Dynamic Adaptation Research Articles

Structural damage identification based on Wasserstein Generative Adversarial Network with gradient penalty and dynamic adversarial adaptation network

Transfer learning-based damage identification is widely explored as it can utilize prior damage knowledge from finite element model (FEM) to identify damage without real structure damage labels. However, lacking a substantial amount of real structure data and overlooking the variances in conditional (local) distributions between source domain (data generated by FEM) and target domain (real structure data) severely restrict its practical application. This paper proposes a transfer learning-based damage identification method by combining Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and Dynamic Adversarial Adaptation Network (DAAN). Firstly, WGAN-GP generative model is employed to augment real structure samples to address the issue of insufficient data. Then, an optimization transfer model with the capability of balancing the importance of marginal and conditional distributions between FEM data and real structure data is proposed based on DAAN transfer model. It dynamically learns damage-invariant features for damage identification. Thirdly, damage identification procedures are provided by combining the generative model WGAN-GP and the transfer model DAAN with moving load induced responses as inputs. Numerical and experimental examples indicate that the proposed method achieves superior transfer effect with limited real structure data and maintains high damage identification accuracy compared to the traditional transfer learning-based methods.

Modular Assembly of Acridines by Integrating Photo-Excitation of o-Alkyl Nitroarenes with Copper-Promoted Cascade Annulation.

Acridine frameworks stand as pivotal architectural elements in pharmaceuticals and photocatalytic applications, owing to their chemical adaptability, biological activity, and unique excited-state dynamics. Conventional synthetic routes often entail specialized starting materials, anaerobic or moisture-free conditions, and elaborate multi-stage manipulations for incorporating diverse functionalities. Herein, we present a convergent approach integrating photo-excitation of readily available ortho-alkyl nitroarenes with copper-promoted cascade annulation. This innovative system enables an aerobic, one-pot reaction of o-alkyl nitroarenes with arylboronic acids, thereby streamlining the modular construction of a wide array of acridine derivatives with various functional groups. This encompasses symmetrical, unsymmetrical and polysubstituted varieties, some of which are otherwise exceptionally difficult to synthesize. Furthermore, it significantly improves the production of structurally varied acridinium salts, featuring enhanced photophysical properties, high excited state potentials (E*red=2.08-3.15 V), and exhibiting superior performance in intricate photoredox transformations.

(Epi)mutation Rates and the Evolution of Composite Trait Architectures.

AbstractMutation rates vary widely along genomes and across inheritance systems. This suggests that complex traits-resulting from the contributions of multiple determinants-might be composite in terms of the underlying mutation rates. Here we investigate through mathematical modeling whether such a heterogeneity may drive changes in a trait's architecture, especially in fluctuating environments, where phenotypic instability can be beneficial. We first identify a convexity principle related to the shape of the trait's fitness function, setting conditions under which composite architectures should be adaptive or, conversely and more commonly, should be selected against. Simulations reveal, however, that applying this principle to realistic evolving populations requires taking into account pervasive epistatic interactions that take place in the system. Indeed, the fate of a mutation affecting the architecture depends on the (epi)genetic background, which itself depends on the current architecture in the population. We tackle this problem by borrowing the adaptive dynamics framework from evolutionary ecology-where it is routinely used to deal with such resident/mutant dependencies-and find that the principle excluding composite architectures generally prevails. Yet the predicted evolutionary trajectories will typically depend on the initial architecture, possibly resulting in historical contingencies. Finally, by relaxing the large population size assumption, we unexpectedly find that not only the strength of selection on a trait's architecture but also its direction depend on population size, revealing a new occurrence of the recently identified phenomenon coined "sign inversion."

Links between Innate and Adaptive Immunity Can Favor Evolutionary Persistence of Immunopathology.

Immunopathology, or the harm caused to an organism's own tissues during the activation of its immune system, carries substantial costs. Moreover, avoiding this self-harm may be an important mechanism underlying tolerance of infection, helping to reducing fitness costs without necessarily clearing parasites. Despite the apparent benefits of minimizing immunopathology, such damage persists across a range of host species. Prior work has explored a trade-off with resistance during a single infection as a potential driver of this persistence, with some collateral damage being unavoidable when killing parasites. Here, we present an additional trade-off that could favor the continued presence of immunopathology: robust immune responses during initial infection (e.g., innate immunity in vertebrates) can induce stronger memory (adaptive immunity), offering protection from future infections. We explore this possibility in an adaptive dynamics framework, using theoretical models parameterized from an ecologically relevant host-parasite system, house finches (Haemorhous mexicanus) infected with the bacterial pathogen, Mycoplasma gallisepticum. We find that some degree of immunopathology is often favored when immunopathology during first infection either reduces susceptibility to or enhances recovery from second infection. Further, interactions among factors like transmission rate, recovery rate, background mortality, and pathogen virulence also shape these evolutionary dynamics. Most notably, the evolutionary stability of investment in immunopathology is highly dependent upon the mechanism by which hosts achieve secondary protection (susceptibility vs. recovery), with the potential for abrupt evolutionary shifts between high and low investment under certain conditions. These results highlight the potential for immune memory to play an important role in the evolutionary persistence of immunopathology and the need for future empirical research to reveal the links between immunopathology during initial infections and longer-term immune protection.

Leeches Predate on Fast-Escaping and Entangling Blackworms by Spiral Entombment.

We investigate how the Helobdella sp. freshwater leeches capture and consume Lumbriculus variegatus blackworms despite the blackworm's ultrafast helical swimming escape reflex and ability to form large tangled "blobs". We describe a spiral "entombment" predation strategy, where Helobdellid leeches latch onto blackworms with their anterior sucker and envelop them in a spiral cocoon. Quantitative analysis shows that larger leeches succeed more often in entombing prey, while longer worms tend to escape. The rate of spiral contraction correlates with entombment outcomes, with slower rates associated with success. These insights highlight the complex interactions between predator and prey in freshwater ecosystems, providing new perspectives on ecological adaptability and predator-prey dynamics.

Saung Angklung Udjo's Strategy For Preserving Culture And Increasing Domestic Tourist Interest

Saung Angklung Udjo (SAU) employs various strategies to preserve angklung art and attract domestic tourists. This research explores SAU's methods, including adapting to consumer behavior, developing new business models, promoting via social media, collaborating with diverse parties, and providing scholarships. SAU innovates with educational programs and music therapy, enhancing engagement through digital promotions and expanding cultural reach through partnerships. Dynamic performance adaptations and involving children are key in attracting audiences and ensuring cultural preservation. Consequently, SAU successfully preserves angklung art while introducing cultural values to the younger generation and broader community through sustainable business strategies.

Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis

Monitoring the health of drilling pumps and diagnosing faults is crucial for the smooth operation of oil drilling activities. However, existing deep learning algorithms struggle with varying speed conditions. To address this, we propose a method that combines physics-driven feature alignment with Dynamic Distribution Adaptation (DDA) for diagnosing drilling pump faults across varying speeds. Our physics-driven feature alignment method reduces discrepancies between samples by aligning amplitude values, angular sampling frequencies, and impulse phases of signals. DDA further improves cross-domain feature matching by dynamically adjusting marginal and conditional distributions during domain adaptation. Experiments on a three-cylinder drilling pump platform under varying speed conditions demonstrate the superior performance of our method compared to state-of-the-art approaches.

