Dynamic Adaptation Research Articles

Spatial sorting and selection within urban agglomerations: a tripartite evolutionary game model approach

Urban agglomerations play a pivotal role in regional economic growth by fostering industrial clustering and attracting firms, thereby enhancing urban productivity and efficiency. However, existing research on spatial sorting and selection in urban settings-primarily based on quantitative spatial models-often overlooks the dynamic strategic adaptations of firms with varying skill levels. This gap leads to an incomplete understanding of how firms interact and adjust their spatial positioning over time in response to evolving urban economic conditions. To address this, we develop a tripartite evolutionary game model to investigate the spatial sorting and selection mechanisms among high-, medium-, and low-skill firms in urban settings. The model simulates firms’ strategic choices-whether to enter or avoid central cities-under varying initial conditions, such as participation willingness, skill disparities, and urban costs. Extensive simulations reveal that although these factors influence the process and equilibrium rates, they do not change the ultimate strategic outcome: high-skill firms consistently gravitate toward central cities, whereas low-skill firms tend to remain in peripheral areas, especially when urban costs are high. These findings provide significant insights for urban policy. The concentration of high-skill firms in large cities poses challenges for smaller cities attempting to attract these industries, potentially exacerbating regional disparities. Policymakers are thus encouraged to prioritize firm quality over quantity to drive sustained economic growth across regions. Additionally, reforming restrictive household registration policies could reduce entry barriers for high-skill labor, improve urban resource allocation, and facilitate more balanced development. The ongoing interaction between spatial sorting and selection fosters agglomeration economies in urban centers, underscoring the need for policy frameworks that accommodate firms’ diverse needs and skill levels across the urban landscape.

Cross-project defect prediction based on autoencoder with dynamic adversarial adaptation

Cross-project defect prediction based on autoencoder with dynamic adversarial adaptation

Intelligent Finance: Emerging Applications and Challenges of Machine Learning in Asset Pricing

This paper explores in depth the impact of big data and machine learning on asset pricing. By addressing the limitations of traditional asset pricing models, it analyzes the challenges and opportunities brought by big data to the financial markets. Traditional models, due to their linear assumptions, struggle to handle the complexity of high-dimensional data. However, machine learning techniques, particularly deep learning, overcome this issue with their powerful data processing capabilities, thereby enhancing the models external validation capacity. This paper summarizes various methods of applying modern machine learning in asset pricing, including feature engineering and end-to-end deep learning, and discusses the adaptive mechanisms of AI technologies to the complexity of asset pricing. Through case analysis, the article also emphasizes the significant effects of AI in improving predictive accuracy and market efficiency. This paper provides a new perspective for asset pricing research and offers practical pathways for the intelligence and dynamic adaptability of financial markets.

A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data

In actual industrial scenarios, collecting a complete dataset with all fault categories under the same conditions is challenging, leading to a loss in fault category knowledge in single-source domains. Deep learning domain adaptation methods face difficulties in multi-source scenarios due to insufficient labeled data and significant distribution differences, hindering domain-specific knowledge transfer and reducing fault diagnosis efficiency. To address these issues, the Dynamic Similarity-guided Multi-source Domain Adaptation Network (DS-MDAN) is proposed. This method leverages incomplete data from multiple-source domains to address distribution disparities in deep domain adaptation. It enhances diagnostic performance in the target domain by transferring knowledge across diverse domains. DS-MDAN uses convolution kernels of different scales to extract multi-scale feature information and achieves feature fusion through upsampling and operations like addition and concatenation. Adversarial training with domain and fault classifiers optimizes feature extraction for widely applicable representations. The similarity between source and target domain data is calculated based on features extracted by a shared-weight network, dynamically adjusting the contribution of different source domain data to minimize distribution differences. Finally, matched source and target domain samples are mapped to the same feature space for fault diagnosis. Experimental validation on various bearing fault datasets shows that DS-MDAN improves performance in multiple fault diagnosis tasks, increasing accuracy and demonstrating good generalization capabilities.

