Dynamic Mechanism Research Articles

Automated Coupon Generator for Subsidy (ACG)

The proposed system is Automated Coupon Generator also known as ACG which introduces a dynamic coupon generation mechanism empowered by machine learning algorithms. This system is there to enhance people's engagement and optimize the coupon distribution process to befair. The coupon distribution process will be in such a way that those farmers who are less privileged will be prioritized. The project involves several key steps. This project involves key stages including data gathering, preprocessing, feature enhancement, and autonomous decision-making logic development, taking into account diverse factors such as farmer segments and budget constraints. The system will interact with HTML, PHP, CSS, and JavaScript, with Python supporting the implementation. It aims to continuously adapt the model to meet evolving customer behaviors and preferences. This technological approach is essential for optimizing coupons effectively and staying responsive to real-time farmer interactions and preferences

Blockchain: A comparative analysis of hybrid consensus algorithm and performance evaluation

The security of data and information is a critical concern in the rapidly evolving IT industry. Blockchain technology emerges as an innovative way of enhancing data security through the secure transmission of digital assets without a trusted third party or central administrator. Blockchain's fundamental principles of decentralization, immutability, and transparency are applicable in a variety of fields, including finance, supply chain management, healthcare, digital voting, and the Internet of Things. This research explores the underlying protocols of blockchain networks, specifically focusing on consensus mechanisms that enable transaction agreement and validation. The widespread adoption of blockchain technology faces significant challenges in achieving scalability and efficient consensus mechanisms. This research addresses these crucial limitations by proposing a novel hybrid consensus approach designed to enhance usability, security, and scalability. The proposed consensus mechanism incorporates a dynamic difficulty adjustment mechanism. The difficulty of validating blocks dynamically adjusts based on transaction volume and overall validator participation. Furthermore, a slashing mechanism is implemented to penalize validators engaging in malicious behavior by reducing their stake. Our analysis demonstrates the effectiveness of our proposed scheme in addressing several key concerns associated with blockchain, including reducing energy consumption, minimizing the probability of forks, and mitigating the level of mining centralization found in the consensus algorithm.

Dynamics of a New Delayed Glucose–Insulin Model with Obesity

In this work, a new glucose–insulin model incorporating time delay and obesity is developed to gain insights of its dynamical mechanisms. Through the method of multiple scales, we theoretically demonstrate that time delay can drive the system to yield Hopf bifurcation, thereby producing oscillating solutions that are consistent with the simulation results. Moreover, obesity changes the level of glucose, but cannot induce oscillations. In particular, it is found that under the combined effect of obesity and time delay, obesity delays the appearance of Hopf bifurcation which is induced by time delay. Results show that a low calorie diet can achieve therapeutic effects including reducing blood glucose fluctuations and insulin resistance, which can be used as an adjuvant for the treatment of diabetes. In addition, our results indicate that the delay, together with an optimal rate of model parameters can cause a variety of dynamics and induce glucose oscillations. The result obtained in this paper may help to better understand the obesity, diabetes, and the interaction between glucose and insulin, so that control strategies can be designed to better regulate blood glucose levels and fluctuations and mitigate the occurrence of type-2 diabetes.

China’s tourism transportation carbon emissions: dynamic mechanisms and multi-regulatory strategies simulation

China’s tourism transportation carbon emissions: dynamic mechanisms and multi-regulatory strategies simulation

A study on the influence of perceived distance on China's inbound Tourism and the interaction of non-economic distance: An analysis of dynamic extended gravity model based on 61 countries' entry data (2004-2018).

In the post-epidemic era, the restart of China's inbound tourism is imminent. However, there are gaps in our current understanding of how distance perception dynamically affects inbound tourism in China. In order to understand the past patterns of inbound tourism in China, we mapped the data of 61 countries of origin from 2004 to 2018 into a dynamic expanding gravity model to understand the effects of cultural distance, institutional distance, geographical distance, and economic distance on inbound tourism in China and revealed the dynamic interaction mechanism of non-economic distance perception on inbound tourism in China. Our research results show that cultural distance has a positive impact on China's inbound tourism, while institutional distance has a negative impact. The significant finding is that the dynamic interaction of the above two kinds of perceived distance can still have a positive impact on China's inbound tourism. Its practical significance is that it can counteract the influence of institutional distance by strengthening the cultural distance. Generally speaking, geographical distance and institutional distance restrict China's inbound tourism flow, while cultural distance, economic distance, and interactive perceptual distance promote China's inbound tourism flow.

Engineering Graphene Phototransistors for High Dynamic Range Applications.

