Dynamic Adaptation Research Articles

Towards a Verifiable Toolchain for Robotics

There is a growing need for autonomous robots to complete complex tasks robustly in dynamic and unstructured environments. However, current robot performance is limited to simple tasks in controlled environments. To improve robot autonomy in complex environments, the robot's deliberation system must be able to synthesise correct plans for a task and generate contingency plans for handling anomalous scenarios that were not expected at design time. The robustness of such a system can be quantified using techniques for formal verification and validation. This paper outlines the progress of EU project CONVINCE (CONtext-aware Verifiable and adaptIve dyNamiC dEliberation), which aims to develop a software toolchain that aids developers in designing, developing, and deploying robot deliberation systems that are fully verified. We describe our modelling approach, each of the toolchain components, and how they interact. We also discuss survey results which demonstrate the demand for a verifiable toolchain among the robotics community.

How host-microbiome/holobiont evolution depends on whether the microbiome affects host lifespan or fecundity

Abstract There is overwhelming evidence that the microbiome can be important to host physiology and fitness. As such, there is interest in and some theoretical work on understanding when hosts and microbiomes (co)evolve so that microbes benefit hosts and hosts favour beneficial microbes. However, the outcome of evolution likely depends on how microbes benefit hosts. Here, we use adaptive dynamics to investigate how host and symbiont evolution depend on whether symbionts increase host lifespan or host reproduction in a simple model of host and symbiont dynamics. In addition, we investigate 2 ways hosts release (and transmit) symbionts: by releasing symbionts steadily during their lifetime or by releasing them at reproduction, potentially increasing symbionts’ chances of infecting the host’s offspring. The former is strict horizontal transmission, whereas the latter is also a form of indirect or “pseudovertical” transmission. Our first key result is that the evolution of symbionts that benefit host fecundity requires pseudovertical transmission, while the evolution of symbionts that benefit host lifespan does not. Furthermore, our second key result is that when investing in host benefits is costly to the free-living symbiont stage, intermediate levels of pseudovertical transmission are needed for selection to favour beneficial symbionts. This is true regardless of fitness effects because release at reproduction increases the free-living symbiont population, which increases competition for hosts. Consequently, hosts could evolve away from traits that favour beneficial symbionts. Generally, our work emphasizes the importance of different forms of vertical transmission and fitness benefits in host, microbiome, and holobiont evolution as highlighted by our prediction that the evolution of fecundity-increasing symbionts requires parent-to-offspring transmission.

Dynamic Adaptability and Prediction Optimization in Power Material Supply Chains Using XGBoost

Dynamic Adaptability and Prediction Optimization in Power Material Supply Chains Using XGBoost

Dynamical theory for adaptive systems

Abstract The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former’s dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

Genome Dynamics of Adaptation and Introgression in Neotropical Palms

ABSTRACTAimThe combined influences of demographic dynamics and gene flow on local adaptation in plants is still poorly understood. Here, we used a genome scan approach on three closely related Neotropical palms, Acrocomia aculeata, A. intumescens and A. totai, to identify the evolutionary processes generating shared and lineage‐specific patterns of differentiation and selection across the genomic landscape.LocationAmazonia, Atlantic Forest, Cerrado and Caatinga ecoregions of Brazil.TaxaArecaceae.MethodsWe used target sequence capture and analysis of climatic correlation, detection of selective sweeps, balancing selection, and spatial and non‐spatial models to identify signatures of natural selection and admixture. We also determined temporal dynamics in spatial distribution and demographic changes.ResultsWe found a higher number of lineage‐specific than shared adaptive sites (SNPs) and no evidence of selective sweeps in shared genes, suggesting lineage specific natural selection across species. Further, evidence of balancing selection in several genes was also identified in the three species. Niche‐based and coalescent models suggest that shifts in spatial range during the Quaternary caused overlapping distributions between species, leading to hybridisation between parapatric localities.Main ConclusionsInterspecific hybridisation may have spread both neutral and adaptive SNPs, which may explain the shared adaptive genes between species. Taken together, we show how genomic adaptation can occur despite introgression, through evolutionary processes that likely drive similar patterns of adaptation in other organisms.

CSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization

With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep learning. Despite achieving high detection accuracy on intra-datasets, these models lack generalization ability when applied to cross-datasets. We propose a deepfake detection model named Channel-Spatial-Triplet Attention Network (CSTAN), which focuses on the difference between real and fake features, thereby enhancing the generality of the detection model. To enhance the feature-learning ability of the model for image forgery regions, we have designed the Channel-Spatial-Triplet (CST) attention mechanism, which extracts subtle local information by capturing feature channels and the spatial correlation of three different scales. Additionally, we propose a novel feature extraction method, OD-ResNet-34, by embedding ODConv into the feature extraction network to enhance its dynamic adaptability to data features. Trained on the FF++ dataset and tested on the Celeb-DF-v1 and Celeb-DF-v2 datasets, the experimental results show that our model has stronger generalization ability in cross-datasets than similar models.

Genetically Engineered Liposwitch-Based Nanomaterials.

Fusion of intrinsically disordered and globular proteins is a powerful strategy to create functional nanomaterials. However, the immutable nature of genetic encoding restricts the dynamic adaptability of nanostructures postexpression. To address this, we envisioned using a myristoyl switch, a protein that combines allostery and post-translational modifications─two strategies that modify protein properties without altering their sequence─to regulate intrinsically disordered protein (IDP)-driven nanoassembly. A typical myristoyl switch, allosterically activated by a stimulus, reveals a sequestered lipid for membrane association. We hypothesize that this conditional exposure of lipids can regulate the assembly of fusion proteins, a concept we term "liposwitching". We tested this by fusing recoverin, a calcium-dependent myristoyl switch, with elastin-like polypeptide, a thermoresponsive model IDP. Biophysical analyses confirmed recoverin's myristoyl-switch functionality, while dynamic light scattering and cryo-transmission electron microscopy showed distinct calcium- and lipidation-dependent phase separation and assembly. This study highlights liposwitching as a viable strategy for controlling DP-driven nanoassembly, enabling applications in synthetic biology and cellular engineering.

A Theoretical Design Framework of Contemporary Vernacular Architecture Based on a Scoping Review of the Best Practices Worldwide

This paper reviews the development of vernacular architecture design and practices worldwide and then focuses on Chinese rural development. It explores the evolution of vernacular architecture and related concepts from a longitudinal perspective, and indicates the dynamic adaptability of vernacular architecture. A scoping review based on archival materials and secondary source literature of the last 10 years is conducted. As a result, 41 design factors have been collected. With a cross-comparative study with related publications, five intangible design factors and ten key tangible design factors are discussed to build a theoretical design framework, which finally provides a foundation for related design practices and further research.

Cooperative control of environmental extremes by artificial intelligent agents.

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging, given the potentially large number of individual and environmental variables involved. This article shows how to address this problem by using fire as the source of extreme events. We implement a simulated environment where fire propagates on a spatial landscape, and a group of artificial agents learn how to harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential artificial intelligence management of complex ecosystems are discussed.

Dynamic Event-Triggered Fuzzy Adaptive Resilient Consensus Control for Nonlinear MASs Under DoS Attacks

Dynamic Event-Triggered Fuzzy Adaptive Resilient Consensus Control for Nonlinear MASs Under DoS Attacks

Inverse stochastic resonance in adaptive small-world neural networks.

