Search

Recently, a mix of traditional and modern approaches have been proposed to detect brain abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the initial/scheduled assessments consider the bio‐signal‐based appraisal, due to its non‐invasive nature and low cost. Further, brain bio‐signal scans can be recorded using a single/multi‐channel electrode setup, which is further evaluated by an experienced doctor, as well as computer software, to identify the nature and severity of abnormality. In this paper, we describe the development of a system for computer supported detection (CSD) of schizophrenia using the electroencephalogram (EEG) signal collected with a 19‐channel electrode array. Schizophrenia is a mental illness that interferes with the way an individual thinks and behaves. It is characterised by psychotic symptoms such as hallucinations or delusions, negative symptoms such as decreased motivation or a lack of interest in daily activities and cognitive symptoms such challenges in processing information to make informed decisions or staying focused. This research has utilized 1142 EEGs (516 normal and 626 schizophrenia) with a frame length of 25 s (6250 samples) for investigation. The work initially converts the EEG signals to images using a spectrogram. Local configuration pattern features were extracted from the images thereafter, and 10‐fold validation technique was used wherein Student's t‐test and z‐score standardization were computed per fold. The highest accuracy of 97.20% was achieved with the K‐nearest neighbour (KNN) classifier. The results obtained confirm that the KNN classifier is helpful in the rapid detection of schizophrenia. This work is one of the first studies to extract local configuration pattern features from spectrogram images, yielding a high accuracy of 97.20%, with reduced computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control. Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization. [ABSTRACT FROM AUTHOR]

The dynamics of a city's structures are determined by the coupling of functional components (such as restaurants) and human population. Yet, there lacks mechanism models to quantify the forces on the spatial distribution of the components. Here, a gradient model is explored to simulate the individual density curves of multiple types of city functional components based on the equilibria of gravitational and repulsive forces along with the urban–rural gradient. The model is concise by relying on four key variables, the attributes of components include net ecosystem service (m) and environmental index (γ); and the attributes of cities include land rent exponent (σ) and population attenuation coefficient (β). The model has been used to simulate the distribution curves of 22 types of components on the urban–rural gradients in 13 cities in two periods. The model reveals a bottom‐up mechanism that the patterns of the components in a city are determined by the economic, ecological, and social attributes of both cities and components. Strongly backed by empirical data, the model can predict the distribution curves of many types of components along with the development of cities. This model provides a general tool for analyzing the distribution of multiple objects on the gradients. [ABSTRACT FROM AUTHOR]

The structure and "metabolism" (movement and conversion of goods and energy) of urban areas has caused cities to be identified as "super‐organisms", placed between ecosystems and the biosphere, in the hierarchy of living systems. Yet most such analogies are weak, and render the super‐organism model ineffective for sustainable development of cities. Via a cluster analysis of 15 shared traits of the hierarchical living system, we found that industrialized cities are more similar to eukaryotic cells than to multicellular organisms; enclosed systems, such as factories and greenhouses, paralleling organelles in eukaryotic cells. We further developed a "super‐cell" industrialized city model: a "eukarcity" with citynucleus (urban area) as a regulating centre, and organaras (enclosed systems, which provide the majority of goods and services) as the functional components, and cityplasm (natural ecosystems and farmlands) as the matrix. This model may improve the vitality and sustainability of cities through planning and management. [ABSTRACT FROM AUTHOR]

In game theory, Parrondo's paradox describes the possibility of achieving winning outcomes by alternating between losing strategies. The framework had been conceptualized from a physical phenomenon termed flashing Brownian ratchets, but has since been useful in understanding a broad range of phenomena in the physical and life sciences, including the behavior of ecological systems and evolutionary trends. A minimal representation of the paradox is that of a pair of games played in random order; unfortunately, closed‐form solutions general in all parameters remain elusive. Here, we present explicit solutions for capital statistics and outcome conditions for a generalized game pair. The methodology is general and can be applied to the development of analytical methods across ratchet‐type models, and of Parrondo's paradox in general, which have wide‐ranging applications across physical and biological systems. [ABSTRACT FROM AUTHOR]

COVID‐19, also known as SARS‐CoV‐2, is a coronavirus that is highly pathogenic and virulent. It spreads very quickly through close contact, and so in response to growing numbers of cases, many countries have imposed lockdown measures to slow its spread around the globe. The purpose of a lockdown is to reduce reproduction, that is, the number of people each confirmed case infects. Lockdown measures have worked to varying extents but they come with a massive price. Nearly every individual, community, business, and economy has been affected. In this paper, switching strategies that take into account the total "cost" borne by a community in response to COVID‐19 are proposed. The proposed cost function takes into account the health and well‐being of the population, as well as the economic impact due to the lockdown. The model allows for a comparative study to investigate the effectiveness of various COVID‐19 suppression strategies. It reveals that both the strategy to implement a lockdown and the strategy to maintain an open community are individually losing in terms of the total "cost" per day. However, switching between these two strategies in a certain manner can paradoxically lead to a winning outcome—a phenomenon attributed to Parrondo's paradox. [ABSTRACT FROM AUTHOR]

The 2019 coronavirus (COVID‐19), also known as SARS‐CoV‐2, is highly pathogenic and virulent, and it spreads very quickly through human‐to‐human contact. In response to the growing number of cases, governments across the spectrum of affected countries have adopted different strategies in implementing control measures, in a hope to reduce the number of new cases. However, 5 months after the first confirmed case, countries like the United States of America (US) seems to be heading towards a trajectory that indicates a health care crisis. This is in stark contrast to the downward trajectory in Europe, China, and elsewhere in Asia, where the number of new cases has seen a decline ahead of an anticipated second wave. A data‐driven approach reveals three key strategies in tackling COVID‐19. Our work here has definitively evaluated these strategies and serves as a warning to the US, and more importantly, a guide for tackling future pandemics. Also see the video abstract here https://youtu.be/gPkCi2_7tWo [ABSTRACT FROM AUTHOR]