Comparison of Muscle Growth and Dynamic Strength Adaptations Induced by Unilateral and Bilateral Resistance Training: A Systematic Review and Meta-analysis.

Currently, great debate exists over the proposed superiority of some resistance exercises to induce muscular adaptations. For example, some argue that unilateral exercise (meaning one limb at a time) is superior to bilateral exercises (meaning both limbs). Of note, an evidence-based answer to this question is yet to be determined, particularly regarding muscle hypertrophy. This systematic review and meta-analysis aimed to compare the effects of unilateral versus bilateral resistance training on muscle hypertrophy and strength gains. A thorough literature search was performed using PubMed, Scopus, and Web of Science databases. The Cochrane Risk of Bias tool 2 (RoBII) tool was used to judge the risk of bias. Meta-analyses were performed using robust variance estimation with small-sample corrections. After retrieving 703 studies, 9 met the criteria and were included in the meta-analyses. We found no significant differences in muscle hypertrophy between bilateral and unilateral training [effect size (ES): - 0.21, 95% confidence interval (95% CI): - 3.56 to 3.13, P = 0.57]. Bilateral training induced a superior increase in bilateral strength (ES: 0.56, 95% CI: 0.16-0.96, P = 0.01). In contrast, unilateral training elicited a superior increase in unilateral strength (ES: - 0.65, 95% CI: - 0.93 to - 0.37, P = 0.001). Overall, studies presented moderate risk of bias. On the basis of the limited literature on the topic, we found no evidence of differential muscle hypertrophy between the two exercise selections. Strength gains appear to follow the principle of specificity.

A pseudo-cubic metal-organic cage with conformationally switchable faces for dynamically adaptive guest encapsulation.

The creation of hosts capable of accommodating different guest molecules may enable these hosts to play useful roles in chemical purifications, among other applications. Metal-organic cages are excellent hosts for various guests, but they generally incorporate rigid structural units that hinder dynamic adaptation to specific guests. Here we report a conformationally adaptable pseudo-cubic cage that can dynamically increase its cavity volume to fit guests with differing sizes. This pseudo-cube incorporates a tetramine subcomponent with 2,6-naphthalene arms that cooperatively adopt a non-planar conformation, enabling the cage faces to switch between endo and exo states. A wide range of guest molecules were observed to bind within the cavity of this cage, spanning a range of sizes from 46% to 154% of the cavity volume of the empty cage. Experimental and computational evidence characterizes the flipping of cage faces from endo to exo, expanding the cavity upon binding of larger guests.

Joint Event Detection with Dynamic Adaptation and Semantic Relevance

Joint Event Detection with Dynamic Adaptation and Semantic Relevance

Decoding Secondary Motor Cortex Neuronal Activity during Cocaine Self-Administration: Insights from Longitudinal in vivo Calcium Imaging.

We recently reported that the risk of cocaine relapse is linked to hyperexcitability in the secondary motor cortex (M2) after prolonged withdrawal following intravenous self-administration (IVSA). However, the neuronal mechanisms underlying drug-taking behaviors and the response of M2 neurons to contingent drug delivery remain poorly understood. Mice received cocaine as reinforcement (RNF) following active lever presses (ALP) but not inactive lever presses (ILP). Using in vivo calcium imaging during cocaine IVSA, we tracked M2 neuronal activity with single-cell resolution. We then analyzed Ca 2+ transients in M2 at the early vs . late stages during the 1-hr daily sessions on IVSA Day 1 and Day 5. M2 neurons adapted to both operant behaviors and drug exposure history. Specifically, saline mice showed a reduction in both saline taking behaviors and Ca 2+ transient frequency with the 1-hr session. In contrast, cocaine mice maintained high ALP and RNF counts, with increased Ca 2+ transient frequency and amplitude on Day 1, persisting through Day 5. Compared to saline controls, cocaine mice exhibited a lower % of positively responsive neurons (Pos) and higher % of negatively responsive neurons (Neg) before ALP and after RNF, a difference not seen before ILP. Furthermore, as drug-taking behaviors progressed during the daily session, cocaine mice showed greater neuronal engagement with a larger population, particularly linked to ALP and RNF, with reduced overlap in neurons associated with ILP. The M2 undergoes dynamic neuronal adaptations during early drug-taking behaviors, indicating its role as a potential substrate mediating the persistence of drug-seeking behaviors in cocaine relapse.

