Probing phase transition in neutron stars via the crust-core interfacial mode

Gravitational waves emitted from the binary neutron star (BNS) systems can carry information about the dense matter phase in these compact stars. The crust-core interfacial mode is an oscillation mode in a neutron star and it depends mostly on the equation of the state of the matter in the crust-core transition region. This mode can be resonantly excited by the tidal field of an inspiraling-in BNS system, thereby affecting the emitted gravitational waves, and hence could be used to probe the equation of state in the crust-core transition region. In this work, we investigate in detail how the first-order phase transition inside the neutron star affects the properties of the crust-core interfacial mode, using a Newtonian fluid perturbation theory on a general relativistic background solution of the stellar structure. Two possible types of phase transitions are considered: (1) the phase transitions happen in the fluid core but near the crust-core interface, which results in density discontinuities; and (2) the strong interaction phase transitions in the dense core (as in the conventional hybrid star case). These phase transitions' impacts on interfacial mode properties are discussed. In particular, the former phase transition has a minor effect on the M-R relation and the adiabatic tidal deformability, but can significantly affect the interfacial mode frequency and thereby could be probed using gravitational waves. For the BNS systems, we discuss the possible observational signatures of these phase transitions in the gravitational waveforms and their detectability. Our work enriches the exploration of the physical properties of the crust-core interfacial mode and provides a promising method for probing the phase transition using the seismology of a compact star.Comment: 18 pages, 14 figure

Comparisons between cross-section and long-axis-section in the quantification of aneurysmal wall enhancement of fusiform intracranial aneurysms in identifying aneurysmal symptoms

BackgroundTo investigate the quantification of aneurysmal wall enhancement (AWE) in fusiform intracranial aneurysms (FIAs) and to compare AWE parameters based on different sections of FIAs in identifying aneurysm symptoms.MethodsConsecutive patients were prospectively recruited from February 2017 to November 2019. Aneurysm-related symptoms were defined as sentinel headache and oculomotor nerve palsy. All patients underwent high resolution magnetic resonance imaging (HR-MRI) protocol, including both pre and post-contrast imaging. CRstalk (signal intensity of FIAs' wall divided by pituitary infundibulum) was evaluated both in the cross-section (CRstalk−cross) and the long-axis section (CRstalk−long) of FIAs. Aneurysm characteristics include the maximal diameter of the cross-section (Dmax), the maximal length of the long-axis section (Lmax), location, type, and mural thrombus. The performance of parameters for differentiating symptomatic and asymptomatic FIAs was obtained and compared by a receiver operating characteristic (ROC) curve.ResultsForty-three FIAs were found in 43 patients. Eighteen (41.9%) patients who presented with aneurysmal symptoms were classified in the symptomatic group. In univariate analysis, male sex (P = 0.133), age (P = 0.013), FIAs type (P = 0.167), mural thrombus (P = 0.130), Lmax (P = 0.066), CRstalk−cross (P = 0.027), and CRstalk−long (P = 0.055) tended to be associated with aneurysmal symptoms. In the cross-section model of multivariate analysis, male (P = 0.038), age (P = 0.018), and CRstalk−cross (P = 0.048) were independently associated with aneurysmal symptoms. In the long-axis section model of multivariate analysis, male (P = 0.040), age (P = 0.010), CRstalk−long (P = 0.046), and Lmax (P = 0.019) were independently associated with aneurysmal symptoms. In the combination model of multivariate analysis, male (P = 0.027), age (P = 0.011), CRstalk−cross (P = 0.030), and Lmax (P = 0.020) were independently associated with aneurysmal symptoms. CRstalk−cross has the highest accuracy in predicting aneurysmal symptoms (AUC = 0.701). The combination of CRstalk−cross and Lmax exhibited the highest performance in discriminating symptomatic from asymptomatic FIAs (AUC = 0.780).ConclusionAneurysmal wall enhancement is associated with symptomatic FIAs. CRstalk−cross and Lmax were independent risk factors for aneurysmal symptoms. The combination of these two factors may improve the predictive performance of aneurysmal symptoms and may also help to stratify the instability of FIAs in future studies

