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Chimp optimization algorithm (CHOA) is a recently developed nature-inspired technique that mimics the swarm intelligence of chimpanzee colonies. However, the original CHOA suffers from slow convergence and a tendency to reach local optima when dealing with multidimensional problems. To address these limitations, we propose TASR-CHOA, a twofold adaptive stochastic reinforced variant. The TASR-CHOA algorithm integrates two novel methodologies: a stochastic approach to improve the speed at which convergence is achieved and a dual adaptive weighting approach to optimize the exploration of early patterns, which refer to initial trends or behaviors in the algorithm's convergence process during the early stages of iterations and the exploitation of subsequent tendencies, indicating how these initial trends develop over time as the algorithm iterates and refines its search. To evaluate TASR-CHOA, we apply it to 29 conventional optimization benchmark functions, 10 IEEE CEC-06 benchmarks, 30 complicated IEEE CEC-BC benchmark functions, and ten well-known benchmark real-world challenges. We evaluate TASR-CHOA against 4 categorical optimization techniques as well as 18 top IEEE CEC-BC algorithms. Based on our broad investigation done using three statistical tests, we claim that TASR-CHOA outperforms the majority of the algorithms since within a position takes the best place, 54 out of 73 evaluation functions and engineering problems. In other cases, the results are almost the same as those of SHADE and CMA-ES over several comparisons. As an illustrative application of this joint approach, a computer-aided fire detection task is performed using a deep convolutional neural network combined with TASR-CHOA. We also outline the algorithm executed in steps, indicating computational complexity, which is O(NI×NV) + O(NI×NV×6) + O(NI×NV + 2×NI×NV) as a function of number of individuals (NI) and dimensions (NV)., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. The authors declare that there is no conflict of interest., (© 2024. The Author(s).)

This work analyzes the implementation of an artificial mechanism inspired by a biological somatic marker that ables a passenger agent to both, react to changes in the service, as well as keep said reactions as a memory for future decisions. An artificial mental model was designed, and the passenger agent was implemented as an autonomous decision-making system, where both, the choice of the transport operator and the evaluation of the received service were fully delegated to the system. The evaluation of the service experience is not only based on rational aspects (such as the cost of the trip) but also on subjective aspects related to the satisfaction level derived from the passenger's experience. The experimental scenario considered 10,000 trip requests simulated within an artificial map that emulates characteristics that are usually present in a city, such as vehicular congestion, the unsafety of certain streets, or the benefits of an area with tourist interest. The results show that the option to travel under a transport operator with a touristic profile is a trend. Unlike current cases in the industry, this research work explores the scenario where the passenger can have as a client a trip profile with memory, differentiated from other clients, and can receive more than one trip proposal for the same trip request, according to the different conditions that the passenger is looking for., (© 2023. Springer Nature Limited.)

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts., (© 2022. The Author(s).)

Alzheimer's disease (AD) is one of the most significant health challenges of our time, affecting a growing number of the elderly population. In recent years, the retina has received increased attention as a candidate for AD biomarkers since it appears to manifest the pathological signatures of the disease. Therefore, its electrical activity may hint at AD-related physiological changes. However, it is unclear how AD affects retinal electrophysiology and what tools are more appropriate to detect these possible changes. In this study, we used entropy tools to estimate the complexity of the dynamics of healthy and diseased retinas at different ages. We recorded microelectroretinogram responses to visual stimuli of different nature from retinas of young and adult, wild-type and 5xFAD-an animal model of AD-mice. To estimate the complexity of signals, we used the multiscale entropy approach, which calculates the entropy at several time scales using a coarse graining procedure. We found that young retinas had more complex responses to different visual stimuli. Further, the responses of young, wild-type retinas to natural-like stimuli exhibited significantly higher complexity than young, 5xFAD retinas. Our findings support a theory of complexity-loss with aging and disease and can have significant implications for early AD diagnosis., (© 2022. The Author(s).)

By studying different sources of temporal variability in central pattern generator (CPG) circuits, we unveil fundamental aspects of the instantaneous balance between flexibility and robustness in sequential dynamics -a property that characterizes many systems that display neural rhythms. Our analysis of the triphasic rhythm of the pyloric CPG (Carcinus maenas) shows strong robustness of transient dynamics in keeping not only the activation sequences but also specific cycle-by-cycle temporal relationships in the form of strong linear correlations between pivotal time intervals, i.e. dynamical invariants. The level of variability and coordination was characterized using intrinsic time references and intervals in long recordings of both regular and irregular rhythms. Out of the many possible combinations of time intervals studied, only two cycle-by-cycle dynamical invariants were identified, existing even outside steady states. While executing a neural sequence, dynamical invariants reflect constraints to optimize functionality by shaping the actual intervals in which activity emerges to build the sequence. Our results indicate that such boundaries to the adaptability arise from the interaction between the rich dynamics of neurons and connections. We suggest that invariant temporal sequence relationships could be present in other networks, including those shaping sequences of functional brain rhythms, and underlie rhythm programming and functionality.

The slice and view approach in electron microscopy defines an ensemble of destructive techniques that is widely used for studying in 3D the structure and chemistry of samples with dimensions ranging from µm to mm. Here, a method is presented for measuring with high resolution and quantitatively the morphology and chemical composition of the surface of a sample in 3D. It is non-destructive and therefore, it is complementary to slice and view methods. The scheme is based on the fusion of conventional scanning electron microscopy (SEM) imaging, multi-view photogrammetry and compositional mapping using energy dispersive X-ray spectroscopy (EDXS). We demonstrate its potential by performing an accurate study of adhesion wear of a tungsten carbide tool that is difficult to obtain using conventional characterization techniques.

An alternative approach for cell-culture end-point protocols is proposed herein. This new technique is suitable for real-time remote sensing. It is based on Electrical Cell-substrate Impedance Spectroscopy (ECIS) and employs the Oscillation-Based Test (OBT) method. Simple and straightforward circuit blocks form the basis of the proposed measurement system. Oscillation parameters - frequency and amplitude - constitute the outcome, directly correlated with the culture status. A user can remotely track the evolution of cell cultures in real time over the complete experiment through a web tool continuously displaying the acquired data. Experiments carried out with commercial electrodes and a well-established cell line (AA8) are described, obtaining the cell number in real time from growth assays. The electrodes have been electrically characterized along the design flow in order to predict the system performance and the sensitivity curves. Curves for 1-week cell growth are reported. The obtained experimental results validate the proposed OBT for cell-culture characterization. Furthermore, the proposed electrode model provides a good approximation for the cell number and the time evolution of the studied cultures.

Physiological stress response is a crucial adaptive mechanism for prey species survival. This paper aims to identify the main environmental and/or individual factors better explaining the stress response in Wood mice, Apodemus sylvaticus. We analyzed alterations in fecal glucocorticoid metabolite (FCM) concentration - extensively used as an accurate measure of the physiological stress response - of wild mice fecal samples seasonally collected during three years. Then, support vector machines were built to predict said concentration according to different stressors. These statistical tools appear to be particularly suitable for small datasets with substantial number of dimensions, corroborating that the stress response is an extremely complex process in which multiple factors can simultaneously partake in a context-dependent manner, i.e., the role of each potential stressor varies in time depending on other stressors. However, air-humidity, temperature and body-weight allowed us to explain the FCM fluctuation in 98% of our samples. The relevance of air-humidity and temperature altering FCM level could be linked to the presence of an abundant vegetation cover and, therefore, to food availability and predation risk perception. Body-weight might be related to the stress produced by reproduction and other intraspecific relationships such as social dominance or territorial behavior.