Regional disparities and dynamic adaptation in the forest health-based industry and ecological environment: A case study of Fujian Province, China

Regional disparities and dynamic adaptation in the forest health-based industry and ecological environment: A case study of Fujian Province, China

Dynamic domain adaptive EEG emotion recognition based on multi-source selection.

Emotion recognition based on electroencephalogram (EEG) has always been a research hotspot. However, due to significant individual variations in EEG signals, cross-subject emotion recognition based on EEG remains a challenging issue to address. In this article, we propose a dynamic domain-adaptive EEG emotion recognition method based on multi-source selection. The method considers each subject as a separate domain, filters suitable source domains from multiple subjects by assessing their resemblance, then further extracts the common and domain-specific features of the source and target domains, and then employs dynamic domain adaptation to mitigate inter-domain discrepancies. Global domain differences and local subdomain differences are also considered, and a dynamic factor is added so that the model training process first focuses on global distribution differences and gradually switches to local subdomain distributions. We conducted cross-subject and cross-session experiments on the SEED and SEED-IV datasets, respectively, and the cross-subject accuracies were 89.76% and 65.28%; the cross-session experiments were 91.63% and 67.83%. The experimental outcomes affirm the efficacy of the EEG emotion recognition approach put forward in this paper.

A stage-related online incremental transfer learning-based remaining useful life prediction method of bearings

Remaining useful life (RUL) prediction of bearings holds significant research value in bearing health management, particularly in the rapidly advancing field of intelligent manufacturing. How to realize the accurate online RUL prediction still faces many challenges, including the diverse degradation trends in bearings, the scarcity of labeled data, and the inconsistencies in the data marginal distributions. This paper proposes a Stage-related Online Incremental Adversarial Domain Adaption (SR-OIADA) transfer learning algorithm for bearing RUL prediction. It employs a dynamic domain adaptation strategy, which integrates incremental learning and transfer learning, facilitating the online acquisition of degradation knowledge aligned with degradation stages. A novel online degradation stage division algorithm is proposed to adaptively detect the health status of online monitored bearings, thereby enhancing the effectiveness of online learning. By incrementally training the deep models in a prediction-to-transfer manner, the model undergoes incremental updates at checkpoints, taking full advantage of new online samples. Experiments are conducted on real rolling bearing datasets to validate the proposed methods, demonstrating excellent predictive performance and highlighting the superiority compare with the non-transfer and offline methods.

PARAMETRIC DESIGN AND ADAPTIVE SIZING OF LATTICE STRUCTURES FOR 3D ADDITIVE MANUFACTURING

The present research is developed into the realm of industrial design engineering and additive manufacturing by introducing a parametric design model and adaptive mechanical analysis for a new lattice structure, with a focus on 3D additive manufacturing of complex parts. Focusing on the land-scape of complex parts additive manufacturing, this research proposes geometric parameterization, mechanical adaptive sizing, and numerical validation of a novel lattice structure to optimize the final printed part volume and mass, as well as its structural rigidity. The topology of the lattice structures exhibited pyramidal geometry. Complete parameterization of the lattice structure ensures that the known geometric parameters adjust to defined restrictions, enabling dynamic adaptability based on its load states and boundary conditions, thereby enhancing its mechanical performance. The core methodology integrates analytical automation with mechanical analysis by employing a model based in two-dimensional beam elements. The dimensioning of the lattice structure is analyzed using rigidity models of its sub-elements, providing an evaluation of its global structural behavior after applying the superposition principle. Numerical validation was performed to validate the proposed analytical model. This step ensures that the analytical model defined for dimensioning the lattice structure adjusts to its real mechanical behavior and allows its validation. The present manuscript aims to advance additive manufacturing methodologies by offering a systematic and adaptive approach to lattice structure design. Parametric and adaptive techniques foster new industrial design engineering methods, enabling the dynamic tailoring of lattice structures to meet their mechanical demands and enhance their overall efficiency and performance. Keywords: Computer-Aided Design, Industrial Design, Innovative Design, Additive Manufacturing, FEM Simulations

