the Shear Stress Transport turbulence model (SSTk-ω) was used to pre-simulate the thermal characteristics of the CLEAR-S double-wall bayonet tube heat exchanger before the experiment. The verification shows that the numerical simulation results of the single bayonet tube structure are in good agreement with the RELAP5 simulation results and experimental data from CLEAR-S. CFD analysis reveals that the non-effective heat exchange area will preheat the liquid water on the side of the tube, so that the overall heat exchange performance decreases by about 4%. The heat conduction of the powder interlayer is the largest heat transfer resistance, which weakened the influence of the lead-bismuth eutectic (LBE) side flow and temperature inhomogeneity on the heat transfer of the tube bundle and significantly reduced the wall radial temperature difference of each bayonet tube. The conclusions of this study can provide a reference for the experiment and design of double-wall bayonet tube heat exchangers.
Misinformation on social media is a nonnegligible phenomenon that causes successive adverse impacts. Numerous scholarly efforts have been devoted to automatic misinformation detection to address this problem. The effective feature is the key to achieving high identification performance. However, the effectiveness of the feature may change in different issues and time considering the manifold social contextual reasons. Most extant literature on misinformation detection does not differentiate between topics, issues or domains. Although some research compares detection across domains, they concentrate on the model's overall performance, neglecting the effectiveness of individual features. Furthermore, the comparison studies mainly incorporate single-domain issues rather than issues that cover multiple domains. It is still difficult to determine which domain's misinformation characteristics will match those of multi-dimensional issues. Since the misinformation nowadays covers multiple domains, finding robust features in misinformation detection over issues and time is an urgent research agenda. In this study, we collected datasets of two issues, climate change and genetically modified organisms (GMOs), between January 1st, 2010 and December 31st, 2020 on Weibo, manually annotated the veracity status of the posts, and compared the performance of the proposed features in identifying misinformation by applying logistic regression. The results demonstrate that (1) the predicting power of content-based features, including topic and sentiment, is relatively robust compared to user-based and propagation-based features across issues and time. (2) The feature effectiveness varied at different time points. Our findings imply that future research could consider focusing more on content-based features, especially implicit features from the content in misinformation detection. Moreover, researchers should evaluate the feature effectiveness at different time stages to improve the efficiency of misinformation detection.
Previous studies have discovered that board-coupled shaft (BCS) can eliminate the plug-holing phenomenon that usually occurs in the traditional shaft and could be applied in shallow-buried and deep-buried tunnels. However, the impact of tunnel burial depth on the smoke exhaust performance with BCS is unknown at present. To better understand this question, 42 simulations were conducted, and the effects of buried depths and board locations on the smoke extraction performance of BCS were investigated. The results indicated that: (a) when the buried depth of the tunnel is large enough (≥10 m) that a plug-holing phenomenon occurs, the overall performance of BCS is better than that of the traditional shaft; (b) the smoke extraction performance of the BCS is controlled by both the buried depth of tunnels and the board locations when the shaft height is lower than 10 m, but mainly dominated by the board position when the shaft height is larger than 30 m; (c) the BCS can achieve a maximum 65.5% improvement in smoke extraction performance over the traditional shaft when the shaft height is 10 m. Besides that, a semi-empirical equation was developed to predict the correlation between non-dimensional volume flow rate and buried depth. These studies can guide extending the application scenarios of shafts and providing technical support.
TiO2 is a typical semiconducting material for photoelectrochemical (PEC) hydrogen generation, but still suffers from poor light capturing capability and slow surface water oxidation kinetics. Herein, a homologous heterojunction has been designed and fabricated based on TiO2 nanorod arrays (NAs) and N-doped titania (Ti0.91O2−xNx) nanosheets (TON NSs). Owing to the narrower band gap, TON broadens light absorption range and enhances absorption intensity. More importantly, the employment of TON for interfacial modulation could accelerate the surface water splitting kinetics due to the gradient band alignment of TON/TiO2. The upshifted valence band could better drive the holes to travel across the surface of photoanode for water oxidation reaction. Consequently, the optimal TON/TiO2 photoanode has achieved a significantly improved photocurrent of 1.62 mA/cm² at 1.23 V vs RHE, which is 2.45 times of that of TiO2. This work provides an effective strategy for the design and construction of homologous heterojunctions for energy catalytic reactions.
