Dynamic Adaptation Research Articles

Machine Learning Defenses: Exploring the integration of machine learning techniques within CAPTCHA systems to dynamically adjust challenge difficulty and thwart adversarial attacks

The utilization of machine learning (ML) techniques within CAPTCHA systems represents a significant advancement in cybersecurity, offering dynamic adaptability to thwart evolving adversarial attacks. This research delves into the integration of ML defenses within CAPTCHA frameworks, focusing on their efficacy in adjusting challenge difficulty dynamically to counter sophisticated attacks. By leveraging ML algorithms, CAPTCHA systems can analyze user behavior patterns and adaptively tailor challenges to deter automated bots while ensuring user accessibility and engagement. This paper aims to provide a comprehensive investigation into the design, implementation, and evaluation of ML-based CAPTCHA defenses. It explores various ML approaches, including supervised learning, reinforcement learning, and deep learning, in dynamically adjusting challenge complexity based on user interactions and environmental factors. Furthermore, the study delves into the assessment of ML-driven CAPTCHA systems' resilience against adversarial attacks, such as machine learning-based algorithms, optical character recognition (OCR) techniques, and adversarial image manipulation. Through empirical analysis and experimentation, this research endeavors to elucidate the effectiveness and limitations of ML-based CAPTCHA defenses in real-world scenarios. By elucidating the intricacies of integrating ML techniques within CAPTCHA systems, this paper seeks to contribute valuable insights to the field of cybersecurity, offering guidance for the development of robust and adaptive CAPTCHA mechanisms capable of mitigating emerging threats in the digital landscape.

Avian Island Radiations Shed Light on the Dynamics of Adaptive and Nonadaptive Radiation.

Understanding the mechanisms underlying species formation and differentiation is a central goal of evolutionary biology and a formidable challenge. This understanding can provide valuable insights into the origins of the astonishing diversity of organisms living on our planet. Avian evolutionary radiations on islands have long fascinated biologists as they provide the ideal variation to study the ecological and evolutionary forces operating on the continuum between incipient lineages to complete speciation. In this review, we summarize the key insights gained from decades of research on adaptive and nonadaptive radiations of both extant and extinct insular bird species. We present a new comprehensive global list of potential avian radiations on oceanic islands, based on published island species checklists, taxonomic studies, and phylogenetic analyses. We demonstrate that our understanding of evolutionary processes is being greatly enhanced through the use of genomic tools. However, to advance the field, it is critical to complement this information with a solid understanding of the ecological and behavioral traits of both extinct and extant avian island species.

MXene-Enabled Pneumatic Multiscale Shape Morphing for Adaptive, Programmable and Multimodal Radar-infrared Compatible Camouflage.

Achieving radar-infrared compatible camouflage with dynamic adaptability has been a long-sought goal, but faces significant challenges owing to the limited dispersion relations of conventional material systems operating in different wavelength ranges. Here, this work proposes the concept of pneumatic multiscale shape morphing and design a periodically arranged pneumatic unit consisting of MXene-based morphable conductors and intake platforms. During gas actuation, the morphable conductor transforms centimeter-scale 2D flat sheets into 3D balloon shapes to enhance microwave absorption behavior, and also reconfigures micrometer-scale MXene wrinkles into smooth planes in combination with cavity-induced low heat transfer to minimize infrared (IR) signatures. Through theory-guided reverse engineering, the final pneumatic matrix shows remarkable frequency tunability (2.64-18.0 GHz), moderate IR emissivity regulation (0.14 at 7-16.5 µm), rapid responsiveness (≈30 ms), wide-angle operation (>45°), and excellent environmental tolerance. Additionally, the multiplexed pneumatic matrix enables over 14 programmable coding sequences that independently alter thermal radiation without compromising radar stealth, and allows multimodal camouflage switching between three distinct compatible states. The approach may facilitate the evolution of camouflage techniques and electromagnetic functional materials toward multispectral, adaptability and intelligence.

Migrants’ perspectives on environmental change and translocal practices in Morocco, Senegal, and the Democratic Republic of Congo

Abstract This qualitative study takes a translocal perspective by considering migrants’ views on environmental change, migration decisions and translocal practices in favour of environmental adaptation. This article addresses the following question: To what extent do migrants’ perceptions of environmental change in their region of origin influence their translocal practices in favour of adaptation to socio-environmental change? Our data show that while environmental change may not be the primary reason that people migrate, nor do they perceive it as such, their translocal practices may have very concrete impacts in environmentally fragile areas, especially with respect to non-migrants in the place of origin. Most practices (e.g. family economic remittances) are spontaneous and unintentionally adaptive to environmental change. In contrast, collective projects initiated through hometown associations, especially in Senegal and Morocco, often have a more intentional and proactive character, resulting in translocal adaptive socio-environmental dynamics.