Systemic immune-inflammation index is associated with aneurysmal wall enhancement in unruptured intracranial fusiform aneurysms

IntroductionInflammation plays a key role in the progression of intracranial aneurysms. Aneurysmal wall enhancement (AWE) correlates well with inflammatory processes in the aneurysmal wall. Understanding the potential associations between blood inflammatory indices and AWE may aid in the further understanding of intracranial aneurysm pathophysiology.MethodsWe retrospectively reviewed 122 patients with intracranial fusiform aneurysms (IFAs) who underwent both high-resolution magnetic resonance imaging and blood laboratory tests. AWE was defined as a contrast ratio of the signal intensity of the aneurysmal wall to that of the pituitary stalk ≥ 0.90. The systemic immune-inflammation (SII) index (neutrophils × platelets/lymphocytes) was calculated from laboratory data and dichotomized based on whether or not the IFA had AWE. Aneurysmal symptoms were defined as sentinel headache or oculomotor nerve palsy. Multivariable logistic regression and receiver operating characteristic curve analyses were performed to determine how well the SII index was able to predict AWE and aneurysmal symptoms. Spearman’s correlation coefficients were used to explore the potential associations between variables.ResultsThis study included 95 patients, of whom 24 (25.3%) presented with AWE. After adjusting for baseline differences in neutrophil to lymphocyte ratios, leukocytes, and neutrophils in the multivariable logistic regression analysis, smoking history (P = 0.002), aneurysmal symptoms (P = 0.047), maximum diameter (P = 0.048), and SII index (P = 0.022) all predicted AWE. The SII index (P = 0.038) was the only independent predictor of aneurysmal symptoms. The receiver operating characteristic curve analysis revealed that the SII index was able to accurately distinguish IFAs with AWE (area under the curve = 0.746) and aneurysmal symptoms (area under the curve = 0.739).DiscussionAn early elevation in the SII index can independently predict AWE in IFAs and is a potential new biomarker for predicting IFA instability

Grey Correlation Analysis between Macro Mechanical Damage and Meso Volume Characteristics of SBS Modified Asphalt Mixture under Freeze-Thaw Cycles

The effect of freeze–thaw (F–T) in the seasonal frozen area would lead to damage to asphalt pavement. After water enters asphalt pavement, the water in voids would expand at a lower temperature, which could change the void content and number, affecting the macro mechanical properties of the asphalt mixture. The rapid development of CT scanning and digital image processing (DIP) provides powerful technical support for the research of asphalt mixture meso volume characteristics. In this paper, the mechanical properties of basalt fiber reinforced asphalt mixture subjected to F–T cycles were tested at different temperatures to clarify the decay law of mechanical properties under F–T cycles. Then, the meso images of the asphalt mixture under various F–T cycles could be obtained by using CT tomography. Based on DIP technology, the meso characteristic parameters of CT images for asphalt mixture were extracted, and the development of asphalt mixture freeze–thaw damage was further analyzed. The test results showed that with the F–T cycle, the macro mechanical properties of the asphalt mixture rapidly declined in the early stage of the F–T cycle and gradually tended to be flat. There would be serious damage inside the asphalt mixture in the late stage of the F–T cycle. The damage to the mechanical properties of the asphalt mixture under the F–T cycle can be attributed to the change in the internal mesostructure of the asphalt mixture. Based on the grey relational analysis theory, the formation of the connected void was the main factor affecting the damage in the early stage of the F–T cycle, while the formation of new voids mainly affected the later development of F-T damage

Compressive and Tensile Fracture Failure Analysis of Asphalt Mixture Subjected to Freeze–Thaw Conditions by Acoustic Emission and CT Scanning Technologies