In this work, the cooperation problem between two populations in a periodically varying environment is discussed. In particular, the two‐population prisoner's dilemma game with periodically oscillating payoffs is discussed, such that the time‐average of these oscillations over the period of environmental variations vanishes. The possible overlaps of these oscillations generate completely new dynamical effects that drastically change the phase space structure of the two‐population evolutionary dynamics. Due to these effects, the emergence of some level of cooperators in both populations is possible under certain conditions on the environmental variations. In the domain of stable coexistence the dynamics of cooperators in each population form stable cycles. Thus, the cooperators in each population promote the existence of cooperators in the other population. However, the survival of cooperators in both populations is not guaranteed by a large initial fraction of them. [ABSTRACT FROM AUTHOR]

Recent studies suggest that the tetracycline antibiotic minocycline, or its cousins, hold therapeutic potential for affective and psychotic disorders. This is proposed on the basis of a direct effect on microglia‐mediated frontocortical synaptic pruning (FSP) during adolescence, perhaps in genetically susceptible individuals harboring risk alleles in the complement component cascade that is involved in this normal process of CNS circuit refinement. In reviewing this field, it is argued that minocycline is actually probing and modulating a deeply evolved and intricate system wherein psychosocial stimuli sculpt the circuitry of the "social brain" underlying adult behavior and personality. Furthermore, this system can generate psychiatric morbidity that is not dependent on genetic variation. This view has important ramifications for understanding "pathologies" of human social behavior and cognition as well as providing long‐sought potential mechanistic links between social experience and susceptibility to mental and physical disease. [ABSTRACT FROM AUTHOR]

It has been shown that the tumor population growth dynamics in a periodically varying environment can drastically differ from the one in a fixed environment. Thus, the environment of a tumor can potentially be manipulated to suppress cancer progression. Diverse evolutionary processes play vital roles in cancer progression and accordingly, understanding the interplay between these processes is essential in optimizing the treatment strategy. Somatic evolution and genetic instability result in intra‐tumor cell heterogeneity. Various models have been developed to analyze the interactions between different types of tumor cells. Here, models of density‐dependent interaction between different types of tumor cells under fast periodical environmental changes are examined. It is illustrated that tumor population densities, which vary on a slow time scale, are affected by fast environmental variations. Finally, the intriguing density‐dependent interactions in metastatic castration‐resistant prostate cancer (mCRPC) in which the different types of tumor cells are defined with respect to the production of and dependence on testosterone are discussed. [ABSTRACT FROM AUTHOR]

Many predators produce dormant offspring to escape harsh environmental conditions, but the evolutionary stability of this adaptation has not been fully explored. Like seed banks in plants, dormancy provides a stable competitive advantage when seasonal variations occur, because the persistence of dormant forms under harsh conditions compensates for the increased cost of producing dormant offspring. However, dormancy also exists in environments with minimal abiotic variation—an observation not accounted for by existing theory. Here it is demonstrated that dormancy can out‐compete perennial activity under conditions of extensive prey density fluctuation caused by overpredation. It is shown that at a critical level of prey density fluctuations, dormancy becomes an evolutionarily stable strategy. This is interpreted as a manifestation of Parrondo's paradox: although neither the active nor dormant forms of a dormancy‐capable predator can individually out‐compete a perennially active predator, alternating between these two losing strategies can paradoxically result in a winning strategy. Parrondo's paradox may thus explain the widespread success of quiescent behavioral strategies such as dormancy, suggesting that dormancy emerges as a natural evolutionary response to the self‐destructive tendencies of overpredation and related biological phenomena. [ABSTRACT FROM AUTHOR]

As the world struggles through the COVID‐19 pandemic, we should also be asking what systems‐level measures will be needed to prevent this or even worse disasters from happening in the future. We argue that the pandemic is merely one of potentially myriad and pleiomorphic future global disasters generated by the same underlying dynamical system. We explain that there are four broad but easily identifiable systemic, pathologically networked conditions that are hurtling civilization toward potential self‐destruction. As long as these conditions are not resolved, we should consider catastrophe as an inevitable emergent endpoint from the dynamics. All four conditions can be reversed with collective action to begin creating an enduring and thriving post‐ COVID‐19 world. This will require maximal application of the precautionary principle. [ABSTRACT FROM AUTHOR]

Parrondo's paradox, in which losing strategies can be combined to produce winning outcomes, has received much attention in mathematics and the physical sciences; a plethora of exciting applications has also been found in biology at an astounding pace. In this review paper, the authors examine a large range of recent developments of Parrondo's paradox in biology, across ecology and evolution, genetics, social and behavioral systems, cellular processes, and disease. Intriguing connections between numerous works are identified and analyzed, culminating in an emergent pattern of nested recurrent mechanics that appear to span the entire biological gamut, from the smallest of spatial and temporal scales to the largest—from the subcellular to the complete biosphere. In analyzing the macro perspective, the pivotal role that the paradox plays in the shaping of biological life becomes apparent, and its identity as a potential universal principle underlying biological diversity and persistence is uncovered. Directions for future research are also discussed in light of this new perspective. [ABSTRACT FROM AUTHOR]