Using the pan-genomic framework for the discovery of genomic islands in the haloarchaeon Halorubrum ezzemoulense.

In this study, we use pan-genomics to characterize the genomic variability of the widely dispersed halophilic archaeal species Halorubrum ezzemoulense (Hez). We include a multi-regional sampling of newly sequenced, high-quality draft genomes. The pan-genome graph of the species reveals 50 genomic islands that represent rare accessory genetic capabilities available to members. Most notably, we observe rearrangements that have led to the insertion/recombination/replacement of mutually exclusive genomic islands in equivalent genome positions ("homeocassettes"). These conflicting islands encode for similar functions, but homologs from islands located between the same core genes exhibit high divergence on the amino acid level, while the neighboring core genes are nearly identical. Both islands of a homeocassette often coexist in the same geographic location, suggesting that either island may be beyond the reach of selective sweeps and that these loci of divergence between Hez members are maintained and persist long term. This implies that subsections of the population have different niche preferences and rare metabolic capabilities. After an evaluation of the gene content in the homeocassettes, we speculate that these islands may play a role in the speciation, niche adaptability, and group selection dynamics in Hez. Though homeocassettes are first described in this study, similar replacements and divergence of genes on genomic islands have been previously reported in other Haloarchaea and distantly related Archaea, suggesting that homeocassettes may be a feature in a wide range of organisms outside of Hez.IMPORTANCEThis study catalogs the rare genes discovered in strains of the species Halorubrum ezzemoulense (Hez), an obligate halophilic archaeon, through the perspective of its pan-genome. These rare genes are often found to be arranged on islands that confer metabolic and transport functions and contain genes that have eluded previous studies. The discovery of divergent, but homologous islands occupying equivalent genome positions ("homeocassettes") in different genomes, reveals significant new information on genome evolution in Hez. Homeocassette pairs encode for similar functions, but their dissimilarity and distribution imply high rates of recombination, different specializations, and niche preferences in Hez. The coexistence of both islands of a homeocassette pair in multiple environments demonstrates that both islands are beyond the reach of selective sweeps and that these genome content differences between strains persist long term. The switch between islands through recombination under different environmental conditions may lead to a greater range of niche adaptability in Hez.

Dynamic adaptation of phytoplankton vertical migration to changing grazing and nutrient conditions

AbstractDespite the ubiquitous occurrence of vertical migration of phytoplankton its quantitative significance remains poorly known. We eliminated a dense Daphnia population in a pond by introducing whitefish fingerlings, and assessed the effects on the vertical migration of dominating motile phytoplankton. At the highest abundance of Daphnia, cryptophytes reduced grazing losses by staying in the hypolimnion day and night, but Mallomonas species armoured by silica bristles remained in the epilimnion. After the fish introduction, phytoplankton was released from Daphnia grazing pressure, allowing cryptophytes to occur in the epilimnion also at noon. At the same time, increased phytoplankton biomass exacerbated the nutrient depletion. Cryptophytes compensated for that by migrating into the anoxic hypolimnion, whereupon their growth rates increased. The collapse of Daphnia was also associated with a temporary increase in nutrient regeneration by enzyme activities and decreases in total nutrient concentration and bacterial biomass in the whole water column. Our results show that cryptophytes can dynamically modify their vertical migration to balance between the exploitation of various nutrient resources and the risk of becoming eaten. Hypolimnetic nutrient resources can be quantitatively more important for phytoplankton than previously assumed.

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

In order to obtain more fine-grained information from multiple sub-feature spaces for domain adaptation, this paper proposes a novel multi-representation joint dynamic domain adaptation network (MJDDAN) and applies it to achieve cross-database facial expression recognition. The MJDDAN uses a hybrid structure to extract multi-representation features and maps the original facial expression features into multiple sub-feature spaces, aligning the expression features of the source domain and target domain in multiple sub-feature spaces from different angles to extract features more comprehensively. Moreover, the MJDDAN proposes the Joint Dynamic Maximum Mean Difference (JD-MMD) model to reduce the difference in feature distribution between different subdomains by simultaneously minimizing the maximum mean difference and local maximum mean difference in each substructure. Three databases, including eNTERFACE, FABO, and RAVDESS, are used to design a large number of cross-database transfer learning facial expression recognition experiments. The accuracy of emotion recognition experiments with eNTERFACE, FABO, and RAVDESS as target domains reach 53.64%, 43.66%, and 35.87%, respectively. Compared to the best comparison method chosen in this article, the accuracy rates were improved by 1.79%, 0.85%, and 1.02%, respectively.

