Adaptive systems frequently incorporate complex structures which can arise spontaneously and which may be nonadaptive in the evolutionary sense. We give examples from phase transition and fractal growth to develop the themes of cooperative phenomena and pattern formation. We discuss RNA interference and transcriptional gene regulation networks, where a major part of the topological properties can be accounted for by mere combinatorics. A discussion of ensemble approaches to biological systems and measures of complexity is presented, and a connection is established between complexity and fitness.
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http://dx.doi.org/10.1007/s12038-013-9394-8 | DOI Listing |
Sensors (Basel)
December 2024
College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.
Rail corrugation intensifies wheel-rail vibrations, often leading to damage in vehicle-track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Key Laboratory of Science and Technology on Micro-System, Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences, Shanghai 200050, China.
Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range-velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Computational Biology Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, India.
Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.
This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output.
View Article and Find Full Text PDFBioengineering (Basel)
November 2024
Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA.
Diabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities, underscoring the need for advanced preventative strategies and technology for effective management. This study integrates Shapley Additive explanations (SHAPs) into ensemble machine learning models to improve the accuracy and efficiency of diabetes predictions. By identifying the most influential features using SHAP, this study examined their role in maintaining high predictive performance while minimizing computational demands.
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