Integrating Advanced Control Strategies for Enhanced Motor Efficiency in Robotics

Motor efficiency requires diversified paradigm integration to facilitate the advancement of robotics applications through precision, energy optimization, and reliability. Advanced control strategies like AI-driven predictive mechanisms, harmonic drive systems, and real-time feedback tools in motor performance underline the efficiency required in robotic technologies. As a collective role for robotic motors, the approaches enable precise torque and speed modulation, dynamic adaptability to environmental changes, and energy-efficient operation in controlled strategies. Understanding theoretical foundations in motor efficiency guides decisions on the selection and implementation of robotic technologies in the automation of industrial and manufacturing processes. Comparative robotic role analyses pinpoint optimization for the technology to harness dynamic responsiveness and energy utilization efficiency. The ways to implement advanced motor controls underscore the potential of combining intelligent algorithms with innovative motor designs to elevate robotics reliance in complex situations. The advanced control strategies showcase the need for efficiency, adaptability, and relevance of robotic solutions in trending technological applications.

Molecular basis for the assembly of the dynein transport machinery on microtubules.

Cytoplasmic dynein-1, a microtubule-based motor protein, requires dynactin and an adaptor to form the processive dynein-dynactin-adaptor (DDA) complex. The role of microtubules in DDA assembly has been elusive. Here, we reveal detailed structural insights into microtubule-mediated DDA assembly using cryo-electron microscopy. We find that an adaptor-independent dynein-dynactin complex (DD) predominantly forms on microtubules in an intrinsic 2:1 stoichiometry, induced by spontaneous parallelization of dynein upon microtubule binding. Adaptors can squeeze in and exchange within the assembled microtubule-bound DD complex, which is enabled by relative rotations between dynein and dynactin, and further facilitated by dynein light intermediate chains that assist in an adaptor 'search' mechanism. Our findings elucidate the dynamic adaptability of the dynein transport machinery, and reveal a new mode for assembly of the motile complex.

Adaptive dynamics of dairy goats in a temperate region

Adaptive dynamics of dairy goats in a temperate region

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

Accurate material demand forecasts are crucial for the effective preparation of power material supply plans. Collaborating with suppliers in the material supply chain and establishing a cooperation mechanism based on mutual trust, information sharing and risk exchange can synchronize operations and enhance management efficiency. Therefore, it is particularly important to thoroughly investigate methods for improving the dynamic adaptability of modern (intelligent) material supply chains and enhancing the management of enterprise material flow. Under this background, we have made research and got the following conclusions: (1) there are six stages in the life cycle of electric power materials. The cost percentage in the planning stage accounts for 5%; the cost percentage in the design stage accounts for 10%; the cost percentage of the manufacturing stage accounts for 37.5%; the cost percentage of the installation stage accounts for 20% and the cost percentage in the operation stage accounts for 15%. Different models predict power materials and XGboost has a better prediction ability. (2) Power material supply chain data quality management is mainly divided into material support system. The first-level index is 0.413, the second-level index system is 0.578 and the weight is 0.7096. The first-level index of logistics timeliness is 0.379, the second-level index is 0.469 and the weight is 0.2577.

Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete.