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

Cognitive approaches to uniformity and variability in morphology

Abstract This special issue of Cognitive Linguistics reexamines the notions of uniformity and variability within morphological systems from a cognitive linguistic standpoint. It challenges traditional perspectives that regard morphological variability as mere deviations from the norm, suggesting instead that such variability is systematic and shaped by external influences including language acquisition and processing constraints. The contributions in this issue promote a shift from isolated analysis to a holistic view of paradigms, classes, and systems, advocating for a framework where morphological structures are seen as integral to communicative and functional aspects of language. By accounting for the broad adaptive dynamics of language systems, the complex interplay between uniformity and variability is revealed as an inherent aspect of language usage.

AI-Driven Network Security: Innovations in Dynamic Threat Adaptation and Time Series Analysis for Proactive Cyber Defense

AI-Driven Network Security: Innovations in Dynamic Threat Adaptation and Time Series Analysis for Proactive Cyber Defense

Design and development of PI controller for DFIG grid integration using neural tuning method ensembled with dense plexus terminals

In a DFIG grid interconnected system, the control of real and reactive power relies on various factors. This paper presents an approach to regulate the flow of real and reactive power using a Neural Tuning Machine (NTM) based on a recurrent neural network. The focus is on controlling the flow of reactive power from the rotor-side converter, which is proportional to the grid-side controller through a coupling voltage. The proposed NTM method leverages neural networks to fine-tune the parameters of the PI controller, optimizing performance for DFIG grid integration. By integrating dense plexus terminals, also known as dense connections, within the neural network, the control system achieves enhanced adaptability, robustness, and nonlinear dynamics, addressing the challenges of the grid. Grid control actions are based on the voltage profile at different bus locations, thereby regulating voltage. This article meticulously examines the analysis in terms of DFIG configuration and highlights the advantages of the neural tuning machine in controlling inner current loop parameters compared to conventional PI controllers. To demonstrate the robustness of the control algorithm, a MATLAB Simulink model is designed, and validation is conducted with three different benchmarking models. All calculations and results presented in the article strictly adhere to IEEE and IEC standards. This research contributes to advancing control methodologies for DFIG grid integration and lays the groundwork for further exploration of neural tuning methods in power system control.

Auto encoders with Cellular Automata for Anomaly Detection

This work combines auto encoders with cellular automata (CA) to present a novel hybrid strategy for anomaly identification. For feature learning, auto encoders are used to identify spatial patterns in the input data. Simultaneously, temporal and geographical dependencies are captured by CA, which improves the model's capacity to identify complicated anomalies. Training on spatially altered data, the auto encoder-CA hybrid model makes use of CA's temporal evolution to reveal dynamic patterns. Reconstruction errors between the input data and its decoded representation are computed to identify anomalies. A comprehensive framework for anomaly identification is provided by the synergy between spatial-temporal analysis of CA and auto encoder-based feature learning. Performance is optimized by fine-tuning the model's parameters, which include the auto encoder architecture and CA setup. The hybrid model's dynamic adaptation ensures robustness over time by accommodating changing data distributions. Evaluation measures show how well the suggested method captures abnormalities that appear in both temporal and geographical dimensions. A promising method for identifying abnormalities in complicated datasets with detailed spatial and temporal patterns is presented by this novel combination of auto encoders and cellular automata. The proposed method is evaluated with various parameters like reconstruction error, precision , recall, F1 score and Area Under the Receiver Operating Characteristic (ROC-AUC). The average accuracy is reported as 97.36% which is promising when compared with baseline methods.

Dynamic Ecosystem Adaptation through Allostasis (DEA-A) Model: Conceptual Presentation of an Integrative Theoretical Framework for Global Health Change

Achieving ambitious goals in Global Health first requires an integrative understanding of how individuals and organizations adapt in a living ecosystem. The absence of a unified framework limits the consideration of the issues in their complexity, which further complicates the planning of Global Health programs aimed at articulating population-based prevention and individual-level (clinical) interventions. The aim of the conceptual contribution is to propose such a model. It introduces the Dynamic Ecosystem of Adaptation through Allostasis (DEA-A) theoretical framework, emphasizing the functional adaptation of individuals and organizations in symbiosis with their living ecosystem. The DEA-A framework articulates two central components to grasp the complexity of adaptation: the internal dynamics (intrasystem level) and the environmental dynamics (ecosystem level). It bridges diverse conceptual approaches, including stress and adaptation models, behavior-change models, and ecosystem-based perspectives. Epistemological considerations raised in the conceptual article prompt a reconsideration of methods and tools for the planning of intervention. Further contributions will present a suitable methodology for the application of the DEA-A framework along with practical recommendations.