The role of Nuclear Magnetic Resonance (NMR) spectroscopy in cattle metabolism

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool that has significantly advanced the understanding of cattle metabolism. Nuclear Magnetic Resonance (NMR) spectroscopy plays a pivotal role in the study of cattle metabolism, offering distinct advantages over other spectrometric methods. NMR spectroscopy is a powerful analytical tool that provides detailed molecular insights by exploiting the magnetic properties of atomic nuclei. Unlike mass spectrometry and infrared spectroscopy, NMR does not require extensive sample preparation or destruction, preserving the integrity of biological samples. This non-invasive nature is particularly beneficial for longitudinal studies in cattle, where metabolic changes over time are of interest. One of the key strengths of NMR spectroscopy is its ability to simultaneously detect and quantify a broad range of metabolites in complex biological matrices, such as blood, urine, and tissue extracts. This comprehensive metabolic profiling is crucial for understanding the biochemical pathways and physiological states in cattle. NMR's high reproducibility and quantitative accuracy further enhance its suitability for metabolic studies, enabling precise monitoring of metabolic fluctuations in response to dietary changes, environmental stressors, or disease conditions. NMR spectroscopy also offers unique advantages in elucidating structural information about metabolites. Through multidimensional NMR techniques, researchers can determine the molecular structure and conformation of metabolites, providing deeper insights into metabolic functions and interactions. This structural elucidation is often challenging with other spectrometric methods, which may lack the resolution or require derivatization of samples. Moreover, NMR spectroscopy's non-destructive nature allows for the analysis of living tissues and in vivo studies, facilitating real-time monitoring of metabolic processes. This capability is instrumental in studying dynamic metabolic responses and adaptations in cattle under different physiological states. Additionally, the development of advanced NMR techniques, such as high-resolution magic angle spinning (HR-MAS) and hyperpolarization, has further expanded the scope of NMR applications in metabolic research. NMR spectroscopy stands out as a superior method for studying cattle metabolism due to its non-destructive approach, comprehensive metabolic profiling, structural elucidation capabilities, and potential for in vivo analysis. These advantages make NMR an indispensable tool in advancing our understanding of cattle metabolism and improving livestock health and productivity. Keywords: Nuclear Magnetic Resonance (NMR), Cattles, Metabolomics.

De-implementation strategy to reduce unnecessary antibiotic prescriptions for ambulatory HIV-infected patients with upper respiratory tract infections in Mozambique: a study protocol of a cluster randomized controlled trial

BackgroundAntibiotics are globally overprescribed for the treatment of upper respiratory tract infections (URTI), especially in persons living with HIV. However, most URTIs are caused by viruses, and antibiotics are not indicated. De-implementation is perceived as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excessive or inappropriate antibiotic use for URTI, through the employment of evidence-based interventions to reduce these practices. Research into strategies that lead to successful de-implementation of unnecessary antibiotic prescriptions within the primary health care setting is limited in Mozambique. In this study, we propose a protocol designed to evaluate the use of a clinical decision support algorithm (CDSA) for promoting the de-implementation of unnecessary antibiotic prescriptions for URTI among ambulatory HIV-infected adult patients in primary healthcare settings.MethodsThis study is a multicenter, two-arm, cluster randomized controlled trial, involving six primary health care facilities in Maputo and Matola municipalities in Mozambique, guided by an innovative implementation science framework, the Dynamic Adaption Process. In total, 380 HIV-infected patients with URTI symptoms will be enrolled, with 190 patients assigned to both the intervention and control arms. For intervention sites, the CDSAs will be posted on either the exam room wall or on the clinician´s exam room desk for ease of reference during clinical visits. Our sample size is powered to detect a reduction in antibiotic use by 15%. We will evaluate the effectiveness and implementation outcomes and examine the effect of multi-level (sites and patients) factors in promoting the de-implementation of unnecessary antibiotic prescriptions. The effectiveness and implementation of our antibiotic de-implementation strategy are the primary outcomes, whereas the clinical endpoints are the secondary outcomes.DiscussionThis research will provide evidence on the effectiveness of the use of the CDSA in promoting the de-implementation of unnecessary antibiotic prescribing in treating acute URTI, among ambulatory HIV-infected patients. Findings will bring evidence for the need to scale up strategies for the de-implementation of unnecessary antibiotic prescription practices in additional healthcare sites within the country.Trial registrationISRCTN, ISRCTN88272350. Registered 16 May 2024, https://www.isrctn.com/ISRCTN88272350