Inverse stochastic resonance (ISR) is a counterintuitive phenomenon where noise reduces the oscillation frequency of an oscillator to a minimum occurring at an intermediate noise intensity, and sometimes even to the complete absence of oscillations. In neuroscience, ISR was first experimentally verified with cerebellar Purkinje neurons [Buchin et al., PLOS Comput. Biol. 12, e1005000 (2016)]. These experiments showed that ISR enables a locally optimal information transfer between the input and output spike train of neurons. Subsequent studies have further demonstrated the efficiency of information processing and transfer in neural networks with small-world network topology. We have conducted a numerical investigation into the impact of adaptivity on ISR in a small-world network of noisy FitzHugh-Nagumo (FHN) neurons, operating in a bi-metastable regime consisting of a metastable fixed point and a metastable limit cycle. Our results show that the degree of ISR is highly dependent on the value of the FHN model's timescale separation parameter ε. The network structure undergoes dynamic adaptation via mechanisms of either spike-time-dependent plasticity (STDP) with potentiation-/depression-domination parameter P or homeostatic structural plasticity (HSP) with rewiring frequency F. We demonstrate that both STDP and HSP amplify the effect of ISR when ε lies within the bi-stability region of FHN neurons. Specifically, at larger values of ε within the bi-stability regime, higher rewiring frequencies F are observed to enhance ISR at intermediate (weak) synaptic noise intensities, while values of P consistent with depression-domination (potentiation-domination) consistently enhance (deteriorate) ISR. Moreover, although STDP and HSP control parameters may jointly enhance ISR, P has a greater impact on improving ISR compared to F. Our findings inform future ISR enhancement strategies in noisy artificial neural circuits, aiming to optimize local information transfer between input and output spike trains in neuromorphic systems and prompt venues for experiments in neural networks.

A Deep Reinforcement Learning‐Based Physical Layer Security Enhancement Design for RIS‐Aided mmWave Communications With Practical Constraints

ABSTRACTReconfigurable intelligent surfaces (RIS) offer new opportunities for enhancing security in millimeter‐wave (mmWave) communications. However, some significant practical challenges still need to be addressed before their extensive implementation in future wireless networks. This article considers the practical constraints in a secure mmWave system aided by distributed RISs, including imperfect channel state information (CSI) in dynamic channel conditions and high complexity of non‐convex optimization in complex environments. To address these challenges, we propose a robust and efficient physical layer security (PLS) enhancement algorithm based on the deep reinforcement learning (DRL) framework to effectively tackle the issues of limited dynamic adaptation and high computational complexity encountered with conventional optimization methods. This algorithm, utilizing an actor‐critic architecture, can dynamically track channel variations and optimize strategies for improved system secrecy rate. Numerical simulations demonstrate that the proposed DRL‐based PLS enhancement algorithm outperforms non‐convex optimization benchmarks in robustness and efficiency for secure mmWave communication systems aided by distributed RISs and affected by imperfect CSI.

A Review on Personalization of Gamified Learning Environments*

This study explores the personalization of gamification in educational contexts, particularly adaptive approaches to meet diverse learner needs. The study examines terminologies and offer definitions for adaptive, tailored, personalized, and customized gamification. Personalization serves as an umbrella term covering any changes in the process. Customization is the changes performed by the user/learner; tailored gamification involves adjustments made at the beginning and adaptive gamification involves system-driven changes as things progress. The analysis of adaptive gamification studies is structured around adaptation criteria, adaptation process, adaptation data, and adaptive intervention. Adaptation criteria includes player types, personality models, learning styles, and hybrid profiles. Adaptation process includes user-controlled and system-controlled adaptation. User-controlled adaptation refers to customization; system-controlled adaptation is examined under static adaptation, dynamic adaptation, and combination of these two. Adaptation data is categorized under explicit and implicit information collection. Lastly, recommendations, and adjusting game elements and mechanics are discussed under adaptation intervention. The review highlights the commonly used player typologies, including Bartle, Hexad, and BrainHex, and personality models such as Big Five and Myers-Briggs Type Indicator. The review identifies limitations in current frameworks, stressing the importance of standardized models and guidelines to implement adaptive gamification and incorporating gamification analytics to sustain adaptation and automation.

Humanising the Nonhuman: An Ecocritical Toolbox for Anthropomorphic Agency

Ecocriticism tends to acknowledge anthropomorphisms as a possible tool to create empathy for nonhumans, but in doing so mostly labels said tool as too sentimental for serious environmental literature. This paper aims to establish a categorisation of anthropomorphisms in media that allows a more diverse and detailed analysis of humanised nonhumans. It seeks to overcome the prevailing idea that anthropomorphic descriptions are limited to nonhuman animals and therefore extends the term to the humanisation of anything that is not human. Following the school of thought suggested by new materialism and material ecocriticism, nonhumans are regarded as having agency and anthropomorphising them allows humans to empathise with nonhumans. The categorisation of anthropomorphism proposed here is divided into each three markers and modes. The markers signify which part of the human can be observed in the anthropomorphised subject, while the modes define how this is realised. This article exemplifies the concept of markers and modes through anthropomorphic trees in literature, but as it is not a static concept, it allows for overlaps between categories and dynamic adaptations for other cases of anthropomorphised subjects. The three markers are Physicality, Sentience, and Language and may appear also in combinations. The modes are Projection, Manifestation, and Hybridity. As anthropomorphisms strongly intersect with theories of nonhuman agency, this, too, will be discussed in the final section of this article.