The cracking of bitumen pavement in seasonal frozen areas has direct and significant influences on its properties. In order to study the compressive and tensile fracture failure features of basalt fiber-reinforced asphalt mix after freeze–thaw (F-T) treatment, the load–displacement curves under the compression and tensile modes of asphalt mixture after F-T conditions were tested. As a real-time detection means, acoustic emission (AE) was used for testing asphalt mix under compression and tensile load modes. X-ray computed tomography (CT) was employed to represent and evaluate the interior void in F-T conditions. The results showed that, as F-T conditions continue, the compressive and tensile strength of the specimens at different temperatures decreases. The amplitude and count of AE signals with the time history of load level show different characteristics of change in various intervals. AE signal indirect parameters reveal that under compressive and tensile load modes there is a gradual deterioration of performance for asphalt mix due to the coupling interactions between tensile and shear cracks. The asphalt mixtures have different behavior in F-T conditions, which are attributable to interior meso-void characteristics based on CT analysis. This study is limited to the type and loading mode of asphalt mixture in order to quantitatively predict the performance of asphalt mixture

Grey Correlation Analysis between Macro Mechanical Damage and Meso Volume Characteristics of SBS Modified Asphalt Mixture under Freeze-Thaw Cycles

The effect of freeze–thaw (F–T) in the seasonal frozen area would lead to damage to asphalt pavement. After water enters asphalt pavement, the water in voids would expand at a lower temperature, which could change the void content and number, affecting the macro mechanical properties of the asphalt mixture. The rapid development of CT scanning and digital image processing (DIP) provides powerful technical support for the research of asphalt mixture meso volume characteristics. In this paper, the mechanical properties of basalt fiber reinforced asphalt mixture subjected to F–T cycles were tested at different temperatures to clarify the decay law of mechanical properties under F–T cycles. Then, the meso images of the asphalt mixture under various F–T cycles could be obtained by using CT tomography. Based on DIP technology, the meso characteristic parameters of CT images for asphalt mixture were extracted, and the development of asphalt mixture freeze–thaw damage was further analyzed. The test results showed that with the F–T cycle, the macro mechanical properties of the asphalt mixture rapidly declined in the early stage of the F–T cycle and gradually tended to be flat. There would be serious damage inside the asphalt mixture in the late stage of the F–T cycle. The damage to the mechanical properties of the asphalt mixture under the F–T cycle can be attributed to the change in the internal mesostructure of the asphalt mixture. Based on the grey relational analysis theory, the formation of the connected void was the main factor affecting the damage in the early stage of the F–T cycle, while the formation of new voids mainly affected the later development of F-T damage

Downhole vibration causing a drill collar failure and solutions

In large borehole drilling of some blocks or formations, due to serious downhole vibration, fatigue failure of a drill collar occurs frequently and most washouts and fractures are in thread root. An analysis of the above failure shows that the drill collar fatigue failure is caused by the cyclic bending stress due to serious downhole vibration. Therefore, downhole vibration modes were theoretically analyzed in terms of axial vibration, lateral vibration, stick-slip, and the physical model established by the mechanical vibration field. Then the resonance damage caused by the actual different downhole vibrations and its theoretical basis were analyzed; and according to the downhole drill string lateral vibration and whirling law, the best area to ensure drilling parameter stability based on the given boundary conditions was figured out, and the theory was clarified that in the best area of drilling, the maximum ROP will be achieved by maintaining the drill string stable or eliminating the vibration/stick-slip, meanwhile the stress fatigue of BHA will be reduced or eliminated especially for drill collar. Finally, solutions were provided as follows: (1) According to the special BHA, drilling conditions, together with physical and mathematical models listed above, downhole resonance speed and related parameters to be avoided can be easily figured out. It was also clarified that resonance speed is exactly the vibration speed that need to be avoided; and that the resonance frequency can be avoided with software for vibration analysis in BHA design and application at well sites; (2) V-Stab is a new and efficient tool which can reduce or eliminate downhole lateral vibration and stick-slip

A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model

Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time

Visual preference of plant features in different living environments using eye tracking and EEG.

Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people's preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people's visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions