Designing a medical information diagnosis platform with IoT integration

In order to enhance the operational efficiency of the healthcare industry, this paper investigates a medical information diagnostic platform through the application of swarm and evolutionary algorithms. This paper begins with an analysis of the current development status of medical information diagnostic platforms based on Chat Generative Pre-trained Transformer (ChatGPT) and Internet of Things (IoT) technology. Subsequently, a comprehensive exploration of the advantages and disadvantages of swarm and evolutionary algorithms within the medical information diagnostic platform is presented. Further, the optimization of the swarm algorithm is achieved through reverse learning and Gaussian functions. The rationality and effectiveness of the proposed optimization algorithm are validated through horizontal comparative experiments. Experimental results demonstrate that the optimized model achieves favorable performance at the levels of minimum, average, and maximum algorithm fitness values. Additionally, preprocessing data in a 10 * 10 server configuration enhances the algorithm's fitness values. The minimum fitness value obtained by the optimized algorithm is 3.56, representing a 3 % improvement compared to the minimum value without sorting. In comparative experiments on algorithm stability, the optimized algorithm exhibits the best stability, with further enhancement observed when using sorting algorithms. Therefore, this paper not only provides a new perspective for the field of medical information diagnostics but also offers effective technical support for practical applications in medical information processing.

Prediction of Rockfill Materials' Shear Strength Using Various Kernel Function-Based Regression Models-A Comparative Perspective

The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R²) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R² = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.

Enhanced resolution impedimetric analysis of cell responses from the distribution of relaxation times

A universal strategy for the sensitive investigation of cell responses to external stimuli, in particular nanosecond pulsed electric fields (nsPEFs), was developed based on electrical impedance spectroscopy (EIS) in combination with a multi-peak analysis for the distribution of relaxation times (DRT). The DRT method provides high resolution for the identification of different polarization processes without a priori assumptions, as they are needed by more conventional approaches, such as an evaluation by equivalent circuit models. Accordingly, the physical properties of cells and their changes due to external stimuli can be uncovered and visualized and dispersion mechanisms introduced by Schwan et al. clearly identified. These are in particular relaxation processes at about 100 kHz that are associated with cell membrane characteristics and dominating respective changes of the distribution function for epithelial cell monolayers after exposure. A relatively moderate evolution at about 10 kHz may represent the polarization of extracellular matrices. Relaxation processes at around 1 MHz were suggested to be associated with intracellular changes. Conversely, the distribution of relaxation times can aid the optimization of the experimental design with respect to intended responses by an external stimulus.

A methodology using Gaussian-based density map approximation to assess sets of cryo-electron microscopy density maps

This article presents a methodology to assess a set of density maps, as used in the Blind Assessment Phase of the 2015/2016 Map Challenge (EMDataBank Validation Challenges). The synthetic and experimental cryo-electron microscopy (cryo-EM) density maps obtained by different single particle analysis protocols and by different participants, submitted in the Challenge Phase for assessment, were analyzed with this methodology and the obtained results are presented and discussed here. The goal of using such a methodology was to blindly identify the density maps with globally similar structural information, meaning the maps with the structural information mostly "reproduced" by different protocols. To this end, the density maps are "coarsened" using Gaussian-based approximations, with the same input approximation parameters for all maps of the target biological complex. The approximated maps are then represented in a common reduced-dimension (here, 3D) space of their correlation-coefficient-based distances, in which close maps mean similar maps. The distance analysis allows identifying maps with the most "reproduced" structural information by different protocols. The obtained results are also discussed taking into account the detailed information about the protocols that has been released after the end of the Blind Assessment Phase.

Complete stability of delayed recurrent neural networks with Gaussian activation functions

This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3^k equilibrium points with 0≤k≤n, among which 2^k and 3^k-2^k equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results.

Denoising of high-resolution single-particle electron-microscopy density maps by their approximation using three-dimensional Gaussian functions

Cryo-electron microscopy (cryo-EM) of frozen-hydrated preparations of isolated macromolecular complexes is the method of choice to obtain the structure of complexes that cannot be easily studied by other experimental methods due to their flexibility or large size. An increasing number of macromolecular structures are currently being obtained at subnanometer resolution but the interpretation of structural details in such EM-derived maps is often difficult because of noise at these high-frequency signal components that reduces their contrast. In this paper, we show that the method for EM density-map approximation using Gaussian functions can be used for denoising of single-particle EM maps of high (typically subnanometer) resolution. We show its denoising performance using simulated and experimental EM density maps of several complexes.