STUDY CONCERNING THE IMPROVEMENT OF SPEED THROUGH BASKETBALL SPECIFIC TRAINING

Background. The major importance of physical education lessons in higher education is achieved through their contribution to maintaining health, to compensating for the increased intellectual efforts generated by passing exams. Basketball training helps in the positive development of the general and specific motor capacity required by the subsequent professional activity, as well as to the achievement of the necessary framework for recreation and the practice of the favorite sport. Therefore, at this level, the achievement of the following objectives will be mainly pursued: – maintaining health and increasing the ability to adapt to changing conditions; – the development of the general motor capacity necessary to carry out the sports activity; – improving the somato-functional indices and optimizing the relationship between them. Physical training is a component of sports training that constitutes the pivot for all other components, constituting the starting point for the entire training process (Ardelean. T, 1996). General physical training has different weightages in training, depending on the experience of the students and for the training of beginner students it is very important that the time allocated is much greater than in the training of students from older years. Objectives. This study aims to find and offer, through specific basketball exercises, how to improve the quality of speed. Speed should be present in the equipment of the "motric luggage" of each student, considering the evolution of our modern society, to dynamic games. This speed quality motor offers several advantages to basketball players: – allows the execution of thechnical and tactic actions in a sustained rhythm. – gives the player a high degree of complexity and dynamic adaptation to playing basketball. – develops the player's thinking, attention, rationality and ability to concentrate. Methods. For this study we used the following research methods: documentation, observation method, statistical method, graphical representation method and experimental method. Results. For checking the motility of students enrolled in educational experiment we chose the following control samples: speed running 15m, speed running 20m and dribbling through cones. Conclusion. The results of this test depend both on the execution speed and the technique in performing the dribbling. We therefore recommend that these results can be improved both by increasing the speed of movement and the execution technique.

Cognitive learning enabled agile optical network

Nonlinear equalization (NLE) is essential for guaranteeing the performance of an optical network (ON). Effective NLE implementation relies on key parameters of the transmission link, including the modulation format (MF) and the launch power. As ONs become more agile, the parameters of fiber optical transmission need to be adaptive and relevant to the routing condition. Therefore, successful NLE implementation relies on the realization of transmission awareness (TA). Although machine learning-enabled optical performance monitoring (OPM) has been extensively investigated in the past few years, current NLE algorithms cannot autonomously perceive transmission parameters. Furthermore, current TA implementation still needs human intervention to guide the NLE. In addition, existing ML-based OPM and NLE cannot be trained autonomously, leading to the incapability of environmental change and mislabeling. Here, we propose cognitive learning (CL) for TA-guided NLE in agile ONs. We perform an experiment involving 32 Gbaud polarization-division-multiplexed (PDM)-quadrature phase shift keying (QPSK)/16-quadrature amplitude modulation (QAM) transmission over 1500 km of standard single-mode fiber (SSMF) with a variable launch power from 0 to 3 dBm. When a deep neural network (DNN) with amplitude histograms (AHs) as inputs and one step per span-learned digital back-propagation (1stps-LDBP) are developed, the CL simultaneously enables both TA and NLE, with the capability of self-learning, mislabeling resistance, and dynamic adaptation. The proof-of-concept experimental results indicate that both the accuracy of TA and the Q-factor of PDM-16QAM can be improved by 34.8% and 0.84 dB, respectively, when the launch power is 3 dBm. Moreover, the accuracy of TA is enhanced by 35.3%, even when the used data has 30% mislabeling. Therefore, the CL framework can be customized to satisfy various NLE implementations, thereby supporting the adaptive transmission of agile ONs.