Phototransistors are light-sensitive devices featuring a high dynamic range, low-light detection, and mechanisms to adapt to different ambient light conditions. These features are of interest for bioinspired applications such as artificial and restored vision. In this work, we report on a graphene-based phototransistor exploiting the photogating effect that features picowatt- to microwatt-level photodetection, a dynamic range covering six orders of magnitude from 7 to 107 lux, and a responsivity of up to 4.7 × 103 A/W. The proposed device offers the highest dynamic range and lowest optical power detected compared to the state of the art in interfacial photogating and further operates air stably. These results have been achieved by a combination of multiple developments. For example, by optimizing the geometry of our devices with respect to the graphene channel aspect ratio and by introducing a semitransparent top-gate electrode, we report a factor 20-30 improvement in responsivity over unoptimized reference devices. Furthermore, we use a built-in dynamic range compression based on a partial logarithmic optical power dependence in combination with control of responsivity. These features enable adaptation to changing lighting conditions and support high dynamic range operation, similar to what is known in human visual perception. The enhanced performance of our devices therefore holds potential for bioinspired applications, such as retinal implants.

ARS-Chain: A Blockchain-Based Anonymous Reputation-Sharing Framework for E-Commerce Platforms

E-commerce platforms incorporate reputation systems that allow buyers to rate sellers after transactions. However, existing reputation systems face challenges such as privacy leakage, linkability, and multiple rating attacks. The feedback data can inadvertently expose user information privacy because they reveal the buyers’ identities and preferences, which deters a significant number of users from providing their ratings. Moreover, malicious actors can exploit data analysis and machine learning techniques to mine user privacy from the rating data, posing serious threats to user security and trust. This study introduces ARS-Chain, a pioneering and secure blockchain-driven anonymous reputation-sharing framework tailored for e-commerce platforms. The core of ARS-Chain is a dynamic ring addition mechanism with linkable ring signatures (LRS), where the number of LRS rings is dynamically added in alignment with the evolving purchase list, and LRS link tags are constructed with the LRS rings and item identifiers. Further, a consortium blockchain is introduced to store these anonymous ratings on e-commerce platforms. As a result, ARS-Chain ensures full anonymity while achieving cross-platform reputation sharing, making rating records unlinkable, and effectively countering multiple rating attacks. The experimental results confirm that ARS-Chain significantly enhances user information privacy protection while maintaining system performance, having an important impact on the construction of trust mechanisms for e-commerce platforms.

Experimental Evidence for the Role of Dynamics in pH-Dependent Enzymatic Activity.

Enzymatic activity is heavily influenced by pH, but the rationale for the dynamical mechanism of pH-dependent enzymatic activity has not been fully understood. In this work, combined neutron scattering techniques, including quasielastic neutron scattering (QENS) and small angle neutron scattering (SANS), are used to study the structural and dynamic changes of a model enzyme, xylanase, under different pH and temperature environments. The QENS results reveal that xylanase at optimal pH exhibits faster relaxational dynamics and a lower energy barrier between conformational substates. The SANS results demonstrate that pH affects both xylanase's stability and monodispersity. Our findings indicate that enzymes have optimized stability and function under their optimal pH conditions, with both structure and dynamics being affected. The current study offers valuable insights into enzymatic functionality mechanisms, allowing for broad industrial applications.

Mechanical Fault Diagnosis of High-Voltage Circuit Breakers with Dynamic Multi-Attention Graph Convolutional Networks Based on Adaptive Graph Construction

With the rapid development of deep learning, its powerful capabilities make it possible to perform mechanical fault diagnosis of high-voltage circuit breakers (HVCBs). Among deep learning approaches, the convolutional neural network is widely used. However, while it can extract features effectively, it also has some limitations. Specifically, it depends on a large number of training data and only takes data information into account without considering structural information. These shortcomings lead to unused information and unsatisfactory model results. To address these shortcomings, this paper proposes AKNN-DMGCN, a novel dynamic multi-attention graph convolutional network based on an adaptively constructed graph, which can achieve high accuracy and robust mechanical fault diagnosis of HVCBs. First, a novel adaptive k-nearest neighbor (AKNN) graph construction method is proposed to construct informative graphs. The AKNN method can mine the relationship between the original data samples and utilize the data and label information. Thus, it has high fault tolerance to noise signals and can construct a structure graph with rich and accurate information, which can improve the overall model performance. Then, a dynamic multi-attention graph convolutional network (DMGCN) is applied for mechanical fault diagnosis of HVCBs. DMGCN fully utilizes structural and numerical information representing HVCB signals to perform classification. DMGCN has a dynamic multi-attention mechanism with strong expressive ability, which allows it to achieve high diagnostic accuracy. The experimental results indicate that the accuracy of AKNN-DMGCN reaches 97.22% on a balanced dataset and 95.01% on an imbalanced dataset, which demonstrates that the proposed method is effective for both balanced and imbalanced samples.