A Review of AI and Multi-Agent Systems for Cloud Performance and Security

Cloud computing has become a critical backbone for distributed systems, offering scalability and flexibility across diverse industries. However, ensuring optimal performance and robust security in such dynamic environments presents significant challenges, including inefficient task scheduling, suboptimal resource utilization, and persistent security threats such as data breaches and Distributed Denial of Service (DDoS) attacks. This paper examines the transformative potential of Artificial Intelligence (AI) and Multi-Agent Systems (MAS) in addressing these complexities. AI-driven solutions, including real-time anomaly detection, predictive analytics, and resource optimization, are combined with MAS frameworks that leverage decentralized, autonomous agents for distributed decision-making and proactive threat mitigation. The integration of AI and MAS enables dynamic adaptation to workload fluctuations, enhances resource efficiency, and provides robust security measures in multi-cloud and large-scale systems. The paper further explores key challenges in implementing these technologies, such as scalability and integration across heterogeneous environments, and identifies promising research directions to advance their adoption. By synthesizing empirical evidence and recent advancements, this study highlights the critical role of AI and MAS in shaping the future of cloud performance and security.

Transformasi Tradisi Mangalehen Tuor dalam Upacara Perkawinan Mandailing Pasca Migrasi ke Kota Medan

This research examines the transformation of the Mangalehen Tuor tradition in Mandailing ethnic wedding ceremonies after migration to Medan City. This tradition has undergone significant adaptation in the context of rapid urbanization and intercultural interactions in urban environments. Changes in the implementation of traditions, such as adjustments to the time and place of implementation, as well as changes in participation and delivery of advice, are the main focus of this research. Factors influencing these changes, such as urbanization, intercultural interactions, the influence of the younger generation, economics, and technology, are analyzed to understand the dynamics of cultural adaptation in a broader context. Despite undergoing transformation, efforts to preserve Mandailing cultural values ??remain a priority, reflected in cultural education efforts, community activities and technology integration. This research provides insight into how ethnic communities manage and maintain their cultural identity amidst the ever-growing challenges of globalization and modernization.

Web-aided data set expansion in deep learning: evaluating trainable activation functions in ResNet for improved image classification

PurposeThe purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.Design/methodology/approachThis research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.FindingsThe findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.Originality/valueThis study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies.

Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method's adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.

A robust domain distribution alignment discriminative network driven by physical samples for rotor-bearing fault diagnosis

The diagnostic performance of most intelligent models depends on having a sufficient number of labeled samples, which are difficult to obtain in real-world scenarios. Physics-guided sample generation emerges as a promising remedy for addressing the scarcity of labeled samples during model training. However, existing studies have tended to overlook the coupling effect between the marginal and conditional distributions of physical and real samples, leading to domain confusion and a subsequent reduction in model generalization and diagnostic accuracy. Therefore, this study introduces a novel approach: the physically driven sample-based domain distribution alignment discriminative network (DDADN), which integrates dynamic modeling and domain adaptation. Specifically, a lumped parameter model is established to generate complete defect samples, subsequently optimized using the Pearson correlation coefficient (PCC). Next, an enhanced joint distribution adaptation (EJDA) mechanism is employed to harmonize distributions across domains, effectively aligning both overall and class-specific distributions within the domains and thus achieving adaptive domain confusion. Meanwhile, to ensure robust feature classification and extraction, an adaptive softmax (A-softmax) loss function is introduced. Finally, in unlabeled real operating conditions, the proposed multitask scheme adeptly transfers knowledge between physical and real signals, yielding impressive classification accuracies of 97.78% and 98.34% for the experimental and Case Western Reserve University (CWRU) datasets, respectively. These results underscore the superiority of the proposed scheme in terms of learning transferable features and maintaining high diagnostic accuracy and generalization capability.

Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites.

Because most proteins have buried active sites, protein tunnels or channels play a crucial role in the transport of small molecules into buried cavities for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, a recent example being energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasted the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a nontrivial system of a haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The most knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting drug development efforts and the rational design of enzymes with buried active sites.

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.

