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The changes in cardiomyocyte action potentials are related to variations in intra- and extracellular ion concentrations. Abnormal ion concentrations can lead to irregular action potentials, subsequently affecting wave propagation in myocardial tissue and potentially resulting in the formation of spiral waves. Therefore, timely monitoring of ion concentration changes is essential. This study presents a novel machine learning classification model that predicts ion concentration changes based on action potential variation data. We conducted simulations using a single-cell model, generating a dataset of 850 action potential variations corresponding to different ion concentration changes. The model demonstrated excellent predictive performance, achieving an accuracy of 0.988 on the test set. Additionally, the causes of spontaneous spiral wave generation in the heart are insufficiently studied. This study presents a new mechanism whereby changes in extracellular potassium ion concentration leads to the spontaneous generation of spiral waves. By constructing composite myocardial tissue containing both myocardial and fibroblast cells, we observed that variations in extracellular potassium ion concentration can either trigger or inhibit cardiomyocyte excitation. We developed three tissue structures, and by appropriately adjusting the extracellular potassium ion concentration, we observed the spontaneous generation of single spiral waves, symmetrical spiral wave pairs, and asymmetrical double spiral waves.

Due to the similar characteristics of chaotic systems and cryptography, public key encryption algorithms based on chaotic systems are worth in-depth research and have high value for the future. Chebyshev polynomials have good properties and are often used in the design of public key algorithms. This paper improves the Bose Multi-Chaotic Public Key Cryptographic Algorithm (BMPKC) by applying Chebyshev polynomials. The proposed algorithm (CMPKC-ki) introduces the selective coefficient ki based on the properties of Chebyshev polynomials, allowing the special functions that need to be negotiated in the original system to be freely and randomly chosen as Chebyshev polynomials, and can also be expanded to m levels. The improved cryptographic algorithm also utilizes chaotic hash functions and logistic mapping to generate pseudo-random sequences and overcomes shortcomings of the Bose algorithm by iteratively iterating the selected Chebyshev polynomials based on the number of 0s or 1s in the pseudo-random sequence, thus providing better security. Analysis and software testing results indicate that this algorithm has strong robustness against brute force attacks, achieving a higher attack time for breaking the private key compared to the CEPKC, BMPKC, and CMPKC algorithms. Compared to the CMPKC algorithm, our proposal algorithm achieves better performance in the encryption and decryption phases. Furthermore, we combine this Multi-Chaotic System Key Exchange Protocol with the Advanced Encryption Standard (AES) algorithm, while providing a demonstration, offering more possibilities for practical applications of this system.

In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving combined control of currents are superior to singular approaches. While previous studies have proposed some combination strategies, further reinforcement and supplementation are required, particularly in the context of controlling arrhythmias through the combined regulation of two potassium ion currents. This study employs the Luo–Rudy phase I cardiac model, modulating the maximum conductance of the time-dependent potassium current and the time-independent potassium current, to investigate the effects of this combined modulation on spiral waves and turbulent states. Numerical simulation results indicate that, compared to modulating a single current, combining reductions in the conductance of two potassium ion currents can rapidly control spiral waves and turbulent states in a short duration. This implies that employing blockers for both potassium ion currents concurrently represents a more efficient control strategy. The control outcomes of this study represent a novel and effective combination for antiarrhythmic interventions, offering potential avenues for new antiarrhythmic drug targets.

Due to its very desirable properties, Chebyshev polynomials are often used in the design of public key cryptographic systems. This paper discretizes the Chebyshev mapping, generalizes the properties of Chebyshev polynomials, and proposes an improved public key encryption algorithm based on Chebyshev chaotic mapping and RSA, i.e., CRPKC−Ki. This algorithm introduces alternative multiplication coefficients Ki, the selection of which is determined by the size of TrTdxmod N=TdTrxmod N, and the specific value selection rules are shared secrets among participants, overcoming the shortcomings of previous schemes. In the key generation and encryption/decryption stages, more complex intermediate processes are used to achieve higher algorithm complexity, making the algorithm more robust against ordinary attacks. The algorithm is also compared with other RSA-based algorithms to demonstrate its effectiveness in terms of performance and security.

In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

Chaotic time series are widely present in practice, but due to their characteristics—such as internal randomness, nonlinearity, and long-term unpredictability—it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.

An energy supply and demand forecasting system can help decision-makers grasp more comprehensive information, make accurate decisions and even plan a carbon-neutral future when adjusting energy structure, developing alternative energy resources and so on. This paper presents a hierarchical design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model called GM(1,1) which covers all the process of data collection, data analysis and data prediction. It mainly consists of three services, namely Crawler Service (CS), Algorithm Service (AS), Data Service (DS). The architecture of multiple loose coupling services makes the system flexible in more data, and more advanced prediction algorithms for future energy forecasting works. In order to make higher prediction accuracy based on GM(1,1), this paper illustrates some basic enhanced methods and their combinations with adaptable variable weights. An implementation for testing the system was applied, where the model was set up for coal, oil and natural gas separately, and the enhanced GM was better with relative error about 9.18% than original GM on validation data between 2010 and 2020. All results are available for reference on adjusting of energy structure and developing alternative energy resources.

With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system.

Percepção do Autoconceito Infantil (PAI) é o nome de um instrumento desenvolvido por Sánchez y Escribano (1999) destinado à avaliação do autoconceito de crianças entre os três e os 10 anos de idade (Pré-primária e 1º Ciclo do Ensino Básico). Tratase de um instrumento de medida de fácil aplicação e correcção, de aplicação tanto individual como colectiva e, que no seu constructo inclui dez dimensões características do auto-conceito infantil. A adaptação portuguesa que agora se apresenta foi desenvolvida no âmbito do Projecto Eficácia Escolar no Ensino da Matemática (3EM), junto de um grupo de 954 alunos do 1º ciclo do ensino básico que fazem parte de uma amostra constituída na região da Cova da Beira. Os resultados obtidos permitem-nos constatar que esta versão de aplicação colectiva apresenta uma fiabilidade razoável (K-R 20 = 0.79) e uma estrutura factorial adequada.

A relação que se faz entre cultura e música popular brasileira promove uma seleção de letras que se abre para um imaginário criativo e constitutivo de suas origens, como nas bases fundantes da história de vida de quem a compôs. Essas músicas em suas representações repercutem os significados deixados como marcas das experiências observadas e vividas sobre o velho, a velhice, a visão do processo de envelhecimento e a criação de lugares a serem ocupados nas formas de inclusão e exclusão social. Foram levantadas para análise músicas em tempos diferenciados, buscando-se um processo comparativo e interativo das visões criadas dos reflexos sociais vivenciados em cada época, procurando-se verificar se houve mudanças nas relações estabelecidas com o ato de se ver velho. Vislumbramos a interação criativa e plástica entre o ato de viver e de morrer, sobre o envelhecimento em sua identidade cultural e a maneira como é observada essa construção partilhada. As músicas em sua sonoridade e letra trazem à tona um construto mítico a mostrar um alguém singularmente transposto para a realidade de mundo criado ao se inserir em um meio cósmico captável por outras pessoas. A melodia apresentada no embalar para com as notas musicais, que surgem como representações dialéticas deixadas pela construção histórica dos personagens, criando espaços para descrever as pessoas em seu convívio, construindo reflexos para com os significados dos termos velho, velhice e envelhecimento, em forma de arte projetada. Palavras-chave: velho; velhice; envelhecimento; música popular brasileira; gerontologia.