Study on the adsorption/desorption characteristics and dynamic diffusion mechanism of rare earth hydrated ions on the kaolinite (001) surface

Coal gangue (CG) contains abundant rare earth elements (REEs), which is a potential REE resource. REEs are mainly present in clay minerals as hydrates. The adsorption, desorption, and diffusion of REEs-hydrates on the surface of clay minerals are the key to regulating the leaching effect of REEs. The paper focuses on the typical clay minerals kaolinite and REEs-hydrates and combines quantum mechanics and molecular dynamics methods to systematically study the adsorption, desorption, and dynamic diffusion behavior of REEs-hydrates in acid solution. The research results indicate that stable REEs-hydrates La(H2O)9 and Y(H2O)7 are formed, with ion bonds between REEs and water molecules. La(H2O)9 and Y(H2O)7 mainly interact with the kaolinite (001) surface through hydrogen bonding. Competitive adsorption is formed between water molecules, hydrated hydrogen ions, and REEs-hydrates. Water molecules and hydrated hydrogen ions occupy the adsorption active sites of REEs-hydrates and form a dense water molecular layer on the surface. The distance between H3O+ and REEs-hydrates and the number of H3O+ in the solvent layer affect the desorption and diffusion behavior of hydrates. The bonding law, dynamic desorption, and diffusion mechanism of REEs-hydrates can provide meaningful references for selective leaching of REEs from CG.

A DYNAMIC PERFORMANCE-BASED PAYMENT MECHANISM FOR PUBLIC-PRIVATE PARTNERSHIP PROJECTS: AN INTEGRATED MODEL FOR PRINCIPAL-AGENT AND MULTI-OBJECTIVE OPTIMIZATION MODELS

Performance-based payment mechanism is one of the key issues to ensure all stakeholders’ benefits in infrastructure Public-Private Partnership (PPP) projects. However, most existing research on performance-based payment with a fixed incentive coefficient can’t play a good incentive role. This study aims to the intrinsic mechanism between the performance appraisal score and the performance-based payment structure, so as to design the dynamic performance-based payment mechanism for infrastructure PPP projects. Firstly, the multi-objective optimal method is used to calculate the unit-payment. Second, principal-agent theory is used to construct the performance-based payment model with a changeable incentive coefficient. The findings of this study show that, the performance-based payment mechanism can effectively motivate participants to provide high-quality and efficient services, because their remuneration directly depends on their performance. When the outcome does not meet expectations, the amount paid can be adjusted accordingly, thus ensuring the maximum protection of public resources as well as the private sector’s profits. They serve a dual purpose, on one hand, they offer insights to rectify the shortcomings in the current unsatisfactory payment structure. On the other hand, the study provides a theoretical reference for the public sector to effectively incentivize the private sector in enhancing project performance.

On the Validity of Granger Causality for Ecological Count Time Series

Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros.

Formation tracking control with disturbance rejection in leader-follower multi-agent systems under dynamic event-triggered mechanism

This paper investigates the problem of time-varying formation tracking (TVFT) in a linear multi-agent systems (MASs) with external disturbance in a type of directed network topology, and proposes a formation control strategy in a leader-follower control framework with disturbance rejection under a dynamic event-triggering mechanism (DETM). Firstly, in order to improve the overall performance of the system, an extended state observer is used to estimate the status of the follower and the presence of external disturbance in real time. Secondly, a DETM is designed to avoid continuous communication between adjacent intelligent agents. And the formation tracking error between adjacent multi-agents is used to formulate a distributed TVFT control strategy. Furthermore, a compensation function is designed to actively offset the impact of time-varying disturbances on system. A Lyapunov function is then established for stability analysis, verifying the absence of Zeno behavior with this DETM. Finally, a comparative numerical simulation was conducted using an unmanned aerial vehicle (UAV) cluster case to verify that the proposed method can save communication resources to a greater extent and has good control performance.

Assessing the change in the epidemiology of seasonal respiratory viruses with the onset of the COVID-19 pandemic.

Background. Respiratory tract infections are among the most important causes of mortality and morbidity in children worldwide. The COVID-19 pandemic has affected the distribution of seasonal respiratory viruses as in all areas of life. In this study, we have aimed to evaluate the changes in the rates of seasonal respiratory viruses with the onset of the pandemic.Methods. This study included patients who were admitted to the Pediatrics Clinic of Eskisehir Osmangazi University Faculty of Medicine Hospital between December 2018 and February 2022 with respiratory tract infections and in whom pathogens were detected from nasopharyngeal swab samples analysed by multiplex PCR method.Results. A total of 833 respiratory tract pathogens were detected in 684 cases consisting of male (55.3 %), and female (44.7 %), patients with a total mean age of 42 months. Single pathogen was revealed in 550, and multiple pathogens in 134 cases. Intensive care was needed in 14 % of the cases. Most frequently influenza A/B, rhinovirus and respiratory syncytial virus (RSV) were detected during the pre-pandemic period, while rhinovirus, RSV, and adenovirus were observed during the lockdown period. In the post-lockdown period, the incidence rates of rhinovirus, RSV, human bocavirus (HboV) (12 %), influenza virus infections increased, and patients with RSV and bocavirus infections required intensive care hospitalization.Conclusion. It is thought that the COVID-9 pandemic lockdown measures may have an impact on the distribution of seasonal respiratory viruses, especially RSV and influenza. Current, prospective and large case series regarding the mechanism of action and dynamics are needed.