Task assignment, the core problem of Spatial Crowdsourcing (SC), is often modeled as an optimization problem with multiple constraints, and the quality and efficiency of its solution determines how well the SC system works. Fairness is a critical aspect of task assignment that affects worker participation and satisfaction. Although the existing studies on SC have noticed the fairness problem, they mainly focus on fairness at the individual level rather than at the group level. However, differences among groups in certain attributes (e.g. race, gender, age) can easily lead to discrimination in task assignment, which triggers ethical issues and even deteriorates the quality of the SC system. Therefore, we study the problem of task assignment with group fairness for SC. According to the principle of fair budget allocation, we define a well-designed constraint that can be considered in the task assignment problem of SC systems, resulting in assignment with group fairness. We mainly consider the task assignment problem in a common One-to-One SC system (O2-SC), and our goal is to maximize the quality of the task assignment while satisfying group fairness and other constraints such as budget and spatial constraints. Specifically, we first give the formal definition of task assignment with group fairness constraint for O2-SC. Then, we prove that it is essentially an NP-hard combinatorial optimization problem. Next, we provide a novel fast algorithm with theoretical guarantees to solve it. Finally, we conduct extensive experiments using both synthetic and real datasets. The experimental results show that the proposed constraint can significantly improve the group fairness level of algorithms, even for a completely random algorithm. The results also show that our algorithm can efficiently and effectively complete the task assignment of SC systems while ensuring group fairness.
Acute pancreatitis is an inflammatory disorder of the pancreas. Medical imaging, such as computed tomography (CT), has been widely used to detect volume changes in the pancreas for acute pancreatitis diagnosis. Many pancreas segmentation methods have been proposed but no methods for pancreas segmentation from acute pancreatitis patients. The segmentation of an inflamed pancreas is more challenging than the normal pancreas due to the following two reasons. 1) The inflamed pancreas invades surrounding organs and causes blurry boundaries. 2) The inflamed pancreas has higher shape, size, and location variability than the normal pancreas. To overcome these challenges, we propose an automated CT pancreas segmentation approach for acute pancreatitis patients by combining a novel object detection approach and U-Net. Our approach includes a detector and a segmenter. Specifically, we develop an FCN-guided region proposal network (RPN) detector to localize the pancreatitis regions. The detector first uses a fully convolutional network (FCN) to reduce the background interference of medical images and generates a fixed feature map containing the acute pancreatitis regions. Then the RPN is employed on the feature map to precisely localize the acute pancreatitis regions. After obtaining the location of pancreatitis, the U-Net segmenter is used on the cropped image according to the bounding box. The proposed approach is validated using a collected clinical dataset with 89 abdominal contrast-enhanced 3D CT scans from acute pancreatitis patients. Compared with other start-of-the-art approaches for normal pancreas segmentation, our method achieves better performance on both localization and segmentation in acute pancreatitis patients.
Bacterial biofilm-associated infection is a life-threatening emergency contributing from drug resistance and immune escape. Herein, a novel non-antibiotic strategy based on the synergy of bionanocatalysts-driven heat-amplified chemodynamic therapy (CDT) and innate immunomodulation is proposed for specific biofilm elimination by the smart design of a biofilm microenvironment (BME)-responsive double-layered metal-organic framework (MOF) bionanocatalysts (MACG) composed of MIL-100 and CuBTC. Once reaching the acidic BME, the acidity-triggered degradation of CuBTC allows the sequential release of glucose oxidase (GOx) and an activable photothermal agent, 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). GOx converts glucose into H2O2 and gluconic acid, which can further acidify the BME to accelerate the CuBTC degradation and GOx/ABTS release. The in vitro and in vivo results show that horseradish peroxidase (HRP)-mimicking MIL-100 in the presence of self-supplied H2O2 can catalyze the oxidation of ABTS into oxABTS to yield a photothermal effect that breaks the biofilm structure via eDNA damage. Simultaneously, the Cu ion released from the degraded CuBTC can deplete glutathione and catalyze the splitting of H2O2 into •OH, which can effectively penetrate the heat-induced loose biofilms and kill sessile bacteria (up to 98.64%), such as E. coli and MRSA. Particularly, MACG-stimulated M1-macrophage polarization suppresses the biofilm regeneration by secreting pro-inflammatory cytokines (e.g., IL-6, TNF-α, etc.) and forming a continuous pro-inflammatory microenvironment in peri-implant biofilm infection animals for at least 14 days. Such BME-responsive strategy has the promise to precisely eliminate refractory peri-implant biofilm infections with extremely few adverse effects.
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