Bangabasi Evening College

For a variety of causes, including membrane stretching and electrostatic softening, the membrane of a microelectromechanical system (MEMS)-based capacitive micromachined ultrasonic transducer (CMUT) exhibits nonlinear behavior when vibrating in response to an AC electrical signal over a DC bias. Effects like harmonic distortion, bi-stability, and frequency shifts are brought about by these occurrences. The higher-order modes that result from the deflections caused by nonlinear coupling are frequently difficult for various analytical models, such as lumped-element techniques, to capture. Using the Föppl–von Kármán equation, a dedicated theory for CMUT nonlinear vibrations is developed in this paper. Geometric nonlinearity, viscous damping, and external force are incorporated into a reduced-order Föppl–von Kármán plate equation. The nonlinear partial differential equations are converted into slow-flow amplitude and phase equations that characterize the membrane’s effective modal dynamics using perturbation-based averaging techniques. A CMUT model is developed for FEM simulation and calibrated with experimental data of resonance frequency. It is performed at sub-resonant (50 kHz), near-resonant (100 kHz), and super-resonant (150 kHz) regimes for a substantial period of time. The resulting time-domain displacement responses are then evaluated using Hilbert-transform-based amplitude. The key characteristics seen in FEM data, such as amplitude saturation, resonance detuning, asymmetric displacement envelopes, and sluggish modulation of oscillation amplitude close to resonance, are satisfactorily explained by the analytical model. The envelope modulation and enhanced phase sensitivity were observed near resonance. The analytical model explained the phenomenon as a consequence of nonlinear frequency pulling and the coexistence of stable and unstable fixed points in the amplitude–phase plane.

Real-world difficulties are defined as discrete or continuous, homogeneous or non-homogeneous, linear or nonlinear, etc. of model systems. The uncertainty results in imprecise system parameters. The values of the parameters may not always be considered real numbers. The real-world system must be emulated for stochastic, interval, fuzzy, intuitionistic, and other imprecise conditions to overcome these issues. Fuzzy sets are extended to intuitionistic fuzzy sets, while it is commonly known that fuzzy differential approaches have been employed in most texts. Intuitionistic fuzzy sets theory is applied in this study to develop a more realistic epidemic model. The population is divided into four categories based on this concept: susceptible (S), exposed (E), infected (I) and recovered (R). By treating each of the coefficients in the suggested model as a distinct type of triangular intuitionistic fuzzy number, we gave all of the epidemiological parameters intuitionistic fuzziness. The intuitionistic SEIR model has been examined using different weight assignments using the Utility Function Method (UFM) technique. The study explores non-negativity, boundedness, and physiologically realistic equilibrium points - all qualitative features of the SEIR model. The stability of the suggested model system has been examined using the intuitionistic fuzzy sets notion. Using MATLAB, all of the outputs and conclusions of the suggested model have been validated both visually and quantitatively.

We introduce a novel technique for sampling particle physics model parameter space using Nested Sampling (NS) enhanced by multiple Machine Learning (ML) networks, including Self-Normalizing Networks (SNN) and Normalizing Flows (Real-NVP). To demonstrate its effectiveness, we apply this approach to the Type-II Seesaw model. Our Bayesian analysis explores the model parameter space while incorporating theoretical constraints and experimental data related to the 125 GeV Higgs boson, the ρ -parameter, and oblique parameters.

A novel tridentate dianionic ligand, [2-(3,5-di-tert-butyl-2-hydroxy-benzylamino) acetic acid], was successfully synthesized and employed in the formation of a new six-coordinate titanium complex with the general formula [TiLCl2THF]. Additionally, the catalytic activity of the complex, in the presence of a сoсatalyst was evaluated in an aqueous medium, revealing efficient catalytic activity for the homo-polymerization of methyl methacrylate (MMA) in aqueous medium under mild reaction conditions. To verify the structure of the complex, various instrumental techniques were employed, including 1H nuclear magnetic resonance (NMR), ultraviolet-visible spectroscopy (UV-visible) and elemental analysis. These analyses provided valuable insights into the composition and bonding of the complex. Furthermore, the properties of the resulting polymethyl methacrylate (PMMA) were thoroughly investigated using a range of techniques, including 1H NMR, Differential scanning calorimetry (DSC), and Gel permeation chromatography (GPC). These analyses helped to elucidate the structural and thermal characteristics and molecular weight of the synthesized PMMA. The reaction conditions for the polymerization of MMA using the complex were also optimized through a response surface methodology approach, ensuring the highest efficiency and yield in the polymerization process. The developed RSM model demonstrated reasonable agreement between predicted and experimental PMMA yield values within the investigated operating range.

Harmful algal blooms (HABs) are a catastrophic phenomenon in aquatic ecosystems globally, occurring in freshwater bodies and sometimes in marine and brackish waters, due to large numbers of cyanobacteria and microalgae, including dinoflagellates. HABs are dangerous and may be fatal to humans and animals due to toxicity and anoxic conditions. They also cause paralytic shellfish poisoning and kill fish in water bodies. Major factors include eutrophication, rising temperatures, stagnant water, and increased sunlight. Promising paths for early intervention are enabled by efficient monitoring and detection techniques, such as molecular biotechnology, artificial intelligence-driven predictive models, and satellite remote sensing. The negative consequences of HABs can be mitigated through sustainable management techniques such as bioremediation, nutrient load reductions, and regulatory measures. Public awareness and community involvement also play a critical role in preventing and mitigating HAB incidents by encouraging ethical farming methods, reducing waste discharge, and bolstering conservation initiatives. In this mini-review, the major causes and mitigation strategies for HABs have been outlined. In addition, modern strategies, policies, and the current status have been discussed.