Online public opinion evaluation through the functional resonance analysis method and deep analysis

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.

Religion and Nature in the Light of Rosa’s Resonance Theory

German sociologist Hartmut Rosa has developed an integral resonance theory, which is a theory of our relationship to the world. This theory has aroused much interest in recent years not only among sociologists but also among representatives of other humanities and social sciences, including representatives of the science of religion. Therefore, the author considers it worth discussing. The article deals with religion and nature in light of Rosa’s theory. Rosa understands religion and nature as two of the main axes and areas of man’s search for vertical resonance in modernity. In the section devoted to religion, the author presents Rosa’s view that the essence of religion is man’s need for a response. In light of resonance theory, the author examines phenomena such as prayer, worship, religious rites, certain holidays (Christmas), and sin. Schleiermacher, Buber, Gerhardt, Luther and Camus are singled out as particularly relevant thinkers and creators. On this basis, he discusses existential violence, which stems from the need for resonance and the rejection of alienation as its opposite. The section devoted to nature is mainly focused on the problems that hinder modern man in their quest to fulfil their longing for resonance with nature. The author explains Rosa’s thesis that the ecological crisis is, at its core and origin, an existential and cultural crisis and not a crisis of resources. The author draws conclusions concerning the importance of religion and modern man’s fear of the loss of resonance.

Vibration-reduction optimization of the point-supporting floating-slab track based on local resonance mechanism

With the continuous increase of subway operating mileage, the problem of subway vibration has become more and more significant. Nowadays, the point-supported floating-slab track is recognized as the best method to control track vibration, which is mainly designed based on the mass-spring-damping theory. How to further improve the vibration control ability of the point-supported floating-slab track? In this paper, a new type of rubber point-supported floating slab track is designed based on the local resonance theory. Through calculation and dynamic test, it is obtained as follows: (1) The band gap of the point support structure by local resonance type depends on the two vertical vibration modes. (2) As the elastic modulus of the cladding layer increases, the bandwidth of the band gap of the corresponding structure increases significantly. (3) The increase of the vibrator density can increase the bandwidth, while reducing the start and stop frequencies, which is beneficial to attenuate the resonance of the floating-slab track. (4) The cushion material parameters of point support structure by local resonance type 2 will not affect the band gap. The increase in sleeve density will reduce the band gap, which is not conducive to vibration reduction. Local resonance type floating-slab track will be the development direction of track vibration-reduction measures in the future.

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

Dissecting the Local Environment of FRB 190608 in the Spiral Arm of its Host Galaxy

We present a high-resolution analysis of the host galaxy of fast radio burst (FRB) 190608, an SB(r)c galaxy at z = 0.11778 (hereafter HG 190608), to dissect its local environment and its contributions to the FRB properties. Our Hubble Space Telescope Wide Field Camera 3 ultraviolet and visible light image reveals that the subarcsecond localization of FRB 190608 is coincident with a knot of star formation (ΣSFR = 1.5 × 10−2 M ⊙ yr−1 kpc−2) in the northwest spiral arm of HG 190608. Using Hβ emission present in our Keck Cosmic Web Imager integral field spectrum of the galaxy with a surface brightness of μ H β = ( 3.36 ± 0.21 ) × 10 − 17 erg s − 1 cm − 2 arcsec − 2 , we infer an extinction-corrected Hα surface brightness and compute a dispersion measure (DM) from the interstellar medium of HG 190608 of DMHost,ISM = 94 ± 38 pc cm−3. The galaxy rotates with a circular velocity v circ = 141 ± 8 km s−1 at an inclination i gas = 37° ± 3°, giving a dynamical mass M halo dyn ≈ 10 11.96 ± 0.08 M ⊙ . This implies a halo contribution to the DM of DMHost,Halo = 55 ± 25 pc cm−3 subject to assumptions on the density profile and fraction of baryons retained. From the galaxy rotation curve, we infer a bar-induced pattern speed of Ω p = 34 ± 6 km s−1 kpc−1 using linear resonance theory. We then calculate the maximum time since star formation for a progenitor using the furthest distance to the arm’s leading edge within the localization, and find t enc = 21 − 6 + 25 Myr. Unlike previous high-resolution studies of FRB environments, we find no evidence of disturbed morphology, emission, or kinematics for FRB 190608.

Prediction of diabetic retinopathy using machine learning techniques

Diabetic Retinopathy (DR) is the complicatedness of diabetes that happens due to macular degeneration among Type II diabetic patients. The early symptom of this disease is predicted through annual eye checkups. Hence, one can save their vision at an early stage. Later on, it prompts retinal detachment. There is a requirement for awareness among diabetic patients about this disease to prevent their life from vision misfortune. Along these lines, there is a need for a computer-assisted method to analyze the disease. The proposed system used Adaptive Histogram Equalization (AHE) technique for image enhancement, Hop Field Neural Network for blood vessel segmentation, and Adaptive Resonance Theory (ART) for blood vessel classification. The proposed system analyzes the disease and classifies the disease level effectively with high accuracy. Also, the system notifies the users about the stages of the disease. The proposed system is evaluated with the clinical as well as open fundus image data sets like DRIVE, STARE, MESSIDOR, HRF, DRIONS, and REVIEW for diabetic retinopathy prediction. Also, physicians evaluated the system and concluded that the proposed system does not deviate from the quality of disease analysis and grading. The proposed techniques accomplished 99.99% accuracy. The system is evaluated by the ophthalmologists and witnesses that the proposed system has not veered off as far as quality.