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.

Ultrafast Primary Dynamics and Isomerization Mechanism of a Far-Red Sensing Cyanobacteriochrome.

Far-red cyanobacteriochromes (CBCRs) are bilin-based photosensory proteins that promise to be novel optical agents in optogenetics and deep tissue imaging. Recent structural studies of a far-red CBCR 2551g3 have revealed a unique all-Z,syn chromophore conformation in the far-red-absorbing Pfr state. Understanding the photoswitching mechanism through bilin photoisomerization is important for developing novel biomedical applications. Here, we employ femtosecond spectroscopy and site-directed mutagenesis to systematically characterize the dynamics of wild-type 2551g3 and four critical mutants in the 15Z Pfr state. We captured local relaxations in several picoseconds and isomerization dynamics in hundreds of picoseconds. Most mutants exhibited faster local relaxation, while their twisting dynamics and photoproducts depend on specific protein-chromophore interactions around the D-ring and C-ring. These results collectively reveal a unique dynamic pattern of excited-state evolution arising from a relatively rigid protein environment, thereby elucidating the molecular mechanism of Pfr-state photoisomerization in far-red CBCRs.

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example.

Label-free detection and profiling of individual solution-phase molecules.

Most chemistry and biology occurs in solution, in which conformational dynamics and complexation underlie behaviour and function. Single-molecule techniques1 are uniquely suited to resolving molecular diversity and new label-free approaches are reshaping the power of single-molecule measurements. A label-free single-molecule method2-16 capable of revealing details of molecular conformation in solution17,18 would allow a new microscopic perspective of unprecedented detail. Here we use the enhanced light-molecule interactions in high-finesse fibre-based Fabry-Pérot microcavities19-21 to detect individual biomolecules as small as 1.2 kDa, a ten-amino-acid peptide, with signal-to-noise ratios (SNRs) >100, even as the molecules are unlabelled and freely diffusing in solution. Our method delivers 2D intensity and temporal profiles, enabling the distinction of subpopulations in mixed samples. Notably, we observe a linear relationship between passage time and molecular radius, unlocking the potential to gather crucial information about diffusion and solution-phase conformation. Furthermore, mixtures of biomolecule isomers of the same molecular weight and composition but different conformation can also be resolved. Detection is based on the creation of a new molecular velocity filter window and a dynamic thermal priming mechanism that make use of the interplay between optical and thermal dynamics22,23 and Pound-Drever-Hall (PDH) cavity locking24 to reveal molecular motion even while suppressing environmental noise. New in vitro ways of revealing molecular conformation, diversity and dynamics can find broad potential for applications in the life and chemical sciences.

Correlation‐savvy sellers

AbstractA multi‐product monopolist sells sequentially to a buyer who privately learns his valuations. Using big data, the monopolist learns the intertemporal correlation of the buyer's valuations. Perfect price discrimination is generally unattainable—even when the seller learns the correlation perfectly, has full commitment, and in the limit where the consumption good about which the buyer has ex ante private information becomes insignificant. This impossibility is due to informational externalities that require information rents for the buyer's later consumption. These rents induce upward and downward distortions, violating the generalized no distortion at the top principle of dynamic mechanism design.

Directional liquid dynamics on superwetting interfaces

Directional liquid dynamics show significant interest across various fields, including energy, environmental remediation, water harvesting, microfluidics, and heat transfer. Nature creatures have developed remarkable abilities to manipulate liquid through their distinct surface structures and chemical compositions, offering valuable insights for human endeavors. Understanding the fundamental principles governing liquid dynamics at the interfaces of liquids, air, and solids is crucial for developing novel technologies and solutions in diverse domains. By elucidating the underlying mechanisms of directional liquid dynamics in natural organisms, we can inspire innovative approaches for practical applications. This review aims to provide a comprehensive understanding of directional liquid dynamics by (1) exploring the fundamental principles governing liquid dynamics at interfaces, (2) introducing the underlying mechanisms of directional liquid dynamics in representative natural organisms, (3) presenting a range of innovations and applications inspired by natural phenomena, and (4) offering our insights into the current unresolved questions and persistent challenges within the realm of directional liquid dynamics on superwetting interfaces. By delineating these objectives, we aim to contribute to the advancement of knowledge in this critical area and pave the way for future research and technological developments.