In today's industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs. These heat maps serve as a flexible and intuitive tool for identifying robust operational points that balance quality, energy efficiency, and productivity. The proposed methodology advances decision-making in welding processes by combining robust predictive modeling with innovative visualization techniques, offering a versatile solution for multiobjective optimization in real-world industrial applications.

Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines.

Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient descent for learning, the need for exact gradients and complex information flow makes its implementation in biological and neuromorphic systems challenging. This has motivated the exploration of alternative learning mechanisms that can operate locally and do not rely on exact gradients. In this work, we introduce a novel approach that leverages noise in the parameters of the system and global reinforcement signals. Using an Ornstein-Uhlenbeck process with adaptive dynamics, our method balances exploration and exploitation during learning, driven by deviations from error predictions, akin to reward prediction error. Operating in continuous time, Ornstein-Uhlenbeck adaptation (OUA) is proposed as a general mechanism for learning in dynamic, time-evolving environments. We validate our approach across a range of different tasks, including supervised learning and reinforcement learning in feedforward and recurrent systems. Additionally, we demonstrate that it can perform meta-learning, adjusting hyper-parameters autonomously. Our results indicate that OUA provides a promising alternative to traditional gradient-based methods, with potential applications in neuromorphic computing. It also hints at a possible mechanism for noise-driven learning in the brain, where stochastic neurotransmitter release may guide synaptic adjustments.

Collaborative governance in stunting interventions at the local level

This study aims to determine and evaluate Collaborative Governance implemented by the government, private sector and the community in handling stunting in Enrekang Regency. The research method used is a descriptive qualitative approach to describe and analyze data obtained through interviews, observations, documentation and literature studies with a purposive sampling method related to the implementation of Collaborative Governance of the Enrekang Regency Government. The data analysis technique used is qualitative analysis through data reduction, data presentation and data verification. The results showed that based on Collaborative Governance by Emerson, it is known that the stakeholders involved have implemented the Dynamics, Actions and Impact and Adaptation of Collaboration well. However, some of the components in it still need to be evaluated and maximized. Among them are the Collaboration Dynamics which still have obstacles in the form of deliberations that have not run optimally and parties who do not know about existing procedures and collective agreements. This certainly affects the Collaborative Action which makes low involvement from several other actors and ultimately also has an impact on the lack of maximum stunting reduction. This study promotes equitable collaboration among all stakeholders to enhance the success of stunting prevention programs and foster active participation from all involved parties.

A hybrid path planning algorithm combining A* and improved ant colony optimization with dynamic window approach for enhancing energy efficiency in warehouse environments

This research presents a novel hybrid path planning algorithm combining A*, ant colony optimization (ACO), and the dynamic window approach (DWA) to enhance energy efficiency in warehouse environments. The proposed algorithm leverages the heuristic capabilities of A*, the optimization strengths of ACO, and the dynamic adaptability of DWA. Experimental results demonstrate that the IACO+A*+DWA approach consistently outperforms other hybrid methods across various metrics. In complex warehouse scenarios, the IACO+A*+DWA algorithm achieved an average energy consumption of 89.8 J, which is 13.3% lower than ACO+A*+DWA, 6.6% lower than GA+A*+DWA, and 25.8% lower than PSO+A*+DWA. The algorithm produced a path length of 95.94 m with 43 turns, compared to 97.36 m with 46 turns for ACO+A*+DWA, 104.43 m with 50 turns for GA+A*+DWA, and 97.84 m with 56 turns for PSO+A*+DWA. Time to goal was 197.93 s, 1.5% faster than GA+A*+DWA. Statistical analysis using ANOVA confirmed the significant differences between the algorithms in terms of energy consumption, path length, number of turns, and time taken, demonstrating the superior performance of IACO+A*+DWA. These results indicate that the IACO+A*+DWA algorithm minimizes energy consumption and produces shorter and more efficient paths, making it highly suitable for real-time applications in dynamic and complex warehouse environments. Future work will focus on further optimizing the algorithm and integrating machine learning techniques for enhanced adaptability and performance.