Microbial evolution-An under-appreciated driver of soil carbon cycling.

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Full wave modeling of radio-frequency beams in tokamaks in the electron cyclotron frequency range

Simulation of full wave, without paraxial approximation, high-resolution solution of wave equations in frequency domain in the electron cyclotron resonance (ECR) frequency range for realistic Tokamak plasma parameters became possible by using recently formulated hybrid iterative algorithm [Svidzinski et al., Phys. Plasmas 25, 082509 (2018)] for numerically solving discretized wave equations. This approach combines time evolution and iterative relaxation techniques into iteration cycles. This algorithm is implemented in 2D code FullWave, solving wave equations in Tokamaks in cold and hot plasma models, and it has been tested in 3D full wave iterative RF beams simulation tool, which is presently being developed to model 3D ECRH RF beams in fusion devices using dynamic grid adaptation. The results of 2D full wave modeling, assuming specified toroidal mode number, of ECRH RF beams in DIII-D plasma, performed in the cold and hot plasma models for outboard and top launch scenarios using FullWave are presented. Nonlocal hot plasma response model, based on accurate numerical solution of linearized Vlasov equation, is used to model beam propagation and absorption in the 2nd electron cyclotron harmonic region. Demonstration of capability of the hybrid iterative algorithm to model ECRH RF beams in 3D is made by simulating a substantial part of realistic beam in DIII-D, launched from outboard side of the machine. All relevant physics of RF beam propagation, most of which is not captured in paraxial approximation, such as beam's divergence, interference between the X and O modes in the beam, X-O mode conversion, beam splitting into the X and O mode beams, transformation of beam's cross section, and absorption at the 2nd electron cyclotron harmonic, is captured in the simulations. A numerical technique to find an optimal beam polarization at the launcher to launch a nearly pure X or O mode beam in plasma is developed and tested.

Auto encoders with Cellular Automata for Anomaly Detection

This work combines auto encoders with cellular automata (CA) to present a novel hybrid strategy for anomaly identification. For feature learning, auto encoders are used to identify spatial patterns in the input data. Simultaneously, temporal and geographical dependencies are captured by CA, which improves the model's capacity to identify complicated anomalies. Training on spatially altered data, the auto encoder-CA hybrid model makes use of CA's temporal evolution to reveal dynamic patterns. Reconstruction errors between the input data and its decoded representation are computed to identify anomalies. A comprehensive framework for anomaly identification is provided by the synergy between spatial-temporal analysis of CA and auto encoder-based feature learning. Performance is optimized by fine-tuning the model's parameters, which include the auto encoder architecture and CA setup. The hybrid model's dynamic adaptation ensures robustness over time by accommodating changing data distributions. Evaluation measures show how well the suggested method captures abnormalities that appear in both temporal and geographical dimensions. A promising method for identifying abnormalities in complicated datasets with detailed spatial and temporal patterns is presented by this novel combination of auto encoders and cellular automata. The proposed method is evaluated with various parameters like reconstruction error, precision , recall, F1 score and Area Under the Receiver Operating Characteristic (ROC-AUC). The average accuracy is reported as 97.36% which is promising when compared with baseline methods.

Information entropy analysis of the relation between climate and thermal adaptation: A case study in hot summer and cold winter region of China

The shift of paradigm from reductionist to holism in the field of thermal adaptation promoted a boom in thermal adaptation theory and provided a systemic perspective on this issue. The thermal adaptation theory confronts twin difficulties of scale and complexity. The complexity and entropy of occupants' thermal adaptation emerging. This paper aims to investigate the information entropy exchange in the coupled climate-building-occupants system. First, we justified that the building-occupants system is an open system and exchanges energy, matter, and entropy with its surrounding climate system. Then, correlation analysis results suggested that climate entropy could influence thermal adaptation entropy. Thermal adaptation entropy fluctuated with climate entropy and the difference varied between −0.5 and 0.5 below 31 °C. A generalized entropy balance model was proposed for quantifying the entropy exchange among coupled climate-building-occupants systems for both free-running and air-conditioning buildings. The quantitative relation between the entropy of thermal adaptation and H_DTR reveals the dynamic occupant's thermal adaptation under the process of entropy exchange with the climate system. The H_DTR and entropy relation models provide a new perspective on thermal adaptation coupled with climate beyond outdoor daily mean temperatures. Control strategies that integrate the dynamic characteristics of DTR can bring more economical, energy-efficient and comfortable effects to the operation of air conditioning or heating systems. Our findings shed some light on the dynamical and complex thermal adaptation. In further studies, more results and methods from meteorological research could be used for thermal adaptation research and interdisciplinary theories could be adopted to help us better understand occupants' thermal adaptation.

Syncretism and Identity: The Integration of Lutheranism among the Mari People of Bashkortostan, Russia

Purpose of the Study: This study explores the intricate process of religious syncretism and identity transformation among the Mari people of Bashkortostan through the integration of Lutheranism into their traditional and Orthodox Christian practices. Methodology: Employing a mixed-methods approach, this research combines qualitative insights from field observations, interviews, and sociological surveys with quantitative data analysis to examine the impact of Lutheranism on the Mari's ethnic identity, cultural adaptation, and social dynamics. Main Findings: The findings reveal that culturally sensitive missionary strategies, including the use of the Mari language and incorporation of ethnic cultural elements into Lutheran worship, have facilitated the acceptance and integration of Lutheranism among the Mari, leading to the formation of a novel religious identity. This identity reflects a dynamic synthesis of traditional Mari beliefs and Lutheranism, embodying the broader phenomena of adaptation and resistance in a globalizing world. Applications of this Study: This study offers insights into the complexity of religious evolution and the role of syncretism in preserving cultural heritage amidst modern challenges. Novelty/Originality of this Study: The study contributes to the discourse on religious syncretism, cultural adaptation, and the resilience of ethnic identities, offering insights into the complexities of religious evolution and the role of syncretism in preserving cultural heritage amidst modern challenges. The research underscores the significance of intercultural dialogue and the adaptability of religious practices, highlighting the potential for mutual enrichment between global religious traditions and local cultural contexts.

Two Phylogenetic Cohorts of the Nucleocapsid Protein NP and Their Correlation with the Host Range of Influenza A Viruses.

Influenza A virus has a wide natural areal among birds, mammals, and humans. One of the main regulatory adaptors of the virus host range is the major NP protein of the viral nucleocapsid. Phylogenetic analysis of the NP protein of different viruses has revealed the existence of two phylogenetic cohorts in human influenza virus population. Cohort I includes classical human viruses that caused epidemics in 1957, 1968, 1977. Cohort II includes the H1N1/2009pdm virus, which had a mixed avian-swine origin but caused global human pandemic. Also, the highly virulent H5N1 avian influenza virus emerged in 2021 and caused outbreaks of lethal infections in mammals including humans, appeared to have the NP gene of the second phylogenetic cohort and, therefore, by the type of adaptation to human is similar to the H1N1/2009pdm virus and seems to possess a high epidemic potential for humans. The data obtained shed light on pathways and dynamics of adaptation of avian influenza viruses to humans and propose phylogenetic algorithm for systemic monitoring of dangerous virus strains to predict epidemic harbingers and take immediate preventive measures.

Scalable implicit solvers with dynamic mesh adaptation for a relativistic drift-kinetic Fokker–Planck–Boltzmann model

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker–Planck operator for small-angle Coulomb collisions, a radiation damping operator, and a secondary knock-on (Boltzmann) collision source. We develop a new scalable fully implicit solver utilizing finite volume and conservative finite difference schemes and dynamic mesh adaptivity. A new data management framework in the PETSc library based on the p4est library is developed to enable simulations with dynamic adaptive mesh refinement (AMR), distributed memory parallelization, and dynamic load balancing of computational work. This framework and the runaway electron solver building on the framework are able to dynamically capture both bulk Maxwellian at the low-energy region and a runaway tail at the high-energy region. To effectively capture features via the AMR algorithm, a new AMR indicator prediction strategy is proposed that is performed alongside the implicit time evolution of the solution. This strategy is complemented by the introduction of computationally cheap feature-based AMR indicators that are analyzed theoretically. Numerical results quantify the advantages of the prediction strategy in better capturing features compared with nonpredictive strategies; and we demonstrate trade-offs regarding computational costs. The robustness with respect to model parameters, algorithmic scalability, and parallel scalability are demonstrated through several benchmark problems including manufactured solutions and solutions of different physics models. We focus on demonstrating the advantages of using implicit time stepping and AMR for runaway electron simulations.