The ability to calibrate learning according to new information is a fundamental component of an organism's ability to adapt to changing conditions. Yet, the exact neural mechanisms guiding dynamic learning rate adjustments remain unclear. Catecholamines appear to play a critical role in adjusting the degree to which we use new information over time, but individuals vary widely in the manner in which they adjust to changes. Here, we studied the effects of a low dose of methamphetamine (MA), and individual differences in these effects, on probabilistic reversal learning dynamics in a within-subject, double-blind, randomized design. Participants first completed a reversal learning task during a drug-free baseline session to provide a measure of baseline performance. Then they completed the task during two sessions, one with MA (20 mg oral) and one with placebo (PL). First, we showed that, relative to PL, MA modulates the ability to dynamically adjust learning from prediction errors. Second, this effect was more pronounced in participants who performed poorly at baseline. These results present novel evidence for the involvement of catecholaminergic transmission on learning flexibility and highlights that baseline performance modulates the effect of the drug.

Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm

Bus passenger flow prediction integrates data analytics and modelling techniques to forecast the number of passengers using bus services, incorporating historical usage patterns, demographics, weather, and events for optimal scheduling and resource allocation. The Bi-LSTM fusion model enhances accuracy by processing past and future features simultaneously, leveraging bidirectional LSTM layers and an attention mechanism to capture temporal dependencies. This approach not only refines insights crucial for urban mobility challenges like traffic management and demand forecasting but also improves route planning and service efficiency. The SMO algorithm initializes with a diverse spider monkey population exploring solution spaces. Through local and global leader phases, it iteratively updates positions based on fitness and probabilistic selections, maintaining a balance between exploration and exploitation. Perturbation-based updates in the local leader phase ensure adaptability, preventing premature convergence, while the global leader phase guides towards better solutions, enhancing efficiency in complex optimization tasks and promoting dynamic adaptation. In Dataset 1, the proposed model achieved a training time of 137 seconds, slightly longer than HA (115s), SARIMA (112s), GRU (123s), and ST-ResNet (113s). It demonstrated superior accuracy at 89%, surpassing HA (66%), SARIMA (68%), GRU (63%), DeepST (78%), and ST-ResNet (84%). In Dataset 2, the model exhibited the lowest RMSE, MAE, and MAPE%, indicating superior predictive accuracy over SVR, CNN, GCN, LSTM, and CONV LSTM models. These findings validate the proposed model's effectiveness in enhancing predictive capabilities for transit forecasting, underscoring its potential to optimize urban mobility and transportation management strategies significantly.

Artificial intelligence (AI) within manufacturing: An investigative exploration for opportunities, challenges, future directions

<p>Artificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.<strong></strong></p>

Perceptual error based on Bayesian cue combination drives implicit motor adaptation.

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one's effector, supported by Bayesian cue integration, underpins the sensorimotor system's implicit adaptation.

Relationship between Climate Change and Business Risk: Strategies for Adaptation and Mitigation: Evidence from a Mediterranean Country

Global corporate operations are facing significant challenges as a result of climate change. Businesses are exposed to a variety of dangers because of their effects, which might include anything from changing regulations to excessive weather. The objective of this research is to examine the connection between business risk and climate change, with a particular emphasis on the success of different adaptation and mitigation tactics used by Greek companies. A cross-sectional study was carried out among 345 enterprises in Greece using a quantitative research methodology. To investigate the relationship involving company risk management, adaptation, and mitigation plans with climate change risks, descriptive, correlational, and regression analyses are employed. Businesses are most distressed about the financial risks associated with climate change, followed by supply chain disruptions and physical hazards. Moreover, developing climate-resilient infrastructure and enhancing catastrophe preparedness have evolved into vital adaptation tactics. Also, converting to renewable energy sources is the most preferred approach to mitigation initiatives. Furthermore, the study demonstrated a significant positive correlation between climate change mitigation strategies and effective business risk management. Nevertheless, as perceived climate change risks increased, business risk management effectiveness decreased. The study suggests that Greek businesses are actively engaging in climate change risk management strategies that embrace both adaptation and mitigation strategies. Yet, the study recommends a continued focus on developing resilient infrastructure and collaborative efforts with local communities, governments, and NGOs. Businesses should also prioritize the shift to renewable energy sources to better mitigate their carbon emissions. This research contributes significantly to understanding how companies can strategically address the challenges of climate change. Furthermore, the study provides valuable insights into the dynamics of climate change adaptation and mitigation in a specific geographical area, thereby enhancing the global dialogue on climate resilience and sustainable business practices.