Diverse prehistoric cattle husbandry strategies in the forests of Central Europe.

During the sixth millennium BCE, the first farmers of Central Europe rapidly expanded across a varied mosaic of forested environments. Such environments would have offered important sources of mineral-rich animal feed and shelter, prompting the question: to what extent did early farmers exploit forests to raise their herds? Here, to resolve this, we have assembled multi-regional datasets, comprising bulk and compound-specific stable isotope values from zooarchaeological remains and pottery, and conducted cross-correlation analyses within a palaeo-environmental framework. Our findings reveal a diversity of pasturing strategies for cattle employed by early farmers, with a notable emphasis on intensive utilization of forests for grazing and seasonal foddering in some regions. This experimentation with forest-based animal feeds by early farmers would have enhanced animal fertility and milk yields for human consumption, concurrently contributing to the expansion of prehistoric farming settlements and the transformation of forest ecosystems. Our study emphasizes the intricate relationship that existed between early farmers and forested landscapes, shedding light on the adaptive dynamics that shaped humans, animals and environments in the past.

Uncovering the dynamics of individual learning and employee adaptability in influencing innovation behavior

The success of organizations in the era of ever-changing business is greatly influenced by the adaptability, creativity, and innovation factors of employees, which are obtained through effective learning. Organizations that implement a learning culture encourage employees to adapt and internalize learning from past experiences. Employees who are active in training become more creative and adaptive, increasing their ability to deal with change. This study aims to explore the relationship between Individual Learning, Employee Adaptability, Employee Creativity, and Employee Innovation Behavior, especially in the State Civil Apparatus (ASN) in South Kalimantan. Through a quantitative approach with analysis using Partial Least Square (PLS), this study found that individual learning significantly affects employee adaptability and creativity, and contributes to their innovative behavior. The results of the study indicate the need for a longitudinal approach to understand the dynamics of changes in employee adaptability and creativity in the context of public services. This study identifies the importance of supporting factors such as management support and work environment that influence employee innovation.

Laozi’s Influence on Contemporary Rap Music: A Study of Cultural Protection and Inheritance

This study investigates the influence of Laozi’s teachings on contemporary rap music, focusing on themes of cultural protection and inheritance. Through a mixed-methods approach, qualitative and quantitative analyses were conducted to explore the extent of Laozi's influence in rap lyrics. Qualitative analysis involved in-depth examination of lyrical content to identify thematic parallels with Laozi's philosophy, while quantitative analysis quantified instances of Laozi references in selected rap songs. The results reveal a steady increase in Laozi references in rap music over time, with prevalent themes including harmony, self-awareness, and balance. Geographic distribution highlights a global reach of Laozi-inspired rap, transcending cultural boundaries. Collaboration patterns, sentiment analysis, and commercial performance further elucidate the multifaceted relationship between Laozi's teachings and contemporary rap culture. This study contributes to understanding the dynamics of cultural transmission and adaptation in the modern music industry, showcasing the enduring relevance of ancient wisdom in shaping artistic expression.

Analysis of control measures inducing the evolution of infectious viral diseases

With the continuation of the infectious disease, pathogens mutate and interact with the resident pathogen. In this paper, we develop an SIR model that considers the evolution of viral virulence under the regulation of different defense strengths. By using infectious disease dynamics and evolutionary adaptive dynamics, we obtain the evolutionary criteria allowing for evolutionary branching and continuously stable strategy across varying intensities of prevention and control measures. Our study finds that robust prevention and control measures can contribute to a rapid evolutionary trend toward higher virulence. We perform a critical function analysis to reveal the evolutionary consequences of trade-offs of arbitrary shape and analyze a diverse range of evolutionary dynamics. Furthermore, we investigate the coexistence of two different strains and construct an evolutionary model to obtain their invasion fitness functions and the evolutionarily singular coalition of the dimorphism. We contemplate the presence of secondary branching on a significantly extended evolutionary time scale. Last, we conduct the numerical simulations to prove our theoretical findings. This reveals the law that the evolutionary state of pathogens is affected by the intensity of prevention and control strategies. The results and methods can be used as references for studying the effects of